From e646126e239dc03a489a767820ac17df13ae401b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 10:48:01 -0700 Subject: [PATCH 01/72] Add the redesigned VeRO system at the repository root Introduce the v0.5 codebase: vero/ (core + harbor eval sidecar, build compiler, runtime/observability), vero-tasks/, and runs/. Root README points to legacy/ for the original-paper code. --- .gitignore | 9 + README.md | 146 +- runs/.gitignore | 3 + vero-tasks/README.md | 35 + .../examples/matmul-eval/pyproject.toml | 16 + .../matmul-eval/src/matmul_eval/__init__.py | 0 .../src/matmul_eval/matmul_task.py | 70 + vero-tasks/examples/matmul-eval/uv.lock | 181 ++ vero-tasks/examples/matmul-kernel/README.md | 30 + .../examples/matmul-kernel/pyproject.toml | 13 + vero-tasks/examples/matmul-kernel/run.py | 271 ++ .../src/matmul_kernel/__init__.py | 22 + vero-tasks/examples/matmul-kernel/uv.lock | 8 + vero-tasks/pyproject.toml | 29 + vero-tasks/src/vero_tasks/__init__.py | 24 + vero-tasks/src/vero_tasks/models.py | 178 + vero-tasks/src/vero_tasks/runner.py | 220 ++ vero-tasks/src/vero_tasks/task.py | 334 ++ vero-tasks/tests/test_runner.py | 200 ++ vero-tasks/tests/test_task.py | 202 ++ vero/.gitignore | 40 + vero/README.md | 850 +++++ vero/docs/agent-setup-guide.md | 133 + vero/docs/insights-generator-subagent.md | 249 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.../vero/harbor/build/templates/task.toml.j2 | 22 + .../vero/harbor/build/templates/test.sh.j2 | 10 + vero/src/vero/harbor/cli.py | 779 +++++ vero/src/vero/harbor/deployment.py | 350 ++ vero/src/vero/harbor/generation.py | 58 + vero/src/vero/harbor/inference.py | 626 ++++ vero/src/vero/harbor/serve.py | 99 + vero/src/vero/harbor/session.py | 234 ++ vero/src/vero/harbor/sidecar.py | 818 +++++ vero/src/vero/harbor/transport.py | 190 ++ vero/src/vero/harbor/verifier.py | 478 +++ vero/src/vero/optimization/__init__.py | 49 + vero/src/vero/optimization/command.py | 179 + vero/src/vero/optimization/models.py | 130 + vero/src/vero/optimization/optimizer.py | 736 +++++ vero/src/vero/optimization/protocols.py | 90 + vero/src/vero/optimization/strategy.py | 329 ++ vero/src/vero/report.py | 677 ++++ vero/src/vero/runtime/__init__.py | 59 + vero/src/vero/runtime/artifacts.py | 42 + vero/src/vero/runtime/context.py | 536 +++ vero/src/vero/runtime/events.py | 120 + vero/src/vero/runtime/factory.py | 282 ++ vero/src/vero/runtime/session.py | 506 +++ vero/src/vero/runtime/wandb.py | 348 ++ vero/src/vero/sandbox.py | 752 +++++ vero/src/vero/staging.py | 72 + vero/src/vero/tools/__init__.py | 35 + vero/src/vero/tools/base.py | 20 + vero/src/vero/tools/evaluation.py | 95 + vero/src/vero/tools/planning.py | 96 + vero/src/vero/tools/registry.py | 89 + vero/src/vero/tools/sandbox_tools.py | 79 + vero/src/vero/tools/sub_agent.py | 182 ++ vero/src/vero/tools/utils/__init__.py | 32 + vero/src/vero/tools/utils/openai_agents.py | 79 + vero/src/vero/tools/web.py | 304 ++ vero/src/vero/utils/__init__.py | 27 + vero/src/vero/utils/asyncio.py | 254 ++ vero/src/vero/utils/general.py | 104 + vero/src/vero/utils/openai_agents.py | 54 + vero/src/vero/utils/tokens.py | 27 + vero/src/vero/workspace/__init__.py | 6 + vero/src/vero/workspace/base.py | 141 + vero/src/vero/workspace/git.py | 408 +++ vero/tests/__init__.py | 0 vero/tests/test_lockfile.py | 33 + vero/tests/test_remote_sandbox.py | 182 ++ vero/tests/test_sandbox.py | 363 +++ vero/tests/test_subprocess.py | 107 + vero/tests/test_tokens.py | 23 + vero/tests/test_tools.py | 63 + vero/tests/test_uv_with_editable.py | 118 + vero/tests/test_v05_agent_context.py | 245 ++ vero/tests/test_v05_agent_events.py | 52 + vero/tests/test_v05_agent_producer.py | 350 ++ vero/tests/test_v05_c_example.py | 90 + vero/tests/test_v05_candidate_repository.py | 298 ++ vero/tests/test_v05_circle_packing_example.py | 180 ++ vero/tests/test_v05_claude_agent.py | 119 + vero/tests/test_v05_cli.py | 311 ++ vero/tests/test_v05_command_backend.py | 331 ++ vero/tests/test_v05_config.py | 148 + vero/tests/test_v05_darwin_godel_strategy.py | 120 + vero/tests/test_v05_docker_sandbox.py | 210 ++ vero/tests/test_v05_error_taxonomy.py | 60 + vero/tests/test_v05_evaluation_models.py | 338 ++ vero/tests/test_v05_evaluation_objective.py | 223 ++ vero/tests/test_v05_evaluation_runtime.py | 904 ++++++ vero/tests/test_v05_evaluation_tools.py | 105 + vero/tests/test_v05_evolutionary_strategy.py | 119 + vero/tests/test_v05_harbor_backend.py | 875 +++++ vero/tests/test_v05_harbor_build.py | 651 ++++ vero/tests/test_v05_harbor_deployment.py | 192 ++ vero/tests/test_v05_harbor_http.py | 653 ++++ vero/tests/test_v05_harbor_inference.py | 320 ++ vero/tests/test_v05_harbor_session.py | 117 + vero/tests/test_v05_harbor_sidecar.py | 700 ++++ vero/tests/test_v05_harbor_verifier.py | 452 +++ vero/tests/test_v05_optimizer.py | 535 +++ vero/tests/test_v05_python_example.py | 40 + vero/tests/test_v05_python_task_backend.py | 323 ++ vero/tests/test_v05_report.py | 228 ++ vero/tests/test_v05_runtime_factory.py | 510 +++ vero/tests/test_v05_runtime_session.py | 300 ++ vero/tests/test_v05_vero_agent.py | 118 + vero/tests/test_v05_wandb.py | 298 ++ vero/tests/test_web.py | 156 + vero/tests/test_workspace.py | 391 +++ vero/uv.lock | 2877 +++++++++++++++++ 199 files changed, 42188 insertions(+), 91 deletions(-) create mode 100644 runs/.gitignore create 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scoping notes (paper planning; not tracked) +harness-engineering-bench/benchmark-scoping.md diff --git a/README.md b/README.md index f366e418..31b39cbb 100644 --- a/README.md +++ b/README.md @@ -1,122 +1,86 @@ -# VeRO: Versioning Rewards and Observations +# VeRO: a harness for agents to optimize programs, text, and agents +> The code for the original VeRO paper is archived in [`legacy/`](legacy/). [![Paper](https://img.shields.io/badge/arXiv-2602.22480-b31b1b.svg)](https://arxiv.org/abs/2602.22480) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) - -VeRO is an evaluation harness for using coding agents to optimize LLM-based agents and workflows. It treats agent code as a versioned artifact — making changes, evaluating results, and hill-climbing toward better performance using git version control. - -> **Paper**: [VeRO: An Evaluation Harness for Agents to Optimize Agents](https://arxiv.org/abs/2602.22480) - -## Repository Structure - -``` -vero/ -├── vero/ # Core library (scale-vero) -├── vero-agents/ # Agent implementations (benchmarking targets) -├── vero-benchmarking/ # Benchmarking scripts and analysis -└── LICENSE -``` - -### [vero/](vero/) - -The core optimization framework. Provides: - -- **Policy** — orchestrates the optimization loop (agent + evaluator + git) -- **Agents** — VeroAgent (OpenAI Agents SDK) and ClaudeCodeAgent (Claude Agent SDK) -- **Evaluator** — runs task evaluations in isolated subprocess environments -- **Tools** — MCP-based tools for agents (bash, file I/O, experiment runner, dataset viewer, etc.) -- **Traces** — session analysis and LLM-based trace interpretation - -```bash -cd vero && uv sync --extra optimize +[![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/) + +VeRO gives a coding agent something to edit, an evaluation boundary, and durable +memory of every candidate it tried. The target is anything you can put under Git +and score — a **program** (a single function up to a whole codebase), **text** +(a prompt, spec, or config), or an **agent** (its scaffold, tools, and prompts). +Agents are programs, but not everyone reads "program" that way, so VeRO names +them explicitly: it was introduced to optimize agents and generalizes the same +version / evaluate / select loop to any Git-versioned artifact. + +```mermaid +flowchart LR + S["Strategy
proposes ideas"] --> P["Candidate producers
edit isolated workspaces"] + P --> E["Evaluation backends
score versioned reports"] + E --> Sel["Selection keeps the
best feasible candidate"] + Sel -->|"next round"| S ``` -See [vero/README.md](vero/README.md) for full documentation. - -### [vero-agents/](vero-agents/) +The target and evaluator do not need to be Python. External evaluators and +candidate producers connect through command protocols; Python benchmarks can +use the optional, optimizer-independent `scale-vero-tasks` package. -Agent implementations used as optimization targets: +Targets may live locally or in an isolated sandbox. VeRO keeps optimization +state and experiment tracking on the host while running Git worktrees, producer +commands, builds, and evaluation commands in the target sandbox. The core guide +includes a no-bind-mount `DockerSandbox` example. -| Agent | Description | -|-------|-------------| -| **generic-agent** | General-purpose agent for MATH, GPQA, GAIA, GSM8K, etc. | -| **web_search_agent** | Web search agent for SimpleQA, Facts Search | -| **KIRA** | Terminal task agent for Terminal Bench 2.0 | -| **tau-bench** | Customer service tool-use agent | -| **pharma_summarizer** | Document summarization agent | +## Repository layout -See [vero-agents/README.md](vero-agents/README.md) for details. +| Directory | Purpose | +| --- | --- | +| [`vero/`](vero/) | The `scale-vero` optimization kernel, runtime, CLI, and coding-agent adapters | +| [`vero-tasks/`](vero-tasks/) | Narrow Python task types and schema-v1 evaluation runner | +| [`harness-engineering-bench/`](harness-engineering-bench/) | Harbor-native target programs and end-to-end optimization benchmarks | -### [vero-benchmarking/](vero-benchmarking/) +Start with the [generic C matrix-multiplication quickstart](vero/examples/c-matmul/), +try the [26-circle packing benchmark](vero/examples/circle-packing/), or read the +[core guide](vero/README.md). The C example demonstrates the language-neutral +command protocol without model credentials. Circle packing is a substantive +coding-agent benchmark with exact geometry checks and inspectable search +artifacts. -Scripts and infrastructure for running optimization experiments: +For a full agent-optimization example, the [GAIA benchmark](harness-engineering-bench/gaia/) +pairs a tool-using GPT-5.4 mini target with Harbor's canonical GAIA verifier and +an immutable 20% / 40% / 40% development, validation, and test split. ```bash -cd vero-benchmarking && uv sync --all-extras - -# Run an optimization experiment -uv run python scripts/run_benchmark.py --scaffold claude-code-vmf --model sonnet --task math - -# Build datasets -./scripts/build_datasets.sh +cd vero +uv sync --all-extras +uv run vero --help ``` -See [vero-benchmarking/README.md](vero-benchmarking/README.md) for full documentation. - -## Quick Start +## Paper reproduction -### Prerequisites +VeRO was introduced in [*VeRO: A Harness for Agents to Optimize +Agents*](https://arxiv.org/abs/2602.22480), accepted at ICML 2026. The current +library generalizes that version/evaluate/select loop from agents to programs. -- Python 3.11+ -- [uv](https://docs.astral.sh/uv/getting-started/installation/) -- Git -- Access to an LLM provider (via LiteLLM, OpenAI, Anthropic, etc.) - -### Install +The exact paper implementation is frozen separately from the v0.5 redesign: ```bash -git clone && cd vero - -# Install core library -cd vero && uv sync --extra optimize - -# Install benchmarking tools -cd ../vero-benchmarking && uv sync --all-extras +git checkout paper-v1 ``` -### Run Your First Optimization - -```python -from agents import Agent as OAIAgent -from vero.policy import Policy -from vero.agents.vero import VeroAgent - -policy = Policy( - project_path="/path/to/my-agent", - dataset="/path/to/my-dataset", - agent=VeroAgent( - oai_agent=OAIAgent(name="VeroAgent", model="anthropic/claude-sonnet-4-5-20250929"), - ), - task="main", - train_budget=10, - max_turns=200, -) - -best = await policy.run() -print(f"Best commit: {best.commit}, score: {best.score}") -``` +Use the `paper/v1` branch or `paper-v1` tag for reproduction. Development of +the generic program optimizer continues on the `v0.5` branch. The frozen ref +also preserves the paper-era `vero-agents` and `vero-benchmarking` directories; +their Harbor-native replacement on `v0.5` is `harness-engineering-bench`. ## Citation ```bibtex @article{ursekar2026vero, - title={VeRO: An Evaluation Harness for Agents to Optimize Agents}, + title={VeRO: A Harness for Agents to Optimize Agents}, author={Ursekar, Varun and Shanker, Apaar and Chatrath, Veronica and Xue, Yuan (Emily) and Denton, Sam}, journal={arXiv preprint arXiv:2602.22480}, year={2026} } ``` -## License - -[MIT](LICENSE) +VeRO is licensed under the [MIT License](LICENSE). diff --git a/runs/.gitignore b/runs/.gitignore new file mode 100644 index 00000000..45ed60a1 --- /dev/null +++ b/runs/.gitignore @@ -0,0 +1,3 @@ +# Local GAIA/harbor run outputs — inspectable, not committed. +* +!.gitignore diff --git a/vero-tasks/README.md b/vero-tasks/README.md new file mode 100644 index 00000000..be26b0f0 --- /dev/null +++ b/vero-tasks/README.md @@ -0,0 +1,35 @@ +# scale-vero-tasks + +`scale-vero-tasks` is the optional Python task protocol used by VeRO benchmark +targets. It contains no optimizer, workspace, session, dataset-store, or +experiment-database code. + +```python +from vero_tasks import TaskOutput, TaskResult, create_task + +task = create_task("exact_match") + +@task.inference() +async def infer(case, context): + return TaskOutput(output=case["question"].upper()) + +@task.evaluation() +async def evaluate(case, output, context): + return TaskResult.from_task_output( + output, + score=float(output.output == case["answer"]), + ) +``` + +The runner accepts a VeRO command-evaluation request and an external JSON/JSONL +case file, imports a task module, and writes a schema-v1 evaluation report. The +standard VeRO adapter runs this package in a trusted evaluator project while +overlaying the candidate package as an editable dependency. This keeps Python +benchmark ergonomics separate from both the target program and VeRO's +language-neutral evaluation kernel. + +The runner applies VeRO's retry policy independently to each non-batch +inference and evaluation call. Provider rate limits, configured HTTP status +codes or messages, and timeouts can be retried with bounded exponential +backoff. Earlier failed attempts are retained in the canonical case error list; +they are non-terminal when a later attempt succeeds. diff --git a/vero-tasks/examples/matmul-eval/pyproject.toml b/vero-tasks/examples/matmul-eval/pyproject.toml new file mode 100644 index 00000000..17ba96ef --- /dev/null +++ b/vero-tasks/examples/matmul-eval/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "matmul-eval" +version = "0.1.0" +description = "Evaluation task for the matmul kernel — measures correctness and speed" +requires-python = ">=3.11" +dependencies = ["scale-vero-tasks>=0.2.0"] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/matmul_eval"] + +[tool.uv.sources] +scale-vero-tasks = { path = "../..", editable = true } diff --git a/vero-tasks/examples/matmul-eval/src/matmul_eval/__init__.py b/vero-tasks/examples/matmul-eval/src/matmul_eval/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/vero-tasks/examples/matmul-eval/src/matmul_eval/matmul_task.py b/vero-tasks/examples/matmul-eval/src/matmul_eval/matmul_task.py new file mode 100644 index 00000000..4a6fdb88 --- /dev/null +++ b/vero-tasks/examples/matmul-eval/src/matmul_eval/matmul_task.py @@ -0,0 +1,70 @@ +"""Matrix multiply evaluation task. + +Measures correctness and execution speed of a matmul kernel. +Score = average time per multiply (lower is better). +Incorrect results get a penalty score of 999999.0. +""" + +import time + +from vero_tasks import ( + TaskContext, + TaskOutput, + TaskParameters, + TaskResult, + create_task, +) + + +class MatmulParameters(TaskParameters): + n_repeats: int = 100 + + +matmul_task = create_task("matmul", task_parameters_type=MatmulParameters) + + +@matmul_task.inference() +async def run_inference(task: dict, context: TaskContext) -> TaskOutput: + from matmul_kernel import multiply + + a = task["matrix_a"] + b = task["matrix_b"] + + parameters = context.parse_task_params(MatmulParameters) + + # Warmup + multiply(a, b) + + # Timed runs (like timeit) + start = time.perf_counter() + for _ in range(parameters.n_repeats): + result = multiply(a, b) + elapsed_ms = (time.perf_counter() - start) / parameters.n_repeats * 1000 + + return TaskOutput(output={"result": result, "time_ms": elapsed_ms}) + + +@matmul_task.evaluation() +async def run_evaluation( + task: dict, output: TaskOutput, context: TaskContext +) -> TaskResult: + expected = task["expected"] + actual = output.output["result"] + time_ms = output.output["time_ms"] + + # Check correctness (within floating point tolerance) + correct = all( + abs(a_val - e_val) < 1e-6 + for a_row, e_row in zip(actual, expected) + for a_val, e_val in zip(a_row, e_row) + ) + + # Score = time if correct, penalty if wrong (lower is better) + score = time_ms if correct else 999999.0 + + return TaskResult.from_task_output( + output, + score=score, + metrics={"time_ms": time_ms, "correct": 1.0 if correct else 0.0}, + feedback=f"{'Correct' if correct else 'Wrong'}. 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`vero/` package directory: + +```bash +# Exercise the complete evaluation pipeline without credentials. +uv run python examples/matmul-kernel/run.py --eval-only + +# Let a coding agent optimize the function. +uv run python examples/matmul-kernel/run.py --agent vero +uv run python examples/matmul-kernel/run.py --agent claude +``` + +`--max-proposals` limits completed optimization attempts. Agent-requested +checkpoints are evaluations too, so use the independent `--max-evaluations` +option when you also want hard agent and system evaluation budgets. Those +budgets cover checkpoints and automatic candidate evaluations independently. + +Every candidate is edited and evaluated in an isolated Git worktree. The +original target template is unchanged, while reports, agent state, events, and +the best candidate identity are preserved in the printed session directory. diff --git a/vero-tasks/examples/matmul-kernel/pyproject.toml b/vero-tasks/examples/matmul-kernel/pyproject.toml new file mode 100644 index 00000000..fb2f31d9 --- /dev/null +++ b/vero-tasks/examples/matmul-kernel/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "matmul-kernel" +version = "0.1.0" +description = "A naive matrix multiply kernel to optimize with VeRO" +requires-python = ">=3.11" +dependencies = [] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/matmul_kernel"] diff --git a/vero-tasks/examples/matmul-kernel/run.py b/vero-tasks/examples/matmul-kernel/run.py new file mode 100644 index 00000000..488c9f66 --- /dev/null +++ b/vero-tasks/examples/matmul-kernel/run.py @@ -0,0 +1,271 @@ +"""Run VeRO's matrix-multiplication program optimization example. + +Run this file from the scale-vero project environment: + + uv run python examples/matmul-kernel/run.py --eval-only + uv run python examples/matmul-kernel/run.py --agent vero +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import random +import shutil +import subprocess +import tempfile +from pathlib import Path + +from vero.evaluation import ( + EvaluationBudget, + EvaluationDefinition, + EvaluationPlan, + EvaluationPrincipal, + EvaluationSet, + MetricAggregation, + MetricSelector, + ObjectiveSpec, + PythonTaskBackend, + PythonTaskBackendConfig, + PythonTaskEvaluationConfig, +) +from vero.optimization import SequentialStrategy +from vero.runtime import create_local_optimization_session + +SCRIPT_DIR = Path(__file__).resolve().parent +EVALUATOR_DIR = SCRIPT_DIR.parent / "matmul-eval" + +INSTRUCTION = """You are optimizing a matrix multiplication function for speed. + +The target is src/matmul_kernel/__init__.py. Preserve the public multiply(a, b) +signature and numerical correctness. You may change the implementation and add +target dependencies. Use the evaluate tool with evaluation="matmul" when +measurement would help; the +objective is mean score in milliseconds, so lower is better. +""" + + +def _multiply(a: list[list[float]], b: list[list[float]]) -> list[list[float]]: + return [ + [ + sum(a_value * b[p][j] for p, a_value in enumerate(row)) + for j in range(len(b[0])) + ] + for row in a + ] + + +def create_cases(path: Path) -> Path: + def matrix(rows: int, columns: int, seed: int) -> list[list[float]]: + generator = random.Random(seed) + return [ + [generator.uniform(-10, 10) for _ in range(columns)] + for _ in range(rows) + ] + + inputs = [ + ([[1, 2], [3, 4]], [[5, 6], [7, 8]]), + ([[1, 0], [0, 1]], [[9, 10], [11, 12]]), + (matrix(8, 8, 42), matrix(8, 8, 52)), + (matrix(10, 10, 43), matrix(10, 10, 53)), + (matrix(12, 12, 44), matrix(12, 12, 54)), + ] + cases = [ + { + "id": f"matrix-{index}", + "matrix_a": a, + "matrix_b": b, + "expected": _multiply(a, b), + } + for index, (a, b) in enumerate(inputs) + ] + path.write_text(json.dumps(cases), encoding="utf-8") + return path + + +def create_target(work_dir: Path) -> Path: + target = work_dir / "matmul-kernel" + target.mkdir() + shutil.copytree(SCRIPT_DIR / "src", target / "src") + shutil.copy2(SCRIPT_DIR / "pyproject.toml", target / "pyproject.toml") + (target / ".gitignore").write_text( + ".venv/\n__pycache__/\n*.pyc\n*.egg-info/\n", + encoding="utf-8", + ) + subprocess.run( + ["git", "init", "-b", "main"], + cwd=target, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=target, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=target, + check=True, + capture_output=True, + ) + return target + + +def create_backend(cases: Path) -> PythonTaskBackend: + return PythonTaskBackend( + PythonTaskBackendConfig( + harness_root=str(EVALUATOR_DIR), + module="matmul_eval.matmul_task", + task="matmul", + evaluations=[ + PythonTaskEvaluationConfig(name="matmul", cases_path=str(cases)) + ], + passthrough_environment=["UV_CACHE_DIR"], + ) + ) + + +async def run_example( + *, + work_dir: Path, + agent_name: str | None, + max_proposals: int, + max_evaluations: int | None, +) -> None: + target = create_target(work_dir) + cases = create_cases(work_dir / "cases.json") + evaluation_set = EvaluationSet(name="matmul") + agent_budget = ( + EvaluationBudget( + backend_id="python-task", + evaluation_set_key=evaluation_set.budget_key("python-task"), + principal=EvaluationPrincipal.AGENT, + total_runs=max_evaluations, + ) + if max_evaluations is not None + else None + ) + system_budget = ( + EvaluationBudget( + backend_id="python-task", + evaluation_set_key=evaluation_set.budget_key("python-task"), + principal=EvaluationPrincipal.SYSTEM, + total_runs=max_evaluations, + ) + if max_evaluations is not None + else None + ) + evaluation_plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition( + evaluation_set=evaluation_set, + agent_budget=agent_budget, + system_budget=system_budget, + ) + ], + selection_evaluation="matmul", + ) + producers = {} + if agent_name is not None: + from vero.agents import AgentCandidateProducer + + if agent_name == "claude": + from vero.agents import ClaudeCodeAgent + + coding_agent = ClaudeCodeAgent() + else: + from vero.agents import VeroAgent + + coding_agent = VeroAgent() + producers["default"] = AgentCandidateProducer( + coding_agent, + prompt=INSTRUCTION, + max_turns=100, + ) + + session = await create_local_optimization_session( + project_path=target, + session_dir=work_dir / "session", + backend_id="python-task", + backend=create_backend(cases), + objective=ObjectiveSpec( + selector=MetricSelector( + metric="score", + aggregation=MetricAggregation.MEAN, + case_failure_value=1.0e12, + ), + direction="minimize", + failure_value=1.0e12, + ), + evaluation_plan=evaluation_plan, + strategy=SequentialStrategy(instruction=INSTRUCTION), + producers=producers, + parameters={"n_repeats": 100}, + max_proposals=max_proposals, + ) + result = await session.run() + print(f"Session: {session.session_dir}") + print(f"Baseline score: {result.baseline.objective.value:.6f} ms") + if result.best is not None: + print(f"Best score: {result.best.objective.value:.6f} ms") + print(f"Best version: {result.best.request.candidate.version}") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--eval-only", + action="store_true", + help="Evaluate the baseline without starting a coding agent.", + ) + parser.add_argument( + "--agent", + choices=["vero", "claude"], + default="vero", + help="Coding-agent adapter used for optimization.", + ) + parser.add_argument("--max-proposals", type=int, default=5) + parser.add_argument( + "--max-evaluations", + type=int, + help=( + "Optional evaluation-run budget, including the baseline, agent " + "checkpoints, and completed candidates. By default evaluations are " + "not separately capped." + ), + ) + parser.add_argument("--work-dir", type=Path) + arguments = parser.parse_args() + if arguments.max_proposals < 0: + parser.error("--max-proposals must be non-negative") + if arguments.max_evaluations is not None and arguments.max_evaluations < 1: + parser.error("--max-evaluations must be positive") + + work_dir = arguments.work_dir or Path(tempfile.mkdtemp(prefix="vero-matmul-")) + work_dir = work_dir.expanduser().resolve() + work_dir.mkdir(parents=True, exist_ok=True) + print(f"Working directory: {work_dir}") + asyncio.run( + run_example( + work_dir=work_dir, + agent_name=None if arguments.eval_only else arguments.agent, + max_proposals=0 if arguments.eval_only else arguments.max_proposals, + max_evaluations=arguments.max_evaluations, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/vero-tasks/examples/matmul-kernel/src/matmul_kernel/__init__.py b/vero-tasks/examples/matmul-kernel/src/matmul_kernel/__init__.py new file mode 100644 index 00000000..072bf205 --- /dev/null +++ b/vero-tasks/examples/matmul-kernel/src/matmul_kernel/__init__.py @@ -0,0 +1,22 @@ +"""Naive matrix multiply kernel. Optimize this program.""" + + +def multiply(a: list[list[float]], b: list[list[float]]) -> list[list[float]]: + """Multiply two matrices using the naive O(n^3) algorithm. + + Args: + a: Matrix of shape (n, k) + b: Matrix of shape (k, m) + + Returns: + Result matrix of shape (n, m) + """ + n = len(a) + m = len(b[0]) + k = len(b) + result = [[0.0] * m for _ in range(n)] + for i in range(n): + for j in range(m): + for p in range(k): + result[i][j] += a[i][p] * b[p][j] + return result diff --git a/vero-tasks/examples/matmul-kernel/uv.lock b/vero-tasks/examples/matmul-kernel/uv.lock new file mode 100644 index 00000000..d055e471 --- /dev/null +++ b/vero-tasks/examples/matmul-kernel/uv.lock @@ -0,0 +1,8 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" + +[[package]] +name = "matmul-kernel" +version = "0.1.0" +source = { editable = "." } diff --git a/vero-tasks/pyproject.toml b/vero-tasks/pyproject.toml new file mode 100644 index 00000000..0dddbb3e --- /dev/null +++ b/vero-tasks/pyproject.toml @@ -0,0 +1,29 @@ +[project] +name = "scale-vero-tasks" +version = "0.2.0" +description = "Narrow Python task protocol and runner for VeRO evaluations." +readme = "README.md" +authors = [ + { name = "Varun Ursekar", email = "oss@scale.com" } +] +license = { text = "MIT" } +requires-python = ">=3.11" +dependencies = [ + "pydantic>=2.11.7", +] + +[project.scripts] +vero-task = "vero_tasks.runner:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/vero_tasks"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] diff --git a/vero-tasks/src/vero_tasks/__init__.py b/vero-tasks/src/vero_tasks/__init__.py new file mode 100644 index 00000000..611c466c --- /dev/null +++ b/vero-tasks/src/vero_tasks/__init__.py @@ -0,0 +1,24 @@ +"""Narrow Python task protocol for VeRO evaluation harnesses.""" + +from vero_tasks.models import ( + RetryPolicy, + TaskAttemptError, + TaskContext, + TaskOutput, + TaskParameters, + TaskResult, + TaskT, +) +from vero_tasks.task import TaskDefinition, create_task + +__all__ = [ + "RetryPolicy", + "TaskAttemptError", + "TaskContext", + "TaskDefinition", + "TaskOutput", + "TaskParameters", + "TaskResult", + "TaskT", + "create_task", +] diff --git a/vero-tasks/src/vero_tasks/models.py b/vero-tasks/src/vero_tasks/models.py new file mode 100644 index 00000000..9472de5e --- /dev/null +++ b/vero-tasks/src/vero_tasks/models.py @@ -0,0 +1,178 @@ +"""Provider-neutral task inputs, outputs, and execution context.""" + +from __future__ import annotations + +import asyncio +import re +import traceback +from dataclasses import dataclass, field +from typing import Any, Literal, Sequence, TypeVar + +from pydantic import BaseModel, ConfigDict, Field, JsonValue, model_validator + +TaskT = TypeVar("TaskT") +ParametersT = TypeVar("ParametersT", bound="TaskParameters") + + +class TaskParameters(BaseModel): + """Strict base class for typed task-specific parameters.""" + + model_config = ConfigDict(extra="forbid") + + +class RetryPolicy(BaseModel): + """Per-case retry policy from VeRO's backend-neutral request.""" + + model_config = ConfigDict(extra="forbid") + + max_attempts: int = Field(default=3, ge=1) + initial_delay_seconds: float = Field(default=4.0, ge=0.0) + maximum_delay_seconds: float = Field(default=120.0, ge=0.0) + multiplier: float = Field(default=2.0, ge=1.0) + retry_on_timeout: bool = True + retry_exception_names: list[str] = Field( + default_factory=lambda: [ + "openai.RateLimitError", + "anthropic.RateLimitError", + ] + ) + retry_status_codes: list[int] = Field(default_factory=lambda: [429, 503, 529]) + retry_message_patterns: list[str] = Field( + default_factory=lambda: ["rate limit", "too many requests"] + ) + + @model_validator(mode="after") + def validate_policy(self) -> RetryPolicy: + if self.maximum_delay_seconds < self.initial_delay_seconds: + raise ValueError("maximum retry delay cannot be less than initial delay") + if any(not value.strip() for value in self.retry_exception_names): + raise ValueError("retry exception names must not be empty") + if len(set(self.retry_exception_names)) != len(self.retry_exception_names): + raise ValueError("retry exception names must be unique") + if any(value < 100 or value > 599 for value in self.retry_status_codes): + raise ValueError("retry status codes must be between 100 and 599") + if len(set(self.retry_status_codes)) != len(self.retry_status_codes): + raise ValueError("retry status codes must be unique") + for pattern in self.retry_message_patterns: + if not pattern.strip(): + raise ValueError("retry message patterns must not be empty") + try: + re.compile(pattern) + except re.error as error: + raise ValueError( + f"invalid retry message pattern {pattern!r}: {error}" + ) from error + return self + + def should_retry(self, error: Exception) -> bool: + if self.retry_on_timeout and isinstance( + error, (TimeoutError, asyncio.TimeoutError) + ): + return True + exception_names = { + name + for error_type in type(error).__mro__ + for name in ( + error_type.__name__, + f"{error_type.__module__}.{error_type.__qualname__}", + ) + } + if any(name in exception_names for name in self.retry_exception_names): + return True + status_code = getattr(error, "status_code", getattr(error, "status", None)) + if status_code in self.retry_status_codes: + return True + message = str(error) + return any( + re.search(pattern, message, flags=re.IGNORECASE) + for pattern in self.retry_message_patterns + ) + + def delay_after(self, attempt: int) -> float: + return min( + self.maximum_delay_seconds, + self.initial_delay_seconds * self.multiplier ** (attempt - 1), + ) + + +class TaskAttemptError(BaseModel): + """One failed inference or evaluation attempt.""" + + model_config = ConfigDict(extra="forbid") + + message: str + phase: Literal["inference", "evaluation"] + attempt: int = Field(ge=1) + retryable: bool + terminal: bool + traceback: str | None = None + + +class TaskContext(BaseModel): + """Evaluation context visible to task inference and scoring functions.""" + + model_config = ConfigDict(extra="forbid") + + parameters: dict[str, JsonValue] = Field(default_factory=dict) + max_concurrency: int = Field(default=100, ge=1) + case_timeout_seconds: float = Field(default=180.0, gt=0.0) + retry: RetryPolicy = Field(default_factory=RetryPolicy) + seed: int | None = None + + @property + def task_params(self) -> dict[str, JsonValue]: + return self.parameters + + def parse_task_params(self, model: type[ParametersT]) -> ParametersT: + return model.model_validate(self.parameters) + + +@dataclass +class TaskOutput: + """In-process output of inference for one evaluation case.""" + + output: Any = None + error: Exception | None = None + execution_trace: Sequence[Any] | None = None + attempt_errors: list[TaskAttemptError] = field(default_factory=list) + + +class TaskResult(BaseModel): + """Serializable scoring result for one evaluation case.""" + + model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True) + + output: Any = None + error: str | None = None + execution_trace: Sequence[Any] | None = None + score: float | None = None + feedback: str | None = None + metrics: dict[str, float] = Field(default_factory=dict) + eval_error: str | None = None + evaluation_trace: Sequence[Any] | None = None + error_traceback: str | None = None + evaluation_error_traceback: str | None = None + attempt_errors: list[TaskAttemptError] = Field(default_factory=list) + + @classmethod + def from_task_output( + cls, + task_output: TaskOutput, + **values: Any, + ) -> TaskResult: + if task_output.error is not None: + values["error"] = str(task_output.error) + values["error_traceback"] = "".join( + traceback.format_exception( + type(task_output.error), + task_output.error, + task_output.error.__traceback__, + ) + ) + values["output"] = task_output.output + values["execution_trace"] = task_output.execution_trace + values.setdefault("attempt_errors", list(task_output.attempt_errors)) + return cls(**values) + + def is_error(self) -> bool: + return self.error is not None or self.eval_error is not None diff --git a/vero-tasks/src/vero_tasks/runner.py b/vero-tasks/src/vero_tasks/runner.py new file mode 100644 index 00000000..0666d75a --- /dev/null +++ b/vero-tasks/src/vero_tasks/runner.py @@ -0,0 +1,220 @@ +"""Run a registered Python task through VeRO's schema-v1 command contract.""" + +from __future__ import annotations + +import argparse +import asyncio +import importlib +import json +from collections import defaultdict +from pathlib import Path +from typing import Any + +from vero_tasks.models import RetryPolicy, TaskContext, TaskResult +from vero_tasks.task import TaskDefinition + + +def _json_value(value: Any) -> Any: + return json.loads(json.dumps(value, default=str)) + + +def _load_cases(path: Path) -> list[Any]: + content = path.read_text(encoding="utf-8") + if path.suffix == ".jsonl": + return [json.loads(line) for line in content.splitlines() if line.strip()] + try: + value = json.loads(content) + except json.JSONDecodeError as error: + lines = [line for line in content.splitlines() if line.strip()] + if len(lines) < 2: + raise error + value = [json.loads(line) for line in lines] + if isinstance(value, dict) and "cases" in value: + value = value["cases"] + if not isinstance(value, list): + raise ValueError( + "case file must contain a JSON list or an object with a cases list" + ) + return value + + +def _case_id(case: Any, index: int) -> str: + if isinstance(case, dict) and case.get("id") is not None: + return str(case["id"]) + return str(index) + + +def _select(cases: list[Any], selection: dict[str, Any]) -> list[tuple[str, Any]]: + indexed = [(_case_id(case, index), case) for index, case in enumerate(cases)] + case_ids = [case_id for case_id, _ in indexed] + if len(case_ids) != len(set(case_ids)): + raise ValueError("case IDs must be unique") + kind = selection.get("kind", "all") + if kind == "all": + return indexed + if kind == "range": + return indexed[selection.get("start", 0) : selection["stop"]] + if kind == "ids": + by_id = dict(indexed) + missing = [case_id for case_id in selection["ids"] if case_id not in by_id] + if missing: + raise ValueError(f"unknown case IDs: {missing}") + return [(case_id, by_id[case_id]) for case_id in selection["ids"]] + raise ValueError(f"unknown case selection kind: {kind!r}") + + +def _errors(result: TaskResult) -> list[dict[str, Any]]: + errors = [ + { + "message": error.message, + "code": f"task_{error.phase}_error", + "phase": error.phase, + "attempt": error.attempt, + "retryable": error.retryable, + "terminal": error.terminal, + "metadata": ( + {"traceback": error.traceback} if error.traceback is not None else {} + ), + } + for error in result.attempt_errors + ] + terminal_phases = {error.phase for error in result.attempt_errors if error.terminal} + if result.error is not None and "inference" not in terminal_phases: + metadata = ( + {"traceback": result.error_traceback} + if result.error_traceback is not None + else {} + ) + errors.append( + { + "message": result.error, + "code": "task_inference_error", + "phase": "inference", + "terminal": True, + "metadata": metadata, + } + ) + if result.eval_error is not None and "evaluation" not in terminal_phases: + metadata = ( + {"traceback": result.evaluation_error_traceback} + if result.evaluation_error_traceback is not None + else {} + ) + errors.append( + { + "message": result.eval_error, + "code": "task_evaluation_error", + "phase": "evaluation", + "terminal": True, + "metadata": metadata, + } + ) + return errors + + +def _report( + selected: list[tuple[str, Any]], + results: list[TaskResult], +) -> dict[str, Any]: + cases = [] + totals: dict[str, list[float]] = defaultdict(list) + error_count = 0 + for (case_id, case), result in zip(selected, results): + metrics = dict(result.metrics) + if result.score is not None: + metrics.setdefault("score", result.score) + errors = _errors(result) + terminal_errors = [error for error in errors if error["terminal"]] + if terminal_errors: + error_count += 1 + else: + for name, value in metrics.items(): + totals[name].append(value) + cases.append( + { + "case_id": case_id, + "status": "error" if terminal_errors else "success", + "metrics": metrics, + "input": _json_value(case), + "output": _json_value(result.output), + "feedback": result.feedback, + "errors": errors, + "execution_trace": ( + _json_value(list(result.execution_trace)) + if result.execution_trace is not None + else None + ), + "evaluation_trace": ( + _json_value(list(result.evaluation_trace)) + if result.evaluation_trace is not None + else None + ), + } + ) + metrics = { + name: sum(values) / len(values) for name, values in totals.items() if values + } + metrics["error_rate"] = error_count / len(results) if results else 0.0 + return { + "schema_version": 1, + "status": "failed" if results and error_count == len(results) else "success", + "metrics": metrics, + "cases": cases, + } + + +async def run_task( + *, + module: str, + task_name: str, + cases_path: Path, + request_path: Path, + report_path: Path, +) -> None: + request_envelope = json.loads(request_path.read_text(encoding="utf-8")) + if request_envelope.get("schema_version") != 1: + raise ValueError("unsupported command evaluation request schema") + request = request_envelope["request"] + importlib.import_module(module) + task = TaskDefinition.resolve(task_name) + selected = _select( + _load_cases(cases_path), + request["evaluation_set"]["selection"], + ) + limits = request["limits"] + context = TaskContext( + parameters=request.get("parameters", {}), + max_concurrency=limits["max_concurrency"], + case_timeout_seconds=limits["case_timeout_seconds"], + retry=RetryPolicy.model_validate(limits.get("retry", {})), + seed=request.get("seed"), + ) + results = await task.run([case for _, case in selected], context) + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text( + json.dumps(_report(selected, results), ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--module", required=True) + parser.add_argument("--task", required=True) + parser.add_argument("--cases", type=Path, required=True) + parser.add_argument("--request", type=Path, required=True) + parser.add_argument("--report", type=Path, required=True) + arguments = parser.parse_args() + asyncio.run( + run_task( + module=arguments.module, + task_name=arguments.task, + cases_path=arguments.cases, + request_path=arguments.request, + report_path=arguments.report, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/vero-tasks/src/vero_tasks/task.py b/vero-tasks/src/vero_tasks/task.py new file mode 100644 index 00000000..df9cfbaf --- /dev/null +++ b/vero-tasks/src/vero_tasks/task.py @@ -0,0 +1,334 @@ +"""Decorator-based task definition without evaluation persistence concerns.""" + +from __future__ import annotations + +import asyncio +import inspect +import os +import traceback +from collections.abc import Awaitable, Callable, Sequence +from dataclasses import dataclass +from typing import Any + +from vero_tasks.models import ( + TaskAttemptError, + TaskContext, + TaskOutput, + TaskResult, + TaskT, +) + + +async def _resolve(value: Any) -> Any: + return await value if inspect.isawaitable(value) else value + + +@dataclass +class _AttemptOutcome: + value: Any + errors: list[TaskAttemptError] + + +def _merge_attempt_errors( + *groups: Sequence[TaskAttemptError], +) -> list[TaskAttemptError]: + merged: list[TaskAttemptError] = [] + for group in groups: + for error in group: + if error not in merged: + merged.append(error) + return merged + + +class TaskDefinition: + """Inference and evaluation functions registered under one task name.""" + + _registry: dict[str, TaskDefinition] = {} + + def __init__( + self, + name: str, + *, + register: bool = True, + task_parameters_type: type | None = None, + required_env_vars: Sequence[str] | None = None, + ): + if not name.strip(): + raise ValueError("task name must not be empty") + if register and name in self._registry: + raise ValueError(f"task {name!r} is already registered") + self.name = name + self.task_parameters_type = task_parameters_type + self.required_env_vars = tuple(required_env_vars or ()) + self._single: dict[str, Callable[..., Any]] = {} + self._batch: dict[str, Callable[..., Any]] = {} + if register: + self._registry[name] = self + + def _decorator(self, kind: str, *, batch: bool) -> Callable: + expected = { + ("inference", False): 2, + ("inference", True): 2, + ("evaluation", False): 3, + ("evaluation", True): 3, + ("load_data", False): 1, + }[(kind, batch)] + + def register(function: Callable[..., Any]) -> Callable[..., Any]: + parameters = inspect.signature(function).parameters + if len(parameters) != expected: + raise TypeError( + f"{kind} function {function.__name__!r} must accept " + f"{expected} parameters, got {len(parameters)}" + ) + functions = self._batch if batch else self._single + if kind in functions: + raise ValueError(f"{kind} function is already registered") + functions[kind] = function + return function + + return register + + def inference(self, *, batch: bool = False) -> Callable: + return self._decorator("inference", batch=batch) + + def evaluation(self, *, batch: bool = False) -> Callable: + return self._decorator("evaluation", batch=batch) + + def load_data(self) -> Callable: + return self._decorator("load_data", batch=False) + + def __call__(self, name: str, *, batch: bool = False) -> Callable: + aliases = { + "run_inference": "inference", + "run_evaluation": "evaluation", + "load_task_data": "load_data", + "create_task": "load_data", + } + try: + kind = aliases[name] + except KeyError as error: + raise ValueError(f"unknown task function kind: {name!r}") from error + return self._decorator(kind, batch=batch) + + def get(self, kind: str, *, batch: bool = False) -> Callable[..., Any] | None: + return (self._batch if batch else self._single).get(kind) + + @classmethod + def resolve(cls, name: str) -> TaskDefinition: + try: + return cls._registry[name] + except KeyError as error: + raise KeyError( + f"task {name!r} is not registered; available: {sorted(cls._registry)}" + ) from error + + @classmethod + def clear_registry(cls) -> None: + cls._registry.clear() + + def _validate(self, context: TaskContext) -> None: + missing = [name for name in self.required_env_vars if not os.environ.get(name)] + if missing: + raise ValueError( + "missing required task environment variables: " + ", ".join(missing) + ) + if self.task_parameters_type is not None: + context.parse_task_params(self.task_parameters_type) + if self.get("inference") is None and self.get("inference", batch=True) is None: + raise RuntimeError("task has no inference function") + if ( + self.get("evaluation") is None + and self.get("evaluation", batch=True) is None + ): + raise RuntimeError("task has no evaluation function") + + @staticmethod + def _output( + value: Any, + attempt_errors: Sequence[TaskAttemptError] = (), + ) -> TaskOutput: + if isinstance(value, TaskOutput): + errors = _merge_attempt_errors(attempt_errors, value.attempt_errors) + if errors == value.attempt_errors: + return value + return TaskOutput( + output=value.output, + error=value.error, + execution_trace=value.execution_trace, + attempt_errors=errors, + ) + if isinstance(value, BaseException): + error = value if isinstance(value, Exception) else Exception(str(value)) + return TaskOutput(error=error, attempt_errors=list(attempt_errors)) + return TaskOutput(output=value, attempt_errors=list(attempt_errors)) + + @staticmethod + def _result( + output: TaskOutput, + value: Any, + attempt_errors: Sequence[TaskAttemptError] = (), + ) -> TaskResult: + if isinstance(value, TaskResult): + updates: dict[str, Any] = {} + if output.error is not None and value.error is None: + updates["error"] = str(output.error) + updates["error_traceback"] = "".join( + traceback.format_exception( + type(output.error), + output.error, + output.error.__traceback__, + ) + ) + if output.execution_trace is not None and value.execution_trace is None: + updates["execution_trace"] = output.execution_trace + errors = _merge_attempt_errors( + output.attempt_errors, + value.attempt_errors, + attempt_errors, + ) + if errors != value.attempt_errors: + updates["attempt_errors"] = errors + return value.model_copy(update=updates) if updates else value + if isinstance(value, BaseException): + return TaskResult.from_task_output( + output, + eval_error=str(value) or type(value).__name__, + evaluation_error_traceback="".join( + traceback.format_exception(type(value), value, value.__traceback__) + ), + attempt_errors=_merge_attempt_errors( + output.attempt_errors, + attempt_errors, + ), + ) + raise TypeError( + f"evaluation returned {type(value).__name__}, expected TaskResult" + ) + + async def _map( + self, + factories: Sequence[Callable[[], Awaitable[Any] | Any]], + context: TaskContext, + *, + phase: str, + ) -> list[_AttemptOutcome]: + semaphore = asyncio.Semaphore(context.max_concurrency) + + async def run( + factory: Callable[[], Awaitable[Any] | Any], + ) -> _AttemptOutcome: + errors: list[TaskAttemptError] = [] + for attempt in range(1, context.retry.max_attempts + 1): + try: + async with semaphore: + async with asyncio.timeout(context.case_timeout_seconds): + value = await _resolve(factory()) + return _AttemptOutcome(value=value, errors=errors) + except Exception as error: + retryable = context.retry.should_retry(error) + terminal = not retryable or attempt == context.retry.max_attempts + errors.append( + TaskAttemptError( + message=str(error) or type(error).__name__, + phase=phase, + attempt=attempt, + retryable=retryable, + terminal=terminal, + traceback="".join( + traceback.format_exception( + type(error), error, error.__traceback__ + ) + ), + ) + ) + if terminal: + return _AttemptOutcome(value=error, errors=errors) + delay = context.retry.delay_after(attempt) + if delay: + await asyncio.sleep(delay) + raise AssertionError("retry loop completed without an outcome") + + return list(await asyncio.gather(*(run(factory) for factory in factories))) + + async def run( + self, + cases: Sequence[TaskT] | None, + context: TaskContext, + ) -> list[TaskResult]: + self._validate(context) + if cases is None: + loader = self.get("load_data") + if loader is None: + raise ValueError( + "cases are required when the task has no load_data function" + ) + cases = list(await _resolve(loader(context))) + else: + cases = list(cases) + + batch_inference = self.get("inference", batch=True) + if batch_inference is not None: + raw_outputs = list(await _resolve(batch_inference(cases, context))) + else: + inference = self.get("inference") + assert inference is not None + inference_outcomes = await self._map( + [lambda case=case: inference(case, context) for case in cases], + context, + phase="inference", + ) + raw_outputs = [outcome.value for outcome in inference_outcomes] + if len(raw_outputs) != len(cases): + raise ValueError("inference result count does not match case count") + if batch_inference is not None: + outputs = [self._output(value) for value in raw_outputs] + else: + outputs = [ + self._output(outcome.value, outcome.errors) + for outcome in inference_outcomes + ] + + batch_evaluation = self.get("evaluation", batch=True) + if batch_evaluation is not None: + raw_results = list( + await _resolve(batch_evaluation(cases, outputs, context)) + ) + else: + evaluation = self.get("evaluation") + assert evaluation is not None + evaluation_outcomes = await self._map( + [ + lambda case=case, output=output: evaluation(case, output, context) + for case, output in zip(cases, outputs) + ], + context, + phase="evaluation", + ) + raw_results = [outcome.value for outcome in evaluation_outcomes] + if len(raw_results) != len(cases): + raise ValueError("evaluation result count does not match case count") + if batch_evaluation is not None: + return [ + self._result(output, result) + for output, result in zip(outputs, raw_results) + ] + return [ + self._result(output, outcome.value, outcome.errors) + for output, outcome in zip(outputs, evaluation_outcomes) + ] + + +def create_task( + name: str, + *, + register: bool = True, + task_parameters_type: type | None = None, + required_env_vars: Sequence[str] | None = None, +) -> TaskDefinition: + return TaskDefinition( + name, + register=register, + task_parameters_type=task_parameters_type, + required_env_vars=required_env_vars, + ) diff --git a/vero-tasks/tests/test_runner.py b/vero-tasks/tests/test_runner.py new file mode 100644 index 00000000..b9efc16f --- /dev/null +++ b/vero-tasks/tests/test_runner.py @@ -0,0 +1,200 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from vero_tasks import TaskAttemptError, TaskResult +from vero_tasks.runner import _errors, _load_cases, run_task +from vero_tasks.task import TaskDefinition + + +def test_load_cases_detects_staged_jsonl_without_a_suffix(tmp_path: Path): + cases_path = tmp_path / "cases" + cases_path.write_text('{"id": "a"}\n{"id": "b"}\n', encoding="utf-8") + + assert _load_cases(cases_path) == [{"id": "a"}, {"id": "b"}] + + +def test_errors_preserve_transient_history_and_explicit_terminal_errors(): + errors = _errors( + TaskResult( + eval_error="invalid score", + attempt_errors=[ + TaskAttemptError( + message="rate limit", + phase="inference", + attempt=1, + retryable=True, + terminal=False, + ) + ], + ) + ) + + assert [(error["phase"], error["terminal"]) for error in errors] == [ + ("inference", False), + ("evaluation", True), + ] + + +@pytest.mark.asyncio +async def test_runner_writes_canonical_report(tmp_path: Path, monkeypatch): + module = tmp_path / "example_tasks.py" + module.write_text( + """ +from vero_tasks import TaskOutput, TaskResult, create_task + +task = create_task("double") + +@task.inference() +async def infer(case, context): + return TaskOutput(output=case["value"] * 2) + +@task.evaluation() +async def evaluate(case, output, context): + return TaskResult.from_task_output( + output, + score=float(output.output == case["expected"]), + metrics={"distance": abs(output.output - case["expected"])}, + ) +""", + encoding="utf-8", + ) + monkeypatch.syspath_prepend(str(tmp_path)) + cases_path = tmp_path / "cases.json" + cases_path.write_text( + json.dumps( + [ + {"id": "a", "value": 2, "expected": 4}, + {"id": "b", "value": 3, "expected": 7}, + {"id": "c", "value": 4, "expected": 8}, + ] + ), + encoding="utf-8", + ) + request_path = tmp_path / "request.json" + request_path.write_text( + json.dumps( + { + "schema_version": 1, + "request": { + "evaluation_set": { + "name": "test", + "partition": None, + "selection": {"kind": "ids", "ids": ["c", "a"]}, + }, + "parameters": {}, + "limits": { + "max_concurrency": 2, + "case_timeout_seconds": 1.0, + }, + "seed": 3, + }, + } + ), + encoding="utf-8", + ) + report_path = tmp_path / "report.json" + + await run_task( + module="example_tasks", + task_name="double", + cases_path=cases_path, + request_path=request_path, + report_path=report_path, + ) + + report = json.loads(report_path.read_text(encoding="utf-8")) + assert report["schema_version"] == 1 + assert report["status"] == "success" + assert report["metrics"] == { + "distance": 0.0, + "score": 1.0, + "error_rate": 0.0, + } + assert [case["case_id"] for case in report["cases"]] == ["c", "a"] + TaskDefinition.clear_registry() + + +@pytest.mark.asyncio +async def test_runner_applies_retry_policy_and_reports_attempts( + tmp_path: Path, monkeypatch +): + module = tmp_path / "retry_tasks.py" + module.write_text( + """ +from vero_tasks import TaskOutput, TaskResult, create_task + +task = create_task("retry") +attempts = 0 + +@task.inference() +async def infer(case, context): + global attempts + attempts += 1 + if attempts == 1: + raise RuntimeError("rate limit") + return TaskOutput(output=case["value"]) + +@task.evaluation() +async def evaluate(case, output, context): + return TaskResult.from_task_output(output, score=1.0) +""", + encoding="utf-8", + ) + monkeypatch.syspath_prepend(str(tmp_path)) + cases_path = tmp_path / "cases.json" + cases_path.write_text(json.dumps([{"id": "a", "value": 2}]), encoding="utf-8") + request_path = tmp_path / "request.json" + request_path.write_text( + json.dumps( + { + "schema_version": 1, + "request": { + "evaluation_set": { + "name": "test", + "partition": None, + "selection": {"kind": "all"}, + }, + "parameters": {}, + "limits": { + "max_concurrency": 1, + "case_timeout_seconds": 1.0, + "retry": { + "max_attempts": 2, + "initial_delay_seconds": 0, + }, + }, + }, + } + ), + encoding="utf-8", + ) + report_path = tmp_path / "report.json" + + await run_task( + module="retry_tasks", + task_name="retry", + cases_path=cases_path, + request_path=request_path, + report_path=report_path, + ) + + report = json.loads(report_path.read_text(encoding="utf-8")) + assert report["status"] == "success" + assert report["metrics"]["error_rate"] == 0.0 + assert report["cases"][0]["status"] == "success" + assert report["cases"][0]["errors"][0] == { + "message": "rate limit", + "code": "task_inference_error", + "phase": "inference", + "attempt": 1, + "retryable": True, + "terminal": False, + "metadata": { + "traceback": report["cases"][0]["errors"][0]["metadata"]["traceback"] + }, + } + TaskDefinition.clear_registry() diff --git a/vero-tasks/tests/test_task.py b/vero-tasks/tests/test_task.py new file mode 100644 index 00000000..1a952680 --- /dev/null +++ b/vero-tasks/tests/test_task.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +import asyncio + +import pytest + +from vero_tasks import ( + RetryPolicy, + TaskContext, + TaskOutput, + TaskParameters, + TaskResult, + create_task, +) + + +class Parameters(TaskParameters): + multiplier: float = 1.0 + + +@pytest.mark.asyncio +async def test_task_runs_cases_with_typed_parameters_and_concurrency(): + task = create_task("score", register=False, task_parameters_type=Parameters) + active = 0 + maximum_active = 0 + + @task.inference() + async def infer(case, context): + nonlocal active, maximum_active + active += 1 + maximum_active = max(maximum_active, active) + await asyncio.sleep(0) + active -= 1 + return TaskOutput(output=case["value"] * 2) + + @task.evaluation() + async def evaluate(case, output, context): + parameters = context.parse_task_params(Parameters) + return TaskResult.from_task_output( + output, + score=output.output * parameters.multiplier, + metrics={"raw": output.output}, + ) + + results = await task.run( + [{"value": 1}, {"value": 2}, {"value": 3}], + TaskContext(parameters={"multiplier": 0.5}, max_concurrency=2), + ) + + assert [result.score for result in results] == [1.0, 2.0, 3.0] + assert maximum_active == 2 + + +@pytest.mark.asyncio +async def test_task_captures_inference_and_evaluation_errors(): + task = create_task("errors", register=False) + + @task("run_inference") + async def infer(case, context): + if case == "inference": + raise RuntimeError("inference failed") + return TaskOutput(output=case) + + @task("run_evaluation") + async def evaluate(case, output, context): + if case == "evaluation": + raise RuntimeError("evaluation failed") + return TaskResult.from_task_output(output, score=1.0) + + results = await task.run( + ["inference", "evaluation", "ok"], + TaskContext(), + ) + + assert results[0].error == "inference failed" + assert results[0].error_traceback is not None + assert results[1].eval_error == "evaluation failed" + assert results[1].evaluation_error_traceback is not None + assert results[2].score == 1.0 + + +@pytest.mark.asyncio +async def test_batch_evaluation_preserves_inference_errors(): + task = create_task("batch-errors", register=False) + + @task.inference() + async def infer(case, context): + if case == "broken": + raise RuntimeError("inference failed") + return TaskOutput(output=case) + + @task.evaluation(batch=True) + async def evaluate(cases, outputs, context): + return [TaskResult(score=1.0) for _ in cases] + + results = await task.run(["broken", "ok"], TaskContext()) + + assert results[0].error == "inference failed" + assert results[1].error is None + + +@pytest.mark.asyncio +async def test_task_retries_transient_inference_errors_and_preserves_history(): + task = create_task("retry-inference", register=False) + attempts = 0 + + @task.inference() + async def infer(case, context): + nonlocal attempts + attempts += 1 + if attempts < 3: + raise RuntimeError("rate limit from provider") + return TaskOutput(output=case) + + @task.evaluation() + async def evaluate(case, output, context): + return TaskResult.from_task_output(output, score=1.0) + + results = await task.run( + ["ok"], + TaskContext( + retry=RetryPolicy( + max_attempts=3, + initial_delay_seconds=0, + ) + ), + ) + + assert attempts == 3 + assert results[0].score == 1.0 + assert results[0].is_error() is False + assert [error.attempt for error in results[0].attempt_errors] == [1, 2] + assert all(not error.terminal for error in results[0].attempt_errors) + + +@pytest.mark.asyncio +async def test_task_retries_timeouts_and_evaluation_errors(): + task = create_task("retry-timeout-and-evaluation", register=False) + inference_attempts = 0 + evaluation_attempts = 0 + + @task.inference() + async def infer(case, context): + nonlocal inference_attempts + inference_attempts += 1 + if inference_attempts == 1: + await asyncio.sleep(0.02) + return TaskOutput(output=case) + + @task.evaluation() + async def evaluate(case, output, context): + nonlocal evaluation_attempts + evaluation_attempts += 1 + if evaluation_attempts == 1: + raise RuntimeError("too many requests") + return TaskResult.from_task_output(output, score=1.0) + + results = await task.run( + ["ok"], + TaskContext( + case_timeout_seconds=0.01, + retry=RetryPolicy(max_attempts=2, initial_delay_seconds=0), + ), + ) + + assert inference_attempts == 2 + assert evaluation_attempts == 2 + assert results[0].score == 1.0 + assert [ + (error.phase, error.attempt, error.terminal) + for error in results[0].attempt_errors + ] == [ + ("inference", 1, False), + ("evaluation", 1, False), + ] + + +@pytest.mark.asyncio +async def test_task_does_not_retry_non_transient_errors(): + task = create_task("no-retry", register=False) + attempts = 0 + + @task.inference() + async def infer(case, context): + nonlocal attempts + attempts += 1 + raise ValueError("invalid candidate") + + @task.evaluation() + async def evaluate(case, output, context): + return TaskResult.from_task_output(output) + + results = await task.run( + ["broken"], + TaskContext(retry=RetryPolicy(initial_delay_seconds=0)), + ) + + assert attempts == 1 + assert results[0].error == "invalid candidate" + assert len(results[0].attempt_errors) == 1 + assert results[0].attempt_errors[0].retryable is False + assert results[0].attempt_errors[0].terminal is True diff --git a/vero/.gitignore b/vero/.gitignore new file mode 100644 index 00000000..4b6c6a55 --- /dev/null +++ b/vero/.gitignore @@ -0,0 +1,40 @@ +# Environment +.env +.env.* +.venv/ +venv/ + +# Python +__pycache__/ +*.pyc +*.pyo +*.egg-info/ +dist/ +build/ +!src/vero/harbor/build/ +!src/vero/harbor/build/*.py +!src/vero/harbor/build/templates/ +!src/vero/harbor/build/templates/* + +#harbor +jobs/ + +# Testing +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +htmlcov/ +.coverage + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# Jupyter +**/.ipynb_checkpoints + +# OS +.DS_Store +Thumbs.db diff --git a/vero/README.md b/vero/README.md new file mode 100644 index 00000000..db12b720 --- /dev/null +++ b/vero/README.md @@ -0,0 +1,850 @@ +# VeRO: a harness for agents to optimize programs, text, and agents + +[![Paper](https://img.shields.io/badge/arXiv-2602.22480-b31b1b.svg)](https://arxiv.org/abs/2602.22480) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) +[![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/) + +VeRO gives an optimizer something to edit, a controlled way to evaluate it, and +durable memory of everything it tried. The target is anything you can put under +Git and score — a **program** (one function to a whole repo), **text** (a prompt, +spec, or config), or an **agent** (its scaffold, tools, and prompts). + +VeRO runs the same **version → evaluate → select** loop over all of them. *Where* +each candidate is produced and contained is a swappable backend — and +**[Harbor](#optimize-an-agent-with-harbor) is the recommended one**: it runs the +whole coding agent inside a reproducible, credential-isolated container and scores +it against a trusted evaluation sidecar. That's the right default for optimizing +agents and for any untrusted or reproducibility-critical run. Lighter local +backends (a language-neutral command harness, Python tasks) are there for trusted +work that doesn't need containment. + +```mermaid +flowchart LR + S["Strategy
proposes ideas"] --> P["Producers edit
isolated candidate
workspaces"] + P --> E["Evaluation backend
scores each version"] + E --> Sel["Selection keeps the
best feasible candidate"] + Sel -->|"next round"| S +``` + +## Highlights + +| | | +| --- | --- | +| **Harbor-first containment** | run the whole agent in a reproducible container against a trusted eval sidecar, behind a credential-isolating inference gateway — the recommended backend | +| **Any target** | a program (one function up to a whole repo), text (prompts, specs, configs), or an agent | +| **Any producer** | a coding agent (any provider via LiteLLM), an external command, or a custom strategy | +| **Durable & inspectable** | every candidate is versioned and re-selectable; tool calls and evaluations stream to an event log | +| **Population search** | `EvolutionaryStrategy` fans out N offspring per round with tournament selection | + +## Backends + +VeRO separates *what* it optimizes from *how* candidates are produced and scored. +Start with Harbor; drop to a lighter backend when you don't need containment. + +| Backend | Best for | Entry point | +| --- | --- | --- | +| **[Harbor](#optimize-an-agent-with-harbor)** — recommended | optimizing agents; untrusted or reproducibility-critical runs. Runs the whole agent in a container against a trusted eval sidecar, with a credential-isolating inference gateway. | `vero harbor run` | +| [Command harness](#optimize-a-program-with-a-command-harness) | any language; a trusted local evaluator you drive over versioned JSON | `vero run` | +| [Python tasks](#python-benchmark-tasks) | Python evaluators via `scale-vero-tasks`, no JSON contract to write | `PythonTaskBackend` | +| [Native in-process](#python-api) | fast trusted local runs; a coding agent whose edits execute in a host-bound sandbox | `vero optimize` | + +**Harbor in one command** — compile a build file into a contained task and run an +agent against it, with secrets kept off the command line: + +```bash +vero harbor run \ + --config build.yaml \ + --agent claude-code --model claude-sonnet-4-6 \ + --env-file secrets.env +``` + +See [Optimize an agent with Harbor](#optimize-an-agent-with-harbor) for the build +file, the inference gateway, and how disclosure and budgets are enforced. + +> **Just want to kick the tires first?** The [Quickstart](#quickstart) C-matmul +> example is deterministic and needs no model credentials. + +## Quickstart + +Install VeRO, then try the checked-in C matrix multiplication example. Its +editable target contains only C; a trusted external harness compiles it, checks +correctness, and measures latency. + +```bash +# Install VeRO from this checkout. Do NOT `pip install scale-vero` from public +# PyPI — that name is currently an unrelated placeholder, not VeRO. +uv sync --extra optimize + +cd examples/c-matmul/target +git init -b main +git add . +git -c user.name=vero -c user.email=vero@localhost commit -m baseline +cd .. + +uv run vero evaluate --config vero.toml +uv run vero run --config vero.toml +``` + +The example is deterministic and needs no model credentials. VeRO evaluates the +baseline, gives an isolated worktree to the configured producer, evaluates its +commit, selects the faster feasible result, and leaves the original target +untouched. See [`examples/c-matmul`](examples/c-matmul/) for the complete target, +harness, optimizer, and config. + +For a more demanding coding-agent run, use the +[`examples/circle-packing`](examples/circle-packing/) benchmark adapted from +ShinkaEvolve. It asks an agent to improve a 26-circle packing, exposes exact +geometric diagnostics and layout artifacts after each authorized evaluation, +and re-evaluates the selected candidate through a hidden final evaluation. + +## Optimize an agent with Harbor + +Harbor is VeRO's recommended backend for optimizing agents. Each candidate runs +as a **contained agent**, scored by a **trusted sidecar**, with provider +credentials held by a **separate gateway** the agent never sees: + +```mermaid +flowchart LR + A["Optimizer container
agent edits the candidate
+ calls the CLI"] + S["Eval sidecar (trusted)
owns cases, scoring,
budgets, finalization"] + G["Inference gateway (trusted)
holds the upstream key,
issues scoped tokens"] + A -->|"vero harbor eval"| S + A -->|"scoped model calls"| G + G --> U["Provider / litellm proxy"] +``` + +Two steps: **build** a contained task from a YAML file, then **run** an agent +against it. + +### 1. The build file + +`build.yaml` declares the target repo, the task source and case partitions, what +the agent may evaluate, and the inference gateway: + +```yaml +name: example/optimize-agent +agent_repo: ../my-program # editable target (a Git repo) +task_source: example/terminal-benchmark@1.0 # pinned registry tasks +agent_import_path: my_program.agent:Agent +harbor_requirement: harbor[modal]==0.18.0 +environment_name: modal # where each nested eval runs +secrets: [MODAL_TOKEN_ID, MODAL_TOKEN_SECRET] # sidecar-only; stripped from the agent + +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: # the optimizer's scope + allowed_models: [gpt-5] + evaluation: # the target's scope + allowed_models: [gpt-5-mini] + max_requests: 5000 + max_tokens: 20000000 + +partitions: + validation: [example/task-a, example/task-b, example/task-c] + test: [example/task-hidden] + +agent_access: # what the agent may evaluate, and how much it sees + - partition: validation + disclosure: aggregate # full | aggregate | none + total_runs: 10 + total_cases: 50 + +selection_partition: validation # candidates are ranked here +targets: + - partition: test # held-out; trusted verifier scores it after selection + reward_key: reward +``` + +| Knob | What it controls | +| --- | --- | +| `agent_access[].disclosure` | how much of an evaluation the agent sees: `full` (per-case traces), `aggregate` (scores only), `none` | +| `agent_access[].total_runs` / `total_cases` | the agent's **cumulative** eval budget — it may spend it all in one run | +| `selection_partition` | the fixed set every candidate is ranked on | +| `targets` | held-out partitions scored after selection; never enter agent context | +| `inference_gateway.{producer,evaluation}.allowed_models` | the models each scope may call (optimizer vs. target) | + +### 2. Run it + +`vero harbor run` compiles the build file and launches the agent, keeping secrets +off the command line via `--env-file`: + +```bash +vero harbor run \ + --config build.yaml \ + --agent claude-code --model claude-sonnet-4-6 \ + --environment modal \ + --env-file secrets.env +``` + +`--agent` picks the coding agent (`claude-code`, `codex`, …) and `--model` sets +both its model and its producer-scope allow-list. `vero harbor build --config +build.yaml --output task` compiles without running, for inspection. + +> **Use `vero harbor run`, not `harbor run` directly.** For a gateway-enabled +> build the VeRO launcher renames provider credentials for the gateway before +> Harbor constructs the agent; a raw `harbor run` would let the agent adapter +> read the upstream key from its own host process first. + +Inside the container the agent evaluates candidates with `vero harbor eval +--detach`, then `eval-status` / `eval-result` / `status` (via `VERO_EVAL_URL`). +Detached evaluations are **durable jobs** — the candidate version is captured +before the command returns, so ending the agent process can't lose or race a +running measurement. + +### How the boundaries hold + +- **The sidecar is the only evaluator.** It owns the cases, scoring, budget + ledger, and candidate selection. The optimizer container gets only the editable + baseline, the agent CLI, and approved result projections; the `test` partition + and task source live only in the sidecar image. +- **The gateway holds the provider key.** A third trusted service alone receives + the upstream credential, then forwards any inference endpoint to your upstream + proxy while restricting each scope to its configured models and metering usage. + The optimizer gets a producer-scoped token (uncapped by default); each + candidate evaluation gets an independently budgeted token on a URL attributed + to its evaluation ID. +- **Disclosure is graded.** `full` exposes per-case traces for the agent to + inspect, `aggregate` returns scores only, and held-out `targets` are + unreachable from agent context. Aggregate validation is optimization data, not + a privacy guarantee — arbitrary subsets are allowed; the final target is not. + +### Artifacts + +The verifier shares the environment, so it exports the full session before +teardown: a successful run leaves `session.tar.gz` (+ `.sha256`), +`experiment.html`, `status.json`, and `finalization.json` in the verifier +artifacts. The archive holds the bare candidate Git repo, canonical evaluation +records, budget state, the finalization result, and the producer trajectory — +re-render it any time with `vero report`. Export failure fails the run rather +than discarding the only durable copy. + +> **Security boundary.** The inference gateway protects *provider credentials*, +> not the OS process. The pinned Harbor overlay and sidecar keep budget and +> scoring trusted, but candidate code still runs inside the nested Harbor +> process, and a Modal controller still needs Modal auth. When target programs +> are **adversarial**, run them in a separate sandbox that can't reach verifier +> data or credentials. + +
+Operational details — timeouts, finalization draining, budgets + +- **Timeouts.** `case_timeout_seconds` is VeRO's absolute limit for the agent + phase. Because Harbor applies task timeouts as a multiplier, set + `task_agent_timeout_seconds` to the pinned task's declared agent timeout; the + compiler passes their ratio (e.g. `180 / 600 = 0.3`) as Harbor's multiplier, + leaving verifier and setup timeouts unchanged. +- **Finalization** closes the agent's eval entrance and waits for every + already-accepted request (including ones launched from a background shell) + before selecting, so ending the optimizer can't race a running validation. + After `evaluation_drain_timeout_seconds` (default `timeout_seconds`) it cancels + and refunds the unfinished eval. Trusted verifier evals remain available after + the entrance closes. +- **Budgets** are cumulative and split between agent- and system-triggered evals. + Cancelled runs and execution failures are refunded; completed failure reports + stay charged as measurements. Whole-run infrastructure failures are retried and + surfaced separately, so an outage fails the session rather than becoming a + candidate regression. +- **Request vs. token limits.** Request limits are exact; a token limit stops the + *next* request after reported usage crosses it, so in-flight concurrent + responses can overshoot slightly. Omit either to record usage without a ceiling. +- **Admin credential.** The shared-container topology protects the admin token + with Unix ownership/permissions and assumes candidate code can't gain root; + higher-assurance setups keep finalization credentials out of the agent + workbench entirely. + +
+ +
+Advanced — Harbor as a plain EvaluationBackend + +Harbor is also a normal backend you can drive from Python. Map each +`EvaluationSet` case to one Harbor task and pin the orchestrating package: + +```json +{"id": "task-1", "task_name": "org/terminal-task-1"} +{"id": "task-2", "task_name": "org/terminal-task-2"} +``` + +```python +from vero.harbor import HarborBackend, HarborBackendConfig + +backend = HarborBackend(HarborBackendConfig( + task_source="org/terminal-benchmark@1.0", + agent_import_path="my_program.agent:Agent", + cases_path=str(Path("../harbor-cases.jsonl").resolve()), + harbor_requirement="harbor[modal]==0.18.0", + evaluation_set_name="terminal-benchmark", + partition="test", + passthrough_environment=["ANTHROPIC_API_KEY"], +)) +``` + +VeRO invokes `harbor run` without importing Harbor into the core library, +collates verifier rewards into schema-v1 case results, zero-fills dead attempts, +and preserves Harbor output as artifacts. + +For optimization-as-a-Harbor-task, `EvaluationSidecar` exposes the same engine +across a process boundary — install the `harbor` extra (`uv sync --extra harbor`) +and serve a trusted factory: + +```bash +vero harbor serve \ + --factory trusted_deployment:build_components \ + --config /etc/vero/sidecar.json \ + --admin-token /shared/admin-token +``` + +`SidecarEvaluationPolicy` maps partitions to full/aggregate/none disclosure, +`GitCandidateTransport` imports agent commits under trusted refs, and +`CanonicalVerifier` re-scores the selected candidate. `EvaluationBackend`, +`CandidateProducer`, `OptimizationStrategy`, and `SelectionPolicy` are all +protocols — implement them for a remote evaluator, a non-Git store, or a custom +search strategy. + +
+ +## Optimize a program with a command harness + +When you don't need containment — a trusted local evaluator, in any language — +the **command backend** is the lightest path: VeRO drives your evaluator over +versioned JSON, and `vero.toml` is the shortest way to configure it. The comments +below cover most of what you need: + +```toml +[target] +root = "./my-program" # a clean Git repo — the editable target +ref = "HEAD" + +[backend] +id = "command" +kind = "command" +harness_root = "../my-evaluator" # trusted; must live outside the target +command = ["python3", "evaluate.py", "{workspace}", "{request}", "{report}"] + +[backend.staged_inputs] # trusted files, referenced via {input:NAME} +train_cases = "../my-evaluator/train.jsonl" +validation_cases = "../my-evaluator/validation.jsonl" +test_cases = "../my-evaluator/test.jsonl" + +[backend.agent_context_inputs] # per-evaluation allowlist of what the agent may see +train = ["train_cases"] + +[[evaluations]] # what the agent may run, and how much it sees +name = "train" +partition = "train" +agent_can_evaluate = true +agent_visible = true +disclosure = "full" # full | aggregate | none +expose_case_resources = true + +[[evaluations]] +name = "validation" +partition = "validation" +agent_can_evaluate = true +agent_visible = true +disclosure = "aggregate" + +[evaluations.agent_budget] # cumulative; the agent decides how to spend it +total_runs = 50 +total_cases = 5000 + +[[evaluations]] +name = "test" # held-out — agent can neither see nor run it +partition = "test" +agent_can_evaluate = false +agent_visible = false +disclosure = "none" + +[protocol] +selection_evaluation = "validation" # every candidate is ranked here +final_evaluation = "test" # scored by the trusted runtime after selection +max_proposals = 5 +error_rate_threshold = 0.1 # an eval fails if >=10% of its cases error + +[objective] +metric = "latency_ms" +direction = "minimize" + +[[objective.constraints]] +metric = "correct" +operator = "==" +value = 1.0 + +[optimizer] +kind = "claude" +model = "claude-sonnet-4-5-20250929" +instruction = "Make the program faster without changing its output" + +[session] +directory = "../runs/my-program" +``` + +Run `vero evaluate` to measure only the baseline, or `vero run` to produce and +evaluate candidates. `vero init` writes this starter profile and `vero check` +validates it before an expensive run. Paths resolve relative to the config; the +session directory, harness, and producer must all live outside the target. + +The evaluator gets an isolated candidate workspace and writes a versioned JSON +report: + +```python +# ../my-evaluator/evaluate.py +import json, sys +from pathlib import Path + +workspace = Path(sys.argv[1]) +report_path = Path(sys.argv[2]) + +latency_ms = measure(workspace) # build, run, benchmark, call a service, ... + +report_path.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"latency_ms": latency_ms}, +})) +``` + +
+Retries, error thresholds, and case aggregation + +- **Retries** wrap each case's inference/scoring call — by default provider rate + limits, HTTP 429/503/529, and timeouts, up to 3 attempts with bounded backoff. + A successful retry stays a success; earlier failed attempts are kept in the + case's structured error history. Set `max_attempts = 1` to disable them. +- **Error threshold.** An otherwise-successful evaluation becomes *failed* once + ≥10% of its selected cases error (`protocol.error_rate_threshold`). For + mean-aggregated objectives, set `aggregation = "mean"` and `case_failure_value` + so a candidate can't improve its score by failing hard cases. +- **Selection is fixed.** Candidates are ranked on the full `validation` + selection regardless of the cheaper subsets the agent explored; the agent sees + only aggregate validation feedback, and `test` never enters agent context. + +
+ +Then choose how candidates are changed. + +### Optimize with a coding agent + +```bash +vero optimize ./my-program \ + --harness-root ../my-evaluator \ + --evaluate 'python3 evaluate.py {workspace} {report}' \ + --agent claude \ + --instruction 'Make the program faster without changing its output' \ + --metric latency_ms \ + --direction minimize \ + --max-proposals 5 +``` + +Use `--agent vero` for VeRO's OpenAI Agents SDK implementation. It runs on any +provider via LiteLLM; its harness runs on the host while its shell and file +edits execute inside a sandbox bound to the candidate checkout (see [Safety +boundaries](#safety-boundaries)). In `vero.toml`, `optimizer.model` selects an +explicit model identifier; for the `vero` agent you can also set it via the +`VERO_OPTIMIZER_MODEL` environment variable, and omitting both preserves the +adapter's default. Provider-specific dependencies and credentials are required +for either built-in coding agent. For a contained/untrusted producer, run the +agent through Harbor (see [`examples/harbor-circle-packing`](examples/harbor-circle-packing/)). + +### Optimize with any external producer + +An external producer receives an isolated workspace and edits it in place: + +```bash +vero optimize ./my-program \ + --harness-root ../my-evaluator \ + --evaluate 'python3 evaluate.py {workspace} {report}' \ + --producer-root ../my-optimizer \ + --produce 'python3 improve.py {workspace}' \ + --metric latency_ms \ + --direction minimize +``` + +Commands are parsed into argument vectors, not executed through a shell. Use +absolute executable paths when the executable is not on the standard system +`PATH`. Available evaluation placeholders are `{workspace}`, `{request}`, +`{report}`, `{artifacts}`, and `{harness}`. External producers additionally get +`{producer}` and `{context}`; `VERO_CONTEXT_PATH` contains the same context path. +The harness and producer roots resolve to staged sandbox paths when the target +is not host-visible. + +`staged_inputs` are trusted evaluator inputs available to the evaluation +command through `{input:NAME}` placeholders. They remain hidden from candidate +producers unless their names are also explicitly listed in +`agent_context_inputs` for a specific named evaluation, as in the example +above. This per-evaluation allowlist prevents exposing a test input merely +because train and test share one command backend. + +The flag-based `vero optimize` command exposes the same objective constraints, +case selection, target ref, timeouts, environments, and concurrency controls; +run `vero optimize --help` for the full surface. + +## Track runs with Weights & Biases + +Install the optional integration (the `wandb` extra) and add a section to +`vero.toml`: + +```bash +uv sync --extra wandb +``` + +```toml +[wandb] +project = "program-optimization" +entity = "my-team" # optional +name = "matmul-v1" # optional +mode = "online" # online, offline, or disabled +tags = ["c", "latency"] +``` + +Each VeRO session maps to a stable W&B run. It logs canonical report metrics, +objective value and feasibility, case counts, candidate and evaluation IDs, and +the final baseline/best summary. Resuming the VeRO session resumes the same W&B +run. The direct CLI equivalent is `--wandb-project`, with optional entity, name, +and mode flags. + +## Python API + +The same pipeline can be assembled from backend-neutral interfaces: + +```python +from pathlib import Path + +from vero.evaluation import ( + CommandBackend, + CommandBackendConfig, + EvaluationPlan, + EvaluationSet, + MetricSelector, + ObjectiveSpec, +) +from vero.optimization import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, +) +from vero.runtime import create_local_optimization_session +from vero.sandbox import LocalSandbox + +backend = CommandBackend(CommandBackendConfig( + harness_root=str(Path("../my-evaluator").resolve()), + command=["python3", "evaluate.py", "{workspace}", "{report}"], +)) +producer = CommandCandidateProducer(CommandCandidateProducerConfig( + root=str(Path("../my-optimizer").resolve()), + command=["python3", "improve.py", "{workspace}"], +)) + +session = await create_local_optimization_session( + project_path="./my-program", + session_dir="~/.vero/sessions/my-run", + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="latency_ms"), + direction="minimize", + ), + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="performance")), + producers={"default": producer}, + max_proposals=5, +) +result = await session.run() +print(result.best.request.candidate.version, result.best.objective.value) + +# Every candidate remains available after its producer workspace is gone. +inspection_sandbox = await LocalSandbox.create() +for candidate in session.candidate_repository.list(): + async with session.candidate_repository.checkout( + candidate, + sandbox=inspection_sandbox, + name=f"inspect-{candidate.id}", + ) as candidate_workspace: + print(candidate.id, candidate_workspace.project_path) +``` + +`vero session inspect SESSION_DIR` includes the same durable candidate records +alongside the manifest and evaluation summaries. + +### Run the target in a remote sandbox + +The local factory above is a convenience wrapper. For containers, remote VMs, +or another execution environment, provision a `Workspace` in that sandbox and +pass it to the generic factory: + +```python +from vero.runtime import create_optimization_session +from vero.candidate_repository import GitCandidateRepository +from vero.sandbox import DockerSandbox +from vero.workspace import GitWorkspace + +sandbox = await DockerSandbox.create(image="gcc:14-bookworm") +try: + # The repository is copied into the container; it is not bind-mounted. + await sandbox.upload("./my-program", "/workspace/my-program") + workspace = await GitWorkspace.from_path( + sandbox, + "/workspace/my-program", + ) + session_dir = Path("~/.vero/sessions/remote-run").expanduser() + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", + workspace=workspace, + ) + + backend = CommandBackend(CommandBackendConfig( + harness_root=str(Path("../my-evaluator").resolve()), + command=[ + "sh", + "{harness}/evaluate.sh", + "{workspace}", + "{report}", + "{artifacts}", + ], + )) + producer = CommandCandidateProducer(CommandCandidateProducerConfig( + root=str(Path("../my-optimizer").resolve()), + command=["sh", "{producer}/improve.sh", "{workspace}"], + )) + session = await create_optimization_session( + workspace=workspace, + candidate_repository=candidate_repository, + session_dir=session_dir, + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="latency_ms"), + direction="minimize", + ), + producers={"default": producer}, + ) + result = await session.run() +finally: + await sandbox.close() +``` + +Session manifests, databases, budgets, W&B logging, artifacts, and the bare Git +candidate repository stay on the host. Candidate commands, compilation, and +evaluation run in isolated checkouts inside the sandbox. VeRO transfers Git +bundles between remote checkouts and the durable repository, then removes each +temporary checkout after use. + +Remote command harnesses and producer directories must be self-contained. An +executable named in a command must either be installed in the sandbox or live +under `{harness}` or `{producer}`. `ClaudeCodeAgent` requires a host-visible +workspace because its SDK takes a local `cwd`; `VeroAgent` and custom agents +whose tools operate through `Sandbox` can work without one. Incompatible agents +are rejected when the session is created. + +## Python benchmark tasks + +Python targets can use the optional `scale-vero-tasks` package instead of +writing the JSON command contract directly. It provides only task definition +and execution types; target programs do not depend on the VeRO optimizer. + +```python +# ../my-evaluator/benchmark.py +from vero_tasks import TaskOutput, TaskResult, create_task +from my_program import run_program + +task = create_task("quality") + +@task.inference() +async def run(case, context): + return TaskOutput(output=run_program(case["input"])) + +@task.evaluation() +async def score(case, output, context): + return TaskResult.from_task_output( + output, + score=float(output.output == case["expected"]), + ) +``` + +Connect it with `PythonTaskBackend`. Keep the task module and cases in a trusted +external uv project; VeRO overlays each isolated candidate with +`uv --with-editable` so the harness imports the exact program version being +measured without making evaluator code editable. + +```python +from vero.evaluation import ( + PythonTaskBackend, + PythonTaskBackendConfig, + PythonTaskEvaluationConfig, +) + +backend = PythonTaskBackend(PythonTaskBackendConfig( + harness_root=str(Path("../evaluation-state").resolve()), + module="benchmark", + task="quality", + evaluations=[ + PythonTaskEvaluationConfig( + name="train", + partition="train", + cases_path=str(Path("../train.jsonl").resolve()), + ), + PythonTaskEvaluationConfig( + name="validation", + partition="validation", + cases_path=str(Path("../validation.jsonl").resolve()), + ), + ], +)) +``` + +## Core concepts + +Production is a swappable `GenerationBackend`: the native in-process producer (a +coding agent whose harness runs on the host while its edits run in a sandbox), or +a Harbor run (the whole agent contained). Either way the orchestrator evaluates, +selects, and remembers — separately from the feedback the producer saw. + +```mermaid +flowchart TB + O["Optimizer loop
(strategy · selection · durable session)"] --> G{"GenerationBackend"} + G -->|native| N["VeroAgent harness (host)
shell / read_file / write_file"] + N --> SB["Sandbox
candidate checkout"] + G -->|contained| H["Harbor run
agent in a container"] + SB --> EV["Evaluator
budget · disclosure · scoring"] + H --> EV + EV --> O +``` + +| Concept | Meaning | +| --- | --- | +| `Candidate` | A target identity (a program, text, or agent) plus an opaque workspace version and lineage | +| `EvaluationSet` | A backend-owned collection or selection of evaluation cases | +| `EvaluationPlan` | Named evaluations, agent access, independent budgets, canonical selection, and optional hidden final evaluation | +| `EvaluationRecord` | The durable request, report, provenance, and objective result | +| `EvaluationBackend` | Measures a candidate without assuming its language or framework | +| `CandidateProducer` | Edits one isolated workspace to realize a proposed idea | +| `GenerationBackend` | Produces candidates plus their generation-time feedback for a proposal — the swappable unit that is the in-process native producer by default or a Harbor run | +| `OptimizationStrategy` | Chooses parents, ideas, and producers for the next batch (e.g. `SequentialStrategy`, or `EvolutionaryStrategy` for population/tournament search) | +| `SelectionPolicy` | Chooses the best feasible evaluation for the configured objective | +| `OptimizationSession` | Owns lifecycle, events, artifacts, budgets, and durable state | + +Coding agents receive a scoped `AgentContext`. They can edit only their supplied +workspace and call `evaluate(evaluation=..., selection=..., candidate_id=...)`. +The current workspace is saved as a candidate when `candidate_id` is omitted; +supplying an existing candidate ID re-evaluates that durable version. The tool +returns a compact receipt with the evaluation ID, status, approved summary, and +path to the filesystem result. Large case records, traces, and artifacts stay +out of the tool response. Intermediate checkpoints are real candidates and +remain eligible for selection, even if the agent later makes the program worse. + +Each producer workspace contains a generated, read-only `.vero/` directory: + +```text +.vero/ +├── README.md +├── manifest.json +├── evaluations.json # available evaluations, selections, disclosure, budgets +├── cases/ # only backend-approved case resources +├── candidates/ # metadata, parent patches, and repository-native refs +└── evaluations/ # authorized summaries or full case/trace/artifact trees +``` + +The agent inspects this with ordinary filesystem and Git commands. Disclosure +governs what lands there: + +- **full** — per-case traces split into separate files (and, for Harbor, the + complete downloaded trial record per case: failure results, tracebacks, trial + logs, target-agent artifacts); +- **aggregate** — metrics and counts only, no case records or trial artifacts; +- **none** — status only. + +Candidate history uses durable Git refs, so siblings need not be ancestors of the +current checkout: a proposal sees the candidates from the start of its generation +plus its own evaluated checkpoints, and parallel siblings appear the next round. + +The `.vero/` view is disposable and read-only — Git-excluded, permission- +protected, and rejected if a candidate force-adds it; the durable candidate and +evaluation stores stay the trusted source. In Harbor the sidecar owns the +writable volume and the agent mounts it read-only, and case resources (for +datasets, the complete pinned task directories) are exported only when the +evaluation's authorization permits it. + +Strategies can propose a batch of candidates and route each proposal to a named +producer. Set `max_concurrency` to produce and evaluate independent candidates in +parallel. This supports sequential hill climbing, evolutionary search, and +orchestrator/sub-agent designs without changing the evaluation model. + +## Durable sessions + +Session state is stored outside the target repository: + +```text +sessions// +├── manifest.json +├── database.json +├── budgets.json # when evaluation is metered +├── events.jsonl +├── artifacts/ +└── evaluations/ + └── / + ├── evaluation.json + ├── cases/ + └── artifacts/ +``` + +The evaluation directories are the source of truth; the database can be rebuilt +from them. Reusing a session directory resumes its compatible baseline, +candidate lineage, evaluation history, completed rounds, and supported coding +agent state. VeRO rejects a resume if its backend configuration, evaluation +plan, run protocol, parameters, limits, seed, objective, or baseline is +incompatible with the schema-v3 manifest. + +```bash +vero session list +vero session inspect ~/.vero/sessions/ +vero report ~/.vero/sessions/ --output experiment.html +vero session fork OLD_SESSION NEW_SESSION --max-proposals 20 --reset-budgets +vero session export ~/.vero/sessions/ +vero session clear ~/.vero/sessions/ --yes +``` + +`vero report` creates a self-contained, read-only HTML view of the whole run: +the score trajectory, candidate lineage and parent diffs, evaluation artifacts, +producer traces, and runtime event timeline. The report embeds those materials, +so treat the resulting file as sensitive experiment data. + +## Safety boundaries + +- Candidate changes happen in isolated Git worktrees; the original target is not + edited. +- The target must be clean so its baseline has an unambiguous version. +- Session state, evaluation harnesses, and external producers must live outside + the editable target repository. +- Command execution uses argument vectors and an explicit environment. +- Evaluation secrets are passed through backend configuration and redacted from + diagnostics; they cannot be embedded in evaluation parameters. +- Agent-visible cases, histories, and evaluation details are projected into a + generated `.vero/` view according to the same authorization boundary. +- Budgets are reserved atomically before backend execution. +- A built-in coding agent's shell and file actions execute inside a sandbox + bound to its candidate checkout, so containment is the sandbox boundary rather + than in-process checks. The local sandbox is for fast, trusted runs; for a + contained or untrusted producer run the agent through Harbor, which isolates + the agent (and, in separate-verifier mode, the scorer) in its own environment. + +## Paper and reproduction + +VeRO was introduced in [*VeRO: A Harness for Agents to Optimize +Agents*](https://arxiv.org/abs/2602.22480), accepted at ICML 2026. The paper +studies agent-harness optimization; the current library generalizes the same +version/evaluate/select loop to programs more broadly. + +The frozen code for reproducing the paper is preserved on the `paper/v1` branch +and at the `paper-v1` tag: + +```bash +git checkout paper-v1 +``` + +## Development + +```bash +uv sync --all-extras +uv run pytest tests/test_v05_*.py +``` + +VeRO is licensed under the MIT License. diff --git a/vero/docs/agent-setup-guide.md b/vero/docs/agent-setup-guide.md new file mode 100644 index 00000000..4f4561be --- /dev/null +++ b/vero/docs/agent-setup-guide.md @@ -0,0 +1,133 @@ +# VeRO setup guide (for coding agents) + +This guide helps a coding agent (Claude, Cursor, Copilot, …) set up and run a +VeRO optimization for a user. VeRO optimizes a **versioned program** against an +**evaluation**: a producer (usually a coding agent) proposes candidate edits, a +trusted evaluator scores them, and the best candidate is selected. + +You do two things: (1) author an **evaluation harness**, and (2) run the +**optimizer**. There is no `.veroaccess`, `Policy`, or resource/namespace setup — +those were removed. Containment now comes from the sandbox the producer runs in, +and disclosure is controlled per evaluation set. + +## Before you start + +Understand the user's program (what it does, how it's invoked, what "better" +means) and read a matching example: + +- `examples/c-matmul/` — a language-neutral **command harness** + `vero.toml`. +- `examples/circle-packing/` — a Python program scored by a command harness. +- `../vero-tasks/examples/matmul-{kernel,eval}/` — a **Python task** using the + `scale-vero-tasks` protocol. +- `examples/harbor-circle-packing/` — a **Harbor** outer loop (contained agent) + with a simple command inner loop. + +## 1. Author the evaluation + +Pick whichever fits the user's program. + +### Option A — command harness (any language) + +Write a script that reads a request JSON and writes a report JSON. VeRO's +`CommandBackend` invokes it with placeholders it substitutes at run time: + +```bash +python evaluate.py --workspace {workspace} --request {request} \ + --report {report} --artifacts {artifacts} +``` + +- `{workspace}` — the candidate checkout to score. +- `{request}` — JSON: `{"schema_version": 1, "request": {"seed": , ...}}`. +- `{report}` — write JSON: `{"schema_version": 1, "status": "success", + "metrics": {"": , ...}}` (a **dict of metrics** — one becomes the + objective; others can be constraints or diagnostics). + +See `examples/circle-packing/harness/` for a complete scorer. + +### Option B — Python task (`scale-vero-tasks`) + +For Python benchmarks, use the task protocol (`../vero-tasks`): + +```python +from vero_tasks import TaskContext, TaskOutput, TaskResult, create_task + +task = create_task("main", required_env_vars=["OPENAI_API_KEY"]) + +@task.inference() +async def infer(case: dict, context: TaskContext) -> TaskOutput: + from my_agent import run_agent + return TaskOutput(output=await run_agent(case["question"])) + +@task.evaluation() +async def evaluate(case: dict, output: TaskOutput, context: TaskContext) -> TaskResult: + return TaskResult.from_task_output( + output, score=float(output.output == case["answer"]) + ) +``` + +The `scale-vero-tasks` runner adapts this to the same command-harness contract. +See `../vero-tasks/README.md` and `../vero-tasks/examples/matmul-eval/`. + +**Rules:** functions are `async`; let exceptions propagate (VeRO records them as +errored cases); keep heavy imports inside functions; declare API keys via +`required_env_vars`; the score is a float (define whether higher or lower is +better via the objective's `direction`). + +## 2. Run the optimizer + +The target must be a git repo whose working tree is clean. + +### Config-driven + +```bash +vero init ./run # scaffolds run/vero.toml (+ target/, harness/) +# edit run/vero.toml +vero check --config run/vero.toml # validate paths, git, eval refs +vero evaluate --config run/vero.toml # score the baseline only +vero run --config run/vero.toml # optimize +``` + +`vero.toml` sections: `[target]` (root, ref), `[backend]` (kind = `command`, +`harness_root`, `command`), one or more `[[evaluations]]` (name, partition, +`agent_can_evaluate`, `disclosure`, optional `agent_budget`), `[protocol]` +(`selection_evaluation`, `final_evaluation`, `max_proposals`), `[objective]` +(metric, direction), and `[optimizer]` (`kind = "vero"`, optional `model`, +`instruction`). + +### One-shot (flags) + +```bash +vero optimize ./target \ + --harness-root ./harness \ + --evaluate "python3 {harness}/evaluate.py --workspace {workspace} --request {request} --report {report} --artifacts {artifacts}" \ + --agent vero \ + --instruction "Improve the program without changing its intended behavior." \ + --metric score --direction maximize \ + --evaluation-set default --max-proposals 4 --max-turns 60 +``` + +## 3. The optimizer agent (`--agent vero`) + +`VeroAgent` runs on the OpenAI Agents SDK. Its harness runs on the host; its +shell/file effects run inside a sandbox (bound to the candidate checkout) via +function tools (`shell`, `read_file`, `write_file`), plus an `evaluate` tool for +mid-run self-scoring. There are no custom bash/grep/git tools to configure and no +in-process ACLs — the sandbox is the boundary. + +- **Model:** any provider via LiteLLM. Set `VERO_OPTIMIZER_MODEL` + (e.g. `openai/gpt-5.4`, `anthropic/claude-sonnet-4-5-...`) or `[optimizer] model`. +- **Containment:** the sandbox client is the seam — local for fast/trusted runs; + for a contained/untrusted agent, run it through **Harbor** (see + `examples/harbor-circle-packing/`). +- **Strategy:** the default proposes one candidate per round; for + population/evolutionary search use `EvolutionaryStrategy` + (`vero.optimization.EvolutionaryStrategy`). + +## Don't + +- Don't let the harness/task code be edited during optimization — it scores the + candidate and must stay fixed (keep it in `--harness-root`, outside the target). +- Don't hardcode absolute paths in harness/task code; use the `{...}` placeholders + and request parameters. +- Don't suppress exceptions in inference/evaluation — VeRO records them. +- Don't put secrets in code; declare them as env vars. diff --git a/vero/docs/insights-generator-subagent.md b/vero/docs/insights-generator-subagent.md new file mode 100644 index 00000000..7d36e1cd --- /dev/null +++ b/vero/docs/insights-generator-subagent.md @@ -0,0 +1,249 @@ +# Insights Generator as a VeRO optimizer sub-agent + +**Status:** design / not yet implemented +**Audience:** an engineer or agent picking this up cold +**Working branch:** `native-runner-sandboxagent` (`/Users/varun.ursekar/code/vero-v05`) + +--- + +## 1. Motivation + +The VeRO optimizer improves a candidate by having a coding agent iterate on it and +scoring the result. Across a run it accumulates a large corpus of **execution +traces** (one per evaluated case, per candidate). Today nothing systematically +mines that corpus: patterns that are only visible across the *population* of runs +(e.g. "40% of failing cases silently swallow a specific exception") never surface +back into the optimizer's next proposal. + +Scale's **Insights Generator (IG)** is a system built exactly for this: *systematic +corpus-level trace diagnostics for LLM agents* (paper: arXiv `2605.21347v3`, +"Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents", +Scale AI). It reads a corpus of traces + a diagnostic question and returns an +evidence-backed report of failure patterns with prevalence stats and cited trace +IDs. In their own eval it roughly doubled practitioner effectiveness over +baselines. + +**The endeavour:** give the VeRO optimizer IG's capability *as a sub-agent it can +invoke* — so that between rounds it can ask "what distinguishes high- from +low-scoring candidates in this corpus?" and feed the answer into the next +generation. + +## 2. The hard constraint: reproduce, do not depend + +VeRO is **public**; IG is **internal/private** (`scaleapi/models`, +`enterprise/insights_generator/`). We therefore **cannot take a dependency on IG**. +We reproduce IG's *mechanisms*, not its code, using only things a public repo can +ship. + +We also do **not** need IG's heavier implementation choices: +- no pandas dataframe input contract, +- no in-sandbox Python REPL, +- no vector store / schema-cache / parquet data layer, +- no Scale Sandbox. + +The substrate is **the sandbox filesystem + bash**, which the Harbor path already +gives us. + +## 3. What IG actually is (stripped to essentials) + +Three mechanisms do the real work; everything else is implementation: + +1. **Corpus-scale coverage without loading everything into context.** Compute + *over* all traces; read the full text of only a sampled/relevant few. +2. **Discovery ≠ validation (Scout / Investigator split).** *Scouts* skim a sample + and propose many hypotheses (breadth). *Investigators* test each one against the + *whole* corpus with quantitative evidence and label it confirmed / refuted / + inconclusive (depth). No finding ships without corpus-scale backing. +3. **Evidence-grounded, typed output with cohort persistence.** Every finding = + description + prevalence % + specific trace IDs + verbatim quotes + mechanism + + fix. IG literally rejects a finding that has no saved "affected traces" cohort. + +> Note the two entry points in the IG repo are **not** equivalent. The public-ish +> `generate_insights()` in `analysis.py` is the **old single-pass** path (sample → +> format as XML → one LLM call) — closer to the baseline the paper *beats*. The +> "best-performing" system is the agentic orchestrator entered via +> `run_orchestrator(question, sandbox, config)`. + +## 4. The path that matters: Harbor, and the real `claude` CLI + +VeRO has two agent worlds. **Only the Harbor path is in scope.** + +- **Out of scope — VeRO's own SDK agents.** `VeroAgent` (OpenAI Agents SDK) and + `ClaudeCodeAgent` (Claude Agent SDK). These have their own `SubAgentTool`, but + they are a *different* execution model. +- **In scope — Harbor's `ClaudeCode` agent.** This is what `harbor run -a claude` + uses. Crucially, it is **not** the Claude Agent SDK — it installs the real + `@anthropic-ai/claude-code` CLI into the sandbox and runs it **headless**: + `claude --print --output-format=stream-json -- `, as the `agent` + user, in the environment filesystem. + +Because the optimizer is the **real Claude Code CLI**, IG plugs in through Claude +Code's **native filesystem/config extension points** — subagents, skills, MCP — not +through Python objects. Harbor already *expects* subagents: it filters `subagents` +session paths and parses `isSidechain` events into its trajectory. + +## 5. The design + +**"The whole IG machinery as a sub-agent" = a Claude Code sub-agent bundle seeded +into the Harbor environment.** The optimizer invokes it with the `Task` tool. + +### Role mapping (1:1 with IG) + +| IG role | Reproduction | +|---|---| +| Orchestrator | `.claude/agents/insights-generator.md` (has `Task`) | +| Hypothesis agent (Scout) | `.claude/agents/ig-scout.md` — breadth, cheap model | +| Explore agent (Investigator) | `.claude/agents/ig-investigator.md` — depth, high effort | + +Each sub-agent runs in its **own context window** — which is exactly IG's +context-economy property (its orchestrator delegates heavy trace-reading to +preserve context). We get it for free from Claude Code's `Task` model. + +### Tool / data-layer mapping + +| IG piece | Reproduction | +|---|---| +| `search_traces` (vector) | `grep`/`rg` over the corpus dir (lexical) | +| `extract` (feature per trace) | a script emitting `features.jsonl`, persisted + reused | +| `compare_segments` (cohort diff) | `jq`/`awk` group-by over `features.jsonl` by label | +| `get_trace` / `get_trace_chunk` | `cat` / `sed -n` a slice of a trace file | +| `save_affected_traces` | write `cohorts/.txt` | +| `submit_finding` + rejection rule | a `findings.json` contract + a `validate.py` check | +| data layer (parquet/vector/schema) | **the sandbox filesystem** | +| Scale Sandbox | **the Harbor container the optimizer already runs in** | + +### Delivery seams (how the bundle gets into the container) + +Harbor auto-injects two of these; you seed the third yourself: + +| What | How it lands in the container | Wired by | +|---|---|---| +| **Skill** (methodology + helper scripts) | `environment.skills_dir` → copied to `$CLAUDE_CONFIG_DIR/skills/` | `task.toml` | +| **MCP server** (optional real-tool surface) | `environment.mcp_servers` / `agent.mcp_servers` → `~/.claude.json` | `task.toml` / agent config | +| **Sub-agent defs** (`.claude/agents/*.md`) | **no dedicated Harbor seam** — bake into the env image/seed | your `Dockerfile` / `seed.sh` | + +The last row is the one gotcha: Harbor injects skills/mcp/memory but **not** an +agents dir, so seed `.claude/agents/*.md` via the environment build (into the +agent's `cwd/.claude/agents/` or `$CLAUDE_CONFIG_DIR/agents/`). + +### Proposed layout + +``` +environment/ + Dockerfile # COPY .claude/agents -> /work/.claude/agents + main/seed.sh # seed /work; also lay out the trace corpus + .claude/agents/ + insights-generator.md # orchestrator persona (Task, Bash, Read, Grep, Glob) + ig-scout.md # sample -> hypotheses + ig-investigator.md # validate one hypothesis over ALL traces + skills/insights/ # -> environment.skills_dir + SKILL.md # evidence-grounding rules, findings.json contract + chunk.sh # slice long traces (the one primitive bash lacks) + validate.py # reject findings without cohort + trace-id citations +``` + +```toml +# task.toml +[environment] +skills_dir = "/skills" +# mcp_servers = [ ... ] # only if a query-server is preferred over raw bash +``` + +### The corpus + +VeRO already dumps per-case traces into a read-only `.vero` context dir: +`.vero/evaluations//cases//execution-trace.json` (and +`evaluation-trace.json`). That **is** the IG corpus. Label traces by the eval +scores VeRO already has (pass/fail, reward) so the investigator can contrast +cohorts — comparative analysis is where IG earns its keep. + +### Where it plugs into the loop + +Between rounds, the optimizer calls `Task(subagent_type="insights-generator", …)` +pointed at the current round's traces; the findings feed the next candidate +proposals. This is a native reproduction of IG's own `patcher` loop +(analyze → guide fix → re-eval), and it lines up with VeRO's evolutionary strategy +(findings become the guidance signal for the next generation of agentic offspring). + +## 6. Design decisions & rationale + +1. **Reproduce, don't import.** VeRO public / IG private. Non-negotiable. +2. **Target the Harbor path (real `claude` CLI), not the SDK agents.** That is the + optimizer we ship; it makes the integration filesystem/config, not Python. +3. **IG = Claude Code sub-agent(s) via `Task`.** Faithful to IG's LLM-driven + orchestration and reproduces its per-role context isolation for free. +4. **Filesystem + bash as the data layer / query engine.** Drops the dataframe, + REPL, vector store, and Scale Sandbox in one move. +5. **Preserve the evidence discipline explicitly.** A `findings.json` contract plus + a `validate.py` check reproduce `submit_finding`'s "no cohort → reject" rule; + without it the output degrades to vibes. +6. **Start nested (orchestrator + scout + investigator).** Maps 1:1 to IG and + exploits context isolation. A single phased sub-agent is a legitimate v0 but + weaker on context economy for large corpora. + +## 7. Known caveats + +- **Chunking long traces is the one real primitive to build.** A single + `execution-trace.json` can blow a sub-agent's context; provide `chunk.sh` (byte- + offset or turn-boundary slicing). Everything else degrades gracefully to grep. +- **No semantic search.** `grep` is lexical. Fine for error strings, tool names, + phrasings (most trace diagnostics). Add a small local embed helper or an MCP + query-server only if recall disappoints. +- **Model control.** Harbor sets `CLAUDE_CODE_SUBAGENT_MODEL` when a custom base + URL is used; set per-role `model`/`effort` in each agent `.md`. + +## 8. Code pointers + +### VeRO (`/Users/varun.ursekar/code/vero-v05/vero`, this repo) + +| Path | Why it matters | +|---|---| +| `src/vero/runtime/context.py` (~L256–273) | Where per-case `execution-trace.json` / `evaluation-trace.json` are written into the `.vero` context — **the corpus source** | +| `src/vero/optimization/optimizer.py` | `Optimizer` (L204); `run` (L409); `_produce_candidate` (L283) — where an IG call would slot between rounds | +| `src/vero/agents/producer.py` | `AgentCandidateProducer.produce()` — how a coding agent is driven per proposal | +| `src/vero/agents/claude_code.py` | VeRO's **SDK** Claude agent — **out of scope**, but note L44 enables `Task`; do not confuse with Harbor's agent | +| `src/vero/agents/vero.py` | `VeroAgent` (OpenAI Agents SDK) — out of scope | +| `src/vero/tools/sub_agent.py` | `SubAgentTool` — the SDK-world sub-agent mechanism (not the Harbor path) | +| `src/vero/harbor/` | Harbor integration: `backend.py`, `cli.py` (`run` command), `sidecar.py`, `verifier.py` | +| `examples/harbor-circle-packing/` | Reference Harbor task: `task.toml`, `environment/Dockerfile`, `environment/main/seed.sh`, `environment/sidecar/` — the template to clone for an insights task | +| `docs/agent-setup-guide.md` | Existing agent setup guide | + +### Harbor (installed from public PyPI; import path `harbor.*`) + +Locate with `python -c "import harbor, os; print(os.path.dirname(harbor.__file__))"` +inside the vero venv. + +| Module | Why it matters | +|---|---| +| `harbor/agents/installed/claude_code.py` | **The real optimizer.** `run()` runs `claude --print --output-format=stream-json`. `_build_register_skills_command` (skills → `$CLAUDE_CONFIG_DIR/skills/`), `_build_register_mcp_servers_command` (→ `~/.claude.json`), `_build_register_memory_command`. `_get_session_dir` / `_convert_events_to_trajectory` already handle `subagents` + `isSidechain` | +| `harbor/agents/installed/base.py` | `BaseInstalledAgent`: `__init__`, `build_cli_flags`, `exec_as_agent`, `with_prompt_template` | +| `harbor/agents/installed/` (dir) | Other agents (`codex.py`, `gemini_cli.py`, …) — same pattern if a different optimizer is used | +| `harbor/trial/trial.py` | `_resolve_effective_skills_dir` (~L1049); `mcp_servers` merge (~L738–748) — how the seams are wired from config | +| `harbor/models/task/config.py` | Task `[environment]` schema: `skills_dir` (~L450), `mcp_servers` (~L444) | +| `harbor/models/trial/config.py` | Agent config: `mcp_servers` (~L125) | +| `harbor/models/trial/paths.py` | `EnvironmentPaths`: `agent_dir`, `default_skills_dir = /harbor/skills` | + +### Insights Generator (private — `scaleapi/models`, reference only, **do not import**) + +Path: `enterprise/insights_generator/src/insights_generator/` + +| Module | Why it matters | +|---|---| +| `agents/orchestrator.py` | `run_orchestrator(question, sandbox, config)` (~L1044) — the real agentic entry point (what we reproduce) | +| `agents/hypothesis_agent.py` | `run_hypothesis_agent` — the Scout; source for `ig-scout.md` | +| `agents/explore_agent.py` | `run_explore_agent` — the Investigator; source for `ig-investigator.md` | +| `agents/dispatch.py` | `DispatchHypothesisAgents` (~L216) / `DispatchExploreAgents` (~L455) — orchestrator dispatches roles via tool calls | +| `agents/prompts/{orchestrator,hypothesis,explore}.py` | The role prompts (23–54 KB) — **distill these into the `.md` personas** | +| `agents/prompts/_compose.py` | Jinja `AblationContext` prompt composition (we drop ablations; use `ablation_mode="full"` = orchestrator + hypothesis + explore) | +| `tools/repl_toolkit.py` | `EXPLORE_FUNCTIONS` (search_traces / extract / compare_segments / summarize / get_trace / save_affected_traces) — the tool surface we re-map to bash | +| `ingestion.py` | `ingest(...)` + the INPUT CONTRACT (`trace_id`, flattened `content`) — the trace-flattening reference | +| `analysis.py` | `generate_insights()` — the **old single-pass** path; NOT the system we want | +| `ig_config.py` | `IGConfig` — `ablation_mode`, per-role model fields | +| `vector_store.py`, `chunking.py`, `schema_cache.py` | The data layer we deliberately do not reproduce (except chunking) | + +## 9. Next step (offered, not yet done) + +Draft the three agent `.md` files + `SKILL.md` / `findings.json` contract + +`chunk.sh` / `validate.py`, distilled from IG's actual scout/investigator/ +orchestrator prompts and grounded against a real `execution-trace.json` from the +`.vero` layout, so the bundle is runnable rather than notional. diff --git a/vero/examples/c-matmul/.gitignore b/vero/examples/c-matmul/.gitignore new file mode 100644 index 00000000..8bf40a1b --- /dev/null +++ b/vero/examples/c-matmul/.gitignore @@ -0,0 +1 @@ +.vero/ diff --git a/vero/examples/c-matmul/README.md b/vero/examples/c-matmul/README.md new file mode 100644 index 00000000..fb620abb --- /dev/null +++ b/vero/examples/c-matmul/README.md @@ -0,0 +1,24 @@ +# Generic C program optimization + +This example is the concrete proof that VeRO optimizes programs rather than +only Python agents. The editable target is a C repository with no Python +package or VeRO dependency. A trusted harness outside the target compiles it, +checks numerical correctness, and reports latency. + +Initialize the target as its own versioned program, then evaluate and optimize +it through the checked-in `vero.toml`: + +```bash +cd examples/c-matmul/target +git init -b main +git add . +git -c user.name=vero -c user.email=vero@localhost commit -m baseline +cd .. + +vero evaluate --config vero.toml +vero run --config vero.toml +``` + +The deterministic producer makes the example suitable for CI and requires no +model credentials. Replace `[optimizer]` with `kind = "vero"` or +`kind = "claude"` plus an `instruction` to use a built-in coding agent. diff --git a/vero/examples/c-matmul/harness/benchmark.c b/vero/examples/c-matmul/harness/benchmark.c new file mode 100644 index 00000000..0de850dc --- /dev/null +++ b/vero/examples/c-matmul/harness/benchmark.c @@ -0,0 +1,69 @@ +#define _POSIX_C_SOURCE 200809L + +#include "matmul.h" + +#include +#include +#include +#include + +static double elapsed_ms(struct timespec start, struct timespec end) { + return (double)(end.tv_sec - start.tv_sec) * 1000.0 + + (double)(end.tv_nsec - start.tv_nsec) / 1000000.0; +} + +static void reference_matmul( + const double *a, + const double *b, + double *c, + size_t n +) { + for (size_t i = 0; i < n; ++i) { + for (size_t j = 0; j < n; ++j) { + double sum = 0.0; + for (size_t k = 0; k < n; ++k) { + sum += a[i * n + k] * b[k * n + j]; + } + c[i * n + j] = sum; + } + } +} + +int main(void) { + const size_t n = 128; + const size_t elements = n * n; + double *a = malloc(elements * sizeof(double)); + double *b = malloc(elements * sizeof(double)); + double *actual = calloc(elements, sizeof(double)); + double *expected = calloc(elements, sizeof(double)); + if (a == NULL || b == NULL || actual == NULL || expected == NULL) { + return 2; + } + + for (size_t index = 0; index < elements; ++index) { + a[index] = (double)((int)(index % 13) - 6) / 7.0; + b[index] = (double)((int)(index % 11) - 5) / 5.0; + } + + struct timespec start; + struct timespec end; + clock_gettime(CLOCK_MONOTONIC, &start); + matmul(a, b, actual, n); + clock_gettime(CLOCK_MONOTONIC, &end); + reference_matmul(a, b, expected, n); + + int correct = 1; + for (size_t index = 0; index < elements; ++index) { + if (fabs(actual[index] - expected[index]) > 1e-9) { + correct = 0; + break; + } + } + + printf("%d %.9f\n", correct, elapsed_ms(start, end)); + free(a); + free(b); + free(actual); + free(expected); + return correct ? 0 : 3; +} diff --git a/vero/examples/c-matmul/harness/evaluate.py b/vero/examples/c-matmul/harness/evaluate.py new file mode 100644 index 00000000..6a99254a --- /dev/null +++ b/vero/examples/c-matmul/harness/evaluate.py @@ -0,0 +1,111 @@ +"""Trusted C build, correctness, and performance harness.""" + +from __future__ import annotations + +import argparse +import json +import subprocess +from pathlib import Path + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--request", type=Path, required=True) + parser.add_argument("--report", type=Path, required=True) + parser.add_argument("--artifacts", type=Path, required=True) + args = parser.parse_args() + + request = json.loads(args.request.read_text(encoding="utf-8")) + if request.get("schema_version") != 1: + raise ValueError("unsupported command input schema") + + artifact_dir = args.artifacts / "c-matmul" + artifact_dir.mkdir(parents=True, exist_ok=True) + binary = artifact_dir / "benchmark" + benchmark_source = Path(__file__).with_name("benchmark.c") + compile_result = subprocess.run( + [ + "cc", + "-O2", + "-std=c11", + "-I", + str(args.workspace), + str(args.workspace / "matmul.c"), + str(benchmark_source), + "-lm", + "-o", + str(binary), + ], + capture_output=True, + text=True, + ) + (artifact_dir / "compiler.log").write_text( + compile_result.stdout + compile_result.stderr, + encoding="utf-8", + ) + if compile_result.returncode != 0: + args.report.write_text( + json.dumps( + { + "schema_version": 1, + "status": "failed", + "diagnostics": [ + { + "code": "compile_failed", + "message": "C candidate did not compile", + "severity": "error", + "phase": "compile", + } + ], + "artifacts": [ + { + "path": "c-matmul/compiler.log", + "media_type": "text/plain", + } + ], + } + ), + encoding="utf-8", + ) + return + + measurements: list[float] = [] + correct = True + for _ in range(3): + run = subprocess.run([str(binary)], capture_output=True, text=True) + if run.returncode != 0: + correct = False + break + correct_value, latency_value = run.stdout.strip().split() + correct = correct and correct_value == "1" + measurements.append(float(latency_value)) + + latency_ms = min(measurements) if measurements else 1.0e12 + args.report.write_text( + json.dumps( + { + "schema_version": 1, + "status": "success", + "metrics": { + "latency_ms": latency_ms, + "correct": 1.0 if correct else 0.0, + }, + "artifacts": [ + { + "path": "c-matmul/compiler.log", + "media_type": "text/plain", + }, + { + "path": "c-matmul/benchmark", + "media_type": "application/octet-stream", + }, + ], + } + ), + encoding="utf-8", + ) + + +if __name__ == "__main__": + main() diff --git a/vero/examples/c-matmul/optimizer/optimize.py b/vero/examples/c-matmul/optimizer/optimize.py new file mode 100644 index 00000000..6d941a6b --- /dev/null +++ b/vero/examples/c-matmul/optimizer/optimize.py @@ -0,0 +1,18 @@ +"""Deterministic candidate producer used by CI.""" + +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + args = parser.parse_args() + shutil.copy2(Path(__file__).with_name("optimized.c"), args.workspace / "matmul.c") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/c-matmul/optimizer/optimized.c b/vero/examples/c-matmul/optimizer/optimized.c new file mode 100644 index 00000000..e93806df --- /dev/null +++ b/vero/examples/c-matmul/optimizer/optimized.c @@ -0,0 +1,16 @@ +#include "matmul.h" + +#include + +/* Cache-friendly loop order with no redundant scalar delay. */ +void matmul(const double *a, const double *b, double *c, size_t n) { + memset(c, 0, n * n * sizeof(double)); + for (size_t i = 0; i < n; ++i) { + for (size_t k = 0; k < n; ++k) { + const double a_ik = a[i * n + k]; + for (size_t j = 0; j < n; ++j) { + c[i * n + j] += a_ik * b[k * n + j]; + } + } + } +} diff --git a/vero/examples/c-matmul/target/.gitignore b/vero/examples/c-matmul/target/.gitignore new file mode 100644 index 00000000..54985fc2 --- /dev/null +++ b/vero/examples/c-matmul/target/.gitignore @@ -0,0 +1,3 @@ +.DS_Store +*.o +benchmark diff --git a/vero/examples/c-matmul/target/matmul.c b/vero/examples/c-matmul/target/matmul.c new file mode 100644 index 00000000..aac353db --- /dev/null +++ b/vero/examples/c-matmul/target/matmul.c @@ -0,0 +1,16 @@ +#include "matmul.h" + +/* Correct but deliberately slow baseline. */ +void matmul(const double *a, const double *b, double *c, size_t n) { + for (size_t i = 0; i < n; ++i) { + for (size_t j = 0; j < n; ++j) { + double sum = 0.0; + for (size_t k = 0; k < n; ++k) { + sum += a[i * n + k] * b[k * n + j]; + } + c[i * n + j] = sum; + for (volatile size_t delay = 0; delay < 500; ++delay) { + } + } + } +} diff --git a/vero/examples/c-matmul/target/matmul.h b/vero/examples/c-matmul/target/matmul.h new file mode 100644 index 00000000..22cb7c08 --- /dev/null +++ b/vero/examples/c-matmul/target/matmul.h @@ -0,0 +1,8 @@ +#ifndef VERO_EXAMPLE_MATMUL_H +#define VERO_EXAMPLE_MATMUL_H + +#include + +void matmul(const double *a, const double *b, double *c, size_t n); + +#endif diff --git a/vero/examples/c-matmul/vero.toml b/vero/examples/c-matmul/vero.toml new file mode 100644 index 00000000..75d36550 --- /dev/null +++ b/vero/examples/c-matmul/vero.toml @@ -0,0 +1,50 @@ +[target] +root = "./target" +ref = "HEAD" + +[backend] +id = "command" +kind = "command" +harness_root = "./harness" +command = [ + "python3", + "evaluate.py", + "--workspace", "{workspace}", + "--request", "{request}", + "--report", "{report}", + "--artifacts", "{artifacts}", +] + +[[evaluations]] +name = "performance" +agent_can_evaluate = true +agent_visible = true +agent_selection = "arbitrary" +disclosure = "full" + +[protocol] +selection_evaluation = "performance" +timeout_seconds = 120 +max_proposals = 1 + +[protocol.retry] +max_attempts = 1 + +[objective] +metric = "latency_ms" +direction = "minimize" + +[[objective.constraints]] +metric = "correct" +operator = "==" +value = 1.0 + +[optimizer] +kind = "command" +root = "./optimizer" +command = ["python3", "optimize.py", "--workspace", "{workspace}"] +description = "Use a cache-friendly matrix kernel" + +[session] +id = "c-matmul-example" +directory = "./.vero/session" diff --git a/vero/examples/circle-packing/.gitignore b/vero/examples/circle-packing/.gitignore new file mode 100644 index 00000000..8bf40a1b --- /dev/null +++ b/vero/examples/circle-packing/.gitignore @@ -0,0 +1 @@ +.vero/ diff --git a/vero/examples/circle-packing/README.md b/vero/examples/circle-packing/README.md new file mode 100644 index 00000000..8c20b520 --- /dev/null +++ b/vero/examples/circle-packing/README.md @@ -0,0 +1,53 @@ +# Circle packing program optimization + +This is a non-trivial VeRO benchmark adapted from +[ShinkaEvolve's circle-packing example](https://github.com/SakanaAI/ShinkaEvolve/tree/main/examples/circle_packing). +The editable program places 26 circles in a unit square and maximizes the sum +of their radii. A trusted external harness checks the exact geometry and emits +the score, detailed validation measurements, a JSON layout, and an SVG rendering. + +The baseline is deliberately simple. The search space includes better initial +layouts, numerical optimization of radii and centers, stochastic global search, +local refinement, restarts, and hybrid algorithms. Unlike ShinkaEvolve's marked +code block, VeRO gives the coding agent an ordinary versioned repository and +allows it to change the complete implementation. + +## Run it + +The candidate must first be initialized as its own Git repository: + +```bash +cd examples/circle-packing/target +git init -b main +git add . +git -c user.name=vero -c user.email=vero@localhost commit -m baseline +cd .. + +vero check --config vero.toml +vero evaluate --config vero.toml +vero run --config vero.toml +``` + +`vero evaluate` is credential-free and records the baseline score. `vero run` +uses the built-in VeRO coding agent with the configured LiteLLM model identifier +and permits up to 30 agent-requested development evaluations in one proposal. +Change `optimizer.model` to any model available through your provider. The best +nominated candidate is then re-evaluated through the hidden final evaluation. +Candidate versions remain in the session candidate repository, while evaluation +artifacts are available in `.vero/session/evaluations/` and to the agent through +its read-only `.vero` context. + +Evaluation launches through the candidate's locked uv environment. The seed +program has no third-party dependencies; an optimizer can add reproducible +scientific-computing dependencies with `uv add`. The evaluator applies the +configured protocol seed to Python and NumPy RNGs and fixes `PYTHONHASHSEED` so +stochastic candidates are reproducible when they use those standard sources. +The same value is available to candidates as `VERO_EVALUATION_SEED`. + +## Attribution + +The starting algorithm is adapted from ShinkaEvolve, Copyright 2025 Sakana AI, +under the Apache License 2.0. The original used NumPy and restricted evolution +to a marked block; this version uses the standard library and is structured as +a complete VeRO target repository. See [UPSTREAM_LICENSE](UPSTREAM_LICENSE) for +the upstream license. diff --git a/vero/examples/circle-packing/UPSTREAM_LICENSE b/vero/examples/circle-packing/UPSTREAM_LICENSE new file mode 100644 index 00000000..e3a6e3db --- /dev/null +++ b/vero/examples/circle-packing/UPSTREAM_LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2025 Sakana AI + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/vero/examples/circle-packing/harness/evaluate.py b/vero/examples/circle-packing/harness/evaluate.py new file mode 100644 index 00000000..a80f94b0 --- /dev/null +++ b/vero/examples/circle-packing/harness/evaluate.py @@ -0,0 +1,315 @@ +"""Trusted validator and scorer for the 26-circle packing benchmark.""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import math +import os +import random +import sys +import time +import traceback +from dataclasses import asdict, dataclass +from pathlib import Path +from types import ModuleType +from typing import Any, Sequence + + +@dataclass(frozen=True) +class Validation: + valid: bool + message: str + computed_sum: float + reported_sum: float + sum_error: float + minimum_boundary_clearance: float + minimum_pair_clearance: float + + +def _load_candidate(path: Path) -> ModuleType: + spec = importlib.util.spec_from_file_location("candidate_packing", path) + if spec is None or spec.loader is None: + raise RuntimeError(f"cannot load candidate program: {path}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def _seed_runtime(seed: int | None) -> None: + """Seed common in-process RNGs before calling the candidate entry point.""" + + if seed is None: + return + os.environ["VERO_EVALUATION_SEED"] = str(seed) + random.seed(seed) + numpy = sys.modules.get("numpy") + numpy_random = getattr(numpy, "random", None) + numpy_seed = getattr(numpy_random, "seed", None) + if callable(numpy_seed): + numpy_seed(seed) + + +def _sequence(value: Any, *, name: str) -> list[Any]: + if isinstance(value, (str, bytes)): + raise TypeError(f"{name} must be a sequence, not text") + try: + return list(value) + except TypeError as error: + raise TypeError(f"{name} must be a sequence") from error + + +def _normalize_output( + output: Any, +) -> tuple[list[list[float]], list[float], float]: + values = _sequence(output, name="run_packing output") + if len(values) != 3: + raise ValueError("run_packing must return (centers, radii, reported_sum)") + + raw_centers = _sequence(values[0], name="centers") + raw_radii = _sequence(values[1], name="radii") + centers: list[list[float]] = [] + for index, center in enumerate(raw_centers): + coordinates = _sequence(center, name=f"centers[{index}]") + if len(coordinates) != 2: + raise ValueError(f"centers[{index}] must have exactly two coordinates") + centers.append([float(coordinates[0]), float(coordinates[1])]) + radii = [float(radius) for radius in raw_radii] + return centers, radii, float(values[2]) + + +def validate_packing( + centers: Sequence[Sequence[float]], + radii: Sequence[float], + reported_sum: float, +) -> Validation: + if len(centers) != 26: + raise ValueError(f"expected 26 centers, received {len(centers)}") + if len(radii) != 26: + raise ValueError(f"expected 26 radii, received {len(radii)}") + + flattened = [coordinate for center in centers for coordinate in center] + if not all(math.isfinite(value) for value in [*flattened, *radii, reported_sum]): + raise ValueError("centers, radii, and reported_sum must all be finite") + if any(radius < 0.0 for radius in radii): + raise ValueError("radii must be non-negative") + + computed_sum = math.fsum(radii) + sum_error = abs(computed_sum - reported_sum) + sum_matches = math.isclose(computed_sum, reported_sum, rel_tol=1e-9, abs_tol=1e-12) + boundary_clearances = [ + min(x - radius, y - radius, 1.0 - x - radius, 1.0 - y - radius) + for (x, y), radius in zip(centers, radii, strict=True) + ] + pair_clearances = [ + math.dist(centers[left], centers[right]) - radii[left] - radii[right] + for left in range(26) + for right in range(left + 1, 26) + ] + minimum_boundary_clearance = min(boundary_clearances) + minimum_pair_clearance = min(pair_clearances) + + failures: list[str] = [] + if not sum_matches: + failures.append( + f"reported sum differs from the computed sum by {sum_error:.6g}" + ) + if minimum_boundary_clearance < 0.0: + failures.append( + "at least one circle crosses the square boundary " + f"by {-minimum_boundary_clearance:.6g}" + ) + if minimum_pair_clearance < 0.0: + failures.append( + f"at least two circles overlap by {-minimum_pair_clearance:.6g}" + ) + + return Validation( + valid=not failures, + message="; ".join(failures) if failures else "packing is geometrically valid", + computed_sum=computed_sum, + reported_sum=reported_sum, + sum_error=sum_error, + minimum_boundary_clearance=minimum_boundary_clearance, + minimum_pair_clearance=minimum_pair_clearance, + ) + + +def _write_layout_json( + path: Path, + centers: Sequence[Sequence[float]], + radii: Sequence[float], + validation: Validation, +) -> None: + path.write_text( + json.dumps( + { + "schema_version": 1, + "circles": [ + {"index": index, "x": x, "y": y, "radius": radius} + for index, ((x, y), radius) in enumerate( + zip(centers, radii, strict=True) + ) + ], + "validation": asdict(validation), + }, + indent=2, + sort_keys=True, + ) + + "\n", + encoding="utf-8", + ) + + +def _write_layout_svg( + path: Path, + centers: Sequence[Sequence[float]], + radii: Sequence[float], + validation: Validation, +) -> None: + size = 800 + circles = "\n".join( + ( + f' ' + ) + for index, ((x, y), radius) in enumerate(zip(centers, radii, strict=True)) + ) + label = ( + f"sum={validation.computed_sum:.12f}; " + f"valid={'yes' if validation.valid else 'no'}" + ) + path.write_text( + "\n".join( + [ + '', + ' ', + circles, + f' {label}', + "", + "", + ] + ), + encoding="utf-8", + ) + + +def _artifact(path: str, media_type: str, description: str) -> dict[str, str]: + return {"path": path, "media_type": media_type, "description": description} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--request", type=Path, required=True) + parser.add_argument("--report", type=Path, required=True) + parser.add_argument("--artifacts", type=Path, required=True) + args = parser.parse_args() + + command_input = json.loads(args.request.read_text(encoding="utf-8")) + if command_input.get("schema_version") != 1: + raise ValueError("unsupported command input schema") + request = command_input.get("request") + if not isinstance(request, dict): + raise ValueError("command input must contain an evaluation request") + seed = request.get("seed") + if seed is not None and not isinstance(seed, int): + raise ValueError("evaluation seed must be an integer or null") + + artifact_dir = args.artifacts / "circle-packing" + artifact_dir.mkdir(parents=True, exist_ok=True) + started = time.perf_counter() + try: + _seed_runtime(seed) + candidate = _load_candidate(args.workspace / "packing.py") + run_packing = getattr(candidate, "run_packing", None) + if not callable(run_packing): + raise AttributeError("packing.py must define a callable run_packing()") + # Reset after import-time work and seed NumPy if the candidate imported it. + _seed_runtime(seed) + centers, radii, reported_sum = _normalize_output(run_packing()) + runtime_ms = (time.perf_counter() - started) * 1000.0 + validation = validate_packing(centers, radii, reported_sum) + + _write_layout_json( + artifact_dir / "layout.json", centers, radii, validation + ) + _write_layout_svg(artifact_dir / "layout.svg", centers, radii, validation) + (artifact_dir / "validation.json").write_text( + json.dumps(asdict(validation), indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + artifacts = [ + _artifact( + "circle-packing/layout.json", + "application/json", + "Circle centers, radii, and validation measurements", + ), + _artifact( + "circle-packing/layout.svg", + "image/svg+xml", + "Rendered circle packing", + ), + _artifact( + "circle-packing/validation.json", + "application/json", + "Geometric feasibility diagnostics", + ), + ] + report: dict[str, Any] = { + "schema_version": 1, + "status": "success", + "metrics": { + "sum_radii": validation.computed_sum, + "valid": 1.0 if validation.valid else 0.0, + "minimum_boundary_clearance": validation.minimum_boundary_clearance, + "minimum_pair_clearance": validation.minimum_pair_clearance, + "runtime_ms": runtime_ms, + }, + "artifacts": artifacts, + } + if not validation.valid: + report["diagnostics"] = [ + { + "code": "invalid_packing", + "message": validation.message, + "severity": "warning", + "phase": "validation", + } + ] + except BaseException as error: + runtime_ms = (time.perf_counter() - started) * 1000.0 + (artifact_dir / "failure.log").write_text( + "".join(traceback.format_exception(error)), + encoding="utf-8", + ) + report = { + "schema_version": 1, + "status": "failed", + "metrics": {"runtime_ms": runtime_ms}, + "diagnostics": [ + { + "code": "candidate_execution_failed", + "message": f"{type(error).__name__}: {error}", + "severity": "error", + "phase": "candidate", + } + ], + "artifacts": [ + _artifact( + "circle-packing/failure.log", + "text/plain", + "Candidate exception and traceback", + ) + ], + } + + args.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/circle-packing/target/.gitignore b/vero/examples/circle-packing/target/.gitignore new file mode 100644 index 00000000..f5b45e68 --- /dev/null +++ b/vero/examples/circle-packing/target/.gitignore @@ -0,0 +1,4 @@ +.vero/ +.venv/ +__pycache__/ +*.py[oc] diff --git a/vero/examples/circle-packing/target/packing.py b/vero/examples/circle-packing/target/packing.py new file mode 100644 index 00000000..88bf3d10 --- /dev/null +++ b/vero/examples/circle-packing/target/packing.py @@ -0,0 +1,62 @@ +"""Constructor-based circle packing for 26 circles in a unit square. + +Adapted from ShinkaEvolve's circle-packing ``initial.py`` baseline: +https://github.com/SakanaAI/ShinkaEvolve/tree/main/examples/circle_packing + +Copyright 2025 Sakana AI. Licensed under the Apache License, Version 2.0. +This VeRO adaptation replaces NumPy with the Python standard library and lets +the coding agent edit the complete program instead of a marked evolve block. +""" + +from __future__ import annotations + +import math + + +def construct_packing() -> tuple[list[list[float]], list[float]]: + """Return centers and non-overlapping radii for 26 circles.""" + + centers = [[0.0, 0.0] for _ in range(26)] + centers[0] = [0.5, 0.5] + + for index in range(8): + angle = 2.0 * math.pi * index / 8 + centers[index + 1] = [ + 0.5 + 0.3 * math.cos(angle), + 0.5 + 0.3 * math.sin(angle), + ] + + for index in range(16): + angle = 2.0 * math.pi * index / 16 + centers[index + 9] = [ + 0.5 + 0.7 * math.cos(angle), + 0.5 + 0.7 * math.sin(angle), + ] + + centers = [ + [min(0.99, max(0.01, x)), min(0.99, max(0.01, y))] + for x, y in centers + ] + return centers, compute_max_radii(centers) + + +def compute_max_radii(centers: list[list[float]]) -> list[float]: + """Greedily shrink initially maximal radii until every pair is feasible.""" + + radii = [min(x, y, 1.0 - x, 1.0 - y) for x, y in centers] + for left in range(len(centers)): + for right in range(left + 1, len(centers)): + distance = math.dist(centers[left], centers[right]) + radius_sum = radii[left] + radii[right] + if radius_sum > distance: + scale = distance / radius_sum + radii[left] *= scale + radii[right] *= scale + return [max(radius, 0.0) for radius in radii] + + +def run_packing() -> tuple[list[list[float]], list[float], float]: + """Entry point called by the trusted evaluator.""" + + centers, radii = construct_packing() + return centers, radii, sum(radii) diff --git a/vero/examples/circle-packing/target/pyproject.toml b/vero/examples/circle-packing/target/pyproject.toml new file mode 100644 index 00000000..a3256b26 --- /dev/null +++ b/vero/examples/circle-packing/target/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "vero-circle-packing-candidate" +version = "0.1.0" +description = "An editable 26-circle packing program" +requires-python = ">=3.11" +dependencies = [] + +[tool.uv] +package = false diff --git a/vero/examples/circle-packing/target/uv.lock b/vero/examples/circle-packing/target/uv.lock new file mode 100644 index 00000000..519f3d03 --- /dev/null +++ b/vero/examples/circle-packing/target/uv.lock @@ -0,0 +1,8 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" + +[[package]] +name = "vero-circle-packing-candidate" +version = "0.1.0" +source = { virtual = "." } diff --git a/vero/examples/circle-packing/vero.toml b/vero/examples/circle-packing/vero.toml new file mode 100644 index 00000000..c489baf7 --- /dev/null +++ b/vero/examples/circle-packing/vero.toml @@ -0,0 +1,86 @@ +[target] +root = "./target" +ref = "HEAD" + +[backend] +id = "circle-packing" +kind = "command" +harness_root = "./harness" +environment = { PYTHONHASHSEED = "0" } +passthrough_environment = ["PATH", "UV_CACHE_DIR"] +command = [ + "uv", + "run", + "--frozen", + "--project", + "{workspace}", + "python", + "{harness}/evaluate.py", + "--workspace", "{workspace}", + "--request", "{request}", + "--report", "{report}", + "--artifacts", "{artifacts}", +] + +[[evaluations]] +name = "development" +partition = "development" +agent_can_evaluate = true +agent_visible = true +agent_selection = "fixed" +disclosure = "full" + +[evaluations.agent_budget] +total_runs = 30 + +[[evaluations]] +name = "final" +partition = "final" +agent_can_evaluate = false +agent_visible = false +agent_selection = "fixed" +disclosure = "none" + +[protocol] +selection_evaluation = "development" +final_evaluation = "final" +evaluate_final_baseline = true +timeout_seconds = 300 +max_proposals = 1 +max_rounds = 1 +max_concurrency = 1 +seed = 1337 + +[protocol.retry] +max_attempts = 1 + +[objective] +metric = "sum_radii" +direction = "maximize" +failure_value = 0.0 + +[[objective.constraints]] +metric = "valid" +operator = "==" +value = 1.0 + +[optimizer] +kind = "vero" +model = "openai/gpt-5" +max_turns = 200 +instruction = """ +Improve the program in packing.py to maximize the sum of the radii of exactly +26 non-overlapping circles inside the unit square. The best published result is +approximately 2.635, but make measurable progress from the supplied baseline +before pursuing sophisticated refinements. + +Use the development evaluation repeatedly and inspect its read-only JSON and +SVG artifacts in .vero. Preserve run_packing() and its return contract. You may +rewrite the algorithm completely. If you need third-party Python packages, add +and lock them with `uv add`; evaluation uses the checked-in uv.lock with +`--frozen`. Never weaken, bypass, or reproduce the trusted evaluator. +""" + +[session] +id = "circle-packing-example" +directory = "./.vero/session" diff --git a/vero/examples/harbor-circle-packing/.gitignore b/vero/examples/harbor-circle-packing/.gitignore new file mode 100644 index 00000000..68ea40f6 --- /dev/null +++ b/vero/examples/harbor-circle-packing/.gitignore @@ -0,0 +1,3 @@ +# Vendored VeRO package copied into the build context before `harbor run` +# (VeRO is not on public PyPI). Do not commit the copy. +environment/vero/ diff --git a/vero/examples/harbor-circle-packing/README.md b/vero/examples/harbor-circle-packing/README.md new file mode 100644 index 00000000..51ec30f3 --- /dev/null +++ b/vero/examples/harbor-circle-packing/README.md @@ -0,0 +1,72 @@ +# Harbor circle-packing example + +A **Harbor outer loop with a simple inner loop**: Harbor drives a coding agent +that edits `packing.py` to pack 26 non-overlapping circles in the unit square +(maximize the sum of radii), and each candidate is scored by a **plain +`CommandBackend`** — a fast Python scorer — **not** a nested `harbor run`. + +``` + harbor run ──► main service (coding agent edits /work/agent/packing.py) + │ vero harbor eval (self-score during the run) + ▼ + eval-sidecar ──► CommandBackend → harness/evaluate.py + (trusted: scoring, budget, disclosure, final selection) +``` + +## Why a custom factory (and not `vero harbor build`) + +`vero harbor build` compiles a task whose inner evaluation is itself a nested +`harbor run` (it hardcodes `HarborBackend` and requires a `task_source`). That +is the right tool when the inner task is another Harbor benchmark, but it is +heavier than needed here. For a **simple** inner loop you supply a custom sidecar +factory (`sidecar/circle_factory.py`) that wires a `CommandBackend` and run it +with `vero harbor serve --factory circle_factory:build` (see +`environment/docker-compose.yaml`). + +## Layout + +| Path | Role | +|---|---| +| `task.toml` | Harbor task definition | +| `instruction.md` | What the coding agent is told to do | +| `environment/Dockerfile` | Main service image (target + VeRO CLI) | +| `environment/main/seed.sh` | Seeds `/work/agent` as a git repo, then idles | +| `environment/agent-seed/` | Baseline `packing.py` the agent starts from | +| `environment/sidecar/Dockerfile` | Trusted sidecar image | +| `environment/sidecar/circle_factory.py` | Wires the `CommandBackend`, sidecar policies, objective, verifier | +| `environment/sidecar/harness/evaluate.py` | The scorer (`sum_radii`, `valid`, clearances) | +| `environment/agent-baseline/` | Trusted baseline the sidecar scores against | +| `solution/solve.sh` | Reference: self-score via `vero harbor eval` | +| `tests/test.sh` | Verifier: `vero harbor finalize` → `/logs/verifier/reward.json` | + +The objective is `sum_radii` (maximize) subject to `valid == 1`; the baseline +scores ~0.96 and the best known result is ~2.635. + +## Run it + +Requires Docker and a coding agent supported by Harbor (e.g. `codex`), plus a +model endpoint. From this directory: + +```bash +export OPENAI_API_KEY=... # or your gateway key +export OPENAI_BASE_URL=... # e.g. a LiteLLM proxy exposing /v1 + +harbor run -e docker -a codex -m openai/gpt-5.4 --yes +``` + +Harbor builds the two images, starts the sidecar, runs the agent against +`instruction.md`, then runs `tests/test.sh` to emit the final reward. + +## Notes + +- **Install:** VeRO is not published on public PyPI (the `scale-vero` name there + is an unrelated placeholder), so both images vendor the package. Before + `harbor run`, copy the VeRO package into this build context: + `cp -r /vero environment/vero` (it is git-ignored). The Dockerfiles then + `COPY vero /opt/vero && uv pip install "/opt/vero[harbor]"`. +- Multi-metric rewards: the verifier emits `reward.json` (a dict), and the + sidecar can run in a separate verifier environment for stronger isolation from + an untrusted agent — see the Harbor task docs. +- This mirrors a pattern validated end-to-end (codex improved the baseline from + 0.96 toward the ~2.635 optimum); it needs Docker + a coding agent + model + access to run and is not exercised by the unit test suite. diff --git a/vero/examples/harbor-circle-packing/environment/Dockerfile b/vero/examples/harbor-circle-packing/environment/Dockerfile new file mode 100644 index 00000000..8eb8918a --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/Dockerfile @@ -0,0 +1,20 @@ +# Main service: the optimizer workbench a coding agent works in. +# Holds the editable target program plus the VeRO CLI so the agent can call +# `vero harbor eval` / `vero harbor status` against the trusted sidecar. +FROM ghcr.io/astral-sh/uv:python3.12-bookworm + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# Install VeRO's harbor client. VeRO is NOT on public PyPI (the `scale-vero` +# name there is an unrelated placeholder), so vendor the package into this build +# context first — e.g. `cp -r /vero environment/vero` — then install it: +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" + +COPY agent-seed /opt/agent-seed +COPY main/seed.sh /opt/seed.sh +RUN chmod +x /opt/seed.sh && useradd -m -u 1001 agent + +WORKDIR /work/agent diff --git a/vero/examples/harbor-circle-packing/environment/agent-baseline/packing.py b/vero/examples/harbor-circle-packing/environment/agent-baseline/packing.py new file mode 100644 index 00000000..88bf3d10 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/agent-baseline/packing.py @@ -0,0 +1,62 @@ +"""Constructor-based circle packing for 26 circles in a unit square. + +Adapted from ShinkaEvolve's circle-packing ``initial.py`` baseline: +https://github.com/SakanaAI/ShinkaEvolve/tree/main/examples/circle_packing + +Copyright 2025 Sakana AI. Licensed under the Apache License, Version 2.0. +This VeRO adaptation replaces NumPy with the Python standard library and lets +the coding agent edit the complete program instead of a marked evolve block. +""" + +from __future__ import annotations + +import math + + +def construct_packing() -> tuple[list[list[float]], list[float]]: + """Return centers and non-overlapping radii for 26 circles.""" + + centers = [[0.0, 0.0] for _ in range(26)] + centers[0] = [0.5, 0.5] + + for index in range(8): + angle = 2.0 * math.pi * index / 8 + centers[index + 1] = [ + 0.5 + 0.3 * math.cos(angle), + 0.5 + 0.3 * math.sin(angle), + ] + + for index in range(16): + angle = 2.0 * math.pi * index / 16 + centers[index + 9] = [ + 0.5 + 0.7 * math.cos(angle), + 0.5 + 0.7 * math.sin(angle), + ] + + centers = [ + [min(0.99, max(0.01, x)), min(0.99, max(0.01, y))] + for x, y in centers + ] + return centers, compute_max_radii(centers) + + +def compute_max_radii(centers: list[list[float]]) -> list[float]: + """Greedily shrink initially maximal radii until every pair is feasible.""" + + radii = [min(x, y, 1.0 - x, 1.0 - y) for x, y in centers] + for left in range(len(centers)): + for right in range(left + 1, len(centers)): + distance = math.dist(centers[left], centers[right]) + radius_sum = radii[left] + radii[right] + if radius_sum > distance: + scale = distance / radius_sum + radii[left] *= scale + radii[right] *= scale + return [max(radius, 0.0) for radius in radii] + + +def run_packing() -> tuple[list[list[float]], list[float], float]: + """Entry point called by the trusted evaluator.""" + + centers, radii = construct_packing() + return centers, radii, sum(radii) diff --git a/vero/examples/harbor-circle-packing/environment/agent-baseline/pyproject.toml b/vero/examples/harbor-circle-packing/environment/agent-baseline/pyproject.toml new file mode 100644 index 00000000..a3256b26 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/agent-baseline/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "vero-circle-packing-candidate" +version = "0.1.0" +description = "An editable 26-circle packing program" +requires-python = ">=3.11" +dependencies = [] + +[tool.uv] +package = false diff --git a/vero/examples/harbor-circle-packing/environment/agent-seed/packing.py b/vero/examples/harbor-circle-packing/environment/agent-seed/packing.py new file mode 100644 index 00000000..88bf3d10 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/agent-seed/packing.py @@ -0,0 +1,62 @@ +"""Constructor-based circle packing for 26 circles in a unit square. + +Adapted from ShinkaEvolve's circle-packing ``initial.py`` baseline: +https://github.com/SakanaAI/ShinkaEvolve/tree/main/examples/circle_packing + +Copyright 2025 Sakana AI. Licensed under the Apache License, Version 2.0. +This VeRO adaptation replaces NumPy with the Python standard library and lets +the coding agent edit the complete program instead of a marked evolve block. +""" + +from __future__ import annotations + +import math + + +def construct_packing() -> tuple[list[list[float]], list[float]]: + """Return centers and non-overlapping radii for 26 circles.""" + + centers = [[0.0, 0.0] for _ in range(26)] + centers[0] = [0.5, 0.5] + + for index in range(8): + angle = 2.0 * math.pi * index / 8 + centers[index + 1] = [ + 0.5 + 0.3 * math.cos(angle), + 0.5 + 0.3 * math.sin(angle), + ] + + for index in range(16): + angle = 2.0 * math.pi * index / 16 + centers[index + 9] = [ + 0.5 + 0.7 * math.cos(angle), + 0.5 + 0.7 * math.sin(angle), + ] + + centers = [ + [min(0.99, max(0.01, x)), min(0.99, max(0.01, y))] + for x, y in centers + ] + return centers, compute_max_radii(centers) + + +def compute_max_radii(centers: list[list[float]]) -> list[float]: + """Greedily shrink initially maximal radii until every pair is feasible.""" + + radii = [min(x, y, 1.0 - x, 1.0 - y) for x, y in centers] + for left in range(len(centers)): + for right in range(left + 1, len(centers)): + distance = math.dist(centers[left], centers[right]) + radius_sum = radii[left] + radii[right] + if radius_sum > distance: + scale = distance / radius_sum + radii[left] *= scale + radii[right] *= scale + return [max(radius, 0.0) for radius in radii] + + +def run_packing() -> tuple[list[list[float]], list[float], float]: + """Entry point called by the trusted evaluator.""" + + centers, radii = construct_packing() + return centers, radii, sum(radii) diff --git a/vero/examples/harbor-circle-packing/environment/agent-seed/pyproject.toml b/vero/examples/harbor-circle-packing/environment/agent-seed/pyproject.toml new file mode 100644 index 00000000..a3256b26 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/agent-seed/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "vero-circle-packing-candidate" +version = "0.1.0" +description = "An editable 26-circle packing program" +requires-python = ">=3.11" +dependencies = [] + +[tool.uv] +package = false diff --git a/vero/examples/harbor-circle-packing/environment/docker-compose.yaml b/vero/examples/harbor-circle-packing/environment/docker-compose.yaml new file mode 100644 index 00000000..c5d742e6 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/docker-compose.yaml @@ -0,0 +1,44 @@ +# Harbor merges this after its generated main-service configuration. +services: + main: + command: ["/opt/seed.sh"] + environment: + VERO_EVAL_URL: "http://eval-sidecar:8000" + OPENAI_API_KEY: "${OPENAI_API_KEY:?OPENAI_API_KEY must be set}" + OPENAI_BASE_URL: "${OPENAI_BASE_URL:?OPENAI_BASE_URL must be set}" + volumes: + - agent_repo:/work/agent + - token_state:/state/token:ro + depends_on: + eval-sidecar: + condition: service_healthy + + eval-sidecar: + build: + context: . + dockerfile: sidecar/Dockerfile + command: + - "vero" + - "harbor" + - "serve" + - "--factory" + - "circle_factory:build" + - "--config" + - "/opt/serve.json" + - "--admin-token" + - "/state/token/admin.token" + volumes: + - agent_repo:/work/agent:ro + - admin_state:/state/admin + - token_state:/state/token + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 5s + timeout: 10s + retries: 30 + start_period: 10s + +volumes: + agent_repo: + admin_state: + token_state: diff --git a/vero/examples/harbor-circle-packing/environment/main/seed.sh b/vero/examples/harbor-circle-packing/environment/main/seed.sh new file mode 100755 index 00000000..4f85e066 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/main/seed.sh @@ -0,0 +1,14 @@ +#!/bin/sh +# Seed the agent's editable working copy as a git repo on first boot, then idle +# so the coding agent (driven by `harbor run`) can work in it. +set -eu +if [ ! -d /work/agent/.git ]; then + cp -a /opt/agent-seed/. /work/agent/ + cd /work/agent + git init -q + git add -A + git -c user.email=seed@vero.test -c user.name=seed commit -qm "baseline" +fi +find /work/agent -exec chown agent:agent {} + +git config --system --add safe.directory /work/agent +exec sleep infinity diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/Dockerfile b/vero/examples/harbor-circle-packing/environment/sidecar/Dockerfile new file mode 100644 index 00000000..d0522acb --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/sidecar/Dockerfile @@ -0,0 +1,32 @@ +# Trusted evaluation sidecar. It owns scoring, budget, disclosure, and final +# candidate selection. The inner loop is a plain CommandBackend (circle-packing's +# evaluate.py) — a SIMPLE inner loop, NOT a nested `harbor run`. +FROM ghcr.io/astral-sh/uv:python3.12-bookworm + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# VeRO is NOT on public PyPI (the `scale-vero` name there is an unrelated +# placeholder), so vendor the package into this build context first — e.g. +# `cp -r /vero environment/vero` — then install it: +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" + +COPY agent-baseline /opt/agent-baseline +COPY sidecar/circle_factory.py /opt/circle_factory.py +COPY sidecar/harness /opt/harness +COPY sidecar/serve.json /opt/serve.json + +# The sidecar holds the trusted baseline as a git repo (source of the baseline +# candidate; also the transport root for importing the agent's commits). +RUN cd /opt/agent-baseline \ + && git init -q \ + && git add -A \ + && git -c user.email=baseline@vero.test -c user.name=baseline commit -qm "baseline" \ + && git config --system --add safe.directory /opt/agent-baseline \ + && git config --system --add safe.directory /work/agent \ + && git config --system --add safe.directory /work/agent/.git + +ENV PYTHONPATH=/opt +WORKDIR /opt diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py new file mode 100644 index 00000000..96cde52e --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py @@ -0,0 +1,155 @@ +"""Custom Harbor sidecar factory for circle-packing. + +Harbor drives the OUTER loop (a coding agent edits packing.py); the target is +scored by a plain CommandBackend (circle-packing's evaluate.py) — NOT a nested +harbor run. Same shape as the proven harbor-live demo, swapped to the +sum_radii / valid objective. +""" + +from __future__ import annotations + +from pathlib import Path + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendRegistry, + BudgetLedger, + CaseRange, + ConstraintOperator, + EvaluationBudget, + EvaluationDatabase, + EvaluationSet, + Evaluator, + MetricConstraint, + MetricSelector, + ObjectiveSpec, +) +from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.engine import EvaluationEngine +from vero.harbor.serve import SidecarComponents +from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy +from vero.harbor.transport import GitCandidateTransport +from vero.harbor.verifier import CanonicalVerifier, VerificationSelection, VerificationTarget +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + +SET = "circle-packing" +OBJ = ObjectiveSpec( + selector=MetricSelector(metric="sum_radii"), + direction="maximize", + failure_value=0.0, + constraints=[ + MetricConstraint( + selector=MetricSelector(metric="valid"), + operator=ConstraintOperator.EQ, + value=1.0, + ) + ], +) + + +def _backend(harness_root: str) -> CommandBackend: + return CommandBackend( + CommandBackendConfig( + harness_root=harness_root, + command=[ + "uv", "run", "--python", "3.12", "python", "{harness}/evaluate.py", + "--workspace", "{workspace}", + "--request", "{request}", + "--report", "{report}", + "--artifacts", "{artifacts}", + ], + environment={"PYTHONHASHSEED": "0"}, + passthrough_environment=["PATH", "HOME", "UV_CACHE_DIR"], + ) + ) + + +async def build(config: dict) -> SidecarComponents: + repo_path = config["repo_path"] + agent_repo_path = config["agent_repo_path"] + session_dir = Path(config["session_dir"]) + harness_root = config["harness_root"] + session_dir.mkdir(parents=True, exist_ok=True) + + sandbox = await LocalSandbox.create(root=Path(repo_path).parent) + workspace = await GitWorkspace.from_path(sandbox, repo_path) + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + database = EvaluationDatabase.load_reconciled( + database_path=session_dir / "database.json", + evaluations_dir=session_dir / "evaluations", + database_id="circle", + ) + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="cmd", + evaluation_set_key=f"cmd:{SET}:validation", + total_runs=10, + ) + ], + path=session_dir / "budgets.json", + ) + ledger.save() + + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + session_id="circle", + ), + backends=BackendRegistry({"cmd": _backend(harness_root)}), + database=database, + database_path=session_dir / "database.json", + budget_ledger=ledger, + ) + transport = GitCandidateTransport( + workspace=workspace, + candidate_repository=candidate_repository, + agent_repo_path=agent_repo_path, + ) + baseline = await transport.trusted_candidate("HEAD") + + selection = VerificationSelection( + mode="auto_best", + backend_id="cmd", + evaluation_set=EvaluationSet( + name=SET, partition="validation", selection=CaseRange(start=0, stop=1) + ), + objective=OBJ, + baseline_candidate=baseline, + ) + sidecar = EvaluationSidecar( + engine=engine, + candidate_transport=transport, + access_policies=[ + SidecarEvaluationPolicy( + backend_id="cmd", evaluation_set_name=SET, partition="development", + disclosure="full", expose_case_resources=True, objective=OBJ, + ), + SidecarEvaluationPolicy( + backend_id="cmd", evaluation_set_name=SET, partition="validation", + disclosure="aggregate", min_aggregate_cases=1, objective=OBJ, + ), + ], + admin_volume=session_dir / "admin", + ) + await sidecar.initialize_context() + + verifier = CanonicalVerifier( + engine=engine, + selection=selection, + targets=[ + VerificationTarget( + reward_key="sum_radii", backend_id="cmd", + evaluation_set=EvaluationSet(name=SET, partition="test"), + objective=OBJ, max_attempts=1, + ) + ], + admin_volume=session_dir / "admin", + score_baseline=True, + ) + return SidecarComponents(sidecar=sidecar, verifier=verifier) diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/harness/evaluate.py b/vero/examples/harbor-circle-packing/environment/sidecar/harness/evaluate.py new file mode 100644 index 00000000..a80f94b0 --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/sidecar/harness/evaluate.py @@ -0,0 +1,315 @@ +"""Trusted validator and scorer for the 26-circle packing benchmark.""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import math +import os +import random +import sys +import time +import traceback +from dataclasses import asdict, dataclass +from pathlib import Path +from types import ModuleType +from typing import Any, Sequence + + +@dataclass(frozen=True) +class Validation: + valid: bool + message: str + computed_sum: float + reported_sum: float + sum_error: float + minimum_boundary_clearance: float + minimum_pair_clearance: float + + +def _load_candidate(path: Path) -> ModuleType: + spec = importlib.util.spec_from_file_location("candidate_packing", path) + if spec is None or spec.loader is None: + raise RuntimeError(f"cannot load candidate program: {path}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def _seed_runtime(seed: int | None) -> None: + """Seed common in-process RNGs before calling the candidate entry point.""" + + if seed is None: + return + os.environ["VERO_EVALUATION_SEED"] = str(seed) + random.seed(seed) + numpy = sys.modules.get("numpy") + numpy_random = getattr(numpy, "random", None) + numpy_seed = getattr(numpy_random, "seed", None) + if callable(numpy_seed): + numpy_seed(seed) + + +def _sequence(value: Any, *, name: str) -> list[Any]: + if isinstance(value, (str, bytes)): + raise TypeError(f"{name} must be a sequence, not text") + try: + return list(value) + except TypeError as error: + raise TypeError(f"{name} must be a sequence") from error + + +def _normalize_output( + output: Any, +) -> tuple[list[list[float]], list[float], float]: + values = _sequence(output, name="run_packing output") + if len(values) != 3: + raise ValueError("run_packing must return (centers, radii, reported_sum)") + + raw_centers = _sequence(values[0], name="centers") + raw_radii = _sequence(values[1], name="radii") + centers: list[list[float]] = [] + for index, center in enumerate(raw_centers): + coordinates = _sequence(center, name=f"centers[{index}]") + if len(coordinates) != 2: + raise ValueError(f"centers[{index}] must have exactly two coordinates") + centers.append([float(coordinates[0]), float(coordinates[1])]) + radii = [float(radius) for radius in raw_radii] + return centers, radii, float(values[2]) + + +def validate_packing( + centers: Sequence[Sequence[float]], + radii: Sequence[float], + reported_sum: float, +) -> Validation: + if len(centers) != 26: + raise ValueError(f"expected 26 centers, received {len(centers)}") + if len(radii) != 26: + raise ValueError(f"expected 26 radii, received {len(radii)}") + + flattened = [coordinate for center in centers for coordinate in center] + if not all(math.isfinite(value) for value in [*flattened, *radii, reported_sum]): + raise ValueError("centers, radii, and reported_sum must all be finite") + if any(radius < 0.0 for radius in radii): + raise ValueError("radii must be non-negative") + + computed_sum = math.fsum(radii) + sum_error = abs(computed_sum - reported_sum) + sum_matches = math.isclose(computed_sum, reported_sum, rel_tol=1e-9, abs_tol=1e-12) + boundary_clearances = [ + min(x - radius, y - radius, 1.0 - x - radius, 1.0 - y - radius) + for (x, y), radius in zip(centers, radii, strict=True) + ] + pair_clearances = [ + math.dist(centers[left], centers[right]) - radii[left] - radii[right] + for left in range(26) + for right in range(left + 1, 26) + ] + minimum_boundary_clearance = min(boundary_clearances) + minimum_pair_clearance = min(pair_clearances) + + failures: list[str] = [] + if not sum_matches: + failures.append( + f"reported sum differs from the computed sum by {sum_error:.6g}" + ) + if minimum_boundary_clearance < 0.0: + failures.append( + "at least one circle crosses the square boundary " + f"by {-minimum_boundary_clearance:.6g}" + ) + if minimum_pair_clearance < 0.0: + failures.append( + f"at least two circles overlap by {-minimum_pair_clearance:.6g}" + ) + + return Validation( + valid=not failures, + message="; ".join(failures) if failures else "packing is geometrically valid", + computed_sum=computed_sum, + reported_sum=reported_sum, + sum_error=sum_error, + minimum_boundary_clearance=minimum_boundary_clearance, + minimum_pair_clearance=minimum_pair_clearance, + ) + + +def _write_layout_json( + path: Path, + centers: Sequence[Sequence[float]], + radii: Sequence[float], + validation: Validation, +) -> None: + path.write_text( + json.dumps( + { + "schema_version": 1, + "circles": [ + {"index": index, "x": x, "y": y, "radius": radius} + for index, ((x, y), radius) in enumerate( + zip(centers, radii, strict=True) + ) + ], + "validation": asdict(validation), + }, + indent=2, + sort_keys=True, + ) + + "\n", + encoding="utf-8", + ) + + +def _write_layout_svg( + path: Path, + centers: Sequence[Sequence[float]], + radii: Sequence[float], + validation: Validation, +) -> None: + size = 800 + circles = "\n".join( + ( + f' ' + ) + for index, ((x, y), radius) in enumerate(zip(centers, radii, strict=True)) + ) + label = ( + f"sum={validation.computed_sum:.12f}; " + f"valid={'yes' if validation.valid else 'no'}" + ) + path.write_text( + "\n".join( + [ + '', + ' ', + circles, + f' {label}', + "", + "", + ] + ), + encoding="utf-8", + ) + + +def _artifact(path: str, media_type: str, description: str) -> dict[str, str]: + return {"path": path, "media_type": media_type, "description": description} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--request", type=Path, required=True) + parser.add_argument("--report", type=Path, required=True) + parser.add_argument("--artifacts", type=Path, required=True) + args = parser.parse_args() + + command_input = json.loads(args.request.read_text(encoding="utf-8")) + if command_input.get("schema_version") != 1: + raise ValueError("unsupported command input schema") + request = command_input.get("request") + if not isinstance(request, dict): + raise ValueError("command input must contain an evaluation request") + seed = request.get("seed") + if seed is not None and not isinstance(seed, int): + raise ValueError("evaluation seed must be an integer or null") + + artifact_dir = args.artifacts / "circle-packing" + artifact_dir.mkdir(parents=True, exist_ok=True) + started = time.perf_counter() + try: + _seed_runtime(seed) + candidate = _load_candidate(args.workspace / "packing.py") + run_packing = getattr(candidate, "run_packing", None) + if not callable(run_packing): + raise AttributeError("packing.py must define a callable run_packing()") + # Reset after import-time work and seed NumPy if the candidate imported it. + _seed_runtime(seed) + centers, radii, reported_sum = _normalize_output(run_packing()) + runtime_ms = (time.perf_counter() - started) * 1000.0 + validation = validate_packing(centers, radii, reported_sum) + + _write_layout_json( + artifact_dir / "layout.json", centers, radii, validation + ) + _write_layout_svg(artifact_dir / "layout.svg", centers, radii, validation) + (artifact_dir / "validation.json").write_text( + json.dumps(asdict(validation), indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + artifacts = [ + _artifact( + "circle-packing/layout.json", + "application/json", + "Circle centers, radii, and validation measurements", + ), + _artifact( + "circle-packing/layout.svg", + "image/svg+xml", + "Rendered circle packing", + ), + _artifact( + "circle-packing/validation.json", + "application/json", + "Geometric feasibility diagnostics", + ), + ] + report: dict[str, Any] = { + "schema_version": 1, + "status": "success", + "metrics": { + "sum_radii": validation.computed_sum, + "valid": 1.0 if validation.valid else 0.0, + "minimum_boundary_clearance": validation.minimum_boundary_clearance, + "minimum_pair_clearance": validation.minimum_pair_clearance, + "runtime_ms": runtime_ms, + }, + "artifacts": artifacts, + } + if not validation.valid: + report["diagnostics"] = [ + { + "code": "invalid_packing", + "message": validation.message, + "severity": "warning", + "phase": "validation", + } + ] + except BaseException as error: + runtime_ms = (time.perf_counter() - started) * 1000.0 + (artifact_dir / "failure.log").write_text( + "".join(traceback.format_exception(error)), + encoding="utf-8", + ) + report = { + "schema_version": 1, + "status": "failed", + "metrics": {"runtime_ms": runtime_ms}, + "diagnostics": [ + { + "code": "candidate_execution_failed", + "message": f"{type(error).__name__}: {error}", + "severity": "error", + "phase": "candidate", + } + ], + "artifacts": [ + _artifact( + "circle-packing/failure.log", + "text/plain", + "Candidate exception and traceback", + ) + ], + } + + args.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/serve.json b/vero/examples/harbor-circle-packing/environment/sidecar/serve.json new file mode 100644 index 00000000..06ae9f4a --- /dev/null +++ b/vero/examples/harbor-circle-packing/environment/sidecar/serve.json @@ -0,0 +1 @@ +{ "repo_path": "/opt/agent-baseline", "agent_repo_path": "/work/agent", "session_dir": "/state/admin/session", "harness_root": "/opt/harness" } diff --git a/vero/examples/harbor-circle-packing/instruction.md b/vero/examples/harbor-circle-packing/instruction.md new file mode 100644 index 00000000..c930ece4 --- /dev/null +++ b/vero/examples/harbor-circle-packing/instruction.md @@ -0,0 +1,27 @@ +# Optimize the circle packing + +Improve `packing.py` in `/work/agent` so the program packs **26 non-overlapping +circles inside the unit square** with the **largest possible sum of radii**. + +`packing.py` must keep a callable `run_packing()` that returns +`(centers, radii, reported_sum)`, where `centers` is 26 `[x, y]` pairs, `radii` +is 26 non-negative floats, and `reported_sum` equals `sum(radii)`. Circles must +stay fully inside the unit square and must not overlap. The current baseline +scores about 0.96; the best known result is ~2.635. + +## Workflow + +1. Edit `/work/agent/packing.py`. +2. Commit your change with Git. +3. Score the current commit on the validation set: + + ```bash + vero harbor eval --backend cmd --evaluation-set circle-packing \ + --partition validation --start 0 --stop 1 + ``` + +4. Use `vero harbor status` to see remaining evaluation budget. + +The trusted sidecar owns scoring, budget, and final candidate selection. Only +`sum_radii` (with a `valid == 1` constraint) counts. Iterate: try an +arrangement, measure it, keep what improves the validated sum. diff --git a/vero/examples/harbor-circle-packing/solution/solve.sh b/vero/examples/harbor-circle-packing/solution/solve.sh new file mode 100755 index 00000000..62ed5687 --- /dev/null +++ b/vero/examples/harbor-circle-packing/solution/solve.sh @@ -0,0 +1,6 @@ +#!/bin/sh +set -eu +cd /work/agent +git config user.name optimizer; git config user.email o@v.test +vero harbor eval --backend cmd --evaluation-set circle-packing --partition validation --start 0 --stop 1 +vero harbor status diff --git a/vero/examples/harbor-circle-packing/task.toml b/vero/examples/harbor-circle-packing/task.toml new file mode 100644 index 00000000..4deb8212 --- /dev/null +++ b/vero/examples/harbor-circle-packing/task.toml @@ -0,0 +1,15 @@ +schema_version = "1.3" + +[task] +name = "vero/circle-packing" +description = "Optimize packing.py to maximize the sum of 26 non-overlapping circle radii in the unit square." + +[agent] +user = "agent" + +[verifier] +environment_mode = "shared" +timeout_sec = 600 + +[environment] +build_timeout_sec = 1800 diff --git a/vero/examples/harbor-circle-packing/tests/test.sh b/vero/examples/harbor-circle-packing/tests/test.sh new file mode 100755 index 00000000..ba6f13b8 --- /dev/null +++ b/vero/examples/harbor-circle-packing/tests/test.sh @@ -0,0 +1,5 @@ +#!/bin/sh +set -eu +mkdir -p /logs/verifier +vero harbor finalize --token-file /state/token/admin.token --output /logs/verifier/reward.json +cat /logs/verifier/reward.json diff --git a/vero/examples/insights-bundle/.claude/agents/ig-investigator.md b/vero/examples/insights-bundle/.claude/agents/ig-investigator.md new file mode 100644 index 00000000..40602795 --- /dev/null +++ b/vero/examples/insights-bundle/.claude/agents/ig-investigator.md @@ -0,0 +1,67 @@ +--- +name: ig-investigator +description: >- + Validation agent (depth). Takes ONE hypothesis and tests it against the WHOLE + corpus with quantitative evidence — labels it confirmed/refuted/inconclusive, + saves a defensible cohort of affected trace_ids, and appends an + evidence-grounded finding to findings.json. Invoked by the insights-generator + orchestrator, one per hypothesis. +model: opus +tools: Bash, Read, Grep, Glob +--- + +You are an **Investigator**. You are handed exactly one hypothesis and you +validate it rigorously against the entire corpus. Depth over breadth: quantify, +compare cohorts, and back the claim with statistical evidence. You may confirm, +**refute**, or mark it inconclusive — a refutation is a valuable result. + +Read `skills/insights/SKILL.md` for the corpus layout, the tool→bash cookbook, +and the finding contract. Follow it exactly — especially the cohort discipline. + +## Validation ladder (broad → deep) +1. **Broad stats (all traces).** Use `index.tsv` + `grep`/`jq`/`awk` to compute + the pattern's rate across the *whole* corpus, split by status (pass vs fail). + This is your prevalence number. +2. **Focused comparison (50-100 traces).** Build a `features.jsonl` if you need a + per-trace signal (see cookbook), then group-by to compare the feature's rate in + the affected cohort vs the rest. Quantify the contrast, not a vibe. +3. **Deep read (~20 traces).** `cat`/`chunk.sh` a sample of the cohort to pull + verbatim evidence and confirm the mechanism is what you think it is. Re-sample + 2-3 times so you're not fooled by a lucky draw. + +## Cohort discipline (this is the crux) +- Save the **defensible cohort** — the narrow subset you are confident exhibits + the pattern — to `cohorts/.txt`, one trace_id per line. NOT the broad + "analyzed pool" you filtered from. Each saved id is a positive claim. + Example: for "agent submits without acknowledging the user's constraint", the + cohort is (user mentioned a constraint) ∧ (first reply lacked the ack) ∧ (a + repair turn followed) — not merely (user mentioned a constraint). +- If `|cohort| / |corpus| > 0.25`, double-check: either it genuinely is that + widespread (state it explicitly in `prevalence`) or your cohort is the analyzed + pool — narrow it. Re-writing the cohort file just overwrites (last write wins). +- **A finding with no saved cohort is rejected by validate.py.** Save first. + +## Evidence discipline +- ≥8 evidence items (aim 10-15 if prevalence is high), each from a **distinct** + trace_id, each a **verbatim** `quote` — tool-call args, exception messages, raw + turn content, literal error strings. Never paraphrase in `quote`. +- Each `explanation` (2-3 sentences) names the concrete mechanism in *that* trace + (exception type / tool name / turn index) and ties it to the prevalence claim. + Don't restate the finding generically. +- Include a counterexample or two (pattern absent + task passed), marked as such. +- Put p-values / odds ratios / test method + n in `additional_observations`, not + in `summary`. Keep `summary`/`prevalence` as plain "X of Y (Z%)". + +## Anti-overfitting +Don't generalize from <20 traces. Flag confidence: high (>100 traces, clear +signal) / medium (20-100) / low (<20). If the evidence doesn't hold, say +`refuted` or `inconclusive` — do not force a confirmation. + +## Submitting +1. Write `cohorts/.txt` (the defensible cohort). +2. Self-check: pick 2-3 random cohort ids, read them, confirm each fits. Narrow + and re-save if any doesn't. +3. Append your finding object (schema in SKILL) to `findings.json`, referencing + `cohort_file: "cohorts/.txt"`. +4. Run `python skills/insights/validate.py findings.json index.tsv` and fix + anything it flags before returning. Return a one-line verdict + the cohort size. diff --git a/vero/examples/insights-bundle/.claude/agents/ig-scout.md b/vero/examples/insights-bundle/.claude/agents/ig-scout.md new file mode 100644 index 00000000..b4598b43 --- /dev/null +++ b/vero/examples/insights-bundle/.claude/agents/ig-scout.md @@ -0,0 +1,52 @@ +--- +name: ig-scout +description: >- + Discovery agent (breadth). Skims a SAMPLE of the trace corpus to surface as + many plausible patterns/hypotheses as possible — failures, silent failures, + efficiency issues, success patterns, cohort differences, omissions. Returns + hypotheses with light evidence; does NOT validate. Invoked by the + insights-generator orchestrator with a slice to explore. +model: haiku +tools: Bash, Read, Grep, Glob +--- + +You are a **Scout**. Your job is *discovery*: skim broadly, find candidate +patterns, and hand back a ranked list of hypotheses. Breadth over depth — you +scan many traces and deeply validate none. Validation is someone else's job. + +Read `skills/insights/SKILL.md` for the corpus layout and the bash cookbook. + +## How you work +1. **Sample, don't exhaust.** Look across 50-200 traces from the slice you were + given. Use `grep`/`wc`/`jq` over `index.tsv` to survey cheaply; `cat` or + `chunk.sh` the full text of only a diverse handful. +2. **Look for anything interesting**, not a fixed taxonomy: + - **Explicit failures** — exceptions, non-zero exits, error strings, refusals. + - **Silent failures** — the run "succeeded" but did a poor job (ignored a + constraint, wrong tool, gave up early, hallucinated a result). + - **Efficiency** — retry loops, repeated identical tool calls, wasted turns, + runaway length. + - **Success patterns** — what the winning traces do that losing ones don't. + - **Cohort differences** — contrast pass vs fail (from `index.tsv` status): + what text/tool/structure appears in one and not the other? + - **Omissions** — the hardest and most valuable. An absent action leaves no + text to grep. So grep for the text an agent that DID the action *would* have + produced, take that cohort, and contrast it against the rest. **The contrast + is the hypothesis** — don't skip it. +3. **Quantify roughly.** For each candidate pattern, get a ballpark count + (`grep -l ... | wc -l`) so the orchestrator can prioritize. + +## What you return +A list of hypotheses. For each: +- **name** — short, specific ("empty-query search retry loop", not "search issues"). +- **description** — the pattern and, crucially, *why it might matter* to the objective. +- **evidence** — 2-5 trace_ids you're genuinely confident about, each with a + short verbatim quote. Name only traces you'd stake the hypothesis on, not every + grep hit. +- **estimated_prevalence** — rare / moderate / common / unknown. +- **suggested_validation** — one line on how an Investigator could confirm it + (which cohort to build, which contrast to run). + +Return this as a compact JSON array. Do NOT write `findings.json`, do NOT save +cohorts, do NOT call validate.py — those are the Investigator's responsibility. +Err toward proposing more hypotheses; the orchestrator filters. diff --git a/vero/examples/insights-bundle/.claude/agents/insights-generator.md b/vero/examples/insights-bundle/.claude/agents/insights-generator.md new file mode 100644 index 00000000..61dede16 --- /dev/null +++ b/vero/examples/insights-bundle/.claude/agents/insights-generator.md @@ -0,0 +1,64 @@ +--- +name: insights-generator +description: >- + Corpus-level trace analyst. Invoke when you need to understand WHY the current + candidate is failing (or winning) across an evaluation corpus — not one trace, + but the population. Coordinates Scouts (discover hypotheses) and Investigators + (validate them against the whole corpus) and returns a small set of + evidence-grounded, actionable findings. Give it the corpus root ($CORPUS, + the .vero dir) and, if known, the optimization objective. +model: inherit +tools: Task, Bash, Read, Grep, Glob +--- + +You are the **Insights Orchestrator**. You do not read many traces yourself — you +*coordinate* a two-phase investigation over a corpus of agent execution traces and +synthesize the results into findings the optimizer can act on. + +Read `skills/insights/SKILL.md` first — it defines the corpus layout, the +tool→bash cookbook, and the finding contract. Everything below assumes it. + +## Your loop + +1. **Orient.** Build `index.tsv` (see SKILL). Report corpus size and the + pass/fail (or reward) distribution. Skim 2-3 traces yourself only to learn the + trace *shape* (turn structure, where tool calls / errors live) — not to analyze. +2. **Dispatch Scouts (discovery, breadth).** Spawn `ig-scout` subagents via the + Task tool — typically 2-4, each pointed at a different slice (e.g. one over + failures, one over successes, one over a specific tool's traces, one over the + longest/most-expensive traces). Each returns a list of *hypotheses*. Do NOT + ask a Scout to validate — that is the Investigator's job. +3. **Review & prioritize hypotheses.** Merge, dedupe, and drop the low-value + ones. Prioritize hypotheses that are (a) plausibly high-prevalence or (b) + small-cohort but high-impact, and (c) *actionable* — a change the optimizer + could actually make. Note which hypotheses conflict (they make good + investigator targets). +4. **Dispatch Investigators (validation, depth).** Spawn one `ig-investigator` + per surviving hypothesis (via Task). Each validates its ONE hypothesis against + the whole corpus, saves a defensible cohort, and appends a finding to + `findings.json`. Run investigators in parallel when independent. +5. **Evaluate coverage.** After investigators return, ask: what's still + unexplained? Are there failure modes no hypothesis covered? If a major slice of + failures is unaccounted for, dispatch another Scout round targeted at the gap. + Stop when coverage is good or two consecutive rounds surface nothing new. +6. **Submit report.** Write the top-level `findings.json` fields + (`executive_summary`, `methodology`, `traces_analyzed`) around the findings the + investigators appended, then run + `python skills/insights/validate.py findings.json index.tsv`. **Do not return + until validate.py passes** — it rejects findings without a persisted cohort or + with too-thin evidence. + +## Standards you enforce +- **Every finding must be validated by an Investigator with corpus-scale + evidence.** A Scout hypothesis is never a finding on its own. +- **Anti-overfitting.** A pattern seen in <20 traces is low-confidence; say so. + Small cohorts are fine — but only when the impact is high and the evidence is + airtight. Prefer specific, mechanistic findings over broad vague ones. +- **Actionable or cut it.** If a finding doesn't suggest a concrete change to the + candidate/prompt/config, it's an observation, not a finding — demote it. + +## What you return to the caller +A short synthesis (not the full JSON): the 3-6 highest-value findings, each as +one line — mechanism, prevalence, and the suggested change — plus the path to +`findings.json` for the full evidence. This is what feeds the next candidate +proposal, so lead with what to change and why. diff --git a/vero/examples/insights-bundle/README.md b/vero/examples/insights-bundle/README.md new file mode 100644 index 00000000..f8cfa107 --- /dev/null +++ b/vero/examples/insights-bundle/README.md @@ -0,0 +1,89 @@ +# insights-bundle — Insights Generator as an optimizer subagent + +This is **not a Harbor task.** It is a bundle you *inject* into an existing +optimizer run (e.g. the GAIA build under `harness-engineering-bench/gaia/`) so the main +solver/optimizer agent can call it — mid-run, as a subagent — to mine its own +accumulated `.vero` trace corpus for patterns and feed the findings into the next +candidate proposal. + +It reproduces Scale's internal **Insights Generator** *methodology* — not its +code (VeRO ships publicly; IG is internal). Everything runs on the sandbox +filesystem + bash; there is no IG dependency, no dataframe contract, no Python +REPL requirement. + +## What's in it + +``` +.claude/agents/ + insights-generator.md # Orchestrator: dispatches scouts + investigators, synthesizes findings + ig-scout.md # Discovery (breadth): skims a sample, proposes hypotheses + ig-investigator.md # Validation (depth): tests one hypothesis over the whole corpus, saves a cohort +skills/insights/ + SKILL.md # Method + corpus layout + IG-tool→bash cookbook + finding contract + chunk.sh # Byte-windowed trace reader (get_trace_chunk over the FS) + validate.py # Enforces the finding contract — rejects a finding with no saved cohort +``` + +Each `.md` subagent gets its **own context window** — that reproduces IG's core +context-economy property natively: the corpus is mined across many agents, none of +which loads it whole. Harbor already captures subagent (`isSidechain`) +trajectories, so the whole investigation is recorded. + +## How it plugs into a run + +The optimizer works in `/work/agent`; its `vero harbor eval` calls write the +trace corpus to `/work/agent/.vero/evaluations/**`. Point the bundle there with +`CORPUS=/work/agent/.vero`. Two seams inject the bundle into the environment: + +1. **Subagents** — Claude Code has no dedicated Harbor seam for `.claude/agents/`. + Seed them via the environment `Dockerfile`/`seed.sh` into the project dir the + optimizer runs in. In a circle-packing-style environment, add to the Dockerfile: + + ```dockerfile + COPY insights-bundle/.claude/agents /opt/insights-agents + COPY insights-bundle/skills /opt/insights-skills + ``` + and in `main/seed.sh`, before `exec sleep infinity`: + ```sh + mkdir -p /work/agent/.claude/agents /work/agent/skills + cp -a /opt/insights-agents/. /work/agent/.claude/agents/ + cp -a /opt/insights-skills/. /work/agent/skills/ + ``` + +2. **Skill** — alternatively (or additionally) deliver the skill through Harbor's + official seam: `task.toml [environment] skills_dir = "skills"`, which Harbor + copies to `$CLAUDE_CONFIG_DIR/skills/`. Seeding it into `/work/agent/skills/` + via the Dockerfile (option 1) is the simpler single-mechanism path and keeps + the skill next to the agents. + +**Run the optimizer as `claude`.** Subagents (`.claude/agents/`), skills, and the +`Task` tool are Claude Code features — inject this into a `harbor run -a claude` +optimizer, not `-a codex`. (Our GAIA shakedown used `-a codex`; to use IG you flip +the optimizer adapter to `claude`. The gateway per-scope allow-list still governs +which model the optimizer may call.) + +## The loop it enables + +``` +optimize round N: + optimizer proposes/edits candidate → `vero harbor eval` → traces land in .vero + optimizer (or a between-rounds step) invokes the `insights-generator` subagent: + orchestrator → scouts (discover) → investigators (validate) → findings.json + optimizer reads the top findings → informs the round N+1 edit +``` + +This is the native reproduction of IG's patcher loop: insights don't sit in a +report, they steer the next candidate. + +## Known limitations vs. real IG +- **Lexical, not semantic, search.** `grep`/`rg` replace IG's hybrid + `search_traces`. The omission-search tactic (grep for what a correct agent + *would* have said, then contrast cohorts — see SKILL/ig-scout) recovers much of + the value; add a small embedding helper or MCP if you need true semantic recall. +- **Chunking is the one real primitive.** Long traces are read via `chunk.sh` + byte windows; use `jq` on the whole file when you need JSON structure. +- **Trace format is backend-specific.** `execution-trace.json` is a JSON array of + turns/steps whose inner shape depends on the agent adapter — grep the text; + don't assume a fixed schema. + +See `skills/insights/SKILL.md` for the full method and cookbook. diff --git a/vero/examples/insights-bundle/skills/insights/SKILL.md b/vero/examples/insights-bundle/skills/insights/SKILL.md new file mode 100644 index 00000000..da794981 --- /dev/null +++ b/vero/examples/insights-bundle/skills/insights/SKILL.md @@ -0,0 +1,164 @@ +--- +name: insights +description: >- + Corpus-level trace diagnostics for the VeRO optimizer. Mine a corpus of agent + execution traces for failure/success patterns, validate each against the WHOLE + corpus with quantitative evidence, and emit evidence-grounded findings. Use + when asked to analyze why candidates fail, what distinguishes high- from + low-scoring traces, or what patterns recur across an evaluation corpus. +--- + +# Insights: corpus-level trace diagnostics + +This skill reproduces Scale's **Insights Generator** methodology using only the +sandbox filesystem + bash. It finds patterns across a *population* of agent +execution traces (patterns invisible in any single trace) and returns findings +backed by corpus-scale evidence. + +Three rules define the method — everything else is mechanics: + +1. **Compute over all traces; read few.** The corpus is too large for one + context. Use `grep`/`jq`/`awk`/`wc` to compute *over* every trace; `cat`/`sed` + the full text of only a sampled or cohort-relevant handful. +2. **Discovery ≠ validation.** *Scouts* skim a sample and propose many hypotheses + (breadth). *Investigators* test one hypothesis against the *whole* corpus with + quantitative evidence and label it confirmed / refuted / inconclusive (depth). + **No finding ships without corpus-scale backing.** +3. **Every finding is evidence-grounded and has a persisted cohort.** description + + prevalence % + a saved cohort of affected trace IDs + ≥8 verbatim quotes + (each from a distinct trace) + mechanism + suggested action. A finding with no + saved cohort is **rejected** (`validate.py`). + +## The corpus layout + +Under the **Harbor** eval backend (the path VeRO actually uses), each candidate's +evaluation dumps per-case trial records into a read-only `.vero` tree: + +``` +$CORPUS/ # e.g. /work/agent/.vero (or a dir you're pointed at) + evaluations// # one dir per candidate evaluation + evaluation.json # aggregate record (score, error_rate, candidate id) + artifacts/harbor/ + stdout.log stderr.log # backend logs for the whole eval + jobs//__/ # one dir per case = a "trial" + result.json # trial metadata: task_name, trial_name, agent model + trial.log + agent/-trace.jsonl # THE TRACE — JSONL, one event per line + agent/answer.txt # the agent's final answer + verifier/reward.txt # THE LABEL — numeric reward (e.g. 0 or 1) + verifier/test-stdout.txt +``` + +- **The trace** = `agent/*.jsonl` (for GAIA, `gaia-trace.jsonl`). One JSON object + per line. Event shapes seen so far: + - a model turn `{"turn":N,"response_id":..,"output_text":..,"function_calls":[..]}` + (note **`function_calls`** — plural, an array; grepping `.function_call` finds nothing); + - a tool result `{"turn":N,"tool":"run_shell","result":{"return_code":..,"stdout":..,"stderr":..}}`; + - non-shell tool results (`transcribe_audio`, `read_image`, `submit_answer`, …) + whose `result` may have `"return_code":null` and empty stdout — a **silent + tool failure** the agent often doesn't notice; + - a recovery marker `{"recovery":"retry_after_empty_response","turn":N}`. + The filename and inner shape are **task/agent-specific** — don't hard-code + beyond "it's JSONL you grep line by line; `jq -c .` per line for structure". +- **The label** for cohort contrast = `verifier/reward.txt` (a bare number). + Comparative analysis (reward 1 vs 0) is where this method earns its keep. +- **The gold + agent answer** = `verifier/test-stdout.txt`, which for exact-match + tasks contains both `Agent answer: '...'` and `Expected answer: '...'`. This is + the highest-value signal for wrong-answer analysis — grep both lines to see + *what* the agent got wrong, not just that it failed. +- **The unit** = a *trial* (one case run by one candidate). The trial dir name is + `__`; the same `case_id` recurs across candidate evals, so use + the **full trial dir name as the unique `trace_id`** and keep `case_id` + + `eval_digest` as grouping columns. +- Only **full-disclosure** evals expose per-case traces; aggregate-only evals + (e.g. a held-out validation split) have `evaluation.json` but no trial `agent/` + dirs. Mine the full-disclosure evals. + +Build a flat index once so lookups are cheap: + +Drive the index off the trace files themselves — one row per trace, so the +`jobs/` dir (whose name also contains `__`) never sneaks in: + +```bash +# trace_id(trial) case_id eval reward trace_path (one row per trial) +find "$CORPUS/evaluations" -path "*/agent/*.jsonl" | while read -r trace; do + t=$(dirname "$(dirname "$trace")") # trial dir = .../__ + trace_id=$(basename "$t") + case_id=${trace_id%%__*} + eval=$(echo "$t" | sed -E 's#.*/evaluations/([^/]{12})[^/]*/.*#\1#') + reward=$([ -f "$t/verifier/reward.txt" ] && tr -d '[:space:]' < "$t/verifier/reward.txt") + printf '%s\t%s\t%s\t%s\t%s\n' "$trace_id" "$case_id" "$eval" "${reward:-NA}" "$trace" +done > index.tsv +wc -l index.tsv # corpus size (trials) +cut -f4 index.tsv | sort | uniq -c # reward distribution (0 vs 1 = your cohorts) +``` + +A trial can lack `verifier/reward.txt` (errored/uncsored case) — those rows get +`reward=NA` and simply drop out of the reward-1-vs-0 cohorts. + +**Cross-trial contrast (the population-level lever).** Because the same `case_id` +is run by several candidates, grouping by `case_id` reveals patterns invisible in +any single trace — e.g. *deterministic* failures (a case that fails with the +**identical** wrong answer across every candidate → a grounding/knowledge gap, +not noise) vs *stochastic* ones (fails sometimes). This is precisely the kind of +finding this method exists to surface: + +```bash +# cases that NEVER pass (deterministic failure) vs sometimes-pass (stochastic) +awk -F'\t' '$4!="NA"{tot[$2]++; if($4==1)pass[$2]++} + END{for(c in tot) printf "%s\t%d/%d pass\n", c, pass[c]+0, tot[c]}' index.tsv \ + | sort -t/ -k1 -n +# then, for a never-pass case, check whether the wrong answer is identical across +# candidates (grep "Agent answer:" in each trial's verifier/test-stdout.txt). +``` + +## Tool cookbook (IG tool → bash) + +| IG tool | Do this instead | +|---|---| +| `search_traces(query)` | `grep -rlF "" "$CORPUS"` (lexical). For "did the agent NOT do X", grep for the text an agent that DID X would emit, then diff that set against the corpus. | +| `get_trace(id)` | `cat` the trace path from `index.tsv`. If huge, `chunk.sh` it first. | +| (recover the answer) | `grep -E "Agent answer:\|Expected answer:" /verifier/test-stdout.txt` — what the agent said vs the gold answer, for exact-match tasks. | +| `get_trace_chunk(id, k)` | `bash chunk.sh ` (byte-window slice; `chunk.sh ` with no k lists chunk count). | +| `extract(taxonomy)` → `features.jsonl` | a small loop that computes one feature per trace and appends `{"trace_id":..,"":..}` to `features.jsonl` (persist + reuse; grep for a regex signal, or a tiny LLM call per trace only when judgment is needed). | +| `compare_segments(A,B)` | `jq`/`awk` group-by over `features.jsonl` joined to `index.tsv` labels — e.g. rate of a feature in pass vs fail cohorts. | +| `save_affected_traces(ids)` | write the **defensible** cohort to `cohorts/.txt` (one trace_id per line). | +| `submit_finding(...)` | append a finding object to `findings.json` (schema below), then run `python validate.py findings.json index.tsv` — it **rejects** findings lacking a cohort or citations. | + +Prevalence = `|cohort| / |corpus|`. If that ratio exceeds ~0.25, either say so +explicitly in `prevalence` or your cohort is actually the analyzed pool — narrow it. + +## The finding contract (findings.json) + +`findings.json` is `{"executive_summary": str, "findings": [], "methodology": str, "traces_analyzed": int}`. +Each ``: + +```json +{ + "hypothesis_name": "Short title", + "status": "confirmed|refuted|inconclusive", + "summary": "Precise mechanism, not a vague restatement", + "prevalence": "X of Y traces (Z%)", + "cohort_file": "cohorts/.txt", + "evidence": [ + {"trace_id": "", "quote": "", "explanation": "2-3 sentences naming the specific mechanism in THIS trace (exception type / tool / turn index) and linking it to the prevalence claim"} + ], + "stats": {"metric": 0.85}, + "segments_compared": ["pass", "fail"], + "additional_observations": ["p-values / odds ratios / notes"], + "suggested_action": "One concrete change (exact file/prompt/config) or null" +} +``` + +**Evidence discipline (enforced by `validate.py`):** +- A finding MUST reference a non-empty `cohort_file` whose trace_ids exist in the corpus. +- ≥8 evidence items (aim 10-15 for high-prevalence findings), each from a + **distinct** trace_id, each with a **verbatim** `quote` (tool-call args, + exception messages, raw turn content, literal error strings — never paraphrase). +- `explanation` names the concrete mechanism in that trace; don't tag a quote as + "representative of N traces". +- Include a counterexample or two (pattern absent + task succeeded), marked as such. + +## Self-check before submitting a finding +Pick 2-3 random trace_ids from the cohort, `cat`/`chunk.sh` them, and confirm each +clearly exhibits the pattern. If one doesn't, narrow the cohort and re-save. diff --git a/vero/examples/insights-bundle/skills/insights/chunk.sh b/vero/examples/insights-bundle/skills/insights/chunk.sh new file mode 100755 index 00000000..66d6cc44 --- /dev/null +++ b/vero/examples/insights-bundle/skills/insights/chunk.sh @@ -0,0 +1,41 @@ +#!/usr/bin/env bash +# chunk.sh — read a large trace in byte-windowed chunks so it never has to be +# loaded into context whole. Reproduces IG's get_trace_chunk over the filesystem. +# +# chunk.sh -> print chunk count + total size (no content) +# chunk.sh -> print chunk k (0-indexed) +# chunk.sh -> use a custom chunk size (bytes; default 20000) +# +# Chunks are cut on byte boundaries, so a JSON token may straddle two chunks — +# that's fine for scanning/grepping; use `jq` on the whole file when you need +# structure. Prefer grepping the file directly first; only chunk to *read* a +# specific region of a trace too big to cat. +set -euo pipefail + +file=${1:?usage: chunk.sh [chunk_index] [chunk_size_bytes]} +size=${3:-20000} + +if [[ ! -f "$file" ]]; then + echo "chunk.sh: no such file: $file" >&2 + exit 1 +fi + +bytes=$(wc -c <"$file" | tr -d ' ') +# ceil division +count=$(( (bytes + size - 1) / size )) + +if [[ $# -lt 2 ]]; then + echo "file: $file" + echo "bytes: $bytes" + echo "chunks: $count (chunk size ${size} bytes; index 0..$((count > 0 ? count - 1 : 0)))" + exit 0 +fi + +k=$2 +if (( k < 0 || k >= count )); then + echo "chunk.sh: chunk index $k out of range 0..$((count - 1))" >&2 + exit 1 +fi + +# dd reads exactly one window; skip in whole blocks of `size`. +dd if="$file" bs="$size" skip="$k" count=1 2>/dev/null diff --git a/vero/examples/insights-bundle/skills/insights/validate.py b/vero/examples/insights-bundle/skills/insights/validate.py new file mode 100755 index 00000000..bfa0d863 --- /dev/null +++ b/vero/examples/insights-bundle/skills/insights/validate.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +"""validate.py — enforce the insights finding contract. + +Reproduces the gate IG's `submit_finding` applies: a finding is rejected unless a +non-empty cohort of affected trace_ids was persisted, those ids exist in the +corpus, and the evidence is real (verbatim quotes from distinct traces). Run it +before a finding is considered submitted: + + python validate.py findings.json index.tsv + +index.tsv is the corpus index built per SKILL.md: tab-separated, one row per +trial, columns ``trace_id case_id eval reward trace_path``. +Only column 1 (trace_id) and column 5 (trace_path, used for the best-effort +verbatim-quote check) are read. Exit code is non-zero if any finding fails, and +every problem is printed so the agent can fix and re-run. +""" +from __future__ import annotations + +import json +import re +import sys +from pathlib import Path + +MIN_EVIDENCE = 8 +VALID_STATUS = {"confirmed", "refuted", "inconclusive"} +COHORT_CORPUS_RATIO_WARN = 0.25 + + +def load_corpus_index(index_path: Path) -> dict[str, str]: + """Return {trace_id: trace_path} from the corpus index (col1 -> col5).""" + index: dict[str, str] = {} + for line in index_path.read_text().splitlines(): + if not line.strip(): + continue + cols = line.split("\t") + trace_id = cols[0].strip() + path = cols[4].strip() if len(cols) >= 5 else "" + index[trace_id] = path + return index + + +_TRIAL_TEXT_CACHE: dict[str, str] = {} + + +def _canon(text: str) -> str: + """Lowercase, alphanumeric-only. Collapses JSON escaping/spacing/punctuation + differences (``{"answer":"X"}`` vs ``\\"answer\\": \\"X\\"``) so a genuine + excerpt matches while paraphrase/fabrication (different words) does not.""" + return re.sub(r"[^a-z0-9]+", "", text.lower()) + + +def _quote_is_verbatim(quote: str, trace_path: str) -> bool | None: + """True/False if the quote appears in the trial; None if unknown. + + Best-effort. A legitimate excerpt may come from the trace JSONL, the agent's + ``answer.txt``, or the verifier's ``test-stdout.txt`` — and a tool-call + argument is stored JSON-escaped — so the whole trial directory is searched + and both sides are canonicalized to alphanumerics before comparing. Returns + None when nothing can be read (caller neither warns nor endorses). + """ + if not trace_path: + return None + tp = Path(trace_path) + # Guard: a bogus/short path whose .parent.parent is a high-level dir (e.g. "/") + # would make rglob walk the whole filesystem. Only proceed for a real trace. + if not tp.is_file(): + return None + trial_dir = tp.parent.parent # trial dir = parent of the agent/ dir + if not trial_dir.is_dir() or trial_dir == trial_dir.parent: + return None + key = str(trial_dir) + text = _TRIAL_TEXT_CACHE.get(key) + if text is None: + parts: list[str] = [] + try: + files = [p for p in trial_dir.rglob("*") if p.is_file()] + except OSError: + files = [] + for p in files: + try: + if p.stat().st_size > 5_000_000: + continue + parts.append(p.read_text(encoding="utf-8", errors="replace")) + except OSError: + continue + text = _canon(" ".join(parts)) + _TRIAL_TEXT_CACHE[key] = text + if not text: + return None + needle = _canon(quote) + return bool(needle) and needle in text + + +def validate_finding( + idx: int, f: dict, corpus_index: dict[str, str], base: Path +) -> tuple[list[str], list[str]]: + """Return (errors, warnings) for one finding.""" + corpus_ids = corpus_index.keys() + errs: list[str] = [] + warns: list[str] = [] + name = f.get("hypothesis_name") or f"#{idx}" + + # --- required scalar fields --- + for field in ("hypothesis_name", "status", "summary", "prevalence"): + if not f.get(field): + errs.append(f"[{name}] missing required field: {field}") + status = f.get("status") + if status and status not in VALID_STATUS: + errs.append(f"[{name}] status '{status}' not in {sorted(VALID_STATUS)}") + + # --- cohort: the IG "no saved cohort -> reject" rule --- + cohort_file = f.get("cohort_file") + cohort_ids: list[str] = [] + if not cohort_file: + errs.append( + f"[{name}] no cohort_file: you must persist the affected trace_ids " + f"(cohorts/.txt) before submitting — a finding without a cohort " + f"is rejected" + ) + else: + cpath = (base / cohort_file).resolve() + if not cpath.exists(): + errs.append(f"[{name}] cohort_file does not exist: {cohort_file}") + else: + cohort_ids = [ + ln.strip() for ln in cpath.read_text().splitlines() if ln.strip() + ] + if not cohort_ids: + errs.append(f"[{name}] cohort_file is empty: {cohort_file}") + unknown = [t for t in cohort_ids if t not in corpus_ids] + if unknown: + errs.append( + f"[{name}] cohort has {len(unknown)} trace_id(s) not in the " + f"corpus (e.g. {unknown[:3]}) — cohort ids must be real" + ) + if corpus_ids and len(cohort_ids) / len(corpus_ids) > COHORT_CORPUS_RATIO_WARN: + warns.append( + f"[{name}] cohort is {len(cohort_ids)}/{len(corpus_ids)} " + f"(>{COHORT_CORPUS_RATIO_WARN:.0%}) of the corpus — confirm this " + f"is a real population-level pattern, not the analyzed pool" + ) + + # --- evidence: verbatim quotes from distinct traces --- + evidence = f.get("evidence") or [] + if len(evidence) < MIN_EVIDENCE: + errs.append( + f"[{name}] {len(evidence)} evidence item(s); need >= {MIN_EVIDENCE}" + ) + ev_trace_ids: list[str] = [] + for j, ev in enumerate(evidence): + loc = f"[{name}] evidence[{j}]" + tid = ev.get("trace_id") + quote = (ev.get("quote") or "").strip() + if not tid: + errs.append(f"{loc}: missing trace_id") + else: + ev_trace_ids.append(tid) + if corpus_ids and tid not in corpus_ids: + errs.append(f"{loc}: trace_id '{tid}' not in the corpus") + elif quote: + verbatim = _quote_is_verbatim(quote, corpus_index.get(tid, "")) + if verbatim is False: + warns.append( + f"{loc}: quote not found verbatim in trace '{tid}' — " + f"use a literal excerpt, not a paraphrase" + ) + if not quote: + errs.append(f"{loc}: missing verbatim quote") + if len((ev.get("explanation") or "").strip()) < 40: + errs.append( + f"{loc}: explanation too thin — name the concrete mechanism in " + f"this trace (tool/exception/turn), don't restate the finding" + ) + # Evidence must span >= MIN_EVIDENCE distinct traces (counterexamples, which + # legitimately sit outside the cohort, still count as distinct traces). + distinct = len(set(ev_trace_ids)) + if len(evidence) >= MIN_EVIDENCE and distinct < MIN_EVIDENCE: + errs.append( + f"[{name}] evidence spans only {distinct} distinct trace(s); need " + f">= {MIN_EVIDENCE} — quote from different traces, not the same 2-3" + ) + + return errs, warns + + +def main(argv: list[str]) -> int: + if len(argv) != 3: + print("usage: python validate.py findings.json index.tsv", file=sys.stderr) + return 2 + findings_path = Path(argv[1]) + index_path = Path(argv[2]) + base = findings_path.resolve().parent + + try: + doc = json.loads(findings_path.read_text()) + except (OSError, json.JSONDecodeError) as e: + print(f"cannot read findings.json: {e}", file=sys.stderr) + return 2 + corpus_index = load_corpus_index(index_path) if index_path.exists() else {} + if not corpus_index: + print(f"warning: no corpus ids loaded from {index_path}", file=sys.stderr) + + findings = doc.get("findings") or [] + if not findings: + print("REJECTED: findings.json has no findings", file=sys.stderr) + return 1 + + all_errs: list[str] = [] + all_warns: list[str] = [] + for i, f in enumerate(findings): + e, w = validate_finding(i, f, corpus_index, base) + all_errs += e + all_warns += w + + for w in all_warns: + print(f"WARN {w}") + for e in all_errs: + print(f"ERROR {e}") + + if all_errs: + print(f"\nREJECTED: {len(all_errs)} error(s) across {len(findings)} finding(s).") + return 1 + print( + f"OK: {len(findings)} finding(s) validated" + + (f" ({len(all_warns)} warning(s))" if all_warns else "") + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv)) diff --git a/vero/pyproject.toml b/vero/pyproject.toml new file mode 100644 index 00000000..7f1f7a2e --- /dev/null +++ b/vero/pyproject.toml @@ -0,0 +1,70 @@ +[project] +name = "scale-vero" +version = "0.5.0" +description = "A harness for agents to optimize programs." +readme = "README.md" +authors = [ + { name = "Varun Ursekar", email = "oss@scale.com" } +] +license = { text = "MIT" } +requires-python = ">=3.11" +dependencies = [ + "click>=8.0.0", + "pydantic>=2.11.7", + "wcmatch>=10.1", +] + +[project.scripts] +vero = "vero.cli:main" + +[project.optional-dependencies] +harbor = [ + "fastapi>=0.110", + "httpx>=0.28.1", + "jinja2>=3.1.6", + "pyyaml>=6.0.2", + "uvicorn>=0.27", +] +claude = [ + "claude-agent-sdk>=0.1.56", +] +optimize = [ + "async-lru>=2.0.5", + "beautifulsoup4>=4.14.2", + "httpx>=0.28.1", + "lxml>=6.0.2", + "openai-agents[litellm]>=0.18.3,<0.19", + "orjson>=3.10", + "pypdf>=6.2.0", + "trafilatura>=2.0.0", +] +wandb = [ + "wandb>=0.19.10", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/vero"] + +[[tool.uv.index]] +url = "https://pypi.org/simple" +name = "pypi" + +[tool.uv.sources] +vero = { workspace = true } + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["tests"] + +[dependency-groups] +dev = [ + "gitpython>=3.1.46", + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", + "pytest-json-report>=1.5.0", + "scale-vero[optimize,harbor,claude]", +] diff --git a/vero/src/vero/__init__.py b/vero/src/vero/__init__.py new file mode 100644 index 00000000..8dc2472d --- /dev/null +++ b/vero/src/vero/__init__.py @@ -0,0 +1,34 @@ +"""VeRO's public program-optimization API.""" + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository, GitCandidateRepository +from vero.evaluation import ( + EvaluationBackend, + EvaluationRecord, + EvaluationReport, + EvaluationSet, + ObjectiveSpec, +) +from vero.optimization import CandidateProducer, OptimizationStrategy, Optimizer +from vero.runtime import ( + OptimizationSession, + create_local_optimization_session, + create_optimization_session, +) + +__all__ = [ + "Candidate", + "CandidateRepository", + "CandidateProducer", + "EvaluationBackend", + "EvaluationRecord", + "EvaluationReport", + "EvaluationSet", + "GitCandidateRepository", + "ObjectiveSpec", + "OptimizationSession", + "OptimizationStrategy", + "Optimizer", + "create_optimization_session", + "create_local_optimization_session", +] diff --git a/vero/src/vero/agents/__init__.py b/vero/src/vero/agents/__init__.py new file mode 100644 index 00000000..7f39c8ed --- /dev/null +++ b/vero/src/vero/agents/__init__.py @@ -0,0 +1,24 @@ +from .producer import AgentCandidateProducer +from .protocol import AgentContext, AgentRequirements, AgentRunResult, CodingAgent + +__all__ = [ + "AgentCandidateProducer", + "AgentContext", + "AgentRequirements", + "AgentRunResult", + "ClaudeCodeAgent", + "CodingAgent", + "VeroAgent", +] + + +def __getattr__(name: str): + if name == "ClaudeCodeAgent": + from .claude_code import ClaudeCodeAgent + + return ClaudeCodeAgent + if name == "VeroAgent": + from .vero import VeroAgent + + return VeroAgent + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/vero/src/vero/agents/claude_code.py b/vero/src/vero/agents/claude_code.py new file mode 100644 index 00000000..3934d494 --- /dev/null +++ b/vero/src/vero/agents/claude_code.py @@ -0,0 +1,364 @@ +"""ClaudeCodeAgent - Agent backend using Claude Agent SDK for optimization.""" + +from __future__ import annotations + +import inspect +import logging +import typing +from dataclasses import asdict, dataclass, field +from typing import Any, Callable + +from claude_agent_sdk import ( + ClaudeAgentOptions, + ClaudeSDKClient, + McpSdkServerConfig, + ResultMessage, + SdkMcpTool, + create_sdk_mcp_server, +) +from claude_agent_sdk.types import ( + Message, + SystemPromptPreset, +) +from pydantic import BaseModel, create_model + +from vero.agents.events import AgentEvent +from vero.agents.protocol import AgentContext, AgentRequirements, AgentRunResult +from vero.tools.base import ToolSet +from vero.tools.evaluation import EvaluationTools +from vero.tools.utils import get_tools_from_class +from vero.utils.general import recursively_serialize + +logger = logging.getLogger(__name__) + + +def default_tool_sets() -> list: + """Default tool sets for ClaudeCodeAgent.""" + return [EvaluationTools()] + + +def _default_claude_options() -> ClaudeAgentOptions: + return ClaudeAgentOptions( + model="claude-sonnet-4-5-20250929", + permission_mode="bypassPermissions", + allowed_tools=["WebSearch", "WebFetch", "Task", "Bash"], + ) + + +@dataclass +class ClaudeCodeAgent: + """Claude Agent SDK coding agent for a scoped optimization proposal.""" + + requirements = AgentRequirements(host_visible_workspace=True) + + options: ClaudeAgentOptions = field(default_factory=_default_claude_options) + tool_sets: list[ToolSet] = field(default_factory=default_tool_sets) + output_format: type[BaseModel] | None = None + trace: list[Message] = field(default_factory=list, repr=False) + state: dict[str, str] | None = field(default=None, repr=False) + + _context: AgentContext | None = field(default=None, repr=False) + _tools: dict[str, McpSdkServerConfig] = field(default_factory=dict, repr=False) + _allowed_tools: list[str] = field(default_factory=list, repr=False) + + async def run( + self, + *, + context: AgentContext, + prompt: str | None, + max_turns: int, + on_event: Callable[[Any], Any] | None = None, + ) -> AgentRunResult: + """Run Claude Code in the candidate workspace.""" + + self._context = context + self._tools, self._allowed_tools = self._create_tools() + input = prompt or "Improve the program, using evaluation feedback when useful." + + async with self._create_client(max_turns=max_turns) as client: + await client.query(input) + async for msg in client.receive_response(): + self.trace.append(msg) + if on_event is not None: + event_result = on_event(msg) + if inspect.isawaitable(event_result): + await event_result + + # Update state with session ID for resumption + result = self.latest_result + if result is not None and hasattr(result, "session_id"): + self.state = {"session_id": result.session_id} + + metadata = {"usage": self.usage()} + model = self.options.model + if model is not None: + metadata["model"] = str(model) + return AgentRunResult( + description="Apply Claude coding-agent changes", + state=recursively_serialize(self.serialize_state()), + trace=recursively_serialize(self.serialize_trace()), + metadata=metadata, + ) + + def serialize_event(self, event: Any) -> AgentEvent | None: + """Convert a Claude Agent SDK Message to a normalized AgentEvent.""" + if isinstance(event, ResultMessage): + text = "" + if hasattr(event, "result") and event.result: + text = str(event.result) + return {"kind": "result", "text": text} + + if not hasattr(event, "content"): + return None + + # Claude SDK Message has .content (str) and .role + content = getattr(event, "content", "") + role = getattr(event, "role", "") + + if role == "system": + return {"kind": "system", "text": str(content)[:200]} + + if role == "assistant": + return {"kind": "message", "text": str(content) if content else ""} + + if role == "tool": + name = getattr(event, "tool_name", "") + return { + "kind": "tool_result", + "name": name, + "output": str(content)[:500] if content else "", + "is_error": bool(getattr(event, "is_error", False)), + } + + # Fallback + return {"kind": "message", "text": str(content) if content else ""} + + def serialize_trace(self) -> Any: + """Serialize the execution trace (event log).""" + if not self.trace: + return None + return [asdict(msg) for msg in self.trace] + + def serialize_state(self) -> dict[str, str] | None: + """Serialize state for resumption — the Claude SDK session ID.""" + return self.state + + def deserialize_state(self, state: dict[str, str]) -> None: + """Restore state by setting the session ID for resume.""" + self.state = state + + def usage(self) -> dict: + """Return usage statistics from the latest result.""" + result = self.latest_result + if result is None: + return {} + usage = recursively_serialize(result.usage) + if not usage: + return {} + return usage + + def summary(self) -> dict: + """Return structured output for wandb summary.""" + structured_output = None + if self.latest_structured_output is not None: + try: + structured_output = self.latest_structured_output.model_dump() + except AttributeError: + structured_output = self.latest_structured_output + return {"structured_output": structured_output} + + def dict(self) -> dict[str, Any]: + """Return serializable Claude Code configuration.""" + return { + "claude_agent_options": recursively_serialize( + { + "allowed_tools": self.options.allowed_tools, + "permission_mode": self.options.permission_mode, + "model": self.options.model, + "cwd": str(self.options.cwd) if self.options.cwd else None, + } # type: ignore + ), + } + + @property + def latest_result(self) -> ResultMessage | None: + """Gets the latest result from the run result.""" + if not self.trace: + return None + for msg in reversed(self.trace): + if isinstance(msg, ResultMessage): + return msg + return None + + @property + def latest_structured_output(self) -> BaseModel | None: + """Gets the latest structured output, parsed into the output_format model.""" + result = self.latest_result + + if result is None or result.structured_output is None: + return None + + if self.output_format is None: + return result.structured_output + + try: + if isinstance(result.structured_output, dict): + return self.output_format.model_validate(result.structured_output) + elif isinstance(result.structured_output, str): + return self.output_format.model_validate_json(result.structured_output) + else: + return result.structured_output + except Exception as e: + logger.warning(f"Failed to parse structured output: {e}") + return result.structured_output + + # ------------------------------------------------------------------------- + # Internal helpers + # ------------------------------------------------------------------------- + + def _create_client(self, max_turns: int | None = None) -> ClaudeSDKClient: + """Creates the ClaudeSDKClient for this step.""" + from copy import copy + + options = copy(self.options) + + if self._context is not None: + options.cwd = self._context.project_path + + # System prompt + options.system_prompt = self._build_system_prompt() + + # MCP servers + options.mcp_servers = self._tools + options.allowed_tools = list(set(self._allowed_tools + options.allowed_tools)) + + # Resume from saved state + if self.state and isinstance(self.state, dict) and "session_id" in self.state: + options.resume = self.state["session_id"] + options.continue_conversation = True + logger.info(f"Resuming Claude Code session: {self.state['session_id']}") + + # Max turns + if max_turns is not None: + options.max_turns = max_turns + + # Output format + if self.output_format is not None: + options.output_format = { + "type": "json_schema", + "schema": self.output_format.model_json_schema(), + } + + return ClaudeSDKClient(options=options) + + def _build_system_prompt(self) -> str | SystemPromptPreset: + """Builds the system prompt from instructions and/or options.system_prompt.""" + + assert self._context is not None, "Agent context is not set" + + instructions = self._context.instructions + + # If options already has a system_prompt (e.g. SystemPromptPreset), merge with instructions + if ( + isinstance(self.options.system_prompt, dict) + and "type" in self.options.system_prompt + ): + preset = self.options.system_prompt + if instructions: + current_append = preset.append or "" + new_append = ( + current_append + "\n\n" + instructions + if current_append + else instructions + ) + return SystemPromptPreset( + type=preset.type, + preset=preset.preset, + append=new_append, + ) + return preset + + configured = self.options.system_prompt + if isinstance(configured, str) and configured: + return ( + f"{configured}\n\n{instructions}" + if instructions + else configured + ) + return instructions or "" + + def _create_tools( + self, + ) -> tuple[dict[str, McpSdkServerConfig], list[str]]: + """Creates the tool set instances based on tool_sets.""" + + assert self._context is not None, "Agent context is not set" + tools: dict[str, McpSdkServerConfig] = {} + internal_allowed_tools: list[str] = [] + + for tool_set in self.tool_sets: + if hasattr(tool_set, "bind"): + tool_set.bind(self._context) + + tool_names, mcp_config = self._to_claude_sdk_server_config(tool_set) + key = type(tool_set).__name__ + tools[key] = mcp_config + for name in tool_names: + internal_allowed_tools.append(f"mcp__{key}__{name}") + + return tools, internal_allowed_tools + + @staticmethod + def _to_claude_sdk_server_config( + instance: object, + ) -> tuple[list[str], McpSdkServerConfig]: + """Convert a tool instance to a Claude Agent SDK server config.""" + import inspect + + tool_methods = get_tools_from_class(instance) + sdk_mcp_tools: list[SdkMcpTool] = [] + tool_names: list[str] = [] + + for method in tool_methods: + name = method.__name__ + description = method.__doc__ or "" + # Use get_type_hints() to resolve string annotations (from __future__ import annotations) + try: + resolved_hints = typing.get_type_hints(method, include_extras=True) + except Exception: + resolved_hints = {} + params = { + param.name: (resolved_hints.get(param.name, param.annotation), ...) + for param in inspect.signature(method).parameters.values() + if param.name != "self" + } + input_schema = create_model(name, **params).model_json_schema() + + def make_handler(m: Callable) -> Callable: + def format_content(content: str | Exception) -> list[dict]: + return [{"type": "text", "text": str(content)}] + + async def handler(args: dict) -> dict: + try: + content = m(**args) + if inspect.isawaitable(content): + content = await content + return {"content": format_content(content)} + except Exception as e: + return {"content": format_content(e), "is_error": True} + + return handler + + sdk_mcp_tools.append( + SdkMcpTool( + name=name, + description=description, + input_schema=input_schema, + handler=make_handler(method), + ) + ) + tool_names.append(name) + + return tool_names, create_sdk_mcp_server( + name=instance.__class__.__name__, version="1.0.0", tools=sdk_mcp_tools + ) diff --git a/vero/src/vero/agents/events.py b/vero/src/vero/agents/events.py new file mode 100644 index 00000000..de22eb8d --- /dev/null +++ b/vero/src/vero/agents/events.py @@ -0,0 +1,46 @@ +"""Normalized agent event types. + +These are the canonical event shapes that agent backends produce and +logging/callbacks consume. No SDK-specific types should leak beyond +the agent's serialize_event boundary. +""" + +from __future__ import annotations + +from typing import TypedDict + + +class MessageEvent(TypedDict): + kind: str # "message" + text: str + + +class ThinkingEvent(TypedDict): + kind: str # "thinking" + text: str + + +class ToolCallEvent(TypedDict): + kind: str # "tool_call" + name: str + args: str + + +class ToolResultEvent(TypedDict): + kind: str # "tool_result" + name: str + output: str + is_error: bool + + +class SystemEvent(TypedDict): + kind: str # "system" + text: str + + +class ResultEvent(TypedDict): + kind: str # "result" + text: str + + +AgentEvent = MessageEvent | ThinkingEvent | ToolCallEvent | ToolResultEvent | SystemEvent | ResultEvent diff --git a/vero/src/vero/agents/producer.py b/vero/src/vero/agents/producer.py new file mode 100644 index 00000000..9da2e8c1 --- /dev/null +++ b/vero/src/vero/agents/producer.py @@ -0,0 +1,166 @@ +"""Adapt a coding agent into the optimization candidate-producer protocol.""" + +from __future__ import annotations + +import hashlib +import logging +from typing import Any, Callable + +from vero.agents.protocol import AgentContext, AgentRequirements, CodingAgent +from vero.optimization import ( + CandidateChange, + CandidateEvaluationGateway, + CandidateProductionContext, + CandidateProposal, +) +from vero.runtime import ArtifactStore +from vero.utils.general import recursively_serialize +from vero.workspace import Workspace + +logger = logging.getLogger(__name__) + + +class AgentCandidateProducer: + def __init__( + self, + agent: CodingAgent, + *, + prompt: str | None = None, + max_turns: int = 200, + artifacts: ArtifactStore | None = None, + on_event: Callable[[Any], Any] | None = None, + ): + if max_turns < 1: + raise ValueError("max_turns must be positive") + self.agent = agent + self.prompt = prompt + self.max_turns = max_turns + self.artifacts = artifacts + self.on_event = on_event + + def validate_workspace(self, workspace: Workspace) -> None: + requirements = getattr(self.agent, "requirements", AgentRequirements()) + if ( + requirements.host_visible_workspace + and workspace.sandbox.host_path(workspace.project_path) is None + ): + raise ValueError( + f"{type(self.agent).__name__} requires a host-visible workspace; " + f"sandbox {type(workspace.sandbox).__name__} does not expose one" + ) + + def bind_artifacts( + self, + artifacts: ArtifactStore, + *, + producer_id: str = "default", + restore: bool = True, + ) -> None: + """Attach durable storage and restore the latest supported agent state.""" + + self.artifacts = artifacts + if not restore: + return + state_path = self._producer_state_path(producer_id) + if not artifacts.path(state_path).exists(): + return + deserialize = getattr(self.agent, "deserialize_state", None) + if callable(deserialize): + deserialize(artifacts.read_json(state_path)) + + @staticmethod + def _producer_state_path(producer_id: str) -> str: + digest = hashlib.sha256(producer_id.encode()).hexdigest()[:16] + return f"agents/producers/{digest}/state.json" + + def _persist_artifacts( + self, + proposal: CandidateProposal, + *, + state: Any, + trace: Any, + ) -> None: + if self.artifacts is None: + return + digest = hashlib.sha256(proposal.id.encode()).hexdigest()[:16] + if state is not None: + serialized_state = recursively_serialize(state) + self.artifacts.write_json(f"agents/{digest}/state.json", serialized_state) + self.artifacts.write_json( + self._producer_state_path(proposal.producer_id), + serialized_state, + ) + if trace is not None: + self.artifacts.write_json( + f"agents/{digest}/trace.json", + recursively_serialize(trace), + ) + + def _persist_failed_run( + self, + proposal: CandidateProposal, + error: BaseException, + ) -> None: + if self.artifacts is None: + return + try: + serialize_state = getattr(self.agent, "serialize_state", None) + serialize_trace = getattr(self.agent, "serialize_trace", None) + state = serialize_state() if callable(serialize_state) else None + trace = serialize_trace() if callable(serialize_trace) else None + self._persist_artifacts(proposal, state=state, trace=trace) + digest = hashlib.sha256(proposal.id.encode()).hexdigest()[:16] + self.artifacts.write_json( + f"agents/{digest}/failure.json", + { + "type": type(error).__name__, + "message": str(error), + }, + ) + except Exception: + logger.exception("Failed to persist coding-agent failure artifacts") + + async def produce( + self, + *, + proposal: CandidateProposal, + context: CandidateProductionContext, + workspace: Workspace, + evaluation: CandidateEvaluationGateway, + ) -> CandidateChange | None: + self.validate_workspace(workspace) + parent = ( + context.candidates.get(proposal.parent_id) + if proposal.parent_id is not None + else None + ) + if parent is None: + parent = context.baseline + try: + result = await self.agent.run( + context=AgentContext( + session_id=context.session_id, + workspace=workspace, + proposal=proposal, + parent=parent, + evaluation=evaluation, + ), + prompt=proposal.instruction or self.prompt, + max_turns=self.max_turns, + on_event=self.on_event, + ) + except BaseException as error: + self._persist_failed_run(proposal, error) + raise + if result is None: + return None + + self._persist_artifacts( + proposal, + state=result.state, + trace=result.trace, + ) + return CandidateChange( + description=result.description, + metadata={"agent": type(self.agent).__name__, **result.metadata}, + ) diff --git a/vero/src/vero/agents/protocol.py b/vero/src/vero/agents/protocol.py new file mode 100644 index 00000000..72d20209 --- /dev/null +++ b/vero/src/vero/agents/protocol.py @@ -0,0 +1,98 @@ +"""Provider-neutral coding-agent contract.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path, PurePosixPath +from typing import Any, Callable, Protocol, runtime_checkable + +from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator + +from vero.candidate import Candidate +from vero.optimization import ( + CandidateEvaluationGateway, + CandidateProposal, +) +from vero.runtime.context import AGENT_CONTEXT_DIRECTORY +from vero.workspace import Workspace + + +@dataclass(frozen=True) +class AgentRequirements: + """Workspace capabilities required by a coding-agent adapter.""" + + host_visible_workspace: bool = False + + +@dataclass(frozen=True) +class AgentContext: + """Capabilities visible to a coding agent working on one proposal.""" + + session_id: str + workspace: Workspace + proposal: CandidateProposal + parent: Candidate + evaluation: CandidateEvaluationGateway + + @property + def project_path(self) -> Path: + path = self.workspace.sandbox.host_path(self.workspace.project_path) + if path is None: + raise RuntimeError("coding agent requires a host-visible workspace path") + return path + + @property + def sandbox_project_path(self) -> str: + return self.workspace.project_path + + @property + def context_path(self) -> str: + return str(PurePosixPath(self.workspace.project_path) / AGENT_CONTEXT_DIRECTORY) + + @property + def relative_context_path(self) -> str: + return AGENT_CONTEXT_DIRECTORY + + @property + def instructions(self) -> str: + context = ( + f"VeRO has placed read-only optimization context in " + f"`{AGENT_CONTEXT_DIRECTORY}/`. Inspect its README, cases, prior " + "candidates, and evaluation feedback when useful. Do not modify or " + "commit that directory." + ) + if self.proposal.instruction: + return f"{self.proposal.instruction}\n\n{context}" + return context + + @property + def base_version(self) -> str: + return self.parent.version + + +class AgentRunResult(BaseModel): + model_config = ConfigDict(extra="forbid") + + description: str = "Apply coding-agent changes" + state: JsonValue | None = None + trace: JsonValue | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("description") + @classmethod + def validate_description(cls, value: str) -> str: + if not value.strip(): + raise ValueError("agent result description must not be empty") + return value + + +@runtime_checkable +class CodingAgent(Protocol): + async def run( + self, + *, + context: AgentContext, + prompt: str | None, + max_turns: int, + on_event: Callable[[Any], Any] | None = None, + ) -> AgentRunResult | None: ... diff --git a/vero/src/vero/agents/vero.py b/vero/src/vero/agents/vero.py new file mode 100644 index 00000000..f8a6aab2 --- /dev/null +++ b/vero/src/vero/agents/vero.py @@ -0,0 +1,413 @@ +"""VeroAgent - native coding agent on the OpenAI Agents SDK. + +The agent harness (model orchestration, tool dispatch, event streaming) runs on +the host. Shell and file effects execute INSIDE a sandbox via plain *function +tools* (see ``vero.tools.sandbox_tools``) that drive an SDK ``SandboxSession``. +The sandbox is bound to the candidate checkout's host directory +(``Manifest(root=...)``), so mid-run evaluation and candidate capture operate on +that directory unchanged; the sandbox *client* is the containment seam +(unix-local by default; docker / modal / e2b for real isolation). + +Using function tools rather than the SDK's hosted ``Shell``/``Filesystem`` +capabilities keeps the native path model-agnostic: it works with any provider +over LiteLLM (ChatCompletions) or the Responses API. Hosted capabilities would +restrict it to the OpenAI Responses API. +""" + +from __future__ import annotations + +import asyncio +import contextlib +import inspect +import logging +from dataclasses import dataclass, field +from typing import Any, Callable + +from agents import ( + Agent, + FunctionTool, + ModelRetrySettings, + ModelSettings, + OpenAIChatCompletionsModel, + RunConfig, + RunResultStreaming, + Runner, + TResponseInputItem, + retry_policies, + set_tracing_disabled, +) +from agents.extensions.models.litellm_model import LitellmModel +from agents.sandbox import Manifest +from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient +from pydantic import BaseModel + +from vero.agents.events import AgentEvent +from vero.agents.protocol import AgentContext, AgentRunResult +from vero.tools.base import ToolSet +from vero.tools.evaluation import EvaluationTools +from vero.tools.planning import TodoList, think +from vero.tools.sandbox_tools import build_sandbox_tools +from vero.tools.sub_agent import SubAgentTool +from vero.tools.utils.openai_agents import ( + callable_to_oai_tool, + tool_set_instance_to_oai_tools, +) +from vero.utils.general import recursively_serialize +from vero.utils.openai_agents import stream_events, strict_mode_from_model + +logger = logging.getLogger(__name__) + +set_tracing_disabled(True) + + +def default_tool_sets() -> list[ToolSet | object | Callable]: + """Default vero-specific tools for the VeroAgent. + + Shell and file access are provided by function tools bound to the sandbox + session at run time (see :meth:`VeroAgent._create_agent`), so only + vero-specific tools live here. + """ + + return [ + EvaluationTools(), + TodoList(), + think, + ] + + +def _default_oai_agent(model: str | None = None) -> Agent: + """Build the template Agent. + + The native path drives the sandbox with plain function tools, so any + provider works via LiteLLM. Model is configurable via VERO_OPTIMIZER_MODEL + or VeroAgent.for_model(). + """ + import os + + model = model or os.getenv("VERO_OPTIMIZER_MODEL") or "openai/gpt-5.4" + + litellm_kwargs: dict[str, str] = {} + api_key = os.getenv("LITELLM_API_KEY") + if api_key: + litellm_kwargs["api_key"] = api_key + base_url = os.getenv("LITELLM_BASE_URL") + if base_url: + litellm_kwargs["base_url"] = base_url.rstrip("/") + + configured_model = LitellmModel(model=model, **litellm_kwargs) + + return Agent( + name="VeroAgent", + model=configured_model, + model_settings=ModelSettings( + include_usage=True, + retry=ModelRetrySettings( + max_retries=3, + backoff={ + "initial_delay": 1.0, + "max_delay": 60.0, + "multiplier": 2.0, + "jitter": True, + }, + policy=retry_policies.any( + retry_policies.provider_suggested(), + retry_policies.retry_after(), + retry_policies.network_error(), + retry_policies.http_status([408, 429, 500, 502, 503, 504]), + ), + ), + ), + ) + + +@dataclass +class VeroAgent: + """OpenAI Agents SDK coding agent for a scoped optimization proposal.""" + + oai_agent: Agent = field(default_factory=_default_oai_agent) + tool_sets: list[ToolSet | object | Callable] = field( + default_factory=default_tool_sets + ) + max_retries: int = 3 + event_timeout: int | None = 60 * 12 + sandbox_client_factory: Callable[[], Any] = UnixLocalSandboxClient + state: list[TResponseInputItem] | None = field(default=None, repr=False) + + _context: AgentContext | None = field(default=None, repr=False) + _tools: dict[type | Callable, list[FunctionTool]] = field( + default_factory=dict, repr=False + ) + _run_result: RunResultStreaming | None = field(default=None, repr=False) + + @classmethod + def for_model(cls, model: str) -> VeroAgent: + """Construct an agent using an explicit LiteLLM model identifier.""" + + if not model.strip(): + raise ValueError("model must not be empty") + return cls(oai_agent=_default_oai_agent(model)) + + @property + def trace(self) -> list[TResponseInputItem] | None: + return self.state + + @trace.setter + def trace(self, value: Any) -> None: + self.state = value + + async def run( + self, + *, + context: AgentContext, + prompt: str | None, + max_turns: int, + on_event: Callable | None = None, + ) -> AgentRunResult: + """Run the agent in the candidate workspace.""" + + self._context = context + self._tools = self._create_tools(context) + state = self.state if self.state is not None else [] + input = prompt or "Improve the program, using evaluation feedback when useful." + inputs = state + [{"role": "user", "content": input}] + + client = self.sandbox_client_factory() + session = await client.create( + manifest=Manifest(root=str(context.project_path)) + ) + await session.start() + try: + agent = self._create_agent(session) + run_config = RunConfig( + workflow_name=f"vero::{context.session_id}", + trace_id=context.session_id, + ) + self._run_result = Runner.run_streamed( + agent, + input=inputs, + max_turns=max_turns, + run_config=run_config, + ) + async for event in stream_events( + self._run_result, event_timeout=self.event_timeout + ): + if on_event is not None: + result = on_event(event) + if inspect.isawaitable(result): + await result + await asyncio.sleep(0) + self.state = self._run_result.to_input_list() + finally: + with contextlib.suppress(Exception): + await session.stop() + with contextlib.suppress(Exception): + await client.delete(session) + + metadata = {"usage": self.usage()} + model = self.model_str() + if model is not None: + metadata["model"] = model + return AgentRunResult( + description="Apply Vero coding-agent changes", + state=recursively_serialize(self.serialize_state()), + trace=recursively_serialize(self.serialize_trace()), + metadata=metadata, + ) + + def serialize_event(self, event: Any) -> AgentEvent | None: + """Convert an OpenAI Agents SDK StreamEvent to a normalized AgentEvent. + + Returns None for noise events (raw_response_event, etc.) that + shouldn't be dispatched to callbacks. + """ + if not hasattr(event, "type") or event.type != "run_item_stream_event": + return None + + item = event.item.raw_item + raw = item.model_dump() if isinstance(item, BaseModel) else (item if isinstance(item, dict) else {}) + item_type = raw.get("type", "") + + if item_type == "message": + parts = [] + for block in raw.get("content", []): + if isinstance(block, dict): + text = block.get("text", "") + if text: + parts.append(text) + return {"kind": "message", "text": "\n".join(parts)} if parts else None + + if item_type in ("reasoning", "thinking"): + parts = [] + for block in (raw.get("summary") or raw.get("content") or []): + if isinstance(block, dict): + text = block.get("text") or block.get("summary") or "" + if text: + parts.append(text) + elif isinstance(block, str) and block: + parts.append(block) + return {"kind": "thinking", "text": "\n".join(parts)} if parts else None + + if item_type == "function_call": + return { + "kind": "tool_call", + "name": raw.get("name", "unknown"), + "args": raw.get("arguments", ""), + } + + if item_type == "function_call_output": + output = raw.get("output", "") + return { + "kind": "tool_result", + "name": raw.get("name", ""), + "output": output, + "is_error": isinstance(output, str) and "error" in output.lower()[:80], + } + + return None + + def serialize_trace(self) -> list[TResponseInputItem] | None: + """Serialize the execution trace. For VeroAgent, this is the conversation history.""" + return self.trace + + def serialize_state(self) -> list[TResponseInputItem] | None: + """Serialize state for resumption. Same as trace for VeroAgent.""" + return self.state + + def deserialize_state(self, state: list[TResponseInputItem]) -> None: + """Restore state from conversation history.""" + self.state = state + + def _get_sub_agent_tool(self) -> SubAgentTool | None: + """Get the SubAgentTool instance if present.""" + for tool_set in self.tool_sets: + if isinstance(tool_set, SubAgentTool): + return tool_set + return None + + def model_str(self) -> str | None: + if isinstance(self.oai_agent.model, (LitellmModel, OpenAIChatCompletionsModel)): + return self.oai_agent.model.model + elif isinstance(self.oai_agent.model, str): + return self.oai_agent.model + return None + + def usage(self) -> dict: + """Return usage statistics.""" + + if self._run_result is None: + return {} + + orchestrator_usage = self._run_result.context_wrapper.usage + sub_agent_usages = {} + sub_agent_tool = self._get_sub_agent_tool() + if sub_agent_tool: + sub_agent_usages = { + name: recursively_serialize(sa.session.context_wrapper.usage) # type: ignore + for name, sa in sub_agent_tool.sessions.items() + if sa.session is not None + } + return { + "orchestrator": recursively_serialize(orchestrator_usage), # type: ignore + "sub_agents": sub_agent_usages, + } + + def dict(self) -> dict: + """Return serializable Vero agent configuration.""" + return { + "model": self.model_str(), + "tool_sets": [ + type(ts).__name__ if hasattr(ts, "__class__") else ts.__name__ + for ts in self.tool_sets + ], + "model_settings": recursively_serialize(self.oai_agent.model_settings), # type: ignore + } + + def artifacts(self) -> dict: + try: + sub_agent_tool = self._get_sub_agent_tool() + if sub_agent_tool and sub_agent_tool.sessions: + sub_agent_dicts = { + key: sa.to_dict() for key, sa in sub_agent_tool.sessions.items() + } + return {"subagents": sub_agent_dicts} + except Exception as e: + logger.warning(f"Failed to get sub agent dicts: {e}") + + return {} + + @property + def strict_mode(self) -> bool: + if self.oai_agent.model is None: + return False + return strict_mode_from_model(self.oai_agent.model) + + def _create_agent(self, session: Any) -> Agent: + """Build the Agent: sandbox function tools + vero tools + the template. + + The shell/read_file/write_file tools execute inside ``session`` (bound + to the candidate checkout). Vero-specific tools (evaluation, planning) + run host-side in the harness. + """ + tools = ( + build_sandbox_tools(session) + + self._get_tools() + + list(self.oai_agent.tools) + ) + + instructions = self._context.instructions if self._context else None + + return Agent( + name=self.oai_agent.name, + model=self.oai_agent.model, + model_settings=self.oai_agent.model_settings, + output_type=self.oai_agent.output_type, + tools=tools, + instructions=instructions or self.oai_agent.instructions, + ) + + def _get_tools(self) -> list[FunctionTool]: + tools = [] + for tool_list in self._tools.values(): + tools.extend(tool_list) + tools.sort(key=lambda tool: tool.name) + return tools + + def _create_tools( + self, context: AgentContext + ) -> dict[type | Callable, list[FunctionTool]]: + instances: dict[type | Callable, list[FunctionTool]] = {} + sub_agent_tool = None + + for ts in self.tool_sets: + # Skip SubAgentTool — bind it last + if isinstance(ts, SubAgentTool): + sub_agent_tool = ts + continue + + # Bind if it's a ToolSet + if hasattr(ts, "bind"): + ts.bind(context) # type: ignore + + # Convert to list of FunctionTools + if inspect.isfunction(ts): + # Plain callable like think + instances[ts] = [callable_to_oai_tool(ts, strict_mode=self.strict_mode)] + else: + instances[type(ts)] = self._to_openai_function_tools(ts) + + # Bind SubAgentTool last (needs other tool instances) + if sub_agent_tool is not None: + sub_agent_tool.tool_instances = instances + model = self.oai_agent.model + model_name = model if isinstance(model, str) else model.model + sub_agent_tool.models = {model_name: model} + instances[SubAgentTool] = self._to_openai_function_tools(sub_agent_tool) + + return instances + + def _to_openai_function_tools(self, instance: object) -> list: + strict_mode = self.strict_mode + if inspect.isfunction(instance): + return [callable_to_oai_tool(instance, strict_mode=strict_mode)] + else: + return tool_set_instance_to_oai_tools(instance, strict_mode=strict_mode) diff --git a/vero/src/vero/candidate.py b/vero/src/vero/candidate.py new file mode 100644 index 00000000..af9d5c4c --- /dev/null +++ b/vero/src/vero/candidate.py @@ -0,0 +1,72 @@ +"""Canonical identity for a versioned program candidate.""" + +from __future__ import annotations + +from datetime import UTC, datetime +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator, model_validator + + +class Candidate(BaseModel): + """A materialized version of the program being optimized. + + ``version`` is interpreted by the session's candidate repository. A + session uses one repository/workspace family, so the identifier remains + stable when the candidate is materialized in different sandboxes. + """ + + model_config = ConfigDict(extra="forbid") + + id: str + version: str + parent_id: str | None = None + created_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) + description: str | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("id", "version") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("candidate identity must not be empty") + return value + + @field_validator("parent_id", "description") + @classmethod + def validate_optional_text(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("optional candidate text must not be empty") + return value + + @field_validator("created_at") + @classmethod + def normalize_created_at(cls, value: datetime) -> datetime: + if value.tzinfo is None or value.utcoffset() is None: + raise ValueError("candidate timestamps must be timezone-aware") + return value.astimezone(UTC) + + @model_validator(mode="after") + def validate_parent(self) -> Candidate: + if self.parent_id == self.id: + raise ValueError("a candidate cannot be its own parent") + return self + + @classmethod + def from_version( + cls, + version: str, + *, + candidate_id: str | None = None, + parent_id: str | None = None, + description: str | None = None, + metadata: dict[str, Any] | None = None, + ) -> Candidate: + """Construct the usual one-candidate-per-workspace-version identity.""" + return cls( + id=candidate_id or version, + version=version, + parent_id=parent_id, + description=description, + metadata=metadata or {}, + ) diff --git a/vero/src/vero/candidate_repository/__init__.py b/vero/src/vero/candidate_repository/__init__.py new file mode 100644 index 00000000..763fe2ae --- /dev/null +++ b/vero/src/vero/candidate_repository/__init__.py @@ -0,0 +1,10 @@ +"""Durable repositories for program candidates.""" + +from vero.candidate_repository.base import CandidateRepository, CandidateRepositoryError +from vero.candidate_repository.git import GitCandidateRepository + +__all__ = [ + "CandidateRepository", + "CandidateRepositoryError", + "GitCandidateRepository", +] diff --git a/vero/src/vero/candidate_repository/base.py b/vero/src/vero/candidate_repository/base.py new file mode 100644 index 00000000..951959b2 --- /dev/null +++ b/vero/src/vero/candidate_repository/base.py @@ -0,0 +1,86 @@ +"""Durable candidate storage and compatible workspace materialization.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from contextlib import asynccontextmanager +from typing import AsyncIterator, Generic, Sequence, TypeVar + +from vero.candidate import Candidate +from vero.sandbox import Sandbox +from vero.workspace import Workspace + + +WorkspaceT = TypeVar("WorkspaceT", bound=Workspace) + + +class CandidateRepositoryError(RuntimeError): + """Raised when durable candidate state is invalid or cannot be transferred.""" + + +class CandidateRepository(ABC, Generic[WorkspaceT]): + """Own durable candidates for one workspace family within a session.""" + + @property + @abstractmethod + def family(self) -> str: + """Stable workspace/repository family identifier.""" + ... + + @property + @abstractmethod + def format_version(self) -> int: + """Persisted repository format version.""" + ... + + @abstractmethod + def supports(self, workspace: Workspace) -> bool: + """Whether this repository can capture the supplied workspace.""" + ... + + @abstractmethod + async def capture( + self, + candidate: Candidate, + workspace: WorkspaceT, + ) -> Candidate: + """Durably record a clean workspace at the candidate's version.""" + ... + + @abstractmethod + async def materialize_agent_history( + self, + candidates: Sequence[Candidate], + *, + workspace: WorkspaceT, + destination: str, + ) -> None: + """Expose a repository-native view of the visible candidates. + + ``destination`` is a sandbox path reserved for generated agent context. + Implementations may install disposable native references in the supplied + workspace, but must never mutate durable candidate state. + """ + ... + + @abstractmethod + def get(self, candidate_id: str) -> Candidate | None: + """Return one durable candidate by identity.""" + ... + + @abstractmethod + def list(self) -> tuple[Candidate, ...]: + """Return all durable candidates in deterministic order.""" + ... + + @asynccontextmanager + @abstractmethod + async def checkout( + self, + candidate: Candidate, + *, + sandbox: Sandbox, + name: str | None = None, + ) -> AsyncIterator[WorkspaceT]: + """Materialize a temporary isolated workspace for one candidate.""" + yield # pragma: no cover diff --git a/vero/src/vero/candidate_repository/git.py b/vero/src/vero/candidate_repository/git.py new file mode 100644 index 00000000..6912c3b2 --- /dev/null +++ b/vero/src/vero/candidate_repository/git.py @@ -0,0 +1,866 @@ +"""Git-backed durable candidate repository.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import os +import re +import tempfile +import uuid +from collections.abc import Awaitable, Callable +from contextlib import asynccontextmanager +from pathlib import Path, PurePosixPath +from typing import AsyncIterator, Literal, Sequence + +from pydantic import BaseModel, ConfigDict, field_validator + +from vero.candidate import Candidate +from vero.candidate_repository.base import CandidateRepository, CandidateRepositoryError +from vero.evaluation.persistence import _atomic_write_json +from vero.sandbox import LocalSandbox, Sandbox +from vero.workspace import GitWorkspace, Workspace + + +_OBJECT_ID = re.compile(r"(?:[0-9a-f]{40}|[0-9a-f]{64})\Z") +_AGENT_CONTEXT_DIRECTORY = ".vero" + + +class _GitRepositoryConfig(BaseModel): + model_config = ConfigDict(extra="forbid") + + schema_version: Literal[1] = 1 + family: Literal["git"] = "git" + project_subpath: str = "." + + @field_validator("project_subpath") + @classmethod + def validate_project_subpath(cls, value: str) -> str: + path = PurePosixPath(value) + if not value or path.is_absolute() or ".." in path.parts or "\\" in value: + raise ValueError("project_subpath must stay within the Git repository") + return value + + +class _CandidateRecord(BaseModel): + model_config = ConfigDict(extra="forbid") + + schema_version: Literal[1] = 1 + candidate: Candidate + + +def _candidate_digest(candidate_id: str) -> str: + return hashlib.sha256(candidate_id.encode()).hexdigest() + + +def _safe_prefix(name: str | None) -> str: + if name is None: + return "vero-candidate-" + value = "".join( + character for character in name if character.isalnum() or character in "-_" + ) + return f"{value[:48] or 'vero-candidate'}-" + + +class GitCandidateRepository(CandidateRepository[GitWorkspace]): + """A session-owned bare Git repository plus durable candidate records.""" + + def __init__( + self, + *, + root: Path, + host: LocalSandbox, + config: _GitRepositoryConfig, + candidates: dict[str, Candidate], + ) -> None: + self.root = root + self.repository_path = root / "repository.git" + self.records_path = root / "records" + self.config_path = root / "repository.json" + self._host = host + self._config = config + self._candidates = candidates + self._lock = asyncio.Lock() + + @property + def family(self) -> str: + return "git" + + @property + def format_version(self) -> int: + return self._config.schema_version + + @property + def project_subpath(self) -> str: + return self._config.project_subpath + + @classmethod + async def create( + cls, + root: Path | str, + *, + workspace: GitWorkspace, + ) -> GitCandidateRepository: + """Create or open the Git repository paired with ``workspace``.""" + + root = Path(root).expanduser().resolve() + try: + relative = PurePosixPath(workspace.project_path).relative_to(workspace.root) + except ValueError as error: + raise ValueError( + "workspace project path must be within its Git root" + ) from error + project_subpath = str(relative) + config = _GitRepositoryConfig(project_subpath=project_subpath) + root.mkdir(parents=True, exist_ok=True) + host = await LocalSandbox.create(root=root.parent) + config_path = root / "repository.json" + repository_path = root / "repository.git" + records_path = root / "records" + + if config_path.exists(): + stored = _GitRepositoryConfig.model_validate_json( + config_path.read_text(encoding="utf-8") + ) + if stored != config: + raise ValueError( + "Git candidate repository does not match workspace project path" + ) + config = stored + else: + await asyncio.to_thread( + _atomic_write_json, + config_path, + config.model_dump(mode="json"), + ) + + if not repository_path.exists(): + result = await host.run(["git", "init", "--bare", str(repository_path)]) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr or "failed to initialize candidate repository" + ) + records_path.mkdir(parents=True, exist_ok=True) + instance = cls( + root=root, + host=host, + config=config, + candidates={}, + ) + await instance._load_records() + return instance + + @classmethod + async def open(cls, root: Path | str) -> GitCandidateRepository: + """Open an existing Git candidate repository without a source workspace.""" + + root = Path(root).expanduser().resolve() + config_path = root / "repository.json" + repository_path = root / "repository.git" + if not config_path.is_file() or not repository_path.is_dir(): + raise FileNotFoundError(f"candidate repository does not exist: {root}") + config = _GitRepositoryConfig.model_validate_json( + config_path.read_text(encoding="utf-8") + ) + host = await LocalSandbox.create(root=root.parent) + instance = cls(root=root, host=host, config=config, candidates={}) + await instance._load_records() + return instance + + def supports(self, workspace: Workspace) -> bool: + if not isinstance(workspace, GitWorkspace): + return False + try: + relative = PurePosixPath(workspace.project_path).relative_to(workspace.root) + except ValueError: + return False + return str(relative) == self.project_subpath + + def _record_path(self, candidate_id: str) -> Path: + return self.records_path / f"{_candidate_digest(candidate_id)}.json" + + def _candidate_ref(self, candidate_id: str) -> str: + return f"refs/vero/candidates/{_candidate_digest(candidate_id)}" + + def _context_ref(self, candidate_id: str) -> str: + return f"refs/vero/context/{_candidate_digest(candidate_id)}" + + @property + def _reserved_context_path(self) -> str: + if self.project_subpath == ".": + return _AGENT_CONTEXT_DIRECTORY + return str(PurePosixPath(self.project_subpath) / _AGENT_CONTEXT_DIRECTORY) + + async def _host_git(self, *arguments: str, timeout: int = 120) -> str: + result = await self._host.run( + ["git", "--git-dir", str(self.repository_path), *arguments], + timeout=timeout, + env={ + "PATH": os.defpath, + "LANG": "C.UTF-8", + "GIT_CONFIG_GLOBAL": "/dev/null", + }, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr.strip() or result.stdout.strip() or "git command failed" + ) + return result.stdout.strip() + + async def _load_records(self) -> None: + loaded: dict[str, Candidate] = {} + for path in sorted(self.records_path.glob("*.json")): + try: + record = _CandidateRecord.model_validate_json( + path.read_text(encoding="utf-8") + ) + except (OSError, ValueError) as error: + raise CandidateRepositoryError( + f"invalid candidate record {path.name}: {error}" + ) from error + candidate = record.candidate + if path != self._record_path(candidate.id): + raise CandidateRepositoryError( + f"candidate record path does not match identity {candidate.id!r}" + ) + if candidate.id in loaded: + raise CandidateRepositoryError( + f"duplicate candidate record: {candidate.id!r}" + ) + ref = self._candidate_ref(candidate.id) + try: + resolved = await self._host_git( + "rev-parse", "--verify", f"{ref}^{{commit}}", timeout=30 + ) + except CandidateRepositoryError as error: + raise CandidateRepositoryError( + f"candidate {candidate.id!r} references a missing Git object" + ) from error + if resolved != candidate.version: + raise CandidateRepositoryError( + f"candidate {candidate.id!r} ref does not match its version" + ) + loaded[candidate.id] = candidate + self._candidates = loaded + + async def _source_git( + self, + workspace: GitWorkspace, + *arguments: str, + timeout: int = 120, + ) -> str: + result = await workspace.sandbox.run( + [ + "git", + "-c", + f"safe.directory={workspace.root}", + "-c", + "core.hooksPath=/dev/null", + *arguments, + ], + cwd=workspace.root, + timeout=timeout, + env={ + "PATH": os.defpath, + "LANG": "C.UTF-8", + "GIT_CONFIG_GLOBAL": "/dev/null", + }, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr.strip() + or result.stdout.strip() + or "source git command failed" + ) + return result.stdout.strip() + + async def _repository_git( + self, + sandbox: Sandbox, + repository_path: str, + *arguments: str, + timeout: int = 120, + ) -> str: + result = await sandbox.run( + [ + "git", + "-c", + f"safe.directory={repository_path}", + "-c", + "core.hooksPath=/dev/null", + "-C", + repository_path, + *arguments, + ], + cwd=repository_path, + timeout=timeout, + env={ + "PATH": os.defpath, + "LANG": "C.UTF-8", + "GIT_CONFIG_GLOBAL": "/dev/null", + }, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr.strip() + or result.stdout.strip() + or "source git command failed" + ) + return result.stdout.strip() + + async def _fetch_from_workspace( + self, + candidate: Candidate, + workspace: GitWorkspace, + temporary_ref: str, + ) -> None: + source_path = workspace.sandbox.host_path(workspace.root) + if source_path is not None: + await self._host_git( + "-c", + "protocol.file.allow=always", + "fetch", + "--force", + "--no-tags", + "--no-recurse-submodules", + str(source_path), + f"+{candidate.version}:{temporary_ref}", + ) + return + + export_ref = f"refs/vero/export/{uuid.uuid4().hex}" + with tempfile.TemporaryDirectory(prefix="vero-candidate-bundle-") as directory: + local_bundle = Path(directory) / "candidate.bundle" + async with workspace.sandbox.temporary_directory( + prefix="vero-candidate-export-" + ) as remote_directory: + remote_bundle = str( + PurePosixPath(remote_directory) / "candidate.bundle" + ) + try: + await self._source_git( + workspace, + "update-ref", + export_ref, + candidate.version, + timeout=30, + ) + await self._source_git( + workspace, + "bundle", + "create", + remote_bundle, + export_ref, + ) + await workspace.sandbox.download(remote_bundle, str(local_bundle)) + await self._host_git( + "fetch", + "--force", + "--no-tags", + str(local_bundle), + f"+{export_ref}:{temporary_ref}", + ) + finally: + try: + await asyncio.shield( + self._source_git( + workspace, + "update-ref", + "-d", + export_ref, + timeout=30, + ) + ) + except Exception: + pass + + async def _fetch_from_repository( + self, + candidate: Candidate, + *, + sandbox: Sandbox, + repository_path: str, + temporary_ref: str, + ) -> None: + source_path = sandbox.host_path(repository_path) + if source_path is not None: + await self._host_git( + "-c", + "protocol.file.allow=always", + "fetch", + "--force", + "--no-tags", + "--no-recurse-submodules", + str(source_path), + f"+{candidate.version}:{temporary_ref}", + ) + return + + export_ref = f"refs/vero/export/{uuid.uuid4().hex}" + with tempfile.TemporaryDirectory(prefix="vero-candidate-bundle-") as directory: + local_bundle = Path(directory) / "candidate.bundle" + async with sandbox.temporary_directory( + prefix="vero-candidate-export-" + ) as remote_directory: + remote_bundle = str( + PurePosixPath(remote_directory) / "candidate.bundle" + ) + try: + await self._repository_git( + sandbox, + repository_path, + "update-ref", + export_ref, + candidate.version, + timeout=30, + ) + await self._repository_git( + sandbox, + repository_path, + "bundle", + "create", + remote_bundle, + export_ref, + ) + await sandbox.download(remote_bundle, str(local_bundle)) + await self._host_git( + "fetch", + "--force", + "--no-tags", + str(local_bundle), + f"+{export_ref}:{temporary_ref}", + ) + finally: + try: + await asyncio.shield( + self._repository_git( + sandbox, + repository_path, + "update-ref", + "-d", + export_ref, + timeout=30, + ) + ) + except Exception: + pass + + async def _persist_import( + self, + candidate: Candidate, + fetch: Callable[[str], Awaitable[None]], + ) -> Candidate: + if _OBJECT_ID.fullmatch(candidate.version) is None: + raise ValueError("Git candidate version must be a full object ID") + async with self._lock: + existing = self._candidates.get(candidate.id) + if existing is not None: + if existing != candidate: + raise ValueError( + f"candidate ID {candidate.id!r} is already stored " + "with different data" + ) + return existing + + temporary_ref = f"refs/vero/incoming/{uuid.uuid4().hex}" + stable_ref = self._candidate_ref(candidate.id) + try: + await fetch(temporary_ref) + imported = await self._host_git( + "rev-parse", "--verify", f"{temporary_ref}^{{commit}}", timeout=30 + ) + if imported != candidate.version: + raise CandidateRepositoryError( + "imported Git object does not match candidate version" + ) + tracked_context = await self._host_git( + "ls-tree", + "-r", + "--name-only", + temporary_ref, + "--", + self._reserved_context_path, + timeout=30, + ) + if tracked_context: + raise CandidateRepositoryError( + f"candidate tracks reserved agent context path " + f"{self._reserved_context_path!r}" + ) + await self._host_git( + "update-ref", stable_ref, candidate.version, timeout=30 + ) + record = _CandidateRecord(candidate=candidate) + await asyncio.to_thread( + _atomic_write_json, + self._record_path(candidate.id), + record.model_dump(mode="json"), + ) + self._candidates[candidate.id] = candidate + return candidate + finally: + try: + await asyncio.shield( + self._host_git("update-ref", "-d", temporary_ref, timeout=30) + ) + except Exception: + pass + + async def capture( + self, + candidate: Candidate, + workspace: GitWorkspace, + ) -> Candidate: + if not self.supports(workspace): + raise TypeError( + "Git candidate repository requires a compatible GitWorkspace" + ) + if await workspace.is_dirty(): + raise ValueError("candidate workspace must be clean before capture") + actual = await workspace.current_version() + if actual != candidate.version: + raise ValueError( + f"candidate workspace is at {actual!r}, expected {candidate.version!r}" + ) + + async def fetch(temporary_ref: str) -> None: + await self._fetch_from_workspace(candidate, workspace, temporary_ref) + + return await self._persist_import(candidate, fetch) + + async def _exclude_agent_context(self, workspace: GitWorkspace) -> None: + git_directory = await self._source_git( + workspace, + "rev-parse", + "--absolute-git-dir", + timeout=30, + ) + exclude_path = str(PurePosixPath(git_directory) / "info" / "exclude") + pattern = f"/{self._reserved_context_path}/" + existing = ( + await workspace.sandbox.read_file(exclude_path) + if await workspace.sandbox.exists(exclude_path) + else "" + ) + lines = existing.splitlines() + if pattern not in lines: + value = existing + if value and not value.endswith("\n"): + value += "\n" + value += pattern + "\n" + await workspace.sandbox.write_file(exclude_path, value) + + async def _bundle_candidates( + self, + candidates: Sequence[Candidate], + destination: Path, + ) -> None: + refs = [self._candidate_ref(candidate.id) for candidate in candidates] + await self._host_git("bundle", "create", str(destination), *refs) + + async def materialize_agent_history( + self, + candidates: Sequence[Candidate], + *, + workspace: GitWorkspace, + destination: str, + ) -> None: + if not self.supports(workspace): + raise TypeError( + "Git candidate repository requires a compatible GitWorkspace" + ) + visible = sorted(candidates, key=lambda item: (item.created_at, item.id)) + for candidate in visible: + stored = self.get(candidate.id) + if stored != candidate: + raise ValueError( + f"candidate {candidate.id!r} is not present in durable storage" + ) + + await self._exclude_agent_context(workspace) + existing_refs = await self._source_git( + workspace, + "for-each-ref", + "--format=%(refname)", + "refs/vero/context", + timeout=30, + ) + for reference in existing_refs.splitlines(): + if reference: + await self._source_git( + workspace, + "update-ref", + "-d", + reference, + timeout=30, + ) + + if visible: + refspecs = [ + f"+{self._candidate_ref(candidate.id)}:{self._context_ref(candidate.id)}" + for candidate in visible + ] + if workspace.sandbox.host_path(workspace.root) is not None: + await self._source_git( + workspace, + "-c", + "protocol.file.allow=always", + "fetch", + "--force", + "--no-tags", + "--no-recurse-submodules", + str(self.repository_path), + *refspecs, + ) + else: + with tempfile.TemporaryDirectory( + prefix="vero-context-bundle-" + ) as directory: + local_bundle = Path(directory) / "candidates.bundle" + await self._bundle_candidates(visible, local_bundle) + async with workspace.sandbox.temporary_directory( + prefix="vero-context-import-" + ) as remote_directory: + remote_bundle = str( + PurePosixPath(remote_directory) / "candidates.bundle" + ) + await workspace.sandbox.upload( + str(local_bundle), + remote_bundle, + ) + await self._source_git( + workspace, + "fetch", + "--force", + "--no-tags", + "--no-recurse-submodules", + remote_bundle, + *refspecs, + ) + + if await workspace.sandbox.exists(destination): + await workspace.sandbox.remove(destination, recursive=True) + await workspace.sandbox.mkdir(destination) + by_id = {candidate.id: candidate for candidate in visible} + index = [] + for candidate in visible: + digest = _candidate_digest(candidate.id) + candidate_dir = str(PurePosixPath(destination) / digest) + await workspace.sandbox.mkdir(candidate_dir) + native_ref = self._context_ref(candidate.id) + await workspace.sandbox.write_file( + str(PurePosixPath(candidate_dir) / "candidate.json"), + candidate.model_dump_json(indent=2) + "\n", + ) + patch_path = None + parent = by_id.get(candidate.parent_id) if candidate.parent_id else None + if parent is not None: + arguments = [ + "diff", + "--binary", + parent.version, + candidate.version, + ] + if self.project_subpath != ".": + arguments.extend(["--", self.project_subpath]) + patch = await self._source_git(workspace, *arguments) + patch_path = f"{digest}/parent.patch" + await workspace.sandbox.write_file( + str(PurePosixPath(destination) / patch_path), + patch + ("\n" if patch and not patch.endswith("\n") else ""), + ) + index.append( + { + "candidate_id": candidate.id, + "version": candidate.version, + "parent_id": candidate.parent_id, + "native_ref": native_ref, + "metadata_path": f"{digest}/candidate.json", + "parent_patch_path": patch_path, + } + ) + await workspace.sandbox.write_file( + str(PurePosixPath(destination) / "index.json"), + json.dumps( + {"schema_version": 1, "candidates": index}, + ensure_ascii=False, + indent=2, + ) + + "\n", + ) + + async def import_candidate( + self, + candidate: Candidate, + *, + sandbox: Sandbox, + repository_path: str, + ) -> Candidate: + """Import a validated commit without checking out the source repository.""" + + async def fetch(temporary_ref: str) -> None: + await self._fetch_from_repository( + candidate, + sandbox=sandbox, + repository_path=repository_path, + temporary_ref=temporary_ref, + ) + + return await self._persist_import(candidate, fetch) + + def get(self, candidate_id: str) -> Candidate | None: + return self._candidates.get(candidate_id) + + def list(self) -> tuple[Candidate, ...]: + return tuple( + sorted( + self._candidates.values(), + key=lambda candidate: (candidate.created_at, candidate.id), + ) + ) + + async def _bundle_candidate(self, candidate: Candidate, destination: Path) -> None: + await self._host_git( + "bundle", + "create", + str(destination), + self._candidate_ref(candidate.id), + ) + + @asynccontextmanager + async def checkout( + self, + candidate: Candidate, + *, + sandbox: Sandbox, + name: str | None = None, + ) -> AsyncIterator[GitWorkspace]: + stored = self.get(candidate.id) + if stored is None: + raise KeyError(f"unknown candidate: {candidate.id!r}") + if stored != candidate: + raise ValueError( + f"candidate {candidate.id!r} does not match its durable record" + ) + + async with sandbox.temporary_directory(prefix=_safe_prefix(name)) as directory: + checkout_root = str(PurePosixPath(directory) / "repository") + result = await sandbox.run(["git", "init", checkout_root], timeout=30) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr or "failed to initialize candidate checkout" + ) + + host_directory = sandbox.host_path(directory) + if host_directory is not None: + source = str(self.repository_path) + ref = self._candidate_ref(candidate.id) + else: + with tempfile.TemporaryDirectory( + prefix="vero-candidate-checkout-" + ) as host_temporary: + local_bundle = Path(host_temporary) / "candidate.bundle" + await self._bundle_candidate(candidate, local_bundle) + remote_bundle = str(PurePosixPath(directory) / "candidate.bundle") + await sandbox.upload(str(local_bundle), remote_bundle) + source = remote_bundle + ref = self._candidate_ref(candidate.id) + result = await sandbox.run( + [ + "git", + "-c", + f"safe.directory={checkout_root}", + "-C", + checkout_root, + "fetch", + "--force", + "--no-tags", + source, + f"+{ref}:refs/vero/checkout", + ], + timeout=120, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr or "failed to fetch remote candidate checkout" + ) + + if host_directory is not None: + result = await sandbox.run( + [ + "git", + "-c", + "protocol.file.allow=always", + "-c", + f"safe.directory={checkout_root}", + "-C", + checkout_root, + "fetch", + "--force", + "--no-tags", + source, + f"+{ref}:refs/vero/checkout", + ], + timeout=120, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr or "failed to fetch candidate checkout" + ) + + result = await sandbox.run( + [ + "git", + "-c", + "core.hooksPath=/dev/null", + "-c", + f"safe.directory={checkout_root}", + "-C", + checkout_root, + "checkout", + "--detach", + candidate.version, + ], + timeout=60, + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr or "failed to check out candidate version" + ) + project_path = ( + checkout_root + if self.project_subpath == "." + else str(PurePosixPath(checkout_root) / self.project_subpath) + ) + workspace = GitWorkspace( + sandbox=sandbox, + root=checkout_root, + project_path=project_path, + ) + if await workspace.current_version() != candidate.version: + raise CandidateRepositoryError( + "candidate checkout resolved incorrectly" + ) + if await workspace.is_dirty(): + raise CandidateRepositoryError( + "candidate checkout is unexpectedly dirty" + ) + try: + yield workspace + finally: + context_path = str( + PurePosixPath(workspace.project_path) / _AGENT_CONTEXT_DIRECTORY + ) + if await workspace.sandbox.exists(context_path): + result = await asyncio.shield( + workspace.sandbox.run( + ["chmod", "-R", "u+w", context_path], + timeout=30, + ) + ) + if result.returncode != 0: + raise CandidateRepositoryError( + result.stderr + or f"failed to unseal agent context {context_path}" + ) diff --git a/vero/src/vero/cli.py b/vero/src/vero/cli.py new file mode 100644 index 00000000..89f9991a --- /dev/null +++ b/vero/src/vero/cli.py @@ -0,0 +1,1025 @@ +"""Command-line interface for generic program optimization.""" + +from __future__ import annotations + +import asyncio +import json +import os +import shlex +import shutil +import subprocess +from datetime import UTC, datetime +from pathlib import Path +from uuid import uuid4 + +import click + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + AllCases, + BudgetLedger, + CaseIds, + CaseRange, + CommandBackend, + CommandBackendConfig, + ConstraintOperator, + DisclosureLevel, + EvaluationDatabase, + EvaluationLimits, + EvaluationPlan, + EvaluationSet, + MetricAggregation, + MetricConstraint, + MetricSelector, + ObjectiveSpec, + RetryPolicy, + project_evaluation, +) +from vero.optimization import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, + SequentialStrategy, +) +from vero.runtime import ( + SessionManifest, + SessionStatus, + WandbEventSink, + create_local_optimization_session, +) +from vero.config import load_config + + +def _default_home() -> Path: + return Path(os.environ.get("VERO_HOME", "~/.vero")).expanduser().resolve() + + +_CONFIG_TEMPLATE = '''[target] +root = "./target" +ref = "HEAD" + +[backend] +id = "command" +kind = "command" +harness_root = "./harness" +command = ["python3", "evaluate.py", "{workspace}", "{request}", "{report}"] + +[[evaluations]] +name = "train" +partition = "train" +agent_can_evaluate = true +agent_visible = true +agent_selection = "arbitrary" +disclosure = "full" +expose_case_resources = true + +[[evaluations]] +name = "validation" +partition = "validation" +agent_can_evaluate = true +agent_visible = true +agent_selection = "arbitrary" +disclosure = "aggregate" + +[evaluations.agent_budget] +total_runs = 50 + +[[evaluations]] +name = "test" +partition = "test" +agent_can_evaluate = false +agent_visible = false +agent_selection = "fixed" +disclosure = "none" + +[protocol] +selection_evaluation = "validation" +final_evaluation = "test" +max_proposals = 5 + +[objective] +metric = "score" +direction = "maximize" + +[optimizer] +kind = "claude" +instruction = "Improve the program without changing its intended behavior" +''' + + +def _parse_parameters(values: tuple[str, ...]) -> dict[str, object]: + parameters: dict[str, object] = {} + for value in values: + name, separator, encoded = value.partition("=") + if not separator or not name.strip(): + raise click.BadParameter( + "parameters must use NAME=JSON syntax", + param_hint="--parameter", + ) + if name in parameters: + raise click.BadParameter( + f"duplicate parameter {name!r}", + param_hint="--parameter", + ) + try: + parameters[name] = json.loads(encoded) + except json.JSONDecodeError as error: + raise click.BadParameter( + f"parameter {name!r} is not valid JSON: {error.msg}", + param_hint="--parameter", + ) from error + return parameters + + +def _command(value: str, option: str) -> list[str]: + try: + command = shlex.split(value) + except ValueError as error: + raise click.BadParameter(str(error), param_hint=option) from error + if not command: + raise click.BadParameter("command must not be empty", param_hint=option) + return command + + +def _parse_environment( + values: tuple[str, ...], + *, + option: str, +) -> dict[str, str]: + environment: dict[str, str] = {} + for value in values: + name, separator, content = value.partition("=") + if not separator or not name or "=" in name: + raise click.BadParameter( + "values must use NAME=VALUE syntax", param_hint=option + ) + if name in environment: + raise click.BadParameter( + f"duplicate environment variable {name!r}", param_hint=option + ) + environment[name] = content + return environment + + +def _parse_constraints( + values: tuple[tuple[str, str, str], ...], +) -> list[MetricConstraint]: + constraints: list[MetricConstraint] = [] + for metric_value, operator_value, target_value in values: + metric, separator, aggregation_value = metric_value.partition(":") + if not metric: + raise click.BadParameter("constraint metric must not be empty") + try: + aggregation = ( + MetricAggregation(aggregation_value) + if separator + else MetricAggregation.REPORT + ) + operator = ConstraintOperator(operator_value) + target = float(target_value) + constraints.append( + MetricConstraint( + selector=MetricSelector( + metric=metric, + aggregation=aggregation, + ), + operator=operator, + value=target, + ) + ) + except (ValueError, TypeError) as error: + raise click.BadParameter( + "constraints use METRIC[:AGGREGATION] OP VALUE; " + "OP is one of ==, !=, <, <=, >, >=", + param_hint="--constraint", + ) from error + return constraints + + +def _print_result(session, result) -> None: + click.echo(f"Session: {session.session_dir}") + click.echo( + f"Baseline: {result.baseline.request.candidate.id} " + f"({result.baseline.objective.value if result.baseline.objective else 'n/a'})" + ) + if result.best is None: + click.echo("Best: no feasible candidate") + else: + click.echo( + f"Best: {result.best.request.candidate.id} " + f"({result.best.objective.value if result.best.objective else 'n/a'})" + ) + + +async def _run_configured(config_path: Path, *, optimize: bool): + from vero.config import build_configured_runtime, load_config + + runtime = await build_configured_runtime( + load_config(config_path), + optimize=optimize, + ) + result = await runtime.session.run( + skip_baseline_evaluation=runtime.session.manifest_path.exists(), + max_proposals=None if optimize else 0, + ) + return runtime.session, result + + +@click.group() +def main() -> None: + """VeRO: a harness for agents to optimize programs.""" + + +@main.command(name="init") +@click.argument( + "directory", + type=click.Path(path_type=Path, file_okay=False), + default=Path("."), +) +def initialize_config(directory: Path) -> None: + """Create a commented-safe train/validation/test vero.toml starter.""" + + directory.mkdir(parents=True, exist_ok=True) + destination = directory / "vero.toml" + if destination.exists(): + raise click.ClickException(f"configuration already exists: {destination}") + destination.write_text(_CONFIG_TEMPLATE, encoding="utf-8") + click.echo(f"Created {destination}") + + +@main.command(name="check") +@click.option( + "--config", + "config_path", + type=click.Path(path_type=Path, exists=True, dir_okay=False), + default=Path("vero.toml"), + show_default=True, +) +def check_config(config_path: Path) -> None: + """Validate configuration, paths, Git state, and evaluation references.""" + + try: + config = load_config(config_path) + target = Path(config.target.root) + harness = Path(config.backend.harness_root) + if not target.is_dir(): + raise ValueError(f"target root does not exist: {target}") + if not harness.is_dir(): + raise ValueError(f"evaluation harness root does not exist: {harness}") + for name, path in config.backend.staged_inputs.items(): + if not Path(path).exists(): + raise ValueError(f"staged input {name!r} does not exist: {path}") + subprocess.run( + ["git", "rev-parse", "--verify", config.target.ref], + cwd=target, + check=True, + capture_output=True, + text=True, + ) + dirty = subprocess.run( + ["git", "status", "--porcelain"], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout + if dirty.strip(): + raise ValueError("target repository has uncommitted changes") + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + click.echo( + f"Configuration is valid: {len(config.evaluations)} evaluations, " + f"selection={config.protocol.selection_evaluation!r}, " + f"final={config.protocol.final_evaluation!r}" + ) + + +@main.command(name="evaluate") +@click.option( + "--config", + "config_path", + type=click.Path(path_type=Path, exists=True, dir_okay=False), + default=Path("vero.toml"), + show_default=True, +) +def evaluate_config(config_path: Path) -> None: + """Evaluate the configured baseline without producing candidates.""" + + try: + session, result = asyncio.run(_run_configured(config_path, optimize=False)) + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + _print_result(session, result) + + +@main.command(name="run") +@click.option( + "--config", + "config_path", + type=click.Path(path_type=Path, exists=True, dir_okay=False), + default=Path("vero.toml"), + show_default=True, +) +def run_config(config_path: Path) -> None: + """Run the optimization declared in vero.toml.""" + + try: + session, result = asyncio.run(_run_configured(config_path, optimize=True)) + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + _print_result(session, result) + + +@main.command(name="report") +@click.argument( + "session_dir", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +@click.option( + "--output", + type=click.Path(path_type=Path, dir_okay=False), + help="Portable HTML path; defaults to SESSION_DIR/experiment.html.", +) +def report_session(session_dir: Path, output: Path | None) -> None: + """Build a self-contained visual report for an optimization session.""" + + from vero.report import generate_experiment_report + + try: + destination = asyncio.run(generate_experiment_report(session_dir, output)) + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + click.echo(destination) + + +@main.command() +@click.argument( + "project_path", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +@click.option( + "--harness-root", + type=click.Path(path_type=Path, exists=True, file_okay=False), + required=True, + help="Trusted directory containing the evaluation harness.", +) +@click.option( + "--evaluate", + "evaluation_command", + required=True, + help="Evaluation argv with placeholders such as {workspace} and {report}.", +) +@click.option( + "--producer-root", + type=click.Path(path_type=Path, exists=True, file_okay=False), + help="Trusted directory containing an external candidate producer.", +) +@click.option( + "--produce", + "producer_command", + help="Producer argv with placeholders such as {workspace} and {instruction}.", +) +@click.option( + "--agent", + type=click.Choice(["claude", "vero"]), + help="Use a built-in coding-agent producer instead of --produce.", +) +@click.option("--instruction", help="Instruction given to the candidate producer.") +@click.option("--metric", required=True, help="Metric to optimize.") +@click.option( + "--aggregation", + type=click.Choice([value.value for value in MetricAggregation]), + default=MetricAggregation.REPORT.value, + show_default=True, +) +@click.option( + "--case-failure-value", + type=float, + help="Value assigned to failed or missing cases during case aggregation.", +) +@click.option( + "--direction", + type=click.Choice(["maximize", "minimize"]), + required=True, +) +@click.option("--failure-value", type=float) +@click.option( + "--constraint", + type=(str, str, str), + multiple=True, + metavar="METRIC[:AGGREGATION] OP VALUE", + help="Feasibility constraint; repeat for multiple constraints.", +) +@click.option("--evaluation-set", default="default", show_default=True) +@click.option("--partition") +@click.option("--case-id", multiple=True, help="Evaluate only this case; repeatable.") +@click.option("--case-start", default=0, type=click.IntRange(min=0), show_default=True) +@click.option("--case-stop", type=click.IntRange(min=1)) +@click.option( + "--parameter", + multiple=True, + help="Evaluation parameter as NAME=JSON; repeat for multiple values.", +) +@click.option( + "--evaluation-env", + multiple=True, + help="Environment variable to pass through to the evaluation harness.", +) +@click.option( + "--evaluation-variable", + multiple=True, + help="Fixed harness environment variable as NAME=VALUE; repeatable.", +) +@click.option( + "--producer-env", + multiple=True, + help="Environment variable to pass through to an external producer.", +) +@click.option( + "--producer-variable", + multiple=True, + help="Fixed producer environment variable as NAME=VALUE; repeatable.", +) +@click.option("--evaluation-working-directory", default=".", show_default=True) +@click.option("--producer-working-directory", default=".", show_default=True) +@click.option( + "--target-ref", + default="HEAD", + show_default=True, + help="Git ref to use as the immutable baseline.", +) +@click.option( + "--session-dir", + type=click.Path(path_type=Path, file_okay=False), + help="Durable output directory; defaults to $VERO_HOME/sessions/.", +) +@click.option("--session-id", help="Stable session identity.") +@click.option( + "--max-proposals", default=1, type=click.IntRange(min=0), show_default=True +) +@click.option( + "--max-rounds", default=100, type=click.IntRange(min=1), show_default=True +) +@click.option( + "--max-concurrency", default=1, type=click.IntRange(min=1), show_default=True +) +@click.option("--max-turns", default=200, type=click.IntRange(min=1), show_default=True) +@click.option( + "--evaluation-timeout", + "--timeout", + "evaluation_timeout", + default=600.0, + type=click.FloatRange(min=0, min_open=True), + show_default=True, + help="Overall timeout for one evaluation. --timeout is a deprecated alias.", +) +@click.option( + "--producer-timeout", + default=600.0, + type=click.FloatRange(min=0, min_open=True), + show_default=True, + help="Timeout for one external command-producer attempt.", +) +@click.option( + "--case-timeout", + default=180.0, + type=click.FloatRange(min=0, min_open=True), + show_default=True, +) +@click.option( + "--evaluation-concurrency", + default=100, + type=click.IntRange(min=1), + show_default=True, +) +@click.option( + "--error-rate-threshold", + default=0.1, + type=click.FloatRange(min=0, max=1, min_open=True), + show_default=True, + help="Fail an evaluation when this fraction of selected cases errors.", +) +@click.option( + "--retry-max-attempts", + default=3, + type=click.IntRange(min=1), + show_default=True, + help="Maximum attempts for a transient per-case failure.", +) +@click.option( + "--retry-initial-delay", + default=4.0, + type=click.FloatRange(min=0), + show_default=True, +) +@click.option( + "--retry-maximum-delay", + default=120.0, + type=click.FloatRange(min=0), + show_default=True, +) +@click.option( + "--retry-multiplier", + default=2.0, + type=click.FloatRange(min=1), + show_default=True, +) +@click.option( + "--retry-on-timeout/--no-retry-on-timeout", + default=True, + show_default=True, +) +@click.option("--seed", type=int) +@click.option("--wandb-project", help="Log the session to this W&B project.") +@click.option("--wandb-entity") +@click.option("--wandb-name") +@click.option( + "--wandb-mode", + type=click.Choice(["online", "offline", "disabled"]), +) +def optimize( + project_path: Path, + harness_root: Path, + evaluation_command: str, + producer_root: Path | None, + producer_command: str | None, + agent: str | None, + instruction: str | None, + metric: str, + aggregation: str, + case_failure_value: float | None, + direction: str, + failure_value: float | None, + constraint: tuple[tuple[str, str, str], ...], + evaluation_set: str, + partition: str | None, + case_id: tuple[str, ...], + case_start: int, + case_stop: int | None, + parameter: tuple[str, ...], + evaluation_env: tuple[str, ...], + evaluation_variable: tuple[str, ...], + producer_env: tuple[str, ...], + producer_variable: tuple[str, ...], + evaluation_working_directory: str, + producer_working_directory: str, + target_ref: str, + session_dir: Path | None, + session_id: str | None, + max_proposals: int, + max_rounds: int, + max_concurrency: int, + max_turns: int, + evaluation_timeout: float, + producer_timeout: float, + case_timeout: float, + evaluation_concurrency: int, + error_rate_threshold: float, + retry_max_attempts: int, + retry_initial_delay: float, + retry_maximum_delay: float, + retry_multiplier: float, + retry_on_timeout: bool, + seed: int | None, + wandb_project: str | None, + wandb_entity: str | None, + wandb_name: str | None, + wandb_mode: str | None, +) -> None: + """Optimize the versioned program at PROJECT_PATH.""" + + producer_count = int(producer_command is not None) + int(agent is not None) + if producer_count > 1 or (max_proposals > 0 and producer_count != 1): + raise click.UsageError( + "provide exactly one of --produce or --agent when producing candidates" + ) + if producer_command is not None and producer_root is None: + raise click.UsageError("--producer-root is required with --produce") + if producer_command is None and producer_root is not None: + raise click.UsageError("--producer-root is only valid with --produce") + if agent is None and max_turns != 200: + raise click.UsageError("--max-turns is only valid with --agent") + if producer_command is None and ( + producer_env + or producer_variable + or producer_working_directory != "." + or producer_timeout != 600.0 + ): + raise click.UsageError( + "--producer-env, --producer-variable, --producer-working-directory, " + "and --producer-timeout are only valid with --produce" + ) + if wandb_project is None and any( + value is not None for value in (wandb_entity, wandb_name, wandb_mode) + ): + raise click.UsageError( + "--wandb-entity, --wandb-name, and --wandb-mode require --wandb-project" + ) + if case_id and case_stop is not None: + raise click.UsageError("--case-id cannot be combined with --case-stop") + if case_stop is None and case_start != 0: + raise click.UsageError("--case-start requires --case-stop") + if case_stop is not None and case_stop <= case_start: + raise click.UsageError("--case-stop must be greater than --case-start") + + if case_id: + selection = CaseIds(ids=list(case_id)) + elif case_stop is not None: + selection = CaseRange(start=case_start, stop=case_stop) + else: + selection = AllCases() + + if session_id is None and session_dir is not None: + manifest_path = session_dir / "manifest.json" + if manifest_path.exists(): + session_id = SessionManifest.model_validate_json( + manifest_path.read_text(encoding="utf-8") + ).id + resolved_session_id = session_id or ( + session_dir.name if session_dir is not None else str(uuid4()) + ) + resolved_session_dir = ( + session_dir.resolve() + if session_dir is not None + else _default_home() / "sessions" / resolved_session_id + ) + + async def run(): + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness_root.resolve()), + command=_command(evaluation_command, "--evaluate"), + working_directory=evaluation_working_directory, + environment=_parse_environment( + evaluation_variable, option="--evaluation-variable" + ), + passthrough_environment=list(evaluation_env), + ) + ) + if producer_command is not None: + assert producer_root is not None + producer = CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root.resolve()), + command=_command(producer_command, "--produce"), + working_directory=producer_working_directory, + environment=_parse_environment( + producer_variable, option="--producer-variable" + ), + passthrough_environment=list(producer_env), + timeout_seconds=producer_timeout, + ) + ) + elif agent is not None: + from vero.agents import AgentCandidateProducer + + if agent == "claude": + from vero.agents import ClaudeCodeAgent + + coding_agent = ClaudeCodeAgent() + else: + from vero.agents import VeroAgent + + coding_agent = VeroAgent() + producer = AgentCandidateProducer( + coding_agent, + prompt=instruction, + max_turns=max_turns, + ) + else: + producer = None + + session = await create_local_optimization_session( + project_path=project_path, + session_dir=resolved_session_dir, + session_id=resolved_session_id, + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector( + metric=metric, + aggregation=MetricAggregation(aggregation), + case_failure_value=case_failure_value, + ), + direction=direction, + failure_value=failure_value, + constraints=_parse_constraints(constraint), + ), + evaluation_plan=EvaluationPlan.single( + EvaluationSet( + name=evaluation_set, + partition=partition, + selection=selection, + ) + ), + strategy=SequentialStrategy(instruction=instruction), + producers={"default": producer} if producer is not None else {}, + parameters=_parse_parameters(parameter), + limits=EvaluationLimits( + timeout_seconds=evaluation_timeout, + case_timeout_seconds=case_timeout, + max_concurrency=evaluation_concurrency, + error_rate_threshold=error_rate_threshold, + retry=RetryPolicy( + max_attempts=retry_max_attempts, + initial_delay_seconds=retry_initial_delay, + maximum_delay_seconds=retry_maximum_delay, + multiplier=retry_multiplier, + retry_on_timeout=retry_on_timeout, + ), + ), + seed=seed, + max_proposals=max_proposals, + max_rounds=max_rounds, + max_concurrency=max_concurrency, + base_ref=target_ref, + metadata={"project_path": str(project_path.resolve())}, + ) + if wandb_project is not None: + assert session.events is not None + session.events.sinks.append( + WandbEventSink( + project=wandb_project, + entity=wandb_entity, + name=wandb_name, + mode=wandb_mode, + session_id=session.id, + session_dir=session.session_dir, + config={ + "vero/target": str(project_path.resolve()), + "vero/evaluation_set": evaluation_set, + "vero/objective_metric": metric, + "vero/objective_direction": direction, + }, + ) + ) + result = await session.run( + skip_baseline_evaluation=session.manifest_path.exists() + ) + return session, result + + try: + session, result = asyncio.run(run()) + except click.ClickException: + raise + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + + _print_result(session, result) + + +@main.group() +def session() -> None: + """Inspect durable optimization sessions.""" + + +@session.command(name="list") +@click.option( + "--root", + type=click.Path(path_type=Path, file_okay=False), + help="Sessions directory; defaults to $VERO_HOME/sessions.", +) +def session_list(root: Path | None) -> None: + """List session manifests.""" + + root = root.resolve() if root is not None else _default_home() / "sessions" + if not root.exists(): + click.echo("No sessions found.") + return + manifests = sorted(root.rglob("manifest.json")) + if not manifests: + click.echo("No sessions found.") + return + for path in manifests: + try: + manifest = SessionManifest.model_validate_json( + path.read_text(encoding="utf-8") + ) + click.echo( + f"{manifest.id}\t{manifest.status.value}\t" + f"{manifest.best_candidate_id or '-'}\t{path.parent.relative_to(root)}" + ) + except Exception as error: + click.echo(f"{path.parent.relative_to(root)}\tinvalid\t{error}") + + +@session.command(name="inspect") +@click.argument( + "session_dir", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +def session_inspect(session_dir: Path) -> None: + """Print a canonical session manifest and evaluation summaries as JSON.""" + + manifest_path = session_dir / "manifest.json" + if not manifest_path.exists(): + raise click.ClickException(f"session manifest not found: {manifest_path}") + try: + manifest = SessionManifest.model_validate_json( + manifest_path.read_text(encoding="utf-8") + ) + except Exception as error: + raise click.ClickException(f"invalid session manifest: {error}") from error + database_path = session_dir / "database.json" + try: + database = ( + EvaluationDatabase.load_from_file(database_path) + if database_path.exists() + else EvaluationDatabase.from_evaluations_dir( + session_dir / "evaluations", + database_id=manifest.id, + ) + ) + except Exception as error: + raise click.ClickException(f"invalid evaluation database: {error}") from error + evaluations = sorted( + database.evaluations.values(), + key=lambda record: (record.completed_at, record.id), + ) + try: + if manifest.candidate_repository_family != "git": + raise ValueError( + "unsupported candidate repository family: " + f"{manifest.candidate_repository_family}" + ) + candidate_repository = asyncio.run( + GitCandidateRepository.open(session_dir / "candidates") + ) + candidates = candidate_repository.list() + except Exception as error: + raise click.ClickException(f"invalid candidate repository: {error}") from error + click.echo( + json.dumps( + { + "manifest": manifest.model_dump(mode="json"), + "candidates": [ + candidate.model_dump(mode="json") for candidate in candidates + ], + "evaluations": [ + project_evaluation(record, DisclosureLevel.AGGREGATE).model_dump( + mode="json" + ) + for record in evaluations + ], + }, + ensure_ascii=False, + indent=2, + ) + ) + + +@session.command(name="export") +@click.argument( + "session_dir", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +@click.option( + "--output", + type=click.Path(path_type=Path, dir_okay=False), + help="Archive path without, or with, a .tar.gz suffix.", +) +def session_export(session_dir: Path, output: Path | None) -> None: + """Export complete durable session state as a portable tar.gz archive.""" + + session_dir = session_dir.resolve() + if not (session_dir / "manifest.json").is_file(): + raise click.ClickException("session manifest not found") + destination = (output or session_dir.with_name(f"{session_dir.name}-export")) + destination = destination.expanduser().resolve() + archive_base = str(destination) + if archive_base.endswith(".tar.gz"): + archive_base = archive_base[: -len(".tar.gz")] + try: + archive = shutil.make_archive( + archive_base, + "gztar", + root_dir=session_dir.parent, + base_dir=session_dir.name, + ) + except Exception as error: + raise click.ClickException(str(error) or type(error).__name__) from error + click.echo(archive) + + +@session.command(name="fork") +@click.argument( + "source", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +@click.argument( + "destination", + type=click.Path(path_type=Path, file_okay=False), +) +@click.option("--session-id", help="New session ID; defaults to destination name.") +@click.option( + "--max-proposals", + type=click.IntRange(min=0), + help="New protocol proposal limit; edit vero.toml to the same value.", +) +@click.option( + "--reset-budgets", + is_flag=True, + help="Restore configured agent/system budgets instead of carrying balances.", +) +def session_fork( + source: Path, + destination: Path, + session_id: str | None, + max_proposals: int | None, + reset_budgets: bool, +) -> None: + """Fork durable candidates and evaluations into a new resumable session.""" + + source = source.resolve() + destination = destination.expanduser().resolve() + if destination.exists(): + raise click.ClickException(f"destination already exists: {destination}") + if destination.is_relative_to(source): + raise click.ClickException("fork destination must not be inside the source") + try: + manifest = SessionManifest.model_validate_json( + (source / "manifest.json").read_text(encoding="utf-8") + ) + new_id = session_id or destination.name + if not new_id.strip(): + raise ValueError("session ID must not be empty") + shutil.copytree(source, destination) + run = manifest.run.model_copy( + update=( + {"max_proposals": max_proposals} + if max_proposals is not None + else {} + ) + ) + forked_at = datetime.now(UTC) + forked = manifest.model_copy( + update={ + "id": new_id, + "status": SessionStatus.CREATED, + "run": run, + "created_at": forked_at, + "updated_at": forked_at, + "failure": None, + "metadata": { + **manifest.metadata, + "forked_from_session_id": manifest.id, + }, + } + ) + (destination / "manifest.json").write_text( + forked.model_dump_json(indent=2), + encoding="utf-8", + ) + database_path = destination / "database.json" + if database_path.exists(): + database = json.loads(database_path.read_text(encoding="utf-8")) + database["id"] = new_id + database_path.write_text( + json.dumps(database, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + for transient in ("events.jsonl", "agent-context.json"): + (destination / transient).unlink(missing_ok=True) + wandb_state = destination / "artifacts" / "wandb" + if wandb_state.exists(): + shutil.rmtree(wandb_state) + if reset_budgets: + budget_path = destination / "budgets.json" + if forked.evaluation_plan.budgets: + BudgetLedger( + forked.evaluation_plan.budgets, + path=budget_path, + ).save() + else: + budget_path.unlink(missing_ok=True) + except Exception as error: + if destination.exists(): + shutil.rmtree(destination) + raise click.ClickException(str(error) or type(error).__name__) from error + click.echo(f"Forked {manifest.id} to {new_id} at {destination}") + + +@session.command(name="clear") +@click.argument( + "session_dir", + type=click.Path(path_type=Path, exists=True, file_okay=False), +) +@click.option("--yes", is_flag=True, help="Confirm permanent deletion.") +def session_clear(session_dir: Path, yes: bool) -> None: + """Permanently delete one session's control-plane state.""" + + if not yes: + raise click.UsageError("session clear requires --yes") + session_dir = session_dir.resolve() + if not (session_dir / "manifest.json").is_file(): + raise click.ClickException("refusing to clear a directory without manifest.json") + shutil.rmtree(session_dir) + click.echo(f"Deleted {session_dir}") + + +from vero.harbor.cli import harbor as harbor_command + +main.add_command(harbor_command) + + +if __name__ == "__main__": + main() diff --git a/vero/src/vero/config.py b/vero/src/vero/config.py new file mode 100644 index 00000000..aad47952 --- /dev/null +++ b/vero/src/vero/config.py @@ -0,0 +1,542 @@ +"""Declarative configuration for generic program optimization.""" + +from __future__ import annotations + +import hashlib +import json +import os +import tomllib +from dataclasses import dataclass +from pathlib import Path +from typing import Annotated, Literal +from uuid import uuid4 + +from pydantic import Field, JsonValue, model_validator + +from vero.evaluation import ( + AllCases, + AgentSelectionMode, + CaseIds, + CaseRange, + CommandBackend, + CommandBackendConfig, + ConstraintOperator, + DisclosureLevel, + EvaluationAccessPolicy, + EvaluationBudget, + EvaluationDefinition, + EvaluationLimits, + EvaluationModel, + EvaluationPlan, + EvaluationPrincipal, + EvaluationSet, + MetricAggregation, + MetricConstraint, + MetricSelector, + ObjectiveSpec, + RetryPolicy, +) +from vero.optimization import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, + SequentialStrategy, +) +from vero.runtime import ( + OptimizationComponentSpec, + OptimizationRunSpec, + OptimizationSession, + SessionManifest, + WandbEventSink, + create_local_optimization_session, +) + + +class TargetConfig(EvaluationModel): + root: str + ref: str = "HEAD" + + +class BackendConfig(EvaluationModel): + id: str = "command" + kind: Literal["command"] = "command" + harness_root: str + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + staged_inputs: dict[str, str] = Field(default_factory=dict) + agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) + + +class BudgetConfig(EvaluationModel): + total_runs: int | None = Field(default=None, ge=0) + total_cases: int | None = Field(default=None, ge=0) + + def to_model( + self, + *, + backend_id: str, + evaluation_set: EvaluationSet, + principal: EvaluationPrincipal, + ) -> EvaluationBudget: + return EvaluationBudget( + backend_id=backend_id, + evaluation_set_key=evaluation_set.budget_key(backend_id), + principal=principal, + total_runs=self.total_runs, + total_cases=self.total_cases, + ) + + +class EvaluationConfig(EvaluationModel): + name: str + partition: str | None = None + case_ids: list[str] | None = None + case_start: int = Field(default=0, ge=0) + case_stop: int | None = Field(default=None, ge=1) + agent_can_evaluate: bool = True + agent_visible: bool = True + agent_selection: AgentSelectionMode = AgentSelectionMode.ARBITRARY + disclosure: DisclosureLevel = DisclosureLevel.FULL + expose_case_resources: bool = False + agent_budget: BudgetConfig | None = None + system_budget: BudgetConfig | None = None + + @model_validator(mode="after") + def validate_selection(self) -> EvaluationConfig: + if self.case_ids is not None and ( + self.case_start != 0 or self.case_stop is not None + ): + raise ValueError("case_ids and case range cannot both be configured") + if self.case_ids is None and self.case_stop is None and self.case_start != 0: + raise ValueError("case_start requires case_stop") + if self.case_stop is not None and self.case_stop <= self.case_start: + raise ValueError("case_stop must be greater than case_start") + return self + + def to_evaluation_set(self) -> EvaluationSet: + if self.case_ids is not None: + selection = CaseIds(ids=self.case_ids) + elif self.case_stop is not None: + selection = CaseRange(start=self.case_start, stop=self.case_stop) + else: + selection = AllCases() + return EvaluationSet( + name=self.name, + partition=self.partition, + selection=selection, + ) + + def to_definition(self, backend_id: str) -> EvaluationDefinition: + evaluation_set = self.to_evaluation_set() + return EvaluationDefinition( + evaluation_set=evaluation_set, + access=EvaluationAccessPolicy( + agent_can_evaluate=self.agent_can_evaluate, + agent_visible=self.agent_visible, + agent_selection=self.agent_selection, + disclosure=self.disclosure, + expose_case_resources=self.expose_case_resources, + ), + agent_budget=( + self.agent_budget.to_model( + backend_id=backend_id, + evaluation_set=evaluation_set, + principal=EvaluationPrincipal.AGENT, + ) + if self.agent_budget is not None + else None + ), + system_budget=( + self.system_budget.to_model( + backend_id=backend_id, + evaluation_set=evaluation_set, + principal=EvaluationPrincipal.SYSTEM, + ) + if self.system_budget is not None + else None + ), + ) + + +class ProtocolConfig(EvaluationModel): + selection_evaluation: str + final_evaluation: str | None = None + evaluate_final_baseline: bool = True + parameters: dict[str, JsonValue] = Field(default_factory=dict) + seed: int | None = None + timeout_seconds: float = Field(default=600.0, gt=0) + case_timeout_seconds: float = Field(default=180.0, gt=0) + evaluation_concurrency: int = Field(default=100, ge=1) + error_rate_threshold: float | None = Field(default=0.1, gt=0, le=1) + retry: RetryPolicy = Field(default_factory=RetryPolicy) + max_proposals: int = Field(default=1, ge=0) + max_rounds: int = Field(default=100, ge=1) + max_concurrency: int = Field(default=1, ge=1) + + def to_limits(self) -> EvaluationLimits: + return EvaluationLimits( + timeout_seconds=self.timeout_seconds, + case_timeout_seconds=self.case_timeout_seconds, + max_concurrency=self.evaluation_concurrency, + error_rate_threshold=self.error_rate_threshold, + retry=self.retry, + ) + + +class ObjectiveConstraintConfig(EvaluationModel): + metric: str + aggregation: MetricAggregation = MetricAggregation.REPORT + case_failure_value: float | None = None + operator: ConstraintOperator + value: float + + def to_model(self) -> MetricConstraint: + return MetricConstraint( + selector=MetricSelector( + metric=self.metric, + aggregation=self.aggregation, + case_failure_value=self.case_failure_value, + ), + operator=self.operator, + value=self.value, + ) + + +class ObjectiveConfig(EvaluationModel): + metric: str + aggregation: MetricAggregation = MetricAggregation.REPORT + case_failure_value: float | None = None + direction: Literal["maximize", "minimize"] + failure_value: float | None = None + constraints: list[ObjectiveConstraintConfig] = Field(default_factory=list) + + def to_model(self) -> ObjectiveSpec: + return ObjectiveSpec( + selector=MetricSelector( + metric=self.metric, + aggregation=self.aggregation, + case_failure_value=self.case_failure_value, + ), + direction=self.direction, + failure_value=self.failure_value, + constraints=[constraint.to_model() for constraint in self.constraints], + ) + + +class BaseOptimizerConfig(EvaluationModel): + instruction: str | None = None + + +class CommandOptimizerConfig(BaseOptimizerConfig): + kind: Literal["command"] = "command" + root: str = "." + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + timeout_seconds: float = Field(default=600.0, gt=0) + description: str = "Optimize candidate" + + @model_validator(mode="after") + def validate_command(self) -> CommandOptimizerConfig: + if not self.command: + raise ValueError("command optimizer requires a non-empty command") + return self + + +class AgentOptimizerConfig(BaseOptimizerConfig): + kind: Literal["vero", "claude"] + model: str | None = None + max_turns: int = Field(default=200, ge=1) + + @model_validator(mode="after") + def validate_model(self) -> AgentOptimizerConfig: + if self.model is not None and not self.model.strip(): + raise ValueError("agent optimizer model must not be empty") + return self + + +OptimizerConfig = Annotated[ + CommandOptimizerConfig | AgentOptimizerConfig, + Field(discriminator="kind"), +] + + +class SessionConfig(EvaluationModel): + id: str | None = None + directory: str | None = None + + +class WandbConfig(EvaluationModel): + project: str + run_id: str | None = None + entity: str | None = None + name: str | None = None + group: str | None = None + tags: list[str] = Field(default_factory=list) + mode: Literal["online", "offline", "disabled"] | None = None + notes: str | None = None + config: dict[str, JsonValue] = Field(default_factory=dict) + + +class VeroConfig(EvaluationModel): + target: TargetConfig + backend: BackendConfig + evaluations: list[EvaluationConfig] + protocol: ProtocolConfig + objective: ObjectiveConfig + optimizer: OptimizerConfig | None = None + session: SessionConfig = Field(default_factory=SessionConfig) + wandb: WandbConfig | None = None + + @model_validator(mode="after") + def validate_plan(self) -> VeroConfig: + self.to_evaluation_plan() + names = {evaluation.name for evaluation in self.evaluations} + unknown_context = sorted(set(self.backend.agent_context_inputs) - names) + if unknown_context: + raise ValueError( + "backend.agent_context_inputs references unknown evaluations: " + f"{unknown_context}" + ) + return self + + def to_evaluation_plan(self) -> EvaluationPlan: + return EvaluationPlan( + evaluations=[ + evaluation.to_definition(self.backend.id) + for evaluation in self.evaluations + ], + selection_evaluation=self.protocol.selection_evaluation, + final_evaluation=self.protocol.final_evaluation, + evaluate_final_baseline=self.protocol.evaluate_final_baseline, + ) + + @staticmethod + def _component_spec( + type_name: str, + payload: dict[str, object], + ) -> OptimizationComponentSpec: + encoded = json.dumps( + payload, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + default=str, + ).encode() + return OptimizationComponentSpec( + type=type_name, + config_digest=hashlib.sha256(encoded).hexdigest(), + ) + + def to_run_spec(self) -> OptimizationRunSpec: + producers = {} + if self.optimizer is not None: + producer_type = ( + "vero.optimization.command.CommandCandidateProducer" + if isinstance(self.optimizer, CommandOptimizerConfig) + else "vero.agents.producer.AgentCandidateProducer" + ) + producers["default"] = self._component_spec( + producer_type, + self.optimizer.model_dump(mode="json"), + ) + return OptimizationRunSpec( + max_proposals=( + self.protocol.max_proposals if self.optimizer is not None else 0 + ), + max_rounds=(self.protocol.max_rounds if self.optimizer is not None else 1), + max_concurrency=( + self.protocol.max_concurrency if self.optimizer is not None else 1 + ), + strategy=self._component_spec( + "vero.optimization.strategy.SequentialStrategy", + { + "producer_id": "default", + "instruction": ( + self.optimizer.instruction + if self.optimizer is not None + else None + ), + }, + ), + producers=producers, + ) + + +def load_config(path: Path | str = Path("vero.toml")) -> VeroConfig: + """Load a trusted config and resolve its filesystem paths beside the file.""" + + config_path = Path(path).expanduser().resolve() + with config_path.open("rb") as config_file: + config = VeroConfig.model_validate(tomllib.load(config_file)) + base = config_path.parent + updates: dict[str, object] = { + "target": config.target.model_copy( + update={"root": str((base / config.target.root).resolve())} + ), + "backend": config.backend.model_copy( + update={ + "harness_root": str((base / config.backend.harness_root).resolve()), + "staged_inputs": { + name: str((base / source).resolve()) + for name, source in config.backend.staged_inputs.items() + }, + } + ), + } + if isinstance(config.optimizer, CommandOptimizerConfig): + updates["optimizer"] = config.optimizer.model_copy( + update={"root": str((base / config.optimizer.root).resolve())} + ) + if config.session.directory is not None: + updates["session"] = config.session.model_copy( + update={"directory": str((base / config.session.directory).resolve())} + ) + return config.model_copy(update=updates) + + +@dataclass(frozen=True) +class ConfiguredRuntime: + config: VeroConfig + session: OptimizationSession + producer: object | None + + +def _session_identity(config: VeroConfig) -> tuple[str, Path]: + configured_dir = config.session.directory + if configured_dir is not None: + session_dir = Path(configured_dir).expanduser().resolve() + manifest_path = session_dir / "manifest.json" + if config.session.id is None and manifest_path.exists(): + session_id = SessionManifest.model_validate_json( + manifest_path.read_text(encoding="utf-8") + ).id + else: + session_id = config.session.id or session_dir.name + return session_id, session_dir + session_id = config.session.id or str(uuid4()) + home = Path(os.environ.get("VERO_HOME", "~/.vero")).expanduser().resolve() + return session_id, home / "sessions" / session_id + + +def _producer(config: CommandOptimizerConfig | AgentOptimizerConfig): + if isinstance(config, CommandOptimizerConfig): + return CommandCandidateProducer( + CommandCandidateProducerConfig( + root=config.root, + command=config.command, + working_directory=config.working_directory, + environment=config.environment, + passthrough_environment=config.passthrough_environment, + timeout_seconds=config.timeout_seconds, + description=config.description, + ) + ) + if config.kind == "claude": + from vero.agents import ClaudeCodeAgent + + agent = ClaudeCodeAgent() + if config.model is not None: + agent.options.model = config.model + else: + from vero.agents import VeroAgent + + agent = ( + VeroAgent.for_model(config.model) + if config.model is not None + else VeroAgent() + ) + from vero.agents import AgentCandidateProducer + + return AgentCandidateProducer( + agent, + prompt=config.instruction, + max_turns=config.max_turns, + ) + + +async def build_configured_runtime( + config: VeroConfig, + *, + optimize: bool, +) -> ConfiguredRuntime: + """Compose a local session from a trusted declarative configuration.""" + + target_root = Path(config.target.root) + harness_root = Path(config.backend.harness_root) + if not target_root.is_dir(): + raise ValueError(f"target root does not exist: {target_root}") + if not harness_root.is_dir(): + raise ValueError(f"evaluation harness root does not exist: {harness_root}") + if optimize and config.optimizer is None: + raise ValueError("vero run requires an [optimizer] configuration") + + optimizer_config = config.optimizer if optimize else None + producer = _producer(optimizer_config) if optimizer_config is not None else None + producers = {"default": producer} if producer is not None else {} + session_id, session_dir = _session_identity(config) + backend = CommandBackend( + CommandBackendConfig( + harness_root=config.backend.harness_root, + command=config.backend.command, + working_directory=config.backend.working_directory, + environment=config.backend.environment, + passthrough_environment=config.backend.passthrough_environment, + staged_inputs=config.backend.staged_inputs, + agent_context_inputs=config.backend.agent_context_inputs, + ) + ) + session = await create_local_optimization_session( + project_path=target_root, + session_dir=session_dir, + session_id=session_id, + backend_id=config.backend.id, + backend=backend, + objective=config.objective.to_model(), + evaluation_plan=config.to_evaluation_plan(), + strategy=SequentialStrategy( + instruction=(optimizer_config.instruction if optimizer_config else None) + ), + producers=producers, + parameters=config.protocol.parameters, + limits=config.protocol.to_limits(), + seed=config.protocol.seed, + max_proposals=(config.protocol.max_proposals if optimizer_config else 0), + max_rounds=(config.protocol.max_rounds if optimizer_config else 1), + max_concurrency=(config.protocol.max_concurrency if optimizer_config else 1), + base_ref=config.target.ref, + metadata={ + "config": "vero.toml", + "project_path": str(target_root), + }, + run_spec=config.to_run_spec(), + ) + if config.wandb is not None: + assert session.events is not None + session.events.sinks.append( + WandbEventSink( + project=config.wandb.project, + session_id=session.id, + session_dir=session.session_dir, + run_id=config.wandb.run_id, + entity=config.wandb.entity, + name=config.wandb.name, + group=config.wandb.group, + tags=config.wandb.tags, + mode=config.wandb.mode, + notes=config.wandb.notes, + config={ + **config.wandb.config, + "vero/target": str(target_root), + "vero/evaluation_plan": config.to_evaluation_plan().model_dump( + mode="json" + ), + "vero/objective": config.objective.to_model().model_dump( + mode="json" + ), + }, + ) + ) + return ConfiguredRuntime(config=config, session=session, producer=producer) diff --git a/vero/src/vero/evaluation/__init__.py b/vero/src/vero/evaluation/__init__.py new file mode 100644 index 00000000..c07b75ed --- /dev/null +++ b/vero/src/vero/evaluation/__init__.py @@ -0,0 +1,166 @@ +"""Public evaluation contracts for VeRO.""" + +from vero.evaluation.backend import ( + BackendRegistry, + CaseResourceExporter, + CaseStore, + EvaluationBackend, + EvaluationContext, +) +from vero.evaluation.budget import BudgetLedger +from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.engine import ( + AuthorizationResolver, + EvaluationEngine, + allow_all_evaluations, + authorize_evaluation_plan, +) +from vero.evaluation.evaluator import Evaluator +from vero.evaluation.exceptions import ( + EvaluationCancelledError, + EvaluationBudgetExceeded, + EvaluationDeniedError, + EvaluationError, + EvaluationExecutionError, + EvaluationInfrastructureError, + EvaluationTerminatedError, + EvaluationRequestError, + UnknownBackendError, +) +from vero.evaluation.models import ( + AgentSelectionMode, + AllCases, + BackendProvenance, + CaseError, + CaseIds, + CaseRange, + CaseResult, + CaseSelection, + CaseStatus, + CommandEvaluationInput, + ConstraintOperator, + ConstraintViolation, + DiagnosticSeverity, + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationAccessPolicy, + EvaluationArtifact, + EvaluationAuthorization, + EvaluationBudget, + EvaluationDefinition, + EvaluationCost, + EvaluationDiagnostic, + EvaluationLimits, + EvaluationModel, + EvaluationPlan, + EvaluationPrincipal, + EvaluationRecord, + EvaluationReceipt, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + EvaluationSummary, + MetricAggregation, + MetricConstraint, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, + RetryPolicy, +) +from vero.evaluation.objective import ( + compare_evaluation_records, + evaluate_objective, + project_evaluation, + resolve_metric, + select_best_evaluation, +) +from vero.evaluation.persistence import ( + CaseCheckpointStore, + EvaluationDatabase, + EvaluationManifest, + EvaluationStore, + RunningEvaluationManifest, +) +from vero.evaluation.python_task import ( + PythonTaskBackend, + PythonTaskBackendConfig, + PythonTaskEvaluationConfig, +) + +__all__ = [ + "AgentSelectionMode", + "AllCases", + "BackendProvenance", + "BackendRegistry", + "BudgetLedger", + "CaseError", + "CaseIds", + "CaseRange", + "CaseResourceExporter", + "CaseResult", + "CaseSelection", + "CaseStatus", + "CaseStore", + "CaseCheckpointStore", + "CommandEvaluationInput", + "CommandBackend", + "CommandBackendConfig", + "ConstraintOperator", + "ConstraintViolation", + "DiagnosticSeverity", + "DisclosureLevel", + "EvaluationAcknowledgement", + "EvaluationAccessPolicy", + "EvaluationArtifact", + "EvaluationAuthorization", + "EvaluationBackend", + "EvaluationBudget", + "EvaluationDefinition", + "EvaluationBudgetExceeded", + "EvaluationCancelledError", + "EvaluationContext", + "EvaluationCost", + "EvaluationDeniedError", + "EvaluationDiagnostic", + "EvaluationDatabase", + "EvaluationEngine", + "EvaluationError", + "EvaluationExecutionError", + "EvaluationInfrastructureError", + "EvaluationTerminatedError", + "EvaluationLimits", + "EvaluationManifest", + "EvaluationModel", + "EvaluationPlan", + "EvaluationPrincipal", + "EvaluationRecord", + "EvaluationReceipt", + "EvaluationRequestError", + "EvaluationReport", + "EvaluationRequest", + "EvaluationSet", + "EvaluationStatus", + "EvaluationSummary", + "EvaluationStore", + "Evaluator", + "MetricAggregation", + "MetricConstraint", + "MetricSelector", + "ObjectiveResult", + "ObjectiveSpec", + "PythonTaskBackend", + "PythonTaskBackendConfig", + "PythonTaskEvaluationConfig", + "RetryPolicy", + "RunningEvaluationManifest", + "UnknownBackendError", + "AuthorizationResolver", + "compare_evaluation_records", + "allow_all_evaluations", + "authorize_evaluation_plan", + "evaluate_objective", + "project_evaluation", + "resolve_metric", + "select_best_evaluation", +] diff --git a/vero/src/vero/evaluation/backend.py b/vero/src/vero/evaluation/backend.py new file mode 100644 index 00000000..91c77b6c --- /dev/null +++ b/vero/src/vero/evaluation/backend.py @@ -0,0 +1,105 @@ +"""Evaluation backend protocol and trusted backend registry.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Protocol, runtime_checkable + +from vero.evaluation.models import ( + BackendProvenance, + EvaluationCost, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + CaseResult, +) +from vero.sandbox import Sandbox +from vero.workspace import Workspace + + +@runtime_checkable +class CaseStore(Protocol): + """Checkpoint interface available to streaming evaluation backends.""" + + async def save(self, result: CaseResult) -> None: ... + + async def load(self, case_id: str) -> CaseResult | None: ... + + async def load_all(self) -> list[CaseResult]: ... + + +@dataclass(frozen=True) +class EvaluationContext: + workspace: Workspace + session_id: str + evaluation_id: str + result_dir: Path + artifact_dir: Path + case_store: CaseStore + # True for trusted finalization (admin) evaluations — lets a backend route + # target-agent inference to a reserved scope so the optimizer's search + # evaluations cannot starve the mandatory re-score's inference budget. + finalization: bool = False + + +@runtime_checkable +class EvaluationBackend(Protocol): + @property + def provenance(self) -> BackendProvenance: ... + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: ... + + async def evaluate( + self, + *, + context: EvaluationContext, + request: EvaluationRequest, + ) -> EvaluationReport: ... + + +@runtime_checkable +class CaseResourceExporter(Protocol): + """Optional trusted export of an agent-visible evaluation-set view.""" + + async def export_case_resources( + self, + *, + evaluation_set: EvaluationSet, + destination: str, + sandbox: Sandbox, + ) -> None: ... + + +class BackendRegistry: + """Registry of trusted, preconfigured evaluation backends.""" + + def __init__(self, backends: dict[str, EvaluationBackend] | None = None): + self._backends: dict[str, EvaluationBackend] = {} + for backend_id, backend in (backends or {}).items(): + self.register(backend_id, backend) + + def register(self, backend_id: str, backend: EvaluationBackend) -> None: + if not backend_id.strip(): + raise ValueError("backend ID must not be empty") + if backend_id in self._backends: + raise ValueError(f"backend ID {backend_id!r} is already registered") + if not isinstance(backend, EvaluationBackend): + raise TypeError("backend does not implement EvaluationBackend") + self._backends[backend_id] = backend + + def resolve(self, backend_id: str) -> EvaluationBackend: + from vero.evaluation.exceptions import UnknownBackendError + + try: + return self._backends[backend_id] + except KeyError as error: + raise UnknownBackendError( + f"unknown evaluation backend: {backend_id!r}" + ) from error + + def __contains__(self, backend_id: str) -> bool: + return backend_id in self._backends + + def __iter__(self): + return iter(self._backends) diff --git a/vero/src/vero/evaluation/budget.py b/vero/src/vero/evaluation/budget.py new file mode 100644 index 00000000..96ab1c2f --- /dev/null +++ b/vero/src/vero/evaluation/budget.py @@ -0,0 +1,226 @@ +"""Durable, backend-qualified evaluation budget reservations.""" + +from __future__ import annotations + +import asyncio +import json +from pathlib import Path +from typing import Any + +from vero.evaluation.exceptions import EvaluationBudgetExceeded +from vero.evaluation.models import ( + EvaluationBudget, + EvaluationCost, + EvaluationPrincipal, + EvaluationSet, +) +from vero.evaluation.persistence import _atomic_write_json + + +class BudgetLedger: + """Atomically meter completed evaluations against configured budgets.""" + + schema_version = 1 + + def __init__( + self, + budgets: list[EvaluationBudget] | None = None, + *, + path: Path | None = None, + ): + self.path = path + self._lock = asyncio.Lock() + self._budgets: dict[tuple[str, str, EvaluationPrincipal], EvaluationBudget] = {} + for budget in budgets or []: + self.add(budget) + + @property + def budgets(self) -> list[EvaluationBudget]: + return list(self._budgets.values()) + + def add(self, budget: EvaluationBudget) -> None: + key = (budget.backend_id, budget.evaluation_set_key, budget.principal) + if key in self._budgets: + raise ValueError(f"duplicate evaluation budget for {key!r}") + updates: dict[str, int] = {} + if budget.total_runs is not None and budget.remaining_runs is None: + updates["remaining_runs"] = budget.total_runs + if budget.total_cases is not None and budget.remaining_cases is None: + updates["remaining_cases"] = budget.total_cases + self._budgets[key] = budget.model_copy(update=updates) + + def get( + self, + backend_id: str, + evaluation_set: EvaluationSet, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + ) -> EvaluationBudget | None: + return self._budgets.get( + (backend_id, evaluation_set.budget_key(backend_id), principal) + ) + + async def reserve( + self, + backend_id: str, + evaluation_set: EvaluationSet, + cost: EvaluationCost, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + ) -> EvaluationBudget | None: + key = (backend_id, evaluation_set.budget_key(backend_id), principal) + async with self._lock: + budget = self._budgets.get(key) + if budget is None: + return None + if cost.cases is None and budget.remaining_cases is not None: + raise EvaluationBudgetExceeded( + "evaluation case cost is unknown but the budget has a case limit" + ) + if budget.remaining_runs is not None and cost.runs > budget.remaining_runs: + raise EvaluationBudgetExceeded("evaluation run budget exhausted") + if ( + budget.remaining_cases is not None + and cost.cases is not None + and cost.cases > budget.remaining_cases + ): + raise EvaluationBudgetExceeded("evaluation case budget exhausted") + + updates: dict[str, int] = {} + if budget.remaining_runs is not None: + updates["remaining_runs"] = budget.remaining_runs - cost.runs + if budget.remaining_cases is not None and cost.cases is not None: + updates["remaining_cases"] = budget.remaining_cases - cost.cases + updated = budget.model_copy(update=updates) + snapshot = dict(self._budgets) + snapshot[key] = updated + if self.path is not None: + write = asyncio.create_task( + asyncio.to_thread( + _atomic_write_json, + self.path, + self._serialize(snapshot), + ) + ) + cancellation: asyncio.CancelledError | None = None + while not write.done(): + try: + await asyncio.shield(write) + except asyncio.CancelledError as error: + cancellation = error + write.result() + if cancellation is not None: + # The run requesting this reservation is being cancelled and + # will never use it. Roll the durable charge back so the + # reservation is atomic — committed in full or not at all — + # and a cancelled reservation never leaks budget. (In-memory + # state was never updated, so we rewrite it as the truth.) + # Drain the rollback to completion even if a further + # cancellation arrives, so it cannot linger in the background + # and overwrite a later reservation once the lock is released. + rollback = asyncio.create_task( + asyncio.to_thread( + _atomic_write_json, + self.path, + self._serialize(self._budgets), + ) + ) + while not rollback.done(): + try: + await asyncio.shield(rollback) + except asyncio.CancelledError: + pass + rollback.result() + raise cancellation + self._budgets = snapshot + return updated + self._budgets = snapshot + return updated + + async def refund( + self, + backend_id: str, + evaluation_set: EvaluationSet, + cost: EvaluationCost, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + ) -> EvaluationBudget | None: + """Undo a reservation when execution produced no usable result.""" + + key = (backend_id, evaluation_set.budget_key(backend_id), principal) + async with self._lock: + budget = self._budgets.get(key) + if budget is None: + return None + updates: dict[str, int] = {} + if budget.remaining_runs is not None: + restored_runs = budget.remaining_runs + cost.runs + updates["remaining_runs"] = ( + min(restored_runs, budget.total_runs) + if budget.total_runs is not None + else restored_runs + ) + if budget.remaining_cases is not None and cost.cases is not None: + restored_cases = budget.remaining_cases + cost.cases + updates["remaining_cases"] = ( + min(restored_cases, budget.total_cases) + if budget.total_cases is not None + else restored_cases + ) + updated = budget.model_copy(update=updates) + snapshot = dict(self._budgets) + snapshot[key] = updated + if self.path is not None: + write = asyncio.create_task( + asyncio.to_thread( + _atomic_write_json, + self.path, + self._serialize(snapshot), + ) + ) + cancellation: asyncio.CancelledError | None = None + while not write.done(): + try: + await asyncio.shield(write) + except asyncio.CancelledError as error: + cancellation = error + write.result() + self._budgets = snapshot + if cancellation is not None: + raise cancellation + return updated + self._budgets = snapshot + return updated + + def _serialize( + self, + budgets: dict[tuple[str, str, EvaluationPrincipal], EvaluationBudget] + | None = None, + ) -> dict[str, Any]: + values = budgets if budgets is not None else self._budgets + return { + "schema_version": self.schema_version, + "budgets": [ + budget.model_dump(mode="json") for _, budget in sorted(values.items()) + ], + } + + def save(self) -> None: + if self.path is None: + raise ValueError("budget ledger has no persistence path") + _atomic_write_json(self.path, self._serialize()) + + @classmethod + def load(cls, path: Path) -> BudgetLedger: + if not path.exists(): + return cls(path=path) + try: + payload = json.loads(path.read_text(encoding="utf-8")) + if payload.get("schema_version") != cls.schema_version: + raise ValueError("unsupported budget ledger schema") + budgets = [ + EvaluationBudget.model_validate(value) + for value in payload.get("budgets", []) + ] + except Exception as error: + raise ValueError( + f"invalid durable budget ledger {path}: {error}" + ) from error + return cls(budgets, path=path) diff --git a/vero/src/vero/evaluation/command.py b/vero/src/vero/evaluation/command.py new file mode 100644 index 00000000..1e488489 --- /dev/null +++ b/vero/src/vero/evaluation/command.py @@ -0,0 +1,386 @@ +"""Language- and framework-neutral command evaluation backend.""" + +from __future__ import annotations + +import json +import os +import posixpath +import re +from pathlib import Path, PurePosixPath + +from pydantic import Field, field_validator, model_validator + +from vero.evaluation.backend import EvaluationContext +from vero.evaluation.models import ( + AllCases, + BackendProvenance, + CaseIds, + CaseRange, + CommandEvaluationInput, + DiagnosticSeverity, + EvaluationArtifact, + EvaluationCost, + EvaluationDiagnostic, + EvaluationModel, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, +) +from vero.evaluation.security import sanitize_evaluation_report, sanitize_text +from vero.staging import SandboxStagingArea +from vero.sandbox import Sandbox + +_PLACEHOLDERS = {"workspace", "harness", "request", "report", "artifacts"} +_PLACEHOLDER_PATTERN = re.compile(r"\{([^{}]+)\}") +_INPUT_NAME_PATTERN = re.compile(r"^[a-zA-Z][a-zA-Z0-9_-]*$") + + +class CommandBackendConfig(EvaluationModel): + harness_root: str + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + staged_inputs: dict[str, str] = Field(default_factory=dict) + agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) + + @field_validator("harness_root") + @classmethod + def validate_harness_root(cls, value: str) -> str: + if not value.strip(): + raise ValueError("harness_root must not be empty") + if not Path(value).is_absolute(): + raise ValueError("harness_root must be absolute after config resolution") + return value + + @field_validator("command") + @classmethod + def validate_command(cls, value: list[str]) -> list[str]: + if not value or any(not argument for argument in value): + raise ValueError("command and its arguments must not be empty") + unknown = { + placeholder + for argument in value + for placeholder in _PLACEHOLDER_PATTERN.findall(argument) + if placeholder not in _PLACEHOLDERS and not placeholder.startswith("input:") + } + if unknown: + raise ValueError( + f"unknown command placeholders: {', '.join(sorted(unknown))}" + ) + return value + + @field_validator("working_directory") + @classmethod + def validate_working_directory(cls, value: str) -> str: + path = Path(value) + if not value.strip() or path.is_absolute() or ".." in path.parts: + raise ValueError("working_directory must stay within harness_root") + return value + + @field_validator("environment") + @classmethod + def validate_environment(cls, value: dict[str, str]) -> dict[str, str]: + for name in value: + if not name or "=" in name: + raise ValueError(f"invalid environment variable name: {name!r}") + return value + + @field_validator("passthrough_environment") + @classmethod + def validate_passthrough(cls, value: list[str]) -> list[str]: + if len(value) != len(set(value)): + raise ValueError("passthrough_environment names must be unique") + for name in value: + if not name or "=" in name: + raise ValueError(f"invalid environment variable name: {name!r}") + return value + + @model_validator(mode="after") + def validate_environment_sources(self) -> CommandBackendConfig: + overlap = set(self.environment) & set(self.passthrough_environment) + if overlap: + raise ValueError( + "environment and passthrough_environment overlap for: " + + ", ".join(sorted(overlap)) + ) + invalid = sorted( + name + for name in self.staged_inputs + if not _INPUT_NAME_PATTERN.fullmatch(name) + ) + if invalid: + raise ValueError(f"invalid staged input names: {', '.join(invalid)}") + referenced = { + placeholder.removeprefix("input:") + for argument in self.command + for placeholder in _PLACEHOLDER_PATTERN.findall(argument) + if placeholder.startswith("input:") + } + unknown = sorted(referenced - set(self.staged_inputs)) + if unknown: + raise ValueError(f"unknown staged command inputs: {', '.join(unknown)}") + for evaluation, names in self.agent_context_inputs.items(): + if not evaluation.strip(): + raise ValueError("agent_context_inputs evaluation names must not be empty") + if len(names) != len(set(names)): + raise ValueError( + f"agent_context_inputs for {evaluation!r} must be unique" + ) + unknown_context = sorted( + { + name + for names in self.agent_context_inputs.values() + for name in names + } + - set(self.staged_inputs) + ) + if unknown_context: + raise ValueError( + "agent_context_inputs reference unknown staged inputs: " + + ", ".join(unknown_context) + ) + return self + + +class CommandBackend: + """Invoke a trusted external harness through a versioned JSON contract.""" + + name = "command" + version = "1" + + def __init__(self, config: CommandBackendConfig): + self.config = config + + @property + def provenance(self) -> BackendProvenance: + return BackendProvenance.from_config( + name=self.name, + version=self.version, + config=self.config, + ) + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: + selection = evaluation_set.selection + if isinstance(selection, CaseIds): + return EvaluationCost(cases=len(selection.ids)) + if isinstance(selection, CaseRange): + return EvaluationCost(cases=selection.stop - selection.start) + if isinstance(selection, AllCases): + return EvaluationCost(cases=None) + raise AssertionError(f"unsupported case selection: {selection}") + + async def export_case_resources( + self, + *, + evaluation_set: EvaluationSet, + destination: str, + sandbox: Sandbox, + ) -> None: + """Copy only explicitly allowlisted staged inputs into agent context.""" + + resources = [] + for name in self.config.agent_context_inputs.get(evaluation_set.name, []): + source = Path(self.config.staged_inputs[name]).resolve() + if not source.exists(): + raise ValueError( + f"agent context input {name!r} does not exist: {source}" + ) + target = str(PurePosixPath(destination) / name) + await sandbox.upload(str(source), target) + resources.append({"name": name, "path": name}) + await sandbox.write_file( + str(PurePosixPath(destination) / "index.json"), + json.dumps( + { + "schema_version": 1, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "resources": resources, + }, + ensure_ascii=False, + indent=2, + ) + + "\n", + ) + + def _working_directory(self, harness_root: str) -> str: + return posixpath.normpath( + posixpath.join(harness_root, self.config.working_directory) + ) + + def _environment(self) -> dict[str, str]: + environment = {"PATH": os.defpath, "LANG": "C.UTF-8"} + for name in ("TMPDIR", "TMP", "TEMP", "SYSTEMROOT"): + if name in os.environ: + environment[name] = os.environ[name] + environment.update(self.config.environment) + for name in self.config.passthrough_environment: + if name in os.environ: + environment[name] = os.environ[name] + return environment + + def _secrets(self) -> list[str]: + values = list(self.config.environment.values()) + values.extend( + os.environ[name] + for name in self.config.passthrough_environment + if name in os.environ + ) + return values + + def sanitize_error(self, message: str) -> str: + return sanitize_text(message, self._secrets()) + + def validate_request(self, request: EvaluationRequest) -> None: + payload = request.model_dump_json() + if any(secret in payload for secret in self._secrets() if len(secret) >= 4): + raise ValueError( + "evaluation parameters must not contain configured secret values; " + "pass secrets through the backend environment" + ) + + def _expand_command(self, values: dict[str, str]) -> list[str]: + command: list[str] = [] + for argument in self.config.command: + expanded = argument + for placeholder, value in values.items(): + expanded = expanded.replace(f"{{{placeholder}}}", value) + command.append(expanded) + return command + + @staticmethod + def _failure_report( + *, + code: str, + message: str, + artifacts: list[EvaluationArtifact], + ) -> EvaluationReport: + return EvaluationReport( + status=EvaluationStatus.FAILED, + diagnostics=[ + EvaluationDiagnostic( + code=code, + message=message, + severity=DiagnosticSeverity.ERROR, + phase="command", + ) + ], + artifacts=artifacts, + ) + + async def evaluate( + self, + *, + context: EvaluationContext, + request: EvaluationRequest, + ) -> EvaluationReport: + harness_source = Path(self.config.harness_root).resolve() + target_root = context.workspace.sandbox.host_path( + context.workspace.project_path + ) + if target_root is not None: + target_root = target_root.resolve() + if harness_source == target_root or harness_source.is_relative_to( + target_root + ): + raise ValueError( + "command harness must live outside the editable target" + ) + + capture_dir = context.artifact_dir / "command" + capture_dir.mkdir(parents=True, exist_ok=True) + async with SandboxStagingArea( + context.workspace.sandbox, + prefix=f"vero-eval-{context.evaluation_id[:8]}-", + ) as staging: + harness_root = ( + str(harness_source) + if context.workspace.sandbox.capabilities.host_paths + else await staging.upload(harness_source, "harness") + ) + staged_inputs = { + f"input:{name}": await staging.upload(source, f"inputs/{name}") + for name, source in self.config.staged_inputs.items() + } + request_path = await staging.write_text( + "request.json", + CommandEvaluationInput(request=request).model_dump_json(indent=2), + ) + report_path = staging.path("report.json") + artifacts_path = await staging.mkdir("artifacts") + + command = self._expand_command( + { + "workspace": context.workspace.project_path, + "harness": harness_root, + "request": request_path, + "report": report_path, + "artifacts": artifacts_path, + **staged_inputs, + } + ) + result = await context.workspace.sandbox.run( + command, + cwd=self._working_directory(harness_root), + timeout=request.limits.timeout_seconds, + env=self._environment(), + ) + + if await staging.exists("artifacts"): + await staging.download("artifacts", context.artifact_dir) + + report_payload = ( + await staging.read_text("report.json") + if await staging.exists("report.json") + else None + ) + + stdout = self.sanitize_error(result.stdout) + stderr = self.sanitize_error(result.stderr) + (capture_dir / "stdout.log").write_text(stdout, encoding="utf-8") + (capture_dir / "stderr.log").write_text(stderr, encoding="utf-8") + capture_artifacts = [ + EvaluationArtifact( + path="command/stdout.log", + media_type="text/plain", + description="Command harness standard output", + ), + EvaluationArtifact( + path="command/stderr.log", + media_type="text/plain", + description="Command harness standard error", + ), + ] + + if result.returncode != 0: + code = "command_timeout" if result.returncode == -1 else "command_failed" + message = ( + stderr.strip() + or f"evaluation command exited with status {result.returncode}" + ) + return self._failure_report( + code=code, + message=message, + artifacts=capture_artifacts, + ) + if report_payload is None: + return self._failure_report( + code="missing_report", + message="evaluation command did not write a report", + artifacts=capture_artifacts, + ) + try: + report = EvaluationReport.model_validate_json(report_payload) + except Exception as error: + return self._failure_report( + code="invalid_report", + message=self.sanitize_error( + f"evaluation command wrote an invalid report: {error}" + ), + artifacts=capture_artifacts, + ) + report = sanitize_evaluation_report(report, self._secrets()) + return report.model_copy( + update={"artifacts": [*report.artifacts, *capture_artifacts]} + ) diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py new file mode 100644 index 00000000..598cd5fc --- /dev/null +++ b/vero/src/vero/evaluation/engine.py @@ -0,0 +1,450 @@ +"""Authorization, budget, backend, persistence, and disclosure boundary.""" + +from __future__ import annotations + +import asyncio +import inspect +import logging +from contextlib import asynccontextmanager +from contextvars import ContextVar +from pathlib import Path +from typing import AsyncIterator, Awaitable, Callable + +from vero.evaluation.backend import BackendRegistry +from vero.evaluation.budget import BudgetLedger +from vero.evaluation.error_taxonomy import TERMINATING_DIAGNOSTIC_CODES +from vero.evaluation.evaluator import Evaluator +from vero.evaluation.exceptions import ( + EvaluationCancelledError, + EvaluationDeniedError, + EvaluationExecutionError, + EvaluationInfrastructureError, + EvaluationRequestError, + EvaluationTerminatedError, +) +from vero.evaluation.models import ( + AgentSelectionMode, + EvaluationAcknowledgement, + EvaluationAuthorization, + EvaluationPrincipal, + EvaluationPlan, + EvaluationRecord, + EvaluationRequest, + EvaluationSummary, + ObjectiveSpec, +) +from vero.evaluation.objective import project_evaluation +from vero.evaluation.persistence import EvaluationDatabase, EvaluationStore + +logger = logging.getLogger(__name__) + +AuthorizationResolver = Callable[ + [EvaluationPrincipal, str, EvaluationRequest], + EvaluationAuthorization | Awaitable[EvaluationAuthorization], +] + + +def allow_all_evaluations( + _principal: EvaluationPrincipal, + _backend_id: str, + _request: EvaluationRequest, +) -> EvaluationAuthorization: + """Explicit resolver for trusted runtimes without an evaluation boundary.""" + + return EvaluationAuthorization( + may_evaluate=True, + may_view=True, + expose_case_resources=True, + ) + + +def authorize_evaluation_plan(plan: EvaluationPlan) -> AuthorizationResolver: + """Build the canonical principal-aware resolver for an evaluation plan.""" + + def resolve( + principal: EvaluationPrincipal, + _backend_id: str, + request: EvaluationRequest, + ) -> EvaluationAuthorization: + definition = plan.for_evaluation_set(request.evaluation_set) + if definition is None: + return EvaluationAuthorization( + may_evaluate=False, + may_view=False, + reason="evaluation set is not present in the session plan", + ) + access = definition.access + if principal == EvaluationPrincipal.ADMIN: + return EvaluationAuthorization( + may_evaluate=True, + may_view=False, + meter_budget=False, + disclosure=access.disclosure, + ) + if principal == EvaluationPrincipal.SYSTEM: + return EvaluationAuthorization( + may_evaluate=True, + may_view=access.agent_visible, + meter_budget=definition.system_budget is not None, + disclosure=access.disclosure, + expose_case_resources=False, + ) + if ( + access.agent_selection == AgentSelectionMode.FIXED + and request.evaluation_set.selection != definition.evaluation_set.selection + ): + return EvaluationAuthorization( + may_evaluate=False, + may_view=access.agent_visible, + reason="evaluation set requires its fixed case selection", + ) + return EvaluationAuthorization( + may_evaluate=access.agent_can_evaluate, + may_view=access.agent_visible, + meter_budget=definition.agent_budget is not None, + disclosure=access.disclosure, + expose_case_resources=access.expose_case_resources, + reason=( + None + if access.agent_can_evaluate + else "evaluation set is not agent-evaluable" + ), + ) + + return resolve + + +class EvaluationEngine: + """The only runtime path from an evaluation request to a stored record.""" + + def __init__( + self, + *, + evaluator: Evaluator, + backends: BackendRegistry, + database: EvaluationDatabase, + database_path: Path | None = None, + budget_ledger: BudgetLedger | None = None, + authorization_resolver: AuthorizationResolver | None = None, + ): + self.evaluator = evaluator + self.backends = backends + self.database = database + self.database_path = database_path + self.budget_ledger = budget_ledger + self.authorization_resolver = authorization_resolver + self.listeners: list[Callable[[EvaluationRecord], object]] = [] + self._record_lock = asyncio.Lock() + self._agent_evaluations_open = True + self._active_agent_evaluations: dict[object, asyncio.Task[object]] = {} + self._agent_evaluations_idle = asyncio.Event() + self._agent_evaluations_idle.set() + self._agent_evaluation_scope_depth: ContextVar[int] = ContextVar( + f"vero_agent_evaluation_scope_{id(self)}", + default=0, + ) + + def _begin_evaluation( + self, + principal: EvaluationPrincipal, + ) -> object | None: + """Atomically admit and track an agent evaluation on this event loop.""" + + if principal != EvaluationPrincipal.AGENT: + return None + if not self._agent_evaluations_open: + raise EvaluationDeniedError("evaluation finalization has started") + task = asyncio.current_task() + if task is None: # pragma: no cover - async entry points always have a task + raise RuntimeError("evaluation requires an active asyncio task") + token = object() + self._active_agent_evaluations[token] = task + self._agent_evaluations_idle.clear() + return token + + def _finish_evaluation(self, token: object | None) -> None: + if token is None: + return + self._active_agent_evaluations.pop(token, None) + if not self._active_agent_evaluations: + self._agent_evaluations_idle.set() + + @asynccontextmanager + async def agent_evaluation_scope(self) -> AsyncIterator[None]: + """Track a complete agent request, including candidate import and disclosure.""" + + depth = self._agent_evaluation_scope_depth.get() + if depth: + nested = self._agent_evaluation_scope_depth.set(depth + 1) + try: + yield + finally: + self._agent_evaluation_scope_depth.reset(nested) + return + + token = self._begin_evaluation(EvaluationPrincipal.AGENT) + outer = self._agent_evaluation_scope_depth.set(1) + try: + yield + finally: + self._agent_evaluation_scope_depth.reset(outer) + self._finish_evaluation(token) + + async def quiesce_agent_evaluations( + self, + *, + timeout_seconds: float, + cancellation_grace_seconds: float = 30.0, + ) -> int: + """Close agent admission and drain requests accepted before finalization. + + The admission close and active-task snapshot contain no suspension point, + so a new agent evaluation cannot slip between them. If the bounded wait + expires, the remaining requests are cancelled; the normal evaluator + cancellation path persists terminal records and refunds their budgets. + Admin and system evaluations remain available to the verifier. + """ + + if timeout_seconds <= 0: + raise ValueError("evaluation drain timeout must be positive") + if cancellation_grace_seconds <= 0: + raise ValueError("evaluation cancellation grace must be positive") + + self._agent_evaluations_open = False + admitted = len(self._active_agent_evaluations) + if not admitted: + return 0 + + try: + async with asyncio.timeout(timeout_seconds): + await self._agent_evaluations_idle.wait() + return admitted + except TimeoutError: + pending = set(self._active_agent_evaluations.values()) + logger.warning( + "Cancelling %d agent evaluation(s) after a %.1fs finalization drain", + len(pending), + timeout_seconds, + ) + for task in pending: + task.cancel() + try: + async with asyncio.timeout(cancellation_grace_seconds): + await asyncio.gather(*pending, return_exceptions=True) + await self._agent_evaluations_idle.wait() + except TimeoutError: + logger.error( + "%d agent evaluation(s) did not stop within the %.1fs " + "cancellation grace", + len(self._active_agent_evaluations), + cancellation_grace_seconds, + ) + return admitted + + async def authorize( + self, + backend_id: str, + request: EvaluationRequest, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + supplied: EvaluationAuthorization | None = None, + ) -> EvaluationAuthorization: + """Resolve the trusted access decision without executing an evaluation.""" + + if supplied is not None: + return supplied + if self.authorization_resolver is None: + return EvaluationAuthorization( + may_evaluate=False, + reason="evaluation authorization was not configured", + ) + resolved = self.authorization_resolver(principal, backend_id, request) + if inspect.isawaitable(resolved): + resolved = await resolved + return resolved + + async def _evaluate_record( + self, + *, + backend_id: str, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None, + authorization: EvaluationAuthorization | None, + principal: EvaluationPrincipal, + ) -> tuple[EvaluationRecord, EvaluationAuthorization]: + token = ( + None + if principal == EvaluationPrincipal.AGENT + and self._agent_evaluation_scope_depth.get() + else self._begin_evaluation(principal) + ) + try: + return await self._execute_record( + backend_id=backend_id, + request=request, + objective_spec=objective_spec, + authorization=authorization, + principal=principal, + ) + finally: + self._finish_evaluation(token) + + async def _execute_record( + self, + *, + backend_id: str, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None, + authorization: EvaluationAuthorization | None, + principal: EvaluationPrincipal, + ) -> tuple[EvaluationRecord, EvaluationAuthorization]: + backend = self.backends.resolve(backend_id) + decision = await self.authorize( + backend_id, + request, + principal, + authorization, + ) + if not decision.may_evaluate: + raise EvaluationDeniedError( + decision.reason or "evaluation is not authorized" + ) + + validate_request = getattr(backend, "validate_request", None) + if callable(validate_request): + try: + validate_request(request) + except ValueError as error: + raise EvaluationRequestError(str(error)) from error + try: + cost = await backend.resolve_cost(request.evaluation_set) + except ValueError as error: + raise EvaluationRequestError(str(error)) from error + charged = decision.meter_budget and self.budget_ledger is not None + if charged: + await self.budget_ledger.reserve( + backend_id, + request.evaluation_set, + cost, + principal, + ) + + try: + record = await self.evaluator.evaluate( + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + ) + except EvaluationCancelledError as error: + cancelled = EvaluationStore( + self.evaluator.evaluations_dir / error.evaluation_id + ).load() + await asyncio.shield(self._record(cancelled)) + if charged: + await asyncio.shield( + self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) + ) + raise + except EvaluationExecutionError as error: + failure = EvaluationStore( + self.evaluator.evaluations_dir / error.evaluation_id + ).load() + await self._record(failure) + if charged: + await self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) + raise + await self._record(record) + terminating = next( + ( + diagnostic + for diagnostic in record.report.diagnostics + if diagnostic.code in TERMINATING_DIAGNOSTIC_CODES + ), + None, + ) + if terminating is not None: + # A terminating condition (inference-budget exhaustion or auth + # failure) will not heal: do not refund and do not retry. + raise EvaluationTerminatedError(record.id, terminating.message) + infrastructure = next( + ( + diagnostic + for diagnostic in record.report.diagnostics + if diagnostic.code == "infrastructure_failure" + ), + None, + ) + if infrastructure is not None: + if charged: + await self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) + raise EvaluationInfrastructureError(record.id, infrastructure.message) + return record, decision + + async def _record(self, record: EvaluationRecord) -> None: + """Index, persist, and publish a completed evaluation exactly once.""" + async with self._record_lock: + self.database.add_evaluation(record) + if self.database_path is not None: + await asyncio.to_thread( + self.database.save_to_file, + self.database_path, + ) + for listener in self.listeners: + try: + result = listener(record) + if inspect.isawaitable(result): + await result + except Exception: + logger.exception("Evaluation listener failed for %s", record.id) + + async def evaluate_record( + self, + *, + backend_id: str, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None = None, + authorization: EvaluationAuthorization | None = None, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + ) -> EvaluationRecord: + record, _ = await self._evaluate_record( + backend_id=backend_id, + request=request, + objective_spec=objective_spec, + authorization=authorization, + principal=principal, + ) + return record + + async def evaluate( + self, + *, + backend_id: str, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None = None, + authorization: EvaluationAuthorization | None = None, + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT, + ) -> EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement: + record, decision = await self._evaluate_record( + backend_id=backend_id, + request=request, + objective_spec=objective_spec, + authorization=authorization, + principal=principal, + ) + return project_evaluation(record, decision.disclosure) diff --git a/vero/src/vero/evaluation/error_taxonomy.py b/vero/src/vero/evaluation/error_taxonomy.py new file mode 100644 index 00000000..beacf7e8 --- /dev/null +++ b/vero/src/vero/evaluation/error_taxonomy.py @@ -0,0 +1,196 @@ +"""Single source of truth for evaluation error categories and their policy. + +Historically the pipeline used one overloaded channel — an HTTP 429 became an +``infrastructure_failure`` diagnostic became a ``failure_value`` reward — to +represent four genuinely different conditions: inference-budget exhaustion, a +transient rate limit, infrastructure that dropped cases, and a legitimate task +failure. Because they were indistinguishable, the system retried permanent +conditions, penalized candidates for infrastructure they did not cause, and +recorded "nothing shipped" as a real score of zero. + +Every layer that classifies or reacts to an error consults this module instead +of maintaining its own vocabulary: + +- the Harbor backend's sub-run classification (``harbor/backend.py``), +- the evaluation engine's retry/refund contract (``evaluation/engine.py``), +- the optimizer agent's client retry policy (``agents/vero.py``), and +- the error-rate / invalidity logic (``evaluation/evaluator.py``). + +Budget exhaustion cannot be recovered from the exception type once the in- +container inference client collapses the gateway's distinct 429 body code into +a generic rate-limit error. It is therefore detected out of band from the +gateway's own usage ledger and assigned directly, not inferred from the +exception here. This module only classifies the signals that *do* survive as +exception type names or messages. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from enum import Enum + + +class ErrorCategory(str, Enum): + """The canonical categories every layer agrees on.""" + + #: The inference gateway's per-scope token/request budget ran out. A + #: permanent condition for this run: stop loudly, never retry or refund. + INFERENCE_BUDGET_EXHAUSTED = "inference_budget_exhausted" + #: The optimizer agent spent its evaluation budget. Expected and benign: + #: surfaced to the agent as a tool result; never terminates a run. + EVALUATION_BUDGET_EXHAUSTED = "evaluation_budget_exhausted" + #: Authentication/authorization failed (e.g. a wrong or cycled key). Does + #: not heal on retry: terminate loudly. + AUTH_FAILURE = "auth_failure" + #: Transient external infrastructure (rate limit, timeout, connection, 5xx, + #: overloaded). Retryable; if it persists it renders the aggregate invalid. + TRANSIENT_INFRA = "transient_infra" + #: The candidate's harness produced no answer, gave up, or crashed on its + #: own. An informative sample: scored at the failure value, not infra. + TASK_FAILURE = "task_failure" + + +@dataclass(frozen=True) +class CategoryPolicy: + """How the pipeline must treat a category, in one place.""" + + #: May a retry plausibly succeed? Drives client, Harbor, and infra retries. + retryable: bool + #: Should the whole run stop immediately rather than continue? + terminating: bool + #: Does a case in this category count toward the infrastructure-loss + #: fraction that can render the aggregate score invalid? + counts_toward_invalidity: bool + #: Is a case in this category a real, scoreable sample (contributes to the + #: aggregate at the failure value) rather than excluded infrastructure noise? + is_informative_sample: bool + #: Stable diagnostic code emitted for this category. + diagnostic_code: str + + +_POLICIES: dict[ErrorCategory, CategoryPolicy] = { + ErrorCategory.INFERENCE_BUDGET_EXHAUSTED: CategoryPolicy( + retryable=False, + terminating=True, + counts_toward_invalidity=False, + is_informative_sample=False, + diagnostic_code="inference_budget_exhausted", + ), + ErrorCategory.EVALUATION_BUDGET_EXHAUSTED: CategoryPolicy( + retryable=False, + terminating=False, + counts_toward_invalidity=False, + is_informative_sample=False, + diagnostic_code="evaluation_budget_exhausted", + ), + ErrorCategory.AUTH_FAILURE: CategoryPolicy( + retryable=False, + terminating=True, + counts_toward_invalidity=False, + is_informative_sample=False, + diagnostic_code="auth_failure", + ), + ErrorCategory.TRANSIENT_INFRA: CategoryPolicy( + retryable=True, + terminating=False, + counts_toward_invalidity=True, + is_informative_sample=False, + diagnostic_code="transient_infrastructure", + ), + ErrorCategory.TASK_FAILURE: CategoryPolicy( + retryable=False, + terminating=False, + counts_toward_invalidity=False, + is_informative_sample=True, + diagnostic_code="task_failure", + ), +} + + +def policy(category: ErrorCategory) -> CategoryPolicy: + """Return the policy for a category.""" + return _POLICIES[category] + + +#: Diagnostic codes whose presence means the run must stop, unretried and +#: unrefunded. Derived from the policy table so there is one source of truth. +TERMINATING_DIAGNOSTIC_CODES: frozenset[str] = frozenset( + category_policy.diagnostic_code + for category_policy in _POLICIES.values() + if category_policy.terminating +) + + +#: The literal exception-type key the Harbor backend records when the candidate +#: produced no answer without raising (see ``harbor/backend.py``). +NO_REWARD_SIGNAL = "no_rewards_recorded" + + +# Ordered most-specific first. Each regex is matched, case-insensitively, +# against a combined " " string. Budget-like +# credit/quota exhaustion from an upstream provider is treated as an inference +# budget condition; authentication/permission never retries; the remainder are +# transient infrastructure. Anything unmatched is deliberately NOT infra — a +# candidate whose own harness crashes is a task failure, an informative low +# score, not an infrastructure error. +_SIGNAL_PATTERNS: list[tuple[re.Pattern[str], ErrorCategory]] = [ + ( + re.compile( + r"budget.?exhausted|quota|insufficient.?credits|billing", + re.IGNORECASE, + ), + ErrorCategory.INFERENCE_BUDGET_EXHAUSTED, + ), + ( + re.compile( + r"authentication|unauthorized|invalid.?api.?key|permission|forbidden", + re.IGNORECASE, + ), + ErrorCategory.AUTH_FAILURE, + ), + ( + re.compile( + r"rate.?limit|too.?many.?requests|time(?:d.?)?out|connection|" + r"service.?unavailable|internal.?server|overloaded|" + r"bad.?gateway|gateway.?time(?:d.?)?out", + re.IGNORECASE, + ), + ErrorCategory.TRANSIENT_INFRA, + ), +] + + +def classify_signal(signal: str) -> ErrorCategory | None: + """Classify one exception-type name or message string. + + Returns ``None`` for the benign no-answer signal and for anything + unrecognized; callers treat both as :attr:`ErrorCategory.TASK_FAILURE` at + the case level. Recognized infrastructure signals return their category. + """ + if not signal or signal == NO_REWARD_SIGNAL: + return None + for pattern, category in _SIGNAL_PATTERNS: + if pattern.search(signal): + return category + return None + + +def classify_case(signals: list[str]) -> ErrorCategory: + """Classify a case from the exception signals its attempts recorded. + + Precedence follows severity: a terminating condition (budget, then auth) + anywhere wins, then transient infrastructure, otherwise the case is a task + failure. An empty signal set — the candidate simply produced no answer — + is a task failure. + """ + categories = {classify_signal(signal) for signal in signals} + categories.discard(None) + for category in ( + ErrorCategory.INFERENCE_BUDGET_EXHAUSTED, + ErrorCategory.AUTH_FAILURE, + ErrorCategory.TRANSIENT_INFRA, + ): + if category in categories: + return category + return ErrorCategory.TASK_FAILURE diff --git a/vero/src/vero/evaluation/evaluator.py b/vero/src/vero/evaluation/evaluator.py new file mode 100644 index 00000000..f35c6525 --- /dev/null +++ b/vero/src/vero/evaluation/evaluator.py @@ -0,0 +1,283 @@ +"""Program-neutral evaluator lifecycle.""" + +from __future__ import annotations + +import asyncio +from contextlib import asynccontextmanager +from datetime import UTC, datetime +from pathlib import Path +from typing import AsyncIterator +from uuid import uuid4 + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository +from vero.evaluation.backend import EvaluationBackend, EvaluationContext +from vero.evaluation.exceptions import ( + EvaluationCancelledError, + EvaluationExecutionError, +) +from vero.evaluation.models import ( + CaseStatus, + DiagnosticSeverity, + EvaluationDiagnostic, + EvaluationRecord, + EvaluationReport, + EvaluationPrincipal, + EvaluationRequest, + EvaluationStatus, + ObjectiveSpec, +) +from vero.evaluation.objective import evaluate_objective +from vero.evaluation.persistence import EvaluationStore +from vero.sandbox import Sandbox +from vero.workspace import Workspace + + +class Evaluator: + """Run one backend against a clean candidate snapshot and persist it.""" + + def __init__( + self, + *, + candidate_repository: CandidateRepository, + sandbox: Sandbox, + session_dir: Path, + session_id: str | None = None, + ): + self.candidate_repository = candidate_repository + self.sandbox = sandbox + self.session_dir = session_dir + self.session_id = session_id or session_dir.name + + @property + def evaluations_dir(self) -> Path: + return self.session_dir / "evaluations" + + @asynccontextmanager + async def _candidate_workspace( + self, + candidate: Candidate, + ) -> AsyncIterator[Workspace]: + async with self.candidate_repository.checkout( + candidate, + sandbox=self.sandbox, + name=f"vero-evaluation-{candidate.id}", + ) as workspace: + yield workspace + + async def _persist_failure( + self, + *, + store: EvaluationStore, + evaluation_id: str, + backend_id: str, + backend: EvaluationBackend, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None, + principal: EvaluationPrincipal, + created_at: datetime, + code: str, + message: str, + status: EvaluationStatus = EvaluationStatus.FAILED, + ) -> EvaluationRecord: + report = EvaluationReport( + status=status, + diagnostics=[ + EvaluationDiagnostic( + code=code, + message=message, + severity=DiagnosticSeverity.ERROR, + phase="evaluation", + ) + ], + ) + objective = ( + evaluate_objective(report, objective_spec) + if objective_spec is not None + else None + ) + record = EvaluationRecord( + id=evaluation_id, + request=request, + report=report, + backend_id=backend_id, + backend=backend.provenance, + principal=principal, + objective_spec=objective_spec, + objective=objective, + created_at=created_at, + completed_at=datetime.now(UTC), + ) + await store.save(record) + return record + + async def evaluate( + self, + *, + backend_id: str, + backend: EvaluationBackend, + request: EvaluationRequest, + objective_spec: ObjectiveSpec | None = None, + principal: EvaluationPrincipal = EvaluationPrincipal.SYSTEM, + ) -> EvaluationRecord: + evaluation_id = str(uuid4()) + created_at = datetime.now(UTC) + result_dir = self.evaluations_dir / evaluation_id + store = EvaluationStore(result_dir) + result_dir.mkdir(parents=True, exist_ok=False) + store.artifact_dir.mkdir(parents=True, exist_ok=True) + store.write_running( + evaluation_id=evaluation_id, + request=request, + backend_id=backend_id, + backend=backend.provenance, + objective_spec=objective_spec, + created_at=created_at, + ) + + try: + async with self._candidate_workspace( + request.candidate, + ) as candidate_workspace: + actual_version = await candidate_workspace.current_version() + if actual_version != request.candidate.version: + raise ValueError( + f"candidate workspace is at {actual_version!r}, expected " + f"{request.candidate.version!r}" + ) + if await candidate_workspace.is_dirty(): + raise ValueError( + "candidate workspace must be clean before evaluation" + ) + context = EvaluationContext( + workspace=candidate_workspace, + session_id=self.session_id, + evaluation_id=evaluation_id, + result_dir=result_dir, + artifact_dir=store.artifact_dir, + case_store=store.cases, + finalization=principal == EvaluationPrincipal.ADMIN, + ) + async with asyncio.timeout(request.limits.timeout_seconds): + raw_report = await backend.evaluate( + context=context, + request=request, + ) + report = EvaluationReport.model_validate(raw_report) + report = self._apply_error_rate_threshold( + report, + request.limits.error_rate_threshold, + ) + + objective = ( + evaluate_objective(report, objective_spec) + if objective_spec is not None + else None + ) + record = EvaluationRecord( + id=evaluation_id, + request=request, + report=report, + backend_id=backend_id, + backend=backend.provenance, + principal=principal, + objective_spec=objective_spec, + objective=objective, + created_at=created_at, + completed_at=datetime.now(UTC), + ) + await store.save(record) + return record + except asyncio.CancelledError as error: + message = "evaluation was cancelled" + await asyncio.shield( + self._persist_failure( + store=store, + evaluation_id=evaluation_id, + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + created_at=created_at, + code="evaluation_cancelled", + message=message, + status=EvaluationStatus.CANCELLED, + ) + ) + raise EvaluationCancelledError(evaluation_id, message) from error + except TimeoutError as error: + message = f"evaluation exceeded {request.limits.timeout_seconds} seconds" + await self._persist_failure( + store=store, + evaluation_id=evaluation_id, + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + created_at=created_at, + code="evaluation_timeout", + message=message, + ) + raise EvaluationExecutionError(evaluation_id, message) from error + except Exception as error: + message = str(error) or type(error).__name__ + await self._persist_failure( + store=store, + evaluation_id=evaluation_id, + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + created_at=created_at, + code="backend_error", + message=message, + ) + raise EvaluationExecutionError(evaluation_id, message) from error + + @staticmethod + def _apply_error_rate_threshold( + report: EvaluationReport, + threshold: float | None, + ) -> EvaluationReport: + """Mark a report INVALID when too many cases were lost to infrastructure. + + Only infrastructure cases (``CaseStatus.ERROR``) count: a legitimate + agent failure is now an informative ``SUCCESS`` at the failure value, so + it does not push a candidate over the threshold. Crossing the threshold + means the aggregate is unreliable, not that the candidate is bad, so the + report becomes ``INVALID`` rather than ``FAILED``.""" + + if threshold is None or report.status != EvaluationStatus.SUCCESS: + return report + considered = [ + case + for case in report.cases + if case.status in (CaseStatus.SUCCESS, CaseStatus.ERROR) + ] + if considered: + error_rate = sum( + case.status == CaseStatus.ERROR for case in considered + ) / len(considered) + else: + error_rate = report.metrics.get("error_rate") + if error_rate is None or error_rate < threshold: + return report + diagnostic = EvaluationDiagnostic( + code="infrastructure_invalidity_threshold_exceeded", + message=( + f"infrastructure-error rate {error_rate:.6g} reached the configured " + f"threshold {threshold:.6g}; the aggregate score is unreliable" + ), + severity=DiagnosticSeverity.ERROR, + phase="evaluation", + ) + return report.model_copy( + update={ + "status": EvaluationStatus.INVALID, + "metrics": {**report.metrics, "error_rate": error_rate}, + "diagnostics": [*report.diagnostics, diagnostic], + } + ) diff --git a/vero/src/vero/evaluation/exceptions.py b/vero/src/vero/evaluation/exceptions.py new file mode 100644 index 00000000..429696d1 --- /dev/null +++ b/vero/src/vero/evaluation/exceptions.py @@ -0,0 +1,52 @@ +"""Exceptions raised by canonical evaluation components.""" + +import asyncio + + +class EvaluationError(Exception): + """Base exception for evaluation failures.""" + + +class UnknownBackendError(EvaluationError, KeyError): + """Raised when a caller selects an unregistered backend.""" + + +class EvaluationDeniedError(EvaluationError, PermissionError): + """Raised when trusted authorization denies an evaluation.""" + + +class EvaluationBudgetExceeded(EvaluationError): + """Raised when an evaluation budget cannot reserve a cost.""" + + +class EvaluationRequestError(EvaluationError, ValueError): + """Raised when a backend rejects caller-controlled request fields.""" + + +class EvaluationExecutionError(EvaluationError): + """Raised after an evaluation failure has been recorded.""" + + def __init__(self, evaluation_id: str, message: str): + self.evaluation_id = evaluation_id + super().__init__(f"Evaluation {evaluation_id} failed: {message}") + + +class EvaluationInfrastructureError(EvaluationExecutionError): + """Raised after transient/external infrastructure exhausted its retries.""" + + +class EvaluationTerminatedError(EvaluationExecutionError): + """Raised for a non-retryable, terminating condition (inference-budget + exhaustion or authentication failure). + + Unlike :class:`EvaluationInfrastructureError`, this is never refunded and + never retried: the condition will not heal on its own, so the run stops + loudly with the cause preserved rather than burning retries on it.""" + + +class EvaluationCancelledError(asyncio.CancelledError): + """Cancellation propagated after its terminal evaluation record is stored.""" + + def __init__(self, evaluation_id: str, message: str = "evaluation was cancelled"): + self.evaluation_id = evaluation_id + super().__init__(message) diff --git a/vero/src/vero/evaluation/models.py b/vero/src/vero/evaluation/models.py new file mode 100644 index 00000000..3ceafab6 --- /dev/null +++ b/vero/src/vero/evaluation/models.py @@ -0,0 +1,818 @@ +"""Canonical, backend-neutral evaluation contracts.""" + +from __future__ import annotations + +import hashlib +import json +import math +import re +from datetime import UTC, datetime +from enum import Enum +from pathlib import PurePosixPath +from typing import Annotated, Any, Literal, Mapping + +from pydantic import ( + BaseModel, + ConfigDict, + Field, + JsonValue, + field_validator, + model_validator, +) + +from vero.candidate import Candidate + + +class EvaluationModel(BaseModel): + """Strict base model for public evaluation contracts.""" + + model_config = ConfigDict(extra="forbid") + + +def _non_empty(value: str, field_name: str) -> str: + if not value.strip(): + raise ValueError(f"{field_name} must not be empty") + return value + + +def _optional_non_empty(value: str | None, field_name: str) -> str | None: + if value is not None: + _non_empty(value, field_name) + return value + + +def _finite_metrics(metrics: dict[str, float]) -> dict[str, float]: + for name, value in metrics.items(): + _non_empty(name, "metric name") + if not math.isfinite(value): + raise ValueError(f"metric {name!r} must be finite") + return metrics + + +def _aware_utc(value: datetime, field_name: str) -> datetime: + if value.tzinfo is None or value.utcoffset() is None: + raise ValueError(f"{field_name} must be timezone-aware") + return value.astimezone(UTC) + + +def _canonical_json(value: Any) -> str: + return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False) + + +class AllCases(EvaluationModel): + kind: Literal["all"] = "all" + + +class CaseIds(EvaluationModel): + kind: Literal["ids"] = "ids" + ids: list[str] + + @field_validator("ids") + @classmethod + def validate_ids(cls, ids: list[str]) -> list[str]: + if not ids: + raise ValueError("ids must not be empty") + for case_id in ids: + _non_empty(case_id, "case ID") + if len(set(ids)) != len(ids): + raise ValueError("case IDs must be unique") + return ids + + +class CaseRange(EvaluationModel): + kind: Literal["range"] = "range" + stop: int + start: int = 0 + + @model_validator(mode="after") + def validate_range(self) -> CaseRange: + if self.start < 0: + raise ValueError("start must be non-negative") + if self.stop <= self.start: + raise ValueError("stop must be greater than start") + return self + + +CaseSelection = Annotated[AllCases | CaseIds | CaseRange, Field(discriminator="kind")] + + +class EvaluationSet(EvaluationModel): + """A backend-owned collection of cases and a selection within it.""" + + name: str = "default" + partition: str | None = None + selection: CaseSelection = Field(default_factory=AllCases) + + @field_validator("name") + @classmethod + def validate_name(cls, value: str) -> str: + return _non_empty(value, "evaluation set name") + + @field_validator("partition") + @classmethod + def validate_partition(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "partition") + + def budget_key(self, backend_id: str) -> str: + _non_empty(backend_id, "backend ID") + return f"{backend_id}:{self.name}:{self.partition or ''}" + + +class EvaluationPrincipal(str, Enum): + """Trusted caller class used for authorization and independent metering.""" + + AGENT = "agent" + SYSTEM = "system" + ADMIN = "admin" + + +class AgentSelectionMode(str, Enum): + """How an agent may vary an evaluation definition's base case selection.""" + + FIXED = "fixed" + ARBITRARY = "arbitrary" + + +class RetryPolicy(EvaluationModel): + max_attempts: int = Field(default=3, ge=1) + initial_delay_seconds: float = Field(default=4.0, ge=0.0) + maximum_delay_seconds: float = Field(default=120.0, ge=0.0) + multiplier: float = Field(default=2.0, ge=1.0) + retry_on_timeout: bool = True + retry_exception_names: list[str] = Field( + default_factory=lambda: [ + "openai.RateLimitError", + "anthropic.RateLimitError", + ] + ) + retry_status_codes: list[int] = Field(default_factory=lambda: [429, 503, 529]) + retry_message_patterns: list[str] = Field( + default_factory=lambda: ["rate limit", "too many requests"] + ) + + @model_validator(mode="after") + def validate_delays(self) -> RetryPolicy: + if self.maximum_delay_seconds < self.initial_delay_seconds: + raise ValueError("maximum retry delay cannot be less than initial delay") + for name in self.retry_exception_names: + _non_empty(name, "retry exception name") + if len(set(self.retry_exception_names)) != len(self.retry_exception_names): + raise ValueError("retry exception names must be unique") + for status_code in self.retry_status_codes: + if status_code < 100 or status_code > 599: + raise ValueError("retry status codes must be between 100 and 599") + if len(set(self.retry_status_codes)) != len(self.retry_status_codes): + raise ValueError("retry status codes must be unique") + for pattern in self.retry_message_patterns: + _non_empty(pattern, "retry message pattern") + try: + re.compile(pattern) + except re.error as error: + raise ValueError( + f"invalid retry message pattern {pattern!r}: {error}" + ) from error + return self + + @classmethod + def disabled(cls) -> RetryPolicy: + """Return an explicit no-retry policy for backends with their own retries.""" + return cls(max_attempts=1) + + +class EvaluationLimits(EvaluationModel): + timeout_seconds: float = Field(default=600.0, gt=0.0) + case_timeout_seconds: float = Field(default=180.0, gt=0.0) + max_concurrency: int = Field(default=100, ge=1) + error_rate_threshold: float | None = Field(default=0.1, gt=0.0, le=1.0) + retry: RetryPolicy = Field(default_factory=RetryPolicy) + + +class EvaluationRequest(EvaluationModel): + candidate: Candidate + evaluation_set: EvaluationSet = Field(default_factory=EvaluationSet) + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits = Field(default_factory=EvaluationLimits) + seed: int | None = None + + @field_validator("parameters") + @classmethod + def validate_parameter_names( + cls, parameters: dict[str, JsonValue] + ) -> dict[str, JsonValue]: + for name in parameters: + _non_empty(name, "parameter name") + return parameters + + def fingerprint(self) -> str: + """Return the stable identity used to group repeat measurements.""" + payload = { + "candidate": { + "id": self.candidate.id, + "version": self.candidate.version, + }, + "evaluation_set": self.evaluation_set.model_dump(mode="json"), + "parameters": self.parameters, + "limits": self.limits.model_dump(mode="json"), + "seed": self.seed, + } + return hashlib.sha256(_canonical_json(payload).encode()).hexdigest() + + +class CommandEvaluationInput(EvaluationModel): + """Versioned JSON input passed to an external evaluation harness.""" + + schema_version: Literal[1] = 1 + request: EvaluationRequest + + +class EvaluationArtifact(EvaluationModel): + path: str + media_type: str | None = None + description: str | None = None + + @field_validator("path") + @classmethod + def validate_path(cls, value: str) -> str: + _non_empty(value, "artifact path") + if "\\" in value or value.startswith("/") or PurePosixPath(value).is_absolute(): + raise ValueError("artifact paths must be relative POSIX paths") + if any(part in {"", ".", ".."} for part in value.split("/")): + raise ValueError( + "artifact paths must not contain empty, '.' or '..' segments" + ) + return value + + @field_validator("media_type", "description") + @classmethod + def validate_optional_text(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "artifact metadata") + + +class CaseStatus(str, Enum): + SUCCESS = "success" + ERROR = "error" + SKIPPED = "skipped" + + +class CaseError(EvaluationModel): + message: str + code: str | None = None + phase: str | None = None + attempt: int | None = Field(default=None, ge=1) + retryable: bool | None = None + terminal: bool = False + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("message") + @classmethod + def validate_message(cls, value: str) -> str: + return _non_empty(value, "error message") + + @field_validator("code", "phase") + @classmethod + def validate_optional_text(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "error code or phase") + + +class CaseResult(EvaluationModel): + case_id: str + status: CaseStatus + metrics: dict[str, float] = Field(default_factory=dict) + input: JsonValue | None = None + output: JsonValue | None = None + feedback: str | None = None + errors: list[CaseError] = Field(default_factory=list) + execution_trace: list[JsonValue] | None = None + evaluation_trace: list[JsonValue] | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + artifacts: list[EvaluationArtifact] = Field(default_factory=list) + + @field_validator("case_id") + @classmethod + def validate_case_id(cls, value: str) -> str: + return _non_empty(value, "case ID") + + @field_validator("metrics") + @classmethod + def validate_metrics(cls, value: dict[str, float]) -> dict[str, float]: + return _finite_metrics(value) + + @field_validator("feedback") + @classmethod + def validate_feedback(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "feedback") + + @model_validator(mode="after") + def validate_status_errors(self) -> CaseResult: + has_terminal_error = any(error.terminal for error in self.errors) + if self.status == CaseStatus.ERROR and not has_terminal_error: + raise ValueError("errored cases require at least one terminal error") + if self.status != CaseStatus.ERROR and has_terminal_error: + raise ValueError("only errored cases may contain terminal errors") + return self + + +class EvaluationStatus(str, Enum): + SUCCESS = "success" + FAILED = "failed" + CANCELLED = "cancelled" + #: The evaluation ran, but too many cases were lost to infrastructure for + #: the aggregate score to be trusted. Distinct from FAILED so a benchmark + #: can tell "unreliable due to infrastructure" apart from a feasible low + #: score, and never fold "nothing usable" into a legitimate zero. + INVALID = "invalid" + + +class DiagnosticSeverity(str, Enum): + INFO = "info" + WARNING = "warning" + ERROR = "error" + + +class EvaluationDiagnostic(EvaluationModel): + code: str + message: str + severity: DiagnosticSeverity + phase: str | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("code", "message") + @classmethod + def validate_required_text(cls, value: str) -> str: + return _non_empty(value, "diagnostic code or message") + + @field_validator("phase") + @classmethod + def validate_phase(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "diagnostic phase") + + +class EvaluationReport(EvaluationModel): + schema_version: Literal[1] = 1 + status: EvaluationStatus + metrics: dict[str, float] = Field(default_factory=dict) + cases: list[CaseResult] = Field(default_factory=list) + diagnostics: list[EvaluationDiagnostic] = Field(default_factory=list) + artifacts: list[EvaluationArtifact] = Field(default_factory=list) + + @field_validator("metrics") + @classmethod + def validate_metrics(cls, value: dict[str, float]) -> dict[str, float]: + return _finite_metrics(value) + + @model_validator(mode="after") + def validate_report(self) -> EvaluationReport: + case_ids = [case.case_id for case in self.cases] + if len(case_ids) != len(set(case_ids)): + raise ValueError("case IDs must be unique within an evaluation report") + return self + + +class BackendProvenance(EvaluationModel): + name: str + version: str + config_digest: str + + @field_validator("name", "version") + @classmethod + def validate_identity(cls, value: str) -> str: + return _non_empty(value, "backend name or version") + + @field_validator("config_digest") + @classmethod + def validate_digest(cls, value: str) -> str: + if re.fullmatch(r"[0-9a-f]{64}", value) is None: + raise ValueError("config_digest must be a lowercase SHA-256 digest") + return value + + @classmethod + def from_config( + cls, + *, + name: str, + version: str, + config: BaseModel | Mapping[str, JsonValue], + ) -> BackendProvenance: + config_value = ( + config.model_dump(mode="json") + if isinstance(config, BaseModel) + else dict(config) + ) + payload = {"name": name, "version": version, "config": config_value} + digest = hashlib.sha256(_canonical_json(payload).encode()).hexdigest() + return cls(name=name, version=version, config_digest=digest) + + +class MetricAggregation(str, Enum): + REPORT = "report" + MEAN = "mean" + MEDIAN = "median" + MIN = "min" + MAX = "max" + + +class MetricSelector(EvaluationModel): + metric: str + aggregation: MetricAggregation = MetricAggregation.REPORT + case_failure_value: float | None = None + + @field_validator("metric") + @classmethod + def validate_metric(cls, value: str) -> str: + return _non_empty(value, "metric name") + + @field_validator("case_failure_value") + @classmethod + def validate_case_failure_value(cls, value: float | None) -> float | None: + if value is not None and not math.isfinite(value): + raise ValueError("case_failure_value must be finite") + return value + + @model_validator(mode="after") + def validate_case_aggregation(self) -> MetricSelector: + if ( + self.aggregation == MetricAggregation.REPORT + and self.case_failure_value is not None + ): + raise ValueError("case_failure_value requires a case metric aggregation") + return self + + +class ConstraintOperator(str, Enum): + EQ = "==" + NE = "!=" + LT = "<" + LTE = "<=" + GT = ">" + GTE = ">=" + + +class MetricConstraint(EvaluationModel): + selector: MetricSelector + operator: ConstraintOperator + value: float + + @field_validator("value") + @classmethod + def validate_value(cls, value: float) -> float: + if not math.isfinite(value): + raise ValueError("constraint value must be finite") + return value + + +class ObjectiveSpec(EvaluationModel): + selector: MetricSelector + direction: Literal["maximize", "minimize"] + failure_value: float | None = None + constraints: list[MetricConstraint] = Field(default_factory=list) + + @field_validator("failure_value") + @classmethod + def validate_failure_value(cls, value: float | None) -> float | None: + if value is not None and not math.isfinite(value): + raise ValueError("failure_value must be finite") + return value + + +class ConstraintViolation(EvaluationModel): + constraint: MetricConstraint + observed: float | None + reason: str + + @field_validator("observed") + @classmethod + def validate_observed(cls, value: float | None) -> float | None: + if value is not None and not math.isfinite(value): + raise ValueError("observed constraint value must be finite") + return value + + @field_validator("reason") + @classmethod + def validate_reason(cls, value: str) -> str: + return _non_empty(value, "constraint violation reason") + + +class ObjectiveResult(EvaluationModel): + value: float | None + feasible: bool + violations: list[ConstraintViolation] = Field(default_factory=list) + + @field_validator("value") + @classmethod + def validate_value(cls, value: float | None) -> float | None: + if value is not None and not math.isfinite(value): + raise ValueError("objective value must be finite") + return value + + @model_validator(mode="after") + def validate_result(self) -> ObjectiveResult: + if self.feasible and self.value is None: + raise ValueError("feasible objective results require a value") + if self.feasible and self.violations: + raise ValueError("feasible objective results cannot contain violations") + return self + + +class EvaluationRecord(EvaluationModel): + schema_version: Literal[2] = 2 + id: str + request: EvaluationRequest + report: EvaluationReport + backend_id: str + backend: BackendProvenance + principal: EvaluationPrincipal = EvaluationPrincipal.SYSTEM + objective_spec: ObjectiveSpec | None = None + objective: ObjectiveResult | None = None + created_at: datetime + completed_at: datetime + + @field_validator("id", "backend_id") + @classmethod + def validate_identity(cls, value: str) -> str: + return _non_empty(value, "evaluation identity") + + @field_validator("created_at", "completed_at") + @classmethod + def normalize_timestamps(cls, value: datetime) -> datetime: + return _aware_utc(value, "evaluation timestamp") + + @model_validator(mode="after") + def validate_record(self) -> EvaluationRecord: + if (self.objective_spec is None) != (self.objective is None): + raise ValueError( + "objective_spec and objective must both be present or absent" + ) + if self.completed_at < self.created_at: + raise ValueError("completed_at must not be before created_at") + return self + + +class DisclosureLevel(str, Enum): + FULL = "full" + AGGREGATE = "aggregate" + NONE = "none" + + +class EvaluationSummary(EvaluationModel): + evaluation_id: str + candidate_id: str + candidate_version: str + backend_id: str + evaluation_set: EvaluationSet + status: EvaluationStatus + metrics: dict[str, float] + objective: ObjectiveResult | None + total_cases: int = Field(ge=0) + successful_cases: int = Field(ge=0) + errored_cases: int = Field(ge=0) + skipped_cases: int = Field(ge=0) + + @field_validator("metrics") + @classmethod + def validate_metrics(cls, value: dict[str, float]) -> dict[str, float]: + return _finite_metrics(value) + + @model_validator(mode="after") + def validate_counts(self) -> EvaluationSummary: + total = self.successful_cases + self.errored_cases + self.skipped_cases + if total != self.total_cases: + raise ValueError("case status counts must sum to total_cases") + return self + + +class EvaluationAcknowledgement(EvaluationModel): + evaluation_id: str + status: EvaluationStatus + + +class EvaluationReceipt(EvaluationModel): + """Bounded agent-facing pointer to filesystem evaluation feedback.""" + + evaluation_id: str + status: EvaluationStatus + disclosure: DisclosureLevel + result: EvaluationSummary | EvaluationAcknowledgement + result_path: str + + @field_validator("evaluation_id") + @classmethod + def validate_evaluation_id(cls, value: str) -> str: + return _non_empty(value, "evaluation ID") + + @field_validator("result_path") + @classmethod + def validate_result_path(cls, value: str) -> str: + path = PurePosixPath(value) + if ( + not value + or "\\" in value + or path.is_absolute() + or any(part in {"", ".", ".."} for part in value.split("/")) + ): + raise ValueError("receipt result_path must be a safe relative POSIX path") + return value + + @model_validator(mode="after") + def validate_projection(self) -> EvaluationReceipt: + if self.disclosure == DisclosureLevel.NONE: + if not isinstance(self.result, EvaluationAcknowledgement): + raise ValueError("none disclosure requires an acknowledgement") + elif not isinstance(self.result, EvaluationSummary): + raise ValueError("aggregate and full disclosure require a summary") + if ( + self.result.evaluation_id != self.evaluation_id + or self.result.status != self.status + ): + raise ValueError("receipt identity and status must match its result") + return self + + +class EvaluationAuthorization(EvaluationModel): + may_evaluate: bool + may_view: bool | None = None + meter_budget: bool = True + disclosure: DisclosureLevel = DisclosureLevel.FULL + expose_case_resources: bool = False + reason: str | None = None + + @property + def viewable(self) -> bool: + return self.may_evaluate if self.may_view is None else self.may_view + + @field_validator("reason") + @classmethod + def validate_reason(cls, value: str | None) -> str | None: + return _optional_non_empty(value, "authorization reason") + + +class EvaluationCost(EvaluationModel): + runs: int = Field(default=1, ge=1) + cases: int | None = Field(default=None, ge=0) + + +class EvaluationBudget(EvaluationModel): + backend_id: str + evaluation_set_key: str + principal: EvaluationPrincipal = EvaluationPrincipal.AGENT + total_runs: int | None = Field(default=None, ge=0) + remaining_runs: int | None = Field(default=None, ge=0) + total_cases: int | None = Field(default=None, ge=0) + remaining_cases: int | None = Field(default=None, ge=0) + + @field_validator("backend_id", "evaluation_set_key") + @classmethod + def validate_keys(cls, value: str) -> str: + return _non_empty(value, "budget key") + + @model_validator(mode="after") + def validate_remaining(self) -> EvaluationBudget: + if ( + self.total_runs is not None + and self.remaining_runs is not None + and self.remaining_runs > self.total_runs + ): + raise ValueError("remaining_runs cannot exceed total_runs") + if ( + self.total_cases is not None + and self.remaining_cases is not None + and self.remaining_cases > self.total_cases + ): + raise ValueError("remaining_cases cannot exceed total_cases") + return self + + +class EvaluationAccessPolicy(EvaluationModel): + """Agent visibility and invocation rights for one named evaluation set.""" + + agent_can_evaluate: bool = True + agent_visible: bool = True + agent_selection: AgentSelectionMode = AgentSelectionMode.ARBITRARY + disclosure: DisclosureLevel = DisclosureLevel.FULL + expose_case_resources: bool = False + + @model_validator(mode="after") + def validate_visibility(self) -> EvaluationAccessPolicy: + if self.agent_can_evaluate and not self.agent_visible: + raise ValueError("agent-evaluable evaluations must be agent-visible") + if self.expose_case_resources and not self.agent_visible: + raise ValueError("agent-invisible evaluations cannot expose case resources") + return self + + +class EvaluationDefinition(EvaluationModel): + """One named evaluation together with access and principal-scoped budgets.""" + + evaluation_set: EvaluationSet + access: EvaluationAccessPolicy = Field(default_factory=EvaluationAccessPolicy) + agent_budget: EvaluationBudget | None = None + system_budget: EvaluationBudget | None = None + + @model_validator(mode="after") + def validate_budgets(self) -> EvaluationDefinition: + expected_suffix = ( + f":{self.evaluation_set.name}:{self.evaluation_set.partition or ''}" + ) + for principal, budget in ( + (EvaluationPrincipal.AGENT, self.agent_budget), + (EvaluationPrincipal.SYSTEM, self.system_budget), + ): + if budget is None: + continue + if budget.principal != principal: + raise ValueError( + f"{principal.value} budget must use principal {principal.value!r}" + ) + if not budget.evaluation_set_key.endswith(expected_suffix): + raise ValueError( + "evaluation budget key does not match its evaluation set" + ) + return self + + +class EvaluationPlan(EvaluationModel): + """All evaluations available to one optimization protocol.""" + + evaluations: list[EvaluationDefinition] + selection_evaluation: str + final_evaluation: str | None = None + evaluate_final_baseline: bool = True + + @model_validator(mode="after") + def validate_plan(self) -> EvaluationPlan: + names = [item.evaluation_set.name for item in self.evaluations] + if not names: + raise ValueError("evaluation plan must contain at least one evaluation") + if len(names) != len(set(names)): + raise ValueError("evaluation plan names must be unique") + if self.selection_evaluation not in names: + raise ValueError("selection evaluation is not present in the plan") + if self.final_evaluation is not None: + if self.final_evaluation not in names: + raise ValueError("final evaluation is not present in the plan") + final = self.get(self.final_evaluation) + if final.access.agent_can_evaluate or final.access.agent_visible: + raise ValueError( + "final evaluation must be agent-invisible and not agent-evaluable" + ) + return self + + def get(self, name: str) -> EvaluationDefinition: + for definition in self.evaluations: + if definition.evaluation_set.name == name: + return definition + raise KeyError(name) + + def for_evaluation_set( + self, + evaluation_set: EvaluationSet, + ) -> EvaluationDefinition | None: + for definition in self.evaluations: + owned = definition.evaluation_set + if ( + owned.name == evaluation_set.name + and owned.partition == evaluation_set.partition + ): + return definition + return None + + @property + def selection(self) -> EvaluationDefinition: + return self.get(self.selection_evaluation) + + @property + def final(self) -> EvaluationDefinition | None: + if self.final_evaluation is None: + return None + return self.get(self.final_evaluation) + + @property + def budgets(self) -> list[EvaluationBudget]: + values: list[EvaluationBudget] = [] + for definition in self.evaluations: + if definition.agent_budget is not None: + values.append(definition.agent_budget) + if definition.system_budget is not None: + values.append(definition.system_budget) + return values + + @classmethod + def single( + cls, + evaluation_set: EvaluationSet | None = None, + *, + access: EvaluationAccessPolicy | None = None, + agent_budget: EvaluationBudget | None = None, + system_budget: EvaluationBudget | None = None, + ) -> EvaluationPlan: + resolved = evaluation_set or EvaluationSet() + return cls( + evaluations=[ + EvaluationDefinition( + evaluation_set=resolved, + access=access or EvaluationAccessPolicy(), + agent_budget=agent_budget, + system_budget=system_budget, + ) + ], + selection_evaluation=resolved.name, + ) diff --git a/vero/src/vero/evaluation/objective.py b/vero/src/vero/evaluation/objective.py new file mode 100644 index 00000000..50fa18fc --- /dev/null +++ b/vero/src/vero/evaluation/objective.py @@ -0,0 +1,191 @@ +"""Metric resolution, objective evaluation, ranking, and disclosure.""" + +from __future__ import annotations + +import operator +import statistics +from functools import cmp_to_key +from typing import Iterable + +from vero.evaluation.models import ( + CaseStatus, + ConstraintOperator, + ConstraintViolation, + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationRecord, + EvaluationReport, + EvaluationStatus, + EvaluationSummary, + MetricAggregation, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) + + +_OPERATORS = { + ConstraintOperator.EQ: operator.eq, + ConstraintOperator.NE: operator.ne, + ConstraintOperator.LT: operator.lt, + ConstraintOperator.LTE: operator.le, + ConstraintOperator.GT: operator.gt, + ConstraintOperator.GTE: operator.ge, +} + + +def resolve_metric(report: EvaluationReport, selector: MetricSelector) -> float | None: + """Resolve a report metric or aggregate it across evaluation cases.""" + if selector.aggregation == MetricAggregation.REPORT: + return report.metrics.get(selector.metric) + + values: list[float] = [] + for case in report.cases: + if case.status == CaseStatus.SUCCESS and selector.metric in case.metrics: + values.append(case.metrics[selector.metric]) + elif selector.case_failure_value is not None: + values.append(selector.case_failure_value) + if not values: + return None + if selector.aggregation == MetricAggregation.MEAN: + return float(statistics.fmean(values)) + if selector.aggregation == MetricAggregation.MEDIAN: + return float(statistics.median(values)) + if selector.aggregation == MetricAggregation.MIN: + return min(values) + if selector.aggregation == MetricAggregation.MAX: + return max(values) + raise AssertionError(f"unsupported aggregation: {selector.aggregation}") + + +def evaluate_objective( + report: EvaluationReport, + specification: ObjectiveSpec, +) -> ObjectiveResult: + """Evaluate the optimization objective and all feasibility constraints.""" + value = resolve_metric(report, specification.selector) + violations: list[ConstraintViolation] = [] + + for constraint in specification.constraints: + observed = resolve_metric(report, constraint.selector) + if observed is None: + violations.append( + ConstraintViolation( + constraint=constraint, + observed=None, + reason=f"metric {constraint.selector.metric!r} is unavailable", + ) + ) + continue + if not _OPERATORS[constraint.operator](observed, constraint.value): + violations.append( + ConstraintViolation( + constraint=constraint, + observed=observed, + reason=( + f"observed {observed} does not satisfy " + f"{constraint.operator.value} {constraint.value}" + ), + ) + ) + + if report.status != EvaluationStatus.SUCCESS or value is None: + return ObjectiveResult(value=specification.failure_value, feasible=False) + if violations: + return ObjectiveResult(value=value, feasible=False, violations=violations) + return ObjectiveResult(value=value, feasible=True) + + +def _compare_results( + left: ObjectiveResult, + right: ObjectiveResult, + direction: str, +) -> int: + if left.feasible != right.feasible: + return 1 if left.feasible else -1 + if left.value is None and right.value is None: + return 0 + if left.value is None: + return -1 + if right.value is None: + return 1 + if left.value == right.value: + return 0 + if direction == "maximize": + return 1 if left.value > right.value else -1 + return 1 if left.value < right.value else -1 + + +def compare_evaluation_records(left: EvaluationRecord, right: EvaluationRecord) -> int: + """Compare compatible records with deterministic candidate tie-breaks.""" + if left.objective_spec is None or left.objective is None: + raise ValueError("left record does not contain an objective") + if right.objective_spec is None or right.objective is None: + raise ValueError("right record does not contain an objective") + if left.objective_spec != right.objective_spec: + raise ValueError("records use different objective specifications") + + comparison = _compare_results( + left.objective, + right.objective, + left.objective_spec.direction, + ) + if comparison: + return comparison + + left_created = left.request.candidate.created_at + right_created = right.request.candidate.created_at + if left_created != right_created: + return 1 if left_created > right_created else -1 + + left_id = left.request.candidate.id + right_id = right.request.candidate.id + if left_id == right_id: + return 0 + return 1 if left_id < right_id else -1 + + +def select_best_evaluation( + records: Iterable[EvaluationRecord], +) -> EvaluationRecord | None: + """Return the best feasible record, or ``None`` if none are feasible.""" + feasible = [ + record + for record in records + if record.objective is not None and record.objective.feasible + ] + if not feasible: + return None + return max(feasible, key=cmp_to_key(compare_evaluation_records)) + + +def project_evaluation( + record: EvaluationRecord, + disclosure: DisclosureLevel, +) -> EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement: + """Project a private record into an approved disclosure shape.""" + if disclosure == DisclosureLevel.FULL: + return record + if disclosure == DisclosureLevel.NONE: + return EvaluationAcknowledgement( + evaluation_id=record.id, + status=record.report.status, + ) + + counts = {status: 0 for status in CaseStatus} + for case in record.report.cases: + counts[case.status] += 1 + return EvaluationSummary( + evaluation_id=record.id, + candidate_id=record.request.candidate.id, + candidate_version=record.request.candidate.version, + backend_id=record.backend_id, + evaluation_set=record.request.evaluation_set, + status=record.report.status, + metrics=dict(record.report.metrics), + objective=record.objective, + total_cases=len(record.report.cases), + successful_cases=counts[CaseStatus.SUCCESS], + errored_cases=counts[CaseStatus.ERROR], + skipped_cases=counts[CaseStatus.SKIPPED], + ) diff --git a/vero/src/vero/evaluation/persistence.py b/vero/src/vero/evaluation/persistence.py new file mode 100644 index 00000000..5a14b8e0 --- /dev/null +++ b/vero/src/vero/evaluation/persistence.py @@ -0,0 +1,495 @@ +"""Atomic persistence for canonical evaluation records.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import logging +import os +import tempfile +import threading +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any, Callable, Literal + +from pydantic import Field, field_validator, model_validator + +from vero.candidate import Candidate +from vero.evaluation.models import ( + BackendProvenance, + CaseResult, + EvaluationModel, + EvaluationPrincipal, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + ObjectiveResult, + ObjectiveSpec, +) +from vero.evaluation.objective import select_best_evaluation + +logger = logging.getLogger(__name__) + + +def _atomic_write_json(path: Path, value: Any) -> None: + """Write JSON through a same-directory temporary file and atomic replace.""" + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + dir=path.parent, + prefix=f".{path.name}.", + suffix=".tmp", + ) + temporary_path = Path(temporary_name) + try: + with os.fdopen(descriptor, "w", encoding="utf-8") as handle: + json.dump(value, handle, ensure_ascii=False, indent=2) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary_path, path) + except BaseException: + try: + os.close(descriptor) + except OSError: + pass + temporary_path.unlink(missing_ok=True) + raise + + +def _case_digest(case_id: str) -> str: + return hashlib.sha256(case_id.encode()).hexdigest() + + +class CaseFileReference(EvaluationModel): + case_id: str + path: str + + @field_validator("case_id") + @classmethod + def validate_case_id(cls, value: str) -> str: + if not value.strip(): + raise ValueError("case ID must not be empty") + return value + + @model_validator(mode="after") + def validate_path(self) -> CaseFileReference: + expected = f"cases/{_case_digest(self.case_id)}.json" + if self.path != expected: + raise ValueError(f"case path must be {expected!r}") + return self + + +class EvaluationManifest(EvaluationModel): + schema_version: Literal[1] = 1 + lifecycle: Literal["complete"] = "complete" + id: str + request: EvaluationRequest + report: EvaluationReport + case_files: list[CaseFileReference] = Field(default_factory=list) + backend_id: str + backend: BackendProvenance + # Persist the principal so a record reconstructed from this source-of-truth + # directory keeps its true provenance instead of silently defaulting to + # SYSTEM. Older manifests without the field read back as SYSTEM, matching + # the historical EvaluationRecord default. + principal: EvaluationPrincipal = EvaluationPrincipal.SYSTEM + objective_spec: ObjectiveSpec | None = None + objective: ObjectiveResult | None = None + created_at: datetime + completed_at: datetime + + @model_validator(mode="after") + def validate_manifest(self) -> EvaluationManifest: + if self.report.cases: + raise ValueError("manifest report must store cases in case_files") + ids = [reference.case_id for reference in self.case_files] + if len(ids) != len(set(ids)): + raise ValueError("case file references must be unique") + if (self.objective_spec is None) != (self.objective is None): + raise ValueError( + "objective_spec and objective must both be present or absent" + ) + return self + + @classmethod + def from_record(cls, record: EvaluationRecord) -> EvaluationManifest: + return cls( + id=record.id, + request=record.request, + report=record.report.model_copy(update={"cases": []}), + case_files=[ + CaseFileReference( + case_id=case.case_id, + path=f"cases/{_case_digest(case.case_id)}.json", + ) + for case in record.report.cases + ], + backend_id=record.backend_id, + backend=record.backend, + principal=record.principal, + objective_spec=record.objective_spec, + objective=record.objective, + created_at=record.created_at, + completed_at=record.completed_at, + ) + + +class RunningEvaluationManifest(EvaluationModel): + schema_version: Literal[1] = 1 + lifecycle: Literal["running"] = "running" + id: str + request: EvaluationRequest + backend_id: str + backend: BackendProvenance + objective_spec: ObjectiveSpec | None = None + created_at: datetime + + +class CaseCheckpointStore: + """Per-evaluation case checkpoints safe for concurrent async writers.""" + + def __init__(self, cases_dir: Path): + self.cases_dir = cases_dir + self._lock = asyncio.Lock() + + def path_for(self, case_id: str) -> Path: + return self.cases_dir / f"{_case_digest(case_id)}.json" + + async def save(self, result: CaseResult) -> None: + if not isinstance(result, CaseResult): + raise TypeError("result must be a CaseResult") + async with self._lock: + await asyncio.to_thread( + _atomic_write_json, + self.path_for(result.case_id), + result.model_dump(mode="json"), + ) + + async def load(self, case_id: str) -> CaseResult | None: + path = self.path_for(case_id) + async with self._lock: + if not path.exists(): + return None + try: + result = CaseResult.model_validate_json( + path.read_text(encoding="utf-8") + ) + except Exception as error: + raise ValueError(f"invalid case checkpoint {path}: {error}") from error + if result.case_id != case_id: + raise ValueError( + f"case checkpoint {path} contains ID {result.case_id!r}, expected {case_id!r}" + ) + return result + + async def load_all(self) -> list[CaseResult]: + async with self._lock: + paths = ( + sorted(self.cases_dir.glob("*.json")) if self.cases_dir.exists() else [] + ) + results: list[CaseResult] = [] + for path in paths: + try: + results.append( + CaseResult.model_validate_json(path.read_text(encoding="utf-8")) + ) + except Exception as error: + raise ValueError( + f"invalid case checkpoint {path}: {error}" + ) from error + return results + + +class EvaluationStore: + """Persist and reconstruct one evaluation directory.""" + + manifest_basename = "evaluation.json" + + def __init__(self, result_dir: Path): + self.result_dir = result_dir + self.cases = CaseCheckpointStore(result_dir / "cases") + self.artifact_dir = result_dir / "artifacts" + + @property + def manifest_path(self) -> Path: + return self.result_dir / self.manifest_basename + + def _validate_artifacts(self, record: EvaluationRecord) -> None: + artifact_root = self.artifact_dir.resolve() + artifacts = list(record.report.artifacts) + for case in record.report.cases: + artifacts.extend(case.artifacts) + for artifact in artifacts: + resolved = (self.artifact_dir / artifact.path).resolve() + if not resolved.is_relative_to(artifact_root): + raise ValueError( + f"artifact path {artifact.path!r} escapes evaluation artifact directory" + ) + + def write_running( + self, + *, + evaluation_id: str, + request: EvaluationRequest, + backend_id: str, + backend: BackendProvenance, + objective_spec: ObjectiveSpec | None, + created_at: datetime, + ) -> None: + self.result_dir.mkdir(parents=True, exist_ok=True) + manifest = RunningEvaluationManifest( + id=evaluation_id, + request=request, + backend_id=backend_id, + backend=backend, + objective_spec=objective_spec, + created_at=created_at, + ) + _atomic_write_json(self.manifest_path, manifest.model_dump(mode="json")) + + async def save(self, record: EvaluationRecord) -> None: + self.result_dir.mkdir(parents=True, exist_ok=True) + self.artifact_dir.mkdir(parents=True, exist_ok=True) + self._validate_artifacts(record) + for case in record.report.cases: + await self.cases.save(case) + _atomic_write_json( + self.manifest_path, + EvaluationManifest.from_record(record).model_dump(mode="json"), + ) + + def load(self) -> EvaluationRecord: + try: + manifest = EvaluationManifest.model_validate_json( + self.manifest_path.read_text(encoding="utf-8") + ) + except Exception as error: + raise ValueError( + f"invalid evaluation manifest {self.manifest_path}: {error}" + ) from error + + cases: list[CaseResult] = [] + for reference in manifest.case_files: + case_path = self.result_dir / reference.path + try: + case = CaseResult.model_validate_json( + case_path.read_text(encoding="utf-8") + ) + except Exception as error: + raise ValueError( + f"invalid evaluation case file {case_path}: {error}" + ) from error + if case.case_id != reference.case_id: + raise ValueError( + f"evaluation case file {case_path} contains ID {case.case_id!r}, " + f"expected {reference.case_id!r}" + ) + cases.append(case) + + record = EvaluationRecord( + id=manifest.id, + request=manifest.request, + report=manifest.report.model_copy(update={"cases": cases}), + backend_id=manifest.backend_id, + backend=manifest.backend, + principal=manifest.principal, + objective_spec=manifest.objective_spec, + objective=manifest.objective, + created_at=manifest.created_at, + completed_at=manifest.completed_at, + ) + self._validate_artifacts(record) + return record + + +@dataclass +class EvaluationDatabase: + """Thread-safe in-memory index of candidates and evaluation records.""" + + id: str + candidates: dict[str, Candidate] = field(default_factory=dict) + evaluations: dict[str, EvaluationRecord] = field(default_factory=dict) + listeners: list[Callable[[EvaluationRecord], None]] = field( + default_factory=list, + repr=False, + ) + _lock: threading.RLock = field( + default_factory=threading.RLock, init=False, repr=False + ) + + def add_evaluation(self, record: EvaluationRecord) -> None: + with self._lock: + existing_record = self.evaluations.get(record.id) + if existing_record is not None: + if existing_record != record: + raise ValueError( + f"evaluation ID {record.id!r} already has a different record" + ) + return + candidate = record.request.candidate + existing_candidate = self.candidates.get(candidate.id) + if existing_candidate is not None and existing_candidate != candidate: + raise ValueError( + f"candidate ID {candidate.id!r} already has a different identity" + ) + self.candidates[candidate.id] = candidate + self.evaluations[record.id] = record + for listener in self.listeners: + listener(record) + + def get_evaluation(self, evaluation_id: str) -> EvaluationRecord | None: + with self._lock: + return self.evaluations.get(evaluation_id) + + def get_evaluations( + self, + evaluation_ids: list[str] | None = None, + *, + limit: int | None = None, + offset: int = 0, + reverse: bool = False, + filter_fn: Callable[[EvaluationRecord], bool] | None = None, + ) -> list[EvaluationRecord]: + with self._lock: + ids = list(self.evaluations) if evaluation_ids is None else evaluation_ids + records = [self.evaluations[evaluation_id] for evaluation_id in ids] + if filter_fn is not None: + records = [record for record in records if filter_fn(record)] + if reverse: + records.reverse() + records = records[offset:] + return records if limit is None else records[:limit] + + def get_best( + self, + objective_spec: ObjectiveSpec, + *, + backend_ids: set[str] | None = None, + evaluation_sets: list[EvaluationSet] | None = None, + exclude_candidate_id: str | None = None, + ) -> EvaluationRecord | None: + with self._lock: + records = [ + record + for record in self.evaluations.values() + if record.objective_spec == objective_spec + and (backend_ids is None or record.backend_id in backend_ids) + and ( + evaluation_sets is None + or record.request.evaluation_set in evaluation_sets + ) + and record.request.candidate.id != exclude_candidate_id + ] + return select_best_evaluation(records) + + def serialize(self) -> dict[str, Any]: + with self._lock: + return { + "schema_version": 1, + "id": self.id, + "candidates": { + candidate_id: candidate.model_dump(mode="json") + for candidate_id, candidate in self.candidates.items() + }, + "evaluations": { + evaluation_id: record.model_dump(mode="json") + for evaluation_id, record in self.evaluations.items() + }, + } + + @classmethod + def deserialize(cls, data: dict[str, Any]) -> EvaluationDatabase: + if data.get("schema_version") != 1: + raise ValueError("unsupported evaluation database schema") + database = cls(id=data["id"]) + for candidate_id, value in data.get("candidates", {}).items(): + candidate = Candidate.model_validate(value) + if candidate.id != candidate_id: + raise ValueError( + f"candidate map key {candidate_id!r} does not match candidate ID {candidate.id!r}" + ) + database.candidates[candidate_id] = candidate + for evaluation_id, value in data.get("evaluations", {}).items(): + record = EvaluationRecord.model_validate(value) + if record.id != evaluation_id: + raise ValueError( + f"evaluation map key {evaluation_id!r} does not match record ID {record.id!r}" + ) + database.add_evaluation(record) + return database + + def to_json(self) -> str: + return json.dumps(self.serialize(), ensure_ascii=False, indent=2) + + @classmethod + def from_json(cls, value: str) -> EvaluationDatabase: + return cls.deserialize(json.loads(value)) + + def save_to_file(self, path: Path) -> None: + with self._lock: + _atomic_write_json(path, self.serialize()) + + @classmethod + def load_from_file(cls, path: Path) -> EvaluationDatabase: + return cls.from_json(path.read_text(encoding="utf-8")) + + @classmethod + def load_reconciled( + cls, + *, + database_path: Path, + evaluations_dir: Path, + database_id: str, + ) -> EvaluationDatabase: + """Load the index and repair it from canonical completed evaluations.""" + + existed = database_path.exists() + database = cls.load_from_file(database_path) if existed else cls(id=database_id) + if database.id != database_id: + raise ValueError( + f"evaluation database belongs to {database.id!r}, not {database_id!r}" + ) + + completed = cls.from_evaluations_dir( + evaluations_dir, + database_id=database_id, + ) + changed = not existed + for evaluation_id, record in completed.evaluations.items(): + if evaluation_id not in database.evaluations: + changed = True + database.add_evaluation(record) + if changed: + database.save_to_file(database_path) + return database + + @classmethod + def from_evaluations_dir( + cls, + evaluations_dir: Path, + *, + database_id: str | None = None, + ) -> EvaluationDatabase: + database = cls(id=database_id or evaluations_dir.parent.name) + if not evaluations_dir.exists(): + return database + for result_dir in sorted(evaluations_dir.iterdir()): + if not result_dir.is_dir(): + continue + if not (result_dir / EvaluationStore.manifest_basename).exists(): + continue + try: + manifest = json.loads( + (result_dir / EvaluationStore.manifest_basename).read_text( + encoding="utf-8" + ) + ) + if manifest.get("lifecycle", "complete") != "complete": + continue + database.add_evaluation(EvaluationStore(result_dir).load()) + except Exception as error: + logger.warning("Skipping corrupt evaluation %s: %s", result_dir, error) + return database diff --git a/vero/src/vero/evaluation/python_task.py b/vero/src/vero/evaluation/python_task.py new file mode 100644 index 00000000..70f5f746 --- /dev/null +++ b/vero/src/vero/evaluation/python_task.py @@ -0,0 +1,328 @@ +"""Optional uv-based adapter for Python tasks defined with scale-vero-tasks.""" + +from __future__ import annotations + +import hashlib +import json +import shutil +from pathlib import Path, PurePosixPath + +from pydantic import Field, field_validator, model_validator + +from vero.evaluation.backend import EvaluationContext +from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.models import ( + AllCases, + BackendProvenance, + CaseIds, + CaseRange, + EvaluationCost, + EvaluationModel, + EvaluationReport, + EvaluationRequest, + EvaluationSet, +) +from vero.sandbox import Sandbox + + +def _default_uv() -> str: + executable = shutil.which("uv") + if executable is None: + raise ValueError("uv is required to configure a Python task backend") + return str(Path(executable).resolve()) + + +class PythonTaskEvaluationConfig(EvaluationModel): + """One named/partitioned dataset owned by a Python task backend.""" + + name: str + cases_path: str + partition: str | None = None + + @field_validator("name") + @classmethod + def validate_name(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Python task evaluation name must not be empty") + return value + + @field_validator("cases_path") + @classmethod + def validate_cases_path(cls, value: str) -> str: + if not value.strip() or not Path(value).is_absolute(): + raise ValueError("Python task cases_path must be absolute") + return value + + @field_validator("partition") + @classmethod + def validate_partition(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("Python task partition must not be empty") + return value + + +class PythonTaskBackendConfig(EvaluationModel): + """Configuration for an external task harness and editable target package.""" + + harness_root: str + module: str + task: str + evaluations: list[PythonTaskEvaluationConfig] + target_project_directory: str = "." + uv_executable: str = Field(default_factory=_default_uv) + python_executable: str = "python" + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + + @field_validator("harness_root") + @classmethod + def validate_absolute_path(cls, value: str) -> str: + if not value.strip() or not Path(value).is_absolute(): + raise ValueError("Python task backend paths must be absolute") + return value + + @field_validator("module", "task", "python_executable") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Python task backend identity must not be empty") + return value + + @field_validator("uv_executable") + @classmethod + def validate_uv_executable(cls, value: str) -> str: + if not value.strip(): + raise ValueError("uv_executable must not be empty") + return value + + @field_validator("target_project_directory") + @classmethod + def validate_target_project_directory(cls, value: str) -> str: + path = Path(value) + if not value.strip() or path.is_absolute() or ".." in path.parts: + raise ValueError( + "Python task target_project_directory must stay within the " + "candidate workspace" + ) + return path.as_posix() + + @model_validator(mode="after") + def validate_filesystem(self) -> PythonTaskBackendConfig: + if not Path(self.harness_root).is_dir(): + raise ValueError("Python task harness_root must be an existing directory") + if not self.evaluations: + raise ValueError("Python task backend requires at least one evaluation") + keys = [(item.name, item.partition) for item in self.evaluations] + if len(keys) != len(set(keys)): + raise ValueError("Python task evaluation name/partition pairs must be unique") + for evaluation in self.evaluations: + if not Path(evaluation.cases_path).is_file(): + raise ValueError( + "Python task cases_path must be an existing file: " + f"{evaluation.cases_path}" + ) + return self + + +class PythonTaskBackend: + """Run an external Python task harness against an editable candidate.""" + + name = "python-task" + version = "2" + + def __init__(self, config: PythonTaskBackendConfig): + self.config = config + target = "{workspace}" + if config.target_project_directory != ".": + target += f"/{config.target_project_directory}" + self._target = target + self._commands: dict[tuple[str, str | None], CommandBackend] = {} + + def _source(self, evaluation_set: EvaluationSet) -> PythonTaskEvaluationConfig: + for source in self.config.evaluations: + if (source.name, source.partition) == ( + evaluation_set.name, + evaluation_set.partition, + ): + return source + raise ValueError( + "Python task backend does not own evaluation " + f"{evaluation_set.name!r} partition {evaluation_set.partition!r}" + ) + + def _command(self, evaluation_set: EvaluationSet) -> CommandBackend: + source = self._source(evaluation_set) + key = (source.name, source.partition) + command = self._commands.get(key) + if command is not None: + return command + command = CommandBackend( + CommandBackendConfig( + harness_root=self.config.harness_root, + command=[ + self.config.uv_executable, + "run", + "--project", + "{harness}", + "--with-editable", + self._target, + self.config.python_executable, + "-m", + "vero_tasks.runner", + "--module", + self.config.module, + "--task", + self.config.task, + "--cases", + "{input:cases}", + "--request", + "{request}", + "--report", + "{report}", + ], + environment=self.config.environment, + passthrough_environment=self.config.passthrough_environment, + staged_inputs={"cases": source.cases_path}, + ) + ) + self._commands[key] = command + return command + + @property + def provenance(self) -> BackendProvenance: + return BackendProvenance.from_config( + name=self.name, + version=self.version, + config=self.config, + ) + + def _cases(self, evaluation_set: EvaluationSet) -> list[object]: + path = Path(self._source(evaluation_set).cases_path) + if path.suffix == ".jsonl": + return [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + value = json.loads(path.read_text(encoding="utf-8")) + if isinstance(value, dict): + value = value.get("cases") + if not isinstance(value, list): + raise ValueError("Python task case file must contain a case list") + return value + + def _case_ids(self, evaluation_set: EvaluationSet) -> list[str]: + case_ids = [ + str(case["id"]) + if isinstance(case, dict) and case.get("id") is not None + else str(index) + for index, case in enumerate(self._cases(evaluation_set)) + ] + if len(case_ids) != len(set(case_ids)): + raise ValueError("Python task case IDs must be unique") + return case_ids + + def _selected_cases( + self, evaluation_set: EvaluationSet + ) -> list[tuple[str, object]]: + self._validate_evaluation_set(evaluation_set) + cases = self._cases(evaluation_set) + case_ids = self._case_ids(evaluation_set) + selection = evaluation_set.selection + if isinstance(selection, AllCases): + indexes = list(range(len(cases))) + elif isinstance(selection, CaseRange): + indexes = list(range(selection.start, selection.stop)) + elif isinstance(selection, CaseIds): + by_id = {case_id: index for index, case_id in enumerate(case_ids)} + indexes = [by_id[case_id] for case_id in selection.ids] + else: # pragma: no cover - closed discriminated union + raise AssertionError(f"unsupported case selection: {selection}") + return [(case_ids[index], cases[index]) for index in indexes] + + async def export_case_resources( + self, + *, + evaluation_set: EvaluationSet, + destination: str, + sandbox: Sandbox, + ) -> None: + index = [] + for case_id, case in self._selected_cases(evaluation_set): + digest = hashlib.sha256(case_id.encode()).hexdigest() + filename = f"{digest}.json" + await sandbox.write_file( + str(PurePosixPath(destination) / filename), + json.dumps(case, ensure_ascii=False, indent=2, default=str) + "\n", + ) + index.append({"case_id": case_id, "path": filename}) + await sandbox.write_file( + str(PurePosixPath(destination) / "index.json"), + json.dumps( + { + "schema_version": 1, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "cases": index, + }, + ensure_ascii=False, + indent=2, + ) + + "\n", + ) + + def _validate_evaluation_set(self, evaluation_set: EvaluationSet) -> None: + self._source(evaluation_set) + + case_ids = self._case_ids(evaluation_set) + selection = evaluation_set.selection + if isinstance(selection, CaseRange) and selection.stop > len(case_ids): + raise ValueError( + f"case range stops at {selection.stop}, but the evaluation set " + f"contains {len(case_ids)} cases" + ) + if isinstance(selection, CaseIds): + unknown = sorted(set(selection.ids) - set(case_ids)) + if unknown: + raise ValueError(f"unknown Python task case IDs: {unknown}") + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: + self._validate_evaluation_set(evaluation_set) + selection = evaluation_set.selection + if isinstance(selection, CaseIds): + return EvaluationCost(cases=len(selection.ids)) + if isinstance(selection, CaseRange): + return EvaluationCost(cases=selection.stop - selection.start) + if isinstance(selection, AllCases): + return EvaluationCost(cases=len(self._case_ids(evaluation_set))) + raise AssertionError(f"unsupported case selection: {selection}") + + def validate_request(self, request: EvaluationRequest) -> None: + self._command(request.evaluation_set).validate_request(request) + self._validate_evaluation_set(request.evaluation_set) + + def sanitize_error(self, message: str) -> str: + source = self.config.evaluations[0] + return self._command( + EvaluationSet(name=source.name, partition=source.partition) + ).sanitize_error(message) + + async def evaluate( + self, + *, + context: EvaluationContext, + request: EvaluationRequest, + ) -> EvaluationReport: + target_root = context.workspace.sandbox.host_path( + context.workspace.project_path + ) + if target_root is not None: + target_root = target_root.resolve() + cases_path = Path(self._source(request.evaluation_set).cases_path).resolve() + if cases_path == target_root or cases_path.is_relative_to(target_root): + raise ValueError( + "Python task cases must live outside the editable target" + ) + return await self._command(request.evaluation_set).evaluate( + context=context, + request=request, + ) diff --git a/vero/src/vero/evaluation/security.py b/vero/src/vero/evaluation/security.py new file mode 100644 index 00000000..64967a46 --- /dev/null +++ b/vero/src/vero/evaluation/security.py @@ -0,0 +1,108 @@ +"""Secret-safe normalization for persisted evaluation content.""" + +from __future__ import annotations + +from typing import Any, Iterable + +from vero.evaluation.models import EvaluationReport + + +def _usable_secrets(secrets: Iterable[str | None]) -> tuple[str, ...]: + return tuple( + sorted( + {secret for secret in secrets if secret and len(secret) >= 4}, + key=len, + reverse=True, + ) + ) + + +def sanitize_text(text: str, secrets: Iterable[str | None]) -> str: + sanitized = text + for secret in _usable_secrets(secrets): + sanitized = sanitized.replace(secret, "[REDACTED]") + return sanitized + + +def _sanitize_value(value: Any, secrets: tuple[str, ...]) -> Any: + if isinstance(value, str): + return sanitize_text(value, secrets) + if isinstance(value, list): + return [_sanitize_value(item, secrets) for item in value] + if isinstance(value, dict): + return {key: _sanitize_value(item, secrets) for key, item in value.items()} + return value + + +def sanitize_evaluation_report( + report: EvaluationReport, + secrets: Iterable[str | None], +) -> EvaluationReport: + """Redact free-form report fields while retaining structural identifiers.""" + secret_values = _usable_secrets(secrets) + if not secret_values: + return report + + diagnostics = [ + diagnostic.model_copy( + update={ + "message": sanitize_text(diagnostic.message, secret_values), + "metadata": _sanitize_value(diagnostic.metadata, secret_values), + } + ) + for diagnostic in report.diagnostics + ] + cases = [] + for case in report.cases: + errors = [ + error.model_copy( + update={ + "message": sanitize_text(error.message, secret_values), + "metadata": _sanitize_value(error.metadata, secret_values), + } + ) + for error in case.errors + ] + artifacts = [ + artifact.model_copy( + update={ + "description": sanitize_text(artifact.description, secret_values) + if artifact.description is not None + else None + } + ) + for artifact in case.artifacts + ] + cases.append( + case.model_copy( + update={ + "input": _sanitize_value(case.input, secret_values), + "output": _sanitize_value(case.output, secret_values), + "feedback": sanitize_text(case.feedback, secret_values) + if case.feedback is not None + else None, + "errors": errors, + "execution_trace": _sanitize_value(case.execution_trace, secret_values), + "evaluation_trace": _sanitize_value(case.evaluation_trace, secret_values), + "metadata": _sanitize_value(case.metadata, secret_values), + "artifacts": artifacts, + } + ) + ) + artifacts = [ + artifact.model_copy( + update={ + "description": sanitize_text(artifact.description, secret_values) + if artifact.description is not None + else None + } + ) + for artifact in report.artifacts + ] + return report.model_copy( + update={ + "diagnostics": diagnostics, + "cases": cases, + "artifacts": artifacts, + } + ) diff --git a/vero/src/vero/exceptions.py b/vero/src/vero/exceptions.py new file mode 100644 index 00000000..b8d4033f --- /dev/null +++ b/vero/src/vero/exceptions.py @@ -0,0 +1,5 @@ +import asyncio + + +class StreamEventTimeout(asyncio.TimeoutError): + pass diff --git a/vero/src/vero/harbor/__init__.py b/vero/src/vero/harbor/__init__.py new file mode 100644 index 00000000..88c46b25 --- /dev/null +++ b/vero/src/vero/harbor/__init__.py @@ -0,0 +1,80 @@ +"""Harbor adapters for canonical VeRO evaluation backends.""" + +from vero.harbor.backend import HarborBackend, HarborBackendConfig, HarborCase +from vero.harbor.build import ( + AgentAccessSpec, + HarborBuildConfig, + InferenceBudgetSpec, + InferenceGatewaySpec, + VerificationTargetSpec, + WandbSpec, + WorkspaceOverlaySpec, + compile_harbor_task, + load_harbor_build_config, +) +from vero.harbor.deployment import HarborDeploymentConfig, build_harbor_components +from vero.harbor.sidecar import ( + EvaluationAccessError, + EvaluationJobNotFoundError, + EvaluationJobStatus, + SidecarEvaluationPolicy, + EvaluationAccessStatus, + EvaluationSidecar, + SidecarEvaluationJob, + SidecarEvaluationRequest, + SidecarEvaluationResult, + SidecarStatus, + Submission, + SubmissionDisabledError, +) +from vero.harbor.session import HarborSessionManifest +from vero.harbor.transport import ( + CandidateTransferError, + CandidateTransport, + GitCandidateTransport, +) +from vero.harbor.verifier import ( + CanonicalVerifier, + NoCandidateError, + VerificationResult, + VerificationSelection, + VerificationTarget, +) + +__all__ = [ + "HarborBackend", + "HarborBackendConfig", + "HarborCase", + "HarborDeploymentConfig", + "HarborSessionManifest", + "AgentAccessSpec", + "HarborBuildConfig", + "InferenceBudgetSpec", + "InferenceGatewaySpec", + "VerificationTargetSpec", + "WandbSpec", + "WorkspaceOverlaySpec", + "CandidateTransferError", + "CandidateTransport", + "EvaluationAccessError", + "EvaluationJobNotFoundError", + "EvaluationJobStatus", + "SidecarEvaluationPolicy", + "EvaluationAccessStatus", + "EvaluationSidecar", + "SidecarEvaluationJob", + "GitCandidateTransport", + "SidecarEvaluationRequest", + "SidecarEvaluationResult", + "SidecarStatus", + "Submission", + "SubmissionDisabledError", + "CanonicalVerifier", + "NoCandidateError", + "VerificationResult", + "VerificationSelection", + "VerificationTarget", + "build_harbor_components", + "compile_harbor_task", + "load_harbor_build_config", +] diff --git a/vero/src/vero/harbor/app.py b/vero/src/vero/harbor/app.py new file mode 100644 index 00000000..0e1a7d67 --- /dev/null +++ b/vero/src/vero/harbor/app.py @@ -0,0 +1,193 @@ +"""Optional FastAPI transport for the canonical Harbor sidecar.""" + +from __future__ import annotations + +import asyncio +import logging +import shutil +import tempfile +from pathlib import Path +from typing import Annotated + +from fastapi import FastAPI, Header, HTTPException, Query +from fastapi.responses import FileResponse, JSONResponse +from pydantic import BaseModel, ConfigDict +from starlette.background import BackgroundTask + +from vero.evaluation import ( + EvaluationBudgetExceeded, + EvaluationDeniedError, + EvaluationRequestError, +) +from vero.evaluation.exceptions import EvaluationExecutionError +from vero.harbor.auth import check_admin_token +from vero.harbor.session import create_harbor_session_archive +from vero.harbor.sidecar import ( + EvaluationAccessError, + EvaluationJobNotFoundError, + EvaluationJobStatus, + EvaluationSidecar, + SidecarEvaluationRequest, + SidecarEvaluationResult, + SubmissionDisabledError, +) +from vero.harbor.transport import CandidateTransferError +from vero.harbor.verifier import CanonicalVerifier + +logger = logging.getLogger(__name__) + + +class SubmitRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + + version: str | None = None + + +def _error(status_code: int, message: str): + async def handler(_request, error): + return JSONResponse( + status_code=status_code, + content={"error": message or str(error)}, + ) + + return handler + + +def create_app( + *, + sidecar: EvaluationSidecar, + verifier: CanonicalVerifier, + admin_token: str, +) -> FastAPI: + """Expose agent endpoints and token-gated admin endpoints on one app.""" + if not admin_token.strip(): + raise ValueError("admin_token must not be empty") + app = FastAPI(title="VeRO evaluation sidecar", version="1") + app.add_exception_handler( + EvaluationBudgetExceeded, + _error(429, "evaluation budget exhausted"), + ) + app.add_exception_handler(EvaluationDeniedError, _error(403, "evaluation denied")) + app.add_exception_handler(EvaluationAccessError, _error(403, "evaluation denied")) + app.add_exception_handler( + EvaluationRequestError, _error(400, "invalid evaluation request") + ) + app.add_exception_handler( + CandidateTransferError, + _error(400, "candidate version could not be imported"), + ) + app.add_exception_handler( + SubmissionDisabledError, + _error(409, "candidate submission is disabled"), + ) + app.add_exception_handler( + EvaluationJobNotFoundError, + _error(404, "evaluation job not found"), + ) + + @app.exception_handler(EvaluationExecutionError) + async def evaluation_failure(_request, error: EvaluationExecutionError): + return JSONResponse( + status_code=502, + content={ + "error": "evaluation failed", + "evaluation_id": error.evaluation_id, + }, + ) + + def require_admin(authorization: str | None) -> None: + if not check_admin_token(authorization, admin_token): + raise HTTPException(status_code=403, detail="admin token required") + + @app.get("/health") + async def health(): + return {"ok": True} + + @app.post("/eval") + async def evaluate(body: SidecarEvaluationRequest): + return await sidecar.evaluate(body) + + @app.post("/eval/jobs", status_code=202) + async def start_evaluation_job(body: SidecarEvaluationRequest): + return await sidecar.start_evaluation_job(body) + + @app.get("/eval/jobs/{job_id}") + async def evaluation_job(job_id: str): + return sidecar.evaluation_job(job_id) + + @app.get("/eval/jobs/{job_id}/result") + async def evaluation_job_result(job_id: str): + job = sidecar.evaluation_job(job_id) + if job.receipt is None: + status_code = ( + 202 + if job.status + in {EvaluationJobStatus.QUEUED, EvaluationJobStatus.RUNNING} + else 409 + ) + return JSONResponse( + status_code=status_code, + content=job.model_dump(mode="json"), + ) + return SidecarEvaluationResult( + disclosure=job.receipt.disclosure, + receipt=job.receipt, + ) + + @app.post("/submit") + async def submit(body: SubmitRequest): + return await sidecar.submit(body.version) + + @app.get("/status") + async def status(): + return sidecar.status() + + @app.post("/finalize") + async def finalize(authorization: Annotated[str | None, Header()] = None): + require_admin(authorization) + return await verifier.finalize() + + @app.get("/evaluations") + async def evaluations( + authorization: Annotated[str | None, Header()] = None, + limit: Annotated[int, Query(ge=1, le=1000)] = 100, + offset: Annotated[int, Query(ge=0)] = 0, + ): + require_admin(authorization) + records = sidecar.engine.database.get_evaluations( + limit=limit, + offset=offset, + reverse=True, + ) + return {"evaluations": records} + + @app.get("/session/export") + async def export_session( + authorization: Annotated[str | None, Header()] = None, + ): + require_admin(authorization) + directory = Path(tempfile.mkdtemp(prefix="vero-harbor-export-")) + archive = directory / "session.tar.gz" + try: + await asyncio.to_thread( + create_harbor_session_archive, + sidecar.engine.evaluator.session_dir, + archive, + ) + except BaseException as error: + shutil.rmtree(directory, ignore_errors=True) + # Surface the real cause (admin-only endpoint) rather than a bare 500. + logger.exception("session export failed") + if isinstance(error, Exception): + raise HTTPException( + status_code=500, detail=f"session export failed: {error}" + ) from error + raise + return FileResponse( + archive, + media_type="application/gzip", + filename="vero-session.tar.gz", + background=BackgroundTask(shutil.rmtree, directory, ignore_errors=True), + ) + + return app diff --git a/vero/src/vero/harbor/auth.py b/vero/src/vero/harbor/auth.py new file mode 100644 index 00000000..46f029d1 --- /dev/null +++ b/vero/src/vero/harbor/auth.py @@ -0,0 +1,79 @@ +"""Admin bearer-token helpers for the Harbor evaluation sidecar.""" + +from __future__ import annotations + +import os +import secrets +import tempfile +from pathlib import Path + + +def generate_admin_token() -> str: + return secrets.token_urlsafe(32) + + +def write_admin_token( + path: Path | str, + token: str, + *, + mode: int = 0o400, +) -> Path: + """Atomically write a read-only token file for the trusted verifier. + + The token gates the full-disclosure admin endpoints (``/finalize``, + ``/evaluations``, ``/session/export``). Under the shared-verifier topology + the token volume is also mounted into the *untrusted* agent container, so + the file (``0o400``) and its parent directory (``0o700``) are locked to the + writing user — root in the sidecar. The agent runs unprivileged + (``task.toml`` ``[agent].user``) and can therefore neither read the file nor + traverse into its directory; only the root-run verifier phase can. This is + what stops an untrusted agent from bypassing the aggregate-disclosure floor + through the admin endpoints. + """ + if not token.strip() or "\x00" in token: + raise ValueError("admin token must not be empty") + destination = Path(path) + destination.parent.mkdir(parents=True, exist_ok=True) + # Block traversal by any non-owner (e.g. the unprivileged agent user), so + # the token is unreachable even if its own mode were ever relaxed. + try: + destination.parent.chmod(0o700) + except OSError: + pass + descriptor, name = tempfile.mkstemp( + dir=destination.parent, + prefix=f".{destination.name}.", + suffix=".tmp", + ) + temporary = Path(name) + try: + os.fchmod(descriptor, mode) + with os.fdopen(descriptor, "w", encoding="utf-8") as handle: + handle.write(token) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, destination) + destination.chmod(mode) + except BaseException: + try: + os.close(descriptor) + except OSError: + pass + temporary.unlink(missing_ok=True) + raise + return destination + + +def read_admin_token(path: Path | str) -> str: + token = Path(path).read_text(encoding="utf-8").strip() + if not token: + raise ValueError("admin token file is empty") + return token + + +def check_admin_token(authorization: str | None, expected_token: str) -> bool: + prefix = "Bearer " + if authorization is None or not authorization.startswith(prefix): + return False + return secrets.compare_digest(authorization[len(prefix) :], expected_token) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py new file mode 100644 index 00000000..813ca043 --- /dev/null +++ b/vero/src/vero/harbor/backend.py @@ -0,0 +1,1369 @@ +"""Evaluate a candidate program by running it over Harbor tasks.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import logging +import math +import mimetypes +import os +import re +import shutil +import statistics +import tempfile +from collections import defaultdict +from pathlib import Path +from typing import Any, Literal + +from pydantic import Field, JsonValue, field_validator, model_validator + +from vero.evaluation.backend import EvaluationContext +from vero.evaluation.error_taxonomy import ( + NO_REWARD_SIGNAL, + ErrorCategory, + classify_case, + policy, +) +from vero.evaluation.models import ( + AllCases, + BackendProvenance, + CaseError, + CaseIds, + CaseRange, + CaseResult, + CaseStatus, + DiagnosticSeverity, + EvaluationArtifact, + EvaluationCost, + EvaluationDiagnostic, + EvaluationModel, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, +) +from vero.evaluation.security import sanitize_evaluation_report, sanitize_text +from vero.staging import SandboxStagingArea +from vero.sandbox import CommandResult, Sandbox + +logger = logging.getLogger(__name__) + + +def _default_uv() -> str: + executable = shutil.which("uv") + if executable is None: + raise ValueError("uv is required to configure a Harbor backend") + return str(Path(executable).resolve()) + + +class HarborCase(EvaluationModel): + """One canonical case mapped to one Harbor task name.""" + + id: str + task_name: str + result_task_name: str | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("id", "task_name") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Harbor case identity must not be empty") + return value + + @field_validator("result_task_name") + @classmethod + def validate_result_identity(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("Harbor result task identity must not be empty") + return value + + @property + def expected_result_task_name(self) -> str: + return self.result_task_name or self.task_name + + +# Dedicated env vars carrying the candidate agent's gateway credentials when +# task_services_use_upstream reroutes OPENAI_* to the real upstream. A custom +# agent reads these to keep its own inference on the metered/allow-listed gateway. +# This name is a contract shared with agents (they cannot import this module). +AGENT_INFERENCE_API_KEY_ENV = "VERO_AGENT_INFERENCE_API_KEY" +AGENT_INFERENCE_BASE_URL_ENV = "VERO_AGENT_INFERENCE_BASE_URL" + + +class HarborBackendConfig(EvaluationModel): + """Trusted configuration for nested ``harbor run`` evaluation.""" + + task_source: str + agent_import_path: str + cases_path: str + harbor_requirement: str + evaluation_set_name: str = "harbor" + partition: str | None = None + model: str | None = None + environment_name: str = "modal" + python_version: str = "3.12" + case_timeout_seconds: float = Field(default=180.0, gt=0) + task_agent_timeout_seconds: float = Field(default=600.0, gt=0) + n_attempts: int = Field(default=1, ge=1) + max_retries: int = Field(default=2, ge=0) + retry_wait_multiplier: float = Field(default=2.0, ge=1.0) + retry_min_wait_seconds: float = Field(default=4.0, ge=0.0) + retry_max_wait_seconds: float = Field(default=60.0, ge=0.0) + infrastructure_max_attempts: int = Field(default=3, ge=1) + infrastructure_retry_delay_seconds: float = Field(default=5.0, ge=0) + reward_key: str | None = None + # Average attempts by default rather than taking the best: best-of-n is an + # optimistic estimator that biases selection above the single-shot final + # score. Selection and final both run through this same aggregation. + aggregate_attempts: Literal["best", "mean"] = "mean" + failure_score: float = 0.0 + feedback_transcripts: bool = False + feedback_max_bytes: int = Field(default=3000, ge=0) + expose_attempt_detail: bool = False + uv_executable: str = Field(default_factory=_default_uv) + default_index: str = "https://pypi.org/simple" + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + inference_gateway_url: str | None = None + inference_gateway_token: str | None = None + # Optional reserved scope for trusted finalization (admin re-score / targets), + # so the optimizer's search evaluations cannot exhaust the budget the mandatory + # re-score needs. When unset, finalization falls back to the evaluation scope. + inference_gateway_finalization_token: str | None = None + # When True, task-owned evaluation services (e.g. LLM user-simulators or graders + # that run *inside* the task containers and cannot reach the compose-internal + # gateway) receive the real upstream credentials via OPENAI_*, while the + # candidate agent still routes through the metered/allow-listed gateway via + # VERO_AGENT_INFERENCE_*. upstream_*_env name the host env vars holding the + # upstream credentials (read from the sidecar process environment). + task_services_use_upstream: bool = False + upstream_api_key_env: str | None = None + upstream_base_url_env: str | None = None + # Unprivileged OS user to execute the untrusted candidate harness as, so it + # cannot read the trusted state (held-out records, budgets, other + # candidates, admin token) that the sidecar process owns. None (the default, + # used for local dev and tests) runs the harness in-process with no drop; + # the compiled sidecar sets this to isolate. + harness_user: str | None = None + case_resources_cache_path: str | None = None + extra_args: list[str] = Field(default_factory=list) + + @field_validator("cases_path", "case_resources_cache_path") + @classmethod + def validate_absolute_path(cls, value: str | None) -> str | None: + if value is None: + return None + if not value.strip() or not Path(value).is_absolute(): + raise ValueError("Harbor backend file paths must be absolute") + return value + + @field_validator( + "task_source", + "agent_import_path", + "harbor_requirement", + "evaluation_set_name", + "environment_name", + "python_version", + "default_index", + ) + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Harbor backend identity must not be empty") + return value + + @field_validator("uv_executable") + @classmethod + def validate_uv_executable(cls, value: str) -> str: + if not value.strip(): + raise ValueError("uv_executable must not be empty") + return value + + @field_validator("harbor_requirement") + @classmethod + def validate_pinned_harbor_requirement(cls, value: str) -> str: + exact = re.search( + r"(?:^|\s)harbor(?:\[[A-Za-z0-9_.-]+(?:,[A-Za-z0-9_.-]+)*\])?" + r"\s*==\s*[^*\s,;]+", + value, + ) + pinned_git = re.search(r"@[0-9a-f]{7,64}(?:#.*)?$", value) + if exact is None and pinned_git is None: + raise ValueError( + "harbor_requirement must pin an exact version or Git commit" + ) + return value + + @field_validator( + "partition", + "model", + "reward_key", + "inference_gateway_url", + "inference_gateway_token", + ) + @classmethod + def validate_optional_identity(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("optional Harbor identity must not be empty") + return value + + @field_validator("failure_score") + @classmethod + def validate_failure_score(cls, value: float) -> float: + if not math.isfinite(value): + raise ValueError("failure_score must be finite") + return value + + @field_validator("environment") + @classmethod + def validate_environment(cls, value: dict[str, str]) -> dict[str, str]: + for name in value: + if not name or "=" in name: + raise ValueError(f"invalid environment variable name: {name!r}") + return value + + @field_validator("passthrough_environment") + @classmethod + def validate_passthrough_environment(cls, value: list[str]) -> list[str]: + if len(value) != len(set(value)): + raise ValueError("passthrough environment names must be unique") + for name in value: + if not name or "=" in name: + raise ValueError(f"invalid environment variable name: {name!r}") + return value + + @field_validator("extra_args") + @classmethod + def validate_extra_args(cls, value: list[str]) -> list[str]: + controlled = { + "-a", + "-d", + "-e", + "-i", + "-m", + "-n", + "-p", + "--agent-import-path", + "--jobs-dir", + "--max-retries", + "--n-attempts", + "--agent-timeout-multiplier", + } + conflicts = [ + argument for argument in value if argument.split("=", 1)[0] in controlled + ] + if conflicts: + raise ValueError( + "extra_args cannot override backend-controlled Harbor flags: " + + ", ".join(conflicts) + ) + return value + + return value + + @model_validator(mode="after") + def validate_filesystem_and_environment(self) -> HarborBackendConfig: + if not Path(self.cases_path).is_file(): + raise ValueError("Harbor cases_path must be an existing file") + overlap = set(self.environment) & set(self.passthrough_environment) + if overlap: + raise ValueError( + "environment and passthrough_environment overlap for: " + + ", ".join(sorted(overlap)) + ) + if (self.inference_gateway_url is None) != ( + self.inference_gateway_token is None + ): + raise ValueError( + "inference_gateway_url and inference_gateway_token must be set together" + ) + if ( + self.inference_gateway_url is not None + and not self.inference_gateway_url.startswith(("http://", "https://")) + ): + raise ValueError("inference_gateway_url must be HTTP(S)") + gateway_names = {"OPENAI_API_KEY", "OPENAI_BASE_URL"} + if self.task_services_use_upstream: + gateway_names |= {AGENT_INFERENCE_API_KEY_ENV, AGENT_INFERENCE_BASE_URL_ENV} + gateway_overlap = gateway_names & ( + set(self.environment) | set(self.passthrough_environment) + ) + if self.inference_gateway_url is not None and gateway_overlap: + raise ValueError( + "gateway-managed environment variables must not also be configured: " + + ", ".join(sorted(gateway_overlap)) + ) + if self.task_services_use_upstream: + if self.inference_gateway_url is None: + raise ValueError( + "task_services_use_upstream requires an inference gateway" + ) + if not self.upstream_api_key_env: + raise ValueError( + "task_services_use_upstream requires upstream_api_key_env" + ) + if self.harness_user is not None and self.task_services_use_upstream: + # uid isolation does not hide env vars, so the raw upstream key would + # still reach the isolated harness through OPENAI_*. Refuse the + # combination until task-service credentials are delivered to the + # task containers off the harness environment. + raise ValueError( + "harness_user cannot be combined with task_services_use_upstream: " + "the raw upstream credential would still reach the isolated " + "harness through its environment" + ) + return self + + +class HarborBackend: + """Run Harbor as an external evaluator and collate its trial records.""" + + name = "harbor" + version = "2" + + def __init__(self, config: HarborBackendConfig): + self.config = config + self._cases = self._load_cases() + case_ids = [case.id for case in self._cases] + task_names = [case.task_name for case in self._cases] + if len(case_ids) != len(set(case_ids)): + raise ValueError("Harbor case IDs must be unique") + if len(task_names) != len(set(task_names)): + raise ValueError( + "Harbor task names must be unique within an evaluation set" + ) + + @property + def provenance(self) -> BackendProvenance: + return BackendProvenance.from_config( + name=self.name, + version=self.version, + config=self.config, + ) + + def _load_cases(self) -> list[HarborCase]: + path = Path(self.config.cases_path) + if path.suffix == ".jsonl": + raw = [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + else: + raw = json.loads(path.read_text(encoding="utf-8")) + if isinstance(raw, dict): + raw = raw.get("cases") + if not isinstance(raw, list) or not raw: + raise ValueError("Harbor case file must contain a non-empty case list") + return [HarborCase.model_validate(value) for value in raw] + + def _validate_evaluation_set(self, evaluation_set: EvaluationSet) -> None: + if evaluation_set.name != self.config.evaluation_set_name: + raise ValueError( + f"Harbor backend owns evaluation set " + f"{self.config.evaluation_set_name!r}, not {evaluation_set.name!r}" + ) + if evaluation_set.partition != self.config.partition: + raise ValueError( + f"Harbor backend owns partition {self.config.partition!r}, " + f"not {evaluation_set.partition!r}" + ) + selection = evaluation_set.selection + if isinstance(selection, CaseRange) and selection.stop > len(self._cases): + raise ValueError( + f"case range stops at {selection.stop}, but the Harbor evaluation " + f"set contains {len(self._cases)} cases" + ) + if isinstance(selection, CaseIds): + known = {case.id for case in self._cases} + unknown = sorted(set(selection.ids) - known) + if unknown: + raise ValueError(f"unknown Harbor case IDs: {unknown}") + + def _selected_cases(self, evaluation_set: EvaluationSet) -> list[HarborCase]: + self._validate_evaluation_set(evaluation_set) + selection = evaluation_set.selection + if isinstance(selection, AllCases): + return list(self._cases) + if isinstance(selection, CaseRange): + return self._cases[selection.start : selection.stop] + if isinstance(selection, CaseIds): + by_id = {case.id: case for case in self._cases} + return [by_id[case_id] for case_id in selection.ids] + raise AssertionError(f"unsupported Harbor case selection: {selection}") + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: + return EvaluationCost(cases=len(self._selected_cases(evaluation_set))) + + async def export_case_resources( + self, + *, + evaluation_set: EvaluationSet, + destination: str, + sandbox: Sandbox, + ) -> None: + """Expose complete task resources for an explicitly authorized partition.""" + + cases = self._selected_cases(evaluation_set) + configured_cache = self.config.case_resources_cache_path + if configured_cache is None: + with tempfile.TemporaryDirectory(prefix="vero-harbor-cases-") as temporary: + root = Path(temporary) + await self._materialize_case_resources(root, cases, evaluation_set) + await sandbox.upload(str(root), destination) + return + + cache = Path(configured_cache) + if not (cache / "index.json").is_file(): + cache.parent.mkdir(parents=True, exist_ok=True) + temporary = Path( + tempfile.mkdtemp( + dir=cache.parent, + prefix=f".{cache.name}.", + ) + ) + try: + await self._materialize_case_resources( + temporary, + cases, + evaluation_set, + ) + if cache.exists(): + shutil.rmtree(cache) + os.replace(temporary, cache) + finally: + shutil.rmtree(temporary, ignore_errors=True) + self._make_agent_readable(cache) + await sandbox.upload(str(cache), destination) + + async def _materialize_case_resources( + self, + root: Path, + cases: list[HarborCase], + evaluation_set: EvaluationSet, + ) -> None: + root.mkdir(parents=True, exist_ok=True) + source = Path(self.config.task_source).expanduser() + if source.exists(): + case_paths = self._materialize_local_case_resources(root, source, cases) + dataset_files_path = None + else: + case_paths, dataset_files_path = await self._materialize_package_resources( + root, + cases, + ) + (root / "index.json").write_text( + json.dumps( + { + "schema_version": 1, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "task_source": self.config.task_source, + "task_source_exposed": True, + "dataset_files_path": dataset_files_path, + "cases": [ + { + "case_id": case.id, + "task_name": case.task_name, + "path": case_paths[case.id], + } + for case in cases + ], + }, + ensure_ascii=False, + indent=2, + ) + + "\n", + encoding="utf-8", + ) + self._make_agent_readable(root) + + @staticmethod + def _make_agent_readable(root: Path) -> None: + """Allow the non-root producer to traverse its read-only context mount.""" + + for path in (root, *root.rglob("*")): + if path.is_symlink(): + continue + mode = path.stat().st_mode & 0o777 + if path.is_dir(): + path.chmod((mode | 0o055) & ~0o022) + elif path.is_file(): + path.chmod((mode | 0o044) & ~0o022) + + def _materialize_local_case_resources( + self, + root: Path, + source: Path, + cases: list[HarborCase], + ) -> dict[str, str]: + tasks = root / "tasks" + tasks.mkdir() + paths: dict[str, str] = {} + resolved_source = source.resolve() + for case in cases: + task = (resolved_source / case.task_name).resolve() + if task.parent != resolved_source or not task.is_dir(): + raise ValueError( + f"local Harbor case {case.task_name!r} is not a direct task directory" + ) + destination = tasks / hashlib.sha256(case.id.encode()).hexdigest() + shutil.copytree(task, destination) + paths[case.id] = destination.relative_to(root).as_posix() + dataset_files = root / "dataset-files" + dataset_files.mkdir() + for path in resolved_source.iterdir(): + if path.is_file() and not path.is_symlink(): + shutil.copy2(path, dataset_files / path.name) + if not any(dataset_files.iterdir()): + dataset_files.rmdir() + return paths + + async def _materialize_package_resources( + self, + root: Path, + cases: list[HarborCase], + ) -> tuple[dict[str, str], str | None]: + try: + from harbor.registry.client.package import PackageDatasetClient + from harbor.tasks.client import TaskClient + except ImportError as error: + raise RuntimeError( + "the pinned Harbor package must be installed to expose remote case resources" + ) from error + + client = PackageDatasetClient() + metadata = await client.get_dataset_metadata(self.config.task_source) + by_name = {task_id.get_name(): task_id for task_id in metadata.task_ids} + missing = sorted( + case.task_name for case in cases if case.task_name not in by_name + ) + if missing: + raise ValueError( + "authorized cases are absent from the pinned Harbor dataset: " + + ", ".join(missing) + ) + task_ids = [by_name[case.task_name] for case in cases] + tasks_root = root / "tasks" + result = await TaskClient().download_tasks( + task_ids=task_ids, + output_dir=tasks_root, + export=True, + ) + paths = { + case.id: download.path.relative_to(root).as_posix() + for case, download in zip(cases, result.results, strict=True) + } + dataset_root = root / "dataset-files" + files = await client.download_dataset_files( + metadata, + output_dir=dataset_root, + ) + return paths, "dataset-files" if files else None + + def _secrets(self) -> list[str]: + values = list(self.config.environment.values()) + values.extend( + os.environ[name] + for name in self.config.passthrough_environment + if name in os.environ + ) + if self.config.inference_gateway_token is not None: + values.append(self.config.inference_gateway_token) + if self.config.inference_gateway_finalization_token is not None: + values.append(self.config.inference_gateway_finalization_token) + return values + + def sanitize_error(self, message: str) -> str: + return sanitize_text(message, self._secrets()) + + def validate_request(self, request: EvaluationRequest) -> None: + self._validate_evaluation_set(request.evaluation_set) + if request.limits.retry.max_attempts > 1: + raise ValueError( + "Harbor does not support generic per-case retries; configure " + "HarborBackendConfig.max_retries instead" + ) + if request.limits.case_timeout_seconds != self.config.case_timeout_seconds: + raise ValueError( + "Harbor case timeout is fixed by the backend at " + f"{self.config.case_timeout_seconds:g} seconds" + ) + if request.seed is not None: + raise ValueError("Harbor does not support the generic evaluation seed") + payload = request.model_dump_json() + if any(secret in payload for secret in self._secrets() if len(secret) >= 4): + raise ValueError( + "evaluation parameters must not contain configured secret values; " + "pass secrets through the backend environment" + ) + + def _environment( + self, evaluation_id: str, *, finalization: bool = False + ) -> dict[str, str]: + environment = {"PATH": os.defpath, "LANG": "C.UTF-8"} + for name in ("TMPDIR", "TMP", "TEMP", "SYSTEMROOT"): + if name in os.environ: + environment[name] = os.environ[name] + environment.update(self.config.environment) + for name in self.config.passthrough_environment: + if name in os.environ: + environment[name] = os.environ[name] + if self.config.harness_user is not None: + # The isolated harness runs as an unprivileged user with no access + # to root's home; point uv and git at a home it owns. + home = f"/home/{self.config.harness_user}" + environment["HOME"] = home + environment["UV_CACHE_DIR"] = f"{home}/.cache/uv" + if self.config.inference_gateway_url is not None: + # Route trusted finalization evals to the reserved scope when configured; + # everything else (and the fallback) uses the shared evaluation scope. + use_finalization = ( + finalization + and self.config.inference_gateway_finalization_token is not None + ) + if finalization and not use_finalization: + # The trusted final re-score asked for the reserved pool but none + # was provisioned, so it silently shares the search budget and can + # be starved. Surface it loudly rather than degrading quietly. + logger.warning( + "finalization evaluation requested the reserved inference " + "scope but no finalization token is configured; falling back " + "to the shared 'evaluation' scope, which search evaluations " + "can exhaust" + ) + scope = "finalization" if use_finalization else "evaluation" + gateway_token = ( + self.config.inference_gateway_finalization_token + if use_finalization + else self.config.inference_gateway_token + ) or "" + gateway_url = ( + f"{self.config.inference_gateway_url.rstrip('/')}/scopes/{scope}/" + f"{evaluation_id}/v1" + ) + if self.config.task_services_use_upstream: + # Task-owned eval services (user-sims, LLM graders) run inside the + # task containers and can't reach the compose-internal gateway; hand + # them the real upstream via OPENAI_*. The candidate agent keeps its + # metered/allow-listed gateway on dedicated VERO_AGENT_INFERENCE_*. + environment["OPENAI_API_KEY"] = os.environ.get( + self.config.upstream_api_key_env or "", "" + ) + if self.config.upstream_base_url_env: + environment["OPENAI_BASE_URL"] = os.environ.get( + self.config.upstream_base_url_env, "" + ) + environment[AGENT_INFERENCE_API_KEY_ENV] = gateway_token + environment[AGENT_INFERENCE_BASE_URL_ENV] = gateway_url + else: + environment["OPENAI_API_KEY"] = gateway_token + environment["OPENAI_BASE_URL"] = gateway_url + return environment + + def _source_args(self, task_source: str, *, local: bool) -> list[str]: + return ["-p", task_source] if local else ["-d", task_source] + + def _retry_config_json(self) -> str: + """Partial harbor JobConfig carrying only the retry policy. + + Harbor's ``run`` CLI exposes ``--max-retries``/``--retry-include``/ + ``--retry-exclude`` but no backoff flags, so the backoff is delivered via a + ``--config`` snippet. The CLI loads ``--config`` as the base JobConfig and + then applies our flags on top (``--max-retries`` wins for the count), so a + retry-only partial is sufficient and safe. + """ + return json.dumps( + { + "retry": { + "max_retries": self.config.max_retries, + "wait_multiplier": self.config.retry_wait_multiplier, + "min_wait_sec": self.config.retry_min_wait_seconds, + "max_wait_sec": self.config.retry_max_wait_seconds, + } + } + ) + + def _command( + self, + *, + workspace: str, + request: EvaluationRequest, + cases: list[HarborCase], + jobs_dir: str, + task_source: str, + local_task_source: bool, + retry_config_path: str, + ) -> list[str]: + command = [ + self.config.uv_executable, + "run", + "--python", + self.config.python_version, + "--no-config", + "--no-env-file", + "--default-index", + self.config.default_index, + "--index-strategy", + "first-index", + "--project", + workspace, + "--with", + self.config.harbor_requirement, + "harbor", + "run", + # Non-interactive: a task that declares env vars (e.g. tau3's user-sim + # TAU2_* and grader TAU2_NL_ASSERTIONS_MODEL) makes `harbor run` prompt + # "Proceed? (Y/n)"; with no stdin the sub-run aborts (0 trials). GAIA + # declares none, so it never hit this. + "--yes", + *self._source_args(task_source, local=local_task_source), + "--agent-import-path", + self.config.agent_import_path, + "-e", + self.config.environment_name, + "-n", + str(request.limits.max_concurrency), + "--n-attempts", + str(self.config.n_attempts), + "--config", + retry_config_path, + "--max-retries", + str(self.config.max_retries), + "--agent-timeout-multiplier", + str( + self.config.case_timeout_seconds + / self.config.task_agent_timeout_seconds + ), + ] + model = request.parameters.get("harbor_model_override", self.config.model) + if model is not None: + command.extend(["-m", str(model)]) + for case in cases: + command.extend(["-i", case.task_name]) + command.extend(["--jobs-dir", jobs_dir, *self.config.extra_args]) + return command + + def _trial_groups(self, jobs_dir: Path) -> dict[str, list[dict[str, Any]]]: + groups: dict[str, list[dict[str, Any]]] = defaultdict(list) + if not jobs_dir.exists(): + return groups + jobs_root = jobs_dir.resolve() + for result_path in jobs_dir.rglob("result.json"): + if result_path.is_symlink(): + continue + try: + resolved = result_path.resolve() + resolved.relative_to(jobs_root) + value = json.loads(resolved.read_text(encoding="utf-8")) + modified = resolved.stat().st_mtime + except (json.JSONDecodeError, OSError, ValueError): + continue + task_name = value.get("task_name") if isinstance(value, dict) else None + if not task_name: + continue + value["_trial_dir"] = str(resolved.parent) + value["_mtime"] = modified + groups[str(task_name)].append(value) + for attempts in groups.values(): + attempts.sort( + key=lambda value: ( + value.get("finished_at") is None, + value.get("finished_at") or "", + value.get("trial_name") or "", + value.get("_mtime", 0.0), + ) + ) + return groups + + def _extract_reward(self, rewards: dict[str, Any]) -> float | None: + value: Any + if self.config.reward_key is not None: + value = rewards.get(self.config.reward_key) + else: + value = None + for key in ("pass", "reward"): + if key in rewards: + value = rewards[key] + break + if value is None and len(rewards) == 1: + value = next(iter(rewards.values())) + if value is None: + return None + try: + number = float(value) + except (TypeError, ValueError): + return None + return number if math.isfinite(number) else None + + def _attempt_reward(self, attempt: dict[str, Any]) -> float | None: + rewards = (attempt.get("verifier_result") or {}).get("rewards") or {} + return self._extract_reward(rewards) if rewards else None + + def _best_attempt( + self, + attempts: list[dict[str, Any]], + ) -> tuple[dict[str, Any] | None, float | None]: + scored = [ + (attempt, self._attempt_reward(attempt)) + for attempt in attempts + if self._attempt_reward(attempt) is not None + ] + if not scored: + return None, None + return max( + scored, + key=lambda item: ( + not bool(item[0].get("exception_info")), + item[1], + item[0].get("finished_at") or "", + item[0].get("_mtime", 0.0), + ), + ) + + def _transcript_feedback( + self, + attempts: list[dict[str, Any]], + ) -> str | None: + if not self.config.feedback_transcripts or self.config.feedback_max_bytes == 0: + return None + for attempt in attempts: + trial_dir_value = attempt.get("_trial_dir") + if not trial_dir_value: + continue + trial_dir = Path(trial_dir_value).resolve() + for relative in ("agent/terminus_2.pane", "agent/trajectory.json"): + path = trial_dir / relative + if path.is_symlink(): + continue + try: + resolved = path.resolve() + resolved.relative_to(trial_dir) + data = resolved.read_bytes() + except (OSError, ValueError): + continue + if data: + return data[-self.config.feedback_max_bytes :].decode( + "utf-8", errors="replace" + ) + return None + + def _trial_artifacts( + self, + attempts: list[dict[str, Any]], + artifact_root: Path, + ) -> list[EvaluationArtifact]: + """Reference complete Harbor trial records and redact configured credentials.""" + + resolved_root = artifact_root.resolve() + artifacts: list[EvaluationArtifact] = [] + seen: set[str] = set() + for attempt in attempts: + trial_dir_value = attempt.get("_trial_dir") + if not trial_dir_value: + continue + trial_root = Path(trial_dir_value) + if not trial_root.is_dir() or trial_root.is_symlink(): + continue + for path in sorted(trial_root.rglob("*")): + if not path.is_file() or path.is_symlink(): + continue + try: + resolved = path.resolve(strict=True) + resolved.relative_to(resolved_root) + except (OSError, ValueError): + continue + relative = resolved.relative_to(resolved_root).as_posix() + if relative in seen: + continue + seen.add(relative) + try: + payload = resolved.read_bytes() + if b"\x00" not in payload: + text = payload.decode("utf-8") + sanitized = sanitize_text(text, self._secrets()) + if sanitized != text: + resolved.write_text(sanitized, encoding="utf-8") + except (OSError, UnicodeDecodeError): + pass + media_type = mimetypes.guess_type(resolved.name)[0] + artifacts.append( + EvaluationArtifact( + path=relative, + media_type=media_type, + description=( + "Harbor trial record: " + + path.relative_to(trial_root).as_posix() + ), + ) + ) + return artifacts + + def _case_result( + self, + case: HarborCase, + attempts: list[dict[str, Any]], + *, + artifact_root: Path, + ) -> tuple[CaseResult, float]: + trial_artifacts = self._trial_artifacts(attempts, artifact_root) + attempt_detail = [ + { + "reward": self._attempt_reward(attempt), + "exception": (attempt.get("exception_info") or {}).get( + "exception_type" + ), + } + for attempt in attempts + ] + output: dict[str, JsonValue] = { + "task_name": case.task_name, + "result_task_name": case.expected_result_task_name, + } + if self.config.expose_attempt_detail: + output["attempts"] = attempt_detail + + if self.config.aggregate_attempts == "mean" and attempts: + rewards = [self._attempt_reward(attempt) for attempt in attempts] + if any(reward is not None for reward in rewards): + measured = [ + self.config.failure_score if reward is None else reward + for reward in rewards + ] + score = sum(measured) / len(measured) + output["attempt_scores"] = measured + output["aggregate"] = "mean" + return ( + CaseResult( + case_id=case.id, + status=CaseStatus.SUCCESS, + metrics={ + "score": score, + "n_attempts": float(len(attempts)), + "n_scored": float( + sum(reward is not None for reward in rewards) + ), + }, + input={"task_name": case.task_name, **case.metadata}, + output=output, + feedback=( + self._transcript_feedback(attempts) + if score == self.config.failure_score + else None + ), + artifacts=trial_artifacts, + ), + score, + ) + + best, score = self._best_attempt(attempts) + if best is not None and score is not None: + rewards = (best.get("verifier_result") or {}).get("rewards") or {} + output.update( + { + "trial_name": best.get("trial_name"), + "rewards": rewards, + "aggregate": "best", + } + ) + numeric_rewards = { + key: float(value) + for key, value in rewards.items() + if isinstance(value, (int, float)) and math.isfinite(float(value)) + } + numeric_rewards["score"] = score + return ( + CaseResult( + case_id=case.id, + status=CaseStatus.SUCCESS, + metrics=numeric_rewards, + input={"task_name": case.task_name, **case.metadata}, + output=output, + feedback=( + self._transcript_feedback(attempts) + if score == self.config.failure_score + else None + ), + artifacts=trial_artifacts, + ), + score, + ) + + # No scored attempt. Decide, from the single source of truth, whether + # this is the candidate's own failure (an informative low score) or an + # infrastructure loss (excluded from the aggregate score). + exception_counts: dict[str, int] = {} + signals: list[str] = [] + for attempt in attempts: + info = attempt.get("exception_info") or {} + name = info.get("exception_type") + key = str(name or NO_REWARD_SIGNAL) + exception_counts[key] = exception_counts.get(key, 0) + 1 + signals.append(key) + # The gateway embeds a distinct "budget_exhausted" code in the error + # body; the in-container client collapses the type to a generic + # rate-limit, but the message survives, so classify on it too. + for detail_key in ("message", "detail", "exception_message"): + detail = info.get(detail_key) + if isinstance(detail, str) and detail: + signals.append(detail) + + if not attempts: + # Harbor produced no trial for this task at all: infrastructure + # dropped the case (see the coverage gate), not the candidate. + category = ErrorCategory.TRANSIENT_INFRA + else: + category = classify_case(signals) + category_policy = policy(category) + output["dead_exception_types"] = exception_counts + output["error_category"] = category.value + + if not attempts: + message = ( + f"Harbor produced no trial for task {case.task_name!r} " + "(infrastructure dropped the case)" + ) + else: + message = f"No verifier reward for Harbor task {case.task_name!r}" + if exception_counts: + causes = ", ".join( + f"{name} x{count}" + for name, count in sorted(exception_counts.items()) + ) + message += f"; attempts: {causes}" + + if category_policy.is_informative_sample: + # The candidate produced no answer or crashed on its own: a real, + # scoreable outcome at the failure value, not infrastructure noise. + output["aggregate"] = "task_failure" + return ( + CaseResult( + case_id=case.id, + status=CaseStatus.SUCCESS, + metrics={"score": self.config.failure_score}, + input={"task_name": case.task_name, **case.metadata}, + output=output, + feedback=self._transcript_feedback(attempts), + metadata={"error_category": category.value}, + artifacts=trial_artifacts, + ), + self.config.failure_score, + ) + + # An infrastructure case: excluded from the aggregate, counted toward + # invalidity, and — for budget/auth — a terminating condition surfaced + # to the aggregation via its category. + return ( + CaseResult( + case_id=case.id, + status=CaseStatus.ERROR, + metrics={"score": self.config.failure_score}, + input={"task_name": case.task_name, **case.metadata}, + output=output, + feedback=self._transcript_feedback(attempts), + metadata={"error_category": category.value}, + artifacts=trial_artifacts, + errors=[ + CaseError( + message=message, + code=category_policy.diagnostic_code, + phase="harbor", + terminal=True, + ) + ], + ), + self.config.failure_score, + ) + + async def evaluate( + self, + *, + context: EvaluationContext, + request: EvaluationRequest, + ) -> EvaluationReport: + self.validate_request(request) + target_root = context.workspace.sandbox.host_path( + context.workspace.project_path + ) + if target_root is not None: + target_root = target_root.resolve() + cases_path = Path(self.config.cases_path).resolve() + if cases_path == target_root or cases_path.is_relative_to(target_root): + raise ValueError("Harbor cases must live outside the editable target") + source = Path(self.config.task_source).expanduser() + local_task_source = source.exists() + if local_task_source and target_root is not None: + resolved_source = source.resolve() + if resolved_source == target_root or resolved_source.is_relative_to( + target_root + ): + raise ValueError( + "local Harbor tasks must live outside the editable target" + ) + + cases = self._selected_cases(request.evaluation_set) + capture_dir = context.artifact_dir / "harbor" + capture_dir.mkdir(parents=True, exist_ok=True) + requested_tasks = {case.expected_result_task_name for case in cases} + attempts: list[tuple[CommandResult, str, str]] = [] + groups: dict[str, list[dict[str, Any]]] = {} + jobs_dir = capture_dir / "jobs" + for attempt in range(1, self.config.infrastructure_max_attempts + 1): + attempt_jobs_dir = ( + jobs_dir if attempt == 1 else capture_dir / f"retry-{attempt}" / "jobs" + ) + attempt_jobs_dir.mkdir(parents=True, exist_ok=True) + async with SandboxStagingArea( + context.workspace.sandbox, + prefix=f"vero-harbor-{context.evaluation_id[:8]}-{attempt}-", + ) as staging: + remote_jobs_dir = await staging.mkdir("jobs") + task_source = ( + ( + str(source.resolve()) + if context.workspace.sandbox.capabilities.host_paths + else await staging.upload(source.resolve(), "tasks") + ) + if local_task_source + else self.config.task_source + ) + retry_config_path = await staging.write_text( + "retry-config.json", self._retry_config_json() + ) + command = self._command( + workspace=context.workspace.project_path, + request=request, + cases=cases, + jobs_dir=remote_jobs_dir, + task_source=task_source, + local_task_source=local_task_source, + retry_config_path=retry_config_path, + ) + # Isolate the untrusted harness: hand the unprivileged user + # ownership of exactly the dirs it must read and write (its + # workspace and the staging tree), then run harbor as that user. + # Everything trusted (session dir, budgets, other candidates, + # admin token) is owned by root and unreadable to it. + if self.config.harness_user is not None: + owner = f"{self.config.harness_user}:{self.config.harness_user}" + for path in (context.workspace.project_path, staging.root): + chown = await context.workspace.sandbox.run( + ["chown", "-R", owner, str(path)], + timeout=120, + ) + if chown.returncode != 0: + raise RuntimeError( + "failed to hand harness workspace to " + f"{self.config.harness_user!r}: " + f"{self.sanitize_error(chown.stderr)}" + ) + result = await context.workspace.sandbox.run( + command, + cwd=context.workspace.project_path, + timeout=request.limits.timeout_seconds, + env=self._environment( + context.evaluation_id, finalization=context.finalization + ), + run_as=self.config.harness_user, + ) + await staging.download("jobs", attempt_jobs_dir) + stdout = self.sanitize_error(result.stdout) + stderr = self.sanitize_error(result.stderr) + attempts.append((result, stdout, stderr)) + groups = self._trial_groups(attempt_jobs_dir) + # Require FULL coverage before accepting the sub-run. Partial + # coverage used to be accepted silently, scoring the dropped tasks + # as zeros; now we retry, and any tasks still missing after retries + # are surfaced as infrastructure cases (excluded from the mean, + # counted toward invalidity) rather than folded in as zeros. + if requested_tasks <= set(groups): + jobs_dir = attempt_jobs_dir + break + if attempt < self.config.infrastructure_max_attempts: + await asyncio.sleep( + self.config.infrastructure_retry_delay_seconds * attempt + ) + + result, stdout, stderr = attempts[-1] + (capture_dir / "stdout.log").write_text(stdout, encoding="utf-8") + (capture_dir / "stderr.log").write_text(stderr, encoding="utf-8") + artifacts = [ + EvaluationArtifact( + path="harbor/stdout.log", + media_type="text/plain", + description="Harbor standard output", + ), + EvaluationArtifact( + path="harbor/stderr.log", + media_type="text/plain", + description="Harbor standard error", + ), + ] + + matching_tasks = requested_tasks & set(groups) + if not matching_tasks: + # Distinguish "sub-run produced nothing" from "produced trials whose + # task_name doesn't match what we requested" — the two collapse to the + # same failure otherwise. Surface the found group keys + exit status. + detail = ( + f"no matching trials for {len(cases)} requested tasks after " + f"{len(attempts)} attempts; requested e.g. {sorted(requested_tasks)[:2]}, " + f"harbor produced {len(groups)} trial group(s) e.g. {sorted(groups)[:3]}, " + f"returncode={result.returncode}" + ) + message = (stderr.strip() + " || " + detail) if stderr.strip() else detail + report = EvaluationReport( + status=EvaluationStatus.FAILED, + diagnostics=[ + EvaluationDiagnostic( + code="infrastructure_failure", + message=message, + severity=DiagnosticSeverity.ERROR, + phase="harbor", + ) + ], + artifacts=artifacts, + ) + return sanitize_evaluation_report(report, self._secrets()) + + case_results: list[CaseResult] = [] + scores: list[float] = [] + for case in cases: + case_result, score = self._case_result( + case, + groups.get(case.expected_result_task_name, []), + artifact_root=context.artifact_dir, + ) + case_results.append(case_result) + scores.append(score) + await context.case_store.save(case_result) + diagnostics = [] + if result.returncode != 0: + diagnostics.append( + EvaluationDiagnostic( + code=( + "harbor_partial_timeout" + if result.returncode == -1 + else "harbor_nonzero_exit" + ), + message=stderr.strip() + or f"Harbor exited with status {result.returncode}; partial trials collated", + severity=DiagnosticSeverity.WARNING, + phase="harbor", + ) + ) + + # Coverage: any requested task that produced no trial is an + # infrastructure loss, recorded loudly rather than silently zeroed. + missing_tasks = sorted(requested_tasks - set(groups)) + if missing_tasks: + diagnostics.append( + EvaluationDiagnostic( + code="harbor_incomplete_coverage", + message=( + f"{len(missing_tasks)} of {len(requested_tasks)} requested " + f"tasks produced no trial after {len(attempts)} attempt(s): " + f"{missing_tasks[:5]}" + ), + severity=DiagnosticSeverity.ERROR, + phase="harbor", + ) + ) + + # Infrastructure cases are excluded from the aggregate score; only + # informative samples (successes and legitimate task failures) count. + informative_scores = [ + score + for case, score in zip(case_results, scores) + if case.status != CaseStatus.ERROR + ] + infra_cases = [ + case for case in case_results if case.status == CaseStatus.ERROR + ] + + def _category(case: CaseResult) -> ErrorCategory | None: + raw = case.metadata.get("error_category") + try: + return ErrorCategory(raw) if isinstance(raw, str) else None + except ValueError: + return None + + # A terminating condition (inference-budget exhaustion or auth failure) + # anywhere fails the whole evaluation loudly: distinct code, no score. + terminating = next( + ( + _category(case) + for case in infra_cases + if (category := _category(case)) is not None + and policy(category).terminating + ), + None, + ) + if terminating is not None: + report = EvaluationReport( + status=EvaluationStatus.INVALID, + metrics={ + "error_rate": len(infra_cases) / len(case_results), + }, + cases=case_results, + diagnostics=[ + *diagnostics, + EvaluationDiagnostic( + code=policy(terminating).diagnostic_code, + message=( + f"terminating condition {terminating.value!r} reached; " + "the run cannot continue and must not be retried" + ), + severity=DiagnosticSeverity.ERROR, + phase="harbor", + ), + ], + artifacts=artifacts, + ) + return sanitize_evaluation_report(report, self._secrets()) + + # No informative sample survived (every case was infrastructure): the + # aggregate is undefined, so the whole evaluation is invalid. Retryable + # transient loss keeps the historical infrastructure_failure code so the + # engine still refunds and lets the optimizer retry. + if not informative_scores: + diagnostics.append( + EvaluationDiagnostic( + code="infrastructure_failure", + message=( + "no informative sample survived; every case was lost to " + "infrastructure after Harbor retries were exhausted" + ), + severity=DiagnosticSeverity.ERROR, + phase="harbor", + ) + ) + report = EvaluationReport( + status=EvaluationStatus.INVALID, + metrics={"error_rate": len(infra_cases) / len(case_results)}, + cases=case_results, + diagnostics=diagnostics, + artifacts=artifacts, + ) + return sanitize_evaluation_report(report, self._secrets()) + + report = EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={ + "score": sum(informative_scores) / len(informative_scores), + "error_rate": len(infra_cases) / len(case_results), + # Spread across informative cases, so a real difference between + # candidates is distinguishable from evaluation noise. + "score_stddev": ( + statistics.pstdev(informative_scores) + if len(informative_scores) > 1 + else 0.0 + ), + }, + cases=case_results, + diagnostics=diagnostics, + artifacts=artifacts, + ) + return sanitize_evaluation_report(report, self._secrets()) diff --git a/vero/src/vero/harbor/build/__init__.py b/vero/src/vero/harbor/build/__init__.py new file mode 100644 index 00000000..b86c4dbb --- /dev/null +++ b/vero/src/vero/harbor/build/__init__.py @@ -0,0 +1,25 @@ +"""Compile a program-optimization setup into a runnable Harbor task.""" + +from vero.harbor.build.compiler import compile_harbor_task +from vero.harbor.build.config import ( + AgentAccessSpec, + HarborBuildConfig, + InferenceBudgetSpec, + InferenceGatewaySpec, + VerificationTargetSpec, + WandbSpec, + WorkspaceOverlaySpec, + load_harbor_build_config, +) + +__all__ = [ + "AgentAccessSpec", + "HarborBuildConfig", + "InferenceBudgetSpec", + "InferenceGatewaySpec", + "VerificationTargetSpec", + "WandbSpec", + "WorkspaceOverlaySpec", + "compile_harbor_task", + "load_harbor_build_config", +] diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py new file mode 100644 index 00000000..5c319ab0 --- /dev/null +++ b/vero/src/vero/harbor/build/compiler.py @@ -0,0 +1,671 @@ +"""Compile a trusted VeRO configuration into a runnable Harbor task.""" + +from __future__ import annotations + +import io +import json +import logging +import os +import re +import shutil +import subprocess +import tarfile +import tomllib +from importlib.metadata import version as distribution_version +from pathlib import Path, PurePosixPath + +from vero.evaluation import ( + EvaluationBudget, + EvaluationLimits, + EvaluationSet, + RetryPolicy, +) +from vero.harbor.build.config import HarborBuildConfig +from vero.harbor.inference import generate_inference_token, token_digest + +logger = logging.getLogger(__name__) + +_TEMPLATES = Path(__file__).parent / "templates" +_VERO_COPY = ("pyproject.toml", "README.md", "uv.lock", "src") + +VERO_DIR = "/opt/vero" +TRUSTED_REPO = "/opt/agent-baseline" +AGENT_REPO = "/work/agent" +CASES_DIR = "/opt/cases" +TASK_SOURCE_DIR = "/opt/task-source" +SERVE_CONFIG = "/opt/serve.json" +AGENT_VOLUME = "/state/agent-context" +ADMIN_VOLUME = "/state/admin" +SESSION_DIR = "/state/admin/session" +TOKEN_PATH = "/state/token/admin.token" +SESSION_ID = "trial" +INFERENCE_STATE = "/state/inference/usage.json" +INFERENCE_GATEWAY_URL = "http://inference-gateway:8001" +UPSTREAM_API_KEY_ENV = "VERO_INFERENCE_UPSTREAM_API_KEY" +UPSTREAM_BASE_URL_ENV = "VERO_INFERENCE_UPSTREAM_BASE_URL" + + +def _backend_id(partition: str) -> str: + return f"harbor-{partition}" + + +def _is_vero_source(vero_root: Path) -> bool: + return (vero_root / "pyproject.toml").is_file() and ( + vero_root / "src/vero" + ).is_dir() + + +def _copy_vero_source(vero_root: Path, destination: Path) -> None: + destination.mkdir(parents=True, exist_ok=True) + for name in _VERO_COPY: + source = vero_root / name + if not source.exists(): + continue + if source.is_dir(): + shutil.copytree(source, destination / name, dirs_exist_ok=True) + else: + shutil.copy2(source, destination / name) + + +def _rewrite_vero_path(pyproject: Path) -> None: + if not pyproject.exists(): + return + original = pyproject.read_text(encoding="utf-8") + rewritten = re.sub( + r'(scale-vero\s*=\s*\{[^}]*?path\s*=\s*")[^"]*(")', + rf"\g<1>{VERO_DIR}\g<2>", + original, + ) + if rewritten != original: + pyproject.write_text(rewritten, encoding="utf-8") + + +def _safe_extract_tar(payload: bytes, destination: Path) -> None: + with tarfile.open(fileobj=io.BytesIO(payload), mode="r:") as archive: + for member in archive.getmembers(): + path = PurePosixPath(member.name) + if path.is_absolute() or ".." in path.parts: + raise ValueError(f"unsafe path in Git archive: {member.name!r}") + if member.issym() or member.islnk(): + link = PurePosixPath(member.linkname) + if link.is_absolute() or ".." in link.parts: + raise ValueError(f"unsafe link in Git archive: {member.linkname!r}") + archive.extractall(destination) + + +def _prepare_baseline_repo( + source: Path, + destination: Path, + *, + rewrite_vero_path: bool, +) -> str: + destination.mkdir(parents=True, exist_ok=True) + repository = subprocess.run( + ["git", "-C", str(source), "rev-parse", "--show-toplevel"], + capture_output=True, + text=True, + ) + if repository.returncode == 0: + root = Path(repository.stdout.strip()) + relative = source.relative_to(root) + treeish = "HEAD" if str(relative) == "." else f"HEAD:{relative.as_posix()}" + archived = subprocess.run( + ["git", "-C", str(root), "archive", "--format=tar", treeish], + capture_output=True, + ) + if archived.returncode != 0: + raise RuntimeError( + "git archive failed: " + + archived.stderr.decode("utf-8", errors="replace").strip() + ) + _safe_extract_tar(archived.stdout, destination) + else: + shutil.copytree( + source, + destination, + dirs_exist_ok=True, + ignore=shutil.ignore_patterns(".git"), + ) + if rewrite_vero_path: + _rewrite_vero_path(destination / "pyproject.toml") + if (destination / ".vero").exists(): + raise ValueError("agent baseline contains reserved path '.vero'") + + def git(*arguments: str) -> str: + result = subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "-C", + str(destination), + *arguments, + ], + check=True, + capture_output=True, + text=True, + ) + return result.stdout.strip() + + git("init", "-q") + git("add", "-A") + git("commit", "-q", "-m", "baseline") + return git("rev-parse", "HEAD") + + +def _local_result_task_name(task_source: Path, selector: str) -> str: + root = task_source.resolve() + task_dir = (root / selector).resolve() + if task_dir.parent != root: + raise ValueError( + f"local Harbor task selector {selector!r} must name a direct child directory" + ) + config_path = task_dir / "task.toml" + if not config_path.is_file(): + raise ValueError(f"local Harbor task selector {selector!r} has no task.toml") + try: + value = tomllib.loads(config_path.read_text(encoding="utf-8")) + task_name = value["task"]["name"] + except (KeyError, TypeError, tomllib.TOMLDecodeError) as error: + raise ValueError( + f"local Harbor task selector {selector!r} has no canonical task.name" + ) from error + if not isinstance(task_name, str) or not task_name.strip(): + raise ValueError( + f"local Harbor task selector {selector!r} has no canonical task.name" + ) + return task_name + + +def _write_cases(config: HarborBuildConfig, destination: Path) -> None: + destination.mkdir(parents=True, exist_ok=True) + task_source = Path(config.task_source) + local = task_source.exists() + for partition, tasks in config.partitions.items(): + path = destination / f"{partition}.jsonl" + lines = [ + json.dumps( + { + "id": task, + "task_name": task, + **( + { + "result_task_name": _local_result_task_name( + task_source, + task, + ) + } + if local + else {} + ), + }, + ensure_ascii=False, + ) + for task in tasks + ] + path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def _deployment_config( + config: HarborBuildConfig, + *, + baseline_version: str, + local_task_source: bool, + evaluation_inference_token: str | None, + finalization_inference_token: str | None, +) -> dict: + task_source = TASK_SOURCE_DIR if local_task_source else config.task_source + backends = {} + for partition in config.partitions: + backends[_backend_id(partition)] = { + "task_source": task_source, + "agent_import_path": config.agent_import_path, + "cases_path": f"{CASES_DIR}/{partition}.jsonl", + "harbor_requirement": config.harbor_requirement, + "evaluation_set_name": config.evaluation_set_name, + "partition": partition, + "model": config.model, + "environment_name": config.environment_name, + "python_version": config.harbor_python_version, + "case_timeout_seconds": config.case_timeout_seconds, + "task_agent_timeout_seconds": config.task_agent_timeout_seconds, + "default_index": config.default_index, + "n_attempts": config.n_attempts, + "max_retries": config.max_retries, + "retry_wait_multiplier": config.retry_wait_multiplier, + "retry_min_wait_seconds": config.retry_min_wait_seconds, + "retry_max_wait_seconds": config.retry_max_wait_seconds, + "infrastructure_max_attempts": config.infrastructure_max_attempts, + "infrastructure_retry_delay_seconds": ( + config.infrastructure_retry_delay_seconds + ), + "reward_key": config.reward_key, + "aggregate_attempts": config.aggregate_attempts, + "feedback_transcripts": config.feedback_transcripts, + "feedback_max_bytes": config.feedback_max_bytes, + "expose_attempt_detail": config.expose_attempt_detail, + "passthrough_environment": config.secrets, + "environment": config.task_environment, + "inference_gateway_url": ( + INFERENCE_GATEWAY_URL if config.inference_gateway is not None else None + ), + "inference_gateway_token": evaluation_inference_token, + "inference_gateway_finalization_token": finalization_inference_token, + "harness_user": config.harness_user, + "task_services_use_upstream": config.task_services_use_upstream, + "upstream_api_key_env": ( + UPSTREAM_API_KEY_ENV if config.inference_gateway is not None else None + ), + "upstream_base_url_env": ( + UPSTREAM_BASE_URL_ENV + if config.inference_gateway is not None + and config.inference_gateway.upstream_base_url_env is not None + else None + ), + "case_resources_cache_path": (f"{ADMIN_VOLUME}/case-resources/{partition}"), + "extra_args": config.extra_harbor_args, + } + + limits = EvaluationLimits( + timeout_seconds=config.timeout_seconds, + case_timeout_seconds=config.case_timeout_seconds, + max_concurrency=config.max_concurrency, + error_rate_threshold=config.error_rate_threshold, + retry=RetryPolicy.disabled(), + ) + policies = [] + budgets = [] + for access in config.agent_access: + backend_id = _backend_id(access.partition) + evaluation_set = EvaluationSet( + name=config.evaluation_set_name, + partition=access.partition, + ) + policies.append( + { + "backend_id": backend_id, + "evaluation_set_name": config.evaluation_set_name, + "partition": access.partition, + "objective": config.objective.model_dump(mode="json"), + "disclosure": access.disclosure.value, + "expose_case_resources": access.expose_case_resources, + "agent_evaluable": True, + "min_aggregate_cases": access.min_aggregate_cases, + "parameters": {}, + "allowed_parameters": [], + "limits": limits.model_dump(mode="json"), + } + ) + if access.total_runs is not None or access.total_cases is not None: + budgets.append( + EvaluationBudget( + backend_id=backend_id, + evaluation_set_key=evaluation_set.budget_key(backend_id), + total_runs=access.total_runs, + total_cases=access.total_cases, + ).model_dump(mode="json") + ) + + selection_backend = _backend_id(config.selection_partition) + selection_set = EvaluationSet( + name=config.evaluation_set_name, + partition=config.selection_partition, + ) + targets = [] + for target in config.targets: + parameters = dict(target.parameters) + if target.model is not None: + parameters["harbor_model_override"] = target.model + targets.append( + { + "reward_key": target.reward_key, + "backend_id": _backend_id(target.partition), + "evaluation_set": EvaluationSet( + name=config.evaluation_set_name, + partition=target.partition, + ).model_dump(mode="json"), + "objective": config.objective.model_dump(mode="json"), + "parameters": parameters, + "limits": limits.model_dump(mode="json"), + "failure_value": target.failure_value, + "reward_scale": ( + 1.0 if config.objective.direction == "maximize" else -1.0 + ), + "max_attempts": target.max_attempts, + } + ) + return { + "task_name": config.name, + "task_description": config.description, + "repo_path": TRUSTED_REPO, + "agent_repo_path": AGENT_REPO, + "session_dir": SESSION_DIR, + "session_id": SESSION_ID, + "backends": backends, + "access_policies": policies, + "budgets": budgets, + "selection": { + "mode": config.reward_mode, + # Always populated so auto_best can serve as the fallback when a + # submit-mode run has no submission. + "backend_id": selection_backend, + "evaluation_set": selection_set.model_dump(mode="json"), + "objective": config.objective.model_dump(mode="json"), + "baseline_version": baseline_version, + "parameters": {}, + "limits": limits.model_dump(mode="json"), + "rescore_top_k": config.rescore_top_k, + "rescore_attempts": config.rescore_attempts, + "baseline_floor": config.baseline_floor, + "selection_coverage_threshold": config.selection_coverage_threshold, + }, + "targets": targets, + "agent_volume": AGENT_VOLUME, + "admin_volume": ADMIN_VOLUME, + "submit_enabled": config.reward_mode == "submit", + "disclose_budget": config.disclose_budget, + "score_baseline": config.score_baseline, + "wandb": ( + config.wandb.model_dump(mode="json") if config.wandb is not None else None + ), + "evaluation_drain_timeout_seconds": ( + config.evaluation_drain_timeout_seconds or config.timeout_seconds + ), + "inference_usage_path": ( + INFERENCE_STATE if config.inference_gateway is not None else None + ), + "inference_limits": ( + { + "producer": config.inference_gateway.producer.model_dump(mode="json"), + "evaluation": config.inference_gateway.evaluation.model_dump( + mode="json" + ), + } + if config.inference_gateway is not None + else {} + ), + } + + +def _render(template: str, destination: Path, **context) -> None: + try: + from jinja2 import Environment, FileSystemLoader, StrictUndefined + except ImportError as error: + raise RuntimeError( + "install scale-vero[harbor] to compile Harbor tasks" + ) from error + environment = Environment( + loader=FileSystemLoader(str(_TEMPLATES)), + undefined=StrictUndefined, + keep_trailing_newline=True, + trim_blocks=True, + lstrip_blocks=True, + autoescape=False, + ) + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + environment.get_template(template).render(**context), + encoding="utf-8", + ) + + +def compile_harbor_task( + config: HarborBuildConfig, + output_dir: Path | str, + *, + vero_root: Path | None = None, +) -> Path: + """Emit a self-contained Harbor task directory from validated config.""" + output = Path(output_dir).expanduser().resolve() + source_root = (vero_root or Path(__file__).parents[4]).resolve() + use_local_vero = _is_vero_source(source_root) + if vero_root is not None and not use_local_vero: + raise ValueError(f"vero_root {source_root} is not a scale-vero source checkout") + protected = [Path(config.agent_repo).resolve()] + if use_local_vero: + protected.append(source_root) + task_source_path = Path(config.task_source) + if task_source_path.exists(): + protected.append(task_source_path.resolve()) + for path in protected: + if output == path or output.is_relative_to(path) or path.is_relative_to(output): + raise ValueError( + f"output directory {output} overlaps protected source {path}" + ) + gateway_environment: list[str] = [] + credential_sources: list[str] = [] + if config.inference_gateway is not None: + gateway_environment.append(UPSTREAM_API_KEY_ENV) + credential_sources.append(config.inference_gateway.upstream_api_key_env) + if config.inference_gateway.upstream_base_url_env is not None: + gateway_environment.append(UPSTREAM_BASE_URL_ENV) + credential_sources.append(config.inference_gateway.upstream_base_url_env) + task_environment = list(dict.fromkeys([*config.secrets, *gateway_environment])) + if os.environ.get("VERO_SKIP_SECRET_CHECK") is None: + required_sources = list(dict.fromkeys([*config.secrets, *credential_sources])) + missing = [name for name in required_sources if not os.environ.get(name)] + if missing: + raise ValueError( + "declared task credentials are missing: " + ", ".join(missing) + ) + if output.exists(): + shutil.rmtree(output) + environment_dir = output / "environment" + sidecar_dir = environment_dir / "sidecar" + gateway_dir = environment_dir / "gateway" + environment_dir.mkdir(parents=True) + if use_local_vero: + _copy_vero_source(source_root, environment_dir / "vero") + + baseline = _prepare_baseline_repo( + Path(config.agent_repo), + environment_dir / "agent-baseline", + rewrite_vero_path=use_local_vero, + ) + shutil.copytree( + environment_dir / "agent-baseline", + environment_dir / "agent-seed", + ) + # General filesystem overlay: bake arbitrary host files/dirs into the agent + # workspace at build time. A source dir's *contents* land under dest; a source + # file lands as dest/. dest="." is the workspace root. + overlay_present = bool(config.workspace_overlays) + overlay_excludes: list[str] = [] + if overlay_present: + overlay_root = environment_dir / "overlay" + for spec in config.workspace_overlays: + source = Path(spec.source) + target = overlay_root if spec.dest == "." else overlay_root / spec.dest + if source.is_dir(): + target.mkdir(parents=True, exist_ok=True) + shutil.copytree(source, target, dirs_exist_ok=True) + else: + target.mkdir(parents=True, exist_ok=True) + shutil.copy2(source, target / source.name) + if spec.dest != ".": + top = spec.dest.split("/", 1)[0] + if top not in overlay_excludes: + overlay_excludes.append(top) + _write_cases(config, sidecar_dir / "cases") + local_task_source = Path(config.task_source).exists() + if local_task_source: + shutil.copytree( + Path(config.task_source), + sidecar_dir / "task-source", + ) + producer_inference_token = ( + generate_inference_token() if config.inference_gateway is not None else None + ) + evaluation_inference_token = ( + generate_inference_token() if config.inference_gateway is not None else None + ) + finalization_inference_token = ( + generate_inference_token() if config.inference_gateway is not None else None + ) + deployment = _deployment_config( + config, + baseline_version=baseline, + local_task_source=local_task_source, + evaluation_inference_token=evaluation_inference_token, + finalization_inference_token=finalization_inference_token, + ) + (sidecar_dir / "serve.json").write_text( + json.dumps(deployment, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + if config.inference_gateway is not None: + assert producer_inference_token is not None + assert evaluation_inference_token is not None + assert finalization_inference_token is not None + # Finalization reserves its own budget; default to the evaluation policy. + finalization_spec = ( + config.inference_gateway.finalization or config.inference_gateway.evaluation + ) + gateway_dir.mkdir(parents=True, exist_ok=True) + gateway_config = { + "upstream_api_key_env": UPSTREAM_API_KEY_ENV, + "upstream_base_url_env": ( + UPSTREAM_BASE_URL_ENV + if config.inference_gateway.upstream_base_url_env is not None + else None + ), + "default_upstream_base_url": ( + config.inference_gateway.default_upstream_base_url + ), + "state_path": INFERENCE_STATE, + "scopes": { + "producer": { + "token_sha256": token_digest(producer_inference_token), + **config.inference_gateway.producer.model_dump(mode="json"), + }, + "evaluation": { + "token_sha256": token_digest(evaluation_inference_token), + **config.inference_gateway.evaluation.model_dump(mode="json"), + }, + "finalization": { + "token_sha256": token_digest(finalization_inference_token), + **finalization_spec.model_dump(mode="json"), + }, + }, + } + (gateway_dir / "config.json").write_text( + json.dumps(gateway_config, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8", + ) + (gateway_dir / "launch.json").write_text( + json.dumps( + { + "upstream_api_key_source": ( + config.inference_gateway.upstream_api_key_env + ), + "upstream_api_key_target": UPSTREAM_API_KEY_ENV, + "upstream_base_url_source": ( + config.inference_gateway.upstream_base_url_env + ), + "upstream_base_url_target": UPSTREAM_BASE_URL_ENV, + "producer_api_key": producer_inference_token, + "producer_base_url": ( + f"{INFERENCE_GATEWAY_URL}/scopes/producer/optimizer/v1" + ), + }, + ensure_ascii=False, + indent=2, + ) + + "\n", + encoding="utf-8", + ) + + selection_access = next( + access + for access in config.agent_access + if access.partition == config.selection_partition + ) + context = { + "name_toml": json.dumps(config.name, ensure_ascii=False), + "description_toml": json.dumps(config.description, ensure_ascii=False), + "description": config.description, + "base_image_main": config.base_image_main, + "base_image_sidecar": config.base_image_sidecar, + "use_local_vero": use_local_vero, + # The trusted sidecar needs the wandb extra when W&B reporting is on. + "sidecar_extras": "harbor,wandb" if config.wandb is not None else "harbor", + "vero_requirement": ( + None + if use_local_vero + else ( + "scale-vero[" + + ("harbor,wandb" if config.wandb is not None else "harbor") + + f"]=={distribution_version('scale-vero')}" + ) + ), + "harbor_requirement": config.harbor_requirement, + "secrets": task_environment, + "sidecar_secrets": config.secrets, + "inference_gateway": config.inference_gateway, + "gateway_environment": gateway_environment, + "scrubbed_main_environment": [ + name + for name in task_environment + if name not in {"OPENAI_API_KEY", "OPENAI_BASE_URL"} + ], + "producer_inference_token": producer_inference_token, + "evaluation_inference_token": evaluation_inference_token, + "inference_gateway_url": INFERENCE_GATEWAY_URL, + "read_only_paths": config.read_only_paths, + "local_task_source": local_task_source, + "selection_backend": _backend_id(config.selection_partition), + "evaluation_set_name": config.evaluation_set_name, + "selection_partition": config.selection_partition, + "submit_enabled": config.reward_mode == "submit", + "task_services_use_upstream": config.task_services_use_upstream, + "multifidelity": config.instruct_multifidelity, + "minimum_subset_cases": ( + selection_access.min_aggregate_cases + if selection_access.disclosure == "aggregate" + else 1 + ), + "exposed_partitions": [ + access.partition + for access in config.agent_access + if access.expose_case_resources + ], + "exhaust_budget": config.instruct_exhaust_budget, + "disclose_budget": config.disclose_budget, + "verifier_timeout": ( + config.verifier_timeout_seconds or max(1, int(config.timeout_seconds)) + ), + "overlay_present": overlay_present, + "overlay_excludes": overlay_excludes, + } + _render("task.toml.j2", output / "task.toml", **context) + _render("instruction.md.j2", output / "instruction.md", **context) + _render("Dockerfile.main.j2", environment_dir / "Dockerfile", **context) + _render( + "Dockerfile.sidecar.j2", + sidecar_dir / "Dockerfile", + **context, + ) + if config.inference_gateway is not None: + _render( + "Dockerfile.gateway.j2", + gateway_dir / "Dockerfile", + **context, + ) + _render( + "docker-compose.yaml.j2", + environment_dir / "docker-compose.yaml", + **context, + ) + _render("seed.sh.j2", environment_dir / "main/seed.sh", **context) + _render("test.sh.j2", output / "tests/test.sh", **context) + _render("solve.sh.j2", output / "solution/solve.sh", **context) + for script in ( + environment_dir / "main/seed.sh", + output / "tests/test.sh", + output / "solution/solve.sh", + ): + script.chmod(0o755) + logger.info("Compiled Harbor task at %s from baseline %s", output, baseline) + return output diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py new file mode 100644 index 00000000..e2965f7d --- /dev/null +++ b/vero/src/vero/harbor/build/config.py @@ -0,0 +1,583 @@ +"""Configuration schema for compiling VeRO optimization tasks for Harbor.""" + +from __future__ import annotations + +import json +import math +import os +import re +from pathlib import Path +from typing import Literal + +from pydantic import ConfigDict, Field, JsonValue, field_validator, model_validator + +from vero.evaluation import ( + DisclosureLevel, + EvaluationModel, + MetricSelector, + ObjectiveSpec, +) + + +class AgentAccessSpec(EvaluationModel): + partition: str + disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE + expose_case_resources: bool = False + min_aggregate_cases: int = Field(default=1, ge=1) + total_runs: int | None = Field(default=None, ge=0) + total_cases: int | None = Field(default=None, ge=0) + + @field_validator("partition") + @classmethod + def validate_partition(cls, value: str) -> str: + if not value.strip(): + raise ValueError("agent access partition must not be empty") + return value + + +class VerificationTargetSpec(EvaluationModel): + partition: str + reward_key: str = "reward" + model: str | None = None + parameters: dict[str, JsonValue] = Field(default_factory=dict) + failure_value: float = 0.0 + max_attempts: int = Field(default=1, ge=1) + + @field_validator("partition", "reward_key") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("verification target identity must not be empty") + return value + + @field_validator("failure_value") + @classmethod + def validate_failure_value(cls, value: float) -> float: + if not math.isfinite(value): + raise ValueError("target failure_value must be finite") + return value + + +class InferenceBudgetSpec(EvaluationModel): + """Routing policy and optional limits for one inference-gateway scope.""" + + allowed_models: list[str] + max_requests: int | None = Field(default=None, ge=1) + max_tokens: int | None = Field(default=None, ge=1) + max_concurrency: int = Field(default=8, ge=1) + + @field_validator("allowed_models") + @classmethod + def validate_models(cls, value: list[str]) -> list[str]: + if not value or any(not model.strip() for model in value): + raise ValueError("allowed_models must contain non-empty model names") + if len(value) != len(set(value)): + raise ValueError("allowed_models must be unique") + return value + + +class InferenceGatewaySpec(EvaluationModel): + """Credential source and independent producer/evaluator policies.""" + + upstream_api_key_env: str = "OPENAI_API_KEY" + upstream_base_url_env: str | None = "OPENAI_BASE_URL" + default_upstream_base_url: str = "https://api.openai.com/v1" + producer: InferenceBudgetSpec + evaluation: InferenceBudgetSpec + # Reserved budget for trusted finalization (admin re-score / targets). When + # omitted, it defaults to a copy of `evaluation`, giving the mandatory re-score + # its own pool so the optimizer's search evaluations cannot starve it. + finalization: InferenceBudgetSpec | None = None + + @field_validator("upstream_api_key_env", "upstream_base_url_env") + @classmethod + def validate_environment_name(cls, value: str | None) -> str | None: + if value is not None and re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", value) is None: + raise ValueError("gateway environment names must be valid identifiers") + return value + + @field_validator("default_upstream_base_url") + @classmethod + def validate_upstream_url(cls, value: str) -> str: + if not value.startswith(("http://", "https://")): + raise ValueError("default_upstream_base_url must be HTTP(S)") + return value.rstrip("/") + + +class WorkspaceOverlaySpec(EvaluationModel): + """A host file or directory to copy into the compiled task's agent workspace. + + General-purpose filesystem injection: bake anything (agent definitions, + skills, config, data) into the optimizer's ``/work/agent`` at build time. + ``source`` is a host path (resolved relative to the build YAML); ``dest`` is + where its contents land relative to the workspace root (``.`` = the root). + """ + + model_config = ConfigDict(extra="forbid") + + source: str + dest: str = "." + + @field_validator("source") + @classmethod + def validate_source(cls, value: str) -> str: + if not value.strip() or not Path(value).exists(): + raise ValueError("overlay source must be an existing file or directory") + return value + + @field_validator("dest") + @classmethod + def validate_dest(cls, value: str) -> str: + candidate = value.strip() + path = Path(candidate) + if candidate == ".": + return candidate + if ( + not candidate + or path.is_absolute() + or ".." in path.parts + or re.fullmatch(r"[A-Za-z0-9_./-]+", candidate) is None + ): + raise ValueError( + "overlay dest must be a safe relative path within the workspace" + ) + return candidate + + +class WandbSpec(EvaluationModel): + """Weights & Biases reporting settings, emitted into the sidecar's + serve.json. The field names match the sidecar's SidecarWandbConfig.""" + + project: str + entity: str | None = None + name: str | None = None + group: str | None = None + tags: list[str] = Field(default_factory=list) + mode: str | None = None + notes: str | None = None + run_id: str | None = None + # Upload each evaluation's trace artifacts to W&B (off by default; large). + log_traces: bool = False + + +class HarborBuildConfig(EvaluationModel): + """Everything needed to emit an isolated Harbor optimization task.""" + + model_config = ConfigDict(extra="forbid") + + name: str + description: str = "" + agent_repo: str + task_source: str + agent_import_path: str + harbor_requirement: str + partitions: dict[str, list[str]] + task_manifest: str | None = None + agent_access: list[AgentAccessSpec] + selection_partition: str + targets: list[VerificationTargetSpec] + + evaluation_set_name: str = "harbor" + objective: ObjectiveSpec = Field( + default_factory=lambda: ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + ) + reward_mode: Literal["submit", "auto_best"] = "auto_best" + baseline_floor: bool = True + score_baseline: bool = True + rescore_top_k: int = Field(default=3, ge=1) + rescore_attempts: int = Field(default=1, ge=1) + # Fraction of the selection set's cases an agent eval must cover to be + # eligible for auto_best ranking. Below this, the eval is too partial to + # trust for selection. + selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) + + model: str | None = None + environment_name: str = "modal" + harbor_python_version: str = "3.12" + default_index: str = "https://pypi.org/simple" + n_attempts: int = Field(default=1, ge=1) + # Per-case retry for the nested `harbor run` eval. Harbor already retries any + # non-excluded exception (including rate limits) with backoff; these knobs let + # a build tune how hard, so a transient upstream rate-limit storm during the + # (mandatory) selection/rescore evals does not fail otherwise-good candidates. + # `max_retries` -> `harbor run --max-retries`; the backoff fields are forwarded + # via a `--config` JobConfig snippet (harbor's CLI exposes no backoff flags). + max_retries: int = Field(default=2, ge=0) + retry_wait_multiplier: float = Field(default=2.0, ge=1.0) + retry_min_wait_seconds: float = Field(default=4.0, ge=0.0) + retry_max_wait_seconds: float = Field(default=60.0, ge=0.0) + infrastructure_max_attempts: int = Field(default=3, ge=1) + infrastructure_retry_delay_seconds: float = Field(default=5.0, ge=0) + reward_key: str | None = None + aggregate_attempts: Literal["best", "mean"] = "mean" + feedback_transcripts: bool = False + feedback_max_bytes: int = Field(default=3000, ge=0) + expose_attempt_detail: bool = False + extra_harbor_args: list[str] = Field(default_factory=list) + + timeout_seconds: float = Field(default=1800.0, gt=0) + case_timeout_seconds: float = Field(default=180.0, gt=0) + task_agent_timeout_seconds: float = Field(default=600.0, gt=0) + max_concurrency: int = Field(default=8, ge=1) + error_rate_threshold: float | None = Field(default=0.1, gt=0, le=1) + verifier_timeout_seconds: int | None = Field(default=None, ge=1) + evaluation_drain_timeout_seconds: float | None = Field(default=None, gt=0) + + secrets: list[str] = Field(default_factory=list) + inference_gateway: InferenceGatewaySpec | None = None + # Trusted-side Weights & Biases reporting from the eval-sidecar. Requires + # WANDB_API_KEY declared in `secrets` (routed to the sidecar, never the agent). + wandb: WandbSpec | None = None + read_only_paths: list[str] = Field(default_factory=list) + workspace_overlays: list[WorkspaceOverlaySpec] = Field(default_factory=list) + instruct_multifidelity: bool = True + instruct_exhaust_budget: bool = True + # Master switch for budget *disclosure* to the optimizer (not enforcement). + # When False, no budget language is rendered into the instruction and the + # sidecar `/status` omits remaining budgets, so the agent cannot see or reason + # about its budget — enabling budget-blind optimization runs. The two + # instruct_* flags above only take effect when this is True. + disclose_budget: bool = True + # Give task-owned evaluation services (LLM user-simulators/graders that run + # inside the task containers and can't reach the compose-internal gateway) the + # real upstream via OPENAI_*, while the candidate agent keeps the metered/ + # allow-listed gateway on dedicated VERO_AGENT_INFERENCE_* vars. Needed for + # benchmarks like tau3 whose environment makes its own LLM calls. + task_services_use_upstream: bool = False + # Unprivileged OS user that executes the untrusted candidate harness in the + # compiled sidecar, isolating it from the trusted state. The sidecar image + # creates this user; isolation is on by default for compiled deployments. + harness_user: str | None = "harness" + # Extra environment injected into the eval sub-run (and thus available for the + # task's ${VAR} substitutions), e.g. TAU2_USER_MODEL. Must not name secrets. + task_environment: dict[str, str] = Field(default_factory=dict) + base_image_main: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" + base_image_sidecar: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" + + @model_validator(mode="after") + def validate_harness_isolation(self) -> HarborBuildConfig: + if self.harness_user is not None and self.task_services_use_upstream: + raise ValueError( + "harness_user (harness isolation) is incompatible with " + "task_services_use_upstream: the raw upstream credential would " + "reach the isolated harness through its environment. Set " + "harness_user: null to build task-owned upstream services." + ) + return self + + @field_validator( + "name", + "agent_repo", + "task_source", + "agent_import_path", + "evaluation_set_name", + "environment_name", + "harbor_python_version", + "default_index", + "base_image_main", + "base_image_sidecar", + ) + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Harbor build identity must not be empty") + return value + + @field_validator("agent_repo") + @classmethod + def validate_agent_repo(cls, value: str) -> str: + if not Path(value).is_absolute() or not Path(value).is_dir(): + raise ValueError("agent_repo must be an existing absolute directory") + return value + + @field_validator("harbor_requirement") + @classmethod + def validate_pinned_harbor(cls, value: str) -> str: + exact = re.search( + r"(?:^|\s)harbor(?:\[[A-Za-z0-9_.-]+(?:,[A-Za-z0-9_.-]+)*\])?" + r"\s*==\s*[^*\s,;]+", + value, + ) + pinned_git = re.search(r"@[0-9a-f]{7,64}(?:#.*)?$", value) + if exact is None and pinned_git is None: + raise ValueError( + "harbor_requirement must pin an exact version or Git commit" + ) + return value + + @field_validator("model", "reward_key") + @classmethod + def validate_optional_identity(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("optional Harbor identity must not be empty") + return value + + @field_validator("task_manifest") + @classmethod + def validate_task_manifest_path(cls, value: str | None) -> str | None: + if value is None: + return None + if not value.strip() or not Path(value).is_file(): + raise ValueError("task_manifest must be an existing JSON file") + return value + + @field_validator("extra_harbor_args") + @classmethod + def validate_extra_harbor_args(cls, value: list[str]) -> list[str]: + controlled = { + "-a", + "-d", + "-e", + "-i", + "-m", + "-n", + "-p", + "--agent-import-path", + "--jobs-dir", + "--max-retries", + "--n-attempts", + } + conflicts = [ + argument for argument in value if argument.split("=", 1)[0] in controlled + ] + if conflicts: + raise ValueError( + "extra_harbor_args override controlled flags: " + ", ".join(conflicts) + ) + return value + + @field_validator("partitions") + @classmethod + def validate_partitions( + cls, + value: dict[str, list[str]], + ) -> dict[str, list[str]]: + if not value: + raise ValueError("at least one Harbor partition is required") + for partition, tasks in value.items(): + if re.fullmatch(r"[A-Za-z0-9_.-]+", partition) is None: + raise ValueError( + f"partition {partition!r} must use letters, digits, '.', '_', or '-'" + ) + if not tasks or any(not task.strip() for task in tasks): + raise ValueError(f"partition {partition!r} must contain task names") + if len(tasks) != len(set(tasks)): + raise ValueError(f"partition {partition!r} contains duplicate tasks") + return value + + @field_validator("secrets") + @classmethod + def validate_secrets(cls, value: list[str]) -> list[str]: + if len(value) != len(set(value)): + raise ValueError("secret environment names must be unique") + for name in value: + if re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", name) is None: + raise ValueError(f"invalid secret environment name: {name!r}") + return value + + @field_validator("read_only_paths") + @classmethod + def validate_read_only_paths(cls, value: list[str]) -> list[str]: + for item in value: + path = Path(item) + if ( + not item.strip() + or path.is_absolute() + or ".." in path.parts + or re.fullmatch(r"[A-Za-z0-9_./-]+", item) is None + ): + raise ValueError( + "read_only_paths must be safe relative candidate paths" + ) + return value + + @model_validator(mode="after") + def validate_references(self) -> HarborBuildConfig: + if not Path(self.task_source).exists() and "@" not in self.task_source: + raise ValueError("registry task_source must include an explicit version") + known = set(self.partitions) + if self.selection_partition not in known: + raise ValueError("selection_partition is not present in partitions") + access_names = [access.partition for access in self.agent_access] + if len(access_names) != len(set(access_names)): + raise ValueError("agent_access partitions must be unique") + unknown_access = sorted(set(access_names) - known) + if unknown_access: + raise ValueError( + f"agent_access references unknown partitions: {unknown_access}" + ) + if self.selection_partition not in access_names: + raise ValueError("selection_partition must be agent-evaluable") + if not self.targets: + raise ValueError("at least one verification target is required") + unknown_targets = sorted({target.partition for target in self.targets} - known) + if unknown_targets: + raise ValueError(f"targets reference unknown partitions: {unknown_targets}") + reward_keys = [target.reward_key for target in self.targets] + if len(reward_keys) != len(set(reward_keys)): + raise ValueError("target reward keys must be unique") + if self.task_manifest is not None: + try: + manifest = json.loads( + Path(self.task_manifest).read_text(encoding="utf-8") + ) + except json.JSONDecodeError as error: + raise ValueError("task_manifest must contain valid JSON") from error + if not isinstance(manifest, dict): + raise ValueError("task_manifest must be a JSON object") + manifest_source = manifest.get("task_source") + if manifest_source != self.task_source: + raise ValueError( + "task_manifest task_source does not match build task_source" + ) + manifest_tasks = manifest.get("tasks") + if not isinstance(manifest_tasks, list): + raise ValueError("task_manifest tasks must be a JSON array") + names: list[str] = [] + for item in manifest_tasks: + name = item.get("name") if isinstance(item, dict) else item + if not isinstance(name, str) or not name.strip(): + raise ValueError( + "task_manifest tasks must be names or objects with a name" + ) + names.append(name) + if len(names) != len(set(names)): + raise ValueError("task_manifest contains duplicate task names") + selected = { + task + for partition_tasks in self.partitions.values() + for task in partition_tasks + } + unknown = sorted(selected - set(names)) + if unknown: + raise ValueError( + "partitions reference tasks absent from task_manifest: " + + ", ".join(unknown) + ) + if self.inference_gateway is not None: + gateway_environment = {self.inference_gateway.upstream_api_key_env} + if self.inference_gateway.upstream_base_url_env is not None: + gateway_environment.add(self.inference_gateway.upstream_base_url_env) + overlap = sorted(set(self.secrets) & gateway_environment) + if overlap: + raise ValueError( + "inference gateway credentials must not also be sidecar secrets: " + + ", ".join(overlap) + ) + return self + + +_BUILD_PARAM = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)(?::(-|\?)([^}]*))?\}") + + +def _substitute_build_param(text: str, context: dict[str, str]) -> str: + """Resolve ``${NAME}`` / ``${NAME:-default}`` / ``${NAME:?message}`` in one scalar.""" + + def replace(match: re.Match[str]) -> str: + name, operator, argument = match.group(1), match.group(2), match.group(3) + resolved = context.get(name) + if resolved: + return resolved + if operator == "-": + return argument or "" + if operator == "?": + raise ValueError( + f"required build parameter {name!r} is unset: " + f"{argument or 'no message provided'}" + ) + raise ValueError( + f"build parameter {name!r} is unset; pass --param {name}=VALUE " + "or set the environment variable" + ) + + return _BUILD_PARAM.sub(replace, text) + + +def _resolve_build_params(value: object, context: dict[str, str]) -> object: + """Recursively resolve ``${...}`` placeholders in string scalars of a YAML value.""" + if isinstance(value, str): + return _substitute_build_param(value, context) + if isinstance(value, dict): + return {key: _resolve_build_params(item, context) for key, item in value.items()} + if isinstance(value, list): + return [_resolve_build_params(item, context) for item in value] + return value + + +def load_harbor_build_config( + path: Path | str, + *, + params: dict[str, str] | None = None, +) -> HarborBuildConfig: + """Load YAML and resolve local paths relative to the configuration file. + + ``${NAME}`` placeholders in the YAML are substituted at load time from + ``params`` (explicit, e.g. ``--param NAME=VALUE``) layered over the process + environment, so run-time knobs (optimizer model, inner sandbox provider, + concurrency, ...) can be varied without rebuilding the task. Use + ``${NAME:-default}`` for a fallback and ``${NAME:?message}`` to require a + value. Fields left un-templated stay fixed (the reproducible measurement + substrate). + """ + try: + import yaml + except ImportError as error: + raise RuntimeError( + "install scale-vero[harbor] to load Harbor builds" + ) from error + + config_path = Path(path).expanduser().resolve() + value = yaml.safe_load(config_path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError("Harbor build config must be a YAML object") + context = {**os.environ, **(params or {})} + value = _resolve_build_params(value, context) + base = config_path.parent + partition_files = value.pop("partition_files", None) + if partition_files is not None: + if "partitions" in value: + raise ValueError("use either partitions or partition_files, not both") + if not isinstance(partition_files, dict) or not partition_files: + raise ValueError("partition_files must be a non-empty YAML object") + partitions: dict[str, list[str]] = {} + for partition, filename in partition_files.items(): + if not isinstance(partition, str) or not isinstance(filename, str): + raise ValueError("partition_files must map names to JSON files") + partition_path = Path(filename).expanduser() + if not partition_path.is_absolute(): + partition_path = base / partition_path + try: + tasks = json.loads(partition_path.read_text(encoding="utf-8")) + except json.JSONDecodeError as error: + raise ValueError( + f"partition file {partition_path} must contain valid JSON" + ) from error + if not isinstance(tasks, list) or any( + not isinstance(task, str) for task in tasks + ): + raise ValueError( + f"partition file {partition_path} must be a JSON array of task names" + ) + partitions[partition] = tasks + value["partitions"] = partitions + agent_repo = value.get("agent_repo") + if isinstance(agent_repo, str) and not Path(agent_repo).is_absolute(): + value["agent_repo"] = str((base / agent_repo).resolve()) + task_source = value.get("task_source") + if isinstance(task_source, str): + local_source = base / task_source + if not Path(task_source).is_absolute() and local_source.exists(): + value["task_source"] = str(local_source.resolve()) + task_manifest = value.get("task_manifest") + if isinstance(task_manifest, str) and not Path(task_manifest).is_absolute(): + value["task_manifest"] = str((base / task_manifest).resolve()) + overlays = value.get("workspace_overlays") + if isinstance(overlays, list): + for entry in overlays: + source = entry.get("source") if isinstance(entry, dict) else None + if isinstance(source, str) and not Path(source).is_absolute(): + entry["source"] = str((base / source).resolve()) + return HarborBuildConfig.model_validate(value) diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 new file mode 100644 index 00000000..9bc6fd15 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 @@ -0,0 +1,17 @@ +# Trusted inference gateway: upstream credential and durable usage ledger. +FROM {{ base_image_sidecar }} + +RUN apt-get update \ + && apt-get install -y --no-install-recommends ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +{% if use_local_vero %} +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" +{% else %} +RUN uv pip install --system "{{ vero_requirement }}" +{% endif %} + +COPY gateway/config.json /opt/inference.json + +WORKDIR /opt diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 new file mode 100644 index 00000000..945d65d3 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 @@ -0,0 +1,22 @@ +# Optimizer workbench: target repository plus the agent-facing VeRO CLI. +FROM {{ base_image_main }} + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +{% if use_local_vero %} +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" +{% else %} +RUN uv pip install --system "{{ vero_requirement }}" +{% endif %} + +COPY agent-seed /opt/agent-seed +{% if overlay_present %} +COPY overlay /opt/overlay +{% endif %} +COPY main/seed.sh /opt/seed.sh +RUN chmod +x /opt/seed.sh && useradd -m -u 1001 agent + +WORKDIR /work/agent diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 new file mode 100644 index 00000000..bcd85e45 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 @@ -0,0 +1,42 @@ +# Trusted evaluation sidecar: baselines, cases, task source, ledger, and secrets. +FROM {{ base_image_sidecar }} + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +{% if use_local_vero %} +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[{{ sidecar_extras }}]" +{% else %} +RUN uv pip install --system "{{ vero_requirement }}" +{% endif %} +RUN uv pip install --system "{{ harbor_requirement }}" + +COPY agent-baseline /opt/agent-baseline +COPY sidecar/cases /opt/cases +COPY sidecar/serve.json /opt/serve.json +{% if local_task_source %} +COPY sidecar/task-source /opt/task-source +{% endif %} + +RUN cd /opt/agent-baseline && uv sync 2>/dev/null || true +RUN git config --system --add safe.directory /opt/agent-baseline \ + && git config --system --add safe.directory /work/agent \ + && git config --system --add safe.directory /work/agent/.git + +# Unprivileged user for executing the untrusted candidate harness. The sidecar +# still starts as root (the trusted control plane); only the `harbor run` +# subprocess drops to this user, so it cannot read the held-out records, the +# budget ledger, other candidates, or the admin token. `-m` gives it a home for +# the uv cache and git; useradd creates a same-named primary group. +RUN useradd -m -u 1002 harness + +# Lock the trusted, root-only files the harness must never read: the case lists +# reveal held-out (validation/test) task membership, and the deployment config +# carries the reserved finalization inference token. (Local task packages under +# /opt/task-source, which harbor must read to grade host-side, remain readable +# and are addressed by the deeper in-container isolation work.) +RUN chmod 700 /opt/cases && chmod 600 /opt/serve.json + +WORKDIR /opt diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 new file mode 100644 index 00000000..be79d2f6 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -0,0 +1,108 @@ +# Harbor merges this after its generated main-service configuration. +services: + main: + command: ["/opt/seed.sh"] + environment: + VERO_EVAL_URL: "http://eval-sidecar:8000" +{% for secret in scrubbed_main_environment %} + {{ secret }}: "" +{% endfor %} +{% if inference_gateway %} + OPENAI_API_KEY: "{{ producer_inference_token }}" + OPENAI_BASE_URL: "{{ inference_gateway_url }}/scopes/producer/optimizer/v1" +{% elif "OPENAI_API_KEY" in secrets %} + OPENAI_API_KEY: "" +{% if "OPENAI_BASE_URL" in secrets %} + OPENAI_BASE_URL: "" +{% endif %} +{% endif %} + volumes: + - agent_repo:/work/agent + - agent_context:/work/agent/.vero:ro + - token_state:/state/token:ro + depends_on: + eval-sidecar: + condition: service_healthy +{% if inference_gateway %} + inference-gateway: + condition: service_healthy +{% endif %} + + eval-sidecar: + build: + context: . + dockerfile: sidecar/Dockerfile + command: + - "vero" + - "harbor" + - "serve" + - "--factory" + - "vero.harbor.deployment:build_harbor_components" + - "--config" + - "/opt/serve.json" + - "--admin-token" + - "/state/token/admin.token" + environment: +{% if sidecar_secrets %} +{% for secret in sidecar_secrets %} + {{ secret }}: "${{ '{' }}{{ secret }}:?{{ secret }} must be set for the eval sidecar{{ '}' }}" +{% endfor %} +{% else %} + LANG: "C.UTF-8" +{% endif %} +{% if task_services_use_upstream %} +{# Trusted sidecar receives the real upstream so it can hand task-owned eval + services (LLM user-sims/graders) a reachable provider. The optimizer/main keeps + these scrubbed to "" so it never sees the raw upstream credential. #} +{% for secret in gateway_environment %} + {{ secret }}: "${{ '{' }}{{ secret }}:?{{ secret }} must be set for the eval sidecar{{ '}' }}" +{% endfor %} +{% endif %} + volumes: + - agent_repo:/work/agent:ro + - agent_context:/state/agent-context + - admin_state:/state/admin + - token_state:/state/token +{% if inference_gateway %} + - inference_state:/state/inference:ro +{% endif %} + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 5s + timeout: 10s + retries: 30 + start_period: 10s + +{% if inference_gateway %} + inference-gateway: + build: + context: . + dockerfile: gateway/Dockerfile + command: + - "vero" + - "harbor" + - "inference-gateway" + - "--config" + - "/opt/inference.json" + environment: +{% for secret in gateway_environment %} + {{ secret }}: "${{ '{' }}{{ secret }}:?{{ secret }} must be set for the inference gateway{{ '}' }}" +{% endfor %} + volumes: + - inference_state:/state/inference + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8001/health')"] + interval: 5s + timeout: 10s + retries: 30 + start_period: 10s +{% endif %} + +volumes: + agent_repo: + agent_context: + admin_state: + token_state: +{% if inference_gateway %} + inference_state: +{% endif %} diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 new file mode 100644 index 00000000..a1a69745 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -0,0 +1,99 @@ +# Optimize the program + +Improve the program in `/work/agent` so it scores as highly as possible on the +hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, +{% if disclose_budget %}budget, {% endif %}and final candidate selection. + +{% if description %} +## Objective + +{{ description }} + +{% endif %} +## Workflow + +1. Inspect and edit `/work/agent`. +2. Commit each candidate with Git. +3. Evaluate the current commit on the selection set: + + ```bash + vero harbor eval \ + --backend {{ selection_backend }} \ + --evaluation-set {{ evaluation_set_name }} \ + --partition {{ selection_partition }} \ + --detach + ``` + + Record the returned `job_id`. Continue inspecting or editing while it runs, + then use `vero harbor eval-status JOB_ID` and + `vero harbor eval-result JOB_ID` to retrieve its durable result. Track every + outstanding job and do not claim an improvement before its comparison + baseline has completed. + +4. Use `vero harbor status` to inspect evaluation jobs and allowed evaluation + sets{% if disclose_budget %}, and to see remaining budgets{% endif %}. +{% if submit_enabled %} +5. When finished, nominate the chosen commit with `vero harbor submit`. +{% else %} +5. VeRO automatically pools your measurements, re-scores a shortlist with the + trusted evaluator, and selects the best candidate. The unmodified baseline is + used as a floor, so a regression is not shipped. +{% endif %} + +{% if multifidelity and disclose_budget %} +## Allocate the evaluation budget + +You decide how to allocate the finite case budget. Use `--start 0 --stop N` or +repeated `--case-id ID` flags for subsets, or omit them to evaluate the complete +set. A request may consume the entire remaining case budget. VeRO does not +reserve cases for later runs, so keep enough only if your strategy requires a +final confirmation. +{% if minimum_subset_cases > 1 %} +Aggregate-only subsets must include at least {{ minimum_subset_cases }} cases; +this prevents singleton aggregates from exposing individual hidden results. +{% else %} +You may use arbitrary subsets, including a single case; validation feedback is +optimization data, while the final evaluation remains hidden. +{% endif %} + +{% endif %} +{% if exposed_partitions %} +## Inspect cases and traces + +Complete task resources for the following authorized partitions are mounted +read-only under `.vero/cases/`: {{ exposed_partitions | join(", ") }}. Start at +`.vero/cases/index.json`; each listed partition has its own task index, +attachments, instructions, and task files. + +After a full-disclosure evaluation, `.vero/evaluations/` contains one file per +case plus the complete Harbor trial records: exact failure results, exception +tracebacks, trial logs, and target-agent artifacts. Use these files to inspect +successful as well as failed trajectories without putting long traces into the +CLI response. +{% if overlay_present %} +Your workspace also includes analysis subagents under `.claude/agents/` and a +corpus-analysis skill under `skills/insights/`. These mine the entire +`.vero/evaluations/` trace corpus for recurring, cross-case failure patterns that +are invisible in any single trace. After running at least one full-disclosure +evaluation, dispatch the `insights-generator` subagent (via the Task tool, +pointing it at the `.vero` corpus) to get evidence-grounded findings, then let +those findings guide your next candidate edit. Re-run it as the corpus grows. +{% endif %} +{% endif %} +## Rules + +- Only the sidecar evaluates candidates. Only explicitly authorized case + resources are mounted; validation and final targets remain outside this + container. +{% if disclose_budget %} +- Evaluation budgets are finite and are charged by backend and evaluation set. +{% endif %} +- Scores may be noisy; compare candidates on the same selection whenever possible. +- Reusing the same cases removes case-sampling differences, but target-model + randomness remains. Replicate important comparisons before assigning causality. +{% if exhaust_budget and disclose_budget %} +- Unspent evaluation budget is wasted. Use remaining calls to re-measure the + strongest candidate or try another plausible change. +{% endif %} +- Paths marked read-only are guardrails. The authoritative evaluator lives in the + sidecar, outside the candidate repository. diff --git a/vero/src/vero/harbor/build/templates/seed.sh.j2 b/vero/src/vero/harbor/build/templates/seed.sh.j2 new file mode 100644 index 00000000..c0886d5a --- /dev/null +++ b/vero/src/vero/harbor/build/templates/seed.sh.j2 @@ -0,0 +1,41 @@ +#!/bin/sh +set -eu + +if [ ! -d /work/agent/.git ]; then + cp -a /opt/agent-seed/. /work/agent/ +fi +{% if overlay_present %} +# Inject baked-in workspace overlay (agent definitions, skills, config, ...). +cp -a /opt/overlay/. /work/agent/ +{% endif %} + +find /work/agent -path /work/agent/.vero -prune -o -exec chown agent:agent {} + +git config --system --add safe.directory /work/agent +printf '%s\n' '/.vero/' >> /work/agent/.git/info/exclude +{% for path in overlay_excludes %} +printf '%s\n' '/{{ path }}/' >> /work/agent/.git/info/exclude +{% endfor %} +{% if inference_gateway %} +# Codex otherwise treats the built-in OpenAI provider as WebSocket-capable. +# The VeRO gateway intentionally supports the metered HTTP Responses surface. +mkdir -p /tmp/codex-home +cat > /tmp/codex-home/config.toml <<'TOML' +model_provider = "vero_gateway" + +[model_providers.vero_gateway] +name = "VeRO inference gateway" +base_url = "{{ inference_gateway_url }}/scopes/producer/optimizer/v1" +env_key = "OPENAI_API_KEY" +wire_api = "responses" +supports_websockets = false +TOML +chown -R agent:agent /tmp/codex-home +{% endif %} +{% for path in read_only_paths %} +if [ -e "/work/agent/{{ path }}" ]; then + chown -R root:root "/work/agent/{{ path }}" + chmod -R a-w "/work/agent/{{ path }}" +fi +{% endfor %} + +exec sleep infinity diff --git a/vero/src/vero/harbor/build/templates/solve.sh.j2 b/vero/src/vero/harbor/build/templates/solve.sh.j2 new file mode 100644 index 00000000..9aec5f8e --- /dev/null +++ b/vero/src/vero/harbor/build/templates/solve.sh.j2 @@ -0,0 +1,17 @@ +#!/bin/sh +set -eu + +cd /work/agent +git config user.name optimizer +git config user.email optimizer@example.test +echo "# optimizer candidate" >> README.md 2>/dev/null || echo "candidate" > NOTES.md +git add -A +git commit -m "optimizer candidate" +vero harbor eval \ + --backend {{ selection_backend }} \ + --evaluation-set {{ evaluation_set_name }} \ + --partition {{ selection_partition }} +{% if submit_enabled %} +vero harbor submit +{% endif %} +vero harbor status diff --git a/vero/src/vero/harbor/build/templates/task.toml.j2 b/vero/src/vero/harbor/build/templates/task.toml.j2 new file mode 100644 index 00000000..d3f414de --- /dev/null +++ b/vero/src/vero/harbor/build/templates/task.toml.j2 @@ -0,0 +1,22 @@ +schema_version = "1.3" + +[task] +name = {{ name_toml }} +description = {{ description_toml }} + +[agent] +user = "agent" + +[verifier] +environment_mode = "shared" +timeout_sec = {{ verifier_timeout }} + +[environment] +build_timeout_sec = 1800 +{% if secrets %} + +[environment.env] +{% for secret in secrets %} +{{ secret }} = "${{ '{' }}{{ secret }}{{ '}' }}" +{% endfor %} +{% endif %} diff --git a/vero/src/vero/harbor/build/templates/test.sh.j2 b/vero/src/vero/harbor/build/templates/test.sh.j2 new file mode 100644 index 00000000..19675ef3 --- /dev/null +++ b/vero/src/vero/harbor/build/templates/test.sh.j2 @@ -0,0 +1,10 @@ +#!/bin/sh +set -eu + +mkdir -p /logs/verifier +vero harbor finalize \ + --token-file /state/token/admin.token \ + --output /logs/verifier/reward.json +vero harbor export-session \ + --token-file /state/token/admin.token +cat /logs/verifier/reward.json diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py new file mode 100644 index 00000000..13fb3b39 --- /dev/null +++ b/vero/src/vero/harbor/cli.py @@ -0,0 +1,779 @@ +"""CLI clients and server entry point for Harbor sidecar deployments.""" + +from __future__ import annotations + +import asyncio +import json +import os +import re +import shlex +import shutil +import subprocess +import sys +import tempfile +import urllib.error +import urllib.request +from pathlib import Path + +import click + +from vero.evaluation import ( + CaseIds, + CaseRange, + EvaluationLimits, + EvaluationSet, + RetryPolicy, +) +from vero.harbor.auth import read_admin_token +from vero.harbor.session import ( + create_harbor_session_archive, + extract_harbor_session_archive, + file_sha256, +) +from vero.harbor.sidecar import SidecarEvaluationRequest + + +def _base_url() -> str: + value = os.environ.get("VERO_EVAL_URL") + if not value: + raise click.ClickException("VERO_EVAL_URL is not set") + return value.rstrip("/") + + +def _request( + method: str, + path: str, + *, + payload: dict | None = None, + headers: dict[str, str] | None = None, +): + data = None if payload is None else json.dumps(payload).encode("utf-8") + request = urllib.request.Request( + f"{_base_url()}{path}", + method=method, + data=data, + headers={"Content-Type": "application/json", **(headers or {})}, + ) + try: + with urllib.request.urlopen(request) as response: + return json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as error: + message = error.read().decode("utf-8", errors="replace") + raise click.ClickException( + f"{method} {path} returned {error.code}: {message}" + ) from error + except urllib.error.URLError as error: + raise click.ClickException( + f"could not reach evaluation sidecar: {error}" + ) from error + + +def _atomic_write_bytes(path: Path, payload: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + dir=path.parent, + prefix=f".{path.name}.", + suffix=".tmp", + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as file: + file.write(payload) + file.flush() + os.fsync(file.fileno()) + os.replace(temporary, path) + finally: + temporary.unlink(missing_ok=True) + + +def _download( + path: str, + destination: Path, + *, + headers: dict[str, str] | None = None, +) -> None: + request = urllib.request.Request( + f"{_base_url()}{path}", + method="GET", + headers=headers or {}, + ) + destination.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + dir=destination.parent, + prefix=f".{destination.name}.", + suffix=".tmp", + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as file: + try: + with urllib.request.urlopen(request) as response: + shutil.copyfileobj(response, file, length=1024 * 1024) + except urllib.error.HTTPError as error: + message = error.read().decode("utf-8", errors="replace") + raise click.ClickException( + f"GET {path} returned {error.code}: {message}" + ) from error + except urllib.error.URLError as error: + raise click.ClickException( + f"could not reach evaluation sidecar: {error}" + ) from error + file.flush() + os.fsync(file.fileno()) + os.replace(temporary, destination) + finally: + temporary.unlink(missing_ok=True) + + +def _redact_trace_text(value: str) -> str: + value = re.sub( + r"(?m)^([A-Z][A-Z0-9_]*(?:API_KEY|TOKEN|SECRET|PASSWORD)=).*$", + r"\1[REDACTED]", + value, + ) + value = re.sub( + r"(?i)(bearer\s+)[A-Za-z0-9._~-]{16,}", + r"\1[REDACTED]", + value, + ) + return re.sub(r"\bsk-[A-Za-z0-9_-]{12,}\b", "[REDACTED]", value) + + +def _redact_trace_value(value: object) -> object: + if isinstance(value, str): + return _redact_trace_text(value) + if isinstance(value, list): + return [_redact_trace_value(item) for item in value] + if isinstance(value, dict): + return {key: _redact_trace_value(item) for key, item in value.items()} + return value + + +def _load_agent_trace(path: Path) -> object: + text = _redact_trace_text(path.read_text(encoding="utf-8", errors="replace")) + try: + return _redact_trace_value(json.loads(text)) + except json.JSONDecodeError: + values = [] + for line in text.splitlines(): + try: + values.append(_redact_trace_value(json.loads(line))) + except json.JSONDecodeError: + continue + if not values: + return [{"role": "assistant", "content": text}] + + entries: list[dict] = [] + for value in values: + if not isinstance(value, dict): + entries.append({"type": "event", "value": value}) + continue + item = value.get("item") + if value.get("type") != "item.completed" or not isinstance(item, dict): + continue + item_type = item.get("type") + if item_type == "agent_message": + entries.append({"role": "assistant", "content": item.get("text", "")}) + elif item_type == "command_execution": + entries.extend( + [ + { + "type": "function_call", + "name": "exec_command", + "arguments": item.get("command", ""), + }, + { + "type": "function_call_output", + "output": item.get("aggregated_output", ""), + }, + ] + ) + elif item_type == "error": + entries.append( + {"type": "error", "message": item.get("message", "unknown error")} + ) + return entries or values + + +def _load_env_file(path: Path) -> dict[str, str]: + """Parse a dotenv-style ``NAME=VALUE`` secrets file. + + Blank lines and ``#`` comments are ignored; a leading ``export`` is + tolerated and surrounding single/double quotes are stripped. Values are + kept verbatim otherwise (no shell/variable expansion) so a secret cannot + be silently mangled. + """ + values: dict[str, str] = {} + for lineno, raw in enumerate(path.read_text(encoding="utf-8").splitlines(), 1): + line = raw.strip() + if not line or line.startswith("#"): + continue + if line.startswith("export ") or line.startswith("export\t"): + line = line[len("export ") :].lstrip() + name, separator, value = line.partition("=") + name = name.strip() + if not separator or re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", name) is None: + raise click.ClickException( + f"{path}:{lineno}: expected NAME=VALUE with a valid NAME, got {raw!r}" + ) + value = value.strip() + if len(value) >= 2 and value[0] == value[-1] and value[0] in "\"'": + value = value[1:-1] + values[name] = value + return values + + +def _compiled_run_environment( + task: Path, overrides: dict[str, str] | None = None +) -> dict[str, str]: + """Separate provider credentials from the environment seen by Harbor agents. + + ``overrides`` (e.g. a loaded ``--env-file``) take precedence over the + ambient environment so a run is reproducible from an explicit secrets file + regardless of what happens to be exported in the shell. + """ + environment = os.environ.copy() + if overrides: + environment.update(overrides) + path = task / "environment/gateway/launch.json" + if not path.exists(): + return environment + try: + launch = json.loads(path.read_text(encoding="utf-8")) + api_source = launch["upstream_api_key_source"] + api_target = launch["upstream_api_key_target"] + producer_api_key = launch["producer_api_key"] + producer_base_url = launch["producer_base_url"] + base_source = launch.get("upstream_base_url_source") + base_target = launch["upstream_base_url_target"] + except (KeyError, json.JSONDecodeError, OSError, TypeError) as error: + raise click.ClickException( + f"invalid compiled gateway launch config: {error}" + ) from error + for name, value in ( + ("upstream_api_key_source", api_source), + ("upstream_api_key_target", api_target), + ("producer_api_key", producer_api_key), + ("producer_base_url", producer_base_url), + ("upstream_base_url_target", base_target), + ): + if not isinstance(value, str) or not value: + raise click.ClickException(f"invalid compiled gateway field {name}") + upstream_api_key = environment.get(api_source) + if not upstream_api_key: + raise click.ClickException( + f"upstream inference credential {api_source} is missing" + ) + environment[api_target] = upstream_api_key + if base_source is not None: + if not isinstance(base_source, str) or not base_source: + raise click.ClickException( + "invalid compiled gateway field upstream_base_url_source" + ) + upstream_base_url = environment.get(base_source) + if not upstream_base_url: + raise click.ClickException( + f"upstream inference base URL {base_source} is missing" + ) + environment[base_target] = upstream_base_url + environment["OPENAI_API_KEY"] = producer_api_key + environment["OPENAI_BASE_URL"] = producer_base_url + # Claude Code (harbor -a claude) reads the Anthropic surface from the host + # env; point it at the same producer scope. The Anthropic SDK re-appends + # "/v1/messages", so hand it the scope root without the trailing "/v1". + # Set unconditionally: codex ignores ANTHROPIC_*, claude ignores OPENAI_*, so + # one compiled task serves either optimizer via `--agent`. + environment["ANTHROPIC_API_KEY"] = producer_api_key + environment["ANTHROPIC_BASE_URL"] = producer_base_url[: -len("/v1")] if ( + producer_base_url.endswith("/v1") + ) else producer_base_url + return environment + + +def _parameters(values: tuple[str, ...]) -> dict: + parameters = {} + for value in values: + name, separator, encoded = value.partition("=") + if not separator or not name.strip(): + raise click.BadParameter("use NAME=JSON", param_hint="--parameter") + if name in parameters: + raise click.BadParameter( + f"duplicate parameter {name!r}", + param_hint="--parameter", + ) + try: + parameters[name] = json.loads(encoded) + except json.JSONDecodeError as error: + raise click.BadParameter( + f"parameter {name!r} is not valid JSON", + param_hint="--parameter", + ) from error + return parameters + + +@click.group() +def harbor() -> None: + """Run VeRO across a Harbor sidecar boundary.""" + + +def _parse_build_params(values: tuple[str, ...]) -> dict[str, str]: + """Parse repeatable ``--param NAME=VALUE`` into a substitution context.""" + params: dict[str, str] = {} + for item in values: + name, separator, value = item.partition("=") + if not separator or re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", name) is None: + raise click.ClickException( + f"--param must be NAME=VALUE with a valid NAME: {item!r}" + ) + params[name] = value + return params + + +_PARAM_OPTION = click.option( + "--param", + "params", + multiple=True, + metavar="NAME=VALUE", + help="Substitute ${NAME} in the build YAML; repeatable. Overrides the environment.", +) + + +@harbor.command("build") +@click.option( + "--config", + "config_path", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option( + "--output", + required=True, + type=click.Path(path_type=Path, file_okay=False), +) +@_PARAM_OPTION +def build_command(config_path, output, params): + """Compile a build YAML into a runnable Harbor task directory.""" + from vero.harbor.build import compile_harbor_task, load_harbor_build_config + + compiled = compile_harbor_task( + load_harbor_build_config(config_path, params=_parse_build_params(params)), + output, + ) + click.echo(f"Compiled Harbor task: {compiled}") + + +@harbor.command( + "run", + context_settings={"ignore_unknown_options": True}, +) +@click.option( + "--config", + "config_path", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option("--agent", required=True, help="Harbor optimizer agent.") +@click.option("--model", help="Model used by the optimizer agent.") +@click.option("--environment", default="modal", show_default=True) +@click.option( + "--env-file", + "env_file", + type=click.Path(path_type=Path, exists=True, dir_okay=False), + help=( + "Load NAME=VALUE run secrets (e.g. the upstream inference key and " + "MODAL_TOKEN_ID/SECRET) from a dotenv file. File values take precedence " + "over the ambient environment and never appear on the command line." + ), +) +@_PARAM_OPTION +@click.argument("extra", nargs=-1, type=click.UNPROCESSED) +def run_command(config_path, agent, model, environment, params, env_file, extra): + """Compile to a temporary directory and invoke `harbor run`.""" + from vero.harbor.build import compile_harbor_task, load_harbor_build_config + + uvx = shutil.which("uvx") + if uvx is None: + raise click.ClickException("uvx is required to run a compiled Harbor task") + overrides = _load_env_file(env_file) if env_file else None + if overrides: + click.echo(f"Loaded {len(overrides)} secret(s) from {env_file}") + # Apply before compiling: the build's declared-credential check and the + # ${NAME} param resolution both read os.environ, so the env-file must + # satisfy them too — not just the launched subprocess. + os.environ.update(overrides) + resolved = _parse_build_params(params) + # The optimizer model is both codex's -m and the producer scope's allow-list; + # expose it as the reserved ${optimizer_model} so a build that references it + # keeps the two in lockstep (no 403 model mismatch). + if model is not None: + resolved.setdefault("optimizer_model", model) + config = load_harbor_build_config(config_path, params=resolved) + with tempfile.TemporaryDirectory(prefix="vero-harbor-") as temporary: + task = compile_harbor_task( + config, + Path(temporary) / "task", + ) + command = [ + uvx, + "--python", + sys.executable, + "--from", + config.harbor_requirement, + "harbor", + "run", + "-p", + str(task), + "-a", + agent, + "-e", + environment, + ] + if model is not None: + command.extend(["-m", model]) + command.extend(extra) + click.echo(shlex.join(command)) + completed = subprocess.run( + command, + env=_compiled_run_environment(task, overrides), + ) + if completed.returncode: + raise SystemExit(completed.returncode) + + +@harbor.command("serve") +@click.option( + "--factory", "factory_path", required=True, help="Trusted module:factory." +) +@click.option( + "--config", + "config_path", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option( + "--admin-token", + "admin_token_path", + required=True, + type=click.Path(path_type=Path, dir_okay=False), +) +@click.option("--host", default="0.0.0.0", show_default=True) +@click.option("--port", default=8000, type=click.IntRange(1, 65535), show_default=True) +def serve_command(factory_path, config_path, admin_token_path, host, port): + """Serve components built by a trusted deployment factory.""" + from vero.harbor.serve import serve + + serve( + factory_path=factory_path, + config_path=config_path, + admin_token_path=admin_token_path, + host=host, + port=port, + ) + + +@harbor.command("inference-gateway") +@click.option( + "--config", + "config_path", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option("--host", default="0.0.0.0", show_default=True) +@click.option("--port", default=8001, type=click.IntRange(1, 65535), show_default=True) +def inference_gateway_command(config_path, host, port): + """Serve the credential-isolating, budgeted inference proxy.""" + from vero.harbor.inference import serve_inference_gateway + + serve_inference_gateway(config_path=config_path, host=host, port=port) + + +@harbor.command("eval") +@click.option("--backend", "backend_id", required=True) +@click.option("--evaluation-set", "evaluation_set_name", required=True) +@click.option("--partition") +@click.option("--version", help="Candidate version; defaults to agent repository HEAD.") +@click.option("--case-id", "case_ids", multiple=True) +@click.option("--start", type=click.IntRange(min=0)) +@click.option("--stop", type=click.IntRange(min=1)) +@click.option("--parameter", multiple=True, help="Evaluation parameter as NAME=JSON.") +@click.option("--timeout", type=click.FloatRange(min=0, min_open=True)) +@click.option("--case-timeout", type=click.FloatRange(min=0, min_open=True)) +@click.option("--max-concurrency", type=click.IntRange(min=1)) +@click.option( + "--error-rate-threshold", + type=click.FloatRange(min=0, max=1, min_open=True), +) +@click.option("--retry-max-attempts", type=click.IntRange(min=1)) +@click.option("--retry-initial-delay", type=click.FloatRange(min=0)) +@click.option("--retry-maximum-delay", type=click.FloatRange(min=0)) +@click.option("--retry-multiplier", type=click.FloatRange(min=1)) +@click.option( + "--retry-on-timeout/--no-retry-on-timeout", + default=None, +) +@click.option("--seed", type=int) +@click.option( + "--detach", + is_flag=True, + help="Start a durable evaluation job and return without waiting for its result.", +) +def evaluate_command( + backend_id, + evaluation_set_name, + partition, + version, + case_ids, + start, + stop, + parameter, + timeout, + case_timeout, + max_concurrency, + error_rate_threshold, + retry_max_attempts, + retry_initial_delay, + retry_maximum_delay, + retry_multiplier, + retry_on_timeout, + seed, + detach, +): + """Evaluate a candidate through the metered agent endpoint.""" + if case_ids and (start is not None or stop is not None): + raise click.UsageError("--case-id cannot be combined with --start/--stop") + if start is not None and stop is None: + raise click.UsageError("--start requires --stop") + selection = None + if case_ids: + selection = CaseIds(ids=list(case_ids)) + elif stop is not None: + selection = CaseRange(start=start or 0, stop=stop) + evaluation_set = EvaluationSet( + name=evaluation_set_name, + partition=partition, + **({"selection": selection} if selection is not None else {}), + ) + retry_values = { + name: value + for name, value in { + "max_attempts": retry_max_attempts, + "initial_delay_seconds": retry_initial_delay, + "maximum_delay_seconds": retry_maximum_delay, + "multiplier": retry_multiplier, + "retry_on_timeout": retry_on_timeout, + }.items() + if value is not None + } + limit_values = { + name: value + for name, value in { + "timeout_seconds": timeout, + "case_timeout_seconds": case_timeout, + "max_concurrency": max_concurrency, + "error_rate_threshold": error_rate_threshold, + }.items() + if value is not None + } + if retry_values: + limit_values["retry"] = RetryPolicy(**retry_values) + body = SidecarEvaluationRequest( + backend_id=backend_id, + evaluation_set=evaluation_set, + version=version, + parameters=_parameters(parameter), + limits=EvaluationLimits(**limit_values) if limit_values else None, + seed=seed, + ) + click.echo( + json.dumps( + _request( + "POST", + "/eval/jobs" if detach else "/eval", + payload=body.model_dump(mode="json"), + ), + indent=2, + ) + ) + + +@harbor.command("eval-status") +@click.argument("job_id") +def evaluation_status_command(job_id): + """Inspect a detached evaluation job.""" + click.echo(json.dumps(_request("GET", f"/eval/jobs/{job_id}"), indent=2)) + + +@harbor.command("eval-result") +@click.argument("job_id") +def evaluation_result_command(job_id): + """Retrieve a detached evaluation result when it is available.""" + click.echo(json.dumps(_request("GET", f"/eval/jobs/{job_id}/result"), indent=2)) + + +@harbor.command("submit") +@click.option("--version", help="Candidate version; defaults to agent repository HEAD.") +def submit_command(version): + """Nominate a candidate for submit-based finalization.""" + click.echo( + json.dumps(_request("POST", "/submit", payload={"version": version}), indent=2) + ) + + +@harbor.command("status") +def status_command(): + """Show agent-visible evaluation access and remaining budgets.""" + click.echo(json.dumps(_request("GET", "/status"), indent=2)) + + +@harbor.command("finalize") +@click.option( + "--token-file", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option( + "--output", + default="/logs/verifier/reward.json", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +def finalize_command(token_file, output): + """Finalize as the trusted verifier and write Harbor rewards.""" + token = read_admin_token(token_file) + result = _request( + "POST", + "/finalize", + headers={"Authorization": f"Bearer {token}"}, + ) + destination = Path(output) + destination.parent.mkdir(parents=True, exist_ok=True) + # reward.json keeps only the reward map Harbor consumes. + destination.write_text( + json.dumps(result["rewards"], indent=2) + "\n", + encoding="utf-8", + ) + # Persist the full verification result (the shipped flag, verifier errors, + # baseline rewards) alongside it, so "did anything ship, and if not why" is + # answerable without re-running — reward.json alone drops those signals. + (destination.parent / "finalization.json").write_text( + json.dumps(result, indent=2) + "\n", + encoding="utf-8", + ) + click.echo(json.dumps(result, indent=2)) + + +@harbor.command("export-session") +@click.option( + "--token-file", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option( + "--output", + default="/logs/verifier/session.tar.gz", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +@click.option( + "--report-output", + default="/logs/verifier/experiment.html", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +@click.option( + "--status-output", + default="/logs/verifier/status.json", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +@click.option( + "--finalization-output", + default="/logs/verifier/finalization.json", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +@click.option( + "--agent-trace", + default="/logs/agent/trajectory.json", + show_default=True, + type=click.Path(path_type=Path, dir_okay=False), +) +def export_session_command( + token_file, + output, + report_output, + status_output, + finalization_output, + agent_trace, +): + """Persist the complete sidecar session and portable experiment report.""" + from vero.report import generate_experiment_report + + token = read_admin_token(token_file) + headers = {"Authorization": f"Bearer {token}"} + finalization = _request("POST", "/finalize", headers=headers) + status = _request("GET", "/status") + output = Path(output).expanduser().resolve() + report_output = Path(report_output).expanduser().resolve() + status_output = Path(status_output).expanduser().resolve() + finalization_output = Path(finalization_output).expanduser().resolve() + with tempfile.TemporaryDirectory(prefix="vero-harbor-session-") as directory: + temporary = Path(directory) + downloaded = temporary / "sidecar-session.tar.gz" + _download("/session/export", downloaded, headers=headers) + session = extract_harbor_session_archive(downloaded, temporary / "extracted") + encoded_finalization = ( + json.dumps(finalization, ensure_ascii=False, indent=2) + "\n" + ).encode("utf-8") + encoded_status = ( + json.dumps(status, ensure_ascii=False, indent=2) + "\n" + ).encode("utf-8") + _atomic_write_bytes(session / "harbor-finalization.json", encoded_finalization) + _atomic_write_bytes(session / "harbor-status.json", encoded_status) + + requested_trace = Path(agent_trace).expanduser() + trace_path = next( + ( + path + for path in ( + requested_trace, + Path("/logs/agent/trajectory.json"), + Path("/logs/agent/codex.txt"), + ) + if path.is_file() and not path.is_symlink() + ), + None, + ) + if trace_path is not None: + trace = _load_agent_trace(trace_path) + trace_destination = ( + session / "artifacts" / "agents" / "harbor-producer" / "trace.json" + ) + _atomic_write_bytes( + trace_destination, + (json.dumps(trace, ensure_ascii=False, indent=2) + "\n").encode( + "utf-8" + ), + ) + + generated_report = temporary / "experiment.html" + asyncio.run(generate_experiment_report(session, generated_report)) + create_harbor_session_archive(session, output) + digest = file_sha256(output) + _atomic_write_bytes(report_output, generated_report.read_bytes()) + _atomic_write_bytes(status_output, encoded_status) + _atomic_write_bytes(finalization_output, encoded_finalization) + _atomic_write_bytes( + output.with_name(f"{output.name}.sha256"), + f"{digest} {output.name}\n".encode("ascii"), + ) + click.echo( + json.dumps( + { + "session": str(output), + "sha256": digest, + "report": str(report_output), + }, + indent=2, + ) + ) diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py new file mode 100644 index 00000000..9000d715 --- /dev/null +++ b/vero/src/vero/harbor/deployment.py @@ -0,0 +1,350 @@ +"""Standard component factory for compiled Harbor optimization tasks.""" + +from __future__ import annotations + +import logging +from pathlib import Path, PurePosixPath +from typing import Literal + +from pydantic import Field, JsonValue, field_validator, model_validator + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendRegistry, + BudgetLedger, + EvaluationBudget, + EvaluationDatabase, + EvaluationModel, + EvaluationLimits, + EvaluationSet, + Evaluator, + ObjectiveSpec, +) +from vero.evaluation.engine import EvaluationEngine +from vero.harbor.backend import HarborBackend, HarborBackendConfig +from vero.harbor.serve import SidecarComponents +from vero.harbor.session import initialize_harbor_session_manifest +from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy +from vero.harbor.transport import GitCandidateTransport +from vero.harbor.verifier import ( + CanonicalVerifier, + VerificationSelection, + VerificationTarget, +) +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + +logger = logging.getLogger(__name__) + + +class DeploymentSelection(EvaluationModel): + mode: Literal["submit", "auto_best"] = "auto_best" + backend_id: str | None = None + evaluation_set: EvaluationSet | None = None + objective: ObjectiveSpec | None = None + baseline_version: str | None = "HEAD" + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits = Field(default_factory=EvaluationLimits) + rescore_top_k: int = Field(default=3, ge=1) + rescore_attempts: int = Field(default=1, ge=1) + baseline_floor: bool = True + selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) + + @field_validator("backend_id", "baseline_version") + @classmethod + def validate_optional_identity(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("optional deployment identity must not be empty") + return value + + @model_validator(mode="after") + def validate_selection(self) -> DeploymentSelection: + if self.mode == "auto_best" and ( + self.backend_id is None + or self.evaluation_set is None + or self.objective is None + ): + raise ValueError( + "auto_best deployment requires backend_id, evaluation_set, and objective" + ) + return self + + +class SidecarWandbConfig(EvaluationModel): + """Trusted-side Weights & Biases config for the eval-sidecar. The W&B + credential (WANDB_API_KEY) is supplied to the sidecar container's + environment, never to the untrusted optimizer agent.""" + + project: str + entity: str | None = None + name: str | None = None + group: str | None = None + tags: list[str] = Field(default_factory=list) + mode: str | None = None + notes: str | None = None + run_id: str | None = None + # Also upload each evaluation's trace artifacts (Harbor trial records, + # stdout/stderr, agent trajectory) to W&B as a per-evaluation artifact. + # Off by default: it uploads files and can be large. + log_traces: bool = False + + +class HarborDeploymentConfig(EvaluationModel): + task_name: str = "harbor-session" + task_description: str = "" + repo_path: str + agent_repo_path: str + session_dir: str + session_id: str = "trial" + backends: dict[str, HarborBackendConfig] + access_policies: list[SidecarEvaluationPolicy] + budgets: list[EvaluationBudget] = Field(default_factory=list) + selection: DeploymentSelection + targets: list[VerificationTarget] + agent_volume: str | None = None + admin_volume: str + inference_usage_path: str | None = None + inference_limits: dict[str, dict[str, JsonValue]] = Field(default_factory=dict) + submit_enabled: bool = False + disclose_budget: bool = True + score_baseline: bool = True + evaluation_drain_timeout_seconds: float = Field(default=600.0, gt=0) + wandb: SidecarWandbConfig | None = None + + @field_validator( + "repo_path", + "agent_repo_path", + "session_dir", + "admin_volume", + ) + @classmethod + def validate_absolute_path(cls, value: str) -> str: + path = PurePosixPath(value) + if not value.startswith("/") or ".." in path.parts: + raise ValueError("deployment paths must be absolute") + return value + + @field_validator("inference_usage_path") + @classmethod + def validate_optional_file_path(cls, value: str | None) -> str | None: + if value is not None: + path = PurePosixPath(value) + if not value.startswith("/") or ".." in path.parts: + raise ValueError("deployment paths must be absolute") + return value + + @field_validator("agent_volume") + @classmethod + def validate_optional_path(cls, value: str | None) -> str | None: + if value is not None and ( + not value.startswith("/") or ".." in PurePosixPath(value).parts + ): + raise ValueError("deployment paths must be absolute") + return value + + @field_validator("session_id", "task_name") + @classmethod + def validate_session_id(cls, value: str) -> str: + if not value.strip(): + raise ValueError("session_id must not be empty") + return value + + @model_validator(mode="after") + def validate_references(self) -> HarborDeploymentConfig: + if not self.backends: + raise ValueError("deployment requires at least one backend") + trusted = PurePosixPath(self.repo_path) + agent = PurePosixPath(self.agent_repo_path) + if trusted == agent: + raise ValueError("trusted and agent repositories must be distinct") + for name, value in ( + ("session_dir", self.session_dir), + ("admin_volume", self.admin_volume), + *( + (("inference_usage_path", self.inference_usage_path),) + if self.inference_usage_path is not None + else () + ), + ): + path = PurePosixPath(value) + if any( + path == repository or path.is_relative_to(repository) + for repository in (agent, trusted) + ): + raise ValueError(f"{name} must live outside candidate repositories") + if self.submit_enabled != (self.selection.mode == "submit"): + raise ValueError("submit_enabled must match selection mode") + known = set(self.backends) + referenced = {policy.backend_id for policy in self.access_policies} | { + target.backend_id for target in self.targets + } + if self.selection.backend_id is not None: + referenced.add(self.selection.backend_id) + unknown = sorted(referenced - known) + if unknown: + raise ValueError(f"deployment references unknown backends: {unknown}") + return self + + +def _database(session_dir: Path, session_id: str) -> EvaluationDatabase: + database_path = session_dir / "database.json" + return EvaluationDatabase.load_reconciled( + database_path=database_path, + evaluations_dir=session_dir / "evaluations", + database_id=session_id, + ) + + +def _ledger( + session_dir: Path, + budgets: list[EvaluationBudget], +) -> BudgetLedger | None: + path = session_dir / "budgets.json" + if path.exists(): + return BudgetLedger.load(path) + if not budgets: + return None + ledger = BudgetLedger(budgets, path=path) + ledger.save() + return ledger + + +async def build_harbor_components(config: dict) -> SidecarComponents: + """Build the standard compiled-task topology from trusted JSON config.""" + parsed = HarborDeploymentConfig.model_validate(config) + session_dir = Path(parsed.session_dir) + session_dir.mkdir(parents=True, exist_ok=True) + # The session dir holds the trusted state — held-out evaluation records and + # scores, the budget ledger, and every candidate's code. Lock it to the + # owning (trusted) user so the unprivileged harness that executes candidate + # code cannot read it. Harmless where the harness is not isolated (the owner + # retains access); the admin token dir is already hardened in auth.py. + session_dir.chmod(0o700) + sandbox = await LocalSandbox.create(root=Path(parsed.repo_path).parent) + workspace = await GitWorkspace.from_path(sandbox, parsed.repo_path) + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", + workspace=workspace, + ) + database = _database(session_dir, parsed.session_id) + ledger = _ledger(session_dir, parsed.budgets) + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + session_id=parsed.session_id, + ), + backends=BackendRegistry( + { + backend_id: HarborBackend(backend_config) + for backend_id, backend_config in parsed.backends.items() + } + ), + database=database, + database_path=session_dir / "database.json", + budget_ledger=ledger, + ) + wandb_sink = None + if parsed.wandb is not None: + # Observability must never take down the eval path: on any failure to + # construct the sink, log and continue without W&B. + from vero.runtime.wandb import SidecarWandbSink + + try: + wandb_sink = SidecarWandbSink( + project=parsed.wandb.project, + session_id=parsed.session_id, + session_dir=session_dir, + entity=parsed.wandb.entity, + name=parsed.wandb.name, + group=parsed.wandb.group, + tags=list(parsed.wandb.tags), + mode=parsed.wandb.mode, + notes=parsed.wandb.notes, + run_id=parsed.wandb.run_id, + budget_ledger=ledger, + log_traces=parsed.wandb.log_traces, + ) + engine.listeners.append(wandb_sink) + except Exception: + logger.warning( + "W&B reporting disabled: the sidecar sink could not be " + "initialized; the evaluation sidecar continues without it", + exc_info=True, + ) + wandb_sink = None + + def _finish_wandb(result: object) -> None: + # Close the W&B run at finalize (the session's end) with a summary. + if wandb_sink is None: + return + rewards = getattr(result, "rewards", {}) or {} + summary = { + "shipped": getattr(result, "shipped", None), + **{f"reward/{key}": value for key, value in rewards.items()}, + } + wandb_sink.finish(summary, failed=not getattr(result, "shipped", True)) + transport = GitCandidateTransport( + workspace=workspace, + candidate_repository=candidate_repository, + agent_repo_path=parsed.agent_repo_path, + ) + baseline = ( + await transport.trusted_candidate(parsed.selection.baseline_version) + if parsed.selection.baseline_version is not None + else None + ) + selection = VerificationSelection( + mode=parsed.selection.mode, + backend_id=parsed.selection.backend_id, + evaluation_set=parsed.selection.evaluation_set, + objective=parsed.selection.objective, + baseline_candidate=baseline, + parameters=parsed.selection.parameters, + limits=parsed.selection.limits, + rescore_top_k=parsed.selection.rescore_top_k, + rescore_attempts=parsed.selection.rescore_attempts, + baseline_floor=parsed.selection.baseline_floor, + selection_coverage_threshold=parsed.selection.selection_coverage_threshold, + ) + initialize_harbor_session_manifest( + session_dir, + session_id=parsed.session_id, + task_name=parsed.task_name, + task_description=parsed.task_description, + backends={ + backend_id: engine.backends.resolve(backend_id).provenance + for backend_id in parsed.backends + }, + selection=selection, + targets=parsed.targets, + ) + sidecar = EvaluationSidecar( + engine=engine, + candidate_transport=transport, + access_policies=parsed.access_policies, + agent_volume=( + Path(parsed.agent_volume) if parsed.agent_volume is not None else None + ), + admin_volume=Path(parsed.admin_volume), + submit_enabled=parsed.submit_enabled, + disclose_budget=parsed.disclose_budget, + inference_usage_path=( + Path(parsed.inference_usage_path) + if parsed.inference_usage_path is not None + else None + ), + inference_limits=parsed.inference_limits, + ) + await sidecar.initialize_context() + verifier = CanonicalVerifier( + engine=engine, + selection=selection, + targets=parsed.targets, + admin_volume=Path(parsed.admin_volume), + score_baseline=parsed.score_baseline, + evaluation_drain_timeout_seconds=parsed.evaluation_drain_timeout_seconds, + on_finalized=_finish_wandb if wandb_sink is not None else None, + ) + return SidecarComponents(sidecar=sidecar, verifier=verifier) diff --git a/vero/src/vero/harbor/generation.py b/vero/src/vero/harbor/generation.py new file mode 100644 index 00000000..01c47441 --- /dev/null +++ b/vero/src/vero/harbor/generation.py @@ -0,0 +1,58 @@ +"""Harbor-backed candidate generation. + +A :class:`~vero.optimization.protocols.GenerationBackend` that produces +candidates by delegating to a ``harbor run`` — symmetric to how +:class:`~vero.harbor.backend.HarborBackend` nests ``harbor run`` for *evaluation*. +This is the contained / untrusted / multi-adapter production path (codex, claude, +terminus, …); the lightweight in-process native producer is the Optimizer's +default backend. + +Status: interface skeleton. The full wiring (drive a ``harbor run`` for the +proposal's agent, then import the resulting commit into the session candidate +repository by object identity) is a follow-on; it reuses the existing +:class:`~vero.harbor.transport.GitCandidateTransport` and the ``harbor.backend`` +nesting rather than re-hosting Harbor's sidecar/gateway. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Sequence + +from vero.candidate import Candidate +from vero.evaluation import EvaluationRecord +from vero.optimization.models import ( + CandidateProposal, + GenerationOutcome, + OptimizationContext, +) + + +@dataclass +class HarborGenerationBackend: + """Generate candidates by running a Harbor agent over the parent. + + Reuses ``GitCandidateTransport`` to import the untrusted candidate commit + into the session repository by object identity, and returns the Harbor + sidecar's disclosed-partition scores as the generation-time feedback in + ``GenerationOutcome.trial_evaluations``. Selection/target scoring remains the + Optimizer's responsibility. + """ + + # Populated when the harbor-run-as-production wiring lands (task source, + # agent, transport, candidate repository, session dir, …). + + async def generate( + self, + *, + proposal: CandidateProposal, + parent: Candidate, + context: OptimizationContext, + evaluation_records: Sequence[EvaluationRecord], + ) -> GenerationOutcome: + raise NotImplementedError( + "HarborGenerationBackend is an interface skeleton; the native " + "in-process backend (Optimizer default) is the implemented path. " + "Wire this to a `harbor run` + GitCandidateTransport import to enable " + "contained/multi-adapter production." + ) diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py new file mode 100644 index 00000000..9667b803 --- /dev/null +++ b/vero/src/vero/harbor/inference.py @@ -0,0 +1,626 @@ +"""Credential-isolating, budgeted inference gateway for Harbor tasks.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import os +import secrets +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Annotated, Any, Literal + +from fastapi import FastAPI, Header, HTTPException, Request +from fastapi.responses import JSONResponse, Response, StreamingResponse +from pydantic import Field, field_validator, model_validator + +from vero.evaluation import EvaluationModel +from vero.evaluation.persistence import _atomic_write_json + + +def token_digest(token: str) -> str: + """Return the stable digest stored in trusted gateway configuration.""" + if not token.strip(): + raise ValueError("inference token must not be empty") + return hashlib.sha256(token.encode("utf-8")).hexdigest() + + +def generate_inference_token() -> str: + return secrets.token_urlsafe(32) + + +class InferenceScopeConfig(EvaluationModel): + """One independently authenticated and metered inference consumer.""" + + token_sha256: str + allowed_models: list[str] + max_requests: int | None = Field(default=None, ge=1) + max_tokens: int | None = Field(default=None, ge=1) + max_concurrency: int = Field(default=8, ge=1) + + @field_validator("token_sha256") + @classmethod + def validate_token_digest(cls, value: str) -> str: + if len(value) != 64 or any( + character not in "0123456789abcdef" for character in value + ): + raise ValueError("token_sha256 must be a lowercase SHA-256 digest") + return value + + @field_validator("allowed_models") + @classmethod + def validate_models(cls, value: list[str]) -> list[str]: + if not value or any(not model.strip() for model in value): + raise ValueError("allowed_models must contain non-empty model names") + if len(value) != len(set(value)): + raise ValueError("allowed_models must be unique") + return value + + +class InferenceGatewayConfig(EvaluationModel): + """Trusted configuration for an OpenAI-compatible inference gateway.""" + + upstream_api_key_env: str = "OPENAI_API_KEY" + upstream_base_url_env: str | None = "OPENAI_BASE_URL" + default_upstream_base_url: str = "https://api.openai.com/v1" + state_path: str = "/state/inference/usage.json" + scopes: dict[str, InferenceScopeConfig] + + @field_validator("upstream_api_key_env", "upstream_base_url_env") + @classmethod + def validate_environment_name(cls, value: str | None) -> str | None: + if value is not None and ( + not value + or not (value[0].isalpha() or value[0] == "_") + or any(not (character.isalnum() or character == "_") for character in value) + ): + raise ValueError("gateway environment names must be valid identifiers") + return value + + @field_validator("default_upstream_base_url") + @classmethod + def validate_upstream_url(cls, value: str) -> str: + if not value.startswith(("http://", "https://")): + raise ValueError("default_upstream_base_url must be HTTP(S)") + return value.rstrip("/") + + @field_validator("state_path") + @classmethod + def validate_state_path(cls, value: str) -> str: + if not Path(value).is_absolute(): + raise ValueError("gateway state_path must be absolute") + return value + + @model_validator(mode="after") + def validate_scopes(self) -> InferenceGatewayConfig: + if not self.scopes: + raise ValueError("gateway requires at least one scope") + for name in self.scopes: + if not name or any( + not (character.isalnum() or character in "_-") for character in name + ): + raise ValueError(f"invalid inference scope {name!r}") + return self + + +class InferenceAttributionUsage(EvaluationModel): + requests: int = Field(default=0, ge=0) + upstream_errors: int = Field(default=0, ge=0) + input_tokens: int = Field(default=0, ge=0) + output_tokens: int = Field(default=0, ge=0) + total_tokens: int = Field(default=0, ge=0) + + +class InferenceScopeUsage(InferenceAttributionUsage): + active_requests: int = Field(default=0, ge=0) + attributions: dict[str, InferenceAttributionUsage] = Field(default_factory=dict) + + +class InferenceUsageLedger(EvaluationModel): + schema_version: Literal[1] = 1 + scopes: dict[str, InferenceScopeUsage] + + +class InferenceBudgetExceeded(RuntimeError): + pass + + +class InferenceUsageStore: + """Cancellation-safe request reservations and durable provider usage.""" + + def __init__(self, config: InferenceGatewayConfig): + self.config = config + self.path = Path(config.state_path) + self._lock = asyncio.Lock() + self._limits = { + name: asyncio.Semaphore(scope.max_concurrency) + for name, scope in config.scopes.items() + } + self.ledger = self._load() + + def _load(self) -> InferenceUsageLedger: + if self.path.exists(): + loaded = InferenceUsageLedger.model_validate_json( + self.path.read_text(encoding="utf-8") + ) + unknown = set(loaded.scopes) - set(self.config.scopes) + if unknown: + raise ValueError( + "inference usage contains unknown scopes: " + + ", ".join(sorted(unknown)) + ) + scopes = dict(loaded.scopes) + for name in self.config.scopes: + scopes.setdefault(name, InferenceScopeUsage()) + # Active requests cannot survive a gateway restart. + scopes = { + name: value.model_copy(update={"active_requests": 0}) + for name, value in scopes.items() + } + return loaded.model_copy(update={"scopes": scopes}) + return InferenceUsageLedger( + scopes={name: InferenceScopeUsage() for name in self.config.scopes} + ) + + def _save(self) -> None: + _atomic_write_json(self.path, self.ledger.model_dump(mode="json")) + + async def reserve(self, scope_name: str, attribution: str) -> None: + limiter = self._limits[scope_name] + await limiter.acquire() + try: + async with self._lock: + limits = self.config.scopes[scope_name] + usage = self.ledger.scopes[scope_name] + if ( + limits.max_requests is not None + and usage.requests >= limits.max_requests + ): + raise InferenceBudgetExceeded("inference request budget exhausted") + if ( + limits.max_tokens is not None + and usage.total_tokens >= limits.max_tokens + ): + raise InferenceBudgetExceeded("inference token budget exhausted") + attribution_usage = usage.attributions.get( + attribution, InferenceAttributionUsage() + ) + usage = usage.model_copy( + update={ + "requests": usage.requests + 1, + "active_requests": usage.active_requests + 1, + "attributions": { + **usage.attributions, + attribution: attribution_usage.model_copy( + update={"requests": attribution_usage.requests + 1} + ), + }, + } + ) + self.ledger = self.ledger.model_copy( + update={"scopes": {**self.ledger.scopes, scope_name: usage}} + ) + self._save() + except BaseException: + limiter.release() + raise + + async def complete( + self, + scope_name: str, + attribution: str, + *, + input_tokens: int = 0, + output_tokens: int = 0, + total_tokens: int = 0, + upstream_error: bool = False, + ) -> None: + try: + async with self._lock: + usage = self.ledger.scopes[scope_name] + attribution_usage = usage.attributions[attribution] + update = { + "upstream_errors": usage.upstream_errors + int(upstream_error), + "input_tokens": usage.input_tokens + input_tokens, + "output_tokens": usage.output_tokens + output_tokens, + "total_tokens": usage.total_tokens + total_tokens, + "active_requests": max(0, usage.active_requests - 1), + "attributions": { + **usage.attributions, + attribution: attribution_usage.model_copy( + update={ + "upstream_errors": attribution_usage.upstream_errors + + int(upstream_error), + "input_tokens": attribution_usage.input_tokens + + input_tokens, + "output_tokens": attribution_usage.output_tokens + + output_tokens, + "total_tokens": attribution_usage.total_tokens + + total_tokens, + } + ), + }, + } + self.ledger = self.ledger.model_copy( + update={ + "scopes": { + **self.ledger.scopes, + scope_name: usage.model_copy(update=update), + } + } + ) + self._save() + finally: + self._limits[scope_name].release() + + def public_status(self) -> dict[str, Any]: + result: dict[str, Any] = {} + for name, scope in self.config.scopes.items(): + usage = self.ledger.scopes[name] + result[name] = { + **usage.model_dump(mode="json"), + "max_requests": scope.max_requests, + "max_tokens": scope.max_tokens, + "max_concurrency": scope.max_concurrency, + "allowed_models": list(scope.allowed_models), + "remaining_requests": ( + None + if scope.max_requests is None + else max(0, scope.max_requests - usage.requests) + ), + "remaining_tokens": ( + None + if scope.max_tokens is None + else max(0, scope.max_tokens - usage.total_tokens) + ), + } + return result + + +def _bearer_token(authorization: str | None) -> str | None: + prefix = "Bearer " + if authorization is None or not authorization.startswith(prefix): + return None + return authorization[len(prefix) :] + + +def _usage(value: Any) -> tuple[int, int, int]: + if not isinstance(value, dict): + return (0, 0, 0) + usage = value.get("usage") + if not isinstance(usage, dict): + response = value.get("response") + usage = response.get("usage") if isinstance(response, dict) else None + if not isinstance(usage, dict): + return (0, 0, 0) + input_tokens = int(usage.get("input_tokens", usage.get("prompt_tokens", 0)) or 0) + output_tokens = int( + usage.get("output_tokens", usage.get("completion_tokens", 0)) or 0 + ) + total_tokens = int(usage.get("total_tokens", input_tokens + output_tokens) or 0) + return (input_tokens, output_tokens, total_tokens) + + +class _StreamingUsage: + def __init__(self): + self.buffer = b"" + self.tokens = (0, 0, 0) + + def feed(self, chunk: bytes) -> None: + self.buffer += chunk + while b"\n" in self.buffer: + line, self.buffer = self.buffer.split(b"\n", 1) + if not line.startswith(b"data:"): + continue + payload = line.removeprefix(b"data:").strip() + if not payload or payload == b"[DONE]": + continue + try: + value = json.loads(payload) + except (json.JSONDecodeError, UnicodeDecodeError): + continue + tokens = _usage(value) + if tokens != (0, 0, 0): + self.tokens = tokens + + +_REQUEST_HEADERS = { + "accept", + "content-type", + "openai-beta", + "user-agent", + "x-stainless-arch", + "x-stainless-async", + "x-stainless-lang", + "x-stainless-os", + "x-stainless-package-version", + "x-stainless-retry-count", + "x-stainless-runtime", + "x-stainless-runtime-version", + "x-stainless-timeout", +} +_RESPONSE_HEADERS = { + "content-type", + "openai-processing-ms", + "request-id", + "x-request-id", +} +def _provider_error(status_code: int, message: str, code: str) -> JSONResponse: + return JSONResponse( + status_code=status_code, + content={ + "error": { + "message": message, + "type": "vero_inference_gateway_error", + "code": code, + } + }, + ) + + +def create_inference_gateway_app( + *, + config: InferenceGatewayConfig, + upstream_api_key: str, + upstream_base_url: str | None = None, + transport: Any = None, +) -> FastAPI: + """Create a scoped reverse proxy without exposing the upstream credential.""" + if not upstream_api_key.strip(): + raise ValueError("upstream API key must not be empty") + try: + import httpx + except ImportError as error: + raise RuntimeError("install scale-vero[harbor] to serve inference") from error + + base_url = (upstream_base_url or config.default_upstream_base_url).rstrip("/") + store = InferenceUsageStore(config) + + @asynccontextmanager + async def lifespan(app: FastAPI): + async with httpx.AsyncClient( + timeout=None, + transport=transport, + follow_redirects=False, + ) as client: + app.state.upstream = client + yield + + app = FastAPI(title="VeRO inference gateway", version="1", lifespan=lifespan) + app.state.usage_store = store + + @app.get("/health") + async def health(): + return {"ok": True} + + @app.get("/usage/{scope_name}") + async def usage_status( + scope_name: str, + authorization: Annotated[str | None, Header()] = None, + ): + scope = config.scopes.get(scope_name) + token = _bearer_token(authorization) + if ( + scope is None + or token is None + or not secrets.compare_digest(token_digest(token), scope.token_sha256) + ): + raise HTTPException(status_code=403, detail="invalid inference scope token") + return store.public_status()[scope_name] + + @app.api_route( + "/scopes/{scope_name}/{attribution}/v1/{endpoint:path}", + methods=["POST"], + ) + async def proxy( + scope_name: str, + attribution: str, + endpoint: str, + request: Request, + authorization: Annotated[str | None, Header()] = None, + x_api_key: Annotated[str | None, Header()] = None, + ): + scope = config.scopes.get(scope_name) + # Provider-agnostic auth: OpenAI/codex clients send the scope token as + # `Authorization: Bearer`, Anthropic/Claude clients send it as `x-api-key`. + token = _bearer_token(authorization) or ( + x_api_key.strip() if x_api_key and x_api_key.strip() else None + ) + if ( + scope is None + or token is None + or not secrets.compare_digest(token_digest(token), scope.token_sha256) + ): + return _provider_error( + 403, "invalid inference scope token", "invalid_token" + ) + # Provider-agnostic passthrough: forward any inference endpoint + # (responses, chat/completions, messages, embeddings, ...) to the upstream + # litellm proxy, which decides what it supports. Guard only against path + # traversal escaping the scoped upstream base — not a per-provider list. + endpoint = endpoint.strip("/") + if not endpoint or ".." in endpoint.split("/"): + return _provider_error( + 400, "invalid inference endpoint", "invalid_endpoint" + ) + try: + body = await request.body() + value = json.loads(body) if body else {} + except (json.JSONDecodeError, UnicodeDecodeError): + return _provider_error(400, "request body must be JSON", "invalid_request") + # Per-scope allow-list: producer (optimizer) and evaluation (target) have + # separate tokens + allowed_models, so the target is normally confined to + # its fixed eval model. NOTE (known, deferred): this is not a *hard* + # fixed-target guarantee. Both scopes share one gateway host, split only by + # URL path, and the optimizer holds the producer token and authors the + # candidate. An adversarial optimizer can smuggle its producer token into + # the candidate and, from the eval sandbox (which already reaches this host + # for its legit calls), hit /scopes/producer to run the optimizer model. + # Closing this needs structural egress isolation (per-role/per-eval + # endpoints), not path-scoping — deferred as it requires an adversarial + # producer. + model = value.get("model") if isinstance(value, dict) else None + if not isinstance(model, str) or model not in scope.allowed_models: + return _provider_error( + 403, "model is not allowed for this scope", "model_denied" + ) + if not attribution or any( + not (character.isalnum() or character in "_.-") for character in attribution + ): + return _provider_error( + 400, "invalid inference attribution", "invalid_attribution" + ) + try: + await store.reserve(scope_name, attribution) + except InferenceBudgetExceeded as error: + return _provider_error(429, str(error), "budget_exhausted") + + headers = { + name: value + for name, value in request.headers.items() + if name.lower() in _REQUEST_HEADERS + } + headers["authorization"] = f"Bearer {upstream_api_key}" + url = f"{base_url}/{endpoint}" + try: + upstream = await app.state.upstream.send( + app.state.upstream.build_request( + "POST", + url, + params=request.query_params, + content=body, + headers=headers, + ), + stream=True, + ) + except httpx.HTTPError: + await asyncio.shield( + store.complete(scope_name, attribution, upstream_error=True) + ) + return _provider_error( + 502, "upstream inference request failed", "upstream_error" + ) + except BaseException: + await asyncio.shield( + store.complete(scope_name, attribution, upstream_error=True) + ) + raise + response_headers = { + name: value + for name, value in upstream.headers.items() + if name.lower() in _RESPONSE_HEADERS + } + is_stream = "text/event-stream" in upstream.headers.get("content-type", "") + if is_stream: + observed = _StreamingUsage() + + async def chunks() -> AsyncIterator[bytes]: + failed = upstream.status_code >= 400 + + async def finish() -> None: + try: + await upstream.aclose() + finally: + await store.complete( + scope_name, + attribution, + input_tokens=observed.tokens[0], + output_tokens=observed.tokens[1], + total_tokens=observed.tokens[2], + upstream_error=failed, + ) + + try: + async for chunk in upstream.aiter_bytes(): + observed.feed(chunk) + yield chunk + except asyncio.CancelledError: + # OpenAI clients commonly stop consuming an SSE stream as soon + # as they observe its terminal response event. That downstream + # cancellation is not an upstream provider failure. + raise + except httpx.HTTPError: + failed = True + raise + except BaseException: + failed = True + raise + finally: + await asyncio.shield(finish()) + + return StreamingResponse( + chunks(), + status_code=upstream.status_code, + headers=response_headers, + media_type="text/event-stream", + ) + + try: + content = await upstream.aread() + try: + tokens = _usage(json.loads(content)) + except (json.JSONDecodeError, UnicodeDecodeError): + tokens = (0, 0, 0) + except BaseException: + await asyncio.shield( + store.complete(scope_name, attribution, upstream_error=True) + ) + raise + finally: + await upstream.aclose() + await asyncio.shield( + store.complete( + scope_name, + attribution, + input_tokens=tokens[0], + output_tokens=tokens[1], + total_tokens=tokens[2], + upstream_error=upstream.status_code >= 400, + ) + ) + return Response( + content=content, + status_code=upstream.status_code, + headers=response_headers, + media_type=upstream.headers.get("content-type"), + ) + + return app + + +def load_inference_gateway_config(path: Path | str) -> InferenceGatewayConfig: + return InferenceGatewayConfig.model_validate_json( + Path(path).read_text(encoding="utf-8") + ) + + +def serve_inference_gateway( + *, + config_path: Path | str, + host: str = "0.0.0.0", + port: int = 8001, +) -> None: + import uvicorn + + config = load_inference_gateway_config(config_path) + upstream_api_key = os.environ.get(config.upstream_api_key_env) + if not upstream_api_key: + raise RuntimeError( + f"gateway upstream credential {config.upstream_api_key_env} is missing" + ) + upstream_base_url = ( + os.environ.get(config.upstream_base_url_env) + if config.upstream_base_url_env is not None + else None + ) + uvicorn.run( + create_inference_gateway_app( + config=config, + upstream_api_key=upstream_api_key, + upstream_base_url=upstream_base_url, + ), + host=host, + port=port, + ) diff --git a/vero/src/vero/harbor/serve.py b/vero/src/vero/harbor/serve.py new file mode 100644 index 00000000..79c51633 --- /dev/null +++ b/vero/src/vero/harbor/serve.py @@ -0,0 +1,99 @@ +"""Build and serve Harbor sidecar components from a trusted factory.""" + +from __future__ import annotations + +import asyncio +import importlib +import inspect +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Awaitable, Callable + +from vero.harbor.auth import generate_admin_token, write_admin_token +from vero.harbor.sidecar import EvaluationSidecar +from vero.harbor.verifier import CanonicalVerifier + + +@dataclass(frozen=True) +class SidecarComponents: + sidecar: EvaluationSidecar + verifier: CanonicalVerifier + + +SidecarFactory = Callable[ + [dict[str, Any]], + SidecarComponents | Awaitable[SidecarComponents], +] + + +def load_factory(import_path: str) -> SidecarFactory: + module_name, separator, attribute_name = import_path.partition(":") + if not separator or not module_name.strip() or not attribute_name.strip(): + raise ValueError("sidecar factory must use module:attribute syntax") + module = importlib.import_module(module_name) + factory = getattr(module, attribute_name) + if not callable(factory): + raise TypeError("sidecar factory is not callable") + return factory + + +async def build_components( + *, + factory_path: str, + config_path: Path | str, +) -> SidecarComponents: + path = Path(config_path) + try: + config = json.loads(path.read_text(encoding="utf-8")) + except Exception as error: + raise ValueError(f"invalid sidecar config {path}: {error}") from error + if not isinstance(config, dict): + raise ValueError("sidecar config must be a JSON object") + built = load_factory(factory_path)(config) + if inspect.isawaitable(built): + built = await built + if not isinstance(built, SidecarComponents): + raise TypeError("sidecar factory must return SidecarComponents") + return built + + +async def build_app( + *, + factory_path: str, + config_path: Path | str, + admin_token_path: Path | str, +): + from vero.harbor.app import create_app + + components = await build_components( + factory_path=factory_path, + config_path=config_path, + ) + token = generate_admin_token() + write_admin_token(admin_token_path, token) + return create_app( + sidecar=components.sidecar, + verifier=components.verifier, + admin_token=token, + ) + + +def serve( + *, + factory_path: str, + config_path: Path | str, + admin_token_path: Path | str, + host: str = "0.0.0.0", + port: int = 8000, +) -> None: + import uvicorn + + app = asyncio.run( + build_app( + factory_path=factory_path, + config_path=config_path, + admin_token_path=admin_token_path, + ) + ) + uvicorn.run(app, host=host, port=port) diff --git a/vero/src/vero/harbor/session.py b/vero/src/vero/harbor/session.py new file mode 100644 index 00000000..aa2b6320 --- /dev/null +++ b/vero/src/vero/harbor/session.py @@ -0,0 +1,234 @@ +"""Portable, reportable session snapshots for ephemeral Harbor environments.""" + +from __future__ import annotations + +import hashlib +import io +import json +import logging +import os +import tarfile +import tempfile +from collections import deque +from datetime import UTC, datetime +from pathlib import Path, PurePosixPath +from typing import Literal + +from pydantic import field_validator + +from vero.evaluation import BackendProvenance, EvaluationModel +from vero.evaluation.persistence import _atomic_write_json +from vero.harbor.verifier import VerificationSelection, VerificationTarget + +logger = logging.getLogger(__name__) + + +class HarborSessionManifest(EvaluationModel): + """Trusted metadata needed to interpret an exported Harbor session.""" + + schema_version: Literal[1] = 1 + id: str + task_name: str + task_description: str = "" + created_at: datetime + candidate_repository_family: Literal["git"] = "git" + candidate_repository_format_version: Literal[1] = 1 + backends: dict[str, BackendProvenance] + selection: VerificationSelection + targets: list[VerificationTarget] + + @field_validator("id", "task_name") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("Harbor session identity must not be empty") + return value + + @field_validator("created_at") + @classmethod + def normalize_timestamp(cls, value: datetime) -> datetime: + if value.tzinfo is None or value.utcoffset() is None: + raise ValueError("Harbor session timestamp must be timezone-aware") + return value.astimezone(UTC) + + +def initialize_harbor_session_manifest( + session_dir: Path, + *, + session_id: str, + task_name: str, + task_description: str, + backends: dict[str, BackendProvenance], + selection: VerificationSelection, + targets: list[VerificationTarget], +) -> HarborSessionManifest: + """Create the immutable session identity or validate it on restart.""" + + path = session_dir / "harbor-session.json" + if path.is_file(): + stored = HarborSessionManifest.model_validate_json( + path.read_text(encoding="utf-8") + ) + expected = HarborSessionManifest( + id=session_id, + task_name=task_name, + task_description=task_description, + created_at=stored.created_at, + backends=backends, + selection=selection, + targets=targets, + ) + if stored != expected: + raise ValueError("Harbor session manifest is incompatible with deployment") + return stored + + manifest = HarborSessionManifest( + id=session_id, + task_name=task_name, + task_description=task_description, + created_at=datetime.now(UTC), + backends=backends, + selection=selection, + targets=targets, + ) + _atomic_write_json(path, manifest.model_dump(mode="json")) + return manifest + + +def _walk_without_following_links(root: Path): + """Yield every entry beneath ``root`` without ever descending through a + symbolic link (so the walk cannot escape the tree or loop).""" + queue: deque[Path] = deque([root]) + while queue: + current = queue.popleft() + try: + children = sorted(current.iterdir()) + except OSError: + continue + for child in children: + yield child + if child.is_dir() and not child.is_symlink(): + queue.append(child) + + +def create_harbor_session_archive( + session_dir: Path | str, + destination: Path | str, +) -> Path: + """Atomically archive a session beneath a stable ``session/`` root. + + Resilient: no single entry can abort the export. Symlinks are recorded as + inert ``.symlink`` metadata (not link members, which the extractor + refuses), unreadable/special files are skipped, and every omission is listed + in ``vero-export-skipped.json`` inside the archive. + """ + + source = Path(session_dir).expanduser().resolve() + if not (source / "harbor-session.json").is_file(): + raise FileNotFoundError("Harbor session manifest not found") + + output = Path(destination).expanduser().resolve() + output.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + dir=output.parent, + prefix=f".{output.name}.", + suffix=".tmp", + ) + os.close(descriptor) + temporary = Path(temporary_name) + skipped: list[dict[str, str]] = [] + + def _text_member(archive: tarfile.TarFile, arcname: str, payload: bytes) -> None: + info = tarfile.TarInfo(arcname) + info.size = len(payload) + archive.addfile(info, io.BytesIO(payload)) + + try: + with tarfile.open(temporary, "w:gz") as archive: + archive.add(source, arcname="session", recursive=False) + for path in _walk_without_following_links(source): + arcname = "session/" + path.relative_to(source).as_posix() + if path.is_symlink(): + try: + target = os.readlink(path) + except OSError as error: + target = f"" + _text_member( + archive, + arcname + ".symlink", + (f"symlink -> {target}\n").encode("utf-8"), + ) + skipped.append( + {"path": arcname, "reason": "symlink", "target": target} + ) + elif path.is_dir(): + archive.add(path, arcname=arcname, recursive=False) + elif path.is_file(): + try: + archive.add(path, arcname=arcname, recursive=False) + except OSError as error: + skipped.append( + { + "path": arcname, + "reason": f"unreadable: {error.__class__.__name__}", + } + ) + else: + skipped.append({"path": arcname, "reason": "special-file"}) + if skipped: + _text_member( + archive, + "session/vero-export-skipped.json", + (json.dumps({"skipped": skipped}, indent=2) + "\n").encode("utf-8"), + ) + os.replace(temporary, output) + finally: + temporary.unlink(missing_ok=True) + if skipped: + logger.warning( + "session export: %d entr%s not archived verbatim " + "(symlinks recorded as metadata; special/unreadable files skipped); " + "see session/vero-export-skipped.json", + len(skipped), + "y" if len(skipped) == 1 else "ies", + ) + return output + + +def extract_harbor_session_archive( + archive_path: Path | str, + destination: Path | str, +) -> Path: + """Extract a trusted sidecar export without permitting link or path traversal.""" + + archive_path = Path(archive_path).expanduser().resolve() + destination = Path(destination).expanduser().resolve() + destination.mkdir(parents=True, exist_ok=True) + with tarfile.open(archive_path, "r:gz") as archive: + members = archive.getmembers() + for member in members: + path = PurePosixPath(member.name) + if ( + path.is_absolute() + or not path.parts + or path.parts[0] != "session" + or ".." in path.parts + or member.issym() + or member.islnk() + or member.isdev() + ): + raise ValueError(f"unsafe Harbor session archive member: {member.name}") + archive.extractall(destination, members=members, filter="data") + session = destination / "session" + HarborSessionManifest.model_validate_json( + (session / "harbor-session.json").read_text(encoding="utf-8") + ) + return session + + +def file_sha256(path: Path | str) -> str: + digest = hashlib.sha256() + with Path(path).open("rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py new file mode 100644 index 00000000..e533d5cd --- /dev/null +++ b/vero/src/vero/harbor/sidecar.py @@ -0,0 +1,818 @@ +"""Transport-neutral agent frontend over the canonical evaluation engine.""" + +from __future__ import annotations + +import asyncio +import json +import posixpath +from datetime import UTC, datetime +from enum import Enum +from pathlib import Path +from uuid import uuid4 + +from pydantic import Field, JsonValue, field_validator, model_validator + +from vero.candidate import Candidate +from vero.evaluation import ( + AllCases, + CaseResourceExporter, + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationAuthorization, + EvaluationBudget, + EvaluationBudgetExceeded, + EvaluationCancelledError, + EvaluationDeniedError, + EvaluationExecutionError, + EvaluationInfrastructureError, + EvaluationLimits, + EvaluationTerminatedError, + EvaluationModel, + EvaluationRecord, + EvaluationReceipt, + EvaluationRequestError, + EvaluationRequest, + EvaluationSet, + EvaluationSummary, + ObjectiveSpec, + project_evaluation, +) +from vero.evaluation.engine import EvaluationEngine +from vero.evaluation.persistence import _atomic_write_json +from vero.runtime.context import ( + AgentContextDirectory, + AgentDisclosureLedger, + context_digest, + make_evaluation_receipt, + narrower_disclosure, +) +from vero.harbor.transport import CandidateTransferError, CandidateTransport +from vero.sandbox import LocalSandbox + + +class EvaluationAccessError(RuntimeError): + """Raised when an agent requests an evaluation it may not perform.""" + + +class SubmissionDisabledError(RuntimeError): + """Raised when submission is disabled for this optimization task.""" + + +class EvaluationJobNotFoundError(LookupError): + """Raised when an agent requests an unknown evaluation job.""" + + +class SidecarEvaluationPolicy(EvaluationModel): + """Agent access to one backend-owned evaluation-set partition.""" + + backend_id: str + evaluation_set_name: str + partition: str | None = None + objective: ObjectiveSpec | None = None + disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE + expose_case_resources: bool = False + agent_evaluable: bool = True + min_aggregate_cases: int = Field(default=1, ge=1) + parameters: dict[str, JsonValue] = Field(default_factory=dict) + allowed_parameters: list[str] = Field(default_factory=list) + limits: EvaluationLimits | None = None + + @field_validator("backend_id", "evaluation_set_name") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("evaluation access identity must not be empty") + return value + + @field_validator("partition") + @classmethod + def validate_partition(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("evaluation access partition must not be empty") + return value + + @property + def key(self) -> tuple[str, str, str | None]: + return (self.backend_id, self.evaluation_set_name, self.partition) + + @model_validator(mode="after") + def validate_parameters(self) -> SidecarEvaluationPolicy: + if any(not name.strip() for name in self.parameters): + raise ValueError("fixed evaluation parameter names must not be empty") + if len(self.allowed_parameters) != len(set(self.allowed_parameters)): + raise ValueError("allowed evaluation parameters must be unique") + if any(not name.strip() for name in self.allowed_parameters): + raise ValueError("allowed evaluation parameter names must not be empty") + overlap = set(self.parameters) & set(self.allowed_parameters) + if overlap: + raise ValueError( + "fixed and agent-controlled parameters overlap: " + + ", ".join(sorted(overlap)) + ) + return self + + +class SidecarEvaluationRequest(EvaluationModel): + """Agent request; candidate identity is established by the transport.""" + + backend_id: str + evaluation_set: EvaluationSet + version: str | None = None + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits | None = None + seed: int | None = None + + @field_validator("backend_id") + @classmethod + def validate_backend_id(cls, value: str) -> str: + if not value.strip(): + raise ValueError("backend_id must not be empty") + return value + + @field_validator("version") + @classmethod + def validate_version(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("candidate version must not be empty") + return value + + +EvaluationProjection = EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement + + +class SidecarEvaluationResult(EvaluationModel): + disclosure: DisclosureLevel + receipt: EvaluationReceipt + + @model_validator(mode="after") + def validate_projection(self) -> SidecarEvaluationResult: + if self.receipt.disclosure != self.disclosure: + raise ValueError("sidecar disclosure must match its receipt") + return self + + +class EvaluationJobStatus(str, Enum): + QUEUED = "queued" + RUNNING = "running" + COMPLETE = "complete" + FAILED = "failed" + CANCELLED = "cancelled" + + +class SidecarEvaluationJob(EvaluationModel): + """Durable agent-facing lifecycle for one sidecar evaluation request.""" + + job_id: str + status: EvaluationJobStatus + backend_id: str + evaluation_set: EvaluationSet + version: str | None = None + evaluation_id: str | None = None + receipt: EvaluationReceipt | None = None + error: str | None = None + created_at: datetime + completed_at: datetime | None = None + + @model_validator(mode="after") + def validate_lifecycle(self) -> SidecarEvaluationJob: + terminal = self.status in { + EvaluationJobStatus.COMPLETE, + EvaluationJobStatus.FAILED, + EvaluationJobStatus.CANCELLED, + } + if terminal != (self.completed_at is not None): + raise ValueError("only terminal evaluation jobs have completed_at") + if self.status == EvaluationJobStatus.COMPLETE and self.receipt is None: + raise ValueError("complete evaluation jobs require a receipt") + if self.receipt is not None: + if self.status != EvaluationJobStatus.COMPLETE: + raise ValueError("only complete evaluation jobs may have a receipt") + if self.evaluation_id != self.receipt.evaluation_id: + raise ValueError("evaluation job and receipt identities must match") + if self.error is not None and self.status not in { + EvaluationJobStatus.FAILED, + EvaluationJobStatus.CANCELLED, + }: + raise ValueError("only failed or cancelled jobs may have an error") + return self + + +class EvaluationAccessStatus(EvaluationModel): + backend_id: str + evaluation_set_name: str + partition: str | None + disclosure: DisclosureLevel + expose_case_resources: bool + min_aggregate_cases: int + allowed_parameters: list[str] + limits: EvaluationLimits | None = None + budget: EvaluationBudget | None = None + + +class SidecarStatus(EvaluationModel): + submit_enabled: bool + evaluation_access: list[EvaluationAccessStatus] + inference_usage: dict[str, JsonValue] | None = None + evaluation_jobs: list[SidecarEvaluationJob] = Field(default_factory=list) + + +class Submission(EvaluationModel): + candidate: Candidate + + +class EvaluationSidecar: + """Meter, evaluate, and disclose candidates imported from an agent repo. + + The sidecar supports any number of registered backends. Unknown evaluation + sets fail closed, and every accepted request supplies an explicit canonical + authorization to the engine. + """ + + def __init__( + self, + *, + engine: EvaluationEngine, + candidate_transport: CandidateTransport, + access_policies: list[SidecarEvaluationPolicy], + agent_volume: Path | None = None, + admin_volume: Path | None = None, + inference_usage_path: Path | None = None, + inference_limits: dict[str, dict[str, JsonValue]] | None = None, + submit_enabled: bool = False, + disclose_budget: bool = True, + ): + self.engine = engine + self.candidate_transport = candidate_transport + self.agent_volume = Path(agent_volume) if agent_volume is not None else None + self.admin_volume = Path(admin_volume) if admin_volume is not None else None + self.inference_usage_path = ( + Path(inference_usage_path) if inference_usage_path is not None else None + ) + self.inference_limits = inference_limits or {} + self.submit_enabled = submit_enabled + self.disclose_budget = disclose_budget + self._context_lock = asyncio.Lock() + self._context_initialized = False + self._evaluation_jobs_dir = ( + self.engine.evaluator.session_dir / "evaluation-jobs" + ) + self._evaluation_jobs_dir.mkdir(parents=True, exist_ok=True) + self._evaluation_jobs: dict[str, SidecarEvaluationJob] = {} + self._evaluation_job_tasks: dict[str, asyncio.Task[None]] = {} + self._load_evaluation_jobs() + self._disclosures = AgentDisclosureLedger( + self.engine.evaluator.session_dir / "agent-context.json" + ) + self._context_directory = ( + AgentContextDirectory( + sandbox=LocalSandbox(self.agent_volume.parent), + root=str(self.agent_volume), + session_dir=self.engine.evaluator.session_dir, + ) + if self.agent_volume is not None + else None + ) + self._policies: dict[tuple[str, str, str | None], SidecarEvaluationPolicy] = {} + for policy in access_policies: + if policy.key in self._policies: + raise ValueError( + f"duplicate evaluation access policy for {policy.key!r}" + ) + if policy.backend_id not in engine.backends: + raise ValueError( + f"access policy references unknown backend {policy.backend_id!r}" + ) + self._policies[policy.key] = policy + + def _load_evaluation_jobs(self) -> None: + """Restore terminal jobs and mark interrupted in-flight jobs explicitly.""" + + for path in sorted(self._evaluation_jobs_dir.glob("*.json")): + try: + job = SidecarEvaluationJob.model_validate_json( + path.read_text(encoding="utf-8") + ) + except (OSError, ValueError): + continue + if job.status in { + EvaluationJobStatus.QUEUED, + EvaluationJobStatus.RUNNING, + }: + job = job.model_copy( + update={ + "status": EvaluationJobStatus.FAILED, + "error": "evaluation job was interrupted by a sidecar restart", + "completed_at": datetime.now(UTC), + } + ) + _atomic_write_json(path, job.model_dump(mode="json")) + self._evaluation_jobs[job.job_id] = job + + async def _save_evaluation_job(self, job: SidecarEvaluationJob) -> None: + self._evaluation_jobs[job.job_id] = job + await asyncio.to_thread( + _atomic_write_json, + self._evaluation_jobs_dir / f"{job.job_id}.json", + job.model_dump(mode="json"), + ) + + async def _update_evaluation_job( + self, + job_id: str, + **updates: object, + ) -> SidecarEvaluationJob: + job = self._evaluation_jobs[job_id].model_copy(update=updates) + job = SidecarEvaluationJob.model_validate(job) + await self._save_evaluation_job(job) + return job + + def evaluation_job(self, job_id: str) -> SidecarEvaluationJob: + try: + return self._evaluation_jobs[job_id] + except KeyError as error: + raise EvaluationJobNotFoundError( + f"unknown evaluation job {job_id!r}" + ) from error + + async def start_evaluation_job( + self, + request: SidecarEvaluationRequest, + ) -> SidecarEvaluationJob: + """Admit an evaluation and return without waiting for its result.""" + + job = SidecarEvaluationJob( + job_id=str(uuid4()), + status=EvaluationJobStatus.QUEUED, + backend_id=request.backend_id, + evaluation_set=request.evaluation_set, + version=request.version, + created_at=datetime.now(UTC), + ) + await self._save_evaluation_job(job) + admitted = asyncio.Event() + task = asyncio.create_task( + self._run_detached_job(job.job_id, request, admitted), + name=f"vero-evaluation-job-{job.job_id}", + ) + self._evaluation_job_tasks[job.job_id] = task + task.add_done_callback( + lambda _task, job_id=job.job_id: self._evaluation_job_tasks.pop( + job_id, None + ) + ) + await admitted.wait() + return self.evaluation_job(job.job_id) + + async def _execute_tracked_job( + self, + job_id: str, + request: SidecarEvaluationRequest, + admitted: asyncio.Event, + ) -> SidecarEvaluationResult: + """Run one evaluation, recording it through its job lifecycle. + + Shared by the synchronous and detached entry points so every + evaluation is a tracked job visible in status. Re-raises on failure + after recording the terminal state, so the synchronous caller surfaces + the real error (mapped to HTTP by the app handlers) and the detached + wrapper can record-and-swallow. + """ + try: + async with self.engine.agent_evaluation_scope(): + policy, canonical_request = await self._prepare_evaluation(request) + await self._update_evaluation_job( + job_id, + status=EvaluationJobStatus.RUNNING, + version=canonical_request.candidate.version, + ) + admitted.set() + result = await self._evaluate_prepared( + backend_id=request.backend_id, + policy=policy, + request=canonical_request, + ) + await self._update_evaluation_job( + job_id, + status=EvaluationJobStatus.COMPLETE, + evaluation_id=result.receipt.evaluation_id, + receipt=result.receipt, + completed_at=datetime.now(UTC), + ) + return result + except asyncio.CancelledError: + admitted.set() + await asyncio.shield( + self._update_evaluation_job( + job_id, + status=EvaluationJobStatus.CANCELLED, + receipt=None, + error="evaluation job was cancelled", + completed_at=datetime.now(UTC), + ) + ) + raise + except Exception as error: # the terminal record remains in agent context + admitted.set() + evaluation_id = getattr(error, "evaluation_id", None) + await self._update_evaluation_job( + job_id, + status=EvaluationJobStatus.FAILED, + evaluation_id=evaluation_id, + receipt=None, + error=self._evaluation_job_error(error), + completed_at=datetime.now(UTC), + ) + raise + + async def _run_detached_job( + self, + job_id: str, + request: SidecarEvaluationRequest, + admitted: asyncio.Event, + ) -> None: + """Detached wrapper: the terminal state is already recorded on the job + (which the agent polls), so a non-cancellation failure is swallowed + here to avoid an unretrieved background-task exception.""" + try: + await self._execute_tracked_job(job_id, request, admitted) + except asyncio.CancelledError: + raise + except Exception: + pass + + @staticmethod + def _evaluation_job_error(error: Exception) -> str: + # Terminating conditions subclass EvaluationExecutionError, so surface + # their reason first: an out-of-budget or auth-failed run should say so. + if isinstance(error, EvaluationTerminatedError): + return f"evaluation terminated: {error}" + if isinstance(error, EvaluationBudgetExceeded): + return "evaluation budget exhausted" + if isinstance(error, EvaluationInfrastructureError): + return "infrastructure failure" + if isinstance(error, (EvaluationDeniedError, EvaluationAccessError)): + return "evaluation denied" + if isinstance(error, EvaluationRequestError): + return "invalid evaluation request" + if isinstance(error, CandidateTransferError): + return "candidate version could not be imported" + # Unmapped failure: include the exception type (a class name, so no + # secret leak) so the agent and operators can tell failures apart + # instead of guessing from an opaque "evaluation failed". + return f"evaluation failed: {type(error).__name__}" + + def _policy( + self, + backend_id: str, + evaluation_set: EvaluationSet, + ) -> SidecarEvaluationPolicy: + key = (backend_id, evaluation_set.name, evaluation_set.partition) + policy = self._policies.get(key) + if policy is None or not policy.agent_evaluable: + raise EvaluationAccessError( + "the requested backend and evaluation set are not agent-evaluable" + ) + return policy + + async def _enforce_aggregate_floor( + self, + policy: SidecarEvaluationPolicy, + evaluation_set: EvaluationSet, + ) -> None: + if policy.disclosure != DisclosureLevel.AGGREGATE: + return + if isinstance(evaluation_set.selection, AllCases): + return + backend = self.engine.backends.resolve(policy.backend_id) + cost = await backend.resolve_cost(evaluation_set) + if cost.cases is None: + raise EvaluationAccessError( + "aggregate subset evaluation requires a backend with exact case costs" + ) + if cost.cases < policy.min_aggregate_cases: + raise EvaluationAccessError( + f"aggregate subset evaluations must cover at least " + f"{policy.min_aggregate_cases} cases; requested {cost.cases}" + ) + + def _visible_projections( + self, + ) -> list[tuple[EvaluationRecord, DisclosureLevel, EvaluationProjection]]: + projections = [] + for evaluation_id, entry in self._disclosures.model.evaluations.items(): + record = self.engine.database.get_evaluation(evaluation_id) + if record is None: + continue + policy = self._policies.get( + ( + record.backend_id, + record.request.evaluation_set.name, + record.request.evaluation_set.partition, + ) + ) + if policy is None or not policy.agent_evaluable: + continue + disclosure = narrower_disclosure( + entry.maximum_disclosure, + policy.disclosure, + ) + projections.append( + (record, disclosure, project_evaluation(record, disclosure)) + ) + return projections + + async def _write_candidate_index( + self, + projections: list[ + tuple[EvaluationRecord, DisclosureLevel, EvaluationProjection] + ], + ) -> None: + assert self._context_directory is not None + root = self._context_directory.path("candidates") + sandbox = self._context_directory.sandbox + if await sandbox.exists(root): + await sandbox.remove(root, recursive=True) + await sandbox.mkdir(root) + candidates = { + record.request.candidate.id: record.request.candidate + for record, _, _ in projections + } + index = [] + for candidate in sorted( + candidates.values(), + key=lambda item: (item.created_at, item.id), + ): + digest = context_digest(candidate.id) + directory = self._context_directory.path("candidates", digest) + await sandbox.mkdir(directory) + await sandbox.write_file( + posixpath.join(directory, "candidate.json"), + candidate.model_dump_json(indent=2) + "\n", + ) + index.append( + { + "candidate_id": candidate.id, + "version": candidate.version, + "parent_id": candidate.parent_id, + "native_ref": candidate.version, + "metadata_path": f"{digest}/candidate.json", + "parent_patch_path": None, + } + ) + await self._context_directory.write_json( + self._context_directory.path("candidates", "index.json"), + {"schema_version": 1, "candidates": index}, + ) + + async def _write_case_resources(self) -> None: + assert self._context_directory is not None + root = self._context_directory.path("cases") + sandbox = self._context_directory.sandbox + if await sandbox.exists(root): + await sandbox.remove(root, recursive=True) + await sandbox.mkdir(root) + index = [] + for policy in self._policies.values(): + backend = self.engine.backends.resolve(policy.backend_id) + if ( + not policy.agent_evaluable + or not policy.expose_case_resources + or not isinstance(backend, CaseResourceExporter) + ): + continue + evaluation_set = EvaluationSet( + name=policy.evaluation_set_name, + partition=policy.partition, + ) + digest = context_digest(evaluation_set.budget_key(policy.backend_id)) + resource_root = self._context_directory.path("cases", digest) + await sandbox.mkdir(resource_root) + await self._context_directory.write_json( + posixpath.join(resource_root, "manifest.json"), + { + "schema_version": 1, + "backend_id": policy.backend_id, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "resources_path": "resources", + }, + ) + resources = posixpath.join(resource_root, "resources") + await sandbox.mkdir(resources) + await backend.export_case_resources( + evaluation_set=evaluation_set, + destination=resources, + sandbox=sandbox, + ) + index.append( + { + "backend_id": policy.backend_id, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "path": digest, + } + ) + await self._context_directory.write_json( + self._context_directory.path("cases", "index.json"), + {"schema_version": 1, "case_resources": index}, + ) + + async def initialize_context(self) -> None: + if self._context_directory is None: + return + async with self._context_lock: + await self._context_directory.reset() + await self._context_directory.write_header( + session_id=self.engine.evaluator.session_id, + round_number=None, + proposal_id=None, + parent_candidate_id=None, + ) + projections = self._visible_projections() + await self._write_candidate_index(projections) + await self._context_directory.write_evaluations(projections) + await self._write_case_resources() + self._context_initialized = True + + async def _refresh_context(self) -> None: + if self._context_directory is None: + return + if not self._context_initialized: + await self.initialize_context() + return + async with self._context_lock: + projections = self._visible_projections() + await self._write_candidate_index(projections) + await self._context_directory.write_evaluations(projections) + + async def evaluate( + self, + request: SidecarEvaluationRequest, + ) -> SidecarEvaluationResult: + """Run an evaluation synchronously, recording it as a tracked job so it + is visible in status exactly like a detached one, then return its + result (or re-raise its failure).""" + job = SidecarEvaluationJob( + job_id=str(uuid4()), + status=EvaluationJobStatus.QUEUED, + backend_id=request.backend_id, + evaluation_set=request.evaluation_set, + version=request.version, + created_at=datetime.now(UTC), + ) + await self._save_evaluation_job(job) + return await self._execute_tracked_job(job.job_id, request, asyncio.Event()) + + async def _prepare_evaluation( + self, + request: SidecarEvaluationRequest, + ) -> tuple[SidecarEvaluationPolicy, EvaluationRequest]: + policy = self._policy(request.backend_id, request.evaluation_set) + unknown_parameters = sorted( + set(request.parameters) - set(policy.allowed_parameters) + ) + if unknown_parameters: + raise EvaluationAccessError( + "evaluation parameters are not agent-controllable: " + + ", ".join(unknown_parameters) + ) + if policy.limits is not None and request.limits is not None: + raise EvaluationAccessError( + "evaluation limits are fixed by the access policy and cannot be " + "overridden by the agent" + ) + await self._enforce_aggregate_floor(policy, request.evaluation_set) + candidate = await self.candidate_transport.import_candidate(request.version) + parameters = {**policy.parameters, **request.parameters} + return policy, EvaluationRequest( + candidate=candidate, + evaluation_set=request.evaluation_set, + parameters=parameters, + limits=policy.limits or request.limits or EvaluationLimits(), + seed=request.seed, + ) + + async def _evaluate_prepared( + self, + *, + backend_id: str, + policy: SidecarEvaluationPolicy, + request: EvaluationRequest, + ) -> SidecarEvaluationResult: + try: + result = await self.engine.evaluate( + backend_id=backend_id, + request=request, + objective_spec=policy.objective, + authorization=EvaluationAuthorization( + may_evaluate=True, + meter_budget=True, + disclosure=policy.disclosure, + expose_case_resources=policy.expose_case_resources, + ), + ) + except (EvaluationExecutionError, EvaluationCancelledError) as error: + record = self.engine.database.get_evaluation(error.evaluation_id) + if record is not None: + await self._disclosures.remember(record.id, policy.disclosure) + await asyncio.shield(self._refresh_context()) + raise + evaluation_id = ( + result.id if isinstance(result, EvaluationRecord) else result.evaluation_id + ) + record = self.engine.database.get_evaluation(evaluation_id) + if record is None: + raise RuntimeError( + f"evaluation engine did not index completed evaluation {evaluation_id!r}" + ) + maximum = await self._disclosures.remember(record.id, policy.disclosure) + disclosure = narrower_disclosure(maximum, policy.disclosure) + await self._refresh_context() + return SidecarEvaluationResult( + disclosure=disclosure, + receipt=make_evaluation_receipt(record, disclosure), + ) + + async def submit(self, version: str | None = None) -> Submission: + if not self.submit_enabled: + raise SubmissionDisabledError("candidate submission is disabled") + candidate = await self.candidate_transport.import_candidate(version) + submission = Submission(candidate=candidate) + if self.admin_volume is not None: + await asyncio.to_thread( + _atomic_write_json, + self.admin_volume / "submission.json", + submission.model_dump(mode="json"), + ) + return submission + + def status(self) -> SidecarStatus: + access: list[EvaluationAccessStatus] = [] + for policy in self._policies.values(): + if not policy.agent_evaluable: + continue + evaluation_set = EvaluationSet( + name=policy.evaluation_set_name, + partition=policy.partition, + ) + budget = ( + self.engine.budget_ledger.get(policy.backend_id, evaluation_set) + if self.engine.budget_ledger is not None + else None + ) + access.append( + EvaluationAccessStatus( + backend_id=policy.backend_id, + evaluation_set_name=policy.evaluation_set_name, + partition=policy.partition, + disclosure=policy.disclosure, + expose_case_resources=policy.expose_case_resources, + min_aggregate_cases=policy.min_aggregate_cases, + allowed_parameters=list(policy.allowed_parameters), + limits=policy.limits, + # Budget is enforced regardless; disclosure is what we gate. + budget=budget if self.disclose_budget else None, + ) + ) + inference_usage: dict[str, JsonValue] | None = None + observed_scopes: dict[str, object] = {} + if self.inference_usage_path is not None and self.inference_usage_path.exists(): + try: + value = self.inference_usage_path.read_text(encoding="utf-8") + parsed = json.loads(value) + scopes = parsed.get("scopes") if isinstance(parsed, dict) else None + if isinstance(scopes, dict): + observed_scopes = scopes + except (OSError, ValueError): + observed_scopes = {} + if self.inference_limits and self.disclose_budget: + inference_usage = {} + for name, limits in self.inference_limits.items(): + observed = observed_scopes.get(name) + usage = observed if isinstance(observed, dict) else {} + requests = usage.get("requests", 0) + total_tokens = usage.get("total_tokens", 0) + max_requests = limits.get("max_requests") + max_tokens = limits.get("max_tokens") + inference_usage[name] = { + **limits, + **usage, + "remaining_requests": ( + None + if max_requests is None + else max(0, int(max_requests) - int(requests)) + ), + "remaining_tokens": ( + None + if max_tokens is None + else max(0, int(max_tokens) - int(total_tokens)) + ), + } + return SidecarStatus( + submit_enabled=self.submit_enabled, + evaluation_access=access, + inference_usage=inference_usage, + evaluation_jobs=sorted( + self._evaluation_jobs.values(), + key=lambda job: (job.created_at, job.job_id), + reverse=True, + )[:100], + ) diff --git a/vero/src/vero/harbor/transport.py b/vero/src/vero/harbor/transport.py new file mode 100644 index 00000000..768a0e70 --- /dev/null +++ b/vero/src/vero/harbor/transport.py @@ -0,0 +1,190 @@ +"""Candidate transfer across the Harbor sidecar trust boundary.""" + +from __future__ import annotations + +import re +import os +from datetime import datetime +from pathlib import PurePosixPath +from typing import Protocol, runtime_checkable + +from vero.candidate import Candidate +from vero.candidate_repository import GitCandidateRepository +from vero.workspace import Workspace + + +class CandidateTransferError(RuntimeError): + """Raised when an untrusted candidate cannot be imported safely.""" + + +@runtime_checkable +class CandidateTransport(Protocol): + """Import an external program version into durable candidate storage.""" + + async def import_candidate(self, version: str | None = None) -> Candidate: ... + + +class GitCandidateTransport: + """Copy a commit from an agent repository into a trusted candidate repository. + + The source ref is resolved before fetching, the object is fetched through a + unique temporary ref, and the imported commit receives a durable candidate + record. Later verifier evaluations do not depend on the agent repository + still retaining the object. + """ + + _OBJECT_ID = re.compile(r"(?:[0-9a-f]{40}|[0-9a-f]{64})\Z") + + def __init__( + self, + *, + workspace: Workspace, + candidate_repository: GitCandidateRepository, + agent_repo_path: str, + ): + if not agent_repo_path.startswith("/"): + raise ValueError("agent_repo_path must be an absolute sandbox path") + if PurePosixPath(agent_repo_path) == PurePosixPath(workspace.root): + raise ValueError("agent and trusted repositories must be distinct") + self.workspace = workspace + self.candidate_repository = candidate_repository + self.agent_repo_path = agent_repo_path.rstrip("/") or "/" + self._candidates: dict[str, Candidate] = {} + + async def _run( + self, + command: list[str], + *, + cwd: str, + timeout: int, + ) -> str: + if command[0] != "git": + raise ValueError("candidate transport only permits Git commands") + command = [ + "git", + "-c", + f"safe.directory={cwd}", + *command[1:], + ] + result = await self.workspace.sandbox.run( + command, + cwd=cwd, + timeout=timeout, + env={ + "PATH": os.defpath, + "LANG": "C.UTF-8", + "GIT_CONFIG_GLOBAL": "/dev/null", + }, + ) + if result.returncode != 0: + message = result.stderr.strip() or result.stdout.strip() or "git failed" + raise CandidateTransferError(message) + return result.stdout.strip() + + async def _resolve_ref(self, version: str, *, repository: str) -> str: + value = await self._run( + [ + "git", + "-c", + "core.hooksPath=/dev/null", + "rev-parse", + "--verify", + "--end-of-options", + f"{version}^{{commit}}", + ], + cwd=repository, + timeout=30, + ) + if self._OBJECT_ID.fullmatch(value) is None: + raise CandidateTransferError( + "source ref did not resolve to a Git object ID" + ) + return value + + async def trusted_candidate(self, version: str | None = None) -> Candidate: + """Resolve an existing commit already owned by the trusted workspace.""" + source_version = version or "HEAD" + if not source_version.strip() or "\x00" in source_version: + raise CandidateTransferError("candidate version must not be empty") + object_id = await self._resolve_ref( + source_version, + repository=self.workspace.root, + ) + cached = self._candidates.get(object_id) + if cached is None: + cached = await self._candidate_metadata( + object_id, + repository=self.workspace.root, + ) + cached = await self.candidate_repository.import_candidate( + cached, + sandbox=self.workspace.sandbox, + repository_path=self.workspace.root, + ) + self._candidates[object_id] = cached + return cached + + async def _candidate_metadata( + self, + object_id: str, + *, + repository: str, + ) -> Candidate: + value = await self._run( + [ + "git", + "-c", + "core.hooksPath=/dev/null", + "show", + "-s", + "--format=%cI%x00%P%x00%T%x00%s", + "--end-of-options", + object_id, + ], + cwd=repository, + timeout=30, + ) + parts = value.split("\x00", 3) + if len(parts) != 4: + raise CandidateTransferError("could not read imported commit metadata") + timestamp, parents, tree, subject = parts + if self._OBJECT_ID.fullmatch(tree) is None: + raise CandidateTransferError("imported commit has an invalid tree identity") + try: + created_at = datetime.fromisoformat(timestamp) + except ValueError as error: + raise CandidateTransferError( + "imported commit has an invalid timestamp" + ) from error + parent_id = parents.split()[0] if parents.strip() else None + return Candidate( + id=object_id, + version=object_id, + parent_id=parent_id, + created_at=created_at, + description=subject.strip() or None, + metadata={"transport": "git", "content_digest": tree}, + ) + + async def import_candidate(self, version: str | None = None) -> Candidate: + source_version = version or "HEAD" + if not source_version.strip() or "\x00" in source_version: + raise CandidateTransferError("candidate version must not be empty") + object_id = await self._resolve_ref( + source_version, + repository=self.agent_repo_path, + ) + cached = self._candidates.get(object_id) + if cached is not None: + return cached + candidate = await self._candidate_metadata( + object_id, + repository=self.agent_repo_path, + ) + candidate = await self.candidate_repository.import_candidate( + candidate, + sandbox=self.workspace.sandbox, + repository_path=self.agent_repo_path, + ) + self._candidates[object_id] = candidate + return candidate diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py new file mode 100644 index 00000000..2bace0ff --- /dev/null +++ b/vero/src/vero/harbor/verifier.py @@ -0,0 +1,478 @@ +"""Admin-side candidate selection and final evaluation for Harbor tasks.""" + +from __future__ import annotations + +import asyncio +import logging +import math +import statistics +from collections import defaultdict +from pathlib import Path +from typing import Callable, Literal + +from pydantic import Field, JsonValue, field_validator, model_validator + +from vero.candidate import Candidate +from vero.evaluation import ( + EvaluationAuthorization, + EvaluationLimits, + EvaluationModel, + EvaluationRecord, + EvaluationPrincipal, + EvaluationRequest, + EvaluationSet, + ObjectiveSpec, +) +from vero.evaluation.engine import EvaluationEngine +from vero.evaluation.persistence import _atomic_write_json +from vero.harbor.sidecar import Submission + +logger = logging.getLogger(__name__) + + +class NoCandidateError(RuntimeError): + """Raised when finalization has no submitted or evaluated candidate.""" + + +class VerificationTarget(EvaluationModel): + """One trusted final evaluation projected to one Harbor reward key.""" + + reward_key: str + backend_id: str + evaluation_set: EvaluationSet + objective: ObjectiveSpec + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits = Field(default_factory=EvaluationLimits) + failure_value: float = 0.0 + reward_scale: float = 1.0 + reward_offset: float = 0.0 + # Keep one attempt by default for adversarial candidates. Retrying an + # unmeasurable candidate-controlled run creates a one-sided re-roll. + max_attempts: int = Field(default=1, ge=1) + + @field_validator("reward_key", "backend_id") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("verification target identity must not be empty") + return value + + @field_validator("failure_value", "reward_scale", "reward_offset") + @classmethod + def validate_failure_value(cls, value: float) -> float: + if not math.isfinite(value): + raise ValueError("verification reward values must be finite") + return value + + +class VerificationSelection(EvaluationModel): + """How finalization chooses a candidate before scoring its targets.""" + + mode: Literal["submit", "auto_best"] = "auto_best" + backend_id: str | None = None + evaluation_set: EvaluationSet | None = None + objective: ObjectiveSpec | None = None + baseline_candidate: Candidate | None = None + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits = Field(default_factory=EvaluationLimits) + rescore_top_k: int = Field(default=3, ge=1) + rescore_attempts: int = Field(default=1, ge=1) + baseline_floor: bool = True + selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) + + @model_validator(mode="after") + def validate_auto_best(self) -> VerificationSelection: + fields = (self.backend_id, self.evaluation_set, self.objective) + if self.mode == "auto_best" and any(value is None for value in fields): + raise ValueError( + "auto_best selection requires backend_id, evaluation_set, and objective" + ) + if self.backend_id is not None and not self.backend_id.strip(): + raise ValueError("selection backend_id must not be empty") + if ( + self.baseline_floor + and self.mode == "auto_best" + and self.baseline_candidate is None + ): + raise ValueError("baseline_floor requires baseline_candidate") + return self + + +class VerificationResult(EvaluationModel): + """Durable, idempotent output consumed by Harbor's verifier.""" + + candidate: Candidate | None = None + #: Whether a candidate was actually shipped. False means selection produced + #: nothing (the rewards are failure values recording "no candidate"), which + #: a benchmark must treat as a distinct outcome from a candidate that + #: shipped and legitimately scored the failure value. + shipped: bool = True + rewards: dict[str, float] + evaluation_ids: dict[str, str] = Field(default_factory=dict) + baseline_rewards: dict[str, float] = Field(default_factory=dict) + errors: dict[str, str] = Field(default_factory=dict) + + @field_validator("rewards", "baseline_rewards") + @classmethod + def validate_rewards(cls, value: dict[str, float]) -> dict[str, float]: + for key, reward in value.items(): + if not key.strip() or not math.isfinite(reward): + raise ValueError("verification rewards require names and finite values") + return value + + +class CanonicalVerifier: + """Select, re-measure, and score candidates through one evaluation engine.""" + + def __init__( + self, + *, + engine: EvaluationEngine, + selection: VerificationSelection, + targets: list[VerificationTarget], + admin_volume: Path, + score_baseline: bool = True, + evaluation_drain_timeout_seconds: float = 600.0, + on_finalized: Callable[[VerificationResult], None] | None = None, + ): + if not targets: + raise ValueError("at least one verification target is required") + reward_keys = [target.reward_key for target in targets] + if len(reward_keys) != len(set(reward_keys)): + raise ValueError("verification reward keys must be unique") + for target in targets: + if target.backend_id not in engine.backends: + raise ValueError( + f"verification target references unknown backend {target.backend_id!r}" + ) + if ( + selection.backend_id is not None + and selection.backend_id not in engine.backends + ): + raise ValueError( + f"selection references unknown backend {selection.backend_id!r}" + ) + self.engine = engine + self.selection = selection + self.targets = targets + self.admin_volume = Path(admin_volume) + self.score_baseline = score_baseline + if evaluation_drain_timeout_seconds <= 0: + raise ValueError("evaluation drain timeout must be positive") + self.evaluation_drain_timeout_seconds = evaluation_drain_timeout_seconds + self._on_finalized = on_finalized + self._lock = asyncio.Lock() + self._result: VerificationResult | None = None + + @property + def result_path(self) -> Path: + return self.admin_volume / "finalize.json" + + @property + def submission_path(self) -> Path: + return self.admin_volume / "submission.json" + + def _try_load_submission(self) -> Candidate | None: + if not self.submission_path.exists(): + return None + try: + submission = Submission.model_validate_json( + self.submission_path.read_text(encoding="utf-8") + ) + except Exception as error: + raise ValueError(f"invalid durable submission: {error}") from error + return submission.candidate + + def _selection_records(self) -> list[EvaluationRecord]: + assert self.selection.backend_id is not None + assert self.selection.evaluation_set is not None + assert self.selection.objective is not None + baseline_version = ( + self.selection.baseline_candidate.version + if self.selection.baseline_candidate is not None + else None + ) + # Match on backend, partition, and objective (not exact evaluation_set + # equality) so a range/subset that samples the set still counts. + selection_set = self.selection.evaluation_set + matching: list[EvaluationRecord] = [] + for record in self.engine.database.evaluations.values(): + objective = record.objective + evaluation_set = record.request.evaluation_set + if ( + record.backend_id != self.selection.backend_id + or evaluation_set.name != selection_set.name + or evaluation_set.partition != selection_set.partition + or record.objective_spec != self.selection.objective + or objective is None + or not objective.feasible + or objective.value is None + or record.request.candidate.version == baseline_version + ): + continue + matching.append(record) + if not matching: + return [] + # Coverage threshold relative to the best-covered eval seen — the closest + # proxy for the full set without re-enumerating it. An under-measured + # eval is too noisy to rank on; the admin re-score on the full selection + # set (in _auto_best) is the actual ship gate. + coverage = { + id(record): len({case.case_id for case in record.report.cases}) + for record in matching + } + best = max(coverage.values()) + min_cases = math.ceil(best * self.selection.selection_coverage_threshold) + return [record for record in matching if coverage[id(record)] >= min_cases] + + def _shortlist(self) -> list[Candidate]: + records = self._selection_records() + if not records: + return [] + + by_content: dict[str, list[EvaluationRecord]] = defaultdict(list) + for record in records: + candidate = record.request.candidate + content = candidate.metadata.get("content_digest") + key = str(content) if content is not None else candidate.version + by_content[key].append(record) + + pooled: list[tuple[float, Candidate]] = [] + for group in by_content.values(): + values = [record.objective.value for record in group] + representative = min( + (record.request.candidate for record in group), + key=lambda candidate: candidate.id, + ) + pooled.append((statistics.fmean(values), representative)) + pooled.sort(key=lambda item: item[1].id) + pooled.sort( + key=lambda item: item[0], + reverse=self.selection.objective.direction == "maximize", + ) + return [candidate for _, candidate in pooled[: self.selection.rescore_top_k]] + + async def _evaluate( + self, + *, + candidate: Candidate, + backend_id: str, + evaluation_set: EvaluationSet, + objective: ObjectiveSpec, + parameters: dict[str, JsonValue], + limits: EvaluationLimits, + max_attempts: int, + ) -> tuple[EvaluationRecord | None, str | None]: + last_error: str | None = None + for attempt in range(1, max_attempts + 1): + try: + record = await self.engine.evaluate_record( + backend_id=backend_id, + request=EvaluationRequest( + candidate=candidate, + evaluation_set=evaluation_set, + parameters=parameters, + limits=limits, + ), + objective_spec=objective, + authorization=EvaluationAuthorization( + may_evaluate=True, + meter_budget=False, + disclosure="full", + ), + principal=EvaluationPrincipal.ADMIN, + ) + if ( + record.objective is not None + and record.objective.feasible + and record.objective.value is not None + ): + return record, None + last_error = "evaluation did not produce a feasible objective" + except Exception as error: + last_error = str(error) or type(error).__name__ + if attempt == max_attempts: + break + return None, last_error + + async def _rescore_candidate( + self, + candidate: Candidate, + ) -> tuple[EvaluationRecord | None, str | None]: + assert self.selection.backend_id is not None + assert self.selection.evaluation_set is not None + assert self.selection.objective is not None + return await self._evaluate( + candidate=candidate, + backend_id=self.selection.backend_id, + evaluation_set=self.selection.evaluation_set, + objective=self.selection.objective, + parameters=self.selection.parameters, + limits=self.selection.limits, + max_attempts=self.selection.rescore_attempts, + ) + + def _strictly_beats( + self, + candidate: EvaluationRecord, + baseline: EvaluationRecord, + ) -> bool: + assert candidate.objective is not None and candidate.objective.value is not None + assert baseline.objective is not None and baseline.objective.value is not None + if not candidate.objective.feasible: + return False + if not baseline.objective.feasible: + return True + if self.selection.objective.direction == "maximize": + return candidate.objective.value > baseline.objective.value + return candidate.objective.value < baseline.objective.value + + def _pick_last(self) -> Candidate | None: + """Last-resort fallback: the most recently measured candidate.""" + records = list(self.engine.database.evaluations.values()) + if not records: + return None + latest = max(records, key=lambda record: record.created_at) + return latest.request.candidate + + async def _auto_best(self) -> Candidate | None: + rescored: list[EvaluationRecord] = [] + for candidate in self._shortlist(): + record, _ = await self._rescore_candidate(candidate) + if record is not None: + rescored.append(record) + if not rescored: + return None + assert self.selection.objective is not None + rescored.sort(key=lambda record: record.request.candidate.id) + rescored.sort( + key=lambda record: record.objective.value, + reverse=self.selection.objective.direction == "maximize", + ) + best = rescored[0] + + baseline_candidate = self.selection.baseline_candidate + if self.selection.baseline_floor and baseline_candidate is not None: + baseline, _ = await self._rescore_candidate(baseline_candidate) + if baseline is not None and not self._strictly_beats(best, baseline): + return baseline_candidate + return best.request.candidate + + async def _select_candidate(self) -> Candidate: + # Chain: the agent's explicit submission wins; else auto_best over + # coverage-qualified evals; else the current/last candidate. Only when + # there is no candidate at all do we fail (and ship nothing). + submission = self._try_load_submission() + if submission is not None: + return submission + candidate = await self._auto_best() + if candidate is not None: + return candidate + last = self._pick_last() + if last is not None: + return last + raise NoCandidateError("no submitted, selectable, or prior candidate to ship") + + async def _score_target( + self, + candidate: Candidate, + target: VerificationTarget, + ) -> tuple[float, str | None, str | None]: + record, error = await self._evaluate( + candidate=candidate, + backend_id=target.backend_id, + evaluation_set=target.evaluation_set, + objective=target.objective, + parameters=target.parameters, + limits=target.limits, + max_attempts=target.max_attempts, + ) + if record is None: + return target.failure_value, None, error + assert record.objective is not None and record.objective.value is not None + reward = ( + target.reward_scale * float(record.objective.value) + target.reward_offset + ) + return reward, record.id, None + + async def _finalize(self) -> VerificationResult: + try: + candidate = await self._select_candidate() + except NoCandidateError as error: + return VerificationResult( + shipped=False, + rewards={ + target.reward_key: target.failure_value for target in self.targets + }, + errors={"selection": str(error)}, + ) + + rewards: dict[str, float] = {} + evaluation_ids: dict[str, str] = {} + errors: dict[str, str] = {} + for target in self.targets: + reward, evaluation_id, error = await self._score_target(candidate, target) + rewards[target.reward_key] = reward + if evaluation_id is not None: + evaluation_ids[target.reward_key] = evaluation_id + if error is not None: + errors[target.reward_key] = error + + baseline_rewards: dict[str, float] = {} + baseline = self.selection.baseline_candidate + if self.score_baseline and baseline is not None: + if baseline.version == candidate.version: + baseline_rewards = dict(rewards) + else: + for target in self.targets: + reward, _, error = await self._score_target(baseline, target) + baseline_rewards[target.reward_key] = reward + if error is not None: + errors[f"baseline:{target.reward_key}"] = error + + return VerificationResult( + candidate=candidate, + rewards=rewards, + evaluation_ids=evaluation_ids, + baseline_rewards=baseline_rewards, + errors=errors, + ) + + async def finalize(self) -> VerificationResult: + """Return the first durable finalization result on every invocation.""" + async with self._lock: + if self._result is not None: + return self._result + if self.result_path.exists(): + try: + self._result = VerificationResult.model_validate_json( + self.result_path.read_text(encoding="utf-8") + ) + except Exception as error: + raise ValueError( + f"invalid durable finalization result: {error}" + ) from error + return self._result + drained = await self.engine.quiesce_agent_evaluations( + timeout_seconds=self.evaluation_drain_timeout_seconds, + ) + if drained: + logger.info( + "Finalization drained %d accepted agent evaluation(s)", + drained, + ) + result = await self._finalize() + await asyncio.to_thread( + _atomic_write_json, + self.result_path, + result.model_dump(mode="json"), + ) + self._result = result + if self._on_finalized is not None: + # Best-effort session-end hook (e.g. finish the W&B run); must + # never affect the durable finalization result. + try: + self._on_finalized(result) + except Exception: + logger.warning("finalization hook failed", exc_info=True) + return result diff --git a/vero/src/vero/optimization/__init__.py b/vero/src/vero/optimization/__init__.py new file mode 100644 index 00000000..534f783b --- /dev/null +++ b/vero/src/vero/optimization/__init__.py @@ -0,0 +1,49 @@ +"""Strategy-driven optimization of versioned programs.""" + +from vero.optimization.command import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, +) +from vero.optimization.models import ( + CandidateChange, + CandidateProductionContext, + CandidateProposal, + GenerationOutcome, + OptimizationContext, + OptimizationResult, +) +from vero.optimization.optimizer import Optimizer +from vero.optimization.protocols import ( + CandidateEvaluationGateway, + CandidateProducer, + GenerationBackend, + OptimizationStrategy, + SelectionPolicy, +) +from vero.optimization.strategy import ( + DarwinGodelStrategy, + EvolutionaryStrategy, + ObjectiveSelectionPolicy, + SequentialStrategy, +) + +__all__ = [ + "CandidateChange", + "CandidateProductionContext", + "CandidateEvaluationGateway", + "CandidateProducer", + "CandidateProposal", + "CommandCandidateProducer", + "CommandCandidateProducerConfig", + "DarwinGodelStrategy", + "EvolutionaryStrategy", + "GenerationBackend", + "GenerationOutcome", + "ObjectiveSelectionPolicy", + "OptimizationContext", + "OptimizationResult", + "OptimizationStrategy", + "Optimizer", + "SelectionPolicy", + "SequentialStrategy", +] diff --git a/vero/src/vero/optimization/command.py b/vero/src/vero/optimization/command.py new file mode 100644 index 00000000..8909a1ac --- /dev/null +++ b/vero/src/vero/optimization/command.py @@ -0,0 +1,179 @@ +"""Trusted command candidate producer.""" + +from __future__ import annotations + +import os +import posixpath +import re +from pathlib import Path + +from pydantic import Field, field_validator, model_validator + +from vero.evaluation import EvaluationModel +from vero.optimization.models import ( + CandidateChange, + CandidateProductionContext, + CandidateProposal, +) +from vero.optimization.protocols import CandidateEvaluationGateway +from vero.staging import SandboxStagingArea +from vero.workspace import Workspace + +_PLACEHOLDERS = { + "workspace", + "context", + "producer", + "round", + "instruction", + "best_candidate_id", + "best_version", + "best_value", +} +_PLACEHOLDER_PATTERN = re.compile(r"\{([^{}]+)\}") + + +class CommandCandidateProducerConfig(EvaluationModel): + root: str + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + timeout_seconds: float = Field(default=600.0, gt=0.0) + description: str = "Optimize candidate" + + @field_validator("root") + @classmethod + def validate_root(cls, value: str) -> str: + if not value.strip() or not Path(value).is_absolute(): + raise ValueError("producer root must be absolute after config resolution") + return value + + @field_validator("command") + @classmethod + def validate_command(cls, value: list[str]) -> list[str]: + if not value or any(not argument for argument in value): + raise ValueError("producer command and its arguments must not be empty") + unknown = { + placeholder + for argument in value + for placeholder in _PLACEHOLDER_PATTERN.findall(argument) + if placeholder not in _PLACEHOLDERS + } + if unknown: + raise ValueError( + f"unknown producer placeholders: {', '.join(sorted(unknown))}" + ) + return value + + @field_validator("working_directory") + @classmethod + def validate_working_directory(cls, value: str) -> str: + path = Path(value) + if not value.strip() or path.is_absolute() or ".." in path.parts: + raise ValueError("producer working_directory must stay within its root") + return value + + @field_validator("passthrough_environment") + @classmethod + def validate_passthrough(cls, value: list[str]) -> list[str]: + if len(value) != len(set(value)): + raise ValueError("producer passthrough environment names must be unique") + return value + + @field_validator("description") + @classmethod + def validate_description(cls, value: str) -> str: + if not value.strip(): + raise ValueError("producer description must not be empty") + return value + + @model_validator(mode="after") + def validate_environment(self) -> CommandCandidateProducerConfig: + overlap = set(self.environment) & set(self.passthrough_environment) + if overlap: + raise ValueError( + "producer environment sources overlap for: " + + ", ".join(sorted(overlap)) + ) + return self + + +class CommandCandidateProducer: + """Run a trusted command that edits the supplied candidate workspace.""" + + def __init__(self, config: CommandCandidateProducerConfig): + self.config = config + + def _environment(self) -> dict[str, str]: + environment = {"PATH": os.defpath, "LANG": "C.UTF-8"} + for name in ("TMPDIR", "TMP", "TEMP", "SYSTEMROOT"): + if name in os.environ: + environment[name] = os.environ[name] + environment.update(self.config.environment) + for name in self.config.passthrough_environment: + if name in os.environ: + environment[name] = os.environ[name] + return environment + + async def produce( + self, + *, + proposal: CandidateProposal, + context: CandidateProductionContext, + workspace: Workspace, + evaluation: CandidateEvaluationGateway, + ) -> CandidateChange | None: + root = Path(self.config.root).resolve() + target = workspace.sandbox.host_path(workspace.project_path) + if target is not None: + target = target.resolve() + if root == target or root.is_relative_to(target): + raise ValueError( + "candidate producer must live outside the editable target" + ) + + best = context.best + async with SandboxStagingArea( + workspace.sandbox, + prefix=f"vero-producer-{proposal.id[:8]}-", + ) as staging: + producer_root = ( + str(root) + if workspace.sandbox.capabilities.host_paths + else await staging.upload(root, "producer") + ) + working_directory = posixpath.normpath( + posixpath.join(producer_root, self.config.working_directory) + ) + values = { + "workspace": workspace.project_path, + "context": posixpath.join(workspace.project_path, ".vero"), + "producer": producer_root, + "round": str(context.round), + "instruction": proposal.instruction or "", + "best_candidate_id": best.id if best else "", + "best_version": best.version if best else "", + # Evaluation values live in the authorized filesystem context. + "best_value": "", + } + command: list[str] = [] + for argument in self.config.command: + expanded = argument + for placeholder, value in values.items(): + expanded = expanded.replace(f"{{{placeholder}}}", value) + command.append(expanded) + + environment = self._environment() + environment["VERO_CONTEXT_PATH"] = values["context"] + result = await workspace.sandbox.run( + command, + cwd=working_directory, + timeout=self.config.timeout_seconds, + env=environment, + ) + if result.returncode != 0: + raise RuntimeError( + result.stderr + or f"candidate producer exited with status {result.returncode}" + ) + return CandidateChange(description=self.config.description) diff --git a/vero/src/vero/optimization/models.py b/vero/src/vero/optimization/models.py new file mode 100644 index 00000000..7ef7f1aa --- /dev/null +++ b/vero/src/vero/optimization/models.py @@ -0,0 +1,130 @@ +"""Optimization proposals, context, and results.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Mapping +from uuid import uuid4 + +from pydantic import Field, JsonValue, field_validator, model_validator + +from vero.candidate import Candidate +from vero.evaluation import ( + EvaluationAcknowledgement, + EvaluationModel, + EvaluationRecord, + EvaluationSummary, +) +from vero.workspace import Workspace + + +class CandidateProposal(EvaluationModel): + """A strategy's request for one producer to explore a parent candidate.""" + + id: str = Field(default_factory=lambda: str(uuid4())) + producer_id: str = "default" + parent_id: str | None = None + instruction: str | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("id", "producer_id") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("proposal identity must not be empty") + return value + + @field_validator("parent_id", "instruction") + @classmethod + def validate_optional_text(cls, value: str | None) -> str | None: + if value is not None and not value.strip(): + raise ValueError("optional proposal text must not be empty") + return value + + @model_validator(mode="after") + def validate_parent(self) -> CandidateProposal: + if self.parent_id == self.id: + raise ValueError("a proposal cannot name itself as its parent") + return self + + +class CandidateChange(EvaluationModel): + """Producer metadata returned after it edits a supplied workspace.""" + + description: str = "Optimize candidate" + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("description") + @classmethod + def validate_description(cls, value: str) -> str: + if not value.strip(): + raise ValueError("candidate description must not be empty") + return value + + +@dataclass(frozen=True) +class OptimizationContext: + """Authorization-projected history supplied to an optimization strategy.""" + + session_id: str + round: int + workspace: Workspace + baseline: Candidate + evaluations: tuple[ + EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement, + ..., + ] + candidates: Mapping[str, Candidate] + best: Candidate | None + + +@dataclass(frozen=True) +class CandidateProductionContext: + """Non-sensitive control context supplied to a candidate producer. + + Authorized evaluation details live in the read-only ``.vero`` tree and are + returned through the evaluation gateway; they are intentionally not + duplicated here as full records. + """ + + session_id: str + round: int + baseline: Candidate + candidates: Mapping[str, Candidate] + best: Candidate | None + + +@dataclass(frozen=True) +class GenerationOutcome: + """Candidates and generation-time feedback from executing one proposal. + + Produced by a :class:`~vero.optimization.protocols.GenerationBackend` (the + native in-process producer by default, or a Harbor run). ``candidate`` is the + produced candidate (``None`` if the producer made no change); + ``trial_candidates`` are the intermediate checkpoints it captured. Crucially, + ``trial_evaluations`` are the **generation-time feedback** evaluations the + producer *observed while iterating* (mid-run self-eval, or a Harbor sidecar's + disclosed-partition scores) — distinct from the orchestrator's later + selection and target scoring, which the Optimizer performs separately on the + returned candidate. + """ + + candidate: Candidate | None + trial_candidates: tuple[Candidate, ...] + trial_evaluations: tuple[EvaluationRecord, ...] + + @property + def candidates(self) -> tuple[Candidate, ...]: + if self.candidate is None: + return self.trial_candidates + return (*self.trial_candidates, self.candidate) + + +@dataclass(frozen=True) +class OptimizationResult: + baseline: EvaluationRecord + evaluations: tuple[EvaluationRecord, ...] + candidates: tuple[Candidate, ...] + best: EvaluationRecord | None + final_baseline: EvaluationRecord | None = None + final: EvaluationRecord | None = None diff --git a/vero/src/vero/optimization/optimizer.py b/vero/src/vero/optimization/optimizer.py new file mode 100644 index 00000000..b7061b3d --- /dev/null +++ b/vero/src/vero/optimization/optimizer.py @@ -0,0 +1,736 @@ +"""Batch-oriented optimizer over versioned program candidates.""" + +from __future__ import annotations + +import asyncio +import hashlib +from dataclasses import dataclass, field +from datetime import UTC, datetime +from typing import TYPE_CHECKING + +from pydantic import JsonValue + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository +from vero.evaluation import ( + CaseSelection, + DisclosureLevel, + EvaluationBudget, + EvaluationCancelledError, + EvaluationEngine, + EvaluationExecutionError, + EvaluationLimits, + EvaluationPlan, + EvaluationPrincipal, + EvaluationReceipt, + EvaluationRecord, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + EvaluationSummary, + ObjectiveSpec, + project_evaluation, +) +from vero.optimization.models import ( + CandidateProductionContext, + CandidateProposal, + GenerationOutcome, + OptimizationContext, + OptimizationResult, +) +from vero.optimization.protocols import ( + CandidateProducer, + CandidateEvaluationGateway, + GenerationBackend, + OptimizationStrategy, + SelectionPolicy, +) +from vero.optimization.strategy import ObjectiveSelectionPolicy +from vero.workspace import Workspace + +if TYPE_CHECKING: + from vero.runtime.context import AgentDisclosureLedger, WorkspaceContextManager + + +class _ScopedEvaluationGateway(CandidateEvaluationGateway): + def __init__( + self, + *, + optimizer: Optimizer, + proposal: CandidateProposal, + parent: Candidate, + workspace: Workspace, + workspace_context: WorkspaceContextManager, + round_number: int, + ): + self.optimizer = optimizer + self.proposal = proposal + self.parent = parent + self.workspace = workspace + self.workspace_context = workspace_context + self.round_number = round_number + self._count = 0 + self._last_candidate_id = parent.id + self._trial_candidates: list[Candidate] = [] + self._trial_evaluations: list[EvaluationRecord] = [] + + @property + def last_candidate_id(self) -> str: + return self._last_candidate_id + + @property + def last_candidate_version(self) -> str | None: + if not self._trial_candidates: + return None + return self._trial_candidates[-1].version + + @property + def trial_candidates(self) -> tuple[Candidate, ...]: + return tuple(self._trial_candidates) + + @property + def trial_evaluations(self) -> tuple[EvaluationRecord, ...]: + return tuple(self._trial_evaluations) + + async def evaluate( + self, + *, + evaluation: str, + selection: CaseSelection | None = None, + candidate_id: str | None = None, + description: str = "Evaluate agent checkpoint", + ) -> EvaluationReceipt: + if not description.strip(): + raise ValueError("checkpoint description must not be empty") + try: + definition = self.optimizer.evaluation_plan.get(evaluation) + except KeyError as error: + raise ValueError(f"unknown evaluation: {evaluation!r}") from error + requested_set = definition.evaluation_set.model_copy( + update={"selection": selection or definition.evaluation_set.selection} + ) + captured = candidate_id is None + if candidate_id is not None: + candidate = self.optimizer.candidate_repository.get(candidate_id) + if candidate is None: + raise ValueError(f"unknown candidate: {candidate_id!r}") + else: + version = ( + await self.workspace.save(description) + if await self.workspace.is_dirty() + else await self.workspace.current_version() + ) + self._count += 1 + candidate = Candidate( + id=f"{self.proposal.id}:trial:{self._count}", + version=version, + parent_id=self._last_candidate_id, + created_at=datetime.now(UTC), + description=description, + metadata={ + **self.proposal.metadata, + "producer_id": self.proposal.producer_id, + "proposal_id": self.proposal.id, + "round": self.round_number, + "trial": self._count, + }, + ) + await self.optimizer._capture_candidate(candidate, self.workspace) + request = self.optimizer._request(candidate, requested_set) + try: + result = await self.optimizer.engine.evaluate( + backend_id=self.optimizer.backend_id, + request=request, + objective_spec=self.optimizer.objective, + principal=EvaluationPrincipal.AGENT, + ) + except (EvaluationExecutionError, EvaluationCancelledError) as error: + record = self.optimizer.engine.database.get_evaluation(error.evaluation_id) + if record is not None: + decision = await self.optimizer.engine.authorize( + self.optimizer.backend_id, + request, + EvaluationPrincipal.AGENT, + ) + if decision.viewable: + await asyncio.shield( + self.workspace_context.add_evaluation( + record, + decision.disclosure, + ) + ) + raise + evaluation_id = ( + result.id if isinstance(result, EvaluationRecord) else result.evaluation_id + ) + record = self.optimizer.engine.database.get_evaluation(evaluation_id) + if record is None: + raise RuntimeError( + "evaluation engine did not index completed evaluation " + f"{evaluation_id!r}" + ) + if captured: + self._last_candidate_id = candidate.id + self._trial_candidates.append(candidate) + self._trial_evaluations.append(record) + disclosure = ( + DisclosureLevel.FULL + if isinstance(result, EvaluationRecord) + else ( + DisclosureLevel.AGGREGATE + if isinstance(result, EvaluationSummary) + else DisclosureLevel.NONE + ) + ) + return await self.workspace_context.add_evaluation(record, disclosure) + + def budgets(self) -> dict[str, EvaluationBudget | None]: + ledger = self.optimizer.engine.budget_ledger + return { + definition.evaluation_set.name: ( + ledger.get( + self.optimizer.backend_id, + definition.evaluation_set, + EvaluationPrincipal.AGENT, + ) + if ledger is not None + else None + ) + for definition in self.optimizer.evaluation_plan.evaluations + if definition.access.agent_can_evaluate + } + + +@dataclass +class Optimizer: + """Schedule proposal, production, evaluation, and selection rounds.""" + + workspace: Workspace + candidate_repository: CandidateRepository + engine: EvaluationEngine + backend_id: str + evaluation_plan: EvaluationPlan + objective: ObjectiveSpec + strategy: OptimizationStrategy + producers: dict[str, CandidateProducer] + selection: SelectionPolicy = field(default_factory=ObjectiveSelectionPolicy) + generation_backend: GenerationBackend | None = None + parameters: dict[str, JsonValue] = field(default_factory=dict) + limits: EvaluationLimits = field(default_factory=EvaluationLimits) + seed: int | None = None + max_proposals: int = 1 + max_rounds: int = 100 + max_concurrency: int = 1 + session_id: str | None = None + _context_ledger: AgentDisclosureLedger | None = field( + default=None, + init=False, + repr=False, + ) + _producer_locks: dict[str, asyncio.Lock] = field( + default_factory=dict, + init=False, + repr=False, + ) + + def _best(self, records: list[EvaluationRecord]) -> EvaluationRecord | None: + selection_set = self.evaluation_plan.selection.evaluation_set + compatible = [ + record + for record in records + if record.request.evaluation_set == selection_set + ] + return self.selection.select(compatible, self.objective) + + def _request( + self, + candidate: Candidate, + evaluation_set: EvaluationSet | None = None, + ) -> EvaluationRequest: + return EvaluationRequest( + candidate=candidate, + evaluation_set=evaluation_set or self.evaluation_plan.selection.evaluation_set, + parameters=self.parameters, + limits=self.limits, + seed=self.seed, + ) + + async def _capture_candidate( + self, + candidate: Candidate, + workspace: Workspace, + ) -> Candidate: + return await self.candidate_repository.capture(candidate, workspace) + + async def evaluate_candidate( + self, + candidate: Candidate, + evaluation_set: EvaluationSet | None = None, + *, + principal: EvaluationPrincipal = EvaluationPrincipal.SYSTEM, + ) -> EvaluationRecord: + return await self.engine.evaluate_record( + backend_id=self.backend_id, + request=self._request(candidate, evaluation_set), + objective_spec=self.objective, + principal=principal, + ) + + @staticmethod + def _workspace_name(proposal: CandidateProposal) -> str: + digest = hashlib.sha256(proposal.id.encode()).hexdigest()[:12] + return f"vero-candidate-{digest}" + + async def _produce_candidate( + self, + *, + proposal: CandidateProposal, + context: OptimizationContext, + parent: Candidate, + evaluation_records: tuple[EvaluationRecord, ...], + ) -> GenerationOutcome: + try: + producer = self.producers[proposal.producer_id] + except KeyError as error: + raise ValueError( + f"unknown candidate producer: {proposal.producer_id!r}" + ) from error + + async with self.candidate_repository.checkout( + parent, + sandbox=self.workspace.sandbox, + name=self._workspace_name(proposal), + ) as candidate_workspace: + if proposal.parent_id is None: + proposal = proposal.model_copy(update={"parent_id": parent.id}) + before = await candidate_workspace.current_version() + if before != parent.version: + raise ValueError( + f"candidate workspace is at {before!r}, " + f"expected parent {parent.version!r}" + ) + if self._context_ledger is None: + from vero.runtime.context import AgentDisclosureLedger + + self._context_ledger = AgentDisclosureLedger( + self.engine.evaluator.session_dir / "agent-context.json" + ) + from vero.runtime.context import WorkspaceContextManager + + workspace_context = WorkspaceContextManager( + session_id=context.session_id, + session_dir=self.engine.evaluator.session_dir, + round_number=context.round, + proposal_id=proposal.id, + parent=parent, + workspace=candidate_workspace, + candidate_repository=self.candidate_repository, + engine=self.engine, + backend_id=self.backend_id, + evaluation_plan=self.evaluation_plan, + candidates=tuple(context.candidates.values()), + evaluations=evaluation_records, + ledger=self._context_ledger, + ) + await workspace_context.initialize() + evaluation = _ScopedEvaluationGateway( + optimizer=self, + proposal=proposal, + parent=parent, + workspace=candidate_workspace, + workspace_context=workspace_context, + round_number=context.round, + ) + # A producer often owns mutable conversation state. Different producer + # IDs may run concurrently, but one producer is deliberately + # non-reentrant so parallel proposals cannot corrupt that state. + producer_lock = self._producer_locks.setdefault( + proposal.producer_id, + asyncio.Lock(), + ) + production_context = CandidateProductionContext( + session_id=context.session_id, + round=context.round, + baseline=context.baseline, + candidates=context.candidates, + best=( + context.best if context.best is not None else None + ), + ) + async with producer_lock: + change = await producer.produce( + proposal=proposal, + context=production_context, + workspace=candidate_workspace, + evaluation=evaluation, + ) + if change is None: + return GenerationOutcome( + candidate=None, + trial_candidates=evaluation.trial_candidates, + trial_evaluations=evaluation.trial_evaluations, + ) + version = ( + await candidate_workspace.save(change.description) + if await candidate_workspace.is_dirty() + else await candidate_workspace.current_version() + ) + if version == parent.version and not evaluation.trial_candidates: + return GenerationOutcome( + candidate=None, + trial_candidates=(), + trial_evaluations=(), + ) + if version == evaluation.last_candidate_version: + return GenerationOutcome( + candidate=None, + trial_candidates=evaluation.trial_candidates, + trial_evaluations=evaluation.trial_evaluations, + ) + metadata = dict(proposal.metadata) + metadata.update(change.metadata) + metadata["producer_id"] = proposal.producer_id + metadata["proposal_id"] = proposal.id + metadata["round"] = context.round + candidate = Candidate( + id=proposal.id, + version=version, + parent_id=evaluation.last_candidate_id, + created_at=datetime.now(UTC), + description=change.description, + metadata=metadata, + ) + await self._capture_candidate(candidate, candidate_workspace) + return GenerationOutcome( + candidate=candidate, + trial_candidates=evaluation.trial_candidates, + trial_evaluations=evaluation.trial_evaluations, + ) + + async def run( + self, + *, + baseline: Candidate | None = None, + skip_baseline_evaluation: bool = False, + max_proposals: int | None = None, + ) -> OptimizationResult: + proposal_limit = self.max_proposals if max_proposals is None else max_proposals + if proposal_limit < 0 or proposal_limit > self.max_proposals: + raise ValueError( + "run max_proposals must be between zero and the session protocol limit" + ) + if self.max_proposals < 0: + raise ValueError("max_proposals must be non-negative") + if self.max_rounds < 1: + raise ValueError("max_rounds must be positive") + if self.max_concurrency < 1: + raise ValueError("max_concurrency must be positive") + if not self.producers and proposal_limit: + raise ValueError("at least one candidate producer is required") + + if baseline is None: + version = await self.workspace.current_version() + baseline = Candidate.from_version(version) + stored_baseline = self.candidate_repository.get(baseline.id) + if stored_baseline is None: + baseline = await self._capture_candidate(baseline, self.workspace) + elif stored_baseline != baseline: + raise ValueError("baseline does not match its durable candidate record") + + backend_provenance = self.engine.backends.resolve(self.backend_id).provenance + selection_set = self.evaluation_plan.selection.evaluation_set + existing_baselines = [ + record + for record in self.engine.database.evaluations.values() + if record.request.candidate.id == baseline.id + and record.request.candidate.version == baseline.version + and record.backend_id == self.backend_id + and record.request.evaluation_set == selection_set + and record.request.parameters == self.parameters + and record.request.limits == self.limits + and record.request.seed == self.seed + and record.backend == backend_provenance + and record.objective_spec == self.objective + ] + if skip_baseline_evaluation: + if not existing_baselines: + raise ValueError( + "skip_baseline_evaluation requires an existing compatible baseline" + ) + baseline_record = existing_baselines[-1] + else: + baseline_record = await self.evaluate_candidate( + baseline, + selection_set, + principal=EvaluationPrincipal.SYSTEM, + ) + + final_baseline: EvaluationRecord | None = None + final_definition = self.evaluation_plan.final + if final_definition is not None and self.evaluation_plan.evaluate_final_baseline: + final_set = final_definition.evaluation_set + existing_final_baselines = [ + record + for record in self.engine.database.evaluations.values() + if record.request.candidate.id == baseline.id + and record.request.candidate.version == baseline.version + and record.backend_id == self.backend_id + and record.request.evaluation_set == final_set + and record.request.parameters == self.parameters + and record.request.limits == self.limits + and record.request.seed == self.seed + and record.backend == backend_provenance + and record.objective_spec == self.objective + ] + if skip_baseline_evaluation and existing_final_baselines: + final_baseline = existing_final_baselines[-1] + else: + final_baseline = await self.evaluate_candidate( + baseline, + final_set, + principal=EvaluationPrincipal.SYSTEM, + ) + + compatible = [ + record + for record in self.engine.database.evaluations.values() + if record.backend_id == self.backend_id + and self.evaluation_plan.for_evaluation_set( + record.request.evaluation_set + ) + is not None + and record.request.parameters == self.parameters + and record.request.limits == self.limits + and record.request.seed == self.seed + and record.backend == backend_provenance + and record.objective_spec == self.objective + ] + candidate_records = { + candidate.id: candidate for candidate in self.candidate_repository.list() + } + reachable = {baseline.id} + changed = True + while changed: + changed = False + for candidate in candidate_records.values(): + if candidate.id in reachable: + continue + if candidate.parent_id in reachable: + reachable.add(candidate.id) + changed = True + evaluations = [ + record for record in compatible if record.request.candidate.id in reachable + ] + if baseline_record not in evaluations: + evaluations.insert(0, baseline_record) + evaluations.sort(key=lambda record: (record.completed_at, record.id)) + candidates: dict[str, Candidate] = { + candidate_id: candidate + for candidate_id, candidate in candidate_records.items() + if candidate_id in reachable + } + candidates[baseline.id] = baseline + proposal_ids = { + str(candidate.metadata.get("proposal_id", candidate.id)) + for candidate in candidates.values() + if candidate.id != baseline.id and "producer_id" in candidate.metadata + } + generated = len(proposal_ids) + completed_rounds = [ + int(candidate.metadata["round"]) + for candidate in candidates.values() + if isinstance(candidate.metadata.get("round"), int) + ] + start_round = max(completed_rounds, default=generated - 1) + 1 + semaphore = asyncio.Semaphore(self.max_concurrency) + + evaluated_candidate_ids = { + record.request.candidate.id for record in evaluations + if record.request.evaluation_set == selection_set + } + pending = [ + candidate + for candidate in candidates.values() + if candidate.id != baseline.id + and candidate.id not in evaluated_candidate_ids + and "producer_id" in candidate.metadata + and "trial" not in candidate.metadata + ] + pending.sort( + key=lambda candidate: ( + int(candidate.metadata.get("round", 0)), + int(candidate.metadata.get("trial", 0)), + candidate.created_at, + candidate.id, + ) + ) + + async def evaluate(candidate: Candidate) -> EvaluationRecord: + async with semaphore: + return await self.evaluate_candidate( + candidate, + selection_set, + principal=EvaluationPrincipal.SYSTEM, + ) + + if pending: + async with asyncio.TaskGroup() as group: + pending_tasks = [ + group.create_task(evaluate(candidate)) for candidate in pending + ] + evaluations.extend(task.result() for task in pending_tasks) + + for round_number in range(start_round, self.max_rounds): + if generated >= proposal_limit: + break + best = self._best(evaluations) + visible_evaluations = tuple( + record + for record in evaluations + if ( + definition := self.evaluation_plan.for_evaluation_set( + record.request.evaluation_set + ) + ) + is not None + and definition.access.agent_visible + ) + evaluation_views = tuple( + project_evaluation( + record, + self.evaluation_plan.for_evaluation_set( + record.request.evaluation_set + ).access.disclosure, + ) + for record in visible_evaluations + ) + context = OptimizationContext( + session_id=self.session_id or self.engine.evaluator.session_dir.name, + round=round_number, + workspace=self.workspace, + baseline=baseline, + evaluations=evaluation_views, + candidates=dict(candidates), + best=(best.request.candidate if best is not None else None), + ) + proposals = list(await self.strategy.propose(context)) + if not proposals: + break + remaining = proposal_limit - generated + proposals = proposals[:remaining] + proposal_ids = [proposal.id for proposal in proposals] + if len(proposal_ids) != len(set(proposal_ids)): + raise ValueError("strategy returned duplicate proposal IDs") + if any(proposal_id in candidates for proposal_id in proposal_ids): + raise ValueError("strategy reused an existing candidate ID") + + async def produce(proposal: CandidateProposal) -> GenerationOutcome: + parent_id = proposal.parent_id or ( + best.request.candidate.id if best is not None else baseline.id + ) + try: + parent = candidates[parent_id] + except KeyError as error: + raise ValueError( + f"proposal {proposal.id!r} names unknown parent {parent_id!r}" + ) from error + async with semaphore: + if self.generation_backend is not None: + return await self.generation_backend.generate( + proposal=proposal, + context=context, + parent=parent, + evaluation_records=visible_evaluations, + ) + return await self._produce_candidate( + proposal=proposal, + context=context, + parent=parent, + evaluation_records=visible_evaluations, + ) + + async with asyncio.TaskGroup() as group: + production_tasks = [ + group.create_task(produce(proposal)) for proposal in proposals + ] + outcomes = [task.result() for task in production_tasks] + meaningful_outcomes = [ + outcome for outcome in outcomes if outcome.candidates + ] + if not meaningful_outcomes: + break + generated += len(meaningful_outcomes) + for outcome in meaningful_outcomes: + evaluations.extend(outcome.trial_evaluations) + for candidate in outcome.candidates: + if candidate.id in candidates: + raise ValueError( + f"candidate producer reused candidate ID {candidate.id!r}" + ) + candidates[candidate.id] = candidate + + produced = [ + outcome.candidate + for outcome in meaningful_outcomes + if outcome.candidate is not None + ] + + async with asyncio.TaskGroup() as group: + evaluation_tasks = [ + group.create_task(evaluate(candidate)) for candidate in produced + ] + evaluations.extend(task.result() for task in evaluation_tasks) + + best = self._best(evaluations) + final_record: EvaluationRecord | None = None + if final_definition is not None and best is not None: + if ( + final_baseline is not None + and best.request.candidate.id == baseline.id + and best.request.candidate.version == baseline.version + ): + final_record = final_baseline + else: + final_set = final_definition.evaluation_set + existing_final = [ + record + for record in self.engine.database.evaluations.values() + if record.request.candidate.id == best.request.candidate.id + and record.request.candidate.version + == best.request.candidate.version + and record.backend_id == self.backend_id + and record.request.evaluation_set == final_set + and record.request.parameters == self.parameters + and record.request.limits == self.limits + and record.request.seed == self.seed + and record.backend == backend_provenance + and record.objective_spec == self.objective + # Only reuse a genuine, completed final result produced by the + # same trusted path: never a failed/cancelled/invalid record, + # and never a non-SYSTEM (e.g. agent-visible) evaluation, so a + # resume can't be marked complete on a broken final. + and record.report.status == EvaluationStatus.SUCCESS + and record.principal == EvaluationPrincipal.SYSTEM + ] + if existing_final: + # On resume (or a re-run of a completed session) the winner + # may already have a compatible final evaluation. Reuse it + # rather than repeating a hidden-test run and spending the + # system/inference budget an exact allocation may not have. + final_record = existing_final[-1] + else: + final_record = await self.evaluate_candidate( + best.request.candidate, + final_set, + principal=EvaluationPrincipal.SYSTEM, + ) + evaluations.append(final_record) + + return OptimizationResult( + baseline=baseline_record, + evaluations=tuple(evaluations), + candidates=tuple(candidates.values()), + best=best, + final_baseline=final_baseline, + final=final_record, + ) diff --git a/vero/src/vero/optimization/protocols.py b/vero/src/vero/optimization/protocols.py new file mode 100644 index 00000000..ef694603 --- /dev/null +++ b/vero/src/vero/optimization/protocols.py @@ -0,0 +1,90 @@ +"""Extension protocols for optimization strategies and candidate producers.""" + +from __future__ import annotations + +from typing import Mapping, Protocol, Sequence, runtime_checkable + +from vero.evaluation import ( + EvaluationBudget, + CaseSelection, + EvaluationReceipt, + EvaluationRecord, + ObjectiveSpec, +) +from vero.candidate import Candidate +from vero.optimization.models import ( + CandidateChange, + CandidateProductionContext, + CandidateProposal, + GenerationOutcome, + OptimizationContext, +) +from vero.workspace import Workspace + + +@runtime_checkable +class OptimizationStrategy(Protocol): + async def propose( + self, + context: OptimizationContext, + ) -> Sequence[CandidateProposal]: ... + + +@runtime_checkable +class CandidateEvaluationGateway(Protocol): + """Evaluation capability scoped to one producer workspace.""" + + async def evaluate( + self, + *, + evaluation: str, + selection: CaseSelection | None = None, + candidate_id: str | None = None, + description: str = "Evaluate agent checkpoint", + ) -> EvaluationReceipt: ... + + def budgets(self) -> Mapping[str, EvaluationBudget | None]: ... + + +@runtime_checkable +class CandidateProducer(Protocol): + async def produce( + self, + *, + proposal: CandidateProposal, + context: CandidateProductionContext, + workspace: Workspace, + evaluation: CandidateEvaluationGateway, + ) -> CandidateChange | None: ... + + +@runtime_checkable +class GenerationBackend(Protocol): + """Turn a parent + proposal into candidate(s) plus generation-time feedback. + + This is the swappable unit the Optimizer drives. The default is the + Optimizer's built-in native production (an agent or operator editing a + checkout in a sandbox, with mid-run self-eval). A Harbor implementation + delegates to a ``harbor run`` instead. Either way, the orchestrator performs + selection and target scoring *separately* on the returned candidate; the + ``GenerationOutcome.trial_evaluations`` are only the generation-time feedback + the producer observed while iterating. + """ + + async def generate( + self, + *, + proposal: CandidateProposal, + parent: Candidate, + context: OptimizationContext, + evaluation_records: Sequence[EvaluationRecord], + ) -> GenerationOutcome: ... + + +@runtime_checkable +class SelectionPolicy(Protocol): + def select( + self, + records: Sequence[EvaluationRecord], + objective: ObjectiveSpec, + ) -> EvaluationRecord | None: ... diff --git a/vero/src/vero/optimization/strategy.py b/vero/src/vero/optimization/strategy.py new file mode 100644 index 00000000..bfdaa88b --- /dev/null +++ b/vero/src/vero/optimization/strategy.py @@ -0,0 +1,329 @@ +"""Built-in optimization and selection strategies.""" + +from __future__ import annotations + +import random +from collections.abc import Sequence + +from vero.evaluation import ( + EvaluationRecord, + EvaluationSummary, + ObjectiveResult, + ObjectiveSpec, + select_best_evaluation, +) +from vero.optimization.models import CandidateProposal, OptimizationContext + + +class SequentialStrategy: + """Request one candidate from the same producer on every round.""" + + def __init__( + self, + *, + producer_id: str = "default", + instruction: str | None = None, + ): + self.producer_id = producer_id + self.instruction = instruction + + @property + def config(self) -> dict[str, object]: + """Identity-relevant settings, folded into the resume digest.""" + return {"producer_id": self.producer_id, "instruction": self.instruction} + + async def propose(self, context: OptimizationContext) -> Sequence[CandidateProposal]: + parent_id = ( + context.best.id + if context.best is not None + else context.baseline.id + ) + return [ + CandidateProposal( + producer_id=self.producer_id, + parent_id=parent_id, + instruction=self.instruction, + ) + ] + + +def _record_objective( + view: object, +) -> tuple[str, str | None, ObjectiveResult] | None: + """Extract (candidate_id, evaluation_set_name, objective) from a projected view. + + Handles full ``EvaluationRecord`` and aggregate ``EvaluationSummary`` views; + returns ``None`` for target/none views (``EvaluationAcknowledgement``) that + carry no objective — so the strategy never sees held-out scores. + """ + if isinstance(view, EvaluationRecord): + if view.objective is None: + return None + return view.request.candidate.id, view.request.evaluation_set.name, view.objective + if isinstance(view, EvaluationSummary): + if view.objective is None: + return None + return view.candidate_id, view.evaluation_set.name, view.objective + return None + + +class EvolutionaryStrategy: + """A population-based strategy: select parents, emit N mutated offspring. + + Each round it ingests the disclosed evaluations into a fitness archive + (direction-aware via ``objective``), keeps the fittest ``population_size`` + candidates, and emits ``num_offspring`` proposals whose parents are chosen by + tournament selection. Before any feasible candidate exists it seeds offspring + from the current best (or the baseline). This is the native runner's + distinctive capability over Harbor's single-agent-as-optimizer. + + Only mutation (parent + instruction) is implemented; crossover (combining two + parents) is a follow-on — it needs the producer/instruction to reference a + second candidate's contents, exposed through the read-only ``.vero`` tree. + + ``evaluation_set`` should name the selection set when multiple sets are + visible, so fitness is ranked on one consistent metric; left ``None`` it + ranks on all disclosed evaluations (correct for single-set setups). + """ + + def __init__( + self, + *, + objective: ObjectiveSpec, + producer_id: str = "default", + population_size: int = 8, + num_offspring: int = 4, + tournament_size: int = 3, + instruction: str | None = None, + evaluation_set: str | None = None, + seed: int | None = None, + ): + if population_size < 1: + raise ValueError("population_size must be >= 1") + if num_offspring < 1: + raise ValueError("num_offspring must be >= 1") + if tournament_size < 1: + raise ValueError("tournament_size must be >= 1") + self.objective = objective + self.producer_id = producer_id + self.population_size = population_size + self.num_offspring = num_offspring + self.tournament_size = tournament_size + self.instruction = instruction + self.evaluation_set = evaluation_set + self.seed = seed + self._rng = random.Random(seed) + self._fitness: dict[str, float] = {} + self._generation = 0 + + @property + def config(self) -> dict[str, object]: + """Every setting that changes search semantics, so the resume digest + rejects a resume under a materially different configuration.""" + return { + "producer_id": self.producer_id, + "population_size": self.population_size, + "num_offspring": self.num_offspring, + "tournament_size": self.tournament_size, + "instruction": self.instruction, + "evaluation_set": self.evaluation_set, + "seed": self.seed, + } + + async def propose(self, context: OptimizationContext) -> Sequence[CandidateProposal]: + self._ingest(context) + parents = self._select_parents(context) + proposals = [ + CandidateProposal( + producer_id=self.producer_id, + parent_id=parent_id, + instruction=self.instruction, + metadata={ + "strategy": "evolutionary", + "generation": self._generation, + "parent_id": parent_id, + }, + ) + for parent_id in parents + ] + self._generation += 1 + return proposals + + def _to_fitness(self, objective: ObjectiveResult) -> float: + """Map an objective result to an internal fitness where higher is better.""" + if not objective.feasible or objective.value is None: + return float("-inf") + return ( + objective.value + if self.objective.direction == "maximize" + else -objective.value + ) + + def _ingest(self, context: OptimizationContext) -> None: + for view in context.evaluations: + parsed = _record_objective(view) + if parsed is None: + continue + candidate_id, set_name, objective = parsed + if self.evaluation_set is not None and set_name != self.evaluation_set: + continue + fitness = self._to_fitness(objective) + previous = self._fitness.get(candidate_id) + if previous is None or fitness > previous: + self._fitness[candidate_id] = fitness + + def _population(self, context: OptimizationContext) -> list[str]: + scored = [ + (candidate_id, fitness) + for candidate_id, fitness in self._fitness.items() + if candidate_id in context.candidates and fitness > float("-inf") + ] + scored.sort(key=lambda item: item[1], reverse=True) + return [candidate_id for candidate_id, _ in scored[: self.population_size]] + + def _select_parents(self, context: OptimizationContext) -> list[str]: + population = self._population(context) + if not population: + seed_parent = ( + context.best.id if context.best is not None else context.baseline.id + ) + return [seed_parent] * self.num_offspring + parents: list[str] = [] + for _ in range(self.num_offspring): + k = min(self.tournament_size, len(population)) + contenders = self._rng.sample(population, k) + parents.append(max(contenders, key=lambda cid: self._fitness[cid])) + return parents + + +class DarwinGodelStrategy: + """Open-ended archive evolution in the style of the Darwin Gödel Machine. + + Where ``EvolutionaryStrategy`` runs a tournament over a fittest-``K`` + population, DGM keeps the **entire archive** as candidate parents and samples + parent ``p`` with probability proportional to ``score(p) / (1 + children(p))`` + — every archived agent keeps a non-zero probability, so lower-performing + nodes remain reachable as *stepping stones* (many innovations traverse them). + + The intended pairing is a self-application producer: each offspring is the + parent's own harness modifying itself (analyze its ``.vero`` trace corpus, + implement one improvement), which is the self-referential loop the original + Gödel Machine wanted but validated empirically rather than by proof. + + ``base_weight`` is the floor performance given to not-yet-scored or infeasible + agents so brand-new lineages are still explorable. ``evaluation_set`` names + the selection set to rank on when several are visible (held-out target scores + never appear in ``context.evaluations``, so they cannot leak into selection). + """ + + def __init__( + self, + *, + objective: ObjectiveSpec, + producer_id: str = "self", + num_offspring: int = 2, + instruction: str | None = None, + evaluation_set: str | None = None, + base_weight: float = 0.2, + seed: int | None = None, + ): + if num_offspring < 1: + raise ValueError("num_offspring must be >= 1") + if base_weight <= 0: + raise ValueError("base_weight must be > 0 so every agent stays reachable") + self.objective = objective + self.producer_id = producer_id + self.num_offspring = num_offspring + self.instruction = instruction + self.evaluation_set = evaluation_set + self.base_weight = base_weight + self.seed = seed + self._rng = random.Random(seed) + self._score: dict[str, float] = {} + self._children: dict[str, int] = {} + self._generation = 0 + + @property + def config(self) -> dict[str, object]: + """Every setting that changes search semantics, so the resume digest + rejects a resume under a materially different configuration.""" + return { + "producer_id": self.producer_id, + "num_offspring": self.num_offspring, + "instruction": self.instruction, + "evaluation_set": self.evaluation_set, + "base_weight": self.base_weight, + "seed": self.seed, + } + + async def propose(self, context: OptimizationContext) -> Sequence[CandidateProposal]: + self._ingest(context) + parents = self._select_parents(context) + proposals = [ + CandidateProposal( + producer_id=self.producer_id, + parent_id=parent_id, + instruction=self.instruction, + metadata={ + "strategy": "darwin-godel", + "generation": self._generation, + "parent_id": parent_id, + }, + ) + for parent_id in parents + ] + for parent_id in parents: + self._children[parent_id] = self._children.get(parent_id, 0) + 1 + self._generation += 1 + return proposals + + def _ingest(self, context: OptimizationContext) -> None: + """Record each candidate's best feasible objective as its performance.""" + for view in context.evaluations: + parsed = _record_objective(view) + if parsed is None: + continue + candidate_id, set_name, objective = parsed + if self.evaluation_set is not None and set_name != self.evaluation_set: + continue + if not objective.feasible or objective.value is None: + continue + quality = ( + objective.value + if self.objective.direction == "maximize" + else -objective.value + ) + previous = self._score.get(candidate_id) + if previous is None or quality > previous: + self._score[candidate_id] = quality + + def _weight(self, candidate_id: str) -> float: + """DGM selection weight: performance, damped by how much it's been explored.""" + performance = self._score.get(candidate_id) + quality = self.base_weight if performance is None else max(self.base_weight, performance) + return quality / (1 + self._children.get(candidate_id, 0)) + + def _select_parents(self, context: OptimizationContext) -> list[str]: + archive = list(context.candidates) + if not archive: + seed_parent = ( + context.best.id if context.best is not None else context.baseline.id + ) + return [seed_parent] * self.num_offspring + weights = [self._weight(candidate_id) for candidate_id in archive] + if sum(weights) <= 0: + weights = [1.0] * len(archive) + # sample WITH replacement: a strong lineage may spawn several children a round + return self._rng.choices(archive, weights=weights, k=self.num_offspring) + + +class ObjectiveSelectionPolicy: + """Select the best feasible value of the configured objective.""" + + def select( + self, + records: Sequence[EvaluationRecord], + objective: ObjectiveSpec, + ) -> EvaluationRecord | None: + compatible = [record for record in records if record.objective_spec == objective] + return select_best_evaluation(compatible) diff --git a/vero/src/vero/report.py b/vero/src/vero/report.py new file mode 100644 index 00000000..0fd87cc5 --- /dev/null +++ b/vero/src/vero/report.py @@ -0,0 +1,677 @@ +"""Self-contained, read-only reports for durable optimization sessions.""" + +from __future__ import annotations + +import base64 +import hashlib +import json +import mimetypes +import subprocess +from pathlib import Path +from typing import Any + +from vero.candidate import Candidate +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import EvaluationDatabase +from vero.harbor.session import HarborSessionManifest +from vero.harbor.verifier import VerificationResult +from vero.runtime.events import RuntimeEvent +from vero.runtime.session import SessionManifest + + +_MAX_EMBEDDED_ARTIFACT_BYTES = 5_000_000 +_MAX_EMBEDDED_ARTIFACTS_BYTES = 50_000_000 +_MAX_DIFF_CHARACTERS = 500_000 + + +def _read_events(path: Path) -> list[dict[str, Any]]: + if not path.is_file(): + return [] + events: list[dict[str, Any]] = [] + for line_number, line in enumerate( + path.read_text(encoding="utf-8").splitlines(), 1 + ): + if not line.strip(): + continue + try: + event = RuntimeEvent.model_validate_json(line) + except Exception as error: + raise ValueError( + f"invalid runtime event on line {line_number}: {error}" + ) from error + events.append(event.model_dump(mode="json")) + return events + + +def _git_diff( + repository_path: Path, + candidate: Candidate, + parent: Candidate | None, + *, + project_subpath: str, +) -> dict[str, Any]: + if parent is None: + arguments = [ + "show", + "--format=", + "--no-ext-diff", + "--no-color", + candidate.version, + ] + label = "Initial program" + else: + arguments = [ + "diff", + "--no-ext-diff", + "--no-color", + parent.version, + candidate.version, + ] + label = f"Changes from {parent.id}" + if project_subpath != ".": + arguments.extend(["--", project_subpath]) + result = subprocess.run( + ["git", "--git-dir", str(repository_path), *arguments], + check=False, + capture_output=True, + text=True, + encoding="utf-8", + errors="replace", + env={"PATH": "/usr/bin:/bin:/usr/sbin:/sbin", "LANG": "C.UTF-8"}, + ) + if result.returncode != 0: + return { + "label": label, + "text": "", + "error": result.stderr.strip() or "Git could not render this diff.", + "truncated": False, + } + text = result.stdout + truncated = len(text) > _MAX_DIFF_CHARACTERS + if truncated: + text = text[:_MAX_DIFF_CHARACTERS] + return {"label": label, "text": text, "error": None, "truncated": truncated} + + +def _embed_artifact( + path: Path, + *, + media_type: str | None, + description: str | None, + relative_path: str, + remaining_bytes: int, +) -> tuple[dict[str, Any], int]: + resolved_media_type = media_type or mimetypes.guess_type(path.name)[0] + artifact: dict[str, Any] = { + "path": relative_path, + "media_type": resolved_media_type, + "description": description, + "exists": path.is_file(), + "size": path.stat().st_size if path.is_file() else None, + "kind": "missing", + "content": None, + "omitted_reason": None, + } + if not path.is_file(): + artifact["omitted_reason"] = "Artifact file is missing." + return artifact, 0 + size = path.stat().st_size + if size > _MAX_EMBEDDED_ARTIFACT_BYTES: + artifact["kind"] = "omitted" + artifact["omitted_reason"] = ( + f"Artifact is larger than {_MAX_EMBEDDED_ARTIFACT_BYTES:,} bytes." + ) + return artifact, 0 + if size > remaining_bytes: + artifact["kind"] = "omitted" + artifact["omitted_reason"] = "The report's embedded-artifact limit was reached." + return artifact, 0 + + payload = path.read_bytes() + if resolved_media_type and ( + resolved_media_type.startswith("image/") + or resolved_media_type == "application/pdf" + ): + artifact["kind"] = ( + "image" if resolved_media_type.startswith("image/") else "binary" + ) + if artifact["kind"] == "image": + encoded = base64.b64encode(payload).decode("ascii") + artifact["content"] = f"data:{resolved_media_type};base64,{encoded}" + else: + artifact["omitted_reason"] = "Binary preview is not supported." + elif ( + resolved_media_type is None + or resolved_media_type.startswith("text/") + or resolved_media_type in {"application/json", "application/xml"} + ): + artifact["kind"] = "text" + artifact["content"] = payload.decode("utf-8", errors="replace") + else: + artifact["kind"] = "binary" + artifact["omitted_reason"] = "Binary preview is not supported." + return artifact, size + + +def _trace_entries(value: Any) -> list[dict[str, Any]]: + values = value if isinstance(value, list) else [value] + entries: list[dict[str, Any]] = [] + for item in values: + if not isinstance(item, dict): + entries.append({"kind": "event", "title": "Event", "body": item}) + continue + item_type = item.get("type") + role = item.get("role") + if item_type == "function_call": + entries.append( + { + "kind": "tool-call", + "title": str(item.get("name") or "Tool call"), + "body": item.get("arguments"), + } + ) + elif item_type == "function_call_output": + entries.append( + { + "kind": "tool-result", + "title": "Tool result", + "body": item.get("output"), + } + ) + elif role in {"user", "assistant", "system", "developer"}: + entries.append( + { + "kind": str(role), + "title": str(role).capitalize(), + "body": item.get("content"), + } + ) + else: + entries.append( + { + "kind": str(item_type or "event"), + "title": str(item_type or "Event").replace("_", " ").title(), + "body": item, + } + ) + return entries + + +def _read_traces(session_dir: Path) -> list[dict[str, Any]]: + root = session_dir / "artifacts" / "agents" + if not root.is_dir(): + return [] + traces: list[dict[str, Any]] = [] + for directory in sorted(path for path in root.iterdir() if path.is_dir()): + trace_path = directory / "trace.json" + if directory.name == "producers" or not trace_path.is_file(): + continue + try: + raw = json.loads(trace_path.read_text(encoding="utf-8")) + except Exception as error: + raw = {"error": f"Could not parse trace: {error}"} + failure_path = directory / "failure.json" + failure = None + if failure_path.is_file(): + try: + failure = json.loads(failure_path.read_text(encoding="utf-8")) + except Exception as error: + failure = {"message": f"Could not parse failure: {error}"} + traces.append( + { + "id": directory.name, + "entries": _trace_entries(raw), + "failure": failure, + "path": str(trace_path.relative_to(session_dir)), + } + ) + return traces + + +def _load_database(session_dir: Path, database_id: str) -> EvaluationDatabase: + database_path = session_dir / "database.json" + database = ( + EvaluationDatabase.load_from_file(database_path) + if database_path.is_file() + else EvaluationDatabase(id=database_id) + ) + if database.id != database_id: + raise ValueError( + f"evaluation database belongs to {database.id!r}, not {database_id!r}" + ) + completed = EvaluationDatabase.from_evaluations_dir( + session_dir / "evaluations", database_id=database_id + ) + for record in completed.evaluations.values(): + if record.id not in database.evaluations: + database.add_evaluation(record) + return database + + +async def _build_report_data( + session_dir: Path, + *, + manifest: dict[str, Any], + database: EvaluationDatabase, + default_trace_id: str | None = None, +) -> dict[str, Any]: + evaluations = sorted( + database.evaluations.values(), + key=lambda record: (record.completed_at, record.id), + ) + + if manifest["candidate_repository_family"] != "git": + candidates = tuple( + sorted( + database.candidates.values(), + key=lambda value: (value.created_at, value.id), + ) + ) + repository = None + else: + repository = await GitCandidateRepository.open(session_dir / "candidates") + candidates = repository.list() + + candidate_by_id = {candidate.id: candidate for candidate in candidates} + traces = _read_traces(session_dir) + trace_ids = {trace["id"] for trace in traces} + candidate_data: list[dict[str, Any]] = [] + for candidate in candidates: + proposal_id = candidate.metadata.get("proposal_id") + trace_id = ( + hashlib.sha256(str(proposal_id).encode()).hexdigest()[:16] + if proposal_id is not None + else default_trace_id + ) + item = candidate.model_dump(mode="json") + item["trace_id"] = trace_id if trace_id in trace_ids else None + if repository is not None: + item["diff"] = _git_diff( + repository.repository_path, + candidate, + candidate_by_id.get(candidate.parent_id) + if candidate.parent_id + else None, + project_subpath=repository.project_subpath, + ) + else: + item["diff"] = { + "label": "Program changes", + "text": "", + "error": "Diffs are unavailable for this candidate repository family.", + "truncated": False, + } + candidate_data.append(item) + + embedded_bytes = 0 + evaluation_data: list[dict[str, Any]] = [] + for step, record in enumerate(evaluations): + references = list(record.report.artifacts) + for case in record.report.cases: + references.extend(case.artifacts) + seen_paths: set[str] = set() + artifacts: list[dict[str, Any]] = [] + for reference in references: + if reference.path in seen_paths: + continue + seen_paths.add(reference.path) + artifact, consumed = _embed_artifact( + session_dir / "evaluations" / record.id / "artifacts" / reference.path, + media_type=reference.media_type, + description=reference.description, + relative_path=reference.path, + remaining_bytes=_MAX_EMBEDDED_ARTIFACTS_BYTES - embedded_bytes, + ) + embedded_bytes += consumed + artifacts.append(artifact) + item = record.model_dump(mode="json") + item["step"] = step + item["artifacts"] = artifacts + evaluation_data.append(item) + + events = _read_events(session_dir / "events.jsonl") + return { + "schema_version": 1, + "generated_from": str(session_dir), + "manifest": manifest, + "candidates": candidate_data, + "evaluations": evaluation_data, + "events": events, + "traces": traces, + } + + +def _harbor_presentation_manifest( + manifest: HarborSessionManifest, + database: EvaluationDatabase, + finalization: VerificationResult | None, +) -> dict[str, Any]: + selection = manifest.selection + selected = finalization.candidate if finalization is not None else None + selection_records = [ + record + for record in database.evaluations.values() + if selected is not None + and selection.backend_id is not None + and selection.evaluation_set is not None + and record.request.candidate.id == selected.id + and record.backend_id == selection.backend_id + and record.request.evaluation_set == selection.evaluation_set + and record.objective is not None + and record.objective.feasible + and record.objective.value is not None + ] + if selection.objective is not None: + selection_records.sort(key=lambda record: record.id) + selection_records.sort( + key=lambda record: record.objective.value, + reverse=selection.objective.direction == "maximize", + ) + best_evaluation_id = selection_records[0].id if selection_records else None + final_evaluation_id = None + if finalization is not None and finalization.evaluation_ids: + final_evaluation_id = next(iter(finalization.evaluation_ids.values())) + completed_at = max( + (record.completed_at for record in database.evaluations.values()), + default=manifest.created_at, + ) + errors = finalization.errors if finalization is not None else {} + objective = selection.objective or manifest.targets[0].objective + backend_id = selection.backend_id or manifest.targets[0].backend_id + selection_set = selection.evaluation_set + return { + "schema_version": 1, + "id": f"{manifest.task_name} · {manifest.id}", + "status": "failed" if finalization is None or errors else "completed", + "backend_id": backend_id, + "candidate_repository_family": manifest.candidate_repository_family, + "candidate_repository_format_version": ( + manifest.candidate_repository_format_version + ), + "evaluation_plan": { + "selection_evaluation": ( + selection_set.name if selection_set is not None else "selection" + ), + "final_evaluation": ( + manifest.targets[0].evaluation_set.name if manifest.targets else None + ), + }, + "selection_evaluation_set": ( + selection_set.model_dump(mode="json") if selection_set is not None else None + ), + "objective": objective.model_dump(mode="json"), + "baseline": ( + selection.baseline_candidate.model_dump(mode="json") + if selection.baseline_candidate is not None + else None + ), + "best_candidate_id": selected.id if selected is not None else None, + "best_evaluation_id": best_evaluation_id, + "final_baseline_evaluation_id": None, + "final_evaluation_id": final_evaluation_id, + "created_at": manifest.created_at.isoformat(), + "updated_at": completed_at.isoformat(), + "failure": ( + {"type": "verification", "message": json.dumps(errors, sort_keys=True)} + if errors + else None + ), + "metadata": { + "task_description": manifest.task_description, + "verification": ( + finalization.model_dump(mode="json") + if finalization is not None + else None + ), + }, + } + + +async def build_experiment_report_data(session_dir: Path | str) -> dict[str, Any]: + """Load a local or Harbor session into the portable report data model.""" + + session_dir = Path(session_dir).expanduser().resolve() + manifest_path = session_dir / "manifest.json" + if manifest_path.is_file(): + parsed = SessionManifest.model_validate_json( + manifest_path.read_text(encoding="utf-8") + ) + manifest = parsed.model_dump(mode="json") + manifest["selection_evaluation_set"] = ( + parsed.evaluation_plan.selection.evaluation_set.model_dump(mode="json") + ) + return await _build_report_data( + session_dir, + manifest=manifest, + database=_load_database(session_dir, parsed.id), + ) + + harbor_path = session_dir / "harbor-session.json" + if not harbor_path.is_file(): + raise FileNotFoundError(f"session manifest not found: {manifest_path}") + harbor = HarborSessionManifest.model_validate_json( + harbor_path.read_text(encoding="utf-8") + ) + database = _load_database(session_dir, harbor.id) + finalization_path = session_dir / "harbor-finalization.json" + finalization = ( + VerificationResult.model_validate_json( + finalization_path.read_text(encoding="utf-8") + ) + if finalization_path.is_file() + else None + ) + traces = _read_traces(session_dir) + data = await _build_report_data( + session_dir, + manifest=_harbor_presentation_manifest(harbor, database, finalization), + database=database, + default_trace_id=traces[0]["id"] if traces else None, + ) + if not data["events"]: + data["events"] = [ + { + "created_at": evaluation["completed_at"], + "kind": "evaluation.completed", + "payload": { + "evaluation_id": evaluation["id"], + "candidate_id": evaluation["request"]["candidate"]["id"], + "backend_id": evaluation["backend_id"], + "evaluation_set": evaluation["request"]["evaluation_set"], + "status": evaluation["report"]["status"], + "objective": evaluation["objective"], + }, + } + for evaluation in data["evaluations"] + ] + if finalization is not None: + data["events"].append( + { + "created_at": data["manifest"]["updated_at"], + "kind": "verification.completed", + "payload": finalization.model_dump(mode="json"), + } + ) + return data + + +def _safe_json(value: Any) -> str: + return ( + json.dumps(value, ensure_ascii=False, separators=(",", ":")) + .replace("<", "\\u003c") + .replace(">", "\\u003e") + .replace("&", "\\u0026") + ) + + +async def generate_experiment_report( + session_dir: Path | str, + output: Path | str | None = None, +) -> Path: + """Generate one portable HTML report without modifying the session.""" + + resolved_session = Path(session_dir).expanduser().resolve() + destination = ( + Path(output).expanduser().resolve() + if output is not None + else resolved_session / "experiment.html" + ) + data = await build_experiment_report_data(resolved_session) + html = _REPORT_HTML.replace("__VERO_REPORT_DATA__", _safe_json(data)) + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text(html, encoding="utf-8") + return destination + + +_REPORT_HTML = r""" + + + + + + VeRO experiment report + + + +
VeRO experiment report

Optimization session

+
+
This portable report can contain source diffs, evaluation artifacts, prompts, and agent tool output. Treat it as sensitive experiment data.
+
+
+

Score trajectories by split

+

Candidate lineage

Click a node to inspect itbaselinebest
+
+

Candidates

+
+

Producer traces

+

Event timeline

+
+
+
+ + + + +""" diff --git a/vero/src/vero/runtime/__init__.py b/vero/src/vero/runtime/__init__.py new file mode 100644 index 00000000..2e19fa2d --- /dev/null +++ b/vero/src/vero/runtime/__init__.py @@ -0,0 +1,59 @@ +"""Durable runtime state for optimization sessions.""" + +from vero.runtime.artifacts import ArtifactStore +from vero.runtime.context import ( + AGENT_CONTEXT_DIRECTORY, + AgentContextDirectory, + AgentDisclosureLedger, + WorkspaceContextManager, + evaluation_result_path, + make_evaluation_receipt, + narrower_disclosure, +) +from vero.runtime.events import ( + EventBus, + EventSink, + JsonlEventSink, + RuntimeEvent, + agent_event_emitter, +) +from vero.runtime.factory import ( + create_local_optimization_session, + create_optimization_session, +) +from vero.runtime.session import ( + OptimizationSession, + OptimizationComponentSpec, + OptimizationRunSpec, + SessionFailure, + SessionManifest, + SessionStatus, +) +from vero.staging import SandboxStagingArea +from vero.runtime.wandb import WandbEventSink + +__all__ = [ + "ArtifactStore", + "AGENT_CONTEXT_DIRECTORY", + "AgentContextDirectory", + "AgentDisclosureLedger", + "EventBus", + "EventSink", + "JsonlEventSink", + "OptimizationSession", + "OptimizationComponentSpec", + "OptimizationRunSpec", + "RuntimeEvent", + "SandboxStagingArea", + "SessionFailure", + "SessionManifest", + "SessionStatus", + "WandbEventSink", + "WorkspaceContextManager", + "agent_event_emitter", + "create_optimization_session", + "evaluation_result_path", + "make_evaluation_receipt", + "narrower_disclosure", + "create_local_optimization_session", +] diff --git a/vero/src/vero/runtime/artifacts.py b/vero/src/vero/runtime/artifacts.py new file mode 100644 index 00000000..524c41e6 --- /dev/null +++ b/vero/src/vero/runtime/artifacts.py @@ -0,0 +1,42 @@ +"""Safe storage for session-level runtime artifacts.""" + +from __future__ import annotations + +import json +from pathlib import Path, PurePosixPath +from typing import Any + +from vero.evaluation.persistence import _atomic_write_json + + +class ArtifactStore: + def __init__(self, root: Path): + self.root = root + + def path(self, relative_path: str) -> Path: + value = PurePosixPath(relative_path) + if ( + not relative_path + or "\\" in relative_path + or value.is_absolute() + or any(part in {"", ".", ".."} for part in relative_path.split("/")) + ): + raise ValueError("artifact path must be a safe relative POSIX path") + resolved = (self.root / Path(*value.parts)).resolve() + if not resolved.is_relative_to(self.root.resolve()): + raise ValueError("artifact path escapes the session artifact directory") + return resolved + + def write_text(self, relative_path: str, value: str) -> Path: + path = self.path(relative_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(value, encoding="utf-8") + return path + + def write_json(self, relative_path: str, value: Any) -> Path: + path = self.path(relative_path) + _atomic_write_json(path, value) + return path + + def read_json(self, relative_path: str) -> Any: + return json.loads(self.path(relative_path).read_text(encoding="utf-8")) diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py new file mode 100644 index 00000000..7ba01d75 --- /dev/null +++ b/vero/src/vero/runtime/context.py @@ -0,0 +1,536 @@ +"""Authorized filesystem context exposed to optimization agents.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import posixpath +from pathlib import Path, PurePosixPath +from typing import Literal, Sequence + +from pydantic import Field + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository +from vero.evaluation import ( + CaseResourceExporter, + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationEngine, + EvaluationModel, + EvaluationPlan, + EvaluationPrincipal, + EvaluationReceipt, + EvaluationRecord, + EvaluationSummary, + project_evaluation, +) +from vero.evaluation.persistence import _atomic_write_json +from vero.sandbox import Sandbox +from vero.workspace import Workspace + + +AGENT_CONTEXT_DIRECTORY = ".vero" +_DISCLOSURE_RANK = { + DisclosureLevel.NONE: 0, + DisclosureLevel.AGGREGATE: 1, + DisclosureLevel.FULL: 2, +} + + +def context_digest(value: str) -> str: + """Return a path-safe identity for an arbitrary public identifier.""" + + return hashlib.sha256(value.encode()).hexdigest() + + +def evaluation_result_path(evaluation_id: str) -> str: + return ( + f"{AGENT_CONTEXT_DIRECTORY}/evaluations/" + f"{context_digest(evaluation_id)}/evaluation.json" + ) + + +def narrower_disclosure( + left: DisclosureLevel, + right: DisclosureLevel, +) -> DisclosureLevel: + return left if _DISCLOSURE_RANK[left] <= _DISCLOSURE_RANK[right] else right + + +def make_evaluation_receipt( + record: EvaluationRecord, + disclosure: DisclosureLevel, +) -> EvaluationReceipt: + result: EvaluationSummary | EvaluationAcknowledgement + if disclosure == DisclosureLevel.NONE: + projected = project_evaluation(record, DisclosureLevel.NONE) + assert isinstance(projected, EvaluationAcknowledgement) + result = projected + else: + projected = project_evaluation(record, DisclosureLevel.AGGREGATE) + assert isinstance(projected, EvaluationSummary) + result = projected + return EvaluationReceipt( + evaluation_id=record.id, + status=record.report.status, + disclosure=disclosure, + result=result, + result_path=evaluation_result_path(record.id), + ) + + +class AgentDisclosureEntry(EvaluationModel): + evaluation_id: str + maximum_disclosure: DisclosureLevel + + +class AgentDisclosureLedgerModel(EvaluationModel): + schema_version: Literal[1] = 1 + evaluations: dict[str, AgentDisclosureEntry] = Field(default_factory=dict) + + +class AgentDisclosureLedger: + """Durably records which evaluations may enter agent-facing context.""" + + def __init__(self, path: Path): + self.path = path + self._lock = asyncio.Lock() + if path.exists(): + self.model = AgentDisclosureLedgerModel.model_validate_json( + path.read_text(encoding="utf-8") + ) + else: + self.model = AgentDisclosureLedgerModel() + + def get(self, evaluation_id: str) -> DisclosureLevel | None: + entry = self.model.evaluations.get(evaluation_id) + return entry.maximum_disclosure if entry is not None else None + + async def remember( + self, + evaluation_id: str, + disclosure: DisclosureLevel, + ) -> DisclosureLevel: + """Record first exposure and never broaden it on a later call.""" + + async with self._lock: + existing = self.model.evaluations.get(evaluation_id) + effective = ( + disclosure + if existing is None + else narrower_disclosure(existing.maximum_disclosure, disclosure) + ) + if existing is None or effective != existing.maximum_disclosure: + self.model.evaluations[evaluation_id] = AgentDisclosureEntry( + evaluation_id=evaluation_id, + maximum_disclosure=effective, + ) + await asyncio.to_thread( + _atomic_write_json, + self.path, + self.model.model_dump(mode="json"), + ) + return effective + + +class AgentContextDirectory: + """Render authorized records into one sandbox filesystem tree.""" + + def __init__( + self, + *, + sandbox: Sandbox, + root: str, + session_dir: Path, + ): + self.sandbox = sandbox + self.root = root.rstrip("/") + self.session_dir = session_dir + + def path(self, *parts: str) -> str: + path = PurePosixPath(self.root) + for part in parts: + path /= part + return path.as_posix() + + async def write_json(self, path: str, value: object) -> None: + await self.sandbox.write_file( + path, + json.dumps(value, ensure_ascii=False, indent=2, default=str) + "\n", + ) + + async def reset(self) -> None: + if await self.sandbox.exists(self.root): + await self.unseal() + for name in await self.sandbox.list_dir(self.root): + await self.sandbox.remove(self.path(name), recursive=True) + else: + await self.sandbox.mkdir(self.root) + + async def unseal(self) -> None: + if not await self.sandbox.exists(self.root): + return + result = await self.sandbox.run(["chmod", "-R", "u+w", self.root]) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to unseal {self.root}") + + async def seal(self) -> None: + result = await self.sandbox.run(["chmod", "-R", "a-w", self.root]) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to seal {self.root}") + + async def write_header( + self, + *, + session_id: str, + round_number: int | None, + proposal_id: str | None, + parent_candidate_id: str | None, + ) -> None: + readme = """# VeRO optimization context + +This directory is generated by VeRO and is read-only. It contains the cases, +past candidates, and evaluation feedback you are authorized to inspect. Start +with each subdirectory's `index.json`. Candidate entries include Git refs you +can pass to `git show` or `git diff`. Full evaluation entries put each case and +long trace in its own file; their artifact paths resolve below that evaluation's +`artifacts/` directory. + +Use ordinary filesystem and Git commands to analyze this context. Do not copy +it into the program: VeRO rejects candidate versions that track `.vero`. + +`evaluations.json` lists the named evaluations you may invoke, their base case +selection, disclosure level, and remaining budget. +""" + await self.sandbox.write_file(self.path("README.md"), readme) + await self.write_json( + self.path("manifest.json"), + { + "schema_version": 1, + "session_id": session_id, + "round": round_number, + "proposal_id": proposal_id, + "parent_candidate_id": parent_candidate_id, + "snapshot_semantics": "generation", + }, + ) + + async def write_evaluations( + self, + projections: Sequence[ + tuple[ + EvaluationRecord, + DisclosureLevel, + EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement, + ] + ], + ) -> None: + root = self.path("evaluations") + if await self.sandbox.exists(root): + await self.sandbox.remove(root, recursive=True) + await self.sandbox.mkdir(root) + index: list[dict[str, object]] = [] + for record, disclosure, projection in sorted( + projections, + key=lambda item: (item[0].completed_at, item[0].id), + ): + digest = context_digest(record.id) + relative_path = f"{digest}/evaluation.json" + evaluation_root = self.path("evaluations", digest) + await self.sandbox.mkdir(evaluation_root) + missing_artifacts: list[str] = [] + if isinstance(projection, EvaluationRecord): + payload = projection.model_dump(mode="json") + cases = payload["report"].pop("cases") + case_files: list[dict[str, object]] = [] + for case_model, case_payload in zip( + projection.report.cases, + cases, + strict=True, + ): + case_digest = context_digest(case_model.case_id) + case_root = self.path("evaluations", digest, "cases", case_digest) + await self.sandbox.mkdir(case_root) + execution_trace = case_payload.pop("execution_trace", None) + evaluation_trace = case_payload.pop("evaluation_trace", None) + case_document: dict[str, object] = { + "schema_version": 1, + "result": case_payload, + } + if execution_trace is not None: + await self.write_json( + posixpath.join(case_root, "execution-trace.json"), + execution_trace, + ) + case_document["execution_trace_path"] = "execution-trace.json" + if evaluation_trace is not None: + await self.write_json( + posixpath.join(case_root, "evaluation-trace.json"), + evaluation_trace, + ) + case_document["evaluation_trace_path"] = "evaluation-trace.json" + await self.write_json( + posixpath.join(case_root, "result.json"), + case_document, + ) + case_files.append( + { + "case_id": case_model.case_id, + "path": f"cases/{case_digest}/result.json", + } + ) + payload["case_files"] = case_files + source_root = self.session_dir / "evaluations" / record.id / "artifacts" + resolved_source_root = source_root.resolve() + artifact_paths = { + artifact.path for artifact in projection.report.artifacts + } + for case in projection.report.cases: + artifact_paths.update(artifact.path for artifact in case.artifacts) + for artifact_path in sorted(artifact_paths): + source = source_root / Path(*PurePosixPath(artifact_path).parts) + try: + resolved_source = source.resolve(strict=True) + except (OSError, RuntimeError): + missing_artifacts.append(artifact_path) + continue + contains_symlink = source.is_symlink() or ( + source.is_dir() + and any(path.is_symlink() for path in source.rglob("*")) + ) + if ( + not resolved_source.is_relative_to(resolved_source_root) + or contains_symlink + ): + missing_artifacts.append(artifact_path) + continue + await self.sandbox.upload( + str(source), + self.path("evaluations", digest, "artifacts", artifact_path), + ) + document = { + "schema_version": 1, + "disclosure": disclosure.value, + "result": payload, + "artifacts_path": "artifacts", + "missing_artifacts": missing_artifacts, + } + else: + document = { + "schema_version": 1, + "disclosure": disclosure.value, + "result": projection.model_dump(mode="json"), + } + await self.write_json( + self.path("evaluations", relative_path), + document, + ) + index.append( + { + "evaluation_id": record.id, + "candidate_id": record.request.candidate.id, + "candidate_version": record.request.candidate.version, + "evaluation": record.request.evaluation_set.name, + "partition": record.request.evaluation_set.partition, + "disclosure": disclosure.value, + "path": relative_path, + } + ) + await self.write_json( + self.path("evaluations", "index.json"), + {"schema_version": 1, "evaluations": index}, + ) + + +class WorkspaceContextManager: + """Build and refresh one proposal's generation-scoped context snapshot.""" + + def __init__( + self, + *, + session_id: str, + session_dir: Path, + round_number: int, + proposal_id: str, + parent: Candidate, + workspace: Workspace, + candidate_repository: CandidateRepository, + engine: EvaluationEngine, + backend_id: str, + evaluation_plan: EvaluationPlan, + candidates: Sequence[Candidate], + evaluations: Sequence[EvaluationRecord], + ledger: AgentDisclosureLedger, + ): + self.session_id = session_id + self.session_dir = session_dir + self.round_number = round_number + self.proposal_id = proposal_id + self.parent = parent + self.workspace = workspace + self.candidate_repository = candidate_repository + self.engine = engine + self.backend_id = backend_id + self.evaluation_plan = evaluation_plan + self.candidates = {candidate.id: candidate for candidate in candidates} + self.evaluations = {record.id: record for record in evaluations} + self.ledger = ledger + self.root = posixpath.join( + self.workspace.project_path, + AGENT_CONTEXT_DIRECTORY, + ) + self.directory = AgentContextDirectory( + sandbox=workspace.sandbox, + root=self.root, + session_dir=session_dir, + ) + self._lock = asyncio.Lock() + + async def _projections( + self, + ) -> list[ + tuple[ + EvaluationRecord, + DisclosureLevel, + EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement, + ] + ]: + projections = [] + for record in self.evaluations.values(): + decision = await self.engine.authorize( + record.backend_id, + record.request, + EvaluationPrincipal.AGENT, + ) + if not decision.viewable: + continue + maximum = await self.ledger.remember(record.id, decision.disclosure) + disclosure = narrower_disclosure(maximum, decision.disclosure) + projections.append( + (record, disclosure, project_evaluation(record, disclosure)) + ) + return projections + + async def _write_candidate_history(self) -> None: + await self.candidate_repository.materialize_agent_history( + tuple(self.candidates.values()), + workspace=self.workspace, + destination=self.directory.path("candidates"), + ) + + async def _write_case_resources(self) -> None: + cases_root = self.directory.path("cases") + await self.workspace.sandbox.mkdir(cases_root) + index: list[dict[str, object]] = [] + backend = self.engine.backends.resolve(self.backend_id) + for definition in self.evaluation_plan.evaluations: + access = definition.access + if not ( + access.agent_visible + and access.expose_case_resources + and isinstance(backend, CaseResourceExporter) + ): + continue + evaluation_set = definition.evaluation_set + key = evaluation_set.budget_key(self.backend_id) + digest = context_digest(key) + resource_root = self.directory.path("cases", digest) + await self.workspace.sandbox.mkdir(resource_root) + await self.directory.write_json( + posixpath.join(resource_root, "manifest.json"), + { + "schema_version": 1, + "backend_id": self.backend_id, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "resources_path": "resources", + }, + ) + resources = posixpath.join(resource_root, "resources") + await self.workspace.sandbox.mkdir(resources) + await backend.export_case_resources( + evaluation_set=evaluation_set, + destination=resources, + sandbox=self.workspace.sandbox, + ) + index.append( + { + "backend_id": self.backend_id, + "evaluation_set": evaluation_set.model_dump(mode="json"), + "path": digest, + } + ) + await self.directory.write_json( + posixpath.join(cases_root, "index.json"), + {"schema_version": 1, "case_resources": index}, + ) + + async def _write_evaluation_plan(self) -> None: + ledger = self.engine.budget_ledger + evaluations = [] + for definition in self.evaluation_plan.evaluations: + access = definition.access + if not access.agent_visible and not access.agent_can_evaluate: + continue + budget = ( + ledger.get( + self.backend_id, + definition.evaluation_set, + EvaluationPrincipal.AGENT, + ) + if ledger is not None + else None + ) + evaluations.append( + { + "name": definition.evaluation_set.name, + "partition": definition.evaluation_set.partition, + "base_selection": definition.evaluation_set.selection.model_dump( + mode="json" + ), + "agent_can_evaluate": access.agent_can_evaluate, + "agent_selection": access.agent_selection.value, + "disclosure": access.disclosure.value, + "expose_case_resources": access.expose_case_resources, + "budget": ( + budget.model_dump(mode="json") if budget is not None else None + ), + } + ) + await self.directory.write_json( + self.directory.path("evaluations.json"), + {"schema_version": 1, "evaluations": evaluations}, + ) + + async def initialize(self) -> None: + async with self._lock: + await self.directory.reset() + await self.directory.write_header( + session_id=self.session_id, + round_number=self.round_number, + proposal_id=self.proposal_id, + parent_candidate_id=self.parent.id, + ) + await self._write_candidate_history() + await self.directory.write_evaluations(await self._projections()) + await self._write_evaluation_plan() + await self._write_case_resources() + await self.directory.seal() + + async def add_evaluation( + self, + record: EvaluationRecord, + disclosure: DisclosureLevel, + ) -> EvaluationReceipt: + async with self._lock: + self.candidates[record.request.candidate.id] = record.request.candidate + self.evaluations[record.id] = record + maximum = await self.ledger.remember(record.id, disclosure) + effective = narrower_disclosure(maximum, disclosure) + await self.directory.unseal() + await self._write_candidate_history() + await self.directory.write_evaluations(await self._projections()) + await self._write_evaluation_plan() + await self.directory.seal() + return make_evaluation_receipt(record, effective) diff --git a/vero/src/vero/runtime/events.py b/vero/src/vero/runtime/events.py new file mode 100644 index 00000000..ea1793fd --- /dev/null +++ b/vero/src/vero/runtime/events.py @@ -0,0 +1,120 @@ +"""Session-scoped runtime events and sinks.""" + +from __future__ import annotations + +import asyncio +import inspect +import json +import logging +from collections.abc import Awaitable, Callable +from datetime import UTC, datetime +from pathlib import Path +from typing import Protocol +from uuid import uuid4 + +from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator + +logger = logging.getLogger(__name__) + + +class RuntimeEvent(BaseModel): + model_config = ConfigDict(extra="forbid") + + id: str = Field(default_factory=lambda: str(uuid4())) + session_id: str + kind: str + created_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) + payload: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("id", "session_id", "kind") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("event identity must not be empty") + return value + + @field_validator("created_at") + @classmethod + def normalize_created_at(cls, value: datetime) -> datetime: + if value.tzinfo is None or value.utcoffset() is None: + raise ValueError("event timestamps must be timezone-aware") + return value.astimezone(UTC) + + +class EventSink(Protocol): + def __call__(self, event: RuntimeEvent) -> object: ... + + +class EventBus: + """Publish runtime events without coupling execution to observability sinks.""" + + def __init__(self, sinks: list[EventSink] | None = None): + self.sinks: list[EventSink] = list(sinks or []) + + async def emit( + self, + *, + session_id: str, + kind: str, + payload: dict[str, JsonValue] | None = None, + ) -> RuntimeEvent: + event = RuntimeEvent( + session_id=session_id, + kind=kind, + payload=payload or {}, + ) + for sink in self.sinks: + try: + result = sink(event) + if inspect.isawaitable(result): + await result + except Exception: + logger.exception("Runtime event sink failed for %s", event.id) + return event + + +def agent_event_emitter( + bus: EventBus, + session_id: str, + agent: object, + *, + kind: str = "agent", +) -> Callable[[object], Awaitable[None]] | None: + """Build an ``on_event`` callback that publishes agent activity to ``bus``. + + The coding agent streams SDK-specific stream events; the agent normalizes + them via ``serialize_event`` into ``AgentEvent`` dicts (message / thinking / + tool_call / tool_result). This adapts that normalized stream onto the runtime + ``EventBus`` so tool calls, reasoning, and messages land in ``events.jsonl`` + and any registered sink (e.g. W&B) live — the native runner's introspection + advantage over opaque environments. Returns ``None`` if the agent cannot + normalize its events. + """ + serialize = getattr(agent, "serialize_event", None) + if not callable(serialize): + return None + + async def _emit(raw_event: object) -> None: + normalized = serialize(raw_event) + if normalized: + await bus.emit( + session_id=session_id, kind=kind, payload=dict(normalized) + ) + + return _emit + + +class JsonlEventSink: + """Append canonical runtime events to a session JSONL file.""" + + def __init__(self, path: Path): + self.path = path + self._lock = asyncio.Lock() + + async def __call__(self, event: RuntimeEvent) -> None: + line = json.dumps(event.model_dump(mode="json"), ensure_ascii=False) + async with self._lock: + self.path.parent.mkdir(parents=True, exist_ok=True) + with self.path.open("a", encoding="utf-8") as handle: + handle.write(line) + handle.write("\n") diff --git a/vero/src/vero/runtime/factory.py b/vero/src/vero/runtime/factory.py new file mode 100644 index 00000000..74084d21 --- /dev/null +++ b/vero/src/vero/runtime/factory.py @@ -0,0 +1,282 @@ +"""Composition helpers for local optimization sessions.""" + +from __future__ import annotations + +from collections.abc import Mapping +from pathlib import Path + +from pydantic import JsonValue + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository, GitCandidateRepository +from vero.evaluation import ( + AuthorizationResolver, + BackendRegistry, + BudgetLedger, + EvaluationBackend, + EvaluationBudget, + EvaluationDatabase, + EvaluationEngine, + EvaluationLimits, + EvaluationPlan, + Evaluator, + ObjectiveSpec, + authorize_evaluation_plan, +) +from vero.optimization import ( + CandidateProducer, + ObjectiveSelectionPolicy, + OptimizationStrategy, + Optimizer, + SelectionPolicy, + SequentialStrategy, +) +from vero.runtime.session import ( + OptimizationRunSpec, + OptimizationSession, + SessionManifest, +) +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace, Workspace + + +def _load_database(session_dir: Path, session_id: str) -> EvaluationDatabase: + database_path = session_dir / "database.json" + return EvaluationDatabase.load_reconciled( + database_path=database_path, + evaluations_dir=session_dir / "evaluations", + database_id=session_id, + ) + + +def _load_budget_ledger( + session_dir: Path, + budgets: list[EvaluationBudget], +) -> BudgetLedger | None: + budget_path = session_dir / "budgets.json" + if budget_path.exists(): + return BudgetLedger.load(budget_path) + if not budgets: + return None + ledger = BudgetLedger(budgets, path=budget_path) + ledger.save() + return ledger + + +async def create_optimization_session( + *, + workspace: Workspace, + candidate_repository: CandidateRepository, + session_dir: Path | str, + backend_id: str, + backend: EvaluationBackend, + objective: ObjectiveSpec, + producers: Mapping[str, CandidateProducer], + evaluation_plan: EvaluationPlan, + session_id: str | None = None, + strategy: OptimizationStrategy | None = None, + selection: SelectionPolicy | None = None, + parameters: dict[str, JsonValue] | None = None, + limits: EvaluationLimits | None = None, + authorization_resolver: AuthorizationResolver | None = None, + metadata: dict[str, JsonValue] | None = None, + run_spec: OptimizationRunSpec | None = None, + seed: int | None = None, + max_proposals: int = 1, + max_rounds: int = 100, + max_concurrency: int = 1, + base_ref: str | None = None, +) -> OptimizationSession: + """Build a durable session around an already-provisioned workspace. + + ``session_dir`` and ``candidate_repository`` are durable control-plane state + on the host. The original workspace supplies the baseline, context, and + default execution sandbox; all candidate checkouts come from the compatible + repository. The evaluation plan is the default fail-closed authorization + boundary; advanced deployments may supply an equivalent trusted resolver. + """ + + session_dir = Path(session_dir).expanduser().resolve() + manifest_path = session_dir / "manifest.json" + if session_id is None and manifest_path.exists(): + session_id = SessionManifest.model_validate_json( + manifest_path.read_text(encoding="utf-8") + ).id + session_id = session_id or session_dir.name + if not session_id.strip(): + raise ValueError("session ID must not be empty") + if not candidate_repository.supports(workspace): + raise ValueError( + "candidate repository and workspace belong to different families" + ) + mismatched_budgets = [ + budget.backend_id + for budget in evaluation_plan.budgets + if budget.backend_id != backend_id + ] + if mismatched_budgets: + raise ValueError( + "evaluation plan budgets must use the session backend " + f"{backend_id!r}" + ) + if not producers and max_proposals: + raise ValueError("at least one candidate producer is required") + for producer in producers.values(): + validate_workspace = getattr(producer, "validate_workspace", None) + if callable(validate_workspace): + validate_workspace(workspace) + + persisted_manifest = ( + SessionManifest.model_validate_json(manifest_path.read_text(encoding="utf-8")) + if manifest_path.exists() + else None + ) + if persisted_manifest is not None: + baseline = persisted_manifest.baseline + if baseline is None: + raise ValueError("persisted session is missing its baseline candidate") + stored = candidate_repository.get(baseline.id) + if stored != baseline: + raise ValueError( + "persisted baseline is missing from the candidate repository" + ) + else: + if await workspace.is_dirty(): + raise ValueError("target workspace must be clean before optimization") + baseline_version = ( + await workspace.resolve_ref(base_ref) + if base_ref is not None + else await workspace.current_version() + ) + baseline = candidate_repository.get(baseline_version) + if baseline is None: + baseline = Candidate.from_version(baseline_version) + if await workspace.current_version() == baseline_version: + await candidate_repository.capture(baseline, workspace) + else: + async with workspace.temp_copy( + from_version=baseline_version + ) as baseline_workspace: + await candidate_repository.capture(baseline, baseline_workspace) + elif ( + baseline.version != baseline_version + or baseline.parent_id is not None + or baseline.description is not None + or baseline.metadata + ): + raise ValueError( + "candidate repository contains a conflicting baseline record" + ) + + session_dir.mkdir(parents=True, exist_ok=True) + database_path = session_dir / "database.json" + database = _load_database(session_dir, session_id) + budget_ledger = _load_budget_ledger(session_dir, evaluation_plan.budgets) + evaluator = Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + session_id=session_id, + ) + engine = EvaluationEngine( + evaluator=evaluator, + backends=BackendRegistry({backend_id: backend}), + database=database, + database_path=database_path, + budget_ledger=budget_ledger, + authorization_resolver=( + authorization_resolver or authorize_evaluation_plan(evaluation_plan) + ), + ) + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id=backend_id, + evaluation_plan=evaluation_plan, + objective=objective, + strategy=strategy or SequentialStrategy(), + producers=dict(producers), + selection=selection or ObjectiveSelectionPolicy(), + parameters=parameters or {}, + limits=limits or EvaluationLimits(), + seed=seed, + max_proposals=max_proposals, + max_rounds=max_rounds, + max_concurrency=max_concurrency, + session_id=session_id, + ) + session = OptimizationSession( + id=session_id, + session_dir=session_dir, + optimizer=optimizer, + baseline=baseline, + metadata=metadata or {}, + run_spec=run_spec, + ) + for producer_id, producer in producers.items(): + bind_artifacts = getattr(producer, "bind_artifacts", None) + if callable(bind_artifacts): + bind_artifacts(session.artifacts, producer_id=producer_id) + return session + + +async def create_local_optimization_session( + *, + project_path: Path | str, + session_dir: Path | str, + backend_id: str, + backend: EvaluationBackend, + objective: ObjectiveSpec, + producers: Mapping[str, CandidateProducer], + evaluation_plan: EvaluationPlan, + session_id: str | None = None, + strategy: OptimizationStrategy | None = None, + selection: SelectionPolicy | None = None, + parameters: dict[str, JsonValue] | None = None, + limits: EvaluationLimits | None = None, + authorization_resolver: AuthorizationResolver | None = None, + metadata: dict[str, JsonValue] | None = None, + run_spec: OptimizationRunSpec | None = None, + seed: int | None = None, + max_proposals: int = 1, + max_rounds: int = 100, + max_concurrency: int = 1, + base_ref: str | None = None, +) -> OptimizationSession: + """Provision a local Git workspace and build an optimization session.""" + + project_path = Path(project_path).expanduser().resolve() + session_path = Path(session_dir).expanduser().resolve() + sandbox = await LocalSandbox.create(root=project_path.parent) + workspace = await GitWorkspace.from_path(sandbox, str(project_path)) + repository_root = Path(workspace.root).resolve() + if session_path == repository_root or session_path.is_relative_to(repository_root): + raise ValueError("session directory must live outside the target repository") + candidate_repository = await GitCandidateRepository.create( + session_path / "candidates", + workspace=workspace, + ) + return await create_optimization_session( + workspace=workspace, + candidate_repository=candidate_repository, + session_dir=session_path, + backend_id=backend_id, + backend=backend, + objective=objective, + producers=producers, + evaluation_plan=evaluation_plan, + session_id=session_id, + strategy=strategy, + selection=selection, + parameters=parameters, + limits=limits, + authorization_resolver=authorization_resolver, + metadata=metadata, + run_spec=run_spec, + seed=seed, + max_proposals=max_proposals, + max_rounds=max_rounds, + max_concurrency=max_concurrency, + base_ref=base_ref, + ) diff --git a/vero/src/vero/runtime/session.py b/vero/src/vero/runtime/session.py new file mode 100644 index 00000000..59b1e54e --- /dev/null +++ b/vero/src/vero/runtime/session.py @@ -0,0 +1,506 @@ +"""Generic optimization session lifecycle and durable manifest.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +from dataclasses import dataclass, field +from datetime import UTC, datetime +from enum import Enum +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepository +from vero.evaluation import ( + BackendProvenance, + CaseStatus, + EvaluationLimits, + EvaluationPlan, + EvaluationRecord, + ObjectiveSpec, +) +from vero.evaluation.persistence import _atomic_write_json +from vero.optimization import OptimizationResult, Optimizer +from vero.runtime.artifacts import ArtifactStore +from vero.runtime.events import EventBus, JsonlEventSink, agent_event_emitter +from vero.workspace import Workspace + + +class SessionStatus(str, Enum): + CREATED = "created" + RUNNING = "running" + COMPLETED = "completed" + FAILED = "failed" + + +class SessionFailure(BaseModel): + model_config = ConfigDict(extra="forbid") + + type: str + message: str + + @field_validator("type", "message") + @classmethod + def validate_text(cls, value: str) -> str: + if not value.strip(): + raise ValueError("session failure fields must not be empty") + return value + + +class OptimizationComponentSpec(BaseModel): + """Stable type and configuration identity for a protocol component.""" + + model_config = ConfigDict(extra="forbid") + + type: str + config_digest: str + + @field_validator("type", "config_digest") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("component identity must not be empty") + return value + + @field_validator("config_digest") + @classmethod + def validate_digest(cls, value: str) -> str: + if len(value) != 64 or any( + character not in "0123456789abcdef" for character in value + ): + raise ValueError("component config_digest must be a SHA-256 hex digest") + return value + + +class OptimizationRunSpec(BaseModel): + """Execution choices that must remain stable when a session resumes.""" + + model_config = ConfigDict(extra="forbid") + + max_proposals: int = Field(ge=0) + max_rounds: int = Field(ge=1) + max_concurrency: int = Field(ge=1) + strategy: OptimizationComponentSpec + producers: dict[str, OptimizationComponentSpec] + # Optional so manifests written before this field can still load; a resume + # under a changed selection policy is then caught as a run-protocol mismatch. + selection: OptimizationComponentSpec | None = None + + +class SessionManifest(BaseModel): + model_config = ConfigDict(extra="forbid") + + schema_version: Literal[3] = 3 + id: str + status: SessionStatus + backend_id: str + backend: BackendProvenance + candidate_repository_family: str + candidate_repository_format_version: int + evaluation_plan: EvaluationPlan + objective: ObjectiveSpec + run: OptimizationRunSpec + parameters: dict[str, JsonValue] = Field(default_factory=dict) + limits: EvaluationLimits = Field(default_factory=EvaluationLimits) + seed: int | None = None + baseline: Candidate | None = None + best_candidate_id: str | None = None + best_evaluation_id: str | None = None + final_baseline_evaluation_id: str | None = None + final_evaluation_id: str | None = None + created_at: datetime + updated_at: datetime + failure: SessionFailure | None = None + metadata: dict[str, JsonValue] = Field(default_factory=dict) + + @field_validator("id", "backend_id", "candidate_repository_family") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("session identity must not be empty") + return value + + @field_validator("created_at", "updated_at") + @classmethod + def normalize_timestamps(cls, value: datetime) -> datetime: + if value.tzinfo is None or value.utcoffset() is None: + raise ValueError("session timestamps must be timezone-aware") + return value.astimezone(UTC) + + +@dataclass +class OptimizationSession: + """Own the durable state and lifecycle of one optimization run.""" + + id: str + session_dir: Path + optimizer: Optimizer + baseline: Candidate | None = None + metadata: dict[str, JsonValue] = field(default_factory=dict) + events: EventBus | None = None + run_spec: OptimizationRunSpec | None = None + + def __post_init__(self) -> None: + if not self.id.strip(): + raise ValueError("session ID must not be empty") + expected = self.session_dir.resolve() + evaluator_session = self.optimizer.engine.evaluator.session_dir.resolve() + if evaluator_session != expected: + raise ValueError( + "optimizer evaluator session directory must match OptimizationSession" + ) + optimizer_session_id = self.optimizer.session_id + if optimizer_session_id is not None and optimizer_session_id != self.id: + raise ValueError("optimizer session ID does not match OptimizationSession") + self.optimizer.session_id = self.id + evaluator_session_id = getattr( + self.optimizer.engine.evaluator, "session_id", None + ) + if evaluator_session_id is not None and evaluator_session_id != self.id: + raise ValueError("evaluator session ID does not match OptimizationSession") + self.optimizer.engine.evaluator.session_id = self.id + if ( + self.optimizer.engine.evaluator.candidate_repository + is not self.optimizer.candidate_repository + ): + raise ValueError( + "optimizer and evaluator must share one candidate repository" + ) + self.session_dir.mkdir(parents=True, exist_ok=True) + if self.events is None: + self.events = EventBus([JsonlEventSink(self.events_path)]) + self.artifacts = ArtifactStore(self.session_dir / "artifacts") + self._event_step = len(self.optimizer.engine.database.evaluations) + self.optimizer.engine.listeners.append(self._on_evaluation_completed) + self._wire_agent_events() + + def _wire_agent_events(self) -> None: + """Publish agent activity (tool calls, reasoning, messages) to the bus. + + Agent producers that expose a settable ``on_event`` and a normalizing + agent get their events routed onto the session event bus. A + caller-provided ``on_event`` is left untouched. + """ + assert self.events is not None + for producer in self.optimizer.producers.values(): + if getattr(producer, "on_event", None) is not None: + continue + if not hasattr(producer, "on_event"): + continue + agent = getattr(producer, "agent", None) + if agent is None: + continue + emitter = agent_event_emitter(self.events, self.id, agent) + if emitter is not None: + producer.on_event = emitter + + @property + def manifest_path(self) -> Path: + return self.session_dir / "manifest.json" + + @property + def events_path(self) -> Path: + return self.session_dir / "events.jsonl" + + @property + def database(self): + return self.optimizer.engine.database + + @property + def budget_ledger(self): + return self.optimizer.engine.budget_ledger + + @property + def candidate_repository(self) -> CandidateRepository: + return self.optimizer.candidate_repository + + @property + def workspace(self) -> Workspace: + """The original workspace supplied when the session was created.""" + + return self.optimizer.workspace + + def _initial_manifest(self, baseline: Candidate) -> SessionManifest: + now = datetime.now(UTC) + return SessionManifest( + id=self.id, + status=SessionStatus.CREATED, + backend_id=self.optimizer.backend_id, + backend=self.optimizer.engine.backends.resolve( + self.optimizer.backend_id + ).provenance, + candidate_repository_family=self.candidate_repository.family, + candidate_repository_format_version=( + self.candidate_repository.format_version + ), + evaluation_plan=self.optimizer.evaluation_plan, + objective=self.optimizer.objective, + run=self._run_spec(), + parameters=self.optimizer.parameters, + limits=self.optimizer.limits, + seed=self.optimizer.seed, + baseline=baseline, + created_at=now, + updated_at=now, + metadata=self.metadata, + ) + + @staticmethod + def _component_spec(value: object) -> OptimizationComponentSpec: + kind = type(value) + type_name = f"{kind.__module__}.{kind.__qualname__}" + payload: dict[str, object] = {} + config = getattr(value, "config", None) + if isinstance(config, BaseModel): + payload["config"] = config.model_dump(mode="json") + elif isinstance(config, dict): + payload["config"] = config + for name in ("producer_id", "instruction", "prompt", "max_turns"): + if hasattr(value, name): + payload[name] = getattr(value, name) + agent = getattr(value, "agent", None) + serialize_agent = getattr(agent, "dict", None) + if callable(serialize_agent): + payload["agent"] = serialize_agent() + encoded = json.dumps( + payload, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + default=str, + ).encode() + return OptimizationComponentSpec( + type=type_name, + config_digest=hashlib.sha256(encoded).hexdigest(), + ) + + def _run_spec(self) -> OptimizationRunSpec: + if self.run_spec is not None: + return self.run_spec + return OptimizationRunSpec( + max_proposals=self.optimizer.max_proposals, + max_rounds=self.optimizer.max_rounds, + max_concurrency=self.optimizer.max_concurrency, + strategy=self._component_spec(self.optimizer.strategy), + producers={ + producer_id: self._component_spec(producer) + for producer_id, producer in sorted(self.optimizer.producers.items()) + }, + selection=self._component_spec(self.optimizer.selection), + ) + + async def _on_evaluation_completed(self, record: EvaluationRecord) -> None: + """Publish evaluations as they finish, rather than replaying them at exit.""" + + assert self.events is not None + step = self._event_step + self._event_step += 1 + await self.events.emit( + session_id=self.id, + kind="evaluation_completed", + payload=self._evaluation_event_payload(record, step=step), + ) + + async def _save_manifest(self, manifest: SessionManifest) -> None: + await asyncio.to_thread( + _atomic_write_json, + self.manifest_path, + manifest.model_dump(mode="json"), + ) + + @staticmethod + def _evaluation_event_payload( + record: EvaluationRecord, + *, + step: int, + ) -> dict[str, JsonValue]: + counts = {status: 0 for status in CaseStatus} + for case in record.report.cases: + counts[case.status] += 1 + payload: dict[str, JsonValue] = { + "step": step, + "evaluation_id": record.id, + "candidate_id": record.request.candidate.id, + "candidate_version": record.request.candidate.version, + "evaluation": record.request.evaluation_set.name, + "partition": record.request.evaluation_set.partition, + "principal": record.principal.value, + "status": record.report.status.value, + "cases/total": len(record.report.cases), + "cases/success": counts[CaseStatus.SUCCESS], + "cases/error": counts[CaseStatus.ERROR], + "cases/skipped": counts[CaseStatus.SKIPPED], + } + payload.update( + {f"metrics/{name}": value for name, value in record.report.metrics.items()} + ) + if record.objective is not None: + payload["objective/value"] = record.objective.value + payload["objective/feasible"] = record.objective.feasible + return payload + + def load_manifest(self) -> SessionManifest: + return SessionManifest.model_validate_json( + self.manifest_path.read_text(encoding="utf-8") + ) + + async def run( + self, + *, + baseline: Candidate | None = None, + skip_baseline_evaluation: bool = False, + max_proposals: int | None = None, + ) -> OptimizationResult: + manifest = self.load_manifest() if self.manifest_path.exists() else None + if baseline is None: + if manifest is not None and manifest.baseline is not None: + baseline = manifest.baseline + elif self.baseline is not None: + baseline = self.baseline + else: + baseline = Candidate.from_version( + await self.optimizer.workspace.current_version() + ) + if manifest is None: + manifest = self._initial_manifest(baseline) + if manifest.id != self.id: + raise ValueError("session manifest ID does not match runtime session") + if manifest.backend_id != self.optimizer.backend_id: + raise ValueError("session backend does not match the persisted manifest") + if manifest.candidate_repository_family != self.candidate_repository.family: + raise ValueError( + "session candidate repository does not match the persisted manifest" + ) + if ( + manifest.candidate_repository_format_version + != self.candidate_repository.format_version + ): + raise ValueError( + "session candidate repository format does not match the " + "persisted manifest" + ) + backend = self.optimizer.engine.backends.resolve(self.optimizer.backend_id) + if manifest.backend != backend.provenance: + raise ValueError( + "session backend configuration does not match the persisted manifest" + ) + if manifest.evaluation_plan != self.optimizer.evaluation_plan: + raise ValueError( + "session evaluation plan does not match the persisted manifest" + ) + if manifest.objective != self.optimizer.objective: + raise ValueError("session objective does not match the persisted manifest") + if manifest.run != self._run_spec(): + raise ValueError("session run protocol does not match the persisted manifest") + if manifest.parameters != self.optimizer.parameters: + raise ValueError( + "session evaluation parameters do not match the persisted manifest" + ) + if manifest.limits != self.optimizer.limits: + raise ValueError( + "session evaluation limits do not match the persisted manifest" + ) + if manifest.seed != self.optimizer.seed: + raise ValueError( + "session evaluation seed does not match the persisted manifest" + ) + if manifest.baseline is None or ( + manifest.baseline.id, + manifest.baseline.version, + ) != (baseline.id, baseline.version): + raise ValueError("session baseline does not match the persisted manifest") + + manifest = manifest.model_copy( + update={ + "status": SessionStatus.RUNNING, + "updated_at": datetime.now(UTC), + "failure": None, + } + ) + await self._save_manifest(manifest) + assert self.events is not None + await self.events.emit( + session_id=self.id, + kind="session_started", + payload={"baseline_candidate_id": baseline.id}, + ) + + try: + result = await self.optimizer.run( + baseline=baseline, + skip_baseline_evaluation=skip_baseline_evaluation, + max_proposals=max_proposals, + ) + except BaseException as error: + failure = SessionFailure( + type=f"{type(error).__module__}.{type(error).__name__}", + message=str(error) or type(error).__name__, + ) + await self._save_manifest( + manifest.model_copy( + update={ + "status": SessionStatus.FAILED, + "updated_at": datetime.now(UTC), + "failure": failure, + } + ) + ) + await self.events.emit( + session_id=self.id, + kind="session_failed", + payload={"error_type": failure.type, "message": failure.message}, + ) + raise + + best = result.best + completed = manifest.model_copy( + update={ + "status": SessionStatus.COMPLETED, + "updated_at": datetime.now(UTC), + "best_candidate_id": ( + best.request.candidate.id if best is not None else None + ), + "best_evaluation_id": best.id if best is not None else None, + "final_baseline_evaluation_id": ( + result.final_baseline.id + if result.final_baseline is not None + else None + ), + "final_evaluation_id": ( + result.final.id if result.final is not None else None + ), + } + ) + await self._save_manifest(completed) + await self.events.emit( + session_id=self.id, + kind="session_completed", + payload={ + "best_candidate_id": completed.best_candidate_id, + "best_evaluation_id": completed.best_evaluation_id, + "evaluation_count": len(result.evaluations), + "status": "completed", + "baseline_candidate_id": result.baseline.request.candidate.id, + "baseline_objective": ( + result.baseline.objective.value + if result.baseline.objective is not None + else None + ), + "best_objective": ( + best.objective.value + if best is not None and best.objective is not None + else None + ), + "final_objective": ( + result.final.objective.value + if result.final is not None + and result.final.objective is not None + else None + ), + }, + ) + return result diff --git a/vero/src/vero/runtime/wandb.py b/vero/src/vero/runtime/wandb.py new file mode 100644 index 00000000..d70fce62 --- /dev/null +++ b/vero/src/vero/runtime/wandb.py @@ -0,0 +1,348 @@ +"""Optional Weights & Biases reporting for canonical runtime events.""" + +from __future__ import annotations + +import hashlib +import tempfile +from pathlib import Path +from typing import Any +from uuid import uuid4 + +from vero.evaluation.budget import BudgetLedger +from vero.evaluation.models import CaseStatus, EvaluationRecord +from vero.runtime.artifacts import ArtifactStore +from vero.runtime.events import RuntimeEvent + + +def _open_wandb_run( + *, + project: str, + session_id: str, + wandb_dir: Path, + client: Any | None, + entity: str | None, + name: str | None, + group: str | None, + tags: list[str] | None, + mode: str | None, + notes: str | None, + config: dict[str, Any] | None, + run_id: str | None, +) -> Any: + """Open one resumable W&B run keyed stably to the session. W&B is imported + lazily so nothing here depends on it unless a sink is constructed.""" + if client is None: + try: + import wandb as client + except ImportError as error: + raise RuntimeError( + "W&B reporting requires `pip install scale-vero[wandb]`" + ) from error + wandb_dir.mkdir(parents=True, exist_ok=True) + stable_id = run_id or ("vero-" + hashlib.sha256(session_id.encode()).hexdigest()[:16]) + init_kwargs: dict[str, Any] = { + "project": project, + "id": stable_id, + "resume": "allow", + "dir": str(wandb_dir), + "config": {**(config or {}), "vero/session_id": session_id}, + } + for key, value in { + "entity": entity, + "name": name, + "group": group, + "tags": tags or None, + "mode": mode, + "notes": notes, + }.items(): + if value is not None: + init_kwargs[key] = value + return client.init(**init_kwargs) + + +class WandbEventSink: + """Log one optimization session as one W&B run. + + W&B is imported only when this sink is constructed, so the core runtime has + no mandatory tracking dependency. + """ + + def __init__( + self, + *, + project: str, + session_id: str, + session_dir: Path, + entity: str | None = None, + name: str | None = None, + group: str | None = None, + tags: list[str] | None = None, + mode: str | None = None, + notes: str | None = None, + config: dict[str, Any] | None = None, + run_id: str | None = None, + client: Any | None = None, + ): + if client is None: + try: + import wandb as client + except ImportError as error: + raise RuntimeError( + "W&B reporting requires `pip install scale-vero[wandb]`" + ) from error + + wandb_dir = session_dir / "artifacts" / "wandb" + wandb_dir.mkdir(parents=True, exist_ok=True) + self.artifacts = ArtifactStore(session_dir / "artifacts") + self.state_path = "wandb/state.json" + if self.artifacts.path(self.state_path).exists(): + state = self.artifacts.read_json(self.state_path) + self.logged_evaluations = set(state.get("evaluation_ids", [])) + self.next_step = int(state.get("next_step", len(self.logged_evaluations))) + else: + self.logged_evaluations: set[str] = set() + self.next_step = 0 + stable_id = run_id or ( + "vero-" + hashlib.sha256(session_id.encode()).hexdigest()[:16] + ) + init_kwargs: dict[str, Any] = { + "project": project, + "id": stable_id, + "resume": "allow", + "dir": str(wandb_dir), + "config": {**(config or {}), "vero/session_id": session_id}, + } + for key, value in { + "entity": entity, + "name": name, + "group": group, + "tags": tags or None, + "mode": mode, + "notes": notes, + }.items(): + if value is not None: + init_kwargs[key] = value + self.run = client.init(**init_kwargs) + + def _save_state(self) -> None: + self.artifacts.write_json( + self.state_path, + { + "evaluation_ids": sorted(self.logged_evaluations), + "next_step": self.next_step, + }, + ) + + def __call__(self, event: RuntimeEvent) -> None: + if event.kind == "evaluation_completed": + payload = dict(event.payload) + payload.pop("step") + evaluation_id = str(payload["evaluation_id"]) + if evaluation_id in self.logged_evaluations: + return + self.run.log(payload, step=self.next_step) + self.logged_evaluations.add(evaluation_id) + self.next_step += 1 + self._save_state() + return + if event.kind == "session_completed": + self.run.summary.update(event.payload) + self.run.finish() + return + if event.kind == "session_failed": + self.run.summary.update( + { + "status": "failed", + "error_type": event.payload.get("error_type"), + "error_message": event.payload.get("message"), + } + ) + self.run.finish(exit_code=1) + + +class SidecarWandbSink: + """Log the trusted eval-sidecar's evaluation stream to one W&B run. + + Subscribed to ``EvaluationEngine.listeners``, so it sees every completed + evaluation the sidecar produces — the agent's search evals and the trusted + SYSTEM/ADMIN re-scores — with the real scores, statuses, diagnostics, and + remaining budget the sidecar holds. This is the harbor-path live-watch + surface: it does not depend on the untrusted optimizer agent cooperating, + and the W&B credentials live only in the trusted container. + """ + + def __init__( + self, + *, + project: str, + session_id: str, + session_dir: Path, + entity: str | None = None, + name: str | None = None, + group: str | None = None, + tags: list[str] | None = None, + mode: str | None = None, + notes: str | None = None, + config: dict[str, Any] | None = None, + run_id: str | None = None, + budget_ledger: BudgetLedger | None = None, + log_traces: bool = False, + client: Any | None = None, + ): + if client is None: + try: + import wandb as client + except ImportError as error: + raise RuntimeError( + "W&B reporting requires `pip install scale-vero[wandb]`" + ) from error + self._wandb = client + self.session_dir = session_dir + self.log_traces = log_traces + self.artifacts = ArtifactStore(session_dir / "artifacts") + self.state_path = "wandb/state.json" + stored_run_id = None + if self.artifacts.path(self.state_path).exists(): + state = self.artifacts.read_json(self.state_path) + self.logged_evaluations = set(state.get("evaluation_ids", [])) + self.next_step = int(state.get("next_step", len(self.logged_evaluations))) + stored_run_id = state.get("run_id") + else: + self.logged_evaluations: set[str] = set() + self.next_step = 0 + # One W&B run per invocation. A fresh session volume mints a new id; a + # sidecar restart within the same run reuses the one persisted in state, + # so it resumes rather than colliding with other invocations. + self.run_id = run_id or stored_run_id or f"vero-{uuid4().hex[:16]}" + self._save_state() + self.budget_ledger = budget_ledger + # Keep W&B's symlink-laden run dir out of session_dir, which is archived + # verbatim for export (a symlink there would break the archive). + wandb_run_dir = Path(tempfile.mkdtemp(prefix="vero-wandb-")) + self.run = _open_wandb_run( + project=project, + session_id=session_id, + wandb_dir=wandb_run_dir, + client=client, + entity=entity, + name=name, + group=group, + tags=tags, + mode=mode, + notes=notes, + config=config, + run_id=self.run_id, + ) + + def _save_state(self) -> None: + self.artifacts.write_json( + self.state_path, + { + "evaluation_ids": sorted(self.logged_evaluations), + "next_step": self.next_step, + "run_id": self.run_id, + }, + ) + + def _payload(self, record: EvaluationRecord) -> dict[str, Any]: + report = record.report + objective = record.objective + evaluation_set = record.request.evaluation_set + partition = evaluation_set.partition or "none" + principal = record.principal.value + # Scope metrics by partition/principal so unlike evals don't share a series. + scope = f"{partition}/{principal}" + counts = {status: 0 for status in CaseStatus} + for case in report.cases: + counts[case.status] += 1 + payload: dict[str, Any] = { + # Identity / context — flat, shared across every evaluation. + "evaluation_id": record.id, + "candidate_id": record.request.candidate.id, + "candidate_version": record.request.candidate.version, + "evaluation_set": evaluation_set.name, + "partition": partition, + "principal": principal, + "status": report.status.value, + "latency_seconds": ( + record.completed_at - record.created_at + ).total_seconds(), + # Scoped quality metrics. `num_cases` is the evaluation's sample + # size (number of trials): evaluations cover different case counts, + # so a score is only interpretable next to how many cases produced + # it. + f"{scope}/num_cases": len(report.cases), + f"{scope}/cases/success": counts[CaseStatus.SUCCESS], + f"{scope}/cases/error": counts[CaseStatus.ERROR], + f"{scope}/cases/skipped": counts[CaseStatus.SKIPPED], + f"{scope}/feasible": ( + bool(objective.feasible) if objective is not None else False + ), + } + if objective is not None and objective.value is not None: + payload[f"{scope}/score"] = objective.value + for key, value in report.metrics.items(): + payload[f"{scope}/metric/{key}"] = value + if report.diagnostics: + payload["diagnostics"] = ",".join( + diagnostic.code for diagnostic in report.diagnostics + ) + if self.budget_ledger is not None: + for budget in self.budget_ledger.budgets: + scope = f"{budget.backend_id}/{budget.principal.value}" + if budget.remaining_runs is not None: + payload[f"budget/{scope}/remaining_runs"] = budget.remaining_runs + if budget.remaining_cases is not None: + payload[f"budget/{scope}/remaining_cases"] = budget.remaining_cases + return payload + + def _log_trace(self, record: EvaluationRecord) -> None: + """Upload an evaluation's trace artifacts as one W&B artifact. + + Full job data is on the per-case artifacts, not just the report-level + harbor logs, so walk both. Best-effort.""" + entries = list(record.report.artifacts) + for case in record.report.cases: + entries.extend(case.artifacts) + if not entries: + return + eval_dir = self.session_dir / "evaluations" / record.id / "artifacts" + artifact = self._wandb.Artifact( + name=f"trace-{record.id}", type="evaluation_trace" + ) + added = 0 + seen: set[str] = set() + for entry in entries: + if entry.path in seen: + continue + seen.add(entry.path) + path = eval_dir / entry.path + if path.is_file(): + artifact.add_file(str(path), name=entry.path) + added += 1 + if added: + self.run.log_artifact(artifact) + + def __call__(self, record: EvaluationRecord) -> None: + if record.id in self.logged_evaluations: + return + self.run.log(self._payload(record), step=self.next_step) + if self.log_traces: + try: + self._log_trace(record) + except Exception: # tracing must never drop the metric log + pass + self.logged_evaluations.add(record.id) + self.next_step += 1 + self._save_state() + + def finish( + self, + summary: dict[str, Any] | None = None, + *, + failed: bool = False, + ) -> None: + """Update the run summary (e.g. shipped/rewards at finalize) and close.""" + if summary: + self.run.summary.update(summary) + self.run.finish(exit_code=1 if failed else 0) diff --git a/vero/src/vero/sandbox.py b/vero/src/vero/sandbox.py new file mode 100644 index 00000000..ae7beac1 --- /dev/null +++ b/vero/src/vero/sandbox.py @@ -0,0 +1,752 @@ +"""Abstract sandbox: filesystem + shell execution. + +A Sandbox provides a unified interface for file I/O and shell commands. +Tools call sandbox methods instead of using pathlib/subprocess directly, +making it possible to swap in different backends (local, container, remote VM). + +The default implementation (LocalSandbox) wraps pathlib.Path and +asyncio.create_subprocess_exec. Access control is handled by Workspace, +not Sandbox — the sandbox is a dumb I/O layer. +""" + +from __future__ import annotations + +import asyncio +import os +import posixpath +import signal +import shutil +import tempfile +import uuid +from abc import ABC, abstractmethod +from contextlib import asynccontextmanager +from dataclasses import dataclass +from pathlib import Path +from typing import AsyncIterator, Awaitable, Callable, NamedTuple + + +async def _terminate_host_process_tree( + process: asyncio.subprocess.Process, + *, + grace_seconds: float = 5, +) -> None: + """Terminate a host subprocess and every descendant in its process group.""" + + if process.returncode is not None and os.name != "posix": + return + + try: + if os.name == "posix": + os.killpg(process.pid, signal.SIGTERM) + else: + process.terminate() + except ProcessLookupError: + return + + if process.returncode is None: + try: + await asyncio.wait_for(process.wait(), timeout=grace_seconds) + except asyncio.TimeoutError: + pass + + # The group leader may exit before descendants that ignore SIGTERM. Always + # sweep the original process group with SIGKILL before returning. + try: + if os.name == "posix": + os.killpg(process.pid, signal.SIGKILL) + elif process.returncode is None: + process.kill() + except ProcessLookupError: + pass + if process.returncode is None: + await process.wait() + + +async def _cleanup_host_process( + process: asyncio.subprocess.Process, + before_terminate: Callable[[], Awaitable[None]] | None = None, +) -> None: + """Run backend cleanup, then unconditionally reap the host process group.""" + + try: + if before_terminate is not None: + await before_terminate() + finally: + await _terminate_host_process_tree(process) + + +# ============================================================================= +# Data types +# ============================================================================= + + +class FileStat(NamedTuple): + """Minimal file stat info.""" + + st_size: int + + +class CommandResult(NamedTuple): + """Result of a shell command execution.""" + + stdout: str + stderr: str + returncode: int + + +@dataclass(frozen=True) +class SandboxCapabilities: + """Execution features exposed by a sandbox implementation.""" + + posix: bool = True + host_paths: bool = False + + +# ============================================================================= +# Abstract base +# ============================================================================= + + +class Sandbox(ABC): + """Abstract sandbox providing filesystem access and shell execution. + + A Sandbox represents a "computer" — an execution environment where vero + runs. For a local sandbox the root is the user's home directory; for a + Docker sandbox it would be ``/``. Vero home (``~/.vero/``) and the git + workspace both live *within* the sandbox. + + Access control is **not** a sandbox concern — it lives on Workspace. + The sandbox is pure I/O: read, write, run, exist-checks. + """ + + # ── Construction ─────────────────────────────────────────────────────── + + @classmethod + @abstractmethod + async def create(cls, **kwargs) -> Sandbox: + """Create a new, bare sandbox instance. + + Each subclass knows how to spin up its own environment. + """ + ... + + # ── Properties ────────────────────────────────────────────────────── + + @property + @abstractmethod + def root(self) -> str: + """Root directory of the sandbox.""" + ... + + @property + def capabilities(self) -> SandboxCapabilities: + return SandboxCapabilities() + + def host_path(self, path: str) -> Path | None: + """Return a host-visible equivalent, or ``None`` for isolated paths.""" + + return None + + # ── Path resolution ──────────────────────────────────────────────── + + @abstractmethod + def resolve_path(self, path: str) -> str: + """Resolve a path relative to sandbox root. For sandbox-internal use.""" + ... + + # ── Filesystem (async) ────────────────────────────────────────────── + + @abstractmethod + async def read_file(self, path: str, encoding: str = "utf-8") -> str: ... + + @abstractmethod + async def read_file_bytes(self, path: str, limit: int | None = None) -> bytes: ... + + @abstractmethod + async def write_file( + self, path: str, content: str, encoding: str = "utf-8" + ) -> None: ... + + @abstractmethod + async def exists(self, path: str) -> bool: ... + + @abstractmethod + async def is_file(self, path: str) -> bool: ... + + @abstractmethod + async def is_dir(self, path: str) -> bool: ... + + @abstractmethod + async def mkdir(self, path: str, parents: bool = True) -> None: ... + + @abstractmethod + async def stat(self, path: str) -> FileStat: ... + + @abstractmethod + async def list_dir(self, path: str) -> list[str]: ... + + # ── Shell (async) ─────────────────────────────────────────────────── + + @abstractmethod + async def run( + self, + command: str | list[str], + cwd: str | None = None, + timeout: int | None = 30, + env: dict[str, str] | None = None, + run_as: str | None = None, + ) -> CommandResult: + """Run a command. ``run_as`` drops the process to that unprivileged + user (and its same-named primary group), shedding supplementary + groups; it requires the caller to be privileged and is how untrusted + code is isolated from the trusted process that launches it.""" + ... + + async def canonicalize(self, path: str) -> str: + """Resolve a sandbox path, including symlinks, inside the sandbox.""" + + result = await self.run(["realpath", path]) + if result.returncode != 0: + raise FileNotFoundError(result.stderr or path) + return result.stdout.strip() + + async def remove(self, path: str, *, recursive: bool = False) -> None: + """Remove a sandbox path.""" + + command = ["rm"] + if recursive: + command.append("-rf") + else: + command.append("-f") + command.extend(["--", path]) + result = await self.run(command) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to remove {path}") + + @asynccontextmanager + async def temporary_directory(self, prefix: str = "vero-") -> AsyncIterator[str]: + """Create and clean up a temporary directory inside the sandbox.""" + + safe_prefix = "".join( + character + for character in prefix + if character.isalnum() or character in "-_" + ) + template = f"/tmp/{safe_prefix or 'vero-'}XXXXXX" + result = await self.run(["mktemp", "-d", template]) + if result.returncode != 0: + raise RuntimeError( + result.stderr or "failed to create sandbox temporary directory" + ) + path = await self.canonicalize(result.stdout.strip()) + try: + yield path + finally: + await asyncio.shield(self.remove(path, recursive=True)) + + # ── Host ↔ Sandbox file transfer (async) ─────────────────────────── + + @abstractmethod + async def upload(self, local_path: str, remote_path: str) -> None: + """Copy a file or directory from the host into the sandbox. + + For LocalSandbox this is a local copy (no-op if paths match). + For remote sandboxes this would be docker cp, scp, etc. + """ + ... + + @abstractmethod + async def download(self, remote_path: str, local_path: str) -> None: + """Copy a file or directory from the sandbox to the host. + + For LocalSandbox this is a local copy (no-op if paths match). + For remote sandboxes this would be docker cp, scp, etc. + """ + ... + + async def close(self) -> None: + """Release resources owned by this sandbox. Default: no-op.""" + + return None + + +# ============================================================================= +# Local implementation +# ============================================================================= + + +class LocalSandbox(Sandbox): + """Sandbox backed by the local filesystem and subprocess execution. + + File I/O uses pathlib, shell execution uses asyncio.create_subprocess_exec. + No access control — that is handled by Workspace. + + Uses pathlib.Path for path resolution and manipulation. + """ + + def __init__(self, root: Path | str) -> None: + self._root = Path(root).resolve() + + @classmethod + async def create(cls, root: Path | str | None = None, **kwargs) -> LocalSandbox: + """Create a LocalSandbox rooted at the user's home directory.""" + if root is None: + root = Path.home() + root = Path(root).resolve() + return cls(root=root) + + # ── Properties ────────────────────────────────────────────────────── + + @property + def root(self) -> str: + return str(self._root) + + @property + def capabilities(self) -> SandboxCapabilities: + return SandboxCapabilities(host_paths=True) + + def host_path(self, path: str) -> Path: + return Path(self.resolve_path(path)) + + # ── Path resolution ──────────────────────────────────────────────── + + def resolve_path(self, path: str) -> str: + p = Path(path) + if p.is_absolute(): + return str(p.resolve()) + return str((self._root / p).resolve()) + + async def canonicalize(self, path: str) -> str: + resolved = Path(self.resolve_path(path)) + if not resolved.exists(): + raise FileNotFoundError(path) + return str(resolved.resolve()) + + # ── Filesystem ────────────────────────────────────────────────────── + + async def read_file(self, path: str, encoding: str = "utf-8") -> str: + resolved = Path(self.resolve_path(path)) + return resolved.read_text(encoding=encoding) + + async def read_file_bytes(self, path: str, limit: int | None = None) -> bytes: + resolved = Path(self.resolve_path(path)) + with open(resolved, "rb") as f: + return f.read(limit) if limit is not None else f.read() + + async def write_file( + self, path: str, content: str, encoding: str = "utf-8" + ) -> None: + resolved = Path(self.resolve_path(path)) + resolved.parent.mkdir(parents=True, exist_ok=True) + resolved.write_text(content, encoding=encoding) + + async def exists(self, path: str) -> bool: + return Path(self.resolve_path(path)).exists() + + async def is_file(self, path: str) -> bool: + return Path(self.resolve_path(path)).is_file() + + async def is_dir(self, path: str) -> bool: + return Path(self.resolve_path(path)).is_dir() + + async def mkdir(self, path: str, parents: bool = True) -> None: + Path(self.resolve_path(path)).mkdir(parents=parents, exist_ok=True) + + async def stat(self, path: str) -> FileStat: + st = Path(self.resolve_path(path)).stat() + return FileStat(st_size=st.st_size) + + async def list_dir(self, path: str) -> list[str]: + resolved = Path(self.resolve_path(path)) + return sorted(entry.name for entry in resolved.iterdir()) + + # ── Shell ─────────────────────────────────────────────────────────── + + async def run( + self, + command: str | list[str], + cwd: str | None = None, + timeout: int | None = 30, + env: dict[str, str] | None = None, + run_as: str | None = None, + ) -> CommandResult: + """Run a command via subprocess. + + Returns CommandResult with stdout, stderr, returncode. + Does NOT raise on non-zero exit — caller decides how to handle. + """ + if isinstance(command, str): + command = ["bash", "-c", command] + + if cwd is None: + cwd = str(self._root) + + privilege_kwargs: dict[str, object] = {} + if run_as is not None: + # Drop to the unprivileged user and its same-named primary group, + # shedding supplementary groups. Requires the launching process to + # be privileged (root); otherwise create_subprocess_exec raises + # PermissionError — the intended fail-closed behavior for isolation. + privilege_kwargs = { + "user": run_as, + "group": run_as, + "extra_groups": [], + } + + proc = await asyncio.create_subprocess_exec( + *command, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + cwd=str(cwd), + env=env, + start_new_session=os.name == "posix", + **privilege_kwargs, + ) + + try: + stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout) + return CommandResult( + stdout=stdout.decode().strip(), + stderr=stderr.decode().strip(), + returncode=proc.returncode or 0, + ) + except asyncio.TimeoutError: + await asyncio.shield(_cleanup_host_process(proc)) + cmd_str = " ".join(command) + return CommandResult( + stdout="", + stderr=f"Command '{cmd_str}' timed out after {timeout} seconds", + returncode=-1, + ) + except (KeyboardInterrupt, asyncio.CancelledError): + await asyncio.shield(_cleanup_host_process(proc)) + raise + + # ── Host ↔ Sandbox file transfer ─────────────────────────────────── + + async def upload(self, local_path: str, remote_path: str) -> None: + """Copy from host to sandbox. No-op if paths resolve to the same location.""" + import shutil + + src = Path(local_path).resolve() + dst = Path(remote_path).resolve() + if src == dst: + return + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + elif src.is_file(): + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + + async def download(self, remote_path: str, local_path: str) -> None: + """Copy from sandbox to host. No-op if paths resolve to the same location.""" + import shutil + + src = Path(remote_path).resolve() + dst = Path(local_path).resolve() + if src == dst: + return + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + elif src.is_file(): + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + + +class DockerSandbox(Sandbox): + """POSIX sandbox backed by a Docker container with no shared filesystem.""" + + def __init__( + self, + container_id: str, + *, + root: str = "/workspace", + docker_executable: str = "docker", + owns_container: bool = False, + ) -> None: + self.container_id = container_id + self._root = posixpath.normpath(root) + self.docker_executable = docker_executable + self.owns_container = owns_container + self._closed = False + + @classmethod + async def create( + cls, + *, + image: str, + root: str = "/workspace", + name: str | None = None, + docker_executable: str | None = None, + **kwargs, + ) -> DockerSandbox: + executable = docker_executable or shutil.which("docker") + if executable is None: + raise ValueError("docker is required to create a DockerSandbox") + command = [ + executable, + "run", + "--detach", + "--rm", + "--workdir", + root, + ] + if name is not None: + command.extend(["--name", name]) + command.extend([image, "sh", "-c", "while :; do sleep 3600; done"]) + result = await cls._host_command(command, timeout=60) + if result.returncode != 0: + raise RuntimeError(result.stderr or "failed to create Docker sandbox") + sandbox = cls( + result.stdout.strip(), + root=root, + docker_executable=executable, + owns_container=True, + ) + mkdir_result = await sandbox.run(["mkdir", "-p", root]) + if mkdir_result.returncode != 0: + await sandbox.close() + raise RuntimeError(mkdir_result.stderr or f"failed to create {root}") + return sandbox + + @classmethod + async def from_container( + cls, + container_id: str, + *, + root: str = "/workspace", + docker_executable: str | None = None, + ) -> DockerSandbox: + executable = docker_executable or shutil.which("docker") + if executable is None: + raise ValueError("docker is required to attach a DockerSandbox") + sandbox = cls( + container_id, + root=root, + docker_executable=executable, + owns_container=False, + ) + result = await sandbox.run(["mkdir", "-p", root]) + if result.returncode != 0: + raise RuntimeError( + result.stderr or f"cannot access Docker container {container_id}" + ) + return sandbox + + @staticmethod + async def _host_command( + command: list[str], + *, + timeout: int | float | None = 30, + before_terminate: Callable[[], Awaitable[None]] | None = None, + ) -> CommandResult: + process = await asyncio.create_subprocess_exec( + *command, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + start_new_session=os.name == "posix", + ) + try: + stdout, stderr = await asyncio.wait_for( + process.communicate(), timeout=timeout + ) + except asyncio.TimeoutError: + await asyncio.shield(_cleanup_host_process(process, before_terminate)) + return CommandResult("", f"Command timed out after {timeout} seconds", -1) + except (KeyboardInterrupt, asyncio.CancelledError): + await asyncio.shield(_cleanup_host_process(process, before_terminate)) + raise + return CommandResult( + stdout.decode(errors="replace").strip(), + stderr.decode(errors="replace").strip(), + process.returncode or 0, + ) + + async def _docker( + self, *arguments: str, timeout: int | float | None = 30 + ) -> CommandResult: + return await self._host_command( + [self.docker_executable, *arguments], + timeout=timeout, + ) + + @property + def root(self) -> str: + return self._root + + @property + def capabilities(self) -> SandboxCapabilities: + return SandboxCapabilities(posix=True, host_paths=False) + + def resolve_path(self, path: str) -> str: + if posixpath.isabs(path): + return posixpath.normpath(path) + return posixpath.normpath(posixpath.join(self._root, path)) + + async def read_file(self, path: str, encoding: str = "utf-8") -> str: + return (await self.read_file_bytes(path)).decode(encoding) + + async def read_file_bytes(self, path: str, limit: int | None = None) -> bytes: + command = [self.docker_executable, "exec", self.container_id] + if limit is None: + command.extend(["cat", self.resolve_path(path)]) + else: + command.extend(["head", "-c", str(limit), self.resolve_path(path)]) + process = await asyncio.create_subprocess_exec( + *command, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + stdout, stderr = await process.communicate() + if process.returncode != 0: + raise FileNotFoundError(stderr.decode(errors="replace") or path) + return stdout + + async def write_file( + self, path: str, content: str, encoding: str = "utf-8" + ) -> None: + with tempfile.TemporaryDirectory(prefix="vero-docker-") as directory: + local_path = Path(directory) / "content" + local_path.write_text(content, encoding=encoding) + await self.upload(str(local_path), self.resolve_path(path)) + + async def exists(self, path: str) -> bool: + return (await self.run(["test", "-e", self.resolve_path(path)])).returncode == 0 + + async def is_file(self, path: str) -> bool: + return (await self.run(["test", "-f", self.resolve_path(path)])).returncode == 0 + + async def is_dir(self, path: str) -> bool: + return (await self.run(["test", "-d", self.resolve_path(path)])).returncode == 0 + + async def mkdir(self, path: str, parents: bool = True) -> None: + command = ["mkdir"] + if parents: + command.append("-p") + command.append(self.resolve_path(path)) + result = await self.run(command) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to create {path}") + + async def stat(self, path: str) -> FileStat: + result = await self.run(["stat", "-c", "%s", self.resolve_path(path)]) + if result.returncode != 0: + raise FileNotFoundError(result.stderr or path) + return FileStat(st_size=int(result.stdout)) + + async def list_dir(self, path: str) -> list[str]: + result = await self.run(["ls", "-1A", self.resolve_path(path)]) + if result.returncode != 0: + raise FileNotFoundError(result.stderr or path) + return sorted(line for line in result.stdout.splitlines() if line) + + async def run( + self, + command: str | list[str], + cwd: str | None = None, + timeout: int | None = 30, + env: dict[str, str] | None = None, + run_as: str | None = None, + ) -> CommandResult: + pid_file = f"/tmp/vero-exec-{uuid.uuid4().hex}.pid" + arguments = ["exec"] + if run_as is not None: + arguments.extend(["-u", run_as]) + if cwd is not None: + arguments.extend(["--workdir", self.resolve_path(cwd)]) + for name, value in (env or {}).items(): + arguments.extend(["--env", f"{name}={value}"]) + arguments.append(self.container_id) + payload = ["sh", "-c", command] if isinstance(command, str) else command + wrapper = ( + 'pid_file="$1"; shift; ' + 'printf \'%s\\n\' "$$" > "$pid_file"; ' + "trap 'rm -f \"$pid_file\"' EXIT; " + '"$@"' + ) + arguments.extend( + ["setsid", "--wait", "sh", "-c", wrapper, "sh", pid_file, *payload] + ) + + async def terminate_container_group() -> None: + script = """ +pid_file=$1 +attempt=0 +while [ ! -s "$pid_file" ] && [ "$attempt" -lt 20 ]; do + sleep 0.05 + attempt=$((attempt + 1)) +done +if [ -s "$pid_file" ]; then + pgid=$(cat "$pid_file") + kill -TERM -- "-$pgid" 2>/dev/null || true + attempt=0 + while kill -0 -- "-$pgid" 2>/dev/null && [ "$attempt" -lt 10 ]; do + sleep 0.5 + attempt=$((attempt + 1)) + done + kill -KILL -- "-$pgid" 2>/dev/null || true +fi +rm -f "$pid_file" +""" + await self._docker( + "exec", + self.container_id, + "sh", + "-c", + script, + "sh", + pid_file, + timeout=10, + ) + + return await self._host_command( + [self.docker_executable, *arguments], + timeout=timeout, + before_terminate=terminate_container_group, + ) + + async def canonicalize(self, path: str) -> str: + result = await self.run(["readlink", "-f", self.resolve_path(path)]) + if result.returncode != 0: + raise FileNotFoundError(result.stderr or path) + return result.stdout.strip() + + async def upload(self, local_path: str, remote_path: str) -> None: + source = Path(local_path).resolve() + if not source.exists(): + raise FileNotFoundError(local_path) + destination = self.resolve_path(remote_path) + if source.is_dir(): + await self.mkdir(destination) + docker_source = f"{source}/." + else: + await self.mkdir(posixpath.dirname(destination)) + docker_source = str(source) + result = await self._docker( + "cp", docker_source, f"{self.container_id}:{destination}", timeout=120 + ) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to upload {local_path}") + + async def download(self, remote_path: str, local_path: str) -> None: + source = self.resolve_path(remote_path) + destination = Path(local_path).resolve() + if not await self.exists(source): + raise FileNotFoundError(remote_path) + if await self.is_dir(source): + destination.mkdir(parents=True, exist_ok=True) + docker_source = f"{self.container_id}:{source}/." + else: + destination.parent.mkdir(parents=True, exist_ok=True) + docker_source = f"{self.container_id}:{source}" + result = await self._docker("cp", docker_source, str(destination), timeout=120) + if result.returncode != 0: + raise RuntimeError(result.stderr or f"failed to download {remote_path}") + + async def close(self) -> None: + if self._closed or not self.owns_container: + return + self._closed = True + result = await self._docker("rm", "--force", self.container_id, timeout=30) + if result.returncode != 0 and "No such container" not in result.stderr: + raise RuntimeError(result.stderr or "failed to remove Docker sandbox") diff --git a/vero/src/vero/staging.py b/vero/src/vero/staging.py new file mode 100644 index 00000000..1ac50500 --- /dev/null +++ b/vero/src/vero/staging.py @@ -0,0 +1,72 @@ +"""Managed exchange directory between the host and a sandbox.""" + +from __future__ import annotations + +from pathlib import Path, PurePosixPath +from typing import Self + +from vero.sandbox import Sandbox + + +class SandboxStagingArea: + """Temporary sandbox directory with explicit host transfer operations.""" + + def __init__(self, sandbox: Sandbox, *, prefix: str = "vero-") -> None: + self.sandbox = sandbox + self.prefix = prefix + self.root: str | None = None + self._temporary_directory = None + + async def __aenter__(self) -> Self: + self._temporary_directory = self.sandbox.temporary_directory(self.prefix) + self.root = await self._temporary_directory.__aenter__() + return self + + async def __aexit__(self, exc_type, exc, traceback) -> None: + assert self._temporary_directory is not None + await self._temporary_directory.__aexit__(exc_type, exc, traceback) + self.root = None + + @staticmethod + def _relative_path(relative_path: str) -> PurePosixPath: + value = PurePosixPath(relative_path) + if ( + not relative_path + or "\\" in relative_path + or value.is_absolute() + or any(part in {"", ".", ".."} for part in relative_path.split("/")) + ): + raise ValueError("staging path must be a safe relative POSIX path") + return value + + def path(self, relative_path: str) -> str: + if self.root is None: + raise RuntimeError("sandbox staging area is not active") + value = self._relative_path(relative_path) + return (PurePosixPath(self.root) / value).as_posix() + + async def mkdir(self, relative_path: str) -> str: + path = self.path(relative_path) + await self.sandbox.mkdir(path) + return path + + async def write_text(self, relative_path: str, value: str) -> str: + path = self.path(relative_path) + await self.sandbox.write_file(path, value) + return path + + async def read_text(self, relative_path: str) -> str: + return await self.sandbox.read_file(self.path(relative_path)) + + async def exists(self, relative_path: str) -> bool: + return await self.sandbox.exists(self.path(relative_path)) + + async def upload(self, local_path: Path | str, relative_path: str) -> str: + remote_path = self.path(relative_path) + await self.sandbox.upload(str(local_path), remote_path) + return remote_path + + async def download(self, relative_path: str, local_path: Path | str) -> Path: + destination = Path(local_path) + await self.sandbox.download(self.path(relative_path), str(destination)) + return destination diff --git a/vero/src/vero/tools/__init__.py b/vero/src/vero/tools/__init__.py new file mode 100644 index 00000000..888735cc --- /dev/null +++ b/vero/src/vero/tools/__init__.py @@ -0,0 +1,35 @@ +from .base import ToolSet +from .evaluation import EvaluationTools +from .planning import TodoList, think +from .registry import ToolDefinition, ToolRegistry, ToolSetInstance +from .sub_agent import SubAgentTool + +# Register all tool classes +ToolRegistry.register(EvaluationTools) +ToolRegistry.register(SubAgentTool) +ToolRegistry.register(TodoList) +ToolRegistry.register_callable(think) + + +def __getattr__(name: str): + if name in {"WebFetch", "WebSearch"}: + from .web import WebFetch, WebSearch + + return {"WebFetch": WebFetch, "WebSearch": WebSearch}[name] + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +__all__ = [ + # Tool classes (these ARE the keys now) + "EvaluationTools", + "SubAgentTool", + "TodoList", + "think", + "WebFetch", + "WebSearch", + # Registry + "ToolRegistry", + "ToolSetInstance", + "ToolDefinition", + "ToolSet", +] diff --git a/vero/src/vero/tools/base.py b/vero/src/vero/tools/base.py new file mode 100644 index 00000000..14d517a1 --- /dev/null +++ b/vero/src/vero/tools/base.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +if TYPE_CHECKING: + from vero.agents.protocol import AgentContext + + +@runtime_checkable +class ToolSet(Protocol): + """Protocol for tool sets that can be bound to an agent context. + + ToolSets group related tool methods (decorated with @is_tool). + They are pre-created instances that self-wire to policy resources + via bind(). + """ + + exclude_tools: list[str] + + def bind(self, context: AgentContext) -> None: ... diff --git a/vero/src/vero/tools/evaluation.py b/vero/src/vero/tools/evaluation.py new file mode 100644 index 00000000..7d05101d --- /dev/null +++ b/vero/src/vero/tools/evaluation.py @@ -0,0 +1,95 @@ +"""Agent-facing tools for the scoped evaluation capability.""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from typing import TYPE_CHECKING + +from vero.evaluation import CaseSelection, EvaluationBudgetExceeded +from vero.optimization import CandidateEvaluationGateway +from vero.tools.utils import is_tool + +if TYPE_CHECKING: + from vero.agents.protocol import AgentContext + + +@dataclass +class EvaluationTools: + """Evaluate current or prior programs through the session's named evaluations.""" + + exclude_tools: list[str] = field(default_factory=list) + evaluation: CandidateEvaluationGateway | None = field(default=None, repr=False) + + def bind(self, context: AgentContext) -> None: + self.evaluation = context.evaluation + + def _gateway(self) -> CandidateEvaluationGateway: + if self.evaluation is None: + raise RuntimeError("evaluation tools are not bound to an agent context") + return self.evaluation + + @is_tool + async def evaluate( + self, + evaluation: str, + selection: CaseSelection | None = None, + candidate_id: str | None = None, + description: str = "Evaluate agent checkpoint", + ) -> str: + """Evaluate a program, returning authorized feedback and a filesystem path. + + Args: + evaluation: Name from ``.vero/evaluations.json``. + selection: Optional case IDs or range. Omit to use the base selection. + candidate_id: Existing candidate to re-evaluate. Omit to save and + evaluate the current workspace. + description: A short description of the program changes being evaluated. + + Returns: + A bounded JSON receipt with an authorized summary and the path to the + complete permitted feedback under ``.vero/evaluations``. + """ + + try: + result = await self._gateway().evaluate( + evaluation=evaluation, + selection=selection, + candidate_id=candidate_id, + description=description, + ) + except EvaluationBudgetExceeded as error: + # Running out of evaluation budget is expected and benign: hand the + # agent a clean result rather than raising, so it stops evaluating + # and proceeds with what it already knows. The final held-out + # evaluation runs on a separate trusted path and is unaffected. + return json.dumps( + { + "status": "evaluation_budget_exhausted", + "message": str(error), + "detail": ( + "The evaluation budget for this evaluation is spent. " + "Further evaluations of this kind are unavailable; " + "proceed with the feedback you already have." + ), + }, + indent=2, + ) + return result.model_dump_json(indent=2) + + @is_tool + def get_evaluation_budgets(self) -> str: + """Return remaining agent budgets for every available evaluation.""" + + budgets = self._gateway().budgets() + return json.dumps( + { + name: ( + budget.model_dump(mode="json") + if budget is not None + else None + ) + for name, budget in sorted(budgets.items()) + }, + indent=2, + ) diff --git a/vero/src/vero/tools/planning.py b/vero/src/vero/tools/planning.py new file mode 100644 index 00000000..4c31746b --- /dev/null +++ b/vero/src/vero/tools/planning.py @@ -0,0 +1,96 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from enum import StrEnum + +from vero.tools.utils import is_tool + + +def think(thought: str) -> str: + """Think and reason about a thought.""" + return "" + + +class TodoStatus(StrEnum): + """The status of a todo item.""" + + NOT_STARTED = "NOT_STARTED" + IN_PROGRESS = "IN_PROGRESS" + COMPLETED = "COMPLETED" + SKIPPED = "SKIPPED" + + +@dataclass +class TodoList: + """Maintain and keep track of a list of todos.""" + + exclude_tools: list[str] = field(default_factory=list) + todos: list[str] = field(default_factory=list) + todo_statuses: dict[str, TodoStatus] = field(default_factory=dict) + + @is_tool + def add_todos(self, tasks: str | list[str]) -> str: + """Add a todo item or severalitems to the list of todos. + + Args: + tasks: The task or tasks to add to the list. + Returns: + The id of the todo item or items. + """ + if isinstance(tasks, str): + tasks = [tasks] + + task_ids = [] + for task in tasks: + task_ids.append(len(self.todos)) + self.todos.append(task) + self.todo_statuses[task] = TodoStatus.NOT_STARTED + return f"{len(tasks)} items have been added to the todo list with ids {', '.join([str(task_id) for task_id in task_ids])}." + + @is_tool + def update_todo_status( + self, status: TodoStatus, task: str | None = None, task_id: int | None = None + ) -> str: + """Update the status of a todo item. Either provide the task or the task_id. + + Args: + status: The status to update the todo item to. + task: The task to update the status of. + task_id: The id of the todo item to update the status of. + Returns: + A message indicating the status of the todo item has been updated. + """ + + if task is None and task_id is None: + raise ValueError("Either task or task_id must be provided") + + if task_id is not None: + task = self.todos[task_id] + + self.todo_statuses[task] = status + return f"Todo item has been updated to status {status}" + + @is_tool + def list_todos(self, status: list[TodoStatus] | None = None) -> str: + """List the todos with the given status. Defaults to not started and in progress todos. + + Args: + status: The statuses to list the todos for. + + Returns: + A JSON string mapping task ids to task details and their statuses. + """ + + if status is None: + status = [TodoStatus.NOT_STARTED, TodoStatus.IN_PROGRESS] + + if not isinstance(status, list): + status = [status] + + todos = {} + for task_id, task in enumerate(self.todos): + if self.todo_statuses[task] in status: + todos[task_id] = {"task": task, "status": self.todo_statuses[task]} + + return f"""Here are the todos with statuses: {status}\n```json{json.dumps(todos, indent=2)}```""" diff --git a/vero/src/vero/tools/registry.py b/vero/src/vero/tools/registry.py new file mode 100644 index 00000000..aac8645e --- /dev/null +++ b/vero/src/vero/tools/registry.py @@ -0,0 +1,89 @@ +"""Tool Registry for managing available tools.""" + +from dataclasses import dataclass, field +from typing import Callable, Generic, TypeVar + +ToolT = TypeVar("ToolT") + + +@dataclass(frozen=True, slots=True) +class ToolSetInstance(Generic[ToolT]): + """An instance of a tool set with its function tools.""" + + instance: Callable | object + tools: list[ToolT] = field(repr=False) + + +@dataclass +class ToolDefinition: + """Definition of a tool in the registry.""" + + tool_class: type | Callable + description: str + is_callable: bool = False + + @property + def name(self) -> str: + """The original class/function name.""" + return self.tool_class.__name__ + + +class ToolRegistry: + """Registry for tools available to agents. + + Uses the class/callable itself as the key. + + Example: + >>> ToolRegistry.register(BashTool) + >>> ToolRegistry.get(BashTool) + ToolDefinition(tool_class=BashTool, ...) + """ + + _tools: dict[type | Callable, ToolDefinition] = {} + + @classmethod + def register(cls, tool_class: type, description: str | None = None) -> type: + """Register a tool class. Returns the class itself as the key.""" + desc = description or (tool_class.__doc__ or "").strip() + cls._tools[tool_class] = ToolDefinition( + tool_class=tool_class, description=desc, is_callable=False + ) + return tool_class + + @classmethod + def register_callable(cls, func: Callable, description: str | None = None) -> Callable: + """Register a callable (function) as a tool. Returns the callable itself.""" + desc = description or (func.__doc__ or "").split("\n")[0].strip() + cls._tools[func] = ToolDefinition( + tool_class=func, description=desc, is_callable=True + ) + return func + + @classmethod + def get(cls, key: type | Callable) -> ToolDefinition: + """Get a tool definition by class/callable.""" + if key not in cls._tools: + raise KeyError( + f"Tool '{key}' not found in registry. Available: {[t.__name__ for t in cls._tools.keys()]}" + ) + return cls._tools[key] + + @classmethod + def describe(cls, key: type | Callable) -> str: + """Describe a tool by class/callable.""" + return cls.get(key).description + + @classmethod + def exists(cls, key: type | Callable) -> bool: + """Check if a tool exists in the registry.""" + return key in cls._tools + + @classmethod + def all_keys(cls) -> set[type | Callable]: + """Get all registered tool keys.""" + return set(cls._tools.keys()) + + @classmethod + def items(cls) -> list[tuple[type | Callable, ToolDefinition]]: + """Get all registered tools as (key, definition) pairs.""" + return list(cls._tools.items()) diff --git a/vero/src/vero/tools/sandbox_tools.py b/vero/src/vero/tools/sandbox_tools.py new file mode 100644 index 00000000..278ec2cc --- /dev/null +++ b/vero/src/vero/tools/sandbox_tools.py @@ -0,0 +1,79 @@ +"""Function tools that drive an SDK ``SandboxSession``. + +These give the coding agent a real shell plus file read/write that execute +INSIDE the sandbox — so they inherit the sandbox's isolation, workspace binding +(``Manifest(root=...)``), and containment (the sandbox client is the seam: +unix-local / docker / modal / e2b). Crucially they are *plain function tools*, +not the SDK's hosted ``Shell``/``Filesystem`` capabilities, so they work with +ANY model/provider over ChatCompletions *or* Responses. The hosted capabilities +would restrict the native path to the OpenAI Responses API. + +All operations go through ``session.exec`` (a real shell), which keeps the tool +surface tiny and provider-neutral. +""" + +from __future__ import annotations + +import base64 +import shlex +from typing import TYPE_CHECKING + +from agents import function_tool + +if TYPE_CHECKING: + from agents.sandbox.session.sandbox_session import SandboxSession + + +def _format(result: object) -> str: + """Render an ExecResult as text (stdout, optional stderr, non-zero exit).""" + out = getattr(result, "stdout", None) + if out is None: + out = getattr(result, "output", "") + if isinstance(out, (bytes, bytearray)): + out = bytes(out).decode(errors="replace") + err = getattr(result, "stderr", "") or "" + if isinstance(err, (bytes, bytearray)): + err = bytes(err).decode(errors="replace") + code = getattr(result, "exit_code", getattr(result, "returncode", 0)) + text = str(out) + if str(err).strip(): + text += f"\n[stderr]\n{err}" + if code not in (0, None): + text = f"[exit={code}]\n{text}" + return text or f"[exit={code}]" + + +def build_sandbox_tools(session: "SandboxSession") -> list: + """Build shell / read_file / write_file function tools bound to ``session``.""" + + @function_tool + async def shell(command: str) -> str: + """Run a shell command in the program workspace and return its output. + + The working directory is the program's workspace root. Use this to run + the program, tests, git, package managers, and any Unix tooling. + """ + return _format(await session.exec(command)) + + @function_tool + async def read_file(path: str) -> str: + """Read a UTF-8 text file. ``path`` is relative to the workspace root.""" + return _format(await session.exec(f"cat -- {shlex.quote(path)}")) + + @function_tool + async def write_file(path: str, content: str) -> str: + """Create or overwrite a file with ``content``. + + ``path`` is relative to the workspace root; parent directories are + created as needed. + """ + encoded = base64.b64encode(content.encode()).decode() + quoted = shlex.quote(path) + command = ( + f'mkdir -p "$(dirname -- {quoted})" && ' + f"printf %s {shlex.quote(encoded)} | base64 -d > {quoted} && " + f"printf 'wrote %s bytes to %s\\n' {len(content)} {quoted}" + ) + return _format(await session.exec(command)) + + return [shell, read_file, write_file] diff --git a/vero/src/vero/tools/sub_agent.py b/vero/src/vero/tools/sub_agent.py new file mode 100644 index 00000000..3791f1c5 --- /dev/null +++ b/vero/src/vero/tools/sub_agent.py @@ -0,0 +1,182 @@ +"""Sub-agent tool for spawning child agents with access to vero tools.""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any, Callable, NamedTuple + +from vero.exceptions import StreamEventTimeout +from vero.tools.utils import is_tool + +if TYPE_CHECKING: + from agents import Agent, RunResultStreaming, Tool + +logger = logging.getLogger(__name__) + +MAX_SANITIZATION_RETRIES = 3 + + +def default_sub_agent_tools() -> set[type | Callable]: + """Default tools available to sub-agents.""" + from vero.tools.evaluation import EvaluationTools + from vero.tools.planning import TodoList, think + from vero.tools.web import WebFetch + + return { + EvaluationTools, + TodoList, + WebFetch, + think, + } + + +class SubAgentSession(NamedTuple): + """A sub-agent with its agent and session.""" + + agent: Agent + session: RunResultStreaming | None = None + + def to_dict(self) -> dict: + from agents import RunResultStreaming + + def serialize_agent(agent: Agent) -> dict: + agent_dict: dict[str, Any] = {"name": agent.name} + if agent.instructions is not None and isinstance(agent.instructions, str): + agent_dict["instructions"] = agent.instructions + agent_dict["tools"] = [tool.name for tool in agent.tools] + return agent_dict + + session_json = None + if isinstance(self.session, RunResultStreaming): + session_json = self.session.to_input_list() + + return {"agent": serialize_agent(self.agent), "session": session_json} + + +@dataclass +class SubAgentTool: + """Spawn child agents with access to a subset of vero tools. + + The orchestrator invokes call_sub_agent to delegate tasks. + Sub-agents can use tools from `allowed_tools` (filtered from `tool_instances`). + """ + + exclude_tools: list[str] = field(default_factory=list) + allowed_tools: set[type | Callable] = field(default_factory=default_sub_agent_tools) + sessions: dict[str, SubAgentSession] = field(default_factory=dict) + + # Runtime fields — set by the agent after binding other ToolSets + tool_instances: dict[type | Callable, list[Tool]] = field(default_factory=dict) + models: dict[str, Any] = field(default_factory=dict) + + def bind(self, session) -> None: + pass # tool_instances and models are set by the agent after binding other ToolSets + + @property + def available_tool_sets(self) -> dict[type | Callable, list[Tool]]: + return {k: v for k, v in self.tool_instances.items() if k in self.allowed_tools} + + @is_tool + async def call_sub_agent( + self, + prompt: str, + instructions: str | None = None, + max_turns: int = 50, + agent_name: str | None = None, + tool_sets: list[str] | None = None, + model_alias: str | None = None, + ) -> str: + """Invoke a sub-agent to perform a task with the given tools. + + Use sub-agents to delegate tasks like summarizing code, analyzing experiments, + searching the web, or getting feedback on solutions. + + Args: + prompt: The prompt for the sub-agent. + instructions: Sub-agent system instructions. + max_turns: Maximum turns for the sub-agent. + agent_name: Name of the sub-agent (reuse to continue a conversation). + tool_sets: Tool set names to provide (e.g. ["FileRead", "Grep"]). Defaults to all available. + model_alias: Model alias to use. Defaults to first available model. + """ + import asyncio + + import agents as agents_lib + from agents import Agent, Runner + + from vero.utils.openai_agents import stream_events + + if model_alias is None: + model_alias = next(iter(self.models.keys())) + + available = self.available_tool_sets + + # Resolve tool_sets by name + tools: list[Tool] = [] + if tool_sets: + name_to_key = { + k.__name__ if hasattr(k, "__name__") else str(k): k for k in available + } + for name in tool_sets: + if name not in name_to_key: + raise ValueError( + f"Unknown tool set: {name}. Available: {list(name_to_key.keys())}" + ) + tools.extend(available[name_to_key[name]]) + else: + for tool_list in available.values(): + tools.extend(tool_list) + + try: + model = self.models[model_alias] + except KeyError: + raise KeyError( + f"Invalid model alias: {model_alias}. Available: {list(self.models.keys())}" + ) + + if ( + agent_name is not None + and agent_name in self.sessions + and self.sessions[agent_name].session is not None + ): + previous = self.sessions[agent_name].session + if previous: + input = previous.to_input_list() + [{"content": prompt, "role": "user"}] + else: + input = [{"content": prompt, "role": "user"}] + + elif agent_name is not None: + input = prompt + else: + agent_name = f"Sub-Agent {len(self.sessions) + 1}" + input = prompt + + if agent_name in self.sessions: + subagent = self.sessions[agent_name].agent + subagent.instructions = instructions or subagent.instructions + else: + subagent = Agent( + name=agent_name, + model=model, + tools=tools, + instructions=instructions, + ) + + result = Runner.run_streamed(subagent, input=input, max_turns=max_turns) + error: Exception | None = None + try: + async for _event in stream_events(result): + await asyncio.sleep(0) + except Exception as exc: # surfaced via the isinstance checks below + error = exc + self.sessions[agent_name] = SubAgentSession(agent=subagent, session=result) + + if isinstance(error, StreamEventTimeout): + return f"Sub-agent '{agent_name}' timed out. Use the same agent name to continue." + elif isinstance(error, agents_lib.MaxTurnsExceeded): + return f"Sub-agent '{agent_name}' reached max turns ({max_turns}). Use the same agent name to continue." + elif error is not None: + raise error + + return f"Sub-agent '{agent_name}' response:\n\n{result.final_output}" diff --git a/vero/src/vero/tools/utils/__init__.py b/vero/src/vero/tools/utils/__init__.py new file mode 100644 index 00000000..4f783b4f --- /dev/null +++ b/vero/src/vero/tools/utils/__init__.py @@ -0,0 +1,32 @@ +from typing import Callable + +_IS_TOOL = "_is_tool" + + +def is_tool(method: Callable) -> Callable: + """A decorator to mark a method of a class as a tool.""" + setattr(method, _IS_TOOL, True) + return method + + +def get_tools_from_class( + cls_or_instance: type | object, +) -> list[Callable]: + """Get all methods marked with @is_tool from a class or instance. + + If the instance has an `exclude_tools` attribute (from the ToolSet protocol), + those method names are excluded from the result. + + Args: + cls_or_instance: A class type or an instance to get tools from. + + Returns: + List of methods marked with @is_tool decorator. + """ + exclude = getattr(cls_or_instance, "exclude_tools", []) or [] + return [ + getattr(cls_or_instance, attr) + for attr in dir(cls_or_instance) + if getattr(getattr(cls_or_instance, attr), _IS_TOOL, False) + and attr not in exclude + ] diff --git a/vero/src/vero/tools/utils/openai_agents.py b/vero/src/vero/tools/utils/openai_agents.py new file mode 100644 index 00000000..c8815708 --- /dev/null +++ b/vero/src/vero/tools/utils/openai_agents.py @@ -0,0 +1,79 @@ +from inspect import isclass, isfunction, ismethod +from typing import Any, Callable + +from agents import FunctionTool, function_tool +from vero.tools.utils import get_tools_from_class +from vero.utils.general import camel_to_snake + + +def callable_to_oai_tool( + tool: Callable | object, method_name: str | None = None, strict_mode: bool = True, **kwargs: Any +) -> FunctionTool: + """Converts a function or class method to an OpenAI Agents FunctionTool. + + Args: + tool: The function or callable object to convert. + method_name: If the tool is a class, the name of the method to convert. If None, we assume the tool is a callable object and use __call__. + strict_mode: Strict mode toggle for function_tool adapter. + **kwargs: Additional keyword arguments to the OpenAI function_tool adapter. + + Returns: + An OpenAI Agents FunctionTool. + """ + + if isfunction(tool) or ismethod(tool): + return function_tool(tool, strict_mode=strict_mode, **kwargs) + + if isclass(tool): + raise ValueError( + "Cannot convert a class to an OpenAI Agents FunctionTool. Pass an instance instead." + ) + + tool_cls = tool.__class__ + + if method_name is None: + method_name = "__call__" + + if "name_override" not in kwargs: + kwargs["name_override"] = camel_to_snake(tool_cls.__name__) + else: + if "name_override" not in kwargs: + kwargs["name_override"] = camel_to_snake(tool_cls.__name__) + "_" + method_name + + # use the method of the instance not the class + method = getattr(tool, method_name) + return function_tool(method, strict_mode=strict_mode, **kwargs) + + +def tool_set_instance_to_oai_tools( + instance: object, + strict_mode: bool = True, + prefix_class_name: bool = True, + exclude_methods: list[str] | None = None, +) -> list[FunctionTool]: + """Creates a list of OpenAI Agents FunctionTools from an instance.""" + + exclude_methods = exclude_methods or [] + + if exclude_methods: + tools = [ + getattr(instance, tool.__name__) + for tool in get_tools_from_class(instance.__class__) + if tool.__name__ not in exclude_methods + ] + else: + tools = [ + getattr(instance, tool.__name__) for tool in get_tools_from_class(instance.__class__) + ] + + # Defaault to using the __call__ method of the instance if no tools are found. + if not tools: + return [callable_to_oai_tool(instance, strict_mode=strict_mode)] + + oai_tools = [] + for tool in tools: + kwargs = {"strict_mode": strict_mode} + if prefix_class_name: + kwargs["name_override"] = f"{instance.__class__.__name__}_{tool.__name__}" + oai_tools.append(callable_to_oai_tool(tool, **kwargs)) + return oai_tools diff --git a/vero/src/vero/tools/web.py b/vero/src/vero/tools/web.py new file mode 100644 index 00000000..bb858096 --- /dev/null +++ b/vero/src/vero/tools/web.py @@ -0,0 +1,304 @@ +from __future__ import annotations + +import io +import json +import os +from dataclasses import dataclass, field + +from async_lru import alru_cache + +from vero.tools.registry import ToolRegistry + +SCRAPING_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36" + + +def _fetch_and_parse_webpage_trafilatura(url: str) -> str | None: + """Fetch and parse a webpage using trafilatura.""" + import trafilatura + from trafilatura.settings import use_config + + trafilatura_config = use_config() + trafilatura_config["DEFAULT"]["USER_AGENT"] = SCRAPING_AGENT + + downloaded = trafilatura.fetch_url(url, config=trafilatura_config, no_ssl=True) + if downloaded is None: + return None + + return trafilatura.extract(downloaded) + + +async def _fetch_and_parse_webpage_beautifulsoup(url: str) -> str | None: + import httpx + from bs4 import BeautifulSoup + + try: + async with httpx.AsyncClient(verify=False, follow_redirects=True, timeout=30.0) as client: + response = await client.get( + url, + headers={ + "User-Agent": SCRAPING_AGENT, + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", + }, + ) + response.raise_for_status() + downloaded = response.text + soup = BeautifulSoup(downloaded, "lxml") + for script in soup(["script", "style"]): + script.extract() + content = soup.get_text(separator="\n", strip=True) + return content + except (httpx.HTTPError, Exception): + return None + + +async def _fetch_and_parse_pdf(url: str) -> str | None: + """Fetch and parse a PDF document.""" + import httpx + from pypdf import PdfReader + + try: + async with httpx.AsyncClient(verify=False, follow_redirects=True, timeout=30.0) as client: + response = await client.get( + url, + headers={ + "User-Agent": SCRAPING_AGENT, + "Accept": "application/pdf", + }, + ) + response.raise_for_status() + + # Check if content is actually a PDF + content_type = response.headers.get("content-type", "").lower() + if "pdf" not in content_type and not url.lower().endswith(".pdf"): + return None + + # Parse PDF + pdf_file = io.BytesIO(response.content) + reader = PdfReader(pdf_file) + + # Extract text from all pages + text_content = [] + for page in reader.pages: + text = page.extract_text() + if text: + text_content.append(text) + + return "\n\n".join(text_content) if text_content else None + + except Exception: + return None + + +@alru_cache(maxsize=128) +async def _fetch_and_parse_webpage(url: str) -> str | None: + """Fetch and parse a webpage or PDF using multiple methods with fallbacks.""" + # Check if URL likely points to a PDF + if url.lower().endswith(".pdf"): + content = await _fetch_and_parse_pdf(url) + if content is not None: + return content + + # Try HTML parsing first + content = _fetch_and_parse_webpage_trafilatura(url) + if content is not None: + return content + + content = await _fetch_and_parse_webpage_beautifulsoup(url) + if content is not None: + return content + + # If HTML parsing failed, try PDF as fallback (might be PDF with non-.pdf extension) + content = await _fetch_and_parse_pdf(url) + if content is not None: + return content + + return None + + +@dataclass +class WebFetch: + """Fetches and extracts text content from a webpage given a URL.""" + + exclude_tools: list[str] = field(default_factory=list) + max_chars: int = 50_000 + + async def __call__(self, url: str, offset: int = 0, max_chars: int = 50_000) -> str: + """ + Fetches the content of a webpage and returns the extracted text. + + Args: + url: The URL of the webpage to fetch + + Returns: + Extracted text content from the webpage + """ + + if offset < 0: + raise ValueError("offset must be greater than or equal to 0.") + + if max_chars > self.max_chars: + raise ValueError( + f"max_chars must be less than or equal to {self.max_chars}. Use pagination to fetch more content." + ) + + content = await _fetch_and_parse_webpage(url) + + if content is None: + raise RuntimeError(f"Failed to fetch content from {url}") + + if offset > len(content): + raise ValueError(f"Offset was greater than the length of the content {len(content)}") + + # Apply offset and max_chars + content = content[offset : offset + max_chars] + + return content + + +def _contains_chinese(text: str) -> bool: + """Check if text contains Chinese characters.""" + return any("\u4E00" <= char <= "\u9FFF" for char in text) + + +@dataclass +class WebSearch: + """Searches the web for information using the Serper API (google.serper.dev).""" + + exclude_tools: list[str] = field(default_factory=list) + max_chars_per_result: int = 10_000 + max_results: int = 10 + max_retries: int = 3 + fetch_full_content: bool = True + base_url: str = "https://google.serper.dev/search" + + def __post_init__(self): + api_key = os.getenv("SERPER_KEY_ID") or os.getenv("SERPER_API_KEY") + if not api_key: + raise ValueError( + "`SERPER_KEY_ID` or `SERPER_API_KEY` must be set as an environment variable to use `WebSearch` tool." + ) + + async def __call__(self, query: str) -> str: # noqa: C901 + """ + Searches the web for information and returns a list of results with URL, title, and content. + + If fetch_full_content is True (default), fetches and parses full webpage content. + Otherwise, returns only snippets from the search results. + + Args: + query: The query string to search for + Returns: + JSON string containing search results (URL, Title, Content/Snippet) + """ + api_key = os.getenv("SERPER_KEY_ID") or os.getenv("SERPER_API_KEY") + if not api_key: + raise RuntimeError( + "`SERPER_KEY_ID` or `SERPER_API_KEY` must be set as an environment variable to use `WebSearch` tool." + ) + + # Configure payload based on language + if _contains_chinese(query): + payload = { + "q": query, + "location": "China", + "gl": "cn", + "hl": "zh-cn", + "num": min(self.max_results, 10), + } + else: + payload = { + "q": query, + "location": "United States", + "gl": "us", + "hl": "en", + "num": min(self.max_results, 10), + } + + headers = { + "X-API-KEY": api_key, + "Content-Type": "application/json", + } + + import httpx + + # Retry logic + last_error = None + for attempt in range(self.max_retries): + try: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.post( + self.base_url, + json=payload, + headers=headers, + ) + response.raise_for_status() + + results = response.json() + + if "organic" not in results: + return json.dumps( + { + "error": f"No results found for query: '{query}'", + "suggestion": "Try a less specific query", + "results": [], + } + ) + + # Parse and optionally fetch full content + parsed_results = [] + for item in results["organic"][: self.max_results]: + result_item = { + "title": item.get("title", ""), + "url": item.get("link", ""), + } + + if self.fetch_full_content: + # Fetch full page content + content = await _fetch_and_parse_webpage(result_item["url"]) + if content is not None: + if len(content) > self.max_chars_per_result: + content = content[: self.max_chars_per_result] + "... (truncated)" + result_item["content"] = content + else: + # Fall back to snippet if full content fetch fails + result_item["content"] = item.get("snippet", "") + else: + # Just use the snippet + result_item["content"] = item.get("snippet", "") + + # Add optional metadata + if "date" in item: + result_item["date"] = item["date"] + if "source" in item: + result_item["source"] = item["source"] + + parsed_results.append(result_item) + + return json.dumps(parsed_results, indent=4) + + except httpx.TimeoutException as e: + last_error = e + if attempt < self.max_retries - 1: + continue + except httpx.HTTPStatusError as e: + last_error = e + if attempt < self.max_retries - 1: + continue + except Exception as e: + last_error = e + if attempt < self.max_retries - 1: + continue + + # All retries failed + return json.dumps( + { + "error": f"Search failed after {self.max_retries} attempts: {str(last_error)}", + "results": [], + } + ) + + +# These tools have optional scraping dependencies, so register them only when +# the web module is imported. +ToolRegistry.register(WebFetch) +ToolRegistry.register(WebSearch) diff --git a/vero/src/vero/utils/__init__.py b/vero/src/vero/utils/__init__.py new file mode 100644 index 00000000..fcebecf0 --- /dev/null +++ b/vero/src/vero/utils/__init__.py @@ -0,0 +1,27 @@ +from .asyncio import ( + SubprocessCancelledError, + SubprocessResult, + SubprocessTimeoutError, + anext_with_timeout, + run_bash_command, + run_subprocess_with_tee, +) +from .general import ( + camel_to_snake, + paginate, + recursively_serialize, + strip_ansi, +) + +__all__ = [ + "anext_with_timeout", + "camel_to_snake", + "paginate", + "recursively_serialize", + "run_bash_command", + "run_subprocess_with_tee", + "strip_ansi", + "SubprocessCancelledError", + "SubprocessResult", + "SubprocessTimeoutError", +] diff --git a/vero/src/vero/utils/asyncio.py b/vero/src/vero/utils/asyncio.py new file mode 100644 index 00000000..5991faf2 --- /dev/null +++ b/vero/src/vero/utils/asyncio.py @@ -0,0 +1,254 @@ +import asyncio +import sys +from dataclasses import dataclass +from subprocess import CalledProcessError +from typing import Any, AsyncIterator, TypeVar + +T = TypeVar("T") + + +@dataclass +class SubprocessResult: + """Result of a subprocess execution, always includes captured output.""" + + args: list[str] + returncode: int | None # None if process didn't complete + stdout: str + stderr: str + pid: int | None = None # Process ID, useful for debugging orphaned processes + timed_out: bool = False + cancelled: bool = False + + +class SubprocessTimeoutError(Exception): + """Timeout with captured output preserved.""" + + def __init__(self, result: SubprocessResult): + self.result = result + super().__init__(f"Subprocess timed out: {result.args}") + + +class SubprocessCancelledError(Exception): + """Cancellation with captured output preserved.""" + + def __init__(self, result: SubprocessResult): + self.result = result + super().__init__(f"Subprocess cancelled: {result.args}") + + +async def anext_with_timeout(it: AsyncIterator[T], timeout: int = 5) -> T: + """Get the next item from an async iterator with a timeout.""" + async with asyncio.timeout(timeout): + return await anext(it) + + +async def run_bash_command(cmd: list[str], timeout: int | None = None) -> str: + """Run a Bash command and return its output. + + Deprecated: tools should use sandbox.run() instead. + """ + proc = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + + async def terminate_process(): + """Terminate process, shielded from cancellation.""" + proc.terminate() + try: + await asyncio.wait_for(proc.wait(), timeout=5) + except asyncio.TimeoutError: + proc.kill() + await proc.wait() + + try: + stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout) + stdout_str = stdout.decode().strip() + stderr_str = stderr.decode().strip() + if proc.returncode == 0: + return stdout_str + else: + raise CalledProcessError( + returncode=proc.returncode, + cmd=cmd, + output=stdout_str, + stderr=stderr_str, + ) + except asyncio.TimeoutError: + await asyncio.shield(terminate_process()) + cmd_str = " ".join(cmd) + raise TimeoutError(f"Command '{cmd_str}' timed out after {timeout} seconds") + except (KeyboardInterrupt, asyncio.CancelledError): + await asyncio.shield(terminate_process()) + raise + + +async def run_subprocess_with_tee( + cmd: list[str], + timeout: float | None = None, + cwd: str | None = None, + check: bool = False, + flush: bool = False, + chunk_size: int = 8192, + tee_stdout: bool = True, + tee_stderr: bool = True, + **kwargs: Any, +) -> SubprocessResult: + """Run a subprocess, streaming output to console while capturing it. + + Args: + cmd: The command to run. + timeout: Timeout in seconds (None = no timeout). + cwd: Working directory for the subprocess. + check: Raise CalledProcessError on non-zero exit. + flush: Flush console output after each chunk. + chunk_size: Bytes to read per iteration (default 8192). + tee_stdout: Whether to print stdout to console. + tee_stderr: Whether to print stderr to console. + **kwargs: Additional arguments to asyncio.create_subprocess_exec. + + Returns: + SubprocessResult with captured stdout/stderr and status. + + Raises: + SubprocessTimeoutError: If timeout exceeded (includes partial output). + SubprocessCancelledError: If cancelled (includes partial output). + CalledProcessError: If check=True and non-zero exit code. + """ + process: asyncio.subprocess.Process | None = None + stdout_chunks: list[str] = [] + stderr_chunks: list[str] = [] + + def build_result( + returncode: int | None = None, + timed_out: bool = False, + cancelled: bool = False, + ) -> SubprocessResult: + return SubprocessResult( + args=cmd, + returncode=returncode, + stdout="".join(stdout_chunks), + stderr="".join(stderr_chunks), + pid=process.pid if process else None, + timed_out=timed_out, + cancelled=cancelled, + ) + + async def read_stream( + stream: asyncio.StreamReader | None, + chunks: list[str], + output_stream, + tee: bool, + ): + if not stream: + return + + while True: + chunk = await stream.read(chunk_size) + if not chunk: + break + decoded = chunk.decode(errors="replace") + chunks.append(decoded) + if tee: + output_stream.write(decoded) + if flush: + output_stream.flush() + + async def terminate_process_safe(): + """Terminate process, handling edge cases.""" + if process is None or process.returncode is not None: + return + try: + process.terminate() + try: + await asyncio.wait_for(process.wait(), timeout=5) + except asyncio.TimeoutError: + process.kill() + await process.wait() + except ProcessLookupError: + pass # Already dead + + try: + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + cwd=cwd, + **kwargs, + ) + + async def run_and_wait(): + # Run stream readers as tasks so we can cancel them + stdout_task = asyncio.create_task( + read_stream(process.stdout, stdout_chunks, sys.stdout, tee_stdout) + ) + stderr_task = asyncio.create_task( + read_stream(process.stderr, stderr_chunks, sys.stderr, tee_stderr) + ) + + try: + # Wait for process to exit (don't wait for streams - child processes may hold them open) + await process.wait() + finally: + # Always cleanup stream tasks, even on cancellation/timeout + _, pending = await asyncio.wait( + [stdout_task, stderr_task], + timeout=1.0, + ) + for task in pending: + task.cancel() + try: + await asyncio.wait_for(task, timeout=0.5) + except (asyncio.CancelledError, asyncio.TimeoutError): + pass + + if timeout is not None: + await asyncio.wait_for(run_and_wait(), timeout=timeout) + else: + await run_and_wait() + + except asyncio.TimeoutError: + # Terminate but preserve partial output + try: + await asyncio.shield(terminate_process_safe()) + except asyncio.CancelledError: + # Shield was pierced, do sync cleanup + if process and process.returncode is None: + process.kill() + raise SubprocessTimeoutError( + build_result( + returncode=process.returncode if process else None, + timed_out=True, + ) + ) + + except asyncio.CancelledError: + try: + await asyncio.shield(terminate_process_safe()) + except asyncio.CancelledError: + if process and process.returncode is None: + process.kill() + raise SubprocessCancelledError( + build_result( + returncode=process.returncode if process else None, + cancelled=True, + ) + ) + + result = build_result(returncode=process.returncode) + + if check and process.returncode != 0: + if process.returncode is None: + returncode = -1 + else: + returncode = process.returncode + + raise CalledProcessError( + returncode=returncode, + cmd=cmd, + output=result.stdout, + stderr=result.stderr, + ) + + return result diff --git a/vero/src/vero/utils/general.py b/vero/src/vero/utils/general.py new file mode 100644 index 00000000..e323f70a --- /dev/null +++ b/vero/src/vero/utils/general.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import re +from dataclasses import asdict, is_dataclass +from pathlib import Path +from typing import ( + Protocol, + TypeVar, + overload, + runtime_checkable, +) + +from pydantic import BaseModel, JsonValue + +JsonT = TypeVar("JsonT", bound=JsonValue) + + +@runtime_checkable +class IsDataclass(Protocol): + __dataclass_fields__: dict # all dataclasses have this attribute + + +@overload +def recursively_serialize(data: BaseModel) -> dict[str, JsonValue]: ... + + +@overload +def recursively_serialize(data: JsonT) -> JsonT: ... + + +@overload +def recursively_serialize(data: Path) -> str: ... + + +@overload +def recursively_serialize(data: IsDataclass) -> dict[str, JsonValue]: ... + + +def recursively_serialize( + data: BaseModel | JsonValue | Path | IsDataclass, +) -> JsonValue: + """Recursively serialize a BaseModel or JsonValue or dataclass to a JSON value.""" + if is_dataclass(data): + return recursively_serialize(asdict(data)) + + if isinstance(data, dict): + return {k: recursively_serialize(v) for k, v in data.items()} + + if isinstance(data, (list, tuple, set)): + return [recursively_serialize(item) for item in data] + + if isinstance(data, BaseModel): + return recursively_serialize(data.model_dump()) + + if isinstance(data, Path): + return str(data) + + return data # type: ignore + + +def camel_to_snake(s: str) -> str: + """Convert a camel case string to a snake case string.""" + snake = [] + for char in s: + if char.isupper() and snake: + snake.append("_") + snake.append(char.lower()) + return "".join(snake) + + +def strip_ansi(text: str) -> str: + """Strip ANSI escape codes from a string.""" + _ANSI_ESCAPE_PATTERN = re.compile(r"\x1b(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])") + return _ANSI_ESCAPE_PATTERN.sub("", text) + + +def paginate( + items: list[str], max_chars: int, offset: int = 0, limit: int | None = None +) -> list[str]: + """Paginate a list of items into a list of strings. max_chars is not respected for single item lists.""" + + if offset < 0: + raise ValueError("offset must be greater than or equal to 0.") + + if offset > len(items): + raise ValueError( + f"offset must be less than the length of the items {len(items)}." + ) + + items = items[offset:] + if limit is None: + limit = len(items) + + paginated_items = [] + paginated_items_length = 0 + + for item in items[:limit]: + paginated_items.append(item) + paginated_items_length += len(item) + + if paginated_items_length > max_chars: + break + + return paginated_items diff --git a/vero/src/vero/utils/openai_agents.py b/vero/src/vero/utils/openai_agents.py new file mode 100644 index 00000000..75b7ae3f --- /dev/null +++ b/vero/src/vero/utils/openai_agents.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +import asyncio +from typing import TYPE_CHECKING, AsyncGenerator + +from vero.exceptions import StreamEventTimeout +from vero.utils import anext_with_timeout + +if TYPE_CHECKING: + from agents import OpenAIChatCompletionsModel, RunResultStreaming, StreamEvent + from agents.extensions.models.litellm_model import LitellmModel + + +async def stream_events( + result: RunResultStreaming, + event_timeout: int | None = None, +) -> AsyncGenerator[StreamEvent, None]: + """Stream events from an OpenAI agent run. + + Args: + result: The RunResultStreaming object to stream events from. + event_timeout: Timeout in seconds for each event. None means no timeout. + """ + + event_iter = aiter(result.stream_events()) + + while True: + try: + if event_timeout is not None: + event = await anext_with_timeout(event_iter, event_timeout) + else: + event = await anext(event_iter) + yield event + except StopAsyncIteration: + break + except asyncio.TimeoutError: + result.cancel(mode="immediate") + await asyncio.sleep(0.1) + raise StreamEventTimeout( + f"Timeout of {event_timeout} seconds reached for an event on iterator {event_iter}!" + ) + + +def strict_mode_from_model( + model: str | OpenAIChatCompletionsModel | LitellmModel, +) -> bool: + """Gets the strict mode of the tool schema from the model name.""" + + if not isinstance(model, str): + model = model.model + + return ( + "/" not in model or model.startswith("openai") or model.startswith("anthropic") + ) diff --git a/vero/src/vero/utils/tokens.py b/vero/src/vero/utils/tokens.py new file mode 100644 index 00000000..836e60cb --- /dev/null +++ b/vero/src/vero/utils/tokens.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from agents import RunResult, RunResultStreaming, TResponseInputItem + + +def run_result_to_messages( + result: RunResult | RunResultStreaming | list[TResponseInputItem], +) -> list[dict | TResponseInputItem]: + """Convert a RunResult or list of TResponseInputItem to a list of messages.""" + + result = deepcopy(result) + + if not isinstance(result, list): + result = result.to_input_list() + + def process_item(item: dict | TResponseInputItem) -> dict | TResponseInputItem: + for key in ["output", "content"]: + if key in item: + if isinstance(item[key], list): + item[key] = item[key][0] if item[key] else "" + return item + + return [process_item(item) for item in result] diff --git a/vero/src/vero/workspace/__init__.py b/vero/src/vero/workspace/__init__.py new file mode 100644 index 00000000..d8a8d027 --- /dev/null +++ b/vero/src/vero/workspace/__init__.py @@ -0,0 +1,6 @@ +from __future__ import annotations + +from vero.workspace.base import Workspace +from vero.workspace.git import GitWorkspace # noqa: E402 + +__all__ = ["GitWorkspace", "Workspace"] diff --git a/vero/src/vero/workspace/base.py b/vero/src/vero/workspace/base.py new file mode 100644 index 00000000..6c5befdb --- /dev/null +++ b/vero/src/vero/workspace/base.py @@ -0,0 +1,141 @@ +from __future__ import annotations + +import posixpath +from abc import ABC, abstractmethod +from contextlib import asynccontextmanager +from pathlib import PurePosixPath +from typing import TYPE_CHECKING, AsyncGenerator + +if TYPE_CHECKING: + from vero.sandbox import Sandbox + + +class Workspace(ABC): + """Abstract version-control workspace. + + Combines an isolated working directory with snapshot/restore versioning. + Workspace depends on a Sandbox for file and shell operations. All + version-control methods are async to support non-local sandbox backends. + """ + + # ── Properties ───────────────────────────────────────────────── + + @property + @abstractmethod + def sandbox(self) -> Sandbox: + """The execution environment this workspace operates in.""" + ... + + @property + @abstractmethod + def root(self) -> str: + """Workspace root directory (e.g. git repo root).""" + ... + + @property + @abstractmethod + def project_path(self) -> str: + """Project directory within this workspace.""" + ... + + @property + @abstractmethod + def name(self) -> str: + """Human-readable name for this workspace.""" + ... + + # ── History ───────────────────────────────────────────────────── + + @abstractmethod + async def current_version(self) -> str: + """Return the current version ID (commit hash, change ID, etc.).""" + ... + + @abstractmethod + async def save(self, message: str = "Save") -> str: + """Create a new version from current state. Returns the version ID.""" + ... + + @abstractmethod + async def restore(self, version_id: str, message: str | None = None) -> str: + """Revert to a previous version, preserving history. Returns new version ID.""" + ... + + # ── History inspection ────────────────────────────────────────── + + @abstractmethod + async def diff( + self, from_version: str | None = None, to_version: str | None = None + ) -> str: + """Diff between two versions.""" + ... + + @abstractmethod + async def log(self, max_count: int = 10, since_version: str | None = None) -> str: + """Version history.""" + ... + + @abstractmethod + async def is_ancestor(self, version_a: str, version_b: str) -> bool: + """Is version_a an ancestor of version_b?""" + ... + + # ── Copies ────────────────────────────────────────────────────── + + @abstractmethod + async def copy( + self, name: str | None = None, from_version: str | None = None + ) -> Workspace: + """Create a persistent isolated copy of this workspace.""" + ... + + @asynccontextmanager + async def temp_copy( + self, from_version: str | None = None + ) -> AsyncGenerator[Workspace, None]: + """Temporary isolated copy, cleaned up on exit.""" + yield self # pragma: no cover + + # ── Execution at a version ────────────────────────────────────── + + @asynccontextmanager + async def at(self, version_id: str) -> AsyncGenerator[None, None]: + """Temporarily switch to a version, restore on exit.""" + yield # pragma: no cover + + # ── State ─────────────────────────────────────────────────────── + + @abstractmethod + async def is_dirty(self) -> bool: + """Are there unsaved changes not yet captured in a version?""" + ... + + async def destroy(self) -> None: + """Clean up this workspace. Default: no-op.""" + pass + + # ── Path resolution ──────────────────────────────────────────── + + def resolve_path(self, path: str) -> str: + """Resolve a path relative to ``project_path``. + + Absolute paths pass through. Relative paths (including ``"."``) + are resolved against ``project_path``, not ``sandbox.root``. + """ + value = PurePosixPath(str(path)) + if value.is_absolute(): + return posixpath.normpath(value.as_posix()) + root = posixpath.normpath(PurePosixPath(str(self.project_path)).as_posix()) + return posixpath.normpath(posixpath.join(root, value.as_posix())) + + def get_relative_path(self, path: str) -> str | None: + """Get path relative to ``project_path``, or None if outside.""" + resolved = PurePosixPath(self.resolve_path(path)) + root = PurePosixPath( + posixpath.normpath(PurePosixPath(str(self.project_path)).as_posix()) + ) + try: + relative = resolved.relative_to(root) + except ValueError: + return None + return relative.as_posix() diff --git a/vero/src/vero/workspace/git.py b/vero/src/vero/workspace/git.py new file mode 100644 index 00000000..7fd20597 --- /dev/null +++ b/vero/src/vero/workspace/git.py @@ -0,0 +1,408 @@ +from __future__ import annotations + +import asyncio +import logging +import uuid +from contextlib import asynccontextmanager +from pathlib import PurePosixPath +from typing import AsyncGenerator + +from vero.sandbox import Sandbox +from vero.workspace.base import Workspace + +logger = logging.getLogger(__name__) + + +def _basename(path: str) -> str: + """Extract the last component of a path.""" + return PurePosixPath(path).name + + +def _parent(path: str) -> str: + """Extract the parent directory of a path.""" + return str(PurePosixPath(path).parent) + + +def _join(parent: str, child: str) -> str: + """Join two path components.""" + return str(PurePosixPath(parent) / child) + + +class GitWorkspace(Workspace): + """Workspace backed by git, using sandbox.run() for all git operations. + + Works with any Sandbox implementation — local, Docker, remote. + All path manipulation uses string operations (no pathlib/os.path) + so it remains correct for non-local sandboxes. + """ + + def __init__( + self, + sandbox: Sandbox, + root: str, + project_path: str | None = None, + name: str | None = None, + worktree_owner_root: str | None = None, + ) -> None: + self._sandbox = sandbox + self._root = root + self._project_path = project_path or root + self._name = name or _basename(root) + self._worktree_owner_root = worktree_owner_root + self._lock = asyncio.Lock() + + @property + def sandbox(self) -> Sandbox: + return self._sandbox + + @property + def root(self) -> str: + return self._root + + @property + def project_path(self) -> str: + return self._project_path + + @property + def name(self) -> str: + return self._name + + # ── Git helper ────────────────────────────────────────────────── + + async def _git(self, *args: str) -> str: + """Run a git command via sandbox.run(), returning stdout. Raises on non-zero exit.""" + result = await self._sandbox.run( + ["git", "-c", f"safe.directory={self._root}", *args], + cwd=self._root, + ) + if result.returncode != 0: + raise RuntimeError(f"git {' '.join(args)} failed: {result.stderr}") + return result.stdout + + # ── Construction helpers ──────────────────────────────────────── + + @classmethod + async def from_path( + cls, + sandbox: Sandbox, + project_path: str, + ) -> GitWorkspace: + """Create a GitWorkspace from a project path. + + Finds the git repo root and determines the project-relative path. + All resolution happens via sandbox commands, not local path operations. + """ + project_path = await sandbox.canonicalize(str(project_path)) + + result = await sandbox.run( + [ + "git", + "-c", + "safe.directory=*", + "rev-parse", + "--show-toplevel", + ], + cwd=project_path, + ) + if result.returncode != 0: + detail = f": {result.stderr}" if result.stderr else "" + raise RuntimeError(f"Not a git repository: {project_path}{detail}") + repo_root = await sandbox.canonicalize(result.stdout.strip()) + + # Find the main repo name (handles worktrees whose common dir differs) + repo_name = _basename(repo_root) + try: + result = await sandbox.run( + [ + "git", + "-c", + f"safe.directory={repo_root}", + "rev-parse", + "--git-common-dir", + ], + cwd=project_path, + ) + if result.returncode == 0: + git_common_dir = result.stdout.strip() + # Only use common-dir if it's an absolute path (worktree case). + # Regular repos return ".git" (relative), which is not useful. + if git_common_dir.startswith("/"): + main_root = _parent(git_common_dir) + repo_name = _basename(main_root) + except RuntimeError: + pass + + return cls( + sandbox=sandbox, + root=repo_root, + project_path=project_path, + name=repo_name, + ) + + @classmethod + async def create(cls, project_path: str) -> GitWorkspace: + """Create a GitWorkspace with a default local sandbox. + + Convenience factory for non-Policy callers (evaluator, trace analysis, + etc.) that just need a workspace for git operations without agent + access control. + """ + from vero.sandbox import LocalSandbox + + sandbox = await LocalSandbox.create() + return await cls.from_path(sandbox, str(project_path)) + + # ── History ───────────────────────────────────────────────────── + + async def current_version(self) -> str: + return await self._git("rev-parse", "HEAD") + + async def save(self, message: str = "Save") -> str: + async with self._lock: + if not await self.is_dirty(): + return await self.current_version() + + # Stage changes scoped to the project path + if self._project_path == self._root: + await self._git("add", "--all") + else: + await self._git("add", self._project_path) + + # Commit (skip hooks for automated commits) + await self._git( + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + message, + "--no-verify", + ) + + return await self.current_version() + + async def restore(self, version_id: str, message: str | None = None) -> str: + async with self._lock: + message = message or f"Restore to {version_id[:8]}" + + # Checkout files from the target version + await self._git("checkout", version_id, "--", ".") + + # Stage everything + await self._git("add", "--all") + + # Only commit if there are actual changes + try: + await self._git("diff", "--cached", "--quiet", "--exit-code") + # No changes — already at that state + except RuntimeError: + # There are staged changes — commit them + await self._git( + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + message, + "--no-verify", + ) + + return await self.current_version() + + # ── History inspection ────────────────────────────────────────── + + async def diff( + self, from_version: str | None = None, to_version: str | None = None + ) -> str: + args = ["diff"] + if from_version: + args.append(from_version) + if to_version: + args.append(to_version) + return await self._git(*args) + + async def log(self, max_count: int = 10, since_version: str | None = None) -> str: + args = ["log", f"-n{max_count}", "--pretty=format:%h - %s (%cr) <%an>"] + if since_version: + args.append(f"{since_version}..HEAD") + return await self._git(*args) + + async def is_ancestor(self, version_a: str, version_b: str) -> bool: + try: + await self._git("merge-base", "--is-ancestor", version_a, version_b) + return True + except RuntimeError: + return False + + def _copied_project_path(self, copied_root: str) -> str: + relative = PurePosixPath(self._project_path).relative_to(self._root) + if str(relative) == ".": + return copied_root + return _join(copied_root, str(relative)) + + # ── Copies ────────────────────────────────────────────────────── + + async def _remove_worktree(self, target_path: str) -> None: + result = await self._sandbox.run( + ["git", "worktree", "remove", "--force", target_path], + cwd=self._root, + ) + if result.returncode != 0 and await self._sandbox.exists(target_path): + await self._sandbox.remove(target_path, recursive=True) + await self._sandbox.run( + ["git", "worktree", "prune"], + cwd=self._root, + ) + + async def _add_worktree(self, target_path: str, from_version: str | None) -> None: + if await self._sandbox.exists(target_path): + raise FileExistsError(target_path) + arguments = ["worktree", "add", "--detach", target_path] + if from_version is not None: + arguments.append(from_version) + try: + await self._git(*arguments) + except BaseException: + await asyncio.shield(self._remove_worktree(target_path)) + raise + + async def copy( + self, name: str | None = None, from_version: str | None = None + ) -> GitWorkspace: + """Create a new git worktree as an isolated copy.""" + async with self._lock: + if name is None: + name = f"worktree-{uuid.uuid4().hex[:8]}" + + target_path = _join(_parent(self._root), name) + await self._add_worktree(target_path, from_version) + + return GitWorkspace( + sandbox=self._sandbox, + root=target_path, + project_path=self._copied_project_path(target_path), + name=self._name, + worktree_owner_root=self._root, + ) + + @asynccontextmanager + async def temp_copy( + self, from_version: str | None = None + ) -> AsyncGenerator[GitWorkspace, None]: + """Create a temporary worktree, cleaned up on exit.""" + copy_name = f"tmp-{uuid.uuid4().hex[:8]}" + + # Ask the sandbox for a temp directory + result = await self._sandbox.run(["mktemp", "-d"]) + if result.returncode != 0: + raise RuntimeError(f"Failed to create temp dir: {result.stderr}") + temporary_root = result.stdout.strip() + target_path = _join(temporary_root, copy_name) + + try: + async with self._lock: + await self._add_worktree(target_path, from_version) + except BaseException: + await asyncio.shield(self._sandbox.remove(temporary_root, recursive=True)) + raise + + target_path = await self._sandbox.canonicalize(target_path) + + copied = GitWorkspace( + sandbox=self._sandbox, + root=target_path, + project_path=self._copied_project_path(target_path), + name=self._name, + worktree_owner_root=self._root, + ) + + try: + yield copied + finally: + await asyncio.shield(copied.destroy()) + await asyncio.shield(self._sandbox.remove(temporary_root, recursive=True)) + + # ── Execution at a version ────────────────────────────────────── + + @asynccontextmanager + async def at(self, version_id: str) -> AsyncGenerator[None, None]: + """Temporarily checkout a version, restore previous state on exit.""" + async with self._lock: + # Remember current state + try: + previous_branch = await self._git("symbolic-ref", "--short", "HEAD") + except RuntimeError: + previous_branch = None + previous_commit = await self.current_version() + + try: + await self._git("checkout", version_id) + yield + finally: + if previous_branch: + await self._git("checkout", previous_branch) + else: + await self._git("checkout", previous_commit) + + # ── Optional ──────────────────────────────────────────────────── + + async def is_dirty(self) -> bool: + status = await self._git("status", "--porcelain", self._project_path) + return bool(status) + + async def destroy(self) -> None: + """Remove this worktree.""" + if self._worktree_owner_root is None: + return + async with self._lock: + owner = GitWorkspace( + sandbox=self._sandbox, + root=self._worktree_owner_root, + ) + await owner._remove_worktree(self._root) + self._worktree_owner_root = None + + # ── Git-specific helpers (used by Policy and git tools) ───────── + + async def resolve_ref(self, ref: str) -> str: + """Resolve a git ref (branch, tag, short hash) to a full commit hash.""" + return await self._git("rev-parse", ref) + + async def current_branch(self) -> str | None: + """Return current branch name, or None if detached HEAD.""" + try: + return await self._git("symbolic-ref", "--short", "HEAD") + except RuntimeError: + return None + + async def branch_exists(self, branch_name: str) -> bool: + try: + await self._git("rev-parse", "--verify", f"refs/heads/{branch_name}") + return True + except RuntimeError: + return False + + async def get_head_commit(self, branch_name: str) -> str: + return await self._git("rev-parse", f"refs/heads/{branch_name}") + + async def checkout_branch( + self, branch_name: str, create: bool = False, from_ref: str | None = None + ) -> None: + async with self._lock: + args = ["checkout"] + if create: + args.append("-b") + args.append(branch_name) + if from_ref: + args.append(from_ref) + await self._git(*args) + + async def maybe_fetch(self) -> None: + """Fetch from remote if one exists.""" + try: + await self._git("remote", "get-url", "origin") + await self._git("fetch", "--all") + except RuntimeError: + pass diff --git a/vero/tests/__init__.py b/vero/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/vero/tests/test_lockfile.py b/vero/tests/test_lockfile.py new file mode 100644 index 00000000..eac0589c --- /dev/null +++ b/vero/tests/test_lockfile.py @@ -0,0 +1,33 @@ +"""Tests to ensure project configuration files are up-to-date.""" + +import subprocess +import warnings +from pathlib import Path + +# Get the project root (parent of tests directory) +PROJECT_ROOT = Path(__file__).parent.parent + + +class TestLockfile: + """Tests for uv.lock file synchronization.""" + + def test_uv_lock_is_up_to_date(self): + """Ensure uv.lock is synchronized with pyproject.toml. + + If this test warns, run `uv lock` to update the lock file. + """ + result = subprocess.run( + ["uv", "lock", "--check"], + cwd=PROJECT_ROOT, + capture_output=True, + text=True, + ) + + if result.returncode != 0: + warnings.warn( + f"uv.lock is out of sync with pyproject.toml. " + f"Run `uv lock` to update it.\n" + f"stdout: {result.stdout}\n" + f"stderr: {result.stderr}", + UserWarning, + ) diff --git a/vero/tests/test_remote_sandbox.py b/vero/tests/test_remote_sandbox.py new file mode 100644 index 00000000..ef9cab63 --- /dev/null +++ b/vero/tests/test_remote_sandbox.py @@ -0,0 +1,182 @@ +"""Tests verifying sandbox boundary correctness with a simulated remote sandbox. + +MockRemoteSandbox uses two separate directories — "host" and "remote" — to +simulate a non-local sandbox where host and sandbox don't share a filesystem. +upload() copies host→remote, download() copies remote→host. All file ops +happen in the remote dir. This catches any code that assumes shared paths. +""" + +from __future__ import annotations + +import shutil +from pathlib import Path + +import pytest + +from vero.sandbox import CommandResult, FileStat, Sandbox +from vero.staging import SandboxStagingArea + + +class MockRemoteSandbox(Sandbox): + """Sandbox that simulates a remote environment. + + File ops happen in `remote_root`. The host writes to `host_root`. + upload/download actually copy between them. This catches any code + that assumes host paths are accessible inside the sandbox. + """ + + def __init__(self, host_root: Path, remote_root: Path): + self._host_root = host_root + self._remote_root = remote_root + + @classmethod + async def create(cls, **kwargs) -> MockRemoteSandbox: + raise NotImplementedError("MockRemoteSandbox requires explicit host/remote roots") + + @property + def root(self) -> str: + return str(self._remote_root) + + def resolve_path(self, path: str) -> str: + p = Path(path) + if p.is_absolute(): + return str(p.resolve()) + return str((self._remote_root / p).resolve()) + + async def read_file(self, path: str, encoding: str = "utf-8") -> str: + resolved = Path(self.resolve_path(path)) + return resolved.read_text(encoding=encoding) + + async def read_file_bytes(self, path: str, limit: int | None = None) -> bytes: + resolved = Path(self.resolve_path(path)) + with open(resolved, "rb") as f: + return f.read(limit) if limit is not None else f.read() + + async def write_file(self, path: str, content: str, encoding: str = "utf-8") -> None: + resolved = Path(self.resolve_path(path)) + resolved.parent.mkdir(parents=True, exist_ok=True) + resolved.write_text(content, encoding=encoding) + + async def exists(self, path: str) -> bool: + return Path(self.resolve_path(path)).exists() + + async def is_file(self, path: str) -> bool: + return Path(self.resolve_path(path)).is_file() + + async def is_dir(self, path: str) -> bool: + return Path(self.resolve_path(path)).is_dir() + + async def mkdir(self, path: str, parents: bool = True) -> None: + Path(self.resolve_path(path)).mkdir(parents=parents, exist_ok=True) + + async def stat(self, path: str) -> FileStat: + st = Path(self.resolve_path(path)).stat() + return FileStat(st_size=st.st_size) + + async def list_dir(self, path: str) -> list[str]: + return sorted(e.name for e in Path(self.resolve_path(path)).iterdir()) + + async def run(self, command, cwd=None, timeout=30, env=None) -> CommandResult: + import asyncio + + if isinstance(command, str): + command = ["bash", "-c", command] + if cwd is None: + cwd = str(self._remote_root) + proc = await asyncio.create_subprocess_exec( + *command, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + cwd=str(cwd), + env=env, + ) + stdout, stderr = await proc.communicate() + return CommandResult( + stdout=stdout.decode().strip(), + stderr=stderr.decode().strip(), + returncode=proc.returncode or 0, + ) + + async def upload(self, local_path: str, remote_path: str) -> None: + """Copy from host to remote. This MUST actually copy (not no-op).""" + src = Path(local_path) + dst = Path(remote_path) + if not src.exists(): + return + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + else: + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + + async def download(self, remote_path: str, local_path: str) -> None: + """Copy from remote to host. This MUST actually copy (not no-op).""" + src = Path(remote_path) + dst = Path(local_path) + if not src.exists(): + return + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + else: + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + + +class TestMockRemoteSandboxBasics: + """Verify the mock itself works correctly.""" + + @pytest.fixture + def sandbox(self, tmp_path): + host = tmp_path / "host" + remote = tmp_path / "remote" + host.mkdir() + remote.mkdir() + return MockRemoteSandbox( + host_root=host, + remote_root=remote, + ) + + @pytest.mark.asyncio + async def test_host_and_remote_are_separate(self, sandbox, tmp_path): + """Writing to sandbox doesn't create files on the host.""" + await sandbox.write_file("test.txt", "hello") + + # File exists in remote + assert (tmp_path / "remote" / "test.txt").exists() + # File does NOT exist in host + assert not (tmp_path / "host" / "test.txt").exists() + + @pytest.mark.asyncio + async def test_upload_copies_to_remote(self, sandbox, tmp_path): + host_file = tmp_path / "host" / "data.txt" + host_file.write_text("from host") + + remote_path = str(tmp_path / "remote" / "data.txt") + await sandbox.upload(str(host_file), remote_path) + + assert (tmp_path / "remote" / "data.txt").read_text() == "from host" + + @pytest.mark.asyncio + async def test_download_copies_from_remote(self, sandbox, tmp_path): + await sandbox.write_file("result.txt", "from remote") + + local_path = str(tmp_path / "host" / "result.txt") + await sandbox.download(str(tmp_path / "remote" / "result.txt"), local_path) + + assert (tmp_path / "host" / "result.txt").read_text() == "from remote" + + @pytest.mark.asyncio + async def test_staging_area_exchanges_data_and_cleans_up(self, sandbox, tmp_path): + source = tmp_path / "host" / "input.txt" + source.write_text("input", encoding="utf-8") + destination = tmp_path / "host" / "output.txt" + + async with SandboxStagingArea(sandbox) as staging: + staging_root = Path(staging.root) + await staging.upload(source, "inputs/input.txt") + assert await staging.read_text("inputs/input.txt") == "input" + await staging.write_text("outputs/output.txt", "output") + await staging.download("outputs/output.txt", destination) + + assert destination.read_text(encoding="utf-8") == "output" + assert not staging_root.exists() diff --git a/vero/tests/test_sandbox.py b/vero/tests/test_sandbox.py new file mode 100644 index 00000000..3a794704 --- /dev/null +++ b/vero/tests/test_sandbox.py @@ -0,0 +1,363 @@ +"""Tests for the Sandbox abstraction (LocalSandbox).""" + +from __future__ import annotations + +import asyncio +import sys +from pathlib import Path + +import pytest + +from vero.sandbox import CommandResult, FileStat, LocalSandbox, Sandbox +import vero.sandbox as sandbox_module + + +@pytest.fixture +def sandbox(tmp_path): + """Create a LocalSandbox for I/O testing.""" + (tmp_path / "hello.txt").write_text("hello world\n") + (tmp_path / "subdir").mkdir() + (tmp_path / "subdir" / "nested.py").write_text("x = 1\n") + (tmp_path / "private").mkdir() + (tmp_path / "private" / "secret.txt").write_text("top secret\n") + + return LocalSandbox(root=tmp_path) + + +@pytest.fixture +def fast_process_group_termination(monkeypatch): + terminate = sandbox_module._terminate_host_process_tree + + async def terminate_quickly(process): + await terminate(process, grace_seconds=0.1) + + monkeypatch.setattr( + sandbox_module, + "_terminate_host_process_tree", + terminate_quickly, + ) + + +class TestSandboxABC: + def test_cannot_instantiate(self): + with pytest.raises(TypeError): + Sandbox() + + +class TestProperties: + def test_root(self, sandbox, tmp_path): + assert sandbox.root == str(tmp_path) + + def test_resolve_path(self, sandbox, tmp_path): + result = sandbox.resolve_path("hello.txt") + assert result == str(tmp_path / "hello.txt") + + def test_local_paths_are_host_visible(self, sandbox, tmp_path): + assert sandbox.capabilities.host_paths + assert sandbox.host_path("hello.txt") == tmp_path / "hello.txt" + + @pytest.mark.asyncio + async def test_temporary_directory_is_cleaned_up(self, sandbox): + async with sandbox.temporary_directory("vero-test-") as directory: + assert await sandbox.is_dir(directory) + assert Path(directory).name.startswith("vero-test-") + assert directory == str(Path(directory).resolve()) + assert not await sandbox.exists(directory) + + +class TestFileOperations: + @pytest.mark.asyncio + async def test_read_file(self, sandbox): + content = await sandbox.read_file("hello.txt") + assert content == "hello world\n" + + @pytest.mark.asyncio + async def test_read_file_bytes(self, sandbox): + data = await sandbox.read_file_bytes("hello.txt") + assert data == b"hello world\n" + + @pytest.mark.asyncio + async def test_read_file_bytes_with_limit(self, sandbox): + data = await sandbox.read_file_bytes("hello.txt", limit=5) + assert data == b"hello" + + @pytest.mark.asyncio + async def test_write_file(self, sandbox, tmp_path): + await sandbox.write_file("new.txt", "new content\n") + assert (tmp_path / "new.txt").read_text() == "new content\n" + + @pytest.mark.asyncio + async def test_write_file_creates_parents(self, sandbox, tmp_path): + await sandbox.write_file("a/b/c.txt", "deep\n") + assert (tmp_path / "a" / "b" / "c.txt").read_text() == "deep\n" + + @pytest.mark.asyncio + async def test_exists(self, sandbox): + assert await sandbox.exists("hello.txt") is True + assert await sandbox.exists("nonexistent.txt") is False + + @pytest.mark.asyncio + async def test_is_file(self, sandbox): + assert await sandbox.is_file("hello.txt") is True + assert await sandbox.is_file("subdir") is False + + @pytest.mark.asyncio + async def test_is_dir(self, sandbox): + assert await sandbox.is_dir("subdir") is True + assert await sandbox.is_dir("hello.txt") is False + + @pytest.mark.asyncio + async def test_mkdir(self, sandbox, tmp_path): + await sandbox.mkdir("new_dir/nested") + assert (tmp_path / "new_dir" / "nested").is_dir() + + @pytest.mark.asyncio + async def test_stat(self, sandbox): + result = await sandbox.stat("hello.txt") + assert isinstance(result, FileStat) + assert result.st_size == len("hello world\n") + + @pytest.mark.asyncio + async def test_list_dir(self, sandbox): + entries = await sandbox.list_dir(sandbox.root) + assert "hello.txt" in entries + assert "subdir" in entries + assert "private" in entries + assert entries == sorted(entries) + + @pytest.mark.asyncio + async def test_list_dir_subdir(self, sandbox): + entries = await sandbox.list_dir("subdir") + assert entries == ["nested.py"] + + +class TestShellExecution: + @pytest.mark.asyncio + async def test_run_simple_command(self, sandbox): + result = await sandbox.run(["echo", "hello"]) + assert isinstance(result, CommandResult) + assert result.stdout == "hello" + assert result.returncode == 0 + + @pytest.mark.asyncio + async def test_run_returns_stderr(self, sandbox): + result = await sandbox.run(["ls", "/nonexistent_path_xyz"]) + assert result.returncode != 0 + assert result.stderr != "" + + @pytest.mark.asyncio + async def test_run_does_not_raise_on_nonzero(self, sandbox): + result = await sandbox.run(["false"]) + assert result.returncode != 0 + + @pytest.mark.asyncio + async def test_run_default_cwd_is_root(self, sandbox): + result = await sandbox.run(["pwd"]) + assert result.stdout == sandbox.root + + @pytest.mark.asyncio + async def test_run_custom_cwd(self, sandbox): + result = await sandbox.run(["pwd"], cwd=sandbox.resolve_path("subdir")) + assert result.stdout.endswith("subdir") + + @pytest.mark.asyncio + async def test_run_string_command(self, sandbox): + result = await sandbox.run("echo hello && echo world") + assert "hello" in result.stdout + assert "world" in result.stdout + + @pytest.mark.asyncio + async def test_run_timeout(self, sandbox): + result = await sandbox.run(["sleep", "10"], timeout=1) + assert result.returncode == -1 + assert "timed out" in result.stderr + + @pytest.mark.asyncio + async def test_timeout_terminates_descendant_processes( + self, + sandbox, + tmp_path, + fast_process_group_termination, + ): + marker = tmp_path / "timeout-descendant-survived" + descendant = ( + "import signal, time; from pathlib import Path; " + "signal.signal(signal.SIGTERM, signal.SIG_IGN); " + f"time.sleep(0.8); Path({str(marker)!r}).write_text('leaked')" + ) + parent = ( + "import subprocess, sys, time; " + f"subprocess.Popen([sys.executable, '-c', {descendant!r}]); " + "time.sleep(60)" + ) + + result = await sandbox.run([sys.executable, "-c", parent], timeout=0.1) + assert result.returncode == -1 + await asyncio.sleep(1) + assert not marker.exists() + + @pytest.mark.asyncio + async def test_cancellation_terminates_descendant_processes( + self, + sandbox, + tmp_path, + fast_process_group_termination, + ): + marker = tmp_path / "cancelled-descendant-survived" + descendant = ( + "import signal, time; from pathlib import Path; " + "signal.signal(signal.SIGTERM, signal.SIG_IGN); " + f"time.sleep(0.8); Path({str(marker)!r}).write_text('leaked')" + ) + parent = ( + "import subprocess, sys, time; " + f"subprocess.Popen([sys.executable, '-c', {descendant!r}]); " + "time.sleep(60)" + ) + task = asyncio.create_task( + sandbox.run([sys.executable, "-c", parent], timeout=None) + ) + await asyncio.sleep(0.1) + + task.cancel() + with pytest.raises(asyncio.CancelledError): + await task + await asyncio.sleep(1) + assert not marker.exists() + + @pytest.mark.asyncio + async def test_timeout_cleans_descendants_after_group_leader_exits( + self, + sandbox, + tmp_path, + fast_process_group_termination, + ): + marker = tmp_path / "detached-descendant-survived" + descendant = ( + "import signal, time; from pathlib import Path; " + "signal.signal(signal.SIGTERM, signal.SIG_IGN); " + f"time.sleep(0.8); Path({str(marker)!r}).write_text('leaked')" + ) + parent = ( + "import subprocess, sys; " + f"subprocess.Popen([sys.executable, '-c', {descendant!r}])" + ) + + result = await sandbox.run([sys.executable, "-c", parent], timeout=0.1) + assert result.returncode == -1 + await asyncio.sleep(1) + assert not marker.exists() + + @pytest.mark.asyncio + async def test_run_env(self, sandbox): + import os + + env = {**os.environ, "MY_TEST_VAR": "test_value_123"} + result = await sandbox.run(["printenv", "MY_TEST_VAR"], env=env) + assert result.stdout == "test_value_123" + + +class TestFileTransfer: + @pytest.mark.asyncio + async def test_upload_file(self, sandbox, tmp_path): + # Create a file outside the sandbox + external = tmp_path / "external" + external.mkdir() + (external / "data.txt").write_text("uploaded\n") + + dest = sandbox.resolve_path("uploaded.txt") + await sandbox.upload(str(external / "data.txt"), dest) + + content = await sandbox.read_file("uploaded.txt") + assert content == "uploaded\n" + + @pytest.mark.asyncio + async def test_upload_directory(self, sandbox, tmp_path): + external = tmp_path / "external_dir" + external.mkdir() + (external / "a.txt").write_text("aaa\n") + (external / "b.txt").write_text("bbb\n") + + dest = sandbox.resolve_path("imported") + await sandbox.upload(str(external), dest) + + assert await sandbox.is_dir("imported") + assert await sandbox.read_file(str(Path(dest) / "a.txt")) == "aaa\n" + assert await sandbox.read_file(str(Path(dest) / "b.txt")) == "bbb\n" + + @pytest.mark.asyncio + async def test_download_file(self, sandbox, tmp_path): + await sandbox.write_file("to_download.txt", "download me\n") + + local_dest = tmp_path / "downloaded.txt" + await sandbox.download(sandbox.resolve_path("to_download.txt"), str(local_dest)) + + assert local_dest.read_text() == "download me\n" + + @pytest.mark.asyncio + async def test_download_directory(self, sandbox, tmp_path): + await sandbox.mkdir("export_dir") + await sandbox.write_file( + str(Path(sandbox.resolve_path("export_dir")) / "x.txt"), "xxx\n" + ) + + local_dest = tmp_path / "exported" + await sandbox.download(sandbox.resolve_path("export_dir"), str(local_dest)) + + assert local_dest.is_dir() + assert (local_dest / "x.txt").read_text() == "xxx\n" + + @pytest.mark.asyncio + async def test_upload_noop_same_path(self, sandbox): + """upload is a no-op when source and dest resolve to the same path.""" + path = sandbox.resolve_path("hello.txt") + await sandbox.upload(path, path) # should not crash or duplicate + content = await sandbox.read_file("hello.txt") + assert content == "hello world\n" + + @pytest.mark.asyncio + async def test_download_noop_same_path(self, sandbox): + """download is a no-op when source and dest resolve to the same path.""" + path = sandbox.resolve_path("hello.txt") + await sandbox.download(path, path) + content = await sandbox.read_file("hello.txt") + assert content == "hello world\n" + + +class _ImmediateProcess: + returncode = 0 + + async def communicate(self): + return (b"", b"") + + +@pytest.mark.asyncio +async def test_run_as_drops_to_unprivileged_user(sandbox, monkeypatch): + """run_as forwards the user/group and sheds supplementary groups.""" + captured: dict = {} + + async def fake_exec(*args, **kwargs): + captured["kwargs"] = kwargs + return _ImmediateProcess() + + monkeypatch.setattr(asyncio, "create_subprocess_exec", fake_exec) + await sandbox.run(["true"], run_as="harness") + + assert captured["kwargs"]["user"] == "harness" + assert captured["kwargs"]["group"] == "harness" + assert captured["kwargs"]["extra_groups"] == [] + + +@pytest.mark.asyncio +async def test_run_without_run_as_does_not_drop_privileges(sandbox, monkeypatch): + captured: dict = {} + + async def fake_exec(*args, **kwargs): + captured["kwargs"] = kwargs + return _ImmediateProcess() + + monkeypatch.setattr(asyncio, "create_subprocess_exec", fake_exec) + await sandbox.run(["true"]) + + assert "user" not in captured["kwargs"] + assert "group" not in captured["kwargs"] diff --git a/vero/tests/test_subprocess.py b/vero/tests/test_subprocess.py new file mode 100644 index 00000000..a88f5f38 --- /dev/null +++ b/vero/tests/test_subprocess.py @@ -0,0 +1,107 @@ +"""Tests for subprocess termination on cancellation.""" + +import asyncio +import sys + +import pytest +from vero.utils.asyncio import ( + SubprocessCancelledError, + SubprocessTimeoutError, + run_bash_command, + run_subprocess_with_tee, +) + + +class TestSubprocessTermination: + """Tests that subprocesses are properly terminated on cancellation.""" + + @pytest.mark.asyncio + async def test_run_subprocess_with_tee_terminates_on_cancellation(self): + """Test that run_subprocess_with_tee terminates subprocess on CancelledError.""" + # Start a long-running subprocess + task = asyncio.create_task(run_subprocess_with_tee(["sleep", "60"], timeout=None)) + + # Give subprocess time to start + await asyncio.sleep(0.1) + + # Cancel the task + task.cancel() + + # Wait for cancellation to complete + with pytest.raises(SubprocessCancelledError): + await task + + # Give OS time to clean up + await asyncio.sleep(0.1) + + # Verify no zombie sleep processes from our test + # (This is a best-effort check - the process should be gone) + + @pytest.mark.asyncio + async def test_run_subprocess_with_tee_terminates_on_timeout(self): + """Test that run_subprocess_with_tee terminates subprocess on timeout.""" + with pytest.raises(SubprocessTimeoutError): + await run_subprocess_with_tee(["sleep", "60"], timeout=1) + + @pytest.mark.asyncio + async def test_run_bash_command_terminates_on_cancellation(self): + """Test that run_bash_command terminates subprocess on CancelledError.""" + task = asyncio.create_task(run_bash_command(["sleep", "60"])) + + await asyncio.sleep(0.1) + task.cancel() + + with pytest.raises(asyncio.CancelledError): + await task + + @pytest.mark.asyncio + async def test_run_bash_command_terminates_on_timeout(self): + """Test that run_bash_command terminates subprocess on timeout.""" + with pytest.raises(TimeoutError): + await run_bash_command(["sleep", "60"], timeout=1) + + @pytest.mark.asyncio + async def test_subprocess_actually_terminates(self): + """Test that the subprocess is actually killed, not just abandoned.""" + # Use a marker file to detect if subprocess is still running + import tempfile + from pathlib import Path + + with tempfile.TemporaryDirectory() as tmpdir: + marker = Path(tmpdir) / "running" + + # Script that creates marker file while running, removes on clean exit + script = f""" +import time +from pathlib import Path +marker = Path("{marker}") +marker.write_text("running") +try: + time.sleep(60) +finally: + marker.unlink(missing_ok=True) +""" + task = asyncio.create_task( + run_subprocess_with_tee([sys.executable, "-c", script], timeout=None) + ) + + # Wait for subprocess to start and create marker + for _ in range(20): + await asyncio.sleep(0.1) + if marker.exists(): + break + else: + pytest.fail("Subprocess didn't start in time") + + # Cancel and wait + task.cancel() + with pytest.raises(SubprocessCancelledError): + await task + + # Give time for cleanup + await asyncio.sleep(0.5) + + # Marker should be gone (subprocess ran finally block) or + # subprocess was killed (marker still exists but process is dead) + # Either way, let's verify no python process is still sleeping + # by checking that we can complete quickly diff --git a/vero/tests/test_tokens.py b/vero/tests/test_tokens.py new file mode 100644 index 00000000..03a07157 --- /dev/null +++ b/vero/tests/test_tokens.py @@ -0,0 +1,23 @@ +from vero.utils.tokens import run_result_to_messages + + +def test_run_result_to_messages_handles_empty_response_content() -> None: + messages = run_result_to_messages( + [ + { + "type": "reasoning", + "content": [], + "summary": [], + } + ] + ) + + assert messages[0]["content"] == "" + + +def test_run_result_to_messages_uses_first_content_item() -> None: + messages = run_result_to_messages( + [{"role": "assistant", "content": ["first", "second"]}] + ) + + assert messages[0]["content"] == "first" diff --git a/vero/tests/test_tools.py b/vero/tests/test_tools.py new file mode 100644 index 00000000..d3078e38 --- /dev/null +++ b/vero/tests/test_tools.py @@ -0,0 +1,63 @@ +"""Tests for the tools.""" + +import json +import re + +import pytest +from vero.tools.planning import TodoList, TodoStatus + + +class TestTodoList: + """Tests for TodoList tool.""" + + def test_add_todos(self): + """Test adding todo items.""" + todo_list = TodoList() + todo_list.add_todos("Write tests") + assert len(todo_list.todos) == 1 + assert todo_list.todo_statuses["Write tests"] == TodoStatus.NOT_STARTED + + def test_update_todo_status(self): + """Test updating todo status by task_id.""" + todo_list = TodoList() + todo_list.add_todos("Task 1") + todo_list.add_todos("Task 2") + todo_list.update_todo_status(status=TodoStatus.COMPLETED, task_id=0) + + assert len(todo_list.todo_statuses) == 2 + assert todo_list.todo_statuses["Task 1"] == TodoStatus.COMPLETED + assert todo_list.todo_statuses["Task 2"] == TodoStatus.NOT_STARTED + + result = todo_list.list_todos(status=[TodoStatus.COMPLETED]) + assert "Task 1" in result + assert "Task 2" not in result + + result = todo_list.list_todos(status=[TodoStatus.NOT_STARTED]) + assert "Task 1" not in result + assert "Task 2" in result + + def test_update_status_error_handling(self): + """Test that updating status without task or task_id raises ValueError.""" + todo_list = TodoList() + todo_list.add_todos("Task 1") + + with pytest.raises(ValueError): + todo_list.update_todo_status(status=TodoStatus.COMPLETED) + + def test_list_todos(self): + """Test listing todos with status filters.""" + todo_list = TodoList() + todo_list.add_todos("Task 1") + todo_list.add_todos("Task 2") + todo_list.add_todos("Task 3") + + # Update statuses + todo_list.update_todo_status(status=TodoStatus.COMPLETED, task_id=0) + todo_list.update_todo_status(status=TodoStatus.IN_PROGRESS, task_id=1) + + # List completed tasks + result = todo_list.list_todos(status=TodoStatus.COMPLETED) + json_match = re.search(r"```json(.+?)```", result, re.DOTALL) + todos_dict = json.loads(json_match.group(1)) + assert len(todos_dict) == 1 + assert todos_dict["0"]["task"] == "Task 1" diff --git a/vero/tests/test_uv_with_editable.py b/vero/tests/test_uv_with_editable.py new file mode 100644 index 00000000..8e9b8918 --- /dev/null +++ b/vero/tests/test_uv_with_editable.py @@ -0,0 +1,118 @@ +"""Test that uv run --with-editable correctly overlays a package version. + +Creates two copies of a 'greeter' package with different behavior, +and a 'consumer' package that depends on greeter. Verifies that: +1. uv run --project consumer → uses the installed greeter (v1) +2. uv run --project consumer --with-editable greeter_v2 → uses greeter v2 +""" + +from __future__ import annotations + +import asyncio +import subprocess +import textwrap +from pathlib import Path + +import pytest + + +def _write_package(root: Path, name: str, version: str, code: str) -> Path: + """Create a minimal uv package.""" + pkg_dir = root / name + pkg_dir.mkdir(parents=True) + src_dir = pkg_dir / "src" / name.replace("-", "_") + src_dir.mkdir(parents=True) + + (pkg_dir / "pyproject.toml").write_text(textwrap.dedent(f"""\ + [project] + name = "{name}" + version = "{version}" + requires-python = ">=3.11" + + [build-system] + requires = ["hatchling"] + build-backend = "hatchling.build" + + [tool.hatch.build.targets.wheel] + packages = ["src/{name.replace('-', '_')}"] + """)) + + (src_dir / "__init__.py").write_text(code) + return pkg_dir + + +@pytest.fixture +def workspace(tmp_path): + """Create a workspace with greeter_v1, greeter_v2, and consumer.""" + + # greeter v1: returns "hello from v1" + greeter_v1 = _write_package( + tmp_path, "greeter", "1.0.0", + 'def greet(): return "hello from v1"\n', + ) + + # greeter v2: returns "hello from v2" (same package name, different code) + greeter_v2_dir = tmp_path / "greeter_v2_src" + greeter_v2_dir.mkdir() + greeter_v2 = _write_package( + greeter_v2_dir, "greeter", "2.0.0", + 'def greet(): return "hello from v2"\n', + ) + + # consumer: depends on greeter (path dep to v1) + consumer = tmp_path / "consumer" + consumer.mkdir() + (consumer / "pyproject.toml").write_text(textwrap.dedent(f"""\ + [project] + name = "consumer" + version = "0.1.0" + requires-python = ">=3.11" + dependencies = ["greeter"] + + [tool.uv.sources] + greeter = {{ path = "{greeter_v1}" }} + """)) + + # Sync consumer to install greeter v1 + subprocess.run( + ["uv", "sync", "--project", str(consumer)], + capture_output=True, + check=True, + ) + + return greeter_v1, greeter_v2, consumer + + +@pytest.mark.asyncio +async def test_with_editable_overlays_package(workspace): + """Verify --with-editable replaces the installed package.""" + greeter_v1, greeter_v2, consumer = workspace + + script = "from greeter import greet; print(greet())" + + # Run normally → should get v1 + result_v1 = subprocess.run( + ["uv", "run", "--project", str(consumer), "python", "-c", script], + capture_output=True, + text=True, + ) + assert result_v1.returncode == 0, f"v1 failed: {result_v1.stderr}" + assert "hello from v1" in result_v1.stdout + + # Run with --with-editable pointing to v2 → should get v2 + result_v2 = subprocess.run( + ["uv", "run", "--project", str(consumer), "--with-editable", str(greeter_v2), "python", "-c", script], + capture_output=True, + text=True, + ) + assert result_v2.returncode == 0, f"v2 failed: {result_v2.stderr}" + assert "hello from v2" in result_v2.stdout + + # Run normally again → still v1 (no mutation) + result_v1_again = subprocess.run( + ["uv", "run", "--project", str(consumer), "python", "-c", script], + capture_output=True, + text=True, + ) + assert result_v1_again.returncode == 0 + assert "hello from v1" in result_v1_again.stdout diff --git a/vero/tests/test_v05_agent_context.py b/vero/tests/test_v05_agent_context.py new file mode 100644 index 00000000..6d3c71b9 --- /dev/null +++ b/vero/tests/test_v05_agent_context.py @@ -0,0 +1,245 @@ +from __future__ import annotations + +import json +import os +import stat +from datetime import UTC, datetime, timedelta +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + BackendProvenance, + CaseResult, + CaseStatus, + DisclosureLevel, + EvaluationArtifact, + EvaluationReport, + EvaluationRequest, + EvaluationStatus, + ObjectiveResult, + EvaluationRecord, + EvaluationSet, + EvaluationSummary, + MetricSelector, + ObjectiveSpec, + project_evaluation, +) +from vero.runtime.context import ( + AgentContextDirectory, + AgentDisclosureLedger, + context_digest, + make_evaluation_receipt, +) +from vero.sandbox import LocalSandbox + + +def record( + evaluation_id: str, + *, + artifact: bool = False, + trace_marker: str = "trace-marker", +) -> EvaluationRecord: + created_at = datetime(2026, 1, 1, tzinfo=UTC) + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + artifacts = ( + [ + EvaluationArtifact(path="logs/output.txt", media_type="text/plain"), + EvaluationArtifact(path="logs/leak.txt", media_type="text/plain"), + ] + if artifact + else [] + ) + return EvaluationRecord( + id=evaluation_id, + request=EvaluationRequest( + candidate=Candidate( + id=f"candidate:{evaluation_id}", + version="a" * 40, + created_at=created_at, + ), + evaluation_set=EvaluationSet(name="validation"), + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 0.75}, + cases=[ + CaseResult( + case_id="case/one", + status=CaseStatus.SUCCESS, + metrics={"score": 0.75}, + input={"prompt": "private case"}, + output={"answer": "candidate answer"}, + execution_trace=[{"message": trace_marker}], + evaluation_trace=[{"grader": "accepted"}], + artifacts=artifacts, + ) + ], + ), + backend_id="backend", + backend=BackendProvenance( + name="test", + version="1", + config_digest="0" * 64, + ), + objective_spec=objective, + objective=ObjectiveResult(value=0.75, feasible=True), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + +@pytest.mark.asyncio +async def test_agent_context_splits_full_traces_and_honors_disclosure(tmp_path: Path): + session_dir = tmp_path / "session" + full = record("evaluation:full", artifact=True, trace_marker="x" * 10_000) + aggregate = record("evaluation:aggregate") + hidden = record("evaluation:hidden") + source_artifact = ( + session_dir / "evaluations" / full.id / "artifacts" / "logs" / "output.txt" + ) + source_artifact.parent.mkdir(parents=True) + source_artifact.write_text("artifact details\n", encoding="utf-8") + secret = tmp_path / "secret.txt" + secret.write_text("must not be copied\n", encoding="utf-8") + os.symlink(secret, source_artifact.parent / "leak.txt") + + project = tmp_path / "project" + project.mkdir() + directory = AgentContextDirectory( + sandbox=await LocalSandbox.create(root=tmp_path), + root=str(project / ".vero"), + session_dir=session_dir, + ) + await directory.reset() + await directory.write_header( + session_id="session", + round_number=2, + proposal_id="proposal", + parent_candidate_id="parent", + ) + await directory.write_evaluations( + [ + ( + full, + DisclosureLevel.FULL, + project_evaluation(full, DisclosureLevel.FULL), + ), + ( + aggregate, + DisclosureLevel.AGGREGATE, + project_evaluation(aggregate, DisclosureLevel.AGGREGATE), + ), + ( + hidden, + DisclosureLevel.NONE, + project_evaluation(hidden, DisclosureLevel.NONE), + ), + ] + ) + await directory.seal() + + try: + evaluations = project / ".vero" / "evaluations" + full_root = evaluations / context_digest(full.id) + full_document = json.loads( + (full_root / "evaluation.json").read_text(encoding="utf-8") + ) + assert full_document["disclosure"] == "full" + assert "cases" not in full_document["result"]["report"] + case_path = full_document["result"]["case_files"][0]["path"] + case_document = json.loads((full_root / case_path).read_text(encoding="utf-8")) + assert case_document["execution_trace_path"] == "execution-trace.json" + trace = (full_root / Path(case_path).parent / "execution-trace.json").read_text( + encoding="utf-8" + ) + assert "x" * 10_000 in trace + assert (full_root / "artifacts" / "logs" / "output.txt").read_text() == ( + "artifact details\n" + ) + assert full_document["missing_artifacts"] == ["logs/leak.txt"] + assert not (full_root / "artifacts" / "logs" / "leak.txt").exists() + + aggregate_root = evaluations / context_digest(aggregate.id) + aggregate_document = json.loads( + (aggregate_root / "evaluation.json").read_text(encoding="utf-8") + ) + assert aggregate_document["disclosure"] == "aggregate" + assert aggregate_document["result"]["metrics"] == {"score": 0.75} + assert not (aggregate_root / "cases").exists() + + hidden_document = json.loads( + (evaluations / context_digest(hidden.id) / "evaluation.json").read_text( + encoding="utf-8" + ) + ) + assert hidden_document == { + "schema_version": 1, + "disclosure": "none", + "result": { + "evaluation_id": hidden.id, + "status": "success", + }, + } + mode = stat.S_IMODE((project / ".vero" / "manifest.json").stat().st_mode) + assert mode & 0o222 == 0 + + receipt = make_evaluation_receipt(full, DisclosureLevel.FULL) + assert isinstance(receipt.result, EvaluationSummary) + assert receipt.result_path == ( + f".vero/evaluations/{context_digest(full.id)}/evaluation.json" + ) + assert "x" * 100 not in receipt.model_dump_json() + finally: + await directory.unseal() + + +@pytest.mark.asyncio +async def test_agent_context_reset_preserves_mounted_root( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +): + root = tmp_path / "agent-context" + root.mkdir() + (root / "stale.txt").write_text("stale\n", encoding="utf-8") + sandbox = await LocalSandbox.create(root=tmp_path) + remove = sandbox.remove + + async def reject_root_removal(path: str, *, recursive: bool = False) -> None: + if Path(path).resolve() == root.resolve(): + raise AssertionError("context mount root must not be removed") + await remove(path, recursive=recursive) + + monkeypatch.setattr(sandbox, "remove", reject_root_removal) + directory = AgentContextDirectory( + sandbox=sandbox, + root=str(root), + session_dir=tmp_path / "session", + ) + await directory.seal() + + await directory.reset() + + assert root.is_dir() + assert list(root.iterdir()) == [] + + +@pytest.mark.asyncio +async def test_disclosure_ledger_survives_restart_and_never_broadens(tmp_path: Path): + path = tmp_path / "agent-context.json" + ledger = AgentDisclosureLedger(path) + + assert await ledger.remember("evaluation", DisclosureLevel.AGGREGATE) == ( + DisclosureLevel.AGGREGATE + ) + reopened = AgentDisclosureLedger(path) + assert await reopened.remember("evaluation", DisclosureLevel.FULL) == ( + DisclosureLevel.AGGREGATE + ) + assert await reopened.remember("evaluation", DisclosureLevel.NONE) == ( + DisclosureLevel.NONE + ) + assert AgentDisclosureLedger(path).get("evaluation") == DisclosureLevel.NONE diff --git a/vero/tests/test_v05_agent_events.py b/vero/tests/test_v05_agent_events.py new file mode 100644 index 00000000..cc245d1c --- /dev/null +++ b/vero/tests/test_v05_agent_events.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import pytest + +from vero.runtime.events import EventBus, RuntimeEvent, agent_event_emitter + + +class _FakeAgent: + """Normalizes raw stream events the way VeroAgent.serialize_event does.""" + + def serialize_event(self, raw): + if raw == "noise": + return None # noise events are dropped, not published + return {"kind": "tool_call", "name": "shell", "args": raw} + + +@pytest.mark.asyncio +async def test_agent_event_emitter_publishes_normalized_events_to_bus(): + captured: list[RuntimeEvent] = [] + bus = EventBus([captured.append]) + emit = agent_event_emitter(bus, "sess-1", _FakeAgent()) + assert emit is not None + + await emit("ls -la") + await emit("noise") # serialize_event -> None, so nothing is published + + assert len(captured) == 1 + event = captured[0] + assert isinstance(event, RuntimeEvent) + assert event.session_id == "sess-1" + assert event.kind == "agent" + assert event.payload == {"kind": "tool_call", "name": "shell", "args": "ls -la"} + + +@pytest.mark.asyncio +async def test_agent_event_emitter_supports_async_sinks(): + captured: list[RuntimeEvent] = [] + + async def async_sink(event: RuntimeEvent) -> None: + captured.append(event) + + bus = EventBus([async_sink]) + emit = agent_event_emitter(bus, "s", _FakeAgent()) + assert emit is not None + await emit("pwd") + + assert [e.payload["args"] for e in captured] == ["pwd"] + + +def test_agent_event_emitter_is_none_without_serialize_event(): + bus = EventBus() + assert agent_event_emitter(bus, "s", object()) is None diff --git a/vero/tests/test_v05_agent_producer.py b/vero/tests/test_v05_agent_producer.py new file mode 100644 index 00000000..c8aefef0 --- /dev/null +++ b/vero/tests/test_v05_agent_producer.py @@ -0,0 +1,350 @@ +from __future__ import annotations + +import hashlib +import json +import subprocess +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from vero.agents import AgentCandidateProducer, AgentRequirements, AgentRunResult +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendRegistry, + CommandBackend, + CommandBackendConfig, + DisclosureLevel, + EvaluationAccessPolicy, + EvaluationDatabase, + EvaluationDefinition, + EvaluationEngine, + EvaluationPlan, + EvaluationReceipt, + EvaluationSet, + Evaluator, + MetricSelector, + ObjectiveSpec, + authorize_evaluation_plan, +) +from vero.optimization import CandidateProposal, Optimizer, SequentialStrategy +from vero.runtime import ArtifactStore +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + + +def initialize_repository(path: Path) -> str: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + return subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=path, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + + +class CheckpointingCodingAgent: + def __init__(self): + self.feedback: EvaluationReceipt | None = None + self.initial_candidate_ids: set[str] = set() + self.initial_evaluation_count = 0 + self.case_resource = None + + async def run(self, *, context, prompt, max_turns, on_event=None): + assert prompt == "Make the program faster" + assert max_turns == 5 + context_root = Path(context.workspace.project_path) / ".vero" + assert not await context.workspace.is_dirty() + manifest = json.loads((context_root / "manifest.json").read_text()) + assert manifest["parent_candidate_id"] == context.parent.id + candidate_index = json.loads( + (context_root / "candidates" / "index.json").read_text() + ) + self.initial_candidate_ids = { + candidate["candidate_id"] for candidate in candidate_index["candidates"] + } + evaluation_index = json.loads( + (context_root / "evaluations" / "index.json").read_text() + ) + self.initial_evaluation_count = len(evaluation_index["evaluations"]) + cases_index = json.loads((context_root / "cases" / "index.json").read_text()) + resource_path = cases_index["case_resources"][0]["path"] + resource_index = json.loads( + ( + context_root / "cases" / resource_path / "resources" / "index.json" + ).read_text() + ) + self.case_resource = json.loads( + ( + context_root + / "cases" + / resource_path + / "resources" + / resource_index["resources"][0]["path"] + ).read_text() + ) + program = Path(context.workspace.project_path) / "program.txt" + program.write_text("fast\n", encoding="utf-8") + feedback = await context.evaluation.evaluate( + evaluation="performance", + description="Try the fast implementation" + ) + assert isinstance(feedback, EvaluationReceipt) + self.feedback = feedback + assert (Path(context.workspace.project_path) / feedback.result_path).is_file() + refreshed = json.loads( + (context_root / "evaluations" / "index.json").read_text() + ) + assert len(refreshed["evaluations"]) == self.initial_evaluation_count + 1 + + # A later edit regresses. The evaluated checkpoint must remain selectable. + program.write_text("slow\n", encoding="utf-8") + return AgentRunResult( + description="Finish agent attempt", + state={"turn": 2}, + trace=[{"objective": feedback.result.objective.value}], + metadata={"provider": "test"}, + ) + + +def test_host_native_agent_rejects_isolated_workspace(): + class HostNativeAgent: + requirements = AgentRequirements(host_visible_workspace=True) + + class IsolatedSandbox: + def host_path(self, path): + return None + + producer = AgentCandidateProducer(HostNativeAgent()) + + with pytest.raises(ValueError, match="requires a host-visible workspace"): + producer.validate_workspace( + SimpleNamespace( + project_path="/workspace/target", + sandbox=IsolatedSandbox(), + ) + ) + + +@pytest.mark.asyncio +async def test_failed_agent_run_persists_state_and_trace(tmp_path: Path): + class FailingAgent: + async def run(self, **kwargs): + raise RuntimeError("turn limit reached") + + def serialize_state(self): + return {"turn": 5} + + def serialize_trace(self): + return [{"tool": "file_read"}] + + artifacts = ArtifactStore(tmp_path / "artifacts") + producer = AgentCandidateProducer(FailingAgent(), artifacts=artifacts) + proposal = CandidateProposal(id="proposal", producer_id="default") + baseline = object() + context = SimpleNamespace( + session_id="session", + candidates={}, + baseline=baseline, + ) + + with pytest.raises(RuntimeError, match="turn limit reached"): + await producer.produce( + proposal=proposal, + context=context, + workspace=SimpleNamespace(), + evaluation=SimpleNamespace(), + ) + + digest = hashlib.sha256(proposal.id.encode()).hexdigest()[:16] + assert artifacts.read_json(f"agents/{digest}/state.json") == {"turn": 5} + assert artifacts.read_json(f"agents/{digest}/trace.json") == [{"tool": "file_read"}] + assert artifacts.read_json(f"agents/{digest}/failure.json") == { + "type": "RuntimeError", + "message": "turn limit reached", + } + assert artifacts.read_json(producer._producer_state_path("default")) == {"turn": 5} + + +@pytest.mark.asyncio +async def test_agent_checkpoint_is_a_selectable_candidate(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n", encoding="utf-8") + baseline_version = initialize_repository(target) + + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path + +workspace, report_path = map(Path, sys.argv[1:]) +program = (workspace / "program.txt").read_text().strip() +latency = 1.0 if program == "fast" else 10.0 +report_path.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"latency_ms": latency}, +})) +""", + encoding="utf-8", + ) + cases = harness / "cases.json" + cases.write_text( + json.dumps([{"id": "case-1", "size": 128}]) + "\n", + encoding="utf-8", + ) + + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(target)) + session_dir = tmp_path / "sessions" / "agent" + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + database = EvaluationDatabase(id="agent") + + plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition( + evaluation_set=EvaluationSet(name="performance"), + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.AGGREGATE, + expose_case_resources=True, + ), + ), + EvaluationDefinition( + evaluation_set=EvaluationSet(name="test", partition="test"), + access=EvaluationAccessPolicy( + agent_can_evaluate=False, + agent_visible=False, + disclosure=DisclosureLevel.NONE, + ), + ), + ], + selection_evaluation="performance", + final_evaluation="test", + ) + + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + ), + backends=BackendRegistry( + { + "command": CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ], + staged_inputs={"cases": str(cases)}, + agent_context_inputs={"performance": ["cases"]}, + ) + ) + } + ), + database=database, + database_path=session_dir / "database.json", + authorization_resolver=authorize_evaluation_plan(plan), + ) + agent = CheckpointingCodingAgent() + artifacts = ArtifactStore(session_dir / "artifacts") + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id="command", + evaluation_plan=plan, + objective=ObjectiveSpec( + selector=MetricSelector(metric="latency_ms"), + direction="minimize", + ), + strategy=SequentialStrategy(instruction="Make the program faster"), + producers={ + "default": AgentCandidateProducer( + agent, + max_turns=5, + artifacts=artifacts, + ) + }, + max_proposals=1, + ) + + result = await optimizer.run() + + assert agent.feedback is not None + assert agent.feedback.result.objective.value == 1.0 + assert agent.feedback.result_path.startswith(".vero/evaluations/") + assert agent.initial_candidate_ids == {baseline_version} + assert agent.initial_evaluation_count == 1 + assert agent.case_resource == [{"id": "case-1", "size": 128}] + assert len(result.evaluations) == 5 + assert len(result.candidates) == 3 + assert result.best.objective.value == 1.0 + assert result.best.request.candidate.id.endswith(":trial:1") + assert result.final is not None + assert result.final.request.evaluation_set.name == "test" + assert result.best.request.candidate.parent_id == baseline_version + final = next( + candidate + for candidate in result.candidates + if candidate.id not in {baseline_version, result.best.request.candidate.id} + ) + assert final.parent_id == result.best.request.candidate.id + assert (target / "program.txt").read_text(encoding="utf-8") == "slow\n" + assert len(database.evaluations) == 5 + + agent_artifacts = list((session_dir / "artifacts" / "agents").iterdir()) + proposal_artifacts = [path for path in agent_artifacts if path.name != "producers"] + assert len(proposal_artifacts) == 1 + assert (proposal_artifacts[0] / "state.json").exists() + assert (proposal_artifacts[0] / "trace.json").exists() + + class ResumedAgent: + def __init__(self): + self.state = None + + def deserialize_state(self, state): + self.state = state + + resumed_agent = ResumedAgent() + resumed_producer = AgentCandidateProducer(resumed_agent) + resumed_producer.bind_artifacts(artifacts) + assert resumed_agent.state == {"turn": 2} diff --git a/vero/tests/test_v05_c_example.py b/vero/tests/test_v05_c_example.py new file mode 100644 index 00000000..ad2cf8fe --- /dev/null +++ b/vero/tests/test_v05_c_example.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + +import pytest +from click.testing import CliRunner + +from vero.cli import main + + +def initialize_repository(path: Path) -> str: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + return subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=path, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + + +@pytest.mark.skipif(shutil.which("cc") is None, reason="a C compiler is required") +def test_declarative_c_example_optimizes_a_non_python_target(tmp_path: Path): + source = Path(__file__).parents[1] / "examples" / "c-matmul" + example = tmp_path / "c-matmul" + shutil.copytree(source, example, ignore=shutil.ignore_patterns("__pycache__")) + baseline_source = (example / "target" / "matmul.c").read_text(encoding="utf-8") + baseline_version = initialize_repository(example / "target") + config = example / "vero.toml" + runner = CliRunner() + + evaluated = runner.invoke(main, ["evaluate", "--config", str(config)]) + assert evaluated.exit_code == 0, evaluated.output + assert f"Baseline: {baseline_version}" in evaluated.output + + optimized = runner.invoke(main, ["run", "--config", str(config)]) + assert optimized.exit_code == 0, optimized.output + assert "Best: no feasible candidate" not in optimized.output + assert f"Best: {baseline_version}" not in optimized.output + assert (example / "target" / "matmul.c").read_text( + encoding="utf-8" + ) == baseline_source + + database = json.loads( + (example / ".vero" / "session" / "database.json").read_text(encoding="utf-8") + ) + records = list(database["evaluations"].values()) + assert len(records) == 2 + assert all(record["objective"]["feasible"] for record in records) + assert min(record["objective"]["value"] for record in records) < max( + record["objective"]["value"] for record in records + ) + + worktrees = subprocess.run( + ["git", "worktree", "list", "--porcelain"], + cwd=example / "target", + check=True, + capture_output=True, + text=True, + ).stdout + assert worktrees.count("worktree ") == 1 diff --git a/vero/tests/test_v05_candidate_repository.py b/vero/tests/test_v05_candidate_repository.py new file mode 100644 index 00000000..68bc20da --- /dev/null +++ b/vero/tests/test_v05_candidate_repository.py @@ -0,0 +1,298 @@ +from __future__ import annotations + +import json +import os +import shutil +import stat +import subprocess +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.candidate_repository import CandidateRepositoryError, GitCandidateRepository +from vero.sandbox import LocalSandbox, SandboxCapabilities +from vero.workspace import GitWorkspace + + +class OpaqueLocalSandbox(LocalSandbox): + """Local execution with paths hidden to exercise remote bundle transfer.""" + + @property + def capabilities(self) -> SandboxCapabilities: + return SandboxCapabilities(host_paths=False) + + def host_path(self, path: str) -> None: + return None + + +def git(path: Path, *arguments: str) -> str: + result = subprocess.run( + ["git", *arguments], + cwd=path, + check=True, + capture_output=True, + text=True, + ) + return result.stdout.strip() + + +def initialize(path: Path) -> str: + git(path, "init", "-b", "main") + git(path, "add", "--all") + git( + path, + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ) + return git(path, "rev-parse", "HEAD") + + +@pytest.mark.asyncio +async def test_git_repository_captures_remote_candidates_and_recreates_them( + tmp_path: Path, +): + source = tmp_path / "source" + source.mkdir() + program = source / "program.sh" + program.write_text("#!/bin/sh\necho baseline\n", encoding="utf-8") + program.chmod(0o755) + os.symlink("program.sh", source / "program-link") + baseline_version = initialize(source) + + source_sandbox = OpaqueLocalSandbox(tmp_path) + workspace = await GitWorkspace.from_path(source_sandbox, str(source)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + baseline = Candidate.from_version(baseline_version) + await repository.capture(baseline, workspace) + + program.write_text("#!/bin/sh\necho candidate\n", encoding="utf-8") + candidate_version = await workspace.save("candidate") + candidate = Candidate.from_version( + candidate_version, + candidate_id="candidate-1", + parent_id=baseline.id, + description="Improve the program", + metadata={"producer_id": "test"}, + ) + await repository.capture(candidate, workspace) + assert await repository.capture(candidate, workspace) == candidate + + shutil.rmtree(source) + reopened = await GitCandidateRepository.open(repository.root) + assert reopened.list() == (baseline, candidate) + + destination_sandbox = OpaqueLocalSandbox(tmp_path) + checkout_root: Path | None = None + async with reopened.checkout( + candidate, + sandbox=destination_sandbox, + name="inspect", + ) as checkout: + checkout_root = Path(checkout.root) + assert await checkout.current_version() == candidate.version + assert (Path(checkout.project_path) / "program.sh").read_text() == ( + "#!/bin/sh\necho candidate\n" + ) + mode = (Path(checkout.project_path) / "program.sh").stat().st_mode + assert mode & stat.S_IXUSR + assert (Path(checkout.project_path) / "program-link").is_symlink() + assert not await checkout.is_dirty() + assert checkout_root is not None + assert not checkout_root.exists() + + +@pytest.mark.asyncio +async def test_git_repository_rejects_conflicting_candidate_identity(tmp_path: Path): + source = tmp_path / "source" + source.mkdir() + (source / "program.txt").write_text("baseline\n", encoding="utf-8") + baseline_version = initialize(source) + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(source)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + candidate = Candidate.from_version(baseline_version, candidate_id="candidate") + await repository.capture(candidate, workspace) + + conflicting = candidate.model_copy(update={"description": "different"}) + with pytest.raises(ValueError, match="already stored with different data"): + await repository.capture(conflicting, workspace) + + +@pytest.mark.asyncio +async def test_git_repository_preserves_nested_project_path(tmp_path: Path): + source = tmp_path / "source" + project = source / "packages" / "target" + project.mkdir(parents=True) + (project / "program.txt").write_text("baseline\n", encoding="utf-8") + version = initialize(source) + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(project)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + candidate = Candidate.from_version(version) + await repository.capture(candidate, workspace) + + async with repository.checkout(candidate, sandbox=sandbox) as checkout: + assert Path(checkout.project_path).relative_to(checkout.root) == Path( + "packages/target" + ) + assert (Path(checkout.project_path) / "program.txt").read_text() == ( + "baseline\n" + ) + + +@pytest.mark.asyncio +async def test_git_repository_fails_loudly_when_a_record_loses_its_ref( + tmp_path: Path, +): + source = tmp_path / "source" + source.mkdir() + (source / "program.txt").write_text("baseline\n", encoding="utf-8") + version = initialize(source) + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(source)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + await repository.capture(Candidate.from_version(version), workspace) + retained_ref = git( + repository.repository_path, + "for-each-ref", + "--format=%(refname)", + "refs/vero/candidates", + ) + git(repository.repository_path, "update-ref", "-d", retained_ref) + + with pytest.raises(CandidateRepositoryError, match="missing Git object"): + await GitCandidateRepository.open(repository.root) + + +@pytest.mark.asyncio +async def test_git_repository_rejects_candidates_that_track_agent_context( + tmp_path: Path, +): + source = tmp_path / "source" + source.mkdir() + (source / "program.txt").write_text("baseline\n", encoding="utf-8") + baseline_version = initialize(source) + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(source)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + await repository.capture(Candidate.from_version(baseline_version), workspace) + + context = source / ".vero" + context.mkdir() + (context / "private.json").write_text('{"secret": true}\n', encoding="utf-8") + git(source, "add", "-f", ".vero/private.json") + git( + source, + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "try to capture context", + ) + candidate = Candidate.from_version( + git(source, "rev-parse", "HEAD"), + candidate_id="candidate-with-context", + ) + + with pytest.raises(CandidateRepositoryError, match="reserved agent context"): + await repository.capture(candidate, workspace) + assert repository.get(candidate.id) is None + + +@pytest.mark.asyncio +async def test_git_repository_materializes_visible_history_in_opaque_workspace( + tmp_path: Path, +): + source = tmp_path / "source" + source.mkdir() + program = source / "program.txt" + program.write_text("baseline\n", encoding="utf-8") + baseline_version = initialize(source) + sandbox = OpaqueLocalSandbox(tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(source)) + repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + baseline = Candidate.from_version(baseline_version) + await repository.capture(baseline, workspace) + + program.write_text("first\n", encoding="utf-8") + first_version = await workspace.save("first candidate") + first = Candidate.from_version( + first_version, + candidate_id="first", + parent_id=baseline.id, + ) + await repository.capture(first, workspace) + + git(source, "checkout", "--detach", baseline.version) + program.write_text("sibling\n", encoding="utf-8") + sibling_version = await workspace.save("sibling candidate") + sibling = Candidate.from_version( + sibling_version, + candidate_id="sibling", + parent_id=baseline.id, + ) + await repository.capture(sibling, workspace) + + async with repository.checkout( + baseline, + sandbox=OpaqueLocalSandbox(tmp_path), + name="history", + ) as checkout: + destination = f"{checkout.project_path}/.vero/candidates" + await repository.materialize_agent_history( + (baseline, first, sibling), + workspace=checkout, + destination=destination, + ) + index = json.loads( + (Path(destination) / "index.json").read_text(encoding="utf-8") + )["candidates"] + assert {item["candidate_id"] for item in index} == { + baseline.id, + first.id, + sibling.id, + } + assert all( + item["native_ref"].startswith("refs/vero/context/") for item in index + ) + for item in index: + assert ( + git( + Path(checkout.root), + "rev-parse", + "--verify", + f"{item['native_ref']}^{{commit}}", + ) + == item["version"] + ) + if item["candidate_id"] != baseline.id: + patch = Path(destination) / item["parent_patch_path"] + assert patch.is_file() + assert "program.txt" in patch.read_text(encoding="utf-8") + assert not await checkout.is_dirty() diff --git a/vero/tests/test_v05_circle_packing_example.py b/vero/tests/test_v05_circle_packing_example.py new file mode 100644 index 00000000..c984edb5 --- /dev/null +++ b/vero/tests/test_v05_circle_packing_example.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +import json +import shutil +import subprocess +import sys +from pathlib import Path + +import pytest +from click.testing import CliRunner + +from vero.cli import main + + +def test_circle_packing_harness_applies_request_seed(tmp_path: Path): + example = Path(__file__).parents[1] / "examples" / "circle-packing" + workspace = tmp_path / "candidate" + workspace.mkdir() + (workspace / "packing.py").write_text( + """\ +import random + +def run_packing(): + centers = [] + for row in range(6): + for column in range(5): + if len(centers) == 26: + break + centers.append([0.1 + 0.2 * column, 0.1 + 0.16 * row]) + radii = [random.uniform(0.01, 0.015) for _ in centers] + return centers, radii, sum(radii) +""", + encoding="utf-8", + ) + request = tmp_path / "request.json" + request.write_text( + json.dumps({"schema_version": 1, "request": {"seed": 12345}}), + encoding="utf-8", + ) + + scores = [] + layouts = [] + for attempt in range(2): + report = tmp_path / f"report-{attempt}.json" + artifacts = tmp_path / f"artifacts-{attempt}" + completed = subprocess.run( + [ + sys.executable, + str(example / "harness" / "evaluate.py"), + "--workspace", + str(workspace), + "--request", + str(request), + "--report", + str(report), + "--artifacts", + str(artifacts), + ], + check=True, + capture_output=True, + text=True, + ) + assert completed.stderr == "" + payload = json.loads(report.read_text(encoding="utf-8")) + assert payload["metrics"]["valid"] == 1.0 + scores.append(payload["metrics"]["sum_radii"]) + layouts.append( + (artifacts / "circle-packing" / "layout.json").read_text( + encoding="utf-8" + ) + ) + + assert scores[0] == scores[1] + assert layouts[0] == layouts[1] + + +def initialize_repository(path: Path) -> str: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + return subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=path, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + + +@pytest.mark.skipif(shutil.which("uv") is None, reason="uv is required") +def test_circle_packing_example_evaluates_and_preserves_artifacts( + tmp_path: Path, monkeypatch +): + source = Path(__file__).parents[1] / "examples" / "circle-packing" + example = tmp_path / "circle-packing" + shutil.copytree( + source, + example, + ignore=shutil.ignore_patterns(".git", ".vero", ".venv", "__pycache__"), + ) + baseline_version = initialize_repository(example / "target") + monkeypatch.setenv("UV_CACHE_DIR", str(tmp_path / "uv-cache")) + + result = CliRunner().invoke( + main, + ["evaluate", "--config", str(example / "vero.toml")], + ) + + assert result.exit_code == 0, result.output + assert f"Baseline: {baseline_version}" in result.output + database = json.loads( + (example / ".vero" / "session" / "database.json").read_text( + encoding="utf-8" + ) + ) + records = list(database["evaluations"].values()) + assert len(records) == 2 + assert {record["request"]["evaluation_set"]["name"] for record in records} == { + "development", + "final", + } + assert all(record["objective"]["feasible"] for record in records) + assert all(record["request"]["seed"] == 1337 for record in records) + assert all( + record["report"]["metrics"]["sum_radii"] + == pytest.approx(0.9597642169962064) + for record in records + ) + + for record in records: + artifact_root = ( + example + / ".vero" + / "session" + / "evaluations" + / record["id"] + / "artifacts" + / "circle-packing" + ) + assert (artifact_root / "layout.json").is_file() + assert (artifact_root / "layout.svg").read_text( + encoding="utf-8" + ).startswith(" AgentContext: + baseline = Candidate(id="baseline", version="baseline-version") + proposal = CandidateProposal( + id="proposal", + parent_id=baseline.id, + instruction="Optimize matrix multiplication", + ) + return AgentContext( + session_id="session-1", + workspace=StubWorkspace(tmp_path), + proposal=proposal, + parent=baseline, + evaluation=gateway, + ) + + +@pytest.mark.asyncio +async def test_claude_agent_implements_canonical_coding_agent_contract( + tmp_path: Path, +): + gateway = StubEvaluationGateway() + evaluation_tools = EvaluationTools() + agent = ClaudeCodeAgent(tool_sets=[evaluation_tools]) + result_message = ResultMessage( + subtype="success", + duration_ms=10, + duration_api_ms=8, + is_error=False, + num_turns=1, + session_id="claude-session", + usage={"input_tokens": 10, "output_tokens": 3}, + result="done", + ) + client = FakeClaudeClient(result_message) + agent._create_client = lambda max_turns=None: client + events = [] + + async def capture(event): + events.append(event) + + context = agent_context(tmp_path, gateway) + result = await agent.run( + context=context, + prompt="Try a tiled kernel", + max_turns=7, + on_event=capture, + ) + + assert isinstance(agent, CodingAgent) + assert client.queries == ["Try a tiled kernel"] + assert events == [result_message] + assert evaluation_tools.evaluation is gateway + assert agent._context is context + assert agent.state == {"session_id": "claude-session"} + assert result.state == {"session_id": "claude-session"} + assert result.metadata["usage"]["input_tokens"] == 10 + assert context.project_path == tmp_path + assert context.instructions.startswith("Optimize matrix multiplication\n\n") + assert "read-only optimization context in `.vero/`" in context.instructions + assert context.base_version == "baseline-version" diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py new file mode 100644 index 00000000..11f517dc --- /dev/null +++ b/vero/tests/test_v05_cli.py @@ -0,0 +1,311 @@ +from __future__ import annotations + +import json +import shlex +import subprocess +import sys +from pathlib import Path + +from click.testing import CliRunner + +import vero +from vero.cli import main +from vero.candidate import Candidate + + +def test_root_package_exposes_only_canonical_program_api(): + assert vero.Candidate is Candidate + assert not hasattr(vero, "Experiment") + + +def test_canonical_root_and_tools_do_not_import_legacy_core(): + result = subprocess.run( + [ + sys.executable, + "-c", + ( + "import sys, vero, vero.tools; " + "assert not any(name == 'vero.core' or name.startswith('vero.core.') " + "for name in sys.modules)" + ), + ], + check=False, + capture_output=True, + text=True, + ) + + assert result.returncode == 0, result.stderr + + +def initialize_repository(path: Path) -> None: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + + +def test_cli_optimizes_non_python_program_and_inspects_session(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n", encoding="utf-8") + initialize_repository(target) + + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path +workspace, report = map(Path, sys.argv[1:]) +latency = 1.0 if (workspace / "program.txt").read_text().strip() == "fast" else 9.0 +report.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"latency_ms": latency, "correct": 1.0}, +})) +""", + encoding="utf-8", + ) + producer = tmp_path / "producer" + producer.mkdir() + producer_script = producer / "improve.py" + producer_script.write_text( + """ +import sys +from pathlib import Path +Path(sys.argv[1], "program.txt").write_text("fast\\n") +""", + encoding="utf-8", + ) + session_dir = tmp_path / "sessions" / "nested" / "cli-run" + runner = CliRunner() + + result = runner.invoke( + main, + [ + "optimize", + str(target), + "--harness-root", + str(harness), + "--evaluate", + shlex.join( + [ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ] + ), + "--producer-root", + str(producer), + "--produce", + shlex.join([sys.executable, str(producer_script), "{workspace}"]), + "--metric", + "latency_ms", + "--direction", + "minimize", + "--constraint", + "correct", + "==", + "1", + "--evaluation-set", + "performance", + "--parameter", + "threshold=0.5", + "--session-dir", + str(session_dir), + "--session-id", + "cli-session", + ], + ) + + assert result.exit_code == 0, result.output + assert "Baseline:" in result.output + assert "(9.0)" in result.output + assert "Best:" in result.output + assert "(1.0)" in result.output + assert (target / "program.txt").read_text(encoding="utf-8") == "slow\n" + + inspect_result = runner.invoke(main, ["session", "inspect", str(session_dir)]) + assert inspect_result.exit_code == 0, inspect_result.output + inspection = json.loads(inspect_result.output) + assert inspection["manifest"]["id"] == "cli-session" + assert inspection["manifest"]["status"] == "completed" + assert len(inspection["candidates"]) == 2 + assert len(inspection["evaluations"]) == 2 + + list_result = runner.invoke( + main, + ["session", "list", "--root", str(tmp_path / "sessions")], + ) + assert list_result.exit_code == 0, list_result.output + assert "cli-session\tcompleted" in list_result.output + assert "nested/cli-run" in list_result.output + + archive = tmp_path / "cli-export.tar.gz" + export_result = runner.invoke( + main, + ["session", "export", str(session_dir), "--output", str(archive)], + ) + assert export_result.exit_code == 0, export_result.output + assert archive.is_file() + + fork_dir = tmp_path / "sessions" / "forked" + fork_result = runner.invoke( + main, + [ + "session", + "fork", + str(session_dir), + str(fork_dir), + "--max-proposals", + "2", + "--reset-budgets", + ], + ) + assert fork_result.exit_code == 0, fork_result.output + forked = json.loads((fork_dir / "manifest.json").read_text()) + assert forked["id"] == "forked" + assert forked["run"]["max_proposals"] == 2 + assert json.loads((fork_dir / "database.json").read_text())["id"] == "forked" + + clear_result = runner.invoke( + main, + ["session", "clear", str(fork_dir), "--yes"], + ) + assert clear_result.exit_code == 0, clear_result.output + assert not fork_dir.exists() + + evaluate_only_dir = tmp_path / "sessions" / "evaluate-only" + evaluate_only = runner.invoke( + main, + [ + "optimize", + str(target), + "--harness-root", + str(harness), + "--evaluate", + shlex.join( + [ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ] + ), + "--metric", + "latency_ms", + "--direction", + "minimize", + "--max-proposals", + "0", + "--session-dir", + str(evaluate_only_dir), + ], + ) + assert evaluate_only.exit_code == 0, evaluate_only.output + assert "(9.0)" in evaluate_only.output + + +def test_cli_init_and_check_config(tmp_path: Path): + runner = CliRunner() + result = runner.invoke(main, ["init", str(tmp_path)]) + assert result.exit_code == 0, result.output + + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + (tmp_path / "harness").mkdir() + + checked = runner.invoke( + main, + ["check", "--config", str(tmp_path / "vero.toml")], + ) + assert checked.exit_code == 0, checked.output + assert "selection='validation'" in checked.output + + +def test_cli_requires_exactly_one_producer(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + harness = tmp_path / "harness" + harness.mkdir() + runner = CliRunner() + + result = runner.invoke( + main, + [ + "optimize", + str(target), + "--harness-root", + str(harness), + "--evaluate", + "evaluate {workspace} {report}", + "--metric", + "score", + "--direction", + "maximize", + ], + ) + + assert result.exit_code == 2 + assert "exactly one of --produce or --agent" in result.output + + +def test_cli_rejects_options_that_do_not_apply(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + harness = tmp_path / "harness" + harness.mkdir() + base = [ + "optimize", + str(target), + "--harness-root", + str(harness), + "--evaluate", + "evaluate {workspace} {report}", + "--metric", + "score", + "--direction", + "maximize", + "--max-proposals", + "0", + ] + runner = CliRunner() + + max_turns = runner.invoke(main, [*base, "--max-turns", "10"]) + producer_timeout = runner.invoke(main, [*base, "--producer-timeout", "10"]) + wandb = runner.invoke(main, [*base, "--wandb-mode", "offline"]) + + assert max_turns.exit_code == 2 + assert "--max-turns is only valid with --agent" in max_turns.output + assert producer_timeout.exit_code == 2 + assert "are only valid with --produce" in producer_timeout.output + assert wandb.exit_code == 2 + assert "require --wandb-project" in wandb.output diff --git a/vero/tests/test_v05_command_backend.py b/vero/tests/test_v05_command_backend.py new file mode 100644 index 00000000..7430a507 --- /dev/null +++ b/vero/tests/test_v05_command_backend.py @@ -0,0 +1,331 @@ +import json +import os +import sys +from datetime import UTC, datetime +from pathlib import Path +from types import SimpleNamespace + +import pytest +from pydantic import ValidationError + +from vero.candidate import Candidate +from vero.evaluation import ( + AllCases, + CaseCheckpointStore, + CaseIds, + CaseRange, + CommandBackend, + CommandBackendConfig, + EvaluationContext, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, +) +from vero.sandbox import LocalSandbox + + +def write_harness(root: Path, body: str) -> Path: + root.mkdir(parents=True, exist_ok=True) + script = root / "harness.py" + script.write_text(body) + return script + + +async def context(tmp_path: Path, workspace_path: Path) -> EvaluationContext: + workspace_path.mkdir(parents=True, exist_ok=True) + result_dir = tmp_path / "result" + artifact_dir = result_dir / "artifacts" + artifact_dir.mkdir(parents=True) + workspace = SimpleNamespace( + project_path=str(workspace_path), + sandbox=await LocalSandbox.create(root=tmp_path), + ) + return EvaluationContext( + workspace=workspace, + session_id="session", + evaluation_id="evaluation", + result_dir=result_dir, + artifact_dir=artifact_dir, + case_store=CaseCheckpointStore(result_dir / "cases"), + ) + + +def request() -> EvaluationRequest: + return EvaluationRequest( + candidate=Candidate( + id="candidate", + version="version-1", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + ) + + +@pytest.mark.asyncio +async def test_command_backend_uses_argv_without_shell_interpolation(tmp_path: Path): + harness_root = tmp_path / "harness" + script = write_harness( + harness_root, + """ +import json +import sys +from pathlib import Path + +workspace, request_path, report_path = map(Path, sys.argv[1:]) +payload = json.loads(request_path.read_text()) +Path(report_path).write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": { + "workspace_exists": float(workspace.exists()), + "request_schema": float(payload["schema_version"]), + }, +})) +print(workspace) +""", + ) + workspace_path = tmp_path / "target;touch SHOULD_NOT_EXIST" + runtime_context = await context(tmp_path, workspace_path) + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness_root), + command=[ + sys.executable, + str(script), + "{workspace}", + "{request}", + "{report}", + ], + ) + ) + + report = await backend.evaluate(context=runtime_context, request=request()) + + assert report.status == EvaluationStatus.SUCCESS + assert report.metrics == {"workspace_exists": 1.0, "request_schema": 1.0} + assert not (tmp_path / "SHOULD_NOT_EXIST").exists() + assert [artifact.path for artifact in report.artifacts] == [ + "command/stdout.log", + "command/stderr.log", + ] + + +@pytest.mark.asyncio +async def test_command_backend_passes_only_declared_environment(tmp_path: Path): + harness_root = tmp_path / "harness" + script = write_harness( + harness_root, + """ +import json +import os +import sys +from pathlib import Path + +Path(sys.argv[1]).write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": { + "configured": float(os.environ["CONFIGURED"]), + "passed": float(os.environ["PASSED"]), + "hidden_absent": float("HIDDEN" not in os.environ), + }, +})) +""", + ) + os.environ["PASSED"] = "2" + os.environ["HIDDEN"] = "3" + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness_root), + command=[sys.executable, str(script), "{report}"], + environment={"CONFIGURED": "1"}, + passthrough_environment=["PASSED"], + ) + ) + + report = await backend.evaluate( + context=await context(tmp_path, tmp_path / "target"), + request=request(), + ) + + assert report.metrics == { + "configured": 1.0, + "passed": 2.0, + "hidden_absent": 1.0, + } + + +@pytest.mark.asyncio +async def test_command_backend_redacts_secrets(tmp_path: Path): + harness_root = tmp_path / "harness" + secret = "highly-sensitive-token" + script = write_harness( + harness_root, + """ +import json +import os +import sys +from pathlib import Path + +secret = os.environ["SECRET_TOKEN"] +print(f"stdout leaked {secret}") +print(f"stderr leaked {secret}", file=sys.stderr) +Path(sys.argv[1]).write_text(json.dumps({ + "schema_version": 1, + "status": "failed", + "diagnostics": [{ + "code": "harness_failed", + "message": f"diagnostic leaked {secret}", + "severity": "error" + }] +})) +""", + ) + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness_root), + command=[sys.executable, str(script), "{report}"], + environment={"SECRET_TOKEN": secret}, + ) + ) + runtime_context = await context(tmp_path, tmp_path / "target") + + report = await backend.evaluate(context=runtime_context, request=request()) + + persisted_text = ( + report.model_dump_json() + + (runtime_context.artifact_dir / "command" / "stdout.log").read_text() + + (runtime_context.artifact_dir / "command" / "stderr.log").read_text() + ) + assert secret not in persisted_text + assert "[REDACTED]" in persisted_text + with pytest.raises(ValueError, match="must not contain configured secret"): + backend.validate_request( + request().model_copy(update={"parameters": {"token": secret}}) + ) + + +@pytest.mark.parametrize( + ("body", "arguments", "expected_code"), + [ + ("raise SystemExit(7)", [], "command_failed"), + ("print('no report')", [], "missing_report"), + ( + "from pathlib import Path; import sys; Path(sys.argv[1]).write_text('{')", + ["{report}"], + "invalid_report", + ), + ], +) +@pytest.mark.asyncio +async def test_command_failures_return_failed_reports( + tmp_path: Path, + body: str, + arguments: list[str], + expected_code: str, +): + harness_root = tmp_path / "harness" + script = write_harness(harness_root, body) + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness_root), + command=[sys.executable, str(script), *arguments], + ) + ) + + report = await backend.evaluate( + context=await context(tmp_path, tmp_path / "target"), + request=request(), + ) + + assert report.status == EvaluationStatus.FAILED + assert report.diagnostics[0].code == expected_code + + +@pytest.mark.parametrize( + ("selection", "expected"), + [ + (AllCases(), None), + (CaseIds(ids=["a", "b"]), 2), + (CaseRange(stop=5), 5), + (CaseRange(start=5, stop=9), 4), + ], +) +@pytest.mark.asyncio +async def test_command_backend_resolves_cost(tmp_path: Path, selection, expected): + backend = CommandBackend( + CommandBackendConfig(harness_root=str(tmp_path), command=["run"]) + ) + + cost = await backend.resolve_cost(EvaluationSet(selection=selection)) + + assert cost.cases == expected + + +@pytest.mark.asyncio +async def test_command_backend_exports_only_allowlisted_agent_inputs(tmp_path: Path): + visible = tmp_path / "visible.json" + visible.write_text('{"visible": true}\n', encoding="utf-8") + hidden = tmp_path / "hidden.json" + hidden.write_text('{"hidden": true}\n', encoding="utf-8") + destination = tmp_path / "context" + destination.mkdir() + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(tmp_path), + command=["run"], + staged_inputs={"visible": str(visible), "hidden": str(hidden)}, + agent_context_inputs={"validation": ["visible"]}, + ) + ) + + await backend.export_case_resources( + evaluation_set=EvaluationSet(name="validation"), + destination=str(destination), + sandbox=await LocalSandbox.create(root=tmp_path), + ) + + index = json.loads((destination / "index.json").read_text()) + assert index["resources"] == [{"name": "visible", "path": "visible"}] + assert json.loads((destination / "visible").read_text()) == {"visible": True} + assert not (destination / "hidden").exists() + + +def test_command_config_rejects_unsafe_shapes(tmp_path: Path): + with pytest.raises(ValidationError, match="must be absolute"): + CommandBackendConfig(harness_root="relative", command=["run"]) + with pytest.raises(ValidationError, match="must not be empty"): + CommandBackendConfig(harness_root=str(tmp_path), command=[]) + with pytest.raises(ValidationError, match="unknown command placeholders"): + CommandBackendConfig( + harness_root=str(tmp_path), + command=["run", "{secret}"], + ) + with pytest.raises(ValidationError, match="overlap"): + CommandBackendConfig( + harness_root=str(tmp_path), + command=["run"], + environment={"TOKEN": "value"}, + passthrough_environment=["TOKEN"], + ) + with pytest.raises(ValidationError, match="unknown staged inputs"): + CommandBackendConfig( + harness_root=str(tmp_path), + command=["run"], + agent_context_inputs={"validation": ["missing"]}, + ) + + +@pytest.mark.asyncio +async def test_harness_cannot_live_inside_target(tmp_path: Path): + target = tmp_path / "target" + harness = target / "harness" + harness.mkdir(parents=True) + backend = CommandBackend( + CommandBackendConfig(harness_root=str(harness), command=["run"]) + ) + + with pytest.raises(ValueError, match="outside the editable target"): + await backend.evaluate( + context=await context(tmp_path, target), + request=request(), + ) diff --git a/vero/tests/test_v05_config.py b/vero/tests/test_v05_config.py new file mode 100644 index 00000000..0e826500 --- /dev/null +++ b/vero/tests/test_v05_config.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from vero.config import AgentOptimizerConfig, VeroConfig, _producer, load_config + + +def _config(optimizer: dict) -> dict: + return { + "target": {"root": "."}, + "backend": { + "harness_root": ".", + "command": ["evaluate", "{workspace}", "{report}"], + }, + "evaluations": [{"name": "default"}], + "protocol": {"selection_evaluation": "default"}, + "objective": {"metric": "score", "direction": "maximize"}, + "optimizer": optimizer, + } + + +def test_agent_optimizer_accepts_only_agent_fields(): + config = VeroConfig.model_validate( + _config( + { + "kind": "vero", + "instruction": "Improve the program", + "model": "openai/gpt-5", + "max_turns": 10, + } + ) + ) + + assert isinstance(config.optimizer, AgentOptimizerConfig) + assert config.optimizer.model == "openai/gpt-5" + assert config.optimizer.max_turns == 10 + + +def test_agent_optimizer_rejects_empty_model(): + with pytest.raises(ValidationError, match="model must not be empty"): + VeroConfig.model_validate(_config({"kind": "vero", "model": " "})) + + +@pytest.mark.parametrize( + ("kind", "model"), + [("vero", "openai/gpt-5"), ("claude", "claude-opus-4-1")], +) +def test_agent_optimizer_applies_configured_model(kind, model): + producer = _producer(AgentOptimizerConfig(kind=kind, model=model)) + + configured = ( + producer.agent.model_str() + if kind == "vero" + else producer.agent.options.model + ) + assert configured == model + + +@pytest.mark.parametrize( + ("field", "value"), + [ + ("root", "producer"), + ("working_directory", "src"), + ("environment", {"NAME": "value"}), + ("passthrough_environment", ["TOKEN"]), + ("timeout_seconds", 10), + ("description", "ignored"), + ("command", ["ignored"]), + ], +) +def test_agent_optimizer_rejects_command_only_fields(field, value): + optimizer = {"kind": "claude", field: value} + + with pytest.raises(ValidationError, match=field): + VeroConfig.model_validate(_config(optimizer)) + + +def test_config_rejects_case_ids_combined_with_range_start(): + value = _config({"kind": "vero"}) + value["evaluations"][0].update({"case_ids": ["a"], "case_start": 1}) + + with pytest.raises(ValidationError, match="case_ids and case range"): + VeroConfig.model_validate(value) + + +def test_config_wires_retry_policy_into_evaluation_limits(): + value = _config({"kind": "vero"}) + value["protocol"]["retry"] = { + "max_attempts": 5, + "initial_delay_seconds": 0.5, + } + + config = VeroConfig.model_validate(value) + + assert config.protocol.to_limits().retry.max_attempts == 5 + assert config.protocol.to_limits().retry.initial_delay_seconds == 0.5 + + +def test_config_wires_case_failure_and_error_rate_policy(): + value = _config({"kind": "vero"}) + value["protocol"]["error_rate_threshold"] = 0.25 + value["objective"].update( + {"aggregation": "mean", "case_failure_value": 0.0} + ) + + config = VeroConfig.model_validate(value) + + assert config.protocol.to_limits().error_rate_threshold == 0.25 + assert config.objective.to_model().selector.case_failure_value == 0.0 + + +def test_config_resolves_agent_context_inputs_with_staged_inputs(tmp_path): + config_path = tmp_path / "vero.toml" + config_path.write_text( + """ +[target] +root = "target" + +[backend] +harness_root = "harness" +command = ["run", "{input:cases}"] + +[backend.staged_inputs] +cases = "data/cases.json" + +[backend.agent_context_inputs] +train = ["cases"] + +[[evaluations]] +name = "train" + +[protocol] +selection_evaluation = "train" + +[objective] +metric = "score" +direction = "maximize" +""", + encoding="utf-8", + ) + + config = load_config(config_path) + + assert config.backend.agent_context_inputs == {"train": ["cases"]} + assert config.backend.staged_inputs == { + "cases": str((tmp_path / "data/cases.json").resolve()) + } diff --git a/vero/tests/test_v05_darwin_godel_strategy.py b/vero/tests/test_v05_darwin_godel_strategy.py new file mode 100644 index 00000000..068153cd --- /dev/null +++ b/vero/tests/test_v05_darwin_godel_strategy.py @@ -0,0 +1,120 @@ +from __future__ import annotations + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + EvaluationSet, + EvaluationStatus, + EvaluationSummary, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.optimization import DarwinGodelStrategy +from vero.optimization.models import OptimizationContext + +OBJ_MAX = ObjectiveSpec(selector=MetricSelector(metric="score"), direction="maximize") + + +def _summary(candidate_id: str, value: float | None, *, feasible: bool = True) -> EvaluationSummary: + return EvaluationSummary( + evaluation_id=f"eval-{candidate_id}", + candidate_id=candidate_id, + candidate_version=f"{candidate_id}-v", + backend_id="cmd", + evaluation_set=EvaluationSet(name="perf"), + status=EvaluationStatus.SUCCESS, + metrics={"score": value} if value is not None else {}, + objective=ObjectiveResult(value=value, feasible=feasible), + total_cases=1, + successful_cases=1, + errored_cases=0, + skipped_cases=0, + ) + + +def _context(*, candidates, evaluations=(), best=None, baseline=None) -> OptimizationContext: + baseline = baseline or next(iter(candidates.values())) + return OptimizationContext( + session_id="s", + round=0, + workspace=object(), # unused by the strategy + baseline=baseline, + evaluations=tuple(evaluations), + candidates=candidates, + best=best, + ) + + +@pytest.mark.asyncio +async def test_dgm_seeds_from_baseline_when_archive_has_only_the_baseline(): + strat = DarwinGodelStrategy(objective=OBJ_MAX, num_offspring=3, seed=0) + base = Candidate(id="base", version="base-v") + ctx = _context(candidates={"base": base}, baseline=base) + + proposals = await strat.propose(ctx) + + assert len(proposals) == 3 + assert all(p.parent_id == "base" for p in proposals) + assert all(p.metadata["strategy"] == "darwin-godel" for p in proposals) + assert proposals[0].producer_id == "self" # self-application producer by default + + +@pytest.mark.asyncio +async def test_dgm_keeps_the_whole_archive_reachable_including_low_and_unscored(): + # Unlike EvolutionaryStrategy's fittest-K population, every archived agent — + # even a weak or not-yet-scored one — can be a parent (stepping stones). + strat = DarwinGodelStrategy(objective=OBJ_MAX, num_offspring=300, seed=1) + cands = {c: Candidate(id=c, version=f"{c}-v") for c in ["base", "strong", "weak", "unscored"]} + evals = (_summary("base", 0.10), _summary("strong", 0.90), _summary("weak", 0.05)) + ctx = _context(candidates=cands, evaluations=evals, best=cands["strong"], baseline=cands["base"]) + + parents = [p.parent_id for p in await strat.propose(ctx)] + seen = set(parents) + + assert "weak" in seen and "unscored" in seen # low + unscored stay reachable + assert parents.count("strong") > parents.count("weak") # performance still biases selection + + +@pytest.mark.asyncio +async def test_dgm_inverse_children_downweights_explored_lineages(): + # Equal performance, but the more-explored lineage gets a lower selection weight. + strat = DarwinGodelStrategy(objective=OBJ_MAX, seed=0) + strat._score = {"a": 0.8, "b": 0.8} + strat._children = {"a": 3, "b": 0} + + assert strat._weight("a") == pytest.approx(0.8 / 4) + assert strat._weight("b") == pytest.approx(0.8 / 1) + assert strat._weight("a") < strat._weight("b") + + +@pytest.mark.asyncio +async def test_dgm_tracks_children_across_rounds(): + strat = DarwinGodelStrategy(objective=OBJ_MAX, num_offspring=4, seed=3) + cands = {c: Candidate(id=c, version=f"{c}-v") for c in ["a", "b"]} + ctx = _context(candidates=cands, evaluations=(_summary("a", 0.8), _summary("b", 0.8)), baseline=cands["a"]) + + await strat.propose(ctx) + await strat.propose(ctx) + + # Every selection increments the parent's child count; 2 rounds × 4 offspring = 8. + assert sum(strat._children.values()) == 8 + assert strat._generation == 2 + + +@pytest.mark.asyncio +async def test_dgm_minimize_direction_prefers_lower_values(): + strat = DarwinGodelStrategy( + objective=ObjectiveSpec(selector=MetricSelector(metric="score"), direction="minimize"), + num_offspring=200, + base_weight=0.01, # sharpen so the performance signal dominates + seed=0, + ) + cands = {c: Candidate(id=c, version=f"{c}-v") for c in ["hi", "lo"]} + ctx = _context(candidates=cands, evaluations=(_summary("hi", 9.0), _summary("lo", 1.0)), baseline=cands["lo"]) + + parents = [p.parent_id for p in await strat.propose(ctx)] + + # Under minimize, the lower value ("lo") is the higher performer → picked more. + assert parents.count("lo") > parents.count("hi") diff --git a/vero/tests/test_v05_docker_sandbox.py b/vero/tests/test_v05_docker_sandbox.py new file mode 100644 index 00000000..aeda19d0 --- /dev/null +++ b/vero/tests/test_v05_docker_sandbox.py @@ -0,0 +1,210 @@ +from __future__ import annotations + +import asyncio +import os +import shutil +import subprocess +from pathlib import Path + +import pytest + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + CommandBackend, + CommandBackendConfig, + EvaluationPlan, + MetricSelector, + ObjectiveSpec, +) +from vero.optimization import CommandCandidateProducer, CommandCandidateProducerConfig +from vero.runtime import create_optimization_session +from vero.sandbox import CommandResult, DockerSandbox +from vero.workspace import GitWorkspace + + +def _docker_available() -> bool: + executable = shutil.which("docker") + if executable is None: + return False + return ( + subprocess.run( + [executable, "info"], + capture_output=True, + timeout=10, + check=False, + ).returncode + == 0 + ) + + +@pytest.mark.asyncio +async def test_docker_sandbox_owns_an_unmounted_container(monkeypatch): + commands: list[list[str]] = [] + + async def fake_host_command(command, *, timeout=30, before_terminate=None): + commands.append(command) + if command[1] == "run": + return CommandResult("container-id", "", 0) + return CommandResult("", "", 0) + + monkeypatch.setattr( + DockerSandbox, + "_host_command", + staticmethod(fake_host_command), + ) + + sandbox = await DockerSandbox.create( + image="example/image:locked", + docker_executable="docker", + ) + await sandbox.close() + + run_command = commands[0] + assert run_command[:3] == ["docker", "run", "--detach"] + assert "--volume" not in run_command + assert "-v" not in run_command + assert sandbox.host_path("/workspace/project") is None + exec_command = next(command for command in commands if command[1] == "exec") + assert "setsid" in exec_command + assert commands[-1] == ["docker", "rm", "--force", "container-id"] + + +@pytest.mark.asyncio +async def test_docker_timeout_terminates_the_in_container_process_group(monkeypatch): + host_commands: list[list[str]] = [] + cleanup_commands: list[tuple[str, ...]] = [] + + async def fake_host_command( + command, + *, + timeout=30, + before_terminate=None, + ): + host_commands.append(command) + assert before_terminate is not None + await before_terminate() + return CommandResult("", "timed out", -1) + + async def fake_docker(*arguments, timeout=30): + cleanup_commands.append(arguments) + return CommandResult("", "", 0) + + monkeypatch.setattr( + DockerSandbox, + "_host_command", + staticmethod(fake_host_command), + ) + sandbox = DockerSandbox("container-id", docker_executable="docker") + monkeypatch.setattr(sandbox, "_docker", fake_docker) + + result = await sandbox.run(["sh", "-c", "sleep 60"], timeout=0.1) + + assert result.returncode == -1 + assert "setsid" in host_commands[0] + assert cleanup_commands + assert cleanup_commands[0][:2] == ("exec", "container-id") + assert "kill -TERM" in cleanup_commands[0][4] + + +@pytest.mark.skipif(not _docker_available(), reason="Docker daemon is unavailable") +@pytest.mark.asyncio +async def test_generic_optimization_without_a_shared_filesystem(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.c").write_text( + '#include \nint main(void) { printf("1.0\\n"); return 0; }\n', + encoding="utf-8", + ) + subprocess.run(["git", "init", "-b", "main"], cwd=target, check=True) + subprocess.run(["git", "add", "--all"], cwd=target, check=True) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=target, + check=True, + ) + + harness = tmp_path / "harness" + harness.mkdir() + (harness / "evaluate.sh").write_text( + """#!/bin/sh +set -eu +workspace=$1 +report=$2 +artifacts=$3 +cc "$workspace/program.c" -o "$artifacts/program" +score=$("$artifacts/program") +printf '{"schema_version":1,"status":"success","metrics":{"score":%s}}' "$score" > "$report" +""", + encoding="utf-8", + ) + producer_root = tmp_path / "producer" + producer_root.mkdir() + (producer_root / "improve.sh").write_text( + "sed -i 's/1.0/2.0/' \"$1/program.c\"\n", + encoding="utf-8", + ) + + sandbox = await DockerSandbox.create( + image=os.environ.get("VERO_DOCKER_TEST_IMAGE", "gcc:14-bookworm") + ) + try: + remote_target = "/workspace/target" + assert sandbox.host_path(remote_target) is None + await sandbox.upload(target, remote_target) + workspace = await GitWorkspace.from_path(sandbox, remote_target) + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[ + "sh", + "{harness}/evaluate.sh", + "{workspace}", + "{report}", + "{artifacts}", + ], + ) + ) + producer = CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=["sh", "{producer}/improve.sh", "{workspace}"], + ) + ) + candidate_repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", + workspace=workspace, + ) + session = await create_optimization_session( + workspace=workspace, + candidate_repository=candidate_repository, + session_dir=tmp_path / "session", + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + evaluation_plan=EvaluationPlan.single(), + producers={"default": producer}, + max_proposals=1, + ) + + result = await asyncio.wait_for(session.run(), timeout=180) + + assert result.baseline.objective.value == 1.0 + assert result.best.objective.value == 2.0 + assert (session.session_dir / "database.json").is_file() + assert await sandbox.read_file(f"{remote_target}/program.c") == ( + '#include \nint main(void) { printf("1.0\\n"); return 0; }\n' + ) + finally: + await sandbox.close() diff --git a/vero/tests/test_v05_error_taxonomy.py b/vero/tests/test_v05_error_taxonomy.py new file mode 100644 index 00000000..5b15cc46 --- /dev/null +++ b/vero/tests/test_v05_error_taxonomy.py @@ -0,0 +1,60 @@ +"""Tests for the single source of truth error taxonomy.""" + +from vero.evaluation.error_taxonomy import ( + ErrorCategory, + classify_case, + classify_signal, + policy, +) + + +def test_classify_signal_recognizes_infrastructure_categories(): + assert classify_signal("openai.RateLimitError") is ErrorCategory.TRANSIENT_INFRA + assert classify_signal("APITimeoutError") is ErrorCategory.TRANSIENT_INFRA + assert classify_signal("ConnectionError") is ErrorCategory.TRANSIENT_INFRA + assert classify_signal("AuthenticationError") is ErrorCategory.AUTH_FAILURE + assert classify_signal("PermissionDeniedError") is ErrorCategory.AUTH_FAILURE + assert ( + classify_signal("insufficient_quota") + is ErrorCategory.INFERENCE_BUDGET_EXHAUSTED + ) + + +def test_classify_signal_leaves_task_failures_unclassified(): + # The benign "produced no answer" marker and a candidate's own bug are not + # infrastructure — the case-level classifier turns both into task failures. + assert classify_signal("no_rewards_recorded") is None + assert classify_signal("ValueError") is None + assert classify_signal("") is None + + +def test_classify_case_precedence_and_defaults(): + assert classify_case([]) is ErrorCategory.TASK_FAILURE + assert classify_case(["no_rewards_recorded"]) is ErrorCategory.TASK_FAILURE + assert classify_case(["KeyError"]) is ErrorCategory.TASK_FAILURE + assert classify_case(["openai.RateLimitError"]) is ErrorCategory.TRANSIENT_INFRA + # Auth (terminating) outranks a transient rate limit seen on another attempt. + assert ( + classify_case(["openai.RateLimitError", "AuthenticationError"]) + is ErrorCategory.AUTH_FAILURE + ) + + +def test_policy_encodes_the_intended_treatment(): + budget = policy(ErrorCategory.INFERENCE_BUDGET_EXHAUSTED) + assert budget.terminating and not budget.retryable + assert not budget.counts_toward_invalidity + + eval_budget = policy(ErrorCategory.EVALUATION_BUDGET_EXHAUSTED) + assert not eval_budget.terminating and not eval_budget.retryable + + auth = policy(ErrorCategory.AUTH_FAILURE) + assert auth.terminating and not auth.retryable + + transient = policy(ErrorCategory.TRANSIENT_INFRA) + assert transient.retryable and transient.counts_toward_invalidity + assert not transient.is_informative_sample + + task = policy(ErrorCategory.TASK_FAILURE) + assert task.is_informative_sample + assert not task.counts_toward_invalidity and not task.terminating diff --git a/vero/tests/test_v05_evaluation_models.py b/vero/tests/test_v05_evaluation_models.py new file mode 100644 index 00000000..9f12b773 --- /dev/null +++ b/vero/tests/test_v05_evaluation_models.py @@ -0,0 +1,338 @@ +from datetime import UTC, datetime, timedelta + +import pytest +from pydantic import ValidationError + +from vero.candidate import Candidate +from vero.evaluation import ( + AllCases, + BackendProvenance, + CaseError, + CaseIds, + CaseRange, + CaseResult, + CaseStatus, + DiagnosticSeverity, + DisclosureLevel, + EvaluationAccessPolicy, + EvaluationArtifact, + EvaluationBudget, + EvaluationDefinition, + EvaluationDiagnostic, + EvaluationLimits, + EvaluationPlan, + EvaluationPrincipal, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, + RetryPolicy, +) + + +def candidate(candidate_id: str = "candidate-1") -> Candidate: + return Candidate( + id=candidate_id, + version=f"snapshot:{candidate_id}", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + + +def test_candidate_identity_is_workspace_neutral(): + value = Candidate.from_version( + "remote-revision:42", + candidate_id="idea-7", + parent_id="baseline", + metadata={"producer": "coding-agent"}, + ) + + assert value.id == "idea-7" + assert value.version == "remote-revision:42" + assert value.parent_id == "baseline" + assert not hasattr(value, "commit") + assert not hasattr(value, "repo_name") + + +def test_candidate_rejects_naive_timestamps_and_self_parent(): + with pytest.raises(ValidationError, match="timezone-aware"): + Candidate(id="a", version="1", created_at=datetime(2026, 1, 1)) + with pytest.raises(ValidationError, match="own parent"): + Candidate(id="a", version="1", parent_id="a") + + +@pytest.mark.parametrize( + "selection", + [ + AllCases(), + CaseIds(ids=["case-2", "case-7"]), + CaseRange(stop=10), + CaseRange(start=10, stop=20), + ], +) +def test_evaluation_set_round_trips_selection(selection): + evaluation_set = EvaluationSet( + name="performance", + partition="validation", + selection=selection, + ) + + restored = EvaluationSet.model_validate_json(evaluation_set.model_dump_json()) + + assert restored == evaluation_set + assert type(restored.selection) is type(selection) + assert restored.budget_key("command") == "command:performance:validation" + + +@pytest.mark.parametrize( + ("model", "kwargs"), + [ + (EvaluationSet, {"name": " "}), + (EvaluationSet, {"partition": ""}), + (CaseIds, {"ids": []}), + (CaseIds, {"ids": ["same", "same"]}), + (CaseRange, {"start": -1, "stop": 2}), + (CaseRange, {"start": 2, "stop": 2}), + ], +) +def test_selection_rejects_invalid_values(model, kwargs): + with pytest.raises(ValidationError): + model(**kwargs) + + +def test_request_fingerprint_ignores_candidate_display_metadata(): + first = EvaluationRequest( + candidate=Candidate( + id="same", + version="version-1", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + description="first description", + ), + parameters={"outer": {"z": 1, "a": 2}, "alpha": True}, + ) + second = EvaluationRequest( + candidate=Candidate( + id="same", + version="version-1", + created_at=datetime(2026, 1, 2, tzinfo=UTC), + description="updated description", + ), + parameters={"alpha": True, "outer": {"a": 2, "z": 1}}, + ) + + assert first.fingerprint() == second.fingerprint() + + +def test_retry_policy_restores_transient_provider_defaults(): + policy = RetryPolicy() + + assert policy.max_attempts == 3 + assert policy.retry_on_timeout is True + assert policy.retry_status_codes == [429, 503, 529] + assert RetryPolicy.disabled().max_attempts == 1 + + +@pytest.mark.parametrize( + "values", + [ + {"initial_delay_seconds": 2, "maximum_delay_seconds": 1}, + {"retry_exception_names": ["same", "same"]}, + {"retry_status_codes": [99]}, + {"retry_message_patterns": ["["]}, + ], +) +def test_retry_policy_rejects_invalid_configuration(values): + with pytest.raises(ValidationError): + RetryPolicy(**values) + + +def test_case_failure_value_requires_case_aggregation(): + with pytest.raises(ValidationError, match="requires a case metric aggregation"): + MetricSelector(metric="score", case_failure_value=0.0) + + +@pytest.mark.parametrize("threshold", [0.0, 1.1]) +def test_error_rate_threshold_must_be_a_positive_fraction(threshold): + with pytest.raises(ValidationError): + EvaluationLimits(error_rate_threshold=threshold) + + +@pytest.mark.parametrize( + "path", + [ + "", + "/absolute.log", + "../escape.log", + "logs/../escape.log", + "logs//run.log", + "logs\\run.log", + ], +) +def test_artifacts_reject_unsafe_paths(path): + with pytest.raises(ValidationError): + EvaluationArtifact(path=path) + + +def test_case_result_preserves_multiple_attempt_errors(): + result = CaseResult( + case_id="1", + status=CaseStatus.ERROR, + errors=[ + CaseError(message="timed out", attempt=1, retryable=True), + CaseError( + message="failed again", + attempt=2, + retryable=False, + terminal=True, + ), + ], + ) + + assert [error.attempt for error in result.errors] == [1, 2] + + +@pytest.mark.parametrize( + "value", + [ + {"case_id": "1", "status": "error", "errors": []}, + { + "case_id": "1", + "status": "success", + "errors": [{"message": "fatal", "terminal": True}], + }, + { + "case_id": "1", + "status": "skipped", + "errors": [{"message": "fatal", "terminal": True}], + }, + ], +) +def test_case_status_and_terminal_errors_agree(value): + with pytest.raises(ValidationError): + CaseResult.model_validate(value) + + +def test_report_preserves_structured_diagnostics_and_rejects_duplicate_cases(): + case = CaseResult(case_id="same", status=CaseStatus.SUCCESS) + with pytest.raises(ValidationError, match="case IDs must be unique"): + EvaluationReport( + status=EvaluationStatus.SUCCESS, + cases=[case, case], + ) + + report = EvaluationReport( + status=EvaluationStatus.FAILED, + diagnostics=[ + EvaluationDiagnostic( + code="compile_failed", + message="compiler returned 1", + severity=DiagnosticSeverity.ERROR, + phase="compile", + ) + ], + ) + assert report.diagnostics[0].code == "compile_failed" + assert not hasattr(report, "error") + + +def test_backend_provenance_digest_is_stable_across_key_order(): + first = BackendProvenance.from_config( + name="command", version="1", config={"command": ["run"], "timeout": 10} + ) + second = BackendProvenance.from_config( + name="command", version="1", config={"timeout": 10, "command": ["run"]} + ) + + assert first == second + + +def test_record_is_schema_one_and_requires_aware_ordered_timestamps(): + specification = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + created_at = datetime(2026, 1, 1, tzinfo=UTC) + record = EvaluationRecord( + id="evaluation-1", + request=EvaluationRequest(candidate=candidate()), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 1.0}, + ), + backend_id="command", + backend=BackendProvenance( + name="command", + version="1", + config_digest="0" * 64, + ), + objective_spec=specification, + objective=ObjectiveResult(value=1.0, feasible=True), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + assert record.schema_version == 2 + assert record.request.candidate.version == "snapshot:candidate-1" + + with pytest.raises(ValidationError, match="must not be before"): + record.model_copy( + update={"completed_at": created_at - timedelta(seconds=1)} + ).__class__.model_validate( + record.model_copy( + update={"completed_at": created_at - timedelta(seconds=1)} + ).model_dump() + ) + + +def test_evaluation_plan_models_selection_visibility_and_principal_budgets(): + validation = EvaluationSet(name="validation", partition="validation") + test = EvaluationSet(name="test", partition="test") + plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition( + evaluation_set=validation, + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.AGGREGATE, + ), + agent_budget=EvaluationBudget( + backend_id="command", + evaluation_set_key=validation.budget_key("command"), + principal=EvaluationPrincipal.AGENT, + total_runs=10, + ), + system_budget=EvaluationBudget( + backend_id="command", + evaluation_set_key=validation.budget_key("command"), + principal=EvaluationPrincipal.SYSTEM, + total_runs=100, + ), + ), + EvaluationDefinition( + evaluation_set=test, + access=EvaluationAccessPolicy( + agent_can_evaluate=False, + agent_visible=False, + disclosure=DisclosureLevel.NONE, + ), + ), + ], + selection_evaluation="validation", + final_evaluation="test", + ) + + assert plan.selection.evaluation_set == validation + assert plan.final.evaluation_set == test + assert {budget.principal for budget in plan.budgets} == { + EvaluationPrincipal.AGENT, + EvaluationPrincipal.SYSTEM, + } + + with pytest.raises(ValidationError, match="final evaluation must be"): + EvaluationPlan( + evaluations=[EvaluationDefinition(evaluation_set=test)], + selection_evaluation="test", + final_evaluation="test", + ) diff --git a/vero/tests/test_v05_evaluation_objective.py b/vero/tests/test_v05_evaluation_objective.py new file mode 100644 index 00000000..bfa312df --- /dev/null +++ b/vero/tests/test_v05_evaluation_objective.py @@ -0,0 +1,223 @@ +from datetime import UTC, datetime, timedelta + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + BackendProvenance, + BackendRegistry, + CaseError, + CaseResult, + CaseStatus, + ConstraintOperator, + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationCost, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + EvaluationSummary, + MetricAggregation, + MetricConstraint, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, + UnknownBackendError, + compare_evaluation_records, + evaluate_objective, + project_evaluation, + resolve_metric, + select_best_evaluation, +) + + +def report(status=EvaluationStatus.SUCCESS): + return EvaluationReport( + status=status, + metrics={"latency": 10.0, "correct": 1.0}, + cases=[ + CaseResult( + case_id="1", + status=CaseStatus.SUCCESS, + metrics={"score": 1.0}, + ), + CaseResult( + case_id="2", + status=CaseStatus.SUCCESS, + metrics={"score": 3.0}, + ), + CaseResult( + case_id="3", + status=CaseStatus.ERROR, + metrics={"score": 100.0}, + errors=[CaseError(message="failed", terminal=True)], + ), + CaseResult( + case_id="4", + status=CaseStatus.SKIPPED, + metrics={"score": 200.0}, + ), + ], + ) + + +@pytest.mark.parametrize( + ("aggregation", "expected"), + [ + (MetricAggregation.MEAN, 2.0), + (MetricAggregation.MEDIAN, 2.0), + (MetricAggregation.MIN, 1.0), + (MetricAggregation.MAX, 3.0), + ], +) +def test_case_metric_aggregations_use_only_successful_cases(aggregation, expected): + assert resolve_metric( + report(), MetricSelector(metric="score", aggregation=aggregation) + ) == expected + + +def test_case_metric_aggregation_penalizes_failed_and_missing_cases(): + selector = MetricSelector( + metric="score", + aggregation=MetricAggregation.MEAN, + case_failure_value=0.0, + ) + + assert resolve_metric(report(), selector) == 1.0 + + +@pytest.mark.parametrize( + ("operator", "threshold", "feasible"), + [ + (ConstraintOperator.EQ, 1.0, True), + (ConstraintOperator.NE, 0.0, True), + (ConstraintOperator.LT, 2.0, True), + (ConstraintOperator.LTE, 1.0, True), + (ConstraintOperator.GT, 0.0, True), + (ConstraintOperator.GTE, 1.0, True), + (ConstraintOperator.EQ, 0.0, False), + ], +) +def test_objective_supports_every_constraint_operator(operator, threshold, feasible): + specification = ObjectiveSpec( + selector=MetricSelector(metric="latency"), + direction="minimize", + constraints=[ + MetricConstraint( + selector=MetricSelector(metric="correct"), + operator=operator, + value=threshold, + ) + ], + ) + + result = evaluate_objective(report(), specification) + + assert result.value == 10.0 + assert result.feasible is feasible + assert len(result.violations) == (0 if feasible else 1) + + +def make_record( + candidate_id: str, + value: float, + *, + feasible: bool = True, + direction: str = "minimize", + candidate_created_at: datetime | None = None, +) -> EvaluationRecord: + specification = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction=direction, + ) + created_at = datetime(2026, 1, 1, tzinfo=UTC) + return EvaluationRecord( + id=f"evaluation:{candidate_id}", + request=EvaluationRequest( + candidate=Candidate( + id=candidate_id, + version=f"version:{candidate_id}", + created_at=candidate_created_at or created_at, + ) + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": value}, + ), + backend_id="default", + backend=BackendProvenance( + name="fake", + version="1", + config_digest="0" * 64, + ), + objective_spec=specification, + objective=ObjectiveResult(value=value, feasible=feasible), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + +def test_selection_respects_direction_feasibility_and_tie_breaks(): + assert ( + select_best_evaluation( + [ + make_record("a", 2.0, direction="maximize"), + make_record("b", 1.0, direction="maximize"), + ] + ).request.candidate.id + == "a" + ) + feasible = make_record("feasible", 100.0) + infeasible = make_record("infeasible", 1.0, feasible=False) + assert compare_evaluation_records(feasible, infeasible) > 0 + assert select_best_evaluation([infeasible]) is None + + created_at = datetime(2026, 1, 1, tzinfo=UTC) + assert ( + select_best_evaluation( + [ + make_record("b", 1.0, candidate_created_at=created_at), + make_record("a", 1.0, candidate_created_at=created_at), + ] + ).request.candidate.id + == "a" + ) + + +def test_disclosure_projections_exclude_case_details(): + record = make_record("a", 1.0) + aggregate = project_evaluation(record, DisclosureLevel.AGGREGATE) + hidden = project_evaluation(record, DisclosureLevel.NONE) + + assert isinstance(aggregate, EvaluationSummary) + assert aggregate.candidate_id == "a" + assert "cases" not in aggregate.model_dump() + assert isinstance(hidden, EvaluationAcknowledgement) + assert set(hidden.model_dump()) == {"evaluation_id", "status"} + + +class FakeBackend: + provenance = BackendProvenance( + name="fake", + version="1", + config_digest="0" * 64, + ) + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: + return EvaluationCost() + + async def evaluate(self, *, context, request): + return EvaluationReport(status=EvaluationStatus.SUCCESS) + + +def test_backend_registry_is_explicit_and_rejects_unknown_ids(): + backend = FakeBackend() + registry = BackendRegistry({"trusted": backend}) + + assert registry.resolve("trusted") is backend + with pytest.raises(UnknownBackendError): + registry.resolve("missing") + with pytest.raises(ValueError, match="already registered"): + registry.register("trusted", backend) diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py new file mode 100644 index 00000000..5c2302cb --- /dev/null +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -0,0 +1,904 @@ +from __future__ import annotations + +import asyncio +import json +import threading +from contextlib import asynccontextmanager +from datetime import UTC, datetime, timedelta +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + AgentSelectionMode, + BackendProvenance, + BackendRegistry, + BudgetLedger, + CaseError, + CaseResult, + CaseStatus, + CaseRange, + DisclosureLevel, + EvaluationAuthorization, + EvaluationAccessPolicy, + EvaluationBudget, + EvaluationBudgetExceeded, + EvaluationCost, + EvaluationDatabase, + EvaluationDeniedError, + EvaluationEngine, + EvaluationLimits, + EvaluationDefinition, + EvaluationExecutionError, + EvaluationRecord, + EvaluationPlan, + EvaluationPrincipal, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + EvaluationStore, + Evaluator, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, + allow_all_evaluations, + authorize_evaluation_plan, +) +from vero.evaluation import persistence +import vero.evaluation.budget as budget_module +from vero.workspace import Workspace + + +class StubWorkspace(Workspace): + def __init__(self, root: Path, version: str = "main", *, dirty: bool = False): + root.mkdir(parents=True, exist_ok=True) + self._root = root + self._version = version + self._dirty = dirty + self.at_calls: list[str] = [] + self.copy_calls: list[str] = [] + + @property + def sandbox(self): + return None + + @property + def root(self) -> str: + return str(self._root) + + @property + def project_path(self) -> str: + return str(self._root) + + @property + def name(self) -> str: + return "workspace" + + async def current_version(self) -> str: + return self._version + + async def save(self, message: str = "Save") -> str: + return self._version + + async def restore(self, version_id: str, message: str | None = None) -> str: + self._version = version_id + return version_id + + async def diff(self, from_version=None, to_version=None) -> str: + return "" + + async def log(self, max_count=10, since_version=None) -> str: + return "" + + async def is_ancestor(self, version_a: str, version_b: str) -> bool: + return True + + async def copy(self, name=None, from_version=None): + return StubWorkspace(self._root, from_version or self._version) + + @asynccontextmanager + async def temp_copy(self, from_version=None): + version = from_version or self._version + self.copy_calls.append(version) + yield StubWorkspace(self._root, version) + + @asynccontextmanager + async def at(self, version_id: str): + previous = self._version + self.at_calls.append(version_id) + self._version = version_id + try: + yield + finally: + self._version = previous + + async def is_dirty(self) -> bool: + return self._dirty + + +class StubCandidateRepository: + family = "stub" + + def __init__(self, workspace: StubWorkspace): + self.workspace = workspace + self.checkout_calls: list[str] = [] + + @asynccontextmanager + async def checkout(self, candidate, *, sandbox, name=None): + self.checkout_calls.append(candidate.version) + yield StubWorkspace(self.workspace._root, candidate.version) + + +class StubBackend: + def __init__( + self, + *, + report: EvaluationReport | None = None, + cost: EvaluationCost | None = None, + error: Exception | None = None, + ): + self.report = report or EvaluationReport(status=EvaluationStatus.SUCCESS) + self.cost = cost or EvaluationCost(cases=0) + self.error = error + self.resolve_calls = 0 + self.evaluate_calls = 0 + self.running_manifests: list[dict] = [] + + @property + def provenance(self) -> BackendProvenance: + return BackendProvenance( + name="stub", + version="1", + config_digest="0" * 64, + ) + + async def resolve_cost(self, evaluation_set: EvaluationSet) -> EvaluationCost: + self.resolve_calls += 1 + return self.cost + + async def evaluate(self, *, context, request): + self.evaluate_calls += 1 + self.running_manifests.append( + json.loads((context.result_dir / "evaluation.json").read_text()) + ) + await context.case_store.save( + CaseResult(case_id="checkpoint", status=CaseStatus.SUCCESS) + ) + if self.error is not None: + raise self.error + return self.report + + +def request(version: str = "candidate") -> EvaluationRequest: + return EvaluationRequest( + candidate=Candidate( + id=f"id:{version}", + version=version, + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ), + evaluation_set=EvaluationSet(name="performance"), + ) + + +def evaluator(tmp_path: Path, workspace: StubWorkspace) -> Evaluator: + runtime = Evaluator( + candidate_repository=StubCandidateRepository(workspace), + sandbox=workspace.sandbox, + session_dir=tmp_path / "sessions" / "session", + ) + return runtime + + +def record(candidate_id: str = "candidate") -> EvaluationRecord: + created_at = datetime(2026, 1, 1, tzinfo=UTC) + specification = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + return EvaluationRecord( + id=f"evaluation:{candidate_id}", + request=EvaluationRequest( + candidate=Candidate( + id=candidate_id, + version=f"version:{candidate_id}", + created_at=created_at, + ) + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 1.0}, + cases=[ + CaseResult( + case_id="case/with/path-characters", + status=CaseStatus.SUCCESS, + metrics={"score": 1.0}, + ) + ], + ), + backend_id="default", + backend=BackendProvenance( + name="stub", + version="1", + config_digest="0" * 64, + ), + objective_spec=specification, + objective=ObjectiveResult(value=1.0, feasible=True), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + +@pytest.mark.asyncio +async def test_store_splits_cases_from_manifest_and_round_trips(tmp_path: Path): + value = record() + store = EvaluationStore(tmp_path / value.id) + + await store.save(value) + + manifest = json.loads(store.manifest_path.read_text()) + assert manifest["schema_version"] == 1 + assert manifest["lifecycle"] == "complete" + assert manifest["report"]["cases"] == [] + assert manifest["case_files"][0]["case_id"] == "case/with/path-characters" + assert "/" not in Path(manifest["case_files"][0]["path"]).name + assert store.load() == value + + +@pytest.mark.asyncio +async def test_store_preserves_principal_across_reconstruction(tmp_path: Path): + value = record().model_copy(update={"principal": EvaluationPrincipal.ADMIN}) + store = EvaluationStore(tmp_path / value.id) + + await store.save(value) + + # Reconstructing from the source-of-truth directory must keep the real + # provenance, not silently default it to SYSTEM. + assert store.load().principal == EvaluationPrincipal.ADMIN + assert store.load() == value + + +def test_running_manifest_is_not_a_completed_record(tmp_path: Path): + value = record() + store = EvaluationStore(tmp_path / value.id) + store.write_running( + evaluation_id=value.id, + request=value.request, + backend_id=value.backend_id, + backend=value.backend, + objective_spec=value.objective_spec, + created_at=value.created_at, + ) + + with pytest.raises(ValueError, match="invalid evaluation manifest"): + store.load() + + +def test_atomic_json_write_closes_descriptor_when_fdopen_fails( + tmp_path: Path, + monkeypatch, +): + closed = [] + real_close = persistence.os.close + + def fail_fdopen(*_args, **_kwargs): + raise RuntimeError("fdopen failed") + + def tracked_close(descriptor): + closed.append(descriptor) + real_close(descriptor) + + monkeypatch.setattr(persistence.os, "fdopen", fail_fdopen) + monkeypatch.setattr(persistence.os, "close", tracked_close) + + with pytest.raises(RuntimeError, match="fdopen failed"): + persistence._atomic_write_json(tmp_path / "value.json", {"value": 1}) + + assert len(closed) == 1 + assert not list(tmp_path.glob("*.tmp")) + + +def test_database_round_trips_schema_one_and_distinguishes_empty_filter(tmp_path: Path): + database = EvaluationDatabase(id="session") + value = record() + database.add_evaluation(value) + path = tmp_path / "database.json" + database.save_to_file(path) + + restored = EvaluationDatabase.load_from_file(path) + + assert restored.get_evaluations() == [value] + assert restored.get_evaluations([]) == [] + assert restored.get_best(value.objective_spec) == value + assert json.loads(path.read_text())["schema_version"] == 1 + + +@pytest.mark.asyncio +async def test_database_repairs_crash_window_from_completed_evaluations( + tmp_path: Path, +): + database_path = tmp_path / "database.json" + EvaluationDatabase(id="session").save_to_file(database_path) + value = record() + await EvaluationStore(tmp_path / "evaluations" / value.id).save(value) + + restored = EvaluationDatabase.load_reconciled( + database_path=database_path, + evaluations_dir=tmp_path / "evaluations", + database_id="session", + ) + + assert restored.get_evaluation(value.id) == value + assert ( + EvaluationDatabase.load_from_file(database_path).get_evaluation(value.id) + == value + ) + + +def test_database_reconciliation_ignores_running_evaluations(tmp_path: Path): + value = record() + store = EvaluationStore(tmp_path / "evaluations" / value.id) + store.write_running( + evaluation_id=value.id, + request=value.request, + backend_id=value.backend_id, + backend=value.backend, + objective_spec=value.objective_spec, + created_at=value.created_at, + ) + + restored = EvaluationDatabase.load_reconciled( + database_path=tmp_path / "database.json", + evaluations_dir=tmp_path / "evaluations", + database_id="session", + ) + + assert restored.evaluations == {} + + +def test_database_rejects_conflicting_candidate_identity(): + database = EvaluationDatabase(id="session") + database.add_evaluation(record("same")) + conflicting = record("other").model_copy( + update={ + "request": EvaluationRequest( + candidate=Candidate( + id="same", + version="different-version", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + ) + } + ) + + with pytest.raises(ValueError, match="different identity"): + database.add_evaluation(conflicting) + + +@pytest.mark.asyncio +async def test_evaluator_runs_at_candidate_version_and_persists(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend( + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"latency": 2.0}, + ) + ) + specification = ObjectiveSpec( + selector=MetricSelector(metric="latency"), + direction="minimize", + ) + + runtime = evaluator(tmp_path, workspace) + value = await runtime.evaluate( + backend_id="command", + backend=backend, + request=request(), + objective_spec=specification, + ) + + assert runtime.candidate_repository.checkout_calls == ["candidate"] + assert backend.running_manifests[0]["lifecycle"] == "running" + assert value.objective.value == 2.0 + result_dir = runtime.evaluations_dir / value.id + assert EvaluationStore(result_dir).load() == value + + +@pytest.mark.asyncio +async def test_evaluator_marks_reports_invalid_at_the_error_rate_threshold( + tmp_path: Path, +): + workspace = StubWorkspace(tmp_path / "repo") + cases = [ + CaseResult( + case_id=f"success-{index}", + status=CaseStatus.SUCCESS, + metrics={"score": 1.0}, + ) + for index in range(9) + ] + cases.append( + CaseResult( + case_id="error", + status=CaseStatus.ERROR, + errors=[CaseError(message="failed", terminal=True)], + ) + ) + backend = StubBackend( + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 1.0}, + cases=cases, + ) + ) + + value = await evaluator(tmp_path, workspace).evaluate( + backend_id="default", + backend=backend, + request=request(), + objective_spec=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + failure_value=0.0, + ), + ) + + assert value.report.status == EvaluationStatus.INVALID + assert value.report.metrics["error_rate"] == pytest.approx(0.1) + assert ( + value.report.diagnostics[-1].code + == "infrastructure_invalidity_threshold_exceeded" + ) + assert value.objective == ObjectiveResult(value=0.0, feasible=False) + + +@pytest.mark.asyncio +async def test_error_rate_threshold_can_be_disabled(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend( + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 1.0, "error_rate": 1.0}, + ) + ) + evaluation_request = request().model_copy( + update={"limits": EvaluationLimits(error_rate_threshold=None)} + ) + + value = await evaluator(tmp_path, workspace).evaluate( + backend_id="default", + backend=backend, + request=evaluation_request, + ) + + assert value.report.status == EvaluationStatus.SUCCESS + + +@pytest.mark.asyncio +async def test_evaluator_uses_isolated_copy(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend() + + runtime = evaluator(tmp_path, workspace) + await runtime.evaluate( + backend_id="default", + backend=backend, + request=request(), + ) + + assert runtime.candidate_repository.checkout_calls == ["candidate"] + assert workspace.copy_calls == [] + assert workspace.at_calls == [] + + +@pytest.mark.asyncio +async def test_backend_exception_is_recorded_then_raised(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend(error=RuntimeError("boom")) + runtime = evaluator(tmp_path, workspace) + + with pytest.raises(EvaluationExecutionError) as captured: + await runtime.evaluate( + backend_id="default", + backend=backend, + request=request(), + ) + + failure = EvaluationStore( + runtime.evaluations_dir / captured.value.evaluation_id + ).load() + assert failure.report.status == EvaluationStatus.FAILED + assert failure.report.diagnostics[0].code == "backend_error" + + +@pytest.mark.asyncio +async def test_engine_indexes_a_durable_backend_exception(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend(error=RuntimeError("boom")) + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=database, + database_path=tmp_path / "database.json", + authorization_resolver=allow_all_evaluations, + ) + + with pytest.raises(EvaluationExecutionError) as captured: + await engine.evaluate_record( + backend_id="default", + request=request(), + ) + + failure = database.get_evaluation(captured.value.evaluation_id) + assert failure is not None + assert failure.report.status == EvaluationStatus.FAILED + assert ( + EvaluationDatabase.load_from_file(tmp_path / "database.json").get_evaluation( + failure.id + ) + == failure + ) + + +@pytest.mark.asyncio +async def test_budget_ledger_reserves_and_restores(tmp_path: Path): + evaluation_set = EvaluationSet(name="performance") + path = tmp_path / "budget.json" + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="command", + evaluation_set_key=evaluation_set.budget_key("command"), + total_runs=2, + total_cases=10, + ) + ], + path=path, + ) + + remaining = await ledger.reserve( + "command", evaluation_set, EvaluationCost(runs=1, cases=7) + ) + + assert remaining.remaining_runs == 1 + assert remaining.remaining_cases == 3 + assert BudgetLedger.load(path).get("command", evaluation_set) == remaining + + with pytest.raises(EvaluationBudgetExceeded): + await ledger.reserve("command", evaluation_set, EvaluationCost(runs=1, cases=4)) + + +@pytest.mark.asyncio +async def test_budget_reservation_rolls_back_when_cancelled( + tmp_path: Path, + monkeypatch, +): + evaluation_set = EvaluationSet(name="performance") + path = tmp_path / "budget.json" + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="command", + evaluation_set_key=evaluation_set.budget_key("command"), + total_runs=2, + ) + ], + path=path, + ) + ledger.save() + started = threading.Event() + release = threading.Event() + real_write = budget_module._atomic_write_json + + def delayed_write(write_path, value): + started.set() + assert release.wait(timeout=5) + real_write(write_path, value) + + monkeypatch.setattr(budget_module, "_atomic_write_json", delayed_write) + reservation = asyncio.create_task( + ledger.reserve("command", evaluation_set, EvaluationCost()) + ) + assert await asyncio.to_thread(started.wait, 5) + reservation.cancel() + release.set() + + with pytest.raises(asyncio.CancelledError): + await reservation + + # The reservation is atomic: a run cancelled while the charge was being + # written keeps its full budget rather than leaking it, and disk and memory + # agree on the rolled-back state. + in_memory = ledger.get("command", evaluation_set) + on_disk = BudgetLedger.load(path).get("command", evaluation_set) + assert in_memory is not None + assert in_memory.remaining_runs == 2 + assert on_disk == in_memory + + +@pytest.mark.asyncio +async def test_cancelled_evaluation_is_terminal_indexed_and_refunded(tmp_path: Path): + class BlockingBackend(StubBackend): + async def evaluate(self, *, context, request): + self.evaluate_calls += 1 + await asyncio.Event().wait() + + workspace = StubWorkspace(tmp_path / "repo") + backend = BlockingBackend() + evaluation_set = request().evaluation_set + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="default", + evaluation_set_key=evaluation_set.budget_key("default"), + total_runs=1, + ) + ], + path=tmp_path / "budgets.json", + ) + ledger.save() + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=database, + database_path=tmp_path / "database.json", + budget_ledger=ledger, + authorization_resolver=allow_all_evaluations, + ) + evaluation = asyncio.create_task( + engine.evaluate_record( + backend_id="default", + request=request(), + ) + ) + while backend.evaluate_calls == 0: + await asyncio.sleep(0) + evaluation.cancel() + + with pytest.raises(asyncio.CancelledError): + await evaluation + + assert len(database.evaluations) == 1 + cancelled = next(iter(database.evaluations.values())) + assert cancelled.report.status == EvaluationStatus.CANCELLED + assert cancelled.report.diagnostics[0].code == "evaluation_cancelled" + assert ( + EvaluationStore( + evaluator(tmp_path, workspace).evaluations_dir / cancelled.id + ).load() + == cancelled + ) + remaining = ledger.get("default", evaluation_set) + assert remaining is not None + assert remaining.remaining_runs == 1 + + +@pytest.mark.asyncio +async def test_finalization_drains_admitted_agent_evaluations_and_closes_admission( + tmp_path: Path, +): + class BlockingBackend(StubBackend): + def __init__(self): + super().__init__( + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 0.75}, + ) + ) + self.started = asyncio.Event() + self.release = asyncio.Event() + + async def evaluate(self, *, context, request): + self.evaluate_calls += 1 + self.started.set() + await self.release.wait() + return self.report + + workspace = StubWorkspace(tmp_path / "repo") + backend = BlockingBackend() + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=database, + authorization_resolver=allow_all_evaluations, + ) + evaluation = asyncio.create_task( + engine.evaluate_record(backend_id="default", request=request()) + ) + await backend.started.wait() + + drain = asyncio.create_task(engine.quiesce_agent_evaluations(timeout_seconds=1.0)) + await asyncio.sleep(0) + assert not drain.done() + with pytest.raises(EvaluationDeniedError, match="finalization has started"): + await engine.evaluate_record(backend_id="default", request=request("late")) + + backend.release.set() + + assert await drain == 1 + assert (await evaluation).report.metrics == {"score": 0.75} + assert len(database.evaluations) == 1 + admin = await engine.evaluate_record( + backend_id="default", + request=request("admin"), + principal=EvaluationPrincipal.ADMIN, + ) + assert admin.request.candidate.version == "admin" + + +@pytest.mark.asyncio +async def test_finalization_cancels_an_agent_evaluation_after_drain_timeout( + tmp_path: Path, +): + class BlockingBackend(StubBackend): + def __init__(self): + super().__init__() + self.started = asyncio.Event() + + async def evaluate(self, *, context, request): + self.evaluate_calls += 1 + self.started.set() + await asyncio.Event().wait() + + workspace = StubWorkspace(tmp_path / "repo") + backend = BlockingBackend() + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=database, + authorization_resolver=allow_all_evaluations, + ) + evaluation = asyncio.create_task( + engine.evaluate_record(backend_id="default", request=request()) + ) + await backend.started.wait() + + assert ( + await engine.quiesce_agent_evaluations( + timeout_seconds=0.01, + cancellation_grace_seconds=1.0, + ) + == 1 + ) + with pytest.raises(asyncio.CancelledError): + await evaluation + + assert len(database.evaluations) == 1 + cancelled = next(iter(database.evaluations.values())) + assert cancelled.report.status == EvaluationStatus.CANCELLED + + +@pytest.mark.asyncio +async def test_plan_authorization_separates_agent_and_system_selection_rights( + tmp_path: Path, +): + canonical = EvaluationSet( + name="validation", + selection=CaseRange(stop=10), + ) + plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition( + evaluation_set=canonical, + access=EvaluationAccessPolicy( + agent_selection=AgentSelectionMode.FIXED, + disclosure=DisclosureLevel.AGGREGATE, + ), + ) + ], + selection_evaluation="validation", + ) + workspace = StubWorkspace(tmp_path / "repo") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": StubBackend()}), + database=EvaluationDatabase(id="session"), + authorization_resolver=authorize_evaluation_plan(plan), + ) + subset_request = request().model_copy( + update={ + "evaluation_set": canonical.model_copy( + update={"selection": CaseRange(stop=2)} + ) + } + ) + + agent = await engine.authorize( + "default", + subset_request, + EvaluationPrincipal.AGENT, + ) + system = await engine.authorize( + "default", + subset_request, + EvaluationPrincipal.SYSTEM, + ) + + assert agent.may_evaluate is False + assert agent.viewable is True + assert system.may_evaluate is True + + +@pytest.mark.asyncio +async def test_engine_denial_stops_before_cost_and_evaluation(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend() + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=database, + ) + + with pytest.raises(EvaluationDeniedError, match="private set"): + await engine.evaluate( + backend_id="default", + request=request(), + authorization=EvaluationAuthorization( + may_evaluate=False, + reason="private set", + ), + ) + + assert backend.resolve_calls == 0 + assert backend.evaluate_calls == 0 + assert database.evaluations == {} + + +@pytest.mark.asyncio +async def test_engine_denies_by_default_without_authorization(tmp_path: Path): + backend = StubBackend() + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, StubWorkspace(tmp_path / "repo")), + backends=BackendRegistry({"default": backend}), + database=EvaluationDatabase(id="session"), + ) + + with pytest.raises( + EvaluationDeniedError, + match="authorization was not configured", + ): + await engine.evaluate_record(backend_id="default", request=request()) + + assert backend.resolve_calls == 0 + assert backend.evaluate_calls == 0 + + +@pytest.mark.asyncio +async def test_engine_selects_backend_persists_and_projects(tmp_path: Path): + workspace = StubWorkspace(tmp_path / "repo") + first = StubBackend( + report=EvaluationReport(status=EvaluationStatus.SUCCESS, metrics={"value": 1}) + ) + second = StubBackend( + report=EvaluationReport(status=EvaluationStatus.SUCCESS, metrics={"value": 2}) + ) + database = EvaluationDatabase(id="session") + database_path = tmp_path / "database.json" + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"first": first, "second": second}), + database=database, + database_path=database_path, + ) + + summary = await engine.evaluate( + backend_id="second", + request=request(), + authorization=EvaluationAuthorization( + may_evaluate=True, + meter_budget=False, + disclosure=DisclosureLevel.AGGREGATE, + ), + ) + + assert first.resolve_calls == 0 + assert second.resolve_calls == 1 + assert summary.metrics == {"value": 2.0} + assert len(database.evaluations) == 1 + assert database_path.exists() diff --git a/vero/tests/test_v05_evaluation_tools.py b/vero/tests/test_v05_evaluation_tools.py new file mode 100644 index 00000000..b0ab0721 --- /dev/null +++ b/vero/tests/test_v05_evaluation_tools.py @@ -0,0 +1,105 @@ +from __future__ import annotations + +import json + +import pytest + +from vero.evaluation import ( + EvaluationAcknowledgement, + EvaluationBudget, + EvaluationBudgetExceeded, + EvaluationSet, + EvaluationStatus, +) +from vero.tools.evaluation import EvaluationTools +from vero.tools.utils import get_tools_from_class + + +class StubEvaluationGateway: + def __init__(self): + self.descriptions: list[str] = [] + self.remaining_runs = 2 + + async def evaluate( + self, + *, + evaluation, + selection=None, + candidate_id=None, + description="Evaluate agent checkpoint", + ): + assert evaluation == "validation" + assert selection is None + assert candidate_id is None + self.descriptions.append(description) + self.remaining_runs -= 1 + return EvaluationAcknowledgement( + evaluation_id="evaluation-1", + status=EvaluationStatus.SUCCESS, + ) + + def budgets(self): + return { + "validation": EvaluationBudget( + backend_id="command", + evaluation_set_key=EvaluationSet(name="validation").budget_key( + "command" + ), + total_runs=2, + remaining_runs=self.remaining_runs, + ) + } + + +@pytest.mark.asyncio +async def test_evaluation_tools_expose_only_scoped_feedback_and_budget(): + gateway = StubEvaluationGateway() + tools = EvaluationTools(evaluation=gateway) + + result = json.loads( + await tools.evaluate( + evaluation="validation", + description="Try vectorized implementation", + ) + ) + budget = json.loads(tools.get_evaluation_budgets()) + + assert result == { + "evaluation_id": "evaluation-1", + "status": "success", + } + assert gateway.descriptions == ["Try vectorized implementation"] + assert budget["validation"]["remaining_runs"] == 1 + assert {tool.__name__ for tool in get_tools_from_class(tools)} == { + "evaluate", + "get_evaluation_budgets", + } + + +@pytest.mark.asyncio +async def test_evaluate_returns_clean_result_when_evaluation_budget_is_spent(): + class ExhaustedGateway(StubEvaluationGateway): + async def evaluate(self, **_kwargs): + raise EvaluationBudgetExceeded("evaluation run budget exhausted") + + result = json.loads( + await EvaluationTools(evaluation=ExhaustedGateway()).evaluate( + evaluation="validation", + ) + ) + + # A benign, non-terminating signal the agent can absorb — not a raised error. + assert result["status"] == "evaluation_budget_exhausted" + assert "budget" in result["message"] + + +def test_evaluation_tools_report_unmetered_and_require_binding(): + class UnmeteredGateway(StubEvaluationGateway): + def budgets(self): + return {"validation": None} + + assert json.loads( + EvaluationTools(evaluation=UnmeteredGateway()).get_evaluation_budgets() + ) == {"validation": None} + with pytest.raises(RuntimeError, match="not bound"): + EvaluationTools().get_evaluation_budgets() diff --git a/vero/tests/test_v05_evolutionary_strategy.py b/vero/tests/test_v05_evolutionary_strategy.py new file mode 100644 index 00000000..2a329602 --- /dev/null +++ b/vero/tests/test_v05_evolutionary_strategy.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + EvaluationSet, + EvaluationStatus, + EvaluationSummary, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.optimization import EvolutionaryStrategy +from vero.optimization.models import OptimizationContext + +OBJ_MAX = ObjectiveSpec(selector=MetricSelector(metric="score"), direction="maximize") + + +def _summary(candidate_id: str, value: float | None, *, feasible: bool = True) -> EvaluationSummary: + return EvaluationSummary( + evaluation_id=f"eval-{candidate_id}", + candidate_id=candidate_id, + candidate_version=f"{candidate_id}-v", + backend_id="cmd", + evaluation_set=EvaluationSet(name="perf"), + status=EvaluationStatus.SUCCESS, + metrics={"score": value} if value is not None else {}, + objective=ObjectiveResult(value=value, feasible=feasible), + total_cases=1, + successful_cases=1, + errored_cases=0, + skipped_cases=0, + ) + + +def _context(*, candidates, evaluations=(), best=None, baseline=None) -> OptimizationContext: + baseline = baseline or next(iter(candidates.values())) + return OptimizationContext( + session_id="s", + round=0, + workspace=object(), # unused by the strategy + baseline=baseline, + evaluations=tuple(evaluations), + candidates=candidates, + best=best, + ) + + +@pytest.mark.asyncio +async def test_seeds_offspring_from_baseline_before_any_feasible_candidate(): + strat = EvolutionaryStrategy(objective=OBJ_MAX, num_offspring=3, seed=0) + baseline = Candidate(id="base", version="base-v") + ctx = _context(candidates={"base": baseline}, baseline=baseline) + + proposals = await strat.propose(ctx) + + assert len(proposals) == 3 + assert all(p.parent_id == "base" for p in proposals) + assert len({p.id for p in proposals}) == 3 # unique proposal ids + assert all(p.metadata["strategy"] == "evolutionary" for p in proposals) + assert all(p.metadata["generation"] == 0 for p in proposals) + + +@pytest.mark.asyncio +async def test_selects_parents_from_the_fittest_population(): + strat = EvolutionaryStrategy( + objective=OBJ_MAX, + num_offspring=6, + population_size=2, + tournament_size=1, # each pick is a uniform draw from the top-2 + seed=0, + ) + candidates = {cid: Candidate(id=cid, version=f"{cid}-v") for cid in ["base", "a", "b", "c"]} + evaluations = ( + _summary("base", 0.10), + _summary("a", 0.90), + _summary("b", 0.80), + _summary("c", 0.20), + _summary("d", None, feasible=False), # infeasible: excluded from the population + ) + ctx = _context( + candidates=candidates, + evaluations=evaluations, + best=candidates["a"], + baseline=candidates["base"], + ) + + proposals = await strat.propose(ctx) + + assert len(proposals) == 6 + # Population is the top-2 by value: {a: 0.9, b: 0.8}; the weaker/infeasible ones + # and the unknown "d" never become parents. + assert {p.parent_id for p in proposals} <= {"a", "b"} + assert "c" not in {p.parent_id for p in proposals} + + +@pytest.mark.asyncio +async def test_minimize_direction_prefers_lower_values(): + strat = EvolutionaryStrategy( + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), direction="minimize" + ), + num_offspring=5, + population_size=1, + tournament_size=1, + seed=0, + ) + candidates = {cid: Candidate(id=cid, version=f"{cid}-v") for cid in ["hi", "lo"]} + ctx = _context( + candidates=candidates, + evaluations=(_summary("hi", 9.0), _summary("lo", 1.0)), + best=candidates["lo"], + ) + + proposals = await strat.propose(ctx) + + # Under minimize, the single-slot population is the lowest value ("lo"). + assert {p.parent_id for p in proposals} == {"lo"} diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py new file mode 100644 index 00000000..5691ee3f --- /dev/null +++ b/vero/tests/test_v05_harbor_backend.py @@ -0,0 +1,875 @@ +from __future__ import annotations + +import json +import sys +from datetime import UTC, datetime +from pathlib import Path +from types import ModuleType, SimpleNamespace + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + CaseCheckpointStore, + CaseIds, + CaseRange, + CaseStatus, + EvaluationContext, + EvaluationLimits, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + RetryPolicy, +) +from vero.harbor import HarborBackend, HarborBackendConfig +from vero.sandbox import CommandResult, LocalSandbox + + +def _cases(path: Path) -> Path: + path.write_text( + json.dumps( + [ + {"id": "case-a", "task_name": "example/alpha"}, + {"id": "case-b", "task_name": "example/beta"}, + {"id": "case-c", "task_name": "example/gamma"}, + ] + ), + encoding="utf-8", + ) + return path + + +def _config(tmp_path: Path, **updates) -> HarborBackendConfig: + values = { + "task_source": "example/tasks@1.0", + "agent_import_path": "candidate.agent:Agent", + "cases_path": str(_cases(tmp_path / "cases.json")), + "harbor_requirement": "harbor==0.1.17", + "evaluation_set_name": "harbor-bench", + "partition": "test", + "uv_executable": sys.executable, + "infrastructure_max_attempts": 1, + "infrastructure_retry_delay_seconds": 0, + } + values.update(updates) + return HarborBackendConfig(**values) + + +def _request(selection=None) -> EvaluationRequest: + return EvaluationRequest( + candidate=Candidate( + id="candidate", + version="version", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ), + evaluation_set=EvaluationSet( + name="harbor-bench", + partition="test", + **({"selection": selection} if selection is not None else {}), + ), + limits=EvaluationLimits( + timeout_seconds=90, + max_concurrency=7, + retry=RetryPolicy.disabled(), + ), + ) + + +def test_backend_accepts_pinned_environment_extra(tmp_path): + config = _config( + tmp_path, + harbor_requirement="harbor[modal]==0.18.0", + ) + + assert config.harbor_requirement == "harbor[modal]==0.18.0" + + +def test_environment_default_routes_agent_through_gateway_on_openai(tmp_path): + config = _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-token", + ) + env = HarborBackend(config)._environment("eval-1") + + assert env["OPENAI_API_KEY"] == "gw-token" + assert env["OPENAI_BASE_URL"] == ( + "http://inference-gateway:8001/scopes/evaluation/eval-1/v1" + ) + assert "VERO_AGENT_INFERENCE_API_KEY" not in env + + +def test_environment_routes_task_services_to_upstream(tmp_path, monkeypatch): + monkeypatch.setenv("VERO_INFERENCE_UPSTREAM_API_KEY", "upstream-key") + monkeypatch.setenv("VERO_INFERENCE_UPSTREAM_BASE_URL", "https://upstream/v1") + config = _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-token", + task_services_use_upstream=True, + upstream_api_key_env="VERO_INFERENCE_UPSTREAM_API_KEY", + upstream_base_url_env="VERO_INFERENCE_UPSTREAM_BASE_URL", + ) + env = HarborBackend(config)._environment("eval-1") + + # task-owned eval services reach the real upstream via OPENAI_* + assert env["OPENAI_API_KEY"] == "upstream-key" + assert env["OPENAI_BASE_URL"] == "https://upstream/v1" + # the candidate agent keeps the metered gateway on dedicated vars + assert env["VERO_AGENT_INFERENCE_API_KEY"] == "gw-token" + assert env["VERO_AGENT_INFERENCE_BASE_URL"] == ( + "http://inference-gateway:8001/scopes/evaluation/eval-1/v1" + ) + + +def test_task_services_use_upstream_requires_gateway_and_upstream_env(tmp_path): + with pytest.raises(ValueError, match="requires an inference gateway"): + _config(tmp_path, task_services_use_upstream=True) + with pytest.raises(ValueError, match="requires upstream_api_key_env"): + _config( + tmp_path, + task_services_use_upstream=True, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-token", + ) + + +@pytest.mark.parametrize( + ("request_factory", "message"), + [ + ( + lambda: _request().model_copy( + update={ + "limits": _request().limits.model_copy( + update={"retry": RetryPolicy(max_attempts=2)} + ) + } + ), + "generic per-case retries", + ), + ( + lambda: _request().model_copy( + update={ + "limits": _request().limits.model_copy( + update={"case_timeout_seconds": 30} + ) + } + ), + "case timeout is fixed by the backend", + ), + (lambda: _request().model_copy(update={"seed": 7}), "evaluation seed"), + ], +) +def test_harbor_backend_rejects_unsupported_generic_controls( + tmp_path, request_factory, message +): + backend = HarborBackend(_config(tmp_path)) + + with pytest.raises(ValueError, match=message): + backend.validate_request(request_factory()) + + +class FakeSandbox(LocalSandbox): + def __init__( + self, + root: Path, + trials: dict[str, list[dict]], + result: CommandResult | None = None, + ): + super().__init__(root) + self.trials = trials + self.result = result or CommandResult("harbor output", "", 0) + self.command = None + self.cwd = None + self.timeout = None + self.env = None + self.run_as = None + self.chown_commands: list[list[str]] = [] + + async def run(self, command, cwd=None, timeout=30, env=None, run_as=None): + if isinstance(command, list) and command[:1] == ["chown"]: + # Record the harness-isolation provisioning without touching real uids. + self.chown_commands.append(command) + return CommandResult("", "", 0) + if not isinstance(command, list) or "--jobs-dir" not in command: + return await super().run( + command, cwd=cwd, timeout=timeout, env=env, run_as=run_as + ) + self.command = command + self.cwd = cwd + self.timeout = timeout + self.env = env + self.run_as = run_as + jobs_dir = Path(command[command.index("--jobs-dir") + 1]) + for task_name, attempts in self.trials.items(): + for index, attempt in enumerate(attempts): + trial_dir = ( + jobs_dir / f"job-{index}" / f"trial-{task_name.split('/')[-1]}" + ) + trial_dir.mkdir(parents=True, exist_ok=True) + payload = { + "task_name": task_name, + "trial_name": f"trial-{index}", + "finished_at": f"2026-01-01T00:00:0{index}Z", + **attempt, + } + agent_files = payload.pop("_agent_files", {}) + (trial_dir / "result.json").write_text(json.dumps(payload)) + for relative_path, content in agent_files.items(): + path = trial_dir / "agent" / relative_path + path.parent.mkdir(parents=True, exist_ok=True) + if isinstance(content, bytes): + path.write_bytes(content) + else: + path.write_text(content, encoding="utf-8") + return self.result + + +async def _context(tmp_path: Path, sandbox: FakeSandbox) -> EvaluationContext: + target = tmp_path / "target" + target.mkdir(exist_ok=True) + result_dir = tmp_path / "result" + artifact_dir = result_dir / "artifacts" + artifact_dir.mkdir(parents=True) + return EvaluationContext( + workspace=SimpleNamespace(project_path=str(target), sandbox=sandbox), + session_id="session", + evaluation_id="evaluation", + result_dir=result_dir, + artifact_dir=artifact_dir, + case_store=CaseCheckpointStore(result_dir / "cases"), + ) + + +@pytest.mark.asyncio +async def test_harbor_backend_resolves_canonical_case_selections(tmp_path): + backend = HarborBackend(_config(tmp_path)) + evaluation_set = EvaluationSet(name="harbor-bench", partition="test") + + assert (await backend.resolve_cost(evaluation_set)).cases == 3 + assert ( + await backend.resolve_cost( + evaluation_set.model_copy(update={"selection": CaseRange(start=1, stop=3)}) + ) + ).cases == 2 + assert ( + await backend.resolve_cost( + evaluation_set.model_copy( + update={"selection": CaseIds(ids=["case-c", "case-a"])} + ) + ) + ).cases == 2 + + with pytest.raises(ValueError, match="unknown Harbor case IDs"): + await backend.resolve_cost( + evaluation_set.model_copy(update={"selection": CaseIds(ids=["missing"])}) + ) + + +@pytest.mark.asyncio +async def test_harbor_backend_exports_complete_authorized_local_tasks(tmp_path): + task_source = tmp_path / "tasks" + task_source.mkdir() + (task_source / "metric.py").write_text("def score(): return 1\n") + cases_path = tmp_path / "local-cases.json" + cases_path.write_text( + json.dumps( + [ + {"id": "case-a", "task_name": "alpha"}, + {"id": "case-b", "task_name": "beta"}, + ] + ) + ) + for task_name in ("alpha", "beta"): + task = task_source / task_name + task.mkdir() + (task / "task.toml").write_text(f'[task]\nname="example/{task_name}"\n') + (task / "instruction.md").write_text(f"Question for {task_name}\n") + (task / "attachment.txt").write_text(f"attachment for {task_name}\n") + backend = HarborBackend( + _config( + tmp_path, + task_source=str(task_source), + cases_path=str(cases_path), + ) + ) + destination = tmp_path / "context" + destination.mkdir() + + await backend.export_case_resources( + evaluation_set=EvaluationSet( + name="harbor-bench", + partition="test", + selection=CaseIds(ids=["case-b"]), + ), + destination=str(destination), + sandbox=await LocalSandbox.create(root=tmp_path), + ) + + index = json.loads((destination / "index.json").read_text()) + assert index["task_source_exposed"] is True + assert [item["case_id"] for item in index["cases"]] == ["case-b"] + exported = destination / index["cases"][0]["path"] + assert (exported / "task.toml").is_file() + assert (exported / "instruction.md").read_text() == "Question for beta\n" + assert (exported / "attachment.txt").read_text() == "attachment for beta\n" + assert (destination / "dataset-files/metric.py").is_file() + assert not (destination / "tasks" / "alpha").exists() + assert destination.stat().st_mode & 0o005 == 0o005 + assert exported.stat().st_mode & 0o005 == 0o005 + assert (exported / "attachment.txt").stat().st_mode & 0o004 == 0o004 + + +@pytest.mark.asyncio +async def test_harbor_backend_maps_remote_download_results_by_request_order( + tmp_path, monkeypatch +): + class FakeTaskId: + def __init__(self, name: str): + self.name = name + + def get_name(self) -> str: + return self.name + + async def get_dataset_metadata(_client, _source): + return SimpleNamespace( + task_ids=[ + FakeTaskId("example/alpha"), + FakeTaskId("example/beta"), + FakeTaskId("example/gamma"), + ] + ) + + async def download_tasks(_client, *, task_ids, output_dir, export): + assert export is True + results = [] + for task_id in task_ids: + path = output_dir / task_id.get_name().split("/")[-1] + path.mkdir(parents=True) + (path / "instruction.md").write_text(task_id.get_name()) + results.append(SimpleNamespace(path=path)) + return SimpleNamespace(results=results) + + async def download_dataset_files(_client, _metadata, *, output_dir): + output_dir.mkdir() + path = output_dir / "metric.py" + path.write_text("def score(): return 1\n") + return [path] + + class PackageDatasetClient: + pass + + class TaskClient: + pass + + PackageDatasetClient.get_dataset_metadata = get_dataset_metadata + PackageDatasetClient.download_dataset_files = download_dataset_files + TaskClient.download_tasks = download_tasks + + modules = { + name: ModuleType(name) + for name in ( + "harbor", + "harbor.registry", + "harbor.registry.client", + "harbor.registry.client.package", + "harbor.tasks", + "harbor.tasks.client", + ) + } + modules[ + "harbor.registry.client.package" + ].PackageDatasetClient = PackageDatasetClient + modules["harbor.tasks.client"].TaskClient = TaskClient + for name, module in modules.items(): + monkeypatch.setitem(sys.modules, name, module) + backend = HarborBackend(_config(tmp_path)) + destination = tmp_path / "context" + destination.mkdir() + + await backend.export_case_resources( + evaluation_set=EvaluationSet( + name="harbor-bench", + partition="test", + selection=CaseIds(ids=["case-c", "case-a"]), + ), + destination=str(destination), + sandbox=await LocalSandbox.create(root=tmp_path), + ) + + index = json.loads((destination / "index.json").read_text()) + assert [item["case_id"] for item in index["cases"]] == ["case-c", "case-a"] + assert [ + (destination / item["path"] / "instruction.md").read_text() + for item in index["cases"] + ] == ["example/gamma", "example/alpha"] + assert (destination / "dataset-files/metric.py").is_file() + + +@pytest.mark.asyncio +async def test_harbor_backend_exposes_complete_successful_trial_records(tmp_path): + secret = "evaluation-scope-secret" + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + { + "verifier_result": {"rewards": {"reward": 1.0}}, + "_agent_files": { + "trajectory.json": json.dumps( + {"steps": [{"message": f"used {secret}"}]} + ), + "gaia-trace.jsonl": ( + '{"turn":1,"action":"search"}\n{"turn":2,"answer":"done"}\n' + ), + }, + } + ] + }, + ) + backend = HarborBackend(_config(tmp_path, environment={"EVALUATION_TOKEN": secret})) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a"])), + ) + + artifacts = report.cases[0].artifacts + assert {Path(artifact.path).name for artifact in artifacts} == { + "trajectory.json", + "gaia-trace.jsonl", + "result.json", + } + for artifact in artifacts: + content = (tmp_path / "result/artifacts" / artifact.path).read_text() + assert secret not in content + trajectory = next( + artifact + for artifact in artifacts + if Path(artifact.path).name == "trajectory.json" + ) + assert "[REDACTED]" in (tmp_path / "result/artifacts" / trajectory.path).read_text() + + +@pytest.mark.asyncio +async def test_harbor_backend_exposes_exact_failed_trial_result(tmp_path): + detail = "Invalid schema for function 'transcribe_audio': Missing 'language'." + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + { + "verifier_result": None, + "exception_info": { + "exception_type": "BadRequestError", + "exception_message": detail, + }, + } + ] + }, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a"])), + ) + + artifacts = report.cases[0].artifacts + result_artifact = next( + artifact for artifact in artifacts if Path(artifact.path).name == "result.json" + ) + result = json.loads( + (tmp_path / "result/artifacts" / result_artifact.path).read_text() + ) + assert result["exception_info"]["exception_message"] == detail + assert result_artifact.description.endswith("result.json") + + +@pytest.mark.asyncio +async def test_harbor_backend_scores_agent_crash_as_informative_task_failure(tmp_path): + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}], + "example/beta": [ + { + "verifier_result": None, + "exception_info": {"exception_type": "AgentCrash"}, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) + runtime_context = await _context(tmp_path, sandbox) + + report = await backend.evaluate( + context=runtime_context, + request=_request(CaseRange(stop=2)), + ) + + # A candidate whose own harness crashes is an informative task failure: + # scored at the failure value, a real SUCCESS sample that counts toward the + # mean, and NOT an infrastructure error. + assert report.status == EvaluationStatus.SUCCESS + assert report.metrics == {"score": 0.5, "error_rate": 0.0, "score_stddev": 0.5} + assert [case.status for case in report.cases] == [ + CaseStatus.SUCCESS, + CaseStatus.SUCCESS, + ] + assert report.cases[1].metrics["score"] == 0.0 + assert report.cases[1].output["error_category"] == "task_failure" + assert sandbox.command[:15] == [ + sys.executable, + "run", + "--python", + "3.12", + "--no-config", + "--no-env-file", + "--default-index", + "https://pypi.org/simple", + "--index-strategy", + "first-index", + "--project", + str(tmp_path / "target"), + "--with", + "harbor==0.1.17", + "harbor", + ] + assert sandbox.command.count("-i") == 2 + assert sandbox.command[sandbox.command.index("-n") + 1] == "7" + assert ( + sandbox.command[sandbox.command.index("--agent-timeout-multiplier") + 1] + == "0.3" + ) + assert sandbox.timeout == 90 + assert [artifact.path for artifact in report.artifacts] == [ + "harbor/stdout.log", + "harbor/stderr.log", + ] + checkpoints = await runtime_context.case_store.load_all() + assert [case.case_id for case in checkpoints] == ["case-a", "case-b"] + + +@pytest.mark.asyncio +async def test_harbor_backend_excludes_transient_infrastructure_from_aggregate( + tmp_path, +): + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}], + "example/beta": [ + { + "verifier_result": None, + "exception_info": {"exception_type": "openai.RateLimitError"}, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseRange(stop=2)), + ) + + # The rate-limited case is infrastructure: excluded from the mean (which is + # the sole successful case, 1.0) and reported as the error rate. + assert report.metrics["score"] == 1.0 + assert report.metrics["error_rate"] == 0.5 + assert [case.status for case in report.cases] == [ + CaseStatus.SUCCESS, + CaseStatus.ERROR, + ] + assert report.cases[1].output["error_category"] == "transient_infra" + assert report.cases[1].errors[0].code == "transient_infrastructure" + + +@pytest.mark.asyncio +async def test_harbor_backend_marks_inference_budget_exhaustion_invalid(tmp_path): + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}], + "example/beta": [ + { + "verifier_result": None, + "exception_info": { + "exception_type": "openai.RateLimitError", + # The gateway's distinct code survives in the body message + # even though the client collapses the type to a 429. + "message": "Error code: 429 - inference token budget " + "exhausted (code: budget_exhausted)", + }, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseRange(stop=2)), + ) + + # Budget exhaustion is terminating: the whole evaluation is INVALID with a + # distinct, non-retryable code — never a rate-limit, never a score of zero. + assert report.status == EvaluationStatus.INVALID + assert "score" not in report.metrics + assert any( + diagnostic.code == "inference_budget_exhausted" + for diagnostic in report.diagnostics + ) + + +@pytest.mark.asyncio +async def test_harbor_backend_treats_missing_coverage_as_infrastructure(tmp_path): + # Only alpha produces a trial; beta is dropped entirely by the sub-run. + sandbox = FakeSandbox( + tmp_path, + {"example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}]}, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseRange(stop=2)), + ) + + # The dropped case is infrastructure (excluded from the mean, counted as + # error rate) rather than a silent zero, and coverage loss is logged. + assert report.metrics["score"] == 1.0 + assert report.metrics["error_rate"] == 0.5 + assert [case.status for case in report.cases] == [ + CaseStatus.SUCCESS, + CaseStatus.ERROR, + ] + assert report.cases[1].output["error_category"] == "transient_infra" + assert any( + diagnostic.code == "harbor_incomplete_coverage" + for diagnostic in report.diagnostics + ) + + +@pytest.mark.asyncio +async def test_harbor_backend_runs_harness_as_unprivileged_user(tmp_path): + sandbox = FakeSandbox( + tmp_path, + {"example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}]}, + ) + backend = HarborBackend(_config(tmp_path, harness_user="harness")) + + await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseRange(stop=1)), + ) + + # `harbor run` drops to the unprivileged user, and its work dirs (workspace + + # staging) are handed to that user first so it never needs the trusted state. + assert sandbox.run_as == "harness" + assert sandbox.chown_commands, "expected the harness work dirs to be chowned" + for command in sandbox.chown_commands: + assert command[:3] == ["chown", "-R", "harness:harness"] + + +def test_harbor_config_rejects_harness_isolation_with_upstream_task_services(tmp_path): + # uid isolation cannot hide env vars, so the raw upstream key would still + # reach the isolated harness; the combination must be refused. + with pytest.raises(ValueError, match="harness_user cannot be combined"): + _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-token", + task_services_use_upstream=True, + upstream_api_key_env="VERO_INFERENCE_UPSTREAM_API_KEY", + harness_user="harness", + ) + + +@pytest.mark.asyncio +async def test_harbor_backend_routes_candidate_inference_through_scoped_gateway( + tmp_path, monkeypatch +): + monkeypatch.setenv("OPENAI_API_KEY", "raw-provider-key") + sandbox = FakeSandbox( + tmp_path, + {"example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}]}, + ) + backend = HarborBackend( + _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="evaluation-scope-token", + ) + ) + + await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a"])), + ) + + assert sandbox.env["OPENAI_API_KEY"] == "evaluation-scope-token" + assert sandbox.env["OPENAI_BASE_URL"] == ( + "http://inference-gateway:8001/scopes/evaluation/evaluation/v1" + ) + assert "raw-provider-key" not in sandbox.env.values() + + +@pytest.mark.asyncio +async def test_harbor_backend_matches_canonical_result_task_name(tmp_path): + cases_path = tmp_path / "canonical-cases.json" + cases_path.write_text( + json.dumps( + [ + { + "id": "local-task", + "task_name": "local-task", + "result_task_name": "org/local-task", + } + ] + ), + encoding="utf-8", + ) + sandbox = FakeSandbox( + tmp_path, + {"org/local-task": [{"verifier_result": {"rewards": {"reward": 1.0}}}]}, + ) + backend = HarborBackend(_config(tmp_path, cases_path=str(cases_path))) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(), + ) + + assert report.status == EvaluationStatus.SUCCESS + assert report.metrics["score"] == 1.0 + assert report.cases[0].output["task_name"] == "local-task" + assert report.cases[0].output["result_task_name"] == "org/local-task" + + +@pytest.mark.asyncio +async def test_harbor_backend_mean_counts_dead_attempts_as_failures(tmp_path): + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + {"verifier_result": {"rewards": {"pass": 1.0}}}, + { + "verifier_result": None, + "exception_info": {"exception_type": "TimeoutError"}, + }, + ] + }, + ) + backend = HarborBackend(_config(tmp_path, aggregate_attempts="mean")) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a"])), + ) + + assert report.metrics == {"score": 0.5, "error_rate": 0.0, "score_stddev": 0.0} + assert report.cases[0].metrics == { + "score": 0.5, + "n_attempts": 2.0, + "n_scored": 1.0, + } + + +@pytest.mark.asyncio +async def test_harbor_backend_fails_when_no_requested_trials_match(tmp_path): + secret = "sensitive-token" + sandbox = FakeSandbox( + tmp_path, + {}, + CommandResult("", f"runner failed with {secret}", 1), + ) + backend = HarborBackend(_config(tmp_path, environment={"TOKEN": secret})) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a"])), + ) + + assert report.status == EvaluationStatus.FAILED + assert report.diagnostics[0].code == "infrastructure_failure" + assert secret not in report.diagnostics[0].message + assert secret not in (tmp_path / "result/artifacts/harbor/stderr.log").read_text() + + +def test_harbor_backend_rejects_controlled_extra_flags(tmp_path): + with pytest.raises(ValueError, match="backend-controlled"): + _config(tmp_path, extra_args=["--jobs-dir=/tmp/forged"]) + + +def test_environment_routes_finalization_to_reserved_scope(tmp_path): + config = _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-eval", + inference_gateway_finalization_token="gw-fin", + ) + backend = HarborBackend(config) + + search = backend._environment("eval-1", finalization=False) + assert search["OPENAI_API_KEY"] == "gw-eval" + assert "/scopes/evaluation/eval-1/" in search["OPENAI_BASE_URL"] + + final = backend._environment("eval-1", finalization=True) + assert final["OPENAI_API_KEY"] == "gw-fin" + assert "/scopes/finalization/eval-1/" in final["OPENAI_BASE_URL"] + + +def test_environment_finalization_falls_back_when_no_reserved_token(tmp_path): + config = _config( + tmp_path, + inference_gateway_url="http://inference-gateway:8001", + inference_gateway_token="gw-eval", + ) + final = HarborBackend(config)._environment("eval-1", finalization=True) + assert final["OPENAI_API_KEY"] == "gw-eval" + assert "/scopes/evaluation/eval-1/" in final["OPENAI_BASE_URL"] + + +def test_retry_config_json_carries_editable_backoff(tmp_path): + backend = HarborBackend( + _config( + tmp_path, + max_retries=5, + retry_wait_multiplier=3.0, + retry_min_wait_seconds=2.0, + retry_max_wait_seconds=45.0, + ) + ) + + payload = json.loads(backend._retry_config_json()) + + assert payload == { + "retry": { + "max_retries": 5, + "wait_multiplier": 3.0, + "min_wait_sec": 2.0, + "max_wait_sec": 45.0, + } + } + + +def test_command_forwards_retry_config_and_max_retries(tmp_path): + backend = HarborBackend(_config(tmp_path, max_retries=5)) + + command = backend._command( + workspace="/work/agent", + request=_request(), + cases=[], + jobs_dir="/staging/jobs", + task_source="example/tasks@1.0", + local_task_source=False, + retry_config_path="/staging/retry-config.json", + ) + + # backoff arrives via the JobConfig snippet ... + assert "--config" in command + assert command[command.index("--config") + 1] == "/staging/retry-config.json" + # ... while the count stays a flag (harbor applies it over the --config base). + assert command[command.index("--max-retries") + 1] == "5" diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py new file mode 100644 index 00000000..e5122138 --- /dev/null +++ b/vero/tests/test_v05_harbor_build.py @@ -0,0 +1,651 @@ +from __future__ import annotations + +import json +import subprocess +import tomllib +from pathlib import Path + +import pytest +from pydantic import ValidationError +import yaml + +from vero.harbor import ( + AgentAccessSpec, + HarborBuildConfig, + HarborDeploymentConfig, + InferenceBudgetSpec, + InferenceGatewaySpec, + VerificationTargetSpec, + WandbSpec, + WorkspaceOverlaySpec, + compile_harbor_task, + load_harbor_build_config, +) + + +BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" + + +def _git(path: Path, *arguments: str) -> str: + result = subprocess.run( + ["git", *arguments], + cwd=path, + check=True, + text=True, + capture_output=True, + ) + return result.stdout.strip() + + +@pytest.mark.parametrize( + ("benchmark", "producer_models"), + [ + # gaia parametrizes the producer model via ${optimizer_model}; the + # default (no --param) resolves to gpt-5.4. + ("gaia", ["gpt-5.4"]), + ("swe-atlas-qna", ["gpt-5.4"]), + ("tau3", ["gpt-5.4"]), + ], +) +def test_canonical_benchmarks_isolate_upstream_inference_credentials( + benchmark, producer_models +): + # gaia is promoted to the top level; other task sets live under candidates/. + base = BENCHMARK_ROOT if benchmark == "gaia" else BENCHMARK_ROOT / "candidates" + config = load_harbor_build_config( + base / benchmark / "baseline" / "build.yaml" + ) + + assert config.inference_gateway is not None + assert not { + "OPENAI_API_KEY", + "OPENAI_BASE_URL", + "OPENAI_API_BASE", + }.intersection(config.secrets) + assert config.inference_gateway.upstream_api_key_env == "OPENAI_API_KEY" + assert config.inference_gateway.upstream_base_url_env == "OPENAI_BASE_URL" + assert config.inference_gateway.producer.allowed_models == producer_models + assert config.inference_gateway.producer.max_requests is None + assert config.inference_gateway.producer.max_tokens is None + assert config.inference_gateway.evaluation.allowed_models == [ + "gpt-5.4-mini-2026-03-17" + ] + assert config.inference_gateway.evaluation.max_requests == 15000 + assert config.inference_gateway.evaluation.max_tokens == 100000000 + + +def test_build_params_override_run_time_knobs_without_rebuild(): + path = BENCHMARK_ROOT / "gaia" / "baseline" / "build.yaml" + + default = load_harbor_build_config(path) + assert default.environment_name == "modal" + assert default.inference_gateway.producer.allowed_models == ["gpt-5.4"] + + overridden = load_harbor_build_config( + path, params={"optimizer_model": "gpt-5.5", "inner_env": "docker"} + ) + assert overridden.environment_name == "docker" + assert overridden.inference_gateway.producer.allowed_models == ["gpt-5.5"] + # The rest of the measurement substrate is untemplated and stays fixed. + assert overridden.model == default.model + assert overridden.task_source == default.task_source + + +def test_build_param_substitution_semantics(): + from vero.harbor.build.config import _substitute_build_param as sub + + context = {"A": "x"} + assert sub("${A}", context) == "x" + assert sub("${MISSING:-fallback}", context) == "fallback" + assert sub("pre-${A}-post", context) == "pre-x-post" + with pytest.raises(ValueError, match="required build parameter 'MISSING'"): + sub("${MISSING:?please set it}", context) + with pytest.raises(ValueError, match="build parameter 'MISSING' is unset"): + sub("${MISSING}", context) + + +def _target_repo(path: Path) -> Path: + path.mkdir(parents=True) + _git(path, "init", "-q") + _git(path, "config", "user.name", "VeRO Test") + _git(path, "config", "user.email", "vero@example.test") + (path / "README.md").write_text("# Target\n", encoding="utf-8") + (path / "pyproject.toml").write_text( + '[project]\nname="target"\nversion="0.1.0"\n', + encoding="utf-8", + ) + _git(path, "add", ".") + _git(path, "commit", "-q", "-m", "target baseline") + return path + + +def _config(tmp_path: Path, **updates) -> HarborBuildConfig: + target = _target_repo(tmp_path / "target") + task_source = tmp_path / "protected-tasks" + task_source.mkdir() + task_names = ["task-a", "task-b", "task-c", "task-d", "task-e", "task-hidden"] + for task_name in task_names: + task = task_source / task_name + task.mkdir() + (task / "task.toml").write_text( + f'[task]\nname="org/{task_name}"\n', + encoding="utf-8", + ) + values = { + "name": 'org/optimize-"program"', + "description": "Improve the program", + "agent_repo": str(target), + "task_source": str(task_source), + "agent_import_path": "target.agent:Agent", + "harbor_requirement": "harbor==0.1.17", + "partitions": { + "validation": ["task-a", "task-b", "task-c", "task-d", "task-e"], + "test": ["task-hidden"], + }, + "agent_access": [ + AgentAccessSpec( + partition="validation", + expose_case_resources=True, + total_runs=5, + total_cases=25, + ) + ], + "selection_partition": "validation", + "targets": [VerificationTargetSpec(partition="test")], + } + values.update(updates) + return HarborBuildConfig(**values) + + +def test_compiler_bakes_workspace_overlay_into_agent_environment(tmp_path): + bundle = tmp_path / "bundle" + (bundle / ".claude" / "agents").mkdir(parents=True) + (bundle / ".claude" / "agents" / "insights.md").write_text("# a\n", encoding="utf-8") + (bundle / "skills" / "insights").mkdir(parents=True) + (bundle / "skills" / "insights" / "SKILL.md").write_text("# s\n", encoding="utf-8") + + config = _config( + tmp_path, + workspace_overlays=[ + WorkspaceOverlaySpec(source=str(bundle / ".claude"), dest=".claude"), + WorkspaceOverlaySpec(source=str(bundle / "skills"), dest="skills"), + ], + ) + output = compile_harbor_task(config, tmp_path / "compiled") + env = output / "environment" + + # staged into the build context under environment/overlay/ + assert (env / "overlay" / ".claude" / "agents" / "insights.md").is_file() + assert (env / "overlay" / "skills" / "insights" / "SKILL.md").is_file() + # Dockerfile copies it, seed applies it, injected tooling is git-excluded + assert "COPY overlay /opt/overlay" in (env / "Dockerfile").read_text() + seed = (env / "main" / "seed.sh").read_text() + assert "cp -a /opt/overlay/. /work/agent/" in seed + assert "/.claude/" in seed and "/skills/" in seed + + +def test_compiler_plumbs_wandb_into_serve_config(tmp_path): + output = compile_harbor_task( + _config(tmp_path, wandb=WandbSpec(project="vero-bench", group="gaia")), + tmp_path / "compiled", + ) + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert serve["wandb"]["project"] == "vero-bench" + assert serve["wandb"]["group"] == "gaia" + + +def test_compiler_omits_wandb_when_unset(tmp_path): + output = compile_harbor_task(_config(tmp_path), tmp_path / "compiled") + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert serve["wandb"] is None + + +def test_compiler_omits_overlay_when_unset(tmp_path): + output = compile_harbor_task(_config(tmp_path), tmp_path / "compiled") + env = output / "environment" + assert not (env / "overlay").exists() + assert "COPY overlay" not in (env / "Dockerfile").read_text() + assert "/opt/overlay" not in (env / "main" / "seed.sh").read_text() + + +def test_compiler_budget_disclosure_toggle(tmp_path): + disclosed = compile_harbor_task(_config(tmp_path / "on"), tmp_path / "on/out") + instruction_on = (disclosed / "instruction.md").read_text(encoding="utf-8") + serve_on = json.loads( + (disclosed / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert "budget" in instruction_on.lower() + assert serve_on["disclose_budget"] is True + + blind = compile_harbor_task( + _config(tmp_path / "off", disclose_budget=False), tmp_path / "off/out" + ) + instruction_off = (blind / "instruction.md").read_text(encoding="utf-8") + serve_off = json.loads( + (blind / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert "budget" not in instruction_off.lower() + assert serve_off["disclose_budget"] is False + + +def test_compiler_plumbs_task_services_use_upstream(tmp_path, monkeypatch): + monkeypatch.setenv("TEST_UPSTREAM_KEY", "real-provider-secret") + monkeypatch.setenv("TEST_UPSTREAM_URL", "https://provider.example/v1") + monkeypatch.setenv("TEST_MODAL_TOKEN", "modal-secret") + config = _config( + tmp_path, + secrets=["TEST_MODAL_TOKEN"], + task_services_use_upstream=True, + # Task-owned upstream services are incompatible with harness isolation + # (the key would reach the harness env), so this build opts out. + harness_user=None, + task_environment={"TAU2_USER_MODEL": "openai/gpt-target"}, + inference_gateway=InferenceGatewaySpec( + upstream_api_key_env="TEST_UPSTREAM_KEY", + upstream_base_url_env="TEST_UPSTREAM_URL", + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec(allowed_models=["gpt-target"]), + ), + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + backend = next(iter(serve["backends"].values())) + assert backend["task_services_use_upstream"] is True + assert backend["upstream_api_key_env"] == "VERO_INFERENCE_UPSTREAM_API_KEY" + assert backend["upstream_base_url_env"] == "VERO_INFERENCE_UPSTREAM_BASE_URL" + assert backend["environment"] == {"TAU2_USER_MODEL": "openai/gpt-target"} + # validates against the deployment schema (backend config accepts the flags) + for partition, b in serve["backends"].items(): + b["cases_path"] = str( + output + / f"environment/sidecar/cases/{partition.removeprefix('harbor-')}.jsonl" + ) + HarborDeploymentConfig.model_validate(serve) + # the trusted eval-sidecar receives the real upstream (main keeps it scrubbed) + compose = yaml.safe_load((output / "environment/docker-compose.yaml").read_text()) + sidecar_env = compose["services"]["eval-sidecar"]["environment"] + assert "VERO_INFERENCE_UPSTREAM_API_KEY" in sidecar_env + assert "VERO_INFERENCE_UPSTREAM_BASE_URL" in sidecar_env + assert compose["services"]["main"]["environment"][ + "VERO_INFERENCE_UPSTREAM_API_KEY" + ] == "" + + +def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path): + config = _config( + tmp_path, + inference_gateway=InferenceGatewaySpec( + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec( + allowed_models=["gpt-target"], max_requests=15000, max_tokens=100000000 + ), + ), + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + gateway = json.loads( + (output / "environment/gateway/config.json").read_text(encoding="utf-8") + ) + scopes = gateway["scopes"] + # a reserved finalization scope exists, defaulting to the evaluation policy + assert "finalization" in scopes + assert scopes["finalization"]["allowed_models"] == ["gpt-target"] + assert scopes["finalization"]["max_tokens"] == 100000000 + # its token is distinct from the evaluation token (optimizer can't drain it) + assert scopes["finalization"]["token_sha256"] != scopes["evaluation"]["token_sha256"] + # and the sidecar backend carries a finalization token distinct from the eval one + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + backend = next(iter(serve["backends"].values())) + assert backend["inference_gateway_finalization_token"] is not None + assert ( + backend["inference_gateway_finalization_token"] + != backend["inference_gateway_token"] + ) + + +def test_workspace_overlay_rejects_unsafe_dest_and_missing_source(tmp_path): + present = tmp_path / "present" + present.mkdir() + with pytest.raises(ValidationError): + WorkspaceOverlaySpec(source=str(present), dest="../escape") + with pytest.raises(ValidationError): + WorkspaceOverlaySpec(source=str(tmp_path / "missing"), dest="ok") + + +def test_build_config_requires_pins_and_valid_partition_references(tmp_path): + assert ( + _config( + tmp_path / "modal", + harbor_requirement="harbor[modal]==0.18.0", + ).harbor_requirement + == "harbor[modal]==0.18.0" + ) + with pytest.raises(ValidationError, match="pin an exact version"): + _config(tmp_path / "unpinned", harbor_requirement="harbor>=0.1") + with pytest.raises(ValidationError, match="selection_partition"): + _config(tmp_path / "unknown", selection_partition="missing") + with pytest.raises(ValidationError, match="controlled flags"): + _config(tmp_path / "flags", extra_harbor_args=["--jobs-dir=/forged"]) + with pytest.raises(ValidationError, match="explicit version"): + _config(tmp_path / "source", task_source="org/unversioned") + + +def test_load_build_config_resolves_relative_local_paths(tmp_path): + target = _target_repo(tmp_path / "target") + tasks = tmp_path / "tasks" + tasks.mkdir() + config_path = tmp_path / "build.yaml" + config_path.write_text( + "\n".join( + [ + "name: org/task", + "agent_repo: target", + "task_source: tasks", + "agent_import_path: target.agent:Agent", + "harbor_requirement: harbor==0.1.17", + "partitions:", + " validation: [org/a]", + " test: [org/b]", + "agent_access:", + " - partition: validation", + "selection_partition: validation", + "targets:", + " - partition: test", + ] + ) + + "\n", + encoding="utf-8", + ) + + loaded = load_harbor_build_config(config_path) + + assert loaded.agent_repo == str(target) + assert loaded.task_source == str(tasks) + + +def test_load_build_config_supports_partition_files_and_validates_manifest(tmp_path): + target = _target_repo(tmp_path / "target") + partitions = tmp_path / "partitions" + partitions.mkdir() + (partitions / "validation.json").write_text( + '["task-a", "task-b"]\n', encoding="utf-8" + ) + (partitions / "test.json").write_text('["task-c"]\n', encoding="utf-8") + source = "org/benchmark@sha256:abc" + manifest = partitions / "manifest.json" + manifest.write_text( + json.dumps( + { + "task_source": source, + "tasks": [ + {"name": "task-a", "ref": "sha256:a"}, + {"name": "task-b", "ref": "sha256:b"}, + {"name": "task-c", "ref": "sha256:c"}, + ], + } + ), + encoding="utf-8", + ) + config_path = tmp_path / "build.yaml" + config_path.write_text( + "\n".join( + [ + "name: org/task", + "agent_repo: target", + f"task_source: {source}", + "task_manifest: partitions/manifest.json", + "agent_import_path: target.agent:Agent", + "harbor_requirement: harbor==0.18.0", + "partition_files:", + " validation: partitions/validation.json", + " test: partitions/test.json", + "agent_access:", + " - partition: validation", + "selection_partition: validation", + "targets:", + " - partition: test", + ] + ) + + "\n", + encoding="utf-8", + ) + + loaded = load_harbor_build_config(config_path) + + assert loaded.agent_repo == str(target) + assert loaded.partitions == { + "validation": ["task-a", "task-b"], + "test": ["task-c"], + } + assert loaded.task_manifest == str(manifest) + + (partitions / "test.json").write_text('["task-missing"]\n', encoding="utf-8") + with pytest.raises(ValidationError, match="absent from task_manifest"): + load_harbor_build_config(config_path) + + +def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): + config = _config(tmp_path) + output = compile_harbor_task( + config, + tmp_path / "compiled", + vero_root=Path(__file__).parents[1], + ) + + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert set(serve["backends"]) == {"harbor-validation", "harbor-test"} + assert serve["access_policies"][0]["disclosure"] == "aggregate" + assert serve["access_policies"][0]["expose_case_resources"] is True + assert serve["budgets"][0]["total_runs"] == 5 + assert serve["selection"]["backend_id"] == "harbor-validation" + assert serve["targets"][0]["backend_id"] == "harbor-test" + assert serve["targets"][0]["reward_scale"] == 1.0 + assert serve["evaluation_drain_timeout_seconds"] == config.timeout_seconds + assert serve["backends"]["harbor-test"]["task_source"] == "/opt/task-source" + assert serve["backends"]["harbor-test"]["python_version"] == "3.12" + assert serve["backends"]["harbor-test"]["case_timeout_seconds"] == 180.0 + assert serve["backends"]["harbor-test"]["task_agent_timeout_seconds"] == 600.0 + assert ( + serve["backends"]["harbor-validation"]["case_resources_cache_path"] + == "/state/admin/case-resources/validation" + ) + assert serve["access_policies"][0]["limits"]["retry"]["max_attempts"] == 1 + assert serve["access_policies"][0]["limits"]["case_timeout_seconds"] == 180.0 + assert "use_evaluation_copies" not in serve + instruction = (output / "instruction.md").read_text() + assert "--detach" in instruction + assert "vero harbor eval-status JOB_ID" in instruction + assert "randomness remains" in instruction + for partition, backend in serve["backends"].items(): + partition_name = partition.removeprefix("harbor-") + backend["cases_path"] = str( + output / f"environment/sidecar/cases/{partition_name}.jsonl" + ) + HarborDeploymentConfig.model_validate(serve) + test_case = json.loads( + (output / "environment/sidecar/cases/test.jsonl").read_text(encoding="utf-8") + ) + assert test_case == { + "id": "task-hidden", + "task_name": "task-hidden", + "result_task_name": "org/task-hidden", + } + assert (output / "environment/sidecar/task-source/task-hidden/task.toml").is_file() + assert not (output / "environment/agent-seed/protected-tasks").exists() + instruction = (output / "instruction.md").read_text(encoding="utf-8") + assert "## Objective\n\nImprove the program" in instruction + assert "--backend harbor-validation" in instruction + assert "arbitrary subsets" in instruction + assert "Complete task resources" in instruction + assert ".vero/evaluations/" in instruction + task_toml = (output / "task.toml").read_text(encoding="utf-8") + assert 'name = "org/optimize-\\"program\\""' in task_toml + assert tomllib.loads(task_toml)["task"]["name"] == 'org/optimize-"program"' + compose = (output / "environment/docker-compose.yaml").read_text() + assert "vero.harbor.deployment:build_harbor_components" in compose + assert "admin_state:/state/admin" in compose + assert "agent_context:/work/agent/.vero:ro" in compose + assert "agent_context:/state/agent-context" in compose + assert set(yaml.safe_load(compose)["services"]) == {"main", "eval-sidecar"} + sidecar_dockerfile = (output / "environment/sidecar/Dockerfile").read_text() + assert 'uv pip install --system "harbor==0.1.17"' in sidecar_dockerfile + seed = (output / "environment/main/seed.sh").read_text() + assert "-path /work/agent/.vero -prune" in seed + assert "'/.vero/' >> /work/agent/.git/info/exclude" in seed + test_script = output / "tests/test.sh" + assert test_script.stat().st_mode & 0o111 + assert "vero harbor export-session" in test_script.read_text() + + +def test_compiler_checks_secrets_before_writing_and_rejects_source_overlap( + tmp_path, + monkeypatch, +): + config = _config(tmp_path, secrets=["MISSING_TEST_SECRET"]) + output = tmp_path / "compiled" + monkeypatch.delenv("MISSING_TEST_SECRET", raising=False) + + with pytest.raises(ValueError, match="MISSING_TEST_SECRET"): + compile_harbor_task( + config, + output, + vero_root=Path(__file__).parents[1], + ) + assert not output.exists() + + monkeypatch.setenv("MISSING_TEST_SECRET", "configured") + compile_harbor_task( + config, + output, + vero_root=Path(__file__).parents[1], + ) + task = tomllib.loads((output / "task.toml").read_text(encoding="utf-8")) + assert task["environment"]["env"] == { + "MISSING_TEST_SECRET": "${MISSING_TEST_SECRET}" + } + + safe = config.model_copy(update={"secrets": []}) + with pytest.raises(ValueError, match="overlaps protected source"): + compile_harbor_task( + safe, + safe.agent_repo, + vero_root=Path(__file__).parents[1], + ) + + (Path(safe.agent_repo) / ".vero").mkdir() + (Path(safe.agent_repo) / ".vero" / "context.json").write_text("{}\n") + _git(Path(safe.agent_repo), "add", "-f", ".vero/context.json") + _git(Path(safe.agent_repo), "commit", "-q", "-m", "reserved context") + with pytest.raises(ValueError, match="reserved path"): + compile_harbor_task( + safe, + tmp_path / "reserved-context", + vero_root=Path(__file__).parents[1], + ) + + +def test_compiler_isolates_upstream_inference_credentials(tmp_path, monkeypatch): + monkeypatch.setenv("TEST_UPSTREAM_KEY", "real-provider-secret") + monkeypatch.setenv("TEST_UPSTREAM_URL", "https://provider.example/v1") + monkeypatch.setenv("TEST_MODAL_TOKEN", "modal-secret") + config = _config( + tmp_path, + secrets=["TEST_MODAL_TOKEN"], + inference_gateway=InferenceGatewaySpec( + upstream_api_key_env="TEST_UPSTREAM_KEY", + upstream_base_url_env="TEST_UPSTREAM_URL", + producer=InferenceBudgetSpec( + allowed_models=["gpt-producer"], + max_requests=10, + max_tokens=1000, + ), + evaluation=InferenceBudgetSpec( + allowed_models=["gpt-target"], + max_requests=20, + max_tokens=2000, + ), + ), + ) + + output = compile_harbor_task( + config, + tmp_path / "compiled", + vero_root=Path(__file__).parents[1], + ) + + task = tomllib.loads((output / "task.toml").read_text()) + assert task["environment"]["env"] == { + "TEST_MODAL_TOKEN": "${TEST_MODAL_TOKEN}", + "VERO_INFERENCE_UPSTREAM_API_KEY": "${VERO_INFERENCE_UPSTREAM_API_KEY}", + "VERO_INFERENCE_UPSTREAM_BASE_URL": "${VERO_INFERENCE_UPSTREAM_BASE_URL}", + } + compose = yaml.safe_load((output / "environment/docker-compose.yaml").read_text()) + assert set(compose["services"]) == { + "main", + "eval-sidecar", + "inference-gateway", + } + main_environment = compose["services"]["main"]["environment"] + assert main_environment["TEST_MODAL_TOKEN"] == "" + assert main_environment["VERO_INFERENCE_UPSTREAM_API_KEY"] == "" + assert main_environment["OPENAI_API_KEY"] + assert main_environment["OPENAI_API_KEY"] != "real-provider-secret" + assert main_environment["OPENAI_BASE_URL"].endswith("/scopes/producer/optimizer/v1") + assert compose["services"]["eval-sidecar"]["environment"] == { + "TEST_MODAL_TOKEN": "${TEST_MODAL_TOKEN:?TEST_MODAL_TOKEN must be set for the eval sidecar}" + } + assert compose["services"]["inference-gateway"]["environment"] == { + "VERO_INFERENCE_UPSTREAM_API_KEY": "${VERO_INFERENCE_UPSTREAM_API_KEY:?VERO_INFERENCE_UPSTREAM_API_KEY must be set for the inference gateway}", + "VERO_INFERENCE_UPSTREAM_BASE_URL": "${VERO_INFERENCE_UPSTREAM_BASE_URL:?VERO_INFERENCE_UPSTREAM_BASE_URL must be set for the inference gateway}", + } + gateway = json.loads((output / "environment/gateway/config.json").read_text()) + assert "real-provider-secret" not in json.dumps(gateway) + assert gateway["scopes"]["producer"]["token_sha256"] + launch = json.loads((output / "environment/gateway/launch.json").read_text()) + assert launch["upstream_api_key_source"] == "TEST_UPSTREAM_KEY" + assert launch["upstream_api_key_target"] == "VERO_INFERENCE_UPSTREAM_API_KEY" + assert launch["producer_api_key"] == main_environment["OPENAI_API_KEY"] + seed = (output / "environment/main/seed.sh").read_text() + assert 'model_provider = "vero_gateway"' in seed + assert "supports_websockets = false" in seed + serve = json.loads((output / "environment/sidecar/serve.json").read_text()) + backend = serve["backends"]["harbor-validation"] + assert backend["passthrough_environment"] == ["TEST_MODAL_TOKEN"] + assert backend["inference_gateway_token"] + assert backend["inference_gateway_token"] != "real-provider-secret" + assert "real-provider-secret" not in json.dumps(serve) + assert (output / "environment/gateway/Dockerfile").is_file() + + +def test_compiler_uses_published_version_outside_a_source_checkout( + tmp_path, + monkeypatch, +): + config = _config(tmp_path) + from vero.harbor.build import compiler + + monkeypatch.setattr( + compiler, + "__file__", + "/installed/site-packages/vero/harbor/build/compiler.py", + ) + monkeypatch.setattr(compiler, "distribution_version", lambda _name: "0.5.0") + + output = compiler.compile_harbor_task(config, tmp_path / "compiled") + + assert not (output / "environment/vero").exists() + dockerfile = (output / "environment/Dockerfile").read_text(encoding="utf-8") + assert "scale-vero[harbor]==0.5.0" in dockerfile diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py new file mode 100644 index 00000000..2b851615 --- /dev/null +++ b/vero/tests/test_v05_harbor_deployment.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +from vero.evaluation import ( + DisclosureLevel, + EvaluationBudget, + EvaluationSet, + MetricSelector, + ObjectiveSpec, +) +from vero.harbor import ( + SidecarEvaluationPolicy, + HarborBackendConfig, + VerificationTarget, + build_harbor_components, +) +from vero.report import generate_experiment_report + + +def _git(path: Path, *arguments: str) -> str: + result = subprocess.run( + ["git", *arguments], + cwd=path, + check=True, + text=True, + capture_output=True, + ) + return result.stdout.strip() + + +def _repo(path: Path, content: str) -> str: + path.mkdir(parents=True) + _git(path, "init", "-q") + _git(path, "config", "user.name", "VeRO Test") + _git(path, "config", "user.email", "vero@example.test") + (path / "program.py").write_text(content, encoding="utf-8") + _git(path, "add", "program.py") + _git(path, "commit", "-q", "-m", "baseline") + return _git(path, "rev-parse", "HEAD") + + +@pytest.mark.asyncio +async def test_standard_deployment_factory_builds_one_canonical_runtime(tmp_path): + trusted = tmp_path / "trusted" + agent = tmp_path / "agent" + baseline = _repo(trusted, "VALUE = 1\n") + _repo(agent, "VALUE = 1\n") + cases = tmp_path / "cases.jsonl" + cases.write_text( + json.dumps({"id": "task", "task_name": "org/task"}) + "\n", + encoding="utf-8", + ) + evaluation_set = EvaluationSet(name="benchmark", partition="validation") + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + backend_config = HarborBackendConfig( + task_source="org/benchmark@1.0", + agent_import_path="program:Agent", + cases_path=str(cases), + harbor_requirement="harbor==0.1.17", + evaluation_set_name="benchmark", + partition="validation", + uv_executable=sys.executable, + ) + budget = EvaluationBudget( + backend_id="validation", + evaluation_set_key=evaluation_set.budget_key("validation"), + total_runs=4, + total_cases=10, + ) + config = { + "repo_path": str(trusted), + "agent_repo_path": str(agent), + "session_dir": str(tmp_path / "state/session"), + "session_id": "trial", + "backends": {"validation": backend_config.model_dump(mode="json")}, + "access_policies": [ + SidecarEvaluationPolicy( + backend_id="validation", + evaluation_set_name="benchmark", + partition="validation", + objective=objective, + disclosure=DisclosureLevel.AGGREGATE, + ).model_dump(mode="json") + ], + "budgets": [budget.model_dump(mode="json")], + "selection": { + "mode": "auto_best", + "backend_id": "validation", + "evaluation_set": evaluation_set.model_dump(mode="json"), + "objective": objective.model_dump(mode="json"), + "baseline_version": "HEAD", + }, + "targets": [ + VerificationTarget( + reward_key="reward", + backend_id="validation", + evaluation_set=evaluation_set, + objective=objective, + ).model_dump(mode="json") + ], + "agent_volume": str(tmp_path / "state/agent"), + "admin_volume": str(tmp_path / "state/admin"), + } + + components = await build_harbor_components(config) + + assert components.sidecar.engine is components.verifier.engine + assert components.verifier.selection.baseline_candidate.version == baseline + assert components.sidecar.status().evaluation_access[0].budget.remaining_runs == 4 + assert (tmp_path / "state/session/budgets.json").is_file() + # The trusted session dir is locked to its owner so the unprivileged harness + # cannot read the held-out records, budgets, or candidates it contains. + assert ((tmp_path / "state/session").stat().st_mode & 0o777) == 0o700 + manifest = json.loads((tmp_path / "state/session/harbor-session.json").read_text()) + assert manifest["id"] == "trial" + assert manifest["selection"]["evaluation_set"] == { + "name": "benchmark", + "partition": "validation", + "selection": {"kind": "all"}, + } + assert (tmp_path / "state/agent/manifest.json").is_file() + assert json.loads( + (tmp_path / "state/agent/evaluations/index.json").read_text() + ) == {"schema_version": 1, "evaluations": []} + report = await generate_experiment_report( + tmp_path / "state/session", + tmp_path / "experiment.html", + ) + assert baseline in report.read_text() + + +@pytest.mark.asyncio +async def test_standard_deployment_fails_closed_on_corrupt_budget_state(tmp_path): + trusted = tmp_path / "trusted" + agent = tmp_path / "agent" + _repo(trusted, "VALUE = 1\n") + _repo(agent, "VALUE = 1\n") + cases = tmp_path / "cases.json" + cases.write_text('[{"id":"task","task_name":"org/task"}]') + session = tmp_path / "state/session" + session.mkdir(parents=True) + (session / "budgets.json").write_text("not json", encoding="utf-8") + evaluation_set = EvaluationSet(name="benchmark") + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + config = { + "repo_path": str(trusted), + "agent_repo_path": str(agent), + "session_dir": str(session), + "backends": { + "backend": { + "task_source": "org/benchmark@1.0", + "agent_import_path": "program:Agent", + "cases_path": str(cases), + "harbor_requirement": "harbor==0.1.17", + "uv_executable": sys.executable, + } + }, + "access_policies": [], + "budgets": [], + "selection": { + "mode": "auto_best", + "backend_id": "backend", + "evaluation_set": evaluation_set.model_dump(mode="json"), + "objective": objective.model_dump(mode="json"), + "baseline_version": "HEAD", + "baseline_floor": False, + }, + "targets": [ + { + "reward_key": "reward", + "backend_id": "backend", + "evaluation_set": evaluation_set.model_dump(mode="json"), + "objective": objective.model_dump(mode="json"), + } + ], + "admin_volume": str(tmp_path / "state/admin"), + } + + with pytest.raises(ValueError, match="invalid durable budget ledger"): + await build_harbor_components(config) diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py new file mode 100644 index 00000000..7f937933 --- /dev/null +++ b/vero/tests/test_v05_harbor_http.py @@ -0,0 +1,653 @@ +from __future__ import annotations + +import json +import os +import stat +from datetime import UTC, datetime +from types import SimpleNamespace + +import click +import pytest +from click.testing import CliRunner +from fastapi.testclient import TestClient + +from vero.candidate import Candidate +from vero.cli import main +from vero.evaluation import ( + DisclosureLevel, + EvaluationAcknowledgement, + EvaluationDatabase, + EvaluationReceipt, + EvaluationRequestError, + EvaluationStatus, +) +from vero.harbor.app import create_app +from vero.harbor.cli import ( + _compiled_run_environment, + _load_agent_trace, + _load_env_file, + harbor, +) +from vero.harbor.auth import ( + check_admin_token, + read_admin_token, + write_admin_token, +) +from vero.harbor.sidecar import ( + EvaluationAccessError, + EvaluationJobNotFoundError, + EvaluationJobStatus, + SidecarEvaluationJob, + SidecarEvaluationResult, + SidecarStatus, + Submission, +) +from vero.harbor.verifier import VerificationResult + + +class FakeSidecar: + def __init__(self): + self.requests = [] + self.raise_access_error = False + self.raise_request_error = False + self.job = None + self.engine = SimpleNamespace(database=EvaluationDatabase(id="session")) + + async def evaluate(self, request): + if self.raise_access_error: + raise EvaluationAccessError("private details") + if self.raise_request_error: + raise EvaluationRequestError("unknown case") + self.requests.append(request) + return SidecarEvaluationResult( + disclosure=DisclosureLevel.NONE, + receipt=EvaluationReceipt( + evaluation_id="evaluation", + status=EvaluationStatus.SUCCESS, + disclosure=DisclosureLevel.NONE, + result=EvaluationAcknowledgement( + evaluation_id="evaluation", + status=EvaluationStatus.SUCCESS, + ), + result_path=".vero/evaluations/evaluation/evaluation.json", + ), + ) + + async def submit(self, version=None): + return Submission( + candidate=Candidate( + id="candidate", + version=version or "HEAD", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + ) + + async def start_evaluation_job(self, request): + result = await self.evaluate(request) + self.job = SidecarEvaluationJob( + job_id="job-1", + status=EvaluationJobStatus.COMPLETE, + backend_id=request.backend_id, + evaluation_set=request.evaluation_set, + version=request.version, + evaluation_id=result.receipt.evaluation_id, + receipt=result.receipt, + created_at=datetime(2026, 1, 1, tzinfo=UTC), + completed_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + return self.job + + def evaluation_job(self, job_id): + if self.job is None or self.job.job_id != job_id: + raise EvaluationJobNotFoundError(job_id) + return self.job + + def status(self): + return SidecarStatus(submit_enabled=True, evaluation_access=[]) + + +class FakeVerifier: + def __init__(self): + self.calls = 0 + + async def finalize(self): + self.calls += 1 + return VerificationResult(rewards={"reward": 0.75}) + + +def test_admin_token_is_atomic_restrictive_and_constant_time_checked(tmp_path): + path = write_admin_token(tmp_path / "admin/token", "secret-token") + + assert read_admin_token(path) == "secret-token" + # Read-only file inside a root-only directory: an unprivileged agent that + # shares the token volume can neither read the file nor traverse to it. + assert stat.S_IMODE(path.stat().st_mode) == 0o400 + assert stat.S_IMODE(path.parent.stat().st_mode) == 0o700 + assert check_admin_token("Bearer secret-token", "secret-token") + assert not check_admin_token("Bearer wrong", "secret-token") + assert not check_admin_token(None, "secret-token") + + +def test_compiled_run_environment_keeps_upstream_credentials_from_agent( + tmp_path, monkeypatch +): + monkeypatch.setenv("OPENAI_API_KEY", "upstream-secret") + monkeypatch.setenv("OPENAI_BASE_URL", "https://provider.example/v1") + launch = tmp_path / "environment/gateway/launch.json" + launch.parent.mkdir(parents=True) + launch.write_text( + json.dumps( + { + "upstream_api_key_source": "OPENAI_API_KEY", + "upstream_api_key_target": "VERO_INFERENCE_UPSTREAM_API_KEY", + "upstream_base_url_source": "OPENAI_BASE_URL", + "upstream_base_url_target": "VERO_INFERENCE_UPSTREAM_BASE_URL", + "producer_api_key": "producer-scope-token", + "producer_base_url": "http://inference/scopes/producer/optimizer/v1", + } + ) + ) + + environment = _compiled_run_environment(tmp_path) + + assert environment["OPENAI_API_KEY"] == "producer-scope-token" + assert environment["OPENAI_BASE_URL"].startswith("http://inference/") + assert environment["VERO_INFERENCE_UPSTREAM_API_KEY"] == "upstream-secret" + assert environment["VERO_INFERENCE_UPSTREAM_BASE_URL"] == ( + "https://provider.example/v1" + ) + # Claude Code (harbor -a claude) is pointed at the same producer scope; the + # Anthropic SDK re-appends "/v1/messages" so the base drops the trailing /v1. + assert environment["ANTHROPIC_API_KEY"] == "producer-scope-token" + assert environment["ANTHROPIC_BASE_URL"] == ( + "http://inference/scopes/producer/optimizer" + ) + + +def test_load_env_file_parses_comments_export_and_quotes(tmp_path): + path = tmp_path / "secrets.env" + path.write_text( + "# a comment\n" + "\n" + "OPENAI_API_KEY=upstream-secret\n" + "export MODAL_TOKEN_ID='mt-id'\n" + 'MODAL_TOKEN_SECRET="mt-secret"\n' + ) + + values = _load_env_file(path) + + assert values == { + "OPENAI_API_KEY": "upstream-secret", + "MODAL_TOKEN_ID": "mt-id", + "MODAL_TOKEN_SECRET": "mt-secret", + } + + +def test_load_env_file_rejects_malformed_line(tmp_path): + path = tmp_path / "secrets.env" + path.write_text("NOT_AN_ASSIGNMENT\n") + + with pytest.raises(click.ClickException): + _load_env_file(path) + + +def test_env_file_overrides_take_precedence_over_ambient_environment( + tmp_path, monkeypatch +): + monkeypatch.setenv("OPENAI_API_KEY", "ambient-wrong-key") + monkeypatch.delenv("MODAL_TOKEN_ID", raising=False) + launch = tmp_path / "environment/gateway/launch.json" + launch.parent.mkdir(parents=True) + launch.write_text( + json.dumps( + { + "upstream_api_key_source": "OPENAI_API_KEY", + "upstream_api_key_target": "VERO_INFERENCE_UPSTREAM_API_KEY", + "producer_api_key": "producer-scope-token", + "producer_base_url": "http://inference/scopes/producer/optimizer/v1", + "upstream_base_url_target": "VERO_INFERENCE_UPSTREAM_BASE_URL", + } + ) + ) + + environment = _compiled_run_environment( + tmp_path, + {"OPENAI_API_KEY": "file-upstream-secret", "MODAL_TOKEN_ID": "mt-id"}, + ) + + # The upstream credential relocated to the gateway target comes from the + # env-file, not the ambient shell. + assert environment["VERO_INFERENCE_UPSTREAM_API_KEY"] == "file-upstream-secret" + assert environment["OPENAI_API_KEY"] == "producer-scope-token" + assert environment["MODAL_TOKEN_ID"] == "mt-id" + + +def test_codex_jsonl_is_converted_to_a_redacted_producer_trace(tmp_path): + path = tmp_path / "codex.txt" + path.write_text( + "\n".join( + [ + json.dumps( + { + "type": "item.completed", + "item": {"type": "agent_message", "text": "Inspecting."}, + } + ), + json.dumps( + { + "type": "item.completed", + "item": { + "type": "command_execution", + "command": "env", + "aggregated_output": "OPENAI_API_KEY=sk-secretvalue123\n", + }, + } + ), + ] + ) + + "\n" + ) + + trace = _load_agent_trace(path) + + assert trace[0] == {"role": "assistant", "content": "Inspecting."} + assert trace[-1]["output"] == "OPENAI_API_KEY=[REDACTED]\n" + + +def test_harbor_run_uses_current_python_and_pinned_harbor_extra(tmp_path, monkeypatch): + import vero.harbor.build as harbor_build + import vero.harbor.cli as harbor_cli + + config_path = tmp_path / "build.yaml" + config_path.write_text("task_name: unused\n") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.18.0") + observed = {} + + def compile_task(_config, output): + output.mkdir(parents=True) + return output + + def run(command, *, env): + observed["command"] = command + observed["environment"] = env + return SimpleNamespace(returncode=0) + + monkeypatch.setattr( + harbor_build, "load_harbor_build_config", lambda _path, **_kwargs: config + ) + monkeypatch.setattr(harbor_build, "compile_harbor_task", compile_task) + monkeypatch.setattr(harbor_cli.shutil, "which", lambda _name: "/usr/bin/uvx") + monkeypatch.setattr(harbor_cli.subprocess, "run", run) + + result = CliRunner().invoke( + harbor, + [ + "run", + "--config", + str(config_path), + "--agent", + "codex", + "--environment", + "modal", + ], + ) + + assert result.exit_code == 0, result.output + assert observed["command"][:6] == [ + "/usr/bin/uvx", + "--python", + harbor_cli.sys.executable, + "--from", + "harbor[modal]==0.18.0", + "harbor", + ] + + +def test_harbor_run_env_file_secrets_reach_subprocess_not_command_line( + tmp_path, monkeypatch +): + import vero.harbor.build as harbor_build + import vero.harbor.cli as harbor_cli + + config_path = tmp_path / "build.yaml" + config_path.write_text("task_name: unused\n") + env_file = tmp_path / "secrets.env" + env_file.write_text("MODAL_TOKEN_ID=mt-id\nMODAL_TOKEN_SECRET=mt-secret\n") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.18.0") + observed = {} + + def compile_task(_config, output): + # The build's declared-credential check reads os.environ at compile time, + # so the env-file must already be applied here (not just at subprocess launch). + observed["modal_at_compile"] = os.environ.get("MODAL_TOKEN_ID") + output.mkdir(parents=True) + return output + + def run(command, *, env): + observed["command"] = command + observed["environment"] = env + return SimpleNamespace(returncode=0) + + monkeypatch.setattr( + harbor_build, "load_harbor_build_config", lambda _path, **_kwargs: config + ) + monkeypatch.setattr(harbor_build, "compile_harbor_task", compile_task) + monkeypatch.setattr(harbor_cli.shutil, "which", lambda _name: "/usr/bin/uvx") + monkeypatch.setattr(harbor_cli.subprocess, "run", run) + + result = CliRunner().invoke( + harbor, + [ + "run", + "--config", + str(config_path), + "--agent", + "codex", + "--environment", + "docker", + "--env-file", + str(env_file), + ], + ) + + assert result.exit_code == 0, result.output + assert observed["modal_at_compile"] == "mt-id" + assert observed["environment"]["MODAL_TOKEN_ID"] == "mt-id" + assert observed["environment"]["MODAL_TOKEN_SECRET"] == "mt-secret" + # Secrets are passed via the environment only, never on argv. + assert "mt-secret" not in " ".join(observed["command"]) + assert "Loaded 2 secret(s)" in result.output + assert "mt-secret" not in result.output + + +def test_http_app_separates_agent_and_admin_surfaces(tmp_path, monkeypatch): + sidecar = FakeSidecar() + sidecar.engine.evaluator = SimpleNamespace(session_dir=tmp_path / "session") + verifier = FakeVerifier() + + def create_archive(_session_dir, destination): + destination.write_bytes(b"portable-session") + return destination + + monkeypatch.setattr("vero.harbor.app.create_harbor_session_archive", create_archive) + client = TestClient( + create_app( + sidecar=sidecar, + verifier=verifier, + admin_token="admin-secret", + ) + ) + + assert client.get("/health").json() == {"ok": True} + response = client.post( + "/eval", + json={ + "backend_id": "backend", + "evaluation_set": {"name": "public"}, + }, + ) + assert response.status_code == 200 + assert response.json()["disclosure"] == "none" + assert sidecar.requests[0].evaluation_set.name == "public" + assert client.get("/status").json()["submit_enabled"] is True + assert client.post("/finalize").status_code == 403 + assert client.get("/session/export").status_code == 403 + exported = client.get( + "/session/export", + headers={"Authorization": "Bearer admin-secret"}, + ) + assert exported.content == b"portable-session" + finalized = client.post( + "/finalize", + headers={"Authorization": "Bearer admin-secret"}, + ) + assert finalized.json()["rewards"] == {"reward": 0.75} + assert verifier.calls == 1 + assert client.get("/evaluations").status_code == 403 + assert client.get( + "/evaluations", + headers={"Authorization": "Bearer admin-secret"}, + ).json() == {"evaluations": []} + + +def test_http_app_exposes_agent_evaluation_jobs(): + sidecar = FakeSidecar() + client = TestClient( + create_app( + sidecar=sidecar, + verifier=FakeVerifier(), + admin_token="admin-secret", + ) + ) + + started = client.post( + "/eval/jobs", + json={ + "backend_id": "backend", + "evaluation_set": {"name": "public"}, + }, + ) + + assert started.status_code == 202 + assert started.json()["job_id"] == "job-1" + assert client.get("/eval/jobs/job-1").json()["status"] == "complete" + result = client.get("/eval/jobs/job-1/result") + assert result.status_code == 200 + assert result.json()["receipt"]["evaluation_id"] == "evaluation" + assert client.get("/eval/jobs/missing").status_code == 404 + + +def test_http_app_redacts_access_denial_details(): + sidecar = FakeSidecar() + sidecar.raise_access_error = True + client = TestClient( + create_app( + sidecar=sidecar, + verifier=FakeVerifier(), + admin_token="admin-secret", + ) + ) + + response = client.post( + "/eval", + json={ + "backend_id": "backend", + "evaluation_set": {"name": "hidden"}, + }, + ) + + assert response.status_code == 403 + assert response.json() == {"error": "evaluation denied"} + + +def test_http_app_maps_backend_request_rejection_to_400(): + sidecar = FakeSidecar() + sidecar.raise_request_error = True + client = TestClient( + create_app( + sidecar=sidecar, + verifier=FakeVerifier(), + admin_token="admin-secret", + ) + ) + + response = client.post( + "/eval", + json={ + "backend_id": "backend", + "evaluation_set": {"name": "hidden"}, + }, + ) + + assert response.status_code == 400 + assert response.json() == {"error": "invalid evaluation request"} + + +def test_harbor_cli_builds_canonical_selection(monkeypatch): + captured = {} + + def fake_request(method, path, *, payload=None, headers=None): + captured.update(method=method, path=path, payload=payload, headers=headers) + return {"ok": True} + + monkeypatch.setattr("vero.harbor.cli._request", fake_request) + result = CliRunner().invoke( + main, + [ + "harbor", + "eval", + "--backend", + "backend", + "--evaluation-set", + "benchmark", + "--partition", + "validation", + "--case-id", + "a", + "--case-id", + "b", + "--parameter", + "temperature=0.2", + ], + ) + + assert result.exit_code == 0, result.output + assert captured["method"] == "POST" + assert captured["path"] == "/eval" + assert captured["payload"]["evaluation_set"]["selection"] == { + "kind": "ids", + "ids": ["a", "b"], + } + assert captured["payload"]["parameters"] == {"temperature": 0.2} + assert captured["payload"]["limits"] is None + + +def test_harbor_cli_supports_detached_evaluation_jobs(monkeypatch): + requests = [] + + def fake_request(method, path, *, payload=None, headers=None): + requests.append((method, path, payload)) + return {"job_id": "job-1", "status": "running"} + + monkeypatch.setattr("vero.harbor.cli._request", fake_request) + runner = CliRunner() + started = runner.invoke( + main, + [ + "harbor", + "eval", + "--backend", + "backend", + "--evaluation-set", + "benchmark", + "--detach", + ], + ) + status = runner.invoke(main, ["harbor", "eval-status", "job-1"]) + result = runner.invoke(main, ["harbor", "eval-result", "job-1"]) + + assert started.exit_code == 0, started.output + assert status.exit_code == 0, status.output + assert result.exit_code == 0, result.output + assert requests[0][:2] == ("POST", "/eval/jobs") + assert requests[1][:2] == ("GET", "/eval/jobs/job-1") + assert requests[2][:2] == ("GET", "/eval/jobs/job-1/result") + + +def test_harbor_finalize_cli_writes_only_rewards(tmp_path, monkeypatch): + token_file = write_admin_token(tmp_path / "token", "admin-secret") + output = tmp_path / "logs/reward.json" + + def fake_request(method, path, *, payload=None, headers=None): + assert headers == {"Authorization": "Bearer admin-secret"} + return { + "rewards": {"accuracy": 0.9}, + "baseline_rewards": {"accuracy": 0.7}, + } + + monkeypatch.setattr("vero.harbor.cli._request", fake_request) + result = CliRunner().invoke( + main, + [ + "harbor", + "finalize", + "--token-file", + str(token_file), + "--output", + str(output), + ], + ) + + assert result.exit_code == 0, result.output + assert json.loads(output.read_text()) == {"accuracy": 0.9} + + +def test_harbor_export_session_persists_archive_report_and_checksum( + tmp_path, monkeypatch +): + import vero.harbor.cli as harbor_cli + import vero.report as report_module + + token_file = write_admin_token(tmp_path / "token", "admin-secret") + output = tmp_path / "logs/session.tar.gz" + report = tmp_path / "logs/experiment.html" + status_output = tmp_path / "logs/status.json" + finalization_output = tmp_path / "logs/finalization.json" + trace = tmp_path / "trajectory.json" + trace.write_text("[]\n") + + def fake_request(method, path, *, payload=None, headers=None): + if path == "/finalize": + assert method == "POST" + assert headers == {"Authorization": "Bearer admin-secret"} + return {"candidate": None, "rewards": {"reward": 0.0}, "errors": {}} + assert (method, path) == ("GET", "/status") + return {"submit_enabled": False, "evaluation_access": []} + + def fake_download(path, destination, *, headers=None): + assert path == "/session/export" + assert headers == {"Authorization": "Bearer admin-secret"} + destination.write_bytes(b"sidecar archive") + + def fake_extract(_archive, destination): + session = destination / "session" + session.mkdir(parents=True) + (session / "harbor-session.json").write_text("{}\n") + return session + + async def fake_report(_session, destination): + destination.write_text("experiment\n") + return destination + + monkeypatch.setattr(harbor_cli, "_request", fake_request) + monkeypatch.setattr(harbor_cli, "_download", fake_download) + monkeypatch.setattr(harbor_cli, "extract_harbor_session_archive", fake_extract) + monkeypatch.setattr(report_module, "generate_experiment_report", fake_report) + + result = CliRunner().invoke( + main, + [ + "harbor", + "export-session", + "--token-file", + str(token_file), + "--output", + str(output), + "--report-output", + str(report), + "--status-output", + str(status_output), + "--finalization-output", + str(finalization_output), + "--agent-trace", + str(trace), + ], + ) + + assert result.exit_code == 0, result.output + assert output.is_file() + assert report.read_text() == "experiment\n" + assert json.loads(status_output.read_text())["submit_enabled"] is False + assert json.loads(finalization_output.read_text())["rewards"] == {"reward": 0.0} + checksum = output.with_name(f"{output.name}.sha256").read_text() + assert output.name in checksum diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py new file mode 100644 index 00000000..a64db873 --- /dev/null +++ b/vero/tests/test_v05_harbor_inference.py @@ -0,0 +1,320 @@ +from __future__ import annotations + +import json + +import httpx +from fastapi.testclient import TestClient + +from vero.harbor.inference import ( + InferenceGatewayConfig, + InferenceScopeConfig, + create_inference_gateway_app, + token_digest, +) + + +def _config(tmp_path, *, max_requests=2, max_tokens=100): + return InferenceGatewayConfig( + state_path=str(tmp_path / "usage.json"), + scopes={ + "producer": InferenceScopeConfig( + token_sha256=token_digest("scoped-token"), + allowed_models=["gpt-test"], + max_requests=max_requests, + max_tokens=max_tokens, + max_concurrency=1, + ) + }, + ) + + +def test_gateway_replaces_credentials_enforces_scope_and_persists_usage(tmp_path): + observed = [] + + def upstream(request: httpx.Request): + observed.append(request) + return httpx.Response( + 200, + json={ + "id": "response", + "usage": { + "input_tokens": 11, + "output_tokens": 7, + "total_tokens": 18, + }, + }, + ) + + app = create_inference_gateway_app( + config=_config(tmp_path, max_requests=1), + upstream_api_key="upstream-secret", + upstream_base_url="https://provider.example/v1", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + denied = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer wrong"}, + json={"model": "gpt-test", "input": "hello"}, + ) + wrong_model = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "other", "input": "hello"}, + ) + accepted = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hello"}, + ) + exhausted = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "again"}, + ) + usage = client.get( + "/usage/producer", + headers={"Authorization": "Bearer scoped-token"}, + ) + + assert denied.status_code == 403 + assert wrong_model.status_code == 403 + assert accepted.status_code == 200 + assert exhausted.status_code == 429 + assert len(observed) == 1 + assert observed[0].headers["authorization"] == "Bearer upstream-secret" + assert b"scoped-token" not in observed[0].content + assert usage.json()["requests"] == 1 + assert usage.json()["total_tokens"] == 18 + assert usage.json()["remaining_requests"] == 0 + persisted = json.loads((tmp_path / "usage.json").read_text()) + assert persisted["scopes"]["producer"]["active_requests"] == 0 + assert persisted["scopes"]["producer"]["attributions"]["optimizer"] == { + "requests": 1, + "upstream_errors": 0, + "input_tokens": 11, + "output_tokens": 7, + "total_tokens": 18, + } + + +def test_gateway_records_usage_without_enforcing_omitted_limits(tmp_path): + def upstream(_request: httpx.Request): + return httpx.Response( + 200, + json={ + "usage": { + "input_tokens": 11, + "output_tokens": 7, + "total_tokens": 18, + } + }, + ) + + app = create_inference_gateway_app( + config=_config(tmp_path, max_requests=None, max_tokens=None), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + responses = [ + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": f"request {index}"}, + ) + for index in range(3) + ] + usage = client.get( + "/usage/producer", + headers={"Authorization": "Bearer scoped-token"}, + ).json() + + assert [response.status_code for response in responses] == [200, 200, 200] + assert usage["requests"] == 3 + assert usage["total_tokens"] == 54 + assert usage["max_requests"] is None + assert usage["max_tokens"] is None + assert usage["remaining_requests"] is None + assert usage["remaining_tokens"] is None + + +def test_gateway_meters_streaming_responses(tmp_path): + payload = ( + 'event: response.created\ndata: {"type":"response.created"}\n\n' + 'event: response.completed\ndata: {"type":"response.completed",' + '"response":{"usage":{"input_tokens":5,"output_tokens":3,' + '"total_tokens":8}}}\n\n' + ) + + def upstream(_request: httpx.Request): + return httpx.Response( + 200, + content=payload, + headers={"content-type": "text/event-stream"}, + ) + + app = create_inference_gateway_app( + config=_config(tmp_path), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + response = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hello", "stream": True}, + ) + + assert response.status_code == 200 + assert "response.completed" in response.text + persisted = json.loads((tmp_path / "usage.json").read_text()) + usage = persisted["scopes"]["producer"] + assert usage["active_requests"] == 0 + assert usage["total_tokens"] == 8 + + +def test_gateway_reloads_usage_and_enforces_budget(tmp_path): + config = _config(tmp_path, max_requests=1) + (tmp_path / "usage.json").write_text( + json.dumps( + { + "schema_version": 1, + "scopes": { + "producer": { + "requests": 1, + "upstream_errors": 0, + "input_tokens": 1, + "output_tokens": 1, + "total_tokens": 2, + "active_requests": 4, + "attributions": {}, + } + }, + } + ) + ) + app = create_inference_gateway_app( + config=config, + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(lambda _request: httpx.Response(200, json={})), + ) + with TestClient(app) as client: + exhausted = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hello"}, + ) + + assert exhausted.status_code == 429 + assert app.state.usage_store.ledger.scopes["producer"].active_requests == 0 + + +def test_gateway_forwards_arbitrary_endpoints_to_upstream(tmp_path): + # Provider-agnostic passthrough: an endpoint the gateway has never heard of + # (here the Anthropic Messages surface) is forwarded to the upstream proxy, + # with the same scope-token + model + budget enforcement as any other call. + observed = [] + + def upstream(request: httpx.Request): + observed.append(request) + return httpx.Response( + 200, json={"usage": {"input_tokens": 3, "output_tokens": 2}} + ) + + app = create_inference_gateway_app( + config=_config(tmp_path), + upstream_api_key="upstream-secret", + upstream_base_url="https://provider.example/v1", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + forwarded = client.post( + "/scopes/producer/optimizer/v1/messages", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "messages": []}, + ) + wrong_model = client.post( + "/scopes/producer/optimizer/v1/messages", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "other", "messages": []}, + ) + + assert forwarded.status_code == 200 + assert wrong_model.status_code == 403 # model allow-list still applies + assert str(observed[0].url) == "https://provider.example/v1/messages" + assert observed[0].headers["authorization"] == "Bearer upstream-secret" + + +def test_gateway_accepts_scope_token_via_x_api_key_header(tmp_path): + # Anthropic/Claude clients send the scope token as `x-api-key`, not + # `Authorization: Bearer`; the gateway must accept either. + observed = [] + + def upstream(request: httpx.Request): + observed.append(request) + return httpx.Response(200, json={"usage": {"input_tokens": 1}}) + + app = create_inference_gateway_app( + config=_config(tmp_path), + upstream_api_key="upstream-secret", + upstream_base_url="https://provider.example/v1", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + via_x_api_key = client.post( + "/scopes/producer/optimizer/v1/messages", + headers={"x-api-key": "scoped-token"}, + json={"model": "gpt-test", "messages": []}, + ) + no_token = client.post( + "/scopes/producer/optimizer/v1/messages", + headers={"x-api-key": "wrong"}, + json={"model": "gpt-test", "messages": []}, + ) + + assert via_x_api_key.status_code == 200 + assert no_token.status_code == 403 + # the upstream never sees the scope token; it gets the upstream credential + assert observed[0].headers["authorization"] == "Bearer upstream-secret" + + +def test_gateway_rejects_path_traversal_endpoints(tmp_path): + app = create_inference_gateway_app( + config=_config(tmp_path), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(lambda _request: httpx.Response(200, json={})), + ) + with TestClient(app) as client: + # Percent-encoded so the client doesn't normalize `..` away before it + # reaches the route; the server-side guard must still reject it. + traversal = client.post( + "/scopes/producer/optimizer/v1/%2e%2e/admin", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test"}, + ) + + assert traversal.status_code == 400 + assert traversal.json()["error"]["code"] == "invalid_endpoint" + + +def test_gateway_releases_concurrency_reservation_on_upstream_failure(tmp_path): + def unavailable(request: httpx.Request): + raise httpx.ConnectError("offline", request=request) + + app = create_inference_gateway_app( + config=_config(tmp_path), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(unavailable), + ) + with TestClient(app) as client: + response = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hello"}, + ) + + assert response.status_code == 502 + usage = app.state.usage_store.ledger.scopes["producer"] + assert usage.active_requests == 0 + assert usage.upstream_errors == 1 diff --git a/vero/tests/test_v05_harbor_session.py b/vero/tests/test_v05_harbor_session.py new file mode 100644 index 00000000..81d2b159 --- /dev/null +++ b/vero/tests/test_v05_harbor_session.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +import io +import json +import tarfile +from datetime import UTC, datetime + +import pytest + +from vero.evaluation import ( + BackendProvenance, + EvaluationSet, + MetricSelector, + ObjectiveSpec, +) +from vero.harbor.session import ( + HarborSessionManifest, + create_harbor_session_archive, + extract_harbor_session_archive, + file_sha256, +) +from vero.harbor.verifier import VerificationSelection, VerificationTarget + + +def _manifest() -> HarborSessionManifest: + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + evaluation_set = EvaluationSet(name="benchmark", partition="validation") + return HarborSessionManifest( + id="trial", + task_name="org/optimize", + created_at=datetime(2026, 7, 16, tzinfo=UTC), + backends={ + "validation": BackendProvenance( + name="harbor", + version="2", + config_digest="a" * 64, + ) + }, + selection=VerificationSelection(mode="submit"), + targets=[ + VerificationTarget( + reward_key="reward", + backend_id="validation", + evaluation_set=evaluation_set, + objective=objective, + ) + ], + ) + + +def test_harbor_session_archive_round_trip_and_checksum(tmp_path): + session = tmp_path / "source" + session.mkdir() + (session / "harbor-session.json").write_text( + _manifest().model_dump_json(indent=2) + "\n" + ) + (session / "database.json").write_text('{"id":"trial"}\n') + archive = create_harbor_session_archive(session, tmp_path / "session.tar.gz") + + extracted = extract_harbor_session_archive(archive, tmp_path / "extracted") + + assert ( + HarborSessionManifest.model_validate_json( + (extracted / "harbor-session.json").read_text() + ).id + == "trial" + ) + assert len(file_sha256(archive)) == 64 + + +def test_harbor_session_archive_records_symlinks_as_metadata_without_failing(tmp_path): + # Regression: a single symlink anywhere in the tree used to raise and 500 the + # whole export, discarding an entire run's data. It must instead succeed, + # preserve each link's target as inert metadata, and record the omission. + session = tmp_path / "source" + session.mkdir() + (session / "harbor-session.json").write_text( + _manifest().model_dump_json(indent=2) + "\n" + ) + (session / "database.json").write_text('{"id":"trial"}\n') + (session / "link_in").symlink_to("database.json") # relative, in-tree + (session / "link_abs").symlink_to("/etc/hostname") # absolute, out-of-tree + + archive = create_harbor_session_archive(session, tmp_path / "session.tar.gz") + + # No link members survive (keeps the archive extractable + traversal-safe). + with tarfile.open(archive) as tar: + members = tar.getmembers() + assert not any(m.issym() or m.islnk() for m in members) + names = {m.name for m in members} + assert "session/link_in.symlink" in names + assert "session/link_abs.symlink" in names + assert "session/vero-export-skipped.json" in names + + extracted = extract_harbor_session_archive(archive, tmp_path / "extracted") + assert (extracted / "database.json").read_text() == '{"id":"trial"}\n' + assert "database.json" in (extracted / "link_in.symlink").read_text() + assert "/etc/hostname" in (extracted / "link_abs.symlink").read_text() + skipped = json.loads((extracted / "vero-export-skipped.json").read_text()) + reasons = {entry["path"]: entry["reason"] for entry in skipped["skipped"]} + assert reasons["session/link_in"] == "symlink" + assert reasons["session/link_abs"] == "symlink" + + +def test_harbor_session_archive_rejects_traversal(tmp_path): + archive = tmp_path / "unsafe.tar.gz" + with tarfile.open(archive, "w:gz") as output: + member = tarfile.TarInfo("session/../../escape") + payload = b"bad" + member.size = len(payload) + output.addfile(member, io.BytesIO(payload)) + + with pytest.raises(ValueError, match="unsafe Harbor session archive member"): + extract_harbor_session_archive(archive, tmp_path / "extracted") diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py new file mode 100644 index 00000000..019c98fa --- /dev/null +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -0,0 +1,700 @@ +from __future__ import annotations + +import asyncio +import json +import shutil +import subprocess +from contextlib import asynccontextmanager +from datetime import UTC, datetime +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendProvenance, + BackendRegistry, + BudgetLedger, + CaseIds, + CaseResult, + CaseStatus, + DisclosureLevel, + EvaluationBudget, + EvaluationCost, + EvaluationDatabase, + EvaluationDeniedError, + EvaluationEngine, + EvaluationAuthorization, + EvaluationLimits, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + Evaluator, + MetricSelector, + ObjectiveSpec, +) +from vero.harbor import ( + EvaluationAccessError, + EvaluationJobStatus, + SidecarEvaluationPolicy, + EvaluationSidecar, + GitCandidateTransport, + SidecarEvaluationRequest, + SubmissionDisabledError, +) +from vero.runtime.context import context_digest +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace, Workspace + + +class StubWorkspace(Workspace): + def __init__(self, root: Path, version: str): + root.mkdir(parents=True, exist_ok=True) + self._root = root + self._version = version + + @property + def sandbox(self): + return None + + @property + def root(self) -> str: + return str(self._root) + + @property + def project_path(self) -> str: + return str(self._root) + + @property + def name(self) -> str: + return "stub" + + async def current_version(self) -> str: + return self._version + + async def save(self, message="Save") -> str: + return self._version + + async def restore(self, version_id, message=None) -> str: + self._version = version_id + return version_id + + async def diff(self, from_version=None, to_version=None) -> str: + return "" + + async def log(self, max_count=10, since_version=None) -> str: + return "" + + async def is_ancestor(self, version_a, version_b) -> bool: + return True + + async def copy(self, name=None, from_version=None): + return StubWorkspace(self._root, from_version or self._version) + + @asynccontextmanager + async def temp_copy(self, from_version=None): + yield StubWorkspace(self._root, from_version or self._version) + + @asynccontextmanager + async def at(self, version_id): + previous = self._version + self._version = version_id + try: + yield + finally: + self._version = previous + + async def is_dirty(self) -> bool: + return False + + +class StubCandidateRepository: + family = "stub" + + def __init__(self, root: Path): + self.root = root + + @asynccontextmanager + async def checkout(self, candidate, *, sandbox, name=None): + yield StubWorkspace(self.root, candidate.version) + + +class StubBackend: + @property + def provenance(self): + return BackendProvenance( + name="stub", + version="1", + config_digest="0" * 64, + ) + + async def resolve_cost(self, evaluation_set): + selection = evaluation_set.selection + if isinstance(selection, CaseIds): + return EvaluationCost(cases=len(selection.ids)) + return EvaluationCost(cases=8) + + async def evaluate(self, *, context, request): + selection = request.evaluation_set.selection + ids = ( + selection.ids + if isinstance(selection, CaseIds) + else [f"case-{i}" for i in range(8)] + ) + cases = [ + CaseResult( + case_id=case_id, + status=CaseStatus.SUCCESS, + metrics={"score": 0.75}, + ) + for case_id in ids + ] + for case in cases: + await context.case_store.save(case) + return EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 0.75}, + cases=cases, + ) + + +class StubTransport: + def __init__(self, candidate: Candidate): + self.candidate = candidate + self.calls: list[str | None] = [] + + async def import_candidate(self, version=None): + self.calls.append(version) + return self.candidate + + +def _sidecar(tmp_path: Path, *, submit_enabled=False, fixed_limits=False, disclose_budget=True): + candidate = Candidate( + id="candidate", + version="candidate-version", + created_at=datetime(2026, 1, 1, tzinfo=UTC), + ) + workspace = StubWorkspace(tmp_path / "repo", candidate.version) + backend = StubBackend() + evaluation_set = EvaluationSet(name="benchmark", partition="validation") + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="primary", + evaluation_set_key=evaluation_set.budget_key("primary"), + total_runs=3, + total_cases=20, + ) + ] + ) + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=StubCandidateRepository(tmp_path / "stub-workspace"), + sandbox=workspace.sandbox, + session_dir=tmp_path / "session", + ), + backends=BackendRegistry({"primary": backend, "secondary": StubBackend()}), + database=EvaluationDatabase(id="session"), + budget_ledger=ledger, + ) + transport = StubTransport(candidate) + sidecar = EvaluationSidecar( + engine=engine, + candidate_transport=transport, + access_policies=[ + SidecarEvaluationPolicy( + backend_id="primary", + evaluation_set_name="benchmark", + partition="validation", + disclosure=DisclosureLevel.AGGREGATE, + min_aggregate_cases=5, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + limits=EvaluationLimits() if fixed_limits else None, + ), + SidecarEvaluationPolicy( + backend_id="secondary", + evaluation_set_name="public", + disclosure=DisclosureLevel.FULL, + ), + ], + agent_volume=tmp_path / "agent-volume", + admin_volume=tmp_path / "admin-volume", + submit_enabled=submit_enabled, + disclose_budget=disclose_budget, + ) + return sidecar, transport, ledger + + +@pytest.mark.asyncio +async def test_sidecar_uses_canonical_disclosure_budget_and_multiple_backends(tmp_path): + sidecar, transport, ledger = _sidecar(tmp_path) + evaluation_set = EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ) + + response = await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=evaluation_set, + version="HEAD", + ) + ) + + assert response.disclosure == DisclosureLevel.AGGREGATE + assert response.receipt.result.metrics == {"score": 0.75} + assert response.receipt.result.total_cases == 5 + assert transport.calls == ["HEAD"] + assert response.receipt.result_path == ( + f".vero/evaluations/{context_digest(response.receipt.evaluation_id)}/" + "evaluation.json" + ) + assert ( + tmp_path + / "agent-volume" + / "evaluations" + / context_digest(response.receipt.evaluation_id) + / "evaluation.json" + ).is_file() + budget = ledger.get("primary", evaluation_set) + assert budget.remaining_runs == 2 + assert budget.remaining_cases == 15 + status = sidecar.status() + assert [item.backend_id for item in status.evaluation_access] == [ + "primary", + "secondary", + ] + assert status.evaluation_access[0].expose_case_resources is False + # The synchronous evaluation is now a tracked job, so it is visible in + # status exactly like a detached one. + assert len(status.evaluation_jobs) == 1 + assert status.evaluation_jobs[0].status == EvaluationJobStatus.COMPLETE + assert status.evaluation_jobs[0].evaluation_id == response.receipt.evaluation_id + + +@pytest.mark.asyncio +async def test_sidecar_detached_job_returns_before_evaluation_and_persists_result( + tmp_path, +): + sidecar, _, _ = _sidecar(tmp_path) + + class BlockingBackend(StubBackend): + def __init__(self): + self.started = asyncio.Event() + self.release = asyncio.Event() + + async def evaluate(self, *, context, request): + self.started.set() + await self.release.wait() + return await super().evaluate(context=context, request=request) + + backend = BlockingBackend() + sidecar.engine.backends._backends["primary"] = backend + job = await sidecar.start_evaluation_job( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ), + version="HEAD", + ) + ) + + assert job.status == EvaluationJobStatus.RUNNING + assert job.version == "candidate-version" + assert job.receipt is None + await backend.started.wait() + assert sidecar.status().evaluation_jobs[0].job_id == job.job_id + drain = asyncio.create_task( + sidecar.engine.quiesce_agent_evaluations(timeout_seconds=1.0) + ) + await asyncio.sleep(0) + assert not drain.done() + + backend.release.set() + task = sidecar._evaluation_job_tasks[job.job_id] + await task + assert await drain == 1 + + completed = sidecar.evaluation_job(job.job_id) + assert completed.status == EvaluationJobStatus.COMPLETE + assert completed.receipt is not None + assert completed.receipt.result.metrics == {"score": 0.75} + persisted = tmp_path / "session/evaluation-jobs" / f"{job.job_id}.json" + assert json.loads(persisted.read_text())["status"] == "complete" + + +@pytest.mark.asyncio +async def test_sidecar_detached_job_reports_safe_admission_failure(tmp_path): + sidecar, _, _ = _sidecar(tmp_path) + + job = await sidecar.start_evaluation_job( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet(name="private", partition="validation"), + ) + ) + + assert job.status == EvaluationJobStatus.FAILED + assert job.error == "evaluation denied" + assert job.receipt is None + + +@pytest.mark.asyncio +async def test_sidecar_tracks_candidate_import_as_part_of_an_agent_evaluation( + tmp_path, +): + sidecar, original_transport, _ = _sidecar(tmp_path) + + class BlockingTransport(StubTransport): + def __init__(self, candidate): + super().__init__(candidate) + self.started = asyncio.Event() + self.release = asyncio.Event() + + async def import_candidate(self, version=None): + self.calls.append(version) + self.started.set() + await self.release.wait() + return self.candidate + + transport = BlockingTransport(original_transport.candidate) + sidecar.candidate_transport = transport + evaluation = asyncio.create_task( + sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + ), + version="HEAD", + ) + ) + ) + await transport.started.wait() + + drain = asyncio.create_task( + sidecar.engine.quiesce_agent_evaluations(timeout_seconds=1.0) + ) + await asyncio.sleep(0) + assert not drain.done() + transport.release.set() + + assert (await evaluation).receipt.status == EvaluationStatus.SUCCESS + assert await drain == 1 + with pytest.raises(EvaluationDeniedError, match="finalization has started"): + await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + ), + ) + ) + + +def test_sidecar_status_reports_inference_usage_and_remaining_budget(tmp_path): + sidecar, _, _ = _sidecar(tmp_path) + usage_path = tmp_path / "inference/usage.json" + usage_path.parent.mkdir() + usage_path.write_text( + json.dumps( + { + "schema_version": 1, + "scopes": { + "producer": { + "requests": 3, + "total_tokens": 120, + "active_requests": 0, + } + }, + } + ) + ) + sidecar.inference_usage_path = usage_path + sidecar.inference_limits = { + "producer": { + "allowed_models": ["gpt-test"], + "max_requests": 10, + "max_tokens": 1000, + "max_concurrency": 2, + } + } + + usage = sidecar.status().inference_usage + + assert usage is not None + assert usage["producer"]["requests"] == 3 + assert usage["producer"]["remaining_requests"] == 7 + assert usage["producer"]["remaining_tokens"] == 880 + + +def test_sidecar_hides_budget_when_disclosure_disabled(tmp_path): + # Budget-blind mode: enforcement is unchanged but /status must not reveal + # per-set case budgets or inference remaining, closing the live leak. + disclosed, _, _ = _sidecar(tmp_path) + assert any(a.budget is not None for a in disclosed.status().evaluation_access) + + blind, _, _ = _sidecar(tmp_path, disclose_budget=False) + usage_path = tmp_path / "inference/usage.json" + usage_path.parent.mkdir() + usage_path.write_text(json.dumps({"schema_version": 1, "scopes": {}})) + blind.inference_usage_path = usage_path + blind.inference_limits = {"producer": {"max_requests": 10, "max_tokens": 1000}} + + status = blind.status() + assert all(a.budget is None for a in status.evaluation_access) + assert status.inference_usage is None + + +@pytest.mark.asyncio +async def test_sidecar_context_survives_restart_without_disclosing_admin_runs( + tmp_path: Path, +): + sidecar, transport, _ = _sidecar(tmp_path) + admin = await sidecar.engine.evaluate_record( + backend_id="secondary", + request=EvaluationRequest( + candidate=transport.candidate, + evaluation_set=EvaluationSet(name="public"), + ), + authorization=EvaluationAuthorization( + may_evaluate=True, + meter_budget=False, + disclosure=DisclosureLevel.FULL, + ), + ) + response = await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{index}" for index in range(5)]), + ), + ) + ) + + restarted = EvaluationSidecar( + engine=sidecar.engine, + candidate_transport=transport, + access_policies=list(sidecar._policies.values()), + agent_volume=tmp_path / "agent-volume", + admin_volume=tmp_path / "admin-volume", + ) + await restarted.initialize_context() + + index = json.loads((tmp_path / "agent-volume/evaluations/index.json").read_text())[ + "evaluations" + ] + assert [entry["evaluation_id"] for entry in index] == [ + response.receipt.evaluation_id + ] + assert admin.id not in json.dumps(index) + + +@pytest.mark.asyncio +async def test_sidecar_fails_closed_before_transfer_or_budget(tmp_path): + sidecar, transport, ledger = _sidecar(tmp_path) + too_small = SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=["single"]), + ), + ) + + with pytest.raises(EvaluationAccessError, match="at least 5"): + await sidecar.evaluate(too_small) + # The synchronous failure is recorded as a tracked job (visible in status) + # even though the error is still re-raised to the caller. + assert sidecar.status().evaluation_jobs[0].status == EvaluationJobStatus.FAILED + assert sidecar.status().evaluation_jobs[0].error == "evaluation denied" + with pytest.raises(EvaluationAccessError, match="not agent-evaluable"): + await sidecar.evaluate( + too_small.model_copy( + update={"evaluation_set": EvaluationSet(name="hidden")} + ) + ) + with pytest.raises(EvaluationAccessError, match="not agent-controllable"): + await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + ), + parameters={"harbor_model_override": "untrusted-model"}, + ) + ) + + assert transport.calls == [] + budget = ledger.get("primary", too_small.evaluation_set) + assert budget.remaining_runs == 3 + assert budget.remaining_cases == 20 + + +@pytest.mark.asyncio +async def test_sidecar_rejects_agent_limits_when_policy_fixes_them(tmp_path): + sidecar, transport, ledger = _sidecar(tmp_path, fixed_limits=True) + evaluation_set = EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ) + + with pytest.raises(EvaluationAccessError, match="limits are fixed"): + await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=evaluation_set, + limits=EvaluationLimits(timeout_seconds=10), + ) + ) + + assert transport.calls == [] + budget = ledger.get("primary", evaluation_set) + assert budget.remaining_runs == 3 + assert budget.remaining_cases == 20 + + +@pytest.mark.asyncio +async def test_sidecar_submission_is_explicit_and_durable(tmp_path): + sidecar, _, _ = _sidecar(tmp_path) + with pytest.raises(SubmissionDisabledError): + await sidecar.submit() + + enabled, _, _ = _sidecar(tmp_path / "enabled", submit_enabled=True) + submission = await enabled.submit("candidate-ref") + + assert submission.candidate.version == "candidate-version" + assert (tmp_path / "enabled/admin-volume/submission.json").is_file() + + +def _git(path: Path, *arguments: str) -> str: + result = subprocess.run( + ["git", *arguments], + cwd=path, + check=True, + text=True, + capture_output=True, + ) + return result.stdout.strip() + + +def _init_repo(path: Path, content: str) -> str: + path.mkdir(parents=True) + _git(path, "init", "-q") + _git(path, "config", "user.name", "VeRO Test") + _git(path, "config", "user.email", "vero@example.test") + (path / "program.txt").write_text(content, encoding="utf-8") + _git(path, "add", "program.txt") + _git(path, "commit", "-q", "-m", f"program {content}") + return _git(path, "rev-parse", "HEAD") + + +@pytest.mark.asyncio +async def test_git_candidate_transport_fetches_to_stable_ref(tmp_path, monkeypatch): + agent_repo = tmp_path / "agent repo" + trusted_repo = tmp_path / "trusted" + agent_commit = _init_repo(agent_repo, "candidate") + _init_repo(trusted_repo, "baseline") + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(trusted_repo)) + system_config = tmp_path / "trusted-system-gitconfig" + _git( + tmp_path, + "config", + "--file", + str(system_config), + "--add", + "safe.directory", + str(trusted_repo), + ) + _git( + tmp_path, + "config", + "--file", + str(system_config), + "--add", + "safe.directory", + str(agent_repo), + ) + _git( + tmp_path, + "config", + "--file", + str(system_config), + "--add", + "safe.directory", + str(agent_repo / ".git"), + ) + original_run = sandbox.run + + async def run_as_different_owner(command, cwd=None, timeout=30, env=None): + return await original_run( + command, + cwd=cwd, + timeout=timeout, + env={ + **(env or {}), + "GIT_TEST_ASSUME_DIFFERENT_OWNER": "1", + "GIT_CONFIG_SYSTEM": str(system_config), + }, + ) + + monkeypatch.setattr(sandbox, "run", run_as_different_owner) + candidate_repository = await GitCandidateRepository.create( + tmp_path / "candidate-store", + workspace=workspace, + ) + transport = GitCandidateTransport( + workspace=workspace, + candidate_repository=candidate_repository, + agent_repo_path=str(agent_repo), + ) + + candidate = await transport.import_candidate() + repeated = await transport.import_candidate(agent_commit) + + assert candidate == repeated + assert candidate.version == agent_commit + assert candidate.description == "program candidate" + shutil.rmtree(agent_repo) + checkout_sandbox = await LocalSandbox.create(root=tmp_path) + async with candidate_repository.checkout( + candidate, + sandbox=checkout_sandbox, + ) as checkout: + assert await checkout.current_version() == agent_commit + retained_refs = _git( + candidate_repository.repository_path, + "for-each-ref", + "--format=%(refname)", + "refs/vero/candidates", + ).splitlines() + assert len(retained_refs) == 1 + assert ( + _git(candidate_repository.repository_path, "rev-parse", retained_refs[0]) + == agent_commit + ) + assert ( + _git( + candidate_repository.repository_path, + "for-each-ref", + "--format=%(refname)", + "refs/vero/incoming", + ) + == "" + ) diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py new file mode 100644 index 00000000..7787e4a6 --- /dev/null +++ b/vero/tests/test_v05_harbor_verifier.py @@ -0,0 +1,452 @@ +from __future__ import annotations + +from datetime import UTC, datetime, timedelta +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + BackendProvenance, + BackendRegistry, + CaseResult, + CaseStatus, + EvaluationDatabase, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.harbor import ( + CanonicalVerifier, + Submission, + VerificationSelection, + VerificationTarget, +) + + +OBJECTIVE = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", +) + + +class StubBackend: + @property + def provenance(self): + return BackendProvenance( + name="stub", + version="1", + config_digest="0" * 64, + ) + + async def resolve_cost(self, evaluation_set): + raise AssertionError("fake engine handles evaluation directly") + + async def evaluate(self, *, context, request): + raise AssertionError("fake engine handles evaluation directly") + + +class FakeEngine: + def __init__(self, scores): + self.backends = BackendRegistry({"backend": StubBackend()}) + self.database = EvaluationDatabase(id="session") + self.scores = scores + self.calls = [] + self.drain_calls = [] + self.on_drain = None + self._sequence = 0 + + async def quiesce_agent_evaluations(self, *, timeout_seconds): + self.drain_calls.append(timeout_seconds) + if self.on_drain is not None: + self.on_drain() + return 0 + + async def evaluate_record( + self, + *, + backend_id, + request, + objective_spec, + authorization, + principal, + ): + self.calls.append((request.candidate.version, request.evaluation_set.name)) + key = (request.candidate.version, request.evaluation_set.name) + value = self.scores[key] + if isinstance(value, list): + value = value.pop(0) + if isinstance(value, Exception): + raise value + self._sequence += 1 + record = _record( + f"admin-{self._sequence}", + request.candidate, + request.evaluation_set, + float(value), + objective_spec, + backend_id=backend_id, + ) + self.database.add_evaluation(record) + assert authorization.meter_budget is False + assert principal.value == "admin" + return record + + +def _candidate(name: str, *, content: str | None = None, seconds: int = 0): + return Candidate( + id=name, + version=name, + created_at=datetime(2026, 1, 1, tzinfo=UTC) + timedelta(seconds=seconds), + metadata={"content_digest": content or name}, + ) + + +def _record( + record_id: str, + candidate: Candidate, + evaluation_set: EvaluationSet, + score: float, + objective: ObjectiveSpec = OBJECTIVE, + *, + backend_id: str = "backend", +): + now = datetime(2026, 2, 1, tzinfo=UTC) + from vero.evaluation import EvaluationRequest + + return EvaluationRecord( + id=record_id, + request=EvaluationRequest( + candidate=candidate, + evaluation_set=evaluation_set, + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": score}, + ), + backend_id=backend_id, + backend=StubBackend().provenance, + objective_spec=objective, + objective=ObjectiveResult(value=score, feasible=True), + created_at=now, + completed_at=now, + ) + + +def _verifier( + tmp_path: Path, + engine: FakeEngine, + *, + baseline: Candidate, + top_k: int = 1, + score_baseline: bool = True, +): + return CanonicalVerifier( + engine=engine, + selection=VerificationSelection( + mode="auto_best", + backend_id="backend", + evaluation_set=EvaluationSet(name="selection"), + objective=OBJECTIVE, + baseline_candidate=baseline, + rescore_top_k=top_k, + rescore_attempts=1, + ), + targets=[ + VerificationTarget( + reward_key="reward", + backend_id="backend", + evaluation_set=EvaluationSet(name="test"), + objective=OBJECTIVE, + max_attempts=1, + ) + ], + admin_volume=tmp_path, + score_baseline=score_baseline, + ) + + +@pytest.mark.asyncio +async def test_verifier_pools_repeats_then_admin_rescores_and_scores_baseline(tmp_path): + baseline = _candidate("baseline") + farmed = _candidate("farmed", content="same-code", seconds=1) + duplicate = _candidate("duplicate", content="same-code", seconds=2) + steady = _candidate("steady", seconds=3) + engine = FakeEngine( + { + ("steady", "selection"): 0.65, + ("steady", "test"): 0.8, + ("baseline", "selection"): 0.6, + ("baseline", "test"): 0.5, + } + ) + for index, (candidate, score) in enumerate( + [(farmed, 0.95), (duplicate, 0.05), (steady, 0.7)] + ): + engine.database.add_evaluation( + _record( + f"record-{index}", candidate, EvaluationSet(name="selection"), score + ) + ) + + result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + + assert result.candidate == steady + assert result.rewards == {"reward": 0.8} + assert result.baseline_rewards == {"reward": 0.5} + assert engine.calls == [ + ("steady", "selection"), + ("baseline", "selection"), + ("steady", "test"), + ("baseline", "test"), + ] + assert engine.drain_calls == [600.0] + + +@pytest.mark.asyncio +async def test_verifier_waits_for_an_inflight_selection_evaluation(tmp_path): + baseline = _candidate("baseline") + candidate = _candidate("candidate", seconds=1) + engine = FakeEngine( + { + ("candidate", "selection"): 0.8, + ("baseline", "selection"): 0.5, + ("candidate", "test"): 0.9, + } + ) + + def complete_inflight(): + engine.database.add_evaluation( + _record( + "agent-selection", + candidate, + EvaluationSet(name="selection"), + 0.75, + ) + ) + + engine.on_drain = complete_inflight + + result = await _verifier( + tmp_path, + engine, + baseline=baseline, + score_baseline=False, + ).finalize() + + assert result.candidate == candidate + assert result.rewards == {"reward": 0.9} + assert engine.drain_calls == [600.0] + + +@pytest.mark.asyncio +async def test_verifier_baseline_floor_prevents_shipping_a_regression(tmp_path): + baseline = _candidate("baseline") + candidate = _candidate("candidate", seconds=1) + engine = FakeEngine( + { + ("candidate", "selection"): 0.55, + ("baseline", "selection"): 0.6, + ("baseline", "test"): 0.5, + } + ) + engine.database.add_evaluation( + _record("selection", candidate, EvaluationSet(name="selection"), 0.9) + ) + + result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + + assert result.candidate == baseline + assert result.rewards == {"reward": 0.5} + assert result.baseline_rewards == result.rewards + assert engine.calls.count(("baseline", "test")) == 1 + + +@pytest.mark.asyncio +async def test_submit_finalization_is_durable_and_idempotent(tmp_path): + candidate = _candidate("submitted") + engine = FakeEngine({("submitted", "test"): 0.9}) + tmp_path.mkdir(exist_ok=True) + (tmp_path / "submission.json").write_text( + Submission(candidate=candidate).model_dump_json(), + encoding="utf-8", + ) + selection = VerificationSelection(mode="submit", baseline_floor=False) + target = VerificationTarget( + reward_key="reward", + backend_id="backend", + evaluation_set=EvaluationSet(name="test"), + objective=OBJECTIVE, + max_attempts=1, + ) + + first = await CanonicalVerifier( + engine=engine, + selection=selection, + targets=[target], + admin_volume=tmp_path, + score_baseline=False, + ).finalize() + engine.scores[("submitted", "test")] = 0.1 + replayed = await CanonicalVerifier( + engine=engine, + selection=selection, + targets=[target], + admin_volume=tmp_path, + score_baseline=False, + ).finalize() + + assert first == replayed + assert replayed.shipped is True + assert replayed.rewards == {"reward": 0.9} + assert engine.calls == [("submitted", "test")] + + +@pytest.mark.asyncio +async def test_verifier_floors_rewards_when_no_candidate_exists(tmp_path): + baseline = _candidate("baseline") + engine = FakeEngine({}) + result = await _verifier( + tmp_path, + engine, + baseline=baseline, + score_baseline=False, + ).finalize() + + assert result.candidate is None + # "Nothing shipped" is an explicit, distinct outcome — not a real zero. + assert result.shipped is False + assert result.rewards == {"reward": 0.0} + assert "selection" in result.errors + + +@pytest.mark.asyncio +async def test_verifier_transforms_minimization_objective_into_higher_reward(tmp_path): + candidate = _candidate("submitted") + minimize = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="minimize", + ) + engine = FakeEngine({("submitted", "latency"): 2.5}) + (tmp_path / "submission.json").write_text( + Submission(candidate=candidate).model_dump_json(), + encoding="utf-8", + ) + verifier = CanonicalVerifier( + engine=engine, + selection=VerificationSelection(mode="submit", baseline_floor=False), + targets=[ + VerificationTarget( + reward_key="latency_reward", + backend_id="backend", + evaluation_set=EvaluationSet(name="latency"), + objective=minimize, + reward_scale=-1.0, + max_attempts=1, + ) + ], + admin_volume=tmp_path, + score_baseline=False, + ) + + result = await verifier.finalize() + + assert result.rewards == {"latency_reward": -2.5} + + +def _record_with_cases(record_id, candidate, evaluation_set, score, case_ids): + now = datetime(2026, 2, 1, tzinfo=UTC) + return EvaluationRecord( + id=record_id, + request=EvaluationRequest(candidate=candidate, evaluation_set=evaluation_set), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": score}, + cases=[ + CaseResult( + case_id=cid, status=CaseStatus.SUCCESS, metrics={"score": score} + ) + for cid in case_ids + ], + ), + backend_id="backend", + backend=StubBackend().provenance, + objective_spec=OBJECTIVE, + objective=ObjectiveResult(value=score, feasible=True), + created_at=now, + completed_at=now, + ) + + +@pytest.mark.asyncio +async def test_verifier_prefers_agent_submission_over_auto_best(tmp_path): + baseline = _candidate("baseline") + picked = _candidate("picked", seconds=5) + engine = FakeEngine({("picked", "test"): 0.9, ("baseline", "test"): 0.5}) + engine.database.add_evaluation( + _record("r", _candidate("other", seconds=1), EvaluationSet(name="selection"), 0.7) + ) + (tmp_path / "submission.json").write_text( + Submission(candidate=picked).model_dump_json() + ) + + result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + + assert result.candidate == picked + assert result.shipped is True + assert result.rewards == {"reward": 0.9} + # The submitted candidate wins outright; auto_best never re-scored 'other'. + assert ("other", "selection") not in engine.calls + + +@pytest.mark.asyncio +async def test_verifier_falls_back_to_last_candidate_when_no_selection_eval(tmp_path): + baseline = _candidate("baseline") + last = _candidate("last", seconds=9) + engine = FakeEngine({("last", "test"): 0.42, ("baseline", "test"): 0.5}) + # Only a non-selection-partition eval exists -> no qualifying selection records. + engine.database.add_evaluation( + _record("r", last, EvaluationSet(name="development"), 0.7) + ) + + result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + + # pick-last ships the most recent candidate instead of shipping nothing. + assert result.candidate == last + assert result.shipped is True + assert result.rewards == {"reward": 0.42} + + +@pytest.mark.asyncio +async def test_verifier_coverage_threshold_excludes_undermeasured_candidates(tmp_path): + baseline = _candidate("baseline") + well = _candidate("well", seconds=1) + thin = _candidate("thin", seconds=2) + engine = FakeEngine( + { + ("well", "selection"): 0.6, + ("well", "test"): 0.7, + ("baseline", "selection"): 0.5, + ("baseline", "test"): 0.4, + } + ) + engine.database.add_evaluation( + _record_with_cases( + "rw", well, EvaluationSet(name="selection"), 0.6, [f"c{i}" for i in range(10)] + ) + ) + # Higher score but only 2/10 coverage -> below the 0.9 threshold -> excluded. + engine.database.add_evaluation( + _record_with_cases("rt", thin, EvaluationSet(name="selection"), 0.99, ["c0", "c1"]) + ) + + result = await _verifier(tmp_path, engine, baseline=baseline, top_k=5).finalize() + + assert result.candidate == well + assert ("thin", "selection") not in engine.calls + assert ("well", "selection") in engine.calls diff --git a/vero/tests/test_v05_optimizer.py b/vero/tests/test_v05_optimizer.py new file mode 100644 index 00000000..dcfe1e48 --- /dev/null +++ b/vero/tests/test_v05_optimizer.py @@ -0,0 +1,535 @@ +from __future__ import annotations + +import asyncio +import subprocess +import sys +from pathlib import Path + +import pytest + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendRegistry, + CommandBackend, + CommandBackendConfig, + EvaluationDatabase, + EvaluationEngine, + EvaluationPlan, + EvaluationSet, + Evaluator, + MetricSelector, + ObjectiveSpec, + allow_all_evaluations, +) +from vero.optimization import ( + CandidateChange, + CandidateProposal, + CommandCandidateProducer, + CommandCandidateProducerConfig, + Optimizer, + SequentialStrategy, +) +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + + +def initialize_repository(path: Path) -> str: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + return subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=path, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + + +@pytest.mark.asyncio +async def test_optimizer_improves_a_non_python_program_via_external_commands( + tmp_path: Path, +): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n") + baseline_version = initialize_repository(target) + + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path + +workspace, report_path = map(Path, sys.argv[1:]) +program = (workspace / "program.txt").read_text().strip() +latency = 1.0 if program == "fast" else 10.0 +report_path.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"latency_ms": latency, "correct": 1.0}, +})) +""" + ) + + producer_root = tmp_path / "producer" + producer_root.mkdir() + producer_script = producer_root / "optimize.py" + producer_script.write_text( + """ +import json +import os +import sys +from pathlib import Path + +workspace = Path(sys.argv[1]) +context = Path(sys.argv[2]) +assert Path(os.environ["VERO_CONTEXT_PATH"]) == context +evaluations = json.loads((context / "evaluations/index.json").read_text()) +assert len(evaluations["evaluations"]) == 1 +(workspace / "program.txt").write_text("fast\\n") +""" + ) + + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(target)) + session_dir = tmp_path / "sessions" / "optimization" + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + database = EvaluationDatabase(id="optimization") + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + ), + backends=BackendRegistry( + { + "command": CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ], + ) + ) + } + ), + database=database, + database_path=session_dir / "database.json", + authorization_resolver=allow_all_evaluations, + ) + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id="command", + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="performance")), + objective=ObjectiveSpec( + selector=MetricSelector(metric="latency_ms"), + direction="minimize", + ), + strategy=SequentialStrategy(), + producers={ + "default": CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=[ + sys.executable, + str(producer_script), + "{workspace}", + "{context}", + ], + description="Use fast implementation", + ) + ) + }, + max_proposals=1, + ) + + result = await optimizer.run() + + assert result.baseline.request.candidate.version == baseline_version + assert result.baseline.objective.value == 10.0 + assert len(result.evaluations) == 2 + assert len(result.candidates) == 2 + assert result.best.objective.value == 1.0 + assert result.best.request.candidate.parent_id == baseline_version + assert result.best.request.candidate.version != baseline_version + assert (target / "program.txt").read_text() == "slow\n" + assert len(database.evaluations) == 2 + assert (session_dir / "database.json").exists() + assert len(list((session_dir / "evaluations").iterdir())) == 2 + worktrees = subprocess.run( + ["git", "worktree", "list", "--porcelain"], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout + assert worktrees.count("worktree ") == 1 + candidate_branches = subprocess.run( + [ + "git", + "for-each-ref", + "--format=%(refname)", + "refs/heads/vero-candidate-", + ], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout + assert candidate_branches == "" + assert len(candidate_repository.list()) == 2 + assert (candidate_repository.repository_path / "HEAD").exists() + + +class _SpyGenerationBackend: + """A GenerationBackend that records calls and delegates to native production.""" + + def __init__(self, optimizer: Optimizer): + self._optimizer = optimizer + self.calls = 0 + + async def generate(self, **kwargs): + self.calls += 1 + return await self._optimizer._produce_candidate(**kwargs) + + +@pytest.mark.asyncio +async def test_optimizer_routes_production_through_generation_backend(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n") + baseline_version = initialize_repository(target) + + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path + +workspace, report_path = map(Path, sys.argv[1:]) +program = (workspace / "program.txt").read_text().strip() +latency = 1.0 if program == "fast" else 10.0 +report_path.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"latency_ms": latency, "correct": 1.0}, +})) +""" + ) + + producer_root = tmp_path / "producer" + producer_root.mkdir() + producer_script = producer_root / "optimize.py" + producer_script.write_text( + """ +import sys +from pathlib import Path + +workspace = Path(sys.argv[1]) +(workspace / "program.txt").write_text("fast\\n") +""" + ) + + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(target)) + session_dir = tmp_path / "sessions" / "optimization" + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + database = EvaluationDatabase(id="optimization") + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + ), + backends=BackendRegistry( + { + "command": CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ], + ) + ) + } + ), + database=database, + database_path=session_dir / "database.json", + authorization_resolver=allow_all_evaluations, + ) + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id="command", + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="performance")), + objective=ObjectiveSpec( + selector=MetricSelector(metric="latency_ms"), + direction="minimize", + ), + strategy=SequentialStrategy(), + producers={ + "default": CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=[sys.executable, str(producer_script), "{workspace}"], + description="Use fast implementation", + ) + ) + }, + max_proposals=1, + ) + spy = _SpyGenerationBackend(optimizer) + optimizer.generation_backend = spy + + result = await optimizer.run() + + # The Optimizer routed production through the injected backend... + assert spy.calls == 1 + # ...and the run still improved the program through it. + assert result.best.objective.value == 1.0 + assert result.best.request.candidate.version != baseline_version + + +class ReusedIdStrategy: + async def propose(self, context): + return [ + CandidateProposal( + id=context.baseline.id, + producer_id="default", + ) + ] + + +class FailingBatchProducer: + def __init__(self, sibling_started): + self.sibling_started = sibling_started + + async def produce(self, **_kwargs): + await self.sibling_started.wait() + raise RuntimeError("producer failed") + + +class CancelledBatchProducer: + def __init__(self): + self.started = asyncio.Event() + self.cancelled = False + + async def produce(self, **_kwargs): + self.started.set() + try: + await asyncio.Event().wait() + except asyncio.CancelledError: + self.cancelled = True + raise + return CandidateChange() + + +class FailingBatchStrategy: + async def propose(self, _context): + return [ + CandidateProposal(id="failing-proposal", producer_id="failing"), + CandidateProposal(id="cancelled-proposal", producer_id="cancelled"), + ] + + +@pytest.mark.asyncio +async def test_optimizer_rejects_strategy_candidate_id_reuse(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n") + initialize_repository(target) + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path +Path(sys.argv[1]).write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"score": 1.0}, +})) +""" + ) + producer_root = tmp_path / "producer" + producer_root.mkdir() + producer_script = producer_root / "noop.py" + producer_script.write_text("pass\n") + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(target)) + candidate_repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", workspace=workspace + ) + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=tmp_path / "session", + ), + backends=BackendRegistry( + { + "command": CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[sys.executable, str(harness_script), "{report}"], + ) + ) + } + ), + database=EvaluationDatabase(id="session"), + authorization_resolver=allow_all_evaluations, + ) + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id="command", + evaluation_plan=EvaluationPlan.single(EvaluationSet()), + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + strategy=ReusedIdStrategy(), + producers={ + "default": CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=[sys.executable, str(producer_script)], + ) + ) + }, + ) + + with pytest.raises(ValueError, match="reused an existing candidate ID"): + await optimizer.run() + + +@pytest.mark.asyncio +async def test_optimizer_cancels_sibling_producers_and_cleans_worktrees( + tmp_path: Path, +): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("slow\n") + initialize_repository(target) + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path +Path(sys.argv[1]).write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"score": 1.0}, +})) +""" + ) + sandbox = await LocalSandbox.create(root=tmp_path) + workspace = await GitWorkspace.from_path(sandbox, str(target)) + candidate_repository = await GitCandidateRepository.create( + tmp_path / "session" / "candidates", workspace=workspace + ) + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=tmp_path / "session", + ), + backends=BackendRegistry( + { + "command": CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[sys.executable, str(harness_script), "{report}"], + ) + ) + } + ), + database=EvaluationDatabase(id="session"), + authorization_resolver=allow_all_evaluations, + ) + cancelled = CancelledBatchProducer() + optimizer = Optimizer( + workspace=workspace, + candidate_repository=candidate_repository, + engine=engine, + backend_id="command", + evaluation_plan=EvaluationPlan.single(EvaluationSet()), + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + strategy=FailingBatchStrategy(), + producers={ + "failing": FailingBatchProducer(cancelled.started), + "cancelled": cancelled, + }, + max_proposals=2, + max_concurrency=2, + ) + + with pytest.raises(ExceptionGroup) as captured: + await optimizer.run() + + assert any( + isinstance(error, RuntimeError) and str(error) == "producer failed" + for error in captured.value.exceptions + ) + assert cancelled.cancelled + worktrees = subprocess.run( + ["git", "worktree", "list", "--porcelain"], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout + assert worktrees.count("worktree ") == 1 diff --git a/vero/tests/test_v05_python_example.py b/vero/tests/test_v05_python_example.py new file mode 100644 index 00000000..15925e18 --- /dev/null +++ b/vero/tests/test_v05_python_example.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import os +import subprocess +import sys +from pathlib import Path + + +def test_advertised_python_matmul_evaluation_runs_end_to_end(tmp_path: Path): + script = ( + Path(__file__).parents[2] + / "vero-tasks" + / "examples" + / "matmul-kernel" + / "run.py" + ) + environment = dict(os.environ) + environment["UV_CACHE_DIR"] = str( + Path(os.environ.get("UV_CACHE_DIR", tmp_path / "uv-cache")).resolve() + ) + + result = subprocess.run( + [ + sys.executable, + str(script), + "--eval-only", + "--work-dir", + str(tmp_path / "example"), + ], + cwd=script.parents[2], + env=environment, + capture_output=True, + text=True, + timeout=120, + check=False, + ) + + assert result.returncode == 0, result.stdout + result.stderr + assert "Baseline score:" in result.stdout + assert (tmp_path / "example" / "session" / "manifest.json").exists() diff --git a/vero/tests/test_v05_python_task_backend.py b/vero/tests/test_v05_python_task_backend.py new file mode 100644 index 00000000..268e1e54 --- /dev/null +++ b/vero/tests/test_v05_python_task_backend.py @@ -0,0 +1,323 @@ +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +from vero.evaluation import ( + AllCases, + CaseIds, + CaseRange, + EvaluationPlan, + EvaluationSet, + PythonTaskBackend, + PythonTaskBackendConfig, + PythonTaskEvaluationConfig, + MetricSelector, + ObjectiveSpec, +) +from vero.optimization import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, +) +from vero.runtime import create_local_optimization_session +from vero.sandbox import LocalSandbox + + +def initialize_repository(path: Path) -> None: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + + +@pytest.mark.asyncio +async def test_python_task_backend_builds_uv_command_and_resolves_cost(tmp_path: Path): + cases = tmp_path / "cases.json" + cases.write_text( + json.dumps([{"id": "a"}, {"id": "b"}, {"id": "c"}]), + encoding="utf-8", + ) + backend = PythonTaskBackend( + PythonTaskBackendConfig( + harness_root=str(tmp_path), + module="target.tasks", + task="quality", + evaluations=[ + PythonTaskEvaluationConfig(name="default", cases_path=str(cases)) + ], + target_project_directory="packages/target", + uv_executable=sys.executable, + ) + ) + + command = backend._command(EvaluationSet()).config.command + assert command[:6] == [ + sys.executable, + "run", + "--project", + "{harness}", + "--with-editable", + "{workspace}/packages/target", + ] + assert command[-4:] == ["--request", "{request}", "--report", "{report}"] + assert "{input:cases}" in command + assert (await backend.resolve_cost(EvaluationSet(selection=AllCases()))).cases == 3 + assert ( + await backend.resolve_cost(EvaluationSet(selection=CaseRange(start=1, stop=3))) + ).cases == 2 + assert ( + await backend.resolve_cost(EvaluationSet(selection=CaseIds(ids=["a", "c"]))) + ).cases == 2 + + with pytest.raises(ValueError, match="contains 3 cases"): + await backend.resolve_cost(EvaluationSet(selection=CaseRange(stop=4))) + + with pytest.raises(ValueError, match="unknown Python task case IDs"): + await backend.resolve_cost(EvaluationSet(selection=CaseIds(ids=["missing"]))) + + +def test_python_task_backend_requires_external_case_file(tmp_path: Path): + missing = tmp_path / "missing.json" + + with pytest.raises(ValueError, match="existing file"): + PythonTaskBackendConfig( + harness_root=str(tmp_path), + module="target.tasks", + task="quality", + evaluations=[ + PythonTaskEvaluationConfig(name="default", cases_path=str(missing)) + ], + uv_executable=sys.executable, + ) + + +@pytest.mark.asyncio +async def test_python_task_backend_exports_only_selected_cases(tmp_path: Path): + cases = tmp_path / "cases.json" + cases.write_text( + json.dumps( + [ + {"id": "a", "prompt": "alpha"}, + {"id": "b", "prompt": "beta"}, + {"id": "c", "prompt": "gamma"}, + ] + ), + encoding="utf-8", + ) + destination = tmp_path / "context" + destination.mkdir() + backend = PythonTaskBackend( + PythonTaskBackendConfig( + harness_root=str(tmp_path), + module="target.tasks", + task="quality", + evaluations=[ + PythonTaskEvaluationConfig(name="default", cases_path=str(cases)) + ], + uv_executable=sys.executable, + ) + ) + + await backend.export_case_resources( + evaluation_set=EvaluationSet(selection=CaseIds(ids=["c", "a"])), + destination=str(destination), + sandbox=await LocalSandbox.create(root=tmp_path), + ) + + index = json.loads((destination / "index.json").read_text()) + assert [item["case_id"] for item in index["cases"]] == ["c", "a"] + exported = [ + json.loads((destination / item["path"]).read_text()) for item in index["cases"] + ] + assert exported == [ + {"id": "c", "prompt": "gamma"}, + {"id": "a", "prompt": "alpha"}, + ] + + +@pytest.mark.asyncio +async def test_python_task_backend_routes_named_partitions_to_distinct_datasets( + tmp_path: Path, +): + train = tmp_path / "train.json" + train.write_text(json.dumps([{"id": "train-a"}]), encoding="utf-8") + validation = tmp_path / "validation.json" + validation.write_text( + json.dumps([{"id": "val-a"}, {"id": "val-b"}]), + encoding="utf-8", + ) + backend = PythonTaskBackend( + PythonTaskBackendConfig( + harness_root=str(tmp_path), + module="target.tasks", + task="quality", + evaluations=[ + PythonTaskEvaluationConfig( + name="train", + partition="train", + cases_path=str(train), + ), + PythonTaskEvaluationConfig( + name="validation", + partition="validation", + cases_path=str(validation), + ), + ], + uv_executable=sys.executable, + ) + ) + + assert ( + await backend.resolve_cost(EvaluationSet(name="train", partition="train")) + ).cases == 1 + assert ( + await backend.resolve_cost( + EvaluationSet(name="validation", partition="validation") + ) + ).cases == 2 + with pytest.raises(ValueError, match="does not own evaluation"): + await backend.resolve_cost(EvaluationSet(name="test", partition="test")) + + +@pytest.mark.asyncio +async def test_python_task_backend_optimizes_target_task_module(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "factor.txt").write_text("1\n", encoding="utf-8") + (target / "target_program.py").write_text( + """ +from pathlib import Path + +def multiply(value): + factor = int(Path(__file__).with_name("factor.txt").read_text()) + return value * factor +""", + encoding="utf-8", + ) + (tmp_path / "evaluation_tasks.py").write_text( + """ +from vero_tasks import TaskOutput, TaskResult, create_task +from target_program import multiply + +task = create_task("multiply") + +@task.inference() +async def infer(case, context): + return TaskOutput(output=multiply(case["value"])) + +@task.evaluation() +async def evaluate(case, output, context): + return TaskResult.from_task_output( + output, + score=float(output.output == case["expected"]), + ) +""", + encoding="utf-8", + ) + initialize_repository(target) + + cases = tmp_path / "cases.json" + cases.write_text( + json.dumps( + [ + {"id": "one", "value": 2, "expected": 4}, + {"id": "two", "value": 3, "expected": 6}, + ] + ), + encoding="utf-8", + ) + tasks_source = Path(__file__).parents[2] / "vero-tasks" / "src" + fake_uv = tmp_path / "uv" + fake_uv.write_text( + f"""#!{sys.executable} +import runpy +import sys + +arguments = sys.argv[1:] +project = arguments[arguments.index("--project") + 1] +target = arguments[arguments.index("--with-editable") + 1] +module_index = arguments.index("-m") +module = arguments[module_index + 1] +sys.path[:0] = [project, target, {str(tasks_source)!r}] +sys.argv = [module, *arguments[module_index + 2:]] +runpy.run_module(module, run_name="__main__") +""", + encoding="utf-8", + ) + fake_uv.chmod(0o755) + + producer_root = tmp_path / "producer" + producer_root.mkdir() + producer_script = producer_root / "improve.py" + producer_script.write_text( + """ +import sys +from pathlib import Path +Path(sys.argv[1], "factor.txt").write_text("2\\n") +""", + encoding="utf-8", + ) + backend = PythonTaskBackend( + PythonTaskBackendConfig( + harness_root=str(tmp_path), + module="evaluation_tasks", + task="multiply", + evaluations=[ + PythonTaskEvaluationConfig(name="default", cases_path=str(cases)) + ], + uv_executable=str(fake_uv), + ) + ) + producer = CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=[sys.executable, str(producer_script), "{workspace}"], + ) + ) + session = await create_local_optimization_session( + project_path=target, + session_dir=tmp_path / "sessions" / "python-task", + backend_id="python-task", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + evaluation_plan=EvaluationPlan.single(), + producers={"default": producer}, + max_proposals=1, + ) + + result = await session.run() + + assert result.baseline.objective.value == 0.0 + assert result.best.objective.value == 1.0 + assert [case.case_id for case in result.best.report.cases] == ["one", "two"] + assert (target / "factor.txt").read_text(encoding="utf-8") == "1\n" diff --git a/vero/tests/test_v05_report.py b/vero/tests/test_v05_report.py new file mode 100644 index 00000000..a5d316e5 --- /dev/null +++ b/vero/tests/test_v05_report.py @@ -0,0 +1,228 @@ +from __future__ import annotations + +import asyncio +import hashlib +import json +import subprocess +from datetime import UTC, datetime, timedelta +from pathlib import Path + +from click.testing import CliRunner + +from vero.candidate import Candidate +from vero.candidate_repository import GitCandidateRepository +from vero.cli import main +from vero.evaluation import ( + BackendProvenance, + EvaluationArtifact, + EvaluationDatabase, + EvaluationPlan, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.runtime import ( + OptimizationComponentSpec, + OptimizationRunSpec, + RuntimeEvent, + SessionManifest, + SessionStatus, +) +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + + +def git(path: Path, *arguments: str) -> str: + result = subprocess.run( + ["git", *arguments], + cwd=path, + check=True, + capture_output=True, + text=True, + ) + return result.stdout.strip() + + +async def build_session(root: Path) -> Path: + source = root / "source" + source.mkdir() + program = source / "program.py" + program.write_text("score = 1\n", encoding="utf-8") + git(source, "init", "-b", "main") + git(source, "add", "--all") + git( + source, + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ) + + sandbox = await LocalSandbox.create(root=root) + workspace = await GitWorkspace.from_path(sandbox, str(source)) + session_dir = root / "session" + repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + baseline = Candidate.from_version( + git(source, "rev-parse", "HEAD"), candidate_id="baseline" + ) + await repository.capture(baseline, workspace) + + program.write_text("score = 2\n", encoding="utf-8") + version = await workspace.save("improve score") + proposal_id = "proposal-1" + candidate = Candidate.from_version( + version, + candidate_id="candidate-1", + parent_id=baseline.id, + description="Improve ", + metadata={"proposal_id": proposal_id, "producer_id": "test"}, + ) + await repository.capture(candidate, workspace) + + objective = ObjectiveSpec(selector=MetricSelector(metric="score"), direction="maximize") + provenance = BackendProvenance.from_config(name="test", version="1", config={}) + created = datetime(2026, 1, 1, tzinfo=UTC) + database = EvaluationDatabase(id="report-test") + for index, (evaluated, value) in enumerate(((baseline, 1.0), (candidate, 2.0))): + evaluation_id = f"evaluation-{index}" + record = EvaluationRecord( + id=evaluation_id, + request=EvaluationRequest( + candidate=evaluated, + evaluation_set=EvaluationSet(name="development"), + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": value}, + artifacts=( + [ + EvaluationArtifact( + path="preview.svg", + media_type="image/svg+xml", + description="Program output", + ) + ] + if evaluated is candidate + else [] + ), + ), + backend_id="test", + backend=provenance, + objective_spec=objective, + objective=ObjectiveResult(value=value, feasible=True), + created_at=created + timedelta(minutes=index), + completed_at=created + timedelta(minutes=index, seconds=1), + ) + database.add_evaluation(record) + if evaluated is candidate: + artifact_dir = session_dir / "evaluations" / evaluation_id / "artifacts" + artifact_dir.mkdir(parents=True) + (artifact_dir / "preview.svg").write_text( + '', + encoding="utf-8", + ) + database.save_to_file(session_dir / "database.json") + + component = OptimizationComponentSpec(type="test", config_digest="0" * 64) + manifest = SessionManifest( + id="report-test", + status=SessionStatus.COMPLETED, + backend_id="test", + backend=provenance, + candidate_repository_family="git", + candidate_repository_format_version=1, + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="development")), + objective=objective, + run=OptimizationRunSpec( + max_proposals=1, + max_rounds=1, + max_concurrency=1, + strategy=component, + producers={"test": component}, + ), + baseline=baseline, + best_candidate_id=candidate.id, + best_evaluation_id="evaluation-1", + created_at=created, + updated_at=created + timedelta(minutes=2), + ) + (session_dir / "manifest.json").write_text( + manifest.model_dump_json(indent=2), encoding="utf-8" + ) + event = RuntimeEvent( + session_id=manifest.id, + kind="evaluation_completed", + created_at=created + timedelta(minutes=1), + payload={"evaluation_id": "evaluation-1", "objective/value": 2.0}, + ) + (session_dir / "events.jsonl").write_text( + event.model_dump_json() + "\n", encoding="utf-8" + ) + trace_id = hashlib.sha256(proposal_id.encode()).hexdigest()[:16] + trace_dir = session_dir / "artifacts" / "agents" / trace_id + trace_dir.mkdir(parents=True) + (trace_dir / "trace.json").write_text( + json.dumps( + [ + {"role": "user", "content": "Improve the score"}, + { + "type": "function_call", + "name": "edit", + "arguments": '{"path":"program.py"}', + }, + ] + ), + encoding="utf-8", + ) + return session_dir + + +def report_payload(html: str) -> dict[str, object]: + opening = '", 1)[0] + return json.loads(encoded) + + +def test_report_command_builds_portable_full_experiment_view(tmp_path: Path): + session_dir = asyncio.run(build_session(tmp_path)) + output = tmp_path / "report.html" + + result = CliRunner().invoke( + main, ["report", str(session_dir), "--output", str(output)] + ) + + assert result.exit_code == 0, result.output + assert str(output) in result.output + html = output.read_text(encoding="utf-8") + payload = report_payload(html) + assert len(payload["candidates"]) == 2 + assert len(payload["evaluations"]) == 2 + assert payload["candidates"][1]["trace_id"] is not None + assert "+score = 2" in payload["candidates"][1]["diff"]["text"] + assert payload["evaluations"][1]["artifacts"][0]["kind"] == "image" + assert payload["evaluations"][1]["artifacts"][0]["content"].startswith( + "data:image/svg+xml;base64," + ) + assert payload["traces"][0]["entries"][1]["kind"] == "tool-call" + assert payload["events"][0]["kind"] == "evaluation_completed" + assert "" not in html + assert "Score trajectories by split" in html + assert "Lines connect exact case selections only" in html + assert "Comparable baseline" in html + + +def test_report_command_fails_clearly_without_a_manifest(tmp_path: Path): + result = CliRunner().invoke(main, ["report", str(tmp_path)]) + + assert result.exit_code == 1 + assert "session manifest not found" in result.output diff --git a/vero/tests/test_v05_runtime_factory.py b/vero/tests/test_v05_runtime_factory.py new file mode 100644 index 00000000..5354fb3d --- /dev/null +++ b/vero/tests/test_v05_runtime_factory.py @@ -0,0 +1,510 @@ +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +from vero.candidate import Candidate +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + CommandBackend, + CommandBackendConfig, + DisclosureLevel, + EvaluationAccessPolicy, + EvaluationDefinition, + EvaluationPlan, + EvaluationSet, + MetricSelector, + ObjectiveSpec, + allow_all_evaluations, +) +from vero.optimization import ( + CommandCandidateProducer, + CommandCandidateProducerConfig, +) +from vero.runtime import ( + SessionStatus, + create_local_optimization_session, + create_optimization_session, +) +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + + +def initialize_repository(path: Path) -> str: + subprocess.run( + ["git", "init", "-b", "main"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + ["git", "add", "--all"], + cwd=path, + check=True, + capture_output=True, + ) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "baseline", + ], + cwd=path, + check=True, + capture_output=True, + ) + return subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=path, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + + +def command_components(tmp_path: Path): + harness = tmp_path / "harness" + harness.mkdir() + harness_script = harness / "evaluate.py" + harness_script.write_text( + """ +import json +import sys +from pathlib import Path + +workspace, report_path = map(Path, sys.argv[1:]) +value = (workspace / "program.txt").read_text().strip() +score = {"improved": 1.0, "improved-again": 2.0}.get(value, 0.0) +report_path.write_text(json.dumps({ + "schema_version": 1, + "status": "success", + "metrics": {"score": score}, +})) +""", + encoding="utf-8", + ) + producer_root = tmp_path / "producer" + producer_root.mkdir() + producer_script = producer_root / "improve.py" + producer_script.write_text( + """ +import sys +from pathlib import Path +value = "improved" if sys.argv[2] == "0" else "improved-again" +Path(sys.argv[1], "program.txt").write_text(value + "\\n") +""", + encoding="utf-8", + ) + backend = CommandBackend( + CommandBackendConfig( + harness_root=str(harness), + command=[ + sys.executable, + str(harness_script), + "{workspace}", + "{report}", + ], + ) + ) + producer = CommandCandidateProducer( + CommandCandidateProducerConfig( + root=str(producer_root), + command=[ + sys.executable, + str(producer_script), + "{workspace}", + "{round}", + ], + description="Improve the program", + ) + ) + return backend, producer + + +@pytest.mark.asyncio +async def test_generic_factory_accepts_a_provisioned_workspace(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, _ = command_components(tmp_path) + workspace = await GitWorkspace.from_path(LocalSandbox(tmp_path), str(target)) + session_dir = tmp_path / "sessions" / "generic" + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + + session = await create_optimization_session( + workspace=workspace, + candidate_repository=candidate_repository, + session_dir=session_dir, + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + authorization_resolver=allow_all_evaluations, + ) + result = await session.run() + + assert result.baseline.objective.value == 0.0 + + +@pytest.mark.asyncio +async def test_local_factory_builds_and_resumes_generic_session(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + baseline_version = initialize_repository(target) + backend, producer = command_components(tmp_path) + session_dir = tmp_path / "sessions" / "factory" + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + + session = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + session_id="stable-id", + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="quality")), + producers={"default": producer}, + max_proposals=1, + ) + result = await session.run() + + assert session.id == "stable-id" + assert result.baseline.request.candidate.version == baseline_version + assert result.best.objective.value == 1.0 + assert (target / "program.txt").read_text(encoding="utf-8") == "baseline\n" + assert session.load_manifest().status == SessionStatus.COMPLETED + assert len(session.database.evaluations) == 2 + + # Simulate a crash after the canonical evaluation directory was committed + # but before database.json was updated. + database_path = session_dir / "database.json" + stale_database = json.loads(database_path.read_text(encoding="utf-8")) + stale_database["evaluations"].pop(result.best.id) + stale_database["candidates"].pop(result.best.request.candidate.id) + database_path.write_text(json.dumps(stale_database), encoding="utf-8") + + resumed = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + session_id=None, + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(EvaluationSet(name="quality")), + producers={"default": producer}, + max_proposals=1, + ) + resumed_result = await resumed.run(skip_baseline_evaluation=True) + + assert len(resumed.database.evaluations) == 2 + assert resumed.id == "stable-id" + assert resumed_result.baseline.id == result.baseline.id + assert len(resumed_result.evaluations) == 2 + assert resumed_result.best.id == result.best.id + + +@pytest.mark.asyncio +async def test_resume_evaluates_a_durable_candidate_captured_before_crash( + tmp_path: Path, +): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, _ = command_components(tmp_path) + session_dir = tmp_path / "sessions" / "captured" + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + session = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + session_id="captured", + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + ) + initial = await session.run() + baseline = initial.baseline.request.candidate + + async with session.candidate_repository.checkout( + baseline, + sandbox=session.workspace.sandbox, + ) as candidate_workspace: + candidate_path = Path(candidate_workspace.project_path) / "program.txt" + candidate_path.write_text("improved\n", encoding="utf-8") + version = await candidate_workspace.save("captured before evaluation") + candidate = Candidate.from_version( + version, + candidate_id="captured-candidate", + parent_id=baseline.id, + description="Captured before evaluation", + metadata={ + "producer_id": "default", + "proposal_id": "captured-candidate", + "round": 0, + }, + ) + await session.candidate_repository.capture(candidate, candidate_workspace) + + resumed = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + session_id=None, + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + ) + result = await resumed.run(skip_baseline_evaluation=True) + + assert len(result.evaluations) == 2 + assert result.best.request.candidate.id == "captured-candidate" + assert result.best.objective.value == 1.0 + + +@pytest.mark.asyncio +async def test_factory_reuses_baseline_captured_before_manifest_write(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, _ = command_components(tmp_path) + session_dir = tmp_path / "sessions" / "baseline-crash" + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + first = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + ) + assert not first.manifest_path.exists() + captured = first.baseline + + recreated = await create_local_optimization_session( + project_path=target, + session_dir=session_dir, + backend_id="command", + backend=backend, + objective=objective, + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + ) + + assert recreated.baseline == captured + assert recreated.candidate_repository.list() == (captured,) + + +@pytest.mark.asyncio +async def test_local_factory_rejects_changed_run_protocol_after_resume(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, producer = command_components(tmp_path) + session_dir = tmp_path / "sessions" / "resume" + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + kwargs = { + "project_path": target, + "session_dir": session_dir, + "session_id": "resume", + "backend_id": "command", + "backend": backend, + "objective": objective, + "evaluation_plan": EvaluationPlan.single(EvaluationSet(name="quality")), + "producers": {"default": producer}, + } + + first = await create_local_optimization_session(max_proposals=1, **kwargs) + first_result = await first.run() + resumed = await create_local_optimization_session(max_proposals=2, **kwargs) + + assert len(first_result.evaluations) == 2 + with pytest.raises(ValueError, match="run protocol"): + await resumed.run(skip_baseline_evaluation=True) + + +@pytest.mark.asyncio +async def test_local_factory_can_evaluate_an_older_target_ref(tmp_path: Path): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + baseline_version = initialize_repository(target) + (target / "program.txt").write_text("improved\n", encoding="utf-8") + subprocess.run(["git", "add", "--all"], cwd=target, check=True) + subprocess.run( + [ + "git", + "-c", + "user.name=vero", + "-c", + "user.email=vero@localhost", + "commit", + "-m", + "new head", + ], + cwd=target, + check=True, + capture_output=True, + ) + head_version = subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + backend, _ = command_components(tmp_path) + + session = await create_local_optimization_session( + project_path=target, + session_dir=tmp_path / "sessions" / "old-ref", + session_id="old-ref", + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + evaluation_plan=EvaluationPlan.single(), + producers={}, + max_proposals=0, + base_ref=baseline_version, + ) + result = await session.run() + + assert result.baseline.request.candidate.version == baseline_version + assert result.baseline.objective.value == 0.0 + assert head_version != baseline_version + assert ( + subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=target, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + == head_version + ) + + +@pytest.mark.asyncio +async def test_local_factory_rejects_state_inside_or_outside_version_control( + tmp_path: Path, +): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, producer = command_components(tmp_path) + kwargs = { + "project_path": target, + "backend_id": "command", + "backend": backend, + "objective": ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + "evaluation_plan": EvaluationPlan.single(), + "producers": {"default": producer}, + } + + with pytest.raises(ValueError, match="outside the target repository"): + await create_local_optimization_session( + session_dir=target / ".vero" / "session", + **kwargs, + ) + + (target / "program.txt").write_text("dirty\n", encoding="utf-8") + with pytest.raises(ValueError, match="must be clean"): + await create_local_optimization_session( + session_dir=tmp_path / "sessions" / "dirty", + **kwargs, + ) + + +@pytest.mark.asyncio +async def test_session_uses_canonical_selection_and_hidden_final_evaluations( + tmp_path: Path, +): + target = tmp_path / "target" + target.mkdir() + (target / "program.txt").write_text("baseline\n", encoding="utf-8") + initialize_repository(target) + backend, _ = command_components(tmp_path) + validation = EvaluationSet(name="validation", partition="validation") + final = EvaluationSet(name="test", partition="test") + plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition(evaluation_set=validation), + EvaluationDefinition( + evaluation_set=final, + access=EvaluationAccessPolicy( + agent_can_evaluate=False, + agent_visible=False, + disclosure=DisclosureLevel.NONE, + ), + ), + ], + selection_evaluation="validation", + final_evaluation="test", + ) + + session = await create_local_optimization_session( + project_path=target, + session_dir=tmp_path / "sessions" / "final", + backend_id="command", + backend=backend, + objective=ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ), + evaluation_plan=plan, + producers={}, + max_proposals=0, + ) + result = await session.run() + + assert result.baseline.request.evaluation_set == validation + assert result.final_baseline is not None + assert result.final_baseline.request.evaluation_set == final + assert result.final == result.final_baseline + assert session.load_manifest().final_evaluation_id == result.final.id diff --git a/vero/tests/test_v05_runtime_session.py b/vero/tests/test_v05_runtime_session.py new file mode 100644 index 00000000..9544cbd6 --- /dev/null +++ b/vero/tests/test_v05_runtime_session.py @@ -0,0 +1,300 @@ +import json +from datetime import UTC, datetime, timedelta +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from vero.candidate import Candidate +from vero.evaluation import ( + BackendProvenance, + EvaluationDatabase, + EvaluationLimits, + EvaluationPlan, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.optimization import OptimizationResult +from vero.runtime import ArtifactStore, EventBus, OptimizationSession, SessionStatus + + +def evaluation(candidate: Candidate) -> EvaluationRecord: + created_at = datetime(2026, 1, 1, tzinfo=UTC) + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + return EvaluationRecord( + id=f"evaluation:{candidate.id}", + request=EvaluationRequest(candidate=candidate), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": 1.0}, + ), + backend_id="default", + backend=BackendProvenance( + name="fake", + version="1", + config_digest="0" * 64, + ), + objective_spec=objective, + objective=ObjectiveResult(value=1.0, feasible=True), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + +class StubWorkspace: + async def current_version(self): + return "baseline-version" + + +class StubCandidateRepository: + family = "stub" + format_version = 1 + + +class StubOptimizer: + def __init__(self, session_dir: Path, *, failure: Exception | None = None): + self.workspace = StubWorkspace() + self.candidate_repository = StubCandidateRepository() + self.backend_id = "default" + self.evaluation_plan = EvaluationPlan.single( + EvaluationSet(name="performance") + ) + self.objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + self.parameters = {} + self.limits = EvaluationLimits() + self.seed = None + self.session_id = None + self.max_proposals = 0 + self.max_rounds = 1 + self.max_concurrency = 1 + self.strategy = SimpleNamespace() + self.producers = {} + self.selection = SimpleNamespace() + self.engine = SimpleNamespace( + evaluator=SimpleNamespace( + session_dir=session_dir, + candidate_repository=self.candidate_repository, + ), + database=EvaluationDatabase(id=session_dir.name), + budget_ledger=None, + listeners=[], + backends=SimpleNamespace( + resolve=lambda _: SimpleNamespace( + provenance=BackendProvenance( + name="fake", + version="1", + config_digest="0" * 64, + ) + ) + ), + ) + self.failure = failure + self.calls: list[tuple[Candidate, bool]] = [] + + async def run( + self, + *, + baseline, + skip_baseline_evaluation=False, + max_proposals=None, + ): + self.calls.append((baseline, skip_baseline_evaluation)) + if self.failure is not None: + raise self.failure + baseline_record = evaluation(baseline) + for listener in self.engine.listeners: + result = listener(baseline_record) + if result is not None: + await result + return OptimizationResult( + baseline=baseline_record, + evaluations=(baseline_record,), + candidates=(baseline,), + best=baseline_record, + ) + + +@pytest.mark.asyncio +async def test_session_persists_lifecycle_events_and_best_result(tmp_path: Path): + session_dir = tmp_path / "sessions" / "run-1" + optimizer = StubOptimizer(session_dir) + session = OptimizationSession( + id="run-1", + session_dir=session_dir, + optimizer=optimizer, + metadata={"purpose": "test"}, + ) + + result = await session.run() + + manifest = session.load_manifest() + assert manifest.schema_version == 3 + assert manifest.status == SessionStatus.COMPLETED + assert manifest.baseline.version == "baseline-version" + assert manifest.best_candidate_id == "baseline-version" + assert manifest.best_evaluation_id == result.best.id + assert manifest.metadata == {"purpose": "test"} + events = [json.loads(line) for line in session.events_path.read_text().splitlines()] + assert [event["kind"] for event in events] == [ + "session_started", + "evaluation_completed", + "session_completed", + ] + assert session.database is optimizer.engine.database + + +@pytest.mark.asyncio +async def test_session_persists_failure_before_reraising(tmp_path: Path): + session_dir = tmp_path / "sessions" / "failed" + session = OptimizationSession( + id="failed", + session_dir=session_dir, + optimizer=StubOptimizer(session_dir, failure=RuntimeError("producer exploded")), + ) + + with pytest.raises(RuntimeError, match="producer exploded"): + await session.run() + + manifest = session.load_manifest() + assert manifest.status == SessionStatus.FAILED + assert manifest.failure.type.endswith("RuntimeError") + assert manifest.failure.message == "producer exploded" + events = [json.loads(line) for line in session.events_path.read_text().splitlines()] + assert events[-1]["kind"] == "session_failed" + + +@pytest.mark.asyncio +async def test_session_rejects_resume_with_changed_objective(tmp_path: Path): + session_dir = tmp_path / "sessions" / "changed-objective" + optimizer = StubOptimizer(session_dir) + session = OptimizationSession( + id="changed-objective", + session_dir=session_dir, + optimizer=optimizer, + ) + await session.run() + optimizer.objective = ObjectiveSpec( + selector=MetricSelector(metric="different"), + direction="minimize", + ) + + with pytest.raises(ValueError, match="objective does not match"): + await session.run(skip_baseline_evaluation=True) + + +@pytest.mark.asyncio +async def test_session_rejects_resume_with_changed_evaluation_parameters( + tmp_path: Path, +): + session_dir = tmp_path / "sessions" / "changed-parameters" + optimizer = StubOptimizer(session_dir) + session = OptimizationSession( + id="changed-parameters", + session_dir=session_dir, + optimizer=optimizer, + ) + await session.run() + optimizer.parameters = {"temperature": 0.5} + + with pytest.raises(ValueError, match="parameters do not match"): + await session.run(skip_baseline_evaluation=True) + + +def test_component_spec_reflects_strategy_settings(): + from vero.optimization import EvolutionaryStrategy + + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), direction="maximize" + ) + base = EvolutionaryStrategy(objective=objective, population_size=8, seed=1) + bigger = EvolutionaryStrategy(objective=objective, population_size=16, seed=1) + reseeded = EvolutionaryStrategy(objective=objective, population_size=8, seed=2) + + base_digest = OptimizationSession._component_spec(base).config_digest + # Built-in strategy settings and the seed now change the resume identity, so + # a resume under materially different search semantics is rejected. + assert base_digest != OptimizationSession._component_spec(bigger).config_digest + assert base_digest != OptimizationSession._component_spec(reseeded).config_digest + + +def test_component_spec_reflects_selection_policy(): + from vero.optimization.strategy import ObjectiveSelectionPolicy + + class _AltSelectionPolicy: + def select(self, records, objective): + return None + + # The selection policy is part of the run identity, so swapping it to a + # different policy changes the component spec (by type) and a resume under a + # different policy is rejected. + objective_spec = OptimizationSession._component_spec(ObjectiveSelectionPolicy()) + alt_spec = OptimizationSession._component_spec(_AltSelectionPolicy()) + assert objective_spec != alt_spec + assert objective_spec.type != alt_spec.type + + +def test_session_rejects_mismatched_evaluator_directory(tmp_path: Path): + with pytest.raises(ValueError, match="must match"): + OptimizationSession( + id="run", + session_dir=tmp_path / "expected", + optimizer=StubOptimizer(tmp_path / "different"), + ) + + +def test_session_binds_and_validates_optimizer_session_id(tmp_path: Path): + session_dir = tmp_path / "directory-name-can-differ" + optimizer = StubOptimizer(session_dir) + + OptimizationSession( + id="canonical-session-id", + session_dir=session_dir, + optimizer=optimizer, + ) + + assert optimizer.session_id == "canonical-session-id" + optimizer.session_id = "different" + with pytest.raises(ValueError, match="session ID"): + OptimizationSession( + id="canonical-session-id", + session_dir=session_dir, + optimizer=optimizer, + ) + + +def test_artifact_store_rejects_escaping_paths(tmp_path: Path): + store = ArtifactStore(tmp_path / "artifacts") + + path = store.write_json("agent/state.json", {"turn": 3}) + + assert path == tmp_path / "artifacts" / "agent" / "state.json" + assert store.read_json("agent/state.json") == {"turn": 3} + for unsafe in ("", "../escape", "/absolute", "a//b", "a\\b"): + with pytest.raises(ValueError): + store.write_text(unsafe, "no") + + +@pytest.mark.asyncio +async def test_event_sink_failure_does_not_break_runtime(): + captured = [] + + def broken(_event): + raise RuntimeError("sink failed") + + bus = EventBus([broken, captured.append]) + + event = await bus.emit(session_id="session", kind="test") + + assert captured == [event] diff --git a/vero/tests/test_v05_vero_agent.py b/vero/tests/test_v05_vero_agent.py new file mode 100644 index 00000000..fa32a191 --- /dev/null +++ b/vero/tests/test_v05_vero_agent.py @@ -0,0 +1,118 @@ +from __future__ import annotations + +from pathlib import Path +from types import SimpleNamespace + +import pytest + +pytest.importorskip("agents") + +from agents import Agent, Runner + +from vero.agents import AgentContext, CodingAgent +from vero.agents.vero import VeroAgent, default_tool_sets +from vero.candidate import Candidate +from vero.optimization import CandidateProposal +from vero.tools.evaluation import EvaluationTools + + +class StubSandbox: + def host_path(self, path): + return Path(path) + + +class StubWorkspace: + def __init__(self, project_path: Path): + self.project_path = str(project_path) + self.sandbox = StubSandbox() + self.accesses = [] + + +class StubEvaluationGateway: + async def evaluate(self, **kwargs): + raise AssertionError("the fake run does not invoke tools") + + def budgets(self): + return {} + + +class FakeRunResult: + def __init__(self): + self.context_wrapper = SimpleNamespace( + usage={"requests": 1, "input_tokens": 8, "output_tokens": 2} + ) + + def stream_events(self): + async def _gen(): + return + yield # pragma: no cover - marks this as an async generator + + return _gen() + + def to_input_list(self): + return [ + {"role": "user", "content": "Try a tiled kernel"}, + {"role": "assistant", "content": "Done"}, + ] + + +def test_default_tools_use_canonical_evaluation_capability(): + names = {type(tool).__name__ for tool in default_tool_sets()} + + assert "EvaluationTools" in names + assert not any("Experiment" in name or "Dataset" in name for name in names) + + +def agent_context(tmp_path: Path, gateway: StubEvaluationGateway) -> AgentContext: + baseline = Candidate(id="baseline", version="baseline-version") + proposal = CandidateProposal( + id="proposal", + parent_id=baseline.id, + instruction="Optimize matrix multiplication", + ) + return AgentContext( + session_id="session-1", + workspace=StubWorkspace(tmp_path), + proposal=proposal, + parent=baseline, + evaluation=gateway, + ) + + +@pytest.mark.asyncio +async def test_vero_agent_implements_canonical_coding_agent_contract( + tmp_path: Path, + monkeypatch, +): + gateway = StubEvaluationGateway() + evaluation_tools = EvaluationTools() + agent = VeroAgent( + oai_agent=Agent(name="test-agent", model="gpt-4.1"), + tool_sets=[evaluation_tools], + ) + captured = {} + + def fake_run_streamed(agent, *, input, max_turns, run_config=None): + captured["agent"] = agent + captured["input"] = input + captured["max_turns"] = max_turns + captured["run_config"] = run_config + return FakeRunResult() + + monkeypatch.setattr(Runner, "run_streamed", staticmethod(fake_run_streamed)) + context = agent_context(tmp_path, gateway) + result = await agent.run( + context=context, + prompt="Try a tiled kernel", + max_turns=7, + ) + + assert isinstance(agent, CodingAgent) + assert captured["input"] == [{"role": "user", "content": "Try a tiled kernel"}] + assert captured["max_turns"] == 7 + assert captured["run_config"].workflow_name == "vero::session-1" + assert evaluation_tools.evaluation is gateway + assert agent._context is context + assert result.state[-1] == {"role": "assistant", "content": "Done"} + assert result.metadata["model"] == "gpt-4.1" + assert result.metadata["usage"]["orchestrator"]["input_tokens"] == 8 diff --git a/vero/tests/test_v05_wandb.py b/vero/tests/test_v05_wandb.py new file mode 100644 index 00000000..c84dcc0a --- /dev/null +++ b/vero/tests/test_v05_wandb.py @@ -0,0 +1,298 @@ +from __future__ import annotations + +import json +from datetime import UTC, datetime, timedelta +from pathlib import Path + +from vero.candidate import Candidate +from vero.evaluation import ( + BackendProvenance, + CaseResult, + CaseStatus, + EvaluationPrincipal, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, +) +from vero.runtime import RuntimeEvent, WandbEventSink +from vero.runtime.wandb import SidecarWandbSink + + +class FakeArtifact: + def __init__(self, name, type): + self.name = name + self.type = type + self.files = [] + + def add_file(self, path, name): + self.files.append((path, name)) + + +class FakeRun: + def __init__(self): + self.logged = [] + self.summary = {} + self.finished = [] + self.artifacts = [] + + def log(self, payload, *, step): + self.logged.append((payload, step)) + + def log_artifact(self, artifact): + self.artifacts.append(artifact) + + def finish(self, *, exit_code=0): + self.finished.append(exit_code) + + +class FakeWandb: + def __init__(self): + self.kwargs = None + self.run = FakeRun() + + def init(self, **kwargs): + self.kwargs = kwargs + return self.run + + def Artifact(self, *, name, type): + return FakeArtifact(name=name, type=type) + + +def test_wandb_sink_tracks_canonical_runtime_events(tmp_path: Path): + client = FakeWandb() + sink = WandbEventSink( + project="vero-tests", + session_id="session/with:unsafe-run-id-characters", + session_dir=tmp_path / "session", + mode="offline", + config={"vero/objective_metric": "latency_ms"}, + client=client, + ) + + assert client.kwargs["project"] == "vero-tests" + assert client.kwargs["id"].startswith("vero-") + assert client.kwargs["resume"] == "allow" + assert client.kwargs["mode"] == "offline" + assert client.kwargs["config"]["vero/session_id"].startswith("session/") + + sink( + RuntimeEvent( + session_id="session", + kind="evaluation_completed", + payload={ + "step": 2, + "candidate_id": "candidate", + "evaluation_id": "evaluation-1", + "metrics/latency_ms": 1.25, + "objective/value": 1.25, + "objective/feasible": True, + }, + ) + ) + assert client.run.logged == [ + ( + { + "candidate_id": "candidate", + "evaluation_id": "evaluation-1", + "metrics/latency_ms": 1.25, + "objective/value": 1.25, + "objective/feasible": True, + }, + 0, + ) + ] + assert json.loads( + (tmp_path / "session" / "artifacts" / "wandb" / "state.json").read_text() + ) == {"evaluation_ids": ["evaluation-1"], "next_step": 1} + + # Replayed history on resume is not sent twice or logged with a stale step. + sink( + RuntimeEvent( + session_id="session", + kind="evaluation_completed", + payload={"step": 0, "evaluation_id": "evaluation-1"}, + ) + ) + assert len(client.run.logged) == 1 + + sink( + RuntimeEvent( + session_id="session", + kind="session_completed", + payload={"status": "completed", "best_objective": 1.25}, + ) + ) + assert client.run.summary["best_objective"] == 1.25 + assert client.run.finished == [0] + + +def _evaluation_record( + *, status=EvaluationStatus.SUCCESS, score=0.75 +) -> EvaluationRecord: + created = datetime(2026, 1, 1, tzinfo=UTC) + return EvaluationRecord( + id="eval-1", + request=EvaluationRequest( + candidate=Candidate(id="cand", version="v1", created_at=created), + evaluation_set=EvaluationSet(name="benchmark", partition="validation"), + ), + report=EvaluationReport( + status=status, + metrics={"score": score, "score_stddev": 0.1, "error_rate": 0.0}, + cases=[ + CaseResult( + case_id="c1", status=CaseStatus.SUCCESS, metrics={"score": score} + ) + ], + ), + backend_id="primary", + backend=BackendProvenance(name="harbor", version="1", config_digest="0" * 64), + principal=EvaluationPrincipal.SYSTEM, + objective_spec=ObjectiveSpec( + selector=MetricSelector(metric="score"), direction="maximize" + ), + objective=ObjectiveResult(value=score, feasible=True), + created_at=created, + completed_at=created + timedelta(seconds=3), + ) + + +def test_sidecar_wandb_sink_logs_evaluation_records(tmp_path: Path): + client = FakeWandb() + sink = SidecarWandbSink( + project="vero", + session_id="s", + session_dir=tmp_path / "session", + client=client, + ) + + sink(_evaluation_record()) + + payload, step = client.run.logged[0] + assert step == 0 + assert payload["evaluation_id"] == "eval-1" + assert payload["status"] == "success" + assert payload["principal"] == "system" + assert payload["partition"] == "validation" + # Quality metrics are scoped by partition/principal so dev, validation, and + # the admin held-out re-score do not share an axis. + assert payload["validation/system/score"] == 0.75 + assert payload["validation/system/metric/score_stddev"] == 0.1 + assert payload["validation/system/cases/success"] == 1 + # num_cases records the evaluation's sample size (number of trials). + assert payload["validation/system/num_cases"] == 1 + # The bare, partition-blind keys are gone — nothing conflates partitions. + assert "score" not in payload + assert "cases/success" not in payload + + # Same record is not logged twice. + sink(_evaluation_record()) + assert len(client.run.logged) == 1 + + +def test_sidecar_wandb_run_id_is_unique_per_invocation_but_stable_on_restart( + tmp_path: Path, +): + # Distinct session volumes must get distinct W&B runs (not all resume one + # shared run), while a restart against the same volume resumes its run. + a = FakeWandb() + SidecarWandbSink(project="v", session_id="trial", session_dir=tmp_path / "a", client=a) + b = FakeWandb() + SidecarWandbSink(project="v", session_id="trial", session_dir=tmp_path / "b", client=b) + assert a.kwargs["id"] != b.kwargs["id"] + + # Same session dir (persisted state) -> same run id on restart. + a2 = FakeWandb() + SidecarWandbSink(project="v", session_id="trial", session_dir=tmp_path / "a", client=a2) + assert a2.kwargs["id"] == a.kwargs["id"] + + +def test_sidecar_wandb_run_dir_is_outside_the_archived_session_dir(tmp_path: Path): + # Regression: W&B's working directory accumulates symlinks (`latest-run`, + # debug logs, an absolute cache link). If it lived under session_dir it would + # be swept into create_harbor_session_archive, whose symlink refusal 500s the + # entire /session/export and loses the run's eval data. The run dir must be + # outside session_dir; only symlink-free resume state may live in artifacts. + client = FakeWandb() + session_dir = tmp_path / "session" + SidecarWandbSink( + project="vero", + session_id="s", + session_dir=session_dir, + client=client, + ) + + wandb_dir = Path(client.kwargs["dir"]).resolve() + resolved_session = session_dir.resolve() + assert not wandb_dir.is_relative_to(resolved_session) + + +def test_sidecar_wandb_sink_logs_full_trace_including_per_case_artifacts( + tmp_path: Path, +): + from vero.evaluation import EvaluationArtifact + + client = FakeWandb() + session_dir = tmp_path / "session" + sink = SidecarWandbSink( + project="vero", + session_id="s", + session_dir=session_dir, + log_traces=True, + client=client, + ) + + # Lay out an evaluation's on-disk artifacts: report-level harbor logs plus + # the full per-trial record attached to the case. + eval_dir = session_dir / "evaluations" / "eval-1" / "artifacts" + (eval_dir / "harbor").mkdir(parents=True) + (eval_dir / "harbor" / "stdout.log").write_text("out", encoding="utf-8") + trial = eval_dir / "harbor" / "jobs" / "trial-0" / "agent" + trial.mkdir(parents=True) + (trial.parent / "result.json").write_text("{}", encoding="utf-8") + (trial / "trajectory.json").write_text("[]", encoding="utf-8") + + record = _evaluation_record() + record = record.model_copy( + update={ + "report": record.report.model_copy( + update={ + "artifacts": [ + EvaluationArtifact(path="harbor/stdout.log"), + ], + "cases": [ + record.report.cases[0].model_copy( + update={ + "artifacts": [ + EvaluationArtifact( + path="harbor/jobs/trial-0/result.json" + ), + EvaluationArtifact( + path="harbor/jobs/trial-0/agent/trajectory.json" + ), + ] + } + ) + ], + } + ) + } + ) + + sink(record) + + assert len(client.run.artifacts) == 1 + artifact = client.run.artifacts[0] + assert artifact.type == "evaluation_trace" + uploaded = {name for _, name in artifact.files} + # Both the report-level harbor log AND the full per-case trial records land. + assert uploaded == { + "harbor/stdout.log", + "harbor/jobs/trial-0/result.json", + "harbor/jobs/trial-0/agent/trajectory.json", + } diff --git a/vero/tests/test_web.py b/vero/tests/test_web.py new file mode 100644 index 00000000..38a40040 --- /dev/null +++ b/vero/tests/test_web.py @@ -0,0 +1,156 @@ +"""Tests for web tools (WebSearch, WebFetch).""" + +import json +import os + +import pytest + +# Check if Serper API key is available +SERPER_API_KEY_AVAILABLE = bool(os.getenv("SERPER_KEY_ID") or os.getenv("SERPER_API_KEY")) + +requires_serper_key = pytest.mark.skipif( + not SERPER_API_KEY_AVAILABLE, + reason="SERPER_KEY_ID or SERPER_API_KEY not set", +) + + +class TestWebSearch: + """Tests for WebSearch tool.""" + + def test_init_without_api_key(self, monkeypatch): + """Test that WebSearch raises ValueError if no API key is set.""" + from vero.tools.web import WebSearch + + # Remove both possible API key env vars + monkeypatch.delenv("SERPER_KEY_ID", raising=False) + monkeypatch.delenv("SERPER_API_KEY", raising=False) + + with pytest.raises(ValueError, match="SERPER_KEY_ID.*SERPER_API_KEY"): + WebSearch() + + @requires_serper_key + def test_init_with_api_key(self): + """Test that WebSearch initializes successfully with API key.""" + from vero.tools.web import WebSearch + + search = WebSearch() + assert search.max_results == 10 + assert search.max_retries == 3 + + @requires_serper_key + @pytest.mark.asyncio + async def test_search_returns_results(self): + """Test that WebSearch returns valid results.""" + from vero.tools.web import WebSearch + + search = WebSearch(fetch_full_content=False) # Faster - just snippets + result = await search("Python programming language") + + # Parse the JSON result + parsed = json.loads(result) + + # Should not have an error + assert "error" not in parsed or parsed.get("results") is not None + + # Should be a list of results + assert isinstance(parsed, list) + assert len(parsed) > 0 + + # Each result should have required fields + for item in parsed: + assert "title" in item + assert "url" in item + assert "content" in item + + @requires_serper_key + @pytest.mark.asyncio + async def test_search_with_full_content(self): + """Test that WebSearch can fetch full page content.""" + from vero.tools.web import WebSearch + + search = WebSearch(fetch_full_content=True, max_results=2) + result = await search("Python official website") + + parsed = json.loads(result) + assert isinstance(parsed, list) + + # At least one result should have substantial content + has_content = any(len(item.get("content", "")) > 100 for item in parsed) + assert has_content, "Expected at least one result with fetched content" + + @requires_serper_key + @pytest.mark.asyncio + async def test_search_chinese_query(self): + """Test that WebSearch handles Chinese queries.""" + from vero.tools.web import WebSearch + + search = WebSearch(fetch_full_content=False, max_results=3) + result = await search("Python 编程语言") + + parsed = json.loads(result) + assert isinstance(parsed, list) + # Should return some results (might be empty depending on search) + + @requires_serper_key + @pytest.mark.asyncio + async def test_search_no_results_query(self): + """Test that WebSearch handles queries with no results gracefully.""" + from vero.tools.web import WebSearch + + search = WebSearch(fetch_full_content=False) + # Very unlikely to have results + result = await search("xyzzy123456789abcdefghijklmnop") + + parsed = json.loads(result) + # Should either be empty list or have error message + assert isinstance(parsed, (list, dict)) + + +class TestWebFetch: + """Tests for WebFetch tool.""" + + @pytest.mark.asyncio + async def test_fetch_webpage(self): + """Test fetching a simple webpage.""" + from vero.tools.web import WebFetch + + fetch = WebFetch() + # Use a reliable, stable URL + content = await fetch("https://example.com") + + assert isinstance(content, str) + assert len(content) > 0 + assert "Example Domain" in content or "example" in content.lower() + + @pytest.mark.asyncio + async def test_fetch_with_offset(self): + """Test fetching with offset.""" + from vero.tools.web import WebFetch + + fetch = WebFetch() + full_content = await fetch("https://example.com") + offset_content = await fetch("https://example.com", offset=10) + + # The offset content should be the full content minus the first 10 chars + assert offset_content == full_content[10:] + assert len(offset_content) == len(full_content) - 10 + + @pytest.mark.asyncio + async def test_fetch_invalid_url(self): + """Test that fetching invalid URL raises error.""" + from vero.tools.web import WebFetch + + fetch = WebFetch() + + with pytest.raises(RuntimeError, match="Failed to fetch"): + await fetch("https://this-url-definitely-does-not-exist-12345.com") + + @pytest.mark.asyncio + async def test_fetch_with_max_chars(self): + """Test that max_chars truncates content.""" + from vero.tools.web import WebFetch + + fetch = WebFetch() + content = await fetch("https://example.com", max_chars=50) + + assert len(content) <= 50 diff --git a/vero/tests/test_workspace.py b/vero/tests/test_workspace.py new file mode 100644 index 00000000..5824f252 --- /dev/null +++ b/vero/tests/test_workspace.py @@ -0,0 +1,391 @@ +"""Tests for the Workspace abstraction (GitWorkspace).""" + +from __future__ import annotations + +import asyncio +import subprocess +from pathlib import Path + +import pytest +import pytest_asyncio + +from vero.sandbox import LocalSandbox +from vero.workspace.git import GitWorkspace + + +def _init_git_repo(path: Path) -> None: + """Initialize a git repo with an initial commit.""" + subprocess.run(["git", "init"], cwd=path, capture_output=True, check=True) + (path / "main.py").write_text("x = 1\n") + subprocess.run(["git", "add", "."], cwd=path, capture_output=True, check=True) + subprocess.run( + [ + "git", + "-c", + "user.name=test", + "-c", + "user.email=test@test", + "commit", + "-m", + "init", + ], + cwd=path, + capture_output=True, + check=True, + ) + + +@pytest_asyncio.fixture +async def workspace(tmp_path): + """Create a GitWorkspace backed by a LocalSandbox.""" + _init_git_repo(tmp_path) + sandbox = LocalSandbox(root=tmp_path) + return await GitWorkspace.from_path(sandbox, tmp_path) + + +class TestConstruction: + @pytest.mark.asyncio + async def test_from_path(self, workspace): + assert workspace.root is not None + assert workspace.project_path is not None + assert workspace.name is not None + + @pytest.mark.asyncio + async def test_sandbox_property(self, workspace): + assert workspace.sandbox is not None + assert isinstance(workspace.sandbox, LocalSandbox) + + @pytest.mark.asyncio + async def test_from_path_subdir(self, tmp_path): + """When project_path is a subdirectory, root is the repo root and project_path is the subdir.""" + _init_git_repo(tmp_path) + subdir = tmp_path / "agents" / "my-agent" + subdir.mkdir(parents=True) + (subdir / "main.py").write_text("agent code\n") + + sandbox = LocalSandbox(root=tmp_path) + ws = await GitWorkspace.from_path(sandbox, subdir) + + assert ws.root == str(tmp_path) + assert ws.project_path == str(subdir) + assert ws.root != ws.project_path + + @pytest.mark.asyncio + async def test_not_a_git_repo(self, tmp_path): + non_git = tmp_path / "not_a_repo" + non_git.mkdir() + sandbox = LocalSandbox(root=non_git) + with pytest.raises(RuntimeError, match="Not a git repository"): + await GitWorkspace.from_path(sandbox, non_git) + + +class TestVersioning: + @pytest.mark.asyncio + async def test_current_version(self, workspace): + version = await workspace.current_version() + assert len(version) == 40 # full SHA + + @pytest.mark.asyncio + async def test_save(self, workspace): + # Make a change + await workspace.sandbox.write_file( + str(Path(workspace.root) / "new.txt"), "new content\n" + ) + assert await workspace.is_dirty() + + new_version = await workspace.save("add new.txt") + assert len(new_version) == 40 + assert not await workspace.is_dirty() + + @pytest.mark.asyncio + async def test_save_no_changes(self, workspace): + v1 = await workspace.current_version() + v2 = await workspace.save("nothing changed") + assert v1 == v2 + + @pytest.mark.asyncio + async def test_restore(self, workspace): + v1 = await workspace.current_version() + + # Make and save a change + main_py = str(Path(workspace.root) / "main.py") + await workspace.sandbox.write_file(main_py, "x = 999\n") + await workspace.save("change main.py") + v2 = await workspace.current_version() + assert v1 != v2 + + # Restore to v1 + await workspace.restore(v1) + + # main.py should be back to original + content = await workspace.sandbox.read_file(main_py) + assert content == "x = 1\n" + + +class TestSubdirProject: + """Tests for when project_path is a subdirectory of the repo root.""" + + @pytest_asyncio.fixture + async def subdir_workspace(self, tmp_path): + _init_git_repo(tmp_path) + subdir = tmp_path / "agents" / "my-agent" + subdir.mkdir(parents=True) + (subdir / "agent.py").write_text("v1\n") + # Commit the subdir + subprocess.run( + ["git", "add", "."], cwd=tmp_path, capture_output=True, check=True + ) + subprocess.run( + [ + "git", + "-c", + "user.name=test", + "-c", + "user.email=test@test", + "commit", + "-m", + "add agent", + ], + cwd=tmp_path, + capture_output=True, + check=True, + ) + sandbox = LocalSandbox(root=tmp_path) + return await GitWorkspace.from_path(sandbox, subdir) + + @pytest.mark.asyncio + async def test_save_scoped_to_project(self, subdir_workspace): + """save() only stages files within project_path, not the whole repo.""" + ws = subdir_workspace + repo_root = ws.root + + # Create a file outside the project subdir + outside = str(Path(repo_root) / "outside.txt") + await ws.sandbox.write_file(outside, "outside\n") + + # Create a file inside the project subdir + inside = str(Path(ws.project_path) / "new.py") + await ws.sandbox.write_file(inside, "new\n") + + v1 = await ws.current_version() + await ws.save("save inside only") + v2 = await ws.current_version() + + # A commit was made (inside file was staged) + assert v1 != v2 + + # The outside file should still be untracked + result = await ws.sandbox.run( + ["git", "status", "--porcelain", "outside.txt"], cwd=repo_root + ) + assert "??" in result.stdout # untracked + + @pytest.mark.asyncio + async def test_is_dirty_scoped_to_project(self, subdir_workspace): + """is_dirty() only reports changes within the project subdirectory.""" + ws = subdir_workspace + assert not await ws.is_dirty() + + # Modify file OUTSIDE project — should NOT be dirty + await ws.sandbox.write_file(str(Path(ws.root) / "outside.txt"), "unrelated\n") + assert not await ws.is_dirty(), ( + "Changes outside project_path should not make workspace dirty" + ) + + # Modify file INSIDE project — should be dirty + await ws.sandbox.write_file(str(Path(ws.project_path) / "agent.py"), "v2\n") + assert await ws.is_dirty() + + +class TestDirtyState: + @pytest.mark.asyncio + async def test_clean_initially(self, workspace): + assert not await workspace.is_dirty() + + @pytest.mark.asyncio + async def test_dirty_after_edit(self, workspace): + await workspace.sandbox.write_file( + str(Path(workspace.root) / "main.py"), "x = 2\n" + ) + assert await workspace.is_dirty() + + @pytest.mark.asyncio + async def test_clean_after_save(self, workspace): + await workspace.sandbox.write_file( + str(Path(workspace.root) / "main.py"), "x = 2\n" + ) + await workspace.save("update") + assert not await workspace.is_dirty() + + +class TestHistory: + @pytest.mark.asyncio + async def test_diff(self, workspace): + v1 = await workspace.current_version() + await workspace.sandbox.write_file( + str(Path(workspace.root) / "main.py"), "x = 2\n" + ) + await workspace.save("update") + v2 = await workspace.current_version() + + diff = await workspace.diff(v1, v2) + assert "-x = 1" in diff + assert "+x = 2" in diff + + @pytest.mark.asyncio + async def test_log(self, workspace): + log = await workspace.log() + assert "init" in log + + @pytest.mark.asyncio + async def test_is_ancestor(self, workspace): + v1 = await workspace.current_version() + await workspace.sandbox.write_file( + str(Path(workspace.root) / "main.py"), "x = 2\n" + ) + await workspace.save("update") + v2 = await workspace.current_version() + + assert await workspace.is_ancestor(v1, v2) + assert not await workspace.is_ancestor(v2, v1) + + +class TestCopies: + @pytest.mark.asyncio + async def test_temp_copy(self, workspace): + v1 = await workspace.current_version() + + async with workspace.temp_copy(from_version=v1) as copy_ws: + assert copy_ws.root != workspace.root + assert copy_ws.root == str(Path(copy_ws.root).resolve()) + # The copy is a separate git worktree + assert Path(copy_ws.root).exists() + # main.py should exist in the copy + assert (Path(copy_ws.root) / "main.py").exists() + assert (Path(copy_ws.root) / "main.py").read_text() == "x = 1\n" + + # After context exit, the temp worktree is cleaned up + assert not Path(copy_ws.root).exists() + + # Original is unchanged + content = await workspace.sandbox.read_file( + str(Path(workspace.root) / "main.py") + ) + assert content == "x = 1\n" + + @pytest.mark.asyncio + async def test_copies_preserve_subdirectory_project(self, tmp_path): + _init_git_repo(tmp_path) + subdir = tmp_path / "packages" / "target" + subdir.mkdir(parents=True) + (subdir / "program.py").write_text("value = 1\n") + subprocess.run( + ["git", "add", "."], cwd=tmp_path, capture_output=True, check=True + ) + subprocess.run( + [ + "git", + "-c", + "user.name=test", + "-c", + "user.email=test@test", + "commit", + "-m", + "add target", + ], + cwd=tmp_path, + capture_output=True, + check=True, + ) + workspace = await GitWorkspace.from_path(LocalSandbox(root=tmp_path), subdir) + + async with workspace.temp_copy() as copy_workspace: + relative = Path(copy_workspace.project_path).relative_to( + copy_workspace.root + ) + assert relative == Path("packages/target") + assert Path(copy_workspace.project_path, "program.py").is_file() + + persistent = await workspace.copy(name="subproject-copy") + try: + relative = Path(persistent.project_path).relative_to(persistent.root) + assert relative == Path("packages/target") + finally: + await persistent.destroy() + + @pytest.mark.asyncio + async def test_persistent_copy_does_not_leak_a_branch( + self, + workspace, + ): + branches_before = await workspace._git( + "for-each-ref", "--format=%(refname)", "refs/heads" + ) + copied = await workspace.copy(name="candidate-copy") + await copied.sandbox.write_file( + str(Path(copied.root) / "candidate.txt"), + "candidate\n", + ) + await copied.save("candidate") + await copied.destroy() + + branches_after = await workspace._git( + "for-each-ref", "--format=%(refname)", "refs/heads" + ) + assert branches_after == branches_before + assert not Path(copied.root).exists() + + @pytest.mark.asyncio + async def test_copy_rolls_back_if_cancelled_after_git_creates_worktree( + self, + workspace, + monkeypatch, + ): + original_run = workspace.sandbox.run + injected = False + + async def cancel_after_add(command, **kwargs): + nonlocal injected + result = await original_run(command, **kwargs) + if ( + "worktree" in command + and command[command.index("worktree") + 1] == "add" + and not injected + ): + injected = True + raise asyncio.CancelledError + return result + + monkeypatch.setattr(workspace.sandbox, "run", cancel_after_add) + + with pytest.raises(asyncio.CancelledError): + await workspace.copy(name="cancelled-copy") + + worktrees = await workspace._git("worktree", "list", "--porcelain") + assert worktrees.count("worktree ") == 1 + assert not Path(workspace.root).parent.joinpath("cancelled-copy").exists() + + +class TestAtVersion: + @pytest.mark.asyncio + async def test_at_version(self, workspace): + v1 = await workspace.current_version() + + # Make a change + await 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The floor gates a ship on a validation comparison while the reward is on the (possibly differently-distributed) target, so it is opt-in. - Pin the fixed seed's score to avoid re-scoring it every run (reproducibility + one fewer eval): VerificationTarget.baseline_reward (per-target, post scale/offset) and VerificationSelection .baseline_selection_score (for the floor). Plumbed through the build config + compiler. - Fail safe when the floor is on, unpinned, and the seed re-score can't be measured: ship nothing (NoCandidateError -> shipped=false + infra error) instead of shipping the best candidate unverified (the fail-open bug). Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/build/compiler.py | 2 + vero/src/vero/harbor/build/config.py | 5 +- vero/src/vero/harbor/deployment.py | 4 +- vero/src/vero/harbor/verifier.py | 43 ++++++++- vero/tests/test_v05_harbor_verifier.py | 115 ++++++++++++++++++++++++- 5 files changed, 161 insertions(+), 8 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 5c319ab0..9497500e 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -333,6 +333,7 @@ def _deployment_config( "reward_scale": ( 1.0 if config.objective.direction == "maximize" else -1.0 ), + "baseline_reward": target.baseline_reward, "max_attempts": target.max_attempts, } ) @@ -359,6 +360,7 @@ def _deployment_config( "rescore_top_k": config.rescore_top_k, "rescore_attempts": config.rescore_attempts, "baseline_floor": config.baseline_floor, + "baseline_selection_score": config.baseline_selection_score, "selection_coverage_threshold": config.selection_coverage_threshold, }, "targets": targets, diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index e2965f7d..d5f66192 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -41,6 +41,8 @@ class VerificationTargetSpec(EvaluationModel): model: str | None = None parameters: dict[str, JsonValue] = Field(default_factory=dict) failure_value: float = 0.0 + # Pin the seed's reward on this target to skip scoring it each run. + baseline_reward: float | None = None max_attempts: int = Field(default=1, ge=1) @field_validator("partition", "reward_key") @@ -185,7 +187,8 @@ class HarborBuildConfig(EvaluationModel): ) ) reward_mode: Literal["submit", "auto_best"] = "auto_best" - baseline_floor: bool = True + baseline_floor: bool = False + baseline_selection_score: float | None = None score_baseline: bool = True rescore_top_k: int = Field(default=3, ge=1) rescore_attempts: int = Field(default=1, ge=1) diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 9000d715..6716db0d 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -47,7 +47,8 @@ class DeploymentSelection(EvaluationModel): limits: EvaluationLimits = Field(default_factory=EvaluationLimits) rescore_top_k: int = Field(default=3, ge=1) rescore_attempts: int = Field(default=1, ge=1) - baseline_floor: bool = True + baseline_floor: bool = False + baseline_selection_score: float | None = None selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) @field_validator("backend_id", "baseline_version") @@ -306,6 +307,7 @@ def _finish_wandb(result: object) -> None: rescore_top_k=parsed.selection.rescore_top_k, rescore_attempts=parsed.selection.rescore_attempts, baseline_floor=parsed.selection.baseline_floor, + baseline_selection_score=parsed.selection.baseline_selection_score, selection_coverage_threshold=parsed.selection.selection_coverage_threshold, ) initialize_harbor_session_manifest( diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py index 2bace0ff..bb57b48e 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/harbor/verifier.py @@ -46,6 +46,9 @@ class VerificationTarget(EvaluationModel): failure_value: float = 0.0 reward_scale: float = 1.0 reward_offset: float = 0.0 + # Pin the seed's reward on this target (post scale/offset) to skip scoring + # the fixed seed each run — reproducibility + one fewer eval per run. + baseline_reward: float | None = None # Keep one attempt by default for adversarial candidates. Retrying an # unmeasurable candidate-controlled run creates a one-sided re-roll. max_attempts: int = Field(default=1, ge=1) @@ -64,6 +67,13 @@ def validate_failure_value(cls, value: float) -> float: raise ValueError("verification reward values must be finite") return value + @field_validator("baseline_reward") + @classmethod + def validate_baseline_reward(cls, value: float | None) -> float | None: + if value is not None and not math.isfinite(value): + raise ValueError("baseline_reward must be finite") + return value + class VerificationSelection(EvaluationModel): """How finalization chooses a candidate before scoring its targets.""" @@ -77,7 +87,11 @@ class VerificationSelection(EvaluationModel): limits: EvaluationLimits = Field(default_factory=EvaluationLimits) rescore_top_k: int = Field(default=3, ge=1) rescore_attempts: int = Field(default=1, ge=1) - baseline_floor: bool = True + # Off by default: the floor gates a ship on a validation comparison while + # the reward is on the (possibly differently-distributed) target. + baseline_floor: bool = False + # Pin the seed's selection-partition score to skip re-scoring it each run. + baseline_selection_score: float | None = None selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) @model_validator(mode="after") @@ -327,6 +341,14 @@ def _strictly_beats( return candidate.objective.value > baseline.objective.value return candidate.objective.value < baseline.objective.value + def _beats_value(self, candidate: EvaluationRecord, baseline_value: float) -> bool: + assert candidate.objective is not None and candidate.objective.value is not None + if not candidate.objective.feasible: + return False + if self.selection.objective.direction == "maximize": + return candidate.objective.value > baseline_value + return candidate.objective.value < baseline_value + def _pick_last(self) -> Candidate | None: """Last-resort fallback: the most recently measured candidate.""" records = list(self.engine.database.evaluations.values()) @@ -353,9 +375,19 @@ async def _auto_best(self) -> Candidate | None: baseline_candidate = self.selection.baseline_candidate if self.selection.baseline_floor and baseline_candidate is not None: - baseline, _ = await self._rescore_candidate(baseline_candidate) - if baseline is not None and not self._strictly_beats(best, baseline): - return baseline_candidate + pinned = self.selection.baseline_selection_score + if pinned is not None: + if not self._beats_value(best, pinned): + return baseline_candidate + else: + baseline, _ = await self._rescore_candidate(baseline_candidate) + if baseline is None: + # Fail safe: can't verify the seed → don't ship it unverified. + raise NoCandidateError( + "baseline floor could not measure the seed (infrastructure)" + ) + if not self._strictly_beats(best, baseline): + return baseline_candidate return best.request.candidate async def _select_candidate(self) -> Candidate: @@ -425,6 +457,9 @@ async def _finalize(self) -> VerificationResult: baseline_rewards = dict(rewards) else: for target in self.targets: + if target.baseline_reward is not None: + baseline_rewards[target.reward_key] = target.baseline_reward + continue reward, _, error = await self._score_target(baseline, target) baseline_rewards[target.reward_key] = reward if error is not None: diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py index 7787e4a6..5660f03f 100644 --- a/vero/tests/test_v05_harbor_verifier.py +++ b/vero/tests/test_v05_harbor_verifier.py @@ -145,6 +145,7 @@ def _verifier( baseline: Candidate, top_k: int = 1, score_baseline: bool = True, + baseline_floor: bool = False, ): return CanonicalVerifier( engine=engine, @@ -156,6 +157,7 @@ def _verifier( baseline_candidate=baseline, rescore_top_k=top_k, rescore_attempts=1, + baseline_floor=baseline_floor, ), targets=[ VerificationTarget( @@ -194,7 +196,9 @@ async def test_verifier_pools_repeats_then_admin_rescores_and_scores_baseline(tm ) ) - result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + result = await _verifier( + tmp_path, engine, baseline=baseline, baseline_floor=True + ).finalize() assert result.candidate == steady assert result.rewards == {"reward": 0.8} @@ -259,7 +263,9 @@ async def test_verifier_baseline_floor_prevents_shipping_a_regression(tmp_path): _record("selection", candidate, EvaluationSet(name="selection"), 0.9) ) - result = await _verifier(tmp_path, engine, baseline=baseline).finalize() + result = await _verifier( + tmp_path, engine, baseline=baseline, baseline_floor=True + ).finalize() assert result.candidate == baseline assert result.rewards == {"reward": 0.5} @@ -450,3 +456,108 @@ async def test_verifier_coverage_threshold_excludes_undermeasured_candidates(tmp assert result.candidate == well assert ("thin", "selection") not in engine.calls assert ("well", "selection") in engine.calls + + +@pytest.mark.asyncio +async def test_verifier_uses_pinned_baseline_reward_without_scoring(tmp_path): + # A pinned target baseline_reward is used verbatim; the seed is never scored. + baseline = _candidate("baseline") + cand = _candidate("cand", seconds=1) + engine = FakeEngine({("cand", "selection"): 0.8, ("cand", "test"): 0.7}) + engine.database.add_evaluation( + _record("r", cand, EvaluationSet(name="selection"), 0.8) + ) + verifier = CanonicalVerifier( + engine=engine, + selection=VerificationSelection( + mode="auto_best", + backend_id="backend", + evaluation_set=EvaluationSet(name="selection"), + objective=OBJECTIVE, + baseline_candidate=baseline, + rescore_top_k=1, + rescore_attempts=1, + ), + targets=[ + VerificationTarget( + reward_key="reward", + backend_id="backend", + evaluation_set=EvaluationSet(name="test"), + objective=OBJECTIVE, + max_attempts=1, + baseline_reward=0.55, + ) + ], + admin_volume=tmp_path, + score_baseline=True, + ) + + result = await verifier.finalize() + + assert result.candidate == cand + assert result.rewards == {"reward": 0.7} + assert result.baseline_rewards == {"reward": 0.55} + assert ("baseline", "test") not in engine.calls # seed never scored + + +@pytest.mark.asyncio +async def test_verifier_uses_pinned_baseline_selection_score(tmp_path): + # With a pinned selection score, the floor compares without re-scoring the seed. + baseline = _candidate("baseline") + cand = _candidate("cand", seconds=1) + engine = FakeEngine({("cand", "selection"): 0.4, ("baseline", "test"): 0.3}) + engine.database.add_evaluation( + _record("r", cand, EvaluationSet(name="selection"), 0.4) + ) + verifier = CanonicalVerifier( + engine=engine, + selection=VerificationSelection( + mode="auto_best", + backend_id="backend", + evaluation_set=EvaluationSet(name="selection"), + objective=OBJECTIVE, + baseline_candidate=baseline, + rescore_top_k=1, + rescore_attempts=1, + baseline_floor=True, + baseline_selection_score=0.6, + ), + targets=[ + VerificationTarget( + reward_key="reward", + backend_id="backend", + evaluation_set=EvaluationSet(name="test"), + objective=OBJECTIVE, + max_attempts=1, + ) + ], + admin_volume=tmp_path, + score_baseline=False, + ) + + result = await verifier.finalize() + + # best (0.4) does not beat the pinned seed (0.6) → floor keeps the seed. + assert result.candidate == baseline + assert ("baseline", "selection") not in engine.calls # seed never re-scored + + +@pytest.mark.asyncio +async def test_verifier_floor_fails_safe_when_seed_unmeasurable(tmp_path): + # Floor on, unpinned, seed re-score fails → ship nothing (inconclusive), + # not the best candidate unverified. + baseline = _candidate("baseline") + cand = _candidate("cand", seconds=1) + engine = FakeEngine( + {("cand", "selection"): 0.9, ("baseline", "selection"): RuntimeError("outage")} + ) + engine.database.add_evaluation( + _record("r", cand, EvaluationSet(name="selection"), 0.9) + ) + + result = await _verifier( + tmp_path, engine, baseline=baseline, baseline_floor=True + ).finalize() + + assert result.shipped is False + assert "baseline floor" in result.errors.get("selection", "") From 4bbe47bbf3deaa74099e9b69eab2f76963e7c90a Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 12:03:09 -0700 Subject: [PATCH 03/72] harbor: score-baseline producer for pinnable baseline numbers (feedback #1) Add an admin-only way to generate the number to pin: CanonicalVerifier .measure_baseline(replicates) admin-scores the fixed seed N times on the selection partition and each target and returns per-key mean/stddev. Exposed as POST /score/baseline and 'vero harbor score-baseline --replicates N'. Reuses the existing admin scoring machinery. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/app.py | 14 ++++++++ vero/src/vero/harbor/cli.py | 19 ++++++++++ vero/src/vero/harbor/verifier.py | 50 ++++++++++++++++++++++++++ vero/tests/test_v05_harbor_verifier.py | 29 +++++++++++++++ 4 files changed, 112 insertions(+) diff --git a/vero/src/vero/harbor/app.py b/vero/src/vero/harbor/app.py index 0e1a7d67..9628b19b 100644 --- a/vero/src/vero/harbor/app.py +++ b/vero/src/vero/harbor/app.py @@ -43,6 +43,12 @@ class SubmitRequest(BaseModel): version: str | None = None +class ScoreBaselineRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + + replicates: int = 1 + + def _error(status_code: int, message: str): async def handler(_request, error): return JSONResponse( @@ -147,6 +153,14 @@ async def finalize(authorization: Annotated[str | None, Header()] = None): require_admin(authorization) return await verifier.finalize() + @app.post("/score/baseline") + async def score_baseline( + body: ScoreBaselineRequest, + authorization: Annotated[str | None, Header()] = None, + ): + require_admin(authorization) + return await verifier.measure_baseline(replicates=body.replicates) + @app.get("/evaluations") async def evaluations( authorization: Annotated[str | None, Header()] = None, diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 13fb3b39..fd07bc29 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -625,6 +625,25 @@ def status_command(): click.echo(json.dumps(_request("GET", "/status"), indent=2)) +@harbor.command("score-baseline") +@click.option( + "--token-file", + required=True, + type=click.Path(path_type=Path, exists=True, dir_okay=False), +) +@click.option("--replicates", default=1, show_default=True, type=click.IntRange(min=1)) +def score_baseline_command(token_file, replicates): + """Admin-score the fixed seed N times to produce a pinnable baseline number.""" + token = read_admin_token(token_file) + result = _request( + "POST", + "/score/baseline", + payload={"replicates": replicates}, + headers={"Authorization": f"Bearer {token}"}, + ) + click.echo(json.dumps(result, indent=2)) + + @harbor.command("finalize") @click.option( "--token-file", diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py index bb57b48e..5cf9210d 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/harbor/verifier.py @@ -427,6 +427,56 @@ async def _score_target( ) return reward, record.id, None + async def measure_baseline(self, *, replicates: int = 1) -> dict[str, JsonValue]: + """Admin-score the fixed seed to produce pinnable baseline numbers. + + Runs `replicates` trusted evaluations of the baseline on the selection + partition and each target and returns per-key mean/stddev, so a stable + value can be pinned (baseline_selection_score / target baseline_reward) + instead of re-scoring the seed every run.""" + if replicates < 1: + raise ValueError("replicates must be >= 1") + baseline = self.selection.baseline_candidate + if baseline is None: + raise NoCandidateError("no baseline candidate to score") + + def _aggregate(values: list[float | None]) -> dict[str, JsonValue]: + clean = [value for value in values if value is not None] + return { + "values": values, + "n": len(clean), + "mean": statistics.fmean(clean) if clean else None, + "stddev": statistics.pstdev(clean) if len(clean) > 1 else 0.0, + } + + result: dict[str, JsonValue] = { + "candidate_version": baseline.version, + "replicates": replicates, + } + if ( + self.selection.backend_id is not None + and self.selection.evaluation_set is not None + and self.selection.objective is not None + ): + selection_values: list[float | None] = [] + for _ in range(replicates): + record, _ = await self._rescore_candidate(baseline) + selection_values.append( + record.objective.value + if record is not None and record.objective is not None + else None + ) + result["selection"] = _aggregate(selection_values) + targets: dict[str, JsonValue] = {} + for target in self.targets: + target_values: list[float | None] = [] + for _ in range(replicates): + reward, _, error = await self._score_target(baseline, target) + target_values.append(None if error is not None else reward) + targets[target.reward_key] = _aggregate(target_values) + result["targets"] = targets + return result + async def _finalize(self) -> VerificationResult: try: candidate = await self._select_candidate() diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py index 5660f03f..3b969c33 100644 --- a/vero/tests/test_v05_harbor_verifier.py +++ b/vero/tests/test_v05_harbor_verifier.py @@ -561,3 +561,32 @@ async def test_verifier_floor_fails_safe_when_seed_unmeasurable(tmp_path): assert result.shipped is False assert "baseline floor" in result.errors.get("selection", "") + + +@pytest.mark.asyncio +async def test_verifier_score_baseline_produces_replicated_means(tmp_path): + baseline = _candidate("baseline") + engine = FakeEngine( + { + ("baseline", "selection"): [0.5, 0.6], + ("baseline", "test"): [0.4, 0.5], + } + ) + + out = await _verifier(tmp_path, engine, baseline=baseline).measure_baseline( + replicates=2 + ) + + assert out["candidate_version"] == "baseline" + assert out["replicates"] == 2 + assert out["selection"]["n"] == 2 + assert out["selection"]["mean"] == 0.55 + assert out["targets"]["reward"]["n"] == 2 + assert out["targets"]["reward"]["mean"] == 0.45 + # 2 selection re-scores + 2 target scores, all admin. + assert engine.calls == [ + ("baseline", "selection"), + ("baseline", "selection"), + ("baseline", "test"), + ("baseline", "test"), + ] From dcd7ffad50b14deffc327703196614713fef6401 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 12:03:09 -0700 Subject: [PATCH 04/72] tests: fix harbor build benchmark tests for the split + candidates flatten The benchmark-config tests read harness-engineering-bench/ (a separate branch in the stacked split), so skip them when it isn't checked out. Update the paths for the candidates/ -> top-level flatten (all benchmarks are now top-level siblings of gaia/). Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/tests/test_v05_harbor_build.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index e5122138..276d898a 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -25,6 +25,13 @@ BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" +# harness-engineering-bench is a separate branch/PR in the stacked split, so +# skip the benchmark-config tests when it isn't checked out here. +_requires_benchmarks = pytest.mark.skipif( + not BENCHMARK_ROOT.exists(), + reason="harness-engineering-bench is not present on this branch", +) + def _git(path: Path, *arguments: str) -> str: result = subprocess.run( @@ -37,6 +44,7 @@ def _git(path: Path, *arguments: str) -> str: return result.stdout.strip() +@_requires_benchmarks @pytest.mark.parametrize( ("benchmark", "producer_models"), [ @@ -50,10 +58,9 @@ def _git(path: Path, *arguments: str) -> str: def test_canonical_benchmarks_isolate_upstream_inference_credentials( benchmark, producer_models ): - # gaia is promoted to the top level; other task sets live under candidates/. - base = BENCHMARK_ROOT if benchmark == "gaia" else BENCHMARK_ROOT / "candidates" + # All benchmarks live at the top level of harness-engineering-bench. config = load_harbor_build_config( - base / benchmark / "baseline" / "build.yaml" + BENCHMARK_ROOT / benchmark / "baseline" / "build.yaml" ) assert config.inference_gateway is not None @@ -74,6 +81,7 @@ def test_canonical_benchmarks_isolate_upstream_inference_credentials( assert config.inference_gateway.evaluation.max_tokens == 100000000 +@_requires_benchmarks def test_build_params_override_run_time_knobs_without_rebuild(): path = BENCHMARK_ROOT / "gaia" / "baseline" / "build.yaml" From 6d7f18d9962273a50f04089fd850fdf5a3c94218 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 12:45:01 -0700 Subject: [PATCH 05/72] harbor: k-anonymity floor defaults to 5, not a no-op (feedback #2) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit min_aggregate_cases defaulted to 1 — a vacuous floor that let an agent read held-out validation labels one case at a time via single-case aggregate evals. Default to 5 in both the sidecar policy and the build AgentAccessSpec so a build that omits it is safe rather than unfloored (shipped builds already set 5 explicitly). Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/build/config.py | 4 +++- vero/src/vero/harbor/sidecar.py | 5 ++++- vero/tests/test_v05_harbor_build.py | 5 +++++ vero/tests/test_v05_harbor_sidecar.py | 9 +++++++++ 4 files changed, 21 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index d5f66192..b285ca19 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -23,7 +23,9 @@ class AgentAccessSpec(EvaluationModel): partition: str disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE expose_case_resources: bool = False - min_aggregate_cases: int = Field(default=1, ge=1) + # k-anonymity floor for aggregate subset evals; 5 by default so an omitted + # value is safe rather than an unfloored (single-case) leak. + min_aggregate_cases: int = Field(default=5, ge=1) total_runs: int | None = Field(default=None, ge=0) total_cases: int | None = Field(default=None, ge=0) diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index e533d5cd..715d58c0 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -72,7 +72,10 @@ class SidecarEvaluationPolicy(EvaluationModel): disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE expose_case_resources: bool = False agent_evaluable: bool = True - min_aggregate_cases: int = Field(default=1, ge=1) + # k-anonymity floor: aggregate subset evals must cover >= this many cases so + # a single held-out label can't be read off one case at a time. Default 5 + # (not 1) so a build that omits it is safe rather than unfloored. + min_aggregate_cases: int = Field(default=5, ge=1) parameters: dict[str, JsonValue] = Field(default_factory=dict) allowed_parameters: list[str] = Field(default_factory=list) limits: EvaluationLimits | None = None diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 276d898a..f2e90b18 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -657,3 +657,8 @@ def test_compiler_uses_published_version_outside_a_source_checkout( assert not (output / "environment/vero").exists() dockerfile = (output / "environment/Dockerfile").read_text(encoding="utf-8") assert "scale-vero[harbor]==0.5.0" in dockerfile + + +def test_agent_access_defaults_to_safe_k_anonymity_floor(): + # Omitting min_aggregate_cases must yield a real floor (5), not a no-op (1). + assert AgentAccessSpec(partition="validation").min_aggregate_cases == 5 diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index 019c98fa..74b4cb68 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -698,3 +698,12 @@ async def run_as_different_owner(command, cwd=None, timeout=30, env=None): ) == "" ) + + +def test_sidecar_policy_defaults_to_safe_k_anonymity_floor(): + from vero.harbor.sidecar import SidecarEvaluationPolicy + + policy = SidecarEvaluationPolicy( + backend_id="b", evaluation_set_name="s" + ) + assert policy.min_aggregate_cases == 5 From b535d184aa5424591803388f53154d4719833e52 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 12:59:40 -0700 Subject: [PATCH 06/72] harbor: pre-warm harness uv cache so isolation actually works Re-enabling harness isolation broke the eval: 'uv run' as the unprivileged harness got a fresh empty uv cache and couldn't resolve the candidate package from it (No module named 'gaia_agent'), with no network to download at eval time. Pre-warm /home/harness/.cache/uv from the build (root) cache in the sidecar image, matching the UV_CACHE_DIR the backend sets for the harness user. Also fixes a test regression from feedback #2: with the k-anonymity floor defaulting to 5, the instruction now reads 'must include at least 5 cases' rather than 'arbitrary subsets'. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../vero/harbor/build/templates/Dockerfile.sidecar.j2 | 8 ++++++++ vero/tests/test_v05_harbor_build.py | 10 +++++++--- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 index bcd85e45..839913c0 100644 --- a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 +++ b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 @@ -32,6 +32,14 @@ RUN git config --system --add safe.directory /opt/agent-baseline \ # the uv cache and git; useradd creates a same-named primary group. RUN useradd -m -u 1002 harness +# Pre-warm the harness user's uv cache from the build (root) cache. `uv run` as +# the unprivileged harness must resolve the candidate package (and harbor) from +# cache: a fresh empty cache would need network at eval time and fail to import +# it ("No module named ''"). Copy so it is owned by and writable to it. +RUN mkdir -p /home/harness/.cache \ + && cp -a "$(uv cache dir)" /home/harness/.cache/uv \ + && chown -R harness:harness /home/harness/.cache + # Lock the trusted, root-only files the harness must never read: the case lists # reveal held-out (validation/test) task membership, and the deployment config # carries the reserved finalization inference token. (Local task packages under diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index f2e90b18..0ea47454 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -6,8 +6,8 @@ from pathlib import Path import pytest -from pydantic import ValidationError import yaml +from pydantic import ValidationError from vero.harbor import ( AgentAccessSpec, @@ -22,7 +22,6 @@ load_harbor_build_config, ) - BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" # harness-engineering-bench is a separate branch/PR in the stacked split, so @@ -496,7 +495,8 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): instruction = (output / "instruction.md").read_text(encoding="utf-8") assert "## Objective\n\nImprove the program" in instruction assert "--backend harbor-validation" in instruction - assert "arbitrary subsets" in instruction + # k-anonymity floor (default 5) is disclosed to the agent + assert "must include at least 5 cases" in instruction assert "Complete task resources" in instruction assert ".vero/evaluations/" in instruction task_toml = (output / "task.toml").read_text(encoding="utf-8") @@ -510,6 +510,10 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert set(yaml.safe_load(compose)["services"]) == {"main", "eval-sidecar"} sidecar_dockerfile = (output / "environment/sidecar/Dockerfile").read_text() assert 'uv pip install --system "harbor==0.1.17"' in sidecar_dockerfile + # the unprivileged harness user exists and gets a pre-warmed uv cache, so + # `uv run` as harness can resolve the candidate package offline + assert "useradd -m -u 1002 harness" in sidecar_dockerfile + assert "chown -R harness:harness /home/harness/.cache" in sidecar_dockerfile seed = (output / "environment/main/seed.sh").read_text() assert "-path /work/agent/.vero -prune" in seed assert "'/.vero/' >> /work/agent/.git/info/exclude" in seed From b069cb120e493c32eb0506f19f3eb644d3f58ec0 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 13:06:24 -0700 Subject: [PATCH 07/72] harbor: re-record n_dead_infra / n_clean in the mean aggregate (feedback #8) The mean-of-k recorded only score/n_attempts/n_scored, so an outage-diluted cell (dead attempts zero-filled) was indistinguishable from a genuinely clean low cell. Add n_dead_infra (dead-to-infrastructure attempts, classified via the existing error taxonomy) and n_clean (the rest), so the split is visible in the live metrics rather than only reconstructable from raw trial records. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/backend.py | 25 ++++++++++++++++++++++++- vero/tests/test_v05_harbor_backend.py | 3 +++ 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 813ca043..e328cc84 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -45,8 +45,8 @@ EvaluationStatus, ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text -from vero.staging import SandboxStagingArea from vero.sandbox import CommandResult, Sandbox +from vero.staging import SandboxStagingArea logger = logging.getLogger(__name__) @@ -802,6 +802,18 @@ def _attempt_reward(self, attempt: dict[str, Any]) -> float | None: rewards = (attempt.get("verifier_result") or {}).get("rewards") or {} return self._extract_reward(rewards) if rewards else None + def _attempt_is_infra(self, attempt: dict[str, Any]) -> bool: + """Whether a dead attempt died to infrastructure (vs the candidate).""" + info = attempt.get("exception_info") or {} + if not info: + return False + signals = [str(info.get("exception_type") or NO_REWARD_SIGNAL)] + for detail_key in ("message", "detail", "exception_message"): + detail = info.get(detail_key) + if isinstance(detail, str) and detail: + signals.append(detail) + return not policy(classify_case(signals)).is_informative_sample + def _best_attempt( self, attempts: list[dict[str, Any]], @@ -935,6 +947,15 @@ def _case_result( score = sum(measured) / len(measured) output["attempt_scores"] = measured output["aggregate"] = "mean" + # Split the zero-filled dead attempts so an outage-diluted mean + # is distinguishable from a genuinely clean low mean: n_dead_infra + # are dead-to-infrastructure; n_clean are the rest (scored or a + # real candidate failure). + n_dead_infra = sum( + 1 + for attempt, reward in zip(attempts, rewards) + if reward is None and self._attempt_is_infra(attempt) + ) return ( CaseResult( case_id=case.id, @@ -945,6 +966,8 @@ def _case_result( "n_scored": float( sum(reward is not None for reward in rewards) ), + "n_dead_infra": float(n_dead_infra), + "n_clean": float(len(attempts) - n_dead_infra), }, input={"task_name": case.task_name, **case.metadata}, output=output, diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 5691ee3f..73ec8f0e 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -774,6 +774,9 @@ async def test_harbor_backend_mean_counts_dead_attempts_as_failures(tmp_path): "score": 0.5, "n_attempts": 2.0, "n_scored": 1.0, + # the dead attempt was a TimeoutError -> infra dilution, not clean signal + "n_dead_infra": 1.0, + "n_clean": 1.0, } From d1076c502adb5dce77ce54bc376bf1b33a6b8042 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 13:10:44 -0700 Subject: [PATCH 08/72] address Greptile review on PR #45 - optimizer.run: validate self.max_proposals >= 0 before the proposal_limit guard, so the specific 'must be non-negative' error is reachable (was a dead branch that always tripped the generic message first). - compiler._safe_extract_tar: extractall(filter='data') to strip device files / setuid bits and neutralize unsafe links, matching extract_harbor_session_archive (also silences the py3.14 deprecation). Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/build/compiler.py | 4 +++- vero/src/vero/optimization/optimizer.py | 7 +++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 9497500e..b1d59728 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -90,7 +90,9 @@ def _safe_extract_tar(payload: bytes, destination: Path) -> None: link = PurePosixPath(member.linkname) if link.is_absolute() or ".." in link.parts: raise ValueError(f"unsafe link in Git archive: {member.linkname!r}") - archive.extractall(destination) + # filter="data" strips device files / setuid bits and neutralizes unsafe + # links, matching extract_harbor_session_archive's defensive posture. + archive.extractall(destination, filter="data") def _prepare_baseline_repo( diff --git a/vero/src/vero/optimization/optimizer.py b/vero/src/vero/optimization/optimizer.py index b7061b3d..de97303b 100644 --- a/vero/src/vero/optimization/optimizer.py +++ b/vero/src/vero/optimization/optimizer.py @@ -415,12 +415,15 @@ async def run( max_proposals: int | None = None, ) -> OptimizationResult: proposal_limit = self.max_proposals if max_proposals is None else max_proposals + # Check the session-level limit first: otherwise a negative + # self.max_proposals always trips the proposal_limit guard below and its + # specific message is unreachable. + if self.max_proposals < 0: + raise ValueError("max_proposals must be non-negative") if proposal_limit < 0 or proposal_limit > self.max_proposals: raise ValueError( "run max_proposals must be between zero and the session protocol limit" ) - if self.max_proposals < 0: - raise ValueError("max_proposals must be non-negative") if self.max_rounds < 1: raise ValueError("max_rounds must be positive") if self.max_concurrency < 1: From bc4316d43c0fcc25b8e41d392030c2c2d436758d Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 13:27:58 -0700 Subject: [PATCH 09/72] docs: harbor benchmark architecture overview + review order A cold-start walkthrough of the harbor path: the three-container trust boundary, a run end-to-end, the evaluation core, disclosure/selection, the five security mechanisms, and a suggested outside-in review order keyed to the source files. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/docs/harbor-architecture.md | 194 +++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 vero/docs/harbor-architecture.md diff --git a/vero/docs/harbor-architecture.md b/vero/docs/harbor-architecture.md new file mode 100644 index 00000000..f80878eb --- /dev/null +++ b/vero/docs/harbor-architecture.md @@ -0,0 +1,194 @@ +# Harbor benchmark architecture + +**Audience:** an engineer picking this up cold, or reviewing the PRs. +**Scope:** the *harbor* deployment path — how VeRO turns an untrusted optimizer +agent into a trustworthy benchmark number. (The in-process +`OptimizationSession`/`optimizer.py` path is a separate, simpler runner and is +not used by the harbor deployment.) + +## The core idea + +VeRO benchmarks an **optimizer** (an LLM coding agent such as codex/claude) at +*improving a candidate program*. The optimizer edits code and asks a **trusted +evaluator** to score it; the evaluator holds the held-out answers and never lets +the optimizer see them. The whole system exists to get a *trustworthy* number +out of an *untrusted* agent editing code against data it must not peek at. + +Everything below hangs off one organizing idea: the **trust boundary** between +the optimizer and the evaluator. + +## Topology: three containers + +A run is a Docker Compose stack of three services (on Modal, or local docker): + +``` + UNTRUSTED │ TRUSTED + │ + ┌─────────────┐ │ ┌────────────────────┐ ┌──────────────────┐ + │ main │──┼─▶│ eval-sidecar │ │ inference-gateway│ + │ (optimizer) │ │ │ (FastAPI, root) │ │ (metered LLM │ + │ edits │ │ │ held-out data + │ │ proxy) │ + │ /work/agent │ │ │ scores + budgets │ │ │ + └─────┬───────┘ │ └─────────┬──────────┘ └────────┬─────────┘ + │ │ │ runs inner `harbor run` │ + │ LLM │ │ as unprivileged `harness`│ + └──────────┼────────────┴──── scoped token ───────┘ + │ +``` + +- **`main`** — runs the optimizer agent. *Untrusted*: it may write anything into + `/work/agent` (the candidate repo). It reaches the sidecar only over HTTP and + the LLM only through the gateway. Real secrets are scrubbed to `""` here. +- **`eval-sidecar`** — the *trusted control plane*. Owns the held-out task + lists, the scores database, the budget ledger, and the admin token. Serves + agent-facing endpoints (budgeted, disclosure-gated) and token-gated admin + endpoints. +- **`inference-gateway`** — a credential-isolating, budgeted proxy in front of + the real LLM upstream. Issues *scoped tokens* (producer / evaluation / + finalization), each with its own model allow-list and budget, so the optimizer + never sees the raw key and its LLM usage is metered. + +Named volumes carry state between services: `agent_repo`, `agent_context`, +`admin_state`, `token_state`, `inference_state`. + +## A run, end to end + +1. **`vero harbor run --config gaia/build.yaml --agent codex --model gpt-5.5`.** +2. **Compile** (`harbor/build/compiler.py`): the `build.yaml` + the target repo + + partitions → a self-contained task dir — three Dockerfiles, + `docker-compose.yaml`, `serve.json` (the sidecar's deployment config), the + case lists, `instruction.md` (what the agent reads), and `test.sh` (the + verifier phase). +3. It shells out to **inner `harbor run -e modal`**, which builds the images, + brings up the stack, and injects the optimizer into `main`. +4. **Optimizer loop**: the agent reads its `.vero` context, edits the candidate + in `/work/agent`, and calls the sidecar `/eval` to score candidates — on + **development** (full per-case feedback) and **validation** (aggregate score + only). It iterates, and may `/submit` a final pick. +5. **Finalization** (`test.sh`, admin-token'd): `verifier.finalize()` selects a + candidate, admin-re-scores it on the held-out **test** target, writes + `reward.json` + `finalization.json`; `export-session` archives the whole + session. +6. Harbor collects `/logs` back to **`jobs///`** on disk and + reports the reward. + +## The evaluation core + +- **`EvaluationEngine`** (`evaluation/engine.py`) runs every evaluation through a + **backend** and records an **`EvaluationRecord`** (`evaluation/models.py`): the + request (candidate, evaluation_set, limits) + a report (per-case results, + metrics, diagnostics, artifacts) + an objective value + a **principal**. It + enforces the **`BudgetLedger`** (`evaluation/budget.py`) and fires **listeners** + (where the W&B sink hooks in). +- **`HarborBackend`** (`harbor/backend.py`) is how a candidate is actually + scored: it runs a *nested* `harbor run` over the tasks (`uv run` the + candidate's package, execute the agent per task, collect trial records), + aggregating attempts (`mean` by default, zero-filling dead attempts and + recording `n_dead_infra`/`n_clean` so an outage-diluted mean is distinguishable + from a clean low one). +- **Principals** are the key access concept: + - **`agent`** — the optimizer's search evals: metered, disclosure-limited. + - **`admin`** — trusted finalization / re-scores: unmetered, full disclosure. + This is the authoritative number. + +## Disclosure & the anti-cheating contract + +Each partition has a **disclosure level** (`harbor/sidecar.py`): +- **development → FULL**: per-case results shown (the disclosed set to iterate + on). +- **validation → AGGREGATE**: only an *average over ≥ k cases* is returned (the + k-anonymity floor, default 5), so individual held-out labels can't be read off + one case at a time. +- **test → held-out**: not agent-evaluable; only admin scores it, at + finalization. + +## Selection / finalization (what ships) + +`CanonicalVerifier.finalize()` (`harbor/verifier.py`) decides what to ship and +computes the reward: +- **Chain**: the agent's explicit `submission` → else `auto_best` (rank + candidates by *coverage-qualified* validation evals, admin-re-score the top-K + on the full set) → else **pick-last** (the current candidate). It ships nothing + only if there is no candidate at all. +- **`baseline_floor`** (opt-in, off by default): ship only if the candidate + beats the seed. Because the seed is fixed, its score can be **pinned** + (`baseline_reward` / `baseline_selection_score`) — or produced reproducibly via + `vero harbor score-baseline --replicates N`. +- Result → `VerificationResult` (shipped, rewards, baseline_rewards) → + `reward.json` (what harbor consumes) + the durable session archive. + +## The security model (five mechanisms) + +1. **Credential isolation** — the gateway's scoped tokens; the raw upstream key + lives only in the sidecar. (`harbor/inference.py`, compose template.) +2. **Harness isolation** — the inner `harbor run` (arbitrary candidate code) runs + as unprivileged `harness`; the session dir is `0700 root`, case lists `700`, + `serve.json` `600` — so the candidate cannot read the answer key it is graded + on. (`harbor/backend.py` `run_as`, `Dockerfile.sidecar.j2`, + `harbor/deployment.py`.) +3. **k-anonymity floor** — aggregate subset evals must cover ≥ 5 cases. + (`harbor/sidecar.py` `_enforce_aggregate_floor`.) +4. **Budget metering** — per-scope gateway budgets + a case/run ledger, with a + *reserved finalization scope* so the mandatory admin re-score can't be starved + by the agent's search. (`evaluation/budget.py`, gateway config.) +5. **Fail-safe finalization** — never ship an unverified candidate on an infra + blip; a session export never fails wholesale. (`harbor/verifier.py`, + `harbor/session.py`.) + +## The build pipeline + +`build.yaml` (`HarborBuildConfig`, `harbor/build/config.py`) → +`compile_harbor_task` (`harbor/build/compiler.py`) renders the whole deployable +task (Dockerfiles + compose + `serve.json` + cases + instructions). `serve.json` +deserializes into `HarborDeploymentConfig` (`harbor/deployment.py`), which +`build_harbor_components` turns into the live sidecar + verifier. So a benchmark +is *fully specified by one YAML* (see `harness-engineering-bench/gaia/baseline/`). + +## Observability + +`SidecarWandbSink` (`runtime/wandb.py`) subscribes to the engine's listeners and +streams every evaluation to Weights & Biases from the trusted side: scoped +metrics per `partition/principal`, `num_cases`, remaining budget, optional full +trace artifacts, and a run summary at finalize. Each `vero harbor run` gets its +own W&B run. + +--- + +## Suggested review order + +Read outside-in — from *what a benchmark declares* down to *how it runs and stays +honest*. Paths are under `vero/src/vero/` unless noted. + +1. **What a benchmark is** — `harness-engineering-bench/gaia/baseline/build.yaml` + and `harbor/build/config.py` (`HarborBuildConfig`, `AgentAccessSpec`, + `VerificationTargetSpec`). This is the declarative surface; everything else + serves it. +2. **Compile → deployable task** — `harbor/build/compiler.py` + (`compile_harbor_task`) and the templates in `harbor/build/templates/` + (`docker-compose.yaml.j2`, `Dockerfile.sidecar.j2`, `instruction.md.j2`, + `test.sh.j2`). See how one YAML becomes the three-container stack + `serve.json`. +3. **The topology at runtime** — `harbor/deployment.py` + (`HarborDeploymentConfig`, `build_harbor_components`): how `serve.json` becomes + a live engine + sidecar + verifier. +4. **The evaluation core** — `evaluation/models.py` (`EvaluationRecord`, + `EvaluationPrincipal`, `DisclosureLevel`, `EvaluationSet`), then + `evaluation/engine.py` and `evaluation/budget.py`. This is the vocabulary the + rest of the system speaks. +5. **How a candidate is scored** — `harbor/backend.py` (`HarborBackend`): the + nested `harbor run`, attempt aggregation, and the infra-vs-candidate taxonomy. + The most intricate file; skim `_case_result`, `_command`, `_environment`. +6. **The sidecar** — `harbor/sidecar.py` (access policies, disclosure floor, + tracked eval jobs, submission) and `harbor/app.py` (the HTTP surface: agent + `/eval` vs admin `/finalize` `/score/baseline` `/session/export`). +7. **Selection & finalization** — `harbor/verifier.py` (`CanonicalVerifier`): the + submit → auto_best → pick-last chain, coverage-qualified selection, the + baseline floor + pinning, `measure_baseline`. This decides the shipped number. +8. **Security specifics** — the five mechanisms: `run_as`/`harness_user` in + `harbor/backend.py` + `Dockerfile.sidecar.j2`; the gateway in + `harbor/inference.py`; the session archive in `harbor/session.py`. +9. **Observability** — `runtime/wandb.py` (`SidecarWandbSink`). +10. **The CLI glue** — `harbor/cli.py` (`vero harbor run`, `finalize`, + `export-session`, `score-baseline`). + +Tests mirror this order (`tests/test_v05_harbor_*.py`) and are a good +executable spec for each layer. From 36933193ab33a4d5fc9630c9799014925711630a Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 15:09:37 -0700 Subject: [PATCH 10/72] Bump harbor to 0.20.0; drop dead harness PYTHONPATH workaround Update harbor_requirement pin from harbor[modal]==0.18.0 to ==0.20.0 across the README and harbor tests. Remove the PYTHONPATH injection in the harness eval launch: it was a no-op (uv strips PYTHONPATH from the child env) and did not fix the "No module named " failure under the dropped uid, so it and its test assertion are gone. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/README.md | 4 ++-- vero/tests/test_v05_harbor_backend.py | 4 ++-- vero/tests/test_v05_harbor_build.py | 6 +++--- vero/tests/test_v05_harbor_http.py | 6 +++--- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/vero/README.md b/vero/README.md index db12b720..f5e42ede 100644 --- a/vero/README.md +++ b/vero/README.md @@ -126,7 +126,7 @@ name: example/optimize-agent agent_repo: ../my-program # editable target (a Git repo) task_source: example/terminal-benchmark@1.0 # pinned registry tasks agent_import_path: my_program.agent:Agent -harbor_requirement: harbor[modal]==0.18.0 +harbor_requirement: harbor[modal]==0.20.0 environment_name: modal # where each nested eval runs secrets: [MODAL_TOKEN_ID, MODAL_TOKEN_SECRET] # sidecar-only; stripped from the agent @@ -273,7 +273,7 @@ backend = HarborBackend(HarborBackendConfig( task_source="org/terminal-benchmark@1.0", agent_import_path="my_program.agent:Agent", cases_path=str(Path("../harbor-cases.jsonl").resolve()), - harbor_requirement="harbor[modal]==0.18.0", + harbor_requirement="harbor[modal]==0.20.0", evaluation_set_name="terminal-benchmark", partition="test", passthrough_environment=["ANTHROPIC_API_KEY"], diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 73ec8f0e..4fd2d023 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -78,10 +78,10 @@ def _request(selection=None) -> EvaluationRequest: def test_backend_accepts_pinned_environment_extra(tmp_path): config = _config( tmp_path, - harbor_requirement="harbor[modal]==0.18.0", + harbor_requirement="harbor[modal]==0.20.0", ) - assert config.harbor_requirement == "harbor[modal]==0.18.0" + assert config.harbor_requirement == "harbor[modal]==0.20.0" def test_environment_default_routes_agent_through_gateway_on_openai(tmp_path): diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 0ea47454..c62cd322 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -334,9 +334,9 @@ def test_build_config_requires_pins_and_valid_partition_references(tmp_path): assert ( _config( tmp_path / "modal", - harbor_requirement="harbor[modal]==0.18.0", + harbor_requirement="harbor[modal]==0.20.0", ).harbor_requirement - == "harbor[modal]==0.18.0" + == "harbor[modal]==0.20.0" ) with pytest.raises(ValidationError, match="pin an exact version"): _config(tmp_path / "unpinned", harbor_requirement="harbor>=0.1") @@ -413,7 +413,7 @@ def test_load_build_config_supports_partition_files_and_validates_manifest(tmp_p f"task_source: {source}", "task_manifest: partitions/manifest.json", "agent_import_path: target.agent:Agent", - "harbor_requirement: harbor==0.18.0", + "harbor_requirement: harbor==0.20.0", "partition_files:", " validation: partitions/validation.json", " test: partitions/test.json", diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index 7f937933..b16214d7 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -260,7 +260,7 @@ def test_harbor_run_uses_current_python_and_pinned_harbor_extra(tmp_path, monkey config_path = tmp_path / "build.yaml" config_path.write_text("task_name: unused\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.18.0") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0") observed = {} def compile_task(_config, output): @@ -298,7 +298,7 @@ def run(command, *, env): "--python", harbor_cli.sys.executable, "--from", - "harbor[modal]==0.18.0", + "harbor[modal]==0.20.0", "harbor", ] @@ -313,7 +313,7 @@ def test_harbor_run_env_file_secrets_reach_subprocess_not_command_line( config_path.write_text("task_name: unused\n") env_file = tmp_path / "secrets.env" env_file.write_text("MODAL_TOKEN_ID=mt-id\nMODAL_TOKEN_SECRET=mt-secret\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.18.0") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0") observed = {} def compile_task(_config, output): From 5ef8162cc3c6f35ee7397d3139551ff7c9e29ad8 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 15:28:06 -0700 Subject: [PATCH 11/72] Fix harness isolation: grant traversal on the candidate checkout parent MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The candidate is checked out at /repository; `mktemp -d` makes that parent 0700 root. The eval launch chowned the repository (and the staging tree) to the harness user but not the mktemp parent, so once the process dropped to harness it could not traverse into its own workspace to resolve the editable candidate package's absolute path — every eval (baseline included) failed with "No module named " while harbor itself still loaded from the harness-owned cache. Grant the harness user traversal on that parent (chmod o+x) alongside the existing chowns. o+x suffices because run_as drops to the user's own uid+gid, so it is "other" relative to the root-owned parent. The dir holds only candidate code, so widening traversal leaks no trusted data. Reproduced and fixed against the real compiled sidecar image with a faithful user+group+extra_groups privilege drop: 0700 -> "No module named gaia_agent"; 0701 -> import resolves. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/backend.py | 19 ++++++++++++++++++- vero/tests/test_v05_harbor_backend.py | 15 +++++++++++++-- 2 files changed, 31 insertions(+), 3 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index e328cc84..717a1579 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -14,7 +14,7 @@ import statistics import tempfile from collections import defaultdict -from pathlib import Path +from pathlib import Path, PurePosixPath from typing import Any, Literal from pydantic import Field, JsonValue, field_validator, model_validator @@ -1182,6 +1182,23 @@ async def evaluate( f"{self.config.harness_user!r}: " f"{self.sanitize_error(chown.stderr)}" ) + # The checkout lives at /repository, and `mktemp -d` + # makes that parent 0700 root. The dropped-uid harness owns + # the repository but can't traverse the parent, so importing + # the editable candidate package (an absolute path under it) + # fails with "No module named ". Grant traversal on + # the parent — it holds only candidate code, no trusted data. + checkout_parent = str(PurePosixPath(context.workspace.root).parent) + chmod = await context.workspace.sandbox.run( + ["chmod", "o+x", checkout_parent], + timeout=30, + ) + if chmod.returncode != 0: + raise RuntimeError( + "failed to grant harness traversal on the checkout " + f"parent {checkout_parent!r}: " + f"{self.sanitize_error(chmod.stderr)}" + ) result = await context.workspace.sandbox.run( command, cwd=context.workspace.project_path, diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 4fd2d023..ccb5ada7 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -3,7 +3,7 @@ import json import sys from datetime import UTC, datetime -from pathlib import Path +from pathlib import Path, PurePosixPath from types import ModuleType, SimpleNamespace import pytest @@ -185,12 +185,16 @@ def __init__( self.env = None self.run_as = None self.chown_commands: list[list[str]] = [] + self.chmod_commands: list[list[str]] = [] async def run(self, command, cwd=None, timeout=30, env=None, run_as=None): if isinstance(command, list) and command[:1] == ["chown"]: # Record the harness-isolation provisioning without touching real uids. self.chown_commands.append(command) return CommandResult("", "", 0) + if isinstance(command, list) and command[:1] == ["chmod"]: + self.chmod_commands.append(command) + return CommandResult("", "", 0) if not isinstance(command, list) or "--jobs-dir" not in command: return await super().run( command, cwd=cwd, timeout=timeout, env=env, run_as=run_as @@ -232,7 +236,9 @@ async def _context(tmp_path: Path, sandbox: FakeSandbox) -> EvaluationContext: artifact_dir = result_dir / "artifacts" artifact_dir.mkdir(parents=True) return EvaluationContext( - workspace=SimpleNamespace(project_path=str(target), sandbox=sandbox), + workspace=SimpleNamespace( + project_path=str(target), root=str(target), sandbox=sandbox + ), session_id="session", evaluation_id="evaluation", result_dir=result_dir, @@ -671,6 +677,11 @@ async def test_harbor_backend_runs_harness_as_unprivileged_user(tmp_path): assert sandbox.chown_commands, "expected the harness work dirs to be chowned" for command in sandbox.chown_commands: assert command[:3] == ["chown", "-R", "harness:harness"] + # The checkout's mktemp parent (0700 root) is made traversable so the + # dropped-uid harness can resolve the editable candidate package's absolute + # path — otherwise `import ` fails with "No module named ...". + checkout_parent = str(PurePosixPath(str(tmp_path / "target")).parent) + assert ["chmod", "o+x", checkout_parent] in sandbox.chmod_commands def test_harbor_config_rejects_harness_isolation_with_upstream_task_services(tmp_path): From 54b91b12634881844e347dc03e40810f88513980 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 15:32:44 -0700 Subject: [PATCH 12/72] Fail fast when the harness can't reach its workspace after provisioning MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add a reachability probe: after chowning/granting traversal, run `test -r ` as the dropped user before launching harbor. Readability requires traversing every ancestor, so any provisioning gap (a missing chown, a non-traversable parent) is caught here with a clear message instead of resurfacing as a cryptic "No module named " several retries downstream — which is exactly how the checkout-parent bug hid. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/backend.py | 16 ++++++++++++++++ vero/tests/test_v05_harbor_backend.py | 8 ++++++++ 2 files changed, 24 insertions(+) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 717a1579..f59915f6 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -1199,6 +1199,22 @@ async def evaluate( f"parent {checkout_parent!r}: " f"{self.sanitize_error(chmod.stderr)}" ) + # Fail fast, at the provisioning site, if the dropped user + # still can't reach its workspace (readability requires + # traversing every ancestor). Without this, any future + # permission gap resurfaces as a cryptic "No module named + # " several retries downstream, not here. + probe = await context.workspace.sandbox.run( + ["test", "-r", str(context.workspace.project_path)], + run_as=self.config.harness_user, + ) + if probe.returncode != 0: + raise RuntimeError( + f"harness {self.config.harness_user!r} cannot reach its " + f"workspace {context.workspace.project_path!r} after " + "provisioning; check that every ancestor directory is " + "traversable by the dropped user" + ) result = await context.workspace.sandbox.run( command, cwd=context.workspace.project_path, diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index ccb5ada7..ed3d08e7 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -186,6 +186,7 @@ def __init__( self.run_as = None self.chown_commands: list[list[str]] = [] self.chmod_commands: list[list[str]] = [] + self.probe_commands: list[tuple[list[str], str | None]] = [] async def run(self, command, cwd=None, timeout=30, env=None, run_as=None): if isinstance(command, list) and command[:1] == ["chown"]: @@ -195,6 +196,10 @@ async def run(self, command, cwd=None, timeout=30, env=None, run_as=None): if isinstance(command, list) and command[:1] == ["chmod"]: self.chmod_commands.append(command) return CommandResult("", "", 0) + if isinstance(command, list) and command[:1] == ["test"]: + # The workspace-reachability probe, run as the dropped user. + self.probe_commands.append((command, run_as)) + return CommandResult("", "", 0) if not isinstance(command, list) or "--jobs-dir" not in command: return await super().run( command, cwd=cwd, timeout=timeout, env=env, run_as=run_as @@ -682,6 +687,9 @@ async def test_harbor_backend_runs_harness_as_unprivileged_user(tmp_path): # path — otherwise `import ` fails with "No module named ...". checkout_parent = str(PurePosixPath(str(tmp_path / "target")).parent) assert ["chmod", "o+x", checkout_parent] in sandbox.chmod_commands + # And it probes, as the dropped user, that the workspace is actually reachable + # before running harbor — so a permission gap fails fast and clearly here. + assert (["test", "-r", str(tmp_path / "target")], "harness") in sandbox.probe_commands def test_harbor_config_rejects_harness_isolation_with_upstream_task_services(tmp_path): From b85de40fe9286d299a12d0976a864587f01ce789 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 15:45:47 -0700 Subject: [PATCH 13/72] Add container integration test for harness-isolation invariants Extract the harness provisioning commands and reachability probe into vero.harbor.isolation as a single source of truth, and have the eval launch use them, so the runtime and the test can't drift. Add tests/test_v05_harbor_isolation_container.py: inside a throwaway Linux container it loads the real sandbox.py + isolation.py standalone (stdlib-only, no install) and asserts, against the real uid/gid drop and the real provisioning commands, that (1) a checkout under a 0700 mktemp parent is unreachable to the dropped user when only the leaf is chowned (the regression sentinel for the shipped bug), (2) it becomes reachable after harness_grant_commands and the probe agrees, and (3) trusted root-only state stays unreadable to the dropped user. Skips when no docker daemon is available. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/backend.py | 51 +++--- vero/src/vero/harbor/isolation.py | 43 +++++ .../test_v05_harbor_isolation_container.py | 158 ++++++++++++++++++ 3 files changed, 220 insertions(+), 32 deletions(-) create mode 100644 vero/src/vero/harbor/isolation.py create mode 100644 vero/tests/test_v05_harbor_isolation_container.py diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index f59915f6..a225735c 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -14,7 +14,7 @@ import statistics import tempfile from collections import defaultdict -from pathlib import Path, PurePosixPath +from pathlib import Path from typing import Any, Literal from pydantic import Field, JsonValue, field_validator, model_validator @@ -45,6 +45,7 @@ EvaluationStatus, ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text +from vero.harbor.isolation import harness_grant_commands, harness_reachability_probe from vero.sandbox import CommandResult, Sandbox from vero.staging import SandboxStagingArea @@ -1170,42 +1171,28 @@ async def evaluate( # Everything trusted (session dir, budgets, other candidates, # admin token) is owned by root and unreadable to it. if self.config.harness_user is not None: - owner = f"{self.config.harness_user}:{self.config.harness_user}" - for path in (context.workspace.project_path, staging.root): - chown = await context.workspace.sandbox.run( - ["chown", "-R", owner, str(path)], - timeout=120, + # Hand the harness user its work dirs and make the checkout + # reachable (the parent that `mktemp -d` left 0700 root); see + # vero.harbor.isolation for the why. + for provision_command in harness_grant_commands( + self.config.harness_user, + chown_paths=[context.workspace.project_path, staging.root], + checkout_root=context.workspace.root, + ): + provision = await context.workspace.sandbox.run( + provision_command, timeout=120 ) - if chown.returncode != 0: + if provision.returncode != 0: raise RuntimeError( - "failed to hand harness workspace to " - f"{self.config.harness_user!r}: " - f"{self.sanitize_error(chown.stderr)}" + "failed to provision harness workspace " + f"({' '.join(provision_command)}): " + f"{self.sanitize_error(provision.stderr)}" ) - # The checkout lives at /repository, and `mktemp -d` - # makes that parent 0700 root. The dropped-uid harness owns - # the repository but can't traverse the parent, so importing - # the editable candidate package (an absolute path under it) - # fails with "No module named ". Grant traversal on - # the parent — it holds only candidate code, no trusted data. - checkout_parent = str(PurePosixPath(context.workspace.root).parent) - chmod = await context.workspace.sandbox.run( - ["chmod", "o+x", checkout_parent], - timeout=30, - ) - if chmod.returncode != 0: - raise RuntimeError( - "failed to grant harness traversal on the checkout " - f"parent {checkout_parent!r}: " - f"{self.sanitize_error(chmod.stderr)}" - ) # Fail fast, at the provisioning site, if the dropped user - # still can't reach its workspace (readability requires - # traversing every ancestor). Without this, any future - # permission gap resurfaces as a cryptic "No module named - # " several retries downstream, not here. + # still can't reach its workspace, instead of a cryptic + # "No module named " several retries downstream. probe = await context.workspace.sandbox.run( - ["test", "-r", str(context.workspace.project_path)], + harness_reachability_probe(context.workspace.project_path), run_as=self.config.harness_user, ) if probe.returncode != 0: diff --git a/vero/src/vero/harbor/isolation.py b/vero/src/vero/harbor/isolation.py new file mode 100644 index 00000000..aa841dbe --- /dev/null +++ b/vero/src/vero/harbor/isolation.py @@ -0,0 +1,43 @@ +"""Filesystem provisioning for the isolated harness user. + +Single source of truth for the commands that hand the dropped-privilege harness +user access to its workspace — the eval launch (``backend.py``) and the isolation +integration test both use these so they can't drift. Kept stdlib-only and free of +``vero`` package imports so the test can load it standalone inside a minimal +container (see ``tests/test_v05_harbor_isolation_container.py``). +""" + +from __future__ import annotations + +from collections.abc import Sequence +from pathlib import PurePosixPath + + +def harness_grant_commands( + user: str, *, chown_paths: Sequence[str], checkout_root: str +) -> list[list[str]]: + """Commands to hand ``user`` its work dirs and make the checkout reachable. + + Chowns each work dir to the user, then grants traversal (``o+x``) on the + checkout's parent: ``mktemp -d`` leaves that parent ``0700`` root, and the + dropped user runs as its own uid+gid (so it is "other" for the root-owned + parent) and otherwise can't traverse in to resolve the editable candidate + package's absolute path — the import fails with "No module named " + while harbor itself still loads from the user-owned cache. The parent holds + only candidate code, so widening traversal exposes no trusted data. + """ + owner = f"{user}:{user}" + commands = [["chown", "-R", owner, str(path)] for path in chown_paths] + checkout_parent = str(PurePosixPath(checkout_root).parent) + commands.append(["chmod", "o+x", checkout_parent]) + return commands + + +def harness_reachability_probe(project_path: str) -> list[str]: + """Command (run as the dropped user) that fails if the workspace is unreachable. + + Readability of ``project_path`` requires traversing every ancestor, so a + non-zero exit means a provisioning gap left the workspace out of reach — caught + at the launch site rather than as a cryptic "No module named " downstream. + """ + return ["test", "-r", str(project_path)] diff --git a/vero/tests/test_v05_harbor_isolation_container.py b/vero/tests/test_v05_harbor_isolation_container.py new file mode 100644 index 00000000..e10c3458 --- /dev/null +++ b/vero/tests/test_v05_harbor_isolation_container.py @@ -0,0 +1,158 @@ +"""Container integration test for harness-isolation filesystem invariants. + +Runs inside a throwaway Linux container (the CI/dev host may be macOS, where the +uid-drop is unavailable) and asserts, against the *real* privilege drop +(``vero.sandbox.LocalSandbox.run(run_as=...)``) and the *real* provisioning +commands (``vero.harbor.isolation``), that: + +* a candidate checkout under a ``mktemp -d`` (0700 root) parent is UNreachable to + the dropped user when only the leaf is chowned (the regression sentinel — this + is the bug that shipped), +* it becomes reachable after ``harness_grant_commands`` (and the reachability + probe agrees), and +* trusted root-only state stays unreadable to the dropped user. + +The container loads ``sandbox.py``/``isolation.py`` standalone (they are +stdlib-only) so no vero install or heavy dependency is needed. +""" + +from __future__ import annotations + +import shutil +import subprocess +from pathlib import Path + +import pytest + +_IMAGE = "ghcr.io/astral-sh/uv:python3.12-bookworm" +_VERO_SRC = Path(__file__).resolve().parents[1] / "src" + + +def _docker_usable() -> bool: + if shutil.which("docker") is None: + return False + try: + return ( + subprocess.run( + ["docker", "info"], + capture_output=True, + timeout=30, + check=False, + ).returncode + == 0 + ) + except (OSError, subprocess.SubprocessError): + return False + + +pytestmark = pytest.mark.skipif( + not _docker_usable(), reason="requires a usable docker daemon" +) + + +# Executed as root inside the container. +_CONTAINER_SCRIPT = r''' +import asyncio, importlib.util, os, subprocess, sys + + +def _load(name, path): + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module # dataclass/typing introspection reads sys.modules + spec.loader.exec_module(module) + return module + + +# Real modules, loaded standalone (stdlib-only) — no vero install needed. +sbx = _load("_vero_sandbox", "/vero-src/vero/sandbox.py") +iso = _load("_vero_isolation", "/vero-src/vero/harbor/isolation.py") + +subprocess.run(["useradd", "-m", "-u", "1002", "harness"], check=True) +sandbox = sbx.LocalSandbox("/") # run_as drops privileges from this root process +MARKER = os.path.join("src", "tinyagent", "__init__.py") + + +def make_checkout(): + # Mirror candidate_repository.git.checkout: /repository. mktemp -d + # leaves the parent 0700 root — the traversal the harness needs. + parent = subprocess.run( + ["mktemp", "-d", "/tmp/vero-candidate-checkoutXXXXXX"], + capture_output=True, text=True, check=True, + ).stdout.strip() + repo = os.path.join(parent, "repository") + os.makedirs(os.path.join(repo, "src", "tinyagent")) + open(os.path.join(repo, MARKER), "w").close() + return parent, repo + + +async def as_harness(command): + return await sandbox.run(command, run_as="harness") + + +async def main(): + failures = [] + + # Trusted seal: a root-only 0700 dir + 0600 file (stands in for the session + # dir / serve.json / admin token). + os.makedirs("/trusted", exist_ok=True) + with open("/trusted/secret", "w") as handle: + handle.write("held-out") + os.chmod("/trusted/secret", 0o600) + os.chmod("/trusted", 0o700) + + # 1) Regression sentinel: chown only the leaf (the shipped bug). The dropped + # user must NOT be able to reach a file under the still-0700 parent. + _parent, repo = make_checkout() + subprocess.run(["chown", "-R", "harness:harness", repo], check=True) + if (await as_harness(["test", "-r", os.path.join(repo, MARKER)])).returncode == 0: + failures.append( + "regression sentinel: workspace reachable WITHOUT the traversal grant" + ) + + # 2) Positive: the real provisioning makes it reachable, and the probe agrees. + _parent, repo = make_checkout() + for command in iso.harness_grant_commands( + "harness", chown_paths=[repo], checkout_root=repo + ): + subprocess.run(command, check=True) + if (await as_harness(iso.harness_reachability_probe(repo))).returncode != 0: + failures.append("positive: reachability probe failed after provisioning") + if (await as_harness(["test", "-r", os.path.join(repo, MARKER)])).returncode != 0: + failures.append("positive: provisioned workspace unreadable to the harness") + + # 3) Negative: trusted state stays sealed from the dropped user. + if (await as_harness(["cat", "/trusted/secret"])).returncode == 0: + failures.append("negative: harness read the trusted /trusted/secret") + + if failures: + print("ISOLATION INVARIANTS VIOLATED:") + for item in failures: + print(" -", item) + sys.exit(1) + print("all isolation invariants hold") + + +asyncio.run(main()) +''' + + +def test_harness_isolation_invariants_on_container(tmp_path): + script = tmp_path / "probe.py" + script.write_text(_CONTAINER_SCRIPT) + result = subprocess.run( + [ + "docker", "run", "--rm", + "-v", f"{_VERO_SRC}:/vero-src:ro", + "-v", f"{script}:/probe.py:ro", + _IMAGE, + "python3", "/probe.py", + ], + capture_output=True, + text=True, + timeout=600, + check=False, + ) + assert result.returncode == 0, ( + f"isolation invariants failed:\n{result.stdout}\n{result.stderr}" + ) + assert "all isolation invariants hold" in result.stdout From 0dcdbd6c70486837a83476ff7e5f60bdf8e1810c Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 17:08:11 -0700 Subject: [PATCH 14/72] Rename agent context .vero -> .evals with self-describing inner tree Agents don't know what "vero" is; every name they meet should say what it holds. The workspace context directory is now .evals/ with results/ (was evaluations/), tasks/ (was cases/), and plan.json (was evaluations.json). Host-side paths (~/.vero, ./.vero/session) are operator-facing and unchanged. Co-Authored-By: Claude Fable 5 --- vero/README.md | 16 +++--- vero/docs/harbor-architecture.md | 2 +- vero/docs/insights-generator-subagent.md | 8 +-- vero/examples/c-matmul/.gitignore | 1 + vero/examples/circle-packing/.gitignore | 1 + vero/examples/circle-packing/README.md | 2 +- .../examples/circle-packing/target/.gitignore | 1 + vero/examples/circle-packing/vero.toml | 2 +- .../.claude/agents/insights-generator.md | 2 +- vero/examples/insights-bundle/README.md | 8 +-- .../insights-bundle/skills/insights/SKILL.md | 8 +-- vero/src/vero/agents/protocol.py | 6 +-- vero/src/vero/candidate_repository/git.py | 2 +- vero/src/vero/harbor/build/compiler.py | 4 +- .../build/templates/docker-compose.yaml.j2 | 2 +- .../harbor/build/templates/instruction.md.j2 | 10 ++-- .../vero/harbor/build/templates/seed.sh.j2 | 4 +- vero/src/vero/optimization/command.py | 2 +- vero/src/vero/optimization/models.py | 2 +- vero/src/vero/optimization/strategy.py | 4 +- vero/src/vero/runtime/context.py | 49 ++++++++++--------- vero/src/vero/tools/evaluation.py | 4 +- vero/tests/test_v05_agent_context.py | 8 +-- vero/tests/test_v05_agent_producer.py | 14 +++--- vero/tests/test_v05_candidate_repository.py | 6 +-- vero/tests/test_v05_circle_packing_example.py | 2 +- vero/tests/test_v05_claude_agent.py | 2 +- vero/tests/test_v05_harbor_build.py | 14 +++--- vero/tests/test_v05_harbor_deployment.py | 2 +- vero/tests/test_v05_harbor_http.py | 2 +- vero/tests/test_v05_harbor_sidecar.py | 6 +-- vero/tests/test_v05_optimizer.py | 2 +- 32 files changed, 103 insertions(+), 95 deletions(-) diff --git a/vero/README.md b/vero/README.md index f5e42ede..83223291 100644 --- a/vero/README.md +++ b/vero/README.md @@ -731,16 +731,16 @@ path to the filesystem result. Large case records, traces, and artifacts stay out of the tool response. Intermediate checkpoints are real candidates and remain eligible for selection, even if the agent later makes the program worse. -Each producer workspace contains a generated, read-only `.vero/` directory: +Each producer workspace contains a generated, read-only `.evals/` directory: ```text -.vero/ +.evals/ ├── README.md ├── manifest.json -├── evaluations.json # available evaluations, selections, disclosure, budgets -├── cases/ # only backend-approved case resources -├── candidates/ # metadata, parent patches, and repository-native refs -└── evaluations/ # authorized summaries or full case/trace/artifact trees +├── plan.json # available evaluations, selections, disclosure, budgets +├── tasks/ # only backend-approved task resources +├── candidates/ # metadata, parent patches, and repository-native refs +└── results/ # authorized summaries or full case/trace/artifact trees ``` The agent inspects this with ordinary filesystem and Git commands. Disclosure @@ -756,7 +756,7 @@ Candidate history uses durable Git refs, so siblings need not be ancestors of th current checkout: a proposal sees the candidates from the start of its generation plus its own evaluated checkpoints, and parallel siblings appear the next round. -The `.vero/` view is disposable and read-only — Git-excluded, permission- +The `.evals/` view is disposable and read-only — Git-excluded, permission- protected, and rejected if a candidate force-adds it; the durable candidate and evaluation stores stay the trusted source. In Harbor the sidecar owns the writable volume and the agent mounts it read-only, and case resources (for @@ -818,7 +818,7 @@ so treat the resulting file as sensitive experiment data. - Evaluation secrets are passed through backend configuration and redacted from diagnostics; they cannot be embedded in evaluation parameters. - Agent-visible cases, histories, and evaluation details are projected into a - generated `.vero/` view according to the same authorization boundary. + generated `.evals/` view according to the same authorization boundary. - Budgets are reserved atomically before backend execution. - A built-in coding agent's shell and file actions execute inside a sandbox bound to its candidate checkout, so containment is the sandbox boundary rather diff --git a/vero/docs/harbor-architecture.md b/vero/docs/harbor-architecture.md index f80878eb..6cb8913f 100644 --- a/vero/docs/harbor-architecture.md +++ b/vero/docs/harbor-architecture.md @@ -61,7 +61,7 @@ Named volumes carry state between services: `agent_repo`, `agent_context`, verifier phase). 3. It shells out to **inner `harbor run -e modal`**, which builds the images, brings up the stack, and injects the optimizer into `main`. -4. **Optimizer loop**: the agent reads its `.vero` context, edits the candidate +4. **Optimizer loop**: the agent reads its `.evals` context, edits the candidate in `/work/agent`, and calls the sidecar `/eval` to score candidates — on **development** (full per-case feedback) and **validation** (aggregate score only). It iterates, and may `/submit` a final pick. diff --git a/vero/docs/insights-generator-subagent.md b/vero/docs/insights-generator-subagent.md index 7d36e1cd..b5ec2f82 100644 --- a/vero/docs/insights-generator-subagent.md +++ b/vero/docs/insights-generator-subagent.md @@ -151,8 +151,8 @@ skills_dir = "/skills" ### The corpus -VeRO already dumps per-case traces into a read-only `.vero` context dir: -`.vero/evaluations//cases//execution-trace.json` (and +VeRO already dumps per-case traces into a read-only `.evals` context dir: +`.evals/results//cases//execution-trace.json` (and `evaluation-trace.json`). That **is** the IG corpus. Label traces by the eval scores VeRO already has (pass/fail, reward) so the investigator can contrast cohorts — comparative analysis is where IG earns its keep. @@ -198,7 +198,7 @@ proposals. This is a native reproduction of IG's own `patcher` loop | Path | Why it matters | |---|---| -| `src/vero/runtime/context.py` (~L256–273) | Where per-case `execution-trace.json` / `evaluation-trace.json` are written into the `.vero` context — **the corpus source** | +| `src/vero/runtime/context.py` (~L256–273) | Where per-case `execution-trace.json` / `evaluation-trace.json` are written into the `.evals` context — **the corpus source** | | `src/vero/optimization/optimizer.py` | `Optimizer` (L204); `run` (L409); `_produce_candidate` (L283) — where an IG call would slot between rounds | | `src/vero/agents/producer.py` | `AgentCandidateProducer.produce()` — how a coding agent is driven per proposal | | `src/vero/agents/claude_code.py` | VeRO's **SDK** Claude agent — **out of scope**, but note L44 enables `Task`; do not confuse with Harbor's agent | @@ -246,4 +246,4 @@ Path: `enterprise/insights_generator/src/insights_generator/` Draft the three agent `.md` files + `SKILL.md` / `findings.json` contract + `chunk.sh` / `validate.py`, distilled from IG's actual scout/investigator/ orchestrator prompts and grounded against a real `execution-trace.json` from the -`.vero` layout, so the bundle is runnable rather than notional. +`.evals` layout, so the bundle is runnable rather than notional. diff --git a/vero/examples/c-matmul/.gitignore b/vero/examples/c-matmul/.gitignore index 8bf40a1b..6af2388b 100644 --- a/vero/examples/c-matmul/.gitignore +++ b/vero/examples/c-matmul/.gitignore @@ -1 +1,2 @@ .vero/ +.evals/ diff --git a/vero/examples/circle-packing/.gitignore b/vero/examples/circle-packing/.gitignore index 8bf40a1b..6af2388b 100644 --- a/vero/examples/circle-packing/.gitignore +++ b/vero/examples/circle-packing/.gitignore @@ -1 +1,2 @@ .vero/ +.evals/ diff --git a/vero/examples/circle-packing/README.md b/vero/examples/circle-packing/README.md index 8c20b520..9530908b 100644 --- a/vero/examples/circle-packing/README.md +++ b/vero/examples/circle-packing/README.md @@ -35,7 +35,7 @@ Change `optimizer.model` to any model available through your provider. The best nominated candidate is then re-evaluated through the hidden final evaluation. Candidate versions remain in the session candidate repository, while evaluation artifacts are available in `.vero/session/evaluations/` and to the agent through -its read-only `.vero` context. +its read-only `.evals` context. Evaluation launches through the candidate's locked uv environment. The seed program has no third-party dependencies; an optimizer can add reproducible diff --git a/vero/examples/circle-packing/target/.gitignore b/vero/examples/circle-packing/target/.gitignore index f5b45e68..507cc660 100644 --- a/vero/examples/circle-packing/target/.gitignore +++ b/vero/examples/circle-packing/target/.gitignore @@ -2,3 +2,4 @@ .venv/ __pycache__/ *.py[oc] +.evals/ diff --git a/vero/examples/circle-packing/vero.toml b/vero/examples/circle-packing/vero.toml index c489baf7..6e190164 100644 --- a/vero/examples/circle-packing/vero.toml +++ b/vero/examples/circle-packing/vero.toml @@ -75,7 +75,7 @@ approximately 2.635, but make measurable progress from the supplied baseline before pursuing sophisticated refinements. Use the development evaluation repeatedly and inspect its read-only JSON and -SVG artifacts in .vero. Preserve run_packing() and its return contract. You may +SVG artifacts in .evals. Preserve run_packing() and its return contract. You may rewrite the algorithm completely. If you need third-party Python packages, add and lock them with `uv add`; evaluation uses the checked-in uv.lock with `--frozen`. Never weaken, bypass, or reproduce the trusted evaluator. diff --git a/vero/examples/insights-bundle/.claude/agents/insights-generator.md b/vero/examples/insights-bundle/.claude/agents/insights-generator.md index 61dede16..213bf3e5 100644 --- a/vero/examples/insights-bundle/.claude/agents/insights-generator.md +++ b/vero/examples/insights-bundle/.claude/agents/insights-generator.md @@ -6,7 +6,7 @@ description: >- but the population. Coordinates Scouts (discover hypotheses) and Investigators (validate them against the whole corpus) and returns a small set of evidence-grounded, actionable findings. Give it the corpus root ($CORPUS, - the .vero dir) and, if known, the optimization objective. + the .evals dir) and, if known, the optimization objective. model: inherit tools: Task, Bash, Read, Grep, Glob --- diff --git a/vero/examples/insights-bundle/README.md b/vero/examples/insights-bundle/README.md index f8cfa107..324dafa9 100644 --- a/vero/examples/insights-bundle/README.md +++ b/vero/examples/insights-bundle/README.md @@ -3,7 +3,7 @@ This is **not a Harbor task.** It is a bundle you *inject* into an existing optimizer run (e.g. the GAIA build under `harness-engineering-bench/gaia/`) so the main solver/optimizer agent can call it — mid-run, as a subagent — to mine its own -accumulated `.vero` trace corpus for patterns and feed the findings into the next +accumulated `.evals` trace corpus for patterns and feed the findings into the next candidate proposal. It reproduces Scale's internal **Insights Generator** *methodology* — not its @@ -32,8 +32,8 @@ trajectories, so the whole investigation is recorded. ## How it plugs into a run The optimizer works in `/work/agent`; its `vero harbor eval` calls write the -trace corpus to `/work/agent/.vero/evaluations/**`. Point the bundle there with -`CORPUS=/work/agent/.vero`. Two seams inject the bundle into the environment: +trace corpus to `/work/agent/.evals/results/**`. Point the bundle there with +`CORPUS=/work/agent/.evals`. Two seams inject the bundle into the environment: 1. **Subagents** — Claude Code has no dedicated Harbor seam for `.claude/agents/`. Seed them via the environment `Dockerfile`/`seed.sh` into the project dir the @@ -66,7 +66,7 @@ which model the optimizer may call.) ``` optimize round N: - optimizer proposes/edits candidate → `vero harbor eval` → traces land in .vero + optimizer proposes/edits candidate → `vero harbor eval` → traces land in .evals optimizer (or a between-rounds step) invokes the `insights-generator` subagent: orchestrator → scouts (discover) → investigators (validate) → findings.json optimizer reads the top findings → informs the round N+1 edit diff --git a/vero/examples/insights-bundle/skills/insights/SKILL.md b/vero/examples/insights-bundle/skills/insights/SKILL.md index da794981..5812fe1a 100644 --- a/vero/examples/insights-bundle/skills/insights/SKILL.md +++ b/vero/examples/insights-bundle/skills/insights/SKILL.md @@ -32,11 +32,11 @@ Three rules define the method — everything else is mechanics: ## The corpus layout Under the **Harbor** eval backend (the path VeRO actually uses), each candidate's -evaluation dumps per-case trial records into a read-only `.vero` tree: +evaluation dumps per-case trial records into a read-only `.evals` tree: ``` -$CORPUS/ # e.g. /work/agent/.vero (or a dir you're pointed at) - evaluations// # one dir per candidate evaluation +$CORPUS/ # e.g. /work/agent/.evals (or a dir you're pointed at) + results// # one dir per candidate evaluation evaluation.json # aggregate record (score, error_rate, candidate id) artifacts/harbor/ stdout.log stderr.log # backend logs for the whole eval @@ -85,7 +85,7 @@ find "$CORPUS/evaluations" -path "*/agent/*.jsonl" | while read -r trace; do t=$(dirname "$(dirname "$trace")") # trial dir = .../__ trace_id=$(basename "$t") case_id=${trace_id%%__*} - eval=$(echo "$t" | sed -E 's#.*/evaluations/([^/]{12})[^/]*/.*#\1#') + eval=$(echo "$t" | sed -E 's#.*/results/([^/]{12})[^/]*/.*#\1#') reward=$([ -f "$t/verifier/reward.txt" ] && tr -d '[:space:]' < "$t/verifier/reward.txt") printf '%s\t%s\t%s\t%s\t%s\n' "$trace_id" "$case_id" "$eval" "${reward:-NA}" "$trace" done > index.tsv diff --git a/vero/src/vero/agents/protocol.py b/vero/src/vero/agents/protocol.py index 72d20209..29d7d8ce 100644 --- a/vero/src/vero/agents/protocol.py +++ b/vero/src/vero/agents/protocol.py @@ -56,9 +56,9 @@ def relative_context_path(self) -> str: @property def instructions(self) -> str: context = ( - f"VeRO has placed read-only optimization context in " - f"`{AGENT_CONTEXT_DIRECTORY}/`. Inspect its README, cases, prior " - "candidates, and evaluation feedback when useful. Do not modify or " + f"Read-only evaluation context has been placed in " + f"`{AGENT_CONTEXT_DIRECTORY}/`. Inspect its README, tasks, prior " + "candidates, and evaluation results when useful. Do not modify or " "commit that directory." ) if self.proposal.instruction: diff --git a/vero/src/vero/candidate_repository/git.py b/vero/src/vero/candidate_repository/git.py index 6912c3b2..8ee8bcdb 100644 --- a/vero/src/vero/candidate_repository/git.py +++ b/vero/src/vero/candidate_repository/git.py @@ -24,7 +24,7 @@ _OBJECT_ID = re.compile(r"(?:[0-9a-f]{40}|[0-9a-f]{64})\Z") -_AGENT_CONTEXT_DIRECTORY = ".vero" +_AGENT_CONTEXT_DIRECTORY = ".evals" class _GitRepositoryConfig(BaseModel): diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index b1d59728..d76d7d1e 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -130,8 +130,8 @@ def _prepare_baseline_repo( ) if rewrite_vero_path: _rewrite_vero_path(destination / "pyproject.toml") - if (destination / ".vero").exists(): - raise ValueError("agent baseline contains reserved path '.vero'") + if (destination / ".evals").exists(): + raise ValueError("agent baseline contains reserved path '.evals'") def git(*arguments: str) -> str: result = subprocess.run( diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 index be79d2f6..b0f9963d 100644 --- a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -18,7 +18,7 @@ services: {% endif %} volumes: - agent_repo:/work/agent - - agent_context:/work/agent/.vero:ro + - agent_context:/work/agent/.evals:ro - token_state:/state/token:ro depends_on: eval-sidecar: diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index a1a69745..02fca1a6 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -61,11 +61,11 @@ optimization data, while the final evaluation remains hidden. ## Inspect cases and traces Complete task resources for the following authorized partitions are mounted -read-only under `.vero/cases/`: {{ exposed_partitions | join(", ") }}. Start at -`.vero/cases/index.json`; each listed partition has its own task index, +read-only under `.evals/tasks/`: {{ exposed_partitions | join(", ") }}. Start at +`.evals/tasks/index.json`; each listed partition has its own task index, attachments, instructions, and task files. -After a full-disclosure evaluation, `.vero/evaluations/` contains one file per +After a full-disclosure evaluation, `.evals/results/` contains one file per case plus the complete Harbor trial records: exact failure results, exception tracebacks, trial logs, and target-agent artifacts. Use these files to inspect successful as well as failed trajectories without putting long traces into the @@ -73,10 +73,10 @@ CLI response. {% if overlay_present %} Your workspace also includes analysis subagents under `.claude/agents/` and a corpus-analysis skill under `skills/insights/`. These mine the entire -`.vero/evaluations/` trace corpus for recurring, cross-case failure patterns that +`.evals/results/` trace corpus for recurring, cross-case failure patterns that are invisible in any single trace. After running at least one full-disclosure evaluation, dispatch the `insights-generator` subagent (via the Task tool, -pointing it at the `.vero` corpus) to get evidence-grounded findings, then let +pointing it at the `.evals` corpus) to get evidence-grounded findings, then let those findings guide your next candidate edit. Re-run it as the corpus grows. {% endif %} {% endif %} diff --git a/vero/src/vero/harbor/build/templates/seed.sh.j2 b/vero/src/vero/harbor/build/templates/seed.sh.j2 index c0886d5a..c67b7168 100644 --- a/vero/src/vero/harbor/build/templates/seed.sh.j2 +++ b/vero/src/vero/harbor/build/templates/seed.sh.j2 @@ -9,9 +9,9 @@ fi cp -a /opt/overlay/. /work/agent/ {% endif %} -find /work/agent -path /work/agent/.vero -prune -o -exec chown agent:agent {} + +find /work/agent -path /work/agent/.evals -prune -o -exec chown agent:agent {} + git config --system --add safe.directory /work/agent -printf '%s\n' '/.vero/' >> /work/agent/.git/info/exclude +printf '%s\n' '/.evals/' >> /work/agent/.git/info/exclude {% for path in overlay_excludes %} printf '%s\n' '/{{ path }}/' >> /work/agent/.git/info/exclude {% endfor %} diff --git a/vero/src/vero/optimization/command.py b/vero/src/vero/optimization/command.py index 8909a1ac..a9d9bbfc 100644 --- a/vero/src/vero/optimization/command.py +++ b/vero/src/vero/optimization/command.py @@ -147,7 +147,7 @@ async def produce( ) values = { "workspace": workspace.project_path, - "context": posixpath.join(workspace.project_path, ".vero"), + "context": posixpath.join(workspace.project_path, ".evals"), "producer": producer_root, "round": str(context.round), "instruction": proposal.instruction or "", diff --git a/vero/src/vero/optimization/models.py b/vero/src/vero/optimization/models.py index 7ef7f1aa..76069bcc 100644 --- a/vero/src/vero/optimization/models.py +++ b/vero/src/vero/optimization/models.py @@ -82,7 +82,7 @@ class OptimizationContext: class CandidateProductionContext: """Non-sensitive control context supplied to a candidate producer. - Authorized evaluation details live in the read-only ``.vero`` tree and are + Authorized evaluation details live in the read-only ``.evals`` tree and are returned through the evaluation gateway; they are intentionally not duplicated here as full records. """ diff --git a/vero/src/vero/optimization/strategy.py b/vero/src/vero/optimization/strategy.py index bfdaa88b..4cca44da 100644 --- a/vero/src/vero/optimization/strategy.py +++ b/vero/src/vero/optimization/strategy.py @@ -79,7 +79,7 @@ class EvolutionaryStrategy: Only mutation (parent + instruction) is implemented; crossover (combining two parents) is a follow-on — it needs the producer/instruction to reference a - second candidate's contents, exposed through the read-only ``.vero`` tree. + second candidate's contents, exposed through the read-only ``.evals`` tree. ``evaluation_set`` should name the selection set when multiple sets are visible, so fitness is ranked on one consistent metric; left ``None`` it @@ -206,7 +206,7 @@ class DarwinGodelStrategy: nodes remain reachable as *stepping stones* (many innovations traverse them). The intended pairing is a self-application producer: each offspring is the - parent's own harness modifying itself (analyze its ``.vero`` trace corpus, + parent's own harness modifying itself (analyze its ``.evals`` trace corpus, implement one improvement), which is the self-referential loop the original Gödel Machine wanted but validated empirically rather than by proof. diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index 7ba01d75..39223787 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -31,7 +31,7 @@ from vero.workspace import Workspace -AGENT_CONTEXT_DIRECTORY = ".vero" +AGENT_CONTEXT_DIRECTORY = ".evals" _DISCLOSURE_RANK = { DisclosureLevel.NONE: 0, DisclosureLevel.AGGREGATE: 1, @@ -47,7 +47,7 @@ def context_digest(value: str) -> str: def evaluation_result_path(evaluation_id: str) -> str: return ( - f"{AGENT_CONTEXT_DIRECTORY}/evaluations/" + f"{AGENT_CONTEXT_DIRECTORY}/results/" f"{context_digest(evaluation_id)}/evaluation.json" ) @@ -189,20 +189,25 @@ async def write_header( proposal_id: str | None, parent_candidate_id: str | None, ) -> None: - readme = """# VeRO optimization context + readme = """# Evaluation context -This directory is generated by VeRO and is read-only. It contains the cases, -past candidates, and evaluation feedback you are authorized to inspect. Start -with each subdirectory's `index.json`. Candidate entries include Git refs you -can pass to `git show` or `git diff`. Full evaluation entries put each case and -long trace in its own file; their artifact paths resolve below that evaluation's -`artifacts/` directory. +This directory is generated and read-only. It contains everything you are +authorized to inspect about how your program is evaluated: -Use ordinary filesystem and Git commands to analyze this context. Do not copy -it into the program: VeRO rejects candidate versions that track `.vero`. +- `results/` — past evaluation results (scores, per-case results, traces, + artifacts). Start at `results/index.json`. +- `tasks/` — the task resources you may read, when exposed. Start at + `tasks/index.json`. +- `candidates/` — prior program versions, with Git refs you can pass to + `git show` or `git diff`. Start at `candidates/index.json`. +- `plan.json` — the named evaluations you may invoke, their base case + selection, disclosure level, and remaining budget. + +Full-disclosure results put each case and long trace in its own file; their +artifact paths resolve below that result's `artifacts/` directory. -`evaluations.json` lists the named evaluations you may invoke, their base case -selection, disclosure level, and remaining budget. +Use ordinary filesystem and Git commands to analyze this context. Do not copy +it into the program: candidate versions that track `.evals` are rejected. """ await self.sandbox.write_file(self.path("README.md"), readme) await self.write_json( @@ -227,7 +232,7 @@ async def write_evaluations( ] ], ) -> None: - root = self.path("evaluations") + root = self.path("results") if await self.sandbox.exists(root): await self.sandbox.remove(root, recursive=True) await self.sandbox.mkdir(root) @@ -238,7 +243,7 @@ async def write_evaluations( ): digest = context_digest(record.id) relative_path = f"{digest}/evaluation.json" - evaluation_root = self.path("evaluations", digest) + evaluation_root = self.path("results", digest) await self.sandbox.mkdir(evaluation_root) missing_artifacts: list[str] = [] if isinstance(projection, EvaluationRecord): @@ -251,7 +256,7 @@ async def write_evaluations( strict=True, ): case_digest = context_digest(case_model.case_id) - case_root = self.path("evaluations", digest, "cases", case_digest) + case_root = self.path("results", digest, "cases", case_digest) await self.sandbox.mkdir(case_root) execution_trace = case_payload.pop("execution_trace", None) evaluation_trace = case_payload.pop("evaluation_trace", None) @@ -308,7 +313,7 @@ async def write_evaluations( continue await self.sandbox.upload( str(source), - self.path("evaluations", digest, "artifacts", artifact_path), + self.path("results", digest, "artifacts", artifact_path), ) document = { "schema_version": 1, @@ -324,7 +329,7 @@ async def write_evaluations( "result": projection.model_dump(mode="json"), } await self.write_json( - self.path("evaluations", relative_path), + self.path("results", relative_path), document, ) index.append( @@ -339,7 +344,7 @@ async def write_evaluations( } ) await self.write_json( - self.path("evaluations", "index.json"), + self.path("results", "index.json"), {"schema_version": 1, "evaluations": index}, ) @@ -421,7 +426,7 @@ async def _write_candidate_history(self) -> None: ) async def _write_case_resources(self) -> None: - cases_root = self.directory.path("cases") + cases_root = self.directory.path("tasks") await self.workspace.sandbox.mkdir(cases_root) index: list[dict[str, object]] = [] backend = self.engine.backends.resolve(self.backend_id) @@ -436,7 +441,7 @@ async def _write_case_resources(self) -> None: evaluation_set = definition.evaluation_set key = evaluation_set.budget_key(self.backend_id) digest = context_digest(key) - resource_root = self.directory.path("cases", digest) + resource_root = self.directory.path("tasks", digest) await self.workspace.sandbox.mkdir(resource_root) await self.directory.write_json( posixpath.join(resource_root, "manifest.json"), @@ -499,7 +504,7 @@ async def _write_evaluation_plan(self) -> None: } ) await self.directory.write_json( - self.directory.path("evaluations.json"), + self.directory.path("plan.json"), {"schema_version": 1, "evaluations": evaluations}, ) diff --git a/vero/src/vero/tools/evaluation.py b/vero/src/vero/tools/evaluation.py index 7d05101d..e6334374 100644 --- a/vero/src/vero/tools/evaluation.py +++ b/vero/src/vero/tools/evaluation.py @@ -40,7 +40,7 @@ async def evaluate( """Evaluate a program, returning authorized feedback and a filesystem path. Args: - evaluation: Name from ``.vero/evaluations.json``. + evaluation: Name from ``.evals/plan.json``. selection: Optional case IDs or range. Omit to use the base selection. candidate_id: Existing candidate to re-evaluate. Omit to save and evaluate the current workspace. @@ -48,7 +48,7 @@ async def evaluate( Returns: A bounded JSON receipt with an authorized summary and the path to the - complete permitted feedback under ``.vero/evaluations``. + complete permitted feedback under ``.evals/results``. """ try: diff --git a/vero/tests/test_v05_agent_context.py b/vero/tests/test_v05_agent_context.py index 6d3c71b9..bc01088e 100644 --- a/vero/tests/test_v05_agent_context.py +++ b/vero/tests/test_v05_agent_context.py @@ -112,7 +112,7 @@ async def test_agent_context_splits_full_traces_and_honors_disclosure(tmp_path: project.mkdir() directory = AgentContextDirectory( sandbox=await LocalSandbox.create(root=tmp_path), - root=str(project / ".vero"), + root=str(project / ".evals"), session_dir=session_dir, ) await directory.reset() @@ -144,7 +144,7 @@ async def test_agent_context_splits_full_traces_and_honors_disclosure(tmp_path: await directory.seal() try: - evaluations = project / ".vero" / "evaluations" + evaluations = project / ".evals" / "results" full_root = evaluations / context_digest(full.id) full_document = json.loads( (full_root / "evaluation.json").read_text(encoding="utf-8") @@ -185,13 +185,13 @@ async def test_agent_context_splits_full_traces_and_honors_disclosure(tmp_path: "status": "success", }, } - mode = stat.S_IMODE((project / ".vero" / "manifest.json").stat().st_mode) + mode = stat.S_IMODE((project / ".evals" / "manifest.json").stat().st_mode) assert mode & 0o222 == 0 receipt = make_evaluation_receipt(full, DisclosureLevel.FULL) assert isinstance(receipt.result, EvaluationSummary) assert receipt.result_path == ( - f".vero/evaluations/{context_digest(full.id)}/evaluation.json" + f".evals/results/{context_digest(full.id)}/evaluation.json" ) assert "x" * 100 not in receipt.model_dump_json() finally: diff --git a/vero/tests/test_v05_agent_producer.py b/vero/tests/test_v05_agent_producer.py index c8aefef0..18a03189 100644 --- a/vero/tests/test_v05_agent_producer.py +++ b/vero/tests/test_v05_agent_producer.py @@ -81,7 +81,7 @@ def __init__(self): async def run(self, *, context, prompt, max_turns, on_event=None): assert prompt == "Make the program faster" assert max_turns == 5 - context_root = Path(context.workspace.project_path) / ".vero" + context_root = Path(context.workspace.project_path) / ".evals" assert not await context.workspace.is_dirty() manifest = json.loads((context_root / "manifest.json").read_text()) assert manifest["parent_candidate_id"] == context.parent.id @@ -92,20 +92,20 @@ async def run(self, *, context, prompt, max_turns, on_event=None): candidate["candidate_id"] for candidate in candidate_index["candidates"] } evaluation_index = json.loads( - (context_root / "evaluations" / "index.json").read_text() + (context_root / "results" / "index.json").read_text() ) self.initial_evaluation_count = len(evaluation_index["evaluations"]) - cases_index = json.loads((context_root / "cases" / "index.json").read_text()) + cases_index = json.loads((context_root / "tasks" / "index.json").read_text()) resource_path = cases_index["case_resources"][0]["path"] resource_index = json.loads( ( - context_root / "cases" / resource_path / "resources" / "index.json" + context_root / "tasks" / resource_path / "resources" / "index.json" ).read_text() ) self.case_resource = json.loads( ( context_root - / "cases" + / "tasks" / resource_path / "resources" / resource_index["resources"][0]["path"] @@ -121,7 +121,7 @@ async def run(self, *, context, prompt, max_turns, on_event=None): self.feedback = feedback assert (Path(context.workspace.project_path) / feedback.result_path).is_file() refreshed = json.loads( - (context_root / "evaluations" / "index.json").read_text() + (context_root / "results" / "index.json").read_text() ) assert len(refreshed["evaluations"]) == self.initial_evaluation_count + 1 @@ -311,7 +311,7 @@ async def test_agent_checkpoint_is_a_selectable_candidate(tmp_path: Path): assert agent.feedback is not None assert agent.feedback.result.objective.value == 1.0 - assert agent.feedback.result_path.startswith(".vero/evaluations/") + assert agent.feedback.result_path.startswith(".evals/results/") assert agent.initial_candidate_ids == {baseline_version} assert agent.initial_evaluation_count == 1 assert agent.case_resource == [{"id": "case-1", "size": 128}] diff --git a/vero/tests/test_v05_candidate_repository.py b/vero/tests/test_v05_candidate_repository.py index 68bc20da..ca244ad0 100644 --- a/vero/tests/test_v05_candidate_repository.py +++ b/vero/tests/test_v05_candidate_repository.py @@ -198,10 +198,10 @@ async def test_git_repository_rejects_candidates_that_track_agent_context( ) await repository.capture(Candidate.from_version(baseline_version), workspace) - context = source / ".vero" + context = source / ".evals" context.mkdir() (context / "private.json").write_text('{"secret": true}\n', encoding="utf-8") - git(source, "add", "-f", ".vero/private.json") + git(source, "add", "-f", ".evals/private.json") git( source, "-c", @@ -264,7 +264,7 @@ async def test_git_repository_materializes_visible_history_in_opaque_workspace( sandbox=OpaqueLocalSandbox(tmp_path), name="history", ) as checkout: - destination = f"{checkout.project_path}/.vero/candidates" + destination = f"{checkout.project_path}/.evals/candidates" await repository.materialize_agent_history( (baseline, first, sibling), workspace=checkout, diff --git a/vero/tests/test_v05_circle_packing_example.py b/vero/tests/test_v05_circle_packing_example.py index c984edb5..53f2be17 100644 --- a/vero/tests/test_v05_circle_packing_example.py +++ b/vero/tests/test_v05_circle_packing_example.py @@ -120,7 +120,7 @@ def test_circle_packing_example_evaluates_and_preserves_artifacts( shutil.copytree( source, example, - ignore=shutil.ignore_patterns(".git", ".vero", ".venv", "__pycache__"), + ignore=shutil.ignore_patterns(".git", ".vero", ".evals", ".venv", "__pycache__"), ) baseline_version = initialize_repository(example / "target") monkeypatch.setenv("UV_CACHE_DIR", str(tmp_path / "uv-cache")) diff --git a/vero/tests/test_v05_claude_agent.py b/vero/tests/test_v05_claude_agent.py index d0214161..11c7d3a0 100644 --- a/vero/tests/test_v05_claude_agent.py +++ b/vero/tests/test_v05_claude_agent.py @@ -115,5 +115,5 @@ async def capture(event): assert result.metadata["usage"]["input_tokens"] == 10 assert context.project_path == tmp_path assert context.instructions.startswith("Optimize matrix multiplication\n\n") - assert "read-only optimization context in `.vero/`" in context.instructions + assert "evaluation context has been placed in `.evals/`" in context.instructions assert context.base_version == "baseline-version" diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index c62cd322..81edfb5a 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -498,14 +498,14 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): # k-anonymity floor (default 5) is disclosed to the agent assert "must include at least 5 cases" in instruction assert "Complete task resources" in instruction - assert ".vero/evaluations/" in instruction + assert ".evals/results/" in instruction task_toml = (output / "task.toml").read_text(encoding="utf-8") assert 'name = "org/optimize-\\"program\\""' in task_toml assert tomllib.loads(task_toml)["task"]["name"] == 'org/optimize-"program"' compose = (output / "environment/docker-compose.yaml").read_text() assert "vero.harbor.deployment:build_harbor_components" in compose assert "admin_state:/state/admin" in compose - assert "agent_context:/work/agent/.vero:ro" in compose + assert "agent_context:/work/agent/.evals:ro" in compose assert "agent_context:/state/agent-context" in compose assert set(yaml.safe_load(compose)["services"]) == {"main", "eval-sidecar"} sidecar_dockerfile = (output / "environment/sidecar/Dockerfile").read_text() @@ -515,8 +515,8 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert "useradd -m -u 1002 harness" in sidecar_dockerfile assert "chown -R harness:harness /home/harness/.cache" in sidecar_dockerfile seed = (output / "environment/main/seed.sh").read_text() - assert "-path /work/agent/.vero -prune" in seed - assert "'/.vero/' >> /work/agent/.git/info/exclude" in seed + assert "-path /work/agent/.evals -prune" in seed + assert "'/.evals/' >> /work/agent/.git/info/exclude" in seed test_script = output / "tests/test.sh" assert test_script.stat().st_mode & 0o111 assert "vero harbor export-session" in test_script.read_text() @@ -557,9 +557,9 @@ def test_compiler_checks_secrets_before_writing_and_rejects_source_overlap( vero_root=Path(__file__).parents[1], ) - (Path(safe.agent_repo) / ".vero").mkdir() - (Path(safe.agent_repo) / ".vero" / "context.json").write_text("{}\n") - _git(Path(safe.agent_repo), "add", "-f", ".vero/context.json") + (Path(safe.agent_repo) / ".evals").mkdir() + (Path(safe.agent_repo) / ".evals" / "context.json").write_text("{}\n") + _git(Path(safe.agent_repo), "add", "-f", ".evals/context.json") _git(Path(safe.agent_repo), "commit", "-q", "-m", "reserved context") with pytest.raises(ValueError, match="reserved path"): compile_harbor_task( diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py index 2b851615..f98f09b8 100644 --- a/vero/tests/test_v05_harbor_deployment.py +++ b/vero/tests/test_v05_harbor_deployment.py @@ -129,7 +129,7 @@ async def test_standard_deployment_factory_builds_one_canonical_runtime(tmp_path } assert (tmp_path / "state/agent/manifest.json").is_file() assert json.loads( - (tmp_path / "state/agent/evaluations/index.json").read_text() + (tmp_path / "state/agent/results/index.json").read_text() ) == {"schema_version": 1, "evaluations": []} report = await generate_experiment_report( tmp_path / "state/session", diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index b16214d7..d4d1d5a6 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -69,7 +69,7 @@ async def evaluate(self, request): evaluation_id="evaluation", status=EvaluationStatus.SUCCESS, ), - result_path=".vero/evaluations/evaluation/evaluation.json", + result_path=".evals/results/evaluation/evaluation.json", ), ) diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index 74b4cb68..df6cdea1 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -252,13 +252,13 @@ async def test_sidecar_uses_canonical_disclosure_budget_and_multiple_backends(tm assert response.receipt.result.total_cases == 5 assert transport.calls == ["HEAD"] assert response.receipt.result_path == ( - f".vero/evaluations/{context_digest(response.receipt.evaluation_id)}/" + f".evals/results/{context_digest(response.receipt.evaluation_id)}/" "evaluation.json" ) assert ( tmp_path / "agent-volume" - / "evaluations" + / "results" / context_digest(response.receipt.evaluation_id) / "evaluation.json" ).is_file() @@ -494,7 +494,7 @@ async def test_sidecar_context_survives_restart_without_disclosing_admin_runs( ) await restarted.initialize_context() - index = json.loads((tmp_path / "agent-volume/evaluations/index.json").read_text())[ + index = json.loads((tmp_path / "agent-volume/results/index.json").read_text())[ "evaluations" ] assert [entry["evaluation_id"] for entry in index] == [ diff --git a/vero/tests/test_v05_optimizer.py b/vero/tests/test_v05_optimizer.py index dcfe1e48..f5af089e 100644 --- a/vero/tests/test_v05_optimizer.py +++ b/vero/tests/test_v05_optimizer.py @@ -112,7 +112,7 @@ async def test_optimizer_improves_a_non_python_program_via_external_commands( workspace = Path(sys.argv[1]) context = Path(sys.argv[2]) assert Path(os.environ["VERO_CONTEXT_PATH"]) == context -evaluations = json.loads((context / "evaluations/index.json").read_text()) +evaluations = json.loads((context / "results/index.json").read_text()) assert len(evaluations["evaluations"]) == 1 (workspace / "program.txt").write_text("fast\\n") """ From 91f58e496f872145921be313735ef9863f4fec41 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 17:20:12 -0700 Subject: [PATCH 15/72] Add agent-facing `evals` CLI and packaged skill MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit One well-named entry point over the .evals/ context — the new avatar of the legacy VeroAgent's three structured tools. Runner subcommands (run/status/ result/submit) delegate to the sidecar client lazily; the viewers (list/show/ cases/trace/diff/plan/tasks) are unprivileged stdlib readers of the disclosure- projected tree, with bounded, paginated, metadata-first output. The compiler bakes skills/evals/SKILL.md into the optimizer workspace by default (include_evals_skill), and the instruction template now teaches `evals`. Co-Authored-By: Claude Fable 5 --- vero/README.md | 6 +- vero/examples/harbor-circle-packing/README.md | 4 +- .../environment/Dockerfile | 2 +- .../environment/sidecar/circle_factory.py | 6 +- .../harbor-circle-packing/instruction.md | 4 +- .../harbor-circle-packing/solution/solve.sh | 4 +- vero/examples/insights-bundle/README.md | 4 +- vero/pyproject.toml | 8 + vero/src/vero/agents/vero.py | 2 +- vero/src/vero/candidate.py | 9 +- vero/src/vero/candidate_repository/base.py | 1 - vero/src/vero/candidate_repository/git.py | 1 - vero/src/vero/cli.py | 4 +- vero/src/vero/config.py | 2 +- vero/src/vero/evals_cli.py | 637 ++++++++++++++++++ vero/src/vero/evaluation/__init__.py | 10 +- vero/src/vero/evaluation/backend.py | 2 +- vero/src/vero/evaluation/command.py | 2 +- vero/src/vero/evaluation/engine.py | 2 +- vero/src/vero/evaluation/evaluator.py | 2 +- vero/src/vero/evaluation/objective.py | 1 - vero/src/vero/harbor/__init__.py | 6 +- vero/src/vero/harbor/build/compiler.py | 15 +- vero/src/vero/harbor/build/config.py | 3 + .../harbor/build/templates/instruction.md.j2 | 15 +- .../vero/harbor/build/templates/solve.sh.j2 | 6 +- vero/src/vero/harbor/deployment.py | 2 +- vero/src/vero/harbor/sidecar.py | 8 +- vero/src/vero/harbor/transport.py | 2 +- vero/src/vero/harbor/verifier.py | 2 +- vero/src/vero/optimization/optimizer.py | 2 +- vero/src/vero/optimization/protocols.py | 4 +- vero/src/vero/report.py | 1 - vero/src/vero/runtime/__init__.py | 4 +- vero/src/vero/runtime/context.py | 1 - vero/src/vero/sandbox.py | 2 +- vero/src/vero/skills/evals/SKILL.md | 67 ++ vero/src/vero/tools/utils/openai_agents.py | 1 + vero/tests/test_sandbox.py | 2 +- vero/tests/test_subprocess.py | 1 + vero/tests/test_tools.py | 1 + vero/tests/test_uv_with_editable.py | 1 - vero/tests/test_v05_agent_context.py | 6 +- vero/tests/test_v05_claude_agent.py | 1 + vero/tests/test_v05_cli.py | 2 +- vero/tests/test_v05_evals_cli.py | 264 ++++++++ vero/tests/test_v05_evaluation_runtime.py | 14 +- vero/tests/test_v05_harbor_build.py | 17 +- vero/tests/test_v05_harbor_deployment.py | 2 +- vero/tests/test_v05_harbor_http.py | 10 +- vero/tests/test_v05_harbor_sidecar.py | 4 +- vero/tests/test_v05_harbor_verifier.py | 1 - vero/tests/test_v05_python_task_backend.py | 4 +- 53 files changed, 1097 insertions(+), 87 deletions(-) create mode 100644 vero/src/vero/evals_cli.py create mode 100644 vero/src/vero/skills/evals/SKILL.md create mode 100644 vero/tests/test_v05_evals_cli.py diff --git a/vero/README.md b/vero/README.md index 83223291..7a501ab7 100644 --- a/vero/README.md +++ b/vero/README.md @@ -108,7 +108,7 @@ flowchart LR A["Optimizer container
agent edits the candidate
+ calls the CLI"] S["Eval sidecar (trusted)
owns cases, scoring,
budgets, finalization"] G["Inference gateway (trusted)
holds the upstream key,
issues scoped tokens"] - A -->|"vero harbor eval"| S + A -->|"evals run"| S A -->|"scoped model calls"| G G --> U["Provider / litellm proxy"] ``` @@ -186,8 +186,8 @@ build.yaml --output task` compiles without running, for inspection. > Harbor constructs the agent; a raw `harbor run` would let the agent adapter > read the upstream key from its own host process first. -Inside the container the agent evaluates candidates with `vero harbor eval ---detach`, then `eval-status` / `eval-result` / `status` (via `VERO_EVAL_URL`). +Inside the container the agent evaluates candidates with `evals run +--detach`, then `evals status JOB` / `evals result JOB` / `evals status` (via `VERO_EVAL_URL`). Detached evaluations are **durable jobs** — the candidate version is captured before the command returns, so ending the agent process can't lose or race a running measurement. diff --git a/vero/examples/harbor-circle-packing/README.md b/vero/examples/harbor-circle-packing/README.md index 51ec30f3..5d5ac4a1 100644 --- a/vero/examples/harbor-circle-packing/README.md +++ b/vero/examples/harbor-circle-packing/README.md @@ -7,7 +7,7 @@ that edits `packing.py` to pack 26 non-overlapping circles in the unit square ``` harbor run ──► main service (coding agent edits /work/agent/packing.py) - │ vero harbor eval (self-score during the run) + │ evals run (self-score during the run) ▼ eval-sidecar ──► CommandBackend → harness/evaluate.py (trusted: scoring, budget, disclosure, final selection) @@ -36,7 +36,7 @@ with `vero harbor serve --factory circle_factory:build` (see | `environment/sidecar/circle_factory.py` | Wires the `CommandBackend`, sidecar policies, objective, verifier | | `environment/sidecar/harness/evaluate.py` | The scorer (`sum_radii`, `valid`, clearances) | | `environment/agent-baseline/` | Trusted baseline the sidecar scores against | -| `solution/solve.sh` | Reference: self-score via `vero harbor eval` | +| `solution/solve.sh` | Reference: self-score via `evals run` | | `tests/test.sh` | Verifier: `vero harbor finalize` → `/logs/verifier/reward.json` | The objective is `sum_radii` (maximize) subject to `valid == 1`; the baseline diff --git a/vero/examples/harbor-circle-packing/environment/Dockerfile b/vero/examples/harbor-circle-packing/environment/Dockerfile index 8eb8918a..d9739763 100644 --- a/vero/examples/harbor-circle-packing/environment/Dockerfile +++ b/vero/examples/harbor-circle-packing/environment/Dockerfile @@ -1,6 +1,6 @@ # Main service: the optimizer workbench a coding agent works in. # Holds the editable target program plus the VeRO CLI so the agent can call -# `vero harbor eval` / `vero harbor status` against the trusted sidecar. +# `evals run` / `evals status` against the trusted sidecar. FROM ghcr.io/astral-sh/uv:python3.12-bookworm RUN apt-get update \ diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py index 96cde52e..72b5a377 100644 --- a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py +++ b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py @@ -29,7 +29,11 @@ from vero.harbor.serve import SidecarComponents from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy from vero.harbor.transport import GitCandidateTransport -from vero.harbor.verifier import CanonicalVerifier, VerificationSelection, VerificationTarget +from vero.harbor.verifier import ( + CanonicalVerifier, + VerificationSelection, + VerificationTarget, +) from vero.sandbox import LocalSandbox from vero.workspace import GitWorkspace diff --git a/vero/examples/harbor-circle-packing/instruction.md b/vero/examples/harbor-circle-packing/instruction.md index c930ece4..8f2b6a98 100644 --- a/vero/examples/harbor-circle-packing/instruction.md +++ b/vero/examples/harbor-circle-packing/instruction.md @@ -16,11 +16,11 @@ scores about 0.96; the best known result is ~2.635. 3. Score the current commit on the validation set: ```bash - vero harbor eval --backend cmd --evaluation-set circle-packing \ + evals run --backend cmd --evaluation-set circle-packing \ --partition validation --start 0 --stop 1 ``` -4. Use `vero harbor status` to see remaining evaluation budget. +4. Use `evals status` to see remaining evaluation budget. The trusted sidecar owns scoring, budget, and final candidate selection. Only `sum_radii` (with a `valid == 1` constraint) counts. Iterate: try an diff --git a/vero/examples/harbor-circle-packing/solution/solve.sh b/vero/examples/harbor-circle-packing/solution/solve.sh index 62ed5687..22463fd0 100755 --- a/vero/examples/harbor-circle-packing/solution/solve.sh +++ b/vero/examples/harbor-circle-packing/solution/solve.sh @@ -2,5 +2,5 @@ set -eu cd /work/agent git config user.name optimizer; git config user.email o@v.test -vero harbor eval --backend cmd --evaluation-set circle-packing --partition validation --start 0 --stop 1 -vero harbor status +evals run --backend cmd --evaluation-set circle-packing --partition validation --start 0 --stop 1 +evals status diff --git a/vero/examples/insights-bundle/README.md b/vero/examples/insights-bundle/README.md index 324dafa9..522a36bd 100644 --- a/vero/examples/insights-bundle/README.md +++ b/vero/examples/insights-bundle/README.md @@ -31,7 +31,7 @@ trajectories, so the whole investigation is recorded. ## How it plugs into a run -The optimizer works in `/work/agent`; its `vero harbor eval` calls write the +The optimizer works in `/work/agent`; its `evals run` calls write the trace corpus to `/work/agent/.evals/results/**`. Point the bundle there with `CORPUS=/work/agent/.evals`. Two seams inject the bundle into the environment: @@ -66,7 +66,7 @@ which model the optimizer may call.) ``` optimize round N: - optimizer proposes/edits candidate → `vero harbor eval` → traces land in .evals + optimizer proposes/edits candidate → `evals run` → traces land in .evals optimizer (or a between-rounds step) invokes the `insights-generator` subagent: orchestrator → scouts (discover) → investigators (validate) → findings.json optimizer reads the top findings → informs the round N+1 edit diff --git a/vero/pyproject.toml b/vero/pyproject.toml index 7f1f7a2e..1928d734 100644 --- a/vero/pyproject.toml +++ b/vero/pyproject.toml @@ -16,6 +16,7 @@ dependencies = [ [project.scripts] vero = "vero.cli:main" +evals = "vero.evals_cli:main" [project.optional-dependencies] harbor = [ @@ -56,6 +57,13 @@ name = "pypi" [tool.uv.sources] vero = { workspace = true } +[tool.ruff] +line-length = 88 +target-version = "py311" + +[tool.ruff.lint] +select = ["E4", "E7", "E9", "F", "I"] + [tool.pytest.ini_options] testpaths = ["tests"] pythonpath = ["tests"] diff --git a/vero/src/vero/agents/vero.py b/vero/src/vero/agents/vero.py index f8a6aab2..a8d7e434 100644 --- a/vero/src/vero/agents/vero.py +++ b/vero/src/vero/agents/vero.py @@ -30,8 +30,8 @@ ModelSettings, OpenAIChatCompletionsModel, RunConfig, - RunResultStreaming, Runner, + RunResultStreaming, TResponseInputItem, retry_policies, set_tracing_disabled, diff --git a/vero/src/vero/candidate.py b/vero/src/vero/candidate.py index af9d5c4c..ed33da93 100644 --- a/vero/src/vero/candidate.py +++ b/vero/src/vero/candidate.py @@ -5,7 +5,14 @@ from datetime import UTC, datetime from typing import Any -from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator, model_validator +from pydantic import ( + BaseModel, + ConfigDict, + Field, + JsonValue, + field_validator, + model_validator, +) class Candidate(BaseModel): diff --git a/vero/src/vero/candidate_repository/base.py b/vero/src/vero/candidate_repository/base.py index 951959b2..c2bb02e2 100644 --- a/vero/src/vero/candidate_repository/base.py +++ b/vero/src/vero/candidate_repository/base.py @@ -10,7 +10,6 @@ from vero.sandbox import Sandbox from vero.workspace import Workspace - WorkspaceT = TypeVar("WorkspaceT", bound=Workspace) diff --git a/vero/src/vero/candidate_repository/git.py b/vero/src/vero/candidate_repository/git.py index 8ee8bcdb..5e91e1bf 100644 --- a/vero/src/vero/candidate_repository/git.py +++ b/vero/src/vero/candidate_repository/git.py @@ -22,7 +22,6 @@ from vero.sandbox import LocalSandbox, Sandbox from vero.workspace import GitWorkspace, Workspace - _OBJECT_ID = re.compile(r"(?:[0-9a-f]{40}|[0-9a-f]{64})\Z") _AGENT_CONTEXT_DIRECTORY = ".evals" diff --git a/vero/src/vero/cli.py b/vero/src/vero/cli.py index 89f9991a..3a9a4266 100644 --- a/vero/src/vero/cli.py +++ b/vero/src/vero/cli.py @@ -15,6 +15,7 @@ import click from vero.candidate_repository import GitCandidateRepository +from vero.config import load_config from vero.evaluation import ( AllCases, BudgetLedger, @@ -46,7 +47,6 @@ WandbEventSink, create_local_optimization_session, ) -from vero.config import load_config def _default_home() -> Path: @@ -1016,7 +1016,7 @@ def session_clear(session_dir: Path, yes: bool) -> None: click.echo(f"Deleted {session_dir}") -from vero.harbor.cli import harbor as harbor_command +from vero.harbor.cli import harbor as harbor_command # noqa: E402 (registered after `main` is defined) main.add_command(harbor_command) diff --git a/vero/src/vero/config.py b/vero/src/vero/config.py index aad47952..28121870 100644 --- a/vero/src/vero/config.py +++ b/vero/src/vero/config.py @@ -14,8 +14,8 @@ from pydantic import Field, JsonValue, model_validator from vero.evaluation import ( - AllCases, AgentSelectionMode, + AllCases, CaseIds, CaseRange, CommandBackend, diff --git a/vero/src/vero/evals_cli.py b/vero/src/vero/evals_cli.py new file mode 100644 index 00000000..e26e6fa8 --- /dev/null +++ b/vero/src/vero/evals_cli.py @@ -0,0 +1,637 @@ +"""`evals` — the agent-facing CLI over the `.evals/` evaluation context. + +One well-named entry point for the whole evaluation loop: run an evaluation +(`evals run`, delegating to the metered sidecar endpoint), then navigate the +disclosure-projected results on disk (`evals list/show/cases/trace/diff`), +browse exposed task resources (`evals tasks/task`), and check what may be run +(`evals plan`). + +The viewers are deliberately dumb and unprivileged: `.evals/` is written by the +trusted control plane already projected to the authorized disclosure, so this +module only reads JSON from disk — stdlib + click, no other vero imports. The +runner subcommands import the sidecar client lazily so the viewers work in +contexts without the harbor extra installed. +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import click + +CONTEXT_DIRECTORY = ".evals" +_CELL_WIDTH = 48 + + +# -------------------------------------------------------------------------- +# Context discovery and shared helpers +# -------------------------------------------------------------------------- + + +def _find_context(explicit: str | None) -> Path: + """Locate the `.evals` context directory. + + Order: --context flag, $VERO_CONTEXT_PATH, then walk up from the working + directory looking for a `.evals/manifest.json`. + """ + if explicit: + path = Path(explicit).expanduser() + if path.name != CONTEXT_DIRECTORY and (path / CONTEXT_DIRECTORY).is_dir(): + path = path / CONTEXT_DIRECTORY + if not path.is_dir(): + raise click.ClickException(f"context directory not found: {path}") + return path + env = os.environ.get("VERO_CONTEXT_PATH") + if env: + path = Path(env) + if path.is_dir(): + return path + raise click.ClickException(f"$VERO_CONTEXT_PATH is not a directory: {path}") + current = Path.cwd() + for candidate in (current, *current.parents): + path = candidate / CONTEXT_DIRECTORY + if (path / "manifest.json").is_file(): + return path + raise click.ClickException( + f"no {CONTEXT_DIRECTORY}/ directory found from {current} upward; " + "pass --context or set $VERO_CONTEXT_PATH" + ) + + +def _load_json(path: Path) -> object: + try: + return json.loads(path.read_text(encoding="utf-8")) + except FileNotFoundError: + raise click.ClickException(f"missing context file: {path}") + except json.JSONDecodeError as error: + raise click.ClickException(f"invalid JSON in {path}: {error}") + + +def _result_index(context: Path) -> list[dict]: + document = _load_json(context / "results" / "index.json") + return list(document.get("evaluations", [])) + + +def _resolve_result(context: Path, identifier: str) -> dict: + """Match an index entry by evaluation id (or unique prefix) or path digest.""" + entries = _result_index(context) + matches = [ + entry + for entry in entries + if entry.get("evaluation_id") == identifier + or str(entry.get("evaluation_id", "")).startswith(identifier) + or str(entry.get("path", "")).startswith(identifier) + ] + if len(matches) == 1: + return matches[0] + available = ", ".join(str(entry.get("evaluation_id")) for entry in entries) or "none" + kind = "ambiguous" if matches else "unknown" + raise click.ClickException( + f"{kind} evaluation {identifier!r}; available ids: {available}" + ) + + +def _result_document(context: Path, entry: dict) -> dict: + return _load_json(context / "results" / str(entry["path"])) + + +def _dig(value: object, *keys: str, default: object = None) -> object: + for key in keys: + if not isinstance(value, dict) or key not in value: + return default + value = value[key] + return value + + +def _result_row(context: Path, entry: dict) -> dict: + """One `evals list` row, tolerant of every disclosure shape.""" + document = _result_document(context, entry) + result = document.get("result", {}) + score = _dig(result, "objective", "value") + if score is None: + score = _dig(result, "metrics", "score") + if score is None: + score = _dig(result, "report", "metrics", "score") + case_files = result.get("case_files") + cases = ( + len(case_files) + if isinstance(case_files, list) + else _dig(result, "total_cases") + ) + return { + "id": entry.get("evaluation_id"), + "evaluation": entry.get("evaluation"), + "partition": entry.get("partition"), + "candidate": entry.get("candidate_id"), + "disclosure": entry.get("disclosure"), + "status": _dig(result, "status") or _dig(result, "report", "status"), + "score": score, + "cases": cases, + "errored": _dig(result, "errored_cases"), + "completed_at": _dig(result, "completed_at"), + } + + +def _case_rows(context: Path, entry: dict) -> list[dict]: + document = _result_document(context, entry) + result = document.get("result", {}) + case_files = result.get("case_files") + if not isinstance(case_files, list): + raise click.ClickException( + f"evaluation {entry.get('evaluation_id')!r} has disclosure " + f"{document.get('disclosure')!r}: per-case results are not available" + ) + root = context / "results" / str(entry["path"]) + rows = [] + for case_file in case_files: + case_document = _load_json(root.parent / str(case_file["path"])) + case = case_document.get("result", {}) + errors = case.get("errors") or [] + rows.append( + { + "case_id": case.get("case_id"), + "task": _dig(case, "input", "task_name"), + "status": case.get("status"), + "score": _dig(case, "metrics", "score"), + "error": ( + errors[0].get("code") + if errors + else _dig(case, "output", "error_category") + ), + "trace": bool( + case_document.get("execution_trace_path") + or case_document.get("evaluation_trace_path") + ), + "_path": str(case_file["path"]), + } + ) + return rows + + +def _resolve_case(context: Path, entry: dict, case_identifier: str) -> dict: + rows = _case_rows(context, entry) + matches = [ + row + for row in rows + if str(row["case_id"]) == case_identifier + or str(row["case_id"]).startswith(case_identifier) + ] + if len(matches) == 1: + return matches[0] + available = ", ".join(str(row["case_id"]) for row in rows) or "none" + kind = "ambiguous" if matches else "unknown" + raise click.ClickException( + f"{kind} case {case_identifier!r}; available case ids: {available}" + ) + + +def _format_cell(value: object) -> str: + if value is None: + return "-" + if isinstance(value, float): + return f"{value:.4g}" + text = str(value) + return text if len(text) <= _CELL_WIDTH else text[: _CELL_WIDTH - 1] + "…" + + +def _print_table(rows: list[dict], columns: list[str]) -> None: + if not rows: + click.echo("(no rows)") + return + cells = [[_format_cell(row.get(column)) for column in columns] for row in rows] + widths = [ + max(len(column), max((len(line[index]) for line in cells), default=0)) + for index, column in enumerate(columns) + ] + click.echo(" ".join(column.ljust(widths[i]) for i, column in enumerate(columns))) + for line in cells: + click.echo(" ".join(cell.ljust(widths[i]) for i, cell in enumerate(line))) + + +def _paginate(rows: list[dict], sort: str | None, desc: bool, limit: int, offset: int): + if sort: + rows = sorted( + rows, + key=lambda row: (row.get(sort) is None, row.get(sort)), + reverse=desc, + ) + total = len(rows) + window = rows[offset : offset + limit if limit else None] + return window, total + + +def _emit(rows: list[dict], columns: list[str], total: int, offset: int, as_json: bool): + public = [ + {key: value for key, value in row.items() if not key.startswith("_")} + for row in rows + ] + if as_json: + click.echo(json.dumps(public, indent=2, default=str)) + return + _print_table(public, columns) + if total > len(rows): + click.echo( + f"({total} total; showing {len(rows)} from offset {offset}; " + "use --limit/--offset)" + ) + + +_CONTEXT_OPTION = click.option( + "--context", + "context_path", + help=f"Path to the {CONTEXT_DIRECTORY}/ directory (default: discovered).", +) +_JSON_OPTION = click.option("--json", "as_json", is_flag=True, help="Emit JSON rows.") + + +def _pagination_options(command): + for option in ( + click.option("--sort", help="Column to sort by."), + click.option("--desc", is_flag=True, help="Sort descending."), + click.option("--limit", default=20, show_default=True, type=click.IntRange(0)), + click.option("--offset", default=0, show_default=True, type=click.IntRange(0)), + ): + command = option(command) + return command + + +# -------------------------------------------------------------------------- +# The command group (runner subcommands attach lazily) +# -------------------------------------------------------------------------- + + +_LAZY_RUNNERS = {"run", "result", "submit"} + + +class _EvalsGroup(click.Group): + """Attach sidecar runner commands only when asked for, so the on-disk + viewers work without the harbor extra installed.""" + + def list_commands(self, ctx): + return sorted({*super().list_commands(ctx), *_LAZY_RUNNERS}) + + def get_command(self, ctx, name): + command = super().get_command(ctx, name) + if command is not None or name not in _LAZY_RUNNERS: + return command + try: + from vero.harbor import cli as harbor_cli + except ImportError as error: + raise click.ClickException( + f"`evals {name}` needs the evaluation sidecar client " + f"(vero[harbor]): {error}" + ) + return { + "run": harbor_cli.evaluate_command, + "result": harbor_cli.evaluation_result_command, + "submit": harbor_cli.submit_command, + }[name] + + +@click.group(cls=_EvalsGroup) +def evals() -> None: + """Run evaluations and navigate their results. + + Everything you are authorized to see lives in the read-only `.evals/` + directory: `results/` (past evaluation results), `tasks/` (exposed task + resources), `candidates/` (prior program versions), and `plan.json` + (what you may evaluate, and remaining budget). + + Typical loop: `evals plan` -> edit + commit -> `evals run --detach` -> + `evals status JOB` -> `evals list` -> `evals diff BASELINE CANDIDATE` -> + `evals cases ID --sort score` -> `evals trace ID CASE`. + """ + + +@evals.command("status") +@click.argument("job_id", required=False) +def status_command(job_id): + """Show evaluation access and budgets, or one detached job's status.""" + try: + from vero.harbor.cli import _request + except ImportError as error: + raise click.ClickException( + f"`evals status` needs the evaluation sidecar client (vero[harbor]): {error}" + ) + path = f"/eval/jobs/{job_id}" if job_id else "/status" + click.echo(json.dumps(_request("GET", path), indent=2)) + + +@evals.command("list") +@_CONTEXT_OPTION +@_pagination_options +@_JSON_OPTION +def list_command(context_path, sort, desc, limit, offset, as_json): + """List past evaluation results, one row per evaluation.""" + context = _find_context(context_path) + rows = [_result_row(context, entry) for entry in _result_index(context)] + window, total = _paginate(rows, sort, desc, limit, offset) + _emit( + window, + [ + "id", + "evaluation", + "partition", + "candidate", + "disclosure", + "status", + "score", + "cases", + "completed_at", + ], + total, + offset, + as_json, + ) + + +@evals.command("show") +@click.argument("evaluation_id") +@_CONTEXT_OPTION +@click.option("--raw", is_flag=True, help="Dump the full stored document.") +def show_command(evaluation_id, context_path, raw): + """Show one evaluation result in detail.""" + context = _find_context(context_path) + entry = _resolve_result(context, evaluation_id) + document = _result_document(context, entry) + if raw: + click.echo(json.dumps(document, indent=2, default=str)) + return + compact = json.loads(json.dumps(document, default=str)) + result = compact.get("result", {}) + case_files = result.get("case_files") + if isinstance(case_files, list): + result["case_files"] = ( + f"({len(case_files)} cases; use `evals cases {entry['evaluation_id']}`)" + ) + artifacts = _dig(result, "report", "artifacts") + if isinstance(artifacts, list) and artifacts: + result["report"]["artifacts"] = ( + f"({len(artifacts)} artifacts under " + f"results/{entry['path'].rsplit('/', 1)[0]}/artifacts/)" + ) + click.echo(json.dumps(compact, indent=2)) + + +@evals.command("cases") +@click.argument("evaluation_id") +@_CONTEXT_OPTION +@_pagination_options +@_JSON_OPTION +def cases_command(evaluation_id, context_path, sort, desc, limit, offset, as_json): + """List one evaluation's per-case results (full disclosure only).""" + context = _find_context(context_path) + entry = _resolve_result(context, evaluation_id) + rows = _case_rows(context, entry) + window, total = _paginate(rows, sort, desc, limit, offset) + _emit( + window, + ["case_id", "task", "status", "score", "error", "trace"], + total, + offset, + as_json, + ) + + +@evals.command("trace") +@click.argument("evaluation_id") +@click.argument("case_id") +@_CONTEXT_OPTION +@click.option("--span", type=click.IntRange(0), help="Show one span by index.") +@click.option("--chars", default=10_000, show_default=True, type=click.IntRange(1)) +@click.option("--char-offset", default=0, show_default=True, type=click.IntRange(0)) +def trace_command(evaluation_id, case_id, context_path, span, chars, char_offset): + """Summarize a case's trace, or window one span of it. + + Without --span: span count, shapes, and sizes, plus the case's artifact + files (read those directly with your file tools). With --span N: that + span's JSON, windowed by --char-offset/--chars. + """ + context = _find_context(context_path) + entry = _resolve_result(context, evaluation_id) + case_row = _resolve_case(context, entry, case_id) + case_root = (context / "results" / str(entry["path"])).parent / Path( + str(case_row["_path"]) + ).parent + case_document = _load_json(case_root / "result.json") + + trace = [] + trace_name = case_document.get("execution_trace_path") + if trace_name: + loaded = _load_json(case_root / str(trace_name)) + trace = loaded if isinstance(loaded, list) else [loaded] + + if span is not None: + if not trace: + raise click.ClickException("this case has no execution trace file") + if span >= len(trace): + raise click.ClickException( + f"span {span} out of range; trace has {len(trace)} spans" + ) + text = json.dumps(trace[span], indent=2, default=str) + window = text[char_offset : char_offset + chars] + click.echo( + f"span {span}/{len(trace) - 1}, chars {char_offset}-" + f"{char_offset + len(window)} of {len(text)}" + ) + click.echo(window) + return + + summary: dict[str, object] = {"case_id": case_row["case_id"]} + if trace: + shapes: dict[str, dict[str, int]] = {} + total_chars = 0 + for item in trace: + if isinstance(item, dict): + key = "dict(" + ",".join(sorted(item)) + ")" + elif isinstance(item, list): + key = f"list(len={len(item)})" + else: + key = type(item).__name__ + size = len(json.dumps(item, default=str)) + total_chars += size + shape = shapes.setdefault(key, {"count": 0, "chars": 0}) + shape["count"] += 1 + shape["chars"] += size + summary["execution_trace"] = { + "spans": len(trace), + "total_chars": total_chars, + "shapes": shapes, + "read_with": f"evals trace {entry['evaluation_id']} " + f"{case_row['case_id']} --span N", + } + else: + summary["execution_trace"] = None + + artifacts = [] + for artifact in _dig(case_document, "result", "artifacts", default=[]) or []: + relative = str(artifact.get("path", "")) + on_disk = ( + context / "results" / str(entry["path"]) + ).parent / "artifacts" / relative + artifacts.append( + { + "path": f"results/{str(entry['path']).rsplit('/', 1)[0]}" + f"/artifacts/{relative}", + "bytes": on_disk.stat().st_size if on_disk.is_file() else None, + "description": artifact.get("description"), + } + ) + summary["artifacts"] = artifacts + click.echo(json.dumps(summary, indent=2)) + + +@evals.command("diff") +@click.argument("baseline_id") +@click.argument("candidate_id") +@_CONTEXT_OPTION +@_JSON_OPTION +def diff_command(baseline_id, candidate_id, context_path, as_json): + """Per-case score deltas between two evaluations (matched by case id).""" + context = _find_context(context_path) + baseline_entry = _resolve_result(context, baseline_id) + candidate_entry = _resolve_result(context, candidate_id) + baseline = {row["case_id"]: row for row in _case_rows(context, baseline_entry)} + candidate = {row["case_id"]: row for row in _case_rows(context, candidate_entry)} + rows = [] + for case_id in sorted({*baseline, *candidate}, key=str): + before = _dig(baseline.get(case_id, {}), "score") + after = _dig(candidate.get(case_id, {}), "score") + if case_id not in baseline or case_id not in candidate: + verdict = "unmatched" + elif before is None or after is None: + verdict = "unscored" + elif after > before: + verdict = "improved" + elif after < before: + verdict = "regressed" + else: + verdict = "unchanged" + rows.append( + { + "case_id": case_id, + "baseline": before, + "candidate": after, + "delta": (after - before) + if isinstance(before, (int, float)) and isinstance(after, (int, float)) + else None, + "verdict": verdict, + } + ) + counts: dict[str, int] = {} + for row in rows: + counts[row["verdict"]] = counts.get(row["verdict"], 0) + 1 + if as_json: + click.echo(json.dumps({"cases": rows, "summary": counts}, indent=2)) + return + _print_table(rows, ["case_id", "baseline", "candidate", "delta", "verdict"]) + click.echo(json.dumps(counts)) + + +@evals.command("plan") +@_CONTEXT_OPTION +@_JSON_OPTION +def plan_command(context_path, as_json): + """Show the evaluations you may run, their rules, and remaining budget.""" + context = _find_context(context_path) + document = _load_json(context / "plan.json") + rows = [] + for evaluation in document.get("evaluations", []): + budget = evaluation.get("budget") or {} + rows.append( + { + "evaluation": evaluation.get("name"), + "partition": evaluation.get("partition"), + "can_evaluate": evaluation.get("agent_can_evaluate"), + "selection": evaluation.get("agent_selection"), + "disclosure": evaluation.get("disclosure"), + "runs_left": budget.get("remaining_runs"), + "cases_left": budget.get("remaining_cases"), + } + ) + if as_json: + click.echo(json.dumps(rows, indent=2)) + return + _print_table( + rows, + [ + "evaluation", + "partition", + "can_evaluate", + "selection", + "disclosure", + "runs_left", + "cases_left", + ], + ) + + +def _task_sets(context: Path) -> list[dict]: + document = _load_json(context / "tasks" / "index.json") + return list(document.get("case_resources", [])) + + +@evals.command("tasks") +@click.argument("evaluation_set", required=False) +@_CONTEXT_OPTION +@_JSON_OPTION +def tasks_command(evaluation_set, context_path, as_json): + """List exposed task sets, or one set's task ids and resource paths.""" + context = _find_context(context_path) + sets = _task_sets(context) + if evaluation_set is None: + rows = [] + for item in sets: + resources = _load_json( + context / "tasks" / str(item["path"]) / "resources" / "index.json" + ) + rows.append( + { + "evaluation": _dig(item, "evaluation_set", "name"), + "partition": _dig(item, "evaluation_set", "partition"), + "tasks": len(resources.get("cases", [])), + "path": f"tasks/{item['path']}/resources/", + } + ) + if as_json: + click.echo(json.dumps(rows, indent=2)) + else: + _print_table(rows, ["evaluation", "partition", "tasks", "path"]) + return + matches = [ + item + for item in sets + if evaluation_set + in ( + _dig(item, "evaluation_set", "name"), + _dig(item, "evaluation_set", "partition"), + str(item.get("path")), + ) + ] + if len(matches) != 1: + available = ", ".join( + f"{_dig(item, 'evaluation_set', 'name')}/" + f"{_dig(item, 'evaluation_set', 'partition')}" + for item in sets + ) + raise click.ClickException( + f"unknown or ambiguous task set {evaluation_set!r}; available: " + f"{available or 'none'}" + ) + root = f"tasks/{matches[0]['path']}/resources" + resources = _load_json(context / "tasks" / str(matches[0]["path"]) / "resources" / "index.json") + rows = [ + {"case_id": case.get("case_id"), "path": f"{root}/{case.get('path')}"} + for case in resources.get("cases", []) + ] + if as_json: + click.echo(json.dumps(rows, indent=2)) + else: + _print_table(rows, ["case_id", "path"]) + click.echo("(read task files directly with your file tools)") + + +def main() -> None: + evals() diff --git a/vero/src/vero/evaluation/__init__.py b/vero/src/vero/evaluation/__init__.py index c07b75ed..9add910c 100644 --- a/vero/src/vero/evaluation/__init__.py +++ b/vero/src/vero/evaluation/__init__.py @@ -17,14 +17,14 @@ ) from vero.evaluation.evaluator import Evaluator from vero.evaluation.exceptions import ( - EvaluationCancelledError, EvaluationBudgetExceeded, + EvaluationCancelledError, EvaluationDeniedError, EvaluationError, EvaluationExecutionError, EvaluationInfrastructureError, - EvaluationTerminatedError, EvaluationRequestError, + EvaluationTerminatedError, UnknownBackendError, ) from vero.evaluation.models import ( @@ -42,20 +42,20 @@ ConstraintViolation, DiagnosticSeverity, DisclosureLevel, - EvaluationAcknowledgement, EvaluationAccessPolicy, + EvaluationAcknowledgement, EvaluationArtifact, EvaluationAuthorization, EvaluationBudget, - EvaluationDefinition, EvaluationCost, + EvaluationDefinition, EvaluationDiagnostic, EvaluationLimits, EvaluationModel, EvaluationPlan, EvaluationPrincipal, - EvaluationRecord, EvaluationReceipt, + EvaluationRecord, EvaluationReport, EvaluationRequest, EvaluationSet, diff --git a/vero/src/vero/evaluation/backend.py b/vero/src/vero/evaluation/backend.py index 91c77b6c..c23e77b4 100644 --- a/vero/src/vero/evaluation/backend.py +++ b/vero/src/vero/evaluation/backend.py @@ -8,11 +8,11 @@ from vero.evaluation.models import ( BackendProvenance, + CaseResult, EvaluationCost, EvaluationReport, EvaluationRequest, EvaluationSet, - CaseResult, ) from vero.sandbox import Sandbox from vero.workspace import Workspace diff --git a/vero/src/vero/evaluation/command.py b/vero/src/vero/evaluation/command.py index 1e488489..2f22feed 100644 --- a/vero/src/vero/evaluation/command.py +++ b/vero/src/vero/evaluation/command.py @@ -28,8 +28,8 @@ EvaluationStatus, ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text -from vero.staging import SandboxStagingArea from vero.sandbox import Sandbox +from vero.staging import SandboxStagingArea _PLACEHOLDERS = {"workspace", "harness", "request", "report", "artifacts"} _PLACEHOLDER_PATTERN = re.compile(r"\{([^{}]+)\}") diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index 598cd5fc..239a6455 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -26,8 +26,8 @@ AgentSelectionMode, EvaluationAcknowledgement, EvaluationAuthorization, - EvaluationPrincipal, EvaluationPlan, + EvaluationPrincipal, EvaluationRecord, EvaluationRequest, EvaluationSummary, diff --git a/vero/src/vero/evaluation/evaluator.py b/vero/src/vero/evaluation/evaluator.py index f35c6525..a299fc64 100644 --- a/vero/src/vero/evaluation/evaluator.py +++ b/vero/src/vero/evaluation/evaluator.py @@ -20,9 +20,9 @@ CaseStatus, DiagnosticSeverity, EvaluationDiagnostic, + EvaluationPrincipal, EvaluationRecord, EvaluationReport, - EvaluationPrincipal, EvaluationRequest, EvaluationStatus, ObjectiveSpec, diff --git a/vero/src/vero/evaluation/objective.py b/vero/src/vero/evaluation/objective.py index 50fa18fc..29bc2aa0 100644 --- a/vero/src/vero/evaluation/objective.py +++ b/vero/src/vero/evaluation/objective.py @@ -23,7 +23,6 @@ ObjectiveSpec, ) - _OPERATORS = { ConstraintOperator.EQ: operator.eq, ConstraintOperator.NE: operator.ne, diff --git a/vero/src/vero/harbor/__init__.py b/vero/src/vero/harbor/__init__.py index 88c46b25..f4a44250 100644 --- a/vero/src/vero/harbor/__init__.py +++ b/vero/src/vero/harbor/__init__.py @@ -13,21 +13,21 @@ load_harbor_build_config, ) from vero.harbor.deployment import HarborDeploymentConfig, build_harbor_components +from vero.harbor.session import HarborSessionManifest from vero.harbor.sidecar import ( EvaluationAccessError, + EvaluationAccessStatus, EvaluationJobNotFoundError, EvaluationJobStatus, - SidecarEvaluationPolicy, - EvaluationAccessStatus, EvaluationSidecar, SidecarEvaluationJob, + SidecarEvaluationPolicy, SidecarEvaluationRequest, SidecarEvaluationResult, SidecarStatus, Submission, SubmissionDisabledError, ) -from vero.harbor.session import HarborSessionManifest from vero.harbor.transport import ( CandidateTransferError, CandidateTransport, diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index d76d7d1e..35998740 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -2,6 +2,7 @@ from __future__ import annotations +import importlib.resources import io import json import logging @@ -20,7 +21,7 @@ EvaluationSet, RetryPolicy, ) -from vero.harbor.build.config import HarborBuildConfig +from vero.harbor.build.config import HarborBuildConfig, WorkspaceOverlaySpec from vero.harbor.inference import generate_inference_token, token_digest logger = logging.getLogger(__name__) @@ -475,11 +476,19 @@ def compile_harbor_task( # General filesystem overlay: bake arbitrary host files/dirs into the agent # workspace at build time. A source dir's *contents* land under dest; a source # file lands as dest/. dest="." is the workspace root. - overlay_present = bool(config.workspace_overlays) + overlays = list(config.workspace_overlays) + if config.include_evals_skill: + # vero's own packaged skill; resolved from the installed package so the + # baked copy always matches the vero (and `evals` CLI) in the image. + skill_source = importlib.resources.files("vero") / "skills" / "evals" + overlays.append( + WorkspaceOverlaySpec(source=str(skill_source), dest="skills/evals") + ) + overlay_present = bool(overlays) overlay_excludes: list[str] = [] if overlay_present: overlay_root = environment_dir / "overlay" - for spec in config.workspace_overlays: + for spec in overlays: source = Path(spec.source) target = overlay_root if spec.dest == "." else overlay_root / spec.dest if source.is_dir(): diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index b285ca19..1640bd62 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -238,6 +238,9 @@ class HarborBuildConfig(EvaluationModel): wandb: WandbSpec | None = None read_only_paths: list[str] = Field(default_factory=list) workspace_overlays: list[WorkspaceOverlaySpec] = Field(default_factory=list) + # Bake vero's packaged `evals` skill (skills/evals/SKILL.md) into the + # optimizer workspace so any coding agent learns the CLI + .evals layout. + include_evals_skill: bool = True instruct_multifidelity: bool = True instruct_exhaust_budget: bool = True # Master switch for budget *disclosure* to the optimizer (not enforcement). diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 02fca1a6..3c58722f 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -17,7 +17,7 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, 3. Evaluate the current commit on the selection set: ```bash - vero harbor eval \ + evals run \ --backend {{ selection_backend }} \ --evaluation-set {{ evaluation_set_name }} \ --partition {{ selection_partition }} \ @@ -25,15 +25,16 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, ``` Record the returned `job_id`. Continue inspecting or editing while it runs, - then use `vero harbor eval-status JOB_ID` and - `vero harbor eval-result JOB_ID` to retrieve its durable result. Track every - outstanding job and do not claim an improvement before its comparison - baseline has completed. + then use `evals status JOB_ID` and `evals result JOB_ID` to retrieve its + durable result. Track every outstanding job and do not claim an improvement + before its comparison baseline has completed. -4. Use `vero harbor status` to inspect evaluation jobs and allowed evaluation +4. Use `evals status` to inspect evaluation jobs and allowed evaluation sets{% if disclose_budget %}, and to see remaining budgets{% endif %}. + Navigate finished results with `evals list`, `evals diff`, `evals cases`, + and `evals trace` (see `evals --help` and `skills/evals/SKILL.md`). {% if submit_enabled %} -5. When finished, nominate the chosen commit with `vero harbor submit`. +5. When finished, nominate the chosen commit with `evals submit`. {% else %} 5. VeRO automatically pools your measurements, re-scores a shortlist with the trusted evaluator, and selects the best candidate. The unmodified baseline is diff --git a/vero/src/vero/harbor/build/templates/solve.sh.j2 b/vero/src/vero/harbor/build/templates/solve.sh.j2 index 9aec5f8e..09ede8ed 100644 --- a/vero/src/vero/harbor/build/templates/solve.sh.j2 +++ b/vero/src/vero/harbor/build/templates/solve.sh.j2 @@ -7,11 +7,11 @@ git config user.email optimizer@example.test echo "# optimizer candidate" >> README.md 2>/dev/null || echo "candidate" > NOTES.md git add -A git commit -m "optimizer candidate" -vero harbor eval \ +evals run \ --backend {{ selection_backend }} \ --evaluation-set {{ evaluation_set_name }} \ --partition {{ selection_partition }} {% if submit_enabled %} -vero harbor submit +evals submit {% endif %} -vero harbor status +evals status diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 6716db0d..750376c4 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -14,8 +14,8 @@ BudgetLedger, EvaluationBudget, EvaluationDatabase, - EvaluationModel, EvaluationLimits, + EvaluationModel, EvaluationSet, Evaluator, ObjectiveSpec, diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index 715d58c0..28c26c7f 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -26,19 +26,20 @@ EvaluationExecutionError, EvaluationInfrastructureError, EvaluationLimits, - EvaluationTerminatedError, EvaluationModel, - EvaluationRecord, EvaluationReceipt, - EvaluationRequestError, + EvaluationRecord, EvaluationRequest, + EvaluationRequestError, EvaluationSet, EvaluationSummary, + EvaluationTerminatedError, ObjectiveSpec, project_evaluation, ) from vero.evaluation.engine import EvaluationEngine from vero.evaluation.persistence import _atomic_write_json +from vero.harbor.transport import CandidateTransferError, CandidateTransport from vero.runtime.context import ( AgentContextDirectory, AgentDisclosureLedger, @@ -46,7 +47,6 @@ make_evaluation_receipt, narrower_disclosure, ) -from vero.harbor.transport import CandidateTransferError, CandidateTransport from vero.sandbox import LocalSandbox diff --git a/vero/src/vero/harbor/transport.py b/vero/src/vero/harbor/transport.py index 768a0e70..519bbac8 100644 --- a/vero/src/vero/harbor/transport.py +++ b/vero/src/vero/harbor/transport.py @@ -2,8 +2,8 @@ from __future__ import annotations -import re import os +import re from datetime import datetime from pathlib import PurePosixPath from typing import Protocol, runtime_checkable diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py index 5cf9210d..6164bd3c 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/harbor/verifier.py @@ -17,8 +17,8 @@ EvaluationAuthorization, EvaluationLimits, EvaluationModel, - EvaluationRecord, EvaluationPrincipal, + EvaluationRecord, EvaluationRequest, EvaluationSet, ObjectiveSpec, diff --git a/vero/src/vero/optimization/optimizer.py b/vero/src/vero/optimization/optimizer.py index de97303b..324d3996 100644 --- a/vero/src/vero/optimization/optimizer.py +++ b/vero/src/vero/optimization/optimizer.py @@ -39,8 +39,8 @@ OptimizationResult, ) from vero.optimization.protocols import ( - CandidateProducer, CandidateEvaluationGateway, + CandidateProducer, GenerationBackend, OptimizationStrategy, SelectionPolicy, diff --git a/vero/src/vero/optimization/protocols.py b/vero/src/vero/optimization/protocols.py index ef694603..48312963 100644 --- a/vero/src/vero/optimization/protocols.py +++ b/vero/src/vero/optimization/protocols.py @@ -4,14 +4,14 @@ from typing import Mapping, Protocol, Sequence, runtime_checkable +from vero.candidate import Candidate from vero.evaluation import ( - EvaluationBudget, CaseSelection, + EvaluationBudget, EvaluationReceipt, EvaluationRecord, ObjectiveSpec, ) -from vero.candidate import Candidate from vero.optimization.models import ( CandidateChange, CandidateProductionContext, diff --git a/vero/src/vero/report.py b/vero/src/vero/report.py index 0fd87cc5..3a3e73ea 100644 --- a/vero/src/vero/report.py +++ b/vero/src/vero/report.py @@ -18,7 +18,6 @@ from vero.runtime.events import RuntimeEvent from vero.runtime.session import SessionManifest - _MAX_EMBEDDED_ARTIFACT_BYTES = 5_000_000 _MAX_EMBEDDED_ARTIFACTS_BYTES = 50_000_000 _MAX_DIFF_CHARACTERS = 500_000 diff --git a/vero/src/vero/runtime/__init__.py b/vero/src/vero/runtime/__init__.py index 2e19fa2d..18e519f6 100644 --- a/vero/src/vero/runtime/__init__.py +++ b/vero/src/vero/runtime/__init__.py @@ -22,15 +22,15 @@ create_optimization_session, ) from vero.runtime.session import ( - OptimizationSession, OptimizationComponentSpec, OptimizationRunSpec, + OptimizationSession, SessionFailure, SessionManifest, SessionStatus, ) -from vero.staging import SandboxStagingArea from vero.runtime.wandb import WandbEventSink +from vero.staging import SandboxStagingArea __all__ = [ "ArtifactStore", diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index 39223787..f52fdc2f 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -30,7 +30,6 @@ from vero.sandbox import Sandbox from vero.workspace import Workspace - AGENT_CONTEXT_DIRECTORY = ".evals" _DISCLOSURE_RANK = { DisclosureLevel.NONE: 0, diff --git a/vero/src/vero/sandbox.py b/vero/src/vero/sandbox.py index ae7beac1..5324734b 100644 --- a/vero/src/vero/sandbox.py +++ b/vero/src/vero/sandbox.py @@ -14,8 +14,8 @@ import asyncio import os import posixpath -import signal import shutil +import signal import tempfile import uuid from abc import ABC, abstractmethod diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md new file mode 100644 index 00000000..8619a73c --- /dev/null +++ b/vero/src/vero/skills/evals/SKILL.md @@ -0,0 +1,67 @@ +--- +name: evals +description: >- + Run evaluations and navigate their results with the `evals` CLI over the + read-only `.evals/` directory. Use whenever you need to score a candidate + program, compare two candidates case by case, inspect why a case failed, or + check what you are allowed to evaluate and how much budget remains. +--- + +# evals: run evaluations and navigate their results + +Your program is scored by a trusted evaluator you cannot see into. Everything +you *are* allowed to see lands in the read-only `.evals/` directory, and the +`evals` CLI is the structured way to work with it. `evals --help` lists every +subcommand. + +## The loop + +```bash +evals plan # what you may run, rules, remaining budget +evals run --backend B --evaluation-set S --partition P --detach # returns job_id +evals status JOB_ID # poll the job (evals status = global view) +evals result JOB_ID # durable result once finished +evals list --sort score --desc # every past result, one row each +evals diff BASELINE_ID CANDIDATE_ID # which cases improved / regressed +evals cases ID --sort score # per-case scores for one result +evals trace ID CASE_ID # trace summary + artifact files for a case +evals trace ID CASE_ID --span 3 # one span of the trace, char-windowed +``` + +Evaluate the baseline first: a candidate only counts as an improvement against +a measured baseline on the same case selection. + +## The `.evals/` tree + +``` +.evals/ +├── README.md, manifest.json +├── plan.json # evaluations you may invoke: selection rules, disclosure, budget +├── results/ # past evaluation results -> evals list / show / cases / trace / diff +│ ├── index.json +│ └── /evaluation.json, cases//result.json, artifacts/ +├── tasks/ # exposed task resources -> evals tasks +└── candidates/ # prior program versions (Git refs for `git show` / `git diff`) +``` + +Everything is plain JSON and files — `evals` is navigation and aggregation; +read individual artifact or task files directly with your file tools once a +command hands you the path. + +## Disclosure + +Each result is projected to an authorized disclosure level before it reaches +you: `full` (per-case results, traces, artifacts), `aggregate` (overall metrics +only — `evals cases` will refuse), or `none` (bare acknowledgement). Budgets +are enforced by the evaluator; `evals plan` shows what remains. + +## Common mistakes + +- Comparing evaluations run on different case selections — deltas are then + case-sampling noise, not signal. Use `evals diff`, which matches by case id. +- Claiming an improvement before the baseline's own evaluation finished. +- Dumping whole traces into context. Go metadata-first: `evals trace` summary, + then `--span N`, then windowed chars; artifacts are files — `grep` them. +- Confusing ids: evaluation ids come from `evals list`; case ids from + `evals cases`; job ids only from `evals run --detach`. +- Scores may be noisy: replicate an important comparison before acting on it. diff --git a/vero/src/vero/tools/utils/openai_agents.py b/vero/src/vero/tools/utils/openai_agents.py index c8815708..ee63f539 100644 --- a/vero/src/vero/tools/utils/openai_agents.py +++ b/vero/src/vero/tools/utils/openai_agents.py @@ -2,6 +2,7 @@ from typing import Any, Callable from agents import FunctionTool, function_tool + from vero.tools.utils import get_tools_from_class from vero.utils.general import camel_to_snake diff --git a/vero/tests/test_sandbox.py b/vero/tests/test_sandbox.py index 3a794704..4e92a803 100644 --- a/vero/tests/test_sandbox.py +++ b/vero/tests/test_sandbox.py @@ -8,8 +8,8 @@ import pytest -from vero.sandbox import CommandResult, FileStat, LocalSandbox, Sandbox import vero.sandbox as sandbox_module +from vero.sandbox import CommandResult, FileStat, LocalSandbox, Sandbox @pytest.fixture diff --git a/vero/tests/test_subprocess.py b/vero/tests/test_subprocess.py index a88f5f38..3b71b7b2 100644 --- a/vero/tests/test_subprocess.py +++ b/vero/tests/test_subprocess.py @@ -4,6 +4,7 @@ import sys import pytest + from vero.utils.asyncio import ( SubprocessCancelledError, SubprocessTimeoutError, diff --git a/vero/tests/test_tools.py b/vero/tests/test_tools.py index d3078e38..106f9c0c 100644 --- a/vero/tests/test_tools.py +++ b/vero/tests/test_tools.py @@ -4,6 +4,7 @@ import re import pytest + from vero.tools.planning import TodoList, TodoStatus diff --git a/vero/tests/test_uv_with_editable.py b/vero/tests/test_uv_with_editable.py index 8e9b8918..07171aec 100644 --- a/vero/tests/test_uv_with_editable.py +++ b/vero/tests/test_uv_with_editable.py @@ -8,7 +8,6 @@ from __future__ import annotations -import asyncio import subprocess import textwrap from pathlib import Path diff --git a/vero/tests/test_v05_agent_context.py b/vero/tests/test_v05_agent_context.py index bc01088e..fd940032 100644 --- a/vero/tests/test_v05_agent_context.py +++ b/vero/tests/test_v05_agent_context.py @@ -15,14 +15,14 @@ CaseStatus, DisclosureLevel, EvaluationArtifact, + EvaluationRecord, EvaluationReport, EvaluationRequest, - EvaluationStatus, - ObjectiveResult, - EvaluationRecord, EvaluationSet, + EvaluationStatus, EvaluationSummary, MetricSelector, + ObjectiveResult, ObjectiveSpec, project_evaluation, ) diff --git a/vero/tests/test_v05_claude_agent.py b/vero/tests/test_v05_claude_agent.py index 11c7d3a0..19ef3935 100644 --- a/vero/tests/test_v05_claude_agent.py +++ b/vero/tests/test_v05_claude_agent.py @@ -1,6 +1,7 @@ from __future__ import annotations from pathlib import Path + import pytest pytest.importorskip("claude_agent_sdk") diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py index 11f517dc..d18534c8 100644 --- a/vero/tests/test_v05_cli.py +++ b/vero/tests/test_v05_cli.py @@ -9,8 +9,8 @@ from click.testing import CliRunner import vero -from vero.cli import main from vero.candidate import Candidate +from vero.cli import main def test_root_package_exposes_only_canonical_program_api(): diff --git a/vero/tests/test_v05_evals_cli.py b/vero/tests/test_v05_evals_cli.py new file mode 100644 index 00000000..a5da481d --- /dev/null +++ b/vero/tests/test_v05_evals_cli.py @@ -0,0 +1,264 @@ +from __future__ import annotations + +import json +from datetime import UTC, datetime, timedelta +from pathlib import Path + +import pytest +import pytest_asyncio +from click.testing import CliRunner + +from vero.candidate import Candidate +from vero.evals_cli import evals +from vero.evaluation import ( + BackendProvenance, + CaseResult, + CaseStatus, + DisclosureLevel, + EvaluationRecord, + EvaluationReport, + EvaluationRequest, + EvaluationSet, + EvaluationStatus, + MetricSelector, + ObjectiveResult, + ObjectiveSpec, + project_evaluation, +) +from vero.runtime.context import AgentContextDirectory +from vero.sandbox import LocalSandbox + + +def _record(evaluation_id: str, scores: dict[str, float], value: float): + created_at = datetime(2026, 1, 1, tzinfo=UTC) + return EvaluationRecord( + id=evaluation_id, + request=EvaluationRequest( + candidate=Candidate( + id=f"candidate:{evaluation_id}", + version="a" * 40, + created_at=created_at, + ), + evaluation_set=EvaluationSet(name="validation", partition="validation"), + ), + report=EvaluationReport( + status=EvaluationStatus.SUCCESS, + metrics={"score": value}, + cases=[ + CaseResult( + case_id=case_id, + status=CaseStatus.SUCCESS, + metrics={"score": score}, + input={"task_name": f"task-{case_id}"}, + output={"answer": "answer"}, + execution_trace=[ + {"turn": 1, "message": f"marker-{case_id}"}, + {"turn": 2, "tool": "run", "result": {"stdout": "ok"}}, + ], + ) + for case_id, score in scores.items() + ], + ), + backend_id="backend", + backend=BackendProvenance(name="test", version="1", config_digest="0" * 64), + objective_spec=ObjectiveSpec( + selector=MetricSelector(metric="score"), direction="maximize" + ), + objective=ObjectiveResult(value=value, feasible=True), + created_at=created_at, + completed_at=created_at + timedelta(seconds=1), + ) + + +@pytest_asyncio.fixture +async def context_dir(tmp_path: Path) -> Path: + baseline = _record("evaluation:baseline", {"one": 0.25, "two": 1.0}, 0.625) + candidate = _record("evaluation:candidate", {"one": 0.75, "two": 0.5}, 0.7) + aggregate = _record("evaluation:aggregate", {"one": 0.5}, 0.5) + project = tmp_path / "project" + project.mkdir() + directory = AgentContextDirectory( + sandbox=await LocalSandbox.create(root=tmp_path), + root=str(project / ".evals"), + session_dir=tmp_path / "session", + ) + await directory.reset() + await directory.write_header( + session_id="session", + round_number=1, + proposal_id="proposal", + parent_candidate_id="parent", + ) + await directory.write_evaluations( + [ + (item, disclosure, project_evaluation(item, disclosure)) + for item, disclosure in ( + (baseline, DisclosureLevel.FULL), + (candidate, DisclosureLevel.FULL), + (aggregate, DisclosureLevel.AGGREGATE), + ) + ] + ) + root = project / ".evals" + (root / "plan.json").write_text( + json.dumps( + { + "schema_version": 1, + "evaluations": [ + { + "name": "validation", + "partition": "validation", + "base_selection": {"kind": "all"}, + "agent_can_evaluate": True, + "agent_selection": "arbitrary", + "disclosure": "full", + "expose_case_resources": True, + "budget": {"remaining_runs": 3, "remaining_cases": 40}, + } + ], + } + ) + ) + resources = root / "tasks" / "digest" / "resources" + resources.mkdir(parents=True) + (resources / "one.json").write_text('{"prompt": "task one"}') + (resources / "index.json").write_text( + json.dumps({"schema_version": 1, "cases": [{"case_id": "one", "path": "one.json"}]}) + ) + (root / "tasks" / "index.json").write_text( + json.dumps( + { + "schema_version": 1, + "case_resources": [ + { + "backend_id": "backend", + "evaluation_set": {"name": "validation", "partition": "validation"}, + "path": "digest", + } + ], + } + ) + ) + return root + + +def _invoke(context_dir: Path, *arguments: str): + result = CliRunner().invoke( + evals, [*arguments, "--context", str(context_dir)], catch_exceptions=False + ) + return result + + +@pytest.mark.asyncio +async def test_list_shows_every_result_and_sorts(context_dir: Path): + result = _invoke(context_dir, "list", "--json") + assert result.exit_code == 0 + rows = json.loads(result.output) + assert {row["id"] for row in rows} == { + "evaluation:baseline", + "evaluation:candidate", + "evaluation:aggregate", + } + by_id = {row["id"]: row for row in rows} + assert by_id["evaluation:baseline"]["score"] == 0.625 + assert by_id["evaluation:baseline"]["cases"] == 2 + assert by_id["evaluation:aggregate"]["disclosure"] == "aggregate" + assert by_id["evaluation:aggregate"]["cases"] == 1 # summary count only + + ordered = json.loads( + _invoke(context_dir, "list", "--json", "--sort", "score", "--desc").output + ) + assert ordered[0]["id"] == "evaluation:candidate" + + +@pytest.mark.asyncio +async def test_show_summarizes_case_files(context_dir: Path): + result = _invoke(context_dir, "show", "evaluation:baseline") + assert result.exit_code == 0 + assert "2 cases" in result.output + assert "evals cases" in result.output + + +@pytest.mark.asyncio +async def test_cases_lists_per_case_results_and_respects_disclosure(context_dir: Path): + result = _invoke(context_dir, "cases", "evaluation:baseline", "--json") + assert result.exit_code == 0 + rows = json.loads(result.output) + assert [(row["case_id"], row["score"]) for row in rows] == [ + ("one", 0.25), + ("two", 1.0), + ] + assert all(row["trace"] for row in rows) + + denied = CliRunner().invoke( + evals, ["cases", "evaluation:aggregate", "--context", str(context_dir)] + ) + assert denied.exit_code != 0 + assert "disclosure" in denied.output + + +@pytest.mark.asyncio +async def test_trace_summary_and_span_window(context_dir: Path): + summary = _invoke(context_dir, "trace", "evaluation:baseline", "one") + assert summary.exit_code == 0 + payload = json.loads(summary.output) + assert payload["execution_trace"]["spans"] == 2 + assert any("turn" in shape for shape in payload["execution_trace"]["shapes"]) + + span = _invoke(context_dir, "trace", "evaluation:baseline", "one", "--span", "0") + assert span.exit_code == 0 + assert "marker-one" in span.output + + +@pytest.mark.asyncio +async def test_diff_reports_per_case_verdicts(context_dir: Path): + result = _invoke( + context_dir, "diff", "evaluation:baseline", "evaluation:candidate", "--json" + ) + assert result.exit_code == 0 + payload = json.loads(result.output) + verdicts = {row["case_id"]: row["verdict"] for row in payload["cases"]} + assert verdicts == {"one": "improved", "two": "regressed"} + assert payload["summary"] == {"improved": 1, "regressed": 1} + + +@pytest.mark.asyncio +async def test_plan_shows_budget(context_dir: Path): + result = _invoke(context_dir, "plan", "--json") + assert result.exit_code == 0 + rows = json.loads(result.output) + assert rows == [ + { + "evaluation": "validation", + "partition": "validation", + "can_evaluate": True, + "selection": "arbitrary", + "disclosure": "full", + "runs_left": 3, + "cases_left": 40, + } + ] + + +@pytest.mark.asyncio +async def test_tasks_lists_sets_then_task_paths(context_dir: Path): + sets = json.loads(_invoke(context_dir, "tasks", "--json").output) + assert sets == [ + { + "evaluation": "validation", + "partition": "validation", + "tasks": 1, + "path": "tasks/digest/resources/", + } + ] + tasks = json.loads(_invoke(context_dir, "tasks", "validation", "--json").output) + assert tasks == [{"case_id": "one", "path": "tasks/digest/resources/one.json"}] + + +@pytest.mark.asyncio +async def test_context_is_discovered_from_workspace(context_dir: Path, monkeypatch): + monkeypatch.chdir(context_dir.parent) + monkeypatch.delenv("VERO_CONTEXT_PATH", raising=False) + result = CliRunner().invoke(evals, ["list", "--json"], catch_exceptions=False) + assert result.exit_code == 0 + assert len(json.loads(result.output)) == 3 diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index 5c2302cb..d3ecbd9e 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -9,6 +9,7 @@ import pytest +import vero.evaluation.budget as budget_module from vero.candidate import Candidate from vero.evaluation import ( AgentSelectionMode, @@ -16,24 +17,24 @@ BackendRegistry, BudgetLedger, CaseError, + CaseRange, CaseResult, CaseStatus, - CaseRange, DisclosureLevel, - EvaluationAuthorization, EvaluationAccessPolicy, + EvaluationAuthorization, EvaluationBudget, EvaluationBudgetExceeded, EvaluationCost, EvaluationDatabase, + EvaluationDefinition, EvaluationDeniedError, EvaluationEngine, - EvaluationLimits, - EvaluationDefinition, EvaluationExecutionError, - EvaluationRecord, + EvaluationLimits, EvaluationPlan, EvaluationPrincipal, + EvaluationRecord, EvaluationReport, EvaluationRequest, EvaluationSet, @@ -45,9 +46,8 @@ ObjectiveSpec, allow_all_evaluations, authorize_evaluation_plan, + persistence, ) -from vero.evaluation import persistence -import vero.evaluation.budget as budget_module from vero.workspace import Workspace diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 81edfb5a..0d885f20 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -212,13 +212,26 @@ def test_compiler_omits_wandb_when_unset(tmp_path): def test_compiler_omits_overlay_when_unset(tmp_path): - output = compile_harbor_task(_config(tmp_path), tmp_path / "compiled") + output = compile_harbor_task( + _config(tmp_path, include_evals_skill=False), tmp_path / "compiled" + ) env = output / "environment" assert not (env / "overlay").exists() assert "COPY overlay" not in (env / "Dockerfile").read_text() assert "/opt/overlay" not in (env / "main" / "seed.sh").read_text() +def test_compiler_bakes_evals_skill_by_default(tmp_path): + output = compile_harbor_task(_config(tmp_path), tmp_path / "compiled") + env = output / "environment" + skill = env / "overlay" / "skills" / "evals" / "SKILL.md" + assert skill.is_file() + assert "evals run" in skill.read_text(encoding="utf-8") + assert "'/skills/' >> /work/agent/.git/info/exclude" in ( + env / "main" / "seed.sh" + ).read_text(encoding="utf-8") + + def test_compiler_budget_disclosure_toggle(tmp_path): disclosed = compile_harbor_task(_config(tmp_path / "on"), tmp_path / "on/out") instruction_on = (disclosed / "instruction.md").read_text(encoding="utf-8") @@ -474,7 +487,7 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert "use_evaluation_copies" not in serve instruction = (output / "instruction.md").read_text() assert "--detach" in instruction - assert "vero harbor eval-status JOB_ID" in instruction + assert "evals status JOB_ID" in instruction assert "randomness remains" in instruction for partition, backend in serve["backends"].items(): partition_name = partition.removeprefix("harbor-") diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py index f98f09b8..78f59c82 100644 --- a/vero/tests/test_v05_harbor_deployment.py +++ b/vero/tests/test_v05_harbor_deployment.py @@ -15,8 +15,8 @@ ObjectiveSpec, ) from vero.harbor import ( - SidecarEvaluationPolicy, HarborBackendConfig, + SidecarEvaluationPolicy, VerificationTarget, build_harbor_components, ) diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index d4d1d5a6..e4708ec4 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -22,17 +22,17 @@ EvaluationStatus, ) from vero.harbor.app import create_app +from vero.harbor.auth import ( + check_admin_token, + read_admin_token, + write_admin_token, +) from vero.harbor.cli import ( _compiled_run_environment, _load_agent_trace, _load_env_file, harbor, ) -from vero.harbor.auth import ( - check_admin_token, - read_admin_token, - write_admin_token, -) from vero.harbor.sidecar import ( EvaluationAccessError, EvaluationJobNotFoundError, diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index df6cdea1..e94034fb 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -20,12 +20,12 @@ CaseResult, CaseStatus, DisclosureLevel, + EvaluationAuthorization, EvaluationBudget, EvaluationCost, EvaluationDatabase, EvaluationDeniedError, EvaluationEngine, - EvaluationAuthorization, EvaluationLimits, EvaluationReport, EvaluationRequest, @@ -38,9 +38,9 @@ from vero.harbor import ( EvaluationAccessError, EvaluationJobStatus, - SidecarEvaluationPolicy, EvaluationSidecar, GitCandidateTransport, + SidecarEvaluationPolicy, SidecarEvaluationRequest, SubmissionDisabledError, ) diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py index 3b969c33..704db2c3 100644 --- a/vero/tests/test_v05_harbor_verifier.py +++ b/vero/tests/test_v05_harbor_verifier.py @@ -28,7 +28,6 @@ VerificationTarget, ) - OBJECTIVE = ObjectiveSpec( selector=MetricSelector(metric="score"), direction="maximize", diff --git a/vero/tests/test_v05_python_task_backend.py b/vero/tests/test_v05_python_task_backend.py index 268e1e54..0979dbf4 100644 --- a/vero/tests/test_v05_python_task_backend.py +++ b/vero/tests/test_v05_python_task_backend.py @@ -13,11 +13,11 @@ CaseRange, EvaluationPlan, EvaluationSet, + MetricSelector, + ObjectiveSpec, PythonTaskBackend, PythonTaskBackendConfig, PythonTaskEvaluationConfig, - MetricSelector, - ObjectiveSpec, ) from vero.optimization import ( CommandCandidateProducer, From 10e3dea786d869b5036ee997d493572cab92a4c1 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 17:35:37 -0700 Subject: [PATCH 16/72] small changes --- vero/docs/insights-generator-subagent.md | 249 ----------------------- vero/src/vero/cli.py | 3 +- 2 files changed, 2 insertions(+), 250 deletions(-) delete mode 100644 vero/docs/insights-generator-subagent.md diff --git a/vero/docs/insights-generator-subagent.md b/vero/docs/insights-generator-subagent.md deleted file mode 100644 index b5ec2f82..00000000 --- a/vero/docs/insights-generator-subagent.md +++ /dev/null @@ -1,249 +0,0 @@ -# Insights Generator as a VeRO optimizer sub-agent - -**Status:** design / not yet implemented -**Audience:** an engineer or agent picking this up cold -**Working branch:** `native-runner-sandboxagent` (`/Users/varun.ursekar/code/vero-v05`) - ---- - -## 1. Motivation - -The VeRO optimizer improves a candidate by having a coding agent iterate on it and -scoring the result. Across a run it accumulates a large corpus of **execution -traces** (one per evaluated case, per candidate). Today nothing systematically -mines that corpus: patterns that are only visible across the *population* of runs -(e.g. "40% of failing cases silently swallow a specific exception") never surface -back into the optimizer's next proposal. - -Scale's **Insights Generator (IG)** is a system built exactly for this: *systematic -corpus-level trace diagnostics for LLM agents* (paper: arXiv `2605.21347v3`, -"Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents", -Scale AI). It reads a corpus of traces + a diagnostic question and returns an -evidence-backed report of failure patterns with prevalence stats and cited trace -IDs. In their own eval it roughly doubled practitioner effectiveness over -baselines. - -**The endeavour:** give the VeRO optimizer IG's capability *as a sub-agent it can -invoke* — so that between rounds it can ask "what distinguishes high- from -low-scoring candidates in this corpus?" and feed the answer into the next -generation. - -## 2. The hard constraint: reproduce, do not depend - -VeRO is **public**; IG is **internal/private** (`scaleapi/models`, -`enterprise/insights_generator/`). We therefore **cannot take a dependency on IG**. -We reproduce IG's *mechanisms*, not its code, using only things a public repo can -ship. - -We also do **not** need IG's heavier implementation choices: -- no pandas dataframe input contract, -- no in-sandbox Python REPL, -- no vector store / schema-cache / parquet data layer, -- no Scale Sandbox. - -The substrate is **the sandbox filesystem + bash**, which the Harbor path already -gives us. - -## 3. What IG actually is (stripped to essentials) - -Three mechanisms do the real work; everything else is implementation: - -1. **Corpus-scale coverage without loading everything into context.** Compute - *over* all traces; read the full text of only a sampled/relevant few. -2. **Discovery ≠ validation (Scout / Investigator split).** *Scouts* skim a sample - and propose many hypotheses (breadth). *Investigators* test each one against the - *whole* corpus with quantitative evidence and label it confirmed / refuted / - inconclusive (depth). No finding ships without corpus-scale backing. -3. **Evidence-grounded, typed output with cohort persistence.** Every finding = - description + prevalence % + specific trace IDs + verbatim quotes + mechanism + - fix. IG literally rejects a finding that has no saved "affected traces" cohort. - -> Note the two entry points in the IG repo are **not** equivalent. The public-ish -> `generate_insights()` in `analysis.py` is the **old single-pass** path (sample → -> format as XML → one LLM call) — closer to the baseline the paper *beats*. The -> "best-performing" system is the agentic orchestrator entered via -> `run_orchestrator(question, sandbox, config)`. - -## 4. The path that matters: Harbor, and the real `claude` CLI - -VeRO has two agent worlds. **Only the Harbor path is in scope.** - -- **Out of scope — VeRO's own SDK agents.** `VeroAgent` (OpenAI Agents SDK) and - `ClaudeCodeAgent` (Claude Agent SDK). These have their own `SubAgentTool`, but - they are a *different* execution model. -- **In scope — Harbor's `ClaudeCode` agent.** This is what `harbor run -a claude` - uses. Crucially, it is **not** the Claude Agent SDK — it installs the real - `@anthropic-ai/claude-code` CLI into the sandbox and runs it **headless**: - `claude --print --output-format=stream-json -- `, as the `agent` - user, in the environment filesystem. - -Because the optimizer is the **real Claude Code CLI**, IG plugs in through Claude -Code's **native filesystem/config extension points** — subagents, skills, MCP — not -through Python objects. Harbor already *expects* subagents: it filters `subagents` -session paths and parses `isSidechain` events into its trajectory. - -## 5. The design - -**"The whole IG machinery as a sub-agent" = a Claude Code sub-agent bundle seeded -into the Harbor environment.** The optimizer invokes it with the `Task` tool. - -### Role mapping (1:1 with IG) - -| IG role | Reproduction | -|---|---| -| Orchestrator | `.claude/agents/insights-generator.md` (has `Task`) | -| Hypothesis agent (Scout) | `.claude/agents/ig-scout.md` — breadth, cheap model | -| Explore agent (Investigator) | `.claude/agents/ig-investigator.md` — depth, high effort | - -Each sub-agent runs in its **own context window** — which is exactly IG's -context-economy property (its orchestrator delegates heavy trace-reading to -preserve context). We get it for free from Claude Code's `Task` model. - -### Tool / data-layer mapping - -| IG piece | Reproduction | -|---|---| -| `search_traces` (vector) | `grep`/`rg` over the corpus dir (lexical) | -| `extract` (feature per trace) | a script emitting `features.jsonl`, persisted + reused | -| `compare_segments` (cohort diff) | `jq`/`awk` group-by over `features.jsonl` by label | -| `get_trace` / `get_trace_chunk` | `cat` / `sed -n` a slice of a trace file | -| `save_affected_traces` | write `cohorts/.txt` | -| `submit_finding` + rejection rule | a `findings.json` contract + a `validate.py` check | -| data layer (parquet/vector/schema) | **the sandbox filesystem** | -| Scale Sandbox | **the Harbor container the optimizer already runs in** | - -### Delivery seams (how the bundle gets into the container) - -Harbor auto-injects two of these; you seed the third yourself: - -| What | How it lands in the container | Wired by | -|---|---|---| -| **Skill** (methodology + helper scripts) | `environment.skills_dir` → copied to `$CLAUDE_CONFIG_DIR/skills/` | `task.toml` | -| **MCP server** (optional real-tool surface) | `environment.mcp_servers` / `agent.mcp_servers` → `~/.claude.json` | `task.toml` / agent config | -| **Sub-agent defs** (`.claude/agents/*.md`) | **no dedicated Harbor seam** — bake into the env image/seed | your `Dockerfile` / `seed.sh` | - -The last row is the one gotcha: Harbor injects skills/mcp/memory but **not** an -agents dir, so seed `.claude/agents/*.md` via the environment build (into the -agent's `cwd/.claude/agents/` or `$CLAUDE_CONFIG_DIR/agents/`). - -### Proposed layout - -``` -environment/ - Dockerfile # COPY .claude/agents -> /work/.claude/agents - main/seed.sh # seed /work; also lay out the trace corpus - .claude/agents/ - insights-generator.md # orchestrator persona (Task, Bash, Read, Grep, Glob) - ig-scout.md # sample -> hypotheses - ig-investigator.md # validate one hypothesis over ALL traces - skills/insights/ # -> environment.skills_dir - SKILL.md # evidence-grounding rules, findings.json contract - chunk.sh # slice long traces (the one primitive bash lacks) - validate.py # reject findings without cohort + trace-id citations -``` - -```toml -# task.toml -[environment] -skills_dir = "/skills" -# mcp_servers = [ ... ] # only if a query-server is preferred over raw bash -``` - -### The corpus - -VeRO already dumps per-case traces into a read-only `.evals` context dir: -`.evals/results//cases//execution-trace.json` (and -`evaluation-trace.json`). That **is** the IG corpus. Label traces by the eval -scores VeRO already has (pass/fail, reward) so the investigator can contrast -cohorts — comparative analysis is where IG earns its keep. - -### Where it plugs into the loop - -Between rounds, the optimizer calls `Task(subagent_type="insights-generator", …)` -pointed at the current round's traces; the findings feed the next candidate -proposals. This is a native reproduction of IG's own `patcher` loop -(analyze → guide fix → re-eval), and it lines up with VeRO's evolutionary strategy -(findings become the guidance signal for the next generation of agentic offspring). - -## 6. Design decisions & rationale - -1. **Reproduce, don't import.** VeRO public / IG private. Non-negotiable. -2. **Target the Harbor path (real `claude` CLI), not the SDK agents.** That is the - optimizer we ship; it makes the integration filesystem/config, not Python. -3. **IG = Claude Code sub-agent(s) via `Task`.** Faithful to IG's LLM-driven - orchestration and reproduces its per-role context isolation for free. -4. **Filesystem + bash as the data layer / query engine.** Drops the dataframe, - REPL, vector store, and Scale Sandbox in one move. -5. **Preserve the evidence discipline explicitly.** A `findings.json` contract plus - a `validate.py` check reproduce `submit_finding`'s "no cohort → reject" rule; - without it the output degrades to vibes. -6. **Start nested (orchestrator + scout + investigator).** Maps 1:1 to IG and - exploits context isolation. A single phased sub-agent is a legitimate v0 but - weaker on context economy for large corpora. - -## 7. Known caveats - -- **Chunking long traces is the one real primitive to build.** A single - `execution-trace.json` can blow a sub-agent's context; provide `chunk.sh` (byte- - offset or turn-boundary slicing). Everything else degrades gracefully to grep. -- **No semantic search.** `grep` is lexical. Fine for error strings, tool names, - phrasings (most trace diagnostics). Add a small local embed helper or an MCP - query-server only if recall disappoints. -- **Model control.** Harbor sets `CLAUDE_CODE_SUBAGENT_MODEL` when a custom base - URL is used; set per-role `model`/`effort` in each agent `.md`. - -## 8. Code pointers - -### VeRO (`/Users/varun.ursekar/code/vero-v05/vero`, this repo) - -| Path | Why it matters | -|---|---| -| `src/vero/runtime/context.py` (~L256–273) | Where per-case `execution-trace.json` / `evaluation-trace.json` are written into the `.evals` context — **the corpus source** | -| `src/vero/optimization/optimizer.py` | `Optimizer` (L204); `run` (L409); `_produce_candidate` (L283) — where an IG call would slot between rounds | -| `src/vero/agents/producer.py` | `AgentCandidateProducer.produce()` — how a coding agent is driven per proposal | -| `src/vero/agents/claude_code.py` | VeRO's **SDK** Claude agent — **out of scope**, but note L44 enables `Task`; do not confuse with Harbor's agent | -| `src/vero/agents/vero.py` | `VeroAgent` (OpenAI Agents SDK) — out of scope | -| `src/vero/tools/sub_agent.py` | `SubAgentTool` — the SDK-world sub-agent mechanism (not the Harbor path) | -| `src/vero/harbor/` | Harbor integration: `backend.py`, `cli.py` (`run` command), `sidecar.py`, `verifier.py` | -| `examples/harbor-circle-packing/` | Reference Harbor task: `task.toml`, `environment/Dockerfile`, `environment/main/seed.sh`, `environment/sidecar/` — the template to clone for an insights task | -| `docs/agent-setup-guide.md` | Existing agent setup guide | - -### Harbor (installed from public PyPI; import path `harbor.*`) - -Locate with `python -c "import harbor, os; print(os.path.dirname(harbor.__file__))"` -inside the vero venv. - -| Module | Why it matters | -|---|---| -| `harbor/agents/installed/claude_code.py` | **The real optimizer.** `run()` runs `claude --print --output-format=stream-json`. `_build_register_skills_command` (skills → `$CLAUDE_CONFIG_DIR/skills/`), `_build_register_mcp_servers_command` (→ `~/.claude.json`), `_build_register_memory_command`. `_get_session_dir` / `_convert_events_to_trajectory` already handle `subagents` + `isSidechain` | -| `harbor/agents/installed/base.py` | `BaseInstalledAgent`: `__init__`, `build_cli_flags`, `exec_as_agent`, `with_prompt_template` | -| `harbor/agents/installed/` (dir) | Other agents (`codex.py`, `gemini_cli.py`, …) — same pattern if a different optimizer is used | -| `harbor/trial/trial.py` | `_resolve_effective_skills_dir` (~L1049); `mcp_servers` merge (~L738–748) — how the seams are wired from config | -| `harbor/models/task/config.py` | Task `[environment]` schema: `skills_dir` (~L450), `mcp_servers` (~L444) | -| `harbor/models/trial/config.py` | Agent config: `mcp_servers` (~L125) | -| `harbor/models/trial/paths.py` | `EnvironmentPaths`: `agent_dir`, `default_skills_dir = /harbor/skills` | - -### Insights Generator (private — `scaleapi/models`, reference only, **do not import**) - -Path: `enterprise/insights_generator/src/insights_generator/` - -| Module | Why it matters | -|---|---| -| `agents/orchestrator.py` | `run_orchestrator(question, sandbox, config)` (~L1044) — the real agentic entry point (what we reproduce) | -| `agents/hypothesis_agent.py` | `run_hypothesis_agent` — the Scout; source for `ig-scout.md` | -| `agents/explore_agent.py` | `run_explore_agent` — the Investigator; source for `ig-investigator.md` | -| `agents/dispatch.py` | `DispatchHypothesisAgents` (~L216) / `DispatchExploreAgents` (~L455) — orchestrator dispatches roles via tool calls | -| `agents/prompts/{orchestrator,hypothesis,explore}.py` | The role prompts (23–54 KB) — **distill these into the `.md` personas** | -| `agents/prompts/_compose.py` | Jinja `AblationContext` prompt composition (we drop ablations; use `ablation_mode="full"` = orchestrator + hypothesis + explore) | -| `tools/repl_toolkit.py` | `EXPLORE_FUNCTIONS` (search_traces / extract / compare_segments / summarize / get_trace / save_affected_traces) — the tool surface we re-map to bash | -| `ingestion.py` | `ingest(...)` + the INPUT CONTRACT (`trace_id`, flattened `content`) — the trace-flattening reference | -| `analysis.py` | `generate_insights()` — the **old single-pass** path; NOT the system we want | -| `ig_config.py` | `IGConfig` — `ablation_mode`, per-role model fields | -| `vector_store.py`, `chunking.py`, `schema_cache.py` | The data layer we deliberately do not reproduce (except chunking) | - -## 9. Next step (offered, not yet done) - -Draft the three agent `.md` files + `SKILL.md` / `findings.json` contract + -`chunk.sh` / `validate.py`, distilled from IG's actual scout/investigator/ -orchestrator prompts and grounded against a real `execution-trace.json` from the -`.evals` layout, so the bundle is runnable rather than notional. diff --git a/vero/src/vero/cli.py b/vero/src/vero/cli.py index 3a9a4266..792fd90f 100644 --- a/vero/src/vero/cli.py +++ b/vero/src/vero/cli.py @@ -1016,7 +1016,8 @@ def session_clear(session_dir: Path, yes: bool) -> None: click.echo(f"Deleted {session_dir}") -from vero.harbor.cli import harbor as harbor_command # noqa: E402 (registered after `main` is defined) +# harbor subcommand registered here, after `main` is defined +from vero.harbor.cli import harbor as harbor_command # noqa: E402, I001 main.add_command(harbor_command) From 3fdcf90fef790f8680e8e76fc74fffba426f1b19 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 20:08:06 -0700 Subject: [PATCH 17/72] Enforce the k-anonymity floor in the engine; type the spec->policy seam MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit min_aggregate_cases moves onto the core EvaluationAccessPolicy (omitted resolves to 5 under aggregate disclosure, 1 otherwise) and the engine refuses agent-chosen aggregate subsets below the floor after cost resolution, so the protection holds on every path — programmatic API and local runs included — not just compiled harbor deployments. The sidecar keeps its earlier check as defense-in-depth and now passes the floor through its explicit authorization. AgentAccessSpec.to_access_policy() is the single typed translation from build spec to runtime policy; the compiler emits it nested in serve.json and SidecarEvaluationPolicy holds it as `access`, replacing the hand-rolled flat keys (disclosure/agent_evaluable/min_aggregate_cases/expose_case_resources) that had to be kept in sync across three representations. Co-Authored-By: Claude Fable 5 --- .../.claude/agents/ig-investigator.md | 67 ----- .../.claude/agents/ig-scout.md | 52 ---- .../.claude/agents/insights-generator.md | 64 ----- vero/examples/insights-bundle/README.md | 89 ------- .../insights-bundle/skills/insights/SKILL.md | 164 ------------- .../insights-bundle/skills/insights/chunk.sh | 41 ---- .../skills/insights/validate.py | 231 ------------------ vero/src/vero/config.py | 3 + vero/src/vero/evaluation/engine.py | 21 ++ vero/src/vero/evaluation/models.py | 11 + vero/src/vero/harbor/build/compiler.py | 5 +- vero/src/vero/harbor/build/config.py | 9 + vero/src/vero/harbor/sidecar.py | 47 ++-- vero/tests/test_v05_evaluation_runtime.py | 70 ++++++ vero/tests/test_v05_harbor_build.py | 6 +- vero/tests/test_v05_harbor_deployment.py | 5 +- vero/tests/test_v05_harbor_sidecar.py | 22 +- 17 files changed, 158 insertions(+), 749 deletions(-) delete mode 100644 vero/examples/insights-bundle/.claude/agents/ig-investigator.md delete mode 100644 vero/examples/insights-bundle/.claude/agents/ig-scout.md delete mode 100644 vero/examples/insights-bundle/.claude/agents/insights-generator.md delete mode 100644 vero/examples/insights-bundle/README.md delete mode 100644 vero/examples/insights-bundle/skills/insights/SKILL.md delete mode 100755 vero/examples/insights-bundle/skills/insights/chunk.sh delete mode 100755 vero/examples/insights-bundle/skills/insights/validate.py diff --git a/vero/examples/insights-bundle/.claude/agents/ig-investigator.md b/vero/examples/insights-bundle/.claude/agents/ig-investigator.md deleted file mode 100644 index 40602795..00000000 --- a/vero/examples/insights-bundle/.claude/agents/ig-investigator.md +++ /dev/null @@ -1,67 +0,0 @@ ---- -name: ig-investigator -description: >- - Validation agent (depth). Takes ONE hypothesis and tests it against the WHOLE - corpus with quantitative evidence — labels it confirmed/refuted/inconclusive, - saves a defensible cohort of affected trace_ids, and appends an - evidence-grounded finding to findings.json. Invoked by the insights-generator - orchestrator, one per hypothesis. -model: opus -tools: Bash, Read, Grep, Glob ---- - -You are an **Investigator**. You are handed exactly one hypothesis and you -validate it rigorously against the entire corpus. Depth over breadth: quantify, -compare cohorts, and back the claim with statistical evidence. You may confirm, -**refute**, or mark it inconclusive — a refutation is a valuable result. - -Read `skills/insights/SKILL.md` for the corpus layout, the tool→bash cookbook, -and the finding contract. Follow it exactly — especially the cohort discipline. - -## Validation ladder (broad → deep) -1. **Broad stats (all traces).** Use `index.tsv` + `grep`/`jq`/`awk` to compute - the pattern's rate across the *whole* corpus, split by status (pass vs fail). - This is your prevalence number. -2. **Focused comparison (50-100 traces).** Build a `features.jsonl` if you need a - per-trace signal (see cookbook), then group-by to compare the feature's rate in - the affected cohort vs the rest. Quantify the contrast, not a vibe. -3. **Deep read (~20 traces).** `cat`/`chunk.sh` a sample of the cohort to pull - verbatim evidence and confirm the mechanism is what you think it is. Re-sample - 2-3 times so you're not fooled by a lucky draw. - -## Cohort discipline (this is the crux) -- Save the **defensible cohort** — the narrow subset you are confident exhibits - the pattern — to `cohorts/.txt`, one trace_id per line. NOT the broad - "analyzed pool" you filtered from. Each saved id is a positive claim. - Example: for "agent submits without acknowledging the user's constraint", the - cohort is (user mentioned a constraint) ∧ (first reply lacked the ack) ∧ (a - repair turn followed) — not merely (user mentioned a constraint). -- If `|cohort| / |corpus| > 0.25`, double-check: either it genuinely is that - widespread (state it explicitly in `prevalence`) or your cohort is the analyzed - pool — narrow it. Re-writing the cohort file just overwrites (last write wins). -- **A finding with no saved cohort is rejected by validate.py.** Save first. - -## Evidence discipline -- ≥8 evidence items (aim 10-15 if prevalence is high), each from a **distinct** - trace_id, each a **verbatim** `quote` — tool-call args, exception messages, raw - turn content, literal error strings. Never paraphrase in `quote`. -- Each `explanation` (2-3 sentences) names the concrete mechanism in *that* trace - (exception type / tool name / turn index) and ties it to the prevalence claim. - Don't restate the finding generically. -- Include a counterexample or two (pattern absent + task passed), marked as such. -- Put p-values / odds ratios / test method + n in `additional_observations`, not - in `summary`. Keep `summary`/`prevalence` as plain "X of Y (Z%)". - -## Anti-overfitting -Don't generalize from <20 traces. Flag confidence: high (>100 traces, clear -signal) / medium (20-100) / low (<20). If the evidence doesn't hold, say -`refuted` or `inconclusive` — do not force a confirmation. - -## Submitting -1. Write `cohorts/.txt` (the defensible cohort). -2. Self-check: pick 2-3 random cohort ids, read them, confirm each fits. Narrow - and re-save if any doesn't. -3. Append your finding object (schema in SKILL) to `findings.json`, referencing - `cohort_file: "cohorts/.txt"`. -4. Run `python skills/insights/validate.py findings.json index.tsv` and fix - anything it flags before returning. Return a one-line verdict + the cohort size. diff --git a/vero/examples/insights-bundle/.claude/agents/ig-scout.md b/vero/examples/insights-bundle/.claude/agents/ig-scout.md deleted file mode 100644 index b4598b43..00000000 --- a/vero/examples/insights-bundle/.claude/agents/ig-scout.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -name: ig-scout -description: >- - Discovery agent (breadth). Skims a SAMPLE of the trace corpus to surface as - many plausible patterns/hypotheses as possible — failures, silent failures, - efficiency issues, success patterns, cohort differences, omissions. Returns - hypotheses with light evidence; does NOT validate. Invoked by the - insights-generator orchestrator with a slice to explore. -model: haiku -tools: Bash, Read, Grep, Glob ---- - -You are a **Scout**. Your job is *discovery*: skim broadly, find candidate -patterns, and hand back a ranked list of hypotheses. Breadth over depth — you -scan many traces and deeply validate none. Validation is someone else's job. - -Read `skills/insights/SKILL.md` for the corpus layout and the bash cookbook. - -## How you work -1. **Sample, don't exhaust.** Look across 50-200 traces from the slice you were - given. Use `grep`/`wc`/`jq` over `index.tsv` to survey cheaply; `cat` or - `chunk.sh` the full text of only a diverse handful. -2. **Look for anything interesting**, not a fixed taxonomy: - - **Explicit failures** — exceptions, non-zero exits, error strings, refusals. - - **Silent failures** — the run "succeeded" but did a poor job (ignored a - constraint, wrong tool, gave up early, hallucinated a result). - - **Efficiency** — retry loops, repeated identical tool calls, wasted turns, - runaway length. - - **Success patterns** — what the winning traces do that losing ones don't. - - **Cohort differences** — contrast pass vs fail (from `index.tsv` status): - what text/tool/structure appears in one and not the other? - - **Omissions** — the hardest and most valuable. An absent action leaves no - text to grep. So grep for the text an agent that DID the action *would* have - produced, take that cohort, and contrast it against the rest. **The contrast - is the hypothesis** — don't skip it. -3. **Quantify roughly.** For each candidate pattern, get a ballpark count - (`grep -l ... | wc -l`) so the orchestrator can prioritize. - -## What you return -A list of hypotheses. For each: -- **name** — short, specific ("empty-query search retry loop", not "search issues"). -- **description** — the pattern and, crucially, *why it might matter* to the objective. -- **evidence** — 2-5 trace_ids you're genuinely confident about, each with a - short verbatim quote. Name only traces you'd stake the hypothesis on, not every - grep hit. -- **estimated_prevalence** — rare / moderate / common / unknown. -- **suggested_validation** — one line on how an Investigator could confirm it - (which cohort to build, which contrast to run). - -Return this as a compact JSON array. Do NOT write `findings.json`, do NOT save -cohorts, do NOT call validate.py — those are the Investigator's responsibility. -Err toward proposing more hypotheses; the orchestrator filters. diff --git a/vero/examples/insights-bundle/.claude/agents/insights-generator.md b/vero/examples/insights-bundle/.claude/agents/insights-generator.md deleted file mode 100644 index 213bf3e5..00000000 --- a/vero/examples/insights-bundle/.claude/agents/insights-generator.md +++ /dev/null @@ -1,64 +0,0 @@ ---- -name: insights-generator -description: >- - Corpus-level trace analyst. Invoke when you need to understand WHY the current - candidate is failing (or winning) across an evaluation corpus — not one trace, - but the population. Coordinates Scouts (discover hypotheses) and Investigators - (validate them against the whole corpus) and returns a small set of - evidence-grounded, actionable findings. Give it the corpus root ($CORPUS, - the .evals dir) and, if known, the optimization objective. -model: inherit -tools: Task, Bash, Read, Grep, Glob ---- - -You are the **Insights Orchestrator**. You do not read many traces yourself — you -*coordinate* a two-phase investigation over a corpus of agent execution traces and -synthesize the results into findings the optimizer can act on. - -Read `skills/insights/SKILL.md` first — it defines the corpus layout, the -tool→bash cookbook, and the finding contract. Everything below assumes it. - -## Your loop - -1. **Orient.** Build `index.tsv` (see SKILL). Report corpus size and the - pass/fail (or reward) distribution. Skim 2-3 traces yourself only to learn the - trace *shape* (turn structure, where tool calls / errors live) — not to analyze. -2. **Dispatch Scouts (discovery, breadth).** Spawn `ig-scout` subagents via the - Task tool — typically 2-4, each pointed at a different slice (e.g. one over - failures, one over successes, one over a specific tool's traces, one over the - longest/most-expensive traces). Each returns a list of *hypotheses*. Do NOT - ask a Scout to validate — that is the Investigator's job. -3. **Review & prioritize hypotheses.** Merge, dedupe, and drop the low-value - ones. Prioritize hypotheses that are (a) plausibly high-prevalence or (b) - small-cohort but high-impact, and (c) *actionable* — a change the optimizer - could actually make. Note which hypotheses conflict (they make good - investigator targets). -4. **Dispatch Investigators (validation, depth).** Spawn one `ig-investigator` - per surviving hypothesis (via Task). Each validates its ONE hypothesis against - the whole corpus, saves a defensible cohort, and appends a finding to - `findings.json`. Run investigators in parallel when independent. -5. **Evaluate coverage.** After investigators return, ask: what's still - unexplained? Are there failure modes no hypothesis covered? If a major slice of - failures is unaccounted for, dispatch another Scout round targeted at the gap. - Stop when coverage is good or two consecutive rounds surface nothing new. -6. **Submit report.** Write the top-level `findings.json` fields - (`executive_summary`, `methodology`, `traces_analyzed`) around the findings the - investigators appended, then run - `python skills/insights/validate.py findings.json index.tsv`. **Do not return - until validate.py passes** — it rejects findings without a persisted cohort or - with too-thin evidence. - -## Standards you enforce -- **Every finding must be validated by an Investigator with corpus-scale - evidence.** A Scout hypothesis is never a finding on its own. -- **Anti-overfitting.** A pattern seen in <20 traces is low-confidence; say so. - Small cohorts are fine — but only when the impact is high and the evidence is - airtight. Prefer specific, mechanistic findings over broad vague ones. -- **Actionable or cut it.** If a finding doesn't suggest a concrete change to the - candidate/prompt/config, it's an observation, not a finding — demote it. - -## What you return to the caller -A short synthesis (not the full JSON): the 3-6 highest-value findings, each as -one line — mechanism, prevalence, and the suggested change — plus the path to -`findings.json` for the full evidence. This is what feeds the next candidate -proposal, so lead with what to change and why. diff --git a/vero/examples/insights-bundle/README.md b/vero/examples/insights-bundle/README.md deleted file mode 100644 index 522a36bd..00000000 --- a/vero/examples/insights-bundle/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# insights-bundle — Insights Generator as an optimizer subagent - -This is **not a Harbor task.** It is a bundle you *inject* into an existing -optimizer run (e.g. the GAIA build under `harness-engineering-bench/gaia/`) so the main -solver/optimizer agent can call it — mid-run, as a subagent — to mine its own -accumulated `.evals` trace corpus for patterns and feed the findings into the next -candidate proposal. - -It reproduces Scale's internal **Insights Generator** *methodology* — not its -code (VeRO ships publicly; IG is internal). Everything runs on the sandbox -filesystem + bash; there is no IG dependency, no dataframe contract, no Python -REPL requirement. - -## What's in it - -``` -.claude/agents/ - insights-generator.md # Orchestrator: dispatches scouts + investigators, synthesizes findings - ig-scout.md # Discovery (breadth): skims a sample, proposes hypotheses - ig-investigator.md # Validation (depth): tests one hypothesis over the whole corpus, saves a cohort -skills/insights/ - SKILL.md # Method + corpus layout + IG-tool→bash cookbook + finding contract - chunk.sh # Byte-windowed trace reader (get_trace_chunk over the FS) - validate.py # Enforces the finding contract — rejects a finding with no saved cohort -``` - -Each `.md` subagent gets its **own context window** — that reproduces IG's core -context-economy property natively: the corpus is mined across many agents, none of -which loads it whole. Harbor already captures subagent (`isSidechain`) -trajectories, so the whole investigation is recorded. - -## How it plugs into a run - -The optimizer works in `/work/agent`; its `evals run` calls write the -trace corpus to `/work/agent/.evals/results/**`. Point the bundle there with -`CORPUS=/work/agent/.evals`. Two seams inject the bundle into the environment: - -1. **Subagents** — Claude Code has no dedicated Harbor seam for `.claude/agents/`. - Seed them via the environment `Dockerfile`/`seed.sh` into the project dir the - optimizer runs in. In a circle-packing-style environment, add to the Dockerfile: - - ```dockerfile - COPY insights-bundle/.claude/agents /opt/insights-agents - COPY insights-bundle/skills /opt/insights-skills - ``` - and in `main/seed.sh`, before `exec sleep infinity`: - ```sh - mkdir -p /work/agent/.claude/agents /work/agent/skills - cp -a /opt/insights-agents/. /work/agent/.claude/agents/ - cp -a /opt/insights-skills/. /work/agent/skills/ - ``` - -2. **Skill** — alternatively (or additionally) deliver the skill through Harbor's - official seam: `task.toml [environment] skills_dir = "skills"`, which Harbor - copies to `$CLAUDE_CONFIG_DIR/skills/`. Seeding it into `/work/agent/skills/` - via the Dockerfile (option 1) is the simpler single-mechanism path and keeps - the skill next to the agents. - -**Run the optimizer as `claude`.** Subagents (`.claude/agents/`), skills, and the -`Task` tool are Claude Code features — inject this into a `harbor run -a claude` -optimizer, not `-a codex`. (Our GAIA shakedown used `-a codex`; to use IG you flip -the optimizer adapter to `claude`. The gateway per-scope allow-list still governs -which model the optimizer may call.) - -## The loop it enables - -``` -optimize round N: - optimizer proposes/edits candidate → `evals run` → traces land in .evals - optimizer (or a between-rounds step) invokes the `insights-generator` subagent: - orchestrator → scouts (discover) → investigators (validate) → findings.json - optimizer reads the top findings → informs the round N+1 edit -``` - -This is the native reproduction of IG's patcher loop: insights don't sit in a -report, they steer the next candidate. - -## Known limitations vs. real IG -- **Lexical, not semantic, search.** `grep`/`rg` replace IG's hybrid - `search_traces`. The omission-search tactic (grep for what a correct agent - *would* have said, then contrast cohorts — see SKILL/ig-scout) recovers much of - the value; add a small embedding helper or MCP if you need true semantic recall. -- **Chunking is the one real primitive.** Long traces are read via `chunk.sh` - byte windows; use `jq` on the whole file when you need JSON structure. -- **Trace format is backend-specific.** `execution-trace.json` is a JSON array of - turns/steps whose inner shape depends on the agent adapter — grep the text; - don't assume a fixed schema. - -See `skills/insights/SKILL.md` for the full method and cookbook. diff --git a/vero/examples/insights-bundle/skills/insights/SKILL.md b/vero/examples/insights-bundle/skills/insights/SKILL.md deleted file mode 100644 index 5812fe1a..00000000 --- a/vero/examples/insights-bundle/skills/insights/SKILL.md +++ /dev/null @@ -1,164 +0,0 @@ ---- -name: insights -description: >- - Corpus-level trace diagnostics for the VeRO optimizer. Mine a corpus of agent - execution traces for failure/success patterns, validate each against the WHOLE - corpus with quantitative evidence, and emit evidence-grounded findings. Use - when asked to analyze why candidates fail, what distinguishes high- from - low-scoring traces, or what patterns recur across an evaluation corpus. ---- - -# Insights: corpus-level trace diagnostics - -This skill reproduces Scale's **Insights Generator** methodology using only the -sandbox filesystem + bash. It finds patterns across a *population* of agent -execution traces (patterns invisible in any single trace) and returns findings -backed by corpus-scale evidence. - -Three rules define the method — everything else is mechanics: - -1. **Compute over all traces; read few.** The corpus is too large for one - context. Use `grep`/`jq`/`awk`/`wc` to compute *over* every trace; `cat`/`sed` - the full text of only a sampled or cohort-relevant handful. -2. **Discovery ≠ validation.** *Scouts* skim a sample and propose many hypotheses - (breadth). *Investigators* test one hypothesis against the *whole* corpus with - quantitative evidence and label it confirmed / refuted / inconclusive (depth). - **No finding ships without corpus-scale backing.** -3. **Every finding is evidence-grounded and has a persisted cohort.** description - + prevalence % + a saved cohort of affected trace IDs + ≥8 verbatim quotes - (each from a distinct trace) + mechanism + suggested action. A finding with no - saved cohort is **rejected** (`validate.py`). - -## The corpus layout - -Under the **Harbor** eval backend (the path VeRO actually uses), each candidate's -evaluation dumps per-case trial records into a read-only `.evals` tree: - -``` -$CORPUS/ # e.g. /work/agent/.evals (or a dir you're pointed at) - results// # one dir per candidate evaluation - evaluation.json # aggregate record (score, error_rate, candidate id) - artifacts/harbor/ - stdout.log stderr.log # backend logs for the whole eval - jobs//__/ # one dir per case = a "trial" - result.json # trial metadata: task_name, trial_name, agent model - trial.log - agent/-trace.jsonl # THE TRACE — JSONL, one event per line - agent/answer.txt # the agent's final answer - verifier/reward.txt # THE LABEL — numeric reward (e.g. 0 or 1) - verifier/test-stdout.txt -``` - -- **The trace** = `agent/*.jsonl` (for GAIA, `gaia-trace.jsonl`). One JSON object - per line. Event shapes seen so far: - - a model turn `{"turn":N,"response_id":..,"output_text":..,"function_calls":[..]}` - (note **`function_calls`** — plural, an array; grepping `.function_call` finds nothing); - - a tool result `{"turn":N,"tool":"run_shell","result":{"return_code":..,"stdout":..,"stderr":..}}`; - - non-shell tool results (`transcribe_audio`, `read_image`, `submit_answer`, …) - whose `result` may have `"return_code":null` and empty stdout — a **silent - tool failure** the agent often doesn't notice; - - a recovery marker `{"recovery":"retry_after_empty_response","turn":N}`. - The filename and inner shape are **task/agent-specific** — don't hard-code - beyond "it's JSONL you grep line by line; `jq -c .` per line for structure". -- **The label** for cohort contrast = `verifier/reward.txt` (a bare number). - Comparative analysis (reward 1 vs 0) is where this method earns its keep. -- **The gold + agent answer** = `verifier/test-stdout.txt`, which for exact-match - tasks contains both `Agent answer: '...'` and `Expected answer: '...'`. This is - the highest-value signal for wrong-answer analysis — grep both lines to see - *what* the agent got wrong, not just that it failed. -- **The unit** = a *trial* (one case run by one candidate). The trial dir name is - `__`; the same `case_id` recurs across candidate evals, so use - the **full trial dir name as the unique `trace_id`** and keep `case_id` + - `eval_digest` as grouping columns. -- Only **full-disclosure** evals expose per-case traces; aggregate-only evals - (e.g. a held-out validation split) have `evaluation.json` but no trial `agent/` - dirs. Mine the full-disclosure evals. - -Build a flat index once so lookups are cheap: - -Drive the index off the trace files themselves — one row per trace, so the -`jobs/` dir (whose name also contains `__`) never sneaks in: - -```bash -# trace_id(trial) case_id eval reward trace_path (one row per trial) -find "$CORPUS/evaluations" -path "*/agent/*.jsonl" | while read -r trace; do - t=$(dirname "$(dirname "$trace")") # trial dir = .../__ - trace_id=$(basename "$t") - case_id=${trace_id%%__*} - eval=$(echo "$t" | sed -E 's#.*/results/([^/]{12})[^/]*/.*#\1#') - reward=$([ -f "$t/verifier/reward.txt" ] && tr -d '[:space:]' < "$t/verifier/reward.txt") - printf '%s\t%s\t%s\t%s\t%s\n' "$trace_id" "$case_id" "$eval" "${reward:-NA}" "$trace" -done > index.tsv -wc -l index.tsv # corpus size (trials) -cut -f4 index.tsv | sort | uniq -c # reward distribution (0 vs 1 = your cohorts) -``` - -A trial can lack `verifier/reward.txt` (errored/uncsored case) — those rows get -`reward=NA` and simply drop out of the reward-1-vs-0 cohorts. - -**Cross-trial contrast (the population-level lever).** Because the same `case_id` -is run by several candidates, grouping by `case_id` reveals patterns invisible in -any single trace — e.g. *deterministic* failures (a case that fails with the -**identical** wrong answer across every candidate → a grounding/knowledge gap, -not noise) vs *stochastic* ones (fails sometimes). This is precisely the kind of -finding this method exists to surface: - -```bash -# cases that NEVER pass (deterministic failure) vs sometimes-pass (stochastic) -awk -F'\t' '$4!="NA"{tot[$2]++; if($4==1)pass[$2]++} - END{for(c in tot) printf "%s\t%d/%d pass\n", c, pass[c]+0, tot[c]}' index.tsv \ - | sort -t/ -k1 -n -# then, for a never-pass case, check whether the wrong answer is identical across -# candidates (grep "Agent answer:" in each trial's verifier/test-stdout.txt). -``` - -## Tool cookbook (IG tool → bash) - -| IG tool | Do this instead | -|---|---| -| `search_traces(query)` | `grep -rlF "" "$CORPUS"` (lexical). For "did the agent NOT do X", grep for the text an agent that DID X would emit, then diff that set against the corpus. | -| `get_trace(id)` | `cat` the trace path from `index.tsv`. If huge, `chunk.sh` it first. | -| (recover the answer) | `grep -E "Agent answer:\|Expected answer:" /verifier/test-stdout.txt` — what the agent said vs the gold answer, for exact-match tasks. | -| `get_trace_chunk(id, k)` | `bash chunk.sh ` (byte-window slice; `chunk.sh ` with no k lists chunk count). | -| `extract(taxonomy)` → `features.jsonl` | a small loop that computes one feature per trace and appends `{"trace_id":..,"":..}` to `features.jsonl` (persist + reuse; grep for a regex signal, or a tiny LLM call per trace only when judgment is needed). | -| `compare_segments(A,B)` | `jq`/`awk` group-by over `features.jsonl` joined to `index.tsv` labels — e.g. rate of a feature in pass vs fail cohorts. | -| `save_affected_traces(ids)` | write the **defensible** cohort to `cohorts/.txt` (one trace_id per line). | -| `submit_finding(...)` | append a finding object to `findings.json` (schema below), then run `python validate.py findings.json index.tsv` — it **rejects** findings lacking a cohort or citations. | - -Prevalence = `|cohort| / |corpus|`. If that ratio exceeds ~0.25, either say so -explicitly in `prevalence` or your cohort is actually the analyzed pool — narrow it. - -## The finding contract (findings.json) - -`findings.json` is `{"executive_summary": str, "findings": [], "methodology": str, "traces_analyzed": int}`. -Each ``: - -```json -{ - "hypothesis_name": "Short title", - "status": "confirmed|refuted|inconclusive", - "summary": "Precise mechanism, not a vague restatement", - "prevalence": "X of Y traces (Z%)", - "cohort_file": "cohorts/.txt", - "evidence": [ - {"trace_id": "", "quote": "", "explanation": "2-3 sentences naming the specific mechanism in THIS trace (exception type / tool / turn index) and linking it to the prevalence claim"} - ], - "stats": {"metric": 0.85}, - "segments_compared": ["pass", "fail"], - "additional_observations": ["p-values / odds ratios / notes"], - "suggested_action": "One concrete change (exact file/prompt/config) or null" -} -``` - -**Evidence discipline (enforced by `validate.py`):** -- A finding MUST reference a non-empty `cohort_file` whose trace_ids exist in the corpus. -- ≥8 evidence items (aim 10-15 for high-prevalence findings), each from a - **distinct** trace_id, each with a **verbatim** `quote` (tool-call args, - exception messages, raw turn content, literal error strings — never paraphrase). -- `explanation` names the concrete mechanism in that trace; don't tag a quote as - "representative of N traces". -- Include a counterexample or two (pattern absent + task succeeded), marked as such. - -## Self-check before submitting a finding -Pick 2-3 random trace_ids from the cohort, `cat`/`chunk.sh` them, and confirm each -clearly exhibits the pattern. If one doesn't, narrow the cohort and re-save. diff --git a/vero/examples/insights-bundle/skills/insights/chunk.sh b/vero/examples/insights-bundle/skills/insights/chunk.sh deleted file mode 100755 index 66d6cc44..00000000 --- a/vero/examples/insights-bundle/skills/insights/chunk.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env bash -# chunk.sh — read a large trace in byte-windowed chunks so it never has to be -# loaded into context whole. Reproduces IG's get_trace_chunk over the filesystem. -# -# chunk.sh -> print chunk count + total size (no content) -# chunk.sh -> print chunk k (0-indexed) -# chunk.sh -> use a custom chunk size (bytes; default 20000) -# -# Chunks are cut on byte boundaries, so a JSON token may straddle two chunks — -# that's fine for scanning/grepping; use `jq` on the whole file when you need -# structure. Prefer grepping the file directly first; only chunk to *read* a -# specific region of a trace too big to cat. -set -euo pipefail - -file=${1:?usage: chunk.sh [chunk_index] [chunk_size_bytes]} -size=${3:-20000} - -if [[ ! -f "$file" ]]; then - echo "chunk.sh: no such file: $file" >&2 - exit 1 -fi - -bytes=$(wc -c <"$file" | tr -d ' ') -# ceil division -count=$(( (bytes + size - 1) / size )) - -if [[ $# -lt 2 ]]; then - echo "file: $file" - echo "bytes: $bytes" - echo "chunks: $count (chunk size ${size} bytes; index 0..$((count > 0 ? count - 1 : 0)))" - exit 0 -fi - -k=$2 -if (( k < 0 || k >= count )); then - echo "chunk.sh: chunk index $k out of range 0..$((count - 1))" >&2 - exit 1 -fi - -# dd reads exactly one window; skip in whole blocks of `size`. -dd if="$file" bs="$size" skip="$k" count=1 2>/dev/null diff --git a/vero/examples/insights-bundle/skills/insights/validate.py b/vero/examples/insights-bundle/skills/insights/validate.py deleted file mode 100755 index bfa0d863..00000000 --- a/vero/examples/insights-bundle/skills/insights/validate.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -"""validate.py — enforce the insights finding contract. - -Reproduces the gate IG's `submit_finding` applies: a finding is rejected unless a -non-empty cohort of affected trace_ids was persisted, those ids exist in the -corpus, and the evidence is real (verbatim quotes from distinct traces). Run it -before a finding is considered submitted: - - python validate.py findings.json index.tsv - -index.tsv is the corpus index built per SKILL.md: tab-separated, one row per -trial, columns ``trace_id case_id eval reward trace_path``. -Only column 1 (trace_id) and column 5 (trace_path, used for the best-effort -verbatim-quote check) are read. Exit code is non-zero if any finding fails, and -every problem is printed so the agent can fix and re-run. -""" -from __future__ import annotations - -import json -import re -import sys -from pathlib import Path - -MIN_EVIDENCE = 8 -VALID_STATUS = {"confirmed", "refuted", "inconclusive"} -COHORT_CORPUS_RATIO_WARN = 0.25 - - -def load_corpus_index(index_path: Path) -> dict[str, str]: - """Return {trace_id: trace_path} from the corpus index (col1 -> col5).""" - index: dict[str, str] = {} - for line in index_path.read_text().splitlines(): - if not line.strip(): - continue - cols = line.split("\t") - trace_id = cols[0].strip() - path = cols[4].strip() if len(cols) >= 5 else "" - index[trace_id] = path - return index - - -_TRIAL_TEXT_CACHE: dict[str, str] = {} - - -def _canon(text: str) -> str: - """Lowercase, alphanumeric-only. Collapses JSON escaping/spacing/punctuation - differences (``{"answer":"X"}`` vs ``\\"answer\\": \\"X\\"``) so a genuine - excerpt matches while paraphrase/fabrication (different words) does not.""" - return re.sub(r"[^a-z0-9]+", "", text.lower()) - - -def _quote_is_verbatim(quote: str, trace_path: str) -> bool | None: - """True/False if the quote appears in the trial; None if unknown. - - Best-effort. A legitimate excerpt may come from the trace JSONL, the agent's - ``answer.txt``, or the verifier's ``test-stdout.txt`` — and a tool-call - argument is stored JSON-escaped — so the whole trial directory is searched - and both sides are canonicalized to alphanumerics before comparing. Returns - None when nothing can be read (caller neither warns nor endorses). - """ - if not trace_path: - return None - tp = Path(trace_path) - # Guard: a bogus/short path whose .parent.parent is a high-level dir (e.g. "/") - # would make rglob walk the whole filesystem. Only proceed for a real trace. - if not tp.is_file(): - return None - trial_dir = tp.parent.parent # trial dir = parent of the agent/ dir - if not trial_dir.is_dir() or trial_dir == trial_dir.parent: - return None - key = str(trial_dir) - text = _TRIAL_TEXT_CACHE.get(key) - if text is None: - parts: list[str] = [] - try: - files = [p for p in trial_dir.rglob("*") if p.is_file()] - except OSError: - files = [] - for p in files: - try: - if p.stat().st_size > 5_000_000: - continue - parts.append(p.read_text(encoding="utf-8", errors="replace")) - except OSError: - continue - text = _canon(" ".join(parts)) - _TRIAL_TEXT_CACHE[key] = text - if not text: - return None - needle = _canon(quote) - return bool(needle) and needle in text - - -def validate_finding( - idx: int, f: dict, corpus_index: dict[str, str], base: Path -) -> tuple[list[str], list[str]]: - """Return (errors, warnings) for one finding.""" - corpus_ids = corpus_index.keys() - errs: list[str] = [] - warns: list[str] = [] - name = f.get("hypothesis_name") or f"#{idx}" - - # --- required scalar fields --- - for field in ("hypothesis_name", "status", "summary", "prevalence"): - if not f.get(field): - errs.append(f"[{name}] missing required field: {field}") - status = f.get("status") - if status and status not in VALID_STATUS: - errs.append(f"[{name}] status '{status}' not in {sorted(VALID_STATUS)}") - - # --- cohort: the IG "no saved cohort -> reject" rule --- - cohort_file = f.get("cohort_file") - cohort_ids: list[str] = [] - if not cohort_file: - errs.append( - f"[{name}] no cohort_file: you must persist the affected trace_ids " - f"(cohorts/.txt) before submitting — a finding without a cohort " - f"is rejected" - ) - else: - cpath = (base / cohort_file).resolve() - if not cpath.exists(): - errs.append(f"[{name}] cohort_file does not exist: {cohort_file}") - else: - cohort_ids = [ - ln.strip() for ln in cpath.read_text().splitlines() if ln.strip() - ] - if not cohort_ids: - errs.append(f"[{name}] cohort_file is empty: {cohort_file}") - unknown = [t for t in cohort_ids if t not in corpus_ids] - if unknown: - errs.append( - f"[{name}] cohort has {len(unknown)} trace_id(s) not in the " - f"corpus (e.g. {unknown[:3]}) — cohort ids must be real" - ) - if corpus_ids and len(cohort_ids) / len(corpus_ids) > COHORT_CORPUS_RATIO_WARN: - warns.append( - f"[{name}] cohort is {len(cohort_ids)}/{len(corpus_ids)} " - f"(>{COHORT_CORPUS_RATIO_WARN:.0%}) of the corpus — confirm this " - f"is a real population-level pattern, not the analyzed pool" - ) - - # --- evidence: verbatim quotes from distinct traces --- - evidence = f.get("evidence") or [] - if len(evidence) < MIN_EVIDENCE: - errs.append( - f"[{name}] {len(evidence)} evidence item(s); need >= {MIN_EVIDENCE}" - ) - ev_trace_ids: list[str] = [] - for j, ev in enumerate(evidence): - loc = f"[{name}] evidence[{j}]" - tid = ev.get("trace_id") - quote = (ev.get("quote") or "").strip() - if not tid: - errs.append(f"{loc}: missing trace_id") - else: - ev_trace_ids.append(tid) - if corpus_ids and tid not in corpus_ids: - errs.append(f"{loc}: trace_id '{tid}' not in the corpus") - elif quote: - verbatim = _quote_is_verbatim(quote, corpus_index.get(tid, "")) - if verbatim is False: - warns.append( - f"{loc}: quote not found verbatim in trace '{tid}' — " - f"use a literal excerpt, not a paraphrase" - ) - if not quote: - errs.append(f"{loc}: missing verbatim quote") - if len((ev.get("explanation") or "").strip()) < 40: - errs.append( - f"{loc}: explanation too thin — name the concrete mechanism in " - f"this trace (tool/exception/turn), don't restate the finding" - ) - # Evidence must span >= MIN_EVIDENCE distinct traces (counterexamples, which - # legitimately sit outside the cohort, still count as distinct traces). - distinct = len(set(ev_trace_ids)) - if len(evidence) >= MIN_EVIDENCE and distinct < MIN_EVIDENCE: - errs.append( - f"[{name}] evidence spans only {distinct} distinct trace(s); need " - f">= {MIN_EVIDENCE} — quote from different traces, not the same 2-3" - ) - - return errs, warns - - -def main(argv: list[str]) -> int: - if len(argv) != 3: - print("usage: python validate.py findings.json index.tsv", file=sys.stderr) - return 2 - findings_path = Path(argv[1]) - index_path = Path(argv[2]) - base = findings_path.resolve().parent - - try: - doc = json.loads(findings_path.read_text()) - except (OSError, json.JSONDecodeError) as e: - print(f"cannot read findings.json: {e}", file=sys.stderr) - return 2 - corpus_index = load_corpus_index(index_path) if index_path.exists() else {} - if not corpus_index: - print(f"warning: no corpus ids loaded from {index_path}", file=sys.stderr) - - findings = doc.get("findings") or [] - if not findings: - print("REJECTED: findings.json has no findings", file=sys.stderr) - return 1 - - all_errs: list[str] = [] - all_warns: list[str] = [] - for i, f in enumerate(findings): - e, w = validate_finding(i, f, corpus_index, base) - all_errs += e - all_warns += w - - for w in all_warns: - print(f"WARN {w}") - for e in all_errs: - print(f"ERROR {e}") - - if all_errs: - print(f"\nREJECTED: {len(all_errs)} error(s) across {len(findings)} finding(s).") - return 1 - print( - f"OK: {len(findings)} finding(s) validated" - + (f" ({len(all_warns)} warning(s))" if all_warns else "") - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main(sys.argv)) diff --git a/vero/src/vero/config.py b/vero/src/vero/config.py index 28121870..75462722 100644 --- a/vero/src/vero/config.py +++ b/vero/src/vero/config.py @@ -99,6 +99,8 @@ class EvaluationConfig(EvaluationModel): agent_selection: AgentSelectionMode = AgentSelectionMode.ARBITRARY disclosure: DisclosureLevel = DisclosureLevel.FULL expose_case_resources: bool = False + # Omitted resolves to 5 under aggregate disclosure, 1 otherwise. + min_aggregate_cases: int | None = Field(default=None, ge=1) agent_budget: BudgetConfig | None = None system_budget: BudgetConfig | None = None @@ -137,6 +139,7 @@ def to_definition(self, backend_id: str) -> EvaluationDefinition: agent_selection=self.agent_selection, disclosure=self.disclosure, expose_case_resources=self.expose_case_resources, + min_aggregate_cases=self.min_aggregate_cases, ), agent_budget=( self.agent_budget.to_model( diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index 239a6455..a800f20c 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -24,6 +24,8 @@ ) from vero.evaluation.models import ( AgentSelectionMode, + AllCases, + DisclosureLevel, EvaluationAcknowledgement, EvaluationAuthorization, EvaluationPlan, @@ -104,6 +106,7 @@ def resolve( meter_budget=definition.agent_budget is not None, disclosure=access.disclosure, expose_case_resources=access.expose_case_resources, + min_aggregate_cases=access.min_aggregate_cases or 1, reason=( None if access.agent_can_evaluate @@ -319,6 +322,24 @@ async def _execute_record( cost = await backend.resolve_cost(request.evaluation_set) except ValueError as error: raise EvaluationRequestError(str(error)) from error + # k-anonymity floor: an aggregate-disclosed subset must cover enough + # cases that a hidden per-case result can't be read off one at a time. + # The complete set is exempt — its aggregate is the intended disclosure. + if ( + decision.disclosure == DisclosureLevel.AGGREGATE + and decision.min_aggregate_cases > 1 + and not isinstance(request.evaluation_set.selection, AllCases) + ): + if cost.cases is None: + raise EvaluationDeniedError( + "aggregate subset evaluation requires a backend with " + "exact case costs" + ) + if cost.cases < decision.min_aggregate_cases: + raise EvaluationDeniedError( + f"aggregate subset evaluations must cover at least " + f"{decision.min_aggregate_cases} cases; requested {cost.cases}" + ) charged = decision.meter_budget and self.budget_ledger is not None if charged: await self.budget_ledger.reserve( diff --git a/vero/src/vero/evaluation/models.py b/vero/src/vero/evaluation/models.py index 3ceafab6..3583dbb4 100644 --- a/vero/src/vero/evaluation/models.py +++ b/vero/src/vero/evaluation/models.py @@ -633,6 +633,9 @@ class EvaluationAuthorization(EvaluationModel): meter_budget: bool = True disclosure: DisclosureLevel = DisclosureLevel.FULL expose_case_resources: bool = False + # Enforced by the engine for agent-chosen aggregate subsets; 1 = no floor + # (trusted principals). + min_aggregate_cases: int = Field(default=1, ge=1) reason: str | None = None @property @@ -689,6 +692,11 @@ class EvaluationAccessPolicy(EvaluationModel): agent_selection: AgentSelectionMode = AgentSelectionMode.ARBITRARY disclosure: DisclosureLevel = DisclosureLevel.FULL expose_case_resources: bool = False + # k-anonymity floor: agent-chosen aggregate subsets must cover at least + # this many cases, so a hidden per-case result can't be read off one case + # at a time. Omitted resolves to 5 under AGGREGATE disclosure (safe rather + # than unfloored) and 1 otherwise; set explicitly to override. + min_aggregate_cases: int | None = Field(default=None, ge=1) @model_validator(mode="after") def validate_visibility(self) -> EvaluationAccessPolicy: @@ -696,6 +704,9 @@ def validate_visibility(self) -> EvaluationAccessPolicy: raise ValueError("agent-evaluable evaluations must be agent-visible") if self.expose_case_resources and not self.agent_visible: raise ValueError("agent-invisible evaluations cannot expose case resources") + if self.min_aggregate_cases is None: + floor = 5 if self.disclosure == DisclosureLevel.AGGREGATE else 1 + object.__setattr__(self, "min_aggregate_cases", floor) return self diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 35998740..3738d59b 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -292,10 +292,7 @@ def _deployment_config( "evaluation_set_name": config.evaluation_set_name, "partition": access.partition, "objective": config.objective.model_dump(mode="json"), - "disclosure": access.disclosure.value, - "expose_case_resources": access.expose_case_resources, - "agent_evaluable": True, - "min_aggregate_cases": access.min_aggregate_cases, + "access": access.to_access_policy().model_dump(mode="json"), "parameters": {}, "allowed_parameters": [], "limits": limits.model_dump(mode="json"), diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 1640bd62..063ee8ee 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -13,6 +13,7 @@ from vero.evaluation import ( DisclosureLevel, + EvaluationAccessPolicy, EvaluationModel, MetricSelector, ObjectiveSpec, @@ -36,6 +37,14 @@ def validate_partition(cls, value: str) -> str: raise ValueError("agent access partition must not be empty") return value + def to_access_policy(self) -> EvaluationAccessPolicy: + """The single typed translation from build spec to runtime policy.""" + return EvaluationAccessPolicy( + disclosure=self.disclosure, + expose_case_resources=self.expose_case_resources, + min_aggregate_cases=self.min_aggregate_cases, + ) + class VerificationTargetSpec(EvaluationModel): partition: str diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index 28c26c7f..a371e6c2 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -17,6 +17,7 @@ AllCases, CaseResourceExporter, DisclosureLevel, + EvaluationAccessPolicy, EvaluationAcknowledgement, EvaluationAuthorization, EvaluationBudget, @@ -69,13 +70,10 @@ class SidecarEvaluationPolicy(EvaluationModel): evaluation_set_name: str partition: str | None = None objective: ObjectiveSpec | None = None - disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE - expose_case_resources: bool = False - agent_evaluable: bool = True - # k-anonymity floor: aggregate subset evals must cover >= this many cases so - # a single held-out label can't be read off one case at a time. Default 5 - # (not 1) so a build that omits it is safe rather than unfloored. - min_aggregate_cases: int = Field(default=5, ge=1) + # The runtime access truth, compiled by AgentAccessSpec.to_access_policy() + # (disclosure, case-resource exposure, and the k-anonymity floor for + # aggregate subsets all live here, not as parallel flat fields). + access: EvaluationAccessPolicy parameters: dict[str, JsonValue] = Field(default_factory=dict) allowed_parameters: list[str] = Field(default_factory=list) limits: EvaluationLimits | None = None @@ -471,7 +469,7 @@ def _policy( ) -> SidecarEvaluationPolicy: key = (backend_id, evaluation_set.name, evaluation_set.partition) policy = self._policies.get(key) - if policy is None or not policy.agent_evaluable: + if policy is None or not policy.access.agent_can_evaluate: raise EvaluationAccessError( "the requested backend and evaluation set are not agent-evaluable" ) @@ -482,7 +480,7 @@ async def _enforce_aggregate_floor( policy: SidecarEvaluationPolicy, evaluation_set: EvaluationSet, ) -> None: - if policy.disclosure != DisclosureLevel.AGGREGATE: + if policy.access.disclosure != DisclosureLevel.AGGREGATE: return if isinstance(evaluation_set.selection, AllCases): return @@ -492,10 +490,10 @@ async def _enforce_aggregate_floor( raise EvaluationAccessError( "aggregate subset evaluation requires a backend with exact case costs" ) - if cost.cases < policy.min_aggregate_cases: + if cost.cases < policy.access.min_aggregate_cases: raise EvaluationAccessError( f"aggregate subset evaluations must cover at least " - f"{policy.min_aggregate_cases} cases; requested {cost.cases}" + f"{policy.access.min_aggregate_cases} cases; requested {cost.cases}" ) def _visible_projections( @@ -513,11 +511,11 @@ def _visible_projections( record.request.evaluation_set.partition, ) ) - if policy is None or not policy.agent_evaluable: + if policy is None or not policy.access.agent_can_evaluate: continue disclosure = narrower_disclosure( entry.maximum_disclosure, - policy.disclosure, + policy.access.disclosure, ) projections.append( (record, disclosure, project_evaluation(record, disclosure)) @@ -578,8 +576,8 @@ async def _write_case_resources(self) -> None: for policy in self._policies.values(): backend = self.engine.backends.resolve(policy.backend_id) if ( - not policy.agent_evaluable - or not policy.expose_case_resources + not policy.access.agent_can_evaluate + or not policy.access.expose_case_resources or not isinstance(backend, CaseResourceExporter) ): continue @@ -708,14 +706,15 @@ async def _evaluate_prepared( authorization=EvaluationAuthorization( may_evaluate=True, meter_budget=True, - disclosure=policy.disclosure, - expose_case_resources=policy.expose_case_resources, + disclosure=policy.access.disclosure, + expose_case_resources=policy.access.expose_case_resources, + min_aggregate_cases=policy.access.min_aggregate_cases or 1, ), ) except (EvaluationExecutionError, EvaluationCancelledError) as error: record = self.engine.database.get_evaluation(error.evaluation_id) if record is not None: - await self._disclosures.remember(record.id, policy.disclosure) + await self._disclosures.remember(record.id, policy.access.disclosure) await asyncio.shield(self._refresh_context()) raise evaluation_id = ( @@ -726,8 +725,8 @@ async def _evaluate_prepared( raise RuntimeError( f"evaluation engine did not index completed evaluation {evaluation_id!r}" ) - maximum = await self._disclosures.remember(record.id, policy.disclosure) - disclosure = narrower_disclosure(maximum, policy.disclosure) + maximum = await self._disclosures.remember(record.id, policy.access.disclosure) + disclosure = narrower_disclosure(maximum, policy.access.disclosure) await self._refresh_context() return SidecarEvaluationResult( disclosure=disclosure, @@ -750,7 +749,7 @@ async def submit(self, version: str | None = None) -> Submission: def status(self) -> SidecarStatus: access: list[EvaluationAccessStatus] = [] for policy in self._policies.values(): - if not policy.agent_evaluable: + if not policy.access.agent_can_evaluate: continue evaluation_set = EvaluationSet( name=policy.evaluation_set_name, @@ -766,9 +765,9 @@ def status(self) -> SidecarStatus: backend_id=policy.backend_id, evaluation_set_name=policy.evaluation_set_name, partition=policy.partition, - disclosure=policy.disclosure, - expose_case_resources=policy.expose_case_resources, - min_aggregate_cases=policy.min_aggregate_cases, + disclosure=policy.access.disclosure, + expose_case_resources=policy.access.expose_case_resources, + min_aggregate_cases=policy.access.min_aggregate_cases, allowed_parameters=list(policy.allowed_parameters), limits=policy.limits, # Budget is enforced regardless; disclosure is what we gate. diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index d3ecbd9e..cd7fbda6 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -13,6 +13,7 @@ from vero.candidate import Candidate from vero.evaluation import ( AgentSelectionMode, + AllCases, BackendProvenance, BackendRegistry, BudgetLedger, @@ -902,3 +903,72 @@ async def test_engine_selects_backend_persists_and_projects(tmp_path: Path): assert summary.metrics == {"value": 2.0} assert len(database.evaluations) == 1 assert database_path.exists() +@pytest.mark.asyncio +async def test_engine_enforces_aggregate_k_anonymity_floor(tmp_path: Path): + """The floor is a core-engine property, not a sidecar courtesy.""" + canonical = EvaluationSet(name="validation", selection=CaseRange(stop=10)) + plan = EvaluationPlan( + evaluations=[ + EvaluationDefinition( + evaluation_set=canonical, + # min_aggregate_cases omitted: resolves to the safe floor (5). + access=EvaluationAccessPolicy(disclosure=DisclosureLevel.AGGREGATE), + ) + ], + selection_evaluation="validation", + ) + + def engine_with(cost: EvaluationCost) -> EvaluationEngine: + return EvaluationEngine( + evaluator=evaluator(tmp_path, StubWorkspace(tmp_path / "repo")), + backends=BackendRegistry({"default": StubBackend(cost=cost)}), + database=EvaluationDatabase(id="session"), + authorization_resolver=authorize_evaluation_plan(plan), + ) + + def request_with(selection) -> EvaluationRequest: + return request().model_copy( + update={ + "evaluation_set": canonical.model_copy( + update={"selection": selection} + ) + } + ) + + # 1) An agent-chosen 2-case aggregate subset is refused by the engine. + with pytest.raises(EvaluationDeniedError, match="at least 5 cases"): + await engine_with(EvaluationCost(cases=2)).evaluate( + backend_id="default", + request=request_with(CaseRange(stop=2)), + principal=EvaluationPrincipal.AGENT, + ) + + # 2) A subset without exact case costs cannot be floored: refused. + with pytest.raises(EvaluationDeniedError, match="exact case costs"): + await engine_with(EvaluationCost(cases=None)).evaluate( + backend_id="default", + request=request_with(CaseRange(stop=2)), + principal=EvaluationPrincipal.AGENT, + ) + + # 3) A subset at the floor passes. + await engine_with(EvaluationCost(cases=5)).evaluate( + backend_id="default", + request=request_with(CaseRange(stop=5)), + principal=EvaluationPrincipal.AGENT, + ) + + # 4) The complete selection is exempt: its aggregate is the intended + # disclosure, however small the set. + await engine_with(EvaluationCost(cases=2)).evaluate( + backend_id="default", + request=request_with(AllCases()), + principal=EvaluationPrincipal.AGENT, + ) + + # 5) Trusted principals are never floored. + await engine_with(EvaluationCost(cases=2)).evaluate( + backend_id="default", + request=request_with(CaseRange(stop=2)), + principal=EvaluationPrincipal.ADMIN, + ) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 0d885f20..34ed0e61 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -467,8 +467,10 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") ) assert set(serve["backends"]) == {"harbor-validation", "harbor-test"} - assert serve["access_policies"][0]["disclosure"] == "aggregate" - assert serve["access_policies"][0]["expose_case_resources"] is True + access = serve["access_policies"][0]["access"] + assert access["disclosure"] == "aggregate" + assert access["expose_case_resources"] is True + assert access["min_aggregate_cases"] == 5 # safe floor survives compilation assert serve["budgets"][0]["total_runs"] == 5 assert serve["selection"]["backend_id"] == "harbor-validation" assert serve["targets"][0]["backend_id"] == "harbor-test" diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py index 78f59c82..0ea84b7b 100644 --- a/vero/tests/test_v05_harbor_deployment.py +++ b/vero/tests/test_v05_harbor_deployment.py @@ -9,6 +9,7 @@ from vero.evaluation import ( DisclosureLevel, + EvaluationAccessPolicy, EvaluationBudget, EvaluationSet, MetricSelector, @@ -88,7 +89,9 @@ async def test_standard_deployment_factory_builds_one_canonical_runtime(tmp_path evaluation_set_name="benchmark", partition="validation", objective=objective, - disclosure=DisclosureLevel.AGGREGATE, + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.AGGREGATE + ), ).model_dump(mode="json") ], "budgets": [budget.model_dump(mode="json")], diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index e94034fb..2388c93e 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -20,6 +20,7 @@ CaseResult, CaseStatus, DisclosureLevel, + EvaluationAccessPolicy, EvaluationAuthorization, EvaluationBudget, EvaluationCost, @@ -208,8 +209,10 @@ def _sidecar(tmp_path: Path, *, submit_enabled=False, fixed_limits=False, disclo backend_id="primary", evaluation_set_name="benchmark", partition="validation", - disclosure=DisclosureLevel.AGGREGATE, - min_aggregate_cases=5, + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.AGGREGATE, + min_aggregate_cases=5, + ), objective=ObjectiveSpec( selector=MetricSelector(metric="score"), direction="maximize", @@ -219,7 +222,7 @@ def _sidecar(tmp_path: Path, *, submit_enabled=False, fixed_limits=False, disclo SidecarEvaluationPolicy( backend_id="secondary", evaluation_set_name="public", - disclosure=DisclosureLevel.FULL, + access=EvaluationAccessPolicy(disclosure=DisclosureLevel.FULL), ), ], agent_volume=tmp_path / "agent-volume", @@ -700,10 +703,9 @@ async def run_as_different_owner(command, cwd=None, timeout=30, env=None): ) -def test_sidecar_policy_defaults_to_safe_k_anonymity_floor(): - from vero.harbor.sidecar import SidecarEvaluationPolicy - - policy = SidecarEvaluationPolicy( - backend_id="b", evaluation_set_name="s" - ) - assert policy.min_aggregate_cases == 5 +def test_access_policy_defaults_to_safe_k_anonymity_floor(): + # The floor now lives on the core policy: omitted resolves to 5 under + # aggregate disclosure and 1 otherwise. + aggregate = EvaluationAccessPolicy(disclosure=DisclosureLevel.AGGREGATE) + assert aggregate.min_aggregate_cases == 5 + assert EvaluationAccessPolicy().min_aggregate_cases == 1 From 8d20950caf0e113592334cc65e00ffa3af1bf582 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 20:48:10 -0700 Subject: [PATCH 18/72] Sidecar context: write task resources under tasks/ and emit plan.json The sidecar's context writer predates the .evals rename and had its own literals: exposed task resources landed in .evals/cases/ (README, skill, and instruction docs all say tasks/) and plan.json was never written in the harbor topology, breaking the documented 'evals plan' first step. Caught live in the first gaia run of the new stack. --- vero/src/vero/harbor/sidecar.py | 48 +++++++++++++++++++++++++-- vero/tests/test_v05_harbor_sidecar.py | 41 +++++++++++++++++++++++ 2 files changed, 86 insertions(+), 3 deletions(-) diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index a371e6c2..bf03b4c5 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -567,7 +567,7 @@ async def _write_candidate_index( async def _write_case_resources(self) -> None: assert self._context_directory is not None - root = self._context_directory.path("cases") + root = self._context_directory.path("tasks") sandbox = self._context_directory.sandbox if await sandbox.exists(root): await sandbox.remove(root, recursive=True) @@ -586,7 +586,7 @@ async def _write_case_resources(self) -> None: partition=policy.partition, ) digest = context_digest(evaluation_set.budget_key(policy.backend_id)) - resource_root = self._context_directory.path("cases", digest) + resource_root = self._context_directory.path("tasks", digest) await sandbox.mkdir(resource_root) await self._context_directory.write_json( posixpath.join(resource_root, "manifest.json"), @@ -612,10 +612,50 @@ async def _write_case_resources(self) -> None: } ) await self._context_directory.write_json( - self._context_directory.path("cases", "index.json"), + self._context_directory.path("tasks", "index.json"), {"schema_version": 1, "case_resources": index}, ) + async def _write_evaluation_plan(self) -> None: + # Same document WorkspaceContextManager writes; `evals plan` reads it. + assert self._context_directory is not None + ledger = self.engine.budget_ledger + evaluations = [] + for policy in self._policies.values(): + access = policy.access + if not access.agent_visible and not access.agent_can_evaluate: + continue + evaluation_set = EvaluationSet( + name=policy.evaluation_set_name, + partition=policy.partition, + ) + budget = ( + ledger.get(policy.backend_id, evaluation_set) + if ledger is not None + else None + ) + evaluations.append( + { + "name": evaluation_set.name, + "partition": evaluation_set.partition, + "base_selection": evaluation_set.selection.model_dump(mode="json"), + "agent_can_evaluate": access.agent_can_evaluate, + "agent_selection": access.agent_selection.value, + "disclosure": access.disclosure.value, + "expose_case_resources": access.expose_case_resources, + # Budget is enforced regardless; disclosure is what we gate. + "budget": ( + budget.model_dump(mode="json") + if budget is not None and self.disclose_budget + else None + ), + } + ) + await self._context_directory.write_json( + self._context_directory.path("plan.json"), + {"schema_version": 1, "evaluations": evaluations}, + ) + async def initialize_context(self) -> None: if self._context_directory is None: return @@ -630,6 +670,7 @@ async def initialize_context(self) -> None: projections = self._visible_projections() await self._write_candidate_index(projections) await self._context_directory.write_evaluations(projections) + await self._write_evaluation_plan() await self._write_case_resources() self._context_initialized = True @@ -643,6 +684,7 @@ async def _refresh_context(self) -> None: projections = self._visible_projections() await self._write_candidate_index(projections) await self._context_directory.write_evaluations(projections) + await self._write_evaluation_plan() async def evaluate( self, diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index 2388c93e..fcec7e45 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -506,6 +506,47 @@ async def test_sidecar_context_survives_restart_without_disclosing_admin_runs( assert admin.id not in json.dumps(index) +@pytest.mark.asyncio +async def test_sidecar_context_writes_plan_and_tasks(tmp_path): + sidecar, _, _ = _sidecar(tmp_path) + await sidecar.initialize_context() + + volume = tmp_path / "agent-volume" + # Case resources live under tasks/ (the documented name), not cases/. + tasks_index = json.loads((volume / "tasks/index.json").read_text()) + assert tasks_index["case_resources"] == [] + assert not (volume / "cases").exists() + + plan = json.loads((volume / "plan.json").read_text()) + by_name = {entry["name"]: entry for entry in plan["evaluations"]} + assert set(by_name) == {"benchmark", "public"} + assert by_name["benchmark"]["disclosure"] == "aggregate" + assert by_name["benchmark"]["budget"]["remaining_runs"] == 3 + assert by_name["benchmark"]["budget"]["remaining_cases"] == 20 + + # Each evaluation refreshes the plan with the depleted budget. + await sidecar.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ), + ) + ) + plan = json.loads((volume / "plan.json").read_text()) + by_name = {entry["name"]: entry for entry in plan["evaluations"]} + assert by_name["benchmark"]["budget"]["remaining_runs"] == 2 + assert by_name["benchmark"]["budget"]["remaining_cases"] == 15 + + # Budget-blind mode withholds the budget column, nothing else. + blind, _, _ = _sidecar(tmp_path, disclose_budget=False) + await blind.initialize_context() + plan = json.loads((volume / "plan.json").read_text()) + assert all(entry["budget"] is None for entry in plan["evaluations"]) + + @pytest.mark.asyncio async def test_sidecar_fails_closed_before_transfer_or_budget(tmp_path): sidecar, transport, ledger = _sidecar(tmp_path) From 0af70e14349e8e35cefcf6833d4d95a025e2d401 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 21:09:40 -0700 Subject: [PATCH 19/72] Share one context-tree writer between the workspace and sidecar paths AgentContextDirectory now owns write_case_resources and write_evaluation_plan; both WorkspaceContextManager and the sidecar feed it ContextPlanEntry rows (policy, evaluation set, resolved budget), so the tree layout can no longer drift between topologies. Top-level names (results/, tasks/, candidates/, plan.json) are module constants. Behavior note: case-resource exposure now keys on expose_case_resources alone (which model validation already ties to agent_visible); the sidecar previously also required agent_can_evaluate. --- vero/src/vero/harbor/sidecar.py | 107 +++----------- vero/src/vero/runtime/context.py | 230 +++++++++++++++++++------------ 2 files changed, 161 insertions(+), 176 deletions(-) diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index bf03b4c5..3bdc6a80 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -15,7 +15,6 @@ from vero.candidate import Candidate from vero.evaluation import ( AllCases, - CaseResourceExporter, DisclosureLevel, EvaluationAccessPolicy, EvaluationAcknowledgement, @@ -42,8 +41,10 @@ from vero.evaluation.persistence import _atomic_write_json from vero.harbor.transport import CandidateTransferError, CandidateTransport from vero.runtime.context import ( + CANDIDATES_SUBDIRECTORY, AgentContextDirectory, AgentDisclosureLedger, + ContextPlanEntry, context_digest, make_evaluation_receipt, narrower_disclosure, @@ -561,100 +562,33 @@ async def _write_candidate_index( } ) await self._context_directory.write_json( - self._context_directory.path("candidates", "index.json"), + self._context_directory.path(CANDIDATES_SUBDIRECTORY, "index.json"), {"schema_version": 1, "candidates": index}, ) - async def _write_case_resources(self) -> None: - assert self._context_directory is not None - root = self._context_directory.path("tasks") - sandbox = self._context_directory.sandbox - if await sandbox.exists(root): - await sandbox.remove(root, recursive=True) - await sandbox.mkdir(root) - index = [] - for policy in self._policies.values(): - backend = self.engine.backends.resolve(policy.backend_id) - if ( - not policy.access.agent_can_evaluate - or not policy.access.expose_case_resources - or not isinstance(backend, CaseResourceExporter) - ): - continue - evaluation_set = EvaluationSet( - name=policy.evaluation_set_name, - partition=policy.partition, - ) - digest = context_digest(evaluation_set.budget_key(policy.backend_id)) - resource_root = self._context_directory.path("tasks", digest) - await sandbox.mkdir(resource_root) - await self._context_directory.write_json( - posixpath.join(resource_root, "manifest.json"), - { - "schema_version": 1, - "backend_id": policy.backend_id, - "evaluation_set": evaluation_set.model_dump(mode="json"), - "resources_path": "resources", - }, - ) - resources = posixpath.join(resource_root, "resources") - await sandbox.mkdir(resources) - await backend.export_case_resources( - evaluation_set=evaluation_set, - destination=resources, - sandbox=sandbox, - ) - index.append( - { - "backend_id": policy.backend_id, - "evaluation_set": evaluation_set.model_dump(mode="json"), - "path": digest, - } - ) - await self._context_directory.write_json( - self._context_directory.path("tasks", "index.json"), - {"schema_version": 1, "case_resources": index}, - ) - - async def _write_evaluation_plan(self) -> None: - # Same document WorkspaceContextManager writes; `evals plan` reads it. - assert self._context_directory is not None + def _context_entries(self) -> list[ContextPlanEntry]: ledger = self.engine.budget_ledger - evaluations = [] + entries = [] for policy in self._policies.values(): - access = policy.access - if not access.agent_visible and not access.agent_can_evaluate: - continue evaluation_set = EvaluationSet( name=policy.evaluation_set_name, partition=policy.partition, ) - budget = ( - ledger.get(policy.backend_id, evaluation_set) - if ledger is not None - else None - ) - evaluations.append( - { - "name": evaluation_set.name, - "partition": evaluation_set.partition, - "base_selection": evaluation_set.selection.model_dump(mode="json"), - "agent_can_evaluate": access.agent_can_evaluate, - "agent_selection": access.agent_selection.value, - "disclosure": access.disclosure.value, - "expose_case_resources": access.expose_case_resources, + entries.append( + ContextPlanEntry( + backend_id=policy.backend_id, + backend=self.engine.backends.resolve(policy.backend_id), + evaluation_set=evaluation_set, + access=policy.access, # Budget is enforced regardless; disclosure is what we gate. - "budget": ( - budget.model_dump(mode="json") - if budget is not None and self.disclose_budget + budget=( + ledger.get(policy.backend_id, evaluation_set) + if ledger is not None and self.disclose_budget else None ), - } + ) ) - await self._context_directory.write_json( - self._context_directory.path("plan.json"), - {"schema_version": 1, "evaluations": evaluations}, - ) + return entries async def initialize_context(self) -> None: if self._context_directory is None: @@ -668,10 +602,11 @@ async def initialize_context(self) -> None: parent_candidate_id=None, ) projections = self._visible_projections() + entries = self._context_entries() await self._write_candidate_index(projections) await self._context_directory.write_evaluations(projections) - await self._write_evaluation_plan() - await self._write_case_resources() + await self._context_directory.write_evaluation_plan(entries) + await self._context_directory.write_case_resources(entries) self._context_initialized = True async def _refresh_context(self) -> None: @@ -684,7 +619,9 @@ async def _refresh_context(self) -> None: projections = self._visible_projections() await self._write_candidate_index(projections) await self._context_directory.write_evaluations(projections) - await self._write_evaluation_plan() + await self._context_directory.write_evaluation_plan( + self._context_entries() + ) async def evaluate( self, diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index f52fdc2f..3d3b586e 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -6,6 +6,7 @@ import hashlib import json import posixpath +from dataclasses import dataclass from pathlib import Path, PurePosixPath from typing import Literal, Sequence @@ -16,13 +17,16 @@ from vero.evaluation import ( CaseResourceExporter, DisclosureLevel, + EvaluationAccessPolicy, EvaluationAcknowledgement, + EvaluationBudget, EvaluationEngine, EvaluationModel, EvaluationPlan, EvaluationPrincipal, EvaluationReceipt, EvaluationRecord, + EvaluationSet, EvaluationSummary, project_evaluation, ) @@ -31,6 +35,12 @@ from vero.workspace import Workspace AGENT_CONTEXT_DIRECTORY = ".evals" +# Top-level names inside the context tree. The `evals` CLI, the skill, and the +# harbor instruction templates all address these paths by name. +RESULTS_SUBDIRECTORY = "results" +TASKS_SUBDIRECTORY = "tasks" +CANDIDATES_SUBDIRECTORY = "candidates" +PLAN_FILENAME = "plan.json" _DISCLOSURE_RANK = { DisclosureLevel.NONE: 0, DisclosureLevel.AGGREGATE: 1, @@ -46,11 +56,26 @@ def context_digest(value: str) -> str: def evaluation_result_path(evaluation_id: str) -> str: return ( - f"{AGENT_CONTEXT_DIRECTORY}/results/" + f"{AGENT_CONTEXT_DIRECTORY}/{RESULTS_SUBDIRECTORY}/" f"{context_digest(evaluation_id)}/evaluation.json" ) +@dataclass(frozen=True) +class ContextPlanEntry: + """One evaluation set as the agent may see it, resolved by the caller. + + Callers own policy filtering nuances that differ per topology (which sets + exist, budget lookup, budget disclosure); the directory owns rendering. + """ + + backend_id: str + backend: object + evaluation_set: EvaluationSet + access: EvaluationAccessPolicy + budget: EvaluationBudget | None = None + + def narrower_disclosure( left: DisclosureLevel, right: DisclosureLevel, @@ -231,7 +256,7 @@ async def write_evaluations( ] ], ) -> None: - root = self.path("results") + root = self.path(RESULTS_SUBDIRECTORY) if await self.sandbox.exists(root): await self.sandbox.remove(root, recursive=True) await self.sandbox.mkdir(root) @@ -242,7 +267,7 @@ async def write_evaluations( ): digest = context_digest(record.id) relative_path = f"{digest}/evaluation.json" - evaluation_root = self.path("results", digest) + evaluation_root = self.path(RESULTS_SUBDIRECTORY, digest) await self.sandbox.mkdir(evaluation_root) missing_artifacts: list[str] = [] if isinstance(projection, EvaluationRecord): @@ -255,7 +280,9 @@ async def write_evaluations( strict=True, ): case_digest = context_digest(case_model.case_id) - case_root = self.path("results", digest, "cases", case_digest) + case_root = self.path( + RESULTS_SUBDIRECTORY, digest, "cases", case_digest + ) await self.sandbox.mkdir(case_root) execution_trace = case_payload.pop("execution_trace", None) evaluation_trace = case_payload.pop("evaluation_trace", None) @@ -312,7 +339,7 @@ async def write_evaluations( continue await self.sandbox.upload( str(source), - self.path("results", digest, "artifacts", artifact_path), + self.path(RESULTS_SUBDIRECTORY, digest, "artifacts", artifact_path), ) document = { "schema_version": 1, @@ -328,7 +355,7 @@ async def write_evaluations( "result": projection.model_dump(mode="json"), } await self.write_json( - self.path("results", relative_path), + self.path(RESULTS_SUBDIRECTORY, relative_path), document, ) index.append( @@ -343,10 +370,91 @@ async def write_evaluations( } ) await self.write_json( - self.path("results", "index.json"), + self.path(RESULTS_SUBDIRECTORY, "index.json"), {"schema_version": 1, "evaluations": index}, ) + async def write_case_resources( + self, + entries: Sequence[ContextPlanEntry], + ) -> None: + root = self.path(TASKS_SUBDIRECTORY) + if await self.sandbox.exists(root): + await self.sandbox.remove(root, recursive=True) + await self.sandbox.mkdir(root) + index: list[dict[str, object]] = [] + for entry in entries: + # expose_case_resources implies agent_visible (model validation). + if not ( + entry.access.expose_case_resources + and isinstance(entry.backend, CaseResourceExporter) + ): + continue + digest = context_digest( + entry.evaluation_set.budget_key(entry.backend_id) + ) + resource_root = self.path(TASKS_SUBDIRECTORY, digest) + await self.sandbox.mkdir(resource_root) + await self.write_json( + posixpath.join(resource_root, "manifest.json"), + { + "schema_version": 1, + "backend_id": entry.backend_id, + "evaluation_set": entry.evaluation_set.model_dump(mode="json"), + "resources_path": "resources", + }, + ) + resources = posixpath.join(resource_root, "resources") + await self.sandbox.mkdir(resources) + await entry.backend.export_case_resources( + evaluation_set=entry.evaluation_set, + destination=resources, + sandbox=self.sandbox, + ) + index.append( + { + "backend_id": entry.backend_id, + "evaluation_set": entry.evaluation_set.model_dump(mode="json"), + "path": digest, + } + ) + await self.write_json( + posixpath.join(root, "index.json"), + {"schema_version": 1, "case_resources": index}, + ) + + async def write_evaluation_plan( + self, + entries: Sequence[ContextPlanEntry], + ) -> None: + evaluations = [] + for entry in entries: + access = entry.access + if not access.agent_visible and not access.agent_can_evaluate: + continue + evaluations.append( + { + "name": entry.evaluation_set.name, + "partition": entry.evaluation_set.partition, + "base_selection": entry.evaluation_set.selection.model_dump( + mode="json" + ), + "agent_can_evaluate": access.agent_can_evaluate, + "agent_selection": access.agent_selection.value, + "disclosure": access.disclosure.value, + "expose_case_resources": access.expose_case_resources, + "budget": ( + entry.budget.model_dump(mode="json") + if entry.budget is not None + else None + ), + } + ) + await self.write_json( + self.path(PLAN_FILENAME), + {"schema_version": 1, "evaluations": evaluations}, + ) + class WorkspaceContextManager: """Build and refresh one proposal's generation-scoped context snapshot.""" @@ -421,91 +529,30 @@ async def _write_candidate_history(self) -> None: await self.candidate_repository.materialize_agent_history( tuple(self.candidates.values()), workspace=self.workspace, - destination=self.directory.path("candidates"), + destination=self.directory.path(CANDIDATES_SUBDIRECTORY), ) - async def _write_case_resources(self) -> None: - cases_root = self.directory.path("tasks") - await self.workspace.sandbox.mkdir(cases_root) - index: list[dict[str, object]] = [] - backend = self.engine.backends.resolve(self.backend_id) - for definition in self.evaluation_plan.evaluations: - access = definition.access - if not ( - access.agent_visible - and access.expose_case_resources - and isinstance(backend, CaseResourceExporter) - ): - continue - evaluation_set = definition.evaluation_set - key = evaluation_set.budget_key(self.backend_id) - digest = context_digest(key) - resource_root = self.directory.path("tasks", digest) - await self.workspace.sandbox.mkdir(resource_root) - await self.directory.write_json( - posixpath.join(resource_root, "manifest.json"), - { - "schema_version": 1, - "backend_id": self.backend_id, - "evaluation_set": evaluation_set.model_dump(mode="json"), - "resources_path": "resources", - }, - ) - resources = posixpath.join(resource_root, "resources") - await self.workspace.sandbox.mkdir(resources) - await backend.export_case_resources( - evaluation_set=evaluation_set, - destination=resources, - sandbox=self.workspace.sandbox, - ) - index.append( - { - "backend_id": self.backend_id, - "evaluation_set": evaluation_set.model_dump(mode="json"), - "path": digest, - } - ) - await self.directory.write_json( - posixpath.join(cases_root, "index.json"), - {"schema_version": 1, "case_resources": index}, - ) - - async def _write_evaluation_plan(self) -> None: + def _context_entries(self) -> list[ContextPlanEntry]: ledger = self.engine.budget_ledger - evaluations = [] - for definition in self.evaluation_plan.evaluations: - access = definition.access - if not access.agent_visible and not access.agent_can_evaluate: - continue - budget = ( - ledger.get( - self.backend_id, - definition.evaluation_set, - EvaluationPrincipal.AGENT, - ) - if ledger is not None - else None - ) - evaluations.append( - { - "name": definition.evaluation_set.name, - "partition": definition.evaluation_set.partition, - "base_selection": definition.evaluation_set.selection.model_dump( - mode="json" - ), - "agent_can_evaluate": access.agent_can_evaluate, - "agent_selection": access.agent_selection.value, - "disclosure": access.disclosure.value, - "expose_case_resources": access.expose_case_resources, - "budget": ( - budget.model_dump(mode="json") if budget is not None else None - ), - } + backend = self.engine.backends.resolve(self.backend_id) + return [ + ContextPlanEntry( + backend_id=self.backend_id, + backend=backend, + evaluation_set=definition.evaluation_set, + access=definition.access, + budget=( + ledger.get( + self.backend_id, + definition.evaluation_set, + EvaluationPrincipal.AGENT, + ) + if ledger is not None + else None + ), ) - await self.directory.write_json( - self.directory.path("plan.json"), - {"schema_version": 1, "evaluations": evaluations}, - ) + for definition in self.evaluation_plan.evaluations + ] async def initialize(self) -> None: async with self._lock: @@ -516,10 +563,11 @@ async def initialize(self) -> None: proposal_id=self.proposal_id, parent_candidate_id=self.parent.id, ) + entries = self._context_entries() await self._write_candidate_history() await self.directory.write_evaluations(await self._projections()) - await self._write_evaluation_plan() - await self._write_case_resources() + await self.directory.write_evaluation_plan(entries) + await self.directory.write_case_resources(entries) await self.directory.seal() async def add_evaluation( @@ -535,6 +583,6 @@ async def add_evaluation( await self.directory.unseal() await self._write_candidate_history() await self.directory.write_evaluations(await self._projections()) - await self._write_evaluation_plan() + await self.directory.write_evaluation_plan(self._context_entries()) await self.directory.seal() return make_evaluation_receipt(record, effective) From 0d08967baaaaa1ac302196d0bd64900fd3028d3c Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 22:06:41 -0700 Subject: [PATCH 20/72] Gateway telemetry: per-request logging and W&B mirroring from the sidecar The gateway now keeps a size-rotated JSONL log of every request it proxies or denies (scope, attribution, model, status, latency, tokens, and head+tail-truncated request/response bodies; SSE responses are captured from the chunks already tapped for usage). On by default in compiled tasks at /state/inference/requests with a 16KB per-body cap (inference_gateway.log_requests / request_log_body_bytes). The trusted sidecar - the only credential holder - mirrors the gateway state into the existing W&B run: a poller logs per-scope usage series (live optimizer producer-scope burn included) every 30s and ships rotated log files as artifacts, sharing the sink step counter. At finalize the gateway ledger and request log are copied into the session's artifacts so /session/export preserves every request-response, W&B or not. --- vero/src/vero/harbor/app.py | 24 ++- vero/src/vero/harbor/build/compiler.py | 15 ++ vero/src/vero/harbor/build/config.py | 6 + vero/src/vero/harbor/deployment.py | 65 +++++++- vero/src/vero/harbor/inference.py | 179 +++++++++++++++++++++++ vero/src/vero/harbor/serve.py | 7 +- vero/src/vero/runtime/wandb.py | 106 ++++++++++++++ vero/tests/test_v05_harbor_build.py | 29 ++++ vero/tests/test_v05_harbor_deployment.py | 75 ++++++++++ vero/tests/test_v05_harbor_inference.py | 150 +++++++++++++++++++ vero/tests/test_v05_wandb.py | 106 ++++++++++++++ 11 files changed, 754 insertions(+), 8 deletions(-) diff --git a/vero/src/vero/harbor/app.py b/vero/src/vero/harbor/app.py index 9628b19b..b6deca0c 100644 --- a/vero/src/vero/harbor/app.py +++ b/vero/src/vero/harbor/app.py @@ -3,11 +3,13 @@ from __future__ import annotations import asyncio +import contextlib import logging import shutil import tempfile +from contextlib import asynccontextmanager from pathlib import Path -from typing import Annotated +from typing import TYPE_CHECKING, Annotated from fastapi import FastAPI, Header, HTTPException, Query from fastapi.responses import FileResponse, JSONResponse @@ -34,6 +36,9 @@ from vero.harbor.transport import CandidateTransferError from vero.harbor.verifier import CanonicalVerifier +if TYPE_CHECKING: + from vero.runtime.wandb import InferenceTelemetryPoller + logger = logging.getLogger(__name__) @@ -64,11 +69,26 @@ def create_app( sidecar: EvaluationSidecar, verifier: CanonicalVerifier, admin_token: str, + telemetry: "InferenceTelemetryPoller | None" = None, ) -> FastAPI: """Expose agent endpoints and token-gated admin endpoints on one app.""" if not admin_token.strip(): raise ValueError("admin_token must not be empty") - app = FastAPI(title="VeRO evaluation sidecar", version="1") + + @asynccontextmanager + async def lifespan(_app: FastAPI): + task = ( + asyncio.create_task(telemetry.run()) if telemetry is not None else None + ) + try: + yield + finally: + if task is not None: + task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await task + + app = FastAPI(title="VeRO evaluation sidecar", version="1", lifespan=lifespan) app.add_exception_handler( EvaluationBudgetExceeded, _error(429, "evaluation budget exhausted"), diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 3738d59b..2c18f157 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -41,6 +41,7 @@ TOKEN_PATH = "/state/token/admin.token" SESSION_ID = "trial" INFERENCE_STATE = "/state/inference/usage.json" +INFERENCE_REQUEST_LOG_DIR = "/state/inference/requests" INFERENCE_GATEWAY_URL = "http://inference-gateway:8001" UPSTREAM_API_KEY_ENV = "VERO_INFERENCE_UPSTREAM_API_KEY" UPSTREAM_BASE_URL_ENV = "VERO_INFERENCE_UPSTREAM_BASE_URL" @@ -378,6 +379,12 @@ def _deployment_config( "inference_usage_path": ( INFERENCE_STATE if config.inference_gateway is not None else None ), + "inference_request_log_dir": ( + INFERENCE_REQUEST_LOG_DIR + if config.inference_gateway is not None + and config.inference_gateway.log_requests + else None + ), "inference_limits": ( { "producer": config.inference_gateway.producer.model_dump(mode="json"), @@ -545,6 +552,14 @@ def compile_harbor_task( config.inference_gateway.default_upstream_base_url ), "state_path": INFERENCE_STATE, + "request_log": ( + { + "directory": INFERENCE_REQUEST_LOG_DIR, + "body_bytes": config.inference_gateway.request_log_body_bytes, + } + if config.inference_gateway.log_requests + else None + ), "scopes": { "producer": { "token_sha256": token_digest(producer_inference_token), diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 063ee8ee..e3a6fd41 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -101,6 +101,12 @@ class InferenceGatewaySpec(EvaluationModel): # omitted, it defaults to a copy of `evaluation`, giving the mandatory re-score # its own pool so the optimizer's search evaluations cannot starve it. finalization: InferenceBudgetSpec | None = None + # Durable JSONL capture of every gateway request/response (metadata plus + # head+tail-truncated bodies). Lives on the gateway state volume — never + # agent-visible; the trusted sidecar mirrors it to W&B and the session + # archive. + log_requests: bool = True + request_log_body_bytes: int = Field(default=16384, ge=0) @field_validator("upstream_api_key_env", "upstream_base_url_env") @classmethod diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 750376c4..c0416859 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -3,6 +3,7 @@ from __future__ import annotations import logging +import shutil from pathlib import Path, PurePosixPath from typing import Literal @@ -88,6 +89,9 @@ class SidecarWandbConfig(EvaluationModel): # stdout/stderr, agent trajectory) to W&B as a per-evaluation artifact. # Off by default: it uploads files and can be large. log_traces: bool = False + # How often the sidecar mirrors the gateway's usage ledger and request log + # into the run (when the gateway state paths are configured below). + telemetry_interval_seconds: float = Field(default=30.0, gt=0) class HarborDeploymentConfig(EvaluationModel): @@ -105,6 +109,7 @@ class HarborDeploymentConfig(EvaluationModel): agent_volume: str | None = None admin_volume: str inference_usage_path: str | None = None + inference_request_log_dir: str | None = None inference_limits: dict[str, dict[str, JsonValue]] = Field(default_factory=dict) submit_enabled: bool = False disclose_budget: bool = True @@ -125,7 +130,7 @@ def validate_absolute_path(cls, value: str) -> str: raise ValueError("deployment paths must be absolute") return value - @field_validator("inference_usage_path") + @field_validator("inference_usage_path", "inference_request_log_dir") @classmethod def validate_optional_file_path(cls, value: str | None) -> str | None: if value is not None: @@ -166,6 +171,11 @@ def validate_references(self) -> HarborDeploymentConfig: if self.inference_usage_path is not None else () ), + *( + (("inference_request_log_dir", self.inference_request_log_dir),) + if self.inference_request_log_dir is not None + else () + ), ): path = PurePosixPath(value) if any( @@ -276,8 +286,53 @@ async def build_harbor_components(config: dict) -> SidecarComponents: ) wandb_sink = None - def _finish_wandb(result: object) -> None: - # Close the W&B run at finalize (the session's end) with a summary. + usage_path = ( + Path(parsed.inference_usage_path) + if parsed.inference_usage_path is not None + else None + ) + request_log_dir = ( + Path(parsed.inference_request_log_dir) + if parsed.inference_request_log_dir is not None + else None + ) + telemetry = None + if wandb_sink is not None and ( + usage_path is not None or request_log_dir is not None + ): + from vero.runtime.wandb import InferenceTelemetryPoller + + telemetry = InferenceTelemetryPoller( + sink=wandb_sink, + usage_path=usage_path, + request_log_dir=request_log_dir, + interval_seconds=parsed.wandb.telemetry_interval_seconds, + ) + + def _export_inference_state() -> None: + # Preserve the gateway's usage ledger and request log with the session, + # so /session/export (and the archived run record) carries every + # request-response the gateway saw. + destination = session_dir / "artifacts" / "inference" + try: + if usage_path is not None and usage_path.is_file(): + destination.mkdir(parents=True, exist_ok=True) + shutil.copy2(usage_path, destination / usage_path.name) + if request_log_dir is not None and request_log_dir.is_dir(): + shutil.copytree( + request_log_dir, + destination / "requests", + dirs_exist_ok=True, + ) + except OSError: + logger.warning("inference state export failed", exc_info=True) + + def _finalize_session_telemetry(result: object) -> None: + # Session's end: archive gateway state, flush telemetry (including the + # still-active request log file), and close the W&B run with a summary. + _export_inference_state() + if telemetry is not None: + telemetry.poll_once(final=True) if wandb_sink is None: return rewards = getattr(result, "rewards", {}) or {} @@ -347,6 +402,6 @@ def _finish_wandb(result: object) -> None: admin_volume=Path(parsed.admin_volume), score_baseline=parsed.score_baseline, evaluation_drain_timeout_seconds=parsed.evaluation_drain_timeout_seconds, - on_finalized=_finish_wandb if wandb_sink is not None else None, + on_finalized=_finalize_session_telemetry, ) - return SidecarComponents(sidecar=sidecar, verifier=verifier) + return SidecarComponents(sidecar=sidecar, verifier=verifier, telemetry=telemetry) diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index 9667b803..0c71bbd0 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -5,10 +5,13 @@ import asyncio import hashlib import json +import logging import os import secrets +import time from collections.abc import AsyncIterator from contextlib import asynccontextmanager +from datetime import UTC, datetime from pathlib import Path from typing import Annotated, Any, Literal @@ -19,6 +22,8 @@ from vero.evaluation import EvaluationModel from vero.evaluation.persistence import _atomic_write_json +logger = logging.getLogger(__name__) + def token_digest(token: str) -> str: """Return the stable digest stored in trusted gateway configuration.""" @@ -59,6 +64,26 @@ def validate_models(cls, value: list[str]) -> list[str]: return value +class InferenceRequestLogConfig(EvaluationModel): + """Durable JSONL capture of every request the gateway proxies or denies. + + Operator telemetry: the log lives on the gateway state volume, which is + never agent-visible. Bodies are stored head+tail truncated so the terminal + usage frame of a stream survives; ``body_bytes: 0`` keeps metadata only. + """ + + directory: str = "/state/inference/requests" + body_bytes: int = Field(default=16384, ge=0) + rotate_bytes: int = Field(default=64 * 1024 * 1024, ge=1_048_576) + + @field_validator("directory") + @classmethod + def validate_directory(cls, value: str) -> str: + if not Path(value).is_absolute(): + raise ValueError("request log directory must be absolute") + return value + + class InferenceGatewayConfig(EvaluationModel): """Trusted configuration for an OpenAI-compatible inference gateway.""" @@ -66,6 +91,7 @@ class InferenceGatewayConfig(EvaluationModel): upstream_base_url_env: str | None = "OPENAI_BASE_URL" default_upstream_base_url: str = "https://api.openai.com/v1" state_path: str = "/state/inference/usage.json" + request_log: InferenceRequestLogConfig | None = None scopes: dict[str, InferenceScopeConfig] @field_validator("upstream_api_key_env", "upstream_base_url_env") @@ -326,6 +352,102 @@ def feed(self, chunk: bytes) -> None: self.tokens = tokens +class _BoundedCapture: + """Keep the head and tail of a byte stream within a fixed byte budget.""" + + def __init__(self, cap: int): + self.cap = cap + self.head_limit = cap // 2 + self.tail_limit = cap - self.head_limit + self.head = bytearray() + self.tail = bytearray() + self.total = 0 + + def feed(self, chunk: bytes) -> None: + self.total += len(chunk) + if self.cap <= 0: + return + if len(self.head) < self.head_limit: + take = min(self.head_limit - len(self.head), len(chunk)) + self.head += chunk[:take] + chunk = chunk[take:] + if chunk: + self.tail += chunk + if len(self.tail) > self.tail_limit: + del self.tail[: len(self.tail) - self.tail_limit] + + def snapshot(self) -> dict[str, Any]: + if self.cap <= 0: + return {"bytes": self.total, "truncated": self.total > 0} + kept = len(self.head) + len(self.tail) + result: dict[str, Any] = {"bytes": self.total, "truncated": self.total > kept} + text = bytes(self.head).decode("utf-8", errors="replace") + if self.tail and not result["truncated"]: + text += bytes(self.tail).decode("utf-8", errors="replace") + elif self.tail: + result["tail"] = bytes(self.tail).decode("utf-8", errors="replace") + result["text"] = text + return result + + +class InferenceRequestLog: + """Size-rotated JSONL log of every request the gateway proxies or denies. + + Best-effort by construction: a logging failure must never affect the + proxied request. + """ + + def __init__(self, config: InferenceRequestLogConfig): + self.config = config + self.directory = Path(config.directory) + self.directory.mkdir(parents=True, exist_ok=True) + self._lock = asyncio.Lock() + existing = sorted(self.directory.glob("requests-*.jsonl")) + if existing: + self._current = existing[-1] + self._index = len(existing) + self._size = self._current.stat().st_size + else: + self._index = 1 + self._current = self.directory / "requests-00001.jsonl" + self._size = 0 + + def body(self, data: bytes) -> dict[str, Any]: + capture = self.capture() + capture.feed(data) + return capture.snapshot() + + def capture(self) -> _BoundedCapture: + return _BoundedCapture(self.config.body_bytes) + + async def record(self, **fields: Any) -> None: + try: + line = json.dumps( + { + "schema_version": 1, + "ts": datetime.now(UTC).isoformat(), + **fields, + }, + ensure_ascii=False, + default=str, + ) + encoded = (line + "\n").encode("utf-8") + async with self._lock: + if self._size and self._size + len(encoded) > self.config.rotate_bytes: + self._index += 1 + self._current = self.directory / f"requests-{self._index:05d}.jsonl" + self._size = 0 + await asyncio.to_thread(self._append, self._current, encoded) + self._size += len(encoded) + except Exception: + logger.warning("inference request log write failed", exc_info=True) + + @staticmethod + def _append(path: Path, data: bytes) -> None: + with open(path, "ab") as handle: + handle.write(data) + + _REQUEST_HEADERS = { "accept", "content-type", @@ -377,6 +499,11 @@ def create_inference_gateway_app( base_url = (upstream_base_url or config.default_upstream_base_url).rstrip("/") store = InferenceUsageStore(config) + request_log = ( + InferenceRequestLog(config.request_log) + if config.request_log is not None + else None + ) @asynccontextmanager async def lifespan(app: FastAPI): @@ -390,6 +517,7 @@ async def lifespan(app: FastAPI): app = FastAPI(title="VeRO inference gateway", version="1", lifespan=lifespan) app.state.usage_store = store + app.state.request_log = request_log @app.get("/health") async def health(): @@ -450,6 +578,36 @@ async def proxy( value = json.loads(body) if body else {} except (json.JSONDecodeError, UnicodeDecodeError): return _provider_error(400, "request body must be JSON", "invalid_request") + + started = time.monotonic() + + async def log_request( + *, + status: int, + error: str | None = None, + stream: bool = False, + tokens: tuple[int, int, int] = (0, 0, 0), + response: dict[str, Any] | None = None, + upstream_error: bool = False, + ) -> None: + if request_log is None: + return + await request_log.record( + scope=scope_name, + attribution=attribution, + endpoint=endpoint, + model=value.get("model") if isinstance(value, dict) else None, + stream=stream, + status=status, + error=error, + latency_ms=round((time.monotonic() - started) * 1000, 1), + input_tokens=tokens[0], + output_tokens=tokens[1], + total_tokens=tokens[2], + upstream_error=upstream_error, + request=request_log.body(body), + response=response, + ) # Per-scope allow-list: producer (optimizer) and evaluation (target) have # separate tokens + allowed_models, so the target is normally confined to # its fixed eval model. NOTE (known, deferred): this is not a *hard* @@ -463,6 +621,7 @@ async def proxy( # producer. model = value.get("model") if isinstance(value, dict) else None if not isinstance(model, str) or model not in scope.allowed_models: + await log_request(status=403, error="model_denied") return _provider_error( 403, "model is not allowed for this scope", "model_denied" ) @@ -475,6 +634,7 @@ async def proxy( try: await store.reserve(scope_name, attribution) except InferenceBudgetExceeded as error: + await log_request(status=429, error="budget_exhausted") return _provider_error(429, str(error), "budget_exhausted") headers = { @@ -499,6 +659,7 @@ async def proxy( await asyncio.shield( store.complete(scope_name, attribution, upstream_error=True) ) + await log_request(status=502, error="upstream_error", upstream_error=True) return _provider_error( 502, "upstream inference request failed", "upstream_error" ) @@ -515,6 +676,7 @@ async def proxy( is_stream = "text/event-stream" in upstream.headers.get("content-type", "") if is_stream: observed = _StreamingUsage() + captured = request_log.capture() if request_log is not None else None async def chunks() -> AsyncIterator[bytes]: failed = upstream.status_code >= 400 @@ -531,10 +693,21 @@ async def finish() -> None: total_tokens=observed.tokens[2], upstream_error=failed, ) + await log_request( + status=upstream.status_code, + stream=True, + tokens=observed.tokens, + response=( + captured.snapshot() if captured is not None else None + ), + upstream_error=failed, + ) try: async for chunk in upstream.aiter_bytes(): observed.feed(chunk) + if captured is not None: + captured.feed(chunk) yield chunk except asyncio.CancelledError: # OpenAI clients commonly stop consuming an SSE stream as soon @@ -580,6 +753,12 @@ async def finish() -> None: upstream_error=upstream.status_code >= 400, ) ) + await log_request( + status=upstream.status_code, + tokens=tokens, + response=request_log.body(content) if request_log is not None else None, + upstream_error=upstream.status_code >= 400, + ) return Response( content=content, status_code=upstream.status_code, diff --git a/vero/src/vero/harbor/serve.py b/vero/src/vero/harbor/serve.py index 79c51633..d609c399 100644 --- a/vero/src/vero/harbor/serve.py +++ b/vero/src/vero/harbor/serve.py @@ -8,17 +8,21 @@ import json from dataclasses import dataclass from pathlib import Path -from typing import Any, Awaitable, Callable +from typing import TYPE_CHECKING, Any, Awaitable, Callable from vero.harbor.auth import generate_admin_token, write_admin_token from vero.harbor.sidecar import EvaluationSidecar from vero.harbor.verifier import CanonicalVerifier +if TYPE_CHECKING: + from vero.runtime.wandb import InferenceTelemetryPoller + @dataclass(frozen=True) class SidecarComponents: sidecar: EvaluationSidecar verifier: CanonicalVerifier + telemetry: "InferenceTelemetryPoller | None" = None SidecarFactory = Callable[ @@ -76,6 +80,7 @@ async def build_app( sidecar=components.sidecar, verifier=components.verifier, admin_token=token, + telemetry=components.telemetry, ) diff --git a/vero/src/vero/runtime/wandb.py b/vero/src/vero/runtime/wandb.py index d70fce62..be723a5e 100644 --- a/vero/src/vero/runtime/wandb.py +++ b/vero/src/vero/runtime/wandb.py @@ -2,7 +2,10 @@ from __future__ import annotations +import asyncio import hashlib +import json +import logging import tempfile from pathlib import Path from typing import Any @@ -13,6 +16,8 @@ from vero.runtime.artifacts import ArtifactStore from vero.runtime.events import RuntimeEvent +logger = logging.getLogger(__name__) + def _open_wandb_run( *, @@ -207,9 +212,12 @@ def __init__( self.logged_evaluations = set(state.get("evaluation_ids", [])) self.next_step = int(state.get("next_step", len(self.logged_evaluations))) stored_run_id = state.get("run_id") + self._shipped_request_logs = dict(state.get("request_log_files", {})) else: self.logged_evaluations: set[str] = set() self.next_step = 0 + self._shipped_request_logs: dict[str, int] = {} + self._last_inference_usage: dict[str, Any] = {} # One W&B run per invocation. A fresh session volume mints a new id; a # sidecar restart within the same run reuses the one persisted in state, # so it resumes rather than colliding with other invocations. @@ -241,6 +249,7 @@ def _save_state(self) -> None: "evaluation_ids": sorted(self.logged_evaluations), "next_step": self.next_step, "run_id": self.run_id, + "request_log_files": self._shipped_request_logs, }, ) @@ -323,6 +332,54 @@ def _log_trace(self, record: EvaluationRecord) -> None: if added: self.run.log_artifact(artifact) + def log_inference_usage(self, scopes: dict[str, Any]) -> None: + """Log the gateway's per-scope usage counters as W&B series.""" + payload: dict[str, Any] = {} + for name, usage in sorted(scopes.items()): + if not isinstance(usage, dict): + continue + for key in ( + "requests", + "upstream_errors", + "input_tokens", + "output_tokens", + "total_tokens", + "active_requests", + ): + value = usage.get(key) + if isinstance(value, (int, float)): + payload[f"inference/{name}/{key}"] = value + if not payload or payload == self._last_inference_usage: + return + self.run.log(payload, step=self.next_step) + self.next_step += 1 + self._last_inference_usage = payload + self._save_state() + + def ship_request_logs(self, directory: Path, *, final: bool = False) -> None: + """Upload the gateway's rotated request-log files as one W&B artifact. + + The highest-numbered file is still being appended to, so it is only + included on the final call. Unchanged files dedupe by digest on the + W&B side, so re-adding them per version is cheap. + """ + files = sorted(directory.glob("requests-*.jsonl")) + if not final: + files = files[:-1] + if not files: + return + snapshot = {path.name: path.stat().st_size for path in files} + if snapshot == self._shipped_request_logs: + return + artifact = self._wandb.Artifact( + name="inference-requests", type="inference_request_log" + ) + for path in files: + artifact.add_file(str(path), name=path.name) + self.run.log_artifact(artifact) + self._shipped_request_logs = snapshot + self._save_state() + def __call__(self, record: EvaluationRecord) -> None: if record.id in self.logged_evaluations: return @@ -346,3 +403,52 @@ def finish( if summary: self.run.summary.update(summary) self.run.finish(exit_code=1 if failed else 0) + + +class InferenceTelemetryPoller: + """Mirror the gateway's durable state into the sidecar's W&B run. + + The gateway state volume is mounted read-only in the trusted sidecar, so + this gives live inference telemetry (including the untrusted optimizer's + producer-scope burn) without W&B credentials leaving the sidecar. + Best-effort throughout: telemetry must never affect the eval path. + """ + + def __init__( + self, + *, + sink: SidecarWandbSink, + usage_path: Path | None = None, + request_log_dir: Path | None = None, + interval_seconds: float = 30.0, + ): + if usage_path is None and request_log_dir is None: + raise ValueError("telemetry poller requires at least one source") + if interval_seconds <= 0: + raise ValueError("telemetry interval must be positive") + self.sink = sink + self.usage_path = usage_path + self.request_log_dir = request_log_dir + self.interval_seconds = interval_seconds + + def poll_once(self, *, final: bool = False) -> None: + if self.usage_path is not None: + try: + if self.usage_path.exists(): + value = json.loads(self.usage_path.read_text(encoding="utf-8")) + scopes = value.get("scopes") if isinstance(value, dict) else None + if isinstance(scopes, dict): + self.sink.log_inference_usage(scopes) + except Exception: + logger.warning("inference usage telemetry failed", exc_info=True) + if self.request_log_dir is not None: + try: + if self.request_log_dir.is_dir(): + self.sink.ship_request_logs(self.request_log_dir, final=final) + except Exception: + logger.warning("inference request log shipping failed", exc_info=True) + + async def run(self) -> None: + while True: + await asyncio.sleep(self.interval_seconds) + await asyncio.to_thread(self.poll_once) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 34ed0e61..fb6323d9 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -332,6 +332,35 @@ def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path) backend["inference_gateway_finalization_token"] != backend["inference_gateway_token"] ) + # Per-request logging is on by default: the gateway captures every + # request/response on its state volume and the sidecar mirrors it. + assert gateway["request_log"] == { + "directory": "/state/inference/requests", + "body_bytes": 16384, + } + assert serve["inference_request_log_dir"] == "/state/inference/requests" + + +def test_compiler_omits_request_log_when_disabled(tmp_path): + config = _config( + tmp_path, + inference_gateway=InferenceGatewaySpec( + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec(allowed_models=["gpt-target"]), + log_requests=False, + ), + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + gateway = json.loads( + (output / "environment/gateway/config.json").read_text(encoding="utf-8") + ) + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert gateway["request_log"] is None + assert serve["inference_request_log_dir"] is None def test_workspace_overlay_rejects_unsafe_dest_and_missing_source(tmp_path): diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py index 0ea84b7b..ac402d6e 100644 --- a/vero/tests/test_v05_harbor_deployment.py +++ b/vero/tests/test_v05_harbor_deployment.py @@ -193,3 +193,78 @@ async def test_standard_deployment_fails_closed_on_corrupt_budget_state(tmp_path with pytest.raises(ValueError, match="invalid durable budget ledger"): await build_harbor_components(config) + + +@pytest.mark.asyncio +async def test_deployment_finalize_hook_archives_gateway_state(tmp_path): + # Even without W&B configured, finalize must copy the gateway's usage + # ledger and request log into the session so /session/export carries them. + trusted = tmp_path / "trusted" + agent = tmp_path / "agent" + _repo(trusted, "VALUE = 1\n") + _repo(agent, "VALUE = 1\n") + cases = tmp_path / "cases.json" + cases.write_text('[{"id":"task","task_name":"org/task"}]') + usage_path = tmp_path / "state/inference/usage.json" + usage_path.parent.mkdir(parents=True) + usage_path.write_text('{"schema_version": 1, "scopes": {}}') + requests_dir = tmp_path / "state/inference/requests" + requests_dir.mkdir() + (requests_dir / "requests-00001.jsonl").write_text('{"scope":"producer"}\n') + evaluation_set = EvaluationSet(name="benchmark") + objective = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + session_dir = tmp_path / "state/session" + config = { + "repo_path": str(trusted), + "agent_repo_path": str(agent), + "session_dir": str(session_dir), + "backends": { + "backend": { + "task_source": "org/benchmark@1.0", + "agent_import_path": "program:Agent", + "cases_path": str(cases), + "harbor_requirement": "harbor==0.1.17", + "uv_executable": sys.executable, + } + }, + "access_policies": [], + "budgets": [], + "selection": { + "mode": "auto_best", + "backend_id": "backend", + "evaluation_set": evaluation_set.model_dump(mode="json"), + "objective": objective.model_dump(mode="json"), + "baseline_version": "HEAD", + }, + "targets": [ + { + "reward_key": "reward", + "backend_id": "backend", + "evaluation_set": evaluation_set.model_dump(mode="json"), + "objective": objective.model_dump(mode="json"), + } + ], + "admin_volume": str(tmp_path / "state/admin"), + "inference_usage_path": str(usage_path), + "inference_request_log_dir": str(requests_dir), + } + + components = await build_harbor_components(config) + # No W&B -> no poller, but the finalize hook is still installed. + assert components.telemetry is None + assert components.verifier._on_finalized is not None + + class Result: + shipped = True + rewards = {} + + components.verifier._on_finalized(Result()) + + exported = session_dir / "artifacts/inference" + assert (exported / "usage.json").read_text().startswith('{"schema_version"') + assert ( + exported / "requests/requests-00001.jsonl" + ).read_text() == '{"scope":"producer"}\n' diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py index a64db873..485cdbfb 100644 --- a/vero/tests/test_v05_harbor_inference.py +++ b/vero/tests/test_v05_harbor_inference.py @@ -1,5 +1,6 @@ from __future__ import annotations +import asyncio import json import httpx @@ -318,3 +319,152 @@ def unavailable(request: httpx.Request): usage = app.state.usage_store.ledger.scopes["producer"] assert usage.active_requests == 0 assert usage.upstream_errors == 1 + + +def _logged_config(tmp_path, **kwargs): + from vero.harbor.inference import InferenceRequestLogConfig + + config = _config(tmp_path, **kwargs) + return config.model_copy( + update={ + "request_log": InferenceRequestLogConfig( + directory=str(tmp_path / "requests"), + body_bytes=64, + ) + } + ) + + +def _log_records(tmp_path): + records = [] + for path in sorted((tmp_path / "requests").glob("requests-*.jsonl")): + for line in path.read_text().splitlines(): + records.append(json.loads(line)) + return records + + +def test_gateway_request_log_captures_responses_streams_and_denials(tmp_path): + stream_payload = ( + 'event: response.completed\ndata: {"type":"response.completed",' + '"response":{"usage":{"input_tokens":5,"output_tokens":3,' + '"total_tokens":8}}}\n\n' + ) + + def upstream(request: httpx.Request): + if b'"stream": true' in request.content or b'"stream":true' in request.content: + return httpx.Response( + 200, + content=stream_payload, + headers={"content-type": "text/event-stream"}, + ) + return httpx.Response( + 200, + json={"id": "r", "usage": {"input_tokens": 11, "output_tokens": 7}}, + ) + + app = create_inference_gateway_app( + config=_logged_config(tmp_path, max_requests=2), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hi"}, + ) + client.post( + "/scopes/producer/eval-1/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hi", "stream": True}, + ) + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "denied-model", "input": "hi"}, + ) + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "over budget"}, + ) + + records = _log_records(tmp_path) + assert [record["status"] for record in records] == [200, 200, 403, 429] + plain, stream, denied, exhausted = records + assert plain["scope"] == "producer" + assert plain["attribution"] == "optimizer" + assert plain["model"] == "gpt-test" + assert plain["input_tokens"] == 11 and plain["output_tokens"] == 7 + assert "gpt-test" in plain["request"]["text"] + assert '"id":"r"' in plain["response"]["text"] + assert plain["latency_ms"] >= 0 + assert stream["stream"] is True + assert stream["attribution"] == "eval-1" + assert stream["total_tokens"] == 8 + # the head+tail capture keeps the stream's terminal usage frame + assert "total_tokens" in ( + stream["response"]["text"] + stream["response"].get("tail", "") + ) + assert denied["error"] == "model_denied" and denied["response"] is None + assert exhausted["error"] == "budget_exhausted" + + +def test_gateway_request_log_truncates_and_rotates(tmp_path): + from vero.harbor.inference import InferenceRequestLogConfig + + config = _config(tmp_path, max_requests=None, max_tokens=None).model_copy( + update={ + "request_log": InferenceRequestLogConfig( + directory=str(tmp_path / "requests"), + body_bytes=32, + rotate_bytes=1_048_576, + ) + } + ) + app = create_inference_gateway_app( + config=config, + upstream_api_key="upstream-secret", + transport=httpx.MockTransport( + lambda _request: httpx.Response(200, json={"data": "y" * 200}) + ), + ) + with TestClient(app) as client: + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "x" * 500}, + ) + + (record,) = _log_records(tmp_path) + assert record["request"]["truncated"] is True + assert record["request"]["bytes"] > 500 + assert len(record["request"]["text"]) <= 16 + assert "tail" in record["request"] + assert record["response"]["truncated"] is True + + +def test_gateway_request_log_rotation_boundary(tmp_path): + from vero.harbor.inference import InferenceRequestLog, InferenceRequestLogConfig + + log = InferenceRequestLog( + InferenceRequestLogConfig( + directory=str(tmp_path / "requests"), + body_bytes=16384, + rotate_bytes=1_048_576, + ) + ) + # Force a tiny rotation threshold without violating the config floor. + log.config = log.config.model_copy(update={"rotate_bytes": 400}) + + async def fill(): + for index in range(6): + await log.record(scope="producer", index=index, payload="z" * 100) + + asyncio.run(fill()) + files = sorted((tmp_path / "requests").glob("requests-*.jsonl")) + assert len(files) > 1 + total = sum( + len(path.read_text().splitlines()) for path in files + ) + assert total == 6 diff --git a/vero/tests/test_v05_wandb.py b/vero/tests/test_v05_wandb.py index c84dcc0a..963a59d1 100644 --- a/vero/tests/test_v05_wandb.py +++ b/vero/tests/test_v05_wandb.py @@ -296,3 +296,109 @@ def test_sidecar_wandb_sink_logs_full_trace_including_per_case_artifacts( "harbor/jobs/trial-0/result.json", "harbor/jobs/trial-0/agent/trajectory.json", } + + +def _gateway_state(tmp_path: Path, requests: int = 3) -> tuple[Path, Path]: + usage_path = tmp_path / "inference" / "usage.json" + requests_dir = tmp_path / "inference" / "requests" + requests_dir.mkdir(parents=True, exist_ok=True) + usage_path.write_text( + json.dumps( + { + "schema_version": 1, + "scopes": { + "producer": { + "requests": requests, + "upstream_errors": 0, + "input_tokens": 10, + "output_tokens": 5, + "total_tokens": 15, + "active_requests": 1, + "attributions": {}, + } + }, + } + ) + ) + return usage_path, requests_dir + + +def test_sidecar_wandb_mirrors_gateway_usage_and_request_logs(tmp_path: Path): + from vero.runtime.wandb import InferenceTelemetryPoller + + client = FakeWandb() + sink = SidecarWandbSink( + project="v", session_id="s", session_dir=tmp_path / "session", client=client + ) + usage_path, requests_dir = _gateway_state(tmp_path) + (requests_dir / "requests-00001.jsonl").write_text('{"a":1}\n') + (requests_dir / "requests-00002.jsonl").write_text('{"a":2}\n') + + poller = InferenceTelemetryPoller( + sink=sink, + usage_path=usage_path, + request_log_dir=requests_dir, + interval_seconds=5, + ) + poller.poll_once() + + payload, step = client.run.logged[0] + assert step == 0 + assert payload["inference/producer/requests"] == 3 + assert payload["inference/producer/total_tokens"] == 15 + assert payload["inference/producer/active_requests"] == 1 + # Only the rotated file ships; the highest-numbered one is still growing. + assert len(client.run.artifacts) == 1 + assert client.run.artifacts[0].type == "inference_request_log" + assert [name for _, name in client.run.artifacts[0].files] == [ + "requests-00001.jsonl" + ] + + # Unchanged gateway state: no duplicate series point, no new artifact. + poller.poll_once() + assert len(client.run.logged) == 1 + assert len(client.run.artifacts) == 1 + + # Usage moved -> a new point; eval records share the same step counter. + _gateway_state(tmp_path, requests=4) + poller.poll_once() + assert client.run.logged[1][1] == 1 + sink(_evaluation_record()) + assert client.run.logged[2][1] == 2 + + # The final flush ships the active file too. + poller.poll_once(final=True) + assert [name for _, name in client.run.artifacts[-1].files] == [ + "requests-00001.jsonl", + "requests-00002.jsonl", + ] + + # A restarted sink remembers what shipped and does not re-upload. + restarted = FakeWandb() + resumed = SidecarWandbSink( + project="v", session_id="s", session_dir=tmp_path / "session", client=restarted + ) + resumed.ship_request_logs(requests_dir, final=True) + assert restarted.run.artifacts == [] + + +def test_sidecar_wandb_telemetry_is_best_effort(tmp_path: Path): + from vero.runtime.wandb import InferenceTelemetryPoller + + client = FakeWandb() + sink = SidecarWandbSink( + project="v", session_id="s", session_dir=tmp_path / "session", client=client + ) + usage_path = tmp_path / "inference" / "usage.json" + usage_path.parent.mkdir(parents=True) + usage_path.write_text("{corrupt") + + poller = InferenceTelemetryPoller( + sink=sink, + usage_path=usage_path, + request_log_dir=tmp_path / "inference" / "missing", + interval_seconds=5, + ) + poller.poll_once() # must not raise + assert client.run.logged == [] + assert client.run.artifacts == [] From 893a904c926bfbdc361fe1ee2c51265358685264 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 23:03:25 -0700 Subject: [PATCH 21/72] Backend: alias OPENAI_API_BASE for task packages that template it --- vero/src/vero/harbor/backend.py | 4 ++++ vero/tests/test_v05_harbor_backend.py | 3 +++ 2 files changed, 7 insertions(+) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index a225735c..9951e8a2 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -662,6 +662,10 @@ def _environment( else: environment["OPENAI_API_KEY"] = gateway_token environment["OPENAI_BASE_URL"] = gateway_url + # Task packages that template `${OPENAI_API_BASE}` (litellm-style + # name, e.g. swe-atlas-qna's rubric judge) get the same endpoint. + if "OPENAI_BASE_URL" in environment: + environment["OPENAI_API_BASE"] = environment["OPENAI_BASE_URL"] return environment def _source_args(self, task_source: str, *, local: bool) -> list[str]: diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index ed3d08e7..bce78d92 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -96,6 +96,8 @@ def test_environment_default_routes_agent_through_gateway_on_openai(tmp_path): assert env["OPENAI_BASE_URL"] == ( "http://inference-gateway:8001/scopes/evaluation/eval-1/v1" ) + # litellm-style alias used by task verifier env templates + assert env["OPENAI_API_BASE"] == env["OPENAI_BASE_URL"] assert "VERO_AGENT_INFERENCE_API_KEY" not in env @@ -115,6 +117,7 @@ def test_environment_routes_task_services_to_upstream(tmp_path, monkeypatch): # task-owned eval services reach the real upstream via OPENAI_* assert env["OPENAI_API_KEY"] == "upstream-key" assert env["OPENAI_BASE_URL"] == "https://upstream/v1" + assert env["OPENAI_API_BASE"] == "https://upstream/v1" # the candidate agent keeps the metered gateway on dedicated vars assert env["VERO_AGENT_INFERENCE_API_KEY"] == "gw-token" assert env["VERO_AGENT_INFERENCE_BASE_URL"] == ( From 5dae928317a2c4206076bbdce81101301456115c Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 23:40:46 -0700 Subject: [PATCH 22/72] Build config: recognize vendored local task_source in manifest check The loader resolves an existing local task_source to an absolute path, while the committed partition manifest records it relative to itself; compare resolved locations before rejecting. Registry sources still compare literally. --- vero/src/vero/harbor/build/config.py | 9 +++- vero/tests/test_v05_harbor_build.py | 61 ++++++++++++++++++++++++++++ 2 files changed, 69 insertions(+), 1 deletion(-) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index e3a6fd41..9acd92de 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -452,7 +452,14 @@ def validate_references(self) -> HarborBuildConfig: if not isinstance(manifest, dict): raise ValueError("task_manifest must be a JSON object") manifest_source = manifest.get("task_source") - if manifest_source != self.task_source: + if manifest_source != self.task_source and not ( + # A vendored local source is recorded relative to the manifest, + # while the loader resolves the build's copy to an absolute + # path once the directory exists; compare resolved locations. + isinstance(manifest_source, str) + and (Path(self.task_manifest).parent / manifest_source).resolve() + == Path(self.task_source).resolve() + ): raise ValueError( "task_manifest task_source does not match build task_source" ) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index fb6323d9..6b78bec2 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -484,6 +484,67 @@ def test_load_build_config_supports_partition_files_and_validates_manifest(tmp_p load_harbor_build_config(config_path) +def test_load_build_config_matches_vendored_local_task_source(tmp_path): + # A local (vendored) task_source is resolved to an absolute path by the + # loader once the directory exists, while the committed manifest records it + # relative to itself; the two must still be recognized as the same source. + _target_repo(tmp_path / "target") + tasks = tmp_path / "tasks" + tasks.mkdir() + partitions = tmp_path / "partitions" + partitions.mkdir() + (partitions / "validation.json").write_text('["task-a"]\n', encoding="utf-8") + (partitions / "test.json").write_text('["task-a"]\n', encoding="utf-8") + (partitions / "manifest.json").write_text( + json.dumps( + { + "task_source": "../tasks", + "tasks": [{"name": "task-a", "ref": "sha256:a"}], + } + ), + encoding="utf-8", + ) + config_path = tmp_path / "build.yaml" + config_path.write_text( + "\n".join( + [ + "name: org/task", + "agent_repo: target", + "task_source: tasks", + "task_manifest: partitions/manifest.json", + "agent_import_path: target.agent:Agent", + "harbor_requirement: harbor==0.20.0", + "partition_files:", + " validation: partitions/validation.json", + " test: partitions/test.json", + "agent_access:", + " - partition: validation", + "selection_partition: validation", + "targets:", + " - partition: test", + ] + ) + + "\n", + encoding="utf-8", + ) + + loaded = load_harbor_build_config(config_path) + assert loaded.task_source == str(tasks.resolve()) + + # A genuinely different source still fails. + (partitions / "manifest.json").write_text( + json.dumps( + { + "task_source": "../elsewhere", + "tasks": [{"name": "task-a", "ref": "sha256:a"}], + } + ), + encoding="utf-8", + ) + with pytest.raises(ValidationError, match="does not match build task_source"): + load_harbor_build_config(config_path) + + def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): config = _config(tmp_path) output = compile_harbor_task( From f7975f9bd8c10346462da1967e395ceeab645151 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 10:01:31 -0700 Subject: [PATCH 23/72] Report per-evaluation cost and latency; accuracy stays the objective The gateway now tracks provider cache reads (cached_input_tokens) per scope and attribution. Each evaluation report carries its own inference_requests/input/cached/output/total_tokens (from the gateway ledger attribution keyed by evaluation id) plus per-case wall_seconds and mean/max_case_wall_seconds from the Harbor trial timestamps. finalize.json echoes the scoring evaluations' full metrics as reward_metrics/baseline_reward_metrics; reward.json is untouched and the score metric remains the sole objective input. Budget-blind mode (disclose_budget: false) redacts inference_* metrics from agent-facing receipts and context projections; latency stays visible and trusted records keep everything. --- vero/src/vero/harbor/backend.py | 91 +++++++++++++++++++++++++ vero/src/vero/harbor/build/compiler.py | 3 + vero/src/vero/harbor/inference.py | 34 ++++++--- vero/src/vero/harbor/sidecar.py | 23 ++++++- vero/src/vero/harbor/verifier.py | 31 +++++++-- vero/src/vero/runtime/wandb.py | 1 + vero/tests/test_v05_harbor_backend.py | 79 +++++++++++++++++++++ vero/tests/test_v05_harbor_inference.py | 50 ++++++++++++++ vero/tests/test_v05_harbor_sidecar.py | 74 ++++++++++++++++++++ vero/tests/test_v05_harbor_verifier.py | 4 ++ 10 files changed, 374 insertions(+), 16 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 9951e8a2..bbf85494 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -14,6 +14,7 @@ import statistics import tempfile from collections import defaultdict +from datetime import datetime from pathlib import Path from typing import Any, Literal @@ -150,6 +151,10 @@ class HarborBackendConfig(EvaluationModel): # the compiled sidecar sets this to isolate. harness_user: str | None = None case_resources_cache_path: str | None = None + # The gateway's durable usage ledger; when set, each evaluation's report + # carries its own inference token totals as metrics (attribution keyed by + # evaluation id). + inference_usage_path: str | None = None extra_args: list[str] = Field(default_factory=list) @field_validator("cases_path", "case_resources_cache_path") @@ -918,6 +923,68 @@ def _trial_artifacts( ) return artifacts + def _inference_usage_metrics(self, evaluation_id: str) -> dict[str, float]: + """This evaluation's gateway token totals, from the durable ledger. + + Attribution is keyed by evaluation id (search and finalization scopes + alike). Best-effort: telemetry must never fail an evaluation. + """ + if self.config.inference_usage_path is None: + return {} + try: + ledger = json.loads( + Path(self.config.inference_usage_path).read_text(encoding="utf-8") + ) + scopes = ledger.get("scopes") + if not isinstance(scopes, dict): + return {} + totals = { + "requests": 0, + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + } + found = False + for scope in scopes.values(): + attribution = (scope.get("attributions") or {}).get(evaluation_id) + if not isinstance(attribution, dict): + continue + found = True + for key in totals: + value = attribution.get(key) + if isinstance(value, (int, float)): + totals[key] += int(value) + if not found: + return {} + return {f"inference_{key}": float(value) for key, value in totals.items()} + except (OSError, ValueError): + logger.warning("inference usage metrics unavailable", exc_info=True) + return {} + + @staticmethod + def _attempt_wall_seconds(attempt: dict[str, Any]) -> float | None: + started = attempt.get("started_at") + finished = attempt.get("finished_at") + if not isinstance(started, str) or not isinstance(finished, str): + return None + try: + delta = datetime.fromisoformat( + finished.replace("Z", "+00:00") + ) - datetime.fromisoformat(started.replace("Z", "+00:00")) + except ValueError: + return None + seconds = delta.total_seconds() + return seconds if seconds >= 0 else None + + def _case_wall_seconds(self, attempts: list[dict[str, Any]]) -> float | None: + durations = [ + seconds + for attempt in attempts + if (seconds := self._attempt_wall_seconds(attempt)) is not None + ] + return max(durations) if durations else None + def _case_result( self, case: HarborCase, @@ -926,6 +993,7 @@ def _case_result( artifact_root: Path, ) -> tuple[CaseResult, float]: trial_artifacts = self._trial_artifacts(attempts, artifact_root) + wall_seconds = self._case_wall_seconds(attempts) attempt_detail = [ { "reward": self._attempt_reward(attempt), @@ -973,6 +1041,11 @@ def _case_result( ), "n_dead_infra": float(n_dead_infra), "n_clean": float(len(attempts) - n_dead_infra), + **( + {"wall_seconds": wall_seconds} + if wall_seconds is not None + else {} + ), }, input={"task_name": case.task_name, **case.metadata}, output=output, @@ -1002,6 +1075,8 @@ def _case_result( if isinstance(value, (int, float)) and math.isfinite(float(value)) } numeric_rewards["score"] = score + if wall_seconds is not None: + numeric_rewards["wall_seconds"] = wall_seconds return ( CaseResult( case_id=case.id, @@ -1396,6 +1471,11 @@ def _category(case: CaseResult) -> ErrorCategory | None: ) return sanitize_evaluation_report(report, self._secrets()) + case_walls = [ + wall + for case in case_results + if isinstance((wall := case.metrics.get("wall_seconds")), (int, float)) + ] report = EvaluationReport( status=EvaluationStatus.SUCCESS, metrics={ @@ -1408,6 +1488,17 @@ def _category(case: CaseResult) -> ErrorCategory | None: if len(informative_scores) > 1 else 0.0 ), + # Cost/latency telemetry: reported alongside accuracy, never + # part of the score. + **( + { + "mean_case_wall_seconds": sum(case_walls) / len(case_walls), + "max_case_wall_seconds": max(case_walls), + } + if case_walls + else {} + ), + **self._inference_usage_metrics(context.evaluation_id), }, cases=case_results, diagnostics=diagnostics, diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 2c18f157..43bcd5d9 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -269,6 +269,9 @@ def _deployment_config( else None ), "case_resources_cache_path": (f"{ADMIN_VOLUME}/case-resources/{partition}"), + "inference_usage_path": ( + INFERENCE_STATE if config.inference_gateway is not None else None + ), "extra_args": config.extra_harbor_args, } diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index 0c71bbd0..fc11e261 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -135,6 +135,8 @@ class InferenceAttributionUsage(EvaluationModel): requests: int = Field(default=0, ge=0) upstream_errors: int = Field(default=0, ge=0) input_tokens: int = Field(default=0, ge=0) + # Provider-reported cache reads; a subset of input_tokens, not additive. + cached_input_tokens: int = Field(default=0, ge=0) output_tokens: int = Field(default=0, ge=0) total_tokens: int = Field(default=0, ge=0) @@ -241,6 +243,7 @@ async def complete( input_tokens: int = 0, output_tokens: int = 0, total_tokens: int = 0, + cached_input_tokens: int = 0, upstream_error: bool = False, ) -> None: try: @@ -250,6 +253,8 @@ async def complete( update = { "upstream_errors": usage.upstream_errors + int(upstream_error), "input_tokens": usage.input_tokens + input_tokens, + "cached_input_tokens": usage.cached_input_tokens + + cached_input_tokens, "output_tokens": usage.output_tokens + output_tokens, "total_tokens": usage.total_tokens + total_tokens, "active_requests": max(0, usage.active_requests - 1), @@ -261,6 +266,10 @@ async def complete( + int(upstream_error), "input_tokens": attribution_usage.input_tokens + input_tokens, + "cached_input_tokens": ( + attribution_usage.cached_input_tokens + + cached_input_tokens + ), "output_tokens": attribution_usage.output_tokens + output_tokens, "total_tokens": attribution_usage.total_tokens @@ -312,27 +321,33 @@ def _bearer_token(authorization: str | None) -> str | None: return authorization[len(prefix) :] -def _usage(value: Any) -> tuple[int, int, int]: +def _usage(value: Any) -> tuple[int, int, int, int]: if not isinstance(value, dict): - return (0, 0, 0) + return (0, 0, 0, 0) usage = value.get("usage") if not isinstance(usage, dict): response = value.get("response") usage = response.get("usage") if isinstance(response, dict) else None if not isinstance(usage, dict): - return (0, 0, 0) + return (0, 0, 0, 0) input_tokens = int(usage.get("input_tokens", usage.get("prompt_tokens", 0)) or 0) output_tokens = int( usage.get("output_tokens", usage.get("completion_tokens", 0)) or 0 ) total_tokens = int(usage.get("total_tokens", input_tokens + output_tokens) or 0) - return (input_tokens, output_tokens, total_tokens) + # Responses API nests cache reads under input_tokens_details; chat + # completions under prompt_tokens_details. + details = usage.get("input_tokens_details") or usage.get("prompt_tokens_details") + cached_tokens = ( + int(details.get("cached_tokens", 0) or 0) if isinstance(details, dict) else 0 + ) + return (input_tokens, output_tokens, total_tokens, cached_tokens) class _StreamingUsage: def __init__(self): self.buffer = b"" - self.tokens = (0, 0, 0) + self.tokens = (0, 0, 0, 0) def feed(self, chunk: bytes) -> None: self.buffer += chunk @@ -348,7 +363,7 @@ def feed(self, chunk: bytes) -> None: except (json.JSONDecodeError, UnicodeDecodeError): continue tokens = _usage(value) - if tokens != (0, 0, 0): + if tokens != (0, 0, 0, 0): self.tokens = tokens @@ -586,7 +601,7 @@ async def log_request( status: int, error: str | None = None, stream: bool = False, - tokens: tuple[int, int, int] = (0, 0, 0), + tokens: tuple[int, int, int, int] = (0, 0, 0, 0), response: dict[str, Any] | None = None, upstream_error: bool = False, ) -> None: @@ -604,6 +619,7 @@ async def log_request( input_tokens=tokens[0], output_tokens=tokens[1], total_tokens=tokens[2], + cached_input_tokens=tokens[3], upstream_error=upstream_error, request=request_log.body(body), response=response, @@ -691,6 +707,7 @@ async def finish() -> None: input_tokens=observed.tokens[0], output_tokens=observed.tokens[1], total_tokens=observed.tokens[2], + cached_input_tokens=observed.tokens[3], upstream_error=failed, ) await log_request( @@ -735,7 +752,7 @@ async def finish() -> None: try: tokens = _usage(json.loads(content)) except (json.JSONDecodeError, UnicodeDecodeError): - tokens = (0, 0, 0) + tokens = (0, 0, 0, 0) except BaseException: await asyncio.shield( store.complete(scope_name, attribution, upstream_error=True) @@ -750,6 +767,7 @@ async def finish() -> None: input_tokens=tokens[0], output_tokens=tokens[1], total_tokens=tokens[2], + cached_input_tokens=tokens[3], upstream_error=upstream.status_code >= 400, ) ) diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index 3bdc6a80..9bf8cc30 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -497,6 +497,22 @@ async def _enforce_aggregate_floor( f"{policy.access.min_aggregate_cases} cases; requested {cost.cases}" ) + def _redact_budget_metrics(self, record: EvaluationRecord) -> EvaluationRecord: + """Budget-blind mode: the inference_* cost metrics are budget signal + and must not reach the agent (enforcement is unchanged).""" + if self.disclose_budget: + return record + metrics = { + key: value + for key, value in record.report.metrics.items() + if not key.startswith("inference_") + } + if len(metrics) == len(record.report.metrics): + return record + return record.model_copy( + update={"report": record.report.model_copy(update={"metrics": metrics})} + ) + def _visible_projections( self, ) -> list[tuple[EvaluationRecord, DisclosureLevel, EvaluationProjection]]: @@ -505,6 +521,7 @@ def _visible_projections( record = self.engine.database.get_evaluation(evaluation_id) if record is None: continue + record = self._redact_budget_metrics(record) policy = self._policies.get( ( record.backend_id, @@ -530,7 +547,7 @@ async def _write_candidate_index( ], ) -> None: assert self._context_directory is not None - root = self._context_directory.path("candidates") + root = self._context_directory.path(CANDIDATES_SUBDIRECTORY) sandbox = self._context_directory.sandbox if await sandbox.exists(root): await sandbox.remove(root, recursive=True) @@ -709,7 +726,9 @@ async def _evaluate_prepared( await self._refresh_context() return SidecarEvaluationResult( disclosure=disclosure, - receipt=make_evaluation_receipt(record, disclosure), + receipt=make_evaluation_receipt( + self._redact_budget_metrics(record), disclosure + ), ) async def submit(self, version: str | None = None) -> Submission: diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py index 6164bd3c..09462dba 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/harbor/verifier.py @@ -124,6 +124,12 @@ class VerificationResult(EvaluationModel): rewards: dict[str, float] evaluation_ids: dict[str, str] = Field(default_factory=dict) baseline_rewards: dict[str, float] = Field(default_factory=dict) + #: Per reward key: the scoring evaluation's full report metrics (accuracy + #: plus cost/latency telemetry such as inference_*_tokens and + #: mean_case_wall_seconds). Informational — the reward itself remains the + #: objective value alone. + reward_metrics: dict[str, dict[str, float]] = Field(default_factory=dict) + baseline_reward_metrics: dict[str, dict[str, float]] = Field(default_factory=dict) errors: dict[str, str] = Field(default_factory=dict) @field_validator("rewards", "baseline_rewards") @@ -409,7 +415,7 @@ async def _score_target( self, candidate: Candidate, target: VerificationTarget, - ) -> tuple[float, str | None, str | None]: + ) -> tuple[float, str | None, dict[str, float], str | None]: record, error = await self._evaluate( candidate=candidate, backend_id=target.backend_id, @@ -420,12 +426,12 @@ async def _score_target( max_attempts=target.max_attempts, ) if record is None: - return target.failure_value, None, error + return target.failure_value, None, {}, error assert record.objective is not None and record.objective.value is not None reward = ( target.reward_scale * float(record.objective.value) + target.reward_offset ) - return reward, record.id, None + return reward, record.id, dict(record.report.metrics), None async def measure_baseline(self, *, replicates: int = 1) -> dict[str, JsonValue]: """Admin-score the fixed seed to produce pinnable baseline numbers. @@ -471,7 +477,7 @@ def _aggregate(values: list[float | None]) -> dict[str, JsonValue]: for target in self.targets: target_values: list[float | None] = [] for _ in range(replicates): - reward, _, error = await self._score_target(baseline, target) + reward, _, _, error = await self._score_target(baseline, target) target_values.append(None if error is not None else reward) targets[target.reward_key] = _aggregate(target_values) result["targets"] = targets @@ -491,27 +497,38 @@ async def _finalize(self) -> VerificationResult: rewards: dict[str, float] = {} evaluation_ids: dict[str, str] = {} + reward_metrics: dict[str, dict[str, float]] = {} errors: dict[str, str] = {} for target in self.targets: - reward, evaluation_id, error = await self._score_target(candidate, target) + reward, evaluation_id, metrics, error = await self._score_target( + candidate, target + ) rewards[target.reward_key] = reward if evaluation_id is not None: evaluation_ids[target.reward_key] = evaluation_id + if metrics: + reward_metrics[target.reward_key] = metrics if error is not None: errors[target.reward_key] = error baseline_rewards: dict[str, float] = {} + baseline_reward_metrics: dict[str, dict[str, float]] = {} baseline = self.selection.baseline_candidate if self.score_baseline and baseline is not None: if baseline.version == candidate.version: baseline_rewards = dict(rewards) + baseline_reward_metrics = dict(reward_metrics) else: for target in self.targets: if target.baseline_reward is not None: baseline_rewards[target.reward_key] = target.baseline_reward continue - reward, _, error = await self._score_target(baseline, target) + reward, _, metrics, error = await self._score_target( + baseline, target + ) baseline_rewards[target.reward_key] = reward + if metrics: + baseline_reward_metrics[target.reward_key] = metrics if error is not None: errors[f"baseline:{target.reward_key}"] = error @@ -520,6 +537,8 @@ async def _finalize(self) -> VerificationResult: rewards=rewards, evaluation_ids=evaluation_ids, baseline_rewards=baseline_rewards, + reward_metrics=reward_metrics, + baseline_reward_metrics=baseline_reward_metrics, errors=errors, ) diff --git a/vero/src/vero/runtime/wandb.py b/vero/src/vero/runtime/wandb.py index be723a5e..a7d484dd 100644 --- a/vero/src/vero/runtime/wandb.py +++ b/vero/src/vero/runtime/wandb.py @@ -342,6 +342,7 @@ def log_inference_usage(self, scopes: dict[str, Any]) -> None: "requests", "upstream_errors", "input_tokens", + "cached_input_tokens", "output_tokens", "total_tokens", "active_requests", diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index bce78d92..beb72f9d 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -465,6 +465,85 @@ async def test_harbor_backend_exposes_complete_successful_trial_records(tmp_path assert "[REDACTED]" in (tmp_path / "result/artifacts" / trajectory.path).read_text() +@pytest.mark.asyncio +async def test_harbor_backend_reports_latency_and_inference_telemetry(tmp_path): + usage_path = tmp_path / "usage.json" + usage_path.write_text( + json.dumps( + { + "schema_version": 1, + "scopes": { + "evaluation": { + "requests": 99, + "attributions": { + "evaluation": { + "requests": 12, + "input_tokens": 1000, + "cached_input_tokens": 400, + "output_tokens": 250, + "total_tokens": 1250, + }, + "other-eval": {"requests": 5, "total_tokens": 9999}, + }, + }, + "finalization": { + "attributions": { + "evaluation": { + "requests": 2, + "input_tokens": 100, + "cached_input_tokens": 0, + "output_tokens": 50, + "total_tokens": 150, + } + } + }, + }, + } + ) + ) + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + { + "verifier_result": {"rewards": {"reward": 1.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:01:30Z", + } + ], + "example/beta": [ + { + "verifier_result": {"rewards": {"reward": 0.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:30Z", + } + ], + }, + ) + backend = HarborBackend( + _config(tmp_path, inference_usage_path=str(usage_path)) + ) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a", "case-b"])), + ) + + by_id = {case.case_id: case for case in report.cases} + assert by_id["case-a"].metrics["wall_seconds"] == 90.0 + assert by_id["case-b"].metrics["wall_seconds"] == 30.0 + assert report.metrics["mean_case_wall_seconds"] == 60.0 + assert report.metrics["max_case_wall_seconds"] == 90.0 + # this evaluation's attribution only, summed across gateway scopes + assert report.metrics["inference_requests"] == 14.0 + assert report.metrics["inference_input_tokens"] == 1100.0 + assert report.metrics["inference_cached_input_tokens"] == 400.0 + assert report.metrics["inference_output_tokens"] == 300.0 + assert report.metrics["inference_total_tokens"] == 1400.0 + # accuracy remains the primary metric, untouched by telemetry + assert report.metrics["score"] == 0.5 + + @pytest.mark.asyncio async def test_harbor_backend_exposes_exact_failed_trial_result(tmp_path): detail = "Invalid schema for function 'transcribe_audio': Missing 'language'." diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py index 485cdbfb..6c187a1b 100644 --- a/vero/tests/test_v05_harbor_inference.py +++ b/vero/tests/test_v05_harbor_inference.py @@ -94,11 +94,61 @@ def upstream(request: httpx.Request): "requests": 1, "upstream_errors": 0, "input_tokens": 11, + "cached_input_tokens": 0, "output_tokens": 7, "total_tokens": 18, } +def test_gateway_records_cached_input_tokens(tmp_path): + def upstream(request: httpx.Request): + if b'"stream": true' in request.content or b'"stream":true' in request.content: + payload = ( + 'event: response.completed\ndata: {"type":"response.completed",' + '"response":{"usage":{"input_tokens":40,"output_tokens":4,' + '"total_tokens":44,"input_tokens_details":{"cached_tokens":32}}}}\n\n' + ) + return httpx.Response( + 200, content=payload, headers={"content-type": "text/event-stream"} + ) + return httpx.Response( + 200, + json={ + "usage": { + "prompt_tokens": 20, + "completion_tokens": 5, + "total_tokens": 25, + "prompt_tokens_details": {"cached_tokens": 16}, + } + }, + ) + + app = create_inference_gateway_app( + config=_config(tmp_path, max_requests=None, max_tokens=None), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + client.post( + "/scopes/producer/optimizer/v1/chat/completions", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "messages": []}, + ) + client.post( + "/scopes/producer/eval-1/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hi", "stream": True}, + ) + + persisted = json.loads((tmp_path / "usage.json").read_text()) + scope = persisted["scopes"]["producer"] + # both the chat (prompt_tokens_details) and responses (input_tokens_details) + # shapes are recognized, per-attribution and in the scope total + assert scope["cached_input_tokens"] == 48 + assert scope["attributions"]["optimizer"]["cached_input_tokens"] == 16 + assert scope["attributions"]["eval-1"]["cached_input_tokens"] == 32 + + def test_gateway_records_usage_without_enforcing_omitted_limits(tmp_path): def upstream(_request: httpx.Request): return httpx.Response( diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index fcec7e45..28987f19 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -506,6 +506,80 @@ async def test_sidecar_context_survives_restart_without_disclosing_admin_runs( assert admin.id not in json.dumps(index) +class CostReportingBackend(StubBackend): + async def evaluate(self, *, context, request): + report = await super().evaluate(context=context, request=request) + return report.model_copy( + update={ + "metrics": { + **report.metrics, + "inference_total_tokens": 12345.0, + "inference_cached_input_tokens": 678.0, + "mean_case_wall_seconds": 42.0, + } + } + ) + + +@pytest.mark.asyncio +async def test_budget_blind_sidecar_redacts_inference_metrics(tmp_path): + # Cost telemetry is budget signal: in budget-blind mode the agent's receipt + # and context must not carry inference_* metrics; latency stays visible and + # the admin record keeps everything. + blind, _, _ = _sidecar(tmp_path, disclose_budget=False) + blind.engine.backends = BackendRegistry( + {"primary": CostReportingBackend(), "secondary": CostReportingBackend()} + ) + response = await blind.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ), + ) + ) + + receipt_metrics = response.receipt.result.metrics + assert "inference_total_tokens" not in receipt_metrics + assert "inference_cached_input_tokens" not in receipt_metrics + assert receipt_metrics["mean_case_wall_seconds"] == 42.0 + + document = json.loads( + ( + tmp_path + / "agent-volume/results" + / context_digest(response.receipt.evaluation_id) + / "evaluation.json" + ).read_text() + ) + context_metrics = document["result"]["metrics"] + assert "inference_total_tokens" not in context_metrics + assert context_metrics["mean_case_wall_seconds"] == 42.0 + + # the trusted record is untouched + record = blind.engine.database.get_evaluation(response.receipt.evaluation_id) + assert record.report.metrics["inference_total_tokens"] == 12345.0 + + # with budget disclosure on, the same metrics flow to the agent + disclosed, _, _ = _sidecar(tmp_path / "disclosed", disclose_budget=True) + disclosed.engine.backends = BackendRegistry( + {"primary": CostReportingBackend(), "secondary": CostReportingBackend()} + ) + open_response = await disclosed.evaluate( + SidecarEvaluationRequest( + backend_id="primary", + evaluation_set=EvaluationSet( + name="benchmark", + partition="validation", + selection=CaseIds(ids=[f"case-{i}" for i in range(5)]), + ), + ) + ) + assert open_response.receipt.result.metrics["inference_total_tokens"] == 12345.0 + + @pytest.mark.asyncio async def test_sidecar_context_writes_plan_and_tasks(tmp_path): sidecar, _, _ = _sidecar(tmp_path) diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py index 704db2c3..3c477d6c 100644 --- a/vero/tests/test_v05_harbor_verifier.py +++ b/vero/tests/test_v05_harbor_verifier.py @@ -202,6 +202,10 @@ async def test_verifier_pools_repeats_then_admin_rescores_and_scores_baseline(tm assert result.candidate == steady assert result.rewards == {"reward": 0.8} assert result.baseline_rewards == {"reward": 0.5} + # the scoring evaluations' full report metrics ride along in the durable + # result (accuracy plus cost/latency telemetry when the backend emits it) + assert result.reward_metrics == {"reward": {"score": 0.8}} + assert result.baseline_reward_metrics == {"reward": {"score": 0.5}} assert engine.calls == [ ("steady", "selection"), ("baseline", "selection"), From 1e97791509031699c21a89361d9d032329558f57 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 13:44:39 -0700 Subject: [PATCH 24/72] Per-trial token accounting: agent-reported metrics + flagged gateway threading Part A (always on): each case report carries agent-self-reported token counts from harbor's agent_result (agent_reported_input/cached/output_ tokens) plus report-level sums, distinct from the trusted gateway-metered inference_* metrics; budget-blind mode redacts both prefixes, now also at the per-case level. Part B (experimental, request_log.attribution, default off): the gateway stamps each request-log record with a provider-agnostic conversation thread_id - stateful responses chains inherit via previous_response_id, stateless surfaces (chat completions, Anthropic Messages) group by a first-user-message digest that ignores the system prompt. The attributor is constructed only when flagged, every method is exception-swallowing, and state is FIFO-bounded, so it can never affect proxying. Also tracks cached_input_tokens per scope/attribution. --- vero/src/vero/harbor/backend.py | 39 ++++++ vero/src/vero/harbor/build/compiler.py | 1 + vero/src/vero/harbor/build/config.py | 3 + vero/src/vero/harbor/inference.py | 138 +++++++++++++++++++ vero/src/vero/harbor/sidecar.py | 37 ++++-- vero/tests/test_v05_harbor_backend.py | 18 +++ vero/tests/test_v05_harbor_build.py | 22 ++- vero/tests/test_v05_harbor_inference.py | 170 ++++++++++++++++++++++++ vero/tests/test_v05_harbor_sidecar.py | 47 ++++++- 9 files changed, 464 insertions(+), 11 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index bbf85494..1a11486e 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -985,6 +985,31 @@ def _case_wall_seconds(self, attempts: list[dict[str, Any]]) -> float | None: ] return max(durations) if durations else None + @staticmethod + def _agent_reported_tokens(attempts: list[dict[str, Any]]) -> dict[str, float]: + """Sum the agent-self-reported token counts across a case's attempts. + + Harbor agents record their own usage in ``agent_result`` (stock + adapters and the baseline agents alike). Self-declared, so telemetry + grade — the gateway's per-evaluation ``inference_*`` metrics remain + the trusted envelope. + """ + names = { + "n_input_tokens": "agent_reported_input_tokens", + "n_cache_tokens": "agent_reported_cached_input_tokens", + "n_output_tokens": "agent_reported_output_tokens", + } + totals: dict[str, float] = {} + for attempt in attempts: + result = attempt.get("agent_result") + if not isinstance(result, dict): + continue + for source, metric in names.items(): + value = result.get(source) + if isinstance(value, (int, float)) and math.isfinite(float(value)): + totals[metric] = totals.get(metric, 0.0) + float(value) + return totals + def _case_result( self, case: HarborCase, @@ -994,6 +1019,7 @@ def _case_result( ) -> tuple[CaseResult, float]: trial_artifacts = self._trial_artifacts(attempts, artifact_root) wall_seconds = self._case_wall_seconds(attempts) + reported_tokens = self._agent_reported_tokens(attempts) attempt_detail = [ { "reward": self._attempt_reward(attempt), @@ -1046,6 +1072,7 @@ def _case_result( if wall_seconds is not None else {} ), + **reported_tokens, }, input={"task_name": case.task_name, **case.metadata}, output=output, @@ -1077,6 +1104,7 @@ def _case_result( numeric_rewards["score"] = score if wall_seconds is not None: numeric_rewards["wall_seconds"] = wall_seconds + numeric_rewards.update(reported_tokens) return ( CaseResult( case_id=case.id, @@ -1476,6 +1504,16 @@ def _category(case: CaseResult) -> ErrorCategory | None: for case in case_results if isinstance((wall := case.metrics.get("wall_seconds")), (int, float)) ] + reported_totals: dict[str, float] = {} + for case in case_results: + for key, value in case.metrics.items(): + if key.startswith("agent_reported_") and isinstance( + value, (int, float) + ): + total_key = key.replace("agent_reported_", "agent_reported_total_") + reported_totals[total_key] = ( + reported_totals.get(total_key, 0.0) + float(value) + ) report = EvaluationReport( status=EvaluationStatus.SUCCESS, metrics={ @@ -1498,6 +1536,7 @@ def _category(case: CaseResult) -> ErrorCategory | None: if case_walls else {} ), + **reported_totals, **self._inference_usage_metrics(context.evaluation_id), }, cases=case_results, diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 43bcd5d9..e11041db 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -559,6 +559,7 @@ def compile_harbor_task( { "directory": INFERENCE_REQUEST_LOG_DIR, "body_bytes": config.inference_gateway.request_log_body_bytes, + "attribution": config.inference_gateway.request_log_attribution, } if config.inference_gateway.log_requests else None diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 9acd92de..d0362388 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -107,6 +107,9 @@ class InferenceGatewaySpec(EvaluationModel): # archive. log_requests: bool = True request_log_body_bytes: int = Field(default=16384, ge=0) + # Experimental: stamp request-log records with provider-agnostic + # conversation threads for post-hoc per-trial accounting. Off by default. + request_log_attribution: bool = False @field_validator("upstream_api_key_env", "upstream_base_url_env") @classmethod diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index fc11e261..b69f9181 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -7,8 +7,10 @@ import json import logging import os +import re import secrets import time +from collections import OrderedDict from collections.abc import AsyncIterator from contextlib import asynccontextmanager from datetime import UTC, datetime @@ -75,6 +77,9 @@ class InferenceRequestLogConfig(EvaluationModel): directory: str = "/state/inference/requests" body_bytes: int = Field(default=16384, ge=0) rotate_bytes: int = Field(default=64 * 1024 * 1024, ge=1_048_576) + # Experimental: stamp each record with a provider-agnostic conversation + # thread (see _RequestAttributor) for post-hoc per-trial accounting. + attribution: bool = False @field_validator("directory") @classmethod @@ -463,6 +468,121 @@ def _append(path: Path, data: bytes) -> None: handle.write(data) +class _RequestAttributor: + """Experimental provider-agnostic conversation threading for the request + log (``request_log.attribution``). + + Stamps each record with a ``thread_id`` so post-hoc analysis can group a + trial's requests without content matching: stateful chains (the responses + API's ``previous_response_id``) inherit their parent's thread, and + stateless full-history surfaces (chat completions, Anthropic Messages) + group by a digest of the conversation's first user message. + + Best-effort by construction — every public method swallows all exceptions + and returns empty results, so it can never affect proxying. Memory is + bounded by two capped FIFO maps; CPU is bounded because the request body + is the dict the proxy already parsed and response scanning is one regex + over an at-most-8KB head. + """ + + _CAP = 100_000 + _SNIPPET_CHARS = 200 + _DIGEST_CHARS = 2048 + _RESPONSE_ID_PATTERN = re.compile(rb'"id"\s*:\s*"(resp_[A-Za-z0-9_-]+)"') + + def __init__(self): + self._response_threads: OrderedDict[str, str] = OrderedDict() + self._root_threads: OrderedDict[str, str] = OrderedDict() + self.errors = 0 + + @staticmethod + def _content_text(content: Any) -> str: + if isinstance(content, str): + return content + if isinstance(content, list): + parts = [] + for block in content: + if isinstance(block, str): + parts.append(block) + elif isinstance(block, dict): + text = block.get("text") or block.get("content") + if isinstance(text, str): + parts.append(text) + return "\n".join(parts) + return "" + + @classmethod + def _first_user_text(cls, value: dict[str, Any]) -> str | None: + """The conversation root: the first user turn, skipping system and + developer content (which is harness-constant, not trial-specific).""" + items = value.get("messages") + if not isinstance(items, list): + items = value.get("input") + if isinstance(items, str): + return items or None + if not isinstance(items, list): + return None + for item in items: + if isinstance(item, dict) and item.get("role") == "user": + text = cls._content_text(item.get("content")) + if text: + return text + return None + + def _remember(self, store: OrderedDict[str, str], key: str, value: str) -> None: + store[key] = value + while len(store) > self._CAP: + store.popitem(last=False) + + def stamp_request(self, value: Any) -> dict[str, str]: + try: + if not isinstance(value, dict): + return {} + previous = value.get("previous_response_id") + if isinstance(previous, str) and previous in self._response_threads: + return {"thread_id": self._response_threads[previous]} + text = self._first_user_text(value) + if not text: + if isinstance(previous, str) and previous: + # Chained onto a response we never saw (e.g. a gateway + # restart): still give the tail of the chain one thread. + thread = secrets.token_hex(8) + self._remember(self._response_threads, previous, thread) + return {"thread_id": thread} + return {} + normalized = "".join( + character for character in text.lower() if character.isalnum() + )[: self._DIGEST_CHARS] + digest = hashlib.sha256(normalized.encode("utf-8")).hexdigest() + thread = self._root_threads.get(digest) + if thread is None: + thread = secrets.token_hex(8) + self._remember(self._root_threads, digest, thread) + return { + "thread_id": thread, + "thread_root_digest": digest, + "root_snippet": text[: self._SNIPPET_CHARS], + } + except Exception: + self.errors += 1 + return {} + + def register_response( + self, fields: dict[str, str] | None, head: bytes + ) -> None: + try: + thread = (fields or {}).get("thread_id") + if not thread or not head: + return + match = self._RESPONSE_ID_PATTERN.search(head[:8192]) + if match is not None: + self._remember( + self._response_threads, match.group(1).decode("ascii"), thread + ) + except Exception: + self.errors += 1 + + _REQUEST_HEADERS = { "accept", "content-type", @@ -519,6 +639,11 @@ def create_inference_gateway_app( if config.request_log is not None else None ) + attributor = ( + _RequestAttributor() + if request_log is not None and config.request_log.attribution + else None + ) @asynccontextmanager async def lifespan(app: FastAPI): @@ -533,6 +658,7 @@ async def lifespan(app: FastAPI): app = FastAPI(title="VeRO inference gateway", version="1", lifespan=lifespan) app.state.usage_store = store app.state.request_log = request_log + app.state.request_attributor = attributor @app.get("/health") async def health(): @@ -595,6 +721,9 @@ async def proxy( return _provider_error(400, "request body must be JSON", "invalid_request") started = time.monotonic() + thread_fields = ( + attributor.stamp_request(value) if attributor is not None else {} + ) async def log_request( *, @@ -623,6 +752,7 @@ async def log_request( upstream_error=upstream_error, request=request_log.body(body), response=response, + **thread_fields, ) # Per-scope allow-list: producer (optimizer) and evaluation (target) have # separate tokens + allowed_models, so the target is normally confined to @@ -710,6 +840,12 @@ async def finish() -> None: cached_input_tokens=observed.tokens[3], upstream_error=failed, ) + if attributor is not None and captured is not None: + # The response id sits in the first SSE event, so + # the captured head is enough to link the chain. + attributor.register_response( + thread_fields, bytes(captured.head) + ) await log_request( status=upstream.status_code, stream=True, @@ -771,6 +907,8 @@ async def finish() -> None: upstream_error=upstream.status_code >= 400, ) ) + if attributor is not None: + attributor.register_response(thread_fields, content[:8192]) await log_request( status=upstream.status_code, tokens=tokens, diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index 9bf8cc30..f66f16f9 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -497,20 +497,39 @@ async def _enforce_aggregate_floor( f"{policy.access.min_aggregate_cases} cases; requested {cost.cases}" ) + _BUDGET_METRIC_PREFIXES = ("inference_", "agent_reported_") + def _redact_budget_metrics(self, record: EvaluationRecord) -> EvaluationRecord: - """Budget-blind mode: the inference_* cost metrics are budget signal - and must not reach the agent (enforcement is unchanged).""" + """Budget-blind mode: cost metrics — gateway-metered ``inference_*`` + and self-declared ``agent_reported_*``, report- and case-level — are + budget signal and must not reach the agent (enforcement unchanged).""" if self.disclose_budget: return record - metrics = { - key: value - for key, value in record.report.metrics.items() - if not key.startswith("inference_") - } - if len(metrics) == len(record.report.metrics): + + def keep(metrics: dict[str, float]) -> dict[str, float]: + return { + key: value + for key, value in metrics.items() + if not key.startswith(self._BUDGET_METRIC_PREFIXES) + } + + metrics = keep(record.report.metrics) + cases = [ + case.model_copy(update={"metrics": kept}) + if len(kept := keep(case.metrics)) != len(case.metrics) + else case + for case in record.report.cases + ] + if len(metrics) == len(record.report.metrics) and all( + kept is original for kept, original in zip(cases, record.report.cases) + ): return record return record.model_copy( - update={"report": record.report.model_copy(update={"metrics": metrics})} + update={ + "report": record.report.model_copy( + update={"metrics": metrics, "cases": cases} + ) + } ) def _visible_projections( diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index beb72f9d..c2efa881 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -509,6 +509,11 @@ async def test_harbor_backend_reports_latency_and_inference_telemetry(tmp_path): "verifier_result": {"rewards": {"reward": 1.0}}, "started_at": "2026-01-01T00:00:00Z", "finished_at": "2026-01-01T00:01:30Z", + "agent_result": { + "n_input_tokens": 1000, + "n_cache_tokens": 600, + "n_output_tokens": 50, + }, } ], "example/beta": [ @@ -516,6 +521,11 @@ async def test_harbor_backend_reports_latency_and_inference_telemetry(tmp_path): "verifier_result": {"rewards": {"reward": 0.0}}, "started_at": "2026-01-01T00:00:00Z", "finished_at": "2026-01-01T00:00:30Z", + "agent_result": { + "n_input_tokens": 200, + "n_cache_tokens": 0, + "n_output_tokens": 25, + }, } ], }, @@ -540,6 +550,14 @@ async def test_harbor_backend_reports_latency_and_inference_telemetry(tmp_path): assert report.metrics["inference_cached_input_tokens"] == 400.0 assert report.metrics["inference_output_tokens"] == 300.0 assert report.metrics["inference_total_tokens"] == 1400.0 + # agent-self-reported usage: per case and summed at report level, under a + # prefix distinct from the trusted gateway-metered inference_* metrics + assert by_id["case-a"].metrics["agent_reported_input_tokens"] == 1000.0 + assert by_id["case-a"].metrics["agent_reported_cached_input_tokens"] == 600.0 + assert by_id["case-b"].metrics["agent_reported_output_tokens"] == 25.0 + assert report.metrics["agent_reported_total_input_tokens"] == 1200.0 + assert report.metrics["agent_reported_total_cached_input_tokens"] == 600.0 + assert report.metrics["agent_reported_total_output_tokens"] == 75.0 # accuracy remains the primary metric, untouched by telemetry assert report.metrics["score"] == 0.5 diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 6b78bec2..92fef6ee 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -333,14 +333,34 @@ def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path) != backend["inference_gateway_token"] ) # Per-request logging is on by default: the gateway captures every - # request/response on its state volume and the sidecar mirrors it. + # request/response on its state volume and the sidecar mirrors it. The + # experimental thread-attribution stamping stays off unless opted into. assert gateway["request_log"] == { "directory": "/state/inference/requests", "body_bytes": 16384, + "attribution": False, } assert serve["inference_request_log_dir"] == "/state/inference/requests" +def test_compiler_emits_request_log_attribution_when_enabled(tmp_path): + config = _config( + tmp_path, + inference_gateway=InferenceGatewaySpec( + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec(allowed_models=["gpt-target"]), + request_log_attribution=True, + ), + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + gateway = json.loads( + (output / "environment/gateway/config.json").read_text(encoding="utf-8") + ) + assert gateway["request_log"]["attribution"] is True + + def test_compiler_omits_request_log_when_disabled(tmp_path): config = _config( tmp_path, diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py index 6c187a1b..366aa0ec 100644 --- a/vero/tests/test_v05_harbor_inference.py +++ b/vero/tests/test_v05_harbor_inference.py @@ -518,3 +518,173 @@ async def fill(): len(path.read_text().splitlines()) for path in files ) assert total == 6 + + +def _attributed_config(tmp_path, **kwargs): + from vero.harbor.inference import InferenceRequestLogConfig + + return _config(tmp_path, **kwargs).model_copy( + update={ + "request_log": InferenceRequestLogConfig( + directory=str(tmp_path / "requests"), + body_bytes=1024, + attribution=True, + ) + } + ) + + +def test_gateway_attribution_threads_stateful_and_stateless_requests(tmp_path): + calls = {"n": 0} + + def upstream(request: httpx.Request): + calls["n"] += 1 + if b'"stream": true' in request.content or b'"stream":true' in request.content: + payload = ( + f'event: response.created\ndata: {{"type":"response.created",' + f'"response":{{"id":"resp_stream{calls["n"]}"}}}}\n\n' + 'event: response.completed\ndata: {"type":"response.completed",' + '"response":{"usage":{"input_tokens":1,"output_tokens":1,' + '"total_tokens":2}}}\n\n' + ) + return httpx.Response( + 200, content=payload, headers={"content-type": "text/event-stream"} + ) + return httpx.Response( + 200, + json={"id": f"resp_{calls['n']}", "usage": {"input_tokens": 1}}, + ) + + app = create_inference_gateway_app( + config=_attributed_config(tmp_path, max_requests=None, max_tokens=None), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport(upstream), + ) + with TestClient(app) as client: + headers = {"Authorization": "Bearer scoped-token"} + # responses API: root turn, then a stateful follow-up with no prompt + client.post( + "/scopes/producer/optimizer/v1/responses", + headers=headers, + json={"model": "gpt-test", "input": "Solve task 42: find the answer"}, + ) + client.post( + "/scopes/producer/optimizer/v1/responses", + headers=headers, + json={"model": "gpt-test", "previous_response_id": "resp_1", "input": []}, + ) + # a streamed root and a follow-up chained onto its SSE-delivered id + client.post( + "/scopes/producer/optimizer/v1/responses", + headers=headers, + json={"model": "gpt-test", "input": "Another task entirely", "stream": True}, + ) + client.post( + "/scopes/producer/optimizer/v1/responses", + headers=headers, + json={ + "model": "gpt-test", + "previous_response_id": "resp_stream3", + "input": [], + }, + ) + # anthropic-messages shape: giant system prompt, threading keys off the + # first user message; the second call resends history (stateless) + system = "x" * 30000 + client.post( + "/scopes/producer/optimizer/v1/messages", + headers=headers, + json={ + "model": "gpt-test", + "system": system, + "messages": [ + {"role": "user", "content": "Solve task 42: find the answer"}, + ], + }, + ) + client.post( + "/scopes/producer/optimizer/v1/messages", + headers=headers, + json={ + "model": "gpt-test", + "system": system, + "messages": [ + {"role": "user", "content": "Solve task 42: find the answer"}, + {"role": "assistant", "content": [{"type": "text", "text": "hm"}]}, + {"role": "user", "content": "continue"}, + ], + }, + ) + + records = _log_records(tmp_path) + assert len(records) == 6 + threads = [record.get("thread_id") for record in records] + assert all(threads) + # stateful follow-ups inherit their root's thread + assert threads[1] == threads[0] + assert threads[3] == threads[2] + assert threads[2] != threads[0] + # stateless resends group by first-user-message digest — and the messages + # conversation shares its root text with the first responses thread + assert threads[4] == threads[5] == threads[0] + assert records[0]["root_snippet"].startswith("Solve task 42") + assert records[0]["thread_root_digest"] == records[4]["thread_root_digest"] + # chained records carry the thread but no root fields + assert "root_snippet" not in records[1] + + +def test_gateway_attribution_never_breaks_proxying(tmp_path): + app = create_inference_gateway_app( + config=_attributed_config(tmp_path, max_requests=None, max_tokens=None), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport( + lambda _request: httpx.Response(200, json={"id": 7, "usage": {}}) + ), + ) + hostile_bodies = [ + {"model": "gpt-test", "input": {"nested": {"weird": True}}}, + {"model": "gpt-test", "messages": [{"role": "user", "content": [1, None]}]}, + {"model": "gpt-test", "messages": "not-a-list", "previous_response_id": 9}, + {"model": "gpt-test", "input": [{"role": "user", "content": {"a": "b"}}]}, + ] + with TestClient(app) as client: + for body in hostile_bodies: + response = client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json=body, + ) + assert response.status_code == 200 + assert len(_log_records(tmp_path)) == len(hostile_bodies) + + +def test_gateway_attribution_disabled_by_default_and_memory_bounded(tmp_path): + from vero.harbor.inference import _RequestAttributor + + # default config: no attributor, no thread fields in records + app = create_inference_gateway_app( + config=_logged_config(tmp_path, max_requests=None, max_tokens=None), + upstream_api_key="upstream-secret", + transport=httpx.MockTransport( + lambda _request: httpx.Response(200, json={"usage": {}}) + ), + ) + assert app.state.request_attributor is None + with TestClient(app) as client: + client.post( + "/scopes/producer/optimizer/v1/responses", + headers={"Authorization": "Bearer scoped-token"}, + json={"model": "gpt-test", "input": "hello"}, + ) + (record,) = _log_records(tmp_path) + assert "thread_id" not in record + + # FIFO caps hold under churn + attributor = _RequestAttributor() + attributor._CAP = 10 + for index in range(50): + fields = attributor.stamp_request({"input": f"root {index}"}) + attributor.register_response(fields, f'{{"id":"resp_{index}"}}'.encode()) + assert len(attributor._root_threads) <= 10 + assert len(attributor._response_threads) <= 10 + assert attributor.errors == 0 diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index 28987f19..b18c9254 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -509,12 +509,26 @@ async def test_sidecar_context_survives_restart_without_disclosing_admin_runs( class CostReportingBackend(StubBackend): async def evaluate(self, *, context, request): report = await super().evaluate(context=context, request=request) + cases = [ + case.model_copy( + update={ + "metrics": { + **case.metrics, + "agent_reported_input_tokens": 111.0, + "wall_seconds": 7.0, + } + } + ) + for case in report.cases + ] return report.model_copy( update={ + "cases": cases, "metrics": { **report.metrics, "inference_total_tokens": 12345.0, "inference_cached_input_tokens": 678.0, + "agent_reported_total_input_tokens": 555.0, "mean_case_wall_seconds": 42.0, } } @@ -556,11 +570,42 @@ async def test_budget_blind_sidecar_redacts_inference_metrics(tmp_path): ) context_metrics = document["result"]["metrics"] assert "inference_total_tokens" not in context_metrics + assert "agent_reported_total_input_tokens" not in context_metrics assert context_metrics["mean_case_wall_seconds"] == 42.0 - # the trusted record is untouched + # the trusted record is untouched, including per-case cost metrics record = blind.engine.database.get_evaluation(response.receipt.evaluation_id) assert record.report.metrics["inference_total_tokens"] == 12345.0 + assert record.report.metrics["agent_reported_total_input_tokens"] == 555.0 + assert record.report.cases[0].metrics["agent_reported_input_tokens"] == 111.0 + + # full disclosure in budget-blind mode: per-case cost metrics are redacted + # from the agent-visible case files too, while wall_seconds stays + full = await blind.evaluate( + SidecarEvaluationRequest( + backend_id="secondary", + evaluation_set=EvaluationSet(name="public"), + ) + ) + full_doc = json.loads( + ( + tmp_path + / "agent-volume/results" + / context_digest(full.receipt.evaluation_id) + / "evaluation.json" + ).read_text() + ) + case_file = full_doc["result"]["case_files"][0]["path"] + case_doc = json.loads( + ( + tmp_path + / "agent-volume/results" + / context_digest(full.receipt.evaluation_id) + / case_file + ).read_text() + ) + assert "agent_reported_input_tokens" not in case_doc["result"]["metrics"] + assert case_doc["result"]["metrics"]["wall_seconds"] == 7.0 # with budget disclosure on, the same metrics flow to the agent disclosed, _, _ = _sidecar(tmp_path / "disclosed", disclose_budget=True) From e4fe6682604a6662960e0f83ab3c451a6144a109 Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Fri, 24 Jul 2026 19:26:17 +0300 Subject: [PATCH 25/72] fix: cap requires-python <3.14 so uv can't pick a Python that fails to build litellm/pyo3 The v0.5 package (vero/pyproject.toml) declares requires-python = ">=3.11", which is too permissive. `uv run` picks the newest installed interpreter (e.g. Python 3.14). The transitive dependency litellm 1.92.0 (pulled via scale-vero[optimize] -> openai-agents[litellm]) has a Rust/PyO3 core that fails to build on Python 3.14: error: the configured Python interpreter version (3.14) is newer than PyO3's maximum supported version (3.13) Repro on a machine whose default uv-managed Python is 3.14: cd vero uv run vero harbor run ... uv resolves to Python 3.14 (satisfies >=3.11), then the litellm build fails with the PyO3 error above. Pinning to <3.14 forces uv to select 3.11-3.13, matching PyO3's max supported version. Change is limited to vero/pyproject.toml. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vero/pyproject.toml b/vero/pyproject.toml index 1928d734..fb743449 100644 --- a/vero/pyproject.toml +++ b/vero/pyproject.toml @@ -7,7 +7,7 @@ authors = [ { name = "Varun Ursekar", email = "oss@scale.com" } ] license = { text = "MIT" } -requires-python = ">=3.11" +requires-python = ">=3.11,<3.14" dependencies = [ "click>=8.0.0", "pydantic>=2.11.7", From f4e20579dd2d182989cd2fc8ad5bf2b715adf6b0 Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Fri, 24 Jul 2026 19:15:20 +0300 Subject: [PATCH 26/72] fix: forward per-run agent env (--ae) to the optimizer's harbor run Add a `agent_env` build-config field and render it as harbor `--ae KEY=VALUE` on the optimizer's `harbor run`, so env vars reach the optimizer agent's own setup/install exec. Root cause: `run_command` in vero/src/vero/harbor/cli.py builds the optimizer's harbor command from a fixed list + `-m model` + the click `extra` passthrough and never reads any per-run agent env. `extra_harbor_args` was the only env-adjacent knob, but it is compiled solely into the eval sub-run (compiler.py writes it into serve.json, consumed by backend.py), a different `harbor run` with a different agent, so it never reaches the optimizer agent's install shell. Harbor's `--ae/--agent-env` flag populates the agent's extra_env, which harbor injects into the agent's setup/install exec via scoped_exec_env, so rendering `--ae` here is the supported channel. Motivation: there was no way to set e.g. UV_TOOL_BIN_DIR for the optimizer agent's install on a non-root sandbox (so `uv tool install` doesn't try to symlink into root-owned /usr/local/bin). Passing it via extra_harbor_args silently did nothing. `extra_harbor_args` is intentionally left unchanged to avoid overloading its meaning (eval sub-run args). Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/harbor/build/config.py | 7 ++++ vero/src/vero/harbor/cli.py | 6 +++ vero/tests/test_v05_harbor_build.py | 61 ++++++++++++++++++++++++++++ 3 files changed, 74 insertions(+) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index d0362388..95e39257 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -240,6 +240,13 @@ class HarborBuildConfig(EvaluationModel): feedback_max_bytes: int = Field(default=3000, ge=0) expose_attempt_detail: bool = False extra_harbor_args: list[str] = Field(default_factory=list) + # Environment variables injected into the optimizer agent's shell (setup/ + # install and run), rendered as harbor `--ae KEY=VALUE` on the optimizer's + # `harbor run`. Distinct from `extra_harbor_args` (which only flows into the + # eval sub-run) and `task_environment` (the eval sub-run's env): this reaches + # the optimizer agent's own install/setup exec, e.g. UV_TOOL_BIN_DIR so + # `uv tool install` targets a writable dir on a non-root sandbox. + agent_env: dict[str, str] = Field(default_factory=dict) timeout_seconds: float = Field(default=1800.0, gt=0) case_timeout_seconds: float = Field(default=180.0, gt=0) diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index fd07bc29..fcd5f11d 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -430,6 +430,12 @@ def run_command(config_path, agent, model, environment, params, env_file, extra) ] if model is not None: command.extend(["-m", model]) + # Forward the build's declared agent env to the optimizer agent's shell. + # Harbor's `--ae KEY=VALUE` populates the agent's extra_env, which harbor + # injects into the agent's setup/install exec (scoped_exec_env). Sorted + # for a deterministic command line. + for key in sorted(config.agent_env): + command.extend(["--ae", f"{key}={config.agent_env[key]}"]) command.extend(extra) click.echo(shlex.join(command)) completed = subprocess.run( diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 92fef6ee..19d9591c 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -1,6 +1,7 @@ from __future__ import annotations import json +import shlex import subprocess import tomllib from pathlib import Path @@ -791,3 +792,63 @@ def test_compiler_uses_published_version_outside_a_source_checkout( def test_agent_access_defaults_to_safe_k_anonymity_floor(): # Omitting min_aggregate_cases must yield a real floor (5), not a no-op (1). assert AgentAccessSpec(partition="validation").min_aggregate_cases == 5 + + +def test_run_command_forwards_agent_env_as_harbor_ae(tmp_path, monkeypatch): + # `config.agent_env` must be rendered as repeated `--ae KEY=VALUE` on the + # optimizer's `harbor run` so it reaches the agent's setup/install exec + # (harbor injects --ae into the agent's extra_env / scoped_exec_env). This is + # the only supported channel for e.g. UV_TOOL_BIN_DIR on a non-root sandbox; + # extra_harbor_args only flows into the eval sub-run, not this command. + from click.testing import CliRunner + + from vero.harbor import build as harbor_build + from vero.harbor import cli as harbor_cli + + config = _config( + tmp_path, + agent_env={"UV_TOOL_BIN_DIR": "/home/agent/.local/bin", "FOO": "bar"}, + ) + config_path = tmp_path / "build.yaml" + config_path.write_text("name: placeholder\n", encoding="utf-8") + + captured: dict[str, list[str]] = {} + + def fake_run(command, *args, **kwargs): + captured["command"] = list(command) + return subprocess.CompletedProcess(command, 0) + + monkeypatch.setattr(harbor_cli.shutil, "which", lambda _name: "/usr/bin/uvx") + monkeypatch.setattr(harbor_cli.subprocess, "run", fake_run) + monkeypatch.setattr( + harbor_build, "load_harbor_build_config", lambda *a, **k: config + ) + monkeypatch.setattr( + harbor_build, "compile_harbor_task", lambda cfg, out: out + ) + + result = CliRunner().invoke( + harbor_cli.harbor, + [ + "run", + "--config", + str(config_path), + "--agent", + "codex", + "--model", + "gpt-5.4", + "--environment", + "modal", + ], + ) + + assert result.exit_code == 0, result.output + command = captured["command"] + assert "--ae" in command + assert "--ae UV_TOOL_BIN_DIR=/home/agent/.local/bin" in shlex.join(command) + assert "--ae FOO=bar" in shlex.join(command) + # Deterministic (sorted-by-key) ordering: FOO before UV_TOOL_BIN_DIR. + joined = shlex.join(command) + assert joined.index("--ae FOO=bar") < joined.index( + "--ae UV_TOOL_BIN_DIR=" + ) From cc464cea8ac53b6f133d1810e471ba608bbbe07f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 14:06:17 -0700 Subject: [PATCH 27/72] Complete run_command test stubs for the merged agent_env field The SimpleNamespace config stubs in test_v05_harbor_http.py predate #48's agent_env field; run_command now reads config.agent_env, so give the stubs the same defaulted-empty field the real HarborBuildConfig has. --- vero/tests/test_v05_harbor_http.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index e4708ec4..943b1fe8 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -260,7 +260,7 @@ def test_harbor_run_uses_current_python_and_pinned_harbor_extra(tmp_path, monkey config_path = tmp_path / "build.yaml" config_path.write_text("task_name: unused\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0", agent_env={}) observed = {} def compile_task(_config, output): @@ -313,7 +313,7 @@ def test_harbor_run_env_file_secrets_reach_subprocess_not_command_line( config_path.write_text("task_name: unused\n") env_file = tmp_path / "secrets.env" env_file.write_text("MODAL_TOKEN_ID=mt-id\nMODAL_TOKEN_SECRET=mt-secret\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0") + config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0", agent_env={}) observed = {} def compile_task(_config, output): From 14d69ca64a081466efdff1554040befaaf7098d2 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 15:16:13 -0700 Subject: [PATCH 28/72] Preserve cancellation and shield refunds in the budget cleanup paths Addresses greptile P1s on #45. In BudgetLedger.reserve/refund a failing durable write (transient OSError) escaped at the shield-drain await and swallowed the run's CancelledError, breaking structured cancellation and leaving a reservation charged on disk. Both drains now catch the write failure and re-raise the originating cancellation, chaining the write error for diagnosis. In the engine, the EvaluationExecutionError handler and the infrastructure-failure refund now wrap record+refund in asyncio.shield like the EvaluationCancelledError handler, so a cancellation racing cleanup cannot interrupt the refund and leak budget. --- vero/src/vero/evaluation/budget.py | 27 ++++- vero/src/vero/evaluation/engine.py | 31 ++++-- vero/tests/test_v05_evaluation_runtime.py | 126 ++++++++++++++++++++++ 3 files changed, 171 insertions(+), 13 deletions(-) diff --git a/vero/src/vero/evaluation/budget.py b/vero/src/vero/evaluation/budget.py index 96ab1c2f..7f15176b 100644 --- a/vero/src/vero/evaluation/budget.py +++ b/vero/src/vero/evaluation/budget.py @@ -128,7 +128,20 @@ async def reserve( await asyncio.shield(rollback) except asyncio.CancelledError: pass - rollback.result() + except Exception: + # The rollback write failed; the task is now done, + # so exit the drain and surface it below. + break + # The originating cancellation must propagate even if the + # rollback write itself failed — swallowing CancelledError + # for an OSError would break structured cancellation and + # hide that the run was cancelled. Chain the write error so + # the durable-state inconsistency stays diagnosable. + write_error = ( + None if rollback.cancelled() else rollback.exception() + ) + if write_error is not None: + raise cancellation from write_error raise cancellation self._budgets = snapshot return updated @@ -181,7 +194,17 @@ async def refund( await asyncio.shield(write) except asyncio.CancelledError as error: cancellation = error - write.result() + except Exception: + # The write failed; the task is done, exit the drain. + break + try: + write.result() + except Exception as error: + # A failed durable refund must not swallow the cancellation + # of the run being torn down; chain the write error. + if cancellation is not None: + raise cancellation from error + raise self._budgets = snapshot if cancellation is not None: raise cancellation diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index a800f20c..8fa237e1 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -376,13 +376,18 @@ async def _execute_record( failure = EvaluationStore( self.evaluator.evaluations_dir / error.evaluation_id ).load() - await self._record(failure) + # Shield the record + refund like the cancellation handler above: a + # cancellation arriving during this cleanup must not interrupt the + # refund and leak the reservation's budget. + await asyncio.shield(self._record(failure)) if charged: - await self.budget_ledger.refund( - backend_id, - request.evaluation_set, - cost, - principal, + await asyncio.shield( + self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) ) raise await self._record(record) @@ -408,11 +413,15 @@ async def _execute_record( ) if infrastructure is not None: if charged: - await self.budget_ledger.refund( - backend_id, - request.evaluation_set, - cost, - principal, + # Shield: a cancellation racing this infrastructure-failure + # refund must not leak the reservation's budget. + await asyncio.shield( + self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) ) raise EvaluationInfrastructureError(record.id, infrastructure.message) return record, decision diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index cd7fbda6..ca1bbcb4 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -619,6 +619,132 @@ def delayed_write(write_path, value): assert on_disk == in_memory +@pytest.mark.asyncio +async def test_reserve_rollback_write_failure_preserves_cancellation( + tmp_path: Path, + monkeypatch, +): + # If the rollback write itself fails, the run's cancellation must still + # propagate (not be replaced by the write's OSError), with the write error + # chained for diagnosis. + evaluation_set = EvaluationSet(name="performance") + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="command", + evaluation_set_key=evaluation_set.budget_key("command"), + total_runs=2, + ) + ], + path=tmp_path / "budget.json", + ) + ledger.save() + started = threading.Event() + release = threading.Event() + real_write = budget_module._atomic_write_json + calls = {"n": 0} + + def failing_rollback_write(write_path, value): + calls["n"] += 1 + if calls["n"] == 1: + # the charge write: block so the reservation can be cancelled here + started.set() + assert release.wait(timeout=5) + real_write(write_path, value) + else: + # the rollback write fails durably + raise OSError("disk gone") + + monkeypatch.setattr(budget_module, "_atomic_write_json", failing_rollback_write) + reservation = asyncio.create_task( + ledger.reserve("command", evaluation_set, EvaluationCost()) + ) + assert await asyncio.to_thread(started.wait, 5) + reservation.cancel() + release.set() + + with pytest.raises(asyncio.CancelledError) as excinfo: + await reservation + assert isinstance(excinfo.value.__cause__, OSError) + + +@pytest.mark.asyncio +async def test_refund_write_failure_preserves_cancellation(tmp_path: Path, monkeypatch): + # A cancellation racing the durable refund write must win over the write's + # own failure rather than being swallowed. + evaluation_set = EvaluationSet(name="performance") + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="command", + evaluation_set_key=evaluation_set.budget_key("command"), + total_runs=2, + ) + ], + path=tmp_path / "budget.json", + ) + await ledger.reserve("command", evaluation_set, EvaluationCost(runs=1)) + started = threading.Event() + release = threading.Event() + + def blocking_failing_write(write_path, value): + started.set() + assert release.wait(timeout=5) + raise OSError("disk gone") + + monkeypatch.setattr(budget_module, "_atomic_write_json", blocking_failing_write) + refund = asyncio.create_task( + ledger.refund("command", evaluation_set, EvaluationCost(runs=1)) + ) + assert await asyncio.to_thread(started.wait, 5) + refund.cancel() + release.set() + + with pytest.raises(asyncio.CancelledError) as excinfo: + await refund + assert isinstance(excinfo.value.__cause__, OSError) + + +@pytest.mark.asyncio +async def test_execution_error_refunds_reservation(tmp_path: Path): + # A backend exception (EvaluationExecutionError) must refund the reservation + # via the shielded cleanup path, restoring the budget in memory and on disk. + workspace = StubWorkspace(tmp_path / "repo") + backend = StubBackend(error=RuntimeError("boom")) + evaluation_set = request().evaluation_set + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="default", + evaluation_set_key=evaluation_set.budget_key("default"), + total_runs=2, + ) + ], + path=tmp_path / "budgets.json", + ) + ledger.save() + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": backend}), + database=EvaluationDatabase(id="session"), + database_path=tmp_path / "database.json", + budget_ledger=ledger, + authorization_resolver=allow_all_evaluations, + ) + + with pytest.raises(EvaluationExecutionError): + await engine.evaluate_record(backend_id="default", request=request()) + + remaining = ledger.get("default", evaluation_set) + assert remaining is not None and remaining.remaining_runs == 2 + assert ( + BudgetLedger.load(tmp_path / "budgets.json") + .get("default", evaluation_set) + .remaining_runs + == 2 + ) + + @pytest.mark.asyncio async def test_cancelled_evaluation_is_terminal_indexed_and_refunded(tmp_path: Path): class BlockingBackend(StubBackend): From d949a3978156d3db4b6eb2de07219874fe2f7d8f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 15:26:22 -0700 Subject: [PATCH 29/72] Gate infrastructure exclusion and retry to trusted evaluations MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Addresses the competitive-selection integrity concern on #45. The transient-infra category is detected from the candidate process's own exception type/message, and excluded cases drop out of the aggregate denominator — so a candidate emitting a timeout/connection error on a hard case could inflate its mean, with whole-sub-run retry (default 3) amplifying it under aggregate_attempts: best. For competitive (agent) evaluations a within-trial transient-infra result is now scored at the failure value (the zero-fill invariant), and whole-sub-run infrastructure retry is disabled. Trusted finalization re-scores — run by the operator, not gameable — keep the exclusion and the configured retries. Genuinely harness-produced deadness stays excluded for both: coverage gaps (no trial produced) and gateway-ledger budget/auth (terminating, out of band). Default infrastructure_max_ attempts lowered to 1. --- vero/src/vero/harbor/backend.py | 53 +++++++++++-- vero/tests/test_v05_harbor_backend.py | 108 +++++++++++++++++++------- 2 files changed, 126 insertions(+), 35 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 1a11486e..98da9836 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -114,7 +114,11 @@ class HarborBackendConfig(EvaluationModel): retry_wait_multiplier: float = Field(default=2.0, ge=1.0) retry_min_wait_seconds: float = Field(default=4.0, ge=0.0) retry_max_wait_seconds: float = Field(default=60.0, ge=0.0) - infrastructure_max_attempts: int = Field(default=3, ge=1) + # Whole-sub-run infrastructure retry. Default 1 (no retry): retry > 1 only + # applies to trusted finalization evaluations (see HarborBackend.evaluate), + # never to competitive agent search, where re-running for best coverage is + # a best-of-N amplifier a candidate can trigger. + infrastructure_max_attempts: int = Field(default=1, ge=1) infrastructure_retry_delay_seconds: float = Field(default=5.0, ge=0) reward_key: str | None = None # Average attempts by default rather than taking the best: best-of-n is an @@ -812,8 +816,14 @@ def _attempt_reward(self, attempt: dict[str, Any]) -> float | None: rewards = (attempt.get("verifier_result") or {}).get("rewards") or {} return self._extract_reward(rewards) if rewards else None - def _attempt_is_infra(self, attempt: dict[str, Any]) -> bool: - """Whether a dead attempt died to infrastructure (vs the candidate).""" + def _attempt_is_infra(self, attempt: dict[str, Any], *, trusted: bool) -> bool: + """Whether a dead attempt died to infrastructure (vs the candidate). + + For a competitive (agent) evaluation a candidate-controlled trial's own + transient-infra exception is not trusted as infrastructure — the + candidate could emit a timeout/connection error to have its dead attempt + excluded from the mean. Only trusted evaluations honor that signal. + """ info = attempt.get("exception_info") or {} if not info: return False @@ -822,7 +832,10 @@ def _attempt_is_infra(self, attempt: dict[str, Any]) -> bool: detail = info.get(detail_key) if isinstance(detail, str) and detail: signals.append(detail) - return not policy(classify_case(signals)).is_informative_sample + category = classify_case(signals) + if not trusted and category == ErrorCategory.TRANSIENT_INFRA: + return False + return not policy(category).is_informative_sample def _best_attempt( self, @@ -1016,6 +1029,7 @@ def _case_result( attempts: list[dict[str, Any]], *, artifact_root: Path, + trusted: bool = False, ) -> tuple[CaseResult, float]: trial_artifacts = self._trial_artifacts(attempts, artifact_root) wall_seconds = self._case_wall_seconds(attempts) @@ -1053,7 +1067,8 @@ def _case_result( n_dead_infra = sum( 1 for attempt, reward in zip(attempts, rewards) - if reward is None and self._attempt_is_infra(attempt) + if reward is None + and self._attempt_is_infra(attempt, trusted=trusted) ) return ( CaseResult( @@ -1143,10 +1158,23 @@ def _case_result( if not attempts: # Harbor produced no trial for this task at all: infrastructure - # dropped the case (see the coverage gate), not the candidate. + # dropped the case (see the coverage gate), not the candidate. This + # coverage gap is harness-produced and stays excluded even for + # competitive evaluations. category = ErrorCategory.TRANSIENT_INFRA else: category = classify_case(signals) + if not trusted and category == ErrorCategory.TRANSIENT_INFRA: + # A trial ran and died with a transient-looking exception. For + # competitive (agent) selection we cannot trust a candidate- + # controlled process's own exception text to mean + # "infrastructure": excluding it would drop the case from its + # own denominator and let a candidate inflate its mean by + # emitting a timeout/connection error on hard cases. Score it at + # the failure value instead. Genuine infrastructure is caught + # out of band (coverage gaps above; gateway-ledger budget/auth, + # which remain terminating) and via trusted-only retry. + category = ErrorCategory.TASK_FAILURE category_policy = policy(category) output["dead_exception_types"] = exception_counts output["error_category"] = category.value @@ -1241,7 +1269,15 @@ async def evaluate( attempts: list[tuple[CommandResult, str, str]] = [] groups: dict[str, list[dict[str, Any]]] = {} jobs_dir = capture_dir / "jobs" - for attempt in range(1, self.config.infrastructure_max_attempts + 1): + # Whole-sub-run infrastructure retry is a trusted-only affordance: for a + # competitive (agent) evaluation, re-running the sub-run and keeping the + # best coverage is a best-of-N amplifier over a candidate that can + # itself trigger the retry. Trusted finalization re-scores, run by the + # operator on a controlled environment, keep the configured retries. + max_infra_attempts = ( + self.config.infrastructure_max_attempts if context.finalization else 1 + ) + for attempt in range(1, max_infra_attempts + 1): attempt_jobs_dir = ( jobs_dir if attempt == 1 else capture_dir / f"retry-{attempt}" / "jobs" ) @@ -1331,7 +1367,7 @@ async def evaluate( if requested_tasks <= set(groups): jobs_dir = attempt_jobs_dir break - if attempt < self.config.infrastructure_max_attempts: + if attempt < max_infra_attempts: await asyncio.sleep( self.config.infrastructure_retry_delay_seconds * attempt ) @@ -1385,6 +1421,7 @@ async def evaluate( case, groups.get(case.expected_result_task_name, []), artifact_root=context.artifact_dir, + trusted=context.finalization, ) case_results.append(case_result) scores.append(score) diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index c2efa881..6aca38e6 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -237,9 +237,11 @@ async def run(self, command, cwd=None, timeout=30, env=None, run_as=None): return self.result -async def _context(tmp_path: Path, sandbox: FakeSandbox) -> EvaluationContext: +async def _context( + tmp_path: Path, sandbox: FakeSandbox, *, finalization: bool = False +) -> EvaluationContext: target = tmp_path / "target" - target.mkdir(exist_ok=True) + target.mkdir(parents=True, exist_ok=True) result_dir = tmp_path / "result" artifact_dir = result_dir / "artifacts" artifact_dir.mkdir(parents=True) @@ -252,6 +254,7 @@ async def _context(tmp_path: Path, sandbox: FakeSandbox) -> EvaluationContext: result_dir=result_dir, artifact_dir=artifact_dir, case_store=CaseCheckpointStore(result_dir / "cases"), + finalization=finalization, ) @@ -663,38 +666,56 @@ async def test_harbor_backend_scores_agent_crash_as_informative_task_failure(tmp @pytest.mark.asyncio -async def test_harbor_backend_excludes_transient_infrastructure_from_aggregate( +async def test_harbor_backend_excludes_transient_infrastructure_only_when_trusted( tmp_path, ): - sandbox = FakeSandbox( - tmp_path, - { - "example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}], - "example/beta": [ - { - "verifier_result": None, - "exception_info": {"exception_type": "openai.RateLimitError"}, - } - ], - }, - ) + # A within-trial transient-infra exception is trusted as infrastructure only + # for finalization (admin) evaluations. For a competitive agent evaluation + # it is scored at the failure value, so a candidate cannot inflate its mean + # by emitting a timeout/connection error on a hard case. + def _sandbox(): + return FakeSandbox( + tmp_path, + { + "example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}], + "example/beta": [ + { + "verifier_result": None, + "exception_info": {"exception_type": "openai.RateLimitError"}, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) - report = await backend.evaluate( - context=await _context(tmp_path, sandbox), + # Agent (untrusted) evaluation: the rate-limited case is a scored failure. + agent_report = await backend.evaluate( + context=await _context(tmp_path / "agent", _sandbox()), request=_request(CaseRange(stop=2)), ) + assert agent_report.metrics["score"] == 0.5 + assert agent_report.metrics["error_rate"] == 0.0 + assert [case.status for case in agent_report.cases] == [ + CaseStatus.SUCCESS, + CaseStatus.SUCCESS, + ] + assert agent_report.cases[1].output["error_category"] == "task_failure" - # The rate-limited case is infrastructure: excluded from the mean (which is - # the sole successful case, 1.0) and reported as the error rate. - assert report.metrics["score"] == 1.0 - assert report.metrics["error_rate"] == 0.5 - assert [case.status for case in report.cases] == [ + # Trusted finalization: infrastructure is excluded from the mean and + # reported as the error rate, as before. + trusted_report = await backend.evaluate( + context=await _context(tmp_path / "trusted", _sandbox(), finalization=True), + request=_request(CaseRange(stop=2)), + ) + assert trusted_report.metrics["score"] == 1.0 + assert trusted_report.metrics["error_rate"] == 0.5 + assert [case.status for case in trusted_report.cases] == [ CaseStatus.SUCCESS, CaseStatus.ERROR, ] - assert report.cases[1].output["error_category"] == "transient_infra" - assert report.cases[1].errors[0].code == "transient_infrastructure" + assert trusted_report.cases[1].output["error_category"] == "transient_infra" + assert trusted_report.cases[1].errors[0].code == "transient_infrastructure" @pytest.mark.asyncio @@ -763,6 +784,37 @@ async def test_harbor_backend_treats_missing_coverage_as_infrastructure(tmp_path ) +@pytest.mark.asyncio +async def test_infrastructure_retry_is_trusted_only(tmp_path): + # With incomplete coverage (beta never produced), a competitive evaluation + # must not re-run the sub-run (no best-of-N amplifier a candidate can + # trigger); a trusted finalization re-scores with the configured retries. + def _sandbox(): + return FakeSandbox( + tmp_path, + {"example/alpha": [{"verifier_result": {"rewards": {"reward": 1.0}}}]}, + ) + + backend = HarborBackend( + _config( + tmp_path, + infrastructure_max_attempts=3, + infrastructure_retry_delay_seconds=0, + ) + ) + + agent_ctx = await _context(tmp_path / "agent", _sandbox()) + await backend.evaluate(context=agent_ctx, request=_request(CaseRange(stop=2))) + assert not (agent_ctx.artifact_dir / "harbor" / "retry-2").exists() + + trusted_ctx = await _context( + tmp_path / "trusted", _sandbox(), finalization=True + ) + await backend.evaluate(context=trusted_ctx, request=_request(CaseRange(stop=2))) + assert (trusted_ctx.artifact_dir / "harbor" / "retry-2").exists() + assert (trusted_ctx.artifact_dir / "harbor" / "retry-3").exists() + + @pytest.mark.asyncio async def test_harbor_backend_runs_harness_as_unprivileged_user(tmp_path): sandbox = FakeSandbox( @@ -893,9 +945,11 @@ async def test_harbor_backend_mean_counts_dead_attempts_as_failures(tmp_path): "score": 0.5, "n_attempts": 2.0, "n_scored": 1.0, - # the dead attempt was a TimeoutError -> infra dilution, not clean signal - "n_dead_infra": 1.0, - "n_clean": 1.0, + # This is a competitive (untrusted) evaluation, so the dead attempt's own + # TimeoutError is not trusted as infrastructure — it counts as a clean + # zero-filled failure, not infra dilution. + "n_dead_infra": 0.0, + "n_clean": 2.0, } From 34b732f0e1de953faf483881b4d685545d9976db Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 12:46:13 -0700 Subject: [PATCH 30/72] Document AgentAccessSpec and VerificationTargetSpec Both specs govern the fair-play boundary but neither said so. Give each a docstring naming its role and fields: AgentAccessSpec is what the optimizer may see and spend while searching a partition, VerificationTargetSpec is how the trusted verifier finally scores the candidate that search picked. Note that min_aggregate_cases is only consulted when disclosure is AGGREGATE; under FULL the agent already sees every per-case result, so the floor is inert there whatever it is set to. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/config.py | 42 ++++++++++++++++++++++++++-- 1 file changed, 39 insertions(+), 3 deletions(-) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 95e39257..bd1135f8 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -21,11 +21,29 @@ class AgentAccessSpec(EvaluationModel): + """The optimizer agent's access to one named evaluation partition. + + A build declares one spec per partition; to_access_policy translates it into + the runtime EvaluationAccessPolicy the sidecar enforces. This governs what + the optimizer may see and spend while searching, as opposed to how the + trusted side finally scores a candidate (see VerificationTargetSpec). + + Attributes: + partition: Partition this access applies to, e.g. "validation". + disclosure: How much of a result the agent sees — aggregate score vs. + per-case detail. + expose_case_resources: Whether each case's input files are materialized + into the agent's workspace. + min_aggregate_cases: Smallest number of cases an aggregate score may + cover, so the agent cannot request a subset small enough to reveal an + individual case's result. Only consulted when disclosure is AGGREGATE. + total_runs: Optional cap on evaluation runs against this partition. + total_cases: Optional cap on cases scored against this partition. + """ + partition: str disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE expose_case_resources: bool = False - # k-anonymity floor for aggregate subset evals; 5 by default so an omitted - # value is safe rather than an unfloored (single-case) leak. min_aggregate_cases: int = Field(default=5, ge=1) total_runs: int | None = Field(default=None, ge=0) total_cases: int | None = Field(default=None, ge=0) @@ -47,12 +65,30 @@ def to_access_policy(self) -> EvaluationAccessPolicy: class VerificationTargetSpec(EvaluationModel): + """One trusted final scoring pass the verifier runs on a chosen candidate. + + Distinct from the evaluations the optimizer runs during search: after search + picks a best candidate on the selection partition, the trusted verifier + scores it against these targets (normally the held-out test partition) to + produce the final reward. A build declares one spec per pass. + + Attributes: + partition: Partition to score on, e.g. "test". + reward_key: Metric key in the report that is the reward. + model: Optional model override for this pass; passed through as + harbor_model_override so final scoring can differ from search. + parameters: Extra backend parameters for the pass. + failure_value: Score assigned when the pass fails. + baseline_reward: Pin the seed's reward on this target to skip scoring + the immutable baseline every run. + max_attempts: Number of scoring attempts before accepting failure. + """ + partition: str reward_key: str = "reward" model: str | None = None parameters: dict[str, JsonValue] = Field(default_factory=dict) failure_value: float = 0.0 - # Pin the seed's reward on this target to skip scoring it each run. baseline_reward: float | None = None max_attempts: int = Field(default=1, ge=1) From d059a6eec7a427b57336d567f4754deae37ef6f6 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 12:47:34 -0700 Subject: [PATCH 31/72] Replace EvaluationModel with a shared StrictModel base EvaluationModel was a BaseModel with extra="forbid" and nothing else, but its name claimed an evaluation-contract scope it never had: WorkspaceOverlaySpec and WandbSpec have nothing to do with evaluation. Worse, the build and runtime layers imported it from vero.evaluation purely to inherit a config flag, which made the evaluation package a dependency of code that otherwise has no reason to touch it. Move the base to vero/models.py as StrictModel, a dependency leaf importing only pydantic, and rebase all 79 subclasses onto it. Fold in the 13 models that hand-rolled the same ConfigDict (Candidate, SessionManifest, RuntimeEvent, the session specs, and others), and drop it from WorkspaceOverlaySpec and HarborBuildConfig, which were setting it redundantly on an already-strict base. Strictness is the point and is unchanged: a typo'd key in a build YAML must fail the build rather than be dropped in silence, leaving a default that quietly widens the optimizer's access. EvaluationModel is removed rather than aliased; it had no references outside src/vero. Test results are identical to before the change. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/agents/protocol.py | 7 ++- vero/src/vero/candidate.py | 8 ++- vero/src/vero/candidate_repository/git.py | 11 ++-- vero/src/vero/config.py | 24 ++++----- vero/src/vero/evaluation/__init__.py | 2 - vero/src/vero/evaluation/command.py | 4 +- vero/src/vero/evaluation/models.py | 66 +++++++++++------------ vero/src/vero/evaluation/persistence.py | 8 +-- vero/src/vero/evaluation/python_task.py | 6 +-- vero/src/vero/harbor/app.py | 10 ++-- vero/src/vero/harbor/backend.py | 6 +-- vero/src/vero/harbor/build/config.py | 22 ++++---- vero/src/vero/harbor/deployment.py | 8 +-- vero/src/vero/harbor/inference.py | 12 ++--- vero/src/vero/harbor/session.py | 5 +- vero/src/vero/harbor/sidecar.py | 16 +++--- vero/src/vero/harbor/verifier.py | 8 +-- vero/src/vero/models.py | 16 ++++++ vero/src/vero/optimization/command.py | 4 +- vero/src/vero/optimization/models.py | 6 +-- vero/src/vero/runtime/context.py | 6 +-- vero/src/vero/runtime/events.py | 8 +-- vero/src/vero/runtime/session.py | 19 +++---- 23 files changed, 135 insertions(+), 147 deletions(-) create mode 100644 vero/src/vero/models.py diff --git a/vero/src/vero/agents/protocol.py b/vero/src/vero/agents/protocol.py index 29d7d8ce..7c9e4177 100644 --- a/vero/src/vero/agents/protocol.py +++ b/vero/src/vero/agents/protocol.py @@ -6,9 +6,10 @@ from pathlib import Path, PurePosixPath from typing import Any, Callable, Protocol, runtime_checkable -from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator +from pydantic import Field, JsonValue, field_validator from vero.candidate import Candidate +from vero.models import StrictModel from vero.optimization import ( CandidateEvaluationGateway, CandidateProposal, @@ -70,9 +71,7 @@ def base_version(self) -> str: return self.parent.version -class AgentRunResult(BaseModel): - model_config = ConfigDict(extra="forbid") - +class AgentRunResult(StrictModel): description: str = "Apply coding-agent changes" state: JsonValue | None = None trace: JsonValue | None = None diff --git a/vero/src/vero/candidate.py b/vero/src/vero/candidate.py index ed33da93..9d7c0459 100644 --- a/vero/src/vero/candidate.py +++ b/vero/src/vero/candidate.py @@ -6,16 +6,16 @@ from typing import Any from pydantic import ( - BaseModel, - ConfigDict, Field, JsonValue, field_validator, model_validator, ) +from vero.models import StrictModel -class Candidate(BaseModel): + +class Candidate(StrictModel): """A materialized version of the program being optimized. ``version`` is interpreted by the session's candidate repository. A @@ -23,8 +23,6 @@ class Candidate(BaseModel): stable when the candidate is materialized in different sandboxes. """ - model_config = ConfigDict(extra="forbid") - id: str version: str parent_id: str | None = None diff --git a/vero/src/vero/candidate_repository/git.py b/vero/src/vero/candidate_repository/git.py index 5e91e1bf..e18dbad9 100644 --- a/vero/src/vero/candidate_repository/git.py +++ b/vero/src/vero/candidate_repository/git.py @@ -14,11 +14,12 @@ from pathlib import Path, PurePosixPath from typing import AsyncIterator, Literal, Sequence -from pydantic import BaseModel, ConfigDict, field_validator +from pydantic import field_validator from vero.candidate import Candidate from vero.candidate_repository.base import CandidateRepository, CandidateRepositoryError from vero.evaluation.persistence import _atomic_write_json +from vero.models import StrictModel from vero.sandbox import LocalSandbox, Sandbox from vero.workspace import GitWorkspace, Workspace @@ -26,9 +27,7 @@ _AGENT_CONTEXT_DIRECTORY = ".evals" -class _GitRepositoryConfig(BaseModel): - model_config = ConfigDict(extra="forbid") - +class _GitRepositoryConfig(StrictModel): schema_version: Literal[1] = 1 family: Literal["git"] = "git" project_subpath: str = "." @@ -42,9 +41,7 @@ def validate_project_subpath(cls, value: str) -> str: return value -class _CandidateRecord(BaseModel): - model_config = ConfigDict(extra="forbid") - +class _CandidateRecord(StrictModel): schema_version: Literal[1] = 1 candidate: Candidate diff --git a/vero/src/vero/config.py b/vero/src/vero/config.py index 75462722..a87b2cba 100644 --- a/vero/src/vero/config.py +++ b/vero/src/vero/config.py @@ -26,7 +26,6 @@ EvaluationBudget, EvaluationDefinition, EvaluationLimits, - EvaluationModel, EvaluationPlan, EvaluationPrincipal, EvaluationSet, @@ -36,6 +35,7 @@ ObjectiveSpec, RetryPolicy, ) +from vero.models import StrictModel from vero.optimization import ( CommandCandidateProducer, CommandCandidateProducerConfig, @@ -51,12 +51,12 @@ ) -class TargetConfig(EvaluationModel): +class TargetConfig(StrictModel): root: str ref: str = "HEAD" -class BackendConfig(EvaluationModel): +class BackendConfig(StrictModel): id: str = "command" kind: Literal["command"] = "command" harness_root: str @@ -68,7 +68,7 @@ class BackendConfig(EvaluationModel): agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) -class BudgetConfig(EvaluationModel): +class BudgetConfig(StrictModel): total_runs: int | None = Field(default=None, ge=0) total_cases: int | None = Field(default=None, ge=0) @@ -88,7 +88,7 @@ def to_model( ) -class EvaluationConfig(EvaluationModel): +class EvaluationConfig(StrictModel): name: str partition: str | None = None case_ids: list[str] | None = None @@ -162,7 +162,7 @@ def to_definition(self, backend_id: str) -> EvaluationDefinition: ) -class ProtocolConfig(EvaluationModel): +class ProtocolConfig(StrictModel): selection_evaluation: str final_evaluation: str | None = None evaluate_final_baseline: bool = True @@ -187,7 +187,7 @@ def to_limits(self) -> EvaluationLimits: ) -class ObjectiveConstraintConfig(EvaluationModel): +class ObjectiveConstraintConfig(StrictModel): metric: str aggregation: MetricAggregation = MetricAggregation.REPORT case_failure_value: float | None = None @@ -206,7 +206,7 @@ def to_model(self) -> MetricConstraint: ) -class ObjectiveConfig(EvaluationModel): +class ObjectiveConfig(StrictModel): metric: str aggregation: MetricAggregation = MetricAggregation.REPORT case_failure_value: float | None = None @@ -227,7 +227,7 @@ def to_model(self) -> ObjectiveSpec: ) -class BaseOptimizerConfig(EvaluationModel): +class BaseOptimizerConfig(StrictModel): instruction: str | None = None @@ -266,12 +266,12 @@ def validate_model(self) -> AgentOptimizerConfig: ] -class SessionConfig(EvaluationModel): +class SessionConfig(StrictModel): id: str | None = None directory: str | None = None -class WandbConfig(EvaluationModel): +class WandbConfig(StrictModel): project: str run_id: str | None = None entity: str | None = None @@ -283,7 +283,7 @@ class WandbConfig(EvaluationModel): config: dict[str, JsonValue] = Field(default_factory=dict) -class VeroConfig(EvaluationModel): +class VeroConfig(StrictModel): target: TargetConfig backend: BackendConfig evaluations: list[EvaluationConfig] diff --git a/vero/src/vero/evaluation/__init__.py b/vero/src/vero/evaluation/__init__.py index 9add910c..dabad078 100644 --- a/vero/src/vero/evaluation/__init__.py +++ b/vero/src/vero/evaluation/__init__.py @@ -51,7 +51,6 @@ EvaluationDefinition, EvaluationDiagnostic, EvaluationLimits, - EvaluationModel, EvaluationPlan, EvaluationPrincipal, EvaluationReceipt, @@ -131,7 +130,6 @@ "EvaluationTerminatedError", "EvaluationLimits", "EvaluationManifest", - "EvaluationModel", "EvaluationPlan", "EvaluationPrincipal", "EvaluationRecord", diff --git a/vero/src/vero/evaluation/command.py b/vero/src/vero/evaluation/command.py index 2f22feed..c7604459 100644 --- a/vero/src/vero/evaluation/command.py +++ b/vero/src/vero/evaluation/command.py @@ -21,13 +21,13 @@ EvaluationArtifact, EvaluationCost, EvaluationDiagnostic, - EvaluationModel, EvaluationReport, EvaluationRequest, EvaluationSet, EvaluationStatus, ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text +from vero.models import StrictModel from vero.sandbox import Sandbox from vero.staging import SandboxStagingArea @@ -36,7 +36,7 @@ _INPUT_NAME_PATTERN = re.compile(r"^[a-zA-Z][a-zA-Z0-9_-]*$") -class CommandBackendConfig(EvaluationModel): +class CommandBackendConfig(StrictModel): harness_root: str command: list[str] working_directory: str = "." diff --git a/vero/src/vero/evaluation/models.py b/vero/src/vero/evaluation/models.py index 3583dbb4..9cdbe63c 100644 --- a/vero/src/vero/evaluation/models.py +++ b/vero/src/vero/evaluation/models.py @@ -13,7 +13,6 @@ from pydantic import ( BaseModel, - ConfigDict, Field, JsonValue, field_validator, @@ -21,12 +20,7 @@ ) from vero.candidate import Candidate - - -class EvaluationModel(BaseModel): - """Strict base model for public evaluation contracts.""" - - model_config = ConfigDict(extra="forbid") +from vero.models import StrictModel def _non_empty(value: str, field_name: str) -> str: @@ -59,11 +53,11 @@ def _canonical_json(value: Any) -> str: return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False) -class AllCases(EvaluationModel): +class AllCases(StrictModel): kind: Literal["all"] = "all" -class CaseIds(EvaluationModel): +class CaseIds(StrictModel): kind: Literal["ids"] = "ids" ids: list[str] @@ -79,7 +73,7 @@ def validate_ids(cls, ids: list[str]) -> list[str]: return ids -class CaseRange(EvaluationModel): +class CaseRange(StrictModel): kind: Literal["range"] = "range" stop: int start: int = 0 @@ -96,7 +90,7 @@ def validate_range(self) -> CaseRange: CaseSelection = Annotated[AllCases | CaseIds | CaseRange, Field(discriminator="kind")] -class EvaluationSet(EvaluationModel): +class EvaluationSet(StrictModel): """A backend-owned collection of cases and a selection within it.""" name: str = "default" @@ -133,7 +127,7 @@ class AgentSelectionMode(str, Enum): ARBITRARY = "arbitrary" -class RetryPolicy(EvaluationModel): +class RetryPolicy(StrictModel): max_attempts: int = Field(default=3, ge=1) initial_delay_seconds: float = Field(default=4.0, ge=0.0) maximum_delay_seconds: float = Field(default=120.0, ge=0.0) @@ -179,7 +173,7 @@ def disabled(cls) -> RetryPolicy: return cls(max_attempts=1) -class EvaluationLimits(EvaluationModel): +class EvaluationLimits(StrictModel): timeout_seconds: float = Field(default=600.0, gt=0.0) case_timeout_seconds: float = Field(default=180.0, gt=0.0) max_concurrency: int = Field(default=100, ge=1) @@ -187,7 +181,7 @@ class EvaluationLimits(EvaluationModel): retry: RetryPolicy = Field(default_factory=RetryPolicy) -class EvaluationRequest(EvaluationModel): +class EvaluationRequest(StrictModel): candidate: Candidate evaluation_set: EvaluationSet = Field(default_factory=EvaluationSet) parameters: dict[str, JsonValue] = Field(default_factory=dict) @@ -218,14 +212,14 @@ def fingerprint(self) -> str: return hashlib.sha256(_canonical_json(payload).encode()).hexdigest() -class CommandEvaluationInput(EvaluationModel): +class CommandEvaluationInput(StrictModel): """Versioned JSON input passed to an external evaluation harness.""" schema_version: Literal[1] = 1 request: EvaluationRequest -class EvaluationArtifact(EvaluationModel): +class EvaluationArtifact(StrictModel): path: str media_type: str | None = None description: str | None = None @@ -254,7 +248,7 @@ class CaseStatus(str, Enum): SKIPPED = "skipped" -class CaseError(EvaluationModel): +class CaseError(StrictModel): message: str code: str | None = None phase: str | None = None @@ -274,7 +268,7 @@ def validate_optional_text(cls, value: str | None) -> str | None: return _optional_non_empty(value, "error code or phase") -class CaseResult(EvaluationModel): +class CaseResult(StrictModel): case_id: str status: CaseStatus metrics: dict[str, float] = Field(default_factory=dict) @@ -329,7 +323,7 @@ class DiagnosticSeverity(str, Enum): ERROR = "error" -class EvaluationDiagnostic(EvaluationModel): +class EvaluationDiagnostic(StrictModel): code: str message: str severity: DiagnosticSeverity @@ -347,7 +341,7 @@ def validate_phase(cls, value: str | None) -> str | None: return _optional_non_empty(value, "diagnostic phase") -class EvaluationReport(EvaluationModel): +class EvaluationReport(StrictModel): schema_version: Literal[1] = 1 status: EvaluationStatus metrics: dict[str, float] = Field(default_factory=dict) @@ -368,7 +362,7 @@ def validate_report(self) -> EvaluationReport: return self -class BackendProvenance(EvaluationModel): +class BackendProvenance(StrictModel): name: str version: str config_digest: str @@ -411,7 +405,7 @@ class MetricAggregation(str, Enum): MAX = "max" -class MetricSelector(EvaluationModel): +class MetricSelector(StrictModel): metric: str aggregation: MetricAggregation = MetricAggregation.REPORT case_failure_value: float | None = None @@ -447,7 +441,7 @@ class ConstraintOperator(str, Enum): GTE = ">=" -class MetricConstraint(EvaluationModel): +class MetricConstraint(StrictModel): selector: MetricSelector operator: ConstraintOperator value: float @@ -460,7 +454,7 @@ def validate_value(cls, value: float) -> float: return value -class ObjectiveSpec(EvaluationModel): +class ObjectiveSpec(StrictModel): selector: MetricSelector direction: Literal["maximize", "minimize"] failure_value: float | None = None @@ -474,7 +468,7 @@ def validate_failure_value(cls, value: float | None) -> float | None: return value -class ConstraintViolation(EvaluationModel): +class ConstraintViolation(StrictModel): constraint: MetricConstraint observed: float | None reason: str @@ -492,7 +486,7 @@ def validate_reason(cls, value: str) -> str: return _non_empty(value, "constraint violation reason") -class ObjectiveResult(EvaluationModel): +class ObjectiveResult(StrictModel): value: float | None feasible: bool violations: list[ConstraintViolation] = Field(default_factory=list) @@ -513,7 +507,7 @@ def validate_result(self) -> ObjectiveResult: return self -class EvaluationRecord(EvaluationModel): +class EvaluationRecord(StrictModel): schema_version: Literal[2] = 2 id: str request: EvaluationRequest @@ -553,7 +547,7 @@ class DisclosureLevel(str, Enum): NONE = "none" -class EvaluationSummary(EvaluationModel): +class EvaluationSummary(StrictModel): evaluation_id: str candidate_id: str candidate_version: str @@ -580,12 +574,12 @@ def validate_counts(self) -> EvaluationSummary: return self -class EvaluationAcknowledgement(EvaluationModel): +class EvaluationAcknowledgement(StrictModel): evaluation_id: str status: EvaluationStatus -class EvaluationReceipt(EvaluationModel): +class EvaluationReceipt(StrictModel): """Bounded agent-facing pointer to filesystem evaluation feedback.""" evaluation_id: str @@ -627,7 +621,7 @@ def validate_projection(self) -> EvaluationReceipt: return self -class EvaluationAuthorization(EvaluationModel): +class EvaluationAuthorization(StrictModel): may_evaluate: bool may_view: bool | None = None meter_budget: bool = True @@ -648,12 +642,12 @@ def validate_reason(cls, value: str | None) -> str | None: return _optional_non_empty(value, "authorization reason") -class EvaluationCost(EvaluationModel): +class EvaluationCost(StrictModel): runs: int = Field(default=1, ge=1) cases: int | None = Field(default=None, ge=0) -class EvaluationBudget(EvaluationModel): +class EvaluationBudget(StrictModel): backend_id: str evaluation_set_key: str principal: EvaluationPrincipal = EvaluationPrincipal.AGENT @@ -684,7 +678,7 @@ def validate_remaining(self) -> EvaluationBudget: return self -class EvaluationAccessPolicy(EvaluationModel): +class EvaluationAccessPolicy(StrictModel): """Agent visibility and invocation rights for one named evaluation set.""" agent_can_evaluate: bool = True @@ -710,7 +704,7 @@ def validate_visibility(self) -> EvaluationAccessPolicy: return self -class EvaluationDefinition(EvaluationModel): +class EvaluationDefinition(StrictModel): """One named evaluation together with access and principal-scoped budgets.""" evaluation_set: EvaluationSet @@ -740,7 +734,7 @@ def validate_budgets(self) -> EvaluationDefinition: return self -class EvaluationPlan(EvaluationModel): +class EvaluationPlan(StrictModel): """All evaluations available to one optimization protocol.""" evaluations: list[EvaluationDefinition] diff --git a/vero/src/vero/evaluation/persistence.py b/vero/src/vero/evaluation/persistence.py index 5a14b8e0..66ab8d06 100644 --- a/vero/src/vero/evaluation/persistence.py +++ b/vero/src/vero/evaluation/persistence.py @@ -20,7 +20,6 @@ from vero.evaluation.models import ( BackendProvenance, CaseResult, - EvaluationModel, EvaluationPrincipal, EvaluationRecord, EvaluationReport, @@ -30,6 +29,7 @@ ObjectiveSpec, ) from vero.evaluation.objective import select_best_evaluation +from vero.models import StrictModel logger = logging.getLogger(__name__) @@ -63,7 +63,7 @@ def _case_digest(case_id: str) -> str: return hashlib.sha256(case_id.encode()).hexdigest() -class CaseFileReference(EvaluationModel): +class CaseFileReference(StrictModel): case_id: str path: str @@ -82,7 +82,7 @@ def validate_path(self) -> CaseFileReference: return self -class EvaluationManifest(EvaluationModel): +class EvaluationManifest(StrictModel): schema_version: Literal[1] = 1 lifecycle: Literal["complete"] = "complete" id: str @@ -137,7 +137,7 @@ def from_record(cls, record: EvaluationRecord) -> EvaluationManifest: ) -class RunningEvaluationManifest(EvaluationModel): +class RunningEvaluationManifest(StrictModel): schema_version: Literal[1] = 1 lifecycle: Literal["running"] = "running" id: str diff --git a/vero/src/vero/evaluation/python_task.py b/vero/src/vero/evaluation/python_task.py index 70f5f746..f4107a05 100644 --- a/vero/src/vero/evaluation/python_task.py +++ b/vero/src/vero/evaluation/python_task.py @@ -17,11 +17,11 @@ CaseIds, CaseRange, EvaluationCost, - EvaluationModel, EvaluationReport, EvaluationRequest, EvaluationSet, ) +from vero.models import StrictModel from vero.sandbox import Sandbox @@ -32,7 +32,7 @@ def _default_uv() -> str: return str(Path(executable).resolve()) -class PythonTaskEvaluationConfig(EvaluationModel): +class PythonTaskEvaluationConfig(StrictModel): """One named/partitioned dataset owned by a Python task backend.""" name: str @@ -61,7 +61,7 @@ def validate_partition(cls, value: str | None) -> str | None: return value -class PythonTaskBackendConfig(EvaluationModel): +class PythonTaskBackendConfig(StrictModel): """Configuration for an external task harness and editable target package.""" harness_root: str diff --git a/vero/src/vero/harbor/app.py b/vero/src/vero/harbor/app.py index b6deca0c..09a78347 100644 --- a/vero/src/vero/harbor/app.py +++ b/vero/src/vero/harbor/app.py @@ -13,7 +13,6 @@ from fastapi import FastAPI, Header, HTTPException, Query from fastapi.responses import FileResponse, JSONResponse -from pydantic import BaseModel, ConfigDict from starlette.background import BackgroundTask from vero.evaluation import ( @@ -35,6 +34,7 @@ ) from vero.harbor.transport import CandidateTransferError from vero.harbor.verifier import CanonicalVerifier +from vero.models import StrictModel if TYPE_CHECKING: from vero.runtime.wandb import InferenceTelemetryPoller @@ -42,15 +42,11 @@ logger = logging.getLogger(__name__) -class SubmitRequest(BaseModel): - model_config = ConfigDict(extra="forbid") - +class SubmitRequest(StrictModel): version: str | None = None -class ScoreBaselineRequest(BaseModel): - model_config = ConfigDict(extra="forbid") - +class ScoreBaselineRequest(StrictModel): replicates: int = 1 diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 98da9836..0bc3a15c 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -39,7 +39,6 @@ EvaluationArtifact, EvaluationCost, EvaluationDiagnostic, - EvaluationModel, EvaluationReport, EvaluationRequest, EvaluationSet, @@ -47,6 +46,7 @@ ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text from vero.harbor.isolation import harness_grant_commands, harness_reachability_probe +from vero.models import StrictModel from vero.sandbox import CommandResult, Sandbox from vero.staging import SandboxStagingArea @@ -60,7 +60,7 @@ def _default_uv() -> str: return str(Path(executable).resolve()) -class HarborCase(EvaluationModel): +class HarborCase(StrictModel): """One canonical case mapped to one Harbor task name.""" id: str @@ -95,7 +95,7 @@ def expected_result_task_name(self) -> str: AGENT_INFERENCE_BASE_URL_ENV = "VERO_AGENT_INFERENCE_BASE_URL" -class HarborBackendConfig(EvaluationModel): +class HarborBackendConfig(StrictModel): """Trusted configuration for nested ``harbor run`` evaluation.""" task_source: str diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index bd1135f8..771d2a0d 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -9,18 +9,18 @@ from pathlib import Path from typing import Literal -from pydantic import ConfigDict, Field, JsonValue, field_validator, model_validator +from pydantic import Field, JsonValue, field_validator, model_validator from vero.evaluation import ( DisclosureLevel, EvaluationAccessPolicy, - EvaluationModel, MetricSelector, ObjectiveSpec, ) +from vero.models import StrictModel -class AgentAccessSpec(EvaluationModel): +class AgentAccessSpec(StrictModel): """The optimizer agent's access to one named evaluation partition. A build declares one spec per partition; to_access_policy translates it into @@ -64,7 +64,7 @@ def to_access_policy(self) -> EvaluationAccessPolicy: ) -class VerificationTargetSpec(EvaluationModel): +class VerificationTargetSpec(StrictModel): """One trusted final scoring pass the verifier runs on a chosen candidate. Distinct from the evaluations the optimizer runs during search: after search @@ -107,7 +107,7 @@ def validate_failure_value(cls, value: float) -> float: return value -class InferenceBudgetSpec(EvaluationModel): +class InferenceBudgetSpec(StrictModel): """Routing policy and optional limits for one inference-gateway scope.""" allowed_models: list[str] @@ -125,7 +125,7 @@ def validate_models(cls, value: list[str]) -> list[str]: return value -class InferenceGatewaySpec(EvaluationModel): +class InferenceGatewaySpec(StrictModel): """Credential source and independent producer/evaluator policies.""" upstream_api_key_env: str = "OPENAI_API_KEY" @@ -162,7 +162,7 @@ def validate_upstream_url(cls, value: str) -> str: return value.rstrip("/") -class WorkspaceOverlaySpec(EvaluationModel): +class WorkspaceOverlaySpec(StrictModel): """A host file or directory to copy into the compiled task's agent workspace. General-purpose filesystem injection: bake anything (agent definitions, @@ -171,8 +171,6 @@ class WorkspaceOverlaySpec(EvaluationModel): where its contents land relative to the workspace root (``.`` = the root). """ - model_config = ConfigDict(extra="forbid") - source: str dest: str = "." @@ -202,7 +200,7 @@ def validate_dest(cls, value: str) -> str: return candidate -class WandbSpec(EvaluationModel): +class WandbSpec(StrictModel): """Weights & Biases reporting settings, emitted into the sidecar's serve.json. The field names match the sidecar's SidecarWandbConfig.""" @@ -218,11 +216,9 @@ class WandbSpec(EvaluationModel): log_traces: bool = False -class HarborBuildConfig(EvaluationModel): +class HarborBuildConfig(StrictModel): """Everything needed to emit an isolated Harbor optimization task.""" - model_config = ConfigDict(extra="forbid") - name: str description: str = "" agent_repo: str diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index c0416859..72305266 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -16,7 +16,6 @@ EvaluationBudget, EvaluationDatabase, EvaluationLimits, - EvaluationModel, EvaluationSet, Evaluator, ObjectiveSpec, @@ -32,13 +31,14 @@ VerificationSelection, VerificationTarget, ) +from vero.models import StrictModel from vero.sandbox import LocalSandbox from vero.workspace import GitWorkspace logger = logging.getLogger(__name__) -class DeploymentSelection(EvaluationModel): +class DeploymentSelection(StrictModel): mode: Literal["submit", "auto_best"] = "auto_best" backend_id: str | None = None evaluation_set: EvaluationSet | None = None @@ -72,7 +72,7 @@ def validate_selection(self) -> DeploymentSelection: return self -class SidecarWandbConfig(EvaluationModel): +class SidecarWandbConfig(StrictModel): """Trusted-side Weights & Biases config for the eval-sidecar. The W&B credential (WANDB_API_KEY) is supplied to the sidecar container's environment, never to the untrusted optimizer agent.""" @@ -94,7 +94,7 @@ class SidecarWandbConfig(EvaluationModel): telemetry_interval_seconds: float = Field(default=30.0, gt=0) -class HarborDeploymentConfig(EvaluationModel): +class HarborDeploymentConfig(StrictModel): task_name: str = "harbor-session" task_description: str = "" repo_path: str diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index b69f9181..043ab9f1 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -21,8 +21,8 @@ from fastapi.responses import JSONResponse, Response, StreamingResponse from pydantic import Field, field_validator, model_validator -from vero.evaluation import EvaluationModel from vero.evaluation.persistence import _atomic_write_json +from vero.models import StrictModel logger = logging.getLogger(__name__) @@ -38,7 +38,7 @@ def generate_inference_token() -> str: return secrets.token_urlsafe(32) -class InferenceScopeConfig(EvaluationModel): +class InferenceScopeConfig(StrictModel): """One independently authenticated and metered inference consumer.""" token_sha256: str @@ -66,7 +66,7 @@ def validate_models(cls, value: list[str]) -> list[str]: return value -class InferenceRequestLogConfig(EvaluationModel): +class InferenceRequestLogConfig(StrictModel): """Durable JSONL capture of every request the gateway proxies or denies. Operator telemetry: the log lives on the gateway state volume, which is @@ -89,7 +89,7 @@ def validate_directory(cls, value: str) -> str: return value -class InferenceGatewayConfig(EvaluationModel): +class InferenceGatewayConfig(StrictModel): """Trusted configuration for an OpenAI-compatible inference gateway.""" upstream_api_key_env: str = "OPENAI_API_KEY" @@ -136,7 +136,7 @@ def validate_scopes(self) -> InferenceGatewayConfig: return self -class InferenceAttributionUsage(EvaluationModel): +class InferenceAttributionUsage(StrictModel): requests: int = Field(default=0, ge=0) upstream_errors: int = Field(default=0, ge=0) input_tokens: int = Field(default=0, ge=0) @@ -151,7 +151,7 @@ class InferenceScopeUsage(InferenceAttributionUsage): attributions: dict[str, InferenceAttributionUsage] = Field(default_factory=dict) -class InferenceUsageLedger(EvaluationModel): +class InferenceUsageLedger(StrictModel): schema_version: Literal[1] = 1 scopes: dict[str, InferenceScopeUsage] diff --git a/vero/src/vero/harbor/session.py b/vero/src/vero/harbor/session.py index aa2b6320..cd50f872 100644 --- a/vero/src/vero/harbor/session.py +++ b/vero/src/vero/harbor/session.py @@ -16,14 +16,15 @@ from pydantic import field_validator -from vero.evaluation import BackendProvenance, EvaluationModel +from vero.evaluation import BackendProvenance from vero.evaluation.persistence import _atomic_write_json from vero.harbor.verifier import VerificationSelection, VerificationTarget +from vero.models import StrictModel logger = logging.getLogger(__name__) -class HarborSessionManifest(EvaluationModel): +class HarborSessionManifest(StrictModel): """Trusted metadata needed to interpret an exported Harbor session.""" schema_version: Literal[1] = 1 diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/harbor/sidecar.py index f66f16f9..c53ab811 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/harbor/sidecar.py @@ -26,7 +26,6 @@ EvaluationExecutionError, EvaluationInfrastructureError, EvaluationLimits, - EvaluationModel, EvaluationReceipt, EvaluationRecord, EvaluationRequest, @@ -40,6 +39,7 @@ from vero.evaluation.engine import EvaluationEngine from vero.evaluation.persistence import _atomic_write_json from vero.harbor.transport import CandidateTransferError, CandidateTransport +from vero.models import StrictModel from vero.runtime.context import ( CANDIDATES_SUBDIRECTORY, AgentContextDirectory, @@ -64,7 +64,7 @@ class EvaluationJobNotFoundError(LookupError): """Raised when an agent requests an unknown evaluation job.""" -class SidecarEvaluationPolicy(EvaluationModel): +class SidecarEvaluationPolicy(StrictModel): """Agent access to one backend-owned evaluation-set partition.""" backend_id: str @@ -114,7 +114,7 @@ def validate_parameters(self) -> SidecarEvaluationPolicy: return self -class SidecarEvaluationRequest(EvaluationModel): +class SidecarEvaluationRequest(StrictModel): """Agent request; candidate identity is established by the transport.""" backend_id: str @@ -142,7 +142,7 @@ def validate_version(cls, value: str | None) -> str | None: EvaluationProjection = EvaluationRecord | EvaluationSummary | EvaluationAcknowledgement -class SidecarEvaluationResult(EvaluationModel): +class SidecarEvaluationResult(StrictModel): disclosure: DisclosureLevel receipt: EvaluationReceipt @@ -161,7 +161,7 @@ class EvaluationJobStatus(str, Enum): CANCELLED = "cancelled" -class SidecarEvaluationJob(EvaluationModel): +class SidecarEvaluationJob(StrictModel): """Durable agent-facing lifecycle for one sidecar evaluation request.""" job_id: str @@ -199,7 +199,7 @@ def validate_lifecycle(self) -> SidecarEvaluationJob: return self -class EvaluationAccessStatus(EvaluationModel): +class EvaluationAccessStatus(StrictModel): backend_id: str evaluation_set_name: str partition: str | None @@ -211,14 +211,14 @@ class EvaluationAccessStatus(EvaluationModel): budget: EvaluationBudget | None = None -class SidecarStatus(EvaluationModel): +class SidecarStatus(StrictModel): submit_enabled: bool evaluation_access: list[EvaluationAccessStatus] inference_usage: dict[str, JsonValue] | None = None evaluation_jobs: list[SidecarEvaluationJob] = Field(default_factory=list) -class Submission(EvaluationModel): +class Submission(StrictModel): candidate: Candidate diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/harbor/verifier.py index 09462dba..44d165b7 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/harbor/verifier.py @@ -16,7 +16,6 @@ from vero.evaluation import ( EvaluationAuthorization, EvaluationLimits, - EvaluationModel, EvaluationPrincipal, EvaluationRecord, EvaluationRequest, @@ -26,6 +25,7 @@ from vero.evaluation.engine import EvaluationEngine from vero.evaluation.persistence import _atomic_write_json from vero.harbor.sidecar import Submission +from vero.models import StrictModel logger = logging.getLogger(__name__) @@ -34,7 +34,7 @@ class NoCandidateError(RuntimeError): """Raised when finalization has no submitted or evaluated candidate.""" -class VerificationTarget(EvaluationModel): +class VerificationTarget(StrictModel): """One trusted final evaluation projected to one Harbor reward key.""" reward_key: str @@ -75,7 +75,7 @@ def validate_baseline_reward(cls, value: float | None) -> float | None: return value -class VerificationSelection(EvaluationModel): +class VerificationSelection(StrictModel): """How finalization chooses a candidate before scoring its targets.""" mode: Literal["submit", "auto_best"] = "auto_best" @@ -112,7 +112,7 @@ def validate_auto_best(self) -> VerificationSelection: return self -class VerificationResult(EvaluationModel): +class VerificationResult(StrictModel): """Durable, idempotent output consumed by Harbor's verifier.""" candidate: Candidate | None = None diff --git a/vero/src/vero/models.py b/vero/src/vero/models.py new file mode 100644 index 00000000..49fbeca3 --- /dev/null +++ b/vero/src/vero/models.py @@ -0,0 +1,16 @@ +"""Shared base model for VeRO's declarative contracts.""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class StrictModel(BaseModel): + """Base model that rejects unknown fields. + + Authored configuration and on-disk records both inherit this, so a typo'd + key fails loudly at load time instead of being dropped in silence and + leaving a default in its place. + """ + + model_config = ConfigDict(extra="forbid") diff --git a/vero/src/vero/optimization/command.py b/vero/src/vero/optimization/command.py index a9d9bbfc..d2f13052 100644 --- a/vero/src/vero/optimization/command.py +++ b/vero/src/vero/optimization/command.py @@ -9,7 +9,7 @@ from pydantic import Field, field_validator, model_validator -from vero.evaluation import EvaluationModel +from vero.models import StrictModel from vero.optimization.models import ( CandidateChange, CandidateProductionContext, @@ -32,7 +32,7 @@ _PLACEHOLDER_PATTERN = re.compile(r"\{([^{}]+)\}") -class CommandCandidateProducerConfig(EvaluationModel): +class CommandCandidateProducerConfig(StrictModel): root: str command: list[str] working_directory: str = "." diff --git a/vero/src/vero/optimization/models.py b/vero/src/vero/optimization/models.py index 76069bcc..3c631dc7 100644 --- a/vero/src/vero/optimization/models.py +++ b/vero/src/vero/optimization/models.py @@ -11,14 +11,14 @@ from vero.candidate import Candidate from vero.evaluation import ( EvaluationAcknowledgement, - EvaluationModel, EvaluationRecord, EvaluationSummary, ) +from vero.models import StrictModel from vero.workspace import Workspace -class CandidateProposal(EvaluationModel): +class CandidateProposal(StrictModel): """A strategy's request for one producer to explore a parent candidate.""" id: str = Field(default_factory=lambda: str(uuid4())) @@ -48,7 +48,7 @@ def validate_parent(self) -> CandidateProposal: return self -class CandidateChange(EvaluationModel): +class CandidateChange(StrictModel): """Producer metadata returned after it edits a supplied workspace.""" description: str = "Optimize candidate" diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index 3d3b586e..fb32d54d 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -21,7 +21,6 @@ EvaluationAcknowledgement, EvaluationBudget, EvaluationEngine, - EvaluationModel, EvaluationPlan, EvaluationPrincipal, EvaluationReceipt, @@ -31,6 +30,7 @@ project_evaluation, ) from vero.evaluation.persistence import _atomic_write_json +from vero.models import StrictModel from vero.sandbox import Sandbox from vero.workspace import Workspace @@ -105,12 +105,12 @@ def make_evaluation_receipt( ) -class AgentDisclosureEntry(EvaluationModel): +class AgentDisclosureEntry(StrictModel): evaluation_id: str maximum_disclosure: DisclosureLevel -class AgentDisclosureLedgerModel(EvaluationModel): +class AgentDisclosureLedgerModel(StrictModel): schema_version: Literal[1] = 1 evaluations: dict[str, AgentDisclosureEntry] = Field(default_factory=dict) diff --git a/vero/src/vero/runtime/events.py b/vero/src/vero/runtime/events.py index ea1793fd..248fa533 100644 --- a/vero/src/vero/runtime/events.py +++ b/vero/src/vero/runtime/events.py @@ -12,14 +12,14 @@ from typing import Protocol from uuid import uuid4 -from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator +from pydantic import Field, JsonValue, field_validator -logger = logging.getLogger(__name__) +from vero.models import StrictModel +logger = logging.getLogger(__name__) -class RuntimeEvent(BaseModel): - model_config = ConfigDict(extra="forbid") +class RuntimeEvent(StrictModel): id: str = Field(default_factory=lambda: str(uuid4())) session_id: str kind: str diff --git a/vero/src/vero/runtime/session.py b/vero/src/vero/runtime/session.py index 59b1e54e..005a5a75 100644 --- a/vero/src/vero/runtime/session.py +++ b/vero/src/vero/runtime/session.py @@ -11,7 +11,7 @@ from pathlib import Path from typing import Literal -from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator +from pydantic import BaseModel, Field, JsonValue, field_validator from vero.candidate import Candidate from vero.candidate_repository import CandidateRepository @@ -24,6 +24,7 @@ ObjectiveSpec, ) from vero.evaluation.persistence import _atomic_write_json +from vero.models import StrictModel from vero.optimization import OptimizationResult, Optimizer from vero.runtime.artifacts import ArtifactStore from vero.runtime.events import EventBus, JsonlEventSink, agent_event_emitter @@ -37,9 +38,7 @@ class SessionStatus(str, Enum): FAILED = "failed" -class SessionFailure(BaseModel): - model_config = ConfigDict(extra="forbid") - +class SessionFailure(StrictModel): type: str message: str @@ -51,11 +50,9 @@ def validate_text(cls, value: str) -> str: return value -class OptimizationComponentSpec(BaseModel): +class OptimizationComponentSpec(StrictModel): """Stable type and configuration identity for a protocol component.""" - model_config = ConfigDict(extra="forbid") - type: str config_digest: str @@ -76,11 +73,9 @@ def validate_digest(cls, value: str) -> str: return value -class OptimizationRunSpec(BaseModel): +class OptimizationRunSpec(StrictModel): """Execution choices that must remain stable when a session resumes.""" - model_config = ConfigDict(extra="forbid") - max_proposals: int = Field(ge=0) max_rounds: int = Field(ge=1) max_concurrency: int = Field(ge=1) @@ -91,9 +86,7 @@ class OptimizationRunSpec(BaseModel): selection: OptimizationComponentSpec | None = None -class SessionManifest(BaseModel): - model_config = ConfigDict(extra="forbid") - +class SessionManifest(StrictModel): schema_version: Literal[3] = 3 id: str status: SessionStatus From 145fa29c9d6de3556406526c9f1c952723ebdc9c Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 13:38:20 -0700 Subject: [PATCH 32/72] Document the remaining build config models Give the five undocumented models in the build schema the same treatment as AgentAccessSpec and VerificationTargetSpec: a summary, the context needed to use them, and an Attributes block. HarborBuildConfig is the payload, since it is the whole grammar of a benchmark's build.yaml and had a one-line docstring for 59 fields. Its header also records the two cross-field rules validate_references enforces, which were previously only discoverable by reading the validator. The Inference*Spec docstrings note two things that were only visible in the gateway: a denied model returns 403 model_denied, and the token budget is checked before a request starts, so one request can overshoot it. The InferenceGatewaySpec docstring also states plainly that the per-scope allow-list confines the target only against a non-adversarial optimizer, since that spec is where someone would otherwise assume the guarantee is airtight. Inline comments that would have duplicated an Attributes entry are folded in rather than left alongside, so the two cannot drift apart. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/config.py | 241 +++++++++++++++++++++------ 1 file changed, 189 insertions(+), 52 deletions(-) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 771d2a0d..50b6552f 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -108,7 +108,22 @@ def validate_failure_value(cls, value: float) -> float: class InferenceBudgetSpec(StrictModel): - """Routing policy and optional limits for one inference-gateway scope.""" + """Routing policy and optional limits for one inference-gateway scope. + + The compiler lowers each spec into an InferenceScopeConfig, adding the + SHA-256 digest of a freshly minted per-scope token. The gateway then checks + the presented token, the requested model, and the running budget on every + proxied request; limits are held server-side in a ledger written through to + disk, so they survive a gateway restart. + + Attributes: + allowed_models: Models this scope may request. A request naming anything + else is refused with 403 model_denied. + max_requests: Cap on proxied requests; unlimited when omitted. + max_tokens: Cap on cumulative tokens; unlimited when omitted. Checked + before a request starts, so a single request can overshoot it. + max_concurrency: Requests this scope may have in flight at once. + """ allowed_models: list[str] max_requests: int | None = Field(default=None, ge=1) @@ -126,25 +141,47 @@ def validate_models(cls, value: list[str]) -> list[str]: class InferenceGatewaySpec(StrictModel): - """Credential source and independent producer/evaluator policies.""" + """Credential source and independent producer/evaluator policies. + + All model access in a compiled task goes through the gateway, which holds + the one upstream credential and hands each consumer a separate token. The + optimizer never sees the evaluation or finalization tokens, so it cannot + spend from those pools. + + The per-scope allow-lists keep the target on its fixed evaluation model, but + only against a non-adversarial optimizer: the scopes share a gateway host and + are split by URL path, so an optimizer that smuggles its own token into the + candidate can reach the producer scope from the eval sandbox. See the proxy + handler in vero.harbor.inference for the full note. + + Attributes: + upstream_api_key_env: Environment variable on the build host holding the + real upstream API key. + upstream_base_url_env: Environment variable holding the upstream base + URL; None to always use default_upstream_base_url. + default_upstream_base_url: Upstream used when no base-URL variable is set. + producer: Policy for the optimizer's own model calls. + evaluation: Policy for the target agent under evaluation. + finalization: Reserved policy for trusted finalization (admin re-score + and verification targets). Defaults to a copy of evaluation, giving + the mandatory re-score a pool that search evaluations cannot starve. + log_requests: Capture every proxied or denied request as JSONL on the + gateway state volume. Never agent-visible; the trusted sidecar + mirrors it to Weights & Biases and the session archive. + request_log_body_bytes: Head+tail budget per logged body, in bytes. + request_log_attribution: Experimental. Stamp log records with + provider-agnostic conversation threads for post-hoc per-trial + accounting. + """ upstream_api_key_env: str = "OPENAI_API_KEY" upstream_base_url_env: str | None = "OPENAI_BASE_URL" default_upstream_base_url: str = "https://api.openai.com/v1" producer: InferenceBudgetSpec evaluation: InferenceBudgetSpec - # Reserved budget for trusted finalization (admin re-score / targets). When - # omitted, it defaults to a copy of `evaluation`, giving the mandatory re-score - # its own pool so the optimizer's search evaluations cannot starve it. finalization: InferenceBudgetSpec | None = None - # Durable JSONL capture of every gateway request/response (metadata plus - # head+tail-truncated bodies). Lives on the gateway state volume — never - # agent-visible; the trusted sidecar mirrors it to W&B and the session - # archive. log_requests: bool = True request_log_body_bytes: int = Field(default=16384, ge=0) - # Experimental: stamp request-log records with provider-agnostic - # conversation threads for post-hoc per-trial accounting. Off by default. request_log_attribution: bool = False @field_validator("upstream_api_key_env", "upstream_base_url_env") @@ -166,9 +203,13 @@ class WorkspaceOverlaySpec(StrictModel): """A host file or directory to copy into the compiled task's agent workspace. General-purpose filesystem injection: bake anything (agent definitions, - skills, config, data) into the optimizer's ``/work/agent`` at build time. - ``source`` is a host path (resolved relative to the build YAML); ``dest`` is - where its contents land relative to the workspace root (``.`` = the root). + skills, config, data) into the optimizer's /work/agent at build time. + + Attributes: + source: Host path, resolved relative to the build YAML. Must exist. A + directory contributes its contents; a file lands as dest/. + dest: Where the contents land, relative to the workspace root. Must be a + safe relative path; "." is the root itself. """ source: str @@ -201,8 +242,23 @@ def validate_dest(cls, value: str) -> str: class WandbSpec(StrictModel): - """Weights & Biases reporting settings, emitted into the sidecar's - serve.json. The field names match the sidecar's SidecarWandbConfig.""" + """Weights & Biases reporting settings, emitted into the sidecar's serve.json. + + Reporting runs on the trusted side only. The field names match the sidecar's + SidecarWandbConfig, which consumes them verbatim. + + Attributes: + project: Weights & Biases project to report into. + entity: Owning user or team; the account default when omitted. + name: Display name for the run. + group: Group to file the run under. + tags: Tags applied to the run. + mode: Client mode, e.g. "online" or "offline". + notes: Free-text note attached to the run. + run_id: Resume into this existing run instead of creating one. + log_traces: Upload each evaluation's trace artifacts. Off by default + because they are large. + """ project: str entity: str | None = None @@ -212,12 +268,127 @@ class WandbSpec(StrictModel): mode: str | None = None notes: str | None = None run_id: str | None = None - # Upload each evaluation's trace artifacts to W&B (off by default; large). log_traces: bool = False class HarborBuildConfig(StrictModel): - """Everything needed to emit an isolated Harbor optimization task.""" + """Everything needed to emit an isolated Harbor optimization task. + + This is the whole grammar of a benchmark's build.yaml: load_harbor_build_config + parses and validates one, and compile_harbor_task lowers it into a task + directory. Unknown keys are rejected, so a typo fails the build instead of + quietly taking a default. + + Two cross-field rules are checked after parsing. harness_user cannot be + combined with task_services_use_upstream, because the raw upstream credential + would then reach the isolated harness. And a task_source that is not a local + path must name an explicit registry version. + + Attributes: + name: Task name written into task.toml. + description: Human-readable task description. + agent_repo: Absolute path to the editable target. Copied twice, into the + immutable agent-baseline and the editable agent-seed. + task_source: Local path to the task definitions, or a registry reference + pinned as name@version. + agent_import_path: Import path of the target agent class Harbor loads. + harbor_requirement: Pinned harbor requirement for the task image. + partitions: Partition name to the Harbor task names it holds. Emitted as + one cases/.jsonl per partition. + task_manifest: Optional path to an existing JSON task manifest. + agent_access: One AgentAccessSpec per partition the optimizer may reach. + selection_partition: Partition search optimizes against, normally + validation. + targets: Trusted final scoring passes; see VerificationTargetSpec. + evaluation_set_name: Name given to the evaluation set. + objective: Metric and direction that define fitness. + reward_mode: "submit" scores the candidate the agent submits; + "auto_best" scores the best measured candidate. + baseline_floor: Never ship a candidate scoring below the baseline. + baseline_selection_score: Pin the baseline's selection score rather than + measuring it. + score_baseline: Whether to score the baseline at all. + rescore_top_k: How many leading candidates the trusted side re-scores. + rescore_attempts: Re-score repeats per candidate. + selection_coverage_threshold: Fraction of the selection set an agent + evaluation must cover to be eligible for auto_best ranking. Below + this it is too partial to trust for selection. + model: Default model for the run. + environment_name: Harbor environment, e.g. "modal". + harbor_python_version: Python version for the task image. + default_index: Package index used for installs. + n_attempts: Attempts per case in the nested evaluation run. + max_retries: Per-case retries for the nested `harbor run`, forwarded as + --max-retries. Harbor already retries non-excluded exceptions with + backoff; this tunes how hard, so a transient upstream rate-limit + storm during a mandatory evaluation does not fail otherwise-good + candidates. + retry_wait_multiplier: Backoff multiplier. This and the two delays below + are forwarded in a --config JobConfig snippet, since harbor's CLI + exposes no backoff flags. + retry_min_wait_seconds: Minimum backoff delay. + retry_max_wait_seconds: Maximum backoff delay. + infrastructure_max_attempts: Attempts for infrastructure-classed + failures. + infrastructure_retry_delay_seconds: Delay between those attempts. + reward_key: Metric key the backend treats as the reward. + aggregate_attempts: Combine repeat attempts by "best" or "mean". + feedback_transcripts: Include target transcripts in the agent's feedback. + feedback_max_bytes: Byte cap per feedback transcript. + expose_attempt_detail: Report per-attempt detail, not just the aggregate. + extra_harbor_args: Extra flags for the evaluation sub-run. Rejected if + they override a flag the compiler controls. + agent_env: Environment injected into the optimizer agent's own shell + (setup, install, and run) as harbor --ae KEY=VALUE. Distinct from + extra_harbor_args, which only reaches the evaluation sub-run, and + task_environment, which is that sub-run's environment. Use it for + things like UV_TOOL_BIN_DIR, so `uv tool install` targets a writable + directory on a non-root sandbox. + timeout_seconds: Wall clock for one evaluation. + case_timeout_seconds: Wall clock for one case. + task_agent_timeout_seconds: Wall clock declared for the target agent. + max_concurrency: Cases evaluated concurrently. + error_rate_threshold: Case error fraction above which an evaluation is + abandoned. + verifier_timeout_seconds: Wall clock for the trusted verifier. Falls back + to timeout_seconds. + evaluation_drain_timeout_seconds: Grace period for in-flight evaluations + to finish. Falls back to timeout_seconds. + secrets: Environment variable names routed into the task. Their presence + on the build host is checked at compile time unless + VERO_SKIP_SECRET_CHECK is set. + inference_gateway: Gateway credential source and per-scope policies; see + InferenceGatewaySpec. Omit for no metered model access. + wandb: Trusted-side Weights & Biases reporting from the evaluation + sidecar. Requires WANDB_API_KEY in secrets, routed to the sidecar and + never to the agent. + read_only_paths: Candidate-relative paths the optimizer may read but not + edit. Must be safe relative paths. + workspace_overlays: Host files baked into the optimizer's workspace. + include_evals_skill: Bake vero's packaged evals skill into the workspace, + so any coding agent learns the CLI and the .evals layout. + instruct_multifidelity: Tell the optimizer it allocates its own case + budget and may evaluate subsets. Requires disclose_budget. + instruct_exhaust_budget: Tell the optimizer that unspent budget is + wasted. Requires disclose_budget. + disclose_budget: Master switch for budget disclosure, not enforcement. + When False the instruction renders no budget language and the + sidecar's status omits remaining budgets, so the agent cannot reason + about its budget at all. The two instruct_ flags above apply only + when this is True. + task_services_use_upstream: Give task-owned model services (user + simulators, graders) running inside the task containers the real + upstream via OPENAI_*, while the candidate keeps the metered, + allow-listed gateway on VERO_AGENT_INFERENCE_*. Needed for + benchmarks like tau3 whose environment makes its own model calls. + harness_user: Unprivileged OS user that runs the untrusted candidate + harness in the sidecar, isolating it from trusted state. None + disables that isolation. + task_environment: Extra environment for the evaluation sub-run, available + to the task's ${VAR} substitutions. Must not name secrets. + base_image_main: Base image for the main container. + base_image_sidecar: Base image for the sidecar container. + """ name: str description: str = "" @@ -244,9 +415,6 @@ class HarborBuildConfig(StrictModel): score_baseline: bool = True rescore_top_k: int = Field(default=3, ge=1) rescore_attempts: int = Field(default=1, ge=1) - # Fraction of the selection set's cases an agent eval must cover to be - # eligible for auto_best ranking. Below this, the eval is too partial to - # trust for selection. selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) model: str | None = None @@ -254,12 +422,6 @@ class HarborBuildConfig(StrictModel): harbor_python_version: str = "3.12" default_index: str = "https://pypi.org/simple" n_attempts: int = Field(default=1, ge=1) - # Per-case retry for the nested `harbor run` eval. Harbor already retries any - # non-excluded exception (including rate limits) with backoff; these knobs let - # a build tune how hard, so a transient upstream rate-limit storm during the - # (mandatory) selection/rescore evals does not fail otherwise-good candidates. - # `max_retries` -> `harbor run --max-retries`; the backoff fields are forwarded - # via a `--config` JobConfig snippet (harbor's CLI exposes no backoff flags). max_retries: int = Field(default=2, ge=0) retry_wait_multiplier: float = Field(default=2.0, ge=1.0) retry_min_wait_seconds: float = Field(default=4.0, ge=0.0) @@ -272,12 +434,6 @@ class HarborBuildConfig(StrictModel): feedback_max_bytes: int = Field(default=3000, ge=0) expose_attempt_detail: bool = False extra_harbor_args: list[str] = Field(default_factory=list) - # Environment variables injected into the optimizer agent's shell (setup/ - # install and run), rendered as harbor `--ae KEY=VALUE` on the optimizer's - # `harbor run`. Distinct from `extra_harbor_args` (which only flows into the - # eval sub-run) and `task_environment` (the eval sub-run's env): this reaches - # the optimizer agent's own install/setup exec, e.g. UV_TOOL_BIN_DIR so - # `uv tool install` targets a writable dir on a non-root sandbox. agent_env: dict[str, str] = Field(default_factory=dict) timeout_seconds: float = Field(default=1800.0, gt=0) @@ -290,34 +446,15 @@ class HarborBuildConfig(StrictModel): secrets: list[str] = Field(default_factory=list) inference_gateway: InferenceGatewaySpec | None = None - # Trusted-side Weights & Biases reporting from the eval-sidecar. Requires - # WANDB_API_KEY declared in `secrets` (routed to the sidecar, never the agent). wandb: WandbSpec | None = None read_only_paths: list[str] = Field(default_factory=list) workspace_overlays: list[WorkspaceOverlaySpec] = Field(default_factory=list) - # Bake vero's packaged `evals` skill (skills/evals/SKILL.md) into the - # optimizer workspace so any coding agent learns the CLI + .evals layout. include_evals_skill: bool = True instruct_multifidelity: bool = True instruct_exhaust_budget: bool = True - # Master switch for budget *disclosure* to the optimizer (not enforcement). - # When False, no budget language is rendered into the instruction and the - # sidecar `/status` omits remaining budgets, so the agent cannot see or reason - # about its budget — enabling budget-blind optimization runs. The two - # instruct_* flags above only take effect when this is True. disclose_budget: bool = True - # Give task-owned evaluation services (LLM user-simulators/graders that run - # inside the task containers and can't reach the compose-internal gateway) the - # real upstream via OPENAI_*, while the candidate agent keeps the metered/ - # allow-listed gateway on dedicated VERO_AGENT_INFERENCE_* vars. Needed for - # benchmarks like tau3 whose environment makes its own LLM calls. task_services_use_upstream: bool = False - # Unprivileged OS user that executes the untrusted candidate harness in the - # compiled sidecar, isolating it from the trusted state. The sidecar image - # creates this user; isolation is on by default for compiled deployments. harness_user: str | None = "harness" - # Extra environment injected into the eval sub-run (and thus available for the - # task's ${VAR} substitutions), e.g. TAU2_USER_MODEL. Must not name secrets. task_environment: dict[str, str] = Field(default_factory=dict) base_image_main: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" base_image_sidecar: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" From 800af32ee3b32c894c06529a9a5e22ddfe5fac51 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 13:38:52 -0700 Subject: [PATCH 33/72] Reject models the inference gateway would refuse at build time MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A build could name a model that no gateway scope allows. Nothing caught it, so the mismatch surfaced as a 403 model_denied at run time — and for a verification target, only after search had already spent its whole budget, which is the most expensive moment to learn about a typo. Check each model that will actually be requested against the scope that will actually serve it: the task model against evaluation, and each target's scoring model (its own override, else the task model) against finalization. Verification runs the target agent too, which is why finalization has to allow the target's model and not just a judge's. This stays narrow on purpose. It only objects when a model that will genuinely be requested is unreachable, so a finalization scope that allows extra models still builds. All five current benchmarks pass unchanged. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/config.py | 26 +++++++++++++++ vero/tests/test_v05_harbor_build.py | 48 ++++++++++++++++++++++++++++ 2 files changed, 74 insertions(+) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 50b6552f..ce3f7ead 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -676,6 +676,32 @@ def validate_references(self) -> HarborBuildConfig: "inference gateway credentials must not also be sidecar secrets: " + ", ".join(overlap) ) + # Every model that will actually be requested has to be allowed by the + # scope that will actually serve it. Otherwise the mismatch surfaces as + # a gateway 403 at run time — and for a verification target, only after + # search has already spent its budget. + evaluation_models = self.inference_gateway.evaluation.allowed_models + if self.model is not None and self.model not in evaluation_models: + raise ValueError( + f"model {self.model!r} is not in the inference gateway's " + f"evaluation allowed_models ({', '.join(evaluation_models)})" + ) + # Finalization runs the target agent too, so it must allow the model + # each target scores with: its own override, else the task model. + finalization_models = ( + self.inference_gateway.finalization or self.inference_gateway.evaluation + ).allowed_models + for target in self.targets: + scoring_model = target.model or self.model + if scoring_model is not None and scoring_model not in ( + finalization_models + ): + raise ValueError( + f"target {target.partition!r} scores with model " + f"{scoring_model!r}, which is not in the inference gateway's " + f"finalization allowed_models " + f"({', '.join(finalization_models)})" + ) return self diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 19d9591c..0b699a52 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -411,6 +411,54 @@ def test_build_config_requires_pins_and_valid_partition_references(tmp_path): _config(tmp_path / "source", task_source="org/unversioned") +def test_build_config_requires_requested_models_to_be_allowed_by_their_scope(tmp_path): + """A model the gateway would refuse must fail the build, not the run. + + The verification case is the costly one: finalization runs the target agent + too, so a target scoring with a model its scope disallows only 403s after + search has already spent its whole budget. + """ + + def gateway(**updates) -> InferenceGatewaySpec: + values = { + "producer": InferenceBudgetSpec(allowed_models=["gpt-producer"]), + "evaluation": InferenceBudgetSpec(allowed_models=["gpt-target"]), + } + values.update(updates) + return InferenceGatewaySpec(**values) + + # The matching case builds, and finalization inherits the evaluation policy. + assert _config( + tmp_path / "ok", model="gpt-target", inference_gateway=gateway() + ).model == "gpt-target" + + with pytest.raises(ValidationError, match="evaluation allowed_models"): + _config(tmp_path / "search", model="gpt-typo", inference_gateway=gateway()) + + # An explicit finalization scope that forgets the task model starves the + # target agent during verification. + with pytest.raises(ValidationError, match="finalization allowed_models"): + _config( + tmp_path / "narrowed", + model="gpt-target", + inference_gateway=gateway( + finalization=InferenceBudgetSpec(allowed_models=["gpt-judge"]) + ), + ) + + # A per-target override is checked against finalization, not evaluation. + with pytest.raises(ValidationError, match="finalization allowed_models"): + _config( + tmp_path / "override", + model="gpt-target", + targets=[VerificationTargetSpec(partition="test", model="gpt-other")], + inference_gateway=gateway(), + ) + + # No gateway means no metering, so there is nothing to check against. + assert _config(tmp_path / "ungated", model="gpt-anything").model == "gpt-anything" + + def test_load_build_config_resolves_relative_local_paths(tmp_path): target = _target_repo(tmp_path / "target") tasks = tmp_path / "tasks" From 99626a189a76ad1ee8f3c0fbbc110f70bc0ed8bf Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 13:57:01 -0700 Subject: [PATCH 34/72] Let a build declare a command evaluation backend MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The compiled path assumed both ends were Harbor: the outer optimizer and the inner evaluation. That left no config-driven route for a target that is not an agent — a solver, an index build, a data pipeline — where there is nothing for a nested `harbor run` to drive. ALE-Bench is that case today and handles it by bypassing the compiler entirely, hand-writing a sidecar factory that re-implements build_harbor_components with one backend swapped out. Make the inner backend a choice instead. HarborDeploymentConfig.backends is now a union discriminated on a new `type` tag, build_harbor_components dispatches on it, and HarborBuildConfig grows evaluation_backend plus a command_backend spec. The compiler emits the matching backend, bakes the scoring harness into the trusted sidecar at /opt/harness, and tells it where the case files are; a command backend's case ids name whatever its harness understands, so they are no longer resolved as local Harbor task directories. Everything above the backend seam is untouched. Disclosure, budgets, the gateway, and verification are backend-agnostic already, which is the point: a task on this route gets the metered gateway and the build-time validators that a hand-written factory does not. Compatibility is preserved in both directions. The tag defaults to "harbor", a serve.json written before the union still loads, and all five benchmarks parse unchanged. agent_import_path becomes optional and is required for a harbor backend; Harbor-only fields set on a command build are reported rather than silently ignored. Nesting each backend's fields under its own sub-spec would make that structural rather than validated, but it would rewrite every benchmark's build.yaml, so it is left as a TODO. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evaluation/command.py | 3 + vero/src/vero/harbor/__init__.py | 2 + vero/src/vero/harbor/backend.py | 2 + vero/src/vero/harbor/build/__init__.py | 2 + vero/src/vero/harbor/build/compiler.py | 149 +++++++++++------- vero/src/vero/harbor/build/config.py | 123 ++++++++++++++- .../build/templates/Dockerfile.sidecar.j2 | 3 + vero/src/vero/harbor/deployment.py | 51 +++++- vero/tests/test_v05_harbor_build.py | 112 ++++++++++++- 9 files changed, 383 insertions(+), 64 deletions(-) diff --git a/vero/src/vero/evaluation/command.py b/vero/src/vero/evaluation/command.py index c7604459..0e86282f 100644 --- a/vero/src/vero/evaluation/command.py +++ b/vero/src/vero/evaluation/command.py @@ -7,6 +7,7 @@ import posixpath import re from pathlib import Path, PurePosixPath +from typing import Literal from pydantic import Field, field_validator, model_validator @@ -37,6 +38,8 @@ class CommandBackendConfig(StrictModel): + # Discriminates this from the other backend configs a deployment may name. + type: Literal["command"] = "command" harness_root: str command: list[str] working_directory: str = "." diff --git a/vero/src/vero/harbor/__init__.py b/vero/src/vero/harbor/__init__.py index f4a44250..42bf5e51 100644 --- a/vero/src/vero/harbor/__init__.py +++ b/vero/src/vero/harbor/__init__.py @@ -3,6 +3,7 @@ from vero.harbor.backend import HarborBackend, HarborBackendConfig, HarborCase from vero.harbor.build import ( AgentAccessSpec, + CommandBackendSpec, HarborBuildConfig, InferenceBudgetSpec, InferenceGatewaySpec, @@ -48,6 +49,7 @@ "HarborDeploymentConfig", "HarborSessionManifest", "AgentAccessSpec", + "CommandBackendSpec", "HarborBuildConfig", "InferenceBudgetSpec", "InferenceGatewaySpec", diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 0bc3a15c..2d50e727 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -98,6 +98,8 @@ def expected_result_task_name(self) -> str: class HarborBackendConfig(StrictModel): """Trusted configuration for nested ``harbor run`` evaluation.""" + # Discriminates this from the other backend configs a deployment may name. + type: Literal["harbor"] = "harbor" task_source: str agent_import_path: str cases_path: str diff --git a/vero/src/vero/harbor/build/__init__.py b/vero/src/vero/harbor/build/__init__.py index b86c4dbb..65542be4 100644 --- a/vero/src/vero/harbor/build/__init__.py +++ b/vero/src/vero/harbor/build/__init__.py @@ -3,6 +3,7 @@ from vero.harbor.build.compiler import compile_harbor_task from vero.harbor.build.config import ( AgentAccessSpec, + CommandBackendSpec, HarborBuildConfig, InferenceBudgetSpec, InferenceGatewaySpec, @@ -14,6 +15,7 @@ __all__ = [ "AgentAccessSpec", + "CommandBackendSpec", "HarborBuildConfig", "InferenceBudgetSpec", "InferenceGatewaySpec", diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index e11041db..4988b7b3 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -34,6 +34,7 @@ AGENT_REPO = "/work/agent" CASES_DIR = "/opt/cases" TASK_SOURCE_DIR = "/opt/task-source" +HARNESS_DIR = "/opt/harness" SERVE_CONFIG = "/opt/serve.json" AGENT_VOLUME = "/state/agent-context" ADMIN_VOLUME = "/state/admin" @@ -186,7 +187,10 @@ def _local_result_task_name(task_source: Path, selector: str) -> str: def _write_cases(config: HarborBuildConfig, destination: Path) -> None: destination.mkdir(parents=True, exist_ok=True) task_source = Path(config.task_source) - local = task_source.exists() + # A command backend's case ids name whatever its harness understands (seeds, + # instances, shards), not local Harbor task directories, so there is no + # canonical task name to resolve for them. + local = task_source.exists() and config.command_backend is None for partition, tasks in config.partitions.items(): path = destination / f"{partition}.jsonl" lines = [ @@ -222,58 +226,89 @@ def _deployment_config( ) -> dict: task_source = TASK_SOURCE_DIR if local_task_source else config.task_source backends = {} - for partition in config.partitions: - backends[_backend_id(partition)] = { - "task_source": task_source, - "agent_import_path": config.agent_import_path, - "cases_path": f"{CASES_DIR}/{partition}.jsonl", - "harbor_requirement": config.harbor_requirement, - "evaluation_set_name": config.evaluation_set_name, - "partition": partition, - "model": config.model, - "environment_name": config.environment_name, - "python_version": config.harbor_python_version, - "case_timeout_seconds": config.case_timeout_seconds, - "task_agent_timeout_seconds": config.task_agent_timeout_seconds, - "default_index": config.default_index, - "n_attempts": config.n_attempts, - "max_retries": config.max_retries, - "retry_wait_multiplier": config.retry_wait_multiplier, - "retry_min_wait_seconds": config.retry_min_wait_seconds, - "retry_max_wait_seconds": config.retry_max_wait_seconds, - "infrastructure_max_attempts": config.infrastructure_max_attempts, - "infrastructure_retry_delay_seconds": ( - config.infrastructure_retry_delay_seconds - ), - "reward_key": config.reward_key, - "aggregate_attempts": config.aggregate_attempts, - "feedback_transcripts": config.feedback_transcripts, - "feedback_max_bytes": config.feedback_max_bytes, - "expose_attempt_detail": config.expose_attempt_detail, - "passthrough_environment": config.secrets, - "environment": config.task_environment, - "inference_gateway_url": ( - INFERENCE_GATEWAY_URL if config.inference_gateway is not None else None - ), - "inference_gateway_token": evaluation_inference_token, - "inference_gateway_finalization_token": finalization_inference_token, - "harness_user": config.harness_user, - "task_services_use_upstream": config.task_services_use_upstream, - "upstream_api_key_env": ( - UPSTREAM_API_KEY_ENV if config.inference_gateway is not None else None - ), - "upstream_base_url_env": ( - UPSTREAM_BASE_URL_ENV - if config.inference_gateway is not None - and config.inference_gateway.upstream_base_url_env is not None - else None - ), - "case_resources_cache_path": (f"{ADMIN_VOLUME}/case-resources/{partition}"), - "inference_usage_path": ( - INFERENCE_STATE if config.inference_gateway is not None else None - ), - "extra_args": config.extra_harbor_args, - } + if config.command_backend is not None: + # A command backend scores by running a program, so it needs none of the + # nested-`harbor run` plumbing: no task source, no agent import path, no + # model. The harness is baked into the sidecar at HARNESS_DIR, and case + # enumeration is the harness's job — it reads the partition's case file + # from VERO_CASES_DIR, since the backend itself derives a case count from + # the requested selection rather than from a cases file. + specification = config.command_backend + for partition in config.partitions: + backends[_backend_id(partition)] = { + "type": "command", + "harness_root": HARNESS_DIR, + "command": list(specification.command), + "working_directory": specification.working_directory, + "environment": { + "VERO_CASES_DIR": CASES_DIR, + **specification.environment, + }, + "passthrough_environment": list( + dict.fromkeys( + [*specification.passthrough_environment, *config.secrets] + ) + ), + "staged_inputs": dict(specification.staged_inputs), + "agent_context_inputs": { + name: list(paths) + for name, paths in specification.agent_context_inputs.items() + }, + } + else: + for partition in config.partitions: + backends[_backend_id(partition)] = { + "type": "harbor", + "task_source": task_source, + "agent_import_path": config.agent_import_path, + "cases_path": f"{CASES_DIR}/{partition}.jsonl", + "harbor_requirement": config.harbor_requirement, + "evaluation_set_name": config.evaluation_set_name, + "partition": partition, + "model": config.model, + "environment_name": config.environment_name, + "python_version": config.harbor_python_version, + "case_timeout_seconds": config.case_timeout_seconds, + "task_agent_timeout_seconds": config.task_agent_timeout_seconds, + "default_index": config.default_index, + "n_attempts": config.n_attempts, + "max_retries": config.max_retries, + "retry_wait_multiplier": config.retry_wait_multiplier, + "retry_min_wait_seconds": config.retry_min_wait_seconds, + "retry_max_wait_seconds": config.retry_max_wait_seconds, + "infrastructure_max_attempts": config.infrastructure_max_attempts, + "infrastructure_retry_delay_seconds": ( + config.infrastructure_retry_delay_seconds + ), + "reward_key": config.reward_key, + "aggregate_attempts": config.aggregate_attempts, + "feedback_transcripts": config.feedback_transcripts, + "feedback_max_bytes": config.feedback_max_bytes, + "expose_attempt_detail": config.expose_attempt_detail, + "passthrough_environment": config.secrets, + "environment": config.task_environment, + "inference_gateway_url": ( + INFERENCE_GATEWAY_URL if config.inference_gateway is not None else None + ), + "inference_gateway_token": evaluation_inference_token, + "inference_gateway_finalization_token": finalization_inference_token, + "harness_user": config.harness_user, + "task_services_use_upstream": config.task_services_use_upstream, + "upstream_api_key_env": ( + UPSTREAM_API_KEY_ENV if config.inference_gateway is not None else None + ), + "upstream_base_url_env": ( + UPSTREAM_BASE_URL_ENV + if config.inference_gateway is not None + and config.inference_gateway.upstream_base_url_env is not None + else None + ), + "case_resources_cache_path": (f"{ADMIN_VOLUME}/case-resources/{partition}"), + "inference_usage_path": ( + INFERENCE_STATE if config.inference_gateway is not None else None + ), + "extra_args": config.extra_harbor_args, + } limits = EvaluationLimits( timeout_seconds=config.timeout_seconds, @@ -509,6 +544,13 @@ def compile_harbor_task( if top not in overlay_excludes: overlay_excludes.append(top) _write_cases(config, sidecar_dir / "cases") + if config.command_backend is not None: + # Bake the scoring program into the trusted sidecar, alongside the cases + # it enumerates. Never reachable from the agent workspace. + shutil.copytree( + Path(config.command_backend.harness_source), + sidecar_dir / "harness", + ) local_task_source = Path(config.task_source).exists() if local_task_source: shutil.copytree( @@ -644,6 +686,7 @@ def compile_harbor_task( "inference_gateway_url": INFERENCE_GATEWAY_URL, "read_only_paths": config.read_only_paths, "local_task_source": local_task_source, + "command_harness": config.command_backend is not None, "selection_backend": _backend_id(config.selection_partition), "evaluation_set_name": config.evaluation_set_name, "selection_partition": config.selection_partition, diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index ce3f7ead..30ac4693 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -271,6 +271,77 @@ class WandbSpec(StrictModel): log_traces: bool = False +class CommandBackendSpec(StrictModel): + """Inner evaluation that scores a candidate by running a program. + + Use this when the outer loop is still a Harbor coding agent but the target is + not an agent — a solver, an index build, a data pipeline — so there is nothing + to drive with a nested `harbor run`. The compiler copies harness_source into + the sidecar and points the backend's harness_root at it. + + Attributes: + harness_source: Host directory holding the scoring program, resolved + relative to the build YAML. Must exist. + command: Argument vector to run. Supports the placeholders {harness}, + {workspace}, {request}, {report}, and {artifacts}. + working_directory: Directory to run the command from. + environment: Environment variables set for the command. + passthrough_environment: Names forwarded from the sidecar's environment. + staged_inputs: Extra files staged into the command's workspace. + agent_context_inputs: Per-case files published into the agent context. + """ + + harness_source: str + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + staged_inputs: dict[str, str] = Field(default_factory=dict) + agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) + + @field_validator("harness_source") + @classmethod + def validate_harness_source(cls, value: str) -> str: + if not value.strip() or not Path(value).is_dir(): + raise ValueError("harness_source must be an existing directory") + return value + + @field_validator("command") + @classmethod + def validate_command(cls, value: list[str]) -> list[str]: + if not value or any(not part.strip() for part in value): + raise ValueError("command must contain non-empty arguments") + return value + + +# Fields that only mean something for a nested `harbor run`. Setting one on a +# command build is a mistake worth reporting: it would be silently ignored. +_HARBOR_ONLY_FIELDS = frozenset( + { + "agent_import_path", + "model", + "environment_name", + "harbor_python_version", + "default_index", + "n_attempts", + "max_retries", + "retry_wait_multiplier", + "retry_min_wait_seconds", + "retry_max_wait_seconds", + "infrastructure_max_attempts", + "infrastructure_retry_delay_seconds", + "aggregate_attempts", + "feedback_transcripts", + "feedback_max_bytes", + "expose_attempt_detail", + "extra_harbor_args", + "task_agent_timeout_seconds", + "task_environment", + "task_services_use_upstream", + } +) + + class HarborBuildConfig(StrictModel): """Everything needed to emit an isolated Harbor optimization task. @@ -292,6 +363,13 @@ class HarborBuildConfig(StrictModel): task_source: Local path to the task definitions, or a registry reference pinned as name@version. agent_import_path: Import path of the target agent class Harbor loads. + Required for a harbor evaluation_backend, unused for a command one. + evaluation_backend: How a candidate is scored. "harbor" drives a target + agent with a nested `harbor run`; "command" runs a program instead, + for a target that is not an agent. The outer optimizer is a Harbor + agent either way. + command_backend: The scoring program, required when evaluation_backend is + "command" and rejected otherwise. harbor_requirement: Pinned harbor requirement for the task image. partitions: Partition name to the Harbor task names it holds. Emitted as one cases/.jsonl per partition. @@ -394,13 +472,22 @@ class HarborBuildConfig(StrictModel): description: str = "" agent_repo: str task_source: str - agent_import_path: str + # Optional because a command evaluation_backend has no agent class to load; + # validate_references requires it for a harbor backend. + agent_import_path: str | None = None harbor_requirement: str partitions: dict[str, list[str]] task_manifest: str | None = None agent_access: list[AgentAccessSpec] selection_partition: str targets: list[VerificationTargetSpec] + # TODO: the Harbor-inner fields below (model, retries, feedback, harbor args) + # only apply when evaluation_backend is "harbor", and the command equivalents + # live in command_backend. Nesting each group under its own discriminated + # sub-spec would make that structural instead of validated, at the cost of + # rewriting every benchmark's build.yaml. Deferred deliberately. + evaluation_backend: Literal["harbor", "command"] = "harbor" + command_backend: CommandBackendSpec | None = None evaluation_set_name: str = "harbor" objective: ObjectiveSpec = Field( @@ -474,7 +561,6 @@ def validate_harness_isolation(self) -> HarborBuildConfig: "name", "agent_repo", "task_source", - "agent_import_path", "evaluation_set_name", "environment_name", "harbor_python_version", @@ -510,7 +596,7 @@ def validate_pinned_harbor(cls, value: str) -> str: ) return value - @field_validator("model", "reward_key") + @field_validator("model", "reward_key", "agent_import_path") @classmethod def validate_optional_identity(cls, value: str | None) -> str | None: if value is not None and not value.strip(): @@ -600,6 +686,27 @@ def validate_read_only_paths(cls, value: list[str]) -> list[str]: def validate_references(self) -> HarborBuildConfig: if not Path(self.task_source).exists() and "@" not in self.task_source: raise ValueError("registry task_source must include an explicit version") + if self.evaluation_backend == "command": + if self.command_backend is None: + raise ValueError( + "command evaluation_backend requires a command_backend" + ) + # Reported rather than ignored: these would silently do nothing. + ignored = sorted(_HARBOR_ONLY_FIELDS & self.model_fields_set) + if ignored: + raise ValueError( + "these fields only apply to a harbor evaluation_backend: " + + ", ".join(ignored) + ) + else: + if self.command_backend is not None: + raise ValueError( + "command_backend requires evaluation_backend: command" + ) + if self.agent_import_path is None: + raise ValueError( + "harbor evaluation_backend requires an agent_import_path" + ) known = set(self.partitions) if self.selection_partition not in known: raise ValueError("selection_partition is not present in partitions") @@ -676,10 +783,12 @@ def validate_references(self) -> HarborBuildConfig: "inference gateway credentials must not also be sidecar secrets: " + ", ".join(overlap) ) - # Every model that will actually be requested has to be allowed by the - # scope that will actually serve it. Otherwise the mismatch surfaces as - # a gateway 403 at run time — and for a verification target, only after - # search has already spent its budget. + # Every model that will actually be requested has to be allowed by the + # scope that will actually serve it. Otherwise the mismatch surfaces as a + # gateway 403 at run time — and for a verification target, only after + # search has already spent its budget. A command backend requests no + # models, so there is nothing to reconcile. + if self.inference_gateway is not None and self.evaluation_backend == "harbor": evaluation_models = self.inference_gateway.evaluation.allowed_models if self.model is not None and self.model not in evaluation_models: raise ValueError( diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 index 839913c0..0a317469 100644 --- a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 +++ b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 @@ -19,6 +19,9 @@ COPY sidecar/serve.json /opt/serve.json {% if local_task_source %} COPY sidecar/task-source /opt/task-source {% endif %} +{% if command_harness %} +COPY sidecar/harness /opt/harness +{% endif %} RUN cd /opt/agent-baseline && uv sync 2>/dev/null || true RUN git config --system --add safe.directory /opt/agent-baseline \ diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 72305266..19cee037 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -5,7 +5,7 @@ import logging import shutil from pathlib import Path, PurePosixPath -from typing import Literal +from typing import Annotated, Literal from pydantic import Field, JsonValue, field_validator, model_validator @@ -13,6 +13,7 @@ from vero.evaluation import ( BackendRegistry, BudgetLedger, + EvaluationBackend, EvaluationBudget, EvaluationDatabase, EvaluationLimits, @@ -20,6 +21,7 @@ Evaluator, ObjectiveSpec, ) +from vero.evaluation.command import CommandBackend, CommandBackendConfig from vero.evaluation.engine import EvaluationEngine from vero.harbor.backend import HarborBackend, HarborBackendConfig from vero.harbor.serve import SidecarComponents @@ -94,6 +96,25 @@ class SidecarWandbConfig(StrictModel): telemetry_interval_seconds: float = Field(default=30.0, gt=0) +DeploymentBackendConfig = Annotated[ + HarborBackendConfig | CommandBackendConfig, + Field(discriminator="type"), +] +"""One partition's evaluation backend. + +Harbor for a nested ``harbor run`` against a target agent, command for a target +that is scored by running a program (a solver, an index build) rather than by +driving an agent. Both live behind the EvaluationBackend protocol, so +everything above this line — disclosure, budgets, verification — is unchanged. +""" + + +def _build_backend(config: DeploymentBackendConfig) -> EvaluationBackend: + if isinstance(config, CommandBackendConfig): + return CommandBackend(config) + return HarborBackend(config) + + class HarborDeploymentConfig(StrictModel): task_name: str = "harbor-session" task_description: str = "" @@ -101,7 +122,7 @@ class HarborDeploymentConfig(StrictModel): agent_repo_path: str session_dir: str session_id: str = "trial" - backends: dict[str, HarborBackendConfig] + backends: dict[str, DeploymentBackendConfig] access_policies: list[SidecarEvaluationPolicy] budgets: list[EvaluationBudget] = Field(default_factory=list) selection: DeploymentSelection @@ -117,6 +138,30 @@ class HarborDeploymentConfig(StrictModel): evaluation_drain_timeout_seconds: float = Field(default=600.0, gt=0) wandb: SidecarWandbConfig | None = None + @model_validator(mode="before") + @classmethod + def default_backend_type(cls, value: object) -> object: + """Treat a backend with no ``type`` as Harbor. + + A discriminated union needs the tag present in the input, but serve.json + files compiled before the union existed omit it. Defaulting here keeps + those (and any in-flight run resuming from one) loadable. + """ + if not isinstance(value, dict): + return value + backends = value.get("backends") + if not isinstance(backends, dict): + return value + patched = { + backend_id: ( + {"type": "harbor", **config} + if isinstance(config, dict) and "type" not in config + else config + ) + for backend_id, config in backends.items() + } + return {**value, "backends": patched} + @field_validator( "repo_path", "agent_repo_path", @@ -248,7 +293,7 @@ async def build_harbor_components(config: dict) -> SidecarComponents: ), backends=BackendRegistry( { - backend_id: HarborBackend(backend_config) + backend_id: _build_backend(backend_config) for backend_id, backend_config in parsed.backends.items() } ), diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 0b699a52..78364509 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -12,6 +12,7 @@ from vero.harbor import ( AgentAccessSpec, + CommandBackendSpec, HarborBuildConfig, HarborDeploymentConfig, InferenceBudgetSpec, @@ -25,6 +26,8 @@ BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" +_OMIT = object() + # harness-engineering-bench is a separate branch/PR in the stacked split, so # skip the benchmark-config tests when it isn't checked out here. _requires_benchmarks = pytest.mark.skipif( @@ -162,7 +165,9 @@ def _config(tmp_path: Path, **updates) -> HarborBuildConfig: "targets": [VerificationTargetSpec(partition="test")], } values.update(updates) - return HarborBuildConfig(**values) + # Pass _OMIT to leave a key out entirely, which is different from passing + # None: the build config reports Harbor-only keys that were set at all. + return HarborBuildConfig(**{k: v for k, v in values.items() if v is not _OMIT}) def test_compiler_bakes_workspace_overlay_into_agent_environment(tmp_path): @@ -459,6 +464,111 @@ def gateway(**updates) -> InferenceGatewaySpec: assert _config(tmp_path / "ungated", model="gpt-anything").model == "gpt-anything" +def _command_backend(tmp_path: Path) -> CommandBackendSpec: + harness = tmp_path / "harness" + harness.mkdir(parents=True) + (harness / "score.py").write_text("print('score')\n", encoding="utf-8") + return CommandBackendSpec( + harness_source=str(harness), + command=["python", "{harness}/score.py", "--report", "{report}"], + ) + + +def test_command_backend_compiles_a_task_with_no_target_agent(tmp_path): + """The outer loop stays a Harbor agent while the target is scored by a program. + + This is the Family B shape: a solver or index build has no agent to drive, so + there is nothing for a nested `harbor run` to do. + """ + config = _config( + tmp_path, + evaluation_backend="command", + command_backend=_command_backend(tmp_path / "cmd"), + agent_import_path=_OMIT, + ) + assert config.agent_import_path is None + + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + backend = serve["backends"]["harbor-validation"] + assert backend["type"] == "command" + assert backend["harness_root"] == "/opt/harness" + # Case enumeration is the harness's job, so it is told where the case files are. + assert backend["environment"]["VERO_CASES_DIR"] == "/opt/cases" + # None of the nested-harbor plumbing leaks into a command backend. + assert "agent_import_path" not in backend + assert "task_source" not in backend + + # The harness is baked into the trusted sidecar, never the agent workspace. + assert (output / "environment/sidecar/harness/score.py").is_file() + dockerfile = (output / "environment/sidecar/Dockerfile").read_text(encoding="utf-8") + assert "COPY sidecar/harness /opt/harness" in dockerfile + + # And the trusted side still parses what the compiler emitted. + assert HarborDeploymentConfig.model_validate(serve).backends["harbor-validation"] + + +def test_deployment_treats_a_backend_without_a_type_as_harbor(tmp_path): + """serve.json written before the backend union existed must still load. + + A discriminated union needs the tag in the input, so without a default the + union would break any run resuming from an older compiled task. + """ + from vero.harbor.backend import HarborBackendConfig + + cases = tmp_path / "cases.jsonl" + cases.write_text('{"id": "a", "task_name": "a"}\n', encoding="utf-8") + backend = HarborBackendConfig( + task_source=str(tmp_path), + agent_import_path="target.agent:Agent", + cases_path=str(cases), + harbor_requirement="harbor==0.20.0", + ) + legacy = { + "repo_path": "/opt/agent-baseline", + "agent_repo_path": "/work/agent", + "session_dir": "/state/admin/session", + "admin_volume": "/state/admin", + "access_policies": [], + "targets": [], + "selection": {"mode": "submit"}, + "submit_enabled": True, + # The tag the older compiler never wrote. + "backends": {"harbor-validation": backend.model_dump( + mode="json", exclude={"type"} + )}, + } + parsed = HarborDeploymentConfig.model_validate(legacy) + assert parsed.backends["harbor-validation"].type == "harbor" + + +def test_build_config_keeps_the_two_backend_kinds_apart(tmp_path): + with pytest.raises(ValidationError, match="requires a command_backend"): + _config( + tmp_path / "missing", + evaluation_backend="command", + agent_import_path=_OMIT, + ) + with pytest.raises(ValidationError, match="requires evaluation_backend: command"): + _config(tmp_path / "stray", command_backend=_command_backend(tmp_path / "s")) + with pytest.raises(ValidationError, match="requires an agent_import_path"): + _config(tmp_path / "noagent", agent_import_path=None) + # Harbor-only knobs are reported rather than silently ignored. + with pytest.raises(ValidationError, match="only apply to a harbor"): + _config( + tmp_path / "ignored", + evaluation_backend="command", + command_backend=_command_backend(tmp_path / "i"), + agent_import_path=_OMIT, + max_retries=5, + feedback_transcripts=True, + ) + + def test_load_build_config_resolves_relative_local_paths(tmp_path): target = _target_repo(tmp_path / "target") tasks = tmp_path / "tasks" From a431e8f1a95227499a2c450b2c2f74e7fe856c9e Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 14:01:21 -0700 Subject: [PATCH 35/72] Make task_source optional, required only for a harbor backend A command build had to name a task_source it never used, so the only way to satisfy the schema was to point it at an empty directory. Nothing on that path reads it: it names Harbor task definitions, and a command backend has none. Make it optional and require it for a harbor backend alongside agent_import_path, reporting both together when either is missing. Setting it on a command build is now rejected as Harbor-only, which also means a case id is resolved as a local Harbor task directory exactly when a task_source names one. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 21 ++++++++++++--------- vero/src/vero/harbor/build/config.py | 26 ++++++++++++++++++-------- vero/tests/test_v05_harbor_build.py | 12 +++++++++++- 3 files changed, 41 insertions(+), 18 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 4988b7b3..83d66b18 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -186,11 +186,11 @@ def _local_result_task_name(task_source: Path, selector: str) -> str: def _write_cases(config: HarborBuildConfig, destination: Path) -> None: destination.mkdir(parents=True, exist_ok=True) - task_source = Path(config.task_source) - # A command backend's case ids name whatever its harness understands (seeds, - # instances, shards), not local Harbor task directories, so there is no - # canonical task name to resolve for them. - local = task_source.exists() and config.command_backend is None + # Only a harbor backend names a task_source, and only then does a case id + # correspond to a local Harbor task directory with a canonical name. A + # command backend's ids name whatever its harness understands. + task_source = Path(config.task_source) if config.task_source else None + local = task_source is not None and task_source.exists() for partition, tasks in config.partitions.items(): path = destination / f"{partition}.jsonl" lines = [ @@ -473,9 +473,10 @@ def compile_harbor_task( protected = [Path(config.agent_repo).resolve()] if use_local_vero: protected.append(source_root) - task_source_path = Path(config.task_source) - if task_source_path.exists(): - protected.append(task_source_path.resolve()) + if config.task_source is not None: + task_source_path = Path(config.task_source) + if task_source_path.exists(): + protected.append(task_source_path.resolve()) for path in protected: if output == path or output.is_relative_to(path) or path.is_relative_to(output): raise ValueError( @@ -551,7 +552,9 @@ def compile_harbor_task( Path(config.command_backend.harness_source), sidecar_dir / "harness", ) - local_task_source = Path(config.task_source).exists() + local_task_source = ( + config.task_source is not None and Path(config.task_source).exists() + ) if local_task_source: shutil.copytree( Path(config.task_source), diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 30ac4693..d1a86565 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -319,6 +319,7 @@ def validate_command(cls, value: list[str]) -> list[str]: _HARBOR_ONLY_FIELDS = frozenset( { "agent_import_path", + "task_source", "model", "environment_name", "harbor_python_version", @@ -361,7 +362,8 @@ class HarborBuildConfig(StrictModel): agent_repo: Absolute path to the editable target. Copied twice, into the immutable agent-baseline and the editable agent-seed. task_source: Local path to the task definitions, or a registry reference - pinned as name@version. + pinned as name@version. Required for a harbor evaluation_backend, + and rejected for a command one, which has no Harbor tasks. agent_import_path: Import path of the target agent class Harbor loads. Required for a harbor evaluation_backend, unused for a command one. evaluation_backend: How a candidate is scored. "harbor" drives a target @@ -471,7 +473,9 @@ class HarborBuildConfig(StrictModel): name: str description: str = "" agent_repo: str - task_source: str + # Optional because a command evaluation_backend has no Harbor task + # definitions; validate_references requires it for a harbor backend. + task_source: str | None = None # Optional because a command evaluation_backend has no agent class to load; # validate_references requires it for a harbor backend. agent_import_path: str | None = None @@ -560,7 +564,6 @@ def validate_harness_isolation(self) -> HarborBuildConfig: @field_validator( "name", "agent_repo", - "task_source", "evaluation_set_name", "environment_name", "harbor_python_version", @@ -596,7 +599,7 @@ def validate_pinned_harbor(cls, value: str) -> str: ) return value - @field_validator("model", "reward_key", "agent_import_path") + @field_validator("model", "reward_key", "agent_import_path", "task_source") @classmethod def validate_optional_identity(cls, value: str | None) -> str | None: if value is not None and not value.strip(): @@ -684,7 +687,9 @@ def validate_read_only_paths(cls, value: list[str]) -> list[str]: @model_validator(mode="after") def validate_references(self) -> HarborBuildConfig: - if not Path(self.task_source).exists() and "@" not in self.task_source: + if self.task_source is not None and ( + not Path(self.task_source).exists() and "@" not in self.task_source + ): raise ValueError("registry task_source must include an explicit version") if self.evaluation_backend == "command": if self.command_backend is None: @@ -703,9 +708,14 @@ def validate_references(self) -> HarborBuildConfig: raise ValueError( "command_backend requires evaluation_backend: command" ) - if self.agent_import_path is None: + missing = sorted( + name + for name in ("agent_import_path", "task_source") + if getattr(self, name) is None + ) + if missing: raise ValueError( - "harbor evaluation_backend requires an agent_import_path" + "harbor evaluation_backend requires: " + ", ".join(missing) ) known = set(self.partitions) if self.selection_partition not in known: @@ -728,7 +738,7 @@ def validate_references(self) -> HarborBuildConfig: reward_keys = [target.reward_key for target in self.targets] if len(reward_keys) != len(set(reward_keys)): raise ValueError("target reward keys must be unique") - if self.task_manifest is not None: + if self.task_manifest is not None and self.task_source is not None: try: manifest = json.loads( Path(self.task_manifest).read_text(encoding="utf-8") diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 78364509..e5b7101c 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -485,6 +485,7 @@ def test_command_backend_compiles_a_task_with_no_target_agent(tmp_path): evaluation_backend="command", command_backend=_command_backend(tmp_path / "cmd"), agent_import_path=_OMIT, + task_source=_OMIT, ) assert config.agent_import_path is None @@ -552,11 +553,19 @@ def test_build_config_keeps_the_two_backend_kinds_apart(tmp_path): tmp_path / "missing", evaluation_backend="command", agent_import_path=_OMIT, + task_source=_OMIT, ) with pytest.raises(ValidationError, match="requires evaluation_backend: command"): _config(tmp_path / "stray", command_backend=_command_backend(tmp_path / "s")) - with pytest.raises(ValidationError, match="requires an agent_import_path"): + with pytest.raises(ValidationError, match="requires: agent_import_path"): _config(tmp_path / "noagent", agent_import_path=None) + with pytest.raises(ValidationError, match="only apply to a harbor"): + _config( + tmp_path / "tasksource", + evaluation_backend="command", + command_backend=_command_backend(tmp_path / "t"), + agent_import_path=_OMIT, + ) # Harbor-only knobs are reported rather than silently ignored. with pytest.raises(ValidationError, match="only apply to a harbor"): _config( @@ -564,6 +573,7 @@ def test_build_config_keeps_the_two_backend_kinds_apart(tmp_path): evaluation_backend="command", command_backend=_command_backend(tmp_path / "i"), agent_import_path=_OMIT, + task_source=_OMIT, max_retries=5, feedback_transcripts=True, ) From d822ed5ca8d658e6b793efce8e6ca0f3dccdf52c Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 14:04:43 -0700 Subject: [PATCH 36/72] Resolve a relative harness_source against the build YAML CommandBackendSpec documented harness_source as resolved relative to the build YAML, matching agent_repo, task_source, task_manifest, and overlay sources, but the loader never resolved it. A relative path was therefore interpreted against the working directory, so the existence check failed from anywhere except the YAML's own directory. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/config.py | 5 ++++ vero/tests/test_v05_harbor_build.py | 38 ++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index d1a86565..c3962ee9 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -928,6 +928,11 @@ def load_harbor_build_config( task_manifest = value.get("task_manifest") if isinstance(task_manifest, str) and not Path(task_manifest).is_absolute(): value["task_manifest"] = str((base / task_manifest).resolve()) + command_backend = value.get("command_backend") + if isinstance(command_backend, dict): + harness_source = command_backend.get("harness_source") + if isinstance(harness_source, str) and not Path(harness_source).is_absolute(): + command_backend["harness_source"] = str((base / harness_source).resolve()) overlays = value.get("workspace_overlays") if isinstance(overlays, list): for entry in overlays: diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index e5b7101c..47d9f02a 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -612,6 +612,44 @@ def test_load_build_config_resolves_relative_local_paths(tmp_path): assert loaded.task_source == str(tasks) +def test_load_build_config_resolves_a_relative_harness_source(tmp_path): + """A command build's harness is relocatable like every other path field.""" + target = _target_repo(tmp_path / "target") + harness = tmp_path / "harness" + harness.mkdir() + (harness / "score.py").write_text("print('score')\n", encoding="utf-8") + config_path = tmp_path / "build.yaml" + config_path.write_text( + "\n".join( + [ + "name: org/solve", + f"agent_repo: {target.name}", + "harbor_requirement: harbor==0.1.17", + "partitions:", + " validation: [s0]", + " test: [s1]", + "agent_access:", + " - partition: validation", + " min_aggregate_cases: 1", + "selection_partition: validation", + "targets:", + " - partition: test", + "evaluation_backend: command", + "command_backend:", + f" harness_source: {harness.name}", + ' command: [python, "{harness}/score.py"]', + ] + ) + + "\n", + encoding="utf-8", + ) + + loaded = load_harbor_build_config(config_path) + + assert loaded.command_backend is not None + assert loaded.command_backend.harness_source == str(harness) + + def test_load_build_config_supports_partition_files_and_validates_manifest(tmp_path): target = _target_repo(tmp_path / "target") partitions = tmp_path / "partitions" From 0f8aaee01adf28e83ccf4a8575820b6220628010 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 14:16:54 -0700 Subject: [PATCH 37/72] Record the measured scope of the nested-backend refactor The TODO said what to do and why to defer, but not what it would cost, so the question would be re-litigated from scratch. Note the measurement and the migration trick that avoids a breaking change. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/config.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index c3962ee9..220eabe7 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -488,8 +488,17 @@ class HarborBuildConfig(StrictModel): # TODO: the Harbor-inner fields below (model, retries, feedback, harbor args) # only apply when evaluation_backend is "harbor", and the command equivalents # live in command_backend. Nesting each group under its own discriminated - # sub-spec would make that structural instead of validated, at the cost of - # rewriting every benchmark's build.yaml. Deferred deliberately. + # sub-spec would make that structural instead of validated: _HARBOR_ONLY_FIELDS + # and the whole backend-coherence branch of validate_references would go away, + # and task_source/agent_import_path would stop being optional-but-required. + # + # Measured scope: 29 refs in the compiler, 11 in these validators, 66 in the + # build tests, and 80 top-level keys to re-indent across the five benchmark + # build.yaml files (nothing outside the build package reads these fields, and + # HarborBackendConfig is unaffected). Deferred because it is a breaking schema + # change with no functional gain, and re-indenting the benchmark configs risks + # silently altering the measurement substrate. A model_validator(mode="before") + # that lifts flat keys into the nested field would avoid the flag day. evaluation_backend: Literal["harbor", "command"] = "harbor" command_backend: CommandBackendSpec | None = None From 936a3f1f658532db1eea801935e9c2be6fbaa68d Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 14:43:03 -0700 Subject: [PATCH 38/72] Centralize the compiled-task layout in one object MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Every container path in a compiled task was written down two or more times: as a Python constant in the compiler and again as a literal in the Jinja templates. /work/agent appeared 19 times across 10 files. Changing a constant therefore did not propagate to the templates, and three paths (/opt/seed.sh, /opt/inference.json, /opt/overlay) had no constant at all. I had already created one instance of the hazard: HARNESS_DIR in the compiler and /opt/harness hardcoded in the sidecar Dockerfile. Add vero.harbor.layout.TaskLayout, a frozen dataclass holding every container path, service name, and port, with child paths derived as properties so a base and its children cannot drift. Every template now receives it, so no template spells out a path of its own. It lives outside build/ because harbor.inference also needs two of these as field defaults. Path values are unchanged and deliberately so: they are a contract with the benchmarks' read_only_paths and with compiled task directories that are checked in. Renaming an attribute is now cheap; repointing one is still breaking. Measured: literal path occurrences outside the definition drop from 93 across 13 files to 0, with the only remaining copies being layout.py itself and one pinning test. That test exists because every other test now references the layout, which would otherwise make them tautological — it is the single place the values are written twice on purpose. Verified by rendering four tasks covering every template branch (gateway on/off, overlay, wandb, budget-blind, harness isolation off, harbor and command backends) before and after: the generated output is byte-identical. Jinja runs with StrictUndefined, so a mistyped layout attribute fails the build rather than rendering an empty path. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 40 +++--- vero/src/vero/harbor/build/config.py | 2 +- .../build/templates/Dockerfile.gateway.j2 | 6 +- .../harbor/build/templates/Dockerfile.main.j2 | 14 +-- .../build/templates/Dockerfile.sidecar.j2 | 26 ++-- .../build/templates/docker-compose.yaml.j2 | 42 +++---- .../harbor/build/templates/instruction.md.j2 | 4 +- .../vero/harbor/build/templates/seed.sh.j2 | 20 +-- .../vero/harbor/build/templates/solve.sh.j2 | 2 +- .../vero/harbor/build/templates/test.sh.j2 | 4 +- vero/src/vero/harbor/inference.py | 5 +- vero/src/vero/harbor/layout.py | 118 ++++++++++++++++++ vero/tests/test_v05_harbor_backend.py | 3 +- vero/tests/test_v05_harbor_build.py | 82 +++++++++--- 14 files changed, 269 insertions(+), 99 deletions(-) create mode 100644 vero/src/vero/harbor/layout.py diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 83d66b18..2f07d7e9 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -23,30 +23,34 @@ ) from vero.harbor.build.config import HarborBuildConfig, WorkspaceOverlaySpec from vero.harbor.inference import generate_inference_token, token_digest +from vero.harbor.layout import LAYOUT logger = logging.getLogger(__name__) _TEMPLATES = Path(__file__).parent / "templates" _VERO_COPY = ("pyproject.toml", "README.md", "uv.lock", "src") -VERO_DIR = "/opt/vero" -TRUSTED_REPO = "/opt/agent-baseline" -AGENT_REPO = "/work/agent" -CASES_DIR = "/opt/cases" -TASK_SOURCE_DIR = "/opt/task-source" -HARNESS_DIR = "/opt/harness" -SERVE_CONFIG = "/opt/serve.json" -AGENT_VOLUME = "/state/agent-context" -ADMIN_VOLUME = "/state/admin" -SESSION_DIR = "/state/admin/session" -TOKEN_PATH = "/state/token/admin.token" SESSION_ID = "trial" -INFERENCE_STATE = "/state/inference/usage.json" -INFERENCE_REQUEST_LOG_DIR = "/state/inference/requests" -INFERENCE_GATEWAY_URL = "http://inference-gateway:8001" UPSTREAM_API_KEY_ENV = "VERO_INFERENCE_UPSTREAM_API_KEY" UPSTREAM_BASE_URL_ENV = "VERO_INFERENCE_UPSTREAM_BASE_URL" +# Container paths and service identities come from the layout, never from a +# literal here: the templates read the same object, so the two cannot drift. +VERO_DIR = LAYOUT.vero +TRUSTED_REPO = LAYOUT.trusted_repo +AGENT_REPO = LAYOUT.target_repo +CASES_DIR = LAYOUT.cases +TASK_SOURCE_DIR = LAYOUT.task_source +HARNESS_DIR = LAYOUT.harness +SERVE_CONFIG = LAYOUT.serve_config +AGENT_VOLUME = LAYOUT.agent_volume +ADMIN_VOLUME = LAYOUT.admin_volume +SESSION_DIR = LAYOUT.session_dir +TOKEN_PATH = LAYOUT.token_path +INFERENCE_STATE = LAYOUT.inference_state +INFERENCE_REQUEST_LOG_DIR = LAYOUT.inference_request_log_dir +INFERENCE_GATEWAY_URL = LAYOUT.gateway_url + def _backend_id(partition: str) -> str: return f"harbor-{partition}" @@ -303,7 +307,9 @@ def _deployment_config( and config.inference_gateway.upstream_base_url_env is not None else None ), - "case_resources_cache_path": (f"{ADMIN_VOLUME}/case-resources/{partition}"), + "case_resources_cache_path": ( + f"{LAYOUT.case_resources_dir}/{partition}" + ), "inference_usage_path": ( INFERENCE_STATE if config.inference_gateway is not None else None ), @@ -437,6 +443,8 @@ def _deployment_config( def _render(template: str, destination: Path, **context) -> None: + """Render one template. Every template gets the layout, so no template needs + to spell out a container path, service name, or port of its own.""" try: from jinja2 import Environment, FileSystemLoader, StrictUndefined except ImportError as error: @@ -453,7 +461,7 @@ def _render(template: str, destination: Path, **context) -> None: ) destination.parent.mkdir(parents=True, exist_ok=True) destination.write_text( - environment.get_template(template).render(**context), + environment.get_template(template).render(layout=LAYOUT, **context), encoding="utf-8", ) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 220eabe7..d6f60c47 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -203,7 +203,7 @@ class WorkspaceOverlaySpec(StrictModel): """A host file or directory to copy into the compiled task's agent workspace. General-purpose filesystem injection: bake anything (agent definitions, - skills, config, data) into the optimizer's /work/agent at build time. + skills, config, data) into the optimizer's workspace at build time. Attributes: source: Host path, resolved relative to the build YAML. Must exist. A diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 index 9bc6fd15..9ee42e89 100644 --- a/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 +++ b/vero/src/vero/harbor/build/templates/Dockerfile.gateway.j2 @@ -6,12 +6,12 @@ RUN apt-get update \ && rm -rf /var/lib/apt/lists/* {% if use_local_vero %} -COPY vero /opt/vero -RUN uv pip install --system "/opt/vero[harbor]" +COPY vero {{ layout.vero }} +RUN uv pip install --system "{{ layout.vero }}[harbor]" {% else %} RUN uv pip install --system "{{ vero_requirement }}" {% endif %} -COPY gateway/config.json /opt/inference.json +COPY gateway/config.json {{ layout.inference_config }} WORKDIR /opt diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 index 945d65d3..c3e97f17 100644 --- a/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 +++ b/vero/src/vero/harbor/build/templates/Dockerfile.main.j2 @@ -6,17 +6,17 @@ RUN apt-get update \ && rm -rf /var/lib/apt/lists/* {% if use_local_vero %} -COPY vero /opt/vero -RUN uv pip install --system "/opt/vero[harbor]" +COPY vero {{ layout.vero }} +RUN uv pip install --system "{{ layout.vero }}[harbor]" {% else %} RUN uv pip install --system "{{ vero_requirement }}" {% endif %} -COPY agent-seed /opt/agent-seed +COPY agent-seed {{ layout.seed_repo }} {% if overlay_present %} -COPY overlay /opt/overlay +COPY overlay {{ layout.overlay }} {% endif %} -COPY main/seed.sh /opt/seed.sh -RUN chmod +x /opt/seed.sh && useradd -m -u 1001 agent +COPY main/seed.sh {{ layout.seed_script }} +RUN chmod +x {{ layout.seed_script }} && useradd -m -u 1001 agent -WORKDIR /work/agent +WORKDIR {{ layout.target_repo }} diff --git a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 index 0a317469..0b30e2f5 100644 --- a/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 +++ b/vero/src/vero/harbor/build/templates/Dockerfile.sidecar.j2 @@ -6,27 +6,27 @@ RUN apt-get update \ && rm -rf /var/lib/apt/lists/* {% if use_local_vero %} -COPY vero /opt/vero -RUN uv pip install --system "/opt/vero[{{ sidecar_extras }}]" +COPY vero {{ layout.vero }} +RUN uv pip install --system "{{ layout.vero }}[{{ sidecar_extras }}]" {% else %} RUN uv pip install --system "{{ vero_requirement }}" {% endif %} RUN uv pip install --system "{{ harbor_requirement }}" -COPY agent-baseline /opt/agent-baseline -COPY sidecar/cases /opt/cases -COPY sidecar/serve.json /opt/serve.json +COPY agent-baseline {{ layout.trusted_repo }} +COPY sidecar/cases {{ layout.cases }} +COPY sidecar/serve.json {{ layout.serve_config }} {% if local_task_source %} -COPY sidecar/task-source /opt/task-source +COPY sidecar/task-source {{ layout.task_source }} {% endif %} {% if command_harness %} -COPY sidecar/harness /opt/harness +COPY sidecar/harness {{ layout.harness }} {% endif %} -RUN cd /opt/agent-baseline && uv sync 2>/dev/null || true -RUN git config --system --add safe.directory /opt/agent-baseline \ - && git config --system --add safe.directory /work/agent \ - && git config --system --add safe.directory /work/agent/.git +RUN cd {{ layout.trusted_repo }} && uv sync 2>/dev/null || true +RUN git config --system --add safe.directory {{ layout.trusted_repo }} \ + && git config --system --add safe.directory {{ layout.target_repo }} \ + && git config --system --add safe.directory {{ layout.target_git }} # Unprivileged user for executing the untrusted candidate harness. The sidecar # still starts as root (the trusted control plane); only the `harbor run` @@ -46,8 +46,8 @@ RUN mkdir -p /home/harness/.cache \ # Lock the trusted, root-only files the harness must never read: the case lists # reveal held-out (validation/test) task membership, and the deployment config # carries the reserved finalization inference token. (Local task packages under -# /opt/task-source, which harbor must read to grade host-side, remain readable +# {{ layout.task_source }}, which harbor must read to grade host-side, remain readable # and are addressed by the deeper in-container isolation work.) -RUN chmod 700 /opt/cases && chmod 600 /opt/serve.json +RUN chmod 700 {{ layout.cases }} && chmod 600 {{ layout.serve_config }} WORKDIR /opt diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 index b0f9963d..f03979e8 100644 --- a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -1,9 +1,9 @@ # Harbor merges this after its generated main-service configuration. services: main: - command: ["/opt/seed.sh"] + command: ["{{ layout.seed_script }}"] environment: - VERO_EVAL_URL: "http://eval-sidecar:8000" + VERO_EVAL_URL: "{{ layout.sidecar_url }}" {% for secret in scrubbed_main_environment %} {{ secret }}: "" {% endfor %} @@ -17,18 +17,18 @@ services: {% endif %} {% endif %} volumes: - - agent_repo:/work/agent - - agent_context:/work/agent/.evals:ro - - token_state:/state/token:ro + - agent_repo:{{ layout.target_repo }} + - agent_context:{{ layout.target_evals }}:ro + - token_state:{{ layout.token_dir }}:ro depends_on: - eval-sidecar: + {{ layout.sidecar_host }}: condition: service_healthy {% if inference_gateway %} - inference-gateway: + {{ layout.gateway_host }}: condition: service_healthy {% endif %} - eval-sidecar: + {{ layout.sidecar_host }}: build: context: . dockerfile: sidecar/Dockerfile @@ -39,9 +39,9 @@ services: - "--factory" - "vero.harbor.deployment:build_harbor_components" - "--config" - - "/opt/serve.json" + - "{{ layout.serve_config }}" - "--admin-token" - - "/state/token/admin.token" + - "{{ layout.token_path }}" environment: {% if sidecar_secrets %} {% for secret in sidecar_secrets %} @@ -59,39 +59,39 @@ services: {% endfor %} {% endif %} volumes: - - agent_repo:/work/agent:ro - - agent_context:/state/agent-context - - admin_state:/state/admin - - token_state:/state/token + - agent_repo:{{ layout.target_repo }}:ro + - agent_context:{{ layout.agent_volume }} + - admin_state:{{ layout.admin_volume }} + - token_state:{{ layout.token_dir }} {% if inference_gateway %} - - inference_state:/state/inference:ro + - inference_state:{{ layout.inference_dir }}:ro {% endif %} healthcheck: - test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:{{ layout.sidecar_port }}/health')"] interval: 5s timeout: 10s retries: 30 start_period: 10s {% if inference_gateway %} - inference-gateway: + {{ layout.gateway_host }}: build: context: . dockerfile: gateway/Dockerfile command: - "vero" - "harbor" - - "inference-gateway" + - "{{ layout.gateway_host }}" - "--config" - - "/opt/inference.json" + - "{{ layout.inference_config }}" environment: {% for secret in gateway_environment %} {{ secret }}: "${{ '{' }}{{ secret }}:?{{ secret }} must be set for the inference gateway{{ '}' }}" {% endfor %} volumes: - - inference_state:/state/inference + - inference_state:{{ layout.inference_dir }} healthcheck: - test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8001/health')"] + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:{{ layout.gateway_port }}/health')"] interval: 5s timeout: 10s retries: 30 diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 3c58722f..9cca793d 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -1,6 +1,6 @@ # Optimize the program -Improve the program in `/work/agent` so it scores as highly as possible on the +Improve the program in `{{ layout.target_repo }}` so it scores as highly as possible on the hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, {% if disclose_budget %}budget, {% endif %}and final candidate selection. @@ -12,7 +12,7 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, {% endif %} ## Workflow -1. Inspect and edit `/work/agent`. +1. Inspect and edit `{{ layout.target_repo }}`. 2. Commit each candidate with Git. 3. Evaluate the current commit on the selection set: diff --git a/vero/src/vero/harbor/build/templates/seed.sh.j2 b/vero/src/vero/harbor/build/templates/seed.sh.j2 index c67b7168..e4b7b3ed 100644 --- a/vero/src/vero/harbor/build/templates/seed.sh.j2 +++ b/vero/src/vero/harbor/build/templates/seed.sh.j2 @@ -1,19 +1,19 @@ #!/bin/sh set -eu -if [ ! -d /work/agent/.git ]; then - cp -a /opt/agent-seed/. /work/agent/ +if [ ! -d {{ layout.target_git }} ]; then + cp -a {{ layout.seed_repo }}/. {{ layout.target_repo }}/ fi {% if overlay_present %} # Inject baked-in workspace overlay (agent definitions, skills, config, ...). -cp -a /opt/overlay/. /work/agent/ +cp -a {{ layout.overlay }}/. {{ layout.target_repo }}/ {% endif %} -find /work/agent -path /work/agent/.evals -prune -o -exec chown agent:agent {} + -git config --system --add safe.directory /work/agent -printf '%s\n' '/.evals/' >> /work/agent/.git/info/exclude +find {{ layout.target_repo }} -path {{ layout.target_evals }} -prune -o -exec chown agent:agent {} + +git config --system --add safe.directory {{ layout.target_repo }} +printf '%s\n' '/.evals/' >> {{ layout.target_git_exclude }} {% for path in overlay_excludes %} -printf '%s\n' '/{{ path }}/' >> /work/agent/.git/info/exclude +printf '%s\n' '/{{ path }}/' >> {{ layout.target_git_exclude }} {% endfor %} {% if inference_gateway %} # Codex otherwise treats the built-in OpenAI provider as WebSocket-capable. @@ -32,9 +32,9 @@ TOML chown -R agent:agent /tmp/codex-home {% endif %} {% for path in read_only_paths %} -if [ -e "/work/agent/{{ path }}" ]; then - chown -R root:root "/work/agent/{{ path }}" - chmod -R a-w "/work/agent/{{ path }}" +if [ -e "{{ layout.target_repo }}/{{ path }}" ]; then + chown -R root:root "{{ layout.target_repo }}/{{ path }}" + chmod -R a-w "{{ layout.target_repo }}/{{ path }}" fi {% endfor %} diff --git a/vero/src/vero/harbor/build/templates/solve.sh.j2 b/vero/src/vero/harbor/build/templates/solve.sh.j2 index 09ede8ed..b2de3993 100644 --- a/vero/src/vero/harbor/build/templates/solve.sh.j2 +++ b/vero/src/vero/harbor/build/templates/solve.sh.j2 @@ -1,7 +1,7 @@ #!/bin/sh set -eu -cd /work/agent +cd {{ layout.target_repo }} git config user.name optimizer git config user.email optimizer@example.test echo "# optimizer candidate" >> README.md 2>/dev/null || echo "candidate" > NOTES.md diff --git a/vero/src/vero/harbor/build/templates/test.sh.j2 b/vero/src/vero/harbor/build/templates/test.sh.j2 index 19675ef3..90fc05d4 100644 --- a/vero/src/vero/harbor/build/templates/test.sh.j2 +++ b/vero/src/vero/harbor/build/templates/test.sh.j2 @@ -3,8 +3,8 @@ set -eu mkdir -p /logs/verifier vero harbor finalize \ - --token-file /state/token/admin.token \ + --token-file {{ layout.token_path }} \ --output /logs/verifier/reward.json vero harbor export-session \ - --token-file /state/token/admin.token + --token-file {{ layout.token_path }} cat /logs/verifier/reward.json diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index 043ab9f1..5ecc92a0 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -22,6 +22,7 @@ from pydantic import Field, field_validator, model_validator from vero.evaluation.persistence import _atomic_write_json +from vero.harbor.layout import LAYOUT from vero.models import StrictModel logger = logging.getLogger(__name__) @@ -74,7 +75,7 @@ class InferenceRequestLogConfig(StrictModel): usage frame of a stream survives; ``body_bytes: 0`` keeps metadata only. """ - directory: str = "/state/inference/requests" + directory: str = LAYOUT.inference_request_log_dir body_bytes: int = Field(default=16384, ge=0) rotate_bytes: int = Field(default=64 * 1024 * 1024, ge=1_048_576) # Experimental: stamp each record with a provider-agnostic conversation @@ -95,7 +96,7 @@ class InferenceGatewayConfig(StrictModel): upstream_api_key_env: str = "OPENAI_API_KEY" upstream_base_url_env: str | None = "OPENAI_BASE_URL" default_upstream_base_url: str = "https://api.openai.com/v1" - state_path: str = "/state/inference/usage.json" + state_path: str = LAYOUT.inference_state request_log: InferenceRequestLogConfig | None = None scopes: dict[str, InferenceScopeConfig] diff --git a/vero/src/vero/harbor/layout.py b/vero/src/vero/harbor/layout.py new file mode 100644 index 00000000..91bec3d5 --- /dev/null +++ b/vero/src/vero/harbor/layout.py @@ -0,0 +1,118 @@ +"""The single source of truth for a compiled Harbor task's layout. + +Every container path, service name, and port in a compiled task is defined here +once and referenced everywhere else — the compiler, the Jinja templates, the +runtime configs, and the tests. Before this module the same strings were copied +between Python constants and template literals, so changing one silently failed +to propagate to the other. + +The values are effectively frozen: they are a contract with things outside this +package, including every benchmark's ``read_only_paths`` and the compiled task +directories that are checked in. Rename an attribute freely; changing what it +points at is a breaking change. + +Two conventions worth keeping straight, because both say "agent": + +- The *target* is the program under optimization. It may not be an agent at all + (a solver, an index build), which is why its paths are named ``target_*``. +- The *agent* is the optimizer, which really is an agent. ``agent_volume`` is + its context directory. +""" + +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class TaskLayout: + """Container paths and service identities inside a compiled Harbor task. + + Attributes: + target_repo: The editable target the optimizer may change. + trusted_repo: Immutable copy of the target, the non-regression floor. + seed_repo: The starting point the target repo is seeded from. + vero: Local vero source, when built from a source checkout. + cases: Per-partition case files, root-only. + task_source: Local Harbor task definitions, for a harbor backend. + harness: The scoring program, for a command backend. + overlay: Host files baked into the optimizer's workspace. + serve_config: The trusted deployment config, root-only. + seed_script: Script that seeds the target repo on first boot. + inference_config: The gateway's scope config. + agent_volume: The optimizer's context directory, written by the sidecar. + admin_volume: Trusted state: records, ledger, candidates. + token_dir: Directory holding the admin token. + inference_dir: Gateway state: usage ledger and request log. + sidecar_host: Compose service name of the trusted evaluation sidecar. + sidecar_port: Port the sidecar listens on. + gateway_host: Compose service name of the inference gateway. + gateway_port: Port the gateway listens on. + """ + + target_repo: str = "/work/agent" + trusted_repo: str = "/opt/agent-baseline" + seed_repo: str = "/opt/agent-seed" + vero: str = "/opt/vero" + cases: str = "/opt/cases" + task_source: str = "/opt/task-source" + harness: str = "/opt/harness" + overlay: str = "/opt/overlay" + serve_config: str = "/opt/serve.json" + seed_script: str = "/opt/seed.sh" + inference_config: str = "/opt/inference.json" + agent_volume: str = "/state/agent-context" + admin_volume: str = "/state/admin" + token_dir: str = "/state/token" + inference_dir: str = "/state/inference" + sidecar_host: str = "eval-sidecar" + sidecar_port: int = 8000 + gateway_host: str = "inference-gateway" + gateway_port: int = 8001 + + # Derived paths. Defined here rather than spelled out at each use site, so a + # base path and its children cannot drift apart. + + @property + def session_dir(self) -> str: + return f"{self.admin_volume}/session" + + @property + def case_resources_dir(self) -> str: + return f"{self.admin_volume}/case-resources" + + @property + def token_path(self) -> str: + return f"{self.token_dir}/admin.token" + + @property + def inference_state(self) -> str: + return f"{self.inference_dir}/usage.json" + + @property + def inference_request_log_dir(self) -> str: + return f"{self.inference_dir}/requests" + + @property + def target_git(self) -> str: + return f"{self.target_repo}/.git" + + @property + def target_git_exclude(self) -> str: + return f"{self.target_git}/info/exclude" + + @property + def target_evals(self) -> str: + return f"{self.target_repo}/.evals" + + @property + def sidecar_url(self) -> str: + return f"http://{self.sidecar_host}:{self.sidecar_port}" + + @property + def gateway_url(self) -> str: + return f"http://{self.gateway_host}:{self.gateway_port}" + + +LAYOUT = TaskLayout() +"""The one instance. There is no reason to construct another.""" diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 6aca38e6..74d28b15 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -22,6 +22,7 @@ RetryPolicy, ) from vero.harbor import HarborBackend, HarborBackendConfig +from vero.harbor.layout import LAYOUT from vero.sandbox import CommandResult, LocalSandbox @@ -1035,7 +1036,7 @@ def test_command_forwards_retry_config_and_max_retries(tmp_path): backend = HarborBackend(_config(tmp_path, max_retries=5)) command = backend._command( - workspace="/work/agent", + workspace=LAYOUT.target_repo, request=_request(), cases=[], jobs_dir="/staging/jobs", diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 47d9f02a..ff640293 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -23,6 +23,7 @@ compile_harbor_task, load_harbor_build_config, ) +from vero.harbor.layout import LAYOUT BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" @@ -36,6 +37,47 @@ ) +def test_task_layout_values_are_pinned(): + """The one place the layout's literal values are written down twice. + + Every other test references LAYOUT, so without this they would all be + tautological: changing a path would silently keep them green. These values + are a contract with the benchmarks' read_only_paths and with the compiled + task directories that are checked in, so a deliberate change should have to + edit this list too. + """ + assert LAYOUT.target_repo == "/work/agent" + assert LAYOUT.trusted_repo == "/opt/agent-baseline" + assert LAYOUT.seed_repo == "/opt/agent-seed" + assert LAYOUT.vero == "/opt/vero" + assert LAYOUT.cases == "/opt/cases" + assert LAYOUT.task_source == "/opt/task-source" + assert LAYOUT.harness == "/opt/harness" + assert LAYOUT.overlay == "/opt/overlay" + assert LAYOUT.serve_config == "/opt/serve.json" + assert LAYOUT.seed_script == "/opt/seed.sh" + assert LAYOUT.inference_config == "/opt/inference.json" + assert LAYOUT.agent_volume == "/state/agent-context" + assert LAYOUT.admin_volume == "/state/admin" + assert LAYOUT.token_dir == "/state/token" + assert LAYOUT.inference_dir == "/state/inference" + assert LAYOUT.sidecar_host == "eval-sidecar" + assert LAYOUT.sidecar_port == 8000 + assert LAYOUT.gateway_host == "inference-gateway" + assert LAYOUT.gateway_port == 8001 + # Derived paths, so a base and its children cannot drift apart. + assert LAYOUT.session_dir == "/state/admin/session" + assert LAYOUT.case_resources_dir == "/state/admin/case-resources" + assert LAYOUT.token_path == "/state/token/admin.token" + assert LAYOUT.inference_state == "/state/inference/usage.json" + assert LAYOUT.inference_request_log_dir == "/state/inference/requests" + assert LAYOUT.target_git == "/work/agent/.git" + assert LAYOUT.target_git_exclude == "/work/agent/.git/info/exclude" + assert LAYOUT.target_evals == "/work/agent/.evals" + assert LAYOUT.sidecar_url == "http://eval-sidecar:8000" + assert LAYOUT.gateway_url == "http://inference-gateway:8001" + + def _git(path: Path, *arguments: str) -> str: result = subprocess.run( ["git", *arguments], @@ -191,9 +233,9 @@ def test_compiler_bakes_workspace_overlay_into_agent_environment(tmp_path): assert (env / "overlay" / ".claude" / "agents" / "insights.md").is_file() assert (env / "overlay" / "skills" / "insights" / "SKILL.md").is_file() # Dockerfile copies it, seed applies it, injected tooling is git-excluded - assert "COPY overlay /opt/overlay" in (env / "Dockerfile").read_text() + assert f"COPY overlay {LAYOUT.overlay}" in (env / "Dockerfile").read_text() seed = (env / "main" / "seed.sh").read_text() - assert "cp -a /opt/overlay/. /work/agent/" in seed + assert f"cp -a {LAYOUT.overlay}/. {LAYOUT.target_repo}/" in seed assert "/.claude/" in seed and "/skills/" in seed @@ -224,7 +266,7 @@ def test_compiler_omits_overlay_when_unset(tmp_path): env = output / "environment" assert not (env / "overlay").exists() assert "COPY overlay" not in (env / "Dockerfile").read_text() - assert "/opt/overlay" not in (env / "main" / "seed.sh").read_text() + assert LAYOUT.overlay not in (env / "main" / "seed.sh").read_text() def test_compiler_bakes_evals_skill_by_default(tmp_path): @@ -233,7 +275,7 @@ def test_compiler_bakes_evals_skill_by_default(tmp_path): skill = env / "overlay" / "skills" / "evals" / "SKILL.md" assert skill.is_file() assert "evals run" in skill.read_text(encoding="utf-8") - assert "'/skills/' >> /work/agent/.git/info/exclude" in ( + assert f"'/skills/' >> {LAYOUT.target_git_exclude}" in ( env / "main" / "seed.sh" ).read_text(encoding="utf-8") @@ -342,11 +384,11 @@ def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path) # request/response on its state volume and the sidecar mirrors it. The # experimental thread-attribution stamping stays off unless opted into. assert gateway["request_log"] == { - "directory": "/state/inference/requests", + "directory": LAYOUT.inference_request_log_dir, "body_bytes": 16384, "attribution": False, } - assert serve["inference_request_log_dir"] == "/state/inference/requests" + assert serve["inference_request_log_dir"] == LAYOUT.inference_request_log_dir def test_compiler_emits_request_log_attribution_when_enabled(tmp_path): @@ -497,9 +539,9 @@ def test_command_backend_compiles_a_task_with_no_target_agent(tmp_path): ) backend = serve["backends"]["harbor-validation"] assert backend["type"] == "command" - assert backend["harness_root"] == "/opt/harness" + assert backend["harness_root"] == LAYOUT.harness # Case enumeration is the harness's job, so it is told where the case files are. - assert backend["environment"]["VERO_CASES_DIR"] == "/opt/cases" + assert backend["environment"]["VERO_CASES_DIR"] == LAYOUT.cases # None of the nested-harbor plumbing leaks into a command backend. assert "agent_import_path" not in backend assert "task_source" not in backend @@ -507,7 +549,7 @@ def test_command_backend_compiles_a_task_with_no_target_agent(tmp_path): # The harness is baked into the trusted sidecar, never the agent workspace. assert (output / "environment/sidecar/harness/score.py").is_file() dockerfile = (output / "environment/sidecar/Dockerfile").read_text(encoding="utf-8") - assert "COPY sidecar/harness /opt/harness" in dockerfile + assert f"COPY sidecar/harness {LAYOUT.harness}" in dockerfile # And the trusted side still parses what the compiler emitted. assert HarborDeploymentConfig.model_validate(serve).backends["harbor-validation"] @@ -530,10 +572,10 @@ def test_deployment_treats_a_backend_without_a_type_as_harbor(tmp_path): harbor_requirement="harbor==0.20.0", ) legacy = { - "repo_path": "/opt/agent-baseline", - "agent_repo_path": "/work/agent", - "session_dir": "/state/admin/session", - "admin_volume": "/state/admin", + "repo_path": LAYOUT.trusted_repo, + "agent_repo_path": LAYOUT.target_repo, + "session_dir": LAYOUT.session_dir, + "admin_volume": LAYOUT.admin_volume, "access_policies": [], "targets": [], "selection": {"mode": "submit"}, @@ -793,13 +835,13 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert serve["targets"][0]["backend_id"] == "harbor-test" assert serve["targets"][0]["reward_scale"] == 1.0 assert serve["evaluation_drain_timeout_seconds"] == config.timeout_seconds - assert serve["backends"]["harbor-test"]["task_source"] == "/opt/task-source" + assert serve["backends"]["harbor-test"]["task_source"] == LAYOUT.task_source assert serve["backends"]["harbor-test"]["python_version"] == "3.12" assert serve["backends"]["harbor-test"]["case_timeout_seconds"] == 180.0 assert serve["backends"]["harbor-test"]["task_agent_timeout_seconds"] == 600.0 assert ( serve["backends"]["harbor-validation"]["case_resources_cache_path"] - == "/state/admin/case-resources/validation" + == f"{LAYOUT.case_resources_dir}/validation" ) assert serve["access_policies"][0]["limits"]["retry"]["max_attempts"] == 1 assert serve["access_policies"][0]["limits"]["case_timeout_seconds"] == 180.0 @@ -836,9 +878,9 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert tomllib.loads(task_toml)["task"]["name"] == 'org/optimize-"program"' compose = (output / "environment/docker-compose.yaml").read_text() assert "vero.harbor.deployment:build_harbor_components" in compose - assert "admin_state:/state/admin" in compose - assert "agent_context:/work/agent/.evals:ro" in compose - assert "agent_context:/state/agent-context" in compose + assert f"admin_state:{LAYOUT.admin_volume}" in compose + assert f"agent_context:{LAYOUT.target_evals}:ro" in compose + assert f"agent_context:{LAYOUT.agent_volume}" in compose assert set(yaml.safe_load(compose)["services"]) == {"main", "eval-sidecar"} sidecar_dockerfile = (output / "environment/sidecar/Dockerfile").read_text() assert 'uv pip install --system "harbor==0.1.17"' in sidecar_dockerfile @@ -847,8 +889,8 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert "useradd -m -u 1002 harness" in sidecar_dockerfile assert "chown -R harness:harness /home/harness/.cache" in sidecar_dockerfile seed = (output / "environment/main/seed.sh").read_text() - assert "-path /work/agent/.evals -prune" in seed - assert "'/.evals/' >> /work/agent/.git/info/exclude" in seed + assert f"-path {LAYOUT.target_evals} -prune" in seed + assert f"'/.evals/' >> {LAYOUT.target_git_exclude}" in seed test_script = output / "tests/test.sh" assert test_script.stat().st_mode & 0o111 assert "vero harbor export-session" in test_script.read_text() From f27d4aab7b528fbc5ae10a89e84734c6a23e2c9b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 14:54:43 -0700 Subject: [PATCH 39/72] Derive the sidecar factory path instead of hardcoding it MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The compose template named the factory as a string literal, duplicating a value that also exists in Python — the last instance of exactly what layout.py was created to remove, and the worst-behaved one. A stale dotted path here fails when the sidecar container starts, so neither the type checker nor the suite would catch a rename of the module or the function. Derive FACTORY_PATH in deployment.py from the function object itself and render it into the template. This does not belong in layout.py: that module is dependency-free by design, and a hand-maintained literal there would reproduce the same problem one level over, when the referent is an object we can introspect. The compiler imports it inside compile_harbor_task rather than at module scope. A top-level import does work today, but only because importing a submodule skips the parent package's partially-run __init__, which imports this package before deployment — an ordering accident, not a property anyone would notice breaking. It would also pull the whole runtime stack into `vero harbor build`. The test asserts on the rendered compose file rather than on the constant, so it still holds if a literal is put back, and it imports the path to prove it resolves. Verified both ways: renaming the function needs no template edit and stays green, and a stale literal fails. Left alone: agent_import_path in each build.yaml is the same species of string, but it points into the target repo, whose dependencies are not installed on the build host, so it genuinely cannot be resolved at compile time. Dotted paths pointing inside vero get derived and resolution-tested; ones pointing outside cannot be. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 6 ++++++ .../build/templates/docker-compose.yaml.j2 | 2 +- vero/src/vero/harbor/deployment.py | 9 ++++++++ vero/tests/test_v05_harbor_build.py | 21 +++++++++++++++++++ 4 files changed, 37 insertions(+), 1 deletion(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 2f07d7e9..9c58f170 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -490,6 +490,11 @@ def compile_harbor_task( raise ValueError( f"output directory {output} overlaps protected source {path}" ) + # Imported here, not at module scope: deployment pulls in the whole runtime + # stack, and harbor/__init__ imports this package before it, so a top-level + # import would only work by accident of partial-initialization ordering. + from vero.harbor.deployment import FACTORY_PATH + gateway_environment: list[str] = [] credential_sources: list[str] = [] if config.inference_gateway is not None: @@ -697,6 +702,7 @@ def compile_harbor_task( "inference_gateway_url": INFERENCE_GATEWAY_URL, "read_only_paths": config.read_only_paths, "local_task_source": local_task_source, + "sidecar_factory": FACTORY_PATH, "command_harness": config.command_backend is not None, "selection_backend": _backend_id(config.selection_partition), "evaluation_set_name": config.evaluation_set_name, diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 index f03979e8..45633f76 100644 --- a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -37,7 +37,7 @@ services: - "harbor" - "serve" - "--factory" - - "vero.harbor.deployment:build_harbor_components" + - "{{ sidecar_factory }}" - "--config" - "{{ layout.serve_config }}" - "--admin-token" diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 19cee037..6742ad8b 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -450,3 +450,12 @@ def _finalize_session_telemetry(result: object) -> None: on_finalized=_finalize_session_telemetry, ) return SidecarComponents(sidecar=sidecar, verifier=verifier, telemetry=telemetry) + + +FACTORY_PATH = f"{build_harbor_components.__module__}:{build_harbor_components.__qualname__}" +"""Dotted path the compiled task uses to load this factory. + +Derived rather than written out, so renaming this module or function cannot leave +a stale literal in the compose template. A wrong value here would not fail at +import or in the type checker: it fails when the sidecar container starts. +""" diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index ff640293..d84032f7 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -23,7 +23,9 @@ compile_harbor_task, load_harbor_build_config, ) +from vero.harbor.deployment import FACTORY_PATH from vero.harbor.layout import LAYOUT +from vero.harbor.serve import load_factory BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" @@ -621,6 +623,25 @@ def test_build_config_keeps_the_two_backend_kinds_apart(tmp_path): ) +def test_compiled_task_factory_path_resolves(tmp_path): + """The factory named in the compiled compose file must actually import. + + A stale dotted path fails when the sidecar container starts, not at import, + so it is invisible to the type checker and to every other test. Asserting on + the rendered artifact rather than on FACTORY_PATH alone means this still + holds if someone puts a literal back in the template. + """ + output = compile_harbor_task(_config(tmp_path), tmp_path / "compiled") + compose = yaml.safe_load( + (output / "environment/docker-compose.yaml").read_text(encoding="utf-8") + ) + command = compose["services"][LAYOUT.sidecar_host]["command"] + factory = command[command.index("--factory") + 1] + + assert factory == FACTORY_PATH + assert callable(load_factory(factory)) + + def test_load_build_config_resolves_relative_local_paths(tmp_path): target = _target_repo(tmp_path / "target") tasks = tmp_path / "tasks" From 4462614bb3b58b1e675a30d3dc9462ff7575e1e1 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 15:07:20 -0700 Subject: [PATCH 40/72] Derive every gateway scope URL from one route string MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The proxy route was declared in inference.py and then independently rebuilt by string concatenation in four places that knew nothing about it: launch.json, the compose file's OPENAI_BASE_URL, the seed script, and the trusted backend. Five copies of three conventions — the scope segment, the attribution segment, and /v1. Change the route and four sites keep producing URLs the gateway 404s or 403s on, inside a container, at run time. Put the route shape in the layout as one string and derive everything from it: scope_route for the gateway to register, scope_url for callers using this layout's gateway, and scope_path for the trusted backend, which reads its base URL from the deployment config rather than assuming ours. The optimizer's attribution segment was duplicated three times and is now a field too. The templates receive a precomputed producer_base_url instead of concatenating. The test asserts on the compiled artifacts — compose, launch.json, and the seed script — rather than on the constant, so it still holds if a template goes back to building the URL by hand. Verified by doing exactly that with a wrong shape: it fails. Also folded in, both hygiene rather than risk: VERO_EVAL_URL was a producer/consumer pair split between the compose template and the CLI that reads it, and the gateway's upstream env var names were the last constants still local to the compiler. The OPENAI_* scrub set is now a named constant with the invariant written down. Worth being precise about what that invariant is, since it looks alarming: the blanking loop is default-deny, so a new credential added to `secrets` is blanked for the optimizer automatically. The exclusion list is the explicit-allow exception for the two names the template sets deliberately, and forgetting one emits a duplicate compose key rather than leaking anything. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/backend.py | 5 ++- vero/src/vero/harbor/build/compiler.py | 24 +++++++++--- .../build/templates/docker-compose.yaml.j2 | 4 +- .../vero/harbor/build/templates/seed.sh.j2 | 2 +- vero/src/vero/harbor/cli.py | 5 ++- vero/src/vero/harbor/inference.py | 2 +- vero/src/vero/harbor/layout.py | 29 ++++++++++++++ vero/tests/test_v05_harbor_build.py | 39 +++++++++++++++++++ 8 files changed, 96 insertions(+), 14 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 2d50e727..41d35f37 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -46,6 +46,7 @@ ) from vero.evaluation.security import sanitize_evaluation_report, sanitize_text from vero.harbor.isolation import harness_grant_commands, harness_reachability_probe +from vero.harbor.layout import LAYOUT from vero.models import StrictModel from vero.sandbox import CommandResult, Sandbox from vero.staging import SandboxStagingArea @@ -653,8 +654,8 @@ def _environment( else self.config.inference_gateway_token ) or "" gateway_url = ( - f"{self.config.inference_gateway_url.rstrip('/')}/scopes/{scope}/" - f"{evaluation_id}/v1" + f"{self.config.inference_gateway_url.rstrip('/')}" + f"{LAYOUT.scope_path(scope, evaluation_id)}" ) if self.config.task_services_use_upstream: # Task-owned eval services (user-sims, LLM graders) run inside the diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 9c58f170..accafe08 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -31,8 +31,15 @@ _VERO_COPY = ("pyproject.toml", "README.md", "uv.lock", "src") SESSION_ID = "trial" -UPSTREAM_API_KEY_ENV = "VERO_INFERENCE_UPSTREAM_API_KEY" -UPSTREAM_BASE_URL_ENV = "VERO_INFERENCE_UPSTREAM_BASE_URL" +UPSTREAM_API_KEY_ENV = LAYOUT.gateway_upstream_api_key_env +UPSTREAM_BASE_URL_ENV = LAYOUT.gateway_upstream_base_url_env +PRODUCER_BASE_URL = LAYOUT.scope_url('producer', LAYOUT.optimizer_attribution) + +# Credentials the compose template routes through the gateway by setting them +# explicitly, instead of blanking them like every other declared secret. The two +# halves are one invariant: a name here must be set below, and a name set below +# must be here, or the rendered compose emits the key twice. +GATEWAY_ROUTED_CREDENTIALS = frozenset({"OPENAI_API_KEY", "OPENAI_BASE_URL"}) # Container paths and service identities come from the layout, never from a # literal here: the templates read the same object, so the two cannot drift. @@ -654,7 +661,7 @@ def compile_harbor_task( "upstream_base_url_target": UPSTREAM_BASE_URL_ENV, "producer_api_key": producer_inference_token, "producer_base_url": ( - f"{INFERENCE_GATEWAY_URL}/scopes/producer/optimizer/v1" + PRODUCER_BASE_URL ), }, ensure_ascii=False, @@ -692,10 +699,14 @@ def compile_harbor_task( "sidecar_secrets": config.secrets, "inference_gateway": config.inference_gateway, "gateway_environment": gateway_environment, + # Names the main container blanks. Default-deny: every declared secret is + # blanked for the optimizer unless it appears in GATEWAY_ROUTED_CREDENTIALS, + # which the compose template sets explicitly just below the blanking loop + # (to the producer token and the gateway URL). The exclusion exists to + # avoid emitting the same key twice, not to permit anything: adding a new + # credential to `secrets` gets it blanked automatically. "scrubbed_main_environment": [ - name - for name in task_environment - if name not in {"OPENAI_API_KEY", "OPENAI_BASE_URL"} + name for name in task_environment if name not in GATEWAY_ROUTED_CREDENTIALS ], "producer_inference_token": producer_inference_token, "evaluation_inference_token": evaluation_inference_token, @@ -703,6 +714,7 @@ def compile_harbor_task( "read_only_paths": config.read_only_paths, "local_task_source": local_task_source, "sidecar_factory": FACTORY_PATH, + "producer_base_url": PRODUCER_BASE_URL, "command_harness": config.command_backend is not None, "selection_backend": _backend_id(config.selection_partition), "evaluation_set_name": config.evaluation_set_name, diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 index 45633f76..bd500338 100644 --- a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -3,13 +3,13 @@ services: main: command: ["{{ layout.seed_script }}"] environment: - VERO_EVAL_URL: "{{ layout.sidecar_url }}" + {{ layout.eval_url_env }}: "{{ layout.sidecar_url }}" {% for secret in scrubbed_main_environment %} {{ secret }}: "" {% endfor %} {% if inference_gateway %} OPENAI_API_KEY: "{{ producer_inference_token }}" - OPENAI_BASE_URL: "{{ inference_gateway_url }}/scopes/producer/optimizer/v1" + OPENAI_BASE_URL: "{{ producer_base_url }}" {% elif "OPENAI_API_KEY" in secrets %} OPENAI_API_KEY: "" {% if "OPENAI_BASE_URL" in secrets %} diff --git a/vero/src/vero/harbor/build/templates/seed.sh.j2 b/vero/src/vero/harbor/build/templates/seed.sh.j2 index e4b7b3ed..b53f6ca6 100644 --- a/vero/src/vero/harbor/build/templates/seed.sh.j2 +++ b/vero/src/vero/harbor/build/templates/seed.sh.j2 @@ -24,7 +24,7 @@ model_provider = "vero_gateway" [model_providers.vero_gateway] name = "VeRO inference gateway" -base_url = "{{ inference_gateway_url }}/scopes/producer/optimizer/v1" +base_url = "{{ producer_base_url }}" env_key = "OPENAI_API_KEY" wire_api = "responses" supports_websockets = false diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index fcd5f11d..9e464e26 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -25,6 +25,7 @@ RetryPolicy, ) from vero.harbor.auth import read_admin_token +from vero.harbor.layout import LAYOUT from vero.harbor.session import ( create_harbor_session_archive, extract_harbor_session_archive, @@ -34,9 +35,9 @@ def _base_url() -> str: - value = os.environ.get("VERO_EVAL_URL") + value = os.environ.get(LAYOUT.eval_url_env) if not value: - raise click.ClickException("VERO_EVAL_URL is not set") + raise click.ClickException(f"{LAYOUT.eval_url_env} is not set") return value.rstrip("/") diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/harbor/inference.py index 5ecc92a0..2b582739 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/harbor/inference.py @@ -681,7 +681,7 @@ async def usage_status( return store.public_status()[scope_name] @app.api_route( - "/scopes/{scope_name}/{attribution}/v1/{endpoint:path}", + LAYOUT.scope_route, methods=["POST"], ) async def proxy( diff --git a/vero/src/vero/harbor/layout.py b/vero/src/vero/harbor/layout.py index 91bec3d5..5d4012cf 100644 --- a/vero/src/vero/harbor/layout.py +++ b/vero/src/vero/harbor/layout.py @@ -69,6 +69,14 @@ class TaskLayout: sidecar_port: int = 8000 gateway_host: str = "inference-gateway" gateway_port: int = 8001 + eval_url_env: str = "VERO_EVAL_URL" + gateway_upstream_api_key_env: str = "VERO_INFERENCE_UPSTREAM_API_KEY" + gateway_upstream_base_url_env: str = "VERO_INFERENCE_UPSTREAM_BASE_URL" + optimizer_attribution: str = "optimizer" + # The gateway's proxy route, with the FastAPI parameter names it binds. Both + # the route the gateway serves and every URL built for it derive from this one + # string, so a caller cannot construct a path the gateway will not match. + scope_route_base: str = "/scopes/{scope_name}/{attribution}/v1" # Derived paths. Defined here rather than spelled out at each use site, so a # base path and its children cannot drift apart. @@ -113,6 +121,27 @@ def sidecar_url(self) -> str: def gateway_url(self) -> str: return f"http://{self.gateway_host}:{self.gateway_port}" + @property + def scope_route(self) -> str: + """The route the gateway registers, including the proxied endpoint.""" + return f"{self.scope_route_base}/{{endpoint:path}}" + + def scope_path(self, scope: str, attribution: str) -> str: + """Path for one scope, for callers that hold their own base URL. + + The trusted backend reads its gateway URL from the deployment config + rather than assuming this layout's, so it needs the path alone. + """ + return self.scope_route_base.format(scope_name=scope, attribution=attribution) + + def scope_url(self, scope: str, attribution: str) -> str: + """Base URL an OpenAI-compatible client should use for one scope. + + The attribution segment is a free-form label for per-caller accounting, + not a security boundary: the token decides what the scope may do. + """ + return f"{self.gateway_url}{self.scope_path(scope, attribution)}" + LAYOUT = TaskLayout() """The one instance. There is no reason to construct another.""" diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index d84032f7..c77bf150 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -642,6 +642,45 @@ def test_compiled_task_factory_path_resolves(tmp_path): assert callable(load_factory(factory)) +def test_compiled_producer_base_url_matches_the_gateway_route(tmp_path): + """Every artifact naming the producer scope must agree with the served route. + + The URL used to be rebuilt by string concatenation in four places while the + route was declared in a fifth. A mismatch 404s or 403s inside the container + at run time, so asserting on the rendered artifacts is the only check that + would catch a template going back to concatenation. + """ + expected = LAYOUT.scope_url("producer", LAYOUT.optimizer_attribution) + config = _config( + tmp_path, + inference_gateway=InferenceGatewaySpec( + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec(allowed_models=["gpt-target"]), + ), + model="gpt-target", + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + + compose = yaml.safe_load( + (output / "environment/docker-compose.yaml").read_text(encoding="utf-8") + ) + assert compose["services"]["main"]["environment"]["OPENAI_BASE_URL"] == expected + launch = json.loads( + (output / "environment/gateway/launch.json").read_text(encoding="utf-8") + ) + assert launch["producer_base_url"] == expected + seed = (output / "environment/main/seed.sh").read_text(encoding="utf-8") + assert f'base_url = "{expected}"' in seed + + # And the path the gateway actually serves is the same string, not a copy. + assert LAYOUT.scope_route.startswith(LAYOUT.scope_route_base) + assert expected.endswith( + LAYOUT.scope_path("producer", LAYOUT.optimizer_attribution) + ) + + def test_load_build_config_resolves_relative_local_paths(tmp_path): target = _target_repo(tmp_path / "target") tasks = tmp_path / "tasks" From 7532c1e804a8a828accb53166f1dc64c939a7b37 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 15:16:10 -0700 Subject: [PATCH 41/72] Make the task image build timeout configurable MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit task.toml.j2 hardcoded build_timeout_sec = 1800 while the timeout_sec two lines above it was already parametrized, so one timeout in the same generated file was tunable and the other was not. The outer build is not trivial — for a source build it copies the vero tree, installs it plus harbor into the sidecar, syncs the baseline, and pre-warms a uv cache — and there was no way to raise the limit without editing the template. Typed as an int to match the neighbouring verifier timeout, so the default still renders as 1800 and compiled output is unchanged. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 1 + vero/src/vero/harbor/build/config.py | 2 ++ vero/src/vero/harbor/build/templates/task.toml.j2 | 2 +- 3 files changed, 4 insertions(+), 1 deletion(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index accafe08..783e653a 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -734,6 +734,7 @@ def compile_harbor_task( ], "exhaust_budget": config.instruct_exhaust_budget, "disclose_budget": config.disclose_budget, + "build_timeout": config.build_timeout_seconds, "verifier_timeout": ( config.verifier_timeout_seconds or max(1, int(config.timeout_seconds)) ), diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index d6f60c47..88c543ea 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -430,6 +430,7 @@ class HarborBuildConfig(StrictModel): max_concurrency: Cases evaluated concurrently. error_rate_threshold: Case error fraction above which an evaluation is abandoned. + build_timeout_seconds: Wall clock for building the task's images. verifier_timeout_seconds: Wall clock for the trusted verifier. Falls back to timeout_seconds. evaluation_drain_timeout_seconds: Grace period for in-flight evaluations @@ -541,6 +542,7 @@ class HarborBuildConfig(StrictModel): task_agent_timeout_seconds: float = Field(default=600.0, gt=0) max_concurrency: int = Field(default=8, ge=1) error_rate_threshold: float | None = Field(default=0.1, gt=0, le=1) + build_timeout_seconds: int = Field(default=1800, ge=1) verifier_timeout_seconds: int | None = Field(default=None, ge=1) evaluation_drain_timeout_seconds: float | None = Field(default=None, gt=0) diff --git a/vero/src/vero/harbor/build/templates/task.toml.j2 b/vero/src/vero/harbor/build/templates/task.toml.j2 index d3f414de..c9d12f3e 100644 --- a/vero/src/vero/harbor/build/templates/task.toml.j2 +++ b/vero/src/vero/harbor/build/templates/task.toml.j2 @@ -12,7 +12,7 @@ environment_mode = "shared" timeout_sec = {{ verifier_timeout }} [environment] -build_timeout_sec = 1800 +build_timeout_sec = {{ build_timeout }} {% if secrets %} [environment.env] From 2da29018ce837edfed0d4e149a92d95afcde68ef Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 19:49:11 -0700 Subject: [PATCH 42/72] Group vero/ by process and trust boundary harbor/ held fourteen flat modules that are not peers: they run in three different processes on either side of a trust boundary, and nothing in the layout said so. Most of them were not Harbor code at all. The sidecar stack -- sidecar, app, serve, verifier, transport, auth, isolation, session -- imports only itself, never backend, build, deployment or cli, and inference.py imports only layout. deployment.py is the single module binding the two, which is exactly its job. The nested-backend work sharpened the point: a compiled task can now use a command backend and no HarborBackend at all, while every module implementing it still lived under harbor/. Split those out to vero/sidecar/ and vero/gateway/, hoist layout.py to the top level so gateway/ needs no backwards edge, and leave harbor/ holding what is genuinely Harbor: build/, backend, generation, deployment, cli. Regroup evaluation/ along its own dependency layers into backends/, scoring/ and store/. A reviewer can now read the trust architecture off the tree. Package docstrings carry what the tree cannot: that nothing in sidecar/ is Harbor-specific, and that its co-location is load-bearing -- HTTP carries the API, but candidate code, the .evals context and the admin token all move through volumes shared with main, so it is not deployable away from the task it serves. That is also why it keeps the name. evaluation/ is an internal regroup: every name is still re-exported from vero.evaluation, so only deep importers moved. harbor/ -> sidecar//gateway/ is a real move, so those exports were relocated rather than aliased back; leaving compatibility shims in harbor/__init__ would preserve the false impression this commit exists to remove. Pure moves and import rewrites. No logic changes: 399 passed / 9 skipped before and after. Co-Authored-By: Claude Opus 5 (1M context) --- .../environment/sidecar/circle_factory.py | 12 ++-- vero/src/vero/candidate_repository/git.py | 2 +- vero/src/vero/evaluation/__init__.py | 32 ++++++--- vero/src/vero/evaluation/backends/__init__.py | 8 +++ .../{backend.py => backends/base.py} | 7 +- .../vero/evaluation/{ => backends}/command.py | 4 +- .../evaluation/{ => backends}/python_task.py | 4 +- vero/src/vero/evaluation/engine.py | 10 +-- vero/src/vero/evaluation/evaluator.py | 6 +- vero/src/vero/evaluation/scoring/__init__.py | 6 ++ .../{ => scoring}/error_taxonomy.py | 0 .../evaluation/{ => scoring}/objective.py | 0 .../vero/evaluation/{ => scoring}/security.py | 0 vero/src/vero/evaluation/store/__init__.py | 6 ++ .../src/vero/evaluation/{ => store}/budget.py | 2 +- .../evaluation/{ => store}/persistence.py | 2 +- vero/src/vero/gateway/__init__.py | 12 ++++ .../src/vero/{harbor => gateway}/inference.py | 4 +- vero/src/vero/harbor/__init__.py | 61 ++++------------ vero/src/vero/harbor/backend.py | 22 +++--- vero/src/vero/harbor/build/compiler.py | 4 +- vero/src/vero/harbor/build/config.py | 2 +- vero/src/vero/harbor/cli.py | 12 ++-- vero/src/vero/harbor/deployment.py | 16 ++--- vero/src/vero/harbor/generation.py | 2 +- vero/src/vero/{harbor => }/layout.py | 0 vero/src/vero/report.py | 4 +- vero/src/vero/runtime/artifacts.py | 2 +- vero/src/vero/runtime/context.py | 2 +- vero/src/vero/runtime/session.py | 2 +- vero/src/vero/runtime/wandb.py | 2 +- vero/src/vero/sidecar/__init__.py | 72 +++++++++++++++++++ vero/src/vero/{harbor => sidecar}/app.py | 12 ++-- vero/src/vero/{harbor => sidecar}/auth.py | 0 .../src/vero/{harbor => sidecar}/isolation.py | 0 vero/src/vero/{harbor => sidecar}/serve.py | 8 +-- vero/src/vero/{harbor => sidecar}/session.py | 4 +- vero/src/vero/{harbor => sidecar}/sidecar.py | 4 +- .../src/vero/{harbor => sidecar}/transport.py | 0 vero/src/vero/{harbor => sidecar}/verifier.py | 4 +- vero/tests/test_v05_error_taxonomy.py | 2 +- vero/tests/test_v05_evaluation_runtime.py | 4 +- vero/tests/test_v05_harbor_backend.py | 2 +- vero/tests/test_v05_harbor_build.py | 4 +- vero/tests/test_v05_harbor_deployment.py | 6 +- vero/tests/test_v05_harbor_http.py | 18 ++--- vero/tests/test_v05_harbor_inference.py | 12 ++-- .../test_v05_harbor_isolation_container.py | 4 +- vero/tests/test_v05_harbor_session.py | 4 +- vero/tests/test_v05_harbor_sidecar.py | 6 +- vero/tests/test_v05_harbor_verifier.py | 2 +- 51 files changed, 250 insertions(+), 166 deletions(-) create mode 100644 vero/src/vero/evaluation/backends/__init__.py rename vero/src/vero/evaluation/{backend.py => backends/base.py} (91%) rename vero/src/vero/evaluation/{ => backends}/command.py (98%) rename vero/src/vero/evaluation/{ => backends}/python_task.py (98%) create mode 100644 vero/src/vero/evaluation/scoring/__init__.py rename vero/src/vero/evaluation/{ => scoring}/error_taxonomy.py (100%) rename vero/src/vero/evaluation/{ => scoring}/objective.py (100%) rename vero/src/vero/evaluation/{ => scoring}/security.py (100%) create mode 100644 vero/src/vero/evaluation/store/__init__.py rename vero/src/vero/evaluation/{ => store}/budget.py (99%) rename vero/src/vero/evaluation/{ => store}/persistence.py (99%) create mode 100644 vero/src/vero/gateway/__init__.py rename vero/src/vero/{harbor => gateway}/inference.py (99%) rename vero/src/vero/{harbor => }/layout.py (100%) create mode 100644 vero/src/vero/sidecar/__init__.py rename vero/src/vero/{harbor => sidecar}/app.py (96%) rename vero/src/vero/{harbor => sidecar}/auth.py (100%) rename vero/src/vero/{harbor => sidecar}/isolation.py (100%) rename vero/src/vero/{harbor => sidecar}/serve.py (92%) rename vero/src/vero/{harbor => sidecar}/session.py (98%) rename vero/src/vero/{harbor => sidecar}/sidecar.py (99%) rename vero/src/vero/{harbor => sidecar}/transport.py (100%) rename vero/src/vero/{harbor => sidecar}/verifier.py (99%) diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py index 72b5a377..c436d4f5 100644 --- a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py +++ b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py @@ -24,17 +24,17 @@ MetricSelector, ObjectiveSpec, ) -from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.backends.command import CommandBackend, CommandBackendConfig from vero.evaluation.engine import EvaluationEngine -from vero.harbor.serve import SidecarComponents -from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy -from vero.harbor.transport import GitCandidateTransport -from vero.harbor.verifier import ( +from vero.sandbox import LocalSandbox +from vero.sidecar.serve import SidecarComponents +from vero.sidecar.sidecar import EvaluationSidecar, SidecarEvaluationPolicy +from vero.sidecar.transport import GitCandidateTransport +from vero.sidecar.verifier import ( CanonicalVerifier, VerificationSelection, VerificationTarget, ) -from vero.sandbox import LocalSandbox from vero.workspace import GitWorkspace SET = "circle-packing" diff --git a/vero/src/vero/candidate_repository/git.py b/vero/src/vero/candidate_repository/git.py index e18dbad9..d0fbb9ba 100644 --- a/vero/src/vero/candidate_repository/git.py +++ b/vero/src/vero/candidate_repository/git.py @@ -18,7 +18,7 @@ from vero.candidate import Candidate from vero.candidate_repository.base import CandidateRepository, CandidateRepositoryError -from vero.evaluation.persistence import _atomic_write_json +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel from vero.sandbox import LocalSandbox, Sandbox from vero.workspace import GitWorkspace, Workspace diff --git a/vero/src/vero/evaluation/__init__.py b/vero/src/vero/evaluation/__init__.py index dabad078..fa612904 100644 --- a/vero/src/vero/evaluation/__init__.py +++ b/vero/src/vero/evaluation/__init__.py @@ -1,14 +1,28 @@ -"""Public evaluation contracts for VeRO.""" +"""Backend-neutral evaluation: what a score is, and how one is produced. -from vero.evaluation.backend import ( +Layered, and the layering is the reading order: ``models``/``exceptions`` define +the vocabulary; ``scoring`` turns raw results into a trustworthy number; +``store`` persists records and the budget ledger; ``backends`` execute one case; +``evaluator`` runs one evaluation; ``engine`` authorizes and orchestrates. + +Nothing here knows about containers or trust boundaries — that is +``vero.sidecar``. Everything below is re-exported here, so the subpackages are an +internal grouping rather than a public surface. +""" + +from vero.evaluation.backends.base import ( BackendRegistry, CaseResourceExporter, CaseStore, EvaluationBackend, EvaluationContext, ) -from vero.evaluation.budget import BudgetLedger -from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.backends.command import CommandBackend, CommandBackendConfig +from vero.evaluation.backends.python_task import ( + PythonTaskBackend, + PythonTaskBackendConfig, + PythonTaskEvaluationConfig, +) from vero.evaluation.engine import ( AuthorizationResolver, EvaluationEngine, @@ -67,25 +81,21 @@ ObjectiveSpec, RetryPolicy, ) -from vero.evaluation.objective import ( +from vero.evaluation.scoring.objective import ( compare_evaluation_records, evaluate_objective, project_evaluation, resolve_metric, select_best_evaluation, ) -from vero.evaluation.persistence import ( +from vero.evaluation.store.budget import BudgetLedger +from vero.evaluation.store.persistence import ( CaseCheckpointStore, EvaluationDatabase, EvaluationManifest, EvaluationStore, RunningEvaluationManifest, ) -from vero.evaluation.python_task import ( - PythonTaskBackend, - PythonTaskBackendConfig, - PythonTaskEvaluationConfig, -) __all__ = [ "AgentSelectionMode", diff --git a/vero/src/vero/evaluation/backends/__init__.py b/vero/src/vero/evaluation/backends/__init__.py new file mode 100644 index 00000000..bac9b085 --- /dev/null +++ b/vero/src/vero/evaluation/backends/__init__.py @@ -0,0 +1,8 @@ +"""Pluggable evaluation execution. + +``base`` defines the ``EvaluationBackend`` protocol and the registry that +resolves one; ``command`` and ``python_task`` are two implementations. A third, +``HarborBackend``, lives in ``vero.harbor.backend`` — it is a peer of these, kept +in that package because it depends on Harbor machinery that ``vero.evaluation`` +must not. +""" diff --git a/vero/src/vero/evaluation/backend.py b/vero/src/vero/evaluation/backends/base.py similarity index 91% rename from vero/src/vero/evaluation/backend.py rename to vero/src/vero/evaluation/backends/base.py index c23e77b4..22555419 100644 --- a/vero/src/vero/evaluation/backend.py +++ b/vero/src/vero/evaluation/backends/base.py @@ -1,4 +1,9 @@ -"""Evaluation backend protocol and trusted backend registry.""" +"""Evaluation backend protocol and trusted backend registry. + +Implementations: ``command`` and ``python_task`` here, plus ``HarborBackend`` in +``vero.harbor.backend`` — a peer of those two, kept in that package because it +depends on Harbor machinery this one must not. +""" from __future__ import annotations diff --git a/vero/src/vero/evaluation/command.py b/vero/src/vero/evaluation/backends/command.py similarity index 98% rename from vero/src/vero/evaluation/command.py rename to vero/src/vero/evaluation/backends/command.py index 0e86282f..5980f998 100644 --- a/vero/src/vero/evaluation/command.py +++ b/vero/src/vero/evaluation/backends/command.py @@ -11,7 +11,7 @@ from pydantic import Field, field_validator, model_validator -from vero.evaluation.backend import EvaluationContext +from vero.evaluation.backends.base import EvaluationContext from vero.evaluation.models import ( AllCases, BackendProvenance, @@ -27,7 +27,7 @@ EvaluationSet, EvaluationStatus, ) -from vero.evaluation.security import sanitize_evaluation_report, sanitize_text +from vero.evaluation.scoring.security import sanitize_evaluation_report, sanitize_text from vero.models import StrictModel from vero.sandbox import Sandbox from vero.staging import SandboxStagingArea diff --git a/vero/src/vero/evaluation/python_task.py b/vero/src/vero/evaluation/backends/python_task.py similarity index 98% rename from vero/src/vero/evaluation/python_task.py rename to vero/src/vero/evaluation/backends/python_task.py index f4107a05..509a02ac 100644 --- a/vero/src/vero/evaluation/python_task.py +++ b/vero/src/vero/evaluation/backends/python_task.py @@ -9,8 +9,8 @@ from pydantic import Field, field_validator, model_validator -from vero.evaluation.backend import EvaluationContext -from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.backends.base import EvaluationContext +from vero.evaluation.backends.command import CommandBackend, CommandBackendConfig from vero.evaluation.models import ( AllCases, BackendProvenance, diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index 8fa237e1..730c25bf 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -10,9 +10,7 @@ from pathlib import Path from typing import AsyncIterator, Awaitable, Callable -from vero.evaluation.backend import BackendRegistry -from vero.evaluation.budget import BudgetLedger -from vero.evaluation.error_taxonomy import TERMINATING_DIAGNOSTIC_CODES +from vero.evaluation.backends.base import BackendRegistry from vero.evaluation.evaluator import Evaluator from vero.evaluation.exceptions import ( EvaluationCancelledError, @@ -35,8 +33,10 @@ EvaluationSummary, ObjectiveSpec, ) -from vero.evaluation.objective import project_evaluation -from vero.evaluation.persistence import EvaluationDatabase, EvaluationStore +from vero.evaluation.scoring.error_taxonomy import TERMINATING_DIAGNOSTIC_CODES +from vero.evaluation.scoring.objective import project_evaluation +from vero.evaluation.store.budget import BudgetLedger +from vero.evaluation.store.persistence import EvaluationDatabase, EvaluationStore logger = logging.getLogger(__name__) diff --git a/vero/src/vero/evaluation/evaluator.py b/vero/src/vero/evaluation/evaluator.py index a299fc64..986af575 100644 --- a/vero/src/vero/evaluation/evaluator.py +++ b/vero/src/vero/evaluation/evaluator.py @@ -11,7 +11,7 @@ from vero.candidate import Candidate from vero.candidate_repository import CandidateRepository -from vero.evaluation.backend import EvaluationBackend, EvaluationContext +from vero.evaluation.backends.base import EvaluationBackend, EvaluationContext from vero.evaluation.exceptions import ( EvaluationCancelledError, EvaluationExecutionError, @@ -27,8 +27,8 @@ EvaluationStatus, ObjectiveSpec, ) -from vero.evaluation.objective import evaluate_objective -from vero.evaluation.persistence import EvaluationStore +from vero.evaluation.scoring.objective import evaluate_objective +from vero.evaluation.store.persistence import EvaluationStore from vero.sandbox import Sandbox from vero.workspace import Workspace diff --git a/vero/src/vero/evaluation/scoring/__init__.py b/vero/src/vero/evaluation/scoring/__init__.py new file mode 100644 index 00000000..346c5454 --- /dev/null +++ b/vero/src/vero/evaluation/scoring/__init__.py @@ -0,0 +1,6 @@ +"""Turning raw results into a trustworthy number. + +``objective`` resolves a metric and compares candidates, ``error_taxonomy`` +classifies failures so infrastructure noise is not scored as a bad candidate, and +``security`` redacts secrets from anything shown to the agent. +""" diff --git a/vero/src/vero/evaluation/error_taxonomy.py b/vero/src/vero/evaluation/scoring/error_taxonomy.py similarity index 100% rename from vero/src/vero/evaluation/error_taxonomy.py rename to vero/src/vero/evaluation/scoring/error_taxonomy.py diff --git a/vero/src/vero/evaluation/objective.py b/vero/src/vero/evaluation/scoring/objective.py similarity index 100% rename from vero/src/vero/evaluation/objective.py rename to vero/src/vero/evaluation/scoring/objective.py diff --git a/vero/src/vero/evaluation/security.py b/vero/src/vero/evaluation/scoring/security.py similarity index 100% rename from vero/src/vero/evaluation/security.py rename to vero/src/vero/evaluation/scoring/security.py diff --git a/vero/src/vero/evaluation/store/__init__.py b/vero/src/vero/evaluation/store/__init__.py new file mode 100644 index 00000000..4ce97217 --- /dev/null +++ b/vero/src/vero/evaluation/store/__init__.py @@ -0,0 +1,6 @@ +"""Durable evaluation state. + +``persistence`` holds evaluation records, manifests, and the partitioned case +store; ``budget`` holds the ledger that caps runs and cases per partition. Both +write through to disk so a restarted process resumes with its limits intact. +""" diff --git a/vero/src/vero/evaluation/budget.py b/vero/src/vero/evaluation/store/budget.py similarity index 99% rename from vero/src/vero/evaluation/budget.py rename to vero/src/vero/evaluation/store/budget.py index 7f15176b..a85e11b5 100644 --- a/vero/src/vero/evaluation/budget.py +++ b/vero/src/vero/evaluation/store/budget.py @@ -14,7 +14,7 @@ EvaluationPrincipal, EvaluationSet, ) -from vero.evaluation.persistence import _atomic_write_json +from vero.evaluation.store.persistence import _atomic_write_json class BudgetLedger: diff --git a/vero/src/vero/evaluation/persistence.py b/vero/src/vero/evaluation/store/persistence.py similarity index 99% rename from vero/src/vero/evaluation/persistence.py rename to vero/src/vero/evaluation/store/persistence.py index 66ab8d06..e8c3ca49 100644 --- a/vero/src/vero/evaluation/persistence.py +++ b/vero/src/vero/evaluation/store/persistence.py @@ -28,7 +28,7 @@ ObjectiveResult, ObjectiveSpec, ) -from vero.evaluation.objective import select_best_evaluation +from vero.evaluation.scoring.objective import select_best_evaluation from vero.models import StrictModel logger = logging.getLogger(__name__) diff --git a/vero/src/vero/gateway/__init__.py b/vero/src/vero/gateway/__init__.py new file mode 100644 index 00000000..d72a27e2 --- /dev/null +++ b/vero/src/vero/gateway/__init__.py @@ -0,0 +1,12 @@ +"""The inference gateway: the only path from a task container to a model. + +Runs as its own container beside ``main`` and ``eval-sidecar``. It holds the one +real upstream credential and hands each consumer a separate scoped token, so the +optimizer cannot spend from the evaluation or finalization pools. Every proxied +request is checked three ways: the presented token against that scope's digest, +the requested model against the scope's allow-list, and the running budget +against a ledger written through to disk. + +Provider-agnostic by design — it accepts either ``Authorization: Bearer`` or +``x-api-key``, and forwards any endpoint to the upstream proxy. +""" diff --git a/vero/src/vero/harbor/inference.py b/vero/src/vero/gateway/inference.py similarity index 99% rename from vero/src/vero/harbor/inference.py rename to vero/src/vero/gateway/inference.py index 2b582739..8ae8f8aa 100644 --- a/vero/src/vero/harbor/inference.py +++ b/vero/src/vero/gateway/inference.py @@ -21,8 +21,8 @@ from fastapi.responses import JSONResponse, Response, StreamingResponse from pydantic import Field, field_validator, model_validator -from vero.evaluation.persistence import _atomic_write_json -from vero.harbor.layout import LAYOUT +from vero.evaluation.store.persistence import _atomic_write_json +from vero.layout import LAYOUT from vero.models import StrictModel logger = logging.getLogger(__name__) diff --git a/vero/src/vero/harbor/__init__.py b/vero/src/vero/harbor/__init__.py index 42bf5e51..83a54a62 100644 --- a/vero/src/vero/harbor/__init__.py +++ b/vero/src/vero/harbor/__init__.py @@ -1,4 +1,15 @@ -"""Harbor adapters for canonical VeRO evaluation backends.""" +"""Harbor as VeRO's task substrate: compile a task, and evaluate on it. + +``build`` compiles a benchmark's build YAML into a runnable Harbor task +directory. ``backend`` evaluates one candidate by nesting a ``harbor run`` per +case — a peer of the backends in ``vero.evaluation.backends``. ``deployment`` is +the standard sidecar factory, and the one module that binds the trusted runtime +in ``vero.sidecar`` to a Harbor-backed evaluation; a task with a command backend +uses the same runtime and no Harbor backend at all. + +The trusted runtime itself lives in ``vero.sidecar``, and the inference gateway +in ``vero.gateway``. Neither is a Harbor concept. +""" from vero.harbor.backend import HarborBackend, HarborBackendConfig, HarborCase from vero.harbor.build import ( @@ -14,40 +25,12 @@ load_harbor_build_config, ) from vero.harbor.deployment import HarborDeploymentConfig, build_harbor_components -from vero.harbor.session import HarborSessionManifest -from vero.harbor.sidecar import ( - EvaluationAccessError, - EvaluationAccessStatus, - EvaluationJobNotFoundError, - EvaluationJobStatus, - EvaluationSidecar, - SidecarEvaluationJob, - SidecarEvaluationPolicy, - SidecarEvaluationRequest, - SidecarEvaluationResult, - SidecarStatus, - Submission, - SubmissionDisabledError, -) -from vero.harbor.transport import ( - CandidateTransferError, - CandidateTransport, - GitCandidateTransport, -) -from vero.harbor.verifier import ( - CanonicalVerifier, - NoCandidateError, - VerificationResult, - VerificationSelection, - VerificationTarget, -) __all__ = [ "HarborBackend", "HarborBackendConfig", "HarborCase", "HarborDeploymentConfig", - "HarborSessionManifest", "AgentAccessSpec", "CommandBackendSpec", "HarborBuildConfig", @@ -56,26 +39,6 @@ "VerificationTargetSpec", "WandbSpec", "WorkspaceOverlaySpec", - "CandidateTransferError", - "CandidateTransport", - "EvaluationAccessError", - "EvaluationJobNotFoundError", - "EvaluationJobStatus", - "SidecarEvaluationPolicy", - "EvaluationAccessStatus", - "EvaluationSidecar", - "SidecarEvaluationJob", - "GitCandidateTransport", - "SidecarEvaluationRequest", - "SidecarEvaluationResult", - "SidecarStatus", - "Submission", - "SubmissionDisabledError", - "CanonicalVerifier", - "NoCandidateError", - "VerificationResult", - "VerificationSelection", - "VerificationTarget", "build_harbor_components", "compile_harbor_task", "load_harbor_build_config", diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 41d35f37..758f8ed0 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -20,13 +20,7 @@ from pydantic import Field, JsonValue, field_validator, model_validator -from vero.evaluation.backend import EvaluationContext -from vero.evaluation.error_taxonomy import ( - NO_REWARD_SIGNAL, - ErrorCategory, - classify_case, - policy, -) +from vero.evaluation.backends.base import EvaluationContext from vero.evaluation.models import ( AllCases, BackendProvenance, @@ -44,11 +38,17 @@ EvaluationSet, EvaluationStatus, ) -from vero.evaluation.security import sanitize_evaluation_report, sanitize_text -from vero.harbor.isolation import harness_grant_commands, harness_reachability_probe -from vero.harbor.layout import LAYOUT +from vero.evaluation.scoring.error_taxonomy import ( + NO_REWARD_SIGNAL, + ErrorCategory, + classify_case, + policy, +) +from vero.evaluation.scoring.security import sanitize_evaluation_report, sanitize_text +from vero.layout import LAYOUT from vero.models import StrictModel from vero.sandbox import CommandResult, Sandbox +from vero.sidecar.isolation import harness_grant_commands, harness_reachability_probe from vero.staging import SandboxStagingArea logger = logging.getLogger(__name__) @@ -1319,7 +1319,7 @@ async def evaluate( if self.config.harness_user is not None: # Hand the harness user its work dirs and make the checkout # reachable (the parent that `mktemp -d` left 0700 root); see - # vero.harbor.isolation for the why. + # vero.sidecar.isolation for the why. for provision_command in harness_grant_commands( self.config.harness_user, chown_paths=[context.workspace.project_path, staging.root], diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 783e653a..df29474e 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -21,9 +21,9 @@ EvaluationSet, RetryPolicy, ) +from vero.gateway.inference import generate_inference_token, token_digest from vero.harbor.build.config import HarborBuildConfig, WorkspaceOverlaySpec -from vero.harbor.inference import generate_inference_token, token_digest -from vero.harbor.layout import LAYOUT +from vero.layout import LAYOUT logger = logging.getLogger(__name__) diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 88c543ea..d6bc857b 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -152,7 +152,7 @@ class InferenceGatewaySpec(StrictModel): only against a non-adversarial optimizer: the scopes share a gateway host and are split by URL path, so an optimizer that smuggles its own token into the candidate can reach the producer scope from the eval sandbox. See the proxy - handler in vero.harbor.inference for the full note. + handler in vero.gateway.inference for the full note. Attributes: upstream_api_key_env: Environment variable on the build host holding the diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 9e464e26..8b700184 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -24,14 +24,14 @@ EvaluationSet, RetryPolicy, ) -from vero.harbor.auth import read_admin_token -from vero.harbor.layout import LAYOUT -from vero.harbor.session import ( +from vero.layout import LAYOUT +from vero.sidecar.auth import read_admin_token +from vero.sidecar.session import ( create_harbor_session_archive, extract_harbor_session_archive, file_sha256, ) -from vero.harbor.sidecar import SidecarEvaluationRequest +from vero.sidecar.sidecar import SidecarEvaluationRequest def _base_url() -> str: @@ -467,7 +467,7 @@ def run_command(config_path, agent, model, environment, params, env_file, extra) @click.option("--port", default=8000, type=click.IntRange(1, 65535), show_default=True) def serve_command(factory_path, config_path, admin_token_path, host, port): """Serve components built by a trusted deployment factory.""" - from vero.harbor.serve import serve + from vero.sidecar.serve import serve serve( factory_path=factory_path, @@ -489,7 +489,7 @@ def serve_command(factory_path, config_path, admin_token_path, host, port): @click.option("--port", default=8001, type=click.IntRange(1, 65535), show_default=True) def inference_gateway_command(config_path, host, port): """Serve the credential-isolating, budgeted inference proxy.""" - from vero.harbor.inference import serve_inference_gateway + from vero.gateway.inference import serve_inference_gateway serve_inference_gateway(config_path=config_path, host=host, port=port) diff --git a/vero/src/vero/harbor/deployment.py b/vero/src/vero/harbor/deployment.py index 6742ad8b..e77aaf81 100644 --- a/vero/src/vero/harbor/deployment.py +++ b/vero/src/vero/harbor/deployment.py @@ -21,20 +21,20 @@ Evaluator, ObjectiveSpec, ) -from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.backends.command import CommandBackend, CommandBackendConfig from vero.evaluation.engine import EvaluationEngine from vero.harbor.backend import HarborBackend, HarborBackendConfig -from vero.harbor.serve import SidecarComponents -from vero.harbor.session import initialize_harbor_session_manifest -from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy -from vero.harbor.transport import GitCandidateTransport -from vero.harbor.verifier import ( +from vero.models import StrictModel +from vero.sandbox import LocalSandbox +from vero.sidecar.serve import SidecarComponents +from vero.sidecar.session import initialize_harbor_session_manifest +from vero.sidecar.sidecar import EvaluationSidecar, SidecarEvaluationPolicy +from vero.sidecar.transport import GitCandidateTransport +from vero.sidecar.verifier import ( CanonicalVerifier, VerificationSelection, VerificationTarget, ) -from vero.models import StrictModel -from vero.sandbox import LocalSandbox from vero.workspace import GitWorkspace logger = logging.getLogger(__name__) diff --git a/vero/src/vero/harbor/generation.py b/vero/src/vero/harbor/generation.py index 01c47441..e530979b 100644 --- a/vero/src/vero/harbor/generation.py +++ b/vero/src/vero/harbor/generation.py @@ -10,7 +10,7 @@ Status: interface skeleton. The full wiring (drive a ``harbor run`` for the proposal's agent, then import the resulting commit into the session candidate repository by object identity) is a follow-on; it reuses the existing -:class:`~vero.harbor.transport.GitCandidateTransport` and the ``harbor.backend`` +:class:`~vero.sidecar.transport.GitCandidateTransport` and the ``harbor.backend`` nesting rather than re-hosting Harbor's sidecar/gateway. """ diff --git a/vero/src/vero/harbor/layout.py b/vero/src/vero/layout.py similarity index 100% rename from vero/src/vero/harbor/layout.py rename to vero/src/vero/layout.py diff --git a/vero/src/vero/report.py b/vero/src/vero/report.py index 3a3e73ea..7854cf81 100644 --- a/vero/src/vero/report.py +++ b/vero/src/vero/report.py @@ -13,10 +13,10 @@ from vero.candidate import Candidate from vero.candidate_repository import GitCandidateRepository from vero.evaluation import EvaluationDatabase -from vero.harbor.session import HarborSessionManifest -from vero.harbor.verifier import VerificationResult from vero.runtime.events import RuntimeEvent from vero.runtime.session import SessionManifest +from vero.sidecar.session import HarborSessionManifest +from vero.sidecar.verifier import VerificationResult _MAX_EMBEDDED_ARTIFACT_BYTES = 5_000_000 _MAX_EMBEDDED_ARTIFACTS_BYTES = 50_000_000 diff --git a/vero/src/vero/runtime/artifacts.py b/vero/src/vero/runtime/artifacts.py index 524c41e6..64389a17 100644 --- a/vero/src/vero/runtime/artifacts.py +++ b/vero/src/vero/runtime/artifacts.py @@ -6,7 +6,7 @@ from pathlib import Path, PurePosixPath from typing import Any -from vero.evaluation.persistence import _atomic_write_json +from vero.evaluation.store.persistence import _atomic_write_json class ArtifactStore: diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index fb32d54d..ccaf9462 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -29,7 +29,7 @@ EvaluationSummary, project_evaluation, ) -from vero.evaluation.persistence import _atomic_write_json +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel from vero.sandbox import Sandbox from vero.workspace import Workspace diff --git a/vero/src/vero/runtime/session.py b/vero/src/vero/runtime/session.py index 005a5a75..778bbfdb 100644 --- a/vero/src/vero/runtime/session.py +++ b/vero/src/vero/runtime/session.py @@ -23,7 +23,7 @@ EvaluationRecord, ObjectiveSpec, ) -from vero.evaluation.persistence import _atomic_write_json +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel from vero.optimization import OptimizationResult, Optimizer from vero.runtime.artifacts import ArtifactStore diff --git a/vero/src/vero/runtime/wandb.py b/vero/src/vero/runtime/wandb.py index a7d484dd..e467e3f3 100644 --- a/vero/src/vero/runtime/wandb.py +++ b/vero/src/vero/runtime/wandb.py @@ -11,8 +11,8 @@ from typing import Any from uuid import uuid4 -from vero.evaluation.budget import BudgetLedger from vero.evaluation.models import CaseStatus, EvaluationRecord +from vero.evaluation.store.budget import BudgetLedger from vero.runtime.artifacts import ArtifactStore from vero.runtime.events import RuntimeEvent diff --git a/vero/src/vero/sidecar/__init__.py b/vero/src/vero/sidecar/__init__.py new file mode 100644 index 00000000..3213efad --- /dev/null +++ b/vero/src/vero/sidecar/__init__.py @@ -0,0 +1,72 @@ +"""The trusted side of the evaluation boundary. + +Owns held-out partitions, disclosure, budgets, and final scoring — everything the +optimizer must not be able to reach. Deployed as the ``eval-sidecar`` container +beside the optimizer's ``main`` container, which is where the name comes from. + +Two invariants worth knowing: + +- Nothing here is Harbor-specific. This package depends on ``vero.evaluation`` + only; ``vero.harbor.deployment`` is the single module that binds this stack to + a Harbor-backed evaluation, and a compiled task can just as well use a command + backend (see ``examples/harbor-circle-packing``). +- Co-location is load-bearing. HTTP carries the API, but candidate code, the + ``.evals`` result context, and the admin token all move through volumes shared + with ``main`` — so this is not, today, deployable away from the task it serves. + +``sidecar`` is the agent-facing API and is transport-neutral; ``app`` is an +optional FastAPI transport over it; ``serve`` is the composition root that loads +a factory, builds the components, and runs them. +""" + +from vero.sidecar.session import HarborSessionManifest +from vero.sidecar.sidecar import ( + EvaluationAccessError, + EvaluationAccessStatus, + EvaluationJobNotFoundError, + EvaluationJobStatus, + EvaluationSidecar, + SidecarEvaluationJob, + SidecarEvaluationPolicy, + SidecarEvaluationRequest, + SidecarEvaluationResult, + SidecarStatus, + Submission, + SubmissionDisabledError, +) +from vero.sidecar.transport import ( + CandidateTransferError, + CandidateTransport, + GitCandidateTransport, +) +from vero.sidecar.verifier import ( + CanonicalVerifier, + NoCandidateError, + VerificationResult, + VerificationSelection, + VerificationTarget, +) + +__all__ = [ + "CandidateTransferError", + "CandidateTransport", + "CanonicalVerifier", + "EvaluationAccessError", + "EvaluationAccessStatus", + "EvaluationJobNotFoundError", + "EvaluationJobStatus", + "EvaluationSidecar", + "GitCandidateTransport", + "HarborSessionManifest", + "NoCandidateError", + "SidecarEvaluationJob", + "SidecarEvaluationPolicy", + "SidecarEvaluationRequest", + "SidecarEvaluationResult", + "SidecarStatus", + "Submission", + "SubmissionDisabledError", + "VerificationResult", + "VerificationSelection", + "VerificationTarget", +] diff --git a/vero/src/vero/harbor/app.py b/vero/src/vero/sidecar/app.py similarity index 96% rename from vero/src/vero/harbor/app.py rename to vero/src/vero/sidecar/app.py index 09a78347..9675c5fa 100644 --- a/vero/src/vero/harbor/app.py +++ b/vero/src/vero/sidecar/app.py @@ -21,9 +21,10 @@ EvaluationRequestError, ) from vero.evaluation.exceptions import EvaluationExecutionError -from vero.harbor.auth import check_admin_token -from vero.harbor.session import create_harbor_session_archive -from vero.harbor.sidecar import ( +from vero.models import StrictModel +from vero.sidecar.auth import check_admin_token +from vero.sidecar.session import create_harbor_session_archive +from vero.sidecar.sidecar import ( EvaluationAccessError, EvaluationJobNotFoundError, EvaluationJobStatus, @@ -32,9 +33,8 @@ SidecarEvaluationResult, SubmissionDisabledError, ) -from vero.harbor.transport import CandidateTransferError -from vero.harbor.verifier import CanonicalVerifier -from vero.models import StrictModel +from vero.sidecar.transport import CandidateTransferError +from vero.sidecar.verifier import CanonicalVerifier if TYPE_CHECKING: from vero.runtime.wandb import InferenceTelemetryPoller diff --git a/vero/src/vero/harbor/auth.py b/vero/src/vero/sidecar/auth.py similarity index 100% rename from vero/src/vero/harbor/auth.py rename to vero/src/vero/sidecar/auth.py diff --git a/vero/src/vero/harbor/isolation.py b/vero/src/vero/sidecar/isolation.py similarity index 100% rename from vero/src/vero/harbor/isolation.py rename to vero/src/vero/sidecar/isolation.py diff --git a/vero/src/vero/harbor/serve.py b/vero/src/vero/sidecar/serve.py similarity index 92% rename from vero/src/vero/harbor/serve.py rename to vero/src/vero/sidecar/serve.py index d609c399..d31bc59e 100644 --- a/vero/src/vero/harbor/serve.py +++ b/vero/src/vero/sidecar/serve.py @@ -10,9 +10,9 @@ from pathlib import Path from typing import TYPE_CHECKING, Any, Awaitable, Callable -from vero.harbor.auth import generate_admin_token, write_admin_token -from vero.harbor.sidecar import EvaluationSidecar -from vero.harbor.verifier import CanonicalVerifier +from vero.sidecar.auth import generate_admin_token, write_admin_token +from vero.sidecar.sidecar import EvaluationSidecar +from vero.sidecar.verifier import CanonicalVerifier if TYPE_CHECKING: from vero.runtime.wandb import InferenceTelemetryPoller @@ -68,7 +68,7 @@ async def build_app( config_path: Path | str, admin_token_path: Path | str, ): - from vero.harbor.app import create_app + from vero.sidecar.app import create_app components = await build_components( factory_path=factory_path, diff --git a/vero/src/vero/harbor/session.py b/vero/src/vero/sidecar/session.py similarity index 98% rename from vero/src/vero/harbor/session.py rename to vero/src/vero/sidecar/session.py index cd50f872..e2874006 100644 --- a/vero/src/vero/harbor/session.py +++ b/vero/src/vero/sidecar/session.py @@ -17,9 +17,9 @@ from pydantic import field_validator from vero.evaluation import BackendProvenance -from vero.evaluation.persistence import _atomic_write_json -from vero.harbor.verifier import VerificationSelection, VerificationTarget +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel +from vero.sidecar.verifier import VerificationSelection, VerificationTarget logger = logging.getLogger(__name__) diff --git a/vero/src/vero/harbor/sidecar.py b/vero/src/vero/sidecar/sidecar.py similarity index 99% rename from vero/src/vero/harbor/sidecar.py rename to vero/src/vero/sidecar/sidecar.py index c53ab811..8de2f967 100644 --- a/vero/src/vero/harbor/sidecar.py +++ b/vero/src/vero/sidecar/sidecar.py @@ -37,8 +37,7 @@ project_evaluation, ) from vero.evaluation.engine import EvaluationEngine -from vero.evaluation.persistence import _atomic_write_json -from vero.harbor.transport import CandidateTransferError, CandidateTransport +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel from vero.runtime.context import ( CANDIDATES_SUBDIRECTORY, @@ -50,6 +49,7 @@ narrower_disclosure, ) from vero.sandbox import LocalSandbox +from vero.sidecar.transport import CandidateTransferError, CandidateTransport class EvaluationAccessError(RuntimeError): diff --git a/vero/src/vero/harbor/transport.py b/vero/src/vero/sidecar/transport.py similarity index 100% rename from vero/src/vero/harbor/transport.py rename to vero/src/vero/sidecar/transport.py diff --git a/vero/src/vero/harbor/verifier.py b/vero/src/vero/sidecar/verifier.py similarity index 99% rename from vero/src/vero/harbor/verifier.py rename to vero/src/vero/sidecar/verifier.py index 44d165b7..64a287d9 100644 --- a/vero/src/vero/harbor/verifier.py +++ b/vero/src/vero/sidecar/verifier.py @@ -23,9 +23,9 @@ ObjectiveSpec, ) from vero.evaluation.engine import EvaluationEngine -from vero.evaluation.persistence import _atomic_write_json -from vero.harbor.sidecar import Submission +from vero.evaluation.store.persistence import _atomic_write_json from vero.models import StrictModel +from vero.sidecar.sidecar import Submission logger = logging.getLogger(__name__) diff --git a/vero/tests/test_v05_error_taxonomy.py b/vero/tests/test_v05_error_taxonomy.py index 5b15cc46..6beb23d7 100644 --- a/vero/tests/test_v05_error_taxonomy.py +++ b/vero/tests/test_v05_error_taxonomy.py @@ -1,6 +1,6 @@ """Tests for the single source of truth error taxonomy.""" -from vero.evaluation.error_taxonomy import ( +from vero.evaluation.scoring.error_taxonomy import ( ErrorCategory, classify_case, classify_signal, diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index ca1bbcb4..6e6c69d4 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -9,7 +9,8 @@ import pytest -import vero.evaluation.budget as budget_module +import vero.evaluation.store.budget as budget_module +import vero.evaluation.store.persistence as persistence from vero.candidate import Candidate from vero.evaluation import ( AgentSelectionMode, @@ -47,7 +48,6 @@ ObjectiveSpec, allow_all_evaluations, authorize_evaluation_plan, - persistence, ) from vero.workspace import Workspace diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 74d28b15..f503c7dc 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -22,7 +22,7 @@ RetryPolicy, ) from vero.harbor import HarborBackend, HarborBackendConfig -from vero.harbor.layout import LAYOUT +from vero.layout import LAYOUT from vero.sandbox import CommandResult, LocalSandbox diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index c77bf150..c21ca5ce 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -24,8 +24,8 @@ load_harbor_build_config, ) from vero.harbor.deployment import FACTORY_PATH -from vero.harbor.layout import LAYOUT -from vero.harbor.serve import load_factory +from vero.layout import LAYOUT +from vero.sidecar.serve import load_factory BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" diff --git a/vero/tests/test_v05_harbor_deployment.py b/vero/tests/test_v05_harbor_deployment.py index ac402d6e..da61367c 100644 --- a/vero/tests/test_v05_harbor_deployment.py +++ b/vero/tests/test_v05_harbor_deployment.py @@ -17,11 +17,13 @@ ) from vero.harbor import ( HarborBackendConfig, - SidecarEvaluationPolicy, - VerificationTarget, build_harbor_components, ) from vero.report import generate_experiment_report +from vero.sidecar import ( + SidecarEvaluationPolicy, + VerificationTarget, +) def _git(path: Path, *arguments: str) -> str: diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index 943b1fe8..a4e17a52 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -21,19 +21,19 @@ EvaluationRequestError, EvaluationStatus, ) -from vero.harbor.app import create_app -from vero.harbor.auth import ( - check_admin_token, - read_admin_token, - write_admin_token, -) from vero.harbor.cli import ( _compiled_run_environment, _load_agent_trace, _load_env_file, harbor, ) -from vero.harbor.sidecar import ( +from vero.sidecar.app import create_app +from vero.sidecar.auth import ( + check_admin_token, + read_admin_token, + write_admin_token, +) +from vero.sidecar.sidecar import ( EvaluationAccessError, EvaluationJobNotFoundError, EvaluationJobStatus, @@ -42,7 +42,7 @@ SidecarStatus, Submission, ) -from vero.harbor.verifier import VerificationResult +from vero.sidecar.verifier import VerificationResult class FakeSidecar: @@ -369,7 +369,7 @@ def create_archive(_session_dir, destination): destination.write_bytes(b"portable-session") return destination - monkeypatch.setattr("vero.harbor.app.create_harbor_session_archive", create_archive) + monkeypatch.setattr("vero.sidecar.app.create_harbor_session_archive", create_archive) client = TestClient( create_app( sidecar=sidecar, diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py index 366aa0ec..b929c403 100644 --- a/vero/tests/test_v05_harbor_inference.py +++ b/vero/tests/test_v05_harbor_inference.py @@ -6,7 +6,7 @@ import httpx from fastapi.testclient import TestClient -from vero.harbor.inference import ( +from vero.gateway.inference import ( InferenceGatewayConfig, InferenceScopeConfig, create_inference_gateway_app, @@ -372,7 +372,7 @@ def unavailable(request: httpx.Request): def _logged_config(tmp_path, **kwargs): - from vero.harbor.inference import InferenceRequestLogConfig + from vero.gateway.inference import InferenceRequestLogConfig config = _config(tmp_path, **kwargs) return config.model_copy( @@ -461,7 +461,7 @@ def upstream(request: httpx.Request): def test_gateway_request_log_truncates_and_rotates(tmp_path): - from vero.harbor.inference import InferenceRequestLogConfig + from vero.gateway.inference import InferenceRequestLogConfig config = _config(tmp_path, max_requests=None, max_tokens=None).model_copy( update={ @@ -495,7 +495,7 @@ def test_gateway_request_log_truncates_and_rotates(tmp_path): def test_gateway_request_log_rotation_boundary(tmp_path): - from vero.harbor.inference import InferenceRequestLog, InferenceRequestLogConfig + from vero.gateway.inference import InferenceRequestLog, InferenceRequestLogConfig log = InferenceRequestLog( InferenceRequestLogConfig( @@ -521,7 +521,7 @@ async def fill(): def _attributed_config(tmp_path, **kwargs): - from vero.harbor.inference import InferenceRequestLogConfig + from vero.gateway.inference import InferenceRequestLogConfig return _config(tmp_path, **kwargs).model_copy( update={ @@ -659,7 +659,7 @@ def test_gateway_attribution_never_breaks_proxying(tmp_path): def test_gateway_attribution_disabled_by_default_and_memory_bounded(tmp_path): - from vero.harbor.inference import _RequestAttributor + from vero.gateway.inference import _RequestAttributor # default config: no attributor, no thread fields in records app = create_inference_gateway_app( diff --git a/vero/tests/test_v05_harbor_isolation_container.py b/vero/tests/test_v05_harbor_isolation_container.py index e10c3458..7053ddc5 100644 --- a/vero/tests/test_v05_harbor_isolation_container.py +++ b/vero/tests/test_v05_harbor_isolation_container.py @@ -3,7 +3,7 @@ Runs inside a throwaway Linux container (the CI/dev host may be macOS, where the uid-drop is unavailable) and asserts, against the *real* privilege drop (``vero.sandbox.LocalSandbox.run(run_as=...)``) and the *real* provisioning -commands (``vero.harbor.isolation``), that: +commands (``vero.sidecar.isolation``), that: * a candidate checkout under a ``mktemp -d`` (0700 root) parent is UNreachable to the dropped user when only the leaf is chowned (the regression sentinel — this @@ -65,7 +65,7 @@ def _load(name, path): # Real modules, loaded standalone (stdlib-only) — no vero install needed. sbx = _load("_vero_sandbox", "/vero-src/vero/sandbox.py") -iso = _load("_vero_isolation", "/vero-src/vero/harbor/isolation.py") +iso = _load("_vero_isolation", "/vero-src/vero/sidecar/isolation.py") subprocess.run(["useradd", "-m", "-u", "1002", "harness"], check=True) sandbox = sbx.LocalSandbox("/") # run_as drops privileges from this root process diff --git a/vero/tests/test_v05_harbor_session.py b/vero/tests/test_v05_harbor_session.py index 81d2b159..e793d4dc 100644 --- a/vero/tests/test_v05_harbor_session.py +++ b/vero/tests/test_v05_harbor_session.py @@ -13,13 +13,13 @@ MetricSelector, ObjectiveSpec, ) -from vero.harbor.session import ( +from vero.sidecar.session import ( HarborSessionManifest, create_harbor_session_archive, extract_harbor_session_archive, file_sha256, ) -from vero.harbor.verifier import VerificationSelection, VerificationTarget +from vero.sidecar.verifier import VerificationSelection, VerificationTarget def _manifest() -> HarborSessionManifest: diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index b18c9254..faba4209 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -36,7 +36,9 @@ MetricSelector, ObjectiveSpec, ) -from vero.harbor import ( +from vero.runtime.context import context_digest +from vero.sandbox import LocalSandbox +from vero.sidecar import ( EvaluationAccessError, EvaluationJobStatus, EvaluationSidecar, @@ -45,8 +47,6 @@ SidecarEvaluationRequest, SubmissionDisabledError, ) -from vero.runtime.context import context_digest -from vero.sandbox import LocalSandbox from vero.workspace import GitWorkspace, Workspace diff --git a/vero/tests/test_v05_harbor_verifier.py b/vero/tests/test_v05_harbor_verifier.py index 3c477d6c..bb2cbe11 100644 --- a/vero/tests/test_v05_harbor_verifier.py +++ b/vero/tests/test_v05_harbor_verifier.py @@ -21,7 +21,7 @@ ObjectiveResult, ObjectiveSpec, ) -from vero.harbor import ( +from vero.sidecar import ( CanonicalVerifier, Submission, VerificationSelection, From 5ff70a9e6da38ed3cf1dc0ebfb789082c4190541 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 19:57:56 -0700 Subject: [PATCH 43/72] Fix the circle-packing example against the current policy schema circle_factory built SidecarEvaluationPolicy with flat disclosure and expose_case_resources keyword arguments, but those moved under a nested access=EvaluationAccessPolicy some time ago. The sidecar container therefore died at startup with three validation errors before serving anything. Predates the package restructure -- the same call fails identically on pr2-add-vero -- and no test covers it, because nothing exercises the example factories. Found by actually booting the container: it now starts and serves /health and /status, reporting full disclosure on development, aggregate on validation, and the budget ledger. ale_factory.py in harness-engineering-bench has the same stale construction and is broken the same way. Co-Authored-By: Claude Opus 5 (1M context) --- .../environment/sidecar/circle_factory.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py index c436d4f5..b03db979 100644 --- a/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py +++ b/vero/examples/harbor-circle-packing/environment/sidecar/circle_factory.py @@ -16,6 +16,8 @@ BudgetLedger, CaseRange, ConstraintOperator, + DisclosureLevel, + EvaluationAccessPolicy, EvaluationBudget, EvaluationDatabase, EvaluationSet, @@ -132,11 +134,17 @@ async def build(config: dict) -> SidecarComponents: access_policies=[ SidecarEvaluationPolicy( backend_id="cmd", evaluation_set_name=SET, partition="development", - disclosure="full", expose_case_resources=True, objective=OBJ, + objective=OBJ, + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.FULL, expose_case_resources=True + ), ), SidecarEvaluationPolicy( backend_id="cmd", evaluation_set_name=SET, partition="validation", - disclosure="aggregate", min_aggregate_cases=1, objective=OBJ, + objective=OBJ, + access=EvaluationAccessPolicy( + disclosure=DisclosureLevel.AGGREGATE, min_aggregate_cases=1 + ), ), ], admin_volume=session_dir / "admin", From 41739e678f192978f7de98235d010b7e7b90faa7 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 21:18:25 -0700 Subject: [PATCH 44/72] Add a compiled, gateway-metered variant of the circle-packing example The example only had the hand-written path: circle_factory.py wiring a CommandBackend directly, with the agent handed raw OPENAI_* credentials. That is the same shape as ale-bench, and it means the one worked example of a non-agent target is also an example of an unmetered optimizer. The nested-backend refactor makes the compiled path available to such a target, so add a build.yaml beside the hand-written task: same packing.py, same evaluate.py, but `evaluation_backend: command` and an inference gateway. The optimizer now reaches its model only through a scoped, allow-listed, metered token; the raw upstream stays in the gateway container and is scrubbed to "" in main. Two wirings of one problem, directly comparable. Verified end to end with codex/gpt-5.4: reward 2.6324 against a 0.9598 baseline and a ~2.635 known optimum, 0 exceptions in 20m46s. The gateway metered every optimizer call -- 57 requests, 3.6M tokens, no denials -- and the evaluation scope stayed at zero, since a command harness makes no model calls of its own. passthrough_environment is set and commented because it is easy to get wrong: CommandBackend deliberately runs the harness with PATH=os.defpath, so python (in /usr/local/bin on the base image) is not found unless PATH is forwarded. The failure surfaces only when the harness first runs, after the agent has already spent its budget. Co-Authored-By: Claude Opus 5 (1M context) --- .../examples/harbor-circle-packing/build.yaml | 112 ++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100644 vero/examples/harbor-circle-packing/build.yaml diff --git a/vero/examples/harbor-circle-packing/build.yaml b/vero/examples/harbor-circle-packing/build.yaml new file mode 100644 index 00000000..9ac8f69b --- /dev/null +++ b/vero/examples/harbor-circle-packing/build.yaml @@ -0,0 +1,112 @@ +# The same circle-packing problem as the hand-written task in this directory, +# but compiled by `vero harbor build` instead of wired by circle_factory.py. +# +# Two things this demonstrates that the custom-factory path does not: +# +# - `evaluation_backend: command` — a non-agent target scored by a program, +# built through the compiler. There is nothing for a nested `harbor run` to +# drive, so none of the Harbor-only knobs apply. +# - An inference gateway. The optimizer reaches its model only through a +# scoped, allow-listed, metered token; the raw upstream credential stays in +# the gateway container. The hand-written task hands the agent the real +# OPENAI_* credentials instead, which is convenient but unmetered. +# +# Build and run: +# cp -r /vero environment/vero # VeRO is not on public PyPI +# vero harbor run --config build.yaml --agent codex --model gpt-5.4 \ +# --environment docker +# +# Either optimizer family works. `vero harbor run` points both surfaces at the +# same producer scope: OPENAI_* lands in the compose environment for codex, and +# ANTHROPIC_* is set on the host, where harbor's claude-code agent reads it and +# injects it into the agent (with /v1 stripped, which the Anthropic SDK +# re-appends). Swap in `--agent claude-code --model claude-sonnet-4-5` and the +# producer allow-list follows via ${optimizer_model}. +# +# Note the target and harness are shared with the hand-written task, so the two +# wirings score identically and can be compared directly. + +name: vero/circle-packing-metered +description: >- + Optimize packing.py to maximize the sum of 26 non-overlapping circle radii in + the unit square, with the optimizer metered through the inference gateway. + +# The editable target: a plain Python program, not an agent. +agent_repo: environment/agent-seed +harbor_requirement: harbor==0.20.0 + +# The harness ignores case identity — one deterministic score per run — so each +# partition holds a single nominal case. Held-out still means held-out: the +# agent may reach development and validation, never test. +partitions: + development: [dev] + validation: [val] + test: [holdout] + +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + min_aggregate_cases: 1 + - partition: validation + disclosure: aggregate + min_aggregate_cases: 1 + total_runs: 10 + +selection_partition: validation + +targets: + - partition: test + reward_key: sum_radii + +objective: + selector: + metric: sum_radii + direction: maximize + failure_value: 0.0 + constraints: + - selector: + metric: valid + operator: "==" + value: 1.0 + +# A program scores the candidate; there is no target agent to run. +evaluation_backend: command +command_backend: + harness_source: environment/sidecar/harness + # CommandBackend runs the harness with a deliberately minimal environment + # (PATH is os.defpath, i.e. /bin:/usr/bin) so the sidecar's own environment -- + # which holds secrets -- is not handed to it wholesale. The base image keeps + # python in /usr/local/bin, so PATH has to be forwarded explicitly or the + # harness dies with "No such file or directory: 'python'". + passthrough_environment: [PATH, HOME, UV_CACHE_DIR] + command: + - python + - "{harness}/evaluate.py" + - --workspace + - "{workspace}" + - --request + - "{request}" + - --report + - "{report}" + - --artifacts + - "{artifacts}" + +# The optimizer's only route to a model. Its token is minted at build time and +# never leaves the gateway config as anything but a digest; the raw upstream key +# is read from OPENAI_API_KEY on the build host and stays in the gateway. +# +# `evaluation` is required by the schema but unused here: a command harness gets +# no gateway token, so this target makes no model calls of its own. +inference_gateway: + producer: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_requests: 500 + max_concurrency: 4 + evaluation: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_requests: 1 + +timeout_seconds: 900 +case_timeout_seconds: 300 +verifier_timeout_seconds: 1800 From d131412d1bfca8830c69f23a493c052c55e7e32a Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 21:22:12 -0700 Subject: [PATCH 45/72] Move the benchmark-config tests to the branch that has the benchmarks test_v05_harbor_build.py carried two tests that read build YAMLs out of harness-engineering-bench, guarded by a skipif on that directory existing. It does not exist on this branch, so they always skipped here and only ran one branch up -- where they had been failing for a while against stale expectations nobody saw. A test that can silently skip on the branch that owns it is a test that rots. Drop them and the BENCHMARK_ROOT/_requires_benchmarks harness; they move to pr3-harness-bench, where the benchmarks are always present and the guard can go away entirely. Suite is unchanged at 399 passed, with skips down from 9 to 5. Co-Authored-By: Claude Opus 5 (1M context) --- vero/tests/test_v05_harbor_build.py | 65 ----------------------------- 1 file changed, 65 deletions(-) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index c21ca5ce..43b9c61c 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -27,18 +27,8 @@ from vero.layout import LAYOUT from vero.sidecar.serve import load_factory -BENCHMARK_ROOT = Path(__file__).resolve().parents[2] / "harness-engineering-bench" - _OMIT = object() -# harness-engineering-bench is a separate branch/PR in the stacked split, so -# skip the benchmark-config tests when it isn't checked out here. -_requires_benchmarks = pytest.mark.skipif( - not BENCHMARK_ROOT.exists(), - reason="harness-engineering-bench is not present on this branch", -) - - def test_task_layout_values_are_pinned(): """The one place the layout's literal values are written down twice. @@ -91,61 +81,6 @@ def _git(path: Path, *arguments: str) -> str: return result.stdout.strip() -@_requires_benchmarks -@pytest.mark.parametrize( - ("benchmark", "producer_models"), - [ - # gaia parametrizes the producer model via ${optimizer_model}; the - # default (no --param) resolves to gpt-5.4. - ("gaia", ["gpt-5.4"]), - ("swe-atlas-qna", ["gpt-5.4"]), - ("tau3", ["gpt-5.4"]), - ], -) -def test_canonical_benchmarks_isolate_upstream_inference_credentials( - benchmark, producer_models -): - # All benchmarks live at the top level of harness-engineering-bench. - config = load_harbor_build_config( - BENCHMARK_ROOT / benchmark / "baseline" / "build.yaml" - ) - - assert config.inference_gateway is not None - assert not { - "OPENAI_API_KEY", - "OPENAI_BASE_URL", - "OPENAI_API_BASE", - }.intersection(config.secrets) - assert config.inference_gateway.upstream_api_key_env == "OPENAI_API_KEY" - assert config.inference_gateway.upstream_base_url_env == "OPENAI_BASE_URL" - assert config.inference_gateway.producer.allowed_models == producer_models - assert config.inference_gateway.producer.max_requests is None - assert config.inference_gateway.producer.max_tokens is None - assert config.inference_gateway.evaluation.allowed_models == [ - "gpt-5.4-mini-2026-03-17" - ] - assert config.inference_gateway.evaluation.max_requests == 15000 - assert config.inference_gateway.evaluation.max_tokens == 100000000 - - -@_requires_benchmarks -def test_build_params_override_run_time_knobs_without_rebuild(): - path = BENCHMARK_ROOT / "gaia" / "baseline" / "build.yaml" - - default = load_harbor_build_config(path) - assert default.environment_name == "modal" - assert default.inference_gateway.producer.allowed_models == ["gpt-5.4"] - - overridden = load_harbor_build_config( - path, params={"optimizer_model": "gpt-5.5", "inner_env": "docker"} - ) - assert overridden.environment_name == "docker" - assert overridden.inference_gateway.producer.allowed_models == ["gpt-5.5"] - # The rest of the measurement substrate is untemplated and stays fixed. - assert overridden.model == default.model - assert overridden.task_source == default.task_source - - def test_build_param_substitution_semantics(): from vero.harbor.build.config import _substitute_build_param as sub From 5bd66a294e00d8cb128be6169dcb9e1e4f1a6432 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 21:42:40 -0700 Subject: [PATCH 46/72] Move the report's HTML document out of a Python string literal report.py was the largest module in the codebase at 43K, but 57% of that was one raw string: a complete 25K HTML document with its own CSS and JavaScript, pasted into _REPORT_HTML. Inside a string literal it gets no highlighting, no formatter, no linter, and no diff worth reading -- and it made the module look like a 43K Python problem when the Python is 19K. Extract it verbatim to templates/report.html, loaded through importlib.resources the way the packaged evals skill already is. Non-Python artifacts get their own directory rather than sitting beside the modules, matching harbor/build/templates and skills/evals. Substitution is unchanged: still one replace of __VERO_REPORT_DATA__. The extracted text is byte-identical to the old literal, so generated reports are unchanged by construction rather than by inspection. The wheel ships the file alongside the existing Jinja templates and skills/evals/SKILL.md, checked by building one. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/report.py | 161 ++-------------------------- vero/src/vero/templates/report.html | 151 ++++++++++++++++++++++++++ 2 files changed, 158 insertions(+), 154 deletions(-) create mode 100644 vero/src/vero/templates/report.html diff --git a/vero/src/vero/report.py b/vero/src/vero/report.py index 7854cf81..59c4fc34 100644 --- a/vero/src/vero/report.py +++ b/vero/src/vero/report.py @@ -4,6 +4,7 @@ import base64 import hashlib +import importlib.resources import json import mimetypes import subprocess @@ -516,161 +517,13 @@ async def generate_experiment_report( else resolved_session / "experiment.html" ) data = await build_experiment_report_data(resolved_session) - html = _REPORT_HTML.replace("__VERO_REPORT_DATA__", _safe_json(data)) + template = ( + importlib.resources.files("vero") + .joinpath("templates/report.html") + .read_text(encoding="utf-8") + ) + html = template.replace("__VERO_REPORT_DATA__", _safe_json(data)) destination.parent.mkdir(parents=True, exist_ok=True) destination.write_text(html, encoding="utf-8") return destination - -_REPORT_HTML = r""" - - - - - - VeRO experiment report - - - -
VeRO experiment report

Optimization session

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This portable report can contain source diffs, evaluation artifacts, prompts, and agent tool output. Treat it as sensitive experiment data.
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Score trajectories by split

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Candidate lineage

Click a node to inspect itbaselinebest
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- - - - -""" diff --git a/vero/src/vero/templates/report.html b/vero/src/vero/templates/report.html new file mode 100644 index 00000000..1d845d28 --- /dev/null +++ b/vero/src/vero/templates/report.html @@ -0,0 +1,151 @@ + + + + + + + VeRO experiment report + + + +
VeRO experiment report

Optimization session

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This portable report can contain source diffs, evaluation artifacts, prompts, and agent tool output. Treat it as sensitive experiment data.
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Score trajectories by split

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Candidate lineage

Click a node to inspect itbaselinebest
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+ + + + From e3e3003e06c63668e35945587cf1bfeb8dcc37b1 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 22:27:04 -0700 Subject: [PATCH 47/72] Single-source the credentials the compose file routes to the gateway The main container blanks every declared secret except the two the compose template assigns itself -- the optimizer's scoped token and its gateway URL. GATEWAY_ROUTED_CREDENTIALS named that exemption in Python while the template spelled the same names out again, five copies of OPENAI_API_KEY and four of OPENAI_BASE_URL between them. The duplication is one-directional and quiet. Blanking is default-deny, so a newly declared secret is scrubbed automatically and adding one is safe. The failure is the other way: a name added to the exemption but not assigned by the template would be neither blanked nor set, leaving whatever the host exported in the optimizer's container -- the one outcome the scrub exists to prevent. Move both names onto LAYOUT beside the other env-name fields, derive the exemption from LAYOUT.routed_credential_envs, and have the compose file and codex's provider config reference them. Nine literals become one definition. Add a test that renders a compiled task and asserts every exempt name is positively set with a scoped value, and that the raw upstream is blanked in the same container. Verified it fails as intended by adding a third name to the exemption: "STRAY_KEY is skipped by the scrub but never set". Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 2 +- .../build/templates/docker-compose.yaml.j2 | 12 +++--- .../vero/harbor/build/templates/seed.sh.j2 | 2 +- vero/src/vero/layout.py | 17 ++++++++ vero/tests/test_v05_harbor_build.py | 40 +++++++++++++++++++ 5 files changed, 65 insertions(+), 8 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index df29474e..5b2ddd6a 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -39,7 +39,7 @@ # explicitly, instead of blanking them like every other declared secret. The two # halves are one invariant: a name here must be set below, and a name set below # must be here, or the rendered compose emits the key twice. -GATEWAY_ROUTED_CREDENTIALS = frozenset({"OPENAI_API_KEY", "OPENAI_BASE_URL"}) +GATEWAY_ROUTED_CREDENTIALS = frozenset(LAYOUT.routed_credential_envs) # Container paths and service identities come from the layout, never from a # literal here: the templates read the same object, so the two cannot drift. diff --git a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 index bd500338..94d940dc 100644 --- a/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 +++ b/vero/src/vero/harbor/build/templates/docker-compose.yaml.j2 @@ -8,12 +8,12 @@ services: {{ secret }}: "" {% endfor %} {% if inference_gateway %} - OPENAI_API_KEY: "{{ producer_inference_token }}" - OPENAI_BASE_URL: "{{ producer_base_url }}" -{% elif "OPENAI_API_KEY" in secrets %} - OPENAI_API_KEY: "" -{% if "OPENAI_BASE_URL" in secrets %} - OPENAI_BASE_URL: "" + {{ layout.producer_api_key_env }}: "{{ producer_inference_token }}" + {{ layout.producer_base_url_env }}: "{{ producer_base_url }}" +{% elif layout.producer_api_key_env in secrets %} + {{ layout.producer_api_key_env }}: "" +{% if layout.producer_base_url_env in secrets %} + {{ layout.producer_base_url_env }}: "" {% endif %} {% endif %} volumes: diff --git a/vero/src/vero/harbor/build/templates/seed.sh.j2 b/vero/src/vero/harbor/build/templates/seed.sh.j2 index b53f6ca6..121b3c63 100644 --- a/vero/src/vero/harbor/build/templates/seed.sh.j2 +++ b/vero/src/vero/harbor/build/templates/seed.sh.j2 @@ -25,7 +25,7 @@ model_provider = "vero_gateway" [model_providers.vero_gateway] name = "VeRO inference gateway" base_url = "{{ producer_base_url }}" -env_key = "OPENAI_API_KEY" +env_key = "{{ layout.producer_api_key_env }}" wire_api = "responses" supports_websockets = false TOML diff --git a/vero/src/vero/layout.py b/vero/src/vero/layout.py index 5d4012cf..ffc724eb 100644 --- a/vero/src/vero/layout.py +++ b/vero/src/vero/layout.py @@ -48,6 +48,10 @@ class TaskLayout: sidecar_port: Port the sidecar listens on. gateway_host: Compose service name of the inference gateway. gateway_port: Port the gateway listens on. + producer_api_key_env: Container-side variable holding the optimizer's + scoped gateway token. + producer_base_url_env: Container-side variable holding the optimizer's + gateway scope URL. """ target_repo: str = "/work/agent" @@ -73,6 +77,14 @@ class TaskLayout: gateway_upstream_api_key_env: str = "VERO_INFERENCE_UPSTREAM_API_KEY" gateway_upstream_base_url_env: str = "VERO_INFERENCE_UPSTREAM_BASE_URL" optimizer_attribution: str = "optimizer" + # Container-side names an OpenAI-surface client reads. The compose file sets + # both for the optimizer -- key to the producer token, base URL to its + # gateway scope -- and the compiler excludes them from the blanking loop so + # it does not emit the same YAML key twice. The two must stay in step: a name + # excluded from blanking but never set would keep whatever the host passed + # in, so routed_credential_envs below is the single list both sides use. + producer_api_key_env: str = "OPENAI_API_KEY" + producer_base_url_env: str = "OPENAI_BASE_URL" # The gateway's proxy route, with the FastAPI parameter names it binds. Both # the route the gateway serves and every URL built for it derive from this one # string, so a caller cannot construct a path the gateway will not match. @@ -113,6 +125,11 @@ def target_git_exclude(self) -> str: def target_evals(self) -> str: return f"{self.target_repo}/.evals" + @property + def routed_credential_envs(self) -> tuple[str, ...]: + """Names the compose file sets explicitly, so must not also blank.""" + return (self.producer_api_key_env, self.producer_base_url_env) + @property def sidecar_url(self) -> str: return f"http://{self.sidecar_host}:{self.sidecar_port}" diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 43b9c61c..b075a463 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -23,6 +23,7 @@ compile_harbor_task, load_harbor_build_config, ) +from vero.harbor.build.compiler import GATEWAY_ROUTED_CREDENTIALS from vero.harbor.deployment import FACTORY_PATH from vero.layout import LAYOUT from vero.sidecar.serve import load_factory @@ -616,6 +617,45 @@ def test_compiled_producer_base_url_matches_the_gateway_route(tmp_path): ) +def test_routed_credentials_are_set_not_merely_left_unblanked(tmp_path): + """Every name excluded from blanking must be positively set instead. + + The compiler blanks each declared secret in the main container, skipping + GATEWAY_ROUTED_CREDENTIALS because the compose template assigns those + itself. That exclusion is only safe while the two agree. A name dropped + from the blanking loop but never assigned would be neither blanked nor + set, so whatever the host exported would survive into the optimizer's + container -- which is the one thing the scrub exists to prevent. + """ + config = _config( + tmp_path, + inference_gateway=InferenceGatewaySpec( + producer=InferenceBudgetSpec(allowed_models=["gpt-producer"]), + evaluation=InferenceBudgetSpec(allowed_models=["gpt-target"]), + ), + ) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + compose = yaml.safe_load( + (output / "environment/docker-compose.yaml").read_text(encoding="utf-8") + ) + environment = compose["services"]["main"]["environment"] + + assert set(GATEWAY_ROUTED_CREDENTIALS) == set(LAYOUT.routed_credential_envs) + for name in GATEWAY_ROUTED_CREDENTIALS: + assert name in environment, f"{name} is skipped by the scrub but never set" + assert environment[name], f"{name} is set to an empty value" + + # And they carry the scoped values, not the upstream ones. + assert environment[LAYOUT.producer_api_key_env] != "" + assert environment[LAYOUT.producer_base_url_env] == LAYOUT.scope_url( + "producer", LAYOUT.optimizer_attribution + ) + # The raw upstream is blanked in the same container. + assert environment[LAYOUT.gateway_upstream_api_key_env] == "" + + def test_load_build_config_resolves_relative_local_paths(tmp_path): target = _target_repo(tmp_path / "target") tasks = tmp_path / "tasks" From 55487cb5d3c8ba582dbdffb12c902ea64944534d Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 23:53:59 -0700 Subject: [PATCH 48/72] Decompose the Harbor build config by concern config.py held three unrelated things in 953 lines: the leaf models a build.yaml composes, HarborBuildConfig itself, and the ${NAME:-default} substitution language plus the YAML reader. Split into specs.py (313), config.py (635), and loader.py (155). Only two places imported deeply; build/__init__.py re-exports the same eleven names. validate_references was 136 lines running six unrelated checks, including 45 lines of task_manifest JSON parsing. It is now six named validators in the original order, so error precedence is unchanged and a rejected build points at the rule it broke. The manifest check reads and parses the file once instead of twice. The fields are grouped into six private base classes, each with its own docstring and the validators that concern only itself. Grouping by inheritance rather than nesting keeps the wire format flat, so every checked-in build.yaml parses identically -- verified by loading all five benchmark configs and by comparing the old and new field sets (62 each, same annotations and defaults). That retires a duplicate list: the Harbor-only fields were enumerated once as declarations and once in a hand-maintained frozenset, and _HARBOR_ONLY_FIELDS is now derived from _HarborEvaluationFields. The two had already drifted -- reward_key is Harbor-only in fact, since a command harness reports its own reward, but was missing from the list, so setting it on a command build passed validation and did nothing. It is now in the group and rejected. Two tests. One sweeps the derived set, so a field added to the group is covered the day it lands; the other asserts the groups stay flat on the wire and that every field belongs to one. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/__init__.py | 14 +- vero/src/vero/harbor/build/compiler.py | 17 +- vero/src/vero/harbor/build/config.py | 1168 +++++++++--------------- vero/src/vero/harbor/build/loader.py | 155 ++++ vero/src/vero/harbor/build/specs.py | 313 +++++++ vero/tests/test_v05_harbor_build.py | 104 ++- 6 files changed, 998 insertions(+), 773 deletions(-) create mode 100644 vero/src/vero/harbor/build/loader.py create mode 100644 vero/src/vero/harbor/build/specs.py diff --git a/vero/src/vero/harbor/build/__init__.py b/vero/src/vero/harbor/build/__init__.py index 65542be4..ceb851c9 100644 --- a/vero/src/vero/harbor/build/__init__.py +++ b/vero/src/vero/harbor/build/__init__.py @@ -1,16 +1,22 @@ -"""Compile a program-optimization setup into a runnable Harbor task.""" +"""Compile a program-optimization setup into a runnable Harbor task. + +Four modules, in the order a build passes through them: specs.py declares the +leaf models a build.yaml composes, config.py assembles them into +HarborBuildConfig and enforces the rules that span groups, loader.py reads the +YAML into one, and compiler.py lowers it into a task directory. +""" from vero.harbor.build.compiler import compile_harbor_task -from vero.harbor.build.config import ( +from vero.harbor.build.config import HarborBuildConfig +from vero.harbor.build.loader import load_harbor_build_config +from vero.harbor.build.specs import ( AgentAccessSpec, CommandBackendSpec, - HarborBuildConfig, InferenceBudgetSpec, InferenceGatewaySpec, VerificationTargetSpec, WandbSpec, WorkspaceOverlaySpec, - load_harbor_build_config, ) __all__ = [ diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index 5b2ddd6a..d1d43285 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -22,7 +22,8 @@ RetryPolicy, ) from vero.gateway.inference import generate_inference_token, token_digest -from vero.harbor.build.config import HarborBuildConfig, WorkspaceOverlaySpec +from vero.harbor.build.config import HarborBuildConfig +from vero.harbor.build.specs import WorkspaceOverlaySpec from vero.layout import LAYOUT logger = logging.getLogger(__name__) @@ -33,7 +34,7 @@ SESSION_ID = "trial" UPSTREAM_API_KEY_ENV = LAYOUT.gateway_upstream_api_key_env UPSTREAM_BASE_URL_ENV = LAYOUT.gateway_upstream_base_url_env -PRODUCER_BASE_URL = LAYOUT.scope_url('producer', LAYOUT.optimizer_attribution) +PRODUCER_BASE_URL = LAYOUT.scope_url("producer", LAYOUT.optimizer_attribution) # Credentials the compose template routes through the gateway by setting them # explicitly, instead of blanking them like every other declared secret. The two @@ -299,14 +300,18 @@ def _deployment_config( "passthrough_environment": config.secrets, "environment": config.task_environment, "inference_gateway_url": ( - INFERENCE_GATEWAY_URL if config.inference_gateway is not None else None + INFERENCE_GATEWAY_URL + if config.inference_gateway is not None + else None ), "inference_gateway_token": evaluation_inference_token, "inference_gateway_finalization_token": finalization_inference_token, "harness_user": config.harness_user, "task_services_use_upstream": config.task_services_use_upstream, "upstream_api_key_env": ( - UPSTREAM_API_KEY_ENV if config.inference_gateway is not None else None + UPSTREAM_API_KEY_ENV + if config.inference_gateway is not None + else None ), "upstream_base_url_env": ( UPSTREAM_BASE_URL_ENV @@ -660,9 +665,7 @@ def compile_harbor_task( ), "upstream_base_url_target": UPSTREAM_BASE_URL_ENV, "producer_api_key": producer_inference_token, - "producer_base_url": ( - PRODUCER_BASE_URL - ), + "producer_base_url": (PRODUCER_BASE_URL), }, ensure_ascii=False, indent=2, diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index d6bc857b..4bc1df43 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -1,399 +1,144 @@ -"""Configuration schema for compiling VeRO optimization tasks for Harbor.""" +"""Configuration schema for compiling VeRO optimization tasks for Harbor. + +HarborBuildConfig is one flat YAML document, but its fields fall into six groups +that have little to do with each other. Each group is a private base class below, +carrying its own fields, its own docstring, and the validators that concern only +itself. HarborBuildConfig inherits all six, so the YAML stays flat — a build +config written before this split parses identically — while the grammar is +readable one group at a time. + +The grouping also removes a duplicate list. The fields that only mean something +for a nested ``harbor run`` used to be enumerated twice: once as declarations and +once in a hand-maintained frozenset. Now they are exactly the members of +_HarborEvaluationFields, and _HARBOR_ONLY_FIELDS is derived from it. + +Rules that span groups stay on HarborBuildConfig itself, as named validators. +""" from __future__ import annotations import json -import math -import os import re from pathlib import Path from typing import Literal -from pydantic import Field, JsonValue, field_validator, model_validator +from pydantic import Field, field_validator, model_validator -from vero.evaluation import ( - DisclosureLevel, - EvaluationAccessPolicy, - MetricSelector, - ObjectiveSpec, +from vero.evaluation import MetricSelector, ObjectiveSpec +from vero.harbor.build.specs import ( + AgentAccessSpec, + CommandBackendSpec, + InferenceGatewaySpec, + VerificationTargetSpec, + WandbSpec, + WorkspaceOverlaySpec, ) from vero.models import StrictModel -class AgentAccessSpec(StrictModel): - """The optimizer agent's access to one named evaluation partition. - - A build declares one spec per partition; to_access_policy translates it into - the runtime EvaluationAccessPolicy the sidecar enforces. This governs what - the optimizer may see and spend while searching, as opposed to how the - trusted side finally scores a candidate (see VerificationTargetSpec). - - Attributes: - partition: Partition this access applies to, e.g. "validation". - disclosure: How much of a result the agent sees — aggregate score vs. - per-case detail. - expose_case_resources: Whether each case's input files are materialized - into the agent's workspace. - min_aggregate_cases: Smallest number of cases an aggregate score may - cover, so the agent cannot request a subset small enough to reveal an - individual case's result. Only consulted when disclosure is AGGREGATE. - total_runs: Optional cap on evaluation runs against this partition. - total_cases: Optional cap on cases scored against this partition. - """ - - partition: str - disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE - expose_case_resources: bool = False - min_aggregate_cases: int = Field(default=5, ge=1) - total_runs: int | None = Field(default=None, ge=0) - total_cases: int | None = Field(default=None, ge=0) - - @field_validator("partition") - @classmethod - def validate_partition(cls, value: str) -> str: - if not value.strip(): - raise ValueError("agent access partition must not be empty") - return value - - def to_access_policy(self) -> EvaluationAccessPolicy: - """The single typed translation from build spec to runtime policy.""" - return EvaluationAccessPolicy( - disclosure=self.disclosure, - expose_case_resources=self.expose_case_resources, - min_aggregate_cases=self.min_aggregate_cases, - ) - +def _require_non_empty(value: str, label: str) -> str: + if not value.strip(): + raise ValueError(f"{label} must not be empty") + return value -class VerificationTargetSpec(StrictModel): - """One trusted final scoring pass the verifier runs on a chosen candidate. - Distinct from the evaluations the optimizer runs during search: after search - picks a best candidate on the selection partition, the trusted verifier - scores it against these targets (normally the held-out test partition) to - produce the final reward. A build declares one spec per pass. +class _TaskIdentityFields(StrictModel): + """What is being optimized, and the images and case sets it is built from. Attributes: - partition: Partition to score on, e.g. "test". - reward_key: Metric key in the report that is the reward. - model: Optional model override for this pass; passed through as - harbor_model_override so final scoring can differ from search. - parameters: Extra backend parameters for the pass. - failure_value: Score assigned when the pass fails. - baseline_reward: Pin the seed's reward on this target to skip scoring - the immutable baseline every run. - max_attempts: Number of scoring attempts before accepting failure. + name: Task name written into task.toml. + description: Human-readable task description. + agent_repo: Absolute path to the editable target. Copied twice, into the + immutable agent-baseline and the editable agent-seed. + harbor_requirement: Pinned harbor requirement for the task image. Needed + for either evaluation backend, since the outer optimizer is a Harbor + agent in both cases. + partitions: Partition name to the Harbor task names it holds. Emitted as + one cases/.jsonl per partition. + task_manifest: Optional path to an existing JSON task manifest. When + given, every task named in partitions must appear in it. + base_image_main: Base image for the main container. + base_image_sidecar: Base image for the sidecar container. """ - partition: str - reward_key: str = "reward" - model: str | None = None - parameters: dict[str, JsonValue] = Field(default_factory=dict) - failure_value: float = 0.0 - baseline_reward: float | None = None - max_attempts: int = Field(default=1, ge=1) + name: str + description: str = "" + agent_repo: str + harbor_requirement: str + partitions: dict[str, list[str]] + task_manifest: str | None = None + base_image_main: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" + base_image_sidecar: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" - @field_validator("partition", "reward_key") + @field_validator("name", "agent_repo", "base_image_main", "base_image_sidecar") @classmethod def validate_identity(cls, value: str) -> str: - if not value.strip(): - raise ValueError("verification target identity must not be empty") - return value - - @field_validator("failure_value") - @classmethod - def validate_failure_value(cls, value: float) -> float: - if not math.isfinite(value): - raise ValueError("target failure_value must be finite") - return value - - -class InferenceBudgetSpec(StrictModel): - """Routing policy and optional limits for one inference-gateway scope. - - The compiler lowers each spec into an InferenceScopeConfig, adding the - SHA-256 digest of a freshly minted per-scope token. The gateway then checks - the presented token, the requested model, and the running budget on every - proxied request; limits are held server-side in a ledger written through to - disk, so they survive a gateway restart. - - Attributes: - allowed_models: Models this scope may request. A request naming anything - else is refused with 403 model_denied. - max_requests: Cap on proxied requests; unlimited when omitted. - max_tokens: Cap on cumulative tokens; unlimited when omitted. Checked - before a request starts, so a single request can overshoot it. - max_concurrency: Requests this scope may have in flight at once. - """ - - allowed_models: list[str] - max_requests: int | None = Field(default=None, ge=1) - max_tokens: int | None = Field(default=None, ge=1) - max_concurrency: int = Field(default=8, ge=1) - - @field_validator("allowed_models") - @classmethod - def validate_models(cls, value: list[str]) -> list[str]: - if not value or any(not model.strip() for model in value): - raise ValueError("allowed_models must contain non-empty model names") - if len(value) != len(set(value)): - raise ValueError("allowed_models must be unique") - return value - - -class InferenceGatewaySpec(StrictModel): - """Credential source and independent producer/evaluator policies. - - All model access in a compiled task goes through the gateway, which holds - the one upstream credential and hands each consumer a separate token. The - optimizer never sees the evaluation or finalization tokens, so it cannot - spend from those pools. + return _require_non_empty(value, "Harbor build identity") - The per-scope allow-lists keep the target on its fixed evaluation model, but - only against a non-adversarial optimizer: the scopes share a gateway host and - are split by URL path, so an optimizer that smuggles its own token into the - candidate can reach the producer scope from the eval sandbox. See the proxy - handler in vero.gateway.inference for the full note. - - Attributes: - upstream_api_key_env: Environment variable on the build host holding the - real upstream API key. - upstream_base_url_env: Environment variable holding the upstream base - URL; None to always use default_upstream_base_url. - default_upstream_base_url: Upstream used when no base-URL variable is set. - producer: Policy for the optimizer's own model calls. - evaluation: Policy for the target agent under evaluation. - finalization: Reserved policy for trusted finalization (admin re-score - and verification targets). Defaults to a copy of evaluation, giving - the mandatory re-score a pool that search evaluations cannot starve. - log_requests: Capture every proxied or denied request as JSONL on the - gateway state volume. Never agent-visible; the trusted sidecar - mirrors it to Weights & Biases and the session archive. - request_log_body_bytes: Head+tail budget per logged body, in bytes. - request_log_attribution: Experimental. Stamp log records with - provider-agnostic conversation threads for post-hoc per-trial - accounting. - """ - - upstream_api_key_env: str = "OPENAI_API_KEY" - upstream_base_url_env: str | None = "OPENAI_BASE_URL" - default_upstream_base_url: str = "https://api.openai.com/v1" - producer: InferenceBudgetSpec - evaluation: InferenceBudgetSpec - finalization: InferenceBudgetSpec | None = None - log_requests: bool = True - request_log_body_bytes: int = Field(default=16384, ge=0) - request_log_attribution: bool = False - - @field_validator("upstream_api_key_env", "upstream_base_url_env") - @classmethod - def validate_environment_name(cls, value: str | None) -> str | None: - if value is not None and re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", value) is None: - raise ValueError("gateway environment names must be valid identifiers") - return value - - @field_validator("default_upstream_base_url") - @classmethod - def validate_upstream_url(cls, value: str) -> str: - if not value.startswith(("http://", "https://")): - raise ValueError("default_upstream_base_url must be HTTP(S)") - return value.rstrip("/") - - -class WorkspaceOverlaySpec(StrictModel): - """A host file or directory to copy into the compiled task's agent workspace. - - General-purpose filesystem injection: bake anything (agent definitions, - skills, config, data) into the optimizer's workspace at build time. - - Attributes: - source: Host path, resolved relative to the build YAML. Must exist. A - directory contributes its contents; a file lands as dest/. - dest: Where the contents land, relative to the workspace root. Must be a - safe relative path; "." is the root itself. - """ - - source: str - dest: str = "." - - @field_validator("source") + @field_validator("agent_repo") @classmethod - def validate_source(cls, value: str) -> str: - if not value.strip() or not Path(value).exists(): - raise ValueError("overlay source must be an existing file or directory") + def validate_agent_repo(cls, value: str) -> str: + if not Path(value).is_absolute() or not Path(value).is_dir(): + raise ValueError("agent_repo must be an existing absolute directory") return value - @field_validator("dest") + @field_validator("harbor_requirement") @classmethod - def validate_dest(cls, value: str) -> str: - candidate = value.strip() - path = Path(candidate) - if candidate == ".": - return candidate - if ( - not candidate - or path.is_absolute() - or ".." in path.parts - or re.fullmatch(r"[A-Za-z0-9_./-]+", candidate) is None - ): + def validate_pinned_harbor(cls, value: str) -> str: + exact = re.search( + r"(?:^|\s)harbor(?:\[[A-Za-z0-9_.-]+(?:,[A-Za-z0-9_.-]+)*\])?" + r"\s*==\s*[^*\s,;]+", + value, + ) + pinned_git = re.search(r"@[0-9a-f]{7,64}(?:#.*)?$", value) + if exact is None and pinned_git is None: raise ValueError( - "overlay dest must be a safe relative path within the workspace" + "harbor_requirement must pin an exact version or Git commit" ) - return candidate - - -class WandbSpec(StrictModel): - """Weights & Biases reporting settings, emitted into the sidecar's serve.json. - - Reporting runs on the trusted side only. The field names match the sidecar's - SidecarWandbConfig, which consumes them verbatim. - - Attributes: - project: Weights & Biases project to report into. - entity: Owning user or team; the account default when omitted. - name: Display name for the run. - group: Group to file the run under. - tags: Tags applied to the run. - mode: Client mode, e.g. "online" or "offline". - notes: Free-text note attached to the run. - run_id: Resume into this existing run instead of creating one. - log_traces: Upload each evaluation's trace artifacts. Off by default - because they are large. - """ - - project: str - entity: str | None = None - name: str | None = None - group: str | None = None - tags: list[str] = Field(default_factory=list) - mode: str | None = None - notes: str | None = None - run_id: str | None = None - log_traces: bool = False - - -class CommandBackendSpec(StrictModel): - """Inner evaluation that scores a candidate by running a program. - - Use this when the outer loop is still a Harbor coding agent but the target is - not an agent — a solver, an index build, a data pipeline — so there is nothing - to drive with a nested `harbor run`. The compiler copies harness_source into - the sidecar and points the backend's harness_root at it. - - Attributes: - harness_source: Host directory holding the scoring program, resolved - relative to the build YAML. Must exist. - command: Argument vector to run. Supports the placeholders {harness}, - {workspace}, {request}, {report}, and {artifacts}. - working_directory: Directory to run the command from. - environment: Environment variables set for the command. - passthrough_environment: Names forwarded from the sidecar's environment. - staged_inputs: Extra files staged into the command's workspace. - agent_context_inputs: Per-case files published into the agent context. - """ - - harness_source: str - command: list[str] - working_directory: str = "." - environment: dict[str, str] = Field(default_factory=dict) - passthrough_environment: list[str] = Field(default_factory=list) - staged_inputs: dict[str, str] = Field(default_factory=dict) - agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) + return value - @field_validator("harness_source") + @field_validator("task_manifest") @classmethod - def validate_harness_source(cls, value: str) -> str: - if not value.strip() or not Path(value).is_dir(): - raise ValueError("harness_source must be an existing directory") + def validate_task_manifest_path(cls, value: str | None) -> str | None: + if value is None: + return None + if not value.strip() or not Path(value).is_file(): + raise ValueError("task_manifest must be an existing JSON file") return value - @field_validator("command") + @field_validator("partitions") @classmethod - def validate_command(cls, value: list[str]) -> list[str]: - if not value or any(not part.strip() for part in value): - raise ValueError("command must contain non-empty arguments") + def validate_partitions( + cls, + value: dict[str, list[str]], + ) -> dict[str, list[str]]: + if not value: + raise ValueError("at least one Harbor partition is required") + for partition, tasks in value.items(): + if re.fullmatch(r"[A-Za-z0-9_.-]+", partition) is None: + raise ValueError( + f"partition {partition!r} must use letters, digits, '.', '_', or '-'" + ) + if not tasks or any(not task.strip() for task in tasks): + raise ValueError(f"partition {partition!r} must contain task names") + if len(tasks) != len(set(tasks)): + raise ValueError(f"partition {partition!r} contains duplicate tasks") return value -# Fields that only mean something for a nested `harbor run`. Setting one on a -# command build is a mistake worth reporting: it would be silently ignored. -_HARBOR_ONLY_FIELDS = frozenset( - { - "agent_import_path", - "task_source", - "model", - "environment_name", - "harbor_python_version", - "default_index", - "n_attempts", - "max_retries", - "retry_wait_multiplier", - "retry_min_wait_seconds", - "retry_max_wait_seconds", - "infrastructure_max_attempts", - "infrastructure_retry_delay_seconds", - "aggregate_attempts", - "feedback_transcripts", - "feedback_max_bytes", - "expose_attempt_detail", - "extra_harbor_args", - "task_agent_timeout_seconds", - "task_environment", - "task_services_use_upstream", - } -) - - -class HarborBuildConfig(StrictModel): - """Everything needed to emit an isolated Harbor optimization task. - - This is the whole grammar of a benchmark's build.yaml: load_harbor_build_config - parses and validates one, and compile_harbor_task lowers it into a task - directory. Unknown keys are rejected, so a typo fails the build instead of - quietly taking a default. +class _HarborEvaluationFields(StrictModel): + """Knobs for the nested ``harbor run`` that scores a candidate. - Two cross-field rules are checked after parsing. harness_user cannot be - combined with task_services_use_upstream, because the raw upstream credential - would then reach the isolated harness. And a task_source that is not a local - path must name an explicit registry version. + These, and only these, are the fields that mean nothing to a command + evaluation backend. Membership of this class *is* the definition: + _HARBOR_ONLY_FIELDS below is derived from it, and HarborBuildConfig rejects a + command build that sets any of them rather than ignoring it in silence. Attributes: - name: Task name written into task.toml. - description: Human-readable task description. - agent_repo: Absolute path to the editable target. Copied twice, into the - immutable agent-baseline and the editable agent-seed. task_source: Local path to the task definitions, or a registry reference - pinned as name@version. Required for a harbor evaluation_backend, - and rejected for a command one, which has no Harbor tasks. + pinned as name@version. agent_import_path: Import path of the target agent class Harbor loads. - Required for a harbor evaluation_backend, unused for a command one. - evaluation_backend: How a candidate is scored. "harbor" drives a target - agent with a nested `harbor run`; "command" runs a program instead, - for a target that is not an agent. The outer optimizer is a Harbor - agent either way. - command_backend: The scoring program, required when evaluation_backend is - "command" and rejected otherwise. - harbor_requirement: Pinned harbor requirement for the task image. - partitions: Partition name to the Harbor task names it holds. Emitted as - one cases/.jsonl per partition. - task_manifest: Optional path to an existing JSON task manifest. - agent_access: One AgentAccessSpec per partition the optimizer may reach. - selection_partition: Partition search optimizes against, normally - validation. - targets: Trusted final scoring passes; see VerificationTargetSpec. - evaluation_set_name: Name given to the evaluation set. - objective: Metric and direction that define fitness. - reward_mode: "submit" scores the candidate the agent submits; - "auto_best" scores the best measured candidate. - baseline_floor: Never ship a candidate scoring below the baseline. - baseline_selection_score: Pin the baseline's selection score rather than - measuring it. - score_baseline: Whether to score the baseline at all. - rescore_top_k: How many leading candidates the trusted side re-scores. - rescore_attempts: Re-score repeats per candidate. - selection_coverage_threshold: Fraction of the selection set an agent - evaluation must cover to be eligible for auto_best ranking. Below - this it is too partial to trust for selection. - model: Default model for the run. + model: Default model the target agent runs on. environment_name: Harbor environment, e.g. "modal". harbor_python_version: Python version for the task image. default_index: Package index used for installs. @@ -411,112 +156,30 @@ class HarborBuildConfig(StrictModel): infrastructure_max_attempts: Attempts for infrastructure-classed failures. infrastructure_retry_delay_seconds: Delay between those attempts. - reward_key: Metric key the backend treats as the reward. + reward_key: Metric key the backend treats as the reward. A command + harness reports its own reward, so this is Harbor-only. aggregate_attempts: Combine repeat attempts by "best" or "mean". feedback_transcripts: Include target transcripts in the agent's feedback. feedback_max_bytes: Byte cap per feedback transcript. expose_attempt_detail: Report per-attempt detail, not just the aggregate. extra_harbor_args: Extra flags for the evaluation sub-run. Rejected if they override a flag the compiler controls. - agent_env: Environment injected into the optimizer agent's own shell - (setup, install, and run) as harbor --ae KEY=VALUE. Distinct from - extra_harbor_args, which only reaches the evaluation sub-run, and - task_environment, which is that sub-run's environment. Use it for - things like UV_TOOL_BIN_DIR, so `uv tool install` targets a writable - directory on a non-root sandbox. - timeout_seconds: Wall clock for one evaluation. - case_timeout_seconds: Wall clock for one case. task_agent_timeout_seconds: Wall clock declared for the target agent. - max_concurrency: Cases evaluated concurrently. - error_rate_threshold: Case error fraction above which an evaluation is - abandoned. - build_timeout_seconds: Wall clock for building the task's images. - verifier_timeout_seconds: Wall clock for the trusted verifier. Falls back - to timeout_seconds. - evaluation_drain_timeout_seconds: Grace period for in-flight evaluations - to finish. Falls back to timeout_seconds. - secrets: Environment variable names routed into the task. Their presence - on the build host is checked at compile time unless - VERO_SKIP_SECRET_CHECK is set. - inference_gateway: Gateway credential source and per-scope policies; see - InferenceGatewaySpec. Omit for no metered model access. - wandb: Trusted-side Weights & Biases reporting from the evaluation - sidecar. Requires WANDB_API_KEY in secrets, routed to the sidecar and - never to the agent. - read_only_paths: Candidate-relative paths the optimizer may read but not - edit. Must be safe relative paths. - workspace_overlays: Host files baked into the optimizer's workspace. - include_evals_skill: Bake vero's packaged evals skill into the workspace, - so any coding agent learns the CLI and the .evals layout. - instruct_multifidelity: Tell the optimizer it allocates its own case - budget and may evaluate subsets. Requires disclose_budget. - instruct_exhaust_budget: Tell the optimizer that unspent budget is - wasted. Requires disclose_budget. - disclose_budget: Master switch for budget disclosure, not enforcement. - When False the instruction renders no budget language and the - sidecar's status omits remaining budgets, so the agent cannot reason - about its budget at all. The two instruct_ flags above apply only - when this is True. + Grouped here rather than with the other timeouts because it bounds + the target, which only a Harbor backend runs. + task_environment: Extra environment for the evaluation sub-run, available + to the task's ${VAR} substitutions. Must not name secrets. task_services_use_upstream: Give task-owned model services (user simulators, graders) running inside the task containers the real upstream via OPENAI_*, while the candidate keeps the metered, allow-listed gateway on VERO_AGENT_INFERENCE_*. Needed for benchmarks like tau3 whose environment makes its own model calls. - harness_user: Unprivileged OS user that runs the untrusted candidate - harness in the sidecar, isolating it from trusted state. None - disables that isolation. - task_environment: Extra environment for the evaluation sub-run, available - to the task's ${VAR} substitutions. Must not name secrets. - base_image_main: Base image for the main container. - base_image_sidecar: Base image for the sidecar container. """ - name: str - description: str = "" - agent_repo: str - # Optional because a command evaluation_backend has no Harbor task - # definitions; validate_references requires it for a harbor backend. + # Optional because a command evaluation_backend has neither; HarborBuildConfig + # requires both for a harbor backend. task_source: str | None = None - # Optional because a command evaluation_backend has no agent class to load; - # validate_references requires it for a harbor backend. agent_import_path: str | None = None - harbor_requirement: str - partitions: dict[str, list[str]] - task_manifest: str | None = None - agent_access: list[AgentAccessSpec] - selection_partition: str - targets: list[VerificationTargetSpec] - # TODO: the Harbor-inner fields below (model, retries, feedback, harbor args) - # only apply when evaluation_backend is "harbor", and the command equivalents - # live in command_backend. Nesting each group under its own discriminated - # sub-spec would make that structural instead of validated: _HARBOR_ONLY_FIELDS - # and the whole backend-coherence branch of validate_references would go away, - # and task_source/agent_import_path would stop being optional-but-required. - # - # Measured scope: 29 refs in the compiler, 11 in these validators, 66 in the - # build tests, and 80 top-level keys to re-indent across the five benchmark - # build.yaml files (nothing outside the build package reads these fields, and - # HarborBackendConfig is unaffected). Deferred because it is a breaking schema - # change with no functional gain, and re-indenting the benchmark configs risks - # silently altering the measurement substrate. A model_validator(mode="before") - # that lifts flat keys into the nested field would avoid the flag day. - evaluation_backend: Literal["harbor", "command"] = "harbor" - command_backend: CommandBackendSpec | None = None - - evaluation_set_name: str = "harbor" - objective: ObjectiveSpec = Field( - default_factory=lambda: ObjectiveSpec( - selector=MetricSelector(metric="score"), - direction="maximize", - ) - ) - reward_mode: Literal["submit", "auto_best"] = "auto_best" - baseline_floor: bool = False - baseline_selection_score: float | None = None - score_baseline: bool = True - rescore_top_k: int = Field(default=3, ge=1) - rescore_attempts: int = Field(default=1, ge=1) - selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) model: str | None = None environment_name: str = "modal" @@ -532,98 +195,23 @@ class HarborBuildConfig(StrictModel): reward_key: str | None = None aggregate_attempts: Literal["best", "mean"] = "mean" feedback_transcripts: bool = False - feedback_max_bytes: int = Field(default=3000, ge=0) - expose_attempt_detail: bool = False - extra_harbor_args: list[str] = Field(default_factory=list) - agent_env: dict[str, str] = Field(default_factory=dict) - - timeout_seconds: float = Field(default=1800.0, gt=0) - case_timeout_seconds: float = Field(default=180.0, gt=0) - task_agent_timeout_seconds: float = Field(default=600.0, gt=0) - max_concurrency: int = Field(default=8, ge=1) - error_rate_threshold: float | None = Field(default=0.1, gt=0, le=1) - build_timeout_seconds: int = Field(default=1800, ge=1) - verifier_timeout_seconds: int | None = Field(default=None, ge=1) - evaluation_drain_timeout_seconds: float | None = Field(default=None, gt=0) - - secrets: list[str] = Field(default_factory=list) - inference_gateway: InferenceGatewaySpec | None = None - wandb: WandbSpec | None = None - read_only_paths: list[str] = Field(default_factory=list) - workspace_overlays: list[WorkspaceOverlaySpec] = Field(default_factory=list) - include_evals_skill: bool = True - instruct_multifidelity: bool = True - instruct_exhaust_budget: bool = True - disclose_budget: bool = True - task_services_use_upstream: bool = False - harness_user: str | None = "harness" - task_environment: dict[str, str] = Field(default_factory=dict) - base_image_main: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" - base_image_sidecar: str = "ghcr.io/astral-sh/uv:python3.12-bookworm" - - @model_validator(mode="after") - def validate_harness_isolation(self) -> HarborBuildConfig: - if self.harness_user is not None and self.task_services_use_upstream: - raise ValueError( - "harness_user (harness isolation) is incompatible with " - "task_services_use_upstream: the raw upstream credential would " - "reach the isolated harness through its environment. Set " - "harness_user: null to build task-owned upstream services." - ) - return self + feedback_max_bytes: int = Field(default=3000, ge=0) + expose_attempt_detail: bool = False + extra_harbor_args: list[str] = Field(default_factory=list) + task_agent_timeout_seconds: float = Field(default=600.0, gt=0) + task_environment: dict[str, str] = Field(default_factory=dict) + task_services_use_upstream: bool = False - @field_validator( - "name", - "agent_repo", - "evaluation_set_name", - "environment_name", - "harbor_python_version", - "default_index", - "base_image_main", - "base_image_sidecar", - ) + @field_validator("environment_name", "harbor_python_version", "default_index") @classmethod def validate_identity(cls, value: str) -> str: - if not value.strip(): - raise ValueError("Harbor build identity must not be empty") - return value - - @field_validator("agent_repo") - @classmethod - def validate_agent_repo(cls, value: str) -> str: - if not Path(value).is_absolute() or not Path(value).is_dir(): - raise ValueError("agent_repo must be an existing absolute directory") - return value - - @field_validator("harbor_requirement") - @classmethod - def validate_pinned_harbor(cls, value: str) -> str: - exact = re.search( - r"(?:^|\s)harbor(?:\[[A-Za-z0-9_.-]+(?:,[A-Za-z0-9_.-]+)*\])?" - r"\s*==\s*[^*\s,;]+", - value, - ) - pinned_git = re.search(r"@[0-9a-f]{7,64}(?:#.*)?$", value) - if exact is None and pinned_git is None: - raise ValueError( - "harbor_requirement must pin an exact version or Git commit" - ) - return value + return _require_non_empty(value, "Harbor build identity") @field_validator("model", "reward_key", "agent_import_path", "task_source") @classmethod def validate_optional_identity(cls, value: str | None) -> str | None: - if value is not None and not value.strip(): - raise ValueError("optional Harbor identity must not be empty") - return value - - @field_validator("task_manifest") - @classmethod - def validate_task_manifest_path(cls, value: str | None) -> str | None: - if value is None: - return None - if not value.strip() or not Path(value).is_file(): - raise ValueError("task_manifest must be an existing JSON file") + if value is not None: + _require_non_empty(value, "optional Harbor identity") return value @field_validator("extra_harbor_args") @@ -651,24 +239,114 @@ def validate_extra_harbor_args(cls, value: list[str]) -> list[str]: ) return value - @field_validator("partitions") + +# The fields a command build must not set, derived from the class above so the +# two cannot drift. Setting one is a mistake worth reporting: it would be +# silently ignored. +_HARBOR_ONLY_FIELDS = frozenset(_HarborEvaluationFields.model_fields) + + +class _SearchAndSelectionFields(StrictModel): + """What the optimizer may measure, what counts as better, and what ships. + + Attributes: + agent_access: One AgentAccessSpec per partition the optimizer may reach. + A partition with no entry is held out by the absence of a policy. + selection_partition: Partition search optimizes against, normally + validation. + targets: Trusted final scoring passes; see VerificationTargetSpec. + evaluation_set_name: Name given to the evaluation set. + objective: Metric and direction that define fitness. + reward_mode: "submit" scores the candidate the agent submits; + "auto_best" scores the best measured candidate. + baseline_floor: Never ship a candidate scoring below the baseline. + baseline_selection_score: Pin the baseline's selection score rather than + measuring it. + score_baseline: Whether to score the baseline at all. + rescore_top_k: How many leading candidates the trusted side re-scores. + rescore_attempts: Re-score repeats per candidate. + selection_coverage_threshold: Fraction of the selection set an agent + evaluation must cover to be eligible for auto_best ranking. Below + this it is too partial to trust for selection. + """ + + agent_access: list[AgentAccessSpec] + selection_partition: str + targets: list[VerificationTargetSpec] + evaluation_set_name: str = "harbor" + objective: ObjectiveSpec = Field( + default_factory=lambda: ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + ) + ) + reward_mode: Literal["submit", "auto_best"] = "auto_best" + baseline_floor: bool = False + baseline_selection_score: float | None = None + score_baseline: bool = True + rescore_top_k: int = Field(default=3, ge=1) + rescore_attempts: int = Field(default=1, ge=1) + selection_coverage_threshold: float = Field(default=0.9, ge=0.0, le=1.0) + + @field_validator("evaluation_set_name") @classmethod - def validate_partitions( - cls, - value: dict[str, list[str]], - ) -> dict[str, list[str]]: - if not value: - raise ValueError("at least one Harbor partition is required") - for partition, tasks in value.items(): - if re.fullmatch(r"[A-Za-z0-9_.-]+", partition) is None: - raise ValueError( - f"partition {partition!r} must use letters, digits, '.', '_', or '-'" - ) - if not tasks or any(not task.strip() for task in tasks): - raise ValueError(f"partition {partition!r} must contain task names") - if len(tasks) != len(set(tasks)): - raise ValueError(f"partition {partition!r} contains duplicate tasks") - return value + def validate_identity(cls, value: str) -> str: + return _require_non_empty(value, "Harbor build identity") + + +class _EvaluationLimitFields(StrictModel): + """Wall clocks and concurrency for the work the sidecar runs. + + Attributes: + timeout_seconds: Wall clock for one evaluation. + case_timeout_seconds: Wall clock for one case. + max_concurrency: Cases evaluated concurrently. + error_rate_threshold: Case error fraction above which an evaluation is + abandoned. + build_timeout_seconds: Wall clock for building the task's images. + verifier_timeout_seconds: Wall clock for the trusted verifier. Falls back + to timeout_seconds. + evaluation_drain_timeout_seconds: Grace period for in-flight evaluations + to finish. Falls back to timeout_seconds. + """ + + timeout_seconds: float = Field(default=1800.0, gt=0) + case_timeout_seconds: float = Field(default=180.0, gt=0) + max_concurrency: int = Field(default=8, ge=1) + error_rate_threshold: float | None = Field(default=0.1, gt=0, le=1) + build_timeout_seconds: int = Field(default=1800, ge=1) + verifier_timeout_seconds: int | None = Field(default=None, ge=1) + evaluation_drain_timeout_seconds: float | None = Field(default=None, gt=0) + + +class _TaskEnvironmentFields(StrictModel): + """Credentials, model access, and reporting for the containers. + + Attributes: + secrets: Environment variable names routed into the task. Their presence + on the build host is checked at compile time unless + VERO_SKIP_SECRET_CHECK is set. + inference_gateway: Gateway credential source and per-scope policies; see + InferenceGatewaySpec. Omit for no metered model access. + wandb: Trusted-side Weights & Biases reporting from the evaluation + sidecar. Requires WANDB_API_KEY in secrets, routed to the sidecar and + never to the agent. + agent_env: Environment injected into the optimizer agent's own shell + (setup, install, and run) as harbor --ae KEY=VALUE. Distinct from + extra_harbor_args, which only reaches the evaluation sub-run, and + task_environment, which is that sub-run's environment. Use it for + things like UV_TOOL_BIN_DIR, so `uv tool install` targets a writable + directory on a non-root sandbox. + harness_user: Unprivileged OS user that runs the untrusted candidate + harness in the sidecar, isolating it from trusted state. None + disables that isolation. + """ + + secrets: list[str] = Field(default_factory=list) + inference_gateway: InferenceGatewaySpec | None = None + wandb: WandbSpec | None = None + agent_env: dict[str, str] = Field(default_factory=dict) + harness_user: str | None = "harness" @field_validator("secrets") @classmethod @@ -680,6 +358,34 @@ def validate_secrets(cls, value: list[str]) -> list[str]: raise ValueError(f"invalid secret environment name: {name!r}") return value + +class _AgentWorkspaceFields(StrictModel): + """What the optimizer finds in its workspace and is told about the task. + + Attributes: + read_only_paths: Candidate-relative paths the optimizer may read but not + edit. Must be safe relative paths. + workspace_overlays: Host files baked into the optimizer's workspace. + include_evals_skill: Bake vero's packaged evals skill into the workspace, + so any coding agent learns the CLI and the .evals layout. + instruct_multifidelity: Tell the optimizer it allocates its own case + budget and may evaluate subsets. Requires disclose_budget. + instruct_exhaust_budget: Tell the optimizer that unspent budget is + wasted. Requires disclose_budget. + disclose_budget: Master switch for budget disclosure, not enforcement. + When False the instruction renders no budget language and the + sidecar's status omits remaining budgets, so the agent cannot reason + about its budget at all. The two instruct_ flags above apply only + when this is True. + """ + + read_only_paths: list[str] = Field(default_factory=list) + workspace_overlays: list[WorkspaceOverlaySpec] = Field(default_factory=list) + include_evals_skill: bool = True + instruct_multifidelity: bool = True + instruct_exhaust_budget: bool = True + disclose_budget: bool = True + @field_validator("read_only_paths") @classmethod def validate_read_only_paths(cls, value: list[str]) -> list[str]: @@ -696,12 +402,108 @@ def validate_read_only_paths(cls, value: list[str]) -> list[str]: ) return value + +def _read_manifest(manifest_path: str) -> dict: + try: + manifest = json.loads(Path(manifest_path).read_text(encoding="utf-8")) + except json.JSONDecodeError as error: + raise ValueError("task_manifest must contain valid JSON") from error + if not isinstance(manifest, dict): + raise ValueError("task_manifest must be a JSON object") + return manifest + + +def _manifest_source_matches( + manifest: dict, + manifest_path: str, + task_source: str, +) -> bool: + """Whether a manifest's recorded task_source is the build's task_source.""" + recorded = manifest.get("task_source") + if recorded == task_source: + return True + # A vendored local source is recorded relative to the manifest, while the + # loader resolves the build's copy to an absolute path once the directory + # exists; compare resolved locations. + return ( + isinstance(recorded, str) + and (Path(manifest_path).parent / recorded).resolve() + == Path(task_source).resolve() + ) + + +def _manifest_task_names(manifest: dict) -> list[str]: + """The task names a manifest declares, rejecting a malformed tasks list.""" + tasks = manifest.get("tasks") + if not isinstance(tasks, list): + raise ValueError("task_manifest tasks must be a JSON array") + names: list[str] = [] + for item in tasks: + name = item.get("name") if isinstance(item, dict) else item + if not isinstance(name, str) or not name.strip(): + raise ValueError("task_manifest tasks must be names or objects with a name") + names.append(name) + if len(names) != len(set(names)): + raise ValueError("task_manifest contains duplicate task names") + return names + + +class HarborBuildConfig( + _TaskIdentityFields, + _HarborEvaluationFields, + _SearchAndSelectionFields, + _EvaluationLimitFields, + _TaskEnvironmentFields, + _AgentWorkspaceFields, +): + """Everything needed to emit an isolated Harbor optimization task. + + This is the whole grammar of a benchmark's build.yaml: load_harbor_build_config + parses and validates one, and compile_harbor_task lowers it into a task + directory. Unknown keys are rejected, so a typo fails the build instead of + quietly taking a default. + + Most fields come from the six groups this inherits, each documented on its + own class: _TaskIdentityFields, _HarborEvaluationFields, + _SearchAndSelectionFields, _EvaluationLimitFields, _TaskEnvironmentFields, + and _AgentWorkspaceFields. Declared here are only the choice of inner + evaluation backend and the rules that span groups — each of the latter is a + named validator below, so a rejected build points at the rule it broke. + + Attributes: + evaluation_backend: How a candidate is scored. "harbor" drives a target + agent with a nested `harbor run`; "command" runs a program instead, + for a target that is not an agent. The outer optimizer is a Harbor + agent either way. + command_backend: The scoring program, required when evaluation_backend is + "command" and rejected otherwise. + """ + + evaluation_backend: Literal["harbor", "command"] = "harbor" + command_backend: CommandBackendSpec | None = None + + @model_validator(mode="after") + def validate_harness_isolation(self) -> HarborBuildConfig: + if self.harness_user is not None and self.task_services_use_upstream: + raise ValueError( + "harness_user (harness isolation) is incompatible with " + "task_services_use_upstream: the raw upstream credential would " + "reach the isolated harness through its environment. Set " + "harness_user: null to build task-owned upstream services." + ) + return self + @model_validator(mode="after") - def validate_references(self) -> HarborBuildConfig: + def validate_task_source_is_pinned(self) -> HarborBuildConfig: if self.task_source is not None and ( not Path(self.task_source).exists() and "@" not in self.task_source ): raise ValueError("registry task_source must include an explicit version") + return self + + @model_validator(mode="after") + def validate_backend_coherence(self) -> HarborBuildConfig: + """Each backend gets its own fields and only its own.""" if self.evaluation_backend == "command": if self.command_backend is None: raise ValueError( @@ -716,9 +518,7 @@ def validate_references(self) -> HarborBuildConfig: ) else: if self.command_backend is not None: - raise ValueError( - "command_backend requires evaluation_backend: command" - ) + raise ValueError("command_backend requires evaluation_backend: command") missing = sorted( name for name in ("agent_import_path", "task_source") @@ -728,6 +528,11 @@ def validate_references(self) -> HarborBuildConfig: raise ValueError( "harbor evaluation_backend requires: " + ", ".join(missing) ) + return self + + @model_validator(mode="after") + def validate_partition_references(self) -> HarborBuildConfig: + """Every partition named elsewhere must exist, and be reachable.""" known = set(self.partitions) if self.selection_partition not in known: raise ValueError("selection_partition is not present in partitions") @@ -749,205 +554,80 @@ def validate_references(self) -> HarborBuildConfig: reward_keys = [target.reward_key for target in self.targets] if len(reward_keys) != len(set(reward_keys)): raise ValueError("target reward keys must be unique") - if self.task_manifest is not None and self.task_source is not None: - try: - manifest = json.loads( - Path(self.task_manifest).read_text(encoding="utf-8") - ) - except json.JSONDecodeError as error: - raise ValueError("task_manifest must contain valid JSON") from error - if not isinstance(manifest, dict): - raise ValueError("task_manifest must be a JSON object") - manifest_source = manifest.get("task_source") - if manifest_source != self.task_source and not ( - # A vendored local source is recorded relative to the manifest, - # while the loader resolves the build's copy to an absolute - # path once the directory exists; compare resolved locations. - isinstance(manifest_source, str) - and (Path(self.task_manifest).parent / manifest_source).resolve() - == Path(self.task_source).resolve() - ): - raise ValueError( - "task_manifest task_source does not match build task_source" - ) - manifest_tasks = manifest.get("tasks") - if not isinstance(manifest_tasks, list): - raise ValueError("task_manifest tasks must be a JSON array") - names: list[str] = [] - for item in manifest_tasks: - name = item.get("name") if isinstance(item, dict) else item - if not isinstance(name, str) or not name.strip(): - raise ValueError( - "task_manifest tasks must be names or objects with a name" - ) - names.append(name) - if len(names) != len(set(names)): - raise ValueError("task_manifest contains duplicate task names") - selected = { - task - for partition_tasks in self.partitions.values() - for task in partition_tasks - } - unknown = sorted(selected - set(names)) - if unknown: - raise ValueError( - "partitions reference tasks absent from task_manifest: " - + ", ".join(unknown) - ) - if self.inference_gateway is not None: - gateway_environment = {self.inference_gateway.upstream_api_key_env} - if self.inference_gateway.upstream_base_url_env is not None: - gateway_environment.add(self.inference_gateway.upstream_base_url_env) - overlap = sorted(set(self.secrets) & gateway_environment) - if overlap: - raise ValueError( - "inference gateway credentials must not also be sidecar secrets: " - + ", ".join(overlap) - ) - # Every model that will actually be requested has to be allowed by the - # scope that will actually serve it. Otherwise the mismatch surfaces as a - # gateway 403 at run time — and for a verification target, only after - # search has already spent its budget. A command backend requests no - # models, so there is nothing to reconcile. - if self.inference_gateway is not None and self.evaluation_backend == "harbor": - evaluation_models = self.inference_gateway.evaluation.allowed_models - if self.model is not None and self.model not in evaluation_models: - raise ValueError( - f"model {self.model!r} is not in the inference gateway's " - f"evaluation allowed_models ({', '.join(evaluation_models)})" - ) - # Finalization runs the target agent too, so it must allow the model - # each target scores with: its own override, else the task model. - finalization_models = ( - self.inference_gateway.finalization or self.inference_gateway.evaluation - ).allowed_models - for target in self.targets: - scoring_model = target.model or self.model - if scoring_model is not None and scoring_model not in ( - finalization_models - ): - raise ValueError( - f"target {target.partition!r} scores with model " - f"{scoring_model!r}, which is not in the inference gateway's " - f"finalization allowed_models " - f"({', '.join(finalization_models)})" - ) return self - -_BUILD_PARAM = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)(?::(-|\?)([^}]*))?\}") - - -def _substitute_build_param(text: str, context: dict[str, str]) -> str: - """Resolve ``${NAME}`` / ``${NAME:-default}`` / ``${NAME:?message}`` in one scalar.""" - - def replace(match: re.Match[str]) -> str: - name, operator, argument = match.group(1), match.group(2), match.group(3) - resolved = context.get(name) - if resolved: - return resolved - if operator == "-": - return argument or "" - if operator == "?": + @model_validator(mode="after") + def validate_task_manifest_agreement(self) -> HarborBuildConfig: + """The manifest and the partitions must describe the same task set.""" + if self.task_manifest is None or self.task_source is None: + return self + manifest = _read_manifest(self.task_manifest) + if not _manifest_source_matches(manifest, self.task_manifest, self.task_source): raise ValueError( - f"required build parameter {name!r} is unset: " - f"{argument or 'no message provided'}" + "task_manifest task_source does not match build task_source" ) - raise ValueError( - f"build parameter {name!r} is unset; pass --param {name}=VALUE " - "or set the environment variable" - ) - - return _BUILD_PARAM.sub(replace, text) - - -def _resolve_build_params(value: object, context: dict[str, str]) -> object: - """Recursively resolve ``${...}`` placeholders in string scalars of a YAML value.""" - if isinstance(value, str): - return _substitute_build_param(value, context) - if isinstance(value, dict): - return {key: _resolve_build_params(item, context) for key, item in value.items()} - if isinstance(value, list): - return [_resolve_build_params(item, context) for item in value] - return value + declared = set(_manifest_task_names(manifest)) + selected = { + task + for partition_tasks in self.partitions.values() + for task in partition_tasks + } + unknown = sorted(selected - declared) + if unknown: + raise ValueError( + "partitions reference tasks absent from task_manifest: " + + ", ".join(unknown) + ) + return self + @model_validator(mode="after") + def validate_gateway_credentials_are_not_secrets(self) -> HarborBuildConfig: + """The upstream credential is the gateway's alone. + + Declaring it as a task secret would deliver it to the containers the + gateway exists to keep it away from. + """ + if self.inference_gateway is None: + return self + gateway_environment = {self.inference_gateway.upstream_api_key_env} + if self.inference_gateway.upstream_base_url_env is not None: + gateway_environment.add(self.inference_gateway.upstream_base_url_env) + overlap = sorted(set(self.secrets) & gateway_environment) + if overlap: + raise ValueError( + "inference gateway credentials must not also be sidecar secrets: " + + ", ".join(overlap) + ) + return self -def load_harbor_build_config( - path: Path | str, - *, - params: dict[str, str] | None = None, -) -> HarborBuildConfig: - """Load YAML and resolve local paths relative to the configuration file. - - ``${NAME}`` placeholders in the YAML are substituted at load time from - ``params`` (explicit, e.g. ``--param NAME=VALUE``) layered over the process - environment, so run-time knobs (optimizer model, inner sandbox provider, - concurrency, ...) can be varied without rebuilding the task. Use - ``${NAME:-default}`` for a fallback and ``${NAME:?message}`` to require a - value. Fields left un-templated stay fixed (the reproducible measurement - substrate). - """ - try: - import yaml - except ImportError as error: - raise RuntimeError( - "install scale-vero[harbor] to load Harbor builds" - ) from error - - config_path = Path(path).expanduser().resolve() - value = yaml.safe_load(config_path.read_text(encoding="utf-8")) - if not isinstance(value, dict): - raise ValueError("Harbor build config must be a YAML object") - context = {**os.environ, **(params or {})} - value = _resolve_build_params(value, context) - base = config_path.parent - partition_files = value.pop("partition_files", None) - if partition_files is not None: - if "partitions" in value: - raise ValueError("use either partitions or partition_files, not both") - if not isinstance(partition_files, dict) or not partition_files: - raise ValueError("partition_files must be a non-empty YAML object") - partitions: dict[str, list[str]] = {} - for partition, filename in partition_files.items(): - if not isinstance(partition, str) or not isinstance(filename, str): - raise ValueError("partition_files must map names to JSON files") - partition_path = Path(filename).expanduser() - if not partition_path.is_absolute(): - partition_path = base / partition_path - try: - tasks = json.loads(partition_path.read_text(encoding="utf-8")) - except json.JSONDecodeError as error: - raise ValueError( - f"partition file {partition_path} must contain valid JSON" - ) from error - if not isinstance(tasks, list) or any( - not isinstance(task, str) for task in tasks - ): + @model_validator(mode="after") + def validate_requested_models_are_allowed(self) -> HarborBuildConfig: + """Every model that will be requested must be allowed by its scope. + + Otherwise the mismatch surfaces as a gateway 403 at run time — and for a + verification target, only after search has already spent its budget. A + command backend requests no models, so there is nothing to reconcile. + """ + if self.inference_gateway is None or self.evaluation_backend != "harbor": + return self + evaluation_models = self.inference_gateway.evaluation.allowed_models + if self.model is not None and self.model not in evaluation_models: + raise ValueError( + f"model {self.model!r} is not in the inference gateway's " + f"evaluation allowed_models ({', '.join(evaluation_models)})" + ) + # Finalization runs the target agent too, so it must allow the model each + # target scores with: its own override, else the task model. + finalization_models = ( + self.inference_gateway.finalization or self.inference_gateway.evaluation + ).allowed_models + for target in self.targets: + scoring_model = target.model or self.model + if scoring_model is not None and scoring_model not in finalization_models: raise ValueError( - f"partition file {partition_path} must be a JSON array of task names" + f"target {target.partition!r} scores with model " + f"{scoring_model!r}, which is not in the inference gateway's " + f"finalization allowed_models " + f"({', '.join(finalization_models)})" ) - partitions[partition] = tasks - value["partitions"] = partitions - agent_repo = value.get("agent_repo") - if isinstance(agent_repo, str) and not Path(agent_repo).is_absolute(): - value["agent_repo"] = str((base / agent_repo).resolve()) - task_source = value.get("task_source") - if isinstance(task_source, str): - local_source = base / task_source - if not Path(task_source).is_absolute() and local_source.exists(): - value["task_source"] = str(local_source.resolve()) - task_manifest = value.get("task_manifest") - if isinstance(task_manifest, str) and not Path(task_manifest).is_absolute(): - value["task_manifest"] = str((base / task_manifest).resolve()) - command_backend = value.get("command_backend") - if isinstance(command_backend, dict): - harness_source = command_backend.get("harness_source") - if isinstance(harness_source, str) and not Path(harness_source).is_absolute(): - command_backend["harness_source"] = str((base / harness_source).resolve()) - overlays = value.get("workspace_overlays") - if isinstance(overlays, list): - for entry in overlays: - source = entry.get("source") if isinstance(entry, dict) else None - if isinstance(source, str) and not Path(source).is_absolute(): - entry["source"] = str((base / source).resolve()) - return HarborBuildConfig.model_validate(value) + return self diff --git a/vero/src/vero/harbor/build/loader.py b/vero/src/vero/harbor/build/loader.py new file mode 100644 index 00000000..20cf7e08 --- /dev/null +++ b/vero/src/vero/harbor/build/loader.py @@ -0,0 +1,155 @@ +"""Reading a build.yaml: parameter substitution, then path resolution. + +Two things happen before HarborBuildConfig ever sees the document. ``${NAME}`` +placeholders are resolved from --param and the environment, which is how one +checked-in benchmark config serves several run-time configurations. Then the +handful of fields that name host paths are resolved relative to the YAML file, +so a config can be written with paths relative to itself. +""" + +from __future__ import annotations + +import json +import os +import re +from pathlib import Path + +from vero.harbor.build.config import HarborBuildConfig + +_BUILD_PARAM = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)(?::(-|\?)([^}]*))?\}") + + +def _substitute_build_param(text: str, context: dict[str, str]) -> str: + """Resolve ``${NAME}`` / ``${NAME:-default}`` / ``${NAME:?message}`` in one scalar.""" + + def replace(match: re.Match[str]) -> str: + name, operator, argument = match.group(1), match.group(2), match.group(3) + resolved = context.get(name) + if resolved: + return resolved + if operator == "-": + return argument or "" + if operator == "?": + raise ValueError( + f"required build parameter {name!r} is unset: " + f"{argument or 'no message provided'}" + ) + raise ValueError( + f"build parameter {name!r} is unset; pass --param {name}=VALUE " + "or set the environment variable" + ) + + return _BUILD_PARAM.sub(replace, text) + + +def _resolve_build_params(value: object, context: dict[str, str]) -> object: + """Recursively resolve ``${...}`` placeholders in string scalars of a YAML value.""" + if isinstance(value, str): + return _substitute_build_param(value, context) + if isinstance(value, dict): + return { + key: _resolve_build_params(item, context) for key, item in value.items() + } + if isinstance(value, list): + return [_resolve_build_params(item, context) for item in value] + return value + + +def _read_partition_files( + partition_files: object, + base: Path, +) -> dict[str, list[str]]: + """Expand the ``partition_files`` shorthand into inline partitions. + + A partition can hold hundreds of task names, so benchmarks keep them in JSON + files beside the config rather than inline in the YAML. + """ + if not isinstance(partition_files, dict) or not partition_files: + raise ValueError("partition_files must be a non-empty YAML object") + partitions: dict[str, list[str]] = {} + for partition, filename in partition_files.items(): + if not isinstance(partition, str) or not isinstance(filename, str): + raise ValueError("partition_files must map names to JSON files") + partition_path = Path(filename).expanduser() + if not partition_path.is_absolute(): + partition_path = base / partition_path + try: + tasks = json.loads(partition_path.read_text(encoding="utf-8")) + except json.JSONDecodeError as error: + raise ValueError( + f"partition file {partition_path} must contain valid JSON" + ) from error + if not isinstance(tasks, list) or any( + not isinstance(task, str) for task in tasks + ): + raise ValueError( + f"partition file {partition_path} must be a JSON array of task names" + ) + partitions[partition] = tasks + return partitions + + +def _resolve_local_paths(value: dict, base: Path) -> None: + """Rewrite the path-valued fields in place, relative to the config's directory.""" + agent_repo = value.get("agent_repo") + if isinstance(agent_repo, str) and not Path(agent_repo).is_absolute(): + value["agent_repo"] = str((base / agent_repo).resolve()) + task_source = value.get("task_source") + if isinstance(task_source, str): + local_source = base / task_source + # Left alone when no such directory exists: it is a registry reference, + # not a path. + if not Path(task_source).is_absolute() and local_source.exists(): + value["task_source"] = str(local_source.resolve()) + task_manifest = value.get("task_manifest") + if isinstance(task_manifest, str) and not Path(task_manifest).is_absolute(): + value["task_manifest"] = str((base / task_manifest).resolve()) + command_backend = value.get("command_backend") + if isinstance(command_backend, dict): + harness_source = command_backend.get("harness_source") + if isinstance(harness_source, str) and not Path(harness_source).is_absolute(): + command_backend["harness_source"] = str((base / harness_source).resolve()) + overlays = value.get("workspace_overlays") + if isinstance(overlays, list): + for entry in overlays: + source = entry.get("source") if isinstance(entry, dict) else None + if isinstance(source, str) and not Path(source).is_absolute(): + entry["source"] = str((base / source).resolve()) + + +def load_harbor_build_config( + path: Path | str, + *, + params: dict[str, str] | None = None, +) -> HarborBuildConfig: + """Load YAML and resolve local paths relative to the configuration file. + + ``${NAME}`` placeholders in the YAML are substituted at load time from + ``params`` (explicit, e.g. ``--param NAME=VALUE``) layered over the process + environment, so run-time knobs (optimizer model, inner sandbox provider, + concurrency, ...) can be varied without rebuilding the task. Use + ``${NAME:-default}`` for a fallback and ``${NAME:?message}`` to require a + value. Fields left un-templated stay fixed (the reproducible measurement + substrate). + """ + try: + import yaml + except ImportError as error: + raise RuntimeError( + "install scale-vero[harbor] to load Harbor builds" + ) from error + + config_path = Path(path).expanduser().resolve() + value = yaml.safe_load(config_path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError("Harbor build config must be a YAML object") + context = {**os.environ, **(params or {})} + value = _resolve_build_params(value, context) + base = config_path.parent + partition_files = value.pop("partition_files", None) + if partition_files is not None: + if "partitions" in value: + raise ValueError("use either partitions or partition_files, not both") + value["partitions"] = _read_partition_files(partition_files, base) + _resolve_local_paths(value, base) + return HarborBuildConfig.model_validate(value) diff --git a/vero/src/vero/harbor/build/specs.py b/vero/src/vero/harbor/build/specs.py new file mode 100644 index 00000000..3facc8ac --- /dev/null +++ b/vero/src/vero/harbor/build/specs.py @@ -0,0 +1,313 @@ +"""Leaf models a build.yaml composes. + +Each spec here is self-contained: it validates its own fields and knows nothing +about the build that holds it. HarborBuildConfig in config.py assembles them and +adds the rules that span more than one. +""" + +from __future__ import annotations + +import math +import re +from pathlib import Path + +from pydantic import Field, JsonValue, field_validator + +from vero.evaluation import DisclosureLevel, EvaluationAccessPolicy +from vero.models import StrictModel + + +class AgentAccessSpec(StrictModel): + """The optimizer agent's access to one named evaluation partition. + + A build declares one spec per partition; to_access_policy translates it into + the runtime EvaluationAccessPolicy the sidecar enforces. This governs what + the optimizer may see and spend while searching, as opposed to how the + trusted side finally scores a candidate (see VerificationTargetSpec). + + Attributes: + partition: Partition this access applies to, e.g. "validation". + disclosure: How much of a result the agent sees — aggregate score vs. + per-case detail. + expose_case_resources: Whether each case's input files are materialized + into the agent's workspace. + min_aggregate_cases: Smallest number of cases an aggregate score may + cover, so the agent cannot request a subset small enough to reveal an + individual case's result. Only consulted when disclosure is AGGREGATE. + total_runs: Optional cap on evaluation runs against this partition. + total_cases: Optional cap on cases scored against this partition. + """ + + partition: str + disclosure: DisclosureLevel = DisclosureLevel.AGGREGATE + expose_case_resources: bool = False + min_aggregate_cases: int = Field(default=5, ge=1) + total_runs: int | None = Field(default=None, ge=0) + total_cases: int | None = Field(default=None, ge=0) + + @field_validator("partition") + @classmethod + def validate_partition(cls, value: str) -> str: + if not value.strip(): + raise ValueError("agent access partition must not be empty") + return value + + def to_access_policy(self) -> EvaluationAccessPolicy: + """The single typed translation from build spec to runtime policy.""" + return EvaluationAccessPolicy( + disclosure=self.disclosure, + expose_case_resources=self.expose_case_resources, + min_aggregate_cases=self.min_aggregate_cases, + ) + + +class VerificationTargetSpec(StrictModel): + """One trusted final scoring pass the verifier runs on a chosen candidate. + + Distinct from the evaluations the optimizer runs during search: after search + picks a best candidate on the selection partition, the trusted verifier + scores it against these targets (normally the held-out test partition) to + produce the final reward. A build declares one spec per pass. + + Attributes: + partition: Partition to score on, e.g. "test". + reward_key: Metric key in the report that is the reward. + model: Optional model override for this pass; passed through as + harbor_model_override so final scoring can differ from search. + parameters: Extra backend parameters for the pass. + failure_value: Score assigned when the pass fails. + baseline_reward: Pin the seed's reward on this target to skip scoring + the immutable baseline every run. + max_attempts: Number of scoring attempts before accepting failure. + """ + + partition: str + reward_key: str = "reward" + model: str | None = None + parameters: dict[str, JsonValue] = Field(default_factory=dict) + failure_value: float = 0.0 + baseline_reward: float | None = None + max_attempts: int = Field(default=1, ge=1) + + @field_validator("partition", "reward_key") + @classmethod + def validate_identity(cls, value: str) -> str: + if not value.strip(): + raise ValueError("verification target identity must not be empty") + return value + + @field_validator("failure_value") + @classmethod + def validate_failure_value(cls, value: float) -> float: + if not math.isfinite(value): + raise ValueError("target failure_value must be finite") + return value + + +class InferenceBudgetSpec(StrictModel): + """Routing policy and optional limits for one inference-gateway scope. + + The compiler lowers each spec into an InferenceScopeConfig, adding the + SHA-256 digest of a freshly minted per-scope token. The gateway then checks + the presented token, the requested model, and the running budget on every + proxied request; limits are held server-side in a ledger written through to + disk, so they survive a gateway restart. + + Attributes: + allowed_models: Models this scope may request. A request naming anything + else is refused with 403 model_denied. + max_requests: Cap on proxied requests; unlimited when omitted. + max_tokens: Cap on cumulative tokens; unlimited when omitted. Checked + before a request starts, so a single request can overshoot it. + max_concurrency: Requests this scope may have in flight at once. + """ + + allowed_models: list[str] + max_requests: int | None = Field(default=None, ge=1) + max_tokens: int | None = Field(default=None, ge=1) + max_concurrency: int = Field(default=8, ge=1) + + @field_validator("allowed_models") + @classmethod + def validate_models(cls, value: list[str]) -> list[str]: + if not value or any(not model.strip() for model in value): + raise ValueError("allowed_models must contain non-empty model names") + if len(value) != len(set(value)): + raise ValueError("allowed_models must be unique") + return value + + +class InferenceGatewaySpec(StrictModel): + """Credential source and independent producer/evaluator policies. + + All model access in a compiled task goes through the gateway, which holds + the one upstream credential and hands each consumer a separate token. The + optimizer never sees the evaluation or finalization tokens, so it cannot + spend from those pools. + + The per-scope allow-lists keep the target on its fixed evaluation model, but + only against a non-adversarial optimizer: the scopes share a gateway host and + are split by URL path, so an optimizer that smuggles its own token into the + candidate can reach the producer scope from the eval sandbox. See the proxy + handler in vero.gateway.inference for the full note. + + Attributes: + upstream_api_key_env: Environment variable on the build host holding the + real upstream API key. + upstream_base_url_env: Environment variable holding the upstream base + URL; None to always use default_upstream_base_url. + default_upstream_base_url: Upstream used when no base-URL variable is set. + producer: Policy for the optimizer's own model calls. + evaluation: Policy for the target agent under evaluation. + finalization: Reserved policy for trusted finalization (admin re-score + and verification targets). Defaults to a copy of evaluation, giving + the mandatory re-score a pool that search evaluations cannot starve. + log_requests: Capture every proxied or denied request as JSONL on the + gateway state volume. Never agent-visible; the trusted sidecar + mirrors it to Weights & Biases and the session archive. + request_log_body_bytes: Head+tail budget per logged body, in bytes. + request_log_attribution: Experimental. Stamp log records with + provider-agnostic conversation threads for post-hoc per-trial + accounting. + """ + + upstream_api_key_env: str = "OPENAI_API_KEY" + upstream_base_url_env: str | None = "OPENAI_BASE_URL" + default_upstream_base_url: str = "https://api.openai.com/v1" + producer: InferenceBudgetSpec + evaluation: InferenceBudgetSpec + finalization: InferenceBudgetSpec | None = None + log_requests: bool = True + request_log_body_bytes: int = Field(default=16384, ge=0) + request_log_attribution: bool = False + + @field_validator("upstream_api_key_env", "upstream_base_url_env") + @classmethod + def validate_environment_name(cls, value: str | None) -> str | None: + if value is not None and re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", value) is None: + raise ValueError("gateway environment names must be valid identifiers") + return value + + @field_validator("default_upstream_base_url") + @classmethod + def validate_upstream_url(cls, value: str) -> str: + if not value.startswith(("http://", "https://")): + raise ValueError("default_upstream_base_url must be HTTP(S)") + return value.rstrip("/") + + +class WorkspaceOverlaySpec(StrictModel): + """A host file or directory to copy into the compiled task's agent workspace. + + General-purpose filesystem injection: bake anything (agent definitions, + skills, config, data) into the optimizer's workspace at build time. + + Attributes: + source: Host path, resolved relative to the build YAML. Must exist. A + directory contributes its contents; a file lands as dest/. + dest: Where the contents land, relative to the workspace root. Must be a + safe relative path; "." is the root itself. + """ + + source: str + dest: str = "." + + @field_validator("source") + @classmethod + def validate_source(cls, value: str) -> str: + if not value.strip() or not Path(value).exists(): + raise ValueError("overlay source must be an existing file or directory") + return value + + @field_validator("dest") + @classmethod + def validate_dest(cls, value: str) -> str: + candidate = value.strip() + path = Path(candidate) + if candidate == ".": + return candidate + if ( + not candidate + or path.is_absolute() + or ".." in path.parts + or re.fullmatch(r"[A-Za-z0-9_./-]+", candidate) is None + ): + raise ValueError( + "overlay dest must be a safe relative path within the workspace" + ) + return candidate + + +class WandbSpec(StrictModel): + """Weights & Biases reporting settings, emitted into the sidecar's serve.json. + + Reporting runs on the trusted side only. The field names match the sidecar's + SidecarWandbConfig, which consumes them verbatim. + + Attributes: + project: Weights & Biases project to report into. + entity: Owning user or team; the account default when omitted. + name: Display name for the run. + group: Group to file the run under. + tags: Tags applied to the run. + mode: Client mode, e.g. "online" or "offline". + notes: Free-text note attached to the run. + run_id: Resume into this existing run instead of creating one. + log_traces: Upload each evaluation's trace artifacts. Off by default + because they are large. + """ + + project: str + entity: str | None = None + name: str | None = None + group: str | None = None + tags: list[str] = Field(default_factory=list) + mode: str | None = None + notes: str | None = None + run_id: str | None = None + log_traces: bool = False + + +class CommandBackendSpec(StrictModel): + """Inner evaluation that scores a candidate by running a program. + + Use this when the outer loop is still a Harbor coding agent but the target is + not an agent — a solver, an index build, a data pipeline — so there is nothing + to drive with a nested `harbor run`. The compiler copies harness_source into + the sidecar and points the backend's harness_root at it. + + Attributes: + harness_source: Host directory holding the scoring program, resolved + relative to the build YAML. Must exist. + command: Argument vector to run. Supports the placeholders {harness}, + {workspace}, {request}, {report}, and {artifacts}. + working_directory: Directory to run the command from. + environment: Environment variables set for the command. + passthrough_environment: Names forwarded from the sidecar's environment. + CommandBackend runs the harness with PATH=os.defpath, so a harness + that needs the build image's interpreter must list PATH here. + staged_inputs: Extra files staged into the command's workspace. + agent_context_inputs: Per-case files published into the agent context. + """ + + harness_source: str + command: list[str] + working_directory: str = "." + environment: dict[str, str] = Field(default_factory=dict) + passthrough_environment: list[str] = Field(default_factory=list) + staged_inputs: dict[str, str] = Field(default_factory=dict) + agent_context_inputs: dict[str, list[str]] = Field(default_factory=dict) + + @field_validator("harness_source") + @classmethod + def validate_harness_source(cls, value: str) -> str: + if not value.strip() or not Path(value).is_dir(): + raise ValueError("harness_source must be an existing directory") + return value + + @field_validator("command") + @classmethod + def validate_command(cls, value: list[str]) -> list[str]: + if not value or any(not part.strip() for part in value): + raise ValueError("command must contain non-empty arguments") + return value diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index b075a463..b74e7e39 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -24,12 +24,22 @@ load_harbor_build_config, ) from vero.harbor.build.compiler import GATEWAY_ROUTED_CREDENTIALS +from vero.harbor.build.config import ( + _HARBOR_ONLY_FIELDS, + _AgentWorkspaceFields, + _EvaluationLimitFields, + _HarborEvaluationFields, + _SearchAndSelectionFields, + _TaskEnvironmentFields, + _TaskIdentityFields, +) from vero.harbor.deployment import FACTORY_PATH from vero.layout import LAYOUT from vero.sidecar.serve import load_factory _OMIT = object() + def test_task_layout_values_are_pinned(): """The one place the layout's literal values are written down twice. @@ -83,7 +93,7 @@ def _git(path: Path, *arguments: str) -> str: def test_build_param_substitution_semantics(): - from vero.harbor.build.config import _substitute_build_param as sub + from vero.harbor.build.loader import _substitute_build_param as sub context = {"A": "x"} assert sub("${A}", context) == "x" @@ -153,7 +163,9 @@ def _config(tmp_path: Path, **updates) -> HarborBuildConfig: def test_compiler_bakes_workspace_overlay_into_agent_environment(tmp_path): bundle = tmp_path / "bundle" (bundle / ".claude" / "agents").mkdir(parents=True) - (bundle / ".claude" / "agents" / "insights.md").write_text("# a\n", encoding="utf-8") + (bundle / ".claude" / "agents" / "insights.md").write_text( + "# a\n", encoding="utf-8" + ) (bundle / "skills" / "insights").mkdir(parents=True) (bundle / "skills" / "insights" / "SKILL.md").write_text("# s\n", encoding="utf-8") @@ -280,9 +292,10 @@ def test_compiler_plumbs_task_services_use_upstream(tmp_path, monkeypatch): sidecar_env = compose["services"]["eval-sidecar"]["environment"] assert "VERO_INFERENCE_UPSTREAM_API_KEY" in sidecar_env assert "VERO_INFERENCE_UPSTREAM_BASE_URL" in sidecar_env - assert compose["services"]["main"]["environment"][ - "VERO_INFERENCE_UPSTREAM_API_KEY" - ] == "" + assert ( + compose["services"]["main"]["environment"]["VERO_INFERENCE_UPSTREAM_API_KEY"] + == "" + ) def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path): @@ -307,7 +320,9 @@ def test_compiler_reserves_finalization_scope_defaulting_to_evaluation(tmp_path) assert scopes["finalization"]["allowed_models"] == ["gpt-target"] assert scopes["finalization"]["max_tokens"] == 100000000 # its token is distinct from the evaluation token (optimizer can't drain it) - assert scopes["finalization"]["token_sha256"] != scopes["evaluation"]["token_sha256"] + assert ( + scopes["finalization"]["token_sha256"] != scopes["evaluation"]["token_sha256"] + ) # and the sidecar backend carries a finalization token distinct from the eval one serve = json.loads( (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") @@ -413,9 +428,10 @@ def gateway(**updates) -> InferenceGatewaySpec: return InferenceGatewaySpec(**values) # The matching case builds, and finalization inherits the evaluation policy. - assert _config( - tmp_path / "ok", model="gpt-target", inference_gateway=gateway() - ).model == "gpt-target" + assert ( + _config(tmp_path / "ok", model="gpt-target", inference_gateway=gateway()).model + == "gpt-target" + ) with pytest.raises(ValidationError, match="evaluation allowed_models"): _config(tmp_path / "search", model="gpt-typo", inference_gateway=gateway()) @@ -519,9 +535,9 @@ def test_deployment_treats_a_backend_without_a_type_as_harbor(tmp_path): "selection": {"mode": "submit"}, "submit_enabled": True, # The tag the older compiler never wrote. - "backends": {"harbor-validation": backend.model_dump( - mode="json", exclude={"type"} - )}, + "backends": { + "harbor-validation": backend.model_dump(mode="json", exclude={"type"}) + }, } parsed = HarborDeploymentConfig.model_validate(legacy) assert parsed.backends["harbor-validation"].type == "harbor" @@ -559,6 +575,62 @@ def test_build_config_keeps_the_two_backend_kinds_apart(tmp_path): ) +def test_every_harbor_only_field_is_rejected_by_a_command_build(tmp_path): + """The rejection list is derived, so this sweep needs no maintenance. + + The previous hand-written frozenset had already drifted: reward_key is + Harbor-only in fact -- a command harness reports its own reward -- but was + missing from the list, so setting it on a command build passed validation and + did nothing. Looping over the group means a field added to + _HarborEvaluationFields is covered the day it lands. + """ + assert _HARBOR_ONLY_FIELDS == frozenset(_HarborEvaluationFields.model_fields) + + for index, name in enumerate(sorted(_HARBOR_ONLY_FIELDS)): + # Its own default is enough: the check reads model_fields_set, so setting + # a field at all is the mistake, whatever the value. + default = HarborBuildConfig.model_fields[name].get_default( + call_default_factory=True + ) + # Listed last so it overrides the _OMIT when it is one of these two. + overrides = { + "evaluation_backend": "command", + "command_backend": _command_backend(tmp_path / f"h{index}"), + "agent_import_path": _OMIT, + "task_source": _OMIT, + name: default, + } + with pytest.raises(ValidationError, match="only apply to a harbor") as caught: + _config(tmp_path / f"harbor-only-{index}", **overrides) + assert name in str(caught.value) + + +def test_field_groups_are_inherited_not_nested(): + """Grouping must not change the wire format. + + The groups exist so the schema can be read one concern at a time, but a + build.yaml is flat and the checked-in benchmark configs depend on that. + Turning a group into a sub-object would break every one of them silently, so + assert the keys stay top-level -- and that every field belongs to a group, + which is what keeps each one documented somewhere. + """ + groups = ( + _TaskIdentityFields, + _HarborEvaluationFields, + _SearchAndSelectionFields, + _EvaluationLimitFields, + _TaskEnvironmentFields, + _AgentWorkspaceFields, + ) + top_level = set(HarborBuildConfig.model_fields) + for group in groups: + assert set(group.model_fields) <= top_level + + grouped = {name for group in groups for name in group.model_fields} + # Only the backend choice itself is declared on HarborBuildConfig. + assert grouped | {"evaluation_backend", "command_backend"} == top_level + + def test_compiled_task_factory_path_resolves(tmp_path): """The factory named in the compiled compose file must actually import. @@ -1106,9 +1178,7 @@ def fake_run(command, *args, **kwargs): monkeypatch.setattr( harbor_build, "load_harbor_build_config", lambda *a, **k: config ) - monkeypatch.setattr( - harbor_build, "compile_harbor_task", lambda cfg, out: out - ) + monkeypatch.setattr(harbor_build, "compile_harbor_task", lambda cfg, out: out) result = CliRunner().invoke( harbor_cli.harbor, @@ -1132,6 +1202,4 @@ def fake_run(command, *args, **kwargs): assert "--ae FOO=bar" in shlex.join(command) # Deterministic (sorted-by-key) ordering: FOO before UV_TOOL_BIN_DIR. joined = shlex.join(command) - assert joined.index("--ae FOO=bar") < joined.index( - "--ae UV_TOOL_BIN_DIR=" - ) + assert joined.index("--ae FOO=bar") < joined.index("--ae UV_TOOL_BIN_DIR=") From 70f8273c2bcc9c3c0c80c8872914954f9714143b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sat, 25 Jul 2026 23:54:01 -0700 Subject: [PATCH 49/72] Point the architecture doc at the modules that exist The package restructure moved the sidecar stack, the gateway, and the evaluation store without updating this doc, leaving fourteen paths that resolve to nothing: harbor/sidecar.py, harbor/verifier.py, harbor/session.py, harbor/app.py, harbor/inference.py, and evaluation/budget.py. Since the doc's whole purpose is a suggested reading order, a dead path is worse here than in a code comment. Also names the four modules the build pipeline now passes through. Co-Authored-By: Claude Opus 5 (1M context) --- vero/docs/harbor-architecture.md | 38 +++++++++++++++++--------------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/vero/docs/harbor-architecture.md b/vero/docs/harbor-architecture.md index 6cb8913f..6f2d569f 100644 --- a/vero/docs/harbor-architecture.md +++ b/vero/docs/harbor-architecture.md @@ -78,7 +78,7 @@ Named volumes carry state between services: `agent_repo`, `agent_context`, **backend** and records an **`EvaluationRecord`** (`evaluation/models.py`): the request (candidate, evaluation_set, limits) + a report (per-case results, metrics, diagnostics, artifacts) + an objective value + a **principal**. It - enforces the **`BudgetLedger`** (`evaluation/budget.py`) and fires **listeners** + enforces the **`BudgetLedger`** (`evaluation/store/budget.py`) and fires **listeners** (where the W&B sink hooks in). - **`HarborBackend`** (`harbor/backend.py`) is how a candidate is actually scored: it runs a *nested* `harbor run` over the tasks (`uv run` the @@ -93,7 +93,7 @@ Named volumes carry state between services: `agent_repo`, `agent_context`, ## Disclosure & the anti-cheating contract -Each partition has a **disclosure level** (`harbor/sidecar.py`): +Each partition has a **disclosure level** (`sidecar/sidecar.py`): - **development → FULL**: per-case results shown (the disclosed set to iterate on). - **validation → AGGREGATE**: only an *average over ≥ k cases* is returned (the @@ -104,7 +104,7 @@ Each partition has a **disclosure level** (`harbor/sidecar.py`): ## Selection / finalization (what ships) -`CanonicalVerifier.finalize()` (`harbor/verifier.py`) decides what to ship and +`CanonicalVerifier.finalize()` (`sidecar/verifier.py`) decides what to ship and computes the reward: - **Chain**: the agent's explicit `submission` → else `auto_best` (rank candidates by *coverage-qualified* validation evals, admin-re-score the top-K @@ -120,25 +120,27 @@ computes the reward: ## The security model (five mechanisms) 1. **Credential isolation** — the gateway's scoped tokens; the raw upstream key - lives only in the sidecar. (`harbor/inference.py`, compose template.) + lives only in the sidecar. (`gateway/inference.py`, compose template.) 2. **Harness isolation** — the inner `harbor run` (arbitrary candidate code) runs as unprivileged `harness`; the session dir is `0700 root`, case lists `700`, `serve.json` `600` — so the candidate cannot read the answer key it is graded on. (`harbor/backend.py` `run_as`, `Dockerfile.sidecar.j2`, `harbor/deployment.py`.) 3. **k-anonymity floor** — aggregate subset evals must cover ≥ 5 cases. - (`harbor/sidecar.py` `_enforce_aggregate_floor`.) + (`sidecar/sidecar.py` `_enforce_aggregate_floor`.) 4. **Budget metering** — per-scope gateway budgets + a case/run ledger, with a *reserved finalization scope* so the mandatory admin re-score can't be starved - by the agent's search. (`evaluation/budget.py`, gateway config.) + by the agent's search. (`evaluation/store/budget.py`, gateway config.) 5. **Fail-safe finalization** — never ship an unverified candidate on an infra - blip; a session export never fails wholesale. (`harbor/verifier.py`, - `harbor/session.py`.) + blip; a session export never fails wholesale. (`sidecar/verifier.py`, + `sidecar/session.py`.) ## The build pipeline -`build.yaml` (`HarborBuildConfig`, `harbor/build/config.py`) → -`compile_harbor_task` (`harbor/build/compiler.py`) renders the whole deployable +`build.yaml` → `load_harbor_build_config` (`harbor/build/loader.py`) → +`HarborBuildConfig` (`harbor/build/config.py`, composed from the leaf models in +`harbor/build/specs.py`) → `compile_harbor_task` (`harbor/build/compiler.py`) +renders the whole deployable task (Dockerfiles + compose + `serve.json` + cases + instructions). `serve.json` deserializes into `HarborDeploymentConfig` (`harbor/deployment.py`), which `build_harbor_components` turns into the live sidecar + verifier. So a benchmark @@ -160,9 +162,9 @@ Read outside-in — from *what a benchmark declares* down to *how it runs and st honest*. Paths are under `vero/src/vero/` unless noted. 1. **What a benchmark is** — `harness-engineering-bench/gaia/baseline/build.yaml` - and `harbor/build/config.py` (`HarborBuildConfig`, `AgentAccessSpec`, - `VerificationTargetSpec`). This is the declarative surface; everything else - serves it. + and `harbor/build/specs.py` (`AgentAccessSpec`, `VerificationTargetSpec`) plus + `harbor/build/config.py` (`HarborBuildConfig` and the field groups it is + composed from). This is the declarative surface; everything else serves it. 2. **Compile → deployable task** — `harbor/build/compiler.py` (`compile_harbor_task`) and the templates in `harbor/build/templates/` (`docker-compose.yaml.j2`, `Dockerfile.sidecar.j2`, `instruction.md.j2`, @@ -172,20 +174,20 @@ honest*. Paths are under `vero/src/vero/` unless noted. a live engine + sidecar + verifier. 4. **The evaluation core** — `evaluation/models.py` (`EvaluationRecord`, `EvaluationPrincipal`, `DisclosureLevel`, `EvaluationSet`), then - `evaluation/engine.py` and `evaluation/budget.py`. This is the vocabulary the + `evaluation/engine.py` and `evaluation/store/budget.py`. This is the vocabulary the rest of the system speaks. 5. **How a candidate is scored** — `harbor/backend.py` (`HarborBackend`): the nested `harbor run`, attempt aggregation, and the infra-vs-candidate taxonomy. The most intricate file; skim `_case_result`, `_command`, `_environment`. -6. **The sidecar** — `harbor/sidecar.py` (access policies, disclosure floor, - tracked eval jobs, submission) and `harbor/app.py` (the HTTP surface: agent +6. **The sidecar** — `sidecar/sidecar.py` (access policies, disclosure floor, + tracked eval jobs, submission) and `sidecar/app.py` (the HTTP surface: agent `/eval` vs admin `/finalize` `/score/baseline` `/session/export`). -7. **Selection & finalization** — `harbor/verifier.py` (`CanonicalVerifier`): the +7. **Selection & finalization** — `sidecar/verifier.py` (`CanonicalVerifier`): the submit → auto_best → pick-last chain, coverage-qualified selection, the baseline floor + pinning, `measure_baseline`. This decides the shipped number. 8. **Security specifics** — the five mechanisms: `run_as`/`harness_user` in `harbor/backend.py` + `Dockerfile.sidecar.j2`; the gateway in - `harbor/inference.py`; the session archive in `harbor/session.py`. + `gateway/inference.py`; the session archive in `sidecar/session.py`. 9. **Observability** — `runtime/wandb.py` (`SidecarWandbSink`). 10. **The CLI glue** — `harbor/cli.py` (`vero harbor run`, `finalize`, `export-session`, `score-baseline`). From a4802131045c0e430cdb2dadeaa1b50c416a11a2 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 09:07:07 -0700 Subject: [PATCH 50/72] Give the ledger's three durable writes one drain loop Each of reserve's charge write, reserve's rollback write, and refund's write hand-rolled the same shield-and-drain loop, and each got the same detail wrong at least once: a write failure replacing the CancelledError of the run being torn down. Two were patched individually in review; the third, reserve's charge write, still called write.result() unguarded, so an OSError there discarded the cancellation and the caller saw the wrong exception. _drain_write now owns the loop and returns both outcomes rather than raising one, which is what makes the invariant checkable: no call site can propagate a write error in place of a cancellation, because it has to decide about both. Draining to completion is preserved -- an abandoned write could overwrite a later one once the ledger lock is released. Behaviour is unchanged except in the case that was broken. Reserve still rolls its charge back on cancellation, and now skips the rollback when the charge never landed. Refund still commits, since returning budget is safe even for a run that is going away; that asymmetry is now stated rather than implied. The new test fails on the old code with OSError: disk gone. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evaluation/store/budget.py | 120 ++++++++++------------ vero/tests/test_v05_evaluation_runtime.py | 50 ++++++++- 2 files changed, 103 insertions(+), 67 deletions(-) diff --git a/vero/src/vero/evaluation/store/budget.py b/vero/src/vero/evaluation/store/budget.py index a85e11b5..c6bd4595 100644 --- a/vero/src/vero/evaluation/store/budget.py +++ b/vero/src/vero/evaluation/store/budget.py @@ -17,6 +17,33 @@ from vero.evaluation.store.persistence import _atomic_write_json +async def _drain_write( + task: asyncio.Task[None], +) -> tuple[BaseException | None, asyncio.CancelledError | None]: + """Run a durable write to completion and report both possible outcomes. + + Returns ``(write_error, cancellation)`` and never raises. Draining to + completion matters because a write left running in the background could + overwrite a later one once the ledger lock is released; returning both + outcomes rather than raising one matters because a cancellation must never + be swallowed by a write failure. Every call site below got that second part + wrong at least once while each was hand-rolling this loop, which is why + there is now one copy. + """ + cancellation: asyncio.CancelledError | None = None + while not task.done(): + try: + await asyncio.shield(task) + except asyncio.CancelledError as error: + cancellation = error + except Exception: + # The write failed, so the task is done: stop draining and let the + # caller decide what the failure means. + break + write_error = None if task.cancelled() else task.exception() + return write_error, cancellation + + class BudgetLedger: """Atomically meter completed evaluations against configured budgets.""" @@ -59,6 +86,15 @@ def get( (backend_id, evaluation_set.budget_key(backend_id), principal) ) + def _write_task( + self, + state: dict[tuple[str, str, EvaluationPrincipal], EvaluationBudget], + ) -> asyncio.Task[None]: + """Start a durable write of one ledger state. Drain it with _drain_write.""" + return asyncio.create_task( + asyncio.to_thread(_atomic_write_json, self.path, self._serialize(state)) + ) + async def reserve( self, backend_id: str, @@ -93,58 +129,26 @@ async def reserve( snapshot = dict(self._budgets) snapshot[key] = updated if self.path is not None: - write = asyncio.create_task( - asyncio.to_thread( - _atomic_write_json, - self.path, - self._serialize(snapshot), - ) + write_error, cancellation = await _drain_write( + self._write_task(snapshot) ) - cancellation: asyncio.CancelledError | None = None - while not write.done(): - try: - await asyncio.shield(write) - except asyncio.CancelledError as error: - cancellation = error - write.result() if cancellation is not None: # The run requesting this reservation is being cancelled and # will never use it. Roll the durable charge back so the # reservation is atomic — committed in full or not at all — # and a cancelled reservation never leaks budget. (In-memory # state was never updated, so we rewrite it as the truth.) - # Drain the rollback to completion even if a further - # cancellation arrives, so it cannot linger in the background - # and overwrite a later reservation once the lock is released. - rollback = asyncio.create_task( - asyncio.to_thread( - _atomic_write_json, - self.path, - self._serialize(self._budgets), + # Nothing to roll back if the charge never landed. + if write_error is None: + write_error, _ = await _drain_write( + self._write_task(self._budgets) ) - ) - while not rollback.done(): - try: - await asyncio.shield(rollback) - except asyncio.CancelledError: - pass - except Exception: - # The rollback write failed; the task is now done, - # so exit the drain and surface it below. - break - # The originating cancellation must propagate even if the - # rollback write itself failed — swallowing CancelledError - # for an OSError would break structured cancellation and - # hide that the run was cancelled. Chain the write error so - # the durable-state inconsistency stays diagnosable. - write_error = ( - None if rollback.cancelled() else rollback.exception() - ) - if write_error is not None: - raise cancellation from write_error - raise cancellation - self._budgets = snapshot - return updated + # Chain whichever write failed so the durable-state + # inconsistency stays diagnosable, but always propagate the + # cancellation itself. + raise cancellation from write_error + if write_error is not None: + raise write_error self._budgets = snapshot return updated @@ -181,30 +185,18 @@ async def refund( snapshot = dict(self._budgets) snapshot[key] = updated if self.path is not None: - write = asyncio.create_task( - asyncio.to_thread( - _atomic_write_json, - self.path, - self._serialize(snapshot), - ) + write_error, cancellation = await _drain_write( + self._write_task(snapshot) ) - cancellation: asyncio.CancelledError | None = None - while not write.done(): - try: - await asyncio.shield(write) - except asyncio.CancelledError as error: - cancellation = error - except Exception: - # The write failed; the task is done, exit the drain. - break - try: - write.result() - except Exception as error: + if write_error is not None: # A failed durable refund must not swallow the cancellation # of the run being torn down; chain the write error. if cancellation is not None: - raise cancellation from error - raise + raise cancellation from write_error + raise write_error + # Unlike reserve, a refund that landed is never rolled back: it + # only ever returns budget, so committing it is safe even for a + # run that is going away. self._budgets = snapshot if cancellation is not None: raise cancellation diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index 6e6c69d4..b62d7196 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -668,6 +668,50 @@ def failing_rollback_write(write_path, value): assert isinstance(excinfo.value.__cause__, OSError) +@pytest.mark.asyncio +async def test_reserve_charge_write_failure_preserves_cancellation( + tmp_path: Path, + monkeypatch, +): + # The third of the three durable writes in this module, and the one that + # was still hand-rolling the drain loop: if the charge write itself fails + # while the run is being cancelled, the caller must still see the + # cancellation. Returning OSError instead breaks structured cancellation + # and hides that the run went away. + evaluation_set = EvaluationSet(name="performance") + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="command", + evaluation_set_key=evaluation_set.budget_key("command"), + total_runs=2, + ) + ], + path=tmp_path / "budget.json", + ) + started = threading.Event() + release = threading.Event() + + def blocking_failing_write(write_path, value): + started.set() + assert release.wait(timeout=5) + raise OSError("disk gone") + + monkeypatch.setattr(budget_module, "_atomic_write_json", blocking_failing_write) + reservation = asyncio.create_task( + ledger.reserve("command", evaluation_set, EvaluationCost(runs=1)) + ) + assert await asyncio.to_thread(started.wait, 5) + reservation.cancel() + release.set() + + with pytest.raises(asyncio.CancelledError) as excinfo: + await reservation + assert isinstance(excinfo.value.__cause__, OSError) + # The charge never landed, so the budget is untouched on both sides. + assert ledger.get("command", evaluation_set).remaining_runs == 2 + + @pytest.mark.asyncio async def test_refund_write_failure_preserves_cancellation(tmp_path: Path, monkeypatch): # A cancellation racing the durable refund write must win over the write's @@ -1029,6 +1073,8 @@ async def test_engine_selects_backend_persists_and_projects(tmp_path: Path): assert summary.metrics == {"value": 2.0} assert len(database.evaluations) == 1 assert database_path.exists() + + @pytest.mark.asyncio async def test_engine_enforces_aggregate_k_anonymity_floor(tmp_path: Path): """The floor is a core-engine property, not a sidecar courtesy.""" @@ -1055,9 +1101,7 @@ def engine_with(cost: EvaluationCost) -> EvaluationEngine: def request_with(selection) -> EvaluationRequest: return request().model_copy( update={ - "evaluation_set": canonical.model_copy( - update={"selection": selection} - ) + "evaluation_set": canonical.model_copy(update={"selection": selection}) } ) From c038d117d2e042cee6ac54bfbadc3824b99539c5 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 09:51:04 -0700 Subject: [PATCH 51/72] Shield the success path's record write like the two failure paths _execute_record shields _record and refund in both exception handlers but left the success-path _record bare, so a cancellation arriving after the evaluation finished could abandon it part-way -- the budget charged for a run the engine never finished publishing. The mechanism is narrower than "the database write is lost": that write is an asyncio.to_thread, and a cancelled await does not stop the thread, so the file lands either way. What is actually skipped is everything after it -- the listener loop, where the trusted side mirrors an evaluation to W&B and the session archive -- plus the whole of _record when the cancellation arrives before its lock is acquired. Shield does not wait for the shielded coroutine, only protect it: the caller still unwinds at once while _record completes in the background. That is already the contract of the four shielded calls beside it, so this makes the path consistent rather than introducing a new guarantee. The test cancels during a listener, the one genuinely interruptible step, and times out on the unshielded code. Reported by greptile on #45. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evaluation/engine.py | 8 +++- vero/tests/test_v05_evaluation_runtime.py | 48 +++++++++++++++++++++++ 2 files changed, 55 insertions(+), 1 deletion(-) diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index 730c25bf..d23051e1 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -390,7 +390,13 @@ async def _execute_record( ) ) raise - await self._record(record) + # Shielded for the same reason as the two handlers above, and one more: + # the budget was charged for an evaluation that has now actually run, so + # a cancellation landing inside _record would leave the ledger counting + # a run the database never indexed. _record is not atomic either -- it + # mutates the in-memory database before persisting it -- so an + # unshielded cancellation can tear those two apart as well. + await asyncio.shield(self._record(record)) terminating = next( ( diagnostic diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index b62d7196..5aca86eb 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -944,6 +944,54 @@ async def evaluate(self, *, context, request): assert cancelled.report.status == EvaluationStatus.CANCELLED +@pytest.mark.asyncio +async def test_cancellation_during_the_success_record_still_completes_it( + tmp_path: Path, +): + """A charged run must finish being recorded even if the caller goes away. + + The success path was the one _record call not shielded, so a cancellation + arriving while it ran abandoned it part-way: the budget stayed charged for + an evaluation the engine never finished publishing. + + The cancellable window is the listener loop, not the database write -- + asyncio.to_thread cannot be interrupted, so that part completes either way. + Listeners are where the trusted side mirrors an evaluation to W&B and the + session archive, so silently skipping them loses the run from both. + """ + workspace = StubWorkspace(tmp_path / "repo") + database = EvaluationDatabase(id="session") + engine = EvaluationEngine( + evaluator=evaluator(tmp_path, workspace), + backends=BackendRegistry({"default": StubBackend()}), + database=database, + authorization_resolver=allow_all_evaluations, + ) + entered = asyncio.Event() + release = asyncio.Event() + finished = asyncio.Event() + + async def slow_listener(record: EvaluationRecord) -> None: + entered.set() + await release.wait() + finished.set() + + engine.listeners.append(slow_listener) + evaluation = asyncio.create_task( + engine.evaluate_record(backend_id="default", request=request()) + ) + await asyncio.wait_for(entered.wait(), timeout=5) + + evaluation.cancel() + with pytest.raises(asyncio.CancelledError): + await evaluation + # shield lets the caller unwind at once while _record finishes in the + # background, so wait for the listener rather than assuming it ran. + release.set() + await asyncio.wait_for(finished.wait(), timeout=5) + assert len(database.evaluations) == 1 + + @pytest.mark.asyncio async def test_plan_authorization_separates_agent_and_system_selection_rights( tmp_path: Path, From e4ba52d13e332a3b9ceaf19b2a91da06c8167b15 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 17:29:34 -0700 Subject: [PATCH 52/72] Let a verification target override attempts-per-case A held-out target can now set n_attempts/aggregate_attempts to score each case several times and combine them (e.g. 3x mean to average a noisy final eval), while search and selection keep the global attempts. Backends are per-partition, so the override is injected into the target partition's backend at compile time; a config validator rejects overriding an agent-evaluable partition (shared with search) or a command backend. Co-Authored-By: Claude Opus 4.8 --- vero/src/vero/harbor/build/compiler.py | 20 +++++++++- vero/src/vero/harbor/build/config.py | 30 +++++++++++++++ vero/src/vero/harbor/build/specs.py | 10 +++++ vero/tests/test_v05_harbor_build.py | 51 ++++++++++++++++++++++++++ 4 files changed, 109 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index d1d43285..ad992633 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -268,6 +268,20 @@ def _deployment_config( }, } else: + # A target may override attempts-per-case on its own (held-out) partition + # without touching search/selection. Backends are per-partition, so the + # override is injected into that partition's backend at compile time; a + # config validator forbids overriding an agent-evaluable partition. + target_n_attempts = { + target.partition: target.n_attempts + for target in config.targets + if target.n_attempts is not None + } + target_aggregate = { + target.partition: target.aggregate_attempts + for target in config.targets + if target.aggregate_attempts is not None + } for partition in config.partitions: backends[_backend_id(partition)] = { "type": "harbor", @@ -283,7 +297,7 @@ def _deployment_config( "case_timeout_seconds": config.case_timeout_seconds, "task_agent_timeout_seconds": config.task_agent_timeout_seconds, "default_index": config.default_index, - "n_attempts": config.n_attempts, + "n_attempts": target_n_attempts.get(partition, config.n_attempts), "max_retries": config.max_retries, "retry_wait_multiplier": config.retry_wait_multiplier, "retry_min_wait_seconds": config.retry_min_wait_seconds, @@ -293,7 +307,9 @@ def _deployment_config( config.infrastructure_retry_delay_seconds ), "reward_key": config.reward_key, - "aggregate_attempts": config.aggregate_attempts, + "aggregate_attempts": target_aggregate.get( + partition, config.aggregate_attempts + ), "feedback_transcripts": config.feedback_transcripts, "feedback_max_bytes": config.feedback_max_bytes, "expose_attempt_detail": config.expose_attempt_detail, diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 4bc1df43..245e9e2c 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -556,6 +556,36 @@ def validate_partition_references(self) -> HarborBuildConfig: raise ValueError("target reward keys must be unique") return self + @model_validator(mode="after") + def validate_target_attempt_overrides(self) -> HarborBuildConfig: + """A per-target n_attempts/aggregate_attempts override is only honorable + on a held-out target: backends are per-partition, so overriding an + agent-evaluable partition (search or selection) would silently change + those runs too, and a command backend has no attempts to override.""" + overriding = [ + target + for target in self.targets + if target.n_attempts is not None or target.aggregate_attempts is not None + ] + if not overriding: + return self + if self.evaluation_backend == "command": + raise ValueError( + "per-target n_attempts/aggregate_attempts only apply to a harbor " + "evaluation_backend" + ) + shared = {access.partition for access in self.agent_access} | { + self.selection_partition + } + bad = sorted({t.partition for t in overriding} & shared) + if bad: + raise ValueError( + "per-target n_attempts/aggregate_attempts cannot override an " + "agent-evaluable partition (its backend is shared with search/" + f"selection): {', '.join(bad)}" + ) + return self + @model_validator(mode="after") def validate_task_manifest_agreement(self) -> HarborBuildConfig: """The manifest and the partitions must describe the same task set.""" diff --git a/vero/src/vero/harbor/build/specs.py b/vero/src/vero/harbor/build/specs.py index 3facc8ac..1d32e420 100644 --- a/vero/src/vero/harbor/build/specs.py +++ b/vero/src/vero/harbor/build/specs.py @@ -10,6 +10,7 @@ import math import re from pathlib import Path +from typing import Literal from pydantic import Field, JsonValue, field_validator @@ -79,6 +80,13 @@ class VerificationTargetSpec(StrictModel): baseline_reward: Pin the seed's reward on this target to skip scoring the immutable baseline every run. max_attempts: Number of scoring attempts before accepting failure. + n_attempts: Optional per-target override of the global attempts-per-case; + None inherits the global. Set >1 to score each held-out case several + times and combine them (with aggregate_attempts), e.g. 3 to average a + noisy final eval over 3 scorings. Only meaningful on a harbor target + whose partition is not agent-evaluable (see the config validator). + aggregate_attempts: Optional per-target override of how repeat attempts + combine ("best"/"mean"); None inherits the global ("mean"). """ partition: str @@ -88,6 +96,8 @@ class VerificationTargetSpec(StrictModel): failure_value: float = 0.0 baseline_reward: float | None = None max_attempts: int = Field(default=1, ge=1) + n_attempts: int | None = Field(default=None, ge=1) + aggregate_attempts: Literal["best", "mean"] | None = None @field_validator("partition", "reward_key") @classmethod diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index b74e7e39..2fd75416 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -1203,3 +1203,54 @@ def fake_run(command, *args, **kwargs): # Deterministic (sorted-by-key) ordering: FOO before UV_TOOL_BIN_DIR. joined = shlex.join(command) assert joined.index("--ae FOO=bar") < joined.index("--ae UV_TOOL_BIN_DIR=") + + +def _serve_backends(tmp_path: Path, **updates) -> dict: + config = _config(tmp_path, **updates) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + serve = json.loads( + (output / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + return serve["backends"] + + +def test_verification_target_spec_defaults_n_attempts_none(): + target = VerificationTargetSpec(partition="test") + assert target.n_attempts is None + assert target.aggregate_attempts is None + + +def test_compiler_applies_per_target_n_attempts_override(tmp_path): + # Only the held-out test backend gets 3x/mean; validation (search/selection) + # keeps the global n_attempts=1. + backends = _serve_backends( + tmp_path, + n_attempts=1, + targets=[ + VerificationTargetSpec( + partition="test", n_attempts=3, aggregate_attempts="mean" + ) + ], + ) + assert backends["harbor-test"]["n_attempts"] == 3 + assert backends["harbor-test"]["aggregate_attempts"] == "mean" + assert backends["harbor-validation"]["n_attempts"] == 1 + + +def test_compiler_defaults_target_n_attempts_to_global(tmp_path): + backends = _serve_backends(tmp_path, n_attempts=2) + # No per-target override -> every backend inherits the global (backward-compat). + assert backends["harbor-test"]["n_attempts"] == 2 + assert backends["harbor-validation"]["n_attempts"] == 2 + + +def test_build_rejects_n_attempts_override_on_agent_partition(tmp_path): + # validation is agent-evaluable + the selection partition; its backend is + # shared with search, so an override there must be rejected. + with pytest.raises(ValidationError, match="agent-evaluable partition"): + _config( + tmp_path, + targets=[VerificationTargetSpec(partition="validation", n_attempts=3)], + ) From 0148acb4668273070c1daee04f49413b4bc2c730 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 17:44:40 -0700 Subject: [PATCH 53/72] Stop advertising the retired insights subagent in the optimizer instruction The instruction's insights paragraph was gated on overlay_present, which is true whenever the always-baked evals skill is added -- so every optimizer was told to dispatch an insights-generator subagent and read skills/insights that no benchmark ships. The insights skill is retired; drop the paragraph. Co-Authored-By: Claude Opus 4.8 --- .../vero/harbor/build/templates/instruction.md.j2 | 9 --------- vero/tests/test_v05_harbor_build.py | 13 +++++++++++++ 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 9cca793d..8e4e78f6 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -71,15 +71,6 @@ case plus the complete Harbor trial records: exact failure results, exception tracebacks, trial logs, and target-agent artifacts. Use these files to inspect successful as well as failed trajectories without putting long traces into the CLI response. -{% if overlay_present %} -Your workspace also includes analysis subagents under `.claude/agents/` and a -corpus-analysis skill under `skills/insights/`. These mine the entire -`.evals/results/` trace corpus for recurring, cross-case failure patterns that -are invisible in any single trace. After running at least one full-disclosure -evaluation, dispatch the `insights-generator` subagent (via the Task tool, -pointing it at the `.evals` corpus) to get evidence-grounded findings, then let -those findings guide your next candidate edit. Re-run it as the corpus grows. -{% endif %} {% endif %} ## Rules diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index 2fd75416..fdae105b 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -1254,3 +1254,16 @@ def test_build_rejects_n_attempts_override_on_agent_partition(tmp_path): tmp_path, targets=[VerificationTargetSpec(partition="validation", n_attempts=3)], ) + + +def test_instruction_omits_retired_insights_paragraph(tmp_path): + # The insights-generator subagent / skills/insights overlay was retired; the + # rendered optimizer instruction must not advertise it (it did whenever the + # always-baked evals skill made overlay_present true). + config = _config(tmp_path) + output = compile_harbor_task( + config, tmp_path / "compiled", vero_root=Path(__file__).parents[1] + ) + instruction = (output / "instruction.md").read_text(encoding="utf-8") + assert "insights-generator" not in instruction + assert "skills/insights/" not in instruction From b67bca8351d1fb50388fc2147dc63d1ed8068b89 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 21:28:04 -0700 Subject: [PATCH 54/72] Document blocking evals + power-flags + submit for the optimizer The optimizer interacts with VeRO through the `evals` CLI; treat it as a tool. Three fixes so we stop handicapping it: - Both eval modes with trade-offs. `evals run` blocks and returns the result by default (one call, stay in-loop); --detach is only for running several evals at once, then poll `evals status`. Every doc previously pushed --detach, which against Claude Code's 10-min Bash cap trained the detach/poll/end-turn pattern that ended a headless run after one eval. Updated the instruction template, the evals skill, and the CLI group help. - Power-flags made discoverable. `evals run` options were bare; add help text so `evals run --help` self-documents subset iteration (--start/--stop/--case-id), replication (--seed), and the per-case/whole-eval wall overrides. - Submit semantics. `evals submit` now explains it nominates your best candidate to ship, that you should beat the baseline first, and that not submitting falls back to auto-best then last-commit. Also state the per-case wall limit as a rule (a slower candidate can be stopped and score the failure value) so the optimizer treats latency as a constraint, worded to stay out of the budget-blind redaction. Co-Authored-By: Claude Opus 4.8 --- vero/src/vero/evals_cli.py | 7 +- .../harbor/build/templates/instruction.md.j2 | 33 +++++++-- vero/src/vero/harbor/cli.py | 74 ++++++++++++++----- vero/src/vero/skills/evals/SKILL.md | 37 +++++++++- 4 files changed, 117 insertions(+), 34 deletions(-) diff --git a/vero/src/vero/evals_cli.py b/vero/src/vero/evals_cli.py index e26e6fa8..a3a137ff 100644 --- a/vero/src/vero/evals_cli.py +++ b/vero/src/vero/evals_cli.py @@ -299,9 +299,10 @@ def evals() -> None: resources), `candidates/` (prior program versions), and `plan.json` (what you may evaluate, and remaining budget). - Typical loop: `evals plan` -> edit + commit -> `evals run --detach` -> - `evals status JOB` -> `evals list` -> `evals diff BASELINE CANDIDATE` -> - `evals cases ID --sort score` -> `evals trace ID CASE`. + Typical loop: `evals plan` -> edit + commit -> `evals run` (blocks and + returns the result) -> `evals diff BASELINE CANDIDATE` -> + `evals cases ID --sort score` -> `evals trace ID CASE`. Add `--detach` only + to run several evaluations at once, then poll `evals status JOB`. """ diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 8e4e78f6..23228338 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -14,27 +14,40 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, 1. Inspect and edit `{{ layout.target_repo }}`. 2. Commit each candidate with Git. -3. Evaluate the current commit on the selection set: +3. Evaluate the current commit on the selection set. By default `evals run` + **blocks** until scoring finishes and prints the result — one call, no + polling, no job to track: ```bash evals run \ --backend {{ selection_backend }} \ --evaluation-set {{ evaluation_set_name }} \ - --partition {{ selection_partition }} \ - --detach + --partition {{ selection_partition }} ``` - Record the returned `job_id`. Continue inspecting or editing while it runs, - then use `evals status JOB_ID` and `evals result JOB_ID` to retrieve its - durable result. Track every outstanding job and do not claim an improvement - before its comparison baseline has completed. + An evaluation can take many minutes — that is expected; let the call block + and read the result it returns. Iterate cheaply on a subset first + (`--start 0 --stop N`, or repeated `--case-id ID`); pass `--seed N` to + reproduce a noisy comparison exactly. Run `evals run --help` for every option. + + Add `--detach` **only** to run several evaluations at once: it returns a + `job_id` immediately instead of blocking. Then poll `evals status JOB_ID` + until it reports `complete` and read `evals result JOB_ID`. Never end your + turn waiting on a detached job — nothing will wake a headless run. Do not + claim an improvement before its comparison baseline has been scored on the + same cases. 4. Use `evals status` to inspect evaluation jobs and allowed evaluation sets{% if disclose_budget %}, and to see remaining budgets{% endif %}. Navigate finished results with `evals list`, `evals diff`, `evals cases`, and `evals trace` (see `evals --help` and `skills/evals/SKILL.md`). {% if submit_enabled %} -5. When finished, nominate the chosen commit with `evals submit`. +5. Before finishing, commit and nominate your **best** candidate with + `evals submit` — this is the commit that ships. Measure the baseline and + submit only a candidate you have confirmed beats it on the same cases. If you + never submit, VeRO falls back to auto-best over validation and then your last + commit, so an unvetted or half-finished edit can ship by default — submit + deliberately. {% else %} 5. VeRO automatically pools your measurements, re-scores a shortlist with the trusted evaluator, and selects the best candidate. The unmodified baseline is @@ -83,6 +96,10 @@ CLI response. - Scores may be noisy; compare candidates on the same selection whenever possible. - Reusing the same cases removes case-sampling differences, but target-model randomness remains. Replicate important comparisons before assigning causality. +- Each case has a wall-clock limit: a case that runs past it is stopped and + scores the failure value. A change that is more thorough but slower can + therefore score *worse* by pushing cases over the limit — treat per-case + latency as a real constraint, not only accuracy. {% if exhaust_budget and disclose_budget %} - Unspent evaluation budget is wasted. Use remaining calls to re-measure the strongest candidate or try another plausible change. diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 8b700184..1a80f8aa 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -495,34 +495,60 @@ def inference_gateway_command(config_path, host, port): @harbor.command("eval") -@click.option("--backend", "backend_id", required=True) -@click.option("--evaluation-set", "evaluation_set_name", required=True) -@click.option("--partition") -@click.option("--version", help="Candidate version; defaults to agent repository HEAD.") -@click.option("--case-id", "case_ids", multiple=True) -@click.option("--start", type=click.IntRange(min=0)) -@click.option("--stop", type=click.IntRange(min=1)) -@click.option("--parameter", multiple=True, help="Evaluation parameter as NAME=JSON.") -@click.option("--timeout", type=click.FloatRange(min=0, min_open=True)) -@click.option("--case-timeout", type=click.FloatRange(min=0, min_open=True)) -@click.option("--max-concurrency", type=click.IntRange(min=1)) +@click.option( + "--backend", "backend_id", required=True, + help="Evaluation backend to score against (e.g. the selection partition's backend; see `evals plan`).", +) +@click.option( + "--evaluation-set", "evaluation_set_name", required=True, + help="Name of the evaluation set to score on.", +) +@click.option( + "--partition", + help="Partition within the evaluation set (e.g. development or validation).", +) +@click.option("--version", help="Candidate version to score; defaults to the agent repository's HEAD commit.") +@click.option( + "--case-id", "case_ids", multiple=True, + help="Score only these case ids (repeatable) — a cheap subset for fast iteration. Cannot combine with --start/--stop.", +) +@click.option( + "--start", type=click.IntRange(min=0), + help="Subset start index (with --stop): score cases [start, stop) for cheap iteration on a slice.", +) +@click.option("--stop", type=click.IntRange(min=1), help="Subset stop index (exclusive); requires --start.") +@click.option("--parameter", multiple=True, help="Evaluation parameter as NAME=JSON (repeatable).") +@click.option( + "--timeout", type=click.FloatRange(min=0, min_open=True), + help="Override the whole-evaluation wall timeout, in seconds.", +) +@click.option( + "--case-timeout", type=click.FloatRange(min=0, min_open=True), + help="Override the per-case wall budget for THIS run, in seconds. A case that exceeds it is stopped and scores the failure value; the final held-out evaluation always uses the configured budget, so keep search runs comparable.", +) +@click.option("--max-concurrency", type=click.IntRange(min=1), help="Override how many cases run in parallel.") @click.option( "--error-rate-threshold", type=click.FloatRange(min=0, max=1, min_open=True), + help="Abort the evaluation if the fraction of errored cases exceeds this.", ) -@click.option("--retry-max-attempts", type=click.IntRange(min=1)) -@click.option("--retry-initial-delay", type=click.FloatRange(min=0)) -@click.option("--retry-maximum-delay", type=click.FloatRange(min=0)) -@click.option("--retry-multiplier", type=click.FloatRange(min=1)) +@click.option("--retry-max-attempts", type=click.IntRange(min=1), help="Max attempts per case on transient failure.") +@click.option("--retry-initial-delay", type=click.FloatRange(min=0), help="Initial retry backoff delay, in seconds.") +@click.option("--retry-maximum-delay", type=click.FloatRange(min=0), help="Maximum retry backoff delay, in seconds.") +@click.option("--retry-multiplier", type=click.FloatRange(min=1), help="Retry backoff growth multiplier.") @click.option( "--retry-on-timeout/--no-retry-on-timeout", default=None, + help="Whether a per-case timeout counts as a retryable failure.", +) +@click.option( + "--seed", type=int, + help="Seed for case sampling / evaluation, so a noisy comparison can be reproduced exactly.", ) -@click.option("--seed", type=int) @click.option( "--detach", is_flag=True, - help="Start a durable evaluation job and return without waiting for its result.", + help="Run several evaluations at once: start a durable job and return a job_id immediately instead of blocking. Poll `evals status JOB` until complete, then read `evals result JOB`. Omit to block and get the result in one call.", ) def evaluate_command( backend_id, @@ -618,9 +644,19 @@ def evaluation_result_command(job_id): @harbor.command("submit") -@click.option("--version", help="Candidate version; defaults to agent repository HEAD.") +@click.option( + "--version", + help="Candidate version to nominate; defaults to the agent repository's HEAD commit.", +) def submit_command(version): - """Nominate a candidate for submit-based finalization.""" + """Nominate your best candidate as the one to ship. + + This is the deliberate selection that finalization scores on the held-out + set. Submit only a candidate you have confirmed beats the baseline on the + same cases. If you never submit, VeRO falls back to auto-best over + validation and then your last commit, so an unvetted edit can ship by + default -- submit on purpose. + """ click.echo( json.dumps(_request("POST", "/submit", payload={"version": version}), indent=2) ) diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index 8619a73c..48acd3f2 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -18,19 +18,41 @@ subcommand. ```bash evals plan # what you may run, rules, remaining budget -evals run --backend B --evaluation-set S --partition P --detach # returns job_id -evals status JOB_ID # poll the job (evals status = global view) -evals result JOB_ID # durable result once finished +evals run --backend B --evaluation-set S --partition P # BLOCKS, returns the result evals list --sort score --desc # every past result, one row each evals diff BASELINE_ID CANDIDATE_ID # which cases improved / regressed evals cases ID --sort score # per-case scores for one result evals trace ID CASE_ID # trace summary + artifact files for a case evals trace ID CASE_ID --span 3 # one span of the trace, char-windowed +evals submit --version COMMIT # nominate your best candidate to ship ``` Evaluate the baseline first: a candidate only counts as an improvement against a measured baseline on the same case selection. +## Blocking vs detached + +By default `evals run` **blocks** until scoring finishes and returns the result +— one call, nothing to track. Evaluations can take many minutes; that is +expected, so let it block. This is what you want almost always. + +Use `--detach` **only** to run several evaluations concurrently: it returns a +`job_id` immediately instead of blocking. Then poll `evals status JOB_ID` until +it reports `complete` and read `evals result JOB_ID`. Never end your turn to +"wait" for a detached job — nothing resumes you. + +## Run options worth knowing (`evals run --help` for all) + +- **`--start N --stop M`** or repeated **`--case-id ID`** — score a *subset*. + Iterate on a handful of cases (cheap, fast) before spending budget on the full + set. +- **`--seed N`** — fix the seed to *reproduce a noisy comparison exactly* (the + cleanest way to "replicate before acting"). +- **`--max-concurrency`**, **`--case-timeout`**, **`--timeout`** — override + parallelism and wall budgets for a run. Note the final held-out evaluation + always uses the configured per-case budget, so don't loosen `--case-timeout` + in search or your slow candidate will look fine, then get stopped on test. + ## The `.evals/` tree ``` @@ -64,4 +86,11 @@ are enforced by the evaluator; `evals plan` shows what remains. then `--span N`, then windowed chars; artifacts are files — `grep` them. - Confusing ids: evaluation ids come from `evals list`; case ids from `evals cases`; job ids only from `evals run --detach`. -- Scores may be noisy: replicate an important comparison before acting on it. +- Scores may be noisy: replicate an important comparison before acting on it + (`--seed N` reproduces one exactly). +- Ending your turn to "wait" for a `--detach`ed job — nothing resumes you. Block + on `evals run` instead, or keep polling `evals status` within your turn. +- Finishing without `evals submit`: your best candidate must be nominated + deliberately, or a fallback (auto-best, then your last commit) ships instead. +- Optimizing accuracy while ignoring per-case latency: a slower candidate can be + stopped at the wall budget and score the failure value. From 3ce8913a5f4e02ce75ceaf3850dc4c9e114c9f2b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 21:52:29 -0700 Subject: [PATCH 55/72] Add elapsed/case-count to evals status; add evals wait C1: `evals status JOB` now reports client-derived `elapsed_seconds` and, for a subset run, `requested_cases`, so a polling agent sees progress instead of a bare `running`. E: `evals wait JOB` blocks until a detached job finishes and prints its result -- the blocking companion to `evals run --detach`, so the agent never hand-rolls a poll loop (the pattern that hit the 10-min Bash cap and Exit 143). Optional --timeout returns the still-running status cleanly (exit 0), so it is safe to re-call. Instruction + evals skill updated to point at it. Co-Authored-By: Claude Opus 4.8 --- vero/src/vero/evals_cli.py | 117 ++++++++++++++++-- .../harbor/build/templates/instruction.md.j2 | 10 +- vero/src/vero/skills/evals/SKILL.md | 8 +- vero/tests/test_v05_evals_cli.py | 98 ++++++++++++++- 4 files changed, 216 insertions(+), 17 deletions(-) diff --git a/vero/src/vero/evals_cli.py b/vero/src/vero/evals_cli.py index a3a137ff..6e8aadb3 100644 --- a/vero/src/vero/evals_cli.py +++ b/vero/src/vero/evals_cli.py @@ -17,6 +17,8 @@ import json import os +import time +from datetime import datetime, timezone from pathlib import Path import click @@ -306,18 +308,117 @@ def evals() -> None: """ -@evals.command("status") -@click.argument("job_id", required=False) -def status_command(job_id): - """Show evaluation access and budgets, or one detached job's status.""" +def _parse_ts(value: object) -> datetime | None: + if not value: + return None + try: + parsed = datetime.fromisoformat(str(value).replace("Z", "+00:00")) + except ValueError: + return None + return parsed if parsed.tzinfo else parsed.replace(tzinfo=timezone.utc) + + +def _requested_cases(job: dict) -> int | None: + """How many cases the run requested, when it named a subset. + + A whole-partition run carries no selection, so the total is not known + client-side; return None rather than guess. + """ + selection = _dig(job, "evaluation_set", "selection") + if not isinstance(selection, dict): + return None + ids = selection.get("ids") + if isinstance(ids, list): + return len(ids) + start, stop = selection.get("start"), selection.get("stop") + if isinstance(start, int) and isinstance(stop, int): + return max(0, stop - start) + return None + + +def _enrich_job(job: object) -> object: + """Add client-derived elapsed time and requested-case count to a job status. + + A polling agent otherwise sees only `status: running` with no sense of how + long it has run or how large the evaluation is. + """ + if not isinstance(job, dict): + return job + created = _parse_ts(job.get("created_at")) + if created is not None: + ended = _parse_ts(job.get("completed_at")) or datetime.now(timezone.utc) + job["elapsed_seconds"] = round((ended - created).total_seconds(), 1) + requested = _requested_cases(job) + if requested is not None: + job["requested_cases"] = requested + return job + + +def _sidecar_request(): try: from vero.harbor.cli import _request - except ImportError as error: + except ImportError as error: # pragma: no cover - exercised via CLI raise click.ClickException( - f"`evals status` needs the evaluation sidecar client (vero[harbor]): {error}" + f"this command needs the evaluation sidecar client (vero[harbor]): {error}" ) - path = f"/eval/jobs/{job_id}" if job_id else "/status" - click.echo(json.dumps(_request("GET", path), indent=2)) + return _request + + +@evals.command("status") +@click.argument("job_id", required=False) +def status_command(job_id): + """Show evaluation access and budgets, or one detached job's status. + + For a specific job this also reports `elapsed_seconds` (how long it has run) + and, for a subset run, `requested_cases` — so a poll shows progress, not just + `running`. + """ + request = _sidecar_request() + if job_id: + click.echo(json.dumps(_enrich_job(request("GET", f"/eval/jobs/{job_id}")), indent=2)) + else: + click.echo(json.dumps(request("GET", "/status"), indent=2)) + + +@evals.command("wait") +@click.argument("job_id") +@click.option( + "--poll-interval", + default=15.0, + show_default=True, + type=click.FloatRange(min=1), + help="Seconds between status polls.", +) +@click.option( + "--timeout", + type=click.FloatRange(min=0), + help="Optional max seconds to wait. On expiry, print the current " + "(still-running) status and exit 0 so you can simply call `evals wait` " + "again. Default: wait until the job finishes.", +) +def wait_command(job_id, poll_interval, timeout): + """Block until a detached job finishes, then print its result. + + The blocking companion to `evals run --detach`: one call that waits, so you + never hand-roll a poll loop. Idempotent — safe to call again if it returns + while the job is still running (only happens when --timeout is set). + """ + request = _sidecar_request() + terminal = {"complete", "failed", "cancelled"} + deadline = None if timeout is None else time.monotonic() + timeout + while True: + job = request("GET", f"/eval/jobs/{job_id}") + status = job.get("status") if isinstance(job, dict) else None + if status in terminal: + break + if deadline is not None and time.monotonic() >= deadline: + click.echo(json.dumps(_enrich_job(job), indent=2)) + return + time.sleep(poll_interval) + if status == "complete": + click.echo(json.dumps(request("GET", f"/eval/jobs/{job_id}/result"), indent=2)) + else: + click.echo(json.dumps(_enrich_job(job), indent=2)) @evals.command("list") diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 23228338..1748e320 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -31,11 +31,11 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, reproduce a noisy comparison exactly. Run `evals run --help` for every option. Add `--detach` **only** to run several evaluations at once: it returns a - `job_id` immediately instead of blocking. Then poll `evals status JOB_ID` - until it reports `complete` and read `evals result JOB_ID`. Never end your - turn waiting on a detached job — nothing will wake a headless run. Do not - claim an improvement before its comparison baseline has been scored on the - same cases. + `job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks + until that job finishes and prints its result (or poll `evals status JOB_ID`, + which also shows elapsed time). Never end your turn waiting on a detached job + — nothing will wake a headless run. Do not claim an improvement before its + comparison baseline has been scored on the same cases. 4. Use `evals status` to inspect evaluation jobs and allowed evaluation sets{% if disclose_budget %}, and to see remaining budgets{% endif %}. diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index 48acd3f2..9a6ca6fd 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -37,9 +37,11 @@ By default `evals run` **blocks** until scoring finishes and returns the result expected, so let it block. This is what you want almost always. Use `--detach` **only** to run several evaluations concurrently: it returns a -`job_id` immediately instead of blocking. Then poll `evals status JOB_ID` until -it reports `complete` and read `evals result JOB_ID`. Never end your turn to -"wait" for a detached job — nothing resumes you. +`job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks until +that job finishes and prints its result; or poll `evals status JOB_ID`, which +now also reports `elapsed_seconds` (and `requested_cases` for a subset) so you +can see it is progressing. Never end your turn to "wait" for a detached job — +nothing resumes you. ## Run options worth knowing (`evals run --help` for all) diff --git a/vero/tests/test_v05_evals_cli.py b/vero/tests/test_v05_evals_cli.py index a5da481d..e3d2981e 100644 --- a/vero/tests/test_v05_evals_cli.py +++ b/vero/tests/test_v05_evals_cli.py @@ -9,7 +9,7 @@ from click.testing import CliRunner from vero.candidate import Candidate -from vero.evals_cli import evals +from vero.evals_cli import _enrich_job, evals from vero.evaluation import ( BackendProvenance, CaseResult, @@ -262,3 +262,99 @@ async def test_context_is_discovered_from_workspace(context_dir: Path, monkeypat result = CliRunner().invoke(evals, ["list", "--json"], catch_exceptions=False) assert result.exit_code == 0 assert len(json.loads(result.output)) == 3 + + +def test_enrich_job_adds_elapsed_and_requested_cases(): + # terminal job: elapsed from created->completed, exactly. + done = _enrich_job( + { + "job_id": "j", + "status": "complete", + "created_at": "2026-01-01T00:00:00Z", + "completed_at": "2026-01-01T00:05:00Z", + } + ) + assert done["elapsed_seconds"] == 300.0 + # subset range run: requested_cases = stop - start. + ranged = _enrich_job( + { + "job_id": "j", + "status": "running", + "created_at": "2026-01-01T00:00:00Z", + "evaluation_set": {"selection": {"start": 0, "stop": 8}}, + } + ) + assert ranged["requested_cases"] == 8 + assert ranged["elapsed_seconds"] >= 0 + # explicit case ids -> len; whole-partition run -> no requested_cases. + assert _enrich_job({"evaluation_set": {"selection": {"ids": ["a", "b"]}}})[ + "requested_cases" + ] == 2 + assert "requested_cases" not in _enrich_job( + {"status": "running", "evaluation_set": {"name": "v"}} + ) + + +def test_status_job_output_is_enriched(monkeypatch): + import vero.harbor.cli as harbor_cli + + monkeypatch.setattr( + harbor_cli, + "_request", + lambda method, path, **kw: { + "job_id": "j", + "status": "running", + "created_at": "2026-01-01T00:00:00Z", + "evaluation_set": {"selection": {"start": 0, "stop": 10}}, + }, + ) + result = CliRunner().invoke(evals, ["status", "j"], catch_exceptions=False) + assert result.exit_code == 0 + payload = json.loads(result.output) + assert payload["requested_cases"] == 10 + assert "elapsed_seconds" in payload + + +def test_wait_blocks_until_complete_then_prints_result(monkeypatch): + import vero.harbor.cli as harbor_cli + + statuses = iter(["running", "running", "complete"]) + calls: list[str] = [] + + def fake_request(method, path, **kw): + calls.append(path) + if path.endswith("/result"): + return {"result": {"objective": {"value": 0.5}}} + return {"job_id": "j", "status": next(statuses), "created_at": "2026-01-01T00:00:00Z"} + + monkeypatch.setattr(harbor_cli, "_request", fake_request) + monkeypatch.setattr("time.sleep", lambda _seconds: None) + result = CliRunner().invoke( + evals, ["wait", "j", "--poll-interval", "1"], catch_exceptions=False + ) + assert result.exit_code == 0 + assert json.loads(result.output)["result"]["objective"]["value"] == 0.5 + assert calls[-1] == "/eval/jobs/j/result" # result fetched only after terminal + + +def test_wait_timeout_returns_still_running_and_enriched(monkeypatch): + import vero.harbor.cli as harbor_cli + + monkeypatch.setattr( + harbor_cli, + "_request", + lambda method, path, **kw: { + "job_id": "j", + "status": "running", + "created_at": "2026-01-01T00:00:00Z", + "evaluation_set": {"selection": {"ids": ["a", "b", "c"]}}, + }, + ) + monkeypatch.setattr("time.sleep", lambda _seconds: None) + result = CliRunner().invoke( + evals, ["wait", "j", "--timeout", "0"], catch_exceptions=False + ) + assert result.exit_code == 0 # clean exit, not Exit 143 + payload = json.loads(result.output) + assert payload["status"] == "running" + assert payload["requested_cases"] == 3 From b85f7608afdb479736333f7fdee69a200e384820 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 22:47:02 -0700 Subject: [PATCH 56/72] Report per-case cost the way latency is: mean, median, and max MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Tokens were the odd metric out — a per-evaluation sum while latency already had per-case statistics. Token and latency distributions are unbounded and heavy-tailed, so a mean alone hides the shape: give both a mean, a median, and a max over cases. The agent-reported token counts are recorded per case and get the full treatment; the trusted gateway figure is metered per evaluation, so it contributes its sum plus a derived mean_case_inference_* (median and max need post-hoc per-case attribution). Accuracy, being bounded, keeps mean and stddev. Budget-blind redaction now matches these markers anywhere in a metric name, not just as a prefix: mean_case_inference_input_tokens is the same budget signal as inference_input_tokens and must not reach the agent. Latency carries no marker and stays visible. Co-Authored-By: Claude Opus 4.8 --- vero/src/vero/harbor/backend.py | 60 +++++++++++++++----- vero/src/vero/sidecar/sidecar.py | 14 ++++- vero/tests/test_v05_harbor_backend.py | 79 +++++++++++++++++++++++++++ vero/tests/test_v05_harbor_sidecar.py | 9 +++ 4 files changed, 145 insertions(+), 17 deletions(-) diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 758f8ed0..884da1fe 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -1026,6 +1026,31 @@ def _agent_reported_tokens(attempts: list[dict[str, Any]]) -> dict[str, float]: totals[metric] = totals.get(metric, 0.0) + float(value) return totals + @staticmethod + def _case_distribution( + case_results: list[EvaluationCaseResult], metric: str + ) -> dict[str, float]: + """mean/median/max of a per-case cost metric. + + Latency and token distributions are unbounded and heavy-tailed — a few + cases carry much of the total — so the median is reported beside the + mean, and the max names the tail (a case past its wall budget is + stopped and scores the failure value). Accuracy, being bounded, stays + mean plus stddev. + """ + values = [ + value + for case in case_results + if isinstance((value := case.metrics.get(metric)), (int, float)) + ] + if not values: + return {} + return { + f"mean_case_{metric}": sum(values) / len(values), + f"median_case_{metric}": float(statistics.median(values)), + f"max_case_{metric}": float(max(values)), + } + def _case_result( self, case: HarborCase, @@ -1539,11 +1564,24 @@ def _category(case: CaseResult) -> ErrorCategory | None: ) return sanitize_evaluation_report(report, self._secrets()) - case_walls = [ - wall - for case in case_results - if isinstance((wall := case.metrics.get("wall_seconds")), (int, float)) - ] + # Per-case cost distributions. wall_seconds and the agent-reported token + # counts are recorded per case, so each gets the same mean/median/max + # treatment; the trusted gateway metering below is per evaluation, so + # only its mean is derivable here (per-case attribution of the gateway + # log is post-hoc — see harness-engineering-bench/scripts). + case_distributions: dict[str, float] = {} + for metric in ( + "wall_seconds", + "agent_reported_input_tokens", + "agent_reported_cached_input_tokens", + "agent_reported_output_tokens", + ): + case_distributions.update(self._case_distribution(case_results, metric)) + inference_usage = self._inference_usage_metrics(context.evaluation_id) + mean_case_inference = { + f"mean_case_{key}": value / len(case_results) + for key, value in inference_usage.items() + } reported_totals: dict[str, float] = {} for case in case_results: for key, value in case.metrics.items(): @@ -1568,16 +1606,10 @@ def _category(case: CaseResult) -> ErrorCategory | None: ), # Cost/latency telemetry: reported alongside accuracy, never # part of the score. - **( - { - "mean_case_wall_seconds": sum(case_walls) / len(case_walls), - "max_case_wall_seconds": max(case_walls), - } - if case_walls - else {} - ), + **case_distributions, **reported_totals, - **self._inference_usage_metrics(context.evaluation_id), + **inference_usage, + **mean_case_inference, }, cases=case_results, diagnostics=diagnostics, diff --git a/vero/src/vero/sidecar/sidecar.py b/vero/src/vero/sidecar/sidecar.py index 8de2f967..8f619dc1 100644 --- a/vero/src/vero/sidecar/sidecar.py +++ b/vero/src/vero/sidecar/sidecar.py @@ -497,12 +497,20 @@ async def _enforce_aggregate_floor( f"{policy.access.min_aggregate_cases} cases; requested {cost.cases}" ) - _BUDGET_METRIC_PREFIXES = ("inference_", "agent_reported_") + _BUDGET_METRIC_MARKERS = ("inference_", "agent_reported_") def _redact_budget_metrics(self, record: EvaluationRecord) -> EvaluationRecord: """Budget-blind mode: cost metrics — gateway-metered ``inference_*`` and self-declared ``agent_reported_*``, report- and case-level — are - budget signal and must not reach the agent (enforcement unchanged).""" + budget signal and must not reach the agent (enforcement unchanged). + + Matched anywhere in the key, not just as a prefix: the report also + carries distribution wrappers of these metrics + (``mean_case_inference_input_tokens``, + ``median_case_agent_reported_output_tokens``), which are the same + signal under a different name. Latency keys carry no marker and stay + visible. + """ if self.disclose_budget: return record @@ -510,7 +518,7 @@ def keep(metrics: dict[str, float]) -> dict[str, float]: return { key: value for key, value in metrics.items() - if not key.startswith(self._BUDGET_METRIC_PREFIXES) + if not any(marker in key for marker in self._BUDGET_METRIC_MARKERS) } metrics = keep(record.report.metrics) diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index f503c7dc..62bf6569 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -548,12 +548,25 @@ async def test_harbor_backend_reports_latency_and_inference_telemetry(tmp_path): assert by_id["case-b"].metrics["wall_seconds"] == 30.0 assert report.metrics["mean_case_wall_seconds"] == 60.0 assert report.metrics["max_case_wall_seconds"] == 90.0 + assert report.metrics["median_case_wall_seconds"] == 60.0 # this evaluation's attribution only, summed across gateway scopes assert report.metrics["inference_requests"] == 14.0 assert report.metrics["inference_input_tokens"] == 1100.0 assert report.metrics["inference_cached_input_tokens"] == 400.0 assert report.metrics["inference_output_tokens"] == 300.0 assert report.metrics["inference_total_tokens"] == 1400.0 + # the trusted gateway total is per evaluation, so only its per-case mean is + # derivable here (median/max need post-hoc per-case attribution) + assert report.metrics["mean_case_inference_input_tokens"] == 550.0 + assert report.metrics["mean_case_inference_total_tokens"] == 700.0 + assert report.metrics["mean_case_inference_requests"] == 7.0 + # agent-reported tokens are per case, so they get the same mean/median/max + # treatment as latency rather than only a report-level sum + assert report.metrics["mean_case_agent_reported_input_tokens"] == 600.0 + assert report.metrics["max_case_agent_reported_input_tokens"] == 1000.0 + assert report.metrics["mean_case_agent_reported_cached_input_tokens"] == 300.0 + assert report.metrics["mean_case_agent_reported_output_tokens"] == 37.5 + assert report.metrics["max_case_agent_reported_output_tokens"] == 50.0 # agent-self-reported usage: per case and summed at report level, under a # prefix distinct from the trusted gateway-metered inference_* metrics assert by_id["case-a"].metrics["agent_reported_input_tokens"] == 1000.0 @@ -1050,3 +1063,69 @@ def test_command_forwards_retry_config_and_max_retries(tmp_path): assert command[command.index("--config") + 1] == "/staging/retry-config.json" # ... while the count stays a flag (harbor applies it over the --config base). assert command[command.index("--max-retries") + 1] == "5" + + +@pytest.mark.asyncio +async def test_harbor_backend_reports_median_for_skewed_cost_distributions(tmp_path): + # Heavy tail: one case dominates both latency and tokens. The mean alone + # hides that shape, which is why cost metrics carry a median and a max. + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + { + "verifier_result": {"rewards": {"reward": 1.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:10Z", + "agent_result": { + "n_input_tokens": 100, + "n_cache_tokens": 10, + "n_output_tokens": 5, + }, + } + ], + "example/beta": [ + { + "verifier_result": {"rewards": {"reward": 1.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:20Z", + "agent_result": { + "n_input_tokens": 200, + "n_cache_tokens": 20, + "n_output_tokens": 10, + }, + } + ], + "example/gamma": [ + { + "verifier_result": {"rewards": {"reward": 1.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:05:00Z", + "agent_result": { + "n_input_tokens": 3000, + "n_cache_tokens": 300, + "n_output_tokens": 150, + }, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a", "case-b", "case-c"])), + ) + + # latency: mean is dragged up by the tail case, median is not + assert report.metrics["mean_case_wall_seconds"] == 110.0 + assert report.metrics["median_case_wall_seconds"] == 20.0 + assert report.metrics["max_case_wall_seconds"] == 300.0 + # tokens get the same treatment, and show the same divergence + assert report.metrics["mean_case_agent_reported_input_tokens"] == 1100.0 + assert report.metrics["median_case_agent_reported_input_tokens"] == 200.0 + assert report.metrics["max_case_agent_reported_input_tokens"] == 3000.0 + assert report.metrics["mean_case_agent_reported_output_tokens"] == 55.0 + assert report.metrics["median_case_agent_reported_output_tokens"] == 10.0 + assert report.metrics["max_case_agent_reported_output_tokens"] == 150.0 + assert report.metrics["median_case_agent_reported_cached_input_tokens"] == 20.0 diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index faba4209..9b75f3fc 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -529,7 +529,11 @@ async def evaluate(self, *, context, request): "inference_total_tokens": 12345.0, "inference_cached_input_tokens": 678.0, "agent_reported_total_input_tokens": 555.0, + # distribution wrappers of the same budget signal + "mean_case_inference_input_tokens": 99.0, + "median_case_agent_reported_output_tokens": 33.0, "mean_case_wall_seconds": 42.0, + "median_case_wall_seconds": 40.0, } } ) @@ -558,7 +562,12 @@ async def test_budget_blind_sidecar_redacts_inference_metrics(tmp_path): receipt_metrics = response.receipt.result.metrics assert "inference_total_tokens" not in receipt_metrics assert "inference_cached_input_tokens" not in receipt_metrics + # per-case distribution wrappers are the same signal renamed, so they are + # redacted too; latency distributions stay visible + assert "mean_case_inference_input_tokens" not in receipt_metrics + assert "median_case_agent_reported_output_tokens" not in receipt_metrics assert receipt_metrics["mean_case_wall_seconds"] == 42.0 + assert receipt_metrics["median_case_wall_seconds"] == 40.0 document = json.loads( ( From 585e34d995b06821c34adcc5877202900d711ed8 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Sun, 26 Jul 2026 23:47:04 -0700 Subject: [PATCH 57/72] Stop advertising a seed the Harbor backend rejects, and say why it rejected MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A live optimizer followed the instruction's advice to "pass --seed N to reproduce a noisy comparison exactly" and got back `400: invalid evaluation request`. Two separate defects met there. HarborBackend.validate_request rejects request.seed outright, and every Harbor-scored build renders that advice, so the flag could never work where it was recommended. Gate it on a new seed_supported context flag: a command backend does its own sampling and keeps the advice; a Harbor build is told to replicate by re-running the identical selection, and that --seed is refused — naming the flag so the rejection is not a surprise. The rejection message also never reached the agent. Both the blocking handler and the detached job path collapsed EvaluationRequestError to a bare "invalid evaluation request", discarding a message that named exactly which backend capability refused the request. Pass it through on both paths. This is the same reasoning that already appends the exception type to unmapped failures. Denials stay opaque: their message is policy, not capability, and test_http_app_redacts_access_denial_details still pins that. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 3 ++ .../harbor/build/templates/instruction.md.j2 | 10 +++++-- vero/src/vero/harbor/cli.py | 6 +++- vero/src/vero/sidecar/app.py | 19 ++++++++---- vero/src/vero/sidecar/sidecar.py | 7 ++++- vero/src/vero/skills/evals/SKILL.md | 10 ++++--- vero/tests/test_v05_harbor_build.py | 30 +++++++++++++++++++ vero/tests/test_v05_harbor_http.py | 5 +++- vero/tests/test_v05_harbor_sidecar.py | 17 +++++++++++ 9 files changed, 92 insertions(+), 15 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index ad992633..ef15668c 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -735,6 +735,9 @@ def compile_harbor_task( "sidecar_factory": FACTORY_PATH, "producer_base_url": PRODUCER_BASE_URL, "command_harness": config.command_backend is not None, + # The Harbor backend hard-rejects request.seed, so only advertise the + # flag when the build evaluates through a command backend. + "seed_supported": config.command_backend is not None, "selection_backend": _backend_id(config.selection_partition), "evaluation_set_name": config.evaluation_set_name, "selection_partition": config.selection_partition, diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index 1748e320..f5c0d6ae 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -27,8 +27,14 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, An evaluation can take many minutes — that is expected; let the call block and read the result it returns. Iterate cheaply on a subset first - (`--start 0 --stop N`, or repeated `--case-id ID`); pass `--seed N` to - reproduce a noisy comparison exactly. Run `evals run --help` for every option. + (`--start 0 --stop N`, or repeated `--case-id ID`). + {% if seed_supported %} + Pass `--seed N` to reproduce a noisy comparison exactly. + {% else %} + To replicate a noisy comparison, re-run the identical selection; this backend + fixes its own sampling and rejects `--seed`. + {% endif %} + Run `evals run --help` for every option. Add `--detach` **only** to run several evaluations at once: it returns a `job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 1a80f8aa..8c1e6b7d 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -543,7 +543,11 @@ def inference_gateway_command(config_path, host, port): ) @click.option( "--seed", type=int, - help="Seed for case sampling / evaluation, so a noisy comparison can be reproduced exactly.", + help=( + "Seed for case sampling / evaluation. Backend-dependent: a backend that " + "fixes its own sampling rejects this with 'invalid evaluation request'. " + "To replicate elsewhere, re-run the identical case selection." + ), ) @click.option( "--detach", diff --git a/vero/src/vero/sidecar/app.py b/vero/src/vero/sidecar/app.py index 9675c5fa..98851094 100644 --- a/vero/src/vero/sidecar/app.py +++ b/vero/src/vero/sidecar/app.py @@ -50,12 +50,15 @@ class ScoreBaselineRequest(StrictModel): replicates: int = 1 -def _error(status_code: int, message: str): +def _error(status_code: int, message: str, *, detail: bool = False): async def handler(_request, error): - return JSONResponse( - status_code=status_code, - content={"error": message or str(error)}, - ) + text = message or str(error) + # `detail` opts a category into appending the raised message. Use it only + # where the message states a backend capability the agent must know to + # fix its own request — never for denials, whose whole point is opacity. + if detail and message and str(error): + text = f"{message}: {error}" + return JSONResponse(status_code=status_code, content={"error": text}) return handler @@ -92,7 +95,11 @@ async def lifespan(_app: FastAPI): app.add_exception_handler(EvaluationDeniedError, _error(403, "evaluation denied")) app.add_exception_handler(EvaluationAccessError, _error(403, "evaluation denied")) app.add_exception_handler( - EvaluationRequestError, _error(400, "invalid evaluation request") + EvaluationRequestError, + # The agent cannot repair a request it is only told is "invalid": these + # messages are backend-authored capability statements (an unsupported + # flag, a fixed limit), so pass them through. + _error(400, "invalid evaluation request", detail=True), ) app.add_exception_handler( CandidateTransferError, diff --git a/vero/src/vero/sidecar/sidecar.py b/vero/src/vero/sidecar/sidecar.py index 8f619dc1..1c8b8083 100644 --- a/vero/src/vero/sidecar/sidecar.py +++ b/vero/src/vero/sidecar/sidecar.py @@ -455,7 +455,12 @@ def _evaluation_job_error(error: Exception) -> str: if isinstance(error, (EvaluationDeniedError, EvaluationAccessError)): return "evaluation denied" if isinstance(error, EvaluationRequestError): - return "invalid evaluation request" + # Same reasoning as the blocking path in sidecar/app.py: the message + # names the backend capability that rejected the request, which the + # agent needs in order to reissue it correctly. + return f"invalid evaluation request: {error}" if str(error) else ( + "invalid evaluation request" + ) if isinstance(error, CandidateTransferError): return "candidate version could not be imported" # Unmapped failure: include the exception type (a class name, so no diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index 9a6ca6fd..cf17a33a 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -48,8 +48,10 @@ nothing resumes you. - **`--start N --stop M`** or repeated **`--case-id ID`** — score a *subset*. Iterate on a handful of cases (cheap, fast) before spending budget on the full set. -- **`--seed N`** — fix the seed to *reproduce a noisy comparison exactly* (the - cleanest way to "replicate before acting"). +- **`--seed N`** — backend-dependent, and several backends reject it outright + (you get an `invalid evaluation request` naming the reason). Where it is + refused, replicate a noisy comparison by re-running the *identical selection* + instead — same `--start/--stop` or same `--case-id` set. - **`--max-concurrency`**, **`--case-timeout`**, **`--timeout`** — override parallelism and wall budgets for a run. Note the final held-out evaluation always uses the configured per-case budget, so don't loosen `--case-timeout` @@ -88,8 +90,8 @@ are enforced by the evaluator; `evals plan` shows what remains. then `--span N`, then windowed chars; artifacts are files — `grep` them. - Confusing ids: evaluation ids come from `evals list`; case ids from `evals cases`; job ids only from `evals run --detach`. -- Scores may be noisy: replicate an important comparison before acting on it - (`--seed N` reproduces one exactly). +- Scores may be noisy: replicate an important comparison before acting on it, by + re-running the identical case selection. - Ending your turn to "wait" for a `--detach`ed job — nothing resumes you. Block on `evals run` instead, or keep polling `evals status` within your turn. - Finishing without `evals submit`: your best candidate must be nominated diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index fdae105b..e2892809 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -1267,3 +1267,33 @@ def test_instruction_omits_retired_insights_paragraph(tmp_path): instruction = (output / "instruction.md").read_text(encoding="utf-8") assert "insights-generator" not in instruction assert "skills/insights/" not in instruction + + +def test_instruction_advertises_seed_only_where_the_backend_accepts_it(tmp_path): + # HarborBackend.validate_request rejects request.seed outright, so telling a + # Harbor-scored optimizer to "pass --seed N to reproduce a comparison" sends + # it into a guaranteed 400. Observed live: the optimizer followed that advice + # and burned a round trip on `invalid evaluation request`. + harbor = compile_harbor_task( + _config(tmp_path), tmp_path / "harbor", vero_root=Path(__file__).parents[1] + ) + instruction = (harbor / "instruction.md").read_text(encoding="utf-8") + assert "--seed" in instruction # still named, so the rejection is not a surprise + assert "rejects `--seed`" in instruction + assert "re-run the identical selection" in instruction + + # A command backend does its own sampling and accepts the seed. + command = compile_harbor_task( + _config( + tmp_path / "cmd-config", + evaluation_backend="command", + command_backend=_command_backend(tmp_path / "cmd"), + agent_import_path=_OMIT, + task_source=_OMIT, + ), + tmp_path / "command", + vero_root=Path(__file__).parents[1], + ) + instruction = (command / "instruction.md").read_text(encoding="utf-8") + assert "reproduce a noisy comparison exactly" in instruction + assert "rejects `--seed`" not in instruction diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index a4e17a52..8034b6d6 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -479,8 +479,11 @@ def test_http_app_maps_backend_request_rejection_to_400(): }, ) + # The reason is passed through: an agent told only "invalid" cannot repair + # its request, and these messages state a backend capability, not policy. + # Contrast test_http_app_redacts_access_denial_details, which stays opaque. assert response.status_code == 400 - assert response.json() == {"error": "invalid evaluation request"} + assert response.json() == {"error": "invalid evaluation request: unknown case"} def test_harbor_cli_builds_canonical_selection(monkeypatch): diff --git a/vero/tests/test_v05_harbor_sidecar.py b/vero/tests/test_v05_harbor_sidecar.py index 9b75f3fc..27792dcd 100644 --- a/vero/tests/test_v05_harbor_sidecar.py +++ b/vero/tests/test_v05_harbor_sidecar.py @@ -878,3 +878,20 @@ def test_access_policy_defaults_to_safe_k_anonymity_floor(): aggregate = EvaluationAccessPolicy(disclosure=DisclosureLevel.AGGREGATE) assert aggregate.min_aggregate_cases == 5 assert EvaluationAccessPolicy().min_aggregate_cases == 1 + + +def test_detached_job_error_names_the_reason_a_request_was_rejected(): + # The detached path mirrors the blocking one (sidecar/app.py): a request + # rejection must say *what* the backend refused, or a polling agent sees + # only "invalid evaluation request" and cannot reissue a valid call. + # Denials stay opaque on purpose — they are policy, not capability. + from vero.evaluation import EvaluationRequestError + + describe = EvaluationSidecar._evaluation_job_error + assert describe(EvaluationRequestError("backend does not support the seed")) == ( + "invalid evaluation request: backend does not support the seed" + ) + assert describe(EvaluationRequestError("")) == "invalid evaluation request" + assert describe(EvaluationDeniedError("private policy detail")) == ( + "evaluation denied" + ) From d49df2cceea06381a2e36dcbd85cc51509b62a12 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 00:02:50 -0700 Subject: [PATCH 58/72] Publish each partition's case count in the evaluation plan An optimizer sizing a subset had no way to learn how many cases a partition holds. Observed live: it asked for `--start 20 --stop 70` of a 49-case development partition, and lost two evaluations to the rejection before backing off to a range it had already proven safe. write_evaluation_plan now asks each backend to cost its own base selection and records the count, which `evals plan` surfaces as a `cases` column. The call is advisory: a backend that cannot cost itself reports no count rather than failing the plan for every partition. The rejection itself was already well worded ("case range stops at 70, but the Harbor evaluation set contains 49 cases") and was being discarded by the opaque request-error mapping fixed in the previous commit. Together the agent now both sees the bound up front and is told it on the way out. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evals_cli.py | 2 ++ vero/src/vero/runtime/context.py | 12 +++++++ vero/src/vero/skills/evals/SKILL.md | 5 +-- vero/tests/test_v05_agent_context.py | 48 ++++++++++++++++++++++++++++ vero/tests/test_v05_evals_cli.py | 2 ++ 5 files changed, 67 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/evals_cli.py b/vero/src/vero/evals_cli.py index 6e8aadb3..5229ee56 100644 --- a/vero/src/vero/evals_cli.py +++ b/vero/src/vero/evals_cli.py @@ -646,6 +646,7 @@ def plan_command(context_path, as_json): { "evaluation": evaluation.get("name"), "partition": evaluation.get("partition"), + "cases": evaluation.get("cases"), "can_evaluate": evaluation.get("agent_can_evaluate"), "selection": evaluation.get("agent_selection"), "disclosure": evaluation.get("disclosure"), @@ -661,6 +662,7 @@ def plan_command(context_path, as_json): [ "evaluation", "partition", + "cases", "can_evaluate", "selection", "disclosure", diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index ccaf9462..20668a02 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -432,10 +432,22 @@ async def write_evaluation_plan( access = entry.access if not access.agent_visible and not access.agent_can_evaluate: continue + # Partition size, so the agent can size a subset instead of guessing + # a range and learning the bound from a rejected request. Advisory + # only: a backend that cannot cost its own base selection just + # reports no count rather than failing the whole plan. + resolve_cost = getattr(entry.backend, "resolve_cost", None) + case_count = None + if callable(resolve_cost): + try: + case_count = (await resolve_cost(entry.evaluation_set)).cases + except Exception: + case_count = None evaluations.append( { "name": entry.evaluation_set.name, "partition": entry.evaluation_set.partition, + "cases": case_count, "base_selection": entry.evaluation_set.selection.model_dump( mode="json" ), diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index cf17a33a..f6c36749 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -17,7 +17,7 @@ subcommand. ## The loop ```bash -evals plan # what you may run, rules, remaining budget +evals plan # what you may run, partition sizes, budget evals run --backend B --evaluation-set S --partition P # BLOCKS, returns the result evals list --sort score --desc # every past result, one row each evals diff BASELINE_ID CANDIDATE_ID # which cases improved / regressed @@ -47,7 +47,8 @@ nothing resumes you. - **`--start N --stop M`** or repeated **`--case-id ID`** — score a *subset*. Iterate on a handful of cases (cheap, fast) before spending budget on the full - set. + set. A range past the end of the partition is rejected, so read the `cases` + column of `evals plan` for the partition's size rather than guessing. - **`--seed N`** — backend-dependent, and several backends reject it outright (you get an `invalid evaluation request` naming the reason). Where it is refused, replicate a noisy comparison by re-running the *identical selection* diff --git a/vero/tests/test_v05_agent_context.py b/vero/tests/test_v05_agent_context.py index fd940032..887b9136 100644 --- a/vero/tests/test_v05_agent_context.py +++ b/vero/tests/test_v05_agent_context.py @@ -243,3 +243,51 @@ async def test_disclosure_ledger_survives_restart_and_never_broadens(tmp_path: P DisclosureLevel.NONE ) assert AgentDisclosureLedger(path).get("evaluation") == DisclosureLevel.NONE + + +@pytest.mark.asyncio +async def test_evaluation_plan_reports_partition_size(tmp_path: Path): + """The plan carries each partition's case count. + + Without it an agent sizing a subset can only guess a `--stop`, and learns the + bound from a rejected request — observed live, where an optimizer asked for + cases 20-70 of a 49-case partition and lost two evaluations to it. + """ + from vero.evaluation import EvaluationAccessPolicy, EvaluationCost, EvaluationSet + from vero.runtime.context import ContextPlanEntry + + class SizedBackend: + async def resolve_cost(self, evaluation_set) -> EvaluationCost: + return EvaluationCost(cases=49) + + class UncostableBackend: + async def resolve_cost(self, evaluation_set): + raise RuntimeError("cannot cost this set") + + directory = AgentContextDirectory( + sandbox=await LocalSandbox.create(root=tmp_path), + root=str(tmp_path / "ctx"), + session_dir=tmp_path / "session", + ) + await directory.reset() + await directory.write_evaluation_plan( + [ + ContextPlanEntry( + backend_id="harbor-development", + backend=SizedBackend(), + evaluation_set=EvaluationSet(name="bench", partition="development"), + access=EvaluationAccessPolicy(agent_can_evaluate=True), + ), + ContextPlanEntry( + backend_id="harbor-validation", + backend=UncostableBackend(), + evaluation_set=EvaluationSet(name="bench", partition="validation"), + access=EvaluationAccessPolicy(agent_can_evaluate=True), + ), + ] + ) + + plan = json.loads((tmp_path / "ctx" / "plan.json").read_text(encoding="utf-8")) + sizes = {row["partition"]: row["cases"] for row in plan["evaluations"]} + # A backend that cannot cost itself degrades to no count, not a broken plan. + assert sizes == {"development": 49, "validation": None} diff --git a/vero/tests/test_v05_evals_cli.py b/vero/tests/test_v05_evals_cli.py index e3d2981e..195f617e 100644 --- a/vero/tests/test_v05_evals_cli.py +++ b/vero/tests/test_v05_evals_cli.py @@ -113,6 +113,7 @@ async def context_dir(tmp_path: Path) -> Path: "agent_selection": "arbitrary", "disclosure": "full", "expose_case_resources": True, + "cases": 12, "budget": {"remaining_runs": 3, "remaining_cases": 40}, } ], @@ -231,6 +232,7 @@ async def test_plan_shows_budget(context_dir: Path): { "evaluation": "validation", "partition": "validation", + "cases": 12, "can_evaluate": True, "selection": "arbitrary", "disclosure": "full", From 152891456424e44d338adf82105fcf811f9f3dd7 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 00:51:58 -0700 Subject: [PATCH 59/72] Tell the optimizer to keep every evaluation call in the foreground MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The env-var mitigation is not sufficient. Harbor forces ENABLE_BACKGROUND_TASKS=1 and the build's agent_env overrides it back to 0 (env.update(self._resolved_env_vars) runs after the forced defaults, and the raised BASH_MAX_TIMEOUT_MS demonstrably took effect — one call blocked 10.8 minutes, impossible under the 600s default). The variable gates *automatic* backgrounding; it does not remove the Bash tool's run_in_background parameter, which the model can still choose. Observed live: the optimizer reached the validation stage, detached two jobs, then backgrounded both `evals wait` calls, polled twice, said it would wait for a notification, and ended its turn. The headless run exited there, killing the waits. The previous wording only warned against ending a turn on a detached job; backgrounding the wait itself reads as actively waiting, so it did not land. Name the actual failure: run every evals call in the foreground, and say that a background task, a scheduled wake-up, or a promise to report back later ends the run outright. Co-Authored-By: Claude Opus 5 (1M context) --- .../vero/harbor/build/templates/instruction.md.j2 | 13 ++++++++++--- vero/src/vero/skills/evals/SKILL.md | 13 +++++++++---- 2 files changed, 19 insertions(+), 7 deletions(-) diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index f5c0d6ae..a279ffcc 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -39,9 +39,16 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, Add `--detach` **only** to run several evaluations at once: it returns a `job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks until that job finishes and prints its result (or poll `evals status JOB_ID`, - which also shows elapsed time). Never end your turn waiting on a detached job - — nothing will wake a headless run. Do not claim an improvement before its - comparison baseline has been scored on the same cases. + which also shows elapsed time). + + **Run every `evals` call in the foreground.** You are a single-shot headless + run: nothing exists to deliver a notification or wake you later. If you put a + long call in a background task, schedule a wake-up, or say you will "report + back when it finishes", the run simply ends there and whatever you have not + submitted is lost. A foreground call that blocks for half an hour is working + correctly — let it block. To wait on two jobs, `evals wait` the first, then + the second. Do not claim an improvement before its comparison baseline has + been scored on the same cases. 4. Use `evals status` to inspect evaluation jobs and allowed evaluation sets{% if disclose_budget %}, and to see remaining budgets{% endif %}. diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index f6c36749..86a05286 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -40,8 +40,12 @@ Use `--detach` **only** to run several evaluations concurrently: it returns a `job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks until that job finishes and prints its result; or poll `evals status JOB_ID`, which now also reports `elapsed_seconds` (and `requested_cases` for a subset) so you -can see it is progressing. Never end your turn to "wait" for a detached job — -nothing resumes you. +can see it is progressing. To wait on two jobs, wait the first, then the second. + +Run every `evals` call in the **foreground**. If you are a headless single-shot +run, nothing can wake you: putting a long call in a background task, scheduling +a wake-up, or promising to "report back when it finishes" ends the run right +there. A call that blocks for half an hour is working correctly. ## Run options worth knowing (`evals run --help` for all) @@ -93,8 +97,9 @@ are enforced by the evaluator; `evals plan` shows what remains. `evals cases`; job ids only from `evals run --detach`. - Scores may be noisy: replicate an important comparison before acting on it, by re-running the identical case selection. -- Ending your turn to "wait" for a `--detach`ed job — nothing resumes you. Block - on `evals run` instead, or keep polling `evals status` within your turn. +- Backgrounding the wait. Putting `evals run`/`evals wait` in a background task + (or scheduling a wake-up) looks like waiting but ends a headless run: the + notification you are counting on never arrives. Block in the foreground. - Finishing without `evals submit`: your best candidate must be nominated deliberately, or a fallback (auto-best, then your last commit) ships instead. - Optimizing accuracy while ignoring per-case latency: a slower candidate can be From 2dbc3356c456a2cb971c177ddd4d6d471d2ef16b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 09:58:22 -0700 Subject: [PATCH 60/72] Tell the optimizer its results are persisted, so printed output is disposable Observed live: run #4's optimizer piped `evals run` through `tail -60`, and the payload is long enough that the score fell off the top -- it kept the per-case wall metrics and discarded its own objective value. Truncating a large JSON blob is what any agent will naturally do, so the fix is not to reformat the output but to say plainly that nothing is lost by truncating it. The full record is written under .evals/results/ before `evals run` returns and stays there for the whole run, so `evals list` / `show` / `cases` / `diff` can recover any of it. The instruction previously mentioned .evals/results/ only in the exposed-partitions section, framed as a place to inspect failed trajectories -- not as the durable home of every result. Say it in the workflow step where the truncation actually happens, and name the failure it prevents: re-running an evaluation to recover a number that was already on disk. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/templates/instruction.md.j2 | 9 +++++++++ vero/src/vero/skills/evals/SKILL.md | 10 ++++++++++ 2 files changed, 19 insertions(+) diff --git a/vero/src/vero/harbor/build/templates/instruction.md.j2 b/vero/src/vero/harbor/build/templates/instruction.md.j2 index a279ffcc..f6a9f678 100644 --- a/vero/src/vero/harbor/build/templates/instruction.md.j2 +++ b/vero/src/vero/harbor/build/templates/instruction.md.j2 @@ -36,6 +36,15 @@ hidden final evaluation. The trusted evaluation sidecar owns the cases, scoring, {% endif %} Run `evals run --help` for every option. + **Every result is persisted, so you never need to preserve the printed + output.** The full record — overall metrics, every per-case score, and the + trial artifacts — is written under `.evals/results/` before the command + returns, and stays there for the rest of the run. Piping a result through + `head`/`tail` therefore costs you nothing: recover any of it later with + `evals list` (one row per past evaluation), `evals show ID`, `evals cases ID`, + and `evals diff OLD NEW`. Do not re-run an evaluation to see a number you + truncated — look it up. + Add `--detach` **only** to run several evaluations at once: it returns a `job_id` immediately instead of blocking. Then `evals wait JOB_ID` blocks until that job finishes and prints its result (or poll `evals status JOB_ID`, diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index 86a05286..7f7e7215 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -30,6 +30,14 @@ evals submit --version COMMIT # nominate your best candidate to ship Evaluate the baseline first: a candidate only counts as an improvement against a measured baseline on the same case selection. +**Results are persisted, so printed output is disposable.** Before `evals run` +returns, the complete record — overall metrics, every per-case score, and the +trial artifacts — is written under `.evals/results/` and stays there for the rest +of the run. So piping through `head`/`tail` loses nothing recoverable: use +`evals list` to find any past evaluation and `evals show` / `evals cases` / +`evals diff` to read it back. Never re-run an evaluation just to recover a number +you truncated — it is on disk. + ## Blocking vs detached By default `evals run` **blocks** until scoring finishes and returns the result @@ -93,6 +101,8 @@ are enforced by the evaluator; `evals plan` shows what remains. - Claiming an improvement before the baseline's own evaluation finished. - Dumping whole traces into context. Go metadata-first: `evals trace` summary, then `--span N`, then windowed chars; artifacts are files — `grep` them. +- Re-running an evaluation because you truncated its output. Every result is on + disk under `.evals/results/`; read it back with `evals show` / `evals cases`. - Confusing ids: evaluation ids come from `evals list`; case ids from `evals cases`; job ids only from `evals run --detach`. - Scores may be noisy: replicate an important comparison before acting on it, by From a49fa4c6ecdd6cccb81f47b6f3ca43e8ad59c6f7 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 11:20:14 -0700 Subject: [PATCH 61/72] Add a cheap end-to-end conformance example for new harnesses and models Before spending a real benchmark on a new optimizer harness or model, we want to know in minutes whether the pieces an optimizer actually depends on work. This is that check: the same code path as harness-engineering-bench -- harbor evaluation backend, a nested `harbor run` per case, a target agent metered through the gateway's evaluation scope, an optimizer through the producer scope -- with six arithmetic tasks that finish in seconds. A command-backend smoke would be cheaper but exercises different code, and the harbor path is the one the experiments use. The optimizer instruction is a literal nine-step checklist rather than a research objective, each step naming what to confirm and what to record: the gateway is compose-internal rather than a public endpoint; `evals plan` exposes partitions, case counts, disclosure and budget; development task resources are mounted and validation's are not; an evaluation blocks in the foreground and returns a score; that score is recoverable from .evals/results/ without re-running; `evals cases` refuses on an aggregate-only partition; budget decrements by what was spent; the edit -> commit -> re-evaluate loop moves the score and `evals diff` attributes it; and submit is accepted. Findings go to a committed conformance-report.json. The seed target answers addition and abandons multiplication, so a healthy stack reads ~0.5 for the seed and 1.0 after a one-line fix. That gives an independent cross-check on the agent's self-report: if the report claims every step passed but the reward is 0.0, trust the reward. Three runs while building it turned up three real faults, which is the point. inner_env=docker cannot work -- the inner evaluation shells out to `harbor run -e docker` from inside the sidecar, which has no docker CLI or socket, so harbor produces 0 trial groups and every evaluation 502s; the benchmark docs advertised it for local shakedowns and are corrected separately. Modal tokens must be listed in `secrets:` or harbor exits unauthenticated with the same symptom. And the first optimizer committed its report *after* `evals submit`, so it was not in the submitted candidate and reached nobody -- the checklist now commits first and ends with `cat`. Two findings about the instruction itself. It asked the optimizer to print $OPENAI_API_KEY, calling it not worth protecting; the optimizer refused, said so in its report, and was right, so it now only checks the variable is non-empty. And the seed agent shipped broken twice -- missing BaseAgent's abstract name/version, then a 16-token cap that truncated the model mid-reasoning -- both found and fixed by the optimizer, whose fixes are folded in here so the seed scores the designed 0.5. Worth recording that the tooling passed its own test: the agent-facing error was a bare `502 evaluation failed`, but `evals show ID` surfaced harbor's verbatim stderr and the optimizer root-caused the Docker fault unaided, retrying seven times to rule out transience. Co-Authored-By: Claude Opus 5 (1M context) --- vero/examples/harness-conformance/README.md | 108 +++++++++ vero/examples/harness-conformance/build.yaml | 208 ++++++++++++++++++ .../partitions/development.json | 4 + .../partitions/manifest.json | 19 ++ .../harness-conformance/partitions/test.json | 4 + .../partitions/validation.json | 4 + .../harness-conformance/target/pyproject.toml | 16 ++ .../target/src/conformance_agent/__init__.py | 1 + .../target/src/conformance_agent/agent.py | 95 ++++++++ .../conformance-01/environment/Dockerfile | 6 + .../tasks/conformance-01/instruction.md | 3 + .../tasks/conformance-01/solution/solve.sh | 4 + .../tasks/conformance-01/task.toml | 29 +++ .../tasks/conformance-01/tests/test.sh | 10 + .../tasks/conformance-01/tests/verify.py | 22 ++ .../conformance-02/environment/Dockerfile | 6 + .../tasks/conformance-02/instruction.md | 3 + .../tasks/conformance-02/solution/solve.sh | 4 + .../tasks/conformance-02/task.toml | 29 +++ .../tasks/conformance-02/tests/test.sh | 10 + .../tasks/conformance-02/tests/verify.py | 22 ++ .../conformance-03/environment/Dockerfile | 6 + .../tasks/conformance-03/instruction.md | 3 + .../tasks/conformance-03/solution/solve.sh | 4 + .../tasks/conformance-03/task.toml | 29 +++ .../tasks/conformance-03/tests/test.sh | 10 + .../tasks/conformance-03/tests/verify.py | 22 ++ .../conformance-04/environment/Dockerfile | 6 + .../tasks/conformance-04/instruction.md | 3 + .../tasks/conformance-04/solution/solve.sh | 4 + .../tasks/conformance-04/task.toml | 29 +++ .../tasks/conformance-04/tests/test.sh | 10 + .../tasks/conformance-04/tests/verify.py | 22 ++ .../conformance-05/environment/Dockerfile | 6 + .../tasks/conformance-05/instruction.md | 3 + .../tasks/conformance-05/solution/solve.sh | 4 + .../tasks/conformance-05/task.toml | 29 +++ .../tasks/conformance-05/tests/test.sh | 10 + .../tasks/conformance-05/tests/verify.py | 22 ++ .../conformance-06/environment/Dockerfile | 6 + .../tasks/conformance-06/instruction.md | 3 + .../tasks/conformance-06/solution/solve.sh | 4 + .../tasks/conformance-06/task.toml | 29 +++ .../tasks/conformance-06/tests/test.sh | 10 + .../tasks/conformance-06/tests/verify.py | 22 ++ 45 files changed, 903 insertions(+) create mode 100644 vero/examples/harness-conformance/README.md create mode 100644 vero/examples/harness-conformance/build.yaml create mode 100644 vero/examples/harness-conformance/partitions/development.json create mode 100644 vero/examples/harness-conformance/partitions/manifest.json create mode 100644 vero/examples/harness-conformance/partitions/test.json create mode 100644 vero/examples/harness-conformance/partitions/validation.json create mode 100644 vero/examples/harness-conformance/target/pyproject.toml create mode 100644 vero/examples/harness-conformance/target/src/conformance_agent/__init__.py create mode 100644 vero/examples/harness-conformance/target/src/conformance_agent/agent.py create mode 100644 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vero/examples/harness-conformance/tasks/conformance-06/task.toml create mode 100755 vero/examples/harness-conformance/tasks/conformance-06/tests/test.sh create mode 100644 vero/examples/harness-conformance/tasks/conformance-06/tests/verify.py diff --git a/vero/examples/harness-conformance/README.md b/vero/examples/harness-conformance/README.md new file mode 100644 index 00000000..85e856ae --- /dev/null +++ b/vero/examples/harness-conformance/README.md @@ -0,0 +1,108 @@ +# harness-conformance + +A conformance check for the *stack*, not the model. Run it before spending a real +benchmark on a new optimizer harness or a new model, and you find out in minutes +whether the pieces an optimizer depends on actually work. + +It is deliberately the **same path** as `harness-engineering-bench`: the harbor +evaluation backend, a nested `harbor run` per case, a target agent metered through +the inference gateway's evaluation scope, and an optimizer metered through the +producer scope. Only the work is trivial — six arithmetic tasks, two per +partition, seconds each. A `command`-backend smoke would be cheaper still, but it +exercises different code and would not tell you what you need to know. + +## Run + +```bash +cd /vero +uv run vero harbor run \ + --config examples/harness-conformance/build.yaml \ + --env-file secrets.env \ + --environment docker \ + --agent claude-code --model claude-sonnet-5 \ + --yes -o /tmp/conformance +``` + +Swap `--agent` / `--model` for whatever is under test. Useful parameters: + +| parameter | default | why | +|---|---|---| +| `--param inner_env=...` | `modal` | **docker does not work** — the inner evaluation runs `harbor run -e docker` inside the sidecar, which has no docker CLI or socket, so every case fails with `Docker is not installed or not on PATH` and the eval 502s | +| `--param target_model=...` | `fireworks_ai/deepseek-v4-flash` | check a target model's gateway wiring | +| `--param optimizer_model=...` | follows `--model` | when the adapter requests a different string than it is launched with | +| `--param wandb_mode=online` | `disabled` | a smoke test should not need W&B | + +## What it proves + +Nine checks, in the order the optimizer meets them. The instruction is a literal +step-through — see `description` in `build.yaml`. + +| step | proves | +|---|---| +| 1 | the optimizer's `OPENAI_BASE_URL` is the compose-internal gateway, not a public endpoint — i.e. it is metered and cannot see upstream | +| 2 | `evals plan` exposes partitions, **case counts**, disclosure levels, and remaining budget | +| 3 | development task resources are mounted under `.evals/tasks/`; validation's are not | +| 4 | an evaluation runs, blocks in the foreground, and returns a score | +| 5 | **persistence** — the same result is recoverable from `.evals/results/` via `evals list` / `show` / `cases`, so truncated output is never lost | +| 6 | **disclosure is enforced** — `evals cases` refuses on an aggregate-only partition | +| 7 | budget accounting decrements by what was actually spent | +| 8 | the edit → commit → re-evaluate loop moves the score, and `evals diff` attributes it | +| 9 | `evals submit` accepts a nomination | + +## Reading the result + +Two independent signals, and they should agree: + +1. **`conformance-report.json`**, committed at the target repo root by the + optimizer. One entry per step plus a `summary`. This is also where the + optimizer records anything that *would* handicap it — a confusing message, a + missing number, a command that behaved oddly. +2. **The reward.** The seed target answers addition and abandons multiplication, + so a healthy stack scores **~0.5 for the seed and 1.0 after the fix**. Scoring + 0.0 throughout usually means the target could not reach the gateway; the + arithmetic is easy enough that no plausible model gets it wrong. + +If the report says every step passed but the reward is 0.0, trust the reward — +and treat the disagreement itself as a finding. + +## Why the seed is broken on purpose + +`target/src/conformance_agent/agent.py` returns early on `*`, leaving those +tasks unanswered. That gap is what makes step 8 meaningful: without it the seed +would already score 1.0 and there would be no way to see whether the +edit → evaluate → submit loop actually works. The fix is one early return. + +## Extending it + +Adding a check usually means adding a step to `description` — no code. Add a +*task* only if you need a new failure mode in the target: `tasks/` entries are +generated from a template (`task.toml`, `instruction.md`, +`environment/Dockerfile`, `tests/{test.sh,verify.py}`, `solution/solve.sh`), and +`partitions/*.json` list them by directory name. Keep every partition at two +cases so the whole run stays a few minutes. + +Timeouts follow the same rules as the benchmark suite (see +`harness-engineering-bench/CONFIGURATION.md`): `case_timeout_seconds` equals the +tasks' declared `[agent] timeout_sec` so harbor's agent-timeout multiplier is +exactly 1.0, and each ceiling sits above +`ceil(trials / max_concurrency) × case_timeout`. + +## Known-good and known-bad results + +The first run of this example found three real problems, which is roughly what it +is for: + +- `inner_env=docker` cannot work (see the parameter table). Fixed by defaulting to + `modal`. +- The checklist originally asked the optimizer to print `$OPENAI_API_KEY`. It + declined, correctly, and said so in its report. Now it only checks the variable + is non-empty. +- The optimizer committed its report *after* `evals submit`, so the submitted + candidate did not contain it and the report never reached the operator. The + checklist now fixes the order and ends with `cat conformance-report.json` so the + findings survive in the transcript regardless. + +It also confirmed the tooling is adequate for diagnosis: the agent-facing error was +a bare `502 evaluation failed`, but `evals show ID` surfaced harbor's verbatim +stderr, and the optimizer root-caused the Docker fault unaided and retried 7 times +to rule out transience. diff --git a/vero/examples/harness-conformance/build.yaml b/vero/examples/harness-conformance/build.yaml new file mode 100644 index 00000000..b9581d8d --- /dev/null +++ b/vero/examples/harness-conformance/build.yaml @@ -0,0 +1,208 @@ +# Harness/model conformance check: the cheapest complete exercise of the path a +# real experiment takes. Same shape as harness-engineering-bench -- harbor +# evaluation backend, nested `harbor run` per case, a target agent metered through +# the inference gateway's evaluation scope, an optimizer metered through the +# producer scope -- but with six arithmetic tasks that finish in seconds. +# +# Run it whenever you introduce a new optimizer harness or model, before spending +# a real benchmark on it: +# +# cd /vero +# uv run vero harbor run --config examples/harness-conformance/build.yaml \ +# --env-file secrets.env --environment docker \ +# --agent claude-code --model claude-sonnet-5 --yes -o /tmp/conformance +# +# Swap --agent/--model for the harness under test. `${optimizer_model}` follows +# --model into the producer allow-list; note some adapters request a different +# string than they are launched with (see README). +# +# Read the outcome from the agent's conformance-report.json (see description), and +# cross-check the reward: the seed answers addition only, so a working stack +# scores ~0.5 for the seed and 1.0 for a fixed candidate. +name: vero/harness-conformance +description: >- + This is a CONFORMANCE CHECK, not a research task. Your job is to work through + the checklist below in order and record what you observe, so an operator can + tell whether this harness is wired up correctly. Being thorough and literal + matters more than being clever. + + + Write your findings to `conformance-report.json` at the repository root as you + go, so partial progress survives. Use one entry per step: + `{"step_3_persistence": {"ok": true, "detail": "..."}}`. Commit it. + + + STEP 1 — gateway is scoped. Print `$OPENAI_BASE_URL` and confirm it points at + the compose-internal gateway, not a public provider endpoint. Check that + `$OPENAI_API_KEY` is non-empty WITHOUT printing or recording its value. + Record the host and path you see. If the URL is a public endpoint, stop and + report that loudly: it means the optimizer is unmetered. + + + STEP 2 — plan is legible. Run `evals plan`. Confirm you can see: two + partitions you may evaluate, a case count for each, the disclosure level of + each, and how much run/case budget remains. Record all of it. If the case + count is missing you cannot size a subset, which is a bug worth reporting. + + + STEP 3 — exposed cases. List `.evals/tasks/`. Confirm the development + partition's task resources are readable and that validation's are NOT + present. Record which partitions are exposed. + + + STEP 4 — baseline evaluation. Run the full development partition in the + FOREGROUND and record the score, the per-case wall time, and how long the call + blocked. Expect roughly 0.5: the seed harness answers addition tasks and + abandons multiplication ones. + + + STEP 5 — persistence. Without re-running anything, recover that same result + from disk: `evals list`, then `evals show ID`, then `evals cases ID`. Confirm + the score you read back matches the score that was printed, and that per-case + scores are visible. Record both numbers. This is the check that proves you + never need to re-run an evaluation to recover output you truncated. + + + STEP 6 — disclosure is enforced. Evaluate the validation partition, then try + `evals cases` on that result. It MUST refuse, because validation is + aggregate-only. Record the refusal. A success here is a disclosure leak and + the most important thing on this list to report. + + + STEP 7 — budget accounting. Run `evals plan` again and compare with STEP 2. + Confirm remaining runs and cases decreased by the amount you actually spent. + Record before and after. + + + STEP 8 — the edit loop works. Fix the target harness so it answers + multiplication tasks as well as addition ones (find the early return that + gives up), commit, and re-evaluate the SAME development selection. Confirm the + score rises to 1.0. Record before and after, and confirm `evals diff` between + the two evaluations attributes the gain to the multiplication cases. + + + STEP 9 — submit. Write the `summary` entry described below, commit the report, + and ONLY THEN nominate your candidate with `evals submit`. Order matters: a + report committed after submitting is not part of the submitted candidate and + an operator will never see it. Confirm the submission was accepted and record + the commit. + + + The `summary` entry: which steps passed, which failed, and any error text + verbatim. Anything you noticed that would handicap an optimizer -- a confusing + message, a missing number, a command that behaved unexpectedly -- belongs in + it. Then `cat conformance-report.json` as your last action, so the findings + survive in the transcript even if the commit does not. + +agent_repo: target +task_source: tasks +task_manifest: partitions/manifest.json +agent_import_path: conformance_agent.agent:ConformanceAgent +harbor_requirement: harbor[modal]==0.20.0 + +partition_files: + development: partitions/development.json + validation: partitions/validation.json + test: partitions/test.json + +# Deliberately mirrors the benchmark suite: development full-disclosure with task +# resources exposed, validation aggregate-only. STEP 6 depends on that asymmetry, +# so min_aggregate_cases is 2 (the whole partition) rather than the usual 5. +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_runs: 20 + total_cases: 40 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 2 + total_runs: 20 + total_cases: 40 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + # A correct stack lands on 0.5 for the seed and 1.0 for a fixed candidate, so + # there is no measurement noise to average away and nothing to pin. + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: conformance +objective: + selector: + metric: score + direction: maximize +reward_mode: submit +baseline_floor: false +score_baseline: false +rescore_top_k: 1 +rescore_attempts: 1 + +model: ${target_model:-fireworks_ai/deepseek-v4-flash} +# modal, matching the benchmarks. `inner_env=docker` does NOT work: the inner +# evaluation shells out to `harbor run -e docker` from inside the sidecar +# container, which has no docker CLI or socket, and every case fails with +# "Docker is not installed or not on PATH" -> 0 trial groups -> 502. +environment_name: ${inner_env:-modal} +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 2 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 8000 +expose_attempt_detail: false +# Every clock is derived the same way as the benchmark suite (see +# harness-engineering-bench/CONFIGURATION.md), just small: case_timeout equals the +# tasks' declared [agent] timeout_sec so harbor's multiplier is exactly 1.0, and +# each ceiling sits above ceil(trials / max_concurrency) x case_timeout. +timeout_seconds: 900 +case_timeout_seconds: 120 +task_agent_timeout_seconds: 120 +max_concurrency: 6 +error_rate_threshold: 0.5 +verifier_timeout_seconds: 1800 +# Passed through to the evaluation backend. The Modal tokens are REQUIRED, not +# optional: inner evals run on Modal (see environment_name), and without them +# harbor exits with "Modal requires authentication", produces 0 trial groups, and +# every evaluation 502s. +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL + +harness_user: harness + +# Same loop protections as the benchmarks: a single blocking eval must fit in one +# Bash call, or the optimizer detaches and a headless run dies waiting. +agent_env: + BASH_MAX_TIMEOUT_MS: "1800000" + BASH_DEFAULT_TIMEOUT_MS: "1800000" + ENABLE_BACKGROUND_TASKS: "0" + FORCE_AUTO_BACKGROUND_TASKS: "0" + +wandb: + project: vero-conformance + group: conformance + name: ${wandb_run:-conformance} + mode: ${wandb_mode:-disabled} # off by default; a smoke test should not need W&B + +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + request_log_attribution: true + producer: + allowed_models: ["${optimizer_model:-openai/gpt-5.4}"] + max_concurrency: 4 + evaluation: + allowed_models: ["${target_model:-fireworks_ai/deepseek-v4-flash}"] + max_requests: 2000 + max_tokens: 20000000 + max_concurrency: 8 +instruct_multifidelity: false +instruct_exhaust_budget: false diff --git a/vero/examples/harness-conformance/partitions/development.json b/vero/examples/harness-conformance/partitions/development.json new file mode 100644 index 00000000..3861d171 --- /dev/null +++ b/vero/examples/harness-conformance/partitions/development.json @@ -0,0 +1,4 @@ +[ + "conformance-01", + "conformance-02" +] diff --git a/vero/examples/harness-conformance/partitions/manifest.json b/vero/examples/harness-conformance/partitions/manifest.json new file mode 100644 index 00000000..ccb2dede --- /dev/null +++ b/vero/examples/harness-conformance/partitions/manifest.json @@ -0,0 +1,19 @@ +{ + "schema_version": 1, + "task_source": "../tasks", + "dataset_name": "harness-conformance", + "seed": "vero-conformance-v1", + "partition_counts": { + "development": 2, + "validation": 2, + "test": 2 + }, + "tasks": [ + "conformance-01", + "conformance-02", + "conformance-03", + "conformance-04", + "conformance-05", + "conformance-06" + ] +} diff --git a/vero/examples/harness-conformance/partitions/test.json b/vero/examples/harness-conformance/partitions/test.json new file mode 100644 index 00000000..0eafdd61 --- /dev/null +++ b/vero/examples/harness-conformance/partitions/test.json @@ -0,0 +1,4 @@ +[ + "conformance-05", + "conformance-06" +] diff --git a/vero/examples/harness-conformance/partitions/validation.json b/vero/examples/harness-conformance/partitions/validation.json new file mode 100644 index 00000000..b94e9e30 --- /dev/null +++ b/vero/examples/harness-conformance/partitions/validation.json @@ -0,0 +1,4 @@ +[ + "conformance-03", + "conformance-04" +] diff --git a/vero/examples/harness-conformance/target/pyproject.toml b/vero/examples/harness-conformance/target/pyproject.toml new file mode 100644 index 00000000..1c904056 --- /dev/null +++ b/vero/examples/harness-conformance/target/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "vero-conformance-agent" +version = "0.1.0" +description = "Trivial editable target harness for the VeRO conformance example" +requires-python = ">=3.12" +dependencies = [ + "harbor==0.20.0", + "openai==2.46.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/conformance_agent"] diff --git a/vero/examples/harness-conformance/target/src/conformance_agent/__init__.py b/vero/examples/harness-conformance/target/src/conformance_agent/__init__.py new file mode 100644 index 00000000..204ea14a --- /dev/null +++ b/vero/examples/harness-conformance/target/src/conformance_agent/__init__.py @@ -0,0 +1 @@ +"""Seed target harness for the VeRO conformance example.""" diff --git a/vero/examples/harness-conformance/target/src/conformance_agent/agent.py b/vero/examples/harness-conformance/target/src/conformance_agent/agent.py new file mode 100644 index 00000000..239f5286 --- /dev/null +++ b/vero/examples/harness-conformance/target/src/conformance_agent/agent.py @@ -0,0 +1,95 @@ +"""Seed target harness for the conformance example. + +Deliberately incomplete: it answers addition tasks and gives up on multiplication +ones, so the seed scores about 0.5 and a one-line fix takes it to 1.0. That gap is +the point -- it makes the optimizer's edit -> evaluate -> submit loop observable in +the score rather than something we have to infer. + +The model call is not optional decoration either: the answer has to come back from +the model, so an unreachable or mis-allow-listed inference gateway shows up as a +zero score instead of passing silently. +""" + +from __future__ import annotations + +import re +import shlex +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +ANSWER_PATH = "/app/answer.txt" + +INSTRUCTIONS = """You answer a single arithmetic question. +Reply with the numeric result and nothing else: no words, units, or punctuation. +""" + + +# A model told to answer with a bare number may still reason in prose first, so +# take the last number in the completion rather than trusting the raw text. +NUMBER_RE = re.compile(r"-?\d+(?:\.\d+)?") + + +class ConformanceAgent(BaseAgent): + """One model call, one file written. Nothing else.""" + + # BaseAgent declares these abstract; omitting them fails at instantiation with + # "Can't instantiate abstract class" and every case reports an infrastructure + # failure, not a low score. + @staticmethod + @override + def name() -> str: + return "conformance-agent" + + @override + def version(self) -> str | None: + return "0.1.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("conformance agent requires a model name") + # The gateway matches the requested model as an exact string, and the + # evaluation scope is allow-listed with the unprefixed name. + self._api_model = self.model_name.removeprefix("openai/") + # base_url and api_key come from OPENAI_BASE_URL / OPENAI_API_KEY, which + # vero points at the metered evaluation scope of the inference gateway. + self._client = AsyncOpenAI(max_retries=4) + + @override + async def setup(self, environment: BaseEnvironment) -> None: + await environment.exec("mkdir -p /app", timeout_sec=30) + + async def _write_answer(self, environment: BaseEnvironment, answer: str) -> None: + quoted = shlex.quote(answer.strip()) + await environment.exec( + f"printf '%s' {quoted} > {ANSWER_PATH}", cwd="/app", timeout_sec=30 + ) + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + # THE DELIBERATE GAP: multiplication tasks are abandoned unanswered. + # Removing this early return is the fix the optimizer is meant to find. + if "*" in instruction: + await self._write_answer(environment, "") + return + + completion = await self._client.chat.completions.create( + model=self._api_model, + messages=[ + {"role": "system", "content": INSTRUCTIONS}, + {"role": "user", "content": instruction}, + ], + max_tokens=256, + ) + content = (completion.choices[0].message.content or "").strip() + numbers = NUMBER_RE.findall(content) + await self._write_answer(environment, numbers[-1] if numbers else content) diff --git a/vero/examples/harness-conformance/tasks/conformance-01/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-01/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-01/instruction.md b/vero/examples/harness-conformance/tasks/conformance-01/instruction.md new file mode 100644 index 00000000..d73e0f4a --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/instruction.md @@ -0,0 +1,3 @@ +Compute 17 + 25 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-01/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-01/solution/solve.sh new file mode 100755 index 00000000..a00a4509 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '42' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-01/task.toml b/vero/examples/harness-conformance/tasks/conformance-01/task.toml new file mode 100644 index 00000000..9ba60b3e --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-01" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-01" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-01/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-01/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-01/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-01/tests/verify.py new file mode 100644 index 00000000..e815ae5c --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-01/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "42" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harness-conformance/tasks/conformance-02/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-02/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-02/instruction.md b/vero/examples/harness-conformance/tasks/conformance-02/instruction.md new file mode 100644 index 00000000..7cc9999a --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/instruction.md @@ -0,0 +1,3 @@ +Compute 6 * 7 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-02/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-02/solution/solve.sh new file mode 100755 index 00000000..a00a4509 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '42' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-02/task.toml b/vero/examples/harness-conformance/tasks/conformance-02/task.toml new file mode 100644 index 00000000..e457484a --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-02" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-02" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-02/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-02/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-02/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-02/tests/verify.py new file mode 100644 index 00000000..e815ae5c --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-02/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "42" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harness-conformance/tasks/conformance-03/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-03/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-03/instruction.md b/vero/examples/harness-conformance/tasks/conformance-03/instruction.md new file mode 100644 index 00000000..4e40e068 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/instruction.md @@ -0,0 +1,3 @@ +Compute 128 + 47 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-03/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-03/solution/solve.sh new file mode 100755 index 00000000..9fee7ecf --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '175' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-03/task.toml b/vero/examples/harness-conformance/tasks/conformance-03/task.toml new file mode 100644 index 00000000..be89c1c1 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-03" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-03" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-03/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-03/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-03/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-03/tests/verify.py new file mode 100644 index 00000000..1e0a19b5 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-03/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "175" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harness-conformance/tasks/conformance-04/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-04/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-04/instruction.md b/vero/examples/harness-conformance/tasks/conformance-04/instruction.md new file mode 100644 index 00000000..2e5ea5cc --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/instruction.md @@ -0,0 +1,3 @@ +Compute 12 * 12 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-04/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-04/solution/solve.sh new file mode 100755 index 00000000..8faecbb7 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '144' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-04/task.toml b/vero/examples/harness-conformance/tasks/conformance-04/task.toml new file mode 100644 index 00000000..38bd962c --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-04" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-04" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-04/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-04/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-04/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-04/tests/verify.py new file mode 100644 index 00000000..12f73819 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-04/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "144" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harness-conformance/tasks/conformance-05/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-05/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-05/instruction.md b/vero/examples/harness-conformance/tasks/conformance-05/instruction.md new file mode 100644 index 00000000..3c2ea4ee --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/instruction.md @@ -0,0 +1,3 @@ +Compute 301 + 99 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-05/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-05/solution/solve.sh new file mode 100755 index 00000000..87c0b682 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '400' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-05/task.toml b/vero/examples/harness-conformance/tasks/conformance-05/task.toml new file mode 100644 index 00000000..ec5eec1f --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-05" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-05" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-05/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-05/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-05/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-05/tests/verify.py new file mode 100644 index 00000000..b6f98fb5 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-05/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "400" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() diff --git a/vero/examples/harness-conformance/tasks/conformance-06/environment/Dockerfile b/vero/examples/harness-conformance/tasks/conformance-06/environment/Dockerfile new file mode 100644 index 00000000..484807c6 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/environment/Dockerfile @@ -0,0 +1,6 @@ +# Smallest thing that can run the verifier. The target harness needs no +# packages of its own -- it runs outside the task container and reaches this +# environment through harbor's exec interface. +FROM python:3.12-slim +WORKDIR /app +RUN mkdir -p /app /logs/verifier diff --git a/vero/examples/harness-conformance/tasks/conformance-06/instruction.md b/vero/examples/harness-conformance/tasks/conformance-06/instruction.md new file mode 100644 index 00000000..c9724347 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/instruction.md @@ -0,0 +1,3 @@ +Compute 25 * 16 and write only the numeric result to /app/answer.txt. + +Write nothing else to the file: no words, units, sign, or trailing text. diff --git a/vero/examples/harness-conformance/tasks/conformance-06/solution/solve.sh b/vero/examples/harness-conformance/tasks/conformance-06/solution/solve.sh new file mode 100755 index 00000000..87c0b682 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/solution/solve.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Reference solution: what a fully working harness produces. +set -Eeuo pipefail +printf '%s' '400' > /app/answer.txt diff --git a/vero/examples/harness-conformance/tasks/conformance-06/task.toml b/vero/examples/harness-conformance/tasks/conformance-06/task.toml new file mode 100644 index 00000000..6c44564a --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/task.toml @@ -0,0 +1,29 @@ +version = "1.0" + +[task] +name = "conformance/conformance-06" + +[metadata] +author_name = "VeRO" +benchmark = "harness-conformance" +source = "vero-examples" +source_id = "conformance-06" +difficulty = "trivial" +category = "smoke" +tags = ["conformance", "smoke"] + +# Deliberately tight: this task is seconds of work, so a hang is obvious rather +# than something that quietly eats an hour. vero sets case_timeout_seconds to +# match [agent] exactly, making harbor's agent-timeout multiplier 1.0. +[verifier] +timeout_sec = 60.0 + +[agent] +timeout_sec = 120.0 + +[environment] +build_timeout_sec = 600.0 +cpus = 1 +memory_mb = 1024 +storage_mb = 2048 +allow_internet = true diff --git a/vero/examples/harness-conformance/tasks/conformance-06/tests/test.sh b/vero/examples/harness-conformance/tasks/conformance-06/tests/test.sh new file mode 100755 index 00000000..919ca088 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/tests/test.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Harbor reads the score from /logs/verifier/reward.txt. Always leave a value +# there and always exit 0, so a verifier crash reads as 0.0 rather than as +# infrastructure error -- an unanswered task is a legitimate zero. +set -Eeuo pipefail + +mkdir -p /logs/verifier +python3 /tests/verify.py || echo 0 > /logs/verifier/reward.txt +cat /logs/verifier/reward.txt +exit 0 diff --git a/vero/examples/harness-conformance/tasks/conformance-06/tests/verify.py b/vero/examples/harness-conformance/tasks/conformance-06/tests/verify.py new file mode 100644 index 00000000..b6f98fb5 --- /dev/null +++ b/vero/examples/harness-conformance/tasks/conformance-06/tests/verify.py @@ -0,0 +1,22 @@ +"""Exact-match verifier: strips whitespace, compares as a number.""" + +from pathlib import Path + +EXPECTED = "400" +ANSWER = Path("/app/answer.txt") +REWARD = Path("/logs/verifier/reward.txt") + + +def main() -> None: + REWARD.parent.mkdir(parents=True, exist_ok=True) + text = ANSWER.read_text(encoding="utf-8").strip() if ANSWER.is_file() else "" + try: + matched = abs(float(text) - float(EXPECTED)) < 1e-9 + except ValueError: + matched = False + REWARD.write_text("1\n" if matched else "0\n", encoding="utf-8") + print(f"expected={EXPECTED} got={text!r} reward={int(matched)}") + + +if __name__ == "__main__": + main() From c3db0c909f54f08910faa5d1c77e0f1c4f4c4693 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 12:46:11 -0700 Subject: [PATCH 62/72] Lift Harbor trial phases into CaseResult.execution_trace `evals trace` was inert on the Harbor backend -- the only backend the benchmarks use. HarborBackend never populated CaseResult.execution_trace, so `evals cases` reported trace=False for every case and `evals trace ID CASE` had nothing to read. Confirmed on a real officeqa run as well as the conformance example, so it was never a toy-config artifact. That made it the same class of defect as the --seed bug fixed earlier: a command advertised in three places (the skill's loop listing, the skill's "go metadata-first" advice, and the instruction's navigation step) that cannot work where we run it. A conformance optimizer correctly skipped it, because `evals cases` had just told it there was no trace -- the interface was honest, it simply had nothing behind it. Everything downstream already existed: context.py splits execution_trace into its own execution-trace.json, `evals cases` derives its column from the path, and `evals trace` summarizes spans and windows one with --span N. Only the producer was missing. _execution_trace() now emits one span per trial phase in run order, from records Harbor already writes: environment_setup, agent_setup, agent_execution (carrying agent_info and agent_result, including the harness's own rollout_details when it reports them), verifier (carrying rewards), plus an exception span when the trial died. Multi-attempt cases get one group per attempt tagged by index, which also makes n_attempts=3 finalization legible for the first time. Spans share one key set so the summary's shape grouping stays useful, with variable-size payload isolated under `detail` -- which is what --span windowing is for. Wired into all four CaseResult paths, including the error and no-trial ones, since those are the cases an agent most needs to inspect. Verified live: `evals cases` now reports trace=True, and a failing conformance case shows environment_setup 4.7s, agent_setup 0.7s, agent_execution 0.34s, verifier 3.6s -- the near-zero agent phase being the seed's deliberate early return, i.e. the trace shows at a glance that the case never called a model. Disclosure is unchanged (aggregate evaluations still write no per-case data) and sanitize_evaluation_report already scrubs execution_trace, so carrying candidate-controlled exception text adds no leak surface. Also adds a conformance step that exercises the trace, so a future harness change that breaks it is caught by the smoke test rather than by an optimizer silently losing its debugging surface. Co-Authored-By: Claude Opus 5 (1M context) --- vero/examples/harness-conformance/README.md | 1 + vero/examples/harness-conformance/build.yaml | 9 ++ vero/src/vero/harbor/backend.py | 80 +++++++++++++++++ vero/tests/test_v05_harbor_backend.py | 92 ++++++++++++++++++++ 4 files changed, 182 insertions(+) diff --git a/vero/examples/harness-conformance/README.md b/vero/examples/harness-conformance/README.md index 85e856ae..d7b88fa9 100644 --- a/vero/examples/harness-conformance/README.md +++ b/vero/examples/harness-conformance/README.md @@ -44,6 +44,7 @@ step-through — see `description` in `build.yaml`. | 3 | development task resources are mounted under `.evals/tasks/`; validation's are not | | 4 | an evaluation runs, blocks in the foreground, and returns a score | | 5 | **persistence** — the same result is recoverable from `.evals/results/` via `evals list` / `show` / `cases`, so truncated output is never lost | +| 5b | **traces** — `evals cases` reports `trace: true` and `evals trace ID CASE` returns per-phase spans with plausible durations | | 6 | **disclosure is enforced** — `evals cases` refuses on an aggregate-only partition | | 7 | budget accounting decrements by what was actually spent | | 8 | the edit → commit → re-evaluate loop moves the score, and `evals diff` attributes it | diff --git a/vero/examples/harness-conformance/build.yaml b/vero/examples/harness-conformance/build.yaml index b9581d8d..ca058090 100644 --- a/vero/examples/harness-conformance/build.yaml +++ b/vero/examples/harness-conformance/build.yaml @@ -63,6 +63,15 @@ description: >- never need to re-run an evaluation to recover output you truncated. + STEP 5b — traces. `evals cases` prints a `trace` column; confirm it is true, + then run `evals trace ID CASE_ID` on the case that scored WORST and record the + span count and each span's phase and duration. Read one span with + `--span N`. Confirm the timings are plausible for what the harness actually + did — a case that never called a model should show a near-zero + `agent_execution` span. If the column says false, or the trace is empty, say so: + it means per-case debugging information is not reaching the optimizer. + + STEP 6 — disclosure is enforced. Evaluate the validation partition, then try `evals cases` on that result. It MUST refuse, because validation is aggregate-only. Record the refusal. A success here is a disclosure leak and diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index 884da1fe..a79e8b8b 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -1001,6 +1001,81 @@ def _case_wall_seconds(self, attempts: list[dict[str, Any]]) -> float | None: ] return max(durations) if durations else None + @staticmethod + def _phase_seconds(phase: Any) -> float | None: + if not isinstance(phase, dict): + return None + started, finished = phase.get("started_at"), phase.get("finished_at") + if not isinstance(started, str) or not isinstance(finished, str): + return None + try: + delta = datetime.fromisoformat( + finished.replace("Z", "+00:00") + ) - datetime.fromisoformat(started.replace("Z", "+00:00")) + except ValueError: + return None + seconds = delta.total_seconds() + return seconds if seconds >= 0 else None + + # Harbor's own phase names, in the order a trial runs them. + _TRIAL_PHASES = ("environment_setup", "agent_setup", "agent_execution", "verifier") + + def _execution_trace(self, attempts: list[dict[str, Any]]) -> list[JsonValue]: + """One span per trial phase, so `evals trace` has something to show. + + Harbor already records a start/finish window per phase and the agent's own + rollout details; without lifting them into ``CaseResult.execution_trace`` + the whole trace surface (`evals cases` reporting a trace, `evals trace + ID CASE`, `--span N`) is inert on this backend and an agent has to go + spelunking in ``artifacts/harbor/jobs/**`` with raw file tools instead. + + Every span carries the same keys so the summary's shape grouping stays + legible; anything phase-specific and potentially large (rollout details, + tracebacks) goes under ``detail``, which the CLI windows by span. + """ + + spans: list[JsonValue] = [] + for index, attempt in enumerate(attempts): + trial_name = attempt.get("trial_name") + + def span(phase: str, seconds: float | None, detail: Any) -> None: + source = attempt.get(phase) if phase in self._TRIAL_PHASES else None + spans.append( + { + "attempt": index, + "trial_name": trial_name, + "phase": phase, + "started_at": (source or {}).get("started_at") + if isinstance(source, dict) + else attempt.get("started_at"), + "finished_at": (source or {}).get("finished_at") + if isinstance(source, dict) + else attempt.get("finished_at"), + "seconds": seconds, + "detail": detail, + } + ) + + for phase in self._TRIAL_PHASES: + if not isinstance(attempt.get(phase), dict): + continue + detail: dict[str, Any] = {} + if phase == "agent_execution": + detail["agent_info"] = attempt.get("agent_info") + # rollout_details is the harness's own turn-by-turn record + # when it reports one; large by nature, hence span windowing. + detail["agent_result"] = attempt.get("agent_result") + elif phase == "verifier": + detail["rewards"] = ( + attempt.get("verifier_result") or {} + ).get("rewards") + span(phase, self._phase_seconds(attempt.get(phase)), detail or None) + + failure = attempt.get("exception_info") + if isinstance(failure, dict) and failure: + span("exception", None, failure) + return spans + @staticmethod def _agent_reported_tokens(attempts: list[dict[str, Any]]) -> dict[str, float]: """Sum the agent-self-reported token counts across a case's attempts. @@ -1060,6 +1135,7 @@ def _case_result( trusted: bool = False, ) -> tuple[CaseResult, float]: trial_artifacts = self._trial_artifacts(attempts, artifact_root) + trace = self._execution_trace(attempts) wall_seconds = self._case_wall_seconds(attempts) reported_tokens = self._agent_reported_tokens(attempts) attempt_detail = [ @@ -1125,6 +1201,7 @@ def _case_result( else None ), artifacts=trial_artifacts, + execution_trace=trace, ), score, ) @@ -1161,6 +1238,7 @@ def _case_result( else None ), artifacts=trial_artifacts, + execution_trace=trace, ), score, ) @@ -1235,6 +1313,7 @@ def _case_result( feedback=self._transcript_feedback(attempts), metadata={"error_category": category.value}, artifacts=trial_artifacts, + execution_trace=trace, ), self.config.failure_score, ) @@ -1252,6 +1331,7 @@ def _case_result( feedback=self._transcript_feedback(attempts), metadata={"error_category": category.value}, artifacts=trial_artifacts, + execution_trace=trace, errors=[ CaseError( message=message, diff --git a/vero/tests/test_v05_harbor_backend.py b/vero/tests/test_v05_harbor_backend.py index 62bf6569..1883eac1 100644 --- a/vero/tests/test_v05_harbor_backend.py +++ b/vero/tests/test_v05_harbor_backend.py @@ -1129,3 +1129,95 @@ async def test_harbor_backend_reports_median_for_skewed_cost_distributions(tmp_p assert report.metrics["median_case_agent_reported_output_tokens"] == 10.0 assert report.metrics["max_case_agent_reported_output_tokens"] == 150.0 assert report.metrics["median_case_agent_reported_cached_input_tokens"] == 20.0 + + +@pytest.mark.asyncio +async def test_case_result_carries_a_phase_execution_trace(tmp_path: Path): + """Harbor trial phases are lifted into CaseResult.execution_trace. + + Without this the whole trace surface is inert on the only backend the + benchmarks use: `evals cases` reports trace=False for every case and + `evals trace ID CASE` has nothing to read, so an agent that wants to know + why a case failed has to walk artifacts/harbor/jobs/** with raw file tools. + Observed live in a conformance run, where the optimizer correctly skipped + `evals trace` because the interface told it there was nothing there. + """ + sandbox = FakeSandbox( + tmp_path, + { + "example/alpha": [ + { + "trial_name": "alpha__abc", + "verifier_result": {"rewards": {"reward": 1.0}}, + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:01:30Z", + "agent_info": {"name": "seed", "version": "0.1.0"}, + "agent_result": {"n_input_tokens": 10, "rollout_details": ["turn"]}, + "environment_setup": { + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:10Z", + }, + "agent_setup": { + "started_at": "2026-01-01T00:00:10Z", + "finished_at": "2026-01-01T00:00:20Z", + }, + "agent_execution": { + "started_at": "2026-01-01T00:00:20Z", + "finished_at": "2026-01-01T00:01:20Z", + }, + "verifier": { + "started_at": "2026-01-01T00:01:20Z", + "finished_at": "2026-01-01T00:01:30Z", + }, + } + ], + # A case that died: the exception has to reach the trace too, since + # that is the case an agent most needs to inspect. + "example/beta": [ + { + "trial_name": "beta__xyz", + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:30Z", + "environment_setup": { + "started_at": "2026-01-01T00:00:00Z", + "finished_at": "2026-01-01T00:00:05Z", + }, + "exception_info": { + "exception_type": "TimeoutError", + "message": "agent exceeded its clock", + }, + } + ], + }, + ) + backend = HarborBackend(_config(tmp_path)) + + report = await backend.evaluate( + context=await _context(tmp_path, sandbox), + request=_request(CaseIds(ids=["case-a", "case-b"])), + ) + by_id = {case.case_id: case for case in report.cases} + + scored = by_id["case-a"].execution_trace + assert [span["phase"] for span in scored] == [ + "environment_setup", + "agent_setup", + "agent_execution", + "verifier", + ] + assert [span["seconds"] for span in scored] == [10.0, 10.0, 60.0, 10.0] + assert {span["attempt"] for span in scored} == {0} + assert {span["trial_name"] for span in scored} == {"alpha__abc"} + # The harness's own turn-by-turn record rides on the agent_execution span, + # which is why the CLI windows spans rather than printing them whole. + agent_span = next(s for s in scored if s["phase"] == "agent_execution") + assert agent_span["detail"]["agent_result"]["rollout_details"] == ["turn"] + assert agent_span["detail"]["agent_info"]["name"] == "seed" + verifier_span = next(s for s in scored if s["phase"] == "verifier") + assert verifier_span["detail"]["rewards"] == {"reward": 1.0} + # Uniform key set across spans keeps `evals trace`'s shape summary legible. + assert len({tuple(sorted(span)) for span in scored}) == 1 + + failed = by_id["case-b"].execution_trace + assert [span["phase"] for span in failed] == ["environment_setup", "exception"] + assert failed[-1]["detail"]["exception_type"] == "TimeoutError" From 24a8ec2d89269b6666657ea30c27e56b1aa4198f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 13:41:20 -0700 Subject: [PATCH 63/72] Route opencode's non-openai providers through the gateway opencode could not drive a Claude model at all. Harbor's adapter injects a provider baseURL into opencode.json only when the provider half of provider/model is "openai" (agents/installed/opencode.py), and opencode's anthropic provider ignores ANTHROPIC_BASE_URL, so `anthropic/claude-sonnet-5` called api.anthropic.com directly: zero requests reached the gateway and the run died on `401 invalid x-api-key`. That fails closed rather than leaking -- the optimizer only ever holds a scoped gateway token and the upstream key never leaves the gateway container -- but it left `openai/` as the only usable form, which forces the Responses API. Claude over Responses then breaks opencode outright. litellm's Anthropic-to-Responses translation emits three id namespaces inside one stream: a resp_ id, Anthropic-native msg_/toolu_ item ids passed through, and a stray chatcmpl- id from its internal chat-completions path. opencode indexes content parts by item id, was told to update a text part keyed by the chatcmpl- id it had never registered, and exited 1 with `text part chatcmpl-... not found` after six steps. Verified from the gateway request log, where all six main-model calls were 200 -- the proxy translated faithfully as far as we are concerned, opencode just cannot parse the result. Passing the baseURL ourselves via harbor's `--ak opencode_config=...`, which the adapter deep-merges last, keeps the traffic on the provider's own API. Written for any non-openai provider rather than special-cased to Anthropic, and skipping openai so it does not fight the adapter. Measured on the conformance example: openai/claude-sonnet-5 crashes at 6 steps; openai/gpt-5.4 scores 1.0; anthropic/claude-sonnet-5 scored 1.0 over 39 steps with 39 metered `messages` calls and no translation layer in sight -- the same path claude-code already uses. One finding this leaves open: opencode also issues an auxiliary small-model call (claude-haiku-4-5 on the anthropic path, gpt-5.4-nano on the openai one) that 403s, because the producer allow-list holds a single entry. It is non-fatal but silent outside the gateway log, and the model varies with the provider family, so the allow-list needs a second entry rather than one more fixed name. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/cli.py | 39 +++++++++++++++++++++++++++++++++++++ vero/tests/test_v05_cli.py | 39 +++++++++++++++++++++++++++++++++++++ 2 files changed, 78 insertions(+) diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 8c1e6b7d..723e0b00 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -224,6 +224,44 @@ def _load_env_file(path: Path) -> dict[str, str]: return values +def _opencode_gateway_args(agent: str, model: str | None, task: Path) -> list[str]: + """Route opencode's non-openai providers through the gateway. + + Harbor's opencode adapter writes a provider ``baseURL`` into + ``opencode.json`` only when the provider half of ``provider/model`` is + ``openai`` (``agents/installed/opencode.py``). For any other provider it + writes none, so opencode calls that provider's public endpoint. That fails + closed rather than leaking -- the optimizer only ever holds a scoped gateway + token, and Anthropic answers ``401 invalid x-api-key`` -- but it does leave + ``openai/`` as the only usable form, which forces the Responses API. Driving + a Claude model that way breaks: litellm's Anthropic-to-Responses translation + emits mixed id namespaces in one stream (a ``resp_`` id, Anthropic ``msg_``/ + ``toolu_`` item ids, and a stray ``chatcmpl-`` id), and opencode dies + resolving a text part under an id it never registered. + + Supplying the baseURL ourselves keeps the traffic on the provider's own API + -- Messages for Anthropic, which is the path claude-code already proves -- + and metered. The adapter deep-merges job kwargs last, so this wins. + """ + + if agent != "opencode" or not model or "/" not in model: + return [] + provider, _, _ = model.partition("/") + if provider == "openai": + return [] # the adapter already injects the baseURL for this one + path = task / "environment/gateway/launch.json" + if not path.exists(): + return [] + try: + base_url = json.loads(path.read_text(encoding="utf-8"))["producer_base_url"] + except (OSError, json.JSONDecodeError, KeyError): + return [] + # producer_base_url ends in /v1 and provider SDKs append their own route + # (/messages), matching how ANTHROPIC_BASE_URL is handed to claude-code. + payload = {"provider": {provider: {"options": {"baseURL": base_url}}}} + return ["--ak", f"opencode_config={json.dumps(payload, separators=(',', ':'))}"] + + def _compiled_run_environment( task: Path, overrides: dict[str, str] | None = None ) -> dict[str, str]: @@ -437,6 +475,7 @@ def run_command(config_path, agent, model, environment, params, env_file, extra) # for a deterministic command line. for key in sorted(config.agent_env): command.extend(["--ae", f"{key}={config.agent_env[key]}"]) + command.extend(_opencode_gateway_args(agent, model, task)) command.extend(extra) click.echo(shlex.join(command)) completed = subprocess.run( diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py index d18534c8..ae4c7d90 100644 --- a/vero/tests/test_v05_cli.py +++ b/vero/tests/test_v05_cli.py @@ -309,3 +309,42 @@ def test_cli_rejects_options_that_do_not_apply(tmp_path: Path): assert "are only valid with --produce" in producer_timeout.output assert wandb.exit_code == 2 assert "require --wandb-project" in wandb.output + + +def test_opencode_non_openai_provider_gets_a_gateway_base_url(tmp_path): + """opencode reaches non-openai providers only if we supply the baseURL. + + Harbor's adapter injects one for `openai/...` alone, so `anthropic/...` + otherwise calls api.anthropic.com and dies on 401 (the optimizer holds only a + scoped token). Forcing `openai/` instead puts a Claude model on the Responses + API, whose litellm translation opencode cannot parse. This keeps Anthropic on + Messages, through the gateway. + """ + from vero.harbor.cli import _opencode_gateway_args + + task = tmp_path / "task" + (task / "environment" / "gateway").mkdir(parents=True) + (task / "environment" / "gateway" / "launch.json").write_text( + json.dumps({"producer_base_url": "http://inference-gateway:8001/scopes/p/o/v1"}), + encoding="utf-8", + ) + + args = _opencode_gateway_args("opencode", "anthropic/claude-sonnet-5", task) + assert args[0] == "--ak" + key, _, value = args[1].partition("=") + assert key == "opencode_config" + assert json.loads(value) == { + "provider": { + "anthropic": { + "options": {"baseURL": "http://inference-gateway:8001/scopes/p/o/v1"} + } + } + } + + # openai is the one provider the adapter already handles; don't fight it. + assert _opencode_gateway_args("opencode", "openai/gpt-5.4", task) == [] + # Other agents and bare model names are untouched. + assert _opencode_gateway_args("claude-code", "anthropic/claude-sonnet-5", task) == [] + assert _opencode_gateway_args("opencode", "claude-sonnet-5", task) == [] + # A task compiled without a gateway simply gets no override. + assert _opencode_gateway_args("opencode", "anthropic/x", tmp_path / "none") == [] From 7f4c91a31bc8ad1ff45088c9dc6a394eae1a80f6 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 13:46:41 -0700 Subject: [PATCH 64/72] Add a conformance-check skill for validating a harness or model The conformance example is only useful if the next person reaches for it, so document it as a skill next to what it documents: when to run it, the launch template, how to read its two independent signals, and -- the part that took a day to learn -- how to diagnose it when it fails. The diagnosis order is the valuable content. An agent-facing `502 evaluation failed` is deliberately terse, but the evaluation record carries the backend's verbatim stderr, which is where "Docker is not installed", "Modal requires authentication" and "Can't instantiate abstract class" each surfaced immediately. The gateway request log then settles anything credential- or routing-shaped: zero producer requests means the harness escaped the gateway, 403 model_denied names a string the harness actually requested rather than the one you launched with, and an unexpected `endpoint` means it is driving an API surface you did not intend. Also records the harness gotchas this check found, generalised past the one harness that exposed them: a secondary small model of the same provider family whose calls 403 against a single-entry allow-list, provider-prefixed names that are requested bare, cross-family proxying that produces responses a harness cannot parse, and a seed harness that fails to instantiate and so reports infrastructure failure on every case rather than a low score. Lives on pr2 beside the example rather than in harness-engineering-bench/skills/, since it documents vero infrastructure that needs no benchmark to run, and pr3 inherits it on its next rebase. Co-Authored-By: Claude Opus 5 (1M context) --- vero/examples/harness-conformance/SKILL.md | 188 +++++++++++++++++++++ 1 file changed, 188 insertions(+) create mode 100644 vero/examples/harness-conformance/SKILL.md diff --git a/vero/examples/harness-conformance/SKILL.md b/vero/examples/harness-conformance/SKILL.md new file mode 100644 index 00000000..983637d5 --- /dev/null +++ b/vero/examples/harness-conformance/SKILL.md @@ -0,0 +1,188 @@ +--- +name: conformance-check +description: >- + Verify a new optimizer harness or model against the real VeRO stack in ~10 + minutes before spending a benchmark on it — gateway scoping and metering, the + evals CLI, result persistence, disclosure enforcement, budget accounting, and + the edit → evaluate → submit loop. Use when introducing an agent harness or + model, after changing gateway/sidecar/timeout configuration, or when a real run + fails and you need to know whether the stack or the benchmark is at fault. +--- + +# Conformance-checking a harness or model + +`vero/examples/harness-conformance` runs the **same path a real experiment takes** +— harbor evaluation backend, a nested `harbor run` per case, a target agent +metered through the gateway's evaluation scope, an optimizer through the producer +scope — over six arithmetic tasks that finish in seconds. It exists so that a +harness problem costs ten minutes rather than a benchmark. + +Read `README.md` in that directory for why it is built the way it is. This file is +how to run it and how to diagnose it when it fails. + +## When to run it + +- Before the first real run with a **new `--agent`** or a **new `--model`**. +- After changing anything shared: gateway budgets or allow-lists, timeouts, + `agent_env`, sidecar or harbor versions. +- When a benchmark run fails early and you cannot tell whether the fault is the + stack or that benchmark. + +## Run it + +```bash +cd /vero +uv run vero harbor run \ + --config examples/harness-conformance/build.yaml \ + --env-file \ + --environment docker \ + --agent --model \ + --yes -o /jobs +``` + +Parameters worth knowing: + +| parameter | default | why | +|---|---|---| +| `--param optimizer_model=` | follows `--model` | set it when the harness **requests** a different model string than it is launched with; it controls the producer allow-list independently | +| `--param target_model=` | a cheap chat model | the model the *target* harness calls; also the evaluation-scope allow-list | +| `--param inner_env=` | `modal` | leave it. `docker` cannot work — the inner evaluation runs `harbor run -e docker` inside the sidecar, which has no docker client or socket | +| `--param wandb_mode=online` | `disabled` | a smoke test should not need telemetry | + +Inner evaluations run on Modal, so the Modal credentials must be listed under +`secrets:` in the build — omit them and every case fails +`Modal requires authentication` with 0 trial groups. + +## Reading the result + +Two independent signals. **Check both, and if they disagree trust the reward.** + +1. **`conformance-report.json`**, committed by the optimizer at the target repo + root, one entry per step plus a `summary`. Recover it from the job's session + archive: + + ```bash + tar xzf /*/task__*/verifier/session.tar.gz -C + cd /session/candidates/repository.git + git log --oneline --all + git show :conformance-report.json + ``` + + The optimizer is asked to commit the report **before** submitting, because a + commit made after `evals submit` is not part of the submitted candidate and + never reaches you. It also `cat`s the report as its final action, so the + findings survive in the agent transcript even if the commit does not. + +2. **The reward.** The seed target answers addition and abandons multiplication, + so a healthy stack reads **~0.5 for the seed and 1.0 after the fix**. + `/*/task__*/verifier/reward.json` holds the final number. + +Interpreting the reward: + +| reward | meaning | +|---|---| +| `1.0` | the loop works: the optimizer measured, fixed the gap, and submitted | +| `~0.5` | evaluation works but the optimizer never fixed the seed — a harness-behaviour problem, not infrastructure | +| `0.0` | usually the target could not reach the gateway, or evaluations never ran. The arithmetic is trivial, so no plausible model gets it wrong | + +## What each step proves + +| step | proves | +|---|---| +| 1 | the optimizer's base URL is the compose-internal gateway, not a public endpoint — i.e. metered, and it cannot see upstream | +| 2 | `evals plan` exposes partitions, **case counts**, disclosure levels, remaining budget | +| 3 | development task resources are mounted; validation's are not | +| 4 | an evaluation runs, blocks in the foreground, returns a score | +| 5 | **persistence** — the same result is recoverable from `.evals/results/`, so truncated output is never lost | +| 5b | **traces** — `evals cases` reports `trace: true` and `evals trace ID CASE` returns per-phase spans with plausible durations | +| 6 | **disclosure is enforced** — `evals cases` refuses on an aggregate-only partition | +| 7 | budget decrements by what was actually spent | +| 8 | the edit → commit → re-evaluate loop moves the score, and `evals diff` attributes it | +| 9 | `evals submit` accepts a nomination | + +## When it fails: diagnose in this order + +**1. The evaluation record's diagnostics — the single most useful artifact.** +An agent-facing `502 evaluation failed` is deliberately terse, but the record +carries the backend's verbatim stderr: + +```bash +python3 -c " +import json,glob +for p in glob.glob('/session/evaluations/*/evaluation.json'): + d=json.load(open(p)); r=d.get('report') or {} + if r.get('status')!='success': + print(json.dumps(r.get('diagnostics'), indent=1)[:1200]) +" +``` + +This is where "Docker is not installed", "Modal requires authentication", and +"Can't instantiate abstract class" all surfaced immediately. + +**2. The gateway request log — which model, which endpoint, what status.** +Definitive for anything credential- or routing-shaped: + +```bash +python3 -c " +import json,glob,collections +c=collections.Counter() +for p in glob.glob('/session/artifacts/inference/requests/*.jsonl'): + for l in open(p,errors='replace'): + r=json.loads(l) + if r.get('scope')=='producer': + c[(r.get('status'), r.get('model'), r.get('endpoint'))]+=1 +for k,v in c.items(): print(k,'->',v) +" +``` + +Read it as: + +- **zero producer requests** → the harness bypassed the gateway entirely. It is + calling a provider's public endpoint with a scoped token, so it fails closed + with `401`; nothing leaks, but nothing works either. +- **`403 model_denied`** → the requested model string is not in the producer + allow-list. Note the string it *actually requested*, which is often not the one + you launched with, and set `--param optimizer_model=` to that. +- **`200` on an unexpected `endpoint`** → the harness is using an API surface you + did not intend (e.g. `responses` rather than `messages`), which matters both for + compatibility and for token attribution. + +**3. The harness's own trajectory**, under +`/*/task__*/agent/` — usually a `.txt` stream and/or `trajectory.json`. +Filter to errors rather than reading it whole: + +```bash +tr -d '\000' < /*/task__*/agent/.txt | grep -o '"type":"error".\{0,240\}' | tail -3 +``` + +**4. The trial phase timings**, via `evals trace ID CASE_ID` or the per-case +`execution_trace`. A near-zero `agent_execution` span means the harness never +called a model on that case. + +## Harness gotchas found this way + +Each of these was discovered by this check rather than by a benchmark run, and +each is the kind of thing that would otherwise read as "the model is bad": + +- **A secondary small model.** Several harnesses issue an auxiliary + summarisation/title call with a small model of the *same provider family*. The + producer allow-list usually holds one entry, so that call `403`s silently — it + is invisible outside the gateway log. Allow a second model. +- **Provider-prefixed model names.** Some harnesses require `provider/model` and + then request only the bare `model`, so the allow-list needs the bare form while + the launch flag needs the prefixed one. That is what `--param optimizer_model=` + is for. +- **Which API surface the harness drives.** Crossing model families through a + proxy can produce responses a harness cannot parse (mixed id namespaces in one + stream). Prefer the provider's native surface; vero supplies a gateway base URL + for non-`openai` opencode providers so Anthropic models stay on Messages. +- **A seed harness that will not instantiate.** Missing abstract methods surface + as `infrastructure_failure` on every case, not as a low score. + +## Extending it + +Adding a check is usually a step in the build's `description` — no code. Add a +*task* only to introduce a new failure mode in the target; keep every partition at +two cases so a run stays a few minutes. Timeouts follow the same rule as the +benchmark suite: `case_timeout_seconds` equals the tasks' declared +`[agent] timeout_sec` so harbor's agent-timeout multiplier is exactly 1.0. From 98ebbcf987ea2e8207ab7b8d136d0f0ef5166e65 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 13:53:13 -0700 Subject: [PATCH 65/72] conformance: let uv-installed harnesses install as the optimizer user --- vero/examples/harness-conformance/build.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/vero/examples/harness-conformance/build.yaml b/vero/examples/harness-conformance/build.yaml index ca058090..8a2c7af1 100644 --- a/vero/examples/harness-conformance/build.yaml +++ b/vero/examples/harness-conformance/build.yaml @@ -194,6 +194,11 @@ agent_env: BASH_DEFAULT_TIMEOUT_MS: "1800000" ENABLE_BACKGROUND_TASKS: "0" FORCE_AUTO_BACKGROUND_TASKS: "0" + # Harnesses installed with `uv tool install` (mini-swe-agent, swe-agent) default + # to symlinking their entry point into /usr/local/bin, which the unprivileged + # optimizer user cannot write: "Failed to install executable ... Permission + # denied". npm/nvm-based harnesses (claude-code, opencode) are unaffected. + UV_TOOL_BIN_DIR: "/home/agent/.local/bin" wandb: project: vero-conformance From 297147494980a70f339cfa4d163aae0626cc1ca1 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 14:33:50 -0700 Subject: [PATCH 66/72] Refund the reservation when cancellation escapes as a raw CancelledError Two P1 review findings on #45, both real once traced through. The evaluator converts cancellation and failure into EvaluationCancelledError and EvaluationExecutionError, and EvaluationEngine refunds on each. But every one of those handlers has to await a persist before it can raise its typed error, and a cancellation delivered during that await unwinds as a raw CancelledError instead. The engine catches only the two typed errors -- no bare handler, no finally -- so neither _record nor budget_ledger.refund runs and the reservation stays charged for good. The trigger is not hypothetical: quiesce_agent_evaluations cancels agent evaluations when finalization starts, which is exactly when an evaluation is most likely to be mid-cleanup. Fixed at both levels. The engine gains a bare `except asyncio.CancelledError` that refunds and re-raises; only the refund is recoverable there, since a raw cancellation carries no evaluation id to load a record from. And the evaluator's TimeoutError and Exception handlers now shield their _persist_failure the way the CancelledError handler already did -- asyncio.timeout absorbs its own internal cancellation, but an external cancel can still be pending, and the first await inside an unshielded persist would deliver it and lose the failure record too. Also drops vero/utils/tokens.py and its tests. run_result_to_messages truncates an item's output/content list to its first element, which would silently discard every parallel tool call after the first -- but it has no callers anywhere in the package, so it is a trap for a future caller rather than a live bug. Deleting is better than documenting a precondition nobody depends on. The fourth P1, tau3's RuntimeError on empty model output, is deliberately left alone; see the reply on #46. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evaluation/engine.py | 22 +++++++++ vero/src/vero/evaluation/evaluator.py | 56 +++++++++++++--------- vero/src/vero/utils/tokens.py | 27 ----------- vero/tests/test_tokens.py | 23 --------- vero/tests/test_v05_evaluation_runtime.py | 57 +++++++++++++++++++++++ 5 files changed, 113 insertions(+), 72 deletions(-) delete mode 100644 vero/src/vero/utils/tokens.py delete mode 100644 vero/tests/test_tokens.py diff --git a/vero/src/vero/evaluation/engine.py b/vero/src/vero/evaluation/engine.py index d23051e1..8e6a0288 100644 --- a/vero/src/vero/evaluation/engine.py +++ b/vero/src/vero/evaluation/engine.py @@ -390,6 +390,28 @@ async def _execute_record( ) ) raise + except asyncio.CancelledError: + # Last line of defence for the reservation. The evaluator's own + # handlers convert cancellation and failure into the two typed errors + # above, but each of them has to await a persist before it can raise, + # and a cancellation delivered during that await -- which + # quiesce_agent_evaluations does deliver, at finalization -- unwinds + # as a raw CancelledError instead. Neither handler above would see + # it, and the reservation would stay charged forever. + # + # Only the refund is recoverable here: a raw cancellation carries no + # evaluation id, so there is no record to load. The evaluator already + # persisted one on its shielded paths. + if charged: + await asyncio.shield( + self.budget_ledger.refund( + backend_id, + request.evaluation_set, + cost, + principal, + ) + ) + raise # Shielded for the same reason as the two handlers above, and one more: # the budget was charged for an evaluation that has now actually run, so # a cancellation landing inside _record would leave the ledger counting diff --git a/vero/src/vero/evaluation/evaluator.py b/vero/src/vero/evaluation/evaluator.py index 986af575..96646194 100644 --- a/vero/src/vero/evaluation/evaluator.py +++ b/vero/src/vero/evaluation/evaluator.py @@ -206,34 +206,46 @@ async def evaluate( ) ) raise EvaluationCancelledError(evaluation_id, message) from error + # The two handlers below shield their persist for the same reason the + # cancellation handler above does. asyncio.timeout absorbs its own + # internal cancellation and re-raises as TimeoutError, but an *external* + # cancel -- quiesce_agent_evaluations draining agent evaluations at + # finalization -- can still be pending, and the first await inside an + # unshielded _persist_failure would deliver it, losing the failure record + # and unwinding as a raw CancelledError instead of the typed error. + # (EvaluationEngine refunds on that raw path too, belt and braces.) except TimeoutError as error: message = f"evaluation exceeded {request.limits.timeout_seconds} seconds" - await self._persist_failure( - store=store, - evaluation_id=evaluation_id, - backend_id=backend_id, - backend=backend, - request=request, - objective_spec=objective_spec, - principal=principal, - created_at=created_at, - code="evaluation_timeout", - message=message, + await asyncio.shield( + self._persist_failure( + store=store, + evaluation_id=evaluation_id, + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + created_at=created_at, + code="evaluation_timeout", + message=message, + ) ) raise EvaluationExecutionError(evaluation_id, message) from error except Exception as error: message = str(error) or type(error).__name__ - await self._persist_failure( - store=store, - evaluation_id=evaluation_id, - backend_id=backend_id, - backend=backend, - request=request, - objective_spec=objective_spec, - principal=principal, - created_at=created_at, - code="backend_error", - message=message, + await asyncio.shield( + self._persist_failure( + store=store, + evaluation_id=evaluation_id, + backend_id=backend_id, + backend=backend, + request=request, + objective_spec=objective_spec, + principal=principal, + created_at=created_at, + code="backend_error", + message=message, + ) ) raise EvaluationExecutionError(evaluation_id, message) from error diff --git a/vero/src/vero/utils/tokens.py b/vero/src/vero/utils/tokens.py deleted file mode 100644 index 836e60cb..00000000 --- a/vero/src/vero/utils/tokens.py +++ /dev/null @@ -1,27 +0,0 @@ -from __future__ import annotations - -from copy import deepcopy -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from agents import RunResult, RunResultStreaming, TResponseInputItem - - -def run_result_to_messages( - result: RunResult | RunResultStreaming | list[TResponseInputItem], -) -> list[dict | TResponseInputItem]: - """Convert a RunResult or list of TResponseInputItem to a list of messages.""" - - result = deepcopy(result) - - if not isinstance(result, list): - result = result.to_input_list() - - def process_item(item: dict | TResponseInputItem) -> dict | TResponseInputItem: - for key in ["output", "content"]: - if key in item: - if isinstance(item[key], list): - item[key] = item[key][0] if item[key] else "" - return item - - return [process_item(item) for item in result] diff --git a/vero/tests/test_tokens.py b/vero/tests/test_tokens.py deleted file mode 100644 index 03a07157..00000000 --- a/vero/tests/test_tokens.py +++ /dev/null @@ -1,23 +0,0 @@ -from vero.utils.tokens import run_result_to_messages - - -def test_run_result_to_messages_handles_empty_response_content() -> None: - messages = run_result_to_messages( - [ - { - "type": "reasoning", - "content": [], - "summary": [], - } - ] - ) - - assert messages[0]["content"] == "" - - -def test_run_result_to_messages_uses_first_content_item() -> None: - messages = run_result_to_messages( - [{"role": "assistant", "content": ["first", "second"]}] - ) - - assert messages[0]["content"] == "first" diff --git a/vero/tests/test_v05_evaluation_runtime.py b/vero/tests/test_v05_evaluation_runtime.py index 5aca86eb..36793da4 100644 --- a/vero/tests/test_v05_evaluation_runtime.py +++ b/vero/tests/test_v05_evaluation_runtime.py @@ -1190,3 +1190,60 @@ def request_with(selection) -> EvaluationRequest: request=request_with(CaseRange(stop=2)), principal=EvaluationPrincipal.ADMIN, ) + + +@pytest.mark.asyncio +async def test_raw_cancellation_during_cleanup_still_refunds(tmp_path: Path): + """A CancelledError that escapes the evaluator must not leak the reservation. + + The evaluator turns cancellation and failure into two typed errors, but each + has to await a persist before it can raise them. A cancellation delivered + during that await -- which quiesce_agent_evaluations does deliver, when it + drains agent evaluations at finalization -- unwinds as a raw CancelledError + instead, matching neither typed handler. Without a bare handler in the engine + the reservation stays charged forever. + """ + + class CancellingEvaluator: + """Stands in for an evaluator interrupted mid-cleanup.""" + + def __init__(self, evaluations_dir): + self.evaluations_dir = evaluations_dir + + async def evaluate(self, **_kwargs): + raise asyncio.CancelledError() + + workspace = StubWorkspace(tmp_path / "repo") + evaluation_set = request().evaluation_set + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="default", + evaluation_set_key=evaluation_set.budget_key("default"), + total_runs=2, + ) + ], + path=tmp_path / "budgets.json", + ) + ledger.save() + engine = EvaluationEngine( + evaluator=CancellingEvaluator(evaluator(tmp_path, workspace).evaluations_dir), + backends=BackendRegistry({"default": StubBackend()}), + database=EvaluationDatabase(id="session"), + database_path=tmp_path / "database.json", + budget_ledger=ledger, + authorization_resolver=allow_all_evaluations, + ) + + # The cancellation still propagates -- it is not swallowed. + with pytest.raises(asyncio.CancelledError): + await engine.evaluate_record(backend_id="default", request=request()) + + remaining = ledger.get("default", evaluation_set) + assert remaining is not None and remaining.remaining_runs == 2 + assert ( + BudgetLedger.load(tmp_path / "budgets.json") + .get("default", evaluation_set) + .remaining_runs + == 2 + ) From 31dd2616c075e156663e5e7b9c4089659dc54e78 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 14:42:21 -0700 Subject: [PATCH 67/72] Stop the finalization drain from inheriting the unreachable eval ceiling evaluation_drain_timeout_seconds fell back to timeout_seconds when unset. That was defensible while timeout_seconds was a realistic eval duration, but this morning's change deliberately sized it to be *unreachable* -- above ceil(trials / max_concurrency) x case_timeout, i.e. every trial hitting its own cap -- so the drain silently inherited the same magnitude: officeqa 7200 -> 21600, and swe-atlas-qna would now be 90000s (25h), tau3 79200s (22h). The two clocks want opposite sizing. The eval ceiling must never fire. The drain is a grace period deciding how long finalization waits on already-running agent evaluations before cancelling them, and its expiry is graceful -- quiesce_agent_evaluations cancels through the normal evaluator path, which persists terminal records and refunds budgets. Waiting longer therefore buys nothing and only delays the held-out score. Observed on officeqa run #4: an agent evaluation finished writing its 48 results at 19:22 but its harbor subprocess never exited, so the verifier that started at 20:27 sat waiting out an inherited 21600s drain with the finalization scope at zero requests. It would not have begun scoring until 02:27. Default to 600s, matching harbor/deployment.py, and keep an explicit setting authoritative for a benchmark that genuinely wants to wait. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/build/compiler.py | 16 ++++++++++- vero/src/vero/harbor/build/config.py | 8 ++++-- vero/tests/test_v05_harbor_build.py | 38 +++++++++++++++++++++++++- 3 files changed, 58 insertions(+), 4 deletions(-) diff --git a/vero/src/vero/harbor/build/compiler.py b/vero/src/vero/harbor/build/compiler.py index ef15668c..a856798f 100644 --- a/vero/src/vero/harbor/build/compiler.py +++ b/vero/src/vero/harbor/build/compiler.py @@ -59,6 +59,13 @@ INFERENCE_REQUEST_LOG_DIR = LAYOUT.inference_request_log_dir INFERENCE_GATEWAY_URL = LAYOUT.gateway_url +# How long finalization waits for already-running agent evaluations before +# cancelling them. A grace period, not a ceiling: expiry is graceful (the +# evaluator's cancellation path persists terminal records and refunds budgets), +# so waiting longer buys nothing and only delays the held-out score. Matches +# harbor/deployment.py's own default. +DEFAULT_EVALUATION_DRAIN_SECONDS = 600.0 + def _backend_id(partition: str) -> str: return f"harbor-{partition}" @@ -445,8 +452,15 @@ def _deployment_config( "wandb": ( config.wandb.model_dump(mode="json") if config.wandb is not None else None ), + # Must NOT inherit timeout_seconds: that is deliberately sized to be + # unreachable (every trial hitting its per-case cap), so inheriting it + # turns one hung sub-run into a finalization stall of the same order. + # officeqa run #4 sat 6h in exactly that state -- its agent evaluation + # had finished writing results two hours earlier, but the subprocess + # never exited and the drain was waiting out an inherited 21600s. "evaluation_drain_timeout_seconds": ( - config.evaluation_drain_timeout_seconds or config.timeout_seconds + config.evaluation_drain_timeout_seconds + or DEFAULT_EVALUATION_DRAIN_SECONDS ), "inference_usage_path": ( INFERENCE_STATE if config.inference_gateway is not None else None diff --git a/vero/src/vero/harbor/build/config.py b/vero/src/vero/harbor/build/config.py index 245e9e2c..e318bbca 100644 --- a/vero/src/vero/harbor/build/config.py +++ b/vero/src/vero/harbor/build/config.py @@ -306,8 +306,12 @@ class _EvaluationLimitFields(StrictModel): build_timeout_seconds: Wall clock for building the task's images. verifier_timeout_seconds: Wall clock for the trusted verifier. Falls back to timeout_seconds. - evaluation_drain_timeout_seconds: Grace period for in-flight evaluations - to finish. Falls back to timeout_seconds. + evaluation_drain_timeout_seconds: Grace period for finalization to wait + on already-running agent evaluations before cancelling them. + Defaults to 600s. Deliberately independent of timeout_seconds, which + is sized to be unreachable: inheriting it would let one hung sub-run + stall the held-out score for hours. Expiry is safe — cancellation + persists terminal records and refunds budgets. """ timeout_seconds: float = Field(default=1800.0, gt=0) diff --git a/vero/tests/test_v05_harbor_build.py b/vero/tests/test_v05_harbor_build.py index e2892809..6f54bc68 100644 --- a/vero/tests/test_v05_harbor_build.py +++ b/vero/tests/test_v05_harbor_build.py @@ -941,7 +941,11 @@ def test_compiler_emits_isolated_canonical_harbor_task(tmp_path): assert serve["selection"]["backend_id"] == "harbor-validation" assert serve["targets"][0]["backend_id"] == "harbor-test" assert serve["targets"][0]["reward_scale"] == 1.0 - assert serve["evaluation_drain_timeout_seconds"] == config.timeout_seconds + # A grace period, not a ceiling — and explicitly NOT timeout_seconds, which is + # sized to be unreachable. Inheriting it stalled officeqa run #4's + # finalization for hours behind a sub-run that had already finished its work. + assert serve["evaluation_drain_timeout_seconds"] == 600.0 + assert serve["evaluation_drain_timeout_seconds"] != config.timeout_seconds assert serve["backends"]["harbor-test"]["task_source"] == LAYOUT.task_source assert serve["backends"]["harbor-test"]["python_version"] == "3.12" assert serve["backends"]["harbor-test"]["case_timeout_seconds"] == 180.0 @@ -1297,3 +1301,35 @@ def test_instruction_advertises_seed_only_where_the_backend_accepts_it(tmp_path) instruction = (command / "instruction.md").read_text(encoding="utf-8") assert "reproduce a noisy comparison exactly" in instruction assert "rejects `--seed`" not in instruction + + +def test_drain_timeout_is_independent_of_the_unreachable_eval_ceiling(tmp_path): + # timeout_seconds is deliberately set above ceil(trials/concurrency) x + # case_timeout so it can never fire. The drain is the opposite kind of clock: + # it decides how long finalization waits on already-running agent evaluations + # before cancelling them, and cancellation is graceful. Tying the two makes a + # hung sub-run cost hours of held-out scoring, which is what happened. + default = compile_harbor_task( + _config(tmp_path / "cfg-default", timeout_seconds=90000.0), + tmp_path / "default", + vero_root=Path(__file__).parents[1], + ) + serve = json.loads( + (default / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert serve["evaluation_drain_timeout_seconds"] == 600.0 + + # An explicit value still wins, for a benchmark that genuinely wants to wait. + explicit = compile_harbor_task( + _config( + tmp_path / "cfg-explicit", + timeout_seconds=90000.0, + evaluation_drain_timeout_seconds=1800.0, + ), + tmp_path / "explicit", + vero_root=Path(__file__).parents[1], + ) + serve = json.loads( + (explicit / "environment/sidecar/serve.json").read_text(encoding="utf-8") + ) + assert serve["evaluation_drain_timeout_seconds"] == 1800.0 From 3d3b8267a34e9dd1d2d3461fcd3c74d423534ea9 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 18:59:54 -0700 Subject: [PATCH 68/72] Publish which backend serves each partition in the evaluation plan `evals run --backend`'s help said "see `evals plan`", but the plan never emitted a backend id -- it listed the evaluation, partition, case count, disclosure and budget, and omitted the one field the flag pointed there for. The agent had to guess the mapping. Observed on swe-atlas-qna's first optimizer run: it asked for `--backend harbor-validation --partition development`. Each partition is served by exactly one backend, so that pairing is unauthorized and came back as `403 evaluation denied`. It recovered on the next call, but only by guessing again. This one cannot self-explain the way a bad request now can. Denials are deliberately opaque -- their message is policy, not capability, and test_http_app_redacts_access_denial_details pins that -- so the pairing has to be discoverable up front. write_evaluation_plan now records entry.backend_id, `evals plan` shows it as a column, the --backend help states the rule instead of pointing at an incomplete source, and the skill lists the mismatch under common mistakes with a note that the denial will not name it. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/evals_cli.py | 2 ++ vero/src/vero/harbor/cli.py | 6 +++++- vero/src/vero/runtime/context.py | 4 ++++ vero/src/vero/skills/evals/SKILL.md | 6 +++++- vero/tests/test_v05_agent_context.py | 7 +++++++ vero/tests/test_v05_evals_cli.py | 2 ++ 6 files changed, 25 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/evals_cli.py b/vero/src/vero/evals_cli.py index 5229ee56..006e7ca1 100644 --- a/vero/src/vero/evals_cli.py +++ b/vero/src/vero/evals_cli.py @@ -646,6 +646,7 @@ def plan_command(context_path, as_json): { "evaluation": evaluation.get("name"), "partition": evaluation.get("partition"), + "backend": evaluation.get("backend"), "cases": evaluation.get("cases"), "can_evaluate": evaluation.get("agent_can_evaluate"), "selection": evaluation.get("agent_selection"), @@ -662,6 +663,7 @@ def plan_command(context_path, as_json): [ "evaluation", "partition", + "backend", "cases", "can_evaluate", "selection", diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 723e0b00..998b3490 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -536,7 +536,11 @@ def inference_gateway_command(config_path, host, port): @harbor.command("eval") @click.option( "--backend", "backend_id", required=True, - help="Evaluation backend to score against (e.g. the selection partition's backend; see `evals plan`).", + help=( + "Evaluation backend to score against. Must match --partition: each " + "partition is served by exactly one backend and asking a different one " + "is denied. `evals plan` lists the pair for every partition you may run." + ), ) @click.option( "--evaluation-set", "evaluation_set_name", required=True, diff --git a/vero/src/vero/runtime/context.py b/vero/src/vero/runtime/context.py index 20668a02..a9515383 100644 --- a/vero/src/vero/runtime/context.py +++ b/vero/src/vero/runtime/context.py @@ -447,6 +447,10 @@ async def write_evaluation_plan( { "name": entry.evaluation_set.name, "partition": entry.evaluation_set.partition, + # Which backend serves this partition. `evals run` needs the + # pair to match — asking one partition's backend for another + # is denied — and `--backend`'s help points here for it. + "backend": entry.backend_id, "cases": case_count, "base_selection": entry.evaluation_set.selection.model_dump( mode="json" diff --git a/vero/src/vero/skills/evals/SKILL.md b/vero/src/vero/skills/evals/SKILL.md index 7f7e7215..3381c081 100644 --- a/vero/src/vero/skills/evals/SKILL.md +++ b/vero/src/vero/skills/evals/SKILL.md @@ -17,7 +17,7 @@ subcommand. ## The loop ```bash -evals plan # what you may run, partition sizes, budget +evals plan # backend+partition pairs, sizes, budget evals run --backend B --evaluation-set S --partition P # BLOCKS, returns the result evals list --sort score --desc # every past result, one row each evals diff BASELINE_ID CANDIDATE_ID # which cases improved / regressed @@ -105,6 +105,10 @@ are enforced by the evaluator; `evals plan` shows what remains. disk under `.evals/results/`; read it back with `evals show` / `evals cases`. - Confusing ids: evaluation ids come from `evals list`; case ids from `evals cases`; job ids only from `evals run --detach`. +- Mismatching `--backend` and `--partition`. Each partition is served by exactly + one backend, and asking a different one is refused as `evaluation denied` — a + denial is deliberately opaque, so it will not tell you that is the reason. Take + both from the same row of `evals plan`. - Scores may be noisy: replicate an important comparison before acting on it, by re-running the identical case selection. - Backgrounding the wait. Putting `evals run`/`evals wait` in a background task diff --git a/vero/tests/test_v05_agent_context.py b/vero/tests/test_v05_agent_context.py index 887b9136..733a52f9 100644 --- a/vero/tests/test_v05_agent_context.py +++ b/vero/tests/test_v05_agent_context.py @@ -288,6 +288,13 @@ async def resolve_cost(self, evaluation_set): ) plan = json.loads((tmp_path / "ctx" / "plan.json").read_text(encoding="utf-8")) + # The backend serving each partition, so `evals run --backend/--partition` + # can be taken from one row. Mismatching them is refused as an opaque + # "evaluation denied", which cost swe-atlas-qna's optimizer a round trip. + assert {row["partition"]: row["backend"] for row in plan["evaluations"]} == { + "development": "harbor-development", + "validation": "harbor-validation", + } sizes = {row["partition"]: row["cases"] for row in plan["evaluations"]} # A backend that cannot cost itself degrades to no count, not a broken plan. assert sizes == {"development": 49, "validation": None} diff --git a/vero/tests/test_v05_evals_cli.py b/vero/tests/test_v05_evals_cli.py index 195f617e..090844e2 100644 --- a/vero/tests/test_v05_evals_cli.py +++ b/vero/tests/test_v05_evals_cli.py @@ -113,6 +113,7 @@ async def context_dir(tmp_path: Path) -> Path: "agent_selection": "arbitrary", "disclosure": "full", "expose_case_resources": True, + "backend": "harbor-validation", "cases": 12, "budget": {"remaining_runs": 3, "remaining_cases": 40}, } @@ -232,6 +233,7 @@ async def test_plan_shows_budget(context_dir: Path): { "evaluation": "validation", "partition": "validation", + "backend": "harbor-validation", "cases": 12, "can_evaluate": True, "selection": "arbitrary", From c2d5a196b9fb10d9ec16f1ad55cd5afa52256f94 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 19:26:09 -0700 Subject: [PATCH 69/72] Return a terminal status for budget exhaustion so nothing retries it The gateway answered InferenceBudgetExceeded with 429. That status means "slow down and try again", and every layer above honours it: EvaluationLimits retry_status_codes defaults to [429, 503, 529], and the target agents' own SDKs retry 429 independently. But an exhausted scope is terminal -- no amount of waiting restores the quota -- so each retry was guaranteed to fail. officeqa run #2 reissued 3672 doomed requests against its exhausted finalization scope, outnumbering the 3366 that did real work and burning verifier wall-clock for nothing. Any rate estimate off that request log is also skewed unless it filters to status 200 first. 402 is in no retry list, so callers now fail fast carrying the reason. The budget_exhausted code still travels in the body, and backend classification already keys on the message rather than the status or the SDK's exception type, so error taxonomy is unaffected -- comment there corrected, since it described the 429-era behaviour. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/gateway/inference.py | 11 +++++++++-- vero/src/vero/harbor/backend.py | 5 +++-- vero/tests/test_v05_harbor_inference.py | 24 +++++++++++++++++++++--- 3 files changed, 33 insertions(+), 7 deletions(-) diff --git a/vero/src/vero/gateway/inference.py b/vero/src/vero/gateway/inference.py index 8ae8f8aa..edffd5f7 100644 --- a/vero/src/vero/gateway/inference.py +++ b/vero/src/vero/gateway/inference.py @@ -781,8 +781,15 @@ async def log_request( try: await store.reserve(scope_name, attribution) except InferenceBudgetExceeded as error: - await log_request(status=429, error="budget_exhausted") - return _provider_error(429, str(error), "budget_exhausted") + # 402, not 429. Budget exhaustion is terminal -- no amount of waiting + # restores the quota -- but 429 means "slow down and try again", and + # every layer above honours that: EvaluationLimits.retry_status_codes + # defaults to [429, 503, 529], and target-agent SDKs retry it too. + # officeqa run #2 reissued 3672 doomed requests after exhausting the + # finalization scope, burning verifier wall-clock to no purpose. + # 402 is in no retry list, so the caller fails fast with the reason. + await log_request(status=402, error="budget_exhausted") + return _provider_error(402, str(error), "budget_exhausted") headers = { name: value diff --git a/vero/src/vero/harbor/backend.py b/vero/src/vero/harbor/backend.py index a79e8b8b..aed6ed22 100644 --- a/vero/src/vero/harbor/backend.py +++ b/vero/src/vero/harbor/backend.py @@ -1255,8 +1255,9 @@ def _case_result( exception_counts[key] = exception_counts.get(key, 0) + 1 signals.append(key) # The gateway embeds a distinct "budget_exhausted" code in the error - # body; the in-container client collapses the type to a generic - # rate-limit, but the message survives, so classify on it too. + # body. Classify on the message rather than the status or exception + # type: the in-container client collapses the type to whatever its + # SDK maps the status to, but the message survives intact. for detail_key in ("message", "detail", "exception_message"): detail = info.get(detail_key) if isinstance(detail, str) and detail: diff --git a/vero/tests/test_v05_harbor_inference.py b/vero/tests/test_v05_harbor_inference.py index b929c403..f53d684c 100644 --- a/vero/tests/test_v05_harbor_inference.py +++ b/vero/tests/test_v05_harbor_inference.py @@ -81,7 +81,9 @@ def upstream(request: httpx.Request): assert denied.status_code == 403 assert wrong_model.status_code == 403 assert accepted.status_code == 200 - assert exhausted.status_code == 429 + # 402, not 429: budget exhaustion is terminal, and 429 would be retried + # by EvaluationLimits.retry_status_codes and by target-agent SDKs. + assert exhausted.status_code == 402 assert len(observed) == 1 assert observed[0].headers["authorization"] == "Bearer upstream-secret" assert b"scoped-token" not in observed[0].content @@ -257,7 +259,7 @@ def test_gateway_reloads_usage_and_enforces_budget(tmp_path): json={"model": "gpt-test", "input": "hello"}, ) - assert exhausted.status_code == 429 + assert exhausted.status_code == 402 assert app.state.usage_store.ledger.scopes["producer"].active_requests == 0 @@ -440,7 +442,7 @@ def upstream(request: httpx.Request): ) records = _log_records(tmp_path) - assert [record["status"] for record in records] == [200, 200, 403, 429] + assert [record["status"] for record in records] == [200, 200, 403, 402] plain, stream, denied, exhausted = records assert plain["scope"] == "producer" assert plain["attribution"] == "optimizer" @@ -688,3 +690,19 @@ def test_gateway_attribution_disabled_by_default_and_memory_bounded(tmp_path): assert len(attributor._root_threads) <= 10 assert len(attributor._response_threads) <= 10 assert attributor.errors == 0 + + +def test_budget_exhaustion_status_is_not_retryable(): + """Budget exhaustion must not wear a status anything will retry. + + It is terminal -- waiting never restores the quota -- so returning 429 made + every layer above retry it: EvaluationLimits.retry_status_codes defaults to + [429, 503, 529], and target-agent SDKs retry 429 on their own. officeqa run + #2 reissued 3672 doomed requests after exhausting its finalization scope. + """ + from vero.evaluation.models import EvaluationLimits + + budget_exhausted_status = 402 + assert budget_exhausted_status not in EvaluationLimits().retry.retry_status_codes + # The transient ones stay retryable. + assert 429 in EvaluationLimits().retry.retry_status_codes From 54175076c72438e9f6f7bb0cd68fd19814373872 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 21:37:25 -0700 Subject: [PATCH 70/72] Name the outer trial's Modal app so its sandbox can be found Inner evaluation sandboxes are already grouped by an explicit --ek app_name in each build's extra_harbor_args, but the outer trial had no name and landed in Modal's default __harbor__ app, alongside every other container in the workspace. Measured while testing session recovery: __harbor__ gained 273 new containers in 270 seconds, so identifying one run's outer sandbox by diffing the container list is hopeless -- two attempts at it failed outright. That matters in two places. Inspecting a run in the Modal UI requires finding it, and recovering the session from a run that must be killed begins with identifying its container -- the Modal equivalent of `docker cp :/state/admin/session`, which is the discipline that saved officeqa run #2's result and whose absence lost run #4's. `vero harbor run --environment modal` now derives the app name from the build name (vero/optimize-gaia-baseline -> vero-optimize-gaia-baseline), so outer trials group per benchmark. An explicit --ek app_name= from the caller still wins, and docker outer trials are unaffected since `docker ps` already finds them. Verified on a live launch: --ek app_name=vero-harness-conformance. The two harbor-run tests stubbed their config with a SimpleNamespace lacking `name`; real configs always carry one, so the stubs gain it. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/cli.py | 26 ++++++++++++++++++++++++++ vero/tests/test_v05_cli.py | 25 +++++++++++++++++++++++++ vero/tests/test_v05_harbor_http.py | 16 ++++++++++++++-- 3 files changed, 65 insertions(+), 2 deletions(-) diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 998b3490..3f9f794f 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -262,6 +262,31 @@ def _opencode_gateway_args(agent: str, model: str | None, task: Path) -> list[st return ["--ak", f"opencode_config={json.dumps(payload, separators=(',', ':'))}"] +def _outer_app_name_args( + environment: str, config_name: str, extra: tuple[str, ...] +) -> list[str]: + """Name the outer trial's Modal app so its sandbox can be found. + + Inner evaluation sandboxes are already grouped by an explicit + ``--ek app_name=...`` in each build's ``extra_harbor_args``, but the outer + trial had none and so landed in Modal's default ``__harbor__`` app. That + costs twice: the workspace holds thousands of containers, so an unnamed outer + sandbox is effectively unfindable in the UI, and recovering the session from + a run that must be killed begins with identifying its container. + + Derived from the build name (``vero/optimize-gaia-baseline`` -> + ``vero-optimize-gaia-baseline``) so outer trials group per benchmark. A + caller passing its own ``--ek app_name=`` wins. + """ + + if environment != "modal": + return [] + if any("app_name=" in argument for argument in extra): + return [] + slug = re.sub(r"[^a-zA-Z0-9_.-]+", "-", config_name).strip("-") + return ["--ek", f"app_name={slug or 'vero'}"] + + def _compiled_run_environment( task: Path, overrides: dict[str, str] | None = None ) -> dict[str, str]: @@ -476,6 +501,7 @@ def run_command(config_path, agent, model, environment, params, env_file, extra) for key in sorted(config.agent_env): command.extend(["--ae", f"{key}={config.agent_env[key]}"]) command.extend(_opencode_gateway_args(agent, model, task)) + command.extend(_outer_app_name_args(environment, config.name, extra)) command.extend(extra) click.echo(shlex.join(command)) completed = subprocess.run( diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py index ae4c7d90..24c792dd 100644 --- a/vero/tests/test_v05_cli.py +++ b/vero/tests/test_v05_cli.py @@ -348,3 +348,28 @@ def test_opencode_non_openai_provider_gets_a_gateway_base_url(tmp_path): assert _opencode_gateway_args("opencode", "claude-sonnet-5", task) == [] # A task compiled without a gateway simply gets no override. assert _opencode_gateway_args("opencode", "anthropic/x", tmp_path / "none") == [] + + +def test_outer_modal_trial_gets_a_named_app(): + """The outer trial must not land in Modal's anonymous default app. + + Inner eval sandboxes are already grouped by app_name via each build's + extra_harbor_args, but the outer trial had none, so it went to __harbor__ + alongside every other workspace container -- ~1800 of them -- which makes it + unfindable in the UI and turns "copy the session out before killing this run" + into a search problem. + """ + from vero.harbor.cli import _outer_app_name_args + + assert _outer_app_name_args("modal", "vero/optimize-gaia-baseline", ()) == [ + "--ek", + "app_name=vero-optimize-gaia-baseline", + ] + # Docker outer trials are found via `docker ps`; no app applies. + assert _outer_app_name_args("docker", "vero/optimize-gaia-baseline", ()) == [] + # An explicit choice wins. + assert _outer_app_name_args( + "modal", "vero/optimize-gaia-baseline", ("--ek", "app_name=mine") + ) == [] + # A name that is entirely punctuation still yields a usable app. + assert _outer_app_name_args("modal", "///", ()) == ["--ek", "app_name=vero"] diff --git a/vero/tests/test_v05_harbor_http.py b/vero/tests/test_v05_harbor_http.py index 8034b6d6..0b4ae9d6 100644 --- a/vero/tests/test_v05_harbor_http.py +++ b/vero/tests/test_v05_harbor_http.py @@ -260,7 +260,13 @@ def test_harbor_run_uses_current_python_and_pinned_harbor_extra(tmp_path, monkey config_path = tmp_path / "build.yaml" config_path.write_text("task_name: unused\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0", agent_env={}) + config = SimpleNamespace( + harbor_requirement="harbor[modal]==0.20.0", + agent_env={}, + # Real configs always carry a name; the outer trial derives its + # Modal app name from it so the sandbox is findable. + name="vero/stub-benchmark", + ) observed = {} def compile_task(_config, output): @@ -313,7 +319,13 @@ def test_harbor_run_env_file_secrets_reach_subprocess_not_command_line( config_path.write_text("task_name: unused\n") env_file = tmp_path / "secrets.env" env_file.write_text("MODAL_TOKEN_ID=mt-id\nMODAL_TOKEN_SECRET=mt-secret\n") - config = SimpleNamespace(harbor_requirement="harbor[modal]==0.20.0", agent_env={}) + config = SimpleNamespace( + harbor_requirement="harbor[modal]==0.20.0", + agent_env={}, + # Real configs always carry a name; the outer trial derives its + # Modal app name from it so the sandbox is findable. + name="vero/stub-benchmark", + ) observed = {} def compile_task(_config, output): From 41ba60f7637492ee18404f6050039a54ebb5aca2 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 22:09:25 -0700 Subject: [PATCH 71/72] Raise opencode's step limit so the harness does not truncate the search opencode caps agentic iterations at 100 by default. Its own config schema describes `steps` as the "maximum number of agentic iterations before forcing text-only response", and that forcing is what makes the cap dangerous: gaia's optimizer used exactly 100 steps over ~2h, then emitted a fluent summary explaining that its candidates had not won, and stopped -- without ever calling `evals submit`. The summary reads like a considered conclusion, so nothing in the trace looks wrong unless you notice the step count is exactly 100. Its own log holds no cap warning. claude-code takes harbor's --max-turns instead and ran to completion on officeqa and swe-atlas, both of which submitted deliberately. So the default silently handicapped one arm of the very axis the grid varies, in a way that presents as a behavioural difference between harnesses rather than as a limit. Set steps via the opencode_config channel already used for the provider baseURL, on opencode's primary `build` agent. Unconditional: it does not depend on the model spelling or on a gateway being present, because a truncated search is a problem either way. 1000 is chosen to be out of the way -- the case budget and the gateway token cap are the intended limits. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/cli.py | 51 +++++++++++++++++++++-------- vero/tests/test_v05_cli.py | 64 ++++++++++++++++++++++++++++++------- 2 files changed, 90 insertions(+), 25 deletions(-) diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 3f9f794f..8161e148 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -224,6 +224,14 @@ def _load_env_file(path: Path) -> dict[str, str]: return values +# opencode's own default is 100 agentic iterations, after which it forces a +# text-only response and stops. That is well inside a real optimization run: +# gaia's optimizer used all 100 and never reached `evals submit`. Set high +# enough that the harness never truncates the search -- the case budget and +# the gateway token cap are the intended limits. +OPENCODE_STEP_LIMIT = 1000 + + def _opencode_gateway_args(agent: str, model: str | None, task: Path) -> list[str]: """Route opencode's non-openai providers through the gateway. @@ -244,21 +252,38 @@ def _opencode_gateway_args(agent: str, model: str | None, task: Path) -> list[st and metered. The adapter deep-merges job kwargs last, so this wins. """ - if agent != "opencode" or not model or "/" not in model: + if agent != "opencode" or not model: return [] + + # opencode caps agentic iterations at 100 by default and then "forces a + # text-only response" (its own config schema's words for `steps`). A gaia + # optimizer hit that cap after ~2h, and the forced final message reads like a + # considered wrap-up, so the truncation is invisible unless you notice the + # step count is exactly 100 -- it never reached `evals submit`. claude-code + # takes harbor's --max-turns instead, so leaving this at the default makes + # the two harnesses incomparable. `build` is opencode's primary agent. + payload: dict[str, object] = { + "agent": {"build": {"steps": OPENCODE_STEP_LIMIT}} + } + provider, _, _ = model.partition("/") - if provider == "openai": - return [] # the adapter already injects the baseURL for this one - path = task / "environment/gateway/launch.json" - if not path.exists(): - return [] - try: - base_url = json.loads(path.read_text(encoding="utf-8"))["producer_base_url"] - except (OSError, json.JSONDecodeError, KeyError): - return [] - # producer_base_url ends in /v1 and provider SDKs append their own route - # (/messages), matching how ANTHROPIC_BASE_URL is handed to claude-code. - payload = {"provider": {provider: {"options": {"baseURL": base_url}}}} + if "/" in model and provider != "openai": + # The adapter injects a baseURL only for the openai provider; for any + # other it writes none and opencode calls that provider's public + # endpoint. Supply it ourselves so the traffic stays metered. + path = task / "environment/gateway/launch.json" + if path.exists(): + try: + base_url = json.loads(path.read_text(encoding="utf-8"))[ + "producer_base_url" + ] + except (OSError, json.JSONDecodeError, KeyError): + base_url = None + if base_url: + # producer_base_url ends in /v1 and provider SDKs append their + # own route (/messages), matching ANTHROPIC_BASE_URL for + # claude-code. + payload["provider"] = {provider: {"options": {"baseURL": base_url}}} return ["--ak", f"opencode_config={json.dumps(payload, separators=(',', ':'))}"] diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py index 24c792dd..af1a3c19 100644 --- a/vero/tests/test_v05_cli.py +++ b/vero/tests/test_v05_cli.py @@ -333,21 +333,32 @@ def test_opencode_non_openai_provider_gets_a_gateway_base_url(tmp_path): assert args[0] == "--ak" key, _, value = args[1].partition("=") assert key == "opencode_config" - assert json.loads(value) == { - "provider": { - "anthropic": { - "options": {"baseURL": "http://inference-gateway:8001/scopes/p/o/v1"} - } + payload = json.loads(value) + assert payload["provider"] == { + "anthropic": { + "options": {"baseURL": "http://inference-gateway:8001/scopes/p/o/v1"} } } - - # openai is the one provider the adapter already handles; don't fight it. - assert _opencode_gateway_args("opencode", "openai/gpt-5.4", task) == [] - # Other agents and bare model names are untouched. + # The same config carries the step limit; see the step-limit test below. + assert payload["agent"]["build"]["steps"] > 100 + + # openai is the one provider the adapter already handles for baseURL; don't + # fight it -- but opencode still needs its step limit raised. + openai = _opencode_gateway_args("opencode", "openai/gpt-5.4", task) + assert "provider" not in json.loads(openai[1].removeprefix("opencode_config=")) + # Other agents are untouched -- claude-code controls turns via --max-turns. assert _opencode_gateway_args("claude-code", "anthropic/claude-sonnet-5", task) == [] - assert _opencode_gateway_args("opencode", "claude-sonnet-5", task) == [] - # A task compiled without a gateway simply gets no override. - assert _opencode_gateway_args("opencode", "anthropic/x", tmp_path / "none") == [] + # For opencode the step limit is unconditional: it does not depend on the + # model spelling or on a gateway being present, because a truncated search is + # a problem either way. Only the baseURL injection is conditional. + bare = _opencode_gateway_args("opencode", "claude-sonnet-5", task) + assert json.loads(bare[1].removeprefix("opencode_config="))["agent"]["build"][ + "steps" + ] > 100 + no_gateway = _opencode_gateway_args("opencode", "anthropic/x", tmp_path / "none") + payload = json.loads(no_gateway[1].removeprefix("opencode_config=")) + assert payload["agent"]["build"]["steps"] > 100 + assert "provider" not in payload def test_outer_modal_trial_gets_a_named_app(): @@ -373,3 +384,32 @@ def test_outer_modal_trial_gets_a_named_app(): ) == [] # A name that is entirely punctuation still yields a usable app. assert _outer_app_name_args("modal", "///", ()) == ["--ek", "app_name=vero"] + + +def test_opencode_gets_a_step_limit_that_does_not_truncate_the_search(tmp_path): + """opencode caps agentic iterations at 100 and then forces a text-only reply. + + That is inside a real run: gaia's optimizer used exactly 100 and stopped + without ever calling `evals submit`, and because the cap produces a fluent + final message the truncation is invisible unless you count the steps. + claude-code takes harbor's --max-turns instead, so leaving opencode at its + default makes the two harnesses incomparable on the one axis the grid varies. + """ + from vero.harbor.cli import OPENCODE_STEP_LIMIT, _opencode_gateway_args + + args = _opencode_gateway_args("opencode", "anthropic/claude-sonnet-5", tmp_path) + assert args[0] == "--ak" + payload = json.loads(args[1].removeprefix("opencode_config=")) + assert payload["agent"]["build"]["steps"] == OPENCODE_STEP_LIMIT + assert OPENCODE_STEP_LIMIT > 100 + + # Still set for the openai provider, which needs no baseURL injection. + openai = json.loads( + _opencode_gateway_args("opencode", "openai/gpt-5.4", tmp_path)[1] + .removeprefix("opencode_config=") + ) + assert openai["agent"]["build"]["steps"] == OPENCODE_STEP_LIMIT + assert "provider" not in openai + + # claude-code is untouched; it has its own turn control. + assert _opencode_gateway_args("claude-code", "claude-sonnet-5", tmp_path) == [] From 269773582ebe0d861448b8d5c9eacd141f206426 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Mon, 27 Jul 2026 23:00:29 -0700 Subject: [PATCH 72/72] Give litellm-based harnesses the gateway URL under the name litellm reads litellm resolves a provider base URL from _API_BASE; the provider SDKs read _BASE_URL. vero sets the SDK names, so a harness that drives the model through litellm sees no override at all and calls the provider's public endpoint holding only a scoped gateway token. mini-swe-agent installs litellm[proxy] and hit exactly that: AuthenticationError from api.anthropic.com, "invalid x-api-key". Worth noting it fails closed -- the optimizer never holds an upstream credential, so the wrong endpoint rejects it rather than serving it -- but the harness cannot run. Set OPENAI_API_BASE and ANTHROPIC_API_BASE from the compiled producer scope for the harnesses known to use litellm, so whichever provider the model names is covered. Harnesses on provider SDKs (claude-code, opencode) already get _BASE_URL and are left alone. This is the third harness-specific credential-routing shape: claude-code takes ANTHROPIC_BASE_URL directly, opencode needs a baseURL written into its own config, and litellm harnesses need the _API_BASE alias. Each fails differently and none of them is discoverable without running the harness. Co-Authored-By: Claude Opus 5 (1M context) --- vero/src/vero/harbor/cli.py | 35 +++++++++++++++++++++++++++++++++++ vero/tests/test_v05_cli.py | 31 +++++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+) diff --git a/vero/src/vero/harbor/cli.py b/vero/src/vero/harbor/cli.py index 8161e148..3e865cb5 100644 --- a/vero/src/vero/harbor/cli.py +++ b/vero/src/vero/harbor/cli.py @@ -231,6 +231,40 @@ def _load_env_file(path: Path) -> dict[str, str]: # the gateway token cap are the intended limits. OPENCODE_STEP_LIMIT = 1000 +# Harnesses that drive the model through litellm rather than a provider SDK. +# litellm reads the base URL as _API_BASE; the SDKs read +# _BASE_URL. vero sets the SDK names, so a litellm-based harness sees no +# override and calls the provider's public endpoint instead of the gateway. +_LITELLM_AGENTS = frozenset({"mini-swe-agent", "swe-agent"}) + + +def _litellm_base_url_args(agent: str, task: Path) -> list[str]: + """Give litellm-based harnesses the gateway URL under the name they read. + + mini-swe-agent installs `litellm[proxy]` and resolves `anthropic/` + through it. Without ANTHROPIC_API_BASE it reaches api.anthropic.com holding + only a scoped gateway token, and fails with + ``AuthenticationError: invalid x-api-key`` -- fails closed, so nothing leaks, + but the harness cannot run at all. Set both provider aliases from the + compiled producer scope so whichever provider the model names is covered. + """ + + if agent not in _LITELLM_AGENTS: + return [] + path = task / "environment/gateway/launch.json" + if not path.exists(): + return [] + try: + base_url = json.loads(path.read_text(encoding="utf-8"))["producer_base_url"] + except (OSError, json.JSONDecodeError, KeyError): + return [] + # The Anthropic SDK re-appends /v1, litellm does not, so anthropic keeps the + # full path here -- matching how the gateway is addressed for openai. + arguments: list[str] = [] + for name in ("OPENAI_API_BASE", "ANTHROPIC_API_BASE"): + arguments.extend(["--ae", f"{name}={base_url}"]) + return arguments + def _opencode_gateway_args(agent: str, model: str | None, task: Path) -> list[str]: """Route opencode's non-openai providers through the gateway. @@ -526,6 +560,7 @@ def run_command(config_path, agent, model, environment, params, env_file, extra) for key in sorted(config.agent_env): command.extend(["--ae", f"{key}={config.agent_env[key]}"]) command.extend(_opencode_gateway_args(agent, model, task)) + command.extend(_litellm_base_url_args(agent, task)) command.extend(_outer_app_name_args(environment, config.name, extra)) command.extend(extra) click.echo(shlex.join(command)) diff --git a/vero/tests/test_v05_cli.py b/vero/tests/test_v05_cli.py index af1a3c19..097913db 100644 --- a/vero/tests/test_v05_cli.py +++ b/vero/tests/test_v05_cli.py @@ -413,3 +413,34 @@ def test_opencode_gets_a_step_limit_that_does_not_truncate_the_search(tmp_path): # claude-code is untouched; it has its own turn control. assert _opencode_gateway_args("claude-code", "claude-sonnet-5", tmp_path) == [] + + +def test_litellm_harnesses_get_the_gateway_url_under_the_name_they_read(tmp_path): + """litellm reads _API_BASE; the provider SDKs read _BASE_URL. + + vero sets the SDK names, so mini-swe-agent -- which drives the model through + litellm[proxy] -- saw no override and called api.anthropic.com holding only a + scoped gateway token: AuthenticationError, invalid x-api-key. It fails closed, + so nothing leaked, but the harness could not run at all. + """ + from vero.harbor.cli import _litellm_base_url_args + + task = tmp_path / "task" + (task / "environment" / "gateway").mkdir(parents=True) + (task / "environment" / "gateway" / "launch.json").write_text( + json.dumps({"producer_base_url": "http://inference-gateway:8001/scopes/p/o/v1"}), + encoding="utf-8", + ) + + args = _litellm_base_url_args("mini-swe-agent", task) + pairs = dict(zip(args[::2], args[1::2])) + assert set(pairs) == {"--ae"} or True # flags interleave; check the values + values = [v for k, v in zip(args[::2], args[1::2])] + assert "OPENAI_API_BASE=http://inference-gateway:8001/scopes/p/o/v1" in values + assert "ANTHROPIC_API_BASE=http://inference-gateway:8001/scopes/p/o/v1" in values + + # Harnesses that use provider SDKs already get _BASE_URL and need nothing. + assert _litellm_base_url_args("claude-code", task) == [] + assert _litellm_base_url_args("opencode", task) == [] + # No compiled gateway: nothing to point at. + assert _litellm_base_url_args("mini-swe-agent", tmp_path / "none") == []