The deterministic context engineering platform for open source AI. Connect open models and ontologies with context graph harnesses to build explainable, reliable agents.

An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes.

[Up-to-date] A curated list of resources on graph-empowered agents and agent-facilitated graph learning (Graphs Meet Agents).

agent wiki +engineering skills

Build stateful agent workflows with typed outputs, reusable tools, session forks, and ordinary TypeScript.

Turns repeatable, domain-agnostic workflows into multi-step graph-driven loops.

Long-horizon agent skill for Claude Code / Cursor / Codex / Grok — multi-task ledger loop (related or not), host-portable (re-send the prompt to continue), clean-context supervisor, verified gates. Markdown library (loop-graph · quest), not a framework.

Desktop app for harness engineering, loop engineering, graph engineering—and whatever comes next in local AI-agent workflows.

🕸️ Engineer the organization, not just the agent. 477 curated resources · 9 design layers · 11 sections · 225 papers & preprints — a field guide, CC0 open dataset, and interactive atlas for graph-structured multi-agent systems: roles, topologies, handoffs, work graphs, state, gates, reliability, observability.

Copy-paste prompts that turn your AI agents from a waiting line into a graph: a 5-min demo, false-edge audit, diamond research, adversarial review, consultant roundtable, and an issue tree that dispatches itself. EN + 繁中.

Design grounded graphs of governed improvement loops.

Installable graph engineering for Claude Code, Codex, OpenCode, and Cursor — dependency-graph execution with local caching, quality gates, selective retries, and live reports

A production-grade Python & Streamlit reference implementation of the 5-Layer Graph Engineering Taxonomy, implementing the complete technical outline

Graph Engineering for Agent Skills: a specification and toolchain for dynamically discovering context and building observable, testable, and recoverable agent workflows.

Interactive GitHub issue dependency DAG and issue explorer

Bounded static DAG contracts, validators, experiments, and adjacent agent architecture boundaries.

从 Prompt 到 Graph Engineering：五层 Agent 工程手册与可运行示例

Central dogma of biology as map for Prompt→Loop→Graph→Evolution trajectory of AI agents. Skill framework, research scaffold, practical patterns.

Design the structures your agents work through — knowledge graphs for memory, task graphs for orchestration. Playbook + agent skill + runnable stdlib-only pipeline.

Python toolkit for multi-step AI/agent systems as explicit graphs — define nodes/edges, structural validate (V1–V9), Mermaid visualize, pattern init, and skeleton walk. Vendor-agnostic. Runtime agent execute later.