From 182c6582c3296db81d313531ef85c9b6e18c1068 Mon Sep 17 00:00:00 2001 From: zmylol <84814703+zmylol@users.noreply.github.com> Date: Sun, 6 Sep 2026 22:22:57 +0800 Subject: [PATCH] fix(s08): recognize Anthropic prompt overflow errors Recognize the documented prompt is too long message in the bounded reactive-compaction path. Synchronize all three chapter READMEs and add regression coverage for recovery, compatible markers, retry exhaustion, and unrelated errors. Validation: 21 targeted tests and 22 subtests passed. Full suite: 486 passed, 1 pre-existing S16 scenario ordering failure, 100 subtests passed. Refs shareAI-lab/learn-claude-code#560. Prepared with AI assistance. --- s08_context_compact/README.ja.md | 5 ++- s08_context_compact/README.md | 5 ++- s08_context_compact/README.zh.md | 5 ++- s08_context_compact/code.py | 3 +- tests/test_s08_context_compact.py | 71 +++++++++++++++++++++++++++++++ 5 files changed, 82 insertions(+), 7 deletions(-) diff --git a/s08_context_compact/README.ja.md b/s08_context_compact/README.ja.md index 07fa610a1..63be990c7 100644 --- a/s08_context_compact/README.ja.md +++ b/s08_context_compact/README.ja.md @@ -20,7 +20,7 @@ Agent が作業を続けると、読み込んだファイル、コマンド結 コンテキストウィンドウは、モデルが現在使っている下書き用紙と考えられます。ユーザーメッセージ、モデルの応答、`tool_use`、`tool_result` が順番に書き込まれます。モデルはタスクを続けるたびに、その内容を読み直します。 -下書き用紙の大きさは固定です。上限を超えると API はリクエストを拒否し、`prompt_too_long` を返します。コーディングタスクでは、ツール結果が多くの領域を占めます。 +下書き用紙の大きさは固定です。上限を超えると API はリクエストを拒否し、`prompt is too long` を返します。コーディングタスクでは、ツール結果が多くの領域を占めます。 - 長いファイルを読むと、その内容がコンテキストに入ります。 - テストやビルドのログは、一度に数十 KB 追加されることがあります。 @@ -202,7 +202,7 @@ if self.estimate_chars(messages) > self.CONTEXT_CHAR_LIMIT: ## API に拒否された後の回復 -文字数はモデルが使う token 数の推定値です。そのため API が `prompt_too_long` を返す可能性は残ります。`reactive_compact` は transcript を保存し、古い履歴を要約して、最新 5 メッセージを保持します。 +文字数はモデルが使う token 数の推定値です。そのため API が `prompt is too long` を返す可能性は残ります。`reactive_compact` は transcript を保存し、古い履歴を要約して、最新 5 メッセージを保持します。 ```python tail_start = max(0, len(messages) - self.KEEP_RECENT_MESSAGES) @@ -236,6 +236,7 @@ def agent_loop(messages, active_request): except Exception as error: message = str(error).lower() too_long = ("prompt_too_long" in message + or "prompt is too long" in message or "too many tokens" in message) if too_long and reactive_retries < MAX_REACTIVE_RETRIES: messages[:] = COMPACTOR.reactive_compact( diff --git a/s08_context_compact/README.md b/s08_context_compact/README.md index 924f88c85..0fcc030ca 100644 --- a/s08_context_compact/README.md +++ b/s08_context_compact/README.md @@ -20,7 +20,7 @@ This lesson adds a four-step compaction pipeline. It first reduces recoverable t Think of the context window as the model's current scratchpad. User messages, model responses, `tool_use`, and `tool_result` blocks are written onto it in order. The model reads that material again whenever it continues the task. -The scratchpad has a fixed size. When a request exceeds it, the API rejects the call with `prompt_too_long`. Tool results usually consume most of the space in coding tasks: +The scratchpad has a fixed size. When a request exceeds it, the API rejects the call with `prompt is too long`. Tool results usually consume most of the space in coding tasks: - Reading a long file puts its contents into the context. - Test and build logs can add tens of kilobytes at once. @@ -202,7 +202,7 @@ Each round therefore starts with the lowest-cost operation whose information is ## Recovering From an API Rejection -A character count can only estimate the tokens used by a model. The API may still return `prompt_too_long`. `reactive_compact` saves a transcript, summarizes older history, and retains the latest 5 messages: +A character count can only estimate the tokens used by a model. The API may still return `prompt is too long`. `reactive_compact` saves a transcript, summarizes older history, and retains the latest 5 messages: ```python tail_start = max(0, len(messages) - self.KEEP_RECENT_MESSAGES) @@ -236,6 +236,7 @@ def agent_loop(messages, active_request): except Exception as error: message = str(error).lower() too_long = ("prompt_too_long" in message + or "prompt is too long" in message or "too many tokens" in message) if too_long and reactive_retries < MAX_REACTIVE_RETRIES: messages[:] = COMPACTOR.reactive_compact( diff --git a/s08_context_compact/README.zh.md b/s08_context_compact/README.zh.md index 013130ab4..1ff435c3e 100644 --- a/s08_context_compact/README.zh.md +++ b/s08_context_compact/README.zh.md @@ -20,7 +20,7 @@ Agent 持续工作时，读过的文件、执行过的命令和模型回复都 可以把上下文窗口看作模型当前使用的一张草稿纸。用户消息、模型回复、`tool_use` 和 `tool_result` 都会按顺序写在这张纸上。模型每次继续工作时，都要重新读取这些内容。 -草稿纸的大小固定。内容超过上限后，API 会拒绝请求并返回 `prompt_too_long`。在代码任务里，工具结果通常占据最多空间： +草稿纸的大小固定。内容超过上限后，API 会拒绝请求并返回 `prompt is too long`。在代码任务里，工具结果通常占据最多空间： - 读取一个长文件会把文件内容放进上下文； - 测试和构建日志可能一次产生几十 KB 文本； @@ -202,7 +202,7 @@ if self.estimate_chars(messages) > self.CONTEXT_CHAR_LIMIT: ## API 拒绝后的补救 -字符数只能估算模型实际使用的 token。API 仍可能返回 `prompt_too_long`。`reactive_compact` 会保存 transcript，总结较早历史，并保留最近 5 条消息： +字符数只能估算模型实际使用的 token。API 仍可能返回 `prompt is too long`。`reactive_compact` 会保存 transcript，总结较早历史，并保留最近 5 条消息： ```python tail_start = max(0, len(messages) - self.KEEP_RECENT_MESSAGES) @@ -236,6 +236,7 @@ def agent_loop(messages, active_request): except Exception as error: message = str(error).lower() too_long = ("prompt_too_long" in message + or "prompt is too long" in message or "too many tokens" in message) if too_long and reactive_retries < MAX_REACTIVE_RETRIES: messages[:] = COMPACTOR.reactive_compact( diff --git a/s08_context_compact/code.py b/s08_context_compact/code.py index 765c99995..668780e15 100644 --- a/s08_context_compact/code.py +++ b/s08_context_compact/code.py @@ -536,7 +536,8 @@ def agent_loop(messages: list, active_request: str): reactive_retries = 0 except Exception as error: too_long = any(text in str(error).lower() - for text in ("prompt_too_long", "too many tokens")) + for text in ("prompt_too_long", "prompt is too long", + "too many tokens")) if too_long and reactive_retries < MAX_REACTIVE_RETRIES: print("[reactive compact]") messages[:] = COMPACTOR.reactive_compact(messages, active_request) diff --git a/tests/test_s08_context_compact.py b/tests/test_s08_context_compact.py index d50a1e4f1..2d7465a8f 100644 --- a/tests/test_s08_context_compact.py +++ b/tests/test_s08_context_compact.py @@ -2,6 +2,9 @@ import sys import types from pathlib import Path +from unittest.mock import Mock + +import pytest ROOT = Path(__file__).resolve().parents[1] @@ -136,3 +139,71 @@ def test_prepare_persists_oversized_unseen_result_before_full_compact( saved_line = next(line for line in content.splitlines() if line.startswith("Full output: ")) assert Path(saved_line.removeprefix("Full output: ")).read_text() == output + + +@pytest.mark.parametrize("error_message", [ + "prompt is too long: 210445 tokens > 200000 maximum", + "prompt_too_long", + "too many tokens", +]) +def test_agent_loop_compacts_and_retries_context_overflow( + tmp_path, monkeypatch, error_message): + lesson = load_lesson(monkeypatch, tmp_path) + messages = [{"role": "user", "content": "continue"}] + compacted = [{"role": "user", "content": "compacted history"}] + requests = [] + response = types.SimpleNamespace( + content=[types.SimpleNamespace(type="text", text="Recovered")]) + + def create(**kwargs): + requests.append(list(kwargs["messages"])) + if len(requests) == 1: + raise RuntimeError(error_message) + return response + + compact = Mock(return_value=compacted) + monkeypatch.setattr(lesson["client"].messages, "create", create) + monkeypatch.setattr(lesson["COMPACTOR"], "reactive_compact", compact) + + lesson["agent_loop"](messages, "continue") + + assert len(requests) == 2 + assert requests[1] == compacted + compact.assert_called_once() + assert compact.call_args.args[1] == "continue" + assert messages == [*compacted, + {"role": "assistant", "content": response.content}] + + +def test_agent_loop_stops_after_one_reactive_retry(tmp_path, monkeypatch): + lesson = load_lesson(monkeypatch, tmp_path) + messages = [{"role": "user", "content": "continue"}] + first_error = RuntimeError("prompt is too long: first request") + retry_error = RuntimeError("prompt is too long: retry") + create = Mock(side_effect=[first_error, retry_error]) + compact = Mock(return_value=list(messages)) + monkeypatch.setattr(lesson["client"].messages, "create", create) + monkeypatch.setattr(lesson["COMPACTOR"], "reactive_compact", compact) + + with pytest.raises(RuntimeError) as caught: + lesson["agent_loop"](messages, "continue") + + assert caught.value is retry_error + assert create.call_count == 2 + compact.assert_called_once() + + +def test_agent_loop_propagates_unrelated_errors(tmp_path, monkeypatch): + lesson = load_lesson(monkeypatch, tmp_path) + error = RuntimeError("invalid API key") + create = Mock(side_effect=error) + compact = Mock() + monkeypatch.setattr(lesson["client"].messages, "create", create) + monkeypatch.setattr(lesson["COMPACTOR"], "reactive_compact", compact) + + with pytest.raises(RuntimeError) as caught: + lesson["agent_loop"]([{"role": "user", "content": "continue"}], "continue") + + assert caught.value is error + create.assert_called_once() + compact.assert_not_called()