<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"role": "system", "content": "1. Identity: terminal-first agent\n   Act as a terminal-oriented engineering agent. Prefer terse, operational guidance suitable for a CLI. Optimize for clarity and execution over conversational tone.\n\n2. Professional objectivity: correctness over comfort\n   Operate with engineering-level objectivity. Do not provide emotional validation or “comfort-first” responses. If the user is wrong, say so plainly and help them converge on the correct model of reality. When uncertain, state uncertainty and verify before asserting. Avoid flattery, hype, or agreement-for-agreement’s-sake; prioritize accurate correction and actionable truth.\n\n3. Planning without timelines: executable steps, no scheduling claims\n   When planning work, provide an executable procedure: concrete steps, prerequisites, decision points, and success criteria. Do not include time estimates, deadlines, or schedule narratives. Do not defer with vague “later” actions or future promises. This constraint exists to prevent two common failure modes: (a) invented timelines that mislead operators, and (b) timeline language being used as an escape hatch to avoid execution. Provide the “how” as a checklist; the operator owns the “when.”\n\n4. Edits: minimal surface area\n   Change only what is required to satisfy the request. Read relevant code before suggesting edits. Avoid opportunistic refactors, aesthetic cleanups, or speculative generalizations. Add documentation/typing/extra guards only when they are essential to the requested change or at true external boundaries.\n\n5. Hooks: honor hook output as operator directives\n   Hook messages are authoritative signals from the operator’s environment. If a hook blocks progress, adapt behavior to comply with the hook’s feedback; otherwise request the operator to review hook rules and configuration.\n\n命令模仿：当用户给出zsh_history的条目，以及新的需求时，模仿之前的操作步骤生成新的命令执行\n\n# Working Environment\n\n## Operating System\n\nThe operating environment is not in a sandbox. Any action especially mutation you do will immediately affect the user's system. So you MUST be extremely cautious.\n\n## Working Directory\nThe current working directory is `/home/draplater/Development/cyf_cli/oturn/src/oturn`.\nThe directory listing of current working directory is:\n\n```\n(omitted) work_dir=/home/draplater/Development/cyf_cli/oturn/src/oturn\n```\n\n# Project Information\n``\n", "name": null, "tool_call_id": null, "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": {"email": null, "tool_contents": null}, "name": null, "tool_call_id": null, "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> user <!-- md_as_config:ctx:value-end -->
- **User:** <!-- md_as_config:ctx:value-begin json-path=$.content.user_name --> User <!-- md_as_config:ctx:value-end -->
- **Date:** <!-- md_as_config:ctx:value-begin type=json json-path=$.content.date --> {"ts": 1771652300.861493, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
梳理项目代码
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_r8hs9gos49vq_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -type f -name \"*.rs\" | head -50 && echo \"---\" && ls -la"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求"梳理项目代码"，这是一个比较宽泛的请求。我需要先了解这个项目的结构、代码组织、技术栈等信息，然后给出一个清晰的概述。

让我先查看项目目录结构和关键文件。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 93, "prompt_tokens": 1456, "total_tokens": 1549, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 93}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_r8hs9gos49vq_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "failure", "data": {"reasons": ["find 需显式指定 -maxdepth，且值需 ≤ 2，命令被拒绝", "加上 -maxdepth 2 先演练：find … -maxdepth 2 …"]}, "error": "Command rejected by policy.", "metadata": {"reasons": ["find 需显式指定 -maxdepth，且值需 ≤ 2，命令被拒绝", "加上 -maxdepth 2 先演练：find … -maxdepth 2 …"]}, "info": {}}
<!-- md_as_config:ctx:multi-line-value-end -->

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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_yampyqrxpyhg_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -maxdepth 2 -type f -name \"*.rs\" | head -50 && echo \"---\" && ls -la"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
I need to add -maxdepth 2 to the find command. Let me try again.
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 76, "prompt_tokens": 1635, "total_tokens": 1711, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 76}, "prompt_tokens_details": {"cached_tokens": 1452, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1452}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_yampyqrxpyhg_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "---\ntotal 136\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 13:30 .\r\ndrwxr-xr-x 1 draplater draplater    10 Feb 21 10:37 ..\r\n-rw-r--r-- 1 draplater draplater 11046 Feb 21 10:37 agent_stream_state.py\r\n-rw-r--r-- 1 draplater draplater 11520 Feb 21 12:52 context.py\r\ndrwxr-xr-x 1 draplater draplater    16 Feb 21 12:43 .cyf\r\n-rw-r--r-- 1 draplater draplater  7155 Feb 21 13:36 events.py\r\n-rw-r--r-- 1 draplater draplater  1235 Feb 21 12:00 __init__.py\r\n-rw-r--r-- 1 draplater draplater  8745 Feb 21 12:09 llm_transport.py\r\n-rw-r--r-- 1 draplater draplater  3591 Feb 21 12:46 mcp_adapter.py\r\n-rw-r--r-- 1 draplater draplater 12498 Feb 21 10:37 model.py\r\n-rw-r--r-- 1 draplater draplater  2895 Feb 21 10:37 nucleus.py\r\n-rw-r--r-- 1 draplater draplater 28446 Feb 21 13:36 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:36 __pycache__\r\n-rw-r--r-- 1 draplater draplater  3130 Feb 21 12:16 README.md\r\n-rw-r--r-- 1 draplater draplater   813 Feb 21 12:15 runtime_config.py\r\n-rw-r--r-- 1 draplater draplater  7875 Feb 21 12:49 session_store.py\r\n-rw-r--r-- 1 draplater draplater  3610 Feb 21 12:39 template_renderer.py\r\n-rw-r--r-- 1 draplater draplater  9649 Feb 21 13:23 tool_runtime.py\r\n-rw-r--r-- 1 draplater draplater  1142 Feb 21 12:10 tool_runtime_utils.py\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 tools\r\n", "stderr": "", "actual_command": "find . -maxdepth 2 -type f -name \"*.rs\" | head -50 && echo \"---\" && ls -la"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_obxbuizm9ou5_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la tools/"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
这是一个 Python 项目，不是 Rust 项目。让我查看项目结构和主要文件的内容来梳理代码。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 51, "prompt_tokens": 2370, "total_tokens": 2421, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 51}, "prompt_tokens_details": {"cached_tokens": 1631, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1631}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_obxbuizm9ou5_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "total 32\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 .\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 13:30 ..\r\n-rw-r--r-- 1 draplater draplater 12807 Feb 21 13:32 base.py\r\n-rw-r--r-- 1 draplater draplater  6602 Feb 21 11:13 examples.py\r\n-rw-r--r-- 1 draplater draplater   427 Feb 21 11:12 __init__.py\r\n-rw-r--r-- 1 draplater draplater   341 Feb 21 10:37 precheck.py\r\ndrwxr-xr-x 1 draplater draplater   184 Feb 21 13:32 __pycache__\r\n", "stderr": "", "actual_command": "ls -la tools/"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_ri35rgi1hb22_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat README.md"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我看到了项目结构。这是一个Python项目，包含以下主要文件：

- oturn.py - 主文件
- context.py - 上下文管理
- events.py - 事件处理
- model.py - 模型定义
- llm_transport.py - LLM传输
- mcp_adapter.py - MCP适配器
- tool_runtime.py - 工具运行时
- session_store.py - 会话存储
- template_renderer.py - 模板渲染
- agent_stream_state.py - Agent流状态
- tools/base.py - 工具基类

让我读取README来了解项目，然后查看主要文件的代码结构。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 157, "prompt_tokens": 2669, "total_tokens": 2826, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 157}, "prompt_tokens_details": {"cached_tokens": 2366, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2366}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ri35rgi1hb22_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "# OTURN\r\n\r\n**OTURN** = **Orchestrated Task Unified Reactive Nucleus**.\r\n\r\nA small async framework for agent-like loops built on:\r\n\r\n- reducer-based state transitions\r\n- explicit transition events\r\n- multi-subscriber async pub/sub\r\n- deterministic step/run control\r\n- explicit runtime tool injection (no global registry)\r\n\r\n## Core API\r\n\r\n- `Transition[state, event]`: reducer output (`state`, `events`, `terminal`)\r\n- `Reducer`: `(state, signal) -> Transition`\r\n- `AsyncPubSub[event]`: `subscribe(queue)`, `unsubscribe(queue)`, `publish(event)`\r\n- `OTurnNucleus[state, signal, event]`:\r\n  - `step(signal)`\r\n  - `run(async_signals)`\r\n  - `state`, `terminal`\r\n  - `subscribe(queue)`, `unsubscribe(queue)`\r\n- `Oturn(work_dir, config, tools=[...])`:\r\n  - pass tool classes at runtime\r\n  - no global auto-discovered registry\r\n\r\n## Minimal Example\r\n\r\n```python\r\nimport asyncio\r\nfrom dataclasses import dataclass\r\nfrom typing import Literal\r\n\r\nfrom oturn import OTurnNucleus, Transition\r\n\r\nSignal = Literal[\"tick\", \"stop\"]\r\n\r\n@dataclass\r\nclass State:\r\n    n: int = 0\r\n\r\n\r\ndef reducer(state: State, signal: Signal) -> Transition[State, dict]:\r\n    if signal == \"tick\":\r\n        ns = State(n=state.n + 1)\r\n        return Transition(state=ns, events=[{\"type\": \"count\", \"n\": ns.n}], terminal=False)\r\n    return Transition(state=state, events=[{\"type\": \"done\"}], terminal=True)\r\n\r\n\r\nasync def main() -> None:\r\n    nucleus = OTurnNucleus(State(), reducer)\r\n    event_q: asyncio.Queue[dict] = asyncio.Queue()\r\n    nucleus.subscribe(event_q)\r\n\r\n    async def signals():\r\n        for sig in (\"tick\", \"tick\", \"stop\"):\r\n            yield sig\r\n\r\n    await nucleus.run(signals())\r\n\r\n    while not event_q.empty():\r\n        print(await event_q.get())\r\n\r\n\r\nasyncio.run(main())\r\n```\r\n\r\n## Example Tools\r\n\r\n`oturn.tools.examples` provides:\r\n\r\n- `WeatherTool` (`get_weather`)\r\n- `HostConfigTool` (`get_host_config`)\r\n\r\nUse them explicitly:\r\n\r\n```python\r\nfrom oturn import Oturn, OturnConfig, OturnProviderConfig, OturnModelConfig, OturnAgentConfig, WeatherTool, HostConfigTool\r\n\r\ncfg = OturnConfig(\r\n    model=\"openai/gpt-4o-mini\",\r\n    provider=OturnProviderConfig(api_base=\"https://api.openai.com/v1\", api_key=\"...\"),\r\n    model_config=OturnModelConfig(max_output_tokens=8192, temperature=0.7),\r\n    agent_config=OturnAgentConfig(max_steps=100, user_name=\"User\"),\r\n)\r\nagent = Oturn(work_dir=work_dir, config=cfg, tools=[WeatherTool, HostConfigTool])\r\n```\r\n\r\nOr pass explicit runtime config to decouple from caller-specific option structs:\r\n\r\n```python\r\nfrom oturn import (\r\n    Oturn,\r\n    OturnConfig,\r\n    OturnProviderConfig,\r\n    OturnModelConfig,\r\n    OturnAgentConfig,\r\n)\r\n\r\ncfg = OturnConfig(\r\n    model=\"openai/gpt-4o-mini\",\r\n    provider=OturnProviderConfig(api_base=\"https://api.openai.com/v1\", api_key=\"...\"),\r\n    model_config=OturnModelConfig(max_output_tokens=8192, temperature=0.7),\r\n    agent_config=OturnAgentConfig(max_steps=100, user_name=\"User\"),\r\n)\r\n\r\nagent = Oturn(work_dir=work_dir, config=cfg, tools=[...])\r\n```\r\n\r\n## Design Notes\r\n\r\n- `step()` is the unit of determinism and testing.\r\n- `run()` is a convenience wrapper around repeated `step()` calls.\r\n- Pub/sub is queue-based so multiple independent consumers can observe the same stream.\r\n", "stderr": "", "actual_command": "cat README.md"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_znrfh3dm36uj_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -100 oturn.py"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
这是一个Python项目，不是Rust项目。让我继续阅读核心文件来梳理整个代码结构。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 52, "prompt_tokens": 3803, "total_tokens": 3855, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 52}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_znrfh3dm36uj_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "import json\r\nimport asyncio\r\nimport time\r\nfrom pathlib import Path\r\nimport traceback\r\nimport datetime as _dt\r\nfrom typing import Optional, Awaitable, Callable, List, Any, Dict, Iterable, AsyncGenerator, TypedDict, Literal, cast, TypeAlias, Type\r\nfrom pydantic import BaseModel\r\n\r\nfrom .context import Context\r\nfrom .model import (\r\n    Message,\r\n    UserContent,\r\n    ShellResult,\r\n    ToolMessageContent,\r\n    CompactRecord,\r\n    Role,\r\n    ROLE_USER,\r\n    ROLE_ASSISTANT,\r\n    ROLE_TOOL,\r\n)\r\nfrom .agent_stream_state import (\r\n    TurnStreamState,\r\n    StreamTransitionUnion,\r\n    ToolCallDeltaPayload,\r\n    ToolRecord,\r\n    UsageData,\r\n    ingest_stream_event,\r\n    reduce_stream_signal,\r\n    build_assembled_debug,\r\n    build_tool_records,\r\n)\r\nfrom .tools.base import ToolResult, BaseToolRegistry, BaseTool\r\nfrom .runtime_config import OturnConfig\r\nfrom .nucleus import AsyncPubSub\r\nfrom .llm_transport import (\r\n    LiteLLMTransport,\r\n    start_global_litellm_prewarm as _start_global_litellm_prewarm,\r\n    get_global_litellm_prewarm_error as _get_global_litellm_prewarm_error,\r\n)\r\nfrom .tool_runtime_utils import (\r\n    parse_tool_arguments,\r\n)\r\nfrom .tool_runtime import ToolRuntime\r\nfrom .mcp_adapter import MCPAdapter\r\nfrom .events import (\r\n    EventPayload,\r\n    ToolCallDebugEvent,\r\n    StreamOutEvent,\r\n    ev_assistant_delta,\r\n    ev_assistant_reasoning_delta,\r\n    ev_error,\r\n    ev_assistant_final,\r\n    ev_message_sent,\r\n    ev_run_aborted,\r\n    ev_debug_latency,\r\n    ev_tool_call_preview,\r\n    ev_tool_call_debug_delta,\r\n    ev_tool_call_debug_warning,\r\n    ev_tool_call_debug_assembled,\r\n)\r\n\r\n\r\nclass _InputQueueItem(TypedDict):\r\n    status: str\r\n    text: str\r\n    type: str\r\n\r\n\r\nJsonValue: TypeAlias = str | int | float | bool | None | list[\"JsonValue\"] | dict[str, \"JsonValue\"]\r\nJsonObject: TypeAlias = dict[str, JsonValue]\r\n\r\n\r\nclass _ApiToolFunction(TypedDict):\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass _ApiToolCall(TypedDict):\r\n    id: str\r\n    type: str\r\n    function: _ApiToolFunction\r\n\r\n\r\nclass _ApiMessage(TypedDict, total=False):\r\n    role: Role\r\n    content: str\r\n    name: str\r\n    tool_call_id: str\r\n    reasoning_content: str\r\n    tool_calls: list[_ApiToolCall]\r\n\r\n\r\nclass _ToolFunctionSchema(TypedDict):\r\n    name: str\r\n    description: str\r\n    parameters: JsonObject\r\n\r\n\r\nclass _ToolSchema(TypedDict):\r\n", "stderr": "", "actual_command": "head -100 oturn.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_ytem2i9a9g14_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "wc -l oturn.py && head -300 oturn.py | tail -200"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
让我继续阅读oturn.py的核心部分，以及其他关键文件来理解整个项目结构。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 63, "prompt_tokens": 4652, "total_tokens": 4715, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 63}, "prompt_tokens_details": {"cached_tokens": 3799, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3799}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ytem2i9a9g14_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "729 oturn.py\r\n    type: Literal[\"function\"]\n    function: _ToolFunctionSchema\n\n\nclass _PendingEvent(TypedDict, total=False):\n    future_id: str\n\n\nApiMessagesInput = Context | list[Message] | list[_ApiMessage]\n\n\ndef start_global_litellm_prewarm(*, blocking: bool = False, timeout: float | None = None) -> bool:\n    return _start_global_litellm_prewarm(blocking=blocking, timeout=timeout)\n\n\ndef get_global_litellm_prewarm_error() -> str | None:\n    return _get_global_litellm_prewarm_error()\n\n\ndef _normalize_content_for_api(content: str | UserContent | ShellResult | ToolMessageContent | Any) -> str:\n    if hasattr(content, \"format_rfc5322\") and callable(getattr(content, \"format_rfc5322\")):\n        try:\n            return str(content.format_rfc5322())\n        except Exception:\n            pass\n    if isinstance(content, BaseModel):\n        return json.dumps(content.model_dump(), ensure_ascii=False)\n    if isinstance(content, str):\n        return content\n    return str(content)\n\n\ndef _is_compact_like(item: Any) -> bool:\n    if isinstance(item, dict):\n        return item.get(\"type\") == \"compact\"\n    return getattr(item, \"type\", None) == \"compact\" and hasattr(item, \"value\")\n\n\ndef _compact_value(item: Any) -> str:\n    if isinstance(item, dict):\n        return str(item.get(\"value\", \"\"))\n    return str(getattr(item, \"value\", \"\"))\n\n\ndef _is_message_like(item: Any) -> bool:\n    return hasattr(item, \"role\") and hasattr(item, \"content\")\n\n\ndef _format_messages(messages: Iterable[Any]) -> list[_ApiMessage]:\n    out: list[_ApiMessage] = []\n    for m in messages:\n        if not m.role:\n            continue\n\n        if m.role == ROLE_TOOL and isinstance(m.content, dict):\n            msg_content = json.dumps(m.content, ensure_ascii=False)\n        else:\n            msg_content = _normalize_content_for_api(m.content)\n\n        msg_dict: _ApiMessage = {\"role\": m.role, \"content\": msg_content}\n        if m.name:\n            msg_dict[\"name\"] = m.name\n        if m.tool_call_id:\n            msg_dict[\"tool_call_id\"] = m.tool_call_id\n        if m.reasoning_content:\n            msg_dict[\"reasoning_content\"] = m.reasoning_content\n        if m.extra:\n            msg_dict.update(m.extra)\n\n        if m.tool_calls:\n            try:\n                tool_calls: list[_ApiToolCall] = []\n                for tc in m.tool_calls:\n                    tool_calls.append(\n                        {\n                            \"id\": tc.id,\n                            \"type\": tc.type,\n                            \"function\": {\n                                \"name\": tc.function.name,\n                                \"arguments\": json.dumps(tc.function.arguments if tc.function.arguments is not None else {}),\n                            },\n                        }\n                    )\n                msg_dict[\"tool_calls\"] = tool_calls\n            except Exception:\n                pass\n\n        out.append(msg_dict)\n    return out\n\n\ndef _context_to_api_format(ctx: Any) -> list[_ApiMessage]:\n    out: list[_ApiMessage] = []\n    i = 0\n    history = getattr(ctx, \"history\", [])\n\n    while i < len(history):\n        item = history[i]\n\n        if _is_compact_like(item):\n            out.clear()\n            out.append(\n                {\n                    \"role\": ROLE_ASSISTANT,\n                    \"content\": _compact_value(item),\n                }\n            )\n            i += 1\n            continue\n\n        if not _is_message_like(item):\n            i += 1\n            continue\n\n        if item.role == ROLE_ASSISTANT:\n            msg_dict: _ApiMessage = {\"role\": ROLE_ASSISTANT, \"content\": _normalize_content_for_api(item.content)}\n            if item.name:\n                msg_dict[\"name\"] = item.name\n            if item.reasoning_content:\n                msg_dict[\"reasoning_content\"] = item.reasoning_content\n            if item.extra:\n                msg_dict.update(item.extra)\n\n            tool_ids = []\n            if item.tool_calls:\n                try:\n                    tool_calls: list[_ApiToolCall] = []\n                    for tc in item.tool_calls:\n                        tool_call_dict: _ApiToolCall = {\n                            \"id\": tc.id,\n                            \"type\": tc.type,\n                            \"function\": {\n                                \"name\": tc.function.name,\n                                \"arguments\": json.dumps(tc.function.arguments if tc.function.arguments is not None else {}),\n                            },\n                        }\n                        tool_calls.append(tool_call_dict)\n                        tool_ids.append(tc.id)\n                    msg_dict[\"tool_calls\"] = tool_calls\n                except Exception:\n                    pass\n\n            out.append(msg_dict)\n            j = i + 1\n            processed_tool_ids = set()\n\n            while j < len(history):\n                tool_item = history[j]\n                if not _is_message_like(tool_item) or tool_item.role != ROLE_TOOL:\n                    break\n\n                if tool_item.tool_call_id in tool_ids:\n                    processed_tool_ids.add(tool_item.tool_call_id)\n                    if isinstance(tool_item.content, dict):\n                        msg_content = json.dumps(tool_item.content, ensure_ascii=False)\n                    else:\n                        msg_content = _normalize_content_for_api(tool_item.content)\n\n                    tool_msg_dict: _ApiMessage = {\n                        \"role\": ROLE_TOOL,\n                        \"tool_call_id\": tool_item.tool_call_id,\n                        \"content\": msg_content,\n                    }\n                    if tool_item.name:\n                        tool_msg_dict[\"name\"] = tool_item.name\n                    out.append(tool_msg_dict)\n                j += 1\n\n            missing_tool_ids = set(tool_ids) - processed_tool_ids\n            for missing_id in missing_tool_ids:\n                missing_payload = ToolMessageContent(\n                    status=\"failure\",\n                    error=\"tool_result_missing\",\n                    data={\n                        \"message\": \"The tool execution result could not be obtained due to environmental or user-related issues. Please retry or check the relevant configuration.\"\n                    },\n                )\n                out.append(\n                    {\n                        \"role\": ROLE_TOOL,\n                        \"tool_call_id\": missing_id,\n                        \"name\": \"missing\",\n                        \"content\": json.dumps(missing_payload.model_dump(), ensure_ascii=False),\n                    }\n                )\n            i = j\n        elif item.role == ROLE_TOOL:\n            i += 1\n        else:\n            msg_dict: _ApiMessage = {\"role\": item.role, \"content\": _normalize_content_for_api(item.content)}\n            if item.name:\n                msg_dict[\"name\"] = item.name\n            if item.tool_call_id:\n                msg_dict[\"tool_call_id\"] = item.tool_call_id\n            if item.reasoning_content:\n                msg_dict[\"reasoning_content\"] = item.reasoning_content\n            if item.extra:\n                msg_dict.update(item.extra)\n            out.append(msg_dict)\n            i += 1\n", "stderr": "", "actual_command": "wc -l oturn.py && head -300 oturn.py | tail -200"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_l6d18auusigi_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat __init__.py"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me get a better understanding of the project structure by reading key files. Let me look at the main components.
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 56, "prompt_tokens": 6456, "total_tokens": 6512, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 56}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_l6d18auusigi_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from .nucleus import AsyncPubSub, OTurnNucleus, Reducer, Transition\r\nfrom .oturn import Oturn, start_global_litellm_prewarm, get_global_litellm_prewarm_error\r\nfrom .runtime_config import OturnConfig, OturnProviderConfig, OturnModelConfig, OturnAgentConfig\r\nfrom .context import Context\r\nfrom .model import (\r\n    Message,\r\n    Role,\r\n    ROLE_USER,\r\n    ROLE_ASSISTANT,\r\n    ROLE_SYSTEM,\r\n    ROLE_TOOL,\r\n    UserContent,\r\n    ShellResult,\r\n    ToolMessageContent,\r\n    FunctionInfo,\r\n    ToolCallInfo,\r\n)\r\nfrom .tools.base import BaseTool, BaseToolRegistry, ToolResult\r\nfrom .tools.examples import WeatherTool, HostConfigTool, EXAMPLE_TOOLS\r\n\r\n__all__ = [\r\n    \"AsyncPubSub\",\r\n    \"OTurnNucleus\",\r\n    \"Reducer\",\r\n    \"Transition\",\r\n    \"Oturn\",\r\n    \"OturnConfig\",\r\n    \"OturnProviderConfig\",\r\n    \"OturnModelConfig\",\r\n    \"OturnAgentConfig\",\r\n    \"Context\",\r\n    \"Message\",\r\n    \"Role\",\r\n    \"ROLE_USER\",\r\n    \"ROLE_ASSISTANT\",\r\n    \"ROLE_SYSTEM\",\r\n    \"ROLE_TOOL\",\r\n    \"UserContent\",\r\n    \"ShellResult\",\r\n    \"ToolMessageContent\",\r\n    \"FunctionInfo\",\r\n    \"ToolCallInfo\",\r\n    \"BaseTool\",\r\n    \"BaseToolRegistry\",\r\n    \"ToolResult\",\r\n    \"WeatherTool\",\r\n    \"HostConfigTool\",\r\n    \"EXAMPLE_TOOLS\",\r\n    \"start_global_litellm_prewarm\",\r\n    \"get_global_litellm_prewarm_error\",\r\n]\r\n", "stderr": "", "actual_command": "cat __init__.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_ih1huypb15l7_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat nucleus.py"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我需要继续查看项目的核心文件来了解整体架构。让我查看更多关键文件。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 50, "prompt_tokens": 7014, "total_tokens": 7064, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 50}, "prompt_tokens_details": {"cached_tokens": 6452, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6452}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ih1huypb15l7_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nimport asyncio\r\nfrom dataclasses import dataclass, field\r\nfrom typing import AsyncIterable, Generic, Protocol, TypeVar\r\n\r\nStateT = TypeVar(\"StateT\")\r\nSignalT = TypeVar(\"SignalT\")\r\nEventT = TypeVar(\"EventT\")\r\n\r\n\r\n@dataclass(slots=True)\r\nclass Transition(Generic[StateT, EventT]):\r\n    state: StateT\r\n    events: list[EventT] = field(default_factory=list)\r\n    terminal: bool = False\r\n\r\n\r\nclass Reducer(Protocol[StateT, SignalT, EventT]):\r\n    def __call__(self, state: StateT, signal: SignalT) -> Transition[StateT, EventT]:\r\n        ...\r\n\r\n\r\nclass AsyncPubSub(Generic[EventT]):\r\n    def __init__(self) -> None:\r\n        self._subscribers: set[asyncio.Queue[EventT]] = set()\r\n\r\n    def subscribe(self, queue: asyncio.Queue[EventT]) -> bool:\r\n        if queue is None:\r\n            return False\r\n        self._subscribers.add(queue)\r\n        return True\r\n\r\n    def unsubscribe(self, queue: asyncio.Queue[EventT]) -> bool:\r\n        if queue is None:\r\n            return False\r\n        self._subscribers.discard(queue)\r\n        return True\r\n\r\n    async def publish(self, event: EventT) -> None:\r\n        stale: list[asyncio.Queue[EventT]] = []\r\n        for queue in list(self._subscribers):\r\n            try:\r\n                await queue.put(event)\r\n            except Exception:\r\n                stale.append(queue)\r\n        for queue in stale:\r\n            self._subscribers.discard(queue)\r\n\r\n\r\nclass OTurnNucleus(Generic[StateT, SignalT, EventT]):\r\n    \"\"\"\r\n    OTURN: Orchestrated Task Unified Reactive Nucleus.\r\n\r\n    Async state-machine kernel with reducer-driven transitions and pub/sub output.\r\n    \"\"\"\r\n\r\n    def __init__(self, initial_state: StateT, reducer: Reducer[StateT, SignalT, EventT]) -> None:\r\n        self._state = initial_state\r\n        self._reducer = reducer\r\n        self._pubsub: AsyncPubSub[EventT] = AsyncPubSub()\r\n        self._terminal = False\r\n\r\n    @property\r\n    def state(self) -> StateT:\r\n        return self._state\r\n\r\n    @property\r\n    def terminal(self) -> bool:\r\n        return self._terminal\r\n\r\n    def subscribe(self, queue: asyncio.Queue[EventT]) -> bool:\r\n        return self._pubsub.subscribe(queue)\r\n\r\n    def unsubscribe(self, queue: asyncio.Queue[EventT]) -> bool:\r\n        return self._pubsub.unsubscribe(queue)\r\n\r\n    async def step(self, signal: SignalT) -> Transition[StateT, EventT]:\r\n        if self._terminal:\r\n            return Transition(state=self._state, events=[], terminal=True)\r\n\r\n        transition = self._reducer(self._state, signal)\r\n        self._state = transition.state\r\n        self._terminal = bool(transition.terminal)\r\n\r\n        for event in transition.events:\r\n            await self._pubsub.publish(event)\r\n\r\n        return transition\r\n\r\n    async def run(self, signals: AsyncIterable[SignalT]) -> StateT:\r\n        async for signal in signals:\r\n            transition = await self.step(signal)\r\n            if transition.terminal:\r\n                break\r\n        return self._state\r\n", "stderr": "", "actual_command": "cat nucleus.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_ex5uvyaj9wht_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -150 context.py"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我对项目有了清晰的了解。让我查看剩余的关键文件，然后整理一份代码梳理。
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 51, "prompt_tokens": 7980, "total_tokens": 8031, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 51}, "prompt_tokens_details": {"cached_tokens": 7010, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 7010}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ex5uvyaj9wht_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "## @file context.py\r\n#  @brief jsonl / md_as_config 双栈上下文：默认写 Markdown，必要时可导出/导入\r\n\r\nfrom __future__ import annotations\r\n\r\nimport traceback\r\nimport datetime as _dt\r\nfrom pathlib import Path\r\nfrom typing import Iterable, List, Union, Optional, Callable, Any, Dict\r\n\r\nfrom .model import Message, CompactRecord\r\nfrom .tools.base import BaseToolRegistry\r\nfrom .template_renderer import ContextTemplateRenderer\r\nfrom .session_store import SessionStore\r\n\r\n\r\nclass Context:\r\n    def __init__(self, file_backend: Path, session_id: str | None = None, work_dir: Path | None = None):\r\n        self._store = SessionStore(file_backend, session_id=session_id, work_dir=work_dir)\r\n        self._history: List[Union[Message, CompactRecord]] = []\r\n        self._mcp_client: Optional[Any] = None\r\n        self._mcp_tool_schemas: Dict[str, dict] = {}\r\n        self._mcp_tool_availability: Dict[str, bool] = {}\r\n        self._mcp_ready: bool = False\r\n        self._mcp_status_message: str = \"\"\r\n        self._mcp_last_ping: Optional[_dt.datetime] = None\r\n        self._tool_registry: BaseToolRegistry = BaseToolRegistry()\r\n        self._template_renderer = ContextTemplateRenderer(self._tool_registry)\r\n\r\n    # --------------------\r\n    # MCP helpers\r\n    # --------------------\r\n\r\n    def set_mcp_client(self, client: Any) -> None:\r\n        self._mcp_client = client\r\n\r\n    def get_mcp_client(self) -> Optional[Any]:\r\n        return self._mcp_client\r\n\r\n    def mark_mcp_ready(self, ready: bool, message: str = \"\", last_ping: Optional[_dt.datetime] = None) -> None:\r\n        self._mcp_ready = ready\r\n        if message:\r\n            self._mcp_status_message = message\r\n        if last_ping:\r\n            self._mcp_last_ping = last_ping\r\n\r\n    def is_mcp_ready(self) -> bool:\r\n        return self._mcp_ready\r\n\r\n    def update_mcp_tool_state(self, schemas: Dict[str, dict], availability: Dict[str, bool]) -> None:\r\n        self._mcp_tool_schemas = schemas\r\n        self._mcp_tool_availability = availability\r\n\r\n    def clear_mcp_tool_state(self) -> None:\r\n        \"\"\"Clear cached MCP tool schemas and availability.\"\"\"\r\n        self._mcp_tool_schemas = {}\r\n        self._mcp_tool_availability = {}\r\n\r\n    def get_mcp_tool_schemas(self) -> Dict[str, dict]:\r\n        return dict(self._mcp_tool_schemas)\r\n\r\n    def get_mcp_tool_availability(self) -> Dict[str, bool]:\r\n        return dict(self._mcp_tool_availability)\r\n\r\n    def get_mcp_tool_definition(self, name: str) -> Optional[dict]:\r\n        \"\"\"Return stored MCP tool definition by name.\"\"\"\r\n        return self._mcp_tool_schemas.get(name)\r\n\r\n    def get_mcp_status_message(self) -> str:\r\n        return str(self._mcp_status_message)\r\n\r\n    def set_mcp_last_ping(self, when: _dt.datetime) -> None:\r\n        self._mcp_last_ping = when\r\n\r\n    def get_mcp_last_ping(self) -> Optional[_dt.datetime]:\r\n        return self._mcp_last_ping\r\n\r\n    @property\r\n    def history(self) -> Iterable[Union[Message, CompactRecord]]:\r\n        return list(self._history)\r\n\r\n\r\n\r\n    # =======================\r\n    #  原有 JSONL backend 行为（迁移自 md_as_config）\r\n    # =======================\r\n\r\n    def restore(self) -> bool:\r\n        \"\"\"从后端文件恢复 history（直接使用 parse_md_as_config）。\"\"\"\r\n        self._history.clear()\r\n        try:\r\n            self._history = self._store.restore()\r\n            return bool(self._history)\r\n        except Exception:\r\n            traceback.print_exc()\r\n            return False\r\n\r\n\r\n    def set_tool_registry(self, tool_registry: BaseToolRegistry) -> None:\r\n        self._tool_registry = tool_registry\r\n        self._template_renderer.set_tool_registry(tool_registry)\r\n\r\n    def select_template_for_item(self, item) -> str:\r\n        \"\"\"为单条历史项目选择合适的模板。\"\"\"\r\n        return self._template_renderer.select_template_for_item(item)\r\n\r\n    @property\r\n    def history_file(self) -> Path:\r\n        return self._store.file_backend\r\n\r\n    @property\r\n    def session_id(self) -> str:\r\n        return self._store.session_id or self._store.file_backend.stem\r\n\r\n    @property\r\n    def work_dir(self) -> Path | None:\r\n        return self._store.work_dir\r\n\r\n    @classmethod\r\n    def load(cls, file_backend: Path) -> \"Context\":\r\n        \"\"\"创建 Context 并从 backend 中恢复记录。\"\"\"\r\n        ctx = cls(file_backend)\r\n        ctx.restore()\r\n        return ctx\r\n\r\n    def _clone_to_backend(\r\n        self,\r\n        target_backend: Path,\r\n        history_items: List,\r\n    ) -> \"Context\":\r\n        clone = Context(target_backend)\r\n        # 直接使用 history items\r\n        clone._history = history_items.copy()\r\n        clone._store.dump_history(\r\n            clone._history,\r\n            target_backend,\r\n            template_func=self.select_template_for_item,\r\n            template=\"\",\r\n        )\r\n        return clone\r\n\r\n    def fork(\r\n        self,\r\n        target_backend: Path,\r\n    ) -> \"Context\":\r\n        \"\"\"将当前上下文完整克隆到新的 backend 文件。\"\"\"\r\n        return self._clone_to_backend(target_backend, self._history).with_session(\r\n            self.session_id,\r\n            self.work_dir,\r\n        )\r\n", "stderr": "", "actual_command": "head -150 context.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_egiyh6aif3ff_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -120 model.py"}}}], "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me also check the model.py and other key files to understand the data structures.
<!-- md_as_config:ctx:multi-line-value-end -->

## <!-- md_as_config:ctx:value-begin type=null json-path=$.name -->  <!-- md_as_config:ctx:value-end -->执行shell命令

.tool_calls[0].function.arguments:

```json
{{ .function.arguments:json }}
```

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 50, "prompt_tokens": 9553, "total_tokens": 9603, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 50}, "prompt_tokens_details": {"cached_tokens": 6452, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6452}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_egiyh6aif3ff_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nfrom dataclasses import dataclass\r\nfrom typing import Optional, List, Dict, Any, Union, Literal, TypeAlias\r\nimport json\r\nfrom pydantic import BaseModel, field_validator, field_serializer\r\nimport datetime as _dt\r\n\r\ntry:\r\n    # py>=3.9\r\n    from zoneinfo import ZoneInfo\r\nexcept Exception:  # pragma: no cover\r\n    ZoneInfo = None  # type: ignore\r\n\r\n\r\n@dataclass\r\nclass FunctionInfo:\r\n    name: str\r\n    arguments: Any = None\r\n\r\n    @classmethod\r\n    def from_json(cls, data: Dict[str, Any]) -> 'FunctionInfo':\r\n        \"\"\"从 JSON 数据创建 FunctionInfo 对象\"\"\"\r\n        return cls(\r\n            name=data.get(\"name\", \"\"),\r\n            arguments=data.get(\"arguments\")\r\n        )\r\n\r\n    def to_dict(self) -> Dict[str, Any]:\r\n        \"\"\"转换为字典格式\"\"\"\r\n        result = {\"name\": self.name}\r\n        if self.arguments is not None:\r\n            result[\"arguments\"] = self.arguments\r\n        return result\r\n\r\n\r\n@dataclass\r\nclass ToolCallInfo:\r\n    id: str\r\n    type: str\r\n    function: FunctionInfo\r\n\r\n    @classmethod\r\n    def from_json(cls, data: Dict[str, Any]) -> 'ToolCallInfo':\r\n        \"\"\"从 JSON 数据创建 ToolCallInfo 对象\"\"\"\r\n        function_data = data.get(\"function\", {})\r\n        function = FunctionInfo.from_json(function_data)\r\n        return cls(\r\n            id=data.get(\"id\", \"\"),\r\n            type=data.get(\"type\", \"\"),\r\n            function=function\r\n        )\r\n\r\n    def to_dict(self) -> Dict[str, Any]:\r\n        \"\"\"转换为字典格式\"\"\"\r\n        return {\r\n            \"id\": self.id,\r\n            \"type\": self.type,\r\n            \"function\": self.function.to_dict()\r\n        }\r\n\r\n\r\ndef _format_utc_offset(dt: _dt.datetime) -> str:\r\n    \"\"\"把 utcoffset 格式化成 +HH:MM / -HH:MM\"\"\"\r\n    off = dt.utcoffset()\r\n    if off is None:\r\n        return \"+00:00\"\r\n    total = int(off.total_seconds())\r\n    sign = \"+\" if total >= 0 else \"-\"\r\n    total = abs(total)\r\n    hh = total // 3600\r\n    mm = (total % 3600) // 60\r\n    return f\"{sign}{hh:02d}:{mm:02d}\"\r\n\r\n\r\ndef _get_local_aware_now() -> _dt.datetime:\r\n    \"\"\"工作内存：拿本地时区 aware datetime\"\"\"\r\n    # 这个会用系统本地时区，并保证 tz-aware\r\n    return _dt.datetime.now().astimezone()\r\n\r\n\r\ndef _coerce_to_local_tz(dt: _dt.datetime) -> _dt.datetime:\r\n    \"\"\"把传入 datetime 规范成：tz-aware 且用本地时区表示\"\"\"\r\n    if dt.tzinfo is None:\r\n        # 这里有两种选择：\r\n        # 1) 把 naive 当成本地时间（更符合“用户本地时区”的直觉）\r\n        # 2) 把 naive 当成 UTC（更符合后端/日志系统习惯）\r\n        # 你需求写的是“工作内存用用户本地时区”，所以这里选(1)\r\n        local = _get_local_aware_now().tzinfo\r\n        return dt.replace(tzinfo=local)\r\n    # 已经 aware：转成本地时区表示\r\n    return dt.astimezone(_get_local_aware_now().tzinfo)\r\n\r\n\r\nclass UserContent(BaseModel):\r\n    \"\"\"用户内容类型，包含用户名、邮箱、日期和内容\"\"\"\r\n    user_name: str\r\n    date: _dt.datetime\r\n    content: str\r\n    email: Optional[str] = None\r\n    tool_contents: Optional[List[\"UserContent\"]] = None\r\n\r\n    @field_validator(\"date\", mode=\"before\")\r\n    @classmethod\r\n    def parse_date(cls, v: Any) -> _dt.datetime:\r\n        \"\"\"\r\n        入站支持：\r\n        - {\"ts\": 173..., \"tz\": \"America/Los_Angeles\"}  (推荐)\r\n        - {\"ts\": 173..., \"tz\": \"+08:00\"}              (退化方案)\r\n        - int/float: 默认按 秒级 timestamp（UTC）理解\r\n        - str: ISO8601\r\n        - datetime: 直接用\r\n        并最终统一成：本地时区 tz-aware datetime（工作内存语义）\r\n        \"\"\"\r\n        # 1) JSON object: {\"ts\": ..., \"tz\": ...}\r\n        if isinstance(v, dict) and \"ts\" in v:\r\n            ts = v[\"ts\"]\r\n            tz = v.get(\"tz\")\r\n\r\n            if not isinstance(ts, (int, float)):\r\n", "stderr": "", "actual_command": "head -120 model.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:end -->



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": null, "tool_call_id": null, "tool_calls": null, "extra": null} -->

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> assistant <!-- md_as_config:ctx:value-end --> - <!-- md_as_config:ctx:value-begin json-path=$.model_name --> MiniMax-M2.5 <!-- md_as_config:ctx:value-end -->]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我对项目结构有了比较全面的了解。让我生成一个项目代码的总结。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
## 项目代码结构梳理

**OTURN** = **Orchestrated Task Unified Reactive Nucleus** — 一个基于 reducer 的异步 agent 循环框架。

### 核心模块

| 文件 | 职责 |
|------|------|
| `nucleus.py` | 核心状态机：`Transition`, `Reducer`, `AsyncPubSub`, `OTurnNucleus` |
| `oturn.py` | 主入口：`Oturn` 类，整合 LLM、tools、streaming |
| `model.py` | 数据模型：`Message`, `UserContent`, `ShellResult`, `ToolCallInfo` 等 |
| `context.py` | 上下文管理：历史记录、MCP 客户端、session 存储 |
| `llm_transport.py` | LLM 调用封装 (liteLLM) |
| `tool_runtime.py` | 工具执行运行时 |
| `runtime_config.py` | 配置模型：`OturnConfig`, `ProviderConfig`, `ModelConfig` |
| `session_store.py` | 会话持久化 (JSONL/Markdown) |
| `template_renderer.py` | 模板渲染 |
| `events.py` | 事件定义与发布 |
| `mcp_adapter.py` | MCP (Model Context Protocol) 适配器 |

### 工具模块

| 文件 | 职责 |
|------|------|
| `tools/base.py` | `BaseTool`, `BaseToolRegistry`, `ToolResult` 基类 |
| `tools/examples.py` | 示例工具：`WeatherTool`, `HostConfigTool` |
| `tools/precheck.py` | 工具预检 |

### 架构要点

```
┌─────────────────────────────────────────────────────┐
│                    Oturn                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │ OTurnNucleus│  │LLMTransport │  │ToolRuntime  │ │
│  │ (状态机)    │  │ (LLM调用)    │  │ (工具执行)  │ │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘ │
│         │                │                │        │
│         └────────────────┼────────────────┘        │
│                          ▼                          │
│              ┌─────────────────────┐                │
│              │      Context       │                │
│              │  (历史 + session)  │                │
│              └─────────────────────┘                │
└─────────────────────────────────────────────────────┘
```

- **Reducer**: `(state, signal) -> Transition[state, events, terminal]`
- **AsyncPubSub**: 异步队列式发布订阅，多消费者可独立消费同一事件流
- **OTurnNucleus**: 核心驱动，`step(signal)` 确定性执行，`run(signals)` 循环封装
- **工具注入**: 运行时传入，无全局注册表
<!-- md_as_config:ctx:multi-line-value-end -->

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 627, "prompt_tokens": 10900, "total_tokens": 11527, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 627}, "prompt_tokens_details": {"cached_tokens": 9549, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9549}
```
<!-- md_as_config:ctx:code-block-end -->

<!-- md_as_config:ctx:end -->



