<!-- 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} -->

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<!-- 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": 1771652034.07911, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

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

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"tool_call_id": null, "tool_calls": [{"id": "call_function_es6oj04w6rt3_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -type f -name \"*.py\" | head -50 && echo \"---\" && ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn"}}}], "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"梳理代码"，这是一个比较模糊的请求。我需要先了解这个项目的结构和代码内容，才能进行有效的梳理。

让我先探索一下项目目录结构，了解代码的基本情况。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
我来先了解项目结构和代码内容，然后进行梳理。
<!-- 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": 135, "prompt_tokens": 1455, "total_tokens": 1590, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 135}, "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_es6oj04w6rt3_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": {}}
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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_bp9nibxuugur_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn && echo \"=== 子目录 ===\" && ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn/*/"}}}], "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 -->
我需要先查看目录结构。让我用ls来查看。
<!-- 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": 94, "prompt_tokens": 1680, "total_tokens": 1774, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 94}, "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_bp9nibxuugur_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 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  7384 Feb 21 13:30 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 27860 Feb 21 13:31 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:32 __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=== 子目录 ===\n/home/draplater/Development/cyf_cli/oturn/src/oturn/.cyf:\r\ntotal 0\r\ndrwxr-xr-x 1 draplater draplater  16 Feb 21 12:43 .\r\ndrwxr-xr-x 1 draplater draplater 450 Feb 21 13:30 ..\r\ndrwxr-xr-x 1 draplater draplater 240 Feb 21 13:33 sessions\r\n\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/__pycache__:\r\ntotal 168\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:32 .\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 13:30 ..\r\n-rw-r--r-- 1 draplater draplater 15751 Feb 21 10:37 agent_stream_state.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 15864 Feb 21 12:55 context.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 10901 Feb 21 13:32 events.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  1181 Feb 21 12:28 __init__.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  9340 Feb 21 12:28 llm_transport.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  4327 Feb 21 12:46 mcp_adapter.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 15731 Feb 21 12:28 model.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  5462 Feb 21 12:28 nucleus.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 36614 Feb 21 13:32 oturn.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  1893 Feb 21 12:28 runtime_config.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 12696 Feb 21 12:53 session_store.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  3732 Feb 21 12:53 template_renderer.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater 11005 Feb 21 13:31 tool_runtime.cpython-312.pyc\r\n-rw-r--r-- 1 draplater draplater  1981 Feb 21 12:28 tool_runtime_utils.cpython-312.pyc\r\n\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tools:\r\ntotal 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 /home/draplater/Development/cyf_cli/oturn/src/oturn && echo \"=== 子目录 ===\" && ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn/*/"}, "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_gtno2e5on20d_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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 -->
现在我了解了项目结构。让我查看各个关键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": 65, "prompt_tokens": 3368, "total_tokens": 3433, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 65}, "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_gtno2e5on20d_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 /home/draplater/Development/cyf_cli/oturn/src/oturn/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_empyr0h1p799_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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 -->
Now I have a good understanding of what OTURN is. Let me examine the key source files to provide a comprehensive code review. Let me read the main files:

1. oturn.py - main entry point
2. nucleus.py - core nucleus
3. model.py - state model
4. events.py - events handling
5. context.py - context management
6. tool_runtime.py - tool runtime
7. tools/base.py - tools base

Let me read these files to understand the codebase.
<!-- 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": 154, "prompt_tokens": 4538, "total_tokens": 4692, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 154}, "prompt_tokens_details": {"cached_tokens": 3364, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3364}
```
<!-- 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_empyr0h1p799_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_usage_total,\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    type: Literal[\"function\"]\r\n    function: _ToolFunctionSchema\r\n\r\n\r\nclass _PendingEvent(TypedDict, total=False):\r\n    future_id: str\r\n\r\n\r\nApiMessagesInput = Context | list[Message] | list[_ApiMessage]\r\n\r\n\r\ndef start_global_litellm_prewarm(*, blocking: bool = False, timeout: float | None = None) -> bool:\r\n    return _start_global_litellm_prewarm(blocking=blocking, timeout=timeout)\r\n\r\n\r\ndef get_global_litellm_prewarm_error() -> str | None:\r\n    return _get_global_litellm_prewarm_error()\r\n\r\n\r\ndef _normalize_content_for_api(content: str | UserContent | ShellResult | ToolMessageContent | Any) -> str:\r\n    if hasattr(content, \"format_rfc5322\") and callable(getattr(content, \"format_rfc5322\")):\r\n        try:\r\n            return str(content.format_rfc5322())\r\n        except Exception:\r\n            pass\r\n    if isinstance(content, BaseModel):\r\n        return json.dumps(content.model_dump(), ensure_ascii=False)\r\n    if isinstance(content, str):\r\n        return content\r\n    return str(content)\r\n\r\n\r\ndef _is_compact_like(item: Any) -> bool:\r\n    if isinstance(item, dict):\r\n        return item.get(\"type\") == \"compact\"\r\n    return getattr(item, \"type\", None) == \"compact\" and hasattr(item, \"value\")\r\n\r\n\r\ndef _compact_value(item: Any) -> str:\r\n    if isinstance(item, dict):\r\n        return str(item.get(\"value\", \"\"))\r\n    return str(getattr(item, \"value\", \"\"))\r\n\r\n\r\ndef _is_message_like(item: Any) -> bool:\r\n    return hasattr(item, \"role\") and hasattr(item, \"content\")\r\n\r\n\r\ndef _format_messages(messages: Iterable[Any]) -> list[_ApiMessage]:\r\n    out: list[_ApiMessage] = []\r\n    for m in messages:\r\n        if not m.role:\r\n            continue\r\n\r\n        if m.role == ROLE_TOOL and isinstance(m.content, dict):\r\n            msg_content = json.dumps(m.content, ensure_ascii=False)\r\n        else:\r\n            msg_content = _normalize_content_for_api(m.content)\r\n\r\n        msg_dict: _ApiMessage = {\"role\": m.role, \"content\": msg_content}\r\n        if m.name:\r\n            msg_dict[\"name\"] = m.name\r\n        if m.tool_call_id:\r\n            msg_dict[\"tool_call_id\"] = m.tool_call_id\r\n        if m.reasoning_content:\r\n            msg_dict[\"reasoning_content\"] = m.reasoning_content\r\n        if m.extra:\r\n            msg_dict.update(m.extra)\r\n\r\n        if m.tool_calls:\r\n            try:\r\n                tool_calls: list[_ApiToolCall] = []\r\n                for tc in m.tool_calls:\r\n                    tool_calls.append(\r\n                        {\r\n                            \"id\": tc.id,\r\n                            \"type\": tc.type,\r\n                            \"function\": {\r\n                                \"name\": tc.function.name,\r\n                                \"arguments\": json.dumps(tc.function.arguments if tc.function.arguments is not None else {}),\r\n                            },\r\n                        }\r\n                    )\r\n                msg_dict[\"tool_calls\"] = tool_calls\r\n            except Exception:\r\n                pass\r\n\r\n        out.append(msg_dict)\r\n    return out\r\n\r\n\r\ndef _context_to_api_format(ctx: Any) -> list[_ApiMessage]:\r\n    out: list[_ApiMessage] = []\r\n    i = 0\r\n    history = getattr(ctx, \"history\", [])\r\n\r\n    while i < len(history):\r\n        item = history[i]\r\n\r\n        if _is_compact_like(item):\r\n            out.clear()\r\n            out.append(\r\n                {\r\n                    \"role\": ROLE_ASSISTANT,\r\n                    \"content\": _compact_value(item),\r\n                }\r\n            )\r\n            i += 1\r\n            continue\r\n\r\n        if not _is_message_like(item):\r\n            i += 1\r\n            continue\r\n\r\n        if item.role == ROLE_ASSISTANT:\r\n            msg_dict: _ApiMessage = {\"role\": ROLE_ASSISTANT, \"content\": _normalize_content_for_api(item.content)}\r\n            if item.name:\r\n                msg_dict[\"name\"] = item.name\r\n            if item.reasoning_content:\r\n                msg_dict[\"reasoning_content\"] = item.reasoning_content\r\n            if item.extra:\r\n                msg_dict.update(item.extra)\r\n\r\n            tool_ids = []\r\n            if item.tool_calls:\r\n                try:\r\n                    tool_calls: list[_ApiToolCall] = []\r\n                    for tc in item.tool_calls:\r\n                        tool_call_dict: _ApiToolCall = {\r\n                            \"id\": tc.id,\r\n                            \"type\": tc.type,\r\n                            \"function\": {\r\n                                \"name\": tc.function.name,\r\n                                \"arguments\": json.dumps(tc.function.arguments if tc.function.arguments is not None else {}),\r\n                            },\r\n                        }\r\n                        tool_calls.append(tool_call_dict)\r\n                        tool_ids.append(tc.id)\r\n                    msg_dict[\"tool_calls\"] = tool_calls\r\n                except Exception:\r\n                    pass\r\n\r\n            out.append(msg_dict)\r\n            j = i + 1\r\n            processed_tool_ids = set()\r\n\r\n            while j < len(history):\r\n                tool_item = history[j]\r\n                if not _is_message_like(tool_item) or tool_item.role != ROLE_TOOL:\r\n                    break\r\n\r\n                if tool_item.tool_call_id in tool_ids:\r\n                    processed_tool_ids.add(tool_item.tool_call_id)\r\n                    if isinstance(tool_item.content, dict):\r\n                        msg_content = json.dumps(tool_item.content, ensure_ascii=False)\r\n                    else:\r\n                        msg_content = _normalize_content_for_api(tool_item.content)\r\n\r\n                    tool_msg_dict: _ApiMessage = {\r\n                        \"role\": ROLE_TOOL,\r\n                        \"tool_call_id\": tool_item.tool_call_id,\r\n                        \"content\": msg_content,\r\n                    }\r\n                    if tool_item.name:\r\n                        tool_msg_dict[\"name\"] = tool_item.name\r\n                    out.append(tool_msg_dict)\r\n                j += 1\r\n\r\n            missing_tool_ids = set(tool_ids) - processed_tool_ids\r\n            for missing_id in missing_tool_ids:\r\n                missing_payload = ToolMessageContent(\r\n                    status=\"failure\",\r\n                    error=\"tool_result_missing\",\r\n                    data={\r\n                        \"message\": \"The tool execution result could not be obtained due to environmental or user-related issues. Please retry or check the relevant configuration.\"\r\n                    },\r\n                )\r\n                out.append(\r\n                    {\r\n                        \"role\": ROLE_TOOL,\r\n                        \"tool_call_id\": missing_id,\r\n                        \"name\": \"missing\",\r\n                        \"content\": json.dumps(missing_payload.model_dump(), ensure_ascii=False),\r\n                    }\r\n                )\r\n            i = j\r\n        elif item.role == ROLE_TOOL:\r\n            i += 1\r\n        else:\r\n            msg_dict: _ApiMessage = {\"role\": item.role, \"content\": _normalize_content_for_api(item.content)}\r\n            if item.name:\r\n                msg_dict[\"name\"] = item.name\r\n            if item.tool_call_id:\r\n                msg_dict[\"tool_call_id\"] = item.tool_call_id\r\n            if item.reasoning_content:\r\n                msg_dict[\"reasoning_content\"] = item.reasoning_content\r\n            if item.extra:\r\n                msg_dict.update(item.extra)\r\n            out.append(msg_dict)\r\n            i += 1\r\n\r\n    return out\r\n\r\n\r\ndef _to_api_messages(messages: ApiMessagesInput | Any) -> list[_ApiMessage]:\r\n    if hasattr(messages, \"history\"):\r\n        return _context_to_api_format(messages)\r\n    if isinstance(messages, list):\r\n        if not messages:\r\n            return []\r\n        if _is_message_like(messages[0]):\r\n            return _format_messages(cast(list[Message], messages))\r\n        if isinstance(messages[0], dict):\r\n            return cast(list[_ApiMessage], messages)\r\n    raise\n\n(...7 KB collapsed...)\n\n     index=tr.payload.get(\"index\"),\r\n                tool_call_id=tr.payload.get(\"id\"),\r\n            )\r\n            tool_class = self._tool_registry.get_tool_class(name)\r\n            if tool_class:\r\n                preview_text = tool_class.get_preview(parsed_args)\r\n                if preview_text:\r\n                    payload[\"preview\"] = preview_text\r\n            return [payload]\r\n        return []\r\n\r\n    async def run_queues(self, ctx: Context) -> None:\r\n        \"\"\"Run the agent loop continuously and broadcast events to subscribers.\"\"\"\r\n        model_name = None\r\n        try:\r\n            if hasattr(ctx, \"set_tool_registry\"):\r\n                ctx.set_tool_registry(self._tool_registry)\r\n        except Exception:\r\n            pass\r\n        try:\r\n            start_global_litellm_prewarm(blocking=False)\r\n        except Exception:\r\n            pass\r\n\r\n        async def _wait_next_input() -> _InputQueueItem:\r\n            queued = await self._input_queue.get()\r\n            if self._input_shadow:\r\n                self._input_shadow.pop(0)\r\n            try:\r\n                result = queued\r\n            except Exception:\r\n                result = None\r\n            if isinstance(result, dict):\r\n                return {\r\n                    \"status\": str(result.get(\"status\") or \"agent\"),\r\n                    \"text\": str(result.get(\"text\") or \"\"),\r\n                    \"type\": str(result.get(\"type\") or \"default\"),\r\n                }\r\n            return {\"status\": \"agent\", \"text\": str(result) if result is not None else \"\", \"type\": \"default\"}\r\n\r\n        while True:\r\n            request_result = await _wait_next_input()\r\n            req_t0 = time.perf_counter()\r\n            status = request_result.get(\"status\", \"agent\")\r\n            user_input = request_result.get(\"text\", \"\")\r\n            if status != \"agent\":\r\n                continue\r\n            if not user_input.strip():\r\n                continue\r\n            persist_task: Optional[asyncio.Task] = None\r\n            try:\r\n                persist_coro = getattr(ctx, \"aensure_persisted\", None)\r\n                if callable(persist_coro):\r\n                    persist_task = asyncio.create_task(persist_coro())\r\n            except Exception:\r\n                persist_task = None\r\n            try:\r\n                tool_contents = []\r\n                for tool_name in self._tool_registry.list_tools():\r\n                    tool_class = self._tool_registry.get_tool_class(tool_name)\r\n                    if not tool_class:\r\n                        continue\r\n                    try:\r\n                        extra = tool_class.user_input_hook()\r\n                    except Exception:\r\n                        extra = None\r\n                    if extra is not None:\r\n                        tool_contents.append(extra)\r\n                if self._debug_loop:\r\n                    await self._debug_put(ev_debug_latency(stage=\"hooks\", ms=round((time.perf_counter() - req_t0) * 1000, 1)))\r\n\r\n                # 使用UserContent类型包装用户输入\r\n                user_content = UserContent(\r\n                    user_name=self.cfg.agent_config.user_name,\r\n                    email=self.cfg.agent_config.email,\r\n                    date=_dt.datetime.now().astimezone(),\r\n                    content=user_input,\r\n                    tool_contents=tool_contents or None,\r\n                )\r\n                if persist_task is not None:\r\n                    await persist_task\r\n                ctx.append_message(Message(role=ROLE_USER, content=user_content))\r\n                if self._debug_loop:\r\n                    await self._debug_put(ev_debug_latency(stage=\"append\", ms=round((time.perf_counter() - req_t0) * 1000, 1)))\r\n            except Exception as e:\r\n                traceback.print_exc()\r\n                error_msg = str(e)\r\n                await self._debug_put(ev_error(error_msg, exception_type=type(e).__name__, traceback_text=traceback.format_exc()))\r\n                await self._debug_put(ev_assistant_final(f\"错误: {error_msg}\"))\r\n                continue\r\n\r\n            for _ in range(self.cfg.agent_config.max_steps):\r\n                # Directly pass ctx to client instead of building messages first\r\n                stream_state = TurnStreamState()\r\n                try:\r\n                    _agen_coro = self._litellm_acreate(\r\n                        ctx,\r\n                        stream=True,\r\n                        tools=self._collect_tool_schemas(ctx),\r\n                        tool_choice=\"auto\",\r\n                        tool_stream=True,\r\n                        debug=self.cfg.provider.debug_request,\r\n                        sse_debug=self.cfg.provider.debug_sse,\r\n                    )  # type: ignore[assignment]\r\n                    await self._debug_put(ev_message_sent())\r\n                    if self._debug_loop:\r\n                        await self._debug_put(\r\n                            ev_debug_latency(stage=\"message_sent\", ms=round((time.perf_counter() - req_t0) * 1000, 1))\r\n                        )\r\n                    agen = await _agen_coro  # type: ignore[assignment]\r\n                except Exception as e:\r\n                    traceback.print_exc()\r\n                    # 发送错误事件并跳出循环等待用户重新输入\r\n                    error_msg = f\"API调用失败: {str(e)}\"\r\n                    await self._debug_put(ev_error(error_msg, exception_type=type(e).__name__, traceback_text=traceback.format_exc()))\r\n                    await self._debug_put(ev_assistant_final(f\"错误: {error_msg}\"))\r\n                    break\r\n                \r\n                if self._cancel_evt.is_set():\r\n                    self._cancel_evt.clear()\r\n                    await self._debug_put(ev_run_aborted(\"cancelled\"))\r\n                    await self._debug_put(ev_assistant_final(\"\"))\r\n                    break\r\n                try:\r\n                    async for ev in agen:  # type: ignore[attr-defined]\r\n                        if self._cancel_evt.is_set():\r\n                            self._cancel_evt.clear()\r\n                            await self._debug_put(ev_run_aborted(\"cancelled\"))\r\n                            await self._debug_put(ev_assistant_final(\"\"))\r\n                            break\r\n                        for signal in ingest_stream_event(ev):\r\n                            if self.cfg.provider.debug_tools and signal.type == \"tool_call_delta\" and isinstance(ev, dict):\r\n                                debug_event: ToolCallDebugEvent = ev_tool_call_debug_delta(cast(ToolCallDeltaPayload, ev))\r\n                                await self._debug_put(debug_event)\r\n                            tr = reduce_stream_signal(stream_state, signal)\r\n                            if tr.kind == \"tool_call_delta_dropped\":\r\n                                if self.cfg.provider.debug_tools:\r\n                                    dropped_event: ToolCallDebugEvent = ev_tool_call_debug_warning(\r\n                                        \"tool_call_delta without output_index/tool_call_id; fragment dropped\"\r\n                                    )\r\n                                    await self._debug_put(dropped_event)\r\n                                continue\r\n                            if tr.kind == \"tool_calls_done\" and self.cfg.provider.debug_tools:\r\n                                assembled = build_assembled_debug(stream_state)\r\n                                assembled_event: ToolCallDebugEvent = ev_tool_call_debug_assembled(assembled)\r\n                                await self._debug_put(assembled_event)\r\n                            for out_ev in self._emit_stream_transition_events(tr):\r\n                                await self._debug_put(out_ev)\r\n                except Exception as e:\r\n                    # 流处理异常，发送错误事件并跳出循环\r\n                    error_msg = f\"流处理失败: {str(e)}\"\r\n                    await self._debug_put(ev_error(error_msg, exception_type=type(e).__name__, traceback_text=traceback.format_exc()))\r\n                    await self._debug_put(ev_assistant_final(f\"错误: {error_msg}\"))\r\n                    break\r\n\r\n                final = stream_state.final\r\n                reasoning = stream_state.reasoning\r\n                reasoning_details = stream_state.reasoning_details\r\n                usage_data = stream_state.usage_data\r\n                if stream_state.model_name is not None:\r\n                    model_name = stream_state.model_name\r\n                tool_records: list[ToolRecord] = []\r\n                if stream_state.tool_phase and stream_state.tool_calls_by_idx:\r\n                    tool_records = build_tool_records(stream_state)\r\n                if tool_records:\r\n                    request_new_input = await self._tool_runtime.process_tool_records(\r\n                        ctx=ctx,\r\n                        tool_records=tool_records,\r\n                        final=final,\r\n                        reasoning=reasoning,\r\n                        reasoning_details=reasoning_details,\r\n                        usage_data=usage_data,\r\n                        model_name=model_name,\r\n                    )\r\n                    if request_new_input:\r\n                        break\r\n\r\n                if not tool_records:\r\n                    if final:\r\n                        await self._debug_put(\r\n                            ev_assistant_final(\r\n                                final,\r\n                                usage_data=usage_data,\r\n                                reasoning=reasoning if reasoning else None,\r\n                            )\r\n                        )\r\n                        try:\r\n                            ctx.append_message(\r\n                                Message(\r\n                                    role=ROLE_ASSISTANT,\r\n                                    content=final,\r\n                                    usage=usage_data,\r\n                                    reasoning_content=reasoning if reasoning else None,\r\n                                    model_name=model_name,\r\n                                    extra={\"reasoning_details\": reasoning_details} if reasoning_details else None,\r\n                                )\r\n                            )\r\n                        except Exception:\r\n                            traceback.print_exc()\r\n                        break\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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_99vjq2ha9zqo_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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 -->
现在我已经看了主要的 oturn.py 文件。让我继续查看其他核心文件来完整理解整个代码架构。
<!-- 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": 72, "prompt_tokens": 9656, "total_tokens": 9728, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 72}, "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_99vjq2ha9zqo_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 /home/draplater/Development/cyf_cli/oturn/src/oturn/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_uik9ebchrewf_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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 -->
Good. Now I have a good understanding of the nucleus. Let me continue reading more files to understand the overall architecture.
<!-- 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": 73, "prompt_tokens": 10660, "total_tokens": 10733, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 73}, "prompt_tokens_details": {"cached_tokens": 9652, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9652}
```
<!-- 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_uik9ebchrewf_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                raise TypeError(f\"date.ts must be int/float seconds, got {type(ts)}\")\r\n\r\n            # 优先用 IANA tz name（能保留 DST 规则）\r\n            if tz and isinstance(tz, str) and ZoneInfo is not None:\r\n                try:\r\n                    dt = _dt.datetime.fromtimestamp(ts, tz=ZoneInfo(tz))\r\n                    return dt  # 已经是目标时区\r\n                except Exception:\r\n                    pass\r\n\r\n            # 退化：tz 是 +HH:MM 这种 offset\r\n            if tz and isinstance(tz, str) and (tz.startswith(\"+\") or tz.startswith(\"-\")):\r\n                try:\r\n                    sign = 1 if tz[0] == \"+\" else -1\r\n                    hh, mm = tz[1:].split(\":\")\r\n                    off = _dt.timedelta(hours=int(hh) * sign, minutes=int(mm) * sign)\r\n                    dt = _dt.datetime.fromtimestamp(ts, tz=_dt.timezone(off))\r\n                    return dt\r\n                except Exception:\r\n                    pass\r\n\r\n            # 再退一步：没 tz 或 tz 不可用，就当 UTC\r\n            return _dt.datetime.fromtimestamp(ts, tz=_dt.timezone.utc)\r\n\r\n        # 2) raw timestamp: seconds, assume UTC then convert to local tz for memory\r\n        if isinstance(v, (int, float)):\r\n            dt = _dt.datetime.fromtimestamp(v, tz=_dt.timezone.utc)\r\n            return dt.astimezone(_get_local_aware_now().tzinfo)\r\n\r\n        # 3) ISO string or datetime: let pydantic parse, then we normalize in after-step below\r\n        if isinstance(v, _dt.datetime):\r\n            return _coerce_to_local_tz(v)\r\n\r\n        # 交给 pydantic 自己解析 str（ISO8601）\r\n        return v\r\n\r\n    @field_validator(\"date\", mode=\"after\")\r\n    @classmethod\r\n    def normalize_date(cls, v: _dt.datetime) -> _dt.datetime:\r\n        \"\"\"确保工作内存里 date 一定是 本地时区的 tz-aware datetime\"\"\"\r\n        return _coerce_to_local_tz(v)\r\n\r\n    @field_serializer(\"date\")\r\n    def dump_date(self, v: _dt.datetime) -> Dict[str, Union[float, str]]:\r\n        \"\"\"\r\n        出站：JSON 同时存 ts + tz\r\n        - ts: 秒级 float\r\n        - tz: 优先存 IANA key（如果拿得到）；否则存 offset(+HH:MM)\r\n        \"\"\"\r\n        if v.tzinfo is None:\r\n            v = _coerce_to_local_tz(v)\r\n\r\n        ts = v.timestamp()\r\n\r\n        # 尝试拿 IANA key（zoneinfo 通常是 ZoneInfo(\"xxx\")）\r\n        tz_name = None\r\n        if ZoneInfo is not None:\r\n            # ZoneInfo 实例一般有 .key 属性\r\n            tz_name = getattr(v.tzinfo, \"key\", None)\r\n\r\n        return {\r\n            \"ts\": ts,\r\n            \"tz\": tz_name if isinstance(tz_name, str) and tz_name else _format_utc_offset(v),\r\n        }\r\n    \r\n    def to_dict(self) -> Dict[str, Any]:\r\n        \"\"\"如果你还要保留 to_dict：建议直接走 model_dump，让 serializer 生效\"\"\"\r\n        return self.model_dump()\r\n    \r\n    @classmethod\r\n    def from_dict(cls, data: Dict[str, Any]) -> 'UserContent':\r\n        \"\"\"从字典创建UserContent对象\"\"\"\r\n        return cls(\r\n            user_name=data.get(\"user_name\", \"\"),\r\n            email=data.get(\"email\"),\r\n            date=data.get(\"date\", _dt.datetime.now()),\r\n            content=data.get(\"content\", \"\"),\r\n            tool_contents=data.get(\"tool_contents\"),\r\n        )\r\n    \r\n    def format_rfc5322(self) -> str:\r\n        \"\"\"格式化为类似RFC5322的格式\"\"\"\r\n        # RFC5322格式：From: user_name <email> <date>\\n\\ncontent\r\n        date_str = self.date.strftime(\"%a, %d %b %Y %H:%M:%S %z\")\r\n        tool_prefix = \"\"\r\n        if self.tool_contents:\r\n            parts = []\r\n            for idx, item in enumerate(self.tool_contents, 1):\r\n                parts.append(f\"[Tool Notice {idx}]\\n{item.format_rfc5322()}\")\r\n            tool_prefix = \"\\n\\n\".join(parts) + \"\\n\\n\"\r\n        if self.email:\r\n            return f\"From: {self.user_name} <{self.email}>\\nDate: {date_str}\\n\\n{tool_prefix}{self.content}\"\r\n        else:\r\n            return f\"From: {self.user_name}\\nDate: {date_str}\\n\\n{tool_prefix}{self.content}\"\r\n\r\n\r\nclass ShellResult(BaseModel):\r\n    \"\"\"Shell命令执行结果类型，包含命令、目录、输出和返回码\"\"\"\r\n    command: str\r\n    cwd: str\r\n    stdout: str\r\n    stderr: str\r\n    retcode: int\r\n    date: _dt.datetime\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：与UserContent相同的日期格式处理\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        # 复用UserContent的日期解析逻辑\r\n        return UserContent.parse_date(v)\r\n\r\n    @field_validator(\"date\", mode=\"after\")\r\n    @classmethod\r\n    def normalize_date(cls, v: _dt.datetime) -> _dt.datetime:\r\n        \"\"\"确保工作内存里 date 一定是 本地时区的 tz-aware datetime\"\"\"\r\n        return _coerce_to_local_tz(v)\r\n\r\n    @field_serializer(\"date\")\r\n    def dump_date(self, v: _dt.datetime) -> Dict[str, Union[float, str]]:\r\n        \"\"\"\r\n        出站：JSON 同时存 ts + tz\r\n        - ts: 秒级 float\r\n        - tz: 优先存 IANA key（如果拿得到）；否则存 offset(+HH:MM)\r\n        \"\"\"\r\n        if v.tzinfo is None:\r\n            v = _coerce_to_local_tz(v)\r\n\r\n        ts = v.timestamp()\r\n\r\n        # 尝试拿 IANA key（zoneinfo 通常是 ZoneInfo(\"xxx\")）\r\n        tz_name = None\r\n        if ZoneInfo is not None:\r\n            # ZoneInfo 实例一般有 .key 属性\r\n            tz_name = getattr(v.tzinfo, \"key\", None)\r\n\r\n        return {\r\n            \"ts\": ts,\r\n            \"tz\": tz_name if isinstance(tz_name, str) and tz_name else _format_utc_offset(v),\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 self.model_dump()\r\n    \r\n    @classmethod\r\n    def from_dict(cls, data: Dict[str, Any]) -> 'ShellResult':\r\n        \"\"\"从字典创建ShellResult对象\"\"\"\r\n        return cls(\r\n            command=data.get(\"command\", \"\"),\r\n            cwd=data.get(\"cwd\", \"\"),\r\n            stdout=data.get(\"stdout\", \"\"),\r\n            stderr=data.get(\"stderr\", \"\"),\r\n            retcode=data.get(\"retcode\", 0),\r\n            date=data.get(\"date\", _dt.datetime.now()),\r\n        )\r\n    \r\n    def format_rfc5322(self) -> str:\r\n        \"\"\"格式化为类似RFC5322的格式\"\"\"\r\n        date_str = self.date.strftime(\"%a, %d %b %Y %H:%M:%S %z\")\r\n        \r\n        # 构建RFC5322格式的shell结果\r\n        result = f\"From: User Shell <user_shell@agent_system.com>\\n\"\r\n        result += f\"Subject: User Shell Execution Report\\n\"\r\n        result += f\"Date: {date_str}\\n\"\r\n        result += f\"X-Exec-Cwd: {self.cwd}\\n\"\r\n        result += f\"X-Exec-Cmd: {self.command}\\n\"\r\n        result += f\"X-Exec-Retcode: {self.retcode}\\n\\n\"\r\n        \r\n        if self.stdout:\r\n            result += \"STDOUT:\\n\"\r\n            result += f\"{self.stdout}\\n\\n\"\r\n        \r\n        if self.stderr:\r\n            result += \"STDERR:\\n\"\r\n            result += f\"{self.stderr}\\n\\n\"\r\n        \r\n        if not self.stdout and not self.stderr:\r\n            result += \"(no output)\\n\"\r\n        \r\n        return result.strip()\r\n\r\n\r\nclass ToolMessageContent(BaseModel):\r\n    \"\"\"工具调用返回内容，统一用 BaseModel 表达\"\"\"\r\n    status: Optional[str] = None\r\n    data: Optional[Any] = None\r\n    error: Optional[str] = None\r\n    metadata: Optional[Dict[str, Any]] = None\r\n    info: Optional[Dict[str, Any]] = None\r\n\r\n\r\nRole: TypeAlias = Literal[\"user\", \"assistant\", \"system\", \"tool\"]\r\nROLE_USER: Role = \"user\"\r\nROLE_ASSISTANT: Role = \"assistant\"\r\nROLE_SYSTEM: Role = \"system\"\r\nROLE_TOOL: Role = \"tool\"\r\n\r\n\r\nclass Message(BaseModel):\r\n    \"\"\"对话消息类型 - 使用Pydantic BaseModel提供更好的序列化支持\"\"\"\r\n    role: Role\r\n    content: Union[str, UserContent, ShellResult, ToolMessageContent]  # 支持字符串、UserContent、ShellResult或工具结果\r\n    name: Optional[str] = None\r\n    tool_call_id: Optional[str] = None\r\n    tool_calls: Optional[List[ToolCallInfo]] = None  # List of ToolCallInfo objects\r\n    usage: Optional[Dict[str, Any]] = None  # complete usage information from API\r\n    reasoning_content: Optional[str] = None  # reasoning details from models\r\n    model_name: Optional[str] = None  # model name that generated this message\r\n    extra: Optional[Dict[str, Any]] = None  # additional extra information\r\n\r\n\r\nclass CompactRecord(BaseModel):\r\n    \"\"\"A compaction marker in history.\r\n\r\n    Indicates that all previous content has been compacted into `value`.\r\n    When building messages for the LLM, clear earlier messages and insert\r\n    a single assistant message with this `value`.\r\n    \"\"\"\r\n    type: str = \"compact\"\r\n    value: str = \"\"\r\n    user_intent: str = \"\"  # Additional user instructions for compaction\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_sijzg3yasx4c_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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 -->
现在我需要查看其他关键模块来完整理解这个项目。让我继续查看:
- agent_stream_state.py - 流状态处理
- context.py - 上下文管理
- tool_runtime.py - 工具运行时
- session_store.py - 会话存储
- events.py - 事件系统
<!-- 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": 110, "prompt_tokens": 14815, "total_tokens": 14925, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 110}, "prompt_tokens_details": {"cached_tokens": 9652, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9652}
```
<!-- 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_sijzg3yasx4c_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\r\n    def truncate_and_fork(\r\n        self,\r\n        split_index: int,\r\n        target_backend: Path,\r\n    ) -> \"Context\":\r\n        \"\"\"截断记录并写入新的 backend，适用于从某个记录开始重新 fork。\"\"\"\r\n        split_index = max(0, min(len(self._history), split_index))\r\n        \r\n        # 截取指定范围的history\r\n        truncated_history = self._history[:split_index]\r\n        \r\n        return self._clone_to_backend(target_backend, truncated_history).with_session(\r\n            self.session_id,\r\n            self.work_dir,\r\n        )\r\n\r\n    def with_session(self, session_id: str | None, work_dir: Path | None) -> \"Context\":\r\n        self._store.session_id = session_id\r\n        self._store.work_dir = work_dir\r\n        return self\r\n\r\n    def adopt(self, other: \"Context\") -> \"Context\":\r\n        self._store = other._store\r\n        self._history = other._history\r\n        return self\r\n\r\n    # =====================\r\n    #  旧的接口：appending messages / compact\r\n    # =====================\r\n\r\n    def append_message(self, message: Message | Iterable[Message]):\r\n        msgs = list(message) if isinstance(message, Iterable) and not isinstance(message, Message) else [message]  # type: ignore[arg-type]\r\n        sanitized: list[Message] = []\r\n        for m in msgs:\r\n            if not isinstance(m, Message):\r\n                continue\r\n            # 检查message是否有有效内容\r\n            # 只有当content为None或空字符串时才认为没有内容，空dict也算有效内容\r\n            has_content = m.content is not None and not (isinstance(m.content, str) and not m.content.strip())\r\n            \r\n            if not (has_content or (getattr(m, \"tool_calls\", None) is not None)):\r\n                continue\r\n            sanitized.append(m)\r\n        if not sanitized:\r\n            return\r\n        for m in sanitized:\r\n            self._history.append(m)\r\n            # 增量写入文件\r\n            try:\r\n                self._append_history_item_to_file(m)\r\n            except:\r\n                traceback.print_exc()\r\n                raise\r\n\r\n    def append_compact(self, summary: str, user_intent: str = \"\") -> None:\r\n        \"\"\"Append a compaction marker to history and persist it.\"\"\"\r\n        rec = CompactRecord(value=str(summary), user_intent=str(user_intent))\r\n        self._history.append(rec)\r\n        # 增量写入文件\r\n        self._append_history_item_to_file(rec)\r\n\r\n    # ============================\r\n    #  新接口：导出 / 导入 md_as_config\r\n    # ============================\r\n\r\n    def export_md_as_config(\r\n        self,\r\n        md_path: Path,\r\n        template: Optional[str] = None,\r\n    ) -> None:\r\n        \"\"\"\r\n        用指定模板把当前记录渲染成 md_as_config markdown。\r\n        \"\"\"\r\n        tmpl = template or \"\"\r\n        self._store.export_md_as_config(self._history, md_path, template_func=self.select_template_for_item, template=tmpl)\r\n\r\n    def import_md_as_config(self, md_path: Path) -> bool:\r\n        \"\"\"\r\n        从 md_as_config markdown 覆盖当前 backend 并同步本地 history。\r\n        \"\"\"\r\n        records = self._store.import_md_as_config(md_path)\r\n        if not records:\r\n            return False\r\n        self._sync_history_from_records(records)\r\n        return True\r\n\r\n    def _sync_history_from_records(self, records: Iterable[Any]) -> None:\r\n        synced: list[Message | CompactRecord] = []\r\n        for item in records:\r\n            if isinstance(item, (Message, CompactRecord)):\r\n                synced.append(item)\r\n        self._history = synced\r\n\r\n    def _append_history_item_to_file(self, item, template_func: Optional[Callable] = None) -> None:\r\n        \"\"\"增量写入单条历史项目到文件末尾\"\"\"\r\n        if template_func is None:\r\n            template_func = self.select_template_for_item\r\n        self._store.append_item(item, template_func=template_func)\r\n\r\n    @classmethod\r\n    def create_session(\r\n        cls,\r\n        work_dir: Path,\r\n        session_id: str | None = None,\r\n        *,\r\n        defer_persist: bool = False,\r\n    ) -> \"Context\":\r\n        store = SessionStore.create_for_workdir(work_dir, session_id, defer_persist=defer_persist)\r\n        ctx = cls(store.file_backend, session_id=store.session_id, work_dir=store.work_dir)\r\n        ctx._store = store\r\n        return ctx\r\n\r\n    def ensure_persisted(self) -> None:\r\n        self._store.ensure_persisted()\r\n\r\n    async def aensure_persisted(self) -> None:\r\n        await self._store.aensure_persisted()\r\n\r\n    @classmethod\r\n    def continue_session(cls, work_dir: Path) -> \"Context\" | None:\r\n        picked = SessionStore.continue_in_workdir(work_dir.absolute())\r\n        if picked is None:\r\n            return None\r\n        latest_session_id, latest_path = picked\r\n        if latest_path.exists():\r\n            ctx = cls(latest_path, session_id=latest_session_id, work_dir=work_dir)\r\n            ctx.restore()\r\n            return ctx\r\n        return None\r\n\r\n    @classmethod\r\n    def resume_session(cls, work_dir: Path, session_id: str) -> \"Context\" | None:\r\n        work_dir = work_dir.absolute()\r\n        history_file = SessionStore.resolve_session_path(work_dir, session_id)\r\n        if history_file is None:\r\n            return None\r\n        ctx = cls(history_file, session_id=session_id, work_dir=work_dir)\r\n        ctx.restore()\r\n        return ctx\r\n\r\n    @classmethod\r\n    def list_sessions(cls, work_dir: Path) -> tuple[list[tuple[str, Path, float]], str | None]:\r\n        return SessionStore.list_sessions(work_dir)\r\n\r\n    @classmethod\r\n    def list_global_sessions(cls, work_dir: Path) -> tuple[list[tuple[str, Path, float]], str | None]:\r\n        return SessionStore.list_global_sessions(work_dir)\r\n\r\n    @classmethod\r\n    def fork_session(cls, work_dir: Path, source_session_id: str, new_session_id: str | None = \"[AUTO]\") -> \"Context\":\r\n        work_dir = work_dir.absolute()\r\n        source = cls.resume_session(work_dir, source_session_id)\r\n        if source is None:\r\n            raise ValueError(f\"Session {source_session_id} not found to fork from\")\r\n        target_id = new_session_id\r\n        if not target_id or target_id == \"\" or target_id == \"[AUTO]\":\r\n            counter = 1\r\n            while True:\r\n                candidate = f\"{source_session_id}-{counter}\"\r\n                if not SessionStore.session_exists(work_dir, candidate):\r\n                    target_id = candidate\r\n                    break\r\n                counter += 1\r\n        new_ctx = cls.create_session(work_dir, target_id)\r\n        source.fork(new_ctx.history_file)\r\n        new_ctx = cls.resume_session(work_dir, target_id)\r\n        return new_ctx\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/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_nvnpig3e0tqa_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.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 -->
现在让我继续查看其他核心模块文件。
<!-- 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": 61, "prompt_tokens": 18210, "total_tokens": 18271, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 61}, "prompt_tokens_details": {"cached_tokens": 14811, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 14811}
```
<!-- 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_nvnpig3e0tqa_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, field\r\nfrom typing import Any, Literal, Optional, TypedDict, TypeAlias, cast\r\n\r\n\r\nclass ReasoningDetail(TypedDict, total=False):\r\n    text: str\r\n\r\n\r\nclass UsageData(TypedDict, total=False):\r\n    prompt_tokens: int\r\n    completion_tokens: int\r\n    total_tokens: int\r\n    cache_read_input_tokens: int\r\n    cache_creation_input_tokens: int\r\n\r\n\r\nclass ToolFunctionPayload(TypedDict):\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass ToolRecord(TypedDict):\r\n    index: int\r\n    id: str\r\n    function: ToolFunctionPayload\r\n\r\n\r\nclass ToolCallDeltaPayload(TypedDict, total=False):\r\n    model: str\r\n    output_index: int\r\n    tool_call_id: str\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass AssistantDeltaPayload(TypedDict, total=False):\r\n    text: str\r\n    model: str\r\n\r\n\r\nclass AssistantReasoningDeltaPayload(TypedDict, total=False):\r\n    text: str\r\n    model: str\r\n    extra: \"ReasoningExtra | None\"\r\n\r\n\r\nclass AssistantDonePayload(TypedDict, total=False):\r\n    usage: UsageData\r\n    model: str\r\n\r\n\r\nclass EmptyPayload(TypedDict):\r\n    pass\r\n\r\n\r\nclass ReasoningExtra(TypedDict, total=False):\r\n    reasoning_details: list[ReasoningDetail]\r\n\r\n\r\n@dataclass\r\nclass ToolCallAccumulator:\r\n    index: int\r\n    id: str\r\n    name: str = \"\"\r\n    arguments: str = \"\"\r\n\r\n\r\n@dataclass\r\nclass TurnStreamState:\r\n    final: str = \"\"\r\n    reasoning: str = \"\"\r\n    reasoning_details: list[ReasoningDetail] = field(default_factory=list)\r\n    tool_phase: bool = False\r\n    usage_data: Optional[UsageData] = None\r\n    model_name: Optional[str] = None\r\n    tool_calls_by_idx: dict[int, ToolCallAccumulator] = field(default_factory=dict)\r\n    tool_calls_by_id: dict[str, ToolCallAccumulator] = field(default_factory=dict)\r\n    next_tool_idx: int = 0\r\n\r\n\r\nSignalType = Literal[\r\n    \"assistant_delta\",\r\n    \"assistant_reasoning_delta\",\r\n    \"assistant_done\",\r\n    \"tool_call_delta\",\r\n    \"tool_calls_done\",\r\n]\r\n\r\n\r\n@dataclass\r\nclass StreamSignal:\r\n    type: Literal[\"assistant_delta\"]\r\n    payload: AssistantDeltaPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass StreamTransition:\r\n    kind: Literal[\"assistant_delta\"]\r\n    payload: AssistantDeltaPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass AssistantReasoningDeltaSignal:\r\n    type: Literal[\"assistant_reasoning_delta\"]\r\n    payload: AssistantReasoningDeltaPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass AssistantDoneSignal:\r\n    type: Literal[\"assistant_done\"]\r\n    payload: AssistantDonePayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass ToolCallDeltaSignal:\r\n    type: Literal[\"tool_call_delta\"]\r\n    payload: ToolCallDeltaPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass ToolCallsDoneSignal:\r\n    type: Literal[\"tool_calls_done\"]\r\n    payload: EmptyPayload = field(default_factory=dict)\r\n\r\n\r\nStreamSignalUnion: TypeAlias = (\r\n    StreamSignal\r\n    | AssistantReasoningDeltaSignal\r\n    | AssistantDoneSignal\r\n    | ToolCallDeltaSignal\r\n    | ToolCallsDoneSignal\r\n)\r\n\r\n\r\nclass AssistantDeltaTransitionPayload(TypedDict):\r\n    text: str\r\n    model: Optional[str]\r\n\r\n\r\nclass AssistantDoneTransitionPayload(TypedDict, total=False):\r\n    usage: UsageData\r\n\r\n\r\nclass ToolCallTransitionPayload(TypedDict):\r\n    index: int\r\n    id: str\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass EmptyTransitionPayload(TypedDict):\r\n    pass\r\n\r\n\r\n@dataclass\r\nclass AssistantDeltaTransition:\r\n    kind: Literal[\"assistant_delta\"]\r\n    payload: AssistantDeltaTransitionPayload\r\n\r\n\r\n@dataclass\r\nclass AssistantReasoningDeltaTransition:\r\n    kind: Literal[\"assistant_reasoning_delta\"]\r\n    payload: AssistantDeltaTransitionPayload\r\n\r\n\r\n@dataclass\r\nclass AssistantDoneTransition:\r\n    kind: Literal[\"assistant_done\"]\r\n    payload: AssistantDoneTransitionPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass ToolCallDeltaTransition:\r\n    kind: Literal[\"tool_call_delta\"]\r\n    payload: ToolCallTransitionPayload\r\n\r\n\r\n@dataclass\r\nclass ToolCallDeltaDroppedTransition:\r\n    kind: Literal[\"tool_call_delta_dropped\"]\r\n    payload: EmptyTransitionPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass ToolCallsDoneTransition:\r\n    kind: Literal[\"tool_calls_done\"]\r\n    payload: EmptyTransitionPayload = field(default_factory=dict)\r\n\r\n\r\n@dataclass\r\nclass NoopTransition:\r\n    kind: Literal[\"noop\"]\r\n    payload: EmptyTransitionPayload = field(default_factory=dict)\r\n\r\n\r\nStreamTransitionUnion: TypeAlias = (\r\n    AssistantDeltaTransition\r\n    | AssistantReasoningDeltaTransition\r\n    | AssistantDoneTransition\r\n    | ToolCallDeltaTransition\r\n    | ToolCallDeltaDroppedTransition\r\n    | ToolCallsDoneTransition\r\n    | NoopTransition\r\n)\r\n\r\n\r\ndef ingest_stream_event(ev: Any) -> list[StreamSignalUnion]:\r\n    if not isinstance(ev, dict):\r\n        return []\r\n    et = ev.get(\"type\")\r\n    if et == \"assistant_delta\":\r\n        payload: AssistantDeltaPayload = {\"text\": ev.get(\"text\", \"\"), \"model\": ev.get(\"model\")}\r\n        return [StreamSignal(type=\"assistant_delta\", payload=payload)]\r\n    if et == \"assistant_reasoning_delta\":\r\n        raw_extra = ev.get(\"extra\")\r\n        extra: ReasoningExtra | None = cast(ReasoningExtra, raw_extra) if isinstance(raw_extra, dict) else None\r\n        payload: AssistantReasoningDeltaPayload = {\r\n            \"text\": ev.get(\"text\", \"\"),\r\n            \"model\": ev.get(\"model\"),\r\n            \"extra\": extra,\r\n        }\r\n        return [\r\n            AssistantReasoningDeltaSignal(\r\n                type=\"assistant_reasoning_delta\",\r\n                payload=payload,\r\n            )\r\n        ]\r\n    if et == \"assistant_done\":\r\n        payload: AssistantDonePayload = {\"usage\": ev.get(\"usage\"), \"model\": ev.get(\"model\")}\r\n        return [AssistantDoneSignal(type=\"assistant_done\", payload=payload)]\r\n    if et == \"tool_call_delta\":\r\n        payload: ToolCallDeltaPayload = {\r\n            \"model\": ev.get(\"model\"),\r\n            \"output_index\": ev.get(\"output_index\"),\r\n            \"tool_call_id\": ev.get(\"tool_call_id\"),\r\n            \"name\": ev.get(\"name\"),\r\n            \"arguments\": ev.get(\"arguments\"),\r\n        }\r\n        return [\r\n            ToolCallDeltaSignal(\r\n                type=\"tool_call_delta\",\r\n                payload=payload,\r\n            )\r\n        ]\r\n    if et == \"tool_calls_done\":\r\n        return [ToolCallsDoneSignal(type=\"tool_calls_done\", payload={})]\r\n    return []\r\n\r\n\r\ndef reduce_stream_signal(state: TurnStreamState, signal: StreamSignalUnion) -> StreamTransitionUnion:\r\n    if signal.type == \"assistant_delta\":\r\n        text = str(signal.payload.get(\"text\", \"\"))\r\n        if text:\r\n            state.final += text\r\n        current_model_name = signal.payload.get(\"model\")\r\n        if current_model_name is not None:\r\n            state.model_name = current_model_name\r\n        return AssistantDeltaTransition(kind=\"assistant_delta\", payload={\"text\": text, \"model\": state.model_name})\r\n\r\n    if signal.type == \"assistant_reasoning_delta\":\r\n        text = str(signal.payload.get(\"text\", \"\"))\r\n        if text:\r\n            state.reasoning += text\r\n        extra = signal.payload.get(\"extra\")\r\n        if isinstance(extra, dict) and extra.get(\"reasoning_details\") is not None:\r\n            details = extra.get(\"reasoning_details\")\r\n            if isinstance(details, list) and details:\r\n                if not state.reasoning_details:\r\n                    first = details[0]\r\n                    if isinstance(first, dict):\r\n                        state.reasoning_details.append(dict(first))\r\n                else:\r\n                    if isinstance(details[0], dict):\r\n                        state.reasoning_details[0][\"text\"] = str(state.reasoning_details[0].get(\"text\", \"\")) + str(\r\n                            details[0].get(\"text\", \"\")\r\n                        )\r\n        current_model_name = signal.payload.get(\"model\")\r\n        if current_model_name is not None:\r\n            state.model_name = current_model_name\r\n        return AssistantReasoningDeltaTransition(\r\n            kind=\"assistant_reasoning_delta\",\r\n            payload={\"text\": text, \"model\": state.model_name},\r\n        )\r\n\r\n    if signal.type == \"assistant_done\":\r\n        usage_data = signal.payload.get(\"usage\")\r\n        if usage_data is not None:\r\n            state.usage_data = usage_data\r\n        current_model_name = signal.payload.get(\"model\")\r\n        if current_model_name is not None:\r\n            state.model_name = current_model_name\r\n        payload: AssistantDoneTransitionPayload = {}\r\n        if usage_data is not None:\r\n            payload[\"usage\"] = usage_data\r\n        return AssistantDoneTransition(kind=\"assistant_done\", payload=payload)\r\n\r\n    if signal.type == \"tool_call_delta\":\r\n        state.tool_phase = True\r\n        current_model_name = signal.payload.get(\"model\")\r\n        if current_model_name is not None:\r\n            state.model_name = current_model_name\r\n        idx = signal.payload.get(\"output_index\")\r\n        tcid = signal.payload.get(\"tool_call_id\")\r\n        rec = None\r\n        if isinstance(idx, int):\r\n            rec = state.tool_calls_by_idx.get(idx)\r\n            if rec is None:\r\n                rec = ToolCallAccumulator(index=idx, id=str(tcid) if tcid else \"\")\r\n                state.tool_calls_by_idx[idx] = rec\r\n            elif (not rec.id) and tcid:\r\n                rec.id = str(tcid)\r\n            if tcid:\r\n                state.tool_calls_by_id[str(tcid)] = rec\r\n            if idx >= state.next_tool_idx:\r\n                state.next_tool_idx = idx + 1\r\n        elif tcid:\r\n            rec = state.tool_calls_by_id.get(str(tcid))\r\n            if rec is None:\r\n                rec = ToolCallAccumulator(index=state.next_tool_idx, id=str(tcid))\r\n                state.tool_calls_by_idx[state.next_tool_idx] = rec\r\n                state.tool_calls_by_id[str(tcid)] = rec\r\n                state.next_tool_idx += 1\r\n\r\n        if rec is None:\r\n            return ToolCallDeltaDroppedTransition(kind=\"tool_call_delta_dropped\")\r\n\r\n        name = signal.payload.get(\"name\")\r\n        if name:\r\n            rec.name = str(name)\r\n        arguments = signal.payload.get(\"arguments\")\r\n        if arguments:\r\n            rec.arguments += str(arguments)\r\n        return ToolCallDeltaTransition(\r\n            kind=\"tool_call_delta\",\r\n            payload={\r\n                \"index\": rec.index,\r\n                \"id\": rec.id,\r\n                \"name\": rec.name,\r\n                \"arguments\": rec.arguments,\r\n            },\r\n        )\r\n\r\n    if signal.type == \"tool_calls_done\":\r\n        state.tool_phase = True\r\n        return ToolCallsDoneTransition(kind=\"tool_calls_done\")\r\n\r\n    return NoopTransition(kind=\"noop\")\r\n\r\n\r\ndef build_assembled_debug(state: TurnStreamState) -> list[ToolRecord]:\r\n    assembled = []\r\n    for k, rec in sorted(state.tool_calls_by_idx.items(), key=lambda x: x[0]):\r\n        assembled.append(\r\n            {\r\n                \"index\": k,\r\n                \"id\": rec.id,\r\n                \"function\": {\"name\": rec.name, \"arguments\": rec.arguments},\r\n            }\r\n        )\r\n    return assembled\r\n\r\n\r\ndef build_tool_records(state: TurnStreamState) -> list[ToolRecord]:\r\n    out: list[ToolRecord] = []\r\n    for _, rec in sorted(state.tool_calls_by_idx.items(), key=lambda x: x[0]):\r\n        out.append(\r\n            {\r\n                \"index\": rec.index,\r\n                \"id\": rec.id,\r\n                \"function\": {\"name\": rec.name, \"arguments\": rec.arguments},\r\n            }\r\n        )\r\n    return out\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.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_t6xlpzdh3s5k_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime.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 -->
Now let me read the remaining key files to complete the overview.
<!-- 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": 21615, "total_tokens": 21678, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 63}, "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_t6xlpzdh3s5k_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\nimport json\r\nimport traceback\r\nfrom dataclasses import dataclass\r\nfrom typing import Any, Awaitable, Callable, Optional\r\n\r\nfrom .agent_stream_state import ToolRecord, UsageData\r\nfrom .context import Context\r\nfrom .model import (\r\n    Message,\r\n    ToolMessageContent,\r\n    FunctionInfo,\r\n    ToolCallInfo,\r\n    ROLE_ASSISTANT,\r\n    ROLE_TOOL,\r\n)\r\nfrom .tool_runtime_utils import (\r\n    JsonObject,\r\n    parse_tool_arguments,\r\n    parse_tool_arguments_with_status,\r\n    summarize_tool_arguments,\r\n)\r\nfrom .tools.base import BaseToolRegistry, ToolResult\r\nfrom .events import (\r\n    EventPayload,\r\n    ev_error,\r\n    ev_assistant_final,\r\n    ev_tool_output,\r\n    ev_tool_exit,\r\n    ev_tool_call_preview,\r\n    ev_tool_call_request,\r\n    ev_tool_call_debug_stage,\r\n)\r\n\r\n\r\n@dataclass(slots=True)\r\nclass ToolRuntime:\r\n    tool_registry: BaseToolRegistry\r\n    publish: Callable[[EventPayload], Awaitable[None]]\r\n    execute_remote_tool: Callable[[Context, str, JsonObject], Awaitable[ToolResult]]\r\n    cancel_event: asyncio.Event\r\n    debug_tools: bool = False\r\n\r\n    async def _emit_tool_denial(\r\n        self,\r\n        ctx: Context,\r\n        name: str,\r\n        tcid: str,\r\n        message: str | None,\r\n        reasons: list[str] | None,\r\n        args: Optional[JsonObject] = None,\r\n        full_metadata: dict | None = None,\r\n    ) -> None:\r\n        detail = message or f\"{name} execution denied.\"\r\n        info: dict[str, Any] = {}\r\n        try:\r\n            tool_class = self.tool_registry.get_tool_class(name)\r\n            if tool_class and hasattr(tool_class, \"on_denial\"):\r\n                info = tool_class.on_denial(args or {})\r\n        except Exception:\r\n            pass\r\n\r\n        payload_metadata = full_metadata if full_metadata else {\"tool\": name, \"reasons\": reasons}\r\n        payload = ToolMessageContent(\r\n            status=\"failure\",\r\n            data=full_metadata,\r\n            error=detail,\r\n            metadata=payload_metadata,\r\n            info=info,\r\n        )\r\n        await self.publish(ev_tool_output(stream=\"stdout\", text=json.dumps(payload.model_dump(), ensure_ascii=False) + \"\\n\"))\r\n        await self.publish(ev_tool_output(stream=\"stderr\", text=detail + \"\\n\"))\r\n        await self.publish(ev_tool_exit(1))\r\n        try:\r\n            ctx.append_message(Message(role=ROLE_TOOL, content=payload, tool_call_id=tcid, name=name))\r\n        except Exception:\r\n            pass\r\n\r\n    async def process_tool_records(\r\n        self,\r\n        *,\r\n        ctx: Context,\r\n        tool_records: list[ToolRecord],\r\n        final: str,\r\n        reasoning: str,\r\n        reasoning_details: Any,\r\n        usage_data: UsageData | None,\r\n        model_name: str | None,\r\n    ) -> bool:\r\n        for rec in tool_records:\r\n            name = rec.get(\"function\", {}).get(\"name\", \"\")\r\n            arg_str = rec.get(\"function\", {}).get(\"arguments\", \"\")\r\n            parsed_args = parse_tool_arguments(arg_str)\r\n            if self.debug_tools:\r\n                await self.publish(\r\n                    ev_tool_call_debug_stage(\r\n                        stage=\"assembled_final\",\r\n                        tool=name,\r\n                        id=rec.get(\"id\") or \"\",\r\n                        index=int(rec.get(\"index\") or 0),\r\n                        args_summary=summarize_tool_arguments(arg_str),\r\n                    )\r\n                )\r\n            preview_id = rec.get(\"id\") or f\"call_{rec.get('index', 0)}\"\r\n            preview_text: Optional[str] = None\r\n            tool_class = self.tool_registry.get_tool_class(name)\r\n            if tool_class:\r\n                preview_text = tool_class.get_preview(parsed_args)\r\n            await self.publish(ev_tool_call_preview(tool=name, args=arg_str, preview_id=preview_id, preview=preview_text))\r\n            loop = asyncio.get_running_loop()\r\n            preview_fut: asyncio.Future = loop.create_future()\r\n            await self.publish(ev_tool_call_request(tool=name, preview_id=preview_id, future=preview_fut))\r\n            await preview_fut\r\n\r\n        try:\r\n            tool_calls_objects: list[ToolCallInfo] = []\r\n            for i, rec in enumerate(tool_records):\r\n                parsed_args = parse_tool_arguments(rec.get(\"function\", {}).get(\"arguments\", \"\"))\r\n                function = FunctionInfo(\r\n                    name=rec.get(\"function\", {}).get(\"name\", \"\"),\r\n                    arguments=parsed_args if parsed_args else None,\r\n                )\r\n                tool_call = ToolCallInfo(\r\n                    id=rec.get(\"id\") or f\"call_{i}\",\r\n                    type=\"function\",\r\n                    function=function,\r\n                )\r\n                tool_calls_objects.append(tool_call)\r\n\r\n            reasoning_content = reasoning if reasoning else None\r\n            ctx.append_message(\r\n                Message(\r\n                    role=ROLE_ASSISTANT,\r\n                    content=final,\r\n                    tool_calls=tool_calls_objects,\r\n                    usage=usage_data,\r\n                    reasoning_content=reasoning_content,\r\n                    model_name=model_name,\r\n                    extra={\"reasoning_details\": reasoning_details} if reasoning_details else None,\r\n                )\r\n            )\r\n        except Exception as e:\r\n            error_msg = f\"工具整理失败: {str(e)}\"\r\n            await self.publish(ev_error(error_msg, exception_type=type(e).__name__, traceback_text=traceback.format_exc()))\r\n            await self.publish(ev_assistant_final(f\"错误: {error_msg}\"))\r\n\r\n        request_new_input = False\r\n        for i, rec in enumerate(tool_records):\r\n            name = rec.get(\"function\", {}).get(\"name\", \"\")\r\n            arg_str = rec.get(\"function\", {}).get(\"arguments\", \"\")\r\n            args, parse_ok = parse_tool_arguments_with_status(arg_str)\r\n            tcid = rec.get(\"id\") or f\"call_{i}\"\r\n\r\n            if arg_str and not parse_ok:\r\n                err = ToolMessageContent(\r\n                    status=\"failure\",\r\n                    error=\"invalid_tool_arguments_json\",\r\n                    data={\r\n                        \"message\": \"Tool arguments are not valid JSON object.\",\r\n                        \"received\": {\"name\": name, \"arguments\": arg_str},\r\n                    },\r\n                )\r\n                await self.publish(\r\n                    ev_tool_call_debug_stage(\r\n                        stage=\"invalid_arguments\",\r\n                        tool=name,\r\n                        id=tcid,\r\n                        index=i,\r\n                        args_summary=summarize_tool_arguments(arg_str),\r\n                    )\r\n                )\r\n                try:\r\n                    ctx.append_message(Message(role=ROLE_TOOL, content=err, tool_call_id=tcid, name=name))\r\n                except Exception:\r\n                    traceback.print_exc()\r\n                continue\r\n\r\n            tool_class = self.tool_registry.get_tool_class(name)\r\n            if tool_class is None:\r\n                if ctx.get_mcp_tool_definition(name):\r\n                    tool_result = await self.execute_remote_tool(ctx, name, args)\r\n                else:\r\n                    available: list[str] = []\r\n                    try:\r\n                        available = self.tool_registry.list_tools()\r\n                    except Exception:\r\n                        available = []\r\n                    err = ToolMessageContent(\r\n                        status=\"failure\",\r\n                        error=\"unknown_tool\",\r\n                        data={\r\n                            \"message\": f\"Unknown tool: {name or '(empty)'}\",\r\n                            \"received\": {\"name\": name, \"arguments\": arg_str},\r\n                            \"available_tools\": [a for a in available if a],\r\n                            \"hint\": \"Ensure the assistant emits tool_calls with a valid function.name that matches a registered tool.\",\r\n                        },\r\n                    )\r\n                    try:\r\n                        ctx.append_message(Message(role=ROLE_TOOL, content=err, tool_call_id=tcid, name=name))\r\n                    except Exception:\r\n                        traceback.print_exc()\r\n                    continue\r\n            else:\r\n                try:\r\n                    tool_result = await tool_class.run(\r\n                        args,\r\n                        cancel_event=self.cancel_event,\r\n                        approval_publish=self.publish,\r\n                        event_publish=self.publish,\r\n                    )\r\n                except Exception as e:\r\n                    traceback.print_exc()\r\n                    tool_result = ToolResult(status=\"failure\", error=str(e))\r\n\r\n            if tool_result.status == \"success\":\r\n                try:\r\n                    tool_content = ToolMessageContent(\r\n                        status=tool_result.status,\r\n                        data=tool_result.content,\r\n                        error=tool_result.error,\r\n                        metadata=tool_result.metadata,\r\n                    )\r\n                    ctx.append_message(Message(role=ROLE_TOOL, content=tool_content, tool_call_id=tcid, name=name))\r\n                except Exception:\r\n                    traceback.print_exc()\r\n                continue\r\n\r\n            reasons = None\r\n            if isinstance(tool_result.metadata, dict):\r\n                reasons = tool_result.metadata.get(\"reasons\")\r\n            detail = tool_result.error or \"Tool execution failed.\"\r\n            await self._emit_tool_denial(\r\n                ctx,\r\n                name,\r\n                tcid,\r\n                detail,\r\n                reasons,\r\n                args,\r\n                full_metadata=tool_result.metadata if isinstance(tool_result.metadata, dict) else None,\r\n            )\r\n            request_new_input = bool(tool_result.needs_user_input)\r\n        return request_new_input\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime.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_h6vvaqg4krjm_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/base.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 -->
现在我有了对整个项目的全面了解。让我继续查看其他几个关键文件，然后提供一个完整的代码梳理。
<!-- 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": 72, "prompt_tokens": 24129, "total_tokens": 24201, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 72}, "prompt_tokens_details": {"cached_tokens": 21611, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 21611}
```
<!-- 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_h6vvaqg4krjm_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": "\"\"\"Base tool interface and abstract classes\"\"\"\r\n\r\nimport abc\r\nimport asyncio\r\nimport inspect\r\nimport json\r\nfrom typing import Dict, Any, Optional, Type, List, TYPE_CHECKING, Callable, Awaitable\r\nfrom dataclasses import dataclass\r\nfrom enum import Enum\r\nfrom uuid import uuid4\r\n\r\nfrom .precheck import ToolPrecheckResult, STATUS_ALLOW, STATUS_DENY, STATUS_NEEDS_APPROVAL\r\nfrom ..events import EventPayload, ev_approval_request, ev_choice_request\r\n\r\nif TYPE_CHECKING:\r\n    from ..model import UserContent\r\n\r\n\r\nclass ToolType(Enum):\r\n    \"\"\"Tool execution types\"\"\"\r\n    SYNC = \"sync\"\r\n    ASYNC = \"async\"\r\n    STREAM = \"stream\"\r\n\r\n\r\n@dataclass\r\nclass ToolResult:\r\n    \"\"\"Standardized tool execution result returned from BaseTool.run\"\"\"\r\n    status: str\r\n    content: Optional[Any] = None\r\n    error: Optional[str] = None\r\n    metadata: Optional[Dict[str, Any]] = None\r\n    needs_user_input: bool = False\r\n\r\n    @property\r\n    def success(self) -> bool:\r\n        return self.status == \"success\"\r\n\r\n\r\n@dataclass\r\nclass ToolStreamEvent:\r\n    \"\"\"Tool streaming event\"\"\"\r\n    type: str  # stdout, stderr, progress, error, done\r\n    data: Any\r\n    metadata: Optional[Dict[str, Any]] = None\r\n\r\n\r\nclass ToolExecutionError(Exception):\r\n    \"\"\"Custom exception that carries metadata for tool failures\"\"\"\r\n\r\n    def __init__(\r\n        self,\r\n        message: str,\r\n        *,\r\n        metadata: Optional[Dict[str, Any]] = None,\r\n        needs_user_input: bool = False,\r\n        content: Optional[Any] = None,\r\n    ) -> None:\r\n        super().__init__(message)\r\n        self.metadata = metadata\r\n        self.needs_user_input = needs_user_input\r\n        self.content = content\r\n\r\n\r\nclass BaseTool(abc.ABC):\r\n    \"\"\"Abstract base class for all tools\"\"\"\r\n    \r\n    def __init_subclass__(cls, **kwargs):\r\n        \"\"\"Subclass hook reserved for future extension.\"\"\"\r\n        super().__init_subclass__(**kwargs)\r\n    \r\n    @property\r\n    @abc.abstractmethod\r\n    def name(self) -> str:\r\n        \"\"\"Tool name (must be unique)\"\"\"\r\n        pass\r\n    \r\n    @property\r\n    @abc.abstractmethod\r\n    def description(self) -> str:\r\n        \"\"\"Tool description for LLM\"\"\"\r\n        pass\r\n    \r\n    @property\r\n    @abc.abstractmethod\r\n    def schema(self) -> Dict[str, Any]:\r\n        \"\"\"OpenAI function calling schema\"\"\"\r\n        pass\r\n    \r\n    @property\r\n    def tool_type(self) -> ToolType:\r\n        \"\"\"Tool execution type\"\"\"\r\n        return ToolType.SYNC\r\n    \r\n    @property\r\n    def requires_permission(self) -> bool:\r\n        \"\"\"Whether this tool requires user permission to execute\"\"\"\r\n        return False\r\n    \r\n    @property\r\n    def is_destructive(self) -> bool:\r\n        \"\"\"Whether this tool can modify system state\"\"\"\r\n        return False\r\n    \r\n    # Standard interface methods that all tools should implement\r\n    @classmethod\r\n    @abc.abstractmethod\r\n    def get_schema(cls) -> Dict[str, Any]:\r\n        \"\"\"Get OpenAI function calling schema\"\"\"\r\n        pass\r\n    \r\n    @abc.abstractstaticmethod\r\n    def execute(**kwargs) -> Any:\r\n        \"\"\"Execute tool\"\"\"\r\n        pass\r\n    \r\n    @classmethod\r\n    @abc.abstractmethod\r\n    def validate_arguments(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        \"\"\"Validate tool arguments before execution\"\"\"\r\n        pass\r\n    \r\n    @classmethod\r\n    @abc.abstractmethod\r\n    def get_preview(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        \"\"\"Get preview text for the tool\"\"\"\r\n        pass\r\n    \r\n    # Optional methods with default implementations\r\n    @classmethod\r\n    def get_precheck(cls, args: Dict[str, Any]):\r\n        \"\"\"Get precheck result (optional method)\"\"\"\r\n        return None\r\n    \r\n    @classmethod\r\n    def get_request_template(cls, prefix: str) -> Optional[str]:\r\n        \"\"\"Get request template (optional method)\"\"\"\r\n        return None\r\n    \r\n    @classmethod\r\n    def get_response_template(cls, item) -> Optional[str]:\r\n        \"\"\"Get response template (optional method)\"\"\"\r\n        return None\r\n\r\n    @classmethod\r\n    def user_input_hook(cls) -> Optional[\"UserContent\"]:\r\n        \"\"\"Optional hook to inject tool status into user input.\"\"\"\r\n        return None\r\n    \r\n    @classmethod\r\n    def on_denial(cls, args: Dict[str, Any]) -> Dict[str, Any]:\r\n        \"\"\"Get denial information (optional method)\r\n        \r\n        Returns a dictionary that will be placed in the content's \"info\" field\r\n        when the tool execution is denied.\r\n        \"\"\"\r\n        return {}\r\n\r\n    # ------------------------------------------------------------------\r\n    # Unified runner that handles validation / precheck / approval / execute\r\n    # ------------------------------------------------------------------\r\n\r\n    @classmethod\r\n    async def run(\r\n        cls,\r\n        args: Dict[str, Any],\r\n        *,\r\n        cancel_event: Optional[asyncio.Event] = None,\r\n        approval_publish: Optional[Callable[[EventPayload], Awaitable[None]]] = None,\r\n        event_publish: Optional[Callable[[EventPayload], Awaitable[None]]] = None,\r\n    ) -> ToolResult:\r\n        \"\"\"Run the tool through validation, optional approval, and execution.\"\"\"\r\n        validation_error = cls.validate_arguments(args)\r\n        if validation_error:\r\n            return ToolResult(status=\"failure\", error=validation_error)\r\n\r\n        precheck = cls.get_precheck(args) or ToolPrecheckResult(status=STATUS_ALLOW)\r\n        if precheck.status == STATUS_DENY:\r\n            return ToolResult(\r\n                status=\"denied\",\r\n                error=precheck.message or \"Tool precheck denied execution.\",\r\n                metadata={\"reasons\": precheck.reasons},\r\n            )\r\n\r\n        if precheck.status == STATUS_NEEDS_APPROVAL:\r\n            if approval_publish is None:\r\n                return ToolResult(\r\n                    status=\"denied\",\r\n                    error=\"Approval required but publisher is unavailable.\",\r\n                    metadata={\"reasons\": precheck.reasons},\r\n                    needs_user_input=True,\r\n                )\r\n            approved = await cls._request_tool_approval_via_publish(approval_publish, args, precheck)\r\n            if not approved:\r\n                return ToolResult(\r\n                    status=\"denied\",\r\n                    error=precheck.message or \"User denied tool approval.\",\r\n                    metadata={\"reasons\": precheck.reasons},\r\n                    needs_user_input=True,\r\n                )\r\n\r\n        loop: Optional[asyncio.AbstractEventLoop] = None\r\n        try:\r\n            loop = asyncio.get_running_loop()\r\n        except Exception:\r\n            loop = None\r\n        kwargs = cls._build_execute_kwargs(\r\n            args,\r\n            cancel_event=cancel_event,\r\n            event_publish=event_publish,\r\n            loop=loop,\r\n        )\r\n\r\n        try:\r\n            out = await cls._call_execute(kwargs)\r\n        except ToolExecutionError as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=str(exc),\r\n                metadata=exc.metadata,\r\n                needs_user_input=exc.needs_user_input,\r\n                content=exc.content,\r\n            )\r\n        except Exception as exc:\r\n            return ToolResult(status=\"failure\", error=str(exc))\r\n\r\n        if isinstance(out, ToolResult):\r\n            return out\r\n        return ToolResult(status=\"success\", content=out)\r\n\r\n    @classmethod\r\n    async def _call_execute(cls, kwargs: Dict[str, Any]) -> Any:\r\n        execute_fn = cls.execute\r\n        if inspect.iscoroutinefunction(execute_fn):\r\n            return await execute_fn(**kwargs)\r\n        # Run sync tools in a worker thread so long-running commands don't block UI/event loop.\r\n        return await asyncio.to_thread(execute_fn, **kwargs)\r\n\r\n    @classmethod\r\n    def _build_execute_kwargs(\r\n        cls,\r\n        args: Dict[str, Any],\r\n        *,\r\n        cancel_event: Optional[asyncio.Event],\r\n        event_publish: Optional[Callable[[EventPayload], Awaitable[None]]] = None,\r\n        loop: Optional[asyncio.AbstractEventLoop] = None,\r\n    ) -> Dict[str, Any]:\r\n        execute_fn = cls.execute\r\n        sig = inspect.signature(execute_fn)\r\n        kwargs = dict(args)\r\n        if \"cancel_event\" in sig.parameters and cancel_event is not None:\r\n            kwargs.setdefault(\"cancel_event\", cancel_event)\r\n        if \"event_publish\" in sig.parameters and event_publish is not None:\r\n            kwargs.setdefault(\"event_publish\", event_publish)\r\n        if \"emit_event\" in sig.parameters and event_publish is not None and loop is not None:\r\n            def _emit_event(payload: EventPayload) -> None:\r\n                try:\r\n                    loop.call_soon_threadsafe(asyncio.create_task, event_publish(payload))\r\n                except Exception:\r\n                    pass\r\n            kwargs.setdefault(\"emit_event\", _emit_event)\r\n        if \"event_loop\" in sig.parameters and loop is not None:\r\n            kwargs.setdefault(\"event_loop\", loop)\r\n        return kwargs\r\n\r\n    @classmethod\r\n    async def _request_tool_approval_via_publish(\r\n        cls,\r\n        publish: Callable[[EventPayload], Awaitable[None]],\r\n        args: Dict[str, Any],\r\n        precheck: ToolPrecheckResult,\r\n    ) -> bool:\r\n        loop = asyncio.get_running_loop()\r\n        approval_fut: asyncio.Future = loop.create_future()\r\n        await publish(\r\n            ev_approval_request(\r\n                id=f\"appr_{uuid4().hex}\",\r\n                tool=cls.name,\r\n                args=args,\r\n                future=approval_fut,\r\n                precheck={\r\n                    \"status\": precheck.status,\r\n                    \"message\": precheck.message,\r\n                    \"reasons\": precheck.reasons,\r\n                    \"metadata\": precheck.metadata,\r\n                },\r\n            )\r\n        )\r\n        try:\r\n            fut_res = await approval_fut\r\n        except Exception:\r\n            fut_res = None\r\n        if isinstance(fut_res, dict):\r\n            return bool(fut_res.get(\"approved\"))\r\n        return bool(fut_res)\r\n\r\n    @classmethod\r\n    async def _request_choice(\r\n        cls,\r\n        publish: Callable[[EventPayload], Awaitable[None]],\r\n        prompt: str,\r\n        choices: list[str],\r\n        *,\r\n        hint: Optional[str] = None,\r\n    ) -> tuple[Optional[str], bool]:\r\n        loop = asyncio.get_running_loop()\r\n        choice_fut: asyncio.Future = loop.create_future()\r\n        await publish(\r\n            ev_choice_request(\r\n                id=f\"choice_{uuid4().hex}\",\r\n                tool=cls.name,\r\n                prompt=prompt,\r\n                choices=choices,\r\n                hint=hint,\r\n                future=choice_fut,\r\n            )\r\n        )\r\n        try:\r\n            fut_res = await choice_fut\r\n        except Exception:\r\n            fut_res = None\r\n        if isinstance(fut_res, dict):\r\n            return fut_res.get(\"selected\"), bool(fut_res.get(\"confirmed\"))\r\n        return None, False\r\n    \r\n    def format_result(self, result: ToolResult) -> str:\r\n        \"\"\"Format tool result for LLM consumption\"\"\"\r\n        if result.status == \"success\":\r\n            if isinstance(result.content, (dict, list)):\r\n                return json.dumps(result.content, ensure_ascii=False, indent=2)\r\n            return str(result.content)\r\n        error_info = {\r\n            \"error\": result.error,\r\n            \"metadata\": result.metadata,\r\n            \"status\": result.status,\r\n        }\r\n        return json.dumps(error_info, ensure_ascii=False, indent=2)\r\n\r\n\r\n# =============================================================================\r\n# Integrated Tool Registry\r\n# =============================================================================\r\n\r\nclass BaseToolRegistry:\r\n    \"\"\"Central registry for all available tools\"\"\"\r\n    \r\n    def __init__(self):\r\n        self._tools: Dict[str, Type[BaseTool]] = {}\r\n    \r\n    def auto_register_tool(self, tool_class: Type[BaseTool]):\r\n        \"\"\"自动注册工具类\"\"\"\r\n        try:\r\n            # 获取工具实例来访问属性\r\n            tool_instance = tool_class()\r\n            \r\n            # 获取基本信息\r\n            name = tool_instance.name\r\n            if not name:\r\n                raise ValueError(f\"Tool class {tool_class.__name__} has no valid name\")\r\n            \r\n            # 检查是否已经注册\r\n            if name in self._tools:\r\n                return\r\n            \r\n            # 注册工具类\r\n            self._tools[name] = tool_class\r\n            \r\n        except Exception as e:\r\n            raise ValueError(f\"Failed to auto-register tool {tool_class.__name__}: {e}\")\r\n    \r\n    def get_tool_class(self, name: str) -> Optional[Type[BaseTool]]:\r\n        \"\"\"Get tool class by name\"\"\"\r\n        return self._tools.get(name)\r\n    \r\n    def list_tools(self) -> List[str]:\r\n        \"\"\"List all available tool names\"\"\"\r\n        return list(self._tools.keys())\r\n    \r\n    def get_schemas(self) -> List[dict]:\r\n        \"\"\"Get all tool schemas for OpenAI function calling\"\"\"\r\n        schemas = []\r\n        for tool_class in self._tools.values():\r\n            try:\r\n                schema = tool_class.get_schema()\r\n                schemas.append(schema)\r\n            except Exception:\r\n                continue\r\n        return schemas\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/base.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_9oqc1jpqoqtf_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/events.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 -->
现在我已经看了主要的代码文件，让我继续查看剩余的文件，包括 events.py、llm_transport.py、session_store.py、template_renderer.py 等，然后给出一个完整的代码结构梳理。
<!-- 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": 87, "prompt_tokens": 27832, "total_tokens": 27919, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 87}, "prompt_tokens_details": {"cached_tokens": 24125, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 24125}
```
<!-- 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_9oqc1jpqoqtf_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 typing import Any, Literal, Optional, TypeAlias, TypedDict\r\n\r\nfrom .agent_stream_state import ToolCallDeltaPayload, ToolRecord, UsageData\r\n\r\n\r\nclass AssistantDeltaEvent(TypedDict):\r\n    type: Literal[\"assistant_delta\"]\r\n    text: str\r\n    model: Optional[str]\r\n\r\n\r\nclass AssistantReasoningDeltaEvent(TypedDict):\r\n    type: Literal[\"assistant_reasoning_delta\"]\r\n    text: str\r\n    model: Optional[str]\r\n\r\n\r\nclass UsageTotalEvent(TypedDict):\r\n    type: Literal[\"usage_total\"]\r\n    usage: UsageData\r\n\r\n\r\nclass ToolCallPreviewEvent(TypedDict, total=False):\r\n    type: Literal[\"tool_call_preview\"]\r\n    tool: str\r\n    args: str\r\n    index: Optional[int]\r\n    tool_call_id: str\r\n    preview_id: str\r\n    preview: str\r\n\r\n\r\nclass ToolCallRequestEvent(TypedDict):\r\n    type: Literal[\"tool_call\"]\r\n    tool: str\r\n    preview_id: str\r\n    future: Any\r\n\r\n\r\nclass ToolOutputEvent(TypedDict):\r\n    type: Literal[\"tool_output\"]\r\n    stream: Literal[\"stdout\", \"stderr\"]\r\n    text: str\r\n\r\n\r\nclass ToolExitEvent(TypedDict):\r\n    type: Literal[\"tool_exit\"]\r\n    code: int\r\n\r\n\r\nclass ApprovalPrecheckPayload(TypedDict, total=False):\r\n    status: str\r\n    message: str | None\r\n    reasons: list[str] | None\r\n    metadata: dict[str, Any] | None\r\n\r\n\r\nclass ApprovalRequestEvent(TypedDict):\r\n    type: Literal[\"approval_request\"]\r\n    id: str\r\n    tool: str\r\n    args: dict[str, Any]\r\n    future: Any\r\n    precheck: ApprovalPrecheckPayload\r\n\r\n\r\nclass ChoiceRequestEvent(TypedDict):\r\n    type: Literal[\"choice_request\"]\r\n    id: str\r\n    tool: str\r\n    prompt: str\r\n    choices: list[str]\r\n    hint: str | None\r\n    future: Any\r\n\r\n\r\nclass ErrorEvent(TypedDict, total=False):\r\n    type: Literal[\"error\"]\r\n    error: str\r\n    exception_type: str\r\n    traceback: str\r\n\r\n\r\nclass AssistantFinalEvent(TypedDict, total=False):\r\n    type: Literal[\"assistant_final\"]\r\n    text: str\r\n    usage_data: Any\r\n    reasoning: str\r\n\r\n\r\nclass MessageSentEvent(TypedDict):\r\n    type: Literal[\"message_sent\"]\r\n\r\n\r\nclass RunAbortedEvent(TypedDict):\r\n    type: Literal[\"run_aborted\"]\r\n    reason: str\r\n\r\n\r\nclass DebugLatencyEvent(TypedDict):\r\n    type: Literal[\"debug_latency\"]\r\n    stage: str\r\n    ms: float\r\n\r\n\r\nclass ToolCallDebugDeltaEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    delta: ToolCallDeltaPayload\r\n\r\n\r\nclass ToolCallDebugWarningEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    warning: str\r\n\r\n\r\nclass ToolCallDebugAssembledEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    assembled: list[ToolRecord]\r\n\r\n\r\nclass ToolCallDebugStageEvent(TypedDict, total=False):\r\n    type: Literal[\"tool_call_debug\"]\r\n    stage: Literal[\"assembled_final\", \"invalid_arguments\"]\r\n    tool: str\r\n    id: str\r\n    index: int\r\n    args_summary: dict[str, Any]\r\n\r\n\r\nToolCallDebugEvent: TypeAlias = (\r\n    ToolCallDebugDeltaEvent\r\n    | ToolCallDebugWarningEvent\r\n    | ToolCallDebugAssembledEvent\r\n    | ToolCallDebugStageEvent\r\n)\r\n\r\n\r\nStreamOutEvent: TypeAlias = AssistantDeltaEvent | AssistantReasoningDeltaEvent | UsageTotalEvent | ToolCallPreviewEvent\r\nCoreEvent: TypeAlias = ErrorEvent | AssistantFinalEvent | MessageSentEvent | RunAbortedEvent | DebugLatencyEvent\r\nToolEvent: TypeAlias = (\r\n    ToolOutputEvent\r\n    | ToolExitEvent\r\n    | ToolCallRequestEvent\r\n    | ToolCallPreviewEvent\r\n    | ToolCallDebugEvent\r\n    | ApprovalRequestEvent\r\n    | ChoiceRequestEvent\r\n)\r\nEventPayload: TypeAlias = StreamOutEvent | CoreEvent | ToolEvent\r\n\r\n\r\ndef ev_assistant_delta(text: str, model: Optional[str] = None) -> AssistantDeltaEvent:\r\n    return {\"type\": \"assistant_delta\", \"text\": text, \"model\": model}\r\n\r\n\r\ndef ev_assistant_reasoning_delta(text: str, model: Optional[str] = None) -> AssistantReasoningDeltaEvent:\r\n    return {\"type\": \"assistant_reasoning_delta\", \"text\": text, \"model\": model}\r\n\r\n\r\ndef ev_usage_total(usage: UsageData) -> UsageTotalEvent:\r\n    return {\"type\": \"usage_total\", \"usage\": usage}\r\n\r\n\r\ndef ev_error(error: str, *, exception_type: Optional[str] = None, traceback_text: Optional[str] = None) -> ErrorEvent:\r\n    payload: ErrorEvent = {\"type\": \"error\", \"error\": error}\r\n    if exception_type:\r\n        payload[\"exception_type\"] = exception_type\r\n    if traceback_text:\r\n        payload[\"traceback\"] = traceback_text\r\n    return payload\r\n\r\n\r\ndef ev_assistant_final(text: str, *, usage_data: Optional[Any] = None, reasoning: Optional[str] = None) -> AssistantFinalEvent:\r\n    payload: AssistantFinalEvent = {\"type\": \"assistant_final\", \"text\": text}\r\n    if usage_data is not None:\r\n        payload[\"usage_data\"] = usage_data\r\n    if reasoning is not None:\r\n        payload[\"reasoning\"] = reasoning\r\n    return payload\r\n\r\n\r\ndef ev_message_sent() -> MessageSentEvent:\r\n    return {\"type\": \"message_sent\"}\r\n\r\n\r\ndef ev_run_aborted(reason: str = \"cancelled\") -> RunAbortedEvent:\r\n    return {\"type\": \"run_aborted\", \"reason\": reason}\r\n\r\n\r\ndef ev_debug_latency(*, stage: str, ms: float) -> DebugLatencyEvent:\r\n    return {\"type\": \"debug_latency\", \"stage\": stage, \"ms\": float(ms)}\r\n\r\n\r\ndef ev_tool_output(*, stream: Literal[\"stdout\", \"stderr\"], text: str) -> ToolOutputEvent:\r\n    return {\"type\": \"tool_output\", \"stream\": stream, \"text\": text}\r\n\r\n\r\ndef ev_tool_exit(code: int) -> ToolExitEvent:\r\n    return {\"type\": \"tool_exit\", \"code\": int(code)}\r\n\r\n\r\ndef ev_tool_call_preview(\r\n    *,\r\n    tool: str,\r\n    args: str,\r\n    preview_id: str,\r\n    index: Optional[int] = None,\r\n    tool_call_id: Optional[str] = None,\r\n    preview: Optional[str] = None,\r\n) -> ToolCallPreviewEvent:\r\n    payload: ToolCallPreviewEvent = {\r\n        \"type\": \"tool_call_preview\",\r\n        \"tool\": tool,\r\n        \"args\": args,\r\n        \"preview_id\": preview_id,\r\n    }\r\n    if index is not None:\r\n        payload[\"index\"] = index\r\n    if tool_call_id:\r\n        payload[\"tool_call_id\"] = tool_call_id\r\n    if preview:\r\n        payload[\"preview\"] = preview\r\n    return payload\r\n\r\n\r\ndef ev_tool_call_request(*, tool: str, preview_id: str, future: Any) -> ToolCallRequestEvent:\r\n    return {\"type\": \"tool_call\", \"tool\": tool, \"preview_id\": preview_id, \"future\": future}\r\n\r\n\r\ndef ev_approval_request(\r\n    *,\r\n    id: str,\r\n    tool: str,\r\n    args: dict[str, Any],\r\n    future: Any,\r\n    precheck: ApprovalPrecheckPayload,\r\n) -> ApprovalRequestEvent:\r\n    return {\r\n        \"type\": \"approval_request\",\r\n        \"id\": id,\r\n        \"tool\": tool,\r\n        \"args\": args,\r\n        \"future\": future,\r\n        \"precheck\": precheck,\r\n    }\r\n\r\n\r\ndef ev_choice_request(\r\n    *,\r\n    id: str,\r\n    tool: str,\r\n    prompt: str,\r\n    choices: list[str],\r\n    hint: str | None,\r\n    future: Any,\r\n) -> ChoiceRequestEvent:\r\n    return {\r\n        \"type\": \"choice_request\",\r\n        \"id\": id,\r\n        \"tool\": tool,\r\n        \"prompt\": prompt,\r\n        \"choices\": choices,\r\n        \"hint\": hint,\r\n        \"future\": future,\r\n    }\r\n\r\n\r\ndef ev_tool_call_debug_delta(delta: ToolCallDeltaPayload) -> ToolCallDebugDeltaEvent:\r\n    return {\"type\": \"tool_call_debug\", \"delta\": delta}\r\n\r\n\r\ndef ev_tool_call_debug_warning(warning: str) -> ToolCallDebugWarningEvent:\r\n    return {\"type\": \"tool_call_debug\", \"warning\": warning}\r\n\r\n\r\ndef ev_tool_call_debug_assembled(assembled: list[ToolRecord]) -> ToolCallDebugAssembledEvent:\r\n    return {\"type\": \"tool_call_debug\", \"assembled\": assembled}\r\n\r\n\r\ndef ev_tool_call_debug_stage(\r\n    *,\r\n    stage: Literal[\"assembled_final\", \"invalid_arguments\"],\r\n    tool: str,\r\n    id: str,\r\n    index: int,\r\n    args_summary: dict[str, Any],\r\n) -> ToolCallDebugStageEvent:\r\n    return {\r\n        \"type\": \"tool_call_debug\",\r\n        \"stage\": stage,\r\n        \"tool\": tool,\r\n        \"id\": id,\r\n        \"index\": index,\r\n        \"args_summary\": args_summary,\r\n    }\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/events.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_ljpx0gahctto_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/llm_transport.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 -->
现在我已经看了大部分核心代码。让我继续查看其他重要文件。
<!-- 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": 67, "prompt_tokens": 30496, "total_tokens": 30563, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 67}, "prompt_tokens_details": {"cached_tokens": 27828, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 27828}
```
<!-- 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_ljpx0gahctto_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 json\r\nimport os\r\nimport threading\r\nfrom typing import Any, AsyncGenerator, Dict, Optional\r\n\r\n_LITELLM_ACOMPLETION = None\r\n_LITELLM_PREWARM_STARTED = False\r\n_LITELLM_PREWARM_LOCK = threading.Lock()\r\n_LITELLM_PREWARM_DONE = threading.Event()\r\n_LITELLM_PREWARM_ERROR: str | None = None\r\n\r\n\r\ndef _obj_get(obj: Any, key: str, default: Any = None) -> Any:\r\n    if isinstance(obj, dict):\r\n        return obj.get(key, default)\r\n    return getattr(obj, key, default)\r\n\r\n\r\ndef _get_litellm_acompletion():\r\n    global _LITELLM_ACOMPLETION, _LITELLM_PREWARM_ERROR\r\n    if _LITELLM_ACOMPLETION is not None:\r\n        return _LITELLM_ACOMPLETION\r\n    os.environ.setdefault(\"LITELLM_LOCAL_MODEL_COST_MAP\", \"true\")\r\n    try:\r\n        import litellm as _litellm\r\n        from litellm.main import acompletion as _acompletion\r\n\r\n        _litellm.suppress_debug_info = True\r\n    except Exception as exc:\r\n        _LITELLM_PREWARM_ERROR = f\"{type(exc).__name__}: {exc}\"\r\n        raise RuntimeError(\r\n            \"litellm is required for model calls but is not available. Install with: uv pip install litellm\"\r\n        ) from exc\r\n    _LITELLM_ACOMPLETION = _acompletion\r\n    _LITELLM_PREWARM_ERROR = None\r\n    _LITELLM_PREWARM_DONE.set()\r\n    return _LITELLM_ACOMPLETION\r\n\r\n\r\ndef start_global_litellm_prewarm(*, blocking: bool = False, timeout: float | None = None) -> bool:\r\n    global _LITELLM_PREWARM_STARTED\r\n    if _LITELLM_ACOMPLETION is not None:\r\n        return True\r\n    with _LITELLM_PREWARM_LOCK:\r\n        if not _LITELLM_PREWARM_STARTED:\r\n            _LITELLM_PREWARM_STARTED = True\r\n\r\n            def _runner() -> None:\r\n                try:\r\n                    _get_litellm_acompletion()\r\n                except Exception:\r\n                    return\r\n                finally:\r\n                    _LITELLM_PREWARM_DONE.set()\r\n\r\n            t = threading.Thread(target=_runner, name=\"cyf-litellm-prewarm\", daemon=True)\r\n            t.start()\r\n    if blocking:\r\n        _LITELLM_PREWARM_DONE.wait(timeout=timeout)\r\n    return _LITELLM_ACOMPLETION is not None\r\n\r\n\r\ndef get_global_litellm_prewarm_error() -> str | None:\r\n    return _LITELLM_PREWARM_ERROR\r\n\r\n\r\nclass LiteLLMTransport:\r\n    def __init__(\r\n        self,\r\n        *,\r\n        model: str,\r\n        timeout: float = 60.0,\r\n        max_tokens: Optional[int] = None,\r\n        temperature: Optional[float] = None,\r\n        extra_body: Optional[dict[str, Any]] = None,\r\n        api_base: str = \"\",\r\n        api_key: Optional[str] = None,\r\n    ) -> None:\r\n        self.model = model\r\n        self.timeout = timeout\r\n        self.max_tokens = max_tokens\r\n        self.temperature = temperature\r\n        self.extra_body = extra_body\r\n        self.api_base = api_base\r\n        self.api_key = api_key\r\n\r\n    async def acreate(\r\n        self,\r\n        api_messages: list[dict[str, Any]],\r\n        *,\r\n        stream: bool = False,\r\n        tools: list | None = None,\r\n        tool_choice: str | dict | None = None,\r\n        tool_stream: bool = False,\r\n        debug: bool = False,\r\n        sse_debug: bool = False,\r\n        **extra: Any,\r\n    ) -> dict | AsyncGenerator[object, None]:\r\n        params: Dict[str, Any] = {\r\n            \"model\": self.model,\r\n            \"messages\": api_messages,\r\n            \"timeout\": self.timeout,\r\n            \"drop_params\": True,\r\n            **({\"tools\": tools} if tools is not None else {}),\r\n            **({\"tool_choice\": tool_choice} if tool_choice is not None else {}),\r\n            **extra,\r\n        }\r\n        if self.max_tokens is not None:\r\n            params[\"max_tokens\"] = int(self.max_tokens)\r\n        if self.temperature is not None:\r\n            params[\"temperature\"] = float(self.temperature)\r\n        if self.extra_body:\r\n            params.update(self.extra_body)\r\n        if self.api_base:\r\n            params[\"api_base\"] = self.api_base\r\n        if self.api_key:\r\n            params[\"api_key\"] = self.api_key\r\n\r\n        if debug:\r\n            import sys as _sys\r\n\r\n            print(\"[debug-litellm] acompletion params:\", file=_sys.stderr)\r\n            print(json.dumps(params, ensure_ascii=False, indent=2), file=_sys.stderr)\r\n\r\n        acompletion = _get_litellm_acompletion()\r\n\r\n        if not stream:\r\n            resp = await acompletion(stream=False, **params)\r\n            if hasattr(resp, \"model_dump\"):\r\n                return resp.model_dump(mode=\"json\")\r\n            if isinstance(resp, dict):\r\n                return resp\r\n            return {\"raw\": str(resp)}\r\n\r\n        async def _agen() -> AsyncGenerator[object, None]:\r\n            stream_resp = await acompletion(\r\n                stream=True,\r\n                stream_options={\"include_usage\": True},\r\n                **params,\r\n            )\r\n            usage_obj = None\r\n            resp_id = None\r\n            model_name_from_resp = None\r\n\r\n            async for chunk in stream_resp:\r\n                if sse_debug:\r\n                    try:\r\n                        import sys as _sys\r\n\r\n                        body = chunk.model_dump(mode=\"json\") if hasattr(chunk, \"model_dump\") else chunk\r\n                        print(\"[sse-debug] chunk:\", json.dumps(body, ensure_ascii=False), file=_sys.stderr)\r\n                    except Exception:\r\n                        pass\r\n\r\n                try:\r\n                    chunk_id = _obj_get(chunk, \"id\")\r\n                    chunk_model = _obj_get(chunk, \"model\")\r\n                    if chunk_id and resp_id is None:\r\n                        resp_id = chunk_id\r\n                    if chunk_model and model_name_from_resp is None:\r\n                        model_name_from_resp = chunk_model\r\n\r\n                    choices = _obj_get(chunk, \"choices\", []) or []\r\n                    for i, choice in enumerate(choices):\r\n                        finish_reason = _obj_get(choice, \"finish_reason\")\r\n                        delta = _obj_get(choice, \"delta\")\r\n                        if not delta:\r\n                            if finish_reason == \"tool_calls\" and tool_stream:\r\n                                yield {\"type\": \"tool_calls_done\"}\r\n                            continue\r\n\r\n                        raw_delta = delta.model_dump(exclude_none=True) if hasattr(delta, \"model_dump\") else delta\r\n\r\n                        tc_list = _obj_get(delta, \"tool_calls\")\r\n                        if tool_stream and tc_list:\r\n                            for j, tc in enumerate(tc_list):\r\n                                tc_index = _obj_get(tc, \"index\")\r\n                                ev = {\r\n                                    \"type\": \"tool_call_delta\",\r\n                                    \"output_index\": tc_index if isinstance(tc_index, int) else j,\r\n                                    \"model\": model_name_from_resp,\r\n                                }\r\n                                tc_id = _obj_get(tc, \"id\")\r\n                                if tc_id:\r\n                                    ev[\"tool_call_id\"] = tc_id\r\n                                func = _obj_get(tc, \"function\")\r\n                                if func is not None:\r\n                                    name = _obj_get(func, \"name\")\r\n                                    if name:\r\n                                        ev[\"name\"] = name\r\n                                    arguments = _obj_get(func, \"arguments\")\r\n                                    if arguments:\r\n                                        ev[\"arguments\"] = arguments\r\n                                yield ev\r\n                            if finish_reason == \"tool_calls\":\r\n                                yield {\"type\": \"tool_calls_done\"}\r\n                            continue\r\n\r\n                        if isinstance(raw_delta, dict):\r\n                            rc = raw_delta.get(\"reasoning_content\") or raw_delta.get(\"reasoning\")\r\n                            if rc:\r\n                                yield {\"type\": \"assistant_reasoning_delta\", \"text\": str(rc), \"model\": model_name_from_resp}\r\n\r\n                        content = _obj_get(delta, \"content\")\r\n                        if content:\r\n                            yield {\"type\": \"assistant_delta\", \"text\": str(content), \"model\": model_name_from_resp}\r\n\r\n                        if finish_reason == \"tool_calls\" and tool_stream:\r\n                            yield {\"type\": \"tool_calls_done\"}\r\n\r\n                    u = _obj_get(chunk, \"usage\")\r\n                    if u is not None:\r\n                        if hasattr(u, \"model_dump\"):\r\n                            usage_obj = u.model_dump(exclude_none=True)\r\n                        elif isinstance(u, dict):\r\n                            usage_obj = u\r\n                except Exception:\r\n                    continue\r\n\r\n            if usage_obj is not None or resp_id is not None or model_name_from_resp is not None:\r\n                yield {\"type\": \"assistant_done\", \"usage\": usage_obj, \"id\": resp_id, \"model\": model_name_from_resp}\r\n\r\n        return _agen()\r\n\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/llm_transport.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_q3q9wtxdk4tr_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/session_store.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 -->
让我继续查看其他文件。
<!-- 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": 57, "prompt_tokens": 32994, "total_tokens": 33051, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 57}, "prompt_tokens_details": {"cached_tokens": 30492, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 30492}
```
<!-- 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_q3q9wtxdk4tr_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\nimport datetime as _dt\r\nimport threading\r\nfrom pathlib import Path\r\nfrom typing import Any, Callable, Optional\r\n\r\nfrom chronml import Chron\r\n\r\nfrom .model import CompactRecord, Message\r\n\r\nMETA_DIRNAME = \".cyf\"\r\nSESSIONS_DIRNAME = \"sessions\"\r\n_SESSION_MARKER_PREFIX = \"md_as_config:ctx:\"\r\n\r\n\r\ndef _session_chron() -> Chron:\r\n    return Chron(marker_prefix=_SESSION_MARKER_PREFIX)\r\n\r\n\r\nclass SessionStore:\r\n    def __init__(self, file_backend: Path, session_id: str | None = None, work_dir: Path | None = None):\r\n        self.file_backend = file_backend\r\n        self.session_id = session_id\r\n        self.work_dir = work_dir\r\n        self._persist_ready: bool = self.file_backend.exists()\r\n        self._persist_lock = threading.Lock()\r\n\r\n    def restore(self) -> list[Message | CompactRecord]:\r\n        if not self.file_backend.exists() or self.file_backend.stat().st_size == 0:\r\n            return []\r\n        records = _session_chron().load(self.file_backend)\r\n        return list(records or [])\r\n\r\n    def append_item(self, item: Any, template_func: Callable[[Any], str]) -> None:\r\n        self.ensure_persisted()\r\n        item_md = _session_chron().dumps([item], template=\"\", template_func=template_func)\r\n        with open(self.file_backend, \"a\", encoding=\"utf-8\") as f:\r\n            f.write(item_md)\r\n            f.write(\"\\n\\n\")\r\n            f.flush()\r\n\r\n    def dump_history(\r\n        self,\r\n        history: list[Any],\r\n        target_path: Path,\r\n        template_func: Callable[[Any], str],\r\n        template: str = \"\",\r\n    ) -> None:\r\n        target_path.parent.mkdir(parents=True, exist_ok=True)\r\n        _session_chron().dump(history, target_path, template=template, template_func=template_func)\r\n\r\n    def export_md_as_config(self, history: list[Any], md_path: Path, template_func: Callable[[Any], str], template: str = \"\") -> None:\r\n        self.dump_history(history, md_path, template_func=template_func, template=template)\r\n\r\n    def import_md_as_config(self, md_path: Path) -> list[Any]:\r\n        if not md_path.exists() or md_path.stat().st_size == 0:\r\n            return []\r\n        records = _session_chron().load(md_path)\r\n        if not records:\r\n            return []\r\n        self.file_backend.parent.mkdir(parents=True, exist_ok=True)\r\n        self.file_backend.write_text(md_path.read_text(encoding=\"utf-8\"), encoding=\"utf-8\")\r\n        return list(records)\r\n\r\n    @staticmethod\r\n    def sessions_dir(work_dir: Path) -> Path:\r\n        return work_dir / META_DIRNAME / SESSIONS_DIRNAME\r\n\r\n    @staticmethod\r\n    def global_sessions_dir(_work_dir: Path) -> Path:\r\n        date_str = _dt.datetime.now().strftime(\"%Y%m%d\")\r\n        return Path.home() / \".cyf\" / \"global_sessions\" / date_str\r\n\r\n    @staticmethod\r\n    def ensure_sessions_dir(work_dir: Path) -> Path:\r\n        p = SessionStore.sessions_dir(work_dir)\r\n        p.mkdir(parents=True, exist_ok=True)\r\n        return p\r\n\r\n    @staticmethod\r\n    def ensure_global_sessions_dir(work_dir: Path) -> Path:\r\n        p = SessionStore.global_sessions_dir(work_dir)\r\n        p.mkdir(parents=True, exist_ok=True)\r\n        return p\r\n\r\n    @staticmethod\r\n    def history_candidates(sessions_dir: Path, session_id: str) -> list[Path]:\r\n        return [sessions_dir / f\"{session_id}.md\"]\r\n\r\n    @classmethod\r\n    def session_exists(cls, work_dir: Path, session_id: str) -> bool:\r\n        sessions_dir = cls.sessions_dir(work_dir)\r\n        for candidate in cls.history_candidates(sessions_dir, session_id):\r\n            if candidate.exists():\r\n                return True\r\n        return False\r\n\r\n    @classmethod\r\n    def create_for_workdir(\r\n        cls,\r\n        work_dir: Path,\r\n        session_id: str | None = None,\r\n        *,\r\n        defer_persist: bool = False,\r\n    ) -> \"SessionStore\":\r\n        work_dir = work_dir.absolute()\r\n        sessions_dir = cls.global_sessions_dir(work_dir) if defer_persist else cls.ensure_global_sessions_dir(work_dir)\r\n        sid = session_id\r\n        if not sid or sid == \"[AUTO]\":\r\n            now = _dt.datetime.now()\r\n            sid = now.strftime(\"%Y%m%d%H%M%S%f\")[:17]\r\n            while cls.session_exists(work_dir, sid):\r\n                sid = f\"{sid}-1\"\r\n        history_file = sessions_dir / f\"{sid}.md\"\r\n        store = cls(history_file, session_id=sid, work_dir=work_dir)\r\n        if not defer_persist:\r\n            store.ensure_persisted()\r\n        return store\r\n\r\n    def ensure_persisted(self) -> None:\r\n        if self._persist_ready:\r\n            return\r\n        with self._persist_lock:\r\n            if self._persist_ready:\r\n                return\r\n            work_dir = self.work_dir\r\n            sid = self.session_id or self.file_backend.stem\r\n            if work_dir is not None and sid:\r\n                self.file_backend.parent.mkdir(parents=True, exist_ok=True)\r\n                self.file_backend.touch(exist_ok=True)\r\n                try:\r\n                    local_dir = self.ensure_sessions_dir(work_dir)\r\n                    local_link = local_dir / f\"{sid}.md\"\r\n                    if not local_link.exists() and not local_link.is_symlink():\r\n                        local_link.symlink_to(self.file_backend.absolute())\r\n                except Exception:\r\n                    pass\r\n            else:\r\n                self.file_backend.parent.mkdir(parents=True, exist_ok=True)\r\n                self.file_backend.touch(exist_ok=True)\r\n            self._persist_ready = True\r\n\r\n    async def aensure_persisted(self) -> None:\r\n        await asyncio.to_thread(self.ensure_persisted)\r\n\r\n    @classmethod\r\n    def continue_in_workdir(cls, work_dir: Path) -> tuple[str, Path] | None:\r\n        sessions, _ = cls.list_sessions(work_dir)\r\n        if not sessions:\r\n            return None\r\n        sid, path, _ = sessions[0]\r\n        return sid, path\r\n\r\n    @classmethod\r\n    def resolve_session_path(cls, work_dir: Path, session_id: str) -> Path | None:\r\n        sessions_dir = cls.sessions_dir(work_dir)\r\n        for candidate in cls.history_candidates(sessions_dir, session_id):\r\n            if candidate.exists():\r\n                return candidate.resolve() if candidate.is_symlink() else candidate\r\n        return None\r\n\r\n    @classmethod\r\n    def list_sessions(cls, work_dir: Path) -> tuple[list[tuple[str, Path, float]], str | None]:\r\n        work_dir = work_dir.absolute()\r\n        sessions_dir = cls.sessions_dir(work_dir)\r\n        entries: dict[str, tuple[Path, float]] = {}\r\n        if sessions_dir.exists():\r\n            for p in sessions_dir.glob(\"*.md\"):\r\n                sid = p.stem\r\n                try:\r\n                    if p.is_symlink():\r\n                        target = p.resolve()\r\n                        ts = target.stat().st_mtime if target.exists() else p.stat().st_mtime\r\n                    else:\r\n                        ts = p.stat().st_mtime\r\n                except Exception:\r\n                    ts = 0.0\r\n                entries[sid] = (p, ts)\r\n        items = [(sid, path, ts) for sid, (path, ts) in entries.items()]\r\n        items.sort(key=lambda x: x[2], reverse=True)\r\n        return items, None\r\n\r\n    @classmethod\r\n    def list_global_sessions(cls, _work_dir: Path) -> tuple[list[tuple[str, Path, float]], str | None]:\r\n        global_sessions_root = Path.home() / \".cyf\" / \"global_sessions\"\r\n        entries: dict[str, tuple[Path, float]] = {}\r\n        if global_sessions_root.exists():\r\n            for date_dir in sorted(global_sessions_root.iterdir(), reverse=True):\r\n                if not date_dir.is_dir():\r\n                    continue\r\n                for p in date_dir.glob(\"*.md\"):\r\n                    sid = p.stem\r\n                    if sid in entries:\r\n                        continue\r\n                    try:\r\n                        ts = p.stat().st_mtime\r\n                    except Exception:\r\n                        ts = 0.0\r\n                    entries[sid] = (p, ts)\r\n        items = [(sid, path, ts) for sid, (path, ts) in entries.items()]\r\n        items.sort(key=lambda x: x[2], reverse=True)\r\n        return items, None\r\n\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/session_store.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_bba26mdkztdy_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/__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 -->
现在我已经阅读了所有主要的源代码文件。让我总结一下这个项目的代码结构和功能。
<!-- 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": 69, "prompt_tokens": 35361, "total_tokens": 35430, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 69}, "prompt_tokens_details": {"cached_tokens": 32990, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 32990}
```
<!-- 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_bba26mdkztdy_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 /home/draplater/Development/cyf_cli/oturn/src/oturn/__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_o4pwozpyx1ld_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/runtime_config.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/template_renderer.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 check a few more files to complete the picture:
<!-- 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": 91, "prompt_tokens": 35955, "total_tokens": 36046, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 91}, "prompt_tokens_details": {"cached_tokens": 35357, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 35357}
```
<!-- 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_o4pwozpyx1ld_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 Any, Optional\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnProviderConfig:\r\n    api_base: str = \"\"\r\n    api_key: Optional[str] = None\r\n    debug_request: bool = False\r\n    debug_sse: bool = False\r\n    debug_tools: bool = False\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnModelConfig:\r\n    max_output_tokens: Optional[int] = None\r\n    temperature: Optional[float] = None\r\n    extra_body: Optional[dict[str, Any]] = None\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnAgentConfig:\r\n    max_steps: int = 100\r\n    user_name: str = \"User\"\r\n    email: Optional[str] = None\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnConfig:\r\n    model: str\r\n    provider: OturnProviderConfig\r\n    model_config: OturnModelConfig\r\n    agent_config: OturnAgentConfig\r\n    debug_loop: bool = False\r\n===\nfrom __future__ import annotations\r\n\r\nfrom .model import CompactRecord, Message, ShellResult, UserContent\r\nfrom .tools.base import BaseToolRegistry\r\n\r\n\r\nclass ContextTemplateRenderer:\r\n    def __init__(self, tool_registry: BaseToolRegistry) -> None:\r\n        self._tool_registry = tool_registry\r\n\r\n    def set_tool_registry(self, tool_registry: BaseToolRegistry) -> None:\r\n        self._tool_registry = tool_registry\r\n\r\n    def select_template_for_item(self, item) -> str:\r\n        if isinstance(item, CompactRecord):\r\n            return \"**Compact Summary:** {.value}\\n\\n**User Intent:** {.user_intent}\\n\\n\"\r\n\r\n        if isinstance(item, Message):\r\n            if item.role == \"system\":\r\n                return \"\"\r\n\r\n            if item.role == \"tool\":\r\n                tool_name = item.name\r\n                if tool_name:\r\n                    tool_class = self._tool_registry.get_tool_class(tool_name)\r\n                    if tool_class:\r\n                        response_template = tool_class.get_response_template(item)\r\n                        if response_template:\r\n                            return response_template\r\n\r\n            if item.role == \"user\":\r\n                content = item.content\r\n                if isinstance(content, UserContent):\r\n                    template = \"# [{.role}\\n\"\r\n                    template += \"- **User:** {.content.user_name}\\n\"\r\n                    if content.email:\r\n                        template += \"- **Email:** {.content.email}\\n\"\r\n                    template += \"- **Date:** {.content.date}\\n]\\n\\n\"\r\n                    template += \"{.content.content:block}\\n\\n\"\r\n                    return template\r\n                if isinstance(content, ShellResult):\r\n                    template = \"# [{.role}\\n\"\r\n                    template += \"- **Command:** {.content.command}\\n\"\r\n                    template += \"- **Working Directory:** {.content.cwd}\\n\"\r\n                    template += \"- **Exit Code:** {.content.retcode}\\n\"\r\n                    template += \"- **Date:** {.content.date}\\n]\\n\\n\"\r\n                    if content.stdout:\r\n                        template += \"**STDOUT:**\\n{.content.stdout:block}\\n\\n\"\r\n                    if content.stderr:\r\n                        template += \"**STDERR:**\\n{.content.stderr:block}\\n\\n\"\r\n                    return template\r\n\r\n            if item.role == \"assistant\" and item.model_name:\r\n                template = \"# [{.role} - {.model_name}]\\n\\n\"\r\n            else:\r\n                template = \"# [{.role}]\\n\\n\"\r\n\r\n            if item.reasoning_content:\r\n                template += \"{.reasoning_content:block}\\n\\n\"\r\n\r\n            if item.content:\r\n                template += \"{.content:block}\\n\\n\"\r\n\r\n            tool_calls = item.tool_calls\r\n            if item.role == \"assistant\" and tool_calls:\r\n                tool_sections = []\r\n                for i, call in enumerate(tool_calls):\r\n                    tool_name = call.function.name if call.function else \"\"\r\n                    if tool_name:\r\n                        prefix = f\".tool_calls[{i}]\"\r\n                        tool_class = self._tool_registry.get_tool_class(tool_name)\r\n                        if tool_class:\r\n                            tool_template = tool_class.get_request_template(prefix)\r\n                            if tool_template:\r\n                                tool_sections.append(tool_template)\r\n                if tool_sections:\r\n                    sections_text = \"\\n\\n\".join(tool_sections)\r\n                    template += sections_text + \"\\n\\n\"\r\n\r\n            if item.usage:\r\n                template += \"Usage:\\n{.usage:code-block-json}\\n\\n\"\r\n\r\n            return template\r\n\r\n        return \"\"\r\n\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/runtime_config.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/template_renderer.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_q6xzhcp7gixu_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime_utils.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/mcp_adapter.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 -->
现在让我看看剩余的几个文件。
<!-- 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": 87, "prompt_tokens": 37302, "total_tokens": 37389, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 87}, "prompt_tokens_details": {"cached_tokens": 35357, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 35357}
```
<!-- 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_q6xzhcp7gixu_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 json\r\nfrom typing import Any, TypedDict\r\n\r\nJsonValue = str | int | float | bool | None | list[\"JsonValue\"] | dict[str, \"JsonValue\"]\r\nJsonObject = dict[str, JsonValue]\r\n\r\n\r\nclass ToolArgsSummary(TypedDict):\r\n    length: int\r\n    parse_ok: bool\r\n    head: str\r\n    tail: str\r\n\r\n\r\ndef parse_tool_arguments_with_status(arg_str: str) -> tuple[JsonObject, bool]:\r\n    text = (arg_str or \"\").strip()\r\n    if not text:\r\n        return {}, True\r\n    try:\r\n        parsed = json.loads(text)\r\n        if isinstance(parsed, dict):\r\n            return parsed, True\r\n        return {}, False\r\n    except Exception:\r\n        return {}, False\r\n\r\n\r\ndef parse_tool_arguments(arg_str: str) -> JsonObject:\r\n    parsed, _ = parse_tool_arguments_with_status(arg_str)\r\n    return parsed\r\n\r\n\r\ndef summarize_tool_arguments(arg_str: str, preview_chars: int = 120) -> ToolArgsSummary:\r\n    text = str(arg_str or \"\")\r\n    _, parse_ok = parse_tool_arguments_with_status(text)\r\n    head = text[:preview_chars]\r\n    tail = text[-preview_chars:] if len(text) > preview_chars else \"\"\r\n    return {\"length\": len(text), \"parse_ok\": parse_ok, \"head\": head, \"tail\": tail}\r\n\r\n===\nfrom __future__ import annotations\r\n\r\nimport os\r\nfrom typing import Any, Optional\r\n\r\nfrom .context import Context\r\nfrom .tool_runtime_utils import JsonObject\r\nfrom .tools.base import ToolResult\r\n\r\n\r\nclass MCPAdapter:\r\n    \"\"\"Experimental MCP integration adapter.\r\n\r\n    Kept out of Oturn core loop so it can be disabled/removed independently.\r\n    \"\"\"\r\n\r\n    def __init__(self, enabled: Optional[bool] = None) -> None:\r\n        self.enabled = (\r\n            bool(enabled)\r\n            if enabled is not None\r\n            else os.getenv(\"ENABLE_MCP\", \"\").strip().lower() in {\"1\", \"true\", \"yes\", \"on\"}\r\n        )\r\n\r\n    def merge_tool_schemas(self, ctx: Context, local_schemas: list[dict[str, Any]]) -> list[dict[str, Any]]:\r\n        if not self.enabled:\r\n            return local_schemas\r\n        remote_schemas: list[dict[str, Any]] = []\r\n        availability = ctx.get_mcp_tool_availability()\r\n        for tool in ctx.get_mcp_tool_schemas().values():\r\n            name = tool.get(\"name\")\r\n            if not name:\r\n                continue\r\n            if not availability.get(name):\r\n                continue\r\n            remote_schemas.append(\r\n                {\r\n                    \"type\": \"function\",\r\n                    \"function\": {\r\n                        \"name\": name,\r\n                        \"description\": tool.get(\"description\") or \"\",\r\n                        \"parameters\": tool.get(\"inputSchema\") or {\"type\": \"object\"},\r\n                    },\r\n                }\r\n            )\r\n        if remote_schemas:\r\n            return local_schemas + remote_schemas\r\n        return local_schemas\r\n\r\n    async def execute_remote_tool(self, ctx: Context, name: str, args: JsonObject) -> ToolResult:\r\n        if not self.enabled:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=\"MCP disabled (experimental; set ENABLE_MCP=1 to enable)\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        client = ctx.get_mcp_client()\r\n        if client is None:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=\"MCP client is not connected\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        try:\r\n            import mcp.types as types\r\n            from mcp.shared.exceptions import McpError\r\n        except Exception as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=f\"MCP runtime unavailable: {exc}\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        params = types.CallToolRequestParams(name=name, arguments=args or None)\r\n        request = types.CallToolRequest(params=params)\r\n        try:\r\n            result = await client.send_request(request, types.CallToolResult)\r\n        except McpError as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=f\"MCP call failed: {exc}\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n        except Exception as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=str(exc),\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        content_blocks = [block.model_dump(mode=\"json\") for block in (result.content or [])]\r\n        payload = {\r\n            \"tool\": name,\r\n            \"content_blocks\": content_blocks,\r\n            \"structuredContent\": result.structuredContent,\r\n            \"isError\": result.isError,\r\n        }\r\n        status = \"failure\" if result.isError else \"success\"\r\n        return ToolResult(status=status, content=payload, metadata={\"tool\": name, \"remote\": True})\r\n\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime_utils.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/mcp_adapter.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_fydbrfx1xl1x_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/__init__.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/precheck.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 -->
现在让我看看 tools 目录的其他文件。
<!-- 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": 90, "prompt_tokens": 38827, "total_tokens": 38917, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 90}, "prompt_tokens_details": {"cached_tokens": 37298, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 37298}
```
<!-- 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_fydbrfx1xl1x_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 tools package.\r\n\r\nNo global registry is created at import time.\r\nTools should be supplied explicitly to ``Oturn(..., tools=[...])``.\r\n\"\"\"\r\n\r\nfrom .base import BaseTool, BaseToolRegistry, ToolResult, ToolType\r\nfrom .examples import WeatherTool, HostConfigTool, EXAMPLE_TOOLS\r\n\r\n__all__ = [\r\n    \"BaseTool\",\r\n    \"BaseToolRegistry\",\r\n    \"ToolResult\",\r\n    \"ToolType\",\r\n    \"WeatherTool\",\r\n    \"HostConfigTool\",\r\n    \"EXAMPLE_TOOLS\",\r\n]\r\n===\nfrom dataclasses import dataclass\r\nfrom typing import Any, Dict, List, Optional\r\n\r\n\r\nSTATUS_ALLOW = \"allow\"\r\nSTATUS_DENY = \"deny\"\r\nSTATUS_NEEDS_APPROVAL = \"needs_approval\"\r\n\r\n\r\n@dataclass\r\nclass ToolPrecheckResult:\r\n    status: str\r\n    message: Optional[str] = None\r\n    reasons: Optional[List[str]] = None\r\n    metadata: Optional[Dict[str, Any]] = None\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/__init__.py && echo \"===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/precheck.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_c88ufums93cx_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/examples.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 -->
现在让我整理一下代码结构，做一个完整的梳理。
<!-- 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": 61, "prompt_tokens": 39289, "total_tokens": 39350, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 61}, "prompt_tokens_details": {"cached_tokens": 38823, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 38823}
```
<!-- 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_c88ufums93cx_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 json\r\nimport os\r\nimport platform\r\nimport socket\r\nimport urllib.parse\r\nimport urllib.request\r\nfrom typing import Any, Dict, Optional\r\n\r\nfrom .base import BaseTool, ToolType\r\n\r\n\r\nclass WeatherTool(BaseTool):\r\n    \"\"\"Example tool: fetch simple weather info from wttr.in.\"\"\"\r\n\r\n    name = \"get_weather\"\r\n    description = \"Get current weather for a city/location.\"\r\n\r\n    @property\r\n    def tool_type(self) -> ToolType:\r\n        return ToolType.SYNC\r\n\r\n    @property\r\n    def schema(self) -> Dict[str, Any]:\r\n        return self.get_schema()\r\n\r\n    @classmethod\r\n    def get_schema(cls) -> Dict[str, Any]:\r\n        return {\r\n            \"type\": \"function\",\r\n            \"function\": {\r\n                \"name\": cls.name,\r\n                \"description\": cls.description,\r\n                \"parameters\": {\r\n                    \"type\": \"object\",\r\n                    \"properties\": {\r\n                        \"location\": {\"type\": \"string\", \"description\": \"City or location, e.g. San Francisco\"},\r\n                        \"lang\": {\"type\": \"string\", \"description\": \"Language code for weather text, default en\"},\r\n                    },\r\n                    \"required\": [\"location\"],\r\n                },\r\n            },\r\n        }\r\n\r\n    @classmethod\r\n    def validate_arguments(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        loc = args.get(\"location\")\r\n        if not isinstance(loc, str) or not loc.strip():\r\n            return \"location must be a non-empty string\"\r\n        lang = args.get(\"lang\")\r\n        if lang is not None and not isinstance(lang, str):\r\n            return \"lang must be a string\"\r\n        return None\r\n\r\n    @classmethod\r\n    def get_preview(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        loc = str(args.get(\"location\") or \"\").strip()\r\n        if not loc:\r\n            return None\r\n        return f\"Fetch weather for: {loc}\"\r\n\r\n    @staticmethod\r\n    def execute(**kwargs) -> Any:\r\n        location = str(kwargs.get(\"location\") or \"\").strip()\r\n        lang = str(kwargs.get(\"lang\") or \"en\").strip() or \"en\"\r\n\r\n        q = urllib.parse.quote(location)\r\n        url = f\"https://wttr.in/{q}?format=j1&lang={urllib.parse.quote(lang)}\"\r\n        req = urllib.request.Request(url, headers={\"User-Agent\": \"oturn-weather/0.1\"})\r\n        with urllib.request.urlopen(req, timeout=10) as resp:\r\n            raw = resp.read().decode(\"utf-8\", errors=\"replace\")\r\n\r\n        data = json.loads(raw)\r\n        cur = (data.get(\"current_condition\") or [{}])[0]\r\n        weather_desc = \"\"\r\n        if isinstance(cur.get(\"weatherDesc\"), list) and cur[\"weatherDesc\"]:\r\n            weather_desc = str(cur[\"weatherDesc\"][0].get(\"value\") or \"\")\r\n\r\n        return {\r\n            \"location\": location,\r\n            \"temperature_c\": cur.get(\"temp_C\"),\r\n            \"feels_like_c\": cur.get(\"FeelsLikeC\"),\r\n            \"humidity\": cur.get(\"humidity\"),\r\n            \"wind_kmph\": cur.get(\"windspeedKmph\"),\r\n            \"description\": weather_desc,\r\n            \"source\": \"wttr.in\",\r\n        }\r\n\r\n\r\nclass HostConfigTool(BaseTool):\r\n    \"\"\"Example tool: inspect local host/runtime configuration.\"\"\"\r\n\r\n    name = \"get_host_config\"\r\n    description = \"Return local host/runtime configuration details.\"\r\n\r\n    @property\r\n    def tool_type(self) -> ToolType:\r\n        return ToolType.SYNC\r\n\r\n    @property\r\n    def schema(self) -> Dict[str, Any]:\r\n        return self.get_schema()\r\n\r\n    @classmethod\r\n    def get_schema(cls) -> Dict[str, Any]:\r\n        return {\r\n            \"type\": \"function\",\r\n            \"function\": {\r\n                \"name\": cls.name,\r\n                \"description\": cls.description,\r\n                \"parameters\": {\r\n                    \"type\": \"object\",\r\n                    \"properties\": {\r\n                        \"include_env\": {\r\n                            \"type\": \"boolean\",\r\n                            \"description\": \"Whether to include selected env vars (HOME, SHELL, USER).\",\r\n                        }\r\n                    },\r\n                    \"required\": [],\r\n                },\r\n            },\r\n        }\r\n\r\n    @classmethod\r\n    def validate_arguments(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        v = args.get(\"include_env\")\r\n        if v is not None and not isinstance(v, bool):\r\n            return \"include_env must be a boolean\"\r\n        return None\r\n\r\n    @classmethod\r\n    def get_preview(cls, args: Dict[str, Any]) -> Optional[str]:\r\n        include_env = bool(args.get(\"include_env\", False))\r\n        return f\"Read host config (include_env={include_env})\"\r\n\r\n    @staticmethod\r\n    def execute(**kwargs) -> Any:\r\n        include_env = bool(kwargs.get(\"include_env\", False))\r\n\r\n        try:\r\n            loadavg = os.getloadavg()\r\n        except Exception:\r\n            loadavg = None\r\n\r\n        info: Dict[str, Any] = {\r\n            \"hostname\": socket.gethostname(),\r\n            \"fqdn\": socket.getfqdn(),\r\n            \"platform\": platform.platform(),\r\n            \"system\": platform.system(),\r\n            \"release\": platform.release(),\r\n            \"machine\": platform.machine(),\r\n            \"processor\": platform.processor(),\r\n            \"python_version\": platform.python_version(),\r\n            \"cpu_count_logical\": os.cpu_count(),\r\n            \"loadavg\": loadavg,\r\n        }\r\n\r\n        # Best-effort physical core count on Linux\r\n        physical_cores = None\r\n        try:\r\n            if platform.system().lower() == \"linux\" and os.path.exists(\"/proc/cpuinfo\"):\r\n                phys_core_pairs = set()\r\n                physical_id = None\r\n                core_id = None\r\n                with open(\"/proc/cpuinfo\", \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\r\n                    for line in f:\r\n                        line = line.strip()\r\n                        if line.startswith(\"physical id\"):\r\n                            physical_id = line.split(\":\", 1)[1].strip()\r\n                        elif line.startswith(\"core id\"):\r\n                            core_id = line.split(\":\", 1)[1].strip()\r\n                        elif not line:\r\n                            if physical_id is not None and core_id is not None:\r\n                                phys_core_pairs.add((physical_id, core_id))\r\n                            physical_id = None\r\n                            core_id = None\r\n                if phys_core_pairs:\r\n                    physical_cores = len(phys_core_pairs)\r\n        except Exception:\r\n            physical_cores = None\r\n\r\n        info[\"cpu_count_physical\"] = physical_cores\r\n\r\n        if include_env:\r\n            info[\"env\"] = {\r\n                \"HOME\": os.environ.get(\"HOME\"),\r\n                \"SHELL\": os.environ.get(\"SHELL\"),\r\n                \"USER\": os.environ.get(\"USER\"),\r\n            }\r\n\r\n        return info\r\n\r\n\r\nEXAMPLE_TOOLS = [WeatherTool, HostConfigTool]\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/examples.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 -->
现在我已经阅读了所有代码文件，可以对整个 oturn 项目进行一个全面的梳理和总结。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
## 代码梳理：OTURN 项目

### 项目概述

**OTURN** (Orchestrated Task Unified Reactive Nucleus) 是一个基于 Python 的异步 Agent 框架，核心设计理念：

- **Reducer 驱动状态转换**
- **显式转换事件 + 异步发布订阅**
- **确定性 step/run 控制**
- **运行时工具注入**（无全局注册表）

---

### 架构分层

```
┌─────────────────────────────────────────────────────────┐
│                    Oturn (主入口)                        │
│        agent_loop + LLM transport + tool runtime         │
├─────────────────────────────────────────────────────────┤
│  Context (会话上下文)  │  SessionStore (持久化)         │
│  - history            │  - md_as_config 格式            │
│  - MCP 集成           │  - 增量追加写入                 │
├─────────────────────────────────────────────────────────┤
│  OTurnNucleus (核心状态机)                              │
│  - Reducer 协议        │  AsyncPubSub                   │
├─────────────────────────────────────────────────────────┤
│  Tools (工具层)                                        │
│  - BaseTool (抽象基类) │ BaseToolRegistry              │
│  - ToolRuntime         │ - 执行/审批/验证流程            │
├─────────────────────────────────────────────────────────┤
│  Model (数据模型)                                       │
│  - Message, UserContent, ShellResult, ToolMessageContent│
│  - TurnStreamState (流式状态)                           │
└─────────────────────────────────────────────────────────┘
```

---

### 核心模块说明

| 文件 | 职责 |
|------|------|
| `nucleus.py` | **核心状态机**：Reducer 协议 + `Transition` 数据结构 + `AsyncPubSub` 发布订阅 |
| `oturn.py` | **主入口**：`Oturn` 类封装 agent_loop，调用 LiteLLM，处理流式响应，管理工具调用循环 |
| `context.py` | **会话上下文**：`Context` 类管理 history，支持 MCP 扩展，模板渲染 |
| `session_store.py` | **持久化**：基于 `chronml` 的 md_as_config 格式存储，支持会话 fork/continue |
| `model.py` | **数据模型**：`Message`, `UserContent`, `ShellResult`, `ToolMessageContent`, `CompactRecord` |
| `agent_stream_state.py` | **流式状态管理**：处理 LLM 流式输出的状态累积（文本/推理/工具调用） |
| `llm_transport.py` | **LLM 传输层**：基于 litellm 的异步调用，流式 SSE 解析 |
| `tool_runtime.py` | **工具运行时**：工具调用编排、执行、审批流程 |
| `tools/base.py` | **工具基类**：`BaseTool` 抽象 + `BaseToolRegistry` 注册表 |
| `events.py` | **事件定义**：各种 TypedDict 事件类型 + 工厂函数 |
| `runtime_config.py` | **配置模型**：`OturnConfig`, `OturnProviderConfig`, `OturnModelConfig`, `OturnAgentConfig` |
| `template_renderer.py` | **模板渲染**：为不同消息类型选择合适的模板 |
| `mcp_adapter.py` | **MCP 实验性适配器**：远程工具调用 |
| `tool_runtime_utils.py` | **工具参数解析**：JSON 解析 + 摘要生成 |

---

### 数据流

```
用户输入 → UserContent → Context.append_message()
                    ↓
           LiteLLM.acreate() 流式调用
                    ↓
           TurnStreamState 状态累积
           - assistant_delta (文本)
           - assistant_reasoning_delta (推理)
           - tool_call_delta (工具调用片段)
           - tool_calls_done
                    ↓
           有工具调用 → ToolRuntime.process_tool_records()
                        ↓
                   工具执行 → 工具结果写入 Context
                        ↓
                   继续下一轮 LLM 调用
                    ↓
           无工具调用 → assistant_final → Context
```

---

### 关键设计点

1. **无全局工具注册表**：工具通过 `Oturn(..., tools=[...])` 显式注入
2. **流式状态累积**：`TurnStreamState` 边接收边累积，支持增量输出
3. **工具审批流程**：支持 precheck → approval → execute 三阶段
4. **会话持久化**：md_as_config 格式（Markdown + JSON 元数据），支持会话分支/继续
5. **MCP 隔离**：MCP 作为实验性适配器独立于核心循环

---

### 入口点 (`__init__.py` 导出)

```python
from oturn import (
    Oturn,              # 主 Agent 类
    OTurnNucleus,       # 底层状态机
    Context,            # 会话上下文
    Message,            # 消息模型
    BaseTool,           # 工具基类
    # ... 配置类
)
```
<!-- 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": 1046, "prompt_tokens": 41320, "total_tokens": 42366, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 1046}, "prompt_tokens_details": {"cached_tokens": 38823, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 38823}
```
<!-- md_as_config:ctx:code-block-end -->

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



