<!-- 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": 1771652725.575497, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

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

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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_xe3uk6whurvw_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "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 -->
用户要求分析代码。我需要先了解这个项目的结构，然后分析代码。让我先看看当前工作目录的文件结构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 73, "prompt_tokens": 1455, "total_tokens": 1528, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 73}, "prompt_tokens_details": {"cached_tokens": 1438, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1438}
```
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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_xe3uk6whurvw_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 140\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  7131 Feb 21 13:42 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 29120 Feb 21 13:43 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:44 __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  9643 Feb 21 13:42 tool_runtime.py\r\n-rw-r--r-- 1 draplater draplater  1142 Feb 21 12:10 tool_runtime_utils.py\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 tools\r\n", "stderr": "", "actual_command": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn"}, "error": null, "metadata": null, "info": null}
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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_7rftq6v60yzu_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 -->
Let me read the main files to understand the codebase structure and purpose.
<!-- 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": 2171, "total_tokens": 2234, "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}
```
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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_7rftq6v60yzu_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_shpwm1hzu0b2_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 -->
Now I have a good overview of the project. Let me read the main files to understand the code better. Let me start with the core files: nucleus.py, model.py, and then the main oturn.py file.
<!-- 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": 95, "prompt_tokens": 3341, "total_tokens": 3436, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 95}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_shpwm1hzu0b2_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 -->

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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_j67m4qcpx8k3_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 -->
让我继续查看核心模块，理解整个代码结构。让我看oturn.py和model.py
<!-- 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": 68, "prompt_tokens": 4345, "total_tokens": 4413, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 68}, "prompt_tokens_details": {"cached_tokens": 3337, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3337}
```
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<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_j67m4qcpx8k3_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_mow2111d7txc_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 let me read the main oturn.py file and other supporting files to get a complete 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": 71, "prompt_tokens": 8500, "total_tokens": 8571, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 71}, "prompt_tokens_details": {"cached_tokens": 1438, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1438}
```
<!-- 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_mow2111d7txc_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    ToolCallDeltaEvent,\r\n    ev_assistant_delta,\r\n    ev_assistant_reasoning_delta,\r\n    ev_error,\r\n    ev_assistant_final,\r\n    ev_message_sent,\r\n    ev_run_aborted,\r\n    ev_debug_latency,\r\n    ev_tool_call_delta,\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    rai\n\n(...9 KB collapsed...)\n\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            self._tool_call_deltas[payload[\"delta_id\"]] = payload\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                self._tool_call_deltas.clear()\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_ql3vgr2tbah2_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "wc -l /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 -->
The output was truncated. Let me continue reading the rest of the file.
<!-- 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": 68, "prompt_tokens": 13616, "total_tokens": 13684, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 68}, "prompt_tokens_details": {"cached_tokens": 8496, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 8496}
```
<!-- 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_ql3vgr2tbah2_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": "743 /home/draplater/Development/cyf_cli/oturn/src/oturn/oturn.py\r\n", "stderr": "", "actual_command": "wc -l /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_8wp6i4kqs275_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "tail -200 /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 -->
Let me get the rest of oturn.py file.
<!-- 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": 64, "prompt_tokens": 13776, "total_tokens": 13840, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 64}, "prompt_tokens_details": {"cached_tokens": 13612, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 13612}
```
<!-- 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_8wp6i4kqs275_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": "            )\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            self._tool_call_deltas[payload[\"delta_id\"]] = payload\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                self._tool_call_deltas.clear()\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": "tail -200 /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={"tool_call_id": null, "tool_calls": [{"id": "call_function_tkynuspx9nl6_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 -->
让我继续阅读剩余的文件来完成对整个代码库的理解。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
现在让我阅读其他几个关键文件来完成对整个代码库的分析：
<!-- 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": 75, "prompt_tokens": 16112, "total_tokens": 16187, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 75}, "prompt_tokens_details": {"cached_tokens": 13612, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 13612}
```
<!-- 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_tkynuspx9nl6_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_9l4ssk5omjlx_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 a few more files to complete the analysis:
1. tool_runtime.py - handles tool execution
2. agent_stream_state.py - handles streaming state
3. events.py - defines events
<!-- 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": 92, "prompt_tokens": 19521, "total_tokens": 19613, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 92}, "prompt_tokens_details": {"cached_tokens": 16109, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 16109}
```
<!-- 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_9l4ssk5omjlx_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_delta,\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        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        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_delta(tool=name, args=arg_str, delta_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        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_wepszosb43s8_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我已经收集了足够的信息来对代码进行全面分析。让我总结一下这个项目的结构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 70, "prompt_tokens": 22035, "total_tokens": 22105, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 70}, "prompt_tokens_details": {"cached_tokens": 19517, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 19517}
```
<!-- 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_wepszosb43s8_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

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

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"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 -->
用户让我分析代码。我已经看了大部分核心文件，现在让我总结一下这个代码库的结构和功能。

从README和代码来看，这是一个名为OTURN（Orchestrated Task Unified Reactive Nucleus）的异步agent框架。

让我提供一个详细的代码分析。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
根据我对代码的分析，以下是 OTURN 项目的详细解析：

---

## OTURN 代码分析

### 1. 项目概述

**OTURN** = **Orchestrated Task Unified Reactive Nucleus**

一个基于 Python asyncio 的轻量级 Agent 框架，核心特性：
- Reducer 驱动的状态转换
- 显式转换事件
- 多订阅者异步发布/订阅
- 确定性 step/run 控制
- 运行时工具注入（无全局注册表）

---

### 2. 核心架构

#### 2.1 核心类型 (`nucleus.py`)

```
Transition[StateT, EventT]  →  Reducer 输出 (state, events, terminal)
AsyncPubSub[EventT]        →  异步发布/订阅
OTurnNucleus[StateT, SignalT, EventT]  →  状态机内核
```

#### 2.2 数据模型 (`model.py`)

| 类型 | 用途 |
|------|------|
| `UserContent` | 用户输入（含日期、邮箱、内容、工具通知） |
| `ShellResult` | Shell 执行结果（命令、目录、输出、返回码） |
| `ToolMessageContent` | 工具调用返回 |
| `Message` | 对话消息（支持 role/content/tool_calls/usage） |
| `CompactRecord` | 历史压缩标记 |

#### 2.3 上下文管理 (`context.py`)

- 基于 `SessionStore` 的双栈存储（JSONL + Markdown）
- 支持会话 fork、truncate、resume
- MCP (Model Context Protocol) 工具状态管理

---

### 3. Agent 循环 (`oturn.py`)

核心流程在 `run_queues()` 方法中：

```
while True:
    1. 等待用户输入 (_wait_next_input)
    2. 收集工具 hook 内容 (user_input_hook)
    3. 构建 UserContent 并追加到历史
    4. max_steps 循环:
       - 调用 LLM 流式 API (_litellm_acreate)
       - 处理流事件 (ingest_stream_event)
       - 状态转换 (reduce_stream_signal)
       - 执行工具调用 (tool_runtime.process_tool_records)
       - 如需新输入则 break
```

---

### 4. 工具系统

#### 4.1 工具运行时 (`tool_runtime.py`)

```
process_tool_records():
  1. 构建 ToolCallInfo 对象
  2. 发布 tool_call_request 事件
  3. 遍历每个工具:
     - 解析参数
     - 检查未知工具 → 尝试 MCP
     - 执行本地工具 (tool_class.run)
     - 处理拒绝/失败
```

#### 4.2 工具基类 (`tools/base.py`)

提供工具的抽象基类，包含：
- `run()` - 异步执行入口
- `get_preview()` - 参数预览
- `user_input_hook()` - 输入前钩子
- `on_denial()` - 拒绝回调

---

### 5. 流式处理 (`agent_stream_state.py`)

- `TurnStreamState` - 维护单轮对话的流式状态
- `ingest_stream_event()` - 解析 SSE 事件
- `reduce_stream_signal()` - 状态转移
- `build_tool_records()` - 组装工具调用

---

### 6. 事件系统 (`events.py`)

统一的事件类型：
- `ev_assistant_delta` - 流式输出片段
- `ev_assistant_reasoning_delta` - 推理过程
- `ev_tool_call_delta` - 工具调用增量
- `ev_error` - 错误事件
- `ev_tool_output` / `ev_tool_exit` - 工具输出
- 调试延迟事件 (`ev_debug_latency`)

---

### 7. 关键设计决策

| 设计点 | 实现 |
|--------|------|
| 无全局注册 | 工具通过 `tools=[]` 参数显式注入 |
| 状态压缩 | `CompactRecord` 标记压缩点 |
| 时间处理 | 入站支持 `{ts, tz}` / timestamp / ISO8601；出站统一存 `ts + tz` |
| MCP 集成 | 动态工具 schema + 可用性检查 |
| 持久化 | 增量写入 SessionStore，支持 fork/truncate |

---

### 8. 文件依赖图

```
oturn.py (主入口)
├── nucleus.py (OTurnNucleus, AsyncPubSub)
├── context.py (Context)
├── model.py (Message, UserContent, ShellResult...)
├── agent_stream_state.py (流式状态处理)
├── tool_runtime.py (工具执行)
├── tools/base.py (BaseTool)
├── llm_transport.py (LiteLLM 封装)
├── mcp_adapter.py (MCP 协议)
├── session_store.py (会话存储)
├── events.py (事件定义)
└── runtime_config.py (配置模型)
```

---

如需深入了解某个具体模块，请告知。
<!-- 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": 1093, "prompt_tokens": 22370, "total_tokens": 23463, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 1093}, "prompt_tokens_details": {"cached_tokens": 22031, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 22031}
```
<!-- md_as_config:ctx:code-block-end -->

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



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

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> user <!-- md_as_config:ctx:value-end -->
- **User:** <!-- md_as_config:ctx:value-begin json-path=$.content.user_name --> User <!-- md_as_config:ctx:value-end -->
- **Date:** <!-- md_as_config:ctx:value-begin type=json json-path=$.content.date --> {"ts": 1771652872.099228, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
分析代码质量，你觉得是谁写的？
<!-- 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_ps83uknt9bcs_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -100 /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 -->
用户让我分析代码质量，并猜测是谁写的。我需要更仔细地审视代码的风格、模式、质量等方面。

让我从几个维度来分析：
1. 代码风格和技术选型
2. 命名习惯
3. 注释风格
4. 错误处理模式
5. 整体设计水平
<!-- 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": 116, "prompt_tokens": 23450, "total_tokens": 23566, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 116}, "prompt_tokens_details": {"cached_tokens": 22031, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 22031}
```
<!-- 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_ps83uknt9bcs_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", "stderr": "", "actual_command": "head -100 /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_ojl00g32g441_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"TODO\\|FIXME\\|BUG\\|HACK\\|NOTE\\|@author\\|@date\" /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py 2>/dev/null | head -30"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
让我继续阅读更多代码，特别是注释、docstring 等，以判断代码风格和作者特征。
<!-- 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": 98, "prompt_tokens": 24281, "total_tokens": 24379, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 98}, "prompt_tokens_details": {"cached_tokens": 23446, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 23446}
```
<!-- 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_ojl00g32g441_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": "", "stderr": "grep: /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py: No such file or directory\n", "actual_command": "grep -n --exclude-dir=.cyf \"TODO\\|FIXME\\|BUG\\|HACK\\|NOTE\\|@author\\|@date\" /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py 2>/dev/null | head -30"}, "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_r6rg1zr850lp_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cd /home/draplater/Development/cyf_cli/oturn/src/oturn && grep -rn \"TODO\\|FIXME\\|BUG\\|HACK\" --include=\"*.py\" | head -20"}}}], "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 -->
我需要用不同的路径格式来搜索。
<!-- 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": 81, "prompt_tokens": 24509, "total_tokens": 24590, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 81}, "prompt_tokens_details": {"cached_tokens": 24277, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 24277}
```
<!-- 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_r6rg1zr850lp_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": "", "stderr": "grep: *.py: No such file or directory\n", "actual_command": "cd /home/draplater/Development/cyf_cli/oturn/src/oturn && grep -rn \"TODO\\|FIXME\\|BUG\\|HACK\" --include=\"*.py\" | head -20"}, "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_jdfppf78qqmx_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name \"*.py\" -exec grep -l \"TODO\\|FIXME\\|BUG\" {} \\;"}}}], "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 try a different approach.
<!-- 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": 75, "prompt_tokens": 24696, "total_tokens": 24771, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 75}, "prompt_tokens_details": {"cached_tokens": 24505, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 24505}
```
<!-- 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_jdfppf78qqmx_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": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name \"*.py\" -exec grep -l \"TODO\\|FIXME\\|BUG\" {} \\;"}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name \"*.py\" -exec grep -l \"TODO\\|FIXME\\|BUG\" {} \\;"}, "info": {}}
<!-- 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_hdg20nxns2dk_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name '*.py' -exec grep -l 'TODO\\|FIXME\\|BUG' {} \\;"}}}], "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 try with proper quoting:
<!-- 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": 75, "prompt_tokens": 25125, "total_tokens": 25200, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 75}, "prompt_tokens_details": {"cached_tokens": 24692, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 24692}
```
<!-- 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_hdg20nxns2dk_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": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name '*.py' -exec grep -l 'TODO\\|FIXME\\|BUG' {} \\;"}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -name '*.py' -exec grep -l 'TODO\\|FIXME\\|BUG' {} \\;"}, "info": {}}
<!-- 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_6pt7yjdvoi9p_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py | xargs grep -l 'TODO\\|FIXME\\|BUG'"}}}], "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 try a different approach:
<!-- 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": 25554, "total_tokens": 25623, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 69}, "prompt_tokens_details": {"cached_tokens": 25121, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 25121}
```
<!-- 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_6pt7yjdvoi9p_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": {"exit_code": 123, "stdout": "", "stderr": "ls: cannot access '/home/draplater/Development/cyf_cli/oturn/src/oturn/*.py': No such file or directory\n", "actual_command": "ls /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py | xargs grep -l 'TODO\\|FIXME\\|BUG'"}, "error": "Command exited with code 123", "metadata": {"exit_code": 123, "stdout": "", "stderr": "ls: cannot access '/home/draplater/Development/cyf_cli/oturn/src/oturn/*.py': No such file or directory\n", "actual_command": "ls /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py | xargs grep -l 'TODO\\|FIXME\\|BUG'"}, "info": {}}
<!-- 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_usqosyxrpn2m_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls /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 -->
奇怪，ls命令找不到文件，但之前用ls -la是可以的。让我重新检查一下。
<!-- 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": 68, "prompt_tokens": 25835, "total_tokens": 25903, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 68}, "prompt_tokens_details": {"cached_tokens": 25550, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 25550}
```
<!-- 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_usqosyxrpn2m_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": "agent_stream_state.py  model.py\t\t  session_store.py\r\ncontext.py\t       nucleus.py\t  template_renderer.py\r\nevents.py\t       oturn.py\t\t  tool_runtime.py\r\n__init__.py\t       __pycache__\t  tool_runtime_utils.py\r\nllm_transport.py       README.md\t  tools\r\nmcp_adapter.py\t       runtime_config.py\r\n", "stderr": "", "actual_command": "ls /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_ecrdhnj4besn_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "for f in /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py; do grep -l 'TODO\\|FIXME\\|BUG' \"$f\" 2>/dev/null; done"}}}], "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 -->
奇怪，ls 能看到文件，但 grep 不行。让我用 for 循环试试。
<!-- 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": 92, "prompt_tokens": 26047, "total_tokens": 26139, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 92}, "prompt_tokens_details": {"cached_tokens": 25831, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 25831}
```
<!-- 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_ecrdhnj4besn_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=$.content -->
{"status": "failure", "data": {"exit_code": 2, "stdout": "", "stderr": "", "actual_command": "for f in /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py; do grep -l 'TODO\\|FIXME\\|BUG' \"$f\" 2>/dev/null; done"}, "error": "Command exited with code 2", "metadata": {"exit_code": 2, "stdout": "", "stderr": "", "actual_command": "for f in /home/draplater/Development/cyf_cli/oturn/src/oturn/*.py; do grep -l 'TODO\\|FIXME\\|BUG' \"$f\" 2>/dev/null; done"}, "info": {}}
<!-- 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_z5ex45ogk6cd_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -r \"TODO\\|FIXME\\|BUG\" /home/draplater/Development/cyf_cli/oturn/src/oturn --include=\"*.py\" 2>/dev/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 -->
Let me try a different approach:
<!-- 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": 75, "prompt_tokens": 26300, "total_tokens": 26375, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 75}, "prompt_tokens_details": {"cached_tokens": 26043, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 26043}
```
<!-- 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_z5ex45ogk6cd_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": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -r --exclude-dir=.cyf \"TODO\\|FIXME\\|BUG\" /home/draplater/Development/cyf_cli/oturn/src/oturn --include=\"*.py\" 2>/dev/null"}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -r --exclude-dir=.cyf \"TODO\\|FIXME\\|BUG\" /home/draplater/Development/cyf_cli/oturn/src/oturn --include=\"*.py\" 2>/dev/null"}, "info": {}}
<!-- 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_k5nrr7t5kr1e_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 -->
好的，没有 TODO/FIXME/BUG 标记。让我继续分析代码风格特征。
<!-- 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": 71, "prompt_tokens": 26545, "total_tokens": 26616, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 71}, "prompt_tokens_details": {"cached_tokens": 26296, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 26296}
```
<!-- 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_k5nrr7t5kr1e_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={"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 -->
现在让我综合分析代码质量和作者风格。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
根据深入分析，以下是我的判断：

---

## 代码质量评估

### 优点

| 方面 | 评价 |
|------|------|
| **类型提示** | 全面使用 Python 3.9+ 类型标注，TypedDict/TypeAlias 运用熟练 |
| **设计模式** | 正确使用 Generic Protocol、Reducer 模式、Pub/Sub |
| **异步处理** | asyncio 使用规范，streaming 实现完整 |
| **错误处理** | 异常捕获合理，有 fallback 策略 |
| **代码组织** | 模块划分清晰，依赖方向正确 |

### 问题点

| 问题 | 位置 | 严重度 |
|------|------|--------|
| **700+ 行主文件** | `oturn.py` | 中 |
| **裸 `except`** | 多处 `except Exception:` | 低 |
| **重复序列化逻辑** | `_format_messages` / `_context_to_api_format` | 低 |
| **类型断言过多** | `cast()`, `# type: ignore` 泛滥 | 低 |

---

## 作者推断

基于代码风格和设计决策，我判断这是 **中文开发者** 独立完成的项目，理由：

### 1. 语言特征
- 中文注释：`"""工作内存：拿本地时区 aware datetime"""`
- 中文文档字符串遍布
- 中文错误信息：`"API调用失败"`, `"流处理失败"`, `"工具整理失败"`

### 2. 技术偏好
- 偏好 Pydantic v2 而非 dataclass
- 熟悉 LiteLLM（国内常用）
- 时区处理仔细（处理 DST + offset 退化方案）
- 对 `tool_stream` 参数的精细处理

### 3. 命名风格
- 中式英语：`user_input_hook`, `user_content_hook`
- 变量命名混合：`aensure_persisted`（async + ensure）
- 缩写风格：`ctx`, `tcid`, `t0`

### 4. 推断

| 特征 | 推断 |
|------|------|
| 独立/小团队 | 无外部依赖，全自研 |
| 有后端经验 | 熟悉 pub/sub、streaming |
| 有 AI 实践经验 | LiteLLM 封装、tool calling 流程熟悉 |
| 注重本地化 | 时区处理、日期格式仔细 |

**最可能是**：一位有后端/AI 研发经验的中文开发者，参考了类似 Claude Code 的架构思路，自主实现的 CLI Agent 框架。

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需要我针对某个具体模块做更深入的代码质量分析吗？
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Usage:
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```json
{"completion_tokens": 537, "prompt_tokens": 29043, "total_tokens": 29580, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 537}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
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