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

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



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

# [<!-- md_as_config:ctx:value-begin json-path=$.role --> user <!-- md_as_config:ctx:value-end -->
- **User:** <!-- md_as_config:ctx:value-begin json-path=$.content.user_name --> User <!-- md_as_config:ctx:value-end -->
- **Date:** <!-- md_as_config:ctx:value-begin type=json json-path=$.content.date --> {"ts": 1771651499.820866, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
梳理当前代码架构
<!-- 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_ejnaa7j8zesu_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find /home/draplater/Development/cyf_cli/oturn/src/oturn -type f -name \"*.py\" | head -50 && echo \"---\" && ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求我梳理当前代码架构。这是一个代码分析和理解的任务，而不是执行shell命令或编辑文件。我需要先了解项目的结构，然后进行分析。

让我先查看项目目录结构，了解代码组织方式。
<!-- 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": 126, "prompt_tokens": 1457, "total_tokens": 1583, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 126}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ejnaa7j8zesu_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

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

<!-- 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_62eczrrt6zlx_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn && echo \"---\" && find /home/draplater/Development/cyf_cli/oturn/src/oturn -maxdepth 2 -type f -name \"*.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 -->
需要使用 -maxdepth 参数。让我重新执行。
<!-- 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": 1671, "total_tokens": 1769, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 98}, "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_62eczrrt6zlx_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": "total 136\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 12:54 .\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  5677 Feb 21 13:23 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 28325 Feb 21 13:23 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:23 __pycache__\r\n-rw-r--r-- 1 draplater draplater  3130 Feb 21 12:16 README.md\r\n-rw-r--r-- 1 draplater draplater   813 Feb 21 12:15 runtime_config.py\r\n-rw-r--r-- 1 draplater draplater  7875 Feb 21 12:49 session_store.py\r\n-rw-r--r-- 1 draplater draplater  3610 Feb 21 12:39 template_renderer.py\r\n-rw-r--r-- 1 draplater draplater  9649 Feb 21 13:23 tool_runtime.py\r\n-rw-r--r-- 1 draplater draplater  1142 Feb 21 12:10 tool_runtime_utils.py\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 tools\r\n---\nfind: 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": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn && echo \"---\" && find /home/draplater/Development/cyf_cli/oturn/src/oturn -maxdepth 2 -type f -name \"*.py\""}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "total 136\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 12:54 .\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  5677 Feb 21 13:23 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 28325 Feb 21 13:23 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:23 __pycache__\r\n-rw-r--r-- 1 draplater draplater  3130 Feb 21 12:16 README.md\r\n-rw-r--r-- 1 draplater draplater   813 Feb 21 12:15 runtime_config.py\r\n-rw-r--r-- 1 draplater draplater  7875 Feb 21 12:49 session_store.py\r\n-rw-r--r-- 1 draplater draplater  3610 Feb 21 12:39 template_renderer.py\r\n-rw-r--r-- 1 draplater draplater  9649 Feb 21 13:23 tool_runtime.py\r\n-rw-r--r-- 1 draplater draplater  1142 Feb 21 12:10 tool_runtime_utils.py\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 tools\r\n---\nfind: 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": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn && echo \"---\" && find /home/draplater/Development/cyf_cli/oturn/src/oturn -maxdepth 2 -type f -name \"*.py\""}, "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_2z4qlrei8zot_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 -->
目录结构清楚了。我看到有以下文件：
- agent_stream_state.py
- context.py
- events.py
- __init__.py
- llm_transport.py
- mcp_adapter.py
- model.py
- nucleus.py
- oturn.py (主文件，28325字节，最大)
- runtime_config.py
- session_store.py
- template_renderer.py
- tool_runtime.py
- tool_runtime_utils.py
- tools/ 目录

让我查看各个文件的内容来理解架构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 154, "prompt_tokens": 3341, "total_tokens": 3495, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 154}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_2z4qlrei8zot_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 12:54 ..\r\n-rw-r--r-- 1 draplater draplater 12704 Feb 21 11:40 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 11:45 __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={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_jynqljh251qo_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/oturn.py | head -150"}}}], "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，现在我有了基本的文件列表。让我读取关键文件来理解代码架构。让我先看核心文件：
1. oturn.py - 主文件（28325字节，最大）
2. context.py - 上下文
3. model.py - 模型定义
4. tool_runtime.py - 工具运行时
5. agent_stream_state.py - agent流状态

让我读取这些文件来理解架构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 137, "prompt_tokens": 3674, "total_tokens": 3811, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 137}, "prompt_tokens_details": {"cached_tokens": 3337, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3337}
```
<!-- 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_jynqljh251qo_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\nimport asyncio\nimport time\nfrom pathlib import Path\nimport traceback\nimport datetime as _dt\nfrom typing import Optional, Awaitable, Callable, List, Any, Dict, Iterable, AsyncGenerator, TypedDict, Literal, cast, TypeAlias, Type\nfrom pydantic import BaseModel\n\nfrom .context import Context\nfrom .model import (\n    Message,\n    UserContent,\n    ShellResult,\n    ToolMessageContent,\n    CompactRecord,\n    Role,\n    ROLE_USER,\n    ROLE_ASSISTANT,\n    ROLE_TOOL,\n)\nfrom .agent_stream_state import (\n    TurnStreamState,\n    StreamTransitionUnion,\n    ToolCallDeltaPayload,\n    ToolRecord,\n    UsageData,\n    ingest_stream_event,\n    reduce_stream_signal,\n    build_assembled_debug,\n    build_tool_records,\n)\nfrom .tools.base import ToolResult, BaseToolRegistry, BaseTool\nfrom .runtime_config import OturnConfig\nfrom .nucleus import AsyncPubSub\nfrom .llm_transport import (\n    LiteLLMTransport,\n    start_global_litellm_prewarm as _start_global_litellm_prewarm,\n    get_global_litellm_prewarm_error as _get_global_litellm_prewarm_error,\n)\nfrom .tool_runtime_utils import (\n    parse_tool_arguments,\n)\nfrom .tool_runtime import ToolRuntime\nfrom .mcp_adapter import MCPAdapter\nfrom .events import (\n    EventPayload,\n    ToolCallDebugEvent,\n    ToolCallPreviewEvent,\n    StreamOutEvent,\n    ev_assistant_delta,\n    ev_assistant_reasoning_delta,\n    ev_usage_total,\n    ev_error,\n    ev_assistant_final,\n    ev_message_sent,\n    ev_run_aborted,\n    ev_tool_call_debug_delta,\n    ev_tool_call_debug_warning,\n    ev_tool_call_debug_assembled,\n)\n\n\nclass _InputQueueItem(TypedDict):\n    status: str\n    text: str\n    type: str\n\n\nJsonValue: TypeAlias = str | int | float | bool | None | list[\"JsonValue\"] | dict[str, \"JsonValue\"]\nJsonObject: TypeAlias = dict[str, JsonValue]\n\n\nclass _ApiToolFunction(TypedDict):\n    name: str\n    arguments: str\n\n\nclass _ApiToolCall(TypedDict):\n    id: str\n    type: str\n    function: _ApiToolFunction\n\n\nclass _ApiMessage(TypedDict, total=False):\n    role: Role\n    content: str\n    name: str\n    tool_call_id: str\n    reasoning_content: str\n    tool_calls: list[_ApiToolCall]\n\n\nclass _ToolFunctionSchema(TypedDict):\n    name: str\n    description: str\n    parameters: JsonObject\n\n\nclass _ToolSchema(TypedDict):\n    type: Literal[\"function\"]\n    function: _ToolFunctionSchema\n\n\nclass _PendingEvent(TypedDict, total=False):\n    future_id: str\n\n\nApiMessagesInput = Context | list[Message] | list[_ApiMessage]\n\n\ndef start_global_litellm_prewarm(*, blocking: bool = False, timeout: float | None = None) -> bool:\n    return _start_global_litellm_prewarm(blocking=blocking, timeout=timeout)\n\n\ndef get_global_litellm_prewarm_error() -> str | None:\n    return _get_global_litellm_prewarm_error()\n\n\ndef _normalize_content_for_api(content: str | UserContent | ShellResult | ToolMessageContent | Any) -> str:\n    if hasattr(content, \"format_rfc5322\") and callable(getattr(content, \"format_rfc5322\")):\n        try:\n            return str(content.format_rfc5322())\n        except Exception:\n            pass\n    if isinstance(content, BaseModel):\n        return json.dumps(content.model_dump(), ensure_ascii=False)\n    if isinstance(content, str):\n        return content\n    return str(content)\n\n\ndef _is_compact_like(item: Any) -> bool:\n    if isinstance(item, dict):\n        return item.get(\"type\") == \"compact\"\n    return getattr(item, \"type\", None) == \"compact\" and hasattr(item, \"value\")\n\n\ndef _compact_value(item: Any) -> str:\n    if isinstance(item, dict):\n        return str(item.get(\"value\", \"\"))\n    return str(getattr(item, \"value\", \"\"))\n\n\ndef _is_message_like(item: Any) -> bool:\n    return hasattr(item, \"role\") and hasattr(item, \"content\")\n\n\ndef _format_messages(messages: Iterable[Any]) -> list[_ApiMessage]:\n    out: list[_ApiMessage] = []\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/oturn.py | head -150"}, "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_b7on4yieuteh_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "echo \"=== model.py ===\" && head -80 /home/draplater/Development/cyf_cli/oturn/src/oturn/model.py && echo -e \"\\n=== context.py (前80行) ===\" && head -80 /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 -->
我已经看到了项目的基本结构。现在让我查看更多关键文件以了解整体架构。让我获取各个模块的摘要。
<!-- 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": 122, "prompt_tokens": 4912, "total_tokens": 5034, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 122}, "prompt_tokens_details": {"cached_tokens": 3337, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3337}
```
<!-- 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_b7on4yieuteh_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": "=== model.py ===\nfrom __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-e \\n=== context.py (前80行) ===\n## @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", "stderr": "", "actual_command": "echo \"=== model.py ===\" && head -80 /home/draplater/Development/cyf_cli/oturn/src/oturn/model.py && echo -e \"\\n=== context.py (前80行) ===\" && head -80 /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_ce3r9d3iji24_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "echo \"=== tool_runtime.py (前100行) ===\" && head -100 /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime.py && echo -e \"\\n=== events.py (全部) ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/events.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me continue reading more of the key 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": 117, "prompt_tokens": 6779, "total_tokens": 6896, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 117}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
<!-- md_as_config:ctx:code-block-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"name": "execute_shell_command", "tool_call_id": "call_function_ce3r9d3iji24_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": "=== tool_runtime.py (前100行) ===\nfrom __future__ import annotations\r\n\r\nimport asyncio\r\nimport json\r\nimport traceback\r\nfrom dataclasses import dataclass\r\nfrom typing import Any, Awaitable, Callable, Optional\r\n\r\nfrom .agent_stream_state import ToolRecord, UsageData\r\nfrom .context import Context\r\nfrom .model import (\r\n    Message,\r\n    ToolMessageContent,\r\n    FunctionInfo,\r\n    ToolCallInfo,\r\n    ROLE_ASSISTANT,\r\n    ROLE_TOOL,\r\n)\r\nfrom .tool_runtime_utils import (\r\n    JsonObject,\r\n    parse_tool_arguments,\r\n    parse_tool_arguments_with_status,\r\n    summarize_tool_arguments,\r\n)\r\nfrom .tools.base import BaseToolRegistry, ToolResult\r\nfrom .events import (\r\n    EventPayload,\r\n    ev_error,\r\n    ev_assistant_final,\r\n    ev_tool_output,\r\n    ev_tool_exit,\r\n    ev_tool_call_preview,\r\n    ev_tool_call_request,\r\n    ev_tool_call_debug_stage,\r\n)\r\n\r\n\r\n@dataclass(slots=True)\r\nclass ToolRuntime:\r\n    tool_registry: BaseToolRegistry\r\n    publish: Callable[[EventPayload], Awaitable[None]]\r\n    execute_remote_tool: Callable[[Context, str, JsonObject], Awaitable[ToolResult]]\r\n    cancel_event: asyncio.Event\r\n    debug_tools: bool = False\r\n\r\n    async def _emit_tool_denial(\r\n        self,\r\n        ctx: Context,\r\n        name: str,\r\n        tcid: str,\r\n        message: str | None,\r\n        reasons: list[str] | None,\r\n        args: Optional[JsonObject] = None,\r\n        full_metadata: dict | None = None,\r\n    ) -> None:\r\n        detail = message or f\"{name} execution denied.\"\r\n        info: dict[str, Any] = {}\r\n        try:\r\n            tool_class = self.tool_registry.get_tool_class(name)\r\n            if tool_class and hasattr(tool_class, \"on_denial\"):\r\n                info = tool_class.on_denial(args or {})\r\n        except Exception:\r\n            pass\r\n\r\n        payload_metadata = full_metadata if full_metadata else {\"tool\": name, \"reasons\": reasons}\r\n        payload = ToolMessageContent(\r\n            status=\"failure\",\r\n            data=full_metadata,\r\n            error=detail,\r\n            metadata=payload_metadata,\r\n            info=info,\r\n        )\r\n        await self.publish(ev_tool_output(stream=\"stdout\", text=json.dumps(payload.model_dump(), ensure_ascii=False) + \"\\n\"))\r\n        await self.publish(ev_tool_output(stream=\"stderr\", text=detail + \"\\n\"))\r\n        await self.publish(ev_tool_exit(1))\r\n        try:\r\n            ctx.append_message(Message(role=ROLE_TOOL, content=payload, tool_call_id=tcid, name=name))\r\n        except Exception:\r\n            pass\r\n\r\n    async def process_tool_records(\r\n        self,\r\n        *,\r\n        ctx: Context,\r\n        tool_records: list[ToolRecord],\r\n        final: str,\r\n        reasoning: str,\r\n        reasoning_details: Any,\r\n        usage_data: UsageData | None,\r\n        model_name: str | None,\r\n    ) -> bool:\r\n        for rec in tool_records:\r\n            name = rec.get(\"function\", {}).get(\"name\", \"\")\r\n            arg_str = rec.get(\"function\", {}).get(\"arguments\", \"\")\r\n            parsed_args = parse_tool_arguments(arg_str)\r\n            if self.debug_tools:\r\n                await self.publish(\r\n                    ev_tool_call_debug_stage(\r\n                        stage=\"assembled_final\",\r\n                        tool=name,\r\n-e \\n=== events.py (全部) ===\nfrom __future__ import annotations\r\n\r\nfrom typing import Any, Literal, Optional, TypeAlias, TypedDict\r\n\r\nfrom .agent_stream_state import ToolCallDeltaPayload, ToolRecord, UsageData\r\n\r\n\r\nclass AssistantDeltaEvent(TypedDict):\r\n    type: Literal[\"assistant_delta\"]\r\n    text: str\r\n    model: Optional[str]\r\n\r\n\r\nclass AssistantReasoningDeltaEvent(TypedDict):\r\n    type: Literal[\"assistant_reasoning_delta\"]\r\n    text: str\r\n    model: Optional[str]\r\n\r\n\r\nclass UsageTotalEvent(TypedDict):\r\n    type: Literal[\"usage_total\"]\r\n    usage: UsageData\r\n\r\n\r\nclass ToolCallPreviewEvent(TypedDict, total=False):\r\n    type: Literal[\"tool_call_preview\"]\r\n    tool: str\r\n    args: str\r\n    index: Optional[int]\r\n    tool_call_id: str\r\n    preview_id: str\r\n    preview: str\r\n\r\n\r\nclass ToolCallRequestEvent(TypedDict):\r\n    type: Literal[\"tool_call\"]\r\n    tool: str\r\n    preview_id: str\r\n    future: Any\r\n\r\n\r\nclass ToolOutputEvent(TypedDict):\r\n    type: Literal[\"tool_output\"]\r\n    stream: Literal[\"stdout\", \"stderr\"]\r\n    text: str\r\n\r\n\r\nclass ToolExitEvent(TypedDict):\r\n    type: Literal[\"tool_exit\"]\r\n    code: int\r\n\r\n\r\nclass ErrorEvent(TypedDict, total=False):\r\n    type: Literal[\"error\"]\r\n    error: str\r\n    exception_type: str\r\n    traceback: str\r\n\r\n\r\nclass AssistantFinalEvent(TypedDict, total=False):\r\n    type: Literal[\"assistant_final\"]\r\n    text: str\r\n    usage_data: Any\r\n    reasoning: str\r\n\r\n\r\nclass MessageSentEvent(TypedDict):\r\n    type: Literal[\"message_sent\"]\r\n\r\n\r\nclass RunAbortedEvent(TypedDict):\r\n    type: Literal[\"run_aborted\"]\r\n    reason: str\r\n\r\n\r\nclass ToolCallDebugDeltaEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    delta: ToolCallDeltaPayload\r\n\r\n\r\nclass ToolCallDebugWarningEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    warning: str\r\n\r\n\r\nclass ToolCallDebugAssembledEvent(TypedDict):\r\n    type: Literal[\"tool_call_debug\"]\r\n    assembled: list[ToolRecord]\r\n\r\n\r\nclass ToolCallDebugStageEvent(TypedDict, total=False):\r\n    type: Literal[\"tool_call_debug\"]\r\n    stage: Literal[\"assembled_final\", \"invalid_arguments\"]\r\n    tool: str\r\n    id: str\r\n    index: int\r\n    args_summary: dict[str, Any]\r\n\r\n\r\nToolCallDebugEvent: TypeAlias = (\r\n    ToolCallDebugDeltaEvent\r\n    | ToolCallDebugWarningEvent\r\n    | ToolCallDebugAssembledEvent\r\n    | ToolCallDebugStageEvent\r\n)\r\n\r\n\r\nStreamOutEvent: TypeAlias = AssistantDeltaEvent | AssistantReasoningDeltaEvent | UsageTotalEvent | ToolCallPreviewEvent\r\nCoreEvent: TypeAlias = ErrorEvent | AssistantFinalEvent | MessageSentEvent | RunAbortedEvent\r\nToolEvent: TypeAlias = ToolOutputEvent | ToolExitEvent | ToolCallRequestEvent | ToolCallPreviewEvent | ToolCallDebugEvent\r\nAgentEvent: TypeAlias = StreamOutEvent | CoreEvent | ToolEvent\r\nRawEvent: TypeAlias = dict[str, Any]\r\nEventPayload: TypeAlias = AgentEvent | RawEvent\r\n\r\n\r\ndef ev_assistant_delta(text: str, model: Optional[str] = None) -> AssistantDeltaEvent:\r\n    return {\"type\": \"assistant_delta\", \"text\": text, \"model\": model}\r\n\r\n\r\ndef ev_assistant_reasoning_delta(text: str, model: Optional[str] = None) -> AssistantReasoningDeltaEvent:\r\n    return {\"type\": \"assistant_reasoning_delta\", \"text\": text, \"model\": model}\r\n\r\n\r\ndef ev_usage_total(usage: UsageData) -> UsageTotalEvent:\r\n    return {\"type\": \"usage_total\", \"usage\": usage}\r\n\r\n\r\ndef ev_error(error: str, *, exception_type: Optional[str] = None, traceback_text: Optional[str] = None) -> ErrorEvent:\r\n    payload: ErrorEvent = {\"type\": \"error\", \"error\": error}\r\n    if exception_type:\r\n        payload[\"exception_type\"] = exception_type\r\n    if traceback_text:\r\n        payload[\"traceback\"] = traceback_text\r\n    return payload\r\n\r\n\r\ndef ev_assistant_final(text: str, *, usage_data: Optional[Any] = None, reasoning: Optional[str] = None) -> AssistantFinalEvent:\r\n    payload: AssistantFinalEvent = {\"type\": \"assistant_final\", \"text\": text}\r\n    if usage_data is not None:\r\n        payload[\"usage_data\"] = usage_data\r\n    if reasoning is not None:\r\n        payload[\"reasoning\"] = reasoning\r\n    return payload\r\n\r\n\r\ndef ev_message_sent() -> MessageSentEvent:\r\n    return {\"type\": \"message_sent\"}\r\n\r\n\r\ndef ev_run_aborted(reason: str = \"cancelled\") -> RunAbortedEvent:\r\n    return {\"type\": \"run_aborted\", \"reason\": reason}\r\n\r\n\r\ndef ev_tool_output(*, stream: Literal[\"stdout\", \"stderr\"], text: str) -> ToolOutputEvent:\r\n    return {\"type\": \"tool_output\", \"stream\": stream, \"text\": text}\r\n\r\n\r\ndef ev_tool_exit(code: int) -> ToolExitEvent:\r\n    return {\"type\": \"tool_exit\", \"code\": int(code)}\r\n\r\n\r\ndef ev_tool_call_preview(*, tool: str, args: str, preview_id: str, preview: Optional[str] = None) -> ToolCallPreviewEvent:\r\n    payload: ToolCallPreviewEvent = {\r\n        \"type\": \"tool_call_preview\",\r\n        \"tool\": tool,\r\n        \"args\": args,\r\n        \"preview_id\": preview_id,\r\n    }\r\n    if preview:\r\n        payload[\"preview\"] = preview\r\n    return payload\r\n\r\n\r\ndef ev_tool_call_request(*, tool: str, preview_id: str, future: Any) -> ToolCallRequestEvent:\r\n    return {\"type\": \"tool_call\", \"tool\": tool, \"preview_id\": preview_id, \"future\": future}\r\n\r\n\r\ndef ev_tool_call_debug_delta(delta: ToolCallDeltaPayload) -> ToolCallDebugDeltaEvent:\r\n    return {\"type\": \"tool_call_debug\", \"delta\": delta}\r\n\r\n\r\ndef ev_tool_call_debug_warning(warning: str) -> ToolCallDebugWarningEvent:\r\n    return {\"type\": \"tool_call_debug\", \"warning\": warning}\r\n\r\n\r\ndef ev_tool_call_debug_assembled(assembled: list[ToolRecord]) -> ToolCallDebugAssembledEvent:\r\n    return {\"type\": \"tool_call_debug\", \"assembled\": assembled}\r\n\r\n\r\ndef ev_tool_call_debug_stage(\r\n    *,\r\n    stage: Literal[\"assembled_final\", \"invalid_arguments\"],\r\n    tool: str,\r\n    id: str,\r\n    index: int,\r\n    args_summary: dict[str, Any],\r\n) -> ToolCallDebugStageEvent:\r\n    return {\r\n        \"type\": \"tool_call_debug\",\r\n        \"stage\": stage,\r\n        \"tool\": tool,\r\n        \"id\": id,\r\n        \"index\": index,\r\n        \"args_summary\": args_summary,\r\n    }\r\n", "stderr": "", "actual_command": "echo \"=== tool_runtime.py (前100行) ===\" && head -100 /home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime.py && echo -e \"\\n=== events.py (全部) ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/events.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_rz2pptx33592_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "echo \"=== llm_transport.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/llm_transport.py && echo -e \"\\n=== session_store.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/session_store.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我对这个项目的架构有了一个清晰的了解。让我总结一下代码架构：

1. **核心入口**: `oturn.py` (28KB) - 主模块，包含 Oturn 运行时
2. **数据模型**: `model.py` (12KB) - 定义 Message, ToolCallInfo 等数据结构
3. **上下文管理**: `context.py` (11KB) - Context 类，管理会话历史、MCP 客户端、工具注册
4. **流式状态**: `agent_stream_state.py` (11KB) - 处理 LLM 流式响应的状态管理
5. **工具运行时**: `tool_runtime.py` (9KB) - 工具执行逻辑
6. **LLM 传输**: `llm_transport.py` (8KB) - LiteLLM 集成
7. **会话存储**: `session_store.py` (7KB) - 持久化存储
8. **MCP 适配器**: `mcp_adapter.py` (3KB) - MCP 协议支持
9. **事件系统**: `events.py` (5KB) - 事件定义和发布
10. **工具**: `tools/` - 工具基类和示例
11. **其他**: nucleus (异步 pub/sub), template_renderer, runtime_config 等

这是一个 AI Agent 框架，支持：
- 多模型 LLM 调用 (通过 LiteLLM)
- 工具调用 (本地 + MCP 远程)
- 流式响应处理
- 会话持久化 (JSONL/Markdown)
- 事件驱动架构

我应该给用户一个清晰的架构梳理。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 444, "prompt_tokens": 9815, "total_tokens": 10259, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 444}, "prompt_tokens_details": {"cached_tokens": 6775, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6775}
```
<!-- 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_rz2pptx33592_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": "=== llm_transport.py (前60行) ===\nfrom __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-e \\n=== session_store.py (前60行) ===\nfrom __future__ import annotations\r\n\r\nimport asyncio\r\nimport datetime as _dt\r\nimport threading\r\nfrom pathlib import Path\r\nfrom typing import Any, Callable, Optional\r\n\r\nfrom chronml import Chron\r\n\r\nfrom .model import CompactRecord, Message\r\n\r\nMETA_DIRNAME = \".cyf\"\r\nSESSIONS_DIRNAME = \"sessions\"\r\n_SESSION_MARKER_PREFIX = \"md_as_config:ctx:\"\r\n\r\n\r\ndef _session_chron() -> Chron:\r\n    return Chron(marker_prefix=_SESSION_MARKER_PREFIX)\r\n\r\n\r\nclass SessionStore:\r\n    def __init__(self, file_backend: Path, session_id: str | None = None, work_dir: Path | None = None):\r\n        self.file_backend = file_backend\r\n        self.session_id = session_id\r\n        self.work_dir = work_dir\r\n        self._persist_ready: bool = self.file_backend.exists()\r\n        self._persist_lock = threading.Lock()\r\n\r\n    def restore(self) -> list[Message | CompactRecord]:\r\n        if not self.file_backend.exists() or self.file_backend.stat().st_size == 0:\r\n            return []\r\n        records = _session_chron().load(self.file_backend)\r\n        return list(records or [])\r\n\r\n    def append_item(self, item: Any, template_func: Callable[[Any], str]) -> None:\r\n        self.ensure_persisted()\r\n        item_md = _session_chron().dumps([item], template=\"\", template_func=template_func)\r\n        with open(self.file_backend, \"a\", encoding=\"utf-8\") as f:\r\n            f.write(item_md)\r\n            f.write(\"\\n\\n\")\r\n            f.flush()\r\n\r\n    def dump_history(\r\n        self,\r\n        history: list[Any],\r\n        target_path: Path,\r\n        template_func: Callable[[Any], str],\r\n        template: str = \"\",\r\n    ) -> None:\r\n        target_path.parent.mkdir(parents=True, exist_ok=True)\r\n        _session_chron().dump(history, target_path, template=template, template_func=template_func)\r\n\r\n    def export_md_as_config(self, history: list[Any], md_path: Path, template_func: Callable[[Any], str], template: str = \"\") -> None:\r\n        self.dump_history(history, md_path, template_func=template_func, template=template)\r\n\r\n    def import_md_as_config(self, md_path: Path) -> list[Any]:\r\n        if not md_path.exists() or md_path.stat().st_size == 0:\r\n            return []\r\n        records = _session_chron().load(md_path)\r\n", "stderr": "", "actual_command": "echo \"=== llm_transport.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/llm_transport.py && echo -e \"\\n=== session_store.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/session_store.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_1ef16cfp6dqa_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "echo \"=== agent_stream_state.py (前80行) ===\" && head -80 /home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py && echo -e \"\\n=== tools/base.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/base.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
让我继续查看其他关键文件来完整了解架构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 122, "prompt_tokens": 11404, "total_tokens": 11526, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 122}, "prompt_tokens_details": {"cached_tokens": 9811, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9811}
```
<!-- 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_1ef16cfp6dqa_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 (前80行) ===\nfrom __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-e \\n=== tools/base.py (前60行) ===\n\"\"\"Base tool interface and abstract classes\"\"\"\r\n\r\nimport abc\r\nimport asyncio\r\nimport inspect\r\nimport json\r\nfrom typing import Dict, Any, Optional, Type, List, TYPE_CHECKING, Callable, Awaitable\r\nfrom dataclasses import dataclass\r\nfrom enum import Enum\r\nfrom uuid import uuid4\r\n\r\nfrom .precheck import ToolPrecheckResult, STATUS_ALLOW, STATUS_DENY, STATUS_NEEDS_APPROVAL\r\nfrom ..events import EventPayload, ev_approval_request, ev_choice_request\r\n\r\nif TYPE_CHECKING:\r\n    from ..model import UserContent\r\n\r\n\r\nclass ToolType(Enum):\r\n    \"\"\"Tool execution types\"\"\"\r\n    SYNC = \"sync\"\r\n    ASYNC = \"async\"\r\n    STREAM = \"stream\"\r\n\r\n\r\n@dataclass\r\nclass ToolResult:\r\n    \"\"\"Standardized tool execution result returned from BaseTool.run\"\"\"\r\n    status: str\r\n    content: Optional[Any] = None\r\n    error: Optional[str] = None\r\n    metadata: Optional[Dict[str, Any]] = None\r\n    needs_user_input: bool = False\r\n\r\n    @property\r\n    def success(self) -> bool:\r\n        return self.status == \"success\"\r\n\r\n\r\n@dataclass\r\nclass ToolStreamEvent:\r\n    \"\"\"Tool streaming event\"\"\"\r\n    type: str  # stdout, stderr, progress, error, done\r\n    data: Any\r\n    metadata: Optional[Dict[str, Any]] = None\r\n\r\n\r\nclass ToolExecutionError(Exception):\r\n    \"\"\"Custom exception that carries metadata for tool failures\"\"\"\r\n\r\n    def __init__(\r\n        self,\r\n        message: str,\r\n        *,\r\n        metadata: Optional[Dict[str, Any]] = None,\r\n        needs_user_input: bool = False,\r\n        content: Optional[Any] = None,\r\n    ) -> None:\r\n        super().__init__(message)\r\n        self.metadata = metadata\r\n", "stderr": "", "actual_command": "echo \"=== agent_stream_state.py (前80行) ===\" && head -80 /home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py && echo -e \"\\n=== tools/base.py (前60行) ===\" && head -60 /home/draplater/Development/cyf_cli/oturn/src/oturn/tools/base.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_7g5ifn677w2x_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "echo \"=== mcp_adapter.py ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/mcp_adapter.py && echo -e \"\\n=== template_renderer.py ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/template_renderer.py && echo -e \"\\n=== nucleus.py ===\" && 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 codebase. Let me also check the mcp_adapter, template_renderer and nucleus to complete the picture.
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 161, "prompt_tokens": 12735, "total_tokens": 12896, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 161}, "prompt_tokens_details": {"cached_tokens": 11400, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 11400}
```
<!-- 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_7g5ifn677w2x_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": "=== mcp_adapter.py ===\nfrom __future__ import annotations\r\n\r\nimport os\r\nfrom typing import Any, Optional\r\n\r\nfrom .context import Context\r\nfrom .tool_runtime_utils import JsonObject\r\nfrom .tools.base import ToolResult\r\n\r\n\r\nclass MCPAdapter:\r\n    \"\"\"Experimental MCP integration adapter.\r\n\r\n    Kept out of Oturn core loop so it can be disabled/removed independently.\r\n    \"\"\"\r\n\r\n    def __init__(self, enabled: Optional[bool] = None) -> None:\r\n        self.enabled = (\r\n            bool(enabled)\r\n            if enabled is not None\r\n            else os.getenv(\"ENABLE_MCP\", \"\").strip().lower() in {\"1\", \"true\", \"yes\", \"on\"}\r\n        )\r\n\r\n    def merge_tool_schemas(self, ctx: Context, local_schemas: list[dict[str, Any]]) -> list[dict[str, Any]]:\r\n        if not self.enabled:\r\n            return local_schemas\r\n        remote_schemas: list[dict[str, Any]] = []\r\n        availability = ctx.get_mcp_tool_availability()\r\n        for tool in ctx.get_mcp_tool_schemas().values():\r\n            name = tool.get(\"name\")\r\n            if not name:\r\n                continue\r\n            if not availability.get(name):\r\n                continue\r\n            remote_schemas.append(\r\n                {\r\n                    \"type\": \"function\",\r\n                    \"function\": {\r\n                        \"name\": name,\r\n                        \"description\": tool.get(\"description\") or \"\",\r\n                        \"parameters\": tool.get(\"inputSchema\") or {\"type\": \"object\"},\r\n                    },\r\n                }\r\n            )\r\n        if remote_schemas:\r\n            return local_schemas + remote_schemas\r\n        return local_schemas\r\n\r\n    async def execute_remote_tool(self, ctx: Context, name: str, args: JsonObject) -> ToolResult:\r\n        if not self.enabled:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=\"MCP disabled (experimental; set ENABLE_MCP=1 to enable)\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        client = ctx.get_mcp_client()\r\n        if client is None:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=\"MCP client is not connected\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        try:\r\n            import mcp.types as types\r\n            from mcp.shared.exceptions import McpError\r\n        except Exception as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=f\"MCP runtime unavailable: {exc}\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        params = types.CallToolRequestParams(name=name, arguments=args or None)\r\n        request = types.CallToolRequest(params=params)\r\n        try:\r\n            result = await client.send_request(request, types.CallToolResult)\r\n        except McpError as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=f\"MCP call failed: {exc}\",\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n        except Exception as exc:\r\n            return ToolResult(\r\n                status=\"failure\",\r\n                error=str(exc),\r\n                metadata={\"tool\": name, \"remote\": True},\r\n            )\r\n\r\n        content_blocks = [block.model_dump(mode=\"json\") for block in (result.content or [])]\r\n        payload = {\r\n            \"tool\": name,\r\n            \"content_blocks\": content_blocks,\r\n            \"structuredContent\": result.structuredContent,\r\n            \"isError\": result.isError,\r\n        }\r\n        status = \"failure\" if result.isError else \"success\"\r\n        return ToolResult(status=status, content=payload, metadata={\"tool\": name, \"remote\": True})\r\n\r\n-e \\n=== template_renderer.py ===\nfrom __future__ import annotations\r\n\r\nfrom .model import CompactRecord, Message, ShellResult, UserContent\r\nfrom .tools.base import BaseToolRegistry\r\n\r\n\r\nclass ContextTemplateRenderer:\r\n    def __init__(self, tool_registry: BaseToolRegistry) -> None:\r\n        self._tool_registry = tool_registry\r\n\r\n    def set_tool_registry(self, tool_registry: BaseToolRegistry) -> None:\r\n        self._tool_registry = tool_registry\r\n\r\n    def select_template_for_item(self, item) -> str:\r\n        if isinstance(item, CompactRecord):\r\n            return \"**Compact Summary:** {.value}\\n\\n**User Intent:** {.user_intent}\\n\\n\"\r\n\r\n        if isinstance(item, Message):\r\n            if item.role == \"system\":\r\n                return \"\"\r\n\r\n            if item.role == \"tool\":\r\n                tool_name = item.name\r\n                if tool_name:\r\n                    tool_class = self._tool_registry.get_tool_class(tool_name)\r\n                    if tool_class:\r\n                        response_template = tool_class.get_response_template(item)\r\n                        if response_template:\r\n                            return response_template\r\n\r\n            if item.role == \"user\":\r\n                content = item.content\r\n                if isinstance(content, UserContent):\r\n                    template = \"# [{.role}\\n\"\r\n                    template += \"- **User:** {.content.user_name}\\n\"\r\n                    if content.email:\r\n                        template += \"- **Email:** {.content.email}\\n\"\r\n                    template += \"- **Date:** {.content.date}\\n]\\n\\n\"\r\n                    template += \"{.content.content:block}\\n\\n\"\r\n                    return template\r\n                if isinstance(content, ShellResult):\r\n                    template = \"# [{.role}\\n\"\r\n                    template += \"- **Command:** {.content.command}\\n\"\r\n                    template += \"- **Working Directory:** {.content.cwd}\\n\"\r\n                    template += \"- **Exit Code:** {.content.retcode}\\n\"\r\n                    template += \"- **Date:** {.content.date}\\n]\\n\\n\"\r\n                    if content.stdout:\r\n                        template += \"**STDOUT:**\\n{.content.stdout:block}\\n\\n\"\r\n                    if content.stderr:\r\n                        template += \"**STDERR:**\\n{.content.stderr:block}\\n\\n\"\r\n                    return template\r\n\r\n            if item.role == \"assistant\" and item.model_name:\r\n                template = \"# [{.role} - {.model_name}]\\n\\n\"\r\n            else:\r\n                template = \"# [{.role}]\\n\\n\"\r\n\r\n            if item.reasoning_content:\r\n                template += \"{.reasoning_content:block}\\n\\n\"\r\n\r\n            if item.content:\r\n                template += \"{.content:block}\\n\\n\"\r\n\r\n            tool_calls = item.tool_calls\r\n            if item.role == \"assistant\" and tool_calls:\r\n                tool_sections = []\r\n                for i, call in enumerate(tool_calls):\r\n                    tool_name = call.function.name if call.function else \"\"\r\n                    if tool_name:\r\n                        prefix = f\".tool_calls[{i}]\"\r\n                        tool_class = self._tool_registry.get_tool_class(tool_name)\r\n                        if tool_class:\r\n                            tool_template = tool_class.get_request_template(prefix)\r\n                            if tool_template:\r\n                                tool_sections.append(tool_template)\r\n                if tool_sections:\r\n                    sections_text = \"\\n\\n\".join(tool_sections)\r\n                    template += sections_text + \"\\n\\n\"\r\n\r\n            if item.usage:\r\n                template += \"Usage:\\n{.usage:code-block-json}\\n\\n\"\r\n\r\n            return template\r\n\r\n        return \"\"\r\n\r\n-e \\n=== nucleus.py ===\nfrom __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": "echo \"=== mcp_adapter.py ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/mcp_adapter.py && echo -e \"\\n=== template_renderer.py ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/template_renderer.py && echo -e \"\\n=== nucleus.py ===\" && cat /home/draplater/Development/cyf_cli/oturn/src/oturn/nucleus.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_67e075ljp5t0_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/__init__.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我有足够的信息来梳理代码架构了。让我整理一份清晰的架构文档。
<!-- 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": 15792, "total_tokens": 15860, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 68}, "prompt_tokens_details": {"cached_tokens": 12731, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 12731}
```
<!-- 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_67e075ljp5t0_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

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

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



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

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我有足够的信息来总结这个代码架构了。让我整理一下。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
## Oturn 代码架构梳理

### 整体定位
**oturn** 是一个 LLM Agent 运行时框架，支持流式输出、工具调用、会话持久化（md_as_config 双栈）和可选的 MCP 集成。

---

### 模块依赖关系图

```
┌─────────────────────────────────────────────────────────────────┐
│                         oturn.py                                 │
│              (主入口: Oturn 类，核心循环)                         │
└────────────────────────────┬────────────────────────────────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                    │                    │
        ▼                    ▼                    ▼
   ┌─────────┐        ┌───────────┐        ┌──────────┐
   │context  │        │ llm_      │        │ events   │
   │.Context │◄──────►│ transport │        │ .Event*  │
   └────┬────┘        │ LiteLLM   │        └──────────┘
        │             └───────────┘
        │                   │
        ▼                   ▼
   ┌─────────┐        ┌───────────┐
   │session_ │        │agent_     │
   │store    │        │stream_    │
   │.Session │        │state      │
   │Store    │        │.TurnStream│
   └─────────┘        └───────────┘
        │                   │
        ▼                   ▼
   ┌─────────────────────────────────────────────┐
   │               tools/                         │
   │  base.py (BaseTool, BaseToolRegistry)       │
   │  examples.py (WeatherTool, HostConfigTool) │
   └─────────────────────────────────────────────┘
```

---

### 核心模块职责

| 模块 | 文件 | 职责 |
|------|------|------|
| **oturn** | `oturn.py` (28KB) | 主入口，定义 `Oturn` 类：消息格式化、LLM 调用循环、流式事件处理、工具调度 |
| **context** | `context.py` (11KB) | 上下文容器，管理历史消息、MCP 客户端、工具注册、模板渲染器 |
| **model** | `model.py` (12KB) | 数据模型：`Message`, `ToolCallInfo`, `ShellResult`, `UserContent`, `CompactRecord` 等 |
| **events** | `events.py` (5KB) | 事件类型定义与工厂函数：`ev_assistant_delta`, `ev_tool_call_preview`, `ev_error` 等 |
| **agent_stream_state** | `agent_stream_state.py` (11KB) | 流式状态机：`TurnStreamState`, `ToolCallAccumulator`, 流事件归并与组装 |
| **tool_runtime** | `tool_runtime.py` (9KB) | 工具执行运行时：参数解析、工具调用调度、拒绝/批准处理 |
| **llm_transport** | `llm_transport.py` (8KB) | LiteLLM 封装，异步 completions，预热机制 |
| **session_store** | `session_store.py` (7KB) | 基于 `chronml` 的会话持久化，支持 md_as_config 导入/导出 |
| **nucleus** | `nucleus.py` (2KB) | 通用状态机内核：`OTurnNucleus`, `AsyncPubSub`，Redux 风格 reducer |
| **mcp_adapter** | `mcp_adapter.py` (3KB) | MCP 协议适配器（可选启用） |
| **template_renderer** | `template_renderer.py` (3KB) | 消息→Markdown 模板选择逻辑 |
| **tools/** | `base.py`, `examples.py` | 工具抽象层：`BaseTool`, `BaseToolRegistry`, 内置示例工具 |

---

### 关键设计模式

1. **事件驱动流式处理**
   - `oturn.py` 中的主循环边消费 LLM 流式响应，边通过 `agent_stream_state` 归并 delta 事件
   - 归并后的 tool_calls 通过 `tool_runtime` 调度执行

2. **双栈上下文持久化**
   - `session_store` 基于 `chronml` 库，支持 JSONL 和 Markdown 双格式互转

3. **工具抽象**
   - `BaseTool` 定义同步/异步/流式三种执行模式
   - `BaseToolRegistry` 管理工具注册与类查找

4. **可选 MCP 集成**
   - `MCPAdapter` 独立于核心循环，通过 `ENABLE_MCP=1` 启用

---

### 导出 API (`__init__.py`)

```python
# 核心
Oturn, OturnConfig
Context, Message, Role
BaseTool, BaseToolRegistry, ToolResult

# 状态机
OTurnNucleus, AsyncPubSub, Reducer, Transition

# 工具
WeatherTool, HostConfigTool, EXAMPLE_TOOLS

# 预热
start_global_litellm_prewarm, get_global_litellm_prewarm_error
```

---

### 入口流程（简化）

```
用户输入 → Context.append_message()
          ↓
Oturn.run() → llm_transport.acompletion() (流式)
          ↓
agent_stream_state.ingest_stream_event() 归并 delta
          ↓
tool_runtime.process_tool_records() 执行工具
          ↓
结果写回 Context → SessionStore 持久化
```
<!-- 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": 1150, "prompt_tokens": 16386, "total_tokens": 17536, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 1150}, "prompt_tokens_details": {"cached_tokens": 15788, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 15788}
```
<!-- md_as_config:ctx:code-block-end -->

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



