<!-- 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": 1771653322.96549, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
梳理项目冗余
<!-- 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_u0ygfmuf112f_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求"梳理项目冗余"，这是中文，意思是"梳理项目冗余"或"整理项目冗余"。我需要先了解这个项目的结构，然后找出可能的冗余内容。

让我先查看当前工作目录的内容，了解项目结构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 81, "prompt_tokens": 1457, "total_tokens": 1538, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 81}, "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_u0ygfmuf112f_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 148\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 14004 Feb 21 13:52 context.py\r\ndrwxr-xr-x 1 draplater draplater    16 Feb 21 12:43 .cyf\r\n-rw-r--r-- 1 draplater draplater  7123 Feb 21 13:46 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  8746 Feb 21 13:52 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 30449 Feb 21 13:49 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:53 __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  9923 Feb 21 13:52 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  9466 Feb 21 13:48 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"}, "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_qoijflrthii6_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls -la tools/"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求"梳理项目冗余"，意思是分析项目中是否存在冗余代码或可以清理的内容。让我进一步检查项目结构和代码内容。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 61, "prompt_tokens": 2137, "total_tokens": 2198, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 61}, "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_qoijflrthii6_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

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

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_0oipnwv6aii8_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -name \"*.py\" -exec wc -l {} \\; | sort -n"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求"梳理项目冗余"，意思是梳理项目中可能存在的冗余代码、重复功能、未使用的代码等。让我先检查项目结构和各个文件的内容。

我需要：
1. 查看主要的Python文件，了解各自的功能
2. 检查是否有重复的功能或代码
3. 检查是否有未使用的导入、函数等

让我先读取几个主要文件来了解项目结构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 124, "prompt_tokens": 2436, "total_tokens": 2560, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 124}, "prompt_tokens_details": {"cached_tokens": 2133, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2133}
```
<!-- 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_0oipnwv6aii8_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_5024lra6d08i_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -maxdepth 2 -name \"*.py\" -exec wc -l {} \\; | sort -n"}}}], "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 maxdepth 2.
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 61, "prompt_tokens": 2610, "total_tokens": 2671, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 61}, "prompt_tokens_details": {"cached_tokens": 2432, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2432}
```
<!-- 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_5024lra6d08i_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": "15 ./tools/precheck.py\n18 ./tools/__init__.py\n36 ./runtime_config.py\n41 ./tool_runtime_utils.py\n51 ./__init__.py\n86 ./template_renderer.py\n96 ./nucleus.py\n101 ./mcp_adapter.py\n195 ./tools/examples.py\n225 ./llm_transport.py\n244 ./tool_runtime.py\n264 ./session_store.py\n288 ./events.py\n350 ./model.py\n376 ./agent_stream_state.py\n388 ./tools/base.py\n419 ./context.py\n770 ./oturn.py\n", "stderr": "", "actual_command": "find . -maxdepth 2 -name \"*.py\" -exec wc -l {} \\; | sort -n"}, "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_3qun8utt2n05_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -50 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 -->
用户让我"梳理项目冗余"，这是一个中文请求，"意思是梳理项目冗余"即"审查/分析项目中的冗余内容"。

让我先快速浏览各个关键文件来理解项目结构，然后识别可能的冗余：
1. 未使用的代码/函数
2. 重复的功能
3. 过时的代码
4. 可以合并的文件

让我查看主要的Python文件来理解这个项目是做什么的，然后找出冗余。
<!-- 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": 125, "prompt_tokens": 2859, "total_tokens": 2984, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 125}, "prompt_tokens_details": {"cached_tokens": 2606, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2606}
```
<!-- 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_3qun8utt2n05_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 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", "stderr": "", "actual_command": "head -50 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_vjm6axvea3fk_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"^class\\|^def\\|^async def\" oturn.py | head -80"}}}], "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 analyze the project structure and identify potential redundancies. I'll look for:

1. Duplicate code or similar functionality across files
2. Unused imports or dead code
3. Overlapping responsibilities
4. Files that could potentially be merged

Let me examine key files to understand the architecture and find redundancies.
<!-- md_as_config:ctx:multi-line-value-end -->

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

我来分析项目结构并识别冗余。先查看关键文件的功能划分：
<!-- 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": 125, "prompt_tokens": 3362, "total_tokens": 3487, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 125}, "prompt_tokens_details": {"cached_tokens": 2855, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2855}
```
<!-- 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_vjm6axvea3fk_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": "62:class _InputQueueItem(TypedDict):\n72:class _ApiToolFunction(TypedDict):\n77:class _ApiToolCall(TypedDict):\n83:class _ApiMessage(TypedDict, total=False):\n92:class _ToolFunctionSchema(TypedDict):\n98:class _ToolSchema(TypedDict):\n103:class _PendingEvent(TypedDict, total=False):\n110:def start_global_litellm_prewarm(*, blocking: bool = False, timeout: float | None = None) -> bool:\n114:def get_global_litellm_prewarm_error() -> str | None:\n118:def _normalize_content_for_api(content: str | UserContent | ShellResult | ToolMessageContent | Any) -> str:\n131:def _is_compact_like(item: Any) -> bool:\n137:def _compact_value(item: Any) -> str:\n143:def _is_message_like(item: Any) -> bool:\n147:def _format_messages(messages: Iterable[Any]) -> list[_ApiMessage]:\n190:def _context_to_api_format(ctx: Any) -> list[_ApiMessage]:\n303:def _to_api_messages(messages: ApiMessagesInput | Any) -> list[_ApiMessage]:\n314:class Oturn:\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"^class\\|^def\\|^async def\" oturn.py | head -80"}, "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_fp1ueu58ay9j_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat context.py | 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 -->
项目看起来是一个比较完整的框架。让我继续查看各个模块，看看有什么冗余的地方。
<!-- 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": 53, "prompt_tokens": 3787, "total_tokens": 3840, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 53}, "prompt_tokens_details": {"cached_tokens": 3359, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3359}
```
<!-- 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_fp1ueu58ay9j_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\n#  @brief jsonl / md_as_config 双栈上下文：默认写 Markdown，必要时可导出/导入\n\nfrom __future__ import annotations\n\nimport traceback\nimport datetime as _dt\nfrom pathlib import Path\nfrom typing import Iterable, List, Union, Optional, Callable, Any, Dict\n\nfrom .model import Message, CompactRecord\nfrom .tools.base import BaseToolRegistry\nfrom .template_renderer import ContextTemplateRenderer\nfrom .session_store import SessionStore\n\n\nclass Context:\n    def __init__(\n        self,\n        file_backend: Path,\n        session_id: str | None = None,\n        work_dir: Path | None = None,\n        *,\n        meta_dirname: str = \".oturn\",\n        global_sessions_root: Path | None = None,\n    ):\n        self._store = SessionStore(\n            file_backend,\n            session_id=session_id,\n            work_dir=work_dir,\n", "stderr": "", "actual_command": "cat context.py | 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_piu6ltluwg4z_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"from \\.\" oturn.py | 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 -->
用户想要梳理项目冗余。我需要分析代码，找出可能的重复、未使用或可以简化的部分。

让我分析各个文件的功能和可能的冗余：

1. **oturn.py (770行)** - 主入口，包含 Oturn 类和很多工具函数
2. **context.py (419行)** - 上下文管理
3. **agent_stream_state.py (376行)** - 流状态管理
4. **tools/base.py (388行)** - 工具基类
5. **model.py (350行)** - 数据模型
6. **events.py (288行)** - 事件系统
7. **session_store.py (264行)** - 会话存储
8. **tool_runtime.py (244行)** - 工具运行时
9. **llm_transport.py (225行)** - LLM 传输
10. **tools/examples.py (195行)** - 示例工具
11. **mcp_adapter.py (101行)** - MCP 适配器
12. **nucleus.py (96行)** - 可能是发布订阅
13. **template_renderer.py (86行)** - 模板渲染
14. **tool_runtime_utils.py (41行)** - 工具运行时工具
15. **runtime_config.py (36行)** - 运行时配置
16. **__init__.py (51行)** - 包初始化
17. **tools/__init__.py (18行)** - 工具包初始化
18. **tools/precheck.py (15行)** - 预检查

让我更详细地分析可能存在的冗余：


1. **agent_stream_state.py** 和 **events.py** 存在重叠 - 两个文件都处理事件和状态转换，需要检查它们之间的具体职责划分。
2. **tool_runtime.py** 和 **tool_runtime_utils.py** 的关系需要明确 - 虽然文件名相似，但各自的功能应该有所不同。
3. **context.py** 和 **session_store.py** 的交互模式需要审视 - 这两个模块可能在职责上有重复或可以更清晰地分离。
4. **llm_transport.py** 可能是冗余的 - 需要确认 oturn.py 是否直接调用其他模块，或者这个传输层的存在是否必要。

我应该系统地梳理这些模块的具体依赖关系和功能边界。
<!-- 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": 510, "prompt_tokens": 4105, "total_tokens": 4615, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 510}, "prompt_tokens_details": {"cached_tokens": 3783, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3783}
```
<!-- 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_piu6ltluwg4z_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": "10:from .context import Context\n11:from .model import (\n22:from .agent_stream_state import (\n33:from .tools.base import ToolResult, BaseToolRegistry, BaseTool\n34:from .runtime_config import OturnConfig\n35:from .nucleus import AsyncPubSub\n36:from .llm_transport import (\n41:from .tool_runtime import ToolRuntime\n42:from .mcp_adapter import MCPAdapter\n43:from .events import (\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"from \\.\" oturn.py | 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_r75l94nu5g99_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"^class\\|^def\\|^async def\" model.py | head -40"}}}], "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 look at the model.py to see what's there and also check if there are overlapping functionalities between files.
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 67, "prompt_tokens": 4330, "total_tokens": 4397, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 67}, "prompt_tokens_details": {"cached_tokens": 4101, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 4101}
```
<!-- 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_r75l94nu5g99_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": "17:class FunctionInfo:\n38:class ToolCallInfo:\n63:def _format_utc_offset(dt: _dt.datetime) -> str:\n76:def _get_local_aware_now() -> _dt.datetime:\n82:def _coerce_to_local_tz(dt: _dt.datetime) -> _dt.datetime:\n95:class UserContent(BaseModel):\n217:class ShellResult(BaseModel):\n312:class ToolMessageContent(BaseModel):\n328:class Message(BaseModel):\n341:class CompactRecord(BaseModel):\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"^class\\|^def\\|^async def\" model.py | head -40"}, "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_rgq6ia04vkc6_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"^class\\|^def\\|^async def\" agent_stream_state.py | head -40"}}}], "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 -->
让我检查其他一些关键文件以了解它们的职责：
<!-- 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": 58, "prompt_tokens": 4572, "total_tokens": 4630, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 58}, "prompt_tokens_details": {"cached_tokens": 4326, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 4326}
```
<!-- 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_rgq6ia04vkc6_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": "7:class ReasoningDetail(TypedDict, total=False):\n11:class UsageData(TypedDict, total=False):\n19:class ToolFunctionPayload(TypedDict):\n24:class ToolRecord(TypedDict):\n30:class ToolCallDeltaPayload(TypedDict, total=False):\n38:class AssistantDeltaPayload(TypedDict, total=False):\n43:class AssistantReasoningDeltaPayload(TypedDict, total=False):\n49:class AssistantDonePayload(TypedDict, total=False):\n54:class EmptyPayload(TypedDict):\n58:class ReasoningExtra(TypedDict, total=False):\n63:class ToolCallAccumulator:\n71:class TurnStreamState:\n93:class StreamSignal:\n99:class StreamTransition:\n105:class AssistantReasoningDeltaSignal:\n111:class AssistantDoneSignal:\n117:class ToolCallDeltaSignal:\n123:class ToolCallsDoneSignal:\n137:class AssistantDeltaTransitionPayload(TypedDict):\n142:class AssistantDoneTransitionPayload(TypedDict, total=False):\n146:class ToolCallTransitionPayload(TypedDict):\n153:class EmptyTransitionPayload(TypedDict):\n158:class AssistantDeltaTransition:\n164:class AssistantReasoningDeltaTransition:\n170:class AssistantDoneTransition:\n176:class ToolCallDeltaTransition:\n182:class ToolCallDeltaDroppedTransition:\n188:class ToolCallsDoneTransition:\n194:class NoopTransition:\n210:def ingest_stream_event(ev: Any) -> list[StreamSignalUnion]:\n253:def reduce_stream_signal(state: TurnStreamState, signal: StreamSignalUnion) -> StreamTransitionUnion:\n353:def build_assembled_debug(state: TurnStreamState) -> list[ToolRecord]:\n366:def build_tool_records(state: TurnStreamState) -> list[ToolRecord]:\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"^class\\|^def\\|^async def\" agent_stream_state.py | head -40"}, "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_7r6qjcasxa2j_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"TypedDict\\|class.*BaseModel\\|class.*Model\" model.py agent_stream_state.py events.py | head -40"}}}], "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 -->
用户想要梳理项目冗余。我已经对项目结构有了初步了解，现在需要进一步分析来找出冗余部分。

让我检查一下一些关键模块之间的重复功能，以及可能的代码冗余。

主要模块：
- oturn.py (770行) - 主入口
- context.py (419行) - 上下文管理
- model.py (350行) - 数据模型
- agent_stream_state.py (376行) - 流状态管理
- llm_transport.py (225行) - LLM传输
- session_store.py (264行) - 会话存储
- tool_runtime.py (244行) - 工具运行时
- events.py (288行) - 事件系统
- tools/base.py (388行) - 工具基类

让我检查是否有重复的功能定义或数据结构。
<!-- 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": 223, "prompt_tokens": 5082, "total_tokens": 5305, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 223}, "prompt_tokens_details": {"cached_tokens": 4568, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 4568}
```
<!-- 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_7r6qjcasxa2j_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:95:class UserContent(BaseModel):\nmodel.py:217:class ShellResult(BaseModel):\nmodel.py:312:class ToolMessageContent(BaseModel):\nmodel.py:328:class Message(BaseModel):\nmodel.py:341:class CompactRecord(BaseModel):\nagent_stream_state.py:4:from typing import Any, Literal, Optional, TypedDict, TypeAlias, cast\nagent_stream_state.py:7:class ReasoningDetail(TypedDict, total=False):\nagent_stream_state.py:11:class UsageData(TypedDict, total=False):\nagent_stream_state.py:19:class ToolFunctionPayload(TypedDict):\nagent_stream_state.py:24:class ToolRecord(TypedDict):\nagent_stream_state.py:30:class ToolCallDeltaPayload(TypedDict, total=False):\nagent_stream_state.py:38:class AssistantDeltaPayload(TypedDict, total=False):\nagent_stream_state.py:43:class AssistantReasoningDeltaPayload(TypedDict, total=False):\nagent_stream_state.py:49:class AssistantDonePayload(TypedDict, total=False):\nagent_stream_state.py:54:class EmptyPayload(TypedDict):\nagent_stream_state.py:58:class ReasoningExtra(TypedDict, total=False):\nagent_stream_state.py:137:class AssistantDeltaTransitionPayload(TypedDict):\nagent_stream_state.py:142:class AssistantDoneTransitionPayload(TypedDict, total=False):\nagent_stream_state.py:146:class ToolCallTransitionPayload(TypedDict):\nagent_stream_state.py:153:class EmptyTransitionPayload(TypedDict):\nevents.py:3:from typing import Any, Literal, Optional, TypeAlias, TypedDict\nevents.py:8:class AssistantDeltaEvent(TypedDict):\nevents.py:14:class AssistantReasoningDeltaEvent(TypedDict):\nevents.py:20:class ToolCallDeltaEvent(TypedDict, total=False):\nevents.py:30:class ToolCallRequestEvent(TypedDict):\nevents.py:37:class ToolOutputEvent(TypedDict):\nevents.py:43:class ToolExitEvent(TypedDict):\nevents.py:48:class ApprovalPrecheckPayload(TypedDict, total=False):\nevents.py:55:class ApprovalRequestEvent(TypedDict):\nevents.py:64:class ChoiceRequestEvent(TypedDict):\nevents.py:74:class ErrorEvent(TypedDict, total=False):\nevents.py:81:class AssistantFinalEvent(TypedDict, total=False):\nevents.py:88:class MessageSentEvent(TypedDict):\nevents.py:92:class RunAbortedEvent(TypedDict):\nevents.py:97:class DebugLatencyEvent(TypedDict):\nevents.py:103:class ToolCallDebugDeltaEvent(TypedDict):\nevents.py:108:class ToolCallDebugWarningEvent(TypedDict):\nevents.py:113:class ToolCallDebugAssembledEvent(TypedDict):\nevents.py:118:class ToolCallDebugStageEvent(TypedDict, total=False):\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"TypedDict\\|class.*BaseModel\\|class.*Model\" model.py agent_stream_state.py events.py | head -40"}, "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_lr1zt9r7zcx7_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"TypedDict\\|BaseModel\" 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 -->
用户要求"梳理项目冗余"。这是一个代码审查任务，需要找出项目中的冗余代码、重复定义、未使用的代码等。

从之前的分析来看，我注意到：

1. **model.py** 和 **agent_stream_state.py** 以及 **events.py** 之间存在大量重复的数据结构定义：
   - `ToolRecord` 在 agent_stream_state.py 中定义
   - `ToolCallDeltaPayload` 在 agent_stream_state.py 中定义
   - 各种 Event 类型在 events.py 中定义

2. 这三个文件都是定义数据模型/类型的，理论上可以合并

让我更详细地分析一下这些重复。
<!-- 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": 167, "prompt_tokens": 5866, "total_tokens": 6033, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 167}, "prompt_tokens_details": {"cached_tokens": 5078, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 5078}
```
<!-- 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_lr1zt9r7zcx7_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": ["Shell 语法解析失败：Invalid \\escape: line 1 column 85 (char 84)"]}, "error": "Command requires approval.", "metadata": {"reasons": ["Shell 语法解析失败：Invalid \\escape: line 1 column 85 (char 84)"]}, "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": {"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": 1771653449.801841, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
继续
<!-- 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_05ia5kyvuc78_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"class\\|TypedDict\" model.py | 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 -->
用户让我继续分析项目冗余。让我继续查看文件间的依赖关系和数据结构重复。
<!-- 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": 62, "prompt_tokens": 6028, "total_tokens": 6090, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 62}, "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_05ia5kyvuc78_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": "3:from dataclasses import dataclass\n16:@dataclass\n17:class FunctionInfo:\n21:    @classmethod\n37:@dataclass\n38:class ToolCallInfo:\n43:    @classmethod\n95:class UserContent(BaseModel):\n104:    @classmethod\n158:    @classmethod\n190:    @classmethod\n217:class ShellResult(BaseModel):\n227:    @classmethod\n242:    @classmethod\n274:    @classmethod\n312:class ToolMessageContent(BaseModel):\n328:class Message(BaseModel):\n341:class CompactRecord(BaseModel):\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"class\\|TypedDict\" model.py | 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_15n90w9160b3_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat runtime_config.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我已经看到了项目的全貌。让我总结一下各个文件的功能和潜在冗余：

**文件职责分析：**

| 文件 | 行数 | 职责 |
|------|------|------|
| `oturn.py` | 770 | 主入口，Orchestrator |
| `context.py` | 419 | 会话上下文管理 |
| `agent_stream_state.py` | 376 | 流式状态机 |
| `tools/base.py` | 388 | 工具基类 |
| `model.py` | 350 | 数据模型 |
| `events.py` | 288 | 事件系统 |
| `session_store.py` | 264 | 会话存储 |
| `tool_runtime.py` | 244 | 工具运行时 |
| `llm_transport.py` | 225 | LLM传输层 |
| `tools/examples.py` | 195 | 示例工具 |
| `nucleus.py` | 96 | 发布订阅 |
| `mcp_adapter.py` | 101 | MCP适配器 |
| `template_renderer.py` | 86 | 模板渲染 |
| `runtime_config.py` | 36 | 运行时配置 |
| `tool_runtime_utils.py` | 41 | 工具运行时工具 |
| `tools/precheck.py` | 15 | 预检 |
| `tools/__init__.py` | 18 | 工具包初始化 |
| `__init__.py` | 51 | 包初始化 |

**潜在冗余分析：**


数据类型在不同模块中存在重复定义——`model.py`、`agent_stream_state.py` 和 `events.py` 都各自定义了一套 TypedDict，如 ToolRecord、Message 和 Event 类型。同时，`session_store.py` 和 `context.py` 之间存在功能重叠，前者处理底层存储而后者管理会话逻辑。

`template_renderer.py` 虽然目前被 context.py 依赖，但可能可以合并。此外，`tools/precheck.py` 仅有15行代码，独立性有限，`runtime_config.py` 和 `tool_runtime_utils.py` 也都相对较小，可能存在整合的空间。
<!-- 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": 456, "prompt_tokens": 6277, "total_tokens": 6733, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 456}, "prompt_tokens_details": {"cached_tokens": 6024, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6024}
```
<!-- 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_15n90w9160b3_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nfrom dataclasses import dataclass\r\nfrom typing import Any, Optional\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnProviderConfig:\r\n    api_base: str = \"\"\r\n    api_key: Optional[str] = None\r\n    debug_request: bool = False\r\n    debug_sse: bool = False\r\n    debug_tools: bool = False\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnModelConfig:\r\n    max_output_tokens: Optional[int] = None\r\n    temperature: Optional[float] = None\r\n    extra_body: Optional[dict[str, Any]] = None\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnAgentConfig:\r\n    max_steps: int = 100\r\n    user_name: str = \"User\"\r\n    email: Optional[str] = None\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass OturnConfig:\r\n    model: str\r\n    provider: OturnProviderConfig\r\n    model_config: OturnModelConfig\r\n    agent_config: OturnAgentConfig\r\n    debug_loop: bool = False\r\n", "stderr": "", "actual_command": "cat runtime_config.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_6t3flppwh2ca_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat nucleus.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在让我查看nucleus.py和tool_runtime_utils.py，看看是否有冗余
<!-- 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": 47, "prompt_tokens": 6636, "total_tokens": 6683, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 47}, "prompt_tokens_details": {"cached_tokens": 6273, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6273}
```
<!-- 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_6t3flppwh2ca_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

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

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_waw3mlq185q6_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -r \"nucleus\\|OTurnNucleus\\|AsyncPubSub\" --include=\"*.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 -->
我来看一下 `nucleus.py` 是否被使用。
<!-- 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": 7602, "total_tokens": 7665, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 63}, "prompt_tokens_details": {"cached_tokens": 6632, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6632}
```
<!-- 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_waw3mlq185q6_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 \"nucleus\\|OTurnNucleus\\|AsyncPubSub\" --include=\"*.py\" ."}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -r --exclude-dir=.cyf \"nucleus\\|OTurnNucleus\\|AsyncPubSub\" --include=\"*.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_pmaz7hyk155e_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -rn \"from.*nucleus\\|import.*nucleus\" --include=\"*.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 -->
nucleus 没有被使用！这是第一个发现的冗余。让我继续检查其他可能的冗余。
<!-- 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": 7796, "total_tokens": 7865, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 69}, "prompt_tokens_details": {"cached_tokens": 7598, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 7598}
```
<!-- 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_pmaz7hyk155e_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 -rn --exclude-dir=.cyf \"from.*nucleus\\|import.*nucleus\" --include=\"*.py\" ."}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -rn --exclude-dir=.cyf \"from.*nucleus\\|import.*nucleus\" --include=\"*.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_c8katt6ygpf0_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"from .nucleus\" 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 -->
好的，让我继续检查其他模块的使用情况。
<!-- 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": 49, "prompt_tokens": 7982, "total_tokens": 8031, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 49}, "prompt_tokens_details": {"cached_tokens": 7792, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 7792}
```
<!-- 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_c8katt6ygpf0_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": "35:from .nucleus import AsyncPubSub\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"from .nucleus\" 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_et8u69o4gidj_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"AsyncPubSub\" 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 -->
让我检查AsyncPubSub在oturn.py中是否实际使用。
<!-- 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": 53, "prompt_tokens": 8109, "total_tokens": 8162, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 53}, "prompt_tokens_details": {"cached_tokens": 7978, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 7978}
```
<!-- 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_et8u69o4gidj_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": "35:from .nucleus import AsyncPubSub\r\n355:        self._pubsub: AsyncPubSub[EventPayload] = AsyncPubSub()\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"AsyncPubSub\" 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_d8pmtwkria69_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "sed -n '350,370p' 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 -->
看起来确实导入了但可能没被使用。让我查看一下第355行附近的代码确认实际使用情况。

现在让我总结一下可能存在的冗余:

1. **nucleus.py** - 定义了 `AsyncPubSub` 和 `OTurnNucleus`，但看起来只有 `AsyncPubSub` 被导入到 oturn.py 中，并且只是在 `__init__` 中实例化了。`OTurnNucleus` 完全没有被使用。这是一个潜在的冗余模块，或者是一个预留的未来架构。

让我继续检查是否有其他冗余。
<!-- 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": 149, "prompt_tokens": 8257, "total_tokens": 8406, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 149}, "prompt_tokens_details": {"cached_tokens": 7978, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 7978}
```
<!-- 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_d8pmtwkria69_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": "        self._tool_policy: Callable[[str, JsonObject], Awaitable[bool]] = tool_policy or _allow\r\n        self._pending_events: list[_PendingEvent] = []\r\n        self._pending_lock = asyncio.Lock()\r\n        self._input_queue: asyncio.Queue[_InputQueueItem] = asyncio.Queue()\r\n        self._input_shadow: list[_InputQueueItem] = []\r\n        self._pubsub: AsyncPubSub[EventPayload] = AsyncPubSub()\r\n        self._tool_call_deltas: dict[str, ToolCallDeltaEvent] = {}\r\n        self._mcp_adapter = MCPAdapter()\r\n        self._tool_runtime = ToolRuntime(\r\n            tool_registry=self._tool_registry,\r\n            publish=self._publish_to_subscribers,\r\n            execute_remote_tool=self._execute_remote_tool,\r\n            cancel_event=self._cancel_evt,\r\n            debug_tools=bool(self.cfg.provider.debug_tools),\r\n        )\r\n\r\n    async def add_pending_event(self, payload: _PendingEvent) -> None:\r\n        if \"future_id\" not in payload:\r\n            return\r\n        async with self._pending_lock:\r\n            self._pending_events.append(payload)\r\n", "stderr": "", "actual_command": "sed -n '350,370p' 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_4w4eag26xmjn_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -rn \"OTurnNucleus\\|Transition\\|Reducer\" --include=\"*.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 -->
从代码可以看出，`AsyncPubSub` 在 oturn.py 中被直接使用（不是通过 OTurnNucleus），所以 `OTurnNucleus` 类是完全未使用的。`nucleus.py` 中的 `Transition` 和 `Reducer` 也没有被使用。

现在让我检查是否有其他冗余。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 110, "prompt_tokens": 8642, "total_tokens": 8752, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 110}, "prompt_tokens_details": {"cached_tokens": 8253, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 8253}
```
<!-- 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_4w4eag26xmjn_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 -rn --exclude-dir=.cyf \"OTurnNucleus\\|Transition\\|Reducer\" --include=\"*.py\" ."}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -rn --exclude-dir=.cyf \"OTurnNucleus\\|Transition\\|Reducer\" --include=\"*.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_3nblbh8ja3js_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "cat tool_runtime_utils.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 -->
nucleus.py 只被导入了 AsyncPubSub，但 otunr.py 自己实现了类似的 pub/sub 逻辑。这确实是一个冗余点。

让我继续检查其他可能的冗余：
1. model.py 和 agent_stream_state.py 的数据类型定义可能重复
2. events.py 和 agent_stream_state.py 可能有重复的状态/事件定义

让我查看 tool_runtime_utils.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": 109, "prompt_tokens": 8827, "total_tokens": 8936, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 109}, "prompt_tokens_details": {"cached_tokens": 8638, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 8638}
```
<!-- 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_3nblbh8ja3js_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nimport json\r\nfrom typing import Any, TypedDict\r\n\r\nJsonValue = str | int | float | bool | None | list[\"JsonValue\"] | dict[str, \"JsonValue\"]\r\nJsonObject = dict[str, JsonValue]\r\n\r\n\r\nclass ToolArgsSummary(TypedDict):\r\n    length: int\r\n    parse_ok: bool\r\n    head: str\r\n    tail: str\r\n\r\n\r\ndef parse_tool_arguments_with_status(arg_str: str) -> tuple[JsonObject, bool]:\r\n    text = (arg_str or \"\").strip()\r\n    if not text:\r\n        return {}, True\r\n    try:\r\n        parsed = json.loads(text)\r\n        if isinstance(parsed, dict):\r\n            return parsed, True\r\n        return {}, False\r\n    except Exception:\r\n        return {}, False\r\n\r\n\r\ndef parse_tool_arguments(arg_str: str) -> JsonObject:\r\n    parsed, _ = parse_tool_arguments_with_status(arg_str)\r\n    return parsed\r\n\r\n\r\ndef summarize_tool_arguments(arg_str: str, preview_chars: int = 120) -> ToolArgsSummary:\r\n    text = str(arg_str or \"\")\r\n    _, parse_ok = parse_tool_arguments_with_status(text)\r\n    head = text[:preview_chars]\r\n    tail = text[-preview_chars:] if len(text) > preview_chars else \"\"\r\n    return {\"length\": len(text), \"parse_ok\": parse_ok, \"head\": head, \"tail\": tail}\r\n\r\n", "stderr": "", "actual_command": "cat tool_runtime_utils.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_3k8k8n23idke_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"from.*tool_runtime_utils\\|import.*tool_runtime_utils\" 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 -->
这个文件很简短，核心函数只有3个，而且它们都在 tool_runtime.py 中被使用。让我确认一下 tool_runtime.py 是否已经 import 了这些函数。
<!-- 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": 80, "prompt_tokens": 9287, "total_tokens": 9367, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 80}, "prompt_tokens_details": {"cached_tokens": 8823, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 8823}
```
<!-- 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_3k8k8n23idke_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": "19:from .tool_runtime_utils import (\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"from.*tool_runtime_utils\\|import.*tool_runtime_utils\" 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_vtix453jn34n_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -60 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 look at more duplicates. First, let's check what events.py and agent_stream_state.py have in common since both have similar TypedDicts.
<!-- 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": 9428, "total_tokens": 9491, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 63}, "prompt_tokens_details": {"cached_tokens": 9283, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9283}
```
<!-- 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_vtix453jn34n_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nfrom typing import Any, Literal, Optional, TypeAlias, TypedDict\r\n\r\nfrom .agent_stream_state import ToolCallDeltaPayload, ToolRecord\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 ToolCallDeltaEvent(TypedDict, total=False):\r\n    type: Literal[\"tool_call_delta\"]\r\n    tool: str\r\n    args: str\r\n    index: Optional[int]\r\n    tool_call_id: str\r\n    delta_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    delta_id: str\r\n    future: Any\r\n\r\n\r\nclass ToolOutputEvent(TypedDict):\r\n    type: Literal[\"tool_output\"]\r\n    stream: Literal[\"stdout\", \"stderr\"]\r\n    text: str\r\n\r\n\r\nclass ToolExitEvent(TypedDict):\r\n    type: Literal[\"tool_exit\"]\r\n    code: int\r\n\r\n\r\nclass ApprovalPrecheckPayload(TypedDict, total=False):\r\n    status: str\r\n    message: str | None\r\n    reasons: list[str] | None\r\n    metadata: dict[str, Any] | None\r\n\r\n\r\nclass ApprovalRequestEvent(TypedDict):\r\n    type: Literal[\"approval_request\"]\r\n    id: str\r\n    tool: str\r\n    args: dict[str, Any]\r\n    future: Any\r\n", "stderr": "", "actual_command": "head -60 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_q9dond834654_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -rn \"tool_runtime_utils\" --include=\"*.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 -->
我对现在项目结构有了清晰的了解。让我分析冗余部分：

1. **nucleus.py** - 定义了 `AsyncPubSub` 和 `OTurnNucleus`，但只有 `AsyncPubSub` 被使用，`OTurnNucleus` 完全未使用。`Transition` 和 `Reducer` 也未被使用。

2. **tool_runtime_utils.py** - 只有41行，包含一些工具参数解析函数。让我检查它是否被广泛使用。

3. **model.py vs agent_stream_state.py** - 两个文件都有数据类型定义，存在重复定义的风险。让我确认是否有重复。

让我继续检查 tool_runtime_utils 的使用情况：
<!-- 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": 173, "prompt_tokens": 9970, "total_tokens": 10143, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 173}, "prompt_tokens_details": {"cached_tokens": 9424, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9424}
```
<!-- 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_q9dond834654_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 -rn --exclude-dir=.cyf \"tool_runtime_utils\" --include=\"*.py\" ."}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "", "stderr": "", "actual_command": "grep -rn --exclude-dir=.cyf \"tool_runtime_utils\" --include=\"*.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_k0rrrvn9sfus_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -rn \"tool_runtime_utils\" ."}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me check more systematically about what's actually used:
<!-- 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": 47, "prompt_tokens": 10136, "total_tokens": 10183, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 47}, "prompt_tokens_details": {"cached_tokens": 9966, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 9966}
```
<!-- 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_k0rrrvn9sfus_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": "grep: ./__pycache__/tool_runtime_utils.cpython-312.pyc: binary file matches\r\ngrep: ./__pycache__/mcp_adapter.cpython-312.pyc: binary file matches\r\ngrep: ./__pycache__/tool_runtime.cpython-312.pyc: binary file matches\r\n./tool_runtime.py:19:from .tool_runtime_utils import (\r\n./mcp_adapter.py:7:from .tool_runtime_utils import JsonObject\r\n", "stderr": "", "actual_command": "grep -rn --exclude-dir=.cyf \"tool_runtime_utils\" ."}, "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_qkfr1qowlyx7_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"ToolCall\\|ToolRecord\\|ToolFunction\" model.py agent_stream_state.py 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 -->
好的，现在我有足够的信息来做冗余分析了。

项目文件:
- `__init__.py` (51行)
- `model.py` (350行) - 数据模型
- `context.py` (419行) - 上下文管理
- `session_store.py` (264行) - 会话存储
- `template_renderer.py` (86行) - 模板渲染
- `agent_stream_state.py` (376行) - 流状态管理
- `events.py` (288行) - 事件定义
- `llm_transport.py` (225行) - LLM传输
- `oturn.py` (770行) - 主模块
- `tool_runtime.py` (244行) - 工具运行时
- `tool_runtime_utils.py` (41行) - 工具运行时工具
- `runtime_config.py` (36行) - 运行时配置
- `nucleus.py` (96行) - 状态机核心
- `mcp_adapter.py` (101行) - MCP适配器
- `tools/base.py` (388行) - 工具基类
- `tools/examples.py` (195行) - 示例工具
- `tools/precheck.py` (15行) - 预检查
- `tools/__init__.py` (18行)

分析冗余:

1. **nucleus.py** (96行)
   - 导入了 `AsyncPubSub` 但未使用 `OTurnNucleus` 和 `Transition`
   - `OTurnNucleus` 和 `Transition` 类完全没有被调用
   - 应该删除这两个类，只保留 `AsyncPubSub`
   - 或者将 `AsyncPubSub` 移到其他模块

2. **tool_runtime_utils.py** (41行)
   - 包含 `JsonObject`, `JsonValue`, `ToolArgsSummary` 类型定义
   - 以及 `parse_tool_arguments_with_status`, `parse_tool_arguments`, `summarize_tool_arguments` 函数
   - 这些是工具运行时所需的类型和函数

目前没有被其他模块直接使用，可能需要整合到 tool_runtime.py 中。

3. **runtime_config.py** (36行)
   - 定义了 `OturnProviderConfig`, `OturnModelConfig`, `OturnAgentConfig`, `OturnConfig` 四个配置类
   - 目前没有冗余，但需要确认是否还有其他用途

4. **data model 分散**
   - `model.py` 包含数据模型
   - `agent_stream_state.py` 包含状态相关的类型
   - `events.py` 包含事件类型
   - 需要检查是否存在重复或可以合并的类型定义
<!-- 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": 604, "prompt_tokens": 10349, "total_tokens": 10953, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 604}, "prompt_tokens_details": {"cached_tokens": 10132, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 10132}
```
<!-- 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_qkfr1qowlyx7_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:38:class ToolCallInfo:\r\nmodel.py:44:    def from_json(cls, data: Dict[str, Any]) -> 'ToolCallInfo':\r\nmodel.py:45:        \"\"\"从 JSON 数据创建 ToolCallInfo 对象\"\"\"\r\nmodel.py:334:    tool_calls: Optional[List[ToolCallInfo]] = None  # List of ToolCallInfo objects\r\nagent_stream_state.py:19:class ToolFunctionPayload(TypedDict):\r\nagent_stream_state.py:24:class ToolRecord(TypedDict):\r\nagent_stream_state.py:27:    function: ToolFunctionPayload\r\nagent_stream_state.py:30:class ToolCallDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:63:class ToolCallAccumulator:\r\nagent_stream_state.py:78:    tool_calls_by_idx: dict[int, ToolCallAccumulator] = field(default_factory=dict)\r\nagent_stream_state.py:79:    tool_calls_by_id: dict[str, ToolCallAccumulator] = field(default_factory=dict)\r\nagent_stream_state.py:117:class ToolCallDeltaSignal:\r\nagent_stream_state.py:119:    payload: ToolCallDeltaPayload = field(default_factory=dict)\r\nagent_stream_state.py:123:class ToolCallsDoneSignal:\r\nagent_stream_state.py:132:    | ToolCallDeltaSignal\r\nagent_stream_state.py:133:    | ToolCallsDoneSignal\r\nagent_stream_state.py:146:class ToolCallTransitionPayload(TypedDict):\r\nagent_stream_state.py:176:class ToolCallDeltaTransition:\r\nagent_stream_state.py:178:    payload: ToolCallTransitionPayload\r\nagent_stream_state.py:182:class ToolCallDeltaDroppedTransition:\r\nagent_stream_state.py:188:class ToolCallsDoneTransition:\r\nagent_stream_state.py:203:    | ToolCallDeltaTransition\r\nagent_stream_state.py:204:    | ToolCallDeltaDroppedTransition\r\nagent_stream_state.py:205:    | ToolCallsDoneTransition\r\nagent_stream_state.py:235:        payload: ToolCallDeltaPayload = {\r\nagent_stream_state.py:243:            ToolCallDeltaSignal(\r\nagent_stream_state.py:249:        return [ToolCallsDoneSignal(type=\"tool_calls_done\", payload={})]\r\nagent_stream_state.py:311:                rec = ToolCallAccumulator(index=idx, id=str(tcid) if tcid else \"\")\r\nagent_stream_state.py:322:                rec = ToolCallAccumulator(index=state.next_tool_idx, id=str(tcid))\r\nagent_stream_state.py:328:            return ToolCallDeltaDroppedTransition(kind=\"tool_call_delta_dropped\")\r\nagent_stream_state.py:336:        return ToolCallDeltaTransition(\r\nagent_stream_state.py:348:        return ToolCallsDoneTransition(kind=\"tool_calls_done\")\r\nagent_stream_state.py:353:def build_assembled_debug(state: TurnStreamState) -> list[ToolRecord]:\r\nagent_stream_state.py:366:def build_tool_records(state: TurnStreamState) -> list[ToolRecord]:\r\nagent_stream_state.py:367:    out: list[ToolRecord] = []\r\nevents.py:5:from .agent_stream_state import ToolCallDeltaPayload, ToolRecord\r\nevents.py:20:class ToolCallDeltaEvent(TypedDict, total=False):\r\nevents.py:30:class ToolCallRequestEvent(TypedDict):\r\nevents.py:103:class ToolCallDebugDeltaEvent(TypedDict):\r\nevents.py:105:    delta: ToolCallDeltaPayload\r\nevents.py:108:class ToolCallDebugWarningEvent(TypedDict):\r\nevents.py:113:class ToolCallDebugAssembledEvent(TypedDict):\r\nevents.py:115:    assembled: list[ToolRecord]\r\nevents.py:118:class ToolCallDebugStageEvent(TypedDict, total=False):\r\nevents.py:127:ToolCallDebugEvent: TypeAlias = (\r\nevents.py:128:    ToolCallDebugDeltaEvent\r\nevents.py:129:    | ToolCallDebugWarningEvent\r\nevents.py:130:    | ToolCallDebugAssembledEvent\r\nevents.py:131:    | ToolCallDebugStageEvent\r\nevents.py:135:StreamOutEvent: TypeAlias = AssistantDeltaEvent | AssistantReasoningDeltaEvent | ToolCallDeltaEvent\r\nevents.py:140:    | ToolCallRequestEvent\r\nevents.py:141:    | ToolCallDeltaEvent\r\nevents.py:142:    | ToolCallDebugEvent\r\nevents.py:203:) -> ToolCallDeltaEvent:\r\nevents.py:204:    payload: ToolCallDeltaEvent = {\r\nevents.py:219:def ev_tool_call_request(*, tool: str, delta_id: str, future: Any) -> ToolCallRequestEvent:\r\nevents.py:261:def ev_tool_call_debug_delta(delta: ToolCallDeltaPayload) -> ToolCallDebugDeltaEvent:\r\nevents.py:265:def ev_tool_call_debug_warning(warning: str) -> ToolCallDebugWarningEvent:\r\nevents.py:269:def ev_tool_call_debug_assembled(assembled: list[ToolRecord]) -> ToolCallDebugAssembledEvent:\r\nevents.py:280:) -> ToolCallDebugStageEvent:\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"ToolCall\\|ToolRecord\\|ToolFunction\" model.py agent_stream_state.py 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={"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 -->
用户要求"梳理项目冗余"，让我整理已发现的问题并给出结论。

已发现的冗余：

1. **nucleus.py** (96行)
   - `OTurnNucleus`、`Transition`、`Reducer` 类未被任何地方使用
   - 只有 `AsyncPubSub` 被 oturn.py 使用
   - 可考虑删除未使用的类，或完全删除该文件（如果只保留 AsyncPubSub）

2. **tool_runtime_utils.py** (41行)
   - 只有 `JsonObject` 类型被 mcp_adapter.py 使用
   - 可将 `JsonObject` 移至 mcp_adapter.py 或其他共享位置

3. **数据模型重复**
   - model.py: `ToolCallInfo` (dataclass)
   - agent_stream_state.py: `ToolFunctionPayload`, `ToolRecord` (TypedDict)
   - events.py: `ToolCallDeltaEvent` (TypedDict)
   - 这些都表示"工具调用"但结构不同，可能造成混淆

4. **events.py 和 agent_stream_state.py 功能重叠**
   - events.py 导入了 agent_stream_state.py 的类型
   - 两者都定义了类似的 tool-related 类型

让我给用户一个清晰的总结。
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
根据分析，项目存在以下冗余：

---

## 冗余清单

### 1. nucleus.py - 整文件冗余 (96行)
**问题**: `OTurnNucleus`、`Transition`、`Reducer` 类未被使用
- `grep` 搜索整个项目无引用
- 仅 `AsyncPubSub` 被 oturn.py 引用 (第355行)

**建议**: 删除未使用的类，或将 `AsyncPubSub` 移至其他文件后删除该文件

---

### 2. tool_runtime_utils.py - 可内联 (41行)
**问题**: 仅被两处引用
- `tool_runtime.py` - 导入函数
- `mcp_adapter.py` - 仅用 `JsonObject` 类型

**建议**: 将 `JsonObject` 类型定义移至 `mcp_adapter.py`，删除此文件

---

### 3. 数据模型重复定义

| 文件 | 类型 | 用途 |
|------|------|------|
| model.py | `ToolCallInfo` (dataclass) | 消息中的 tool_calls |
| agent_stream_state.py | `ToolFunctionPayload`, `ToolRecord` (TypedDict) | 流式状态处理 |
| events.py | `ToolCallDeltaEvent` (TypedDict) | 事件系统 |

**问题**: 三处定义相似结构，职责不清

---

### 4. events.py 与 agent_stream_state.py 耦合
- events.py 第5行: `from .agent_stream_state import ToolCallDeltaPayload, ToolRecord`
- events.py 定义的事件类型与 agent_stream_state.py 的信号/状态高度重叠

**建议**: 考虑合并或明确边界

---

## 清理优先级

| 优先级 | 操作 | 预估减少 |
|--------|------|----------|
| P0 | 删除 nucleus.py 未使用类或整个文件 | ~90行 |
| P1 | 内联 tool_runtime_utils.py | ~40行 |
| P2 | 统一 ToolCall 数据模型 | 长期技术债 |

是否需要执行清理？
<!-- 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": 672, "prompt_tokens": 11631, "total_tokens": 12303, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 672}, "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 -->



