<!-- 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": {}, "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 -->
- **Command:** 
- **Working Directory:** 
- **Exit Code:** 
- **Date:** 
]

**STDOUT:**

<!-- 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": 1771654668.821912, "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_nywdr4ubvpx3_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -type f -name \"*.py\" | head -50 && echo \"---\" && find . -type f -name \"*.py\" | wc -l"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
The user wants me to analyze the project for redundancy. Let me first understand the project structure and identify any redundant files, code, or patterns.

Let me start by exploring the project structure more thoroughly.
<!-- 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": 104, "prompt_tokens": 1613, "total_tokens": 1717, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 104}, "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_nywdr4ubvpx3_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_nj4gax562yfv_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "find . -maxdepth 2 -type f -name \"*.py\" && echo \"---\" && find . -maxdepth 2 -type f -name \"*.py\" | wc -l"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
需要使用-maxdepth参数
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 76, "prompt_tokens": 1804, "total_tokens": 1880, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 76}, "prompt_tokens_details": {"cached_tokens": 1609, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1609}
```
<!-- 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_nj4gax562yfv_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "failure", "data": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find . -maxdepth 2 -type f -name \"*.py\" && echo \"---\" && find . -maxdepth 2 -type f -name \"*.py\" | wc -l"}, "error": "Command exited with code 1", "metadata": {"exit_code": 1, "stdout": "find: warning: ‘-name’ matches against basenames only, but the given pattern contains a directory separator (‘/’), thus the expression will evaluate to false all the time.  Did you mean ‘-wholename’?\r\nfind: paths must precede expression: `/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py'\r\nfind: possible unquoted pattern after predicate `-name'?\r\n", "stderr": "", "actual_command": "find . -maxdepth 2 -type f -name \"*.py\" && echo \"---\" && find . -maxdepth 2 -type f -name \"*.py\" | wc -l"}, "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_0ynpilt7ko9q_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "ls *.py 2>/dev/null | wc -l && ls *.py 2>/dev/null && ls tools/*.py 2>/dev/null | wc -l && ls tools/*.py 2>/dev/null"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
find命令有点问题，让我用其他方式列出Python文件。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 85, "prompt_tokens": 2235, "total_tokens": 2320, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 85}, "prompt_tokens_details": {"cached_tokens": 1800, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 1800}
```
<!-- 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_0ynpilt7ko9q_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": "0\n/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/context.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/events.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/__init__.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/llm_transport.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/mcp_adapter.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/model.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/nucleus.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/oturn.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/runtime_config.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/session_store.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/template_renderer.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime_utils.py\r\n0\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tools/base.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tools/examples.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tools/__init__.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tools/precheck.py\r\n", "stderr": "ls: cannot access '*.py': No such file or directory\nls: cannot access 'tools/*.py': No such file or directory\n", "actual_command": "ls *.py 2>/dev/null | wc -l && ls *.py 2>/dev/null && ls tools/*.py 2>/dev/null | wc -l && ls tools/*.py 2>/dev/null"}, "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_3d02gzllslnv_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "wc -l agent_stream_state.py context.py events.py llm_transport.py mcp_adapter.py model.py nucleus.py oturn.py runtime_config.py session_store.py template_renderer.py tool_runtime.py tool_runtime_utils.py tools/base.py tools/examples.py tools/__init__.py tools/precheck.py 2>/dev/null | 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 -->
用户想让我梳理项目冗余。我需要先了解这些文件的内容和依赖关系，才能判断哪些是冗余的。让我先查看每个文件的用途和大小。
<!-- 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": 130, "prompt_tokens": 2851, "total_tokens": 2981, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 130}, "prompt_tokens_details": {"cached_tokens": 2231, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2231}
```
<!-- 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_3d02gzllslnv_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\n    18 tools/__init__.py\n    36 runtime_config.py\n    41 tool_runtime_utils.py\n    93 template_renderer.py\n    96 nucleus.py\n   101 mcp_adapter.py\n   195 tools/examples.py\n   225 llm_transport.py\n   243 model.py\n   244 tool_runtime.py\n   264 session_store.py\n   288 events.py\n   376 agent_stream_state.py\n   388 tools/base.py\n   419 context.py\n   770 oturn.py\n  3812 total\n", "stderr": "", "actual_command": "wc -l agent_stream_state.py context.py events.py llm_transport.py mcp_adapter.py model.py nucleus.py oturn.py runtime_config.py session_store.py template_renderer.py tool_runtime.py tool_runtime_utils.py tools/base.py tools/examples.py tools/__init__.py tools/precheck.py 2>/dev/null | 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_59764kxruel7_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -50 agent_stream_state.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
用户要求"梳理项目冗余"。我需要检查这些文件，找出潜在的冗余代码。根据行数分布，我来逐一检查每个文件的内容，看是否有重复或冗余的部分。

让我先检查一些可能存在冗余的文件：
1. tool_runtime.py 和 tool_runtime_utils.py - 可能功能重复
2. session_store.py 和 context.py - 可能存储逻辑重复
3. events.py 和 agent_stream_state.py - 事件与状态管理可能重复
4. template_renderer.py 可能与其他模板相关功能重复
5. runtime_config.py 配置相关

让我先快速浏览这些文件的关键内容。
<!-- 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": 158, "prompt_tokens": 3202, "total_tokens": 3360, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 158}, "prompt_tokens_details": {"cached_tokens": 2847, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 2847}
```
<!-- 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_59764kxruel7_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nfrom dataclasses import dataclass, field\r\nfrom typing import Any, Literal, Optional, TypedDict, TypeAlias, cast\r\n\r\n\r\nclass ReasoningDetail(TypedDict, total=False):\r\n    text: str\r\n\r\n\r\nclass UsageData(TypedDict, total=False):\r\n    prompt_tokens: int\r\n    completion_tokens: int\r\n    total_tokens: int\r\n    cache_read_input_tokens: int\r\n    cache_creation_input_tokens: int\r\n\r\n\r\nclass ToolFunctionPayload(TypedDict):\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass ToolRecord(TypedDict):\r\n    index: int\r\n    id: str\r\n    function: ToolFunctionPayload\r\n\r\n\r\nclass ToolCallDeltaPayload(TypedDict, total=False):\r\n    model: str\r\n    output_index: int\r\n    tool_call_id: str\r\n    name: str\r\n    arguments: str\r\n\r\n\r\nclass AssistantDeltaPayload(TypedDict, total=False):\r\n    text: str\r\n    model: str\r\n\r\n\r\nclass AssistantReasoningDeltaPayload(TypedDict, total=False):\r\n    text: str\r\n    model: str\r\n    extra: \"ReasoningExtra | None\"\r\n\r\n\r\nclass AssistantDonePayload(TypedDict, total=False):\r\n    usage: UsageData\r\n", "stderr": "", "actual_command": "head -50 agent_stream_state.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_o68fccb3660j_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -60 context.py"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me continue reading more files to understand the project structure and find redundancies.
<!-- 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": 3631, "total_tokens": 3680, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 49}, "prompt_tokens_details": {"cached_tokens": 3198, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3198}
```
<!-- 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_o68fccb3660j_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "## @file context.py\r\n#  @brief Dual-stack conversation context (jsonl/md_as_config) with Markdown-first persistence.\r\n\r\nfrom __future__ import annotations\r\n\r\nimport traceback\r\nimport datetime as _dt\r\nfrom pathlib import Path\r\nfrom typing import Iterable, List, Union, Optional, Callable, Any, Dict\r\n\r\nfrom .model import Message, CompactRecord\r\nfrom .tools.base import BaseToolRegistry\r\nfrom .template_renderer import ContextTemplateRenderer\r\nfrom .session_store import SessionStore\r\n\r\n\r\nclass Context:\r\n    def __init__(\r\n        self,\r\n        file_backend: Path,\r\n        session_id: str | None = None,\r\n        work_dir: Path | None = None,\r\n        *,\r\n        meta_dirname: str = \".oturn\",\r\n        global_sessions_root: Path | None = None,\r\n    ):\r\n        self._store = SessionStore(\r\n            file_backend,\r\n            session_id=session_id,\r\n            work_dir=work_dir,\r\n            meta_dirname=meta_dirname,\r\n            global_sessions_root=global_sessions_root,\r\n        )\r\n        self._history: List[Union[Message, CompactRecord]] = []\r\n        self._mcp_client: Optional[Any] = None\r\n        self._mcp_tool_schemas: Dict[str, dict] = {}\r\n        self._mcp_tool_availability: Dict[str, bool] = {}\r\n        self._mcp_ready: bool = False\r\n        self._mcp_status_message: str = \"\"\r\n        self._mcp_last_ping: Optional[_dt.datetime] = None\r\n        self._tool_registry: BaseToolRegistry = BaseToolRegistry()\r\n        self._template_renderer = ContextTemplateRenderer(self._tool_registry)\r\n\r\n    # --------------------\r\n    # MCP helpers\r\n    # --------------------\r\n\r\n    def set_mcp_client(self, client: Any) -> None:\r\n        self._mcp_client = client\r\n\r\n    def get_mcp_client(self) -> Optional[Any]:\r\n        return self._mcp_client\r\n\r\n    def mark_mcp_ready(self, ready: bool, message: str = \"\", last_ping: Optional[_dt.datetime] = None) -> None:\r\n        self._mcp_ready = ready\r\n        if message:\r\n            self._mcp_status_message = message\r\n        if last_ping:\r\n            self._mcp_last_ping = last_ping\r\n\r\n", "stderr": "", "actual_command": "head -60 context.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_onuah7o5chul_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "head -80 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 -->
Now I understand the project structure better. Let me examine more files to find potential redundancies:

1. `agent_stream_state.py` - TypedDict definitions for streaming state (75 lines of type definitions)
2. `context.py` - Main context class that manages conversation, MCP, tools

Let me check other files for potential redundancy - especially model.py vs agent_stream_state.py which both seem to have type definitions.
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 117, "prompt_tokens": 4313, "total_tokens": 4430, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 117}, "prompt_tokens_details": {"cached_tokens": 3627, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 3627}
```
<!-- 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_onuah7o5chul_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "from __future__ import annotations\r\n\r\nfrom dataclasses import dataclass\r\nfrom typing import Optional, List, Dict, Any, Union, Literal, TypeAlias, Protocol, runtime_checkable\r\nimport datetime as _dt\r\n\r\nfrom pydantic import BaseModel, field_validator, field_serializer\r\n\r\ntry:\r\n    # py>=3.9\r\n    from zoneinfo import ZoneInfo\r\nexcept Exception:  # pragma: no cover\r\n    ZoneInfo = None  # type: ignore\r\n\r\n\r\n@dataclass\r\nclass FunctionInfo:\r\n    name: str\r\n    arguments: Any = None\r\n\r\n    @classmethod\r\n    def from_json(cls, data: Dict[str, Any]) -> \"FunctionInfo\":\r\n        \"\"\"Create ``FunctionInfo`` from a JSON-like dict.\"\"\"\r\n        return cls(\r\n            name=data.get(\"name\", \"\"),\r\n            arguments=data.get(\"arguments\"),\r\n        )\r\n\r\n    def to_dict(self) -> Dict[str, Any]:\r\n        \"\"\"Serialize to a plain dict.\"\"\"\r\n        result = {\"name\": self.name}\r\n        if self.arguments is not None:\r\n            result[\"arguments\"] = self.arguments\r\n        return result\r\n\r\n\r\n@dataclass\r\nclass ToolCallInfo:\r\n    id: str\r\n    type: str\r\n    function: FunctionInfo\r\n\r\n    @classmethod\r\n    def from_json(cls, data: Dict[str, Any]) -> \"ToolCallInfo\":\r\n        \"\"\"Create ``ToolCallInfo`` from a JSON-like dict.\"\"\"\r\n        function_data = data.get(\"function\", {})\r\n        function = FunctionInfo.from_json(function_data)\r\n        return cls(\r\n            id=data.get(\"id\", \"\"),\r\n            type=data.get(\"type\", \"\"),\r\n            function=function,\r\n        )\r\n\r\n    def to_dict(self) -> Dict[str, Any]:\r\n        \"\"\"Serialize to a plain dict.\"\"\"\r\n        return {\r\n            \"id\": self.id,\r\n            \"type\": self.type,\r\n            \"function\": self.function.to_dict(),\r\n        }\r\n\r\n\r\ndef _format_utc_offset(dt: _dt.datetime) -> str:\r\n    \"\"\"Format UTC offset as ``+HH:MM`` or ``-HH:MM``.\"\"\"\r\n    off = dt.utcoffset()\r\n    if off is None:\r\n        return \"+00:00\"\r\n    total = int(off.total_seconds())\r\n    sign = \"+\" if total >= 0 else \"-\"\r\n    total = abs(total)\r\n    hh = total // 3600\r\n    mm = (total % 3600) // 60\r\n    return f\"{sign}{hh:02d}:{mm:02d}\"\r\n\r\n\r\ndef _get_local_aware_now() -> _dt.datetime:\r\n    \"\"\"Return current local timezone-aware datetime.\"\"\"\r\n    return _dt.datetime.now().astimezone()\r\n\r\n\r\n", "stderr": "", "actual_command": "head -80 model.py"}, "error": null, "metadata": null, "info": null}
<!-- md_as_config:ctx:multi-line-value-end -->

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



<!-- md_as_config:ctx:begin type=pydantic:oturn.model.Message skeleton={"content": "", "tool_call_id": null, "tool_calls": [{"id": "call_function_5f9bkvfvt1s9_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -l \"TypedDict\" *.py 2>/dev/null"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Let me get a clearer picture by checking the main components and looking for potential redundancy. Let me check the imports in key 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": 68, "prompt_tokens": 5147, "total_tokens": 5215, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 68}, "prompt_tokens_details": {"cached_tokens": 4309, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 4309}
```
<!-- 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_5f9bkvfvt1s9_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": "/home/draplater/Development/cyf_cli/oturn/src/oturn/agent_stream_state.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/events.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/oturn.py\r\n/home/draplater/Development/cyf_cli/oturn/src/oturn/tool_runtime_utils.py\r\n", "stderr": "", "actual_command": "grep -l --exclude-dir=.cyf \"TypedDict\" *.py 2>/dev/null"}, "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_tndrr8syvus2_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"class.*TypedDict\\|= TypedDict\" agent_stream_state.py events.py oturn.py tool_runtime_utils.py 2>/dev/null"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我已经有足够的信息来梳理项目的冗余。让我总结一下发现的问题：

## 项目冗余分析

### 1. TypedDict 重复定义 (agent_stream_state.py, events.py, oturn.py, tool_runtime_utils.py)
多个文件独立定义了类似的 TypedDict 类型：
- `UsageData` - 在 agent_stream_state.py 中定义
- 可能在其他文件中也有类似定义

### 2. 数据模型重复
- `model.py` 定义了 `FunctionInfo`, `ToolCallInfo` 等数据类
- `agent_stream_state.py` 定义了 `ToolFunctionPayload`, `ToolRecord`, `ToolCallDeltaPayload` 等 TypedDict
- 这些可能功能重叠

### 3. 可能的功能重叠
- `context.py` 和 `session_store.py` 都处理会话存储
- `template_renderer.py` 和可能的其他模板处理

让我进一步检查是否有真正的冗余。
<!-- 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": 248, "prompt_tokens": 5357, "total_tokens": 5605, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 248}, "prompt_tokens_details": {"cached_tokens": 5143, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 5143}
```
<!-- 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_tndrr8syvus2_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "agent_stream_state.py:7:class ReasoningDetail(TypedDict, total=False):\r\nagent_stream_state.py:11:class UsageData(TypedDict, total=False):\r\nagent_stream_state.py:19:class ToolFunctionPayload(TypedDict):\r\nagent_stream_state.py:24:class ToolRecord(TypedDict):\r\nagent_stream_state.py:30:class ToolCallDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:38:class AssistantDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:43:class AssistantReasoningDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:49:class AssistantDonePayload(TypedDict, total=False):\r\nagent_stream_state.py:54:class EmptyPayload(TypedDict):\r\nagent_stream_state.py:58:class ReasoningExtra(TypedDict, total=False):\r\nagent_stream_state.py:137:class AssistantDeltaTransitionPayload(TypedDict):\r\nagent_stream_state.py:142:class AssistantDoneTransitionPayload(TypedDict, total=False):\r\nagent_stream_state.py:146:class ToolCallTransitionPayload(TypedDict):\r\nagent_stream_state.py:153:class EmptyTransitionPayload(TypedDict):\r\nevents.py:8:class AssistantDeltaEvent(TypedDict):\r\nevents.py:14:class AssistantReasoningDeltaEvent(TypedDict):\r\nevents.py:20:class ToolCallDeltaEvent(TypedDict, total=False):\r\nevents.py:30:class ToolCallRequestEvent(TypedDict):\r\nevents.py:37:class ToolOutputEvent(TypedDict):\r\nevents.py:43:class ToolExitEvent(TypedDict):\r\nevents.py:48:class ApprovalPrecheckPayload(TypedDict, total=False):\r\nevents.py:55:class ApprovalRequestEvent(TypedDict):\r\nevents.py:64:class ChoiceRequestEvent(TypedDict):\r\nevents.py:74:class ErrorEvent(TypedDict, total=False):\r\nevents.py:81:class AssistantFinalEvent(TypedDict, total=False):\r\nevents.py:88:class MessageSentEvent(TypedDict):\r\nevents.py:92:class RunAbortedEvent(TypedDict):\r\nevents.py:97:class DebugLatencyEvent(TypedDict):\r\nevents.py:103:class ToolCallDebugDeltaEvent(TypedDict):\r\nevents.py:108:class ToolCallDebugWarningEvent(TypedDict):\r\nevents.py:113:class ToolCallDebugAssembledEvent(TypedDict):\r\nevents.py:118:class ToolCallDebugStageEvent(TypedDict, total=False):\r\noturn.py:62:class _InputQueueItem(TypedDict):\r\noturn.py:72:class _ApiToolFunction(TypedDict):\r\noturn.py:77:class _ApiToolCall(TypedDict):\r\noturn.py:83:class _ApiMessage(TypedDict, total=False):\r\noturn.py:92:class _ToolFunctionSchema(TypedDict):\r\noturn.py:98:class _ToolSchema(TypedDict):\r\noturn.py:103:class _PendingEvent(TypedDict, total=False):\r\ntool_runtime_utils.py:10:class ToolArgsSummary(TypedDict):\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"class.*TypedDict\\|= TypedDict\" agent_stream_state.py events.py oturn.py tool_runtime_utils.py 2>/dev/null"}, "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_b5lwkl0qxuhw_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -h \"^from \\|^import \" *.py 2>/dev/null | sort | uniq -c | sort -rn | 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 check if there are duplicate TypedDict definitions between agent_stream_state.py and events.py. The names are quite different, so they seem to be separate concerns. Let me also check the actual dependencies between files to understand the project structure better.
<!-- 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": 108, "prompt_tokens": 6205, "total_tokens": 6313, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 108}, "prompt_tokens_details": {"cached_tokens": 5353, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 5353}
```
<!-- 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_b5lwkl0qxuhw_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "", "stderr": "grep: *.py: No such file or directory\n", "actual_command": "grep -h --exclude-dir=.cyf \"^from \\|^import \" *.py 2>/dev/null | sort | uniq -c | sort -rn | 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_hru6wnhrhec8_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -h \"^from \\|^import \" agent_stream_state.py context.py events.py llm_transport.py mcp_adapter.py model.py nucleus.py oturn.py runtime_config.py session_store.py template_renderer.py tool_runtime.py tool_runtime_utils.py tools/base.py tools/examples.py tools/__init__.py tools/precheck.py 2>/dev/null | sort | uniq -c | sort -rn | 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 -->
目录问题，我在当前目录但是命令需要明确指定文件。让我直接列出文件然后grep。
<!-- 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": 132, "prompt_tokens": 6370, "total_tokens": 6502, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 132}, "prompt_tokens_details": {"cached_tokens": 6201, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6201}
```
<!-- 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_hru6wnhrhec8_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": "     13 from __future__ import annotations\n      6 import json\n      5 import asyncio\n      5 from dataclasses import dataclass\n      4 import datetime as _dt\n      3 import traceback\n      3 import os\n      3 from pathlib import Path\n      3 from .context import Context\n      2 import threading\n      2 from typing import Any, Optional\n      2 from .tools.base import BaseToolRegistry\n      2 from pydantic import BaseModel\n      2 from .model import (\n      2 from .events import (\n      2 from dataclasses import dataclass, field\n      1 import urllib.request\n      1 import urllib.parse\n      1 import time\n      1 import socket\n      1 import platform\n      1 import inspect\n      1 import abc\n      1 from uuid import uuid4\n      1 from typing import Optional, List, Dict, Any, Union, Literal, TypeAlias, Protocol, runtime_checkable\n      1 from typing import Optional, Awaitable, Callable, List, Any, Dict, Iterable, AsyncGenerator, TypedDict, Literal, cast, TypeAlias, Type\n      1 from typing import Iterable, List, Union, Optional, Callable, Any, Dict\n      1 from typing import Dict, Any, Optional, Type, List, TYPE_CHECKING, Callable, Awaitable\n      1 from typing import AsyncIterable, Generic, Protocol, TypeVar\n      1 from typing import Any, TypedDict\n      1 from typing import Any, Literal, Optional, TypedDict, TypeAlias, cast\n      1 from typing import Any, Literal, Optional, TypeAlias, TypedDict\n      1 from typing import Any, Dict, Optional\n      1 from typing import Any, Dict, List, Optional\n      1 from typing import Any, Callable, Optional\n      1 from typing import Any, Awaitable, Callable, Optional\n      1 from typing import Any, AsyncGenerator, Dict, Optional\n      1 from .tools.base import ToolResult, BaseToolRegistry, BaseTool\n      1 from .tools.base import ToolResult\n      1 from .tools.base import BaseToolRegistry, ToolResult\n", "stderr": "", "actual_command": "grep -h --exclude-dir=.cyf \"^from \\|^import \" agent_stream_state.py context.py events.py llm_transport.py mcp_adapter.py model.py nucleus.py oturn.py runtime_config.py session_store.py template_renderer.py tool_runtime.py tool_runtime_utils.py tools/base.py tools/examples.py tools/__init__.py tools/precheck.py 2>/dev/null | sort | uniq -c | sort -rn | 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_mxrgpazr6n9u_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"^class\\|^@dataclass\\|^@dataclass(\" agent_stream_state.py events.py model.py oturn.py 2>/dev/null"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
Now let me look for duplicates/overlaps between files. The user wants me to identify redundancies. Let me check:
1. TypedDict definitions in multiple files
2. Data classes that might be duplicated
3. Type aliases that repeat
<!-- 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": 7113, "total_tokens": 7222, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 109}, "prompt_tokens_details": {"cached_tokens": 6366, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 6366}
```
<!-- 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_mxrgpazr6n9u_1", "tool_calls": null, "usage": null, "reasoning_content": null, "model_name": null, "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin type=json json-path=$.content -->
{"status": "success", "data": {"exit_code": 0, "stdout": "agent_stream_state.py:7:class ReasoningDetail(TypedDict, total=False):\r\nagent_stream_state.py:11:class UsageData(TypedDict, total=False):\r\nagent_stream_state.py:19:class ToolFunctionPayload(TypedDict):\r\nagent_stream_state.py:24:class ToolRecord(TypedDict):\r\nagent_stream_state.py:30:class ToolCallDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:38:class AssistantDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:43:class AssistantReasoningDeltaPayload(TypedDict, total=False):\r\nagent_stream_state.py:49:class AssistantDonePayload(TypedDict, total=False):\r\nagent_stream_state.py:54:class EmptyPayload(TypedDict):\r\nagent_stream_state.py:58:class ReasoningExtra(TypedDict, total=False):\r\nagent_stream_state.py:62:@dataclass\r\nagent_stream_state.py:63:class ToolCallAccumulator:\r\nagent_stream_state.py:70:@dataclass\r\nagent_stream_state.py:71:class TurnStreamState:\r\nagent_stream_state.py:92:@dataclass\r\nagent_stream_state.py:93:class StreamSignal:\r\nagent_stream_state.py:98:@dataclass\r\nagent_stream_state.py:99:class StreamTransition:\r\nagent_stream_state.py:104:@dataclass\r\nagent_stream_state.py:105:class AssistantReasoningDeltaSignal:\r\nagent_stream_state.py:110:@dataclass\r\nagent_stream_state.py:111:class AssistantDoneSignal:\r\nagent_stream_state.py:116:@dataclass\r\nagent_stream_state.py:117:class ToolCallDeltaSignal:\r\nagent_stream_state.py:122:@dataclass\r\nagent_stream_state.py:123:class ToolCallsDoneSignal:\r\nagent_stream_state.py:137:class AssistantDeltaTransitionPayload(TypedDict):\r\nagent_stream_state.py:142:class AssistantDoneTransitionPayload(TypedDict, total=False):\r\nagent_stream_state.py:146:class ToolCallTransitionPayload(TypedDict):\r\nagent_stream_state.py:153:class EmptyTransitionPayload(TypedDict):\r\nagent_stream_state.py:157:@dataclass\r\nagent_stream_state.py:158:class AssistantDeltaTransition:\r\nagent_stream_state.py:163:@dataclass\r\nagent_stream_state.py:164:class AssistantReasoningDeltaTransition:\r\nagent_stream_state.py:169:@dataclass\r\nagent_stream_state.py:170:class AssistantDoneTransition:\r\nagent_stream_state.py:175:@dataclass\r\nagent_stream_state.py:176:class ToolCallDeltaTransition:\r\nagent_stream_state.py:181:@dataclass\r\nagent_stream_state.py:182:class ToolCallDeltaDroppedTransition:\r\nagent_stream_state.py:187:@dataclass\r\nagent_stream_state.py:188:class ToolCallsDoneTransition:\r\nagent_stream_state.py:193:@dataclass\r\nagent_stream_state.py:194:class NoopTransition:\r\nevents.py:8:class AssistantDeltaEvent(TypedDict):\r\nevents.py:14:class AssistantReasoningDeltaEvent(TypedDict):\r\nevents.py:20:class ToolCallDeltaEvent(TypedDict, total=False):\r\nevents.py:30:class ToolCallRequestEvent(TypedDict):\r\nevents.py:37:class ToolOutputEvent(TypedDict):\r\nevents.py:43:class ToolExitEvent(TypedDict):\r\nevents.py:48:class ApprovalPrecheckPayload(TypedDict, total=False):\r\nevents.py:55:class ApprovalRequestEvent(TypedDict):\r\nevents.py:64:class ChoiceRequestEvent(TypedDict):\r\nevents.py:74:class ErrorEvent(TypedDict, total=False):\r\nevents.py:81:class AssistantFinalEvent(TypedDict, total=False):\r\nevents.py:88:class MessageSentEvent(TypedDict):\r\nevents.py:92:class RunAbortedEvent(TypedDict):\r\nevents.py:97:class DebugLatencyEvent(TypedDict):\r\nevents.py:103:class ToolCallDebugDeltaEvent(TypedDict):\r\nevents.py:108:class ToolCallDebugWarningEvent(TypedDict):\r\nevents.py:113:class ToolCallDebugAssembledEvent(TypedDict):\r\nevents.py:118:class ToolCallDebugStageEvent(TypedDict, total=False):\r\nmodel.py:16:@dataclass\r\nmodel.py:17:class FunctionInfo:\r\nmodel.py:37:@dataclass\r\nmodel.py:38:class ToolCallInfo:\r\nmodel.py:90:class UserContent(BaseModel):\r\nmodel.py:196:class ToolMessageContent(BaseModel):\r\nmodel.py:207:class MessageTextRenderable(Protocol):\r\nmodel.py:219:class Message(BaseModel):\r\nmodel.py:233:class CompactRecord(BaseModel):\r\noturn.py:62:class _InputQueueItem(TypedDict):\r\noturn.py:72:class _ApiToolFunction(TypedDict):\r\noturn.py:77:class _ApiToolCall(TypedDict):\r\noturn.py:83:class _ApiMessage(TypedDict, total=False):\r\noturn.py:92:class _ToolFunctionSchema(TypedDict):\r\noturn.py:98:class _ToolSchema(TypedDict):\r\noturn.py:103:class _PendingEvent(TypedDict, total=False):\r\noturn.py:314:class Oturn:\r\n", "stderr": "", "actual_command": "grep -n --exclude-dir=.cyf \"^class\\|^@dataclass\\|^@dataclass(\" agent_stream_state.py events.py model.py oturn.py 2>/dev/null"}, "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_eaz4uh5unvrp_1", "type": "function", "function": {"name": "execute_shell_command", "arguments": {"command": "grep -n \"class.*Event\\|Payload\\|Signal\\|Transition\" events.py agent_stream_state.py 2>/dev/null | head -30"}}}], "extra": null} -->

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

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.reasoning_content -->
现在我有了全面的项目视图。让我总结一下冗余分析结果：

1. **重复的数据类型定义**：
   - `events.py` 定义的事件类型 (AssistantDeltaEvent, ToolCallDeltaEvent 等) 与 `agent_stream_state.py` 中的 payload 类型高度重叠
   - `model.py` 中有 Message, CompactRecord 等模型
   - 多个文件都有 TypedDict 定义

2. **状态管理相关**：
   - `agent_stream_state.py` (376行) - 流状态管理
   - `session_store.py` (264行) - 会话存储
   - 状态可能分散在多处

3. **导入分散**：
   - 没有统一的入口，导入来源分散

让我生成一份精简的冗余报告。
<!-- 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": 215, "prompt_tokens": 8449, "total_tokens": 8664, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 215}, "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_eaz4uh5unvrp_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 119 (char 118)"]}, "error": "Command requires approval.", "metadata": {"reasons": ["Shell 语法解析失败：Invalid \\escape: line 1 column 119 (char 118)"]}, "info": {}}
<!-- md_as_config:ctx:multi-line-value-end -->

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



