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

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# [<!-- md_as_config:ctx: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": 1771651368.280687, "tz": "+08:00"} <!-- md_as_config:ctx:value-end -->
]

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content.content -->
查询系统架构
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# [<!-- md_as_config:ctx:value-begin json-path=$.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 -->
用户想要查询系统架构。我需要了解这个项目的结构。让我先看看当前目录的文件结构。
<!-- md_as_config:ctx:multi-line-value-end -->

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

.tool_calls[0].function.arguments:

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

{{ .content:code-block }}

Usage:
<!-- md_as_config:ctx:code-block-begin lang=json type=json json-path=$.usage -->
```json
{"completion_tokens": 69, "prompt_tokens": 1456, "total_tokens": 1525, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 69}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
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# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

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{"status": "success", "data": {"exit_code": 0, "stdout": "total 136\r\ndrwxr-xr-x 1 draplater draplater   450 Feb 21 12:54 .\r\ndrwxr-xr-x 1 draplater draplater    10 Feb 21 10:37 ..\r\n-rw-r--r-- 1 draplater draplater 11046 Feb 21 10:37 agent_stream_state.py\r\n-rw-r--r-- 1 draplater draplater 11520 Feb 21 12:52 context.py\r\ndrwxr-xr-x 1 draplater draplater    16 Feb 21 12:43 .cyf\r\n-rw-r--r-- 1 draplater draplater  4881 Feb 21 13:20 events.py\r\n-rw-r--r-- 1 draplater draplater  1235 Feb 21 12:00 __init__.py\r\n-rw-r--r-- 1 draplater draplater  8745 Feb 21 12:09 llm_transport.py\r\n-rw-r--r-- 1 draplater draplater  3591 Feb 21 12:46 mcp_adapter.py\r\n-rw-r--r-- 1 draplater draplater 12498 Feb 21 10:37 model.py\r\n-rw-r--r-- 1 draplater draplater  2895 Feb 21 10:37 nucleus.py\r\n-rw-r--r-- 1 draplater draplater 28280 Feb 21 13:20 oturn.py\r\ndrwxr-xr-x 1 draplater draplater   756 Feb 21 13:21 __pycache__\r\n-rw-r--r-- 1 draplater draplater  3130 Feb 21 12:16 README.md\r\n-rw-r--r-- 1 draplater draplater   813 Feb 21 12:15 runtime_config.py\r\n-rw-r--r-- 1 draplater draplater  7875 Feb 21 12:49 session_store.py\r\n-rw-r--r-- 1 draplater draplater  3610 Feb 21 12:39 template_renderer.py\r\n-rw-r--r-- 1 draplater draplater  9633 Feb 21 13:21 tool_runtime.py\r\n-rw-r--r-- 1 draplater draplater  1142 Feb 21 12:10 tool_runtime_utils.py\r\ndrwxr-xr-x 1 draplater draplater   102 Feb 21 11:13 tools\r\n", "stderr": "", "actual_command": "ls -la /home/draplater/Development/cyf_cli/oturn/src/oturn"}, "error": null, "metadata": null, "info": null}
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# [<!-- md_as_config:ctx: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=$.reasoning_content -->
The user is asking about the system architecture. Let me look at the key files to understand the architecture better - particularly the main entry point (oturn.py) and the README.
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## <!-- md_as_config:ctx:value-begin type=null json-path=$.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=$.usage -->
```json
{"completion_tokens": 85, "prompt_tokens": 2172, "total_tokens": 2257, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 85}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
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# [<!-- md_as_config:ctx:value-begin json-path=$.role --> tool <!-- md_as_config:ctx:value-end -->]

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{"status": "success", "data": {"exit_code": 0, "stdout": "# OTURN\r\n\r\n**OTURN** = **Orchestrated Task Unified Reactive Nucleus**.\r\n\r\nA small async framework for agent-like loops built on:\r\n\r\n- reducer-based state transitions\r\n- explicit transition events\r\n- multi-subscriber async pub/sub\r\n- deterministic step/run control\r\n- explicit runtime tool injection (no global registry)\r\n\r\n## Core API\r\n\r\n- `Transition[state, event]`: reducer output (`state`, `events`, `terminal`)\r\n- `Reducer`: `(state, signal) -> Transition`\r\n- `AsyncPubSub[event]`: `subscribe(queue)`, `unsubscribe(queue)`, `publish(event)`\r\n- `OTurnNucleus[state, signal, event]`:\r\n  - `step(signal)`\r\n  - `run(async_signals)`\r\n  - `state`, `terminal`\r\n  - `subscribe(queue)`, `unsubscribe(queue)`\r\n- `Oturn(work_dir, config, tools=[...])`:\r\n  - pass tool classes at runtime\r\n  - no global auto-discovered registry\r\n\r\n## Minimal Example\r\n\r\n```python\r\nimport asyncio\r\nfrom dataclasses import dataclass\r\nfrom typing import Literal\r\n\r\nfrom oturn import OTurnNucleus, Transition\r\n\r\nSignal = Literal[\"tick\", \"stop\"]\r\n\r\n@dataclass\r\nclass State:\r\n    n: int = 0\r\n\r\n\r\ndef reducer(state: State, signal: Signal) -> Transition[State, dict]:\r\n    if signal == \"tick\":\r\n        ns = State(n=state.n + 1)\r\n        return Transition(state=ns, events=[{\"type\": \"count\", \"n\": ns.n}], terminal=False)\r\n    return Transition(state=state, events=[{\"type\": \"done\"}], terminal=True)\r\n\r\n\r\nasync def main() -> None:\r\n    nucleus = OTurnNucleus(State(), reducer)\r\n    event_q: asyncio.Queue[dict] = asyncio.Queue()\r\n    nucleus.subscribe(event_q)\r\n\r\n    async def signals():\r\n        for sig in (\"tick\", \"tick\", \"stop\"):\r\n            yield sig\r\n\r\n    await nucleus.run(signals())\r\n\r\n    while not event_q.empty():\r\n        print(await event_q.get())\r\n\r\n\r\nasyncio.run(main())\r\n```\r\n\r\n## Example Tools\r\n\r\n`oturn.tools.examples` provides:\r\n\r\n- `WeatherTool` (`get_weather`)\r\n- `HostConfigTool` (`get_host_config`)\r\n\r\nUse them explicitly:\r\n\r\n```python\r\nfrom oturn import Oturn, OturnConfig, OturnProviderConfig, OturnModelConfig, OturnAgentConfig, WeatherTool, HostConfigTool\r\n\r\ncfg = OturnConfig(\r\n    model=\"openai/gpt-4o-mini\",\r\n    provider=OturnProviderConfig(api_base=\"https://api.openai.com/v1\", api_key=\"...\"),\r\n    model_config=OturnModelConfig(max_output_tokens=8192, temperature=0.7),\r\n    agent_config=OturnAgentConfig(max_steps=100, user_name=\"User\"),\r\n)\r\nagent = Oturn(work_dir=work_dir, config=cfg, tools=[WeatherTool, HostConfigTool])\r\n```\r\n\r\nOr pass explicit runtime config to decouple from caller-specific option structs:\r\n\r\n```python\r\nfrom oturn import (\r\n    Oturn,\r\n    OturnConfig,\r\n    OturnProviderConfig,\r\n    OturnModelConfig,\r\n    OturnAgentConfig,\r\n)\r\n\r\ncfg = OturnConfig(\r\n    model=\"openai/gpt-4o-mini\",\r\n    provider=OturnProviderConfig(api_base=\"https://api.openai.com/v1\", api_key=\"...\"),\r\n    model_config=OturnModelConfig(max_output_tokens=8192, temperature=0.7),\r\n    agent_config=OturnAgentConfig(max_steps=100, user_name=\"User\"),\r\n)\r\n\r\nagent = Oturn(work_dir=work_dir, config=cfg, tools=[...])\r\n```\r\n\r\n## Design Notes\r\n\r\n- `step()` is the unit of determinism and testing.\r\n- `run()` is a convenience wrapper around repeated `step()` calls.\r\n- Pub/sub is queue-based so multiple independent consumers can observe the same stream.\r\n", "stderr": "", "actual_command": "cat /home/draplater/Development/cyf_cli/oturn/src/oturn/README.md"}, "error": null, "metadata": null, "info": null}
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# [<!-- 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 is architecture of the ot asking about the systemurn project. Let me summarize what I've found:

From the README and file listing, this is OTURN (Orchestrated Task Unified Reactive Nucleus) - a small async framework for agent-like loops.

Key architectural components:
1. Core API:
   - `Transition[state, event]` - reducer output
   - `Reducer` - function that maps (state, signal) to Transition
   - `AsyncPubSub[event]` - async pub/sub with subscribe/unsubscribe/publish
   - `OTurnNucleus[state, signal, event]` - the main nucleus with step/run methods
   - `Oturn` - higher-level wrapper that takes work_dir, config, and tools

2. Key files in the project:
   - `oturn.py` - main entry point (28280 bytes, largest file)
   - `nucleus.py` - OTurnNucleus implementation
   - `model.py` - model configuration
   - `context.py` - context management
   - `events.py` - event handling
   - `llm_transport.py` - LLM transport layer
   - `mcp_adapter.py` - MCP adapter
   - `session_store.py` - session storage
   - `template_renderer.py` - template rendering
   - `tool_runtime.py` - tool runtime
   - `agent_stream_state.py` - agent stream state management
   - `tool_runtime_utils.py` - tool runtime utilities
   - `runtime_config.py` - runtime configuration
   - `tools/` - tools directory

Let me provide a clear summary of the architecture.
<!-- md_as_config:ctx:multi-line-value-end -->

<!-- md_as_config:ctx:multi-line-value-begin json-path=$.content -->
## OTURN 系统架构

**OTURN** = Orchestrated Task Unified Reactive Nucleus

一个基于以下构建的轻量级异步 agent 框架：

| 核心概念 | 说明 |
|---------|------|
| **Transition[state, event]** | reducer 输出 (state, events, terminal) |
| **Reducer** | 函数 `(state, signal) -> Transition` |
| **AsyncPubSub[event]** | 异步发布/订阅 (subscribe/unsubscribe/publish) |
| **OTurnNucleus** | 核心引擎：step() / run() / state / terminal |
| **Oturn** | 高层封装：传入 work_dir, config, tools[] |

### 项目文件结构

```
oturn/
├── oturn.py           # 主入口 (28KB)
├── nucleus.py         # OTurnNucleus 核心实现
├── model.py           # 模型配置
├── context.py         # 上下文管理
├── events.py          # 事件处理
├── llm_transport.py   # LLM 传输层
├── mcp_adapter.py     # MCP 适配器
├── session_store.py   # 会话存储
├── template_renderer.py
├── tool_runtime.py    # 工具运行时
├── agent_stream_state.py
└── tools/             # 工具目录
```

### 架构特点

- **reducer 状态转换** - 确定性的 step 测试单元
- **显式转换事件** - 多订阅者异步 pub/sub
- **运行时工具注入** - 无全局注册表
- **队列式 pub/sub** - 支持多独立消费者观察同一流
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Usage:
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```json
{"completion_tokens": 681, "prompt_tokens": 3342, "total_tokens": 4023, "completion_tokens_details": {"reasoning_tokens": 0, "text_tokens": 681}, "prompt_tokens_details": {"cached_tokens": 0, "cache_creation_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}
```
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