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95 lines
3.5 KiB
Markdown
95 lines
3.5 KiB
Markdown
# s04: Subagents (子智能体)
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`s01 > s02 > s03 > [ s04 ] s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"大任务拆小, 每个小任务干净的上下文"* -- 子智能体用独立 messages[], 不污染主对话。
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## 问题
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智能体工作越久, messages 数组越胖。每次读文件、跑命令的输出都永久留在上下文里。"这个项目用什么测试框架?" 可能要读 5 个文件, 但父智能体只需要一个词: "pytest。"
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## 解决方案
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```
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Parent agent Subagent
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+------------------+ +------------------+
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| messages=[...] | | messages=[] | <-- fresh
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| | dispatch | |
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| tool: task | ----------> | while tool_use: |
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| prompt="..." | | call tools |
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| | summary | append results |
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| result = "..." | <---------- | return last text |
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+------------------+ +------------------+
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Parent context stays clean. Subagent context is discarded.
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```
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## 工作原理
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1. 父智能体有一个 `task` 工具。子智能体拥有除 `task` 外的所有基础工具 (禁止递归生成)。
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```python
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PARENT_TOOLS = CHILD_TOOLS + [
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{"name": "task",
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"description": "Spawn a subagent with fresh context.",
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"input_schema": {
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"type": "object",
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"properties": {"prompt": {"type": "string"}},
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"required": ["prompt"],
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}},
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]
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```
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2. 子智能体以 `messages=[]` 启动, 运行自己的循环。只有最终文本返回给父智能体。
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```python
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def run_subagent(prompt: str) -> str:
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sub_messages = [{"role": "user", "content": prompt}]
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for _ in range(30): # safety limit
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response = client.messages.create(
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model=MODEL, system=SUBAGENT_SYSTEM,
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messages=sub_messages,
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tools=CHILD_TOOLS, max_tokens=8000,
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)
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sub_messages.append({"role": "assistant",
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"content": response.content})
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if response.stop_reason != "tool_use":
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break
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results = []
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for block in response.content:
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if block.type == "tool_use":
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handler = TOOL_HANDLERS.get(block.name)
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output = handler(**block.input)
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results.append({"type": "tool_result",
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"tool_use_id": block.id,
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"content": str(output)[:50000]})
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sub_messages.append({"role": "user", "content": results})
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return "".join(
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b.text for b in response.content if hasattr(b, "text")
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) or "(no summary)"
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```
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子智能体可能跑了 30+ 次工具调用, 但整个消息历史直接丢弃。父智能体收到的只是一段摘要文本, 作为普通 `tool_result` 返回。
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## 相对 s03 的变更
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| 组件 | 之前 (s03) | 之后 (s04) |
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|----------------|------------------|-------------------------------|
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| Tools | 5 | 5 (基础) + task (仅父端) |
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| 上下文 | 单一共享 | 父 + 子隔离 |
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| Subagent | 无 | `run_subagent()` 函数 |
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| 返回值 | 不适用 | 仅摘要文本 |
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## 试一试
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```sh
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cd learn-claude-code
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python agents/s04_subagent.py
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```
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试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
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1. `Use a subtask to find what testing framework this project uses`
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2. `Delegate: read all .py files and summarize what each one does`
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3. `Use a task to create a new module, then verify it from here`
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