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Comprehensive rewrite establishing the harness engineering narrative across the entire repository. README (EN/ZH/JA): added "The Model IS the Agent" manifesto with historical proof (DQN, OpenAI Five, AlphaStar, Tencent Jueyu), "What an Agent Is NOT" critique, harness engineer role definition, "Why Claude Code" as masterclass in harness design, and universe vision. Consistent framing: model = driver, harness = vehicle. docs (36 files, 3 languages): injected one-line "Harness layer" callout after the motto in every session document (s01-s12). agents (13 Python files): added harness framing comment before each module docstring. skills/agent-philosophy.md: full rewrite aligned with harness narrative.
130 lines
4.6 KiB
Markdown
130 lines
4.6 KiB
Markdown
# s09: Agent Teams (智能体团队)
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`s01 > s02 > s03 > s04 > s05 > s06 | s07 > s08 > [ s09 ] s10 > s11 > s12`
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> *"任务太大一个人干不完, 要能分给队友"* -- 持久化队友 + JSONL 邮箱。
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>
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> **Harness 层**: 团队邮箱 -- 多个模型, 通过文件协调。
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## 问题
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子智能体 (s04) 是一次性的: 生成、干活、返回摘要、消亡。没有身份, 没有跨调用的记忆。后台任务 (s08) 能跑 shell 命令, 但做不了 LLM 引导的决策。
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真正的团队协作需要三样东西: (1) 能跨多轮对话存活的持久智能体, (2) 身份和生命周期管理, (3) 智能体之间的通信通道。
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## 解决方案
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```
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Teammate lifecycle:
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spawn -> WORKING -> IDLE -> WORKING -> ... -> SHUTDOWN
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Communication:
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.team/
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config.json <- team roster + statuses
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inbox/
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alice.jsonl <- append-only, drain-on-read
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bob.jsonl
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lead.jsonl
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+--------+ send("alice","bob","...") +--------+
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| alice | -----------------------------> | bob |
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| loop | bob.jsonl << {json_line} | loop |
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+--------+ +--------+
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^ |
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| BUS.read_inbox("alice") |
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+---- alice.jsonl -> read + drain ---------+
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```
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## 工作原理
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1. TeammateManager 通过 config.json 维护团队名册。
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```python
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class TeammateManager:
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def __init__(self, team_dir: Path):
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self.dir = team_dir
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self.dir.mkdir(exist_ok=True)
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self.config_path = self.dir / "config.json"
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self.config = self._load_config()
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self.threads = {}
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```
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2. `spawn()` 创建队友并在线程中启动 agent loop。
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```python
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def spawn(self, name: str, role: str, prompt: str) -> str:
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member = {"name": name, "role": role, "status": "working"}
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self.config["members"].append(member)
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self._save_config()
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thread = threading.Thread(
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target=self._teammate_loop,
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args=(name, role, prompt), daemon=True)
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thread.start()
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return f"Spawned teammate '{name}' (role: {role})"
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```
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3. MessageBus: append-only 的 JSONL 收件箱。`send()` 追加一行; `read_inbox()` 读取全部并清空。
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```python
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class MessageBus:
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def send(self, sender, to, content, msg_type="message", extra=None):
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msg = {"type": msg_type, "from": sender,
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"content": content, "timestamp": time.time()}
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if extra:
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msg.update(extra)
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with open(self.dir / f"{to}.jsonl", "a") as f:
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f.write(json.dumps(msg) + "\n")
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def read_inbox(self, name):
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path = self.dir / f"{name}.jsonl"
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if not path.exists(): return "[]"
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msgs = [json.loads(l) for l in path.read_text().strip().splitlines() if l]
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path.write_text("") # drain
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return json.dumps(msgs, indent=2)
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```
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4. 每个队友在每次 LLM 调用前检查收件箱, 将消息注入上下文。
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```python
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def _teammate_loop(self, name, role, prompt):
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messages = [{"role": "user", "content": prompt}]
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for _ in range(50):
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inbox = BUS.read_inbox(name)
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if inbox != "[]":
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messages.append({"role": "user",
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"content": f"<inbox>{inbox}</inbox>"})
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messages.append({"role": "assistant",
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"content": "Noted inbox messages."})
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response = client.messages.create(...)
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if response.stop_reason != "tool_use":
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break
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# execute tools, append results...
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self._find_member(name)["status"] = "idle"
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```
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## 相对 s08 的变更
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| 组件 | 之前 (s08) | 之后 (s09) |
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|----------------|------------------|------------------------------------|
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| Tools | 6 | 9 (+spawn/send/read_inbox) |
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| 智能体数量 | 单一 | 领导 + N 个队友 |
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| 持久化 | 无 | config.json + JSONL 收件箱 |
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| 线程 | 后台命令 | 每线程完整 agent loop |
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| 生命周期 | 一次性 | idle -> working -> idle |
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| 通信 | 无 | message + broadcast |
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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/s09_agent_teams.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. `Spawn alice (coder) and bob (tester). Have alice send bob a message.`
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2. `Broadcast "status update: phase 1 complete" to all teammates`
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3. `Check the lead inbox for any messages`
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4. 输入 `/team` 查看团队名册和状态
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5. 输入 `/inbox` 手动检查领导的收件箱
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