better doc
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CrazyBoyM
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# s09: Agent Teams (智能体团队)
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> 持久化的队友通过 JSONL 收件箱提供了一种教学协议, 将孤立的智能体转变为可通信的团队 -- spawn、message、broadcast 和 drain。
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`s01 > s02 > s03 > s04 > s05 > s06 | s07 > s08 > [ s09 ] s10 > s11 > s12`
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> *"Append to send, drain to read"* -- 追加即发送, 排空即读取: 异步邮箱让队友能持久通信。
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## 问题
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子智能体 (s04) 是一次性的: 生成、工作、返回摘要、消亡。它们没有身份, 没有跨调用的记忆, 也无法接收后续指令。后台任务 (s08) 运行 shell 命令, 但不能做 LLM 引导的决策或交流发现。
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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) 存活时间超过单次 prompt 的持久化智能体, (2) 身份和生命周期管理, (3) 智能体之间的通信通道。没有消息机制, 即使持久化的队友也是又聋又哑的 -- 它们可以并行工作但永远无法协调。
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解决方案将 TeammateManager (用于生成持久化的命名智能体) 与使用 JSONL 收件箱文件的 MessageBus 结合。每个队友在独立线程中运行自己的 agent loop, 每次 LLM 调用前检查收件箱, 可以向任何其他队友或领导发送消息。
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关于 s06 到 s07 的桥梁: s03 的 TodoManager 条目随压缩 (s06) 消亡。基于文件的任务 (s07) 因为存储在磁盘上而能存活压缩。团队建立在同样的原则上 -- config.json 和收件箱文件持久化在上下文窗口之外。
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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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@@ -26,27 +24,18 @@ Communication:
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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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5 message types:
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+-------------------------+------------------------------+
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| message | Normal text between agents |
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| broadcast | Sent to all teammates |
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| shutdown_request | Request graceful shutdown |
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| shutdown_response | Approve/reject shutdown |
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| plan_approval_response | Approve/reject plan |
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+-------------------------+------------------------------+
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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。每个成员有名称、角色和状态。
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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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@@ -58,58 +47,44 @@ class TeammateManager:
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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其 agent loop。重新 spawn 一个 idle 状态的队友会将其重新激活。
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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 = self._find_member(name)
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if member:
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if member["status"] not in ("idle", "shutdown"):
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return f"Error: '{name}' is currently {member['status']}"
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member["status"] = "working"
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else:
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member = {"name": name, "role": role, "status": "working"}
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self.config["members"].append(member)
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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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self.threads[name] = thread
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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 处理 JSONL 收件箱文件。`send()` 追加一行 JSON; `read_inbox()` 读取所有行并清空文件。
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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,
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msg_type="message", extra=None):
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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,
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"timestamp": time.time()}
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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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return f"Sent {msg_type} to {to}"
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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():
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return "[]"
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msgs = [json.loads(l)
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for l in path.read_text().strip().splitlines()
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if l]
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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, 将收到的消息注入对话上下文。
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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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sys_prompt = f"You are '{name}', role: {role}, at {WORKDIR}."
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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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@@ -118,65 +93,11 @@ def _teammate_loop(self, name, role, prompt):
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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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model=MODEL, system=sys_prompt,
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messages=messages, tools=TOOLS)
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messages.append({"role": "assistant",
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"content": response.content})
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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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self._save_config()
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```
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5. `broadcast()` 向除发送者外的所有队友发送相同消息。
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```python
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def broadcast(self, sender, content, teammates):
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count = 0
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for name in teammates:
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if name != sender:
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self.send(sender, name, content, "broadcast")
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count += 1
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return f"Broadcast to {count} teammates"
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```
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## 核心代码
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TeammateManager + MessageBus 核心 (来自 `agents/s09_agent_teams.py`):
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```python
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class TeammateManager:
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def spawn(self, name, role, prompt):
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member = self._find_member(name) or {
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"name": name, "role": role, "status": "working"
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}
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member["status"] = "working"
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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 '{name}'"
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class MessageBus:
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def send(self, sender, to, content,
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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: 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)
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for l in path.read_text().strip().splitlines()
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if l]
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path.write_text("")
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return json.dumps(msgs, indent=2)
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```
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## 相对 s08 的变更
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@@ -188,13 +109,7 @@ class MessageBus:
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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 | 5 种消息类型 + broadcast |
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教学简化说明: 此实现未使用文件锁来保护收件箱访问。在生产中, 多个写入者并发追加需要文件锁或原子重命名。这里使用的单写入者-per-收件箱模式在教学场景下是安全的。
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## 设计原理
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基于文件的邮箱 (追加式 JSONL) 在教学代码中具有可观察、易理解的优势。"读取时排空" 模式 (读取全部, 截断) 用很少的机制就能实现批量传递。代价是延迟 -- 消息只在下一次轮询时才被看到 -- 但对于每轮需要数秒推理时间的 LLM 驱动智能体来说, 本课程中该延迟是可接受的。
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| 通信 | 无 | message + broadcast |
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## 试一试
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@@ -203,10 +118,10 @@ cd learn-claude-code
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python agents/s09_agent_teams.py
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```
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可以尝试的提示:
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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` 查看带状态的团队名册
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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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