# Google ADK 多智能体示例：Agent Transfer模式
from google.adk import Agent, Runner
from google.adk.tools import agent_transfer

# 定义专业智能体
billing_agent = Agent(
    name="billing_specialist",
    model="gemini-2.5-pro",
    instruction="处理账单查询和退款请求",
    tools=[query_billing_db, process_refund]
)

technical_agent = Agent(
    name="technical_support",
    model="gemini-2.5-pro",
    instruction="处理技术故障排查",
    tools=[diagnose_system, search_knowledge_base]
)

# 定义路由智能体
router_agent = Agent(
    name="customer_service_router",
    model="gemini-2.5-pro",
    instruction="根据用户意图路由到合适的专业智能体",
    tools=[
        agent_transfer(billing_agent),
        agent_transfer(technical_agent)
    ]
)

# 运行多智能体系统
runner = Runner(agent=router_agent)
result = runner.run("我的账单有误，同时App也无法登录")

# Blackboard模式实现示例
from dataclasses import dataclass, field
from typing import Any
import asyncio

@dataclass
class Blackboard:
    """共享知识空间"""
    data: dict[str, Any] = field(default_factory=dict)
    history: list[dict] = field(default_factory=list)

    def write(self, agent_name: str, key: str, value: Any):
        self.data[key] = value
        self.history.append({
            "agent": agent_name,
            "action": "write",
            "key": key,
            "timestamp": datetime.now()
        })

    def read(self, key: str) -> Any:
        return self.data.get(key)

class BlackboardController:
    """控制模块：决定哪个Agent何时执行"""
    def __init__(self, blackboard: Blackboard, agents: list):
        self.bb = blackboard
        self.agents = agents

    async def run(self, max_iterations: int = 10):
        for i in range(max_iterations):
            # 找出有能力且有新信息可处理的Agent
            eligible = [
                a for a in self.agents 
                if a.can_contribute(self.bb)
            ]
            if not eligible:
                break  # 没有Agent有新贡献，终止

            # 并行执行所有合格Agent
            results = await asyncio.gather(*[
                a.execute(self.bb) for a in eligible
            ])

            # 检查终止条件
            if self.is_solution_complete(self.bb):
                break

# Mem0多智能体共享记忆示例
from mem0 import Memory

# 创建共享记忆实例
shared_memory = Memory()

# Agent A写入经验
shared_memory.add(
    "用户偏好简短回答，不要超过3个要点",
    user_id="user_123",
    agent_id="research_agent",
    metadata={"category": "preference"}
)

# Agent B读取Agent A写入的经验
memories = shared_memory.search(
    "用户沟通偏好",
    user_id="user_123",
    agent_id="writer_agent"  # 不同Agent可以读取
)

# 输出: [{"memory": "用户偏好简短回答...", "score": 0.92}]

// AG-UI 前端集成示例（React + CopilotKit）
import { useCoAgent, useAgentAction } from "@copilotkit/react-core";

function MultiAgentDashboard() {
  // 连接多个智能体
  const researchAgent = useCoAgent({ name: "research_agent" });
  const analysisAgent = useCoAgent({ name: "analysis_agent" });

  // 监听智能体间的Agent Transfer事件
  const { transferTo } = useAgentAction({
    name: "transfer_agent",
    handler: ({ target_agent, reason }) => {
      console.log(`转移到 ${target_agent}: ${reason}`);
    }
  });

  return (
    <div>
      <CoAgentRender agent={researchAgent} />
      <CoAgentRender agent={analysisAgent} />
    </div>
  );
}

# AG2 事件驱动多智能体示例
import ag2
from ag2 import AssistantAgent, UserProxyAgent, GroupChat

# 定义事件驱动的智能体团队
researcher = AssistantAgent(
    name="researcher",
    system_message="你是一位深度调研分析师",
    llm_config={"model": "gpt-4.1", "temperature": 0.3}
)

coder = AssistantAgent(
    name="coder", 
    system_message="你是一位高级Python工程师",
    llm_config={"model": "claude-sonnet-4-6", "temperature": 0}
)

reviewer = AssistantAgent(
    name="reviewer",
    system_message="你是代码审查专家，负责质量把关",
    llm_config={"model": "gpt-4.1", "temperature": 0.1}
)

# GroupChat支持多种Speaker Selection策略
group_chat = GroupChat(
    agents=[researcher, coder, reviewer],
    messages=[],
    max_round=12,
    speaker_selection_method="auto",  # auto/round_robin/manual
    allow_repeat_speaker=False
)

# 事件钩子：监控智能体间通信
@group_chat.on("agent_message")
async def on_message(event):
    print(f"[{event.sender} → {event.receiver}] {event.content[:100]}")

@group_chat.on("tool_call")
async def on_tool_call(event):
    print(f"[工具调用] {event.tool_name}({event.arguments})")