Multi-Agent 协作：从单兵作战到智能组织

class MarketResearchOrchestrator:
    """
    主管 Agent：协调多个研究员 Agent 生成市场报告
    """
    
    def __init__(self):
        self.supervisor = SupervisorAgent()
        self.researchers = {
            "industry": ResearcherAgent(expertise="industry_analysis"),
            "competitor": ResearcherAgent(expertise="competitive_analysis"),
            "customer": ResearcherAgent(expertise="customer_research"),
            "trend": ResearcherAgent(expertise="trend_forecasting")
        }
    
    async def generate_report(self, topic: str) -> Report:
        # 1. 主管分解任务
        subtasks = self.supervisor.decompose_task(topic)
        
        # 2. 并行分配给研究员
        results = await asyncio.gather(*[
            self.researchers[task.type].research(task)
            for task in subtasks
        ])
        
        # 3. 主管汇总整合
        report = self.supervisor.synthesize(results)
        
        return report

class CodeReviewCommittee:
    """
    多个专家 Agent 共同评审代码
    """
    
    def __init__(self):
        self.reviewers = [
            SecurityReviewer(),
            PerformanceReviewer(),
            MaintainabilityReviewer(),
            BusinessLogicReviewer()
        ]
    
    async def review(self, code: Code) -> ReviewDecision:
        # 每个评审员独立评审
        reviews = await asyncio.gather(*[
            reviewer.review(code) for reviewer in self.reviewers
        ])
        
        # 讨论和协商
        consensus = await self.discuss_and_resolve(reviews)
        
        return consensus
    
    async def discuss_and_resolve(self, reviews: List[Review]) -> ReviewDecision:
        """通过多轮讨论达成共识"""
        discussion = Discussion()
        
        for round in range(max_rounds):
            # 每个评审员回应其他人的观点
            for reviewer in self.reviewers:
                response = await reviewer.respond_to(reviews, discussion)
                discussion.add(response)
            
            # 检查是否达成共识
            if self.check_consensus(discussion):
                break
        
        return self.compile_decision(discussion)

class VirtualSalesOrganization:
    """
    模拟完整销售组织的 Multi-Agent 系统
    """
    
    def __init__(self):
        # 顶层：战略层
        self.ceo = CEOAgent()
        
        # 中层：部门层
        self.vps = {
            "sales": VPSales(),
            "marketing": VPMarketing(),
            "customer_success": VPCustomerSuccess()
        }
        
        # 基层：执行层
        self.sales_team = [SalesRepAgent() for _ in range(10)]
        self.marketing_team = [MarketingSpecialistAgent() for _ in range(5)]
    
    async def quarterly_planning(self):
        # CEO 制定战略目标
        strategy = await self.ceo.set_quarterly_strategy()
        
        # VP 们制定战术计划
        plans = await asyncio.gather(*[
            vp.create_plan(strategy) for vp in self.vps.values()
        ])
        
        # 执行团队落实
        execution_results = await self.execute_plans(plans)
        
        return execution_results

class MessageBus:
    """
    Agent 间通信的总线
    """
    
    def __init__(self):
        self.subscribers = defaultdict(list)
        self.message_history = []
    
    def subscribe(self, topic: str, agent: Agent):
        """订阅主题"""
        self.subscribers[topic].append(agent)
    
    def publish(self, message: Message):
        """发布消息"""
        self.message_history.append(message)
        
        # 广播给订阅者
        for agent in self.subscribers.get(message.topic, []):
            if agent.id != message.sender_id:  # 不发给发送者自己
                asyncio.create_task(agent.receive(message))
    
    def send_direct(self, message: Message, recipient_id: str):
        """点对点发送"""
        recipient = self.get_agent(recipient_id)
        asyncio.create_task(recipient.receive(message))

@dataclass
class Message:
    id: str
    sender_id: str
    recipient_id: Optional[str]  # None 表示广播
    topic: str
    content: str
    message_type: str  # task, response, notification, request_help
    priority: int  # 1-5
    timestamp: datetime
    context: dict  # 关联的上下文信息

class CapabilityBasedAssignment:
    """
    根据 Agent 能力匹配任务
    """
    
    def assign(self, task: Task, available_agents: List[Agent]) -> Agent:
        # 计算每个 Agent 的能力匹配度
        scores = []
        for agent in available_agents:
            score = self.calculate_match_score(task, agent)
            scores.append((agent, score))
        
        # 选择匹配度最高的
        scores.sort(key=lambda x: x[1], reverse=True)
        return scores[0][0]
    
    def calculate_match_score(self, task: Task, agent: Agent) -> float:
        score = 0.0
        
        # 技能匹配
        for required_skill in task.required_skills:
            if required_skill in agent.skills:
                score += agent.skills[required_skill]
        
        # 负载均衡（避免过度分配给同一个 Agent）
        current_load = agent.get_current_load()
        score *= (1 - current_load)
        
        # 历史表现
        past_performance = agent.get_performance_on(task.type)
        score *= past_performance
        
        return score

class AuctionBasedAssignment:
    """
    Agent 竞拍任务，出价基于能力和负载
    """
    
    async def assign_via_auction(self, task: Task, agents: List[Agent]) -> Agent:
        # 收集投标
        bids = await asyncio.gather(*[
            agent.submit_bid(task) for agent in agents
        ])
        
        # 选择最优投标
        # 考虑：价格、质量、交付时间
        winner = self.select_winner(bids)
        
        return winner

class ConflictResolver:
    """
    Multi-Agent 冲突解决器
    """
    
    def resolve(self, conflict: Conflict) -> Resolution:
        if conflict.type == "RESOURCE_CONTENTION":
            return self.resolve_resource_contention(conflict)
        elif conflict.type == "DECISION_DISAGREEMENT":
            return self.resolve_decision_disagreement(conflict)
        elif conflict.type == "PRIORITY":
            return self.resolve_priority_conflict(conflict)
    
    def resolve_decision_disagreement(self, conflict: Conflict) -> Resolution:
        """
        决策分歧：通过投票或上级裁决
        """
        agents = conflict.involved_agents
        
        # 尝试达成共识
        consensus = self.attempt_consensus(agents)
        if consensus:
            return Resolution(type="CONSENSUS", decision=consensus)
        
        # 无法达成共识，升级给上级
        if conflict.supervisor:
            decision = conflict.supervisor.arbitrate(conflict)
            return Resolution(type="ARBITRATION", decision=decision)
        
        # 最终手段：投票
        votes = self.collect_votes(agents)
        winner = max(votes, key=votes.get)
        return Resolution(type="VOTING", decision=winner)

class MapReducePattern:
    """
    将大任务分解为小任务并行处理，然后汇总结果
    """
    
    async def execute(self, task: LargeTask, workers: List[Agent]) -> Result:
        # Map 阶段：分解任务
        subtasks = self.decompose(task)
        
        # 并行执行
        partial_results = await asyncio.gather(*[
            workers[i % len(workers)].execute(subtask)
            for i, subtask in enumerate(subtasks)
        ])
        
        # Reduce 阶段：汇总结果
        final_result = self.aggregate(partial_results)
        
        return final_result

class DebatePattern:
    """
    多个 Agent 就一个问题展开辩论，最后达成共识或投票
    """
    
    async def debate(self, topic: str, debaters: List[Agent], rounds: int = 3):
        discussion = []
        
        for round in range(rounds):
            round_arguments = []
            
            for debater in debaters:
                # 每个 debater 基于之前的讨论发表观点
                argument = await debater.argue(topic, discussion)
                round_arguments.append(argument)
            
            discussion.extend(round_arguments)
            
            # 检查是否达成共识
            if self.check_consensus(discussion):
                break
        
        return self.compile_conclusion(discussion)

class VotingPattern:
    """
    多个 Agent 对选项进行投票
    """
    
    async def vote(self, options: List[Option], voters: List[Agent]) -> Option:
        votes = defaultdict(int)
        
        for voter in voters:
            vote = await voter.vote(options)
            votes[vote] += voter.weight  # 权重可以基于专业能力
        
        winner = max(votes, key=votes.get)
        return winner

class PipelinePattern:
    """
    Agent 按顺序处理，每个 Agent 的输出是下一个的输入
    """
    
    async def execute(self, input_data: Any, stages: List[Agent]) -> Any:
        data = input_data
        
        for stage in stages:
            data = await stage.process(data)
            
            # 质量检查
            if not self.quality_check(data):
                # 回退或重试
                data = await self.handle_failure(stage, data)
        
        return data