MCP 在多 Agent 系统中的角色及代码级落地实现 原创

class MultiAgentMCPFramework:
    def __init__(self):
        self.agents = {}
        self.mcp_hub = MCPCoordinationHub()
        self.context_store = SharedContextStore()


    async def register_agent(self, agent_id: str, agent_type: str, capabilities: list):
        """注册新的 Agent 到系统中"""
        agent_profile = {
            'id': agent_id,
            'type': agent_type,
            'capabilities': capabilities,
            'mcp_endpoint': f'mcp://agents/{agent_id}',
            'status': 'active',
            'last_seen': datetime.now()
        }


        self.agents[agent_id] = agent_profile


        # 通过 MCP 广播新 Agent 加入
        await self.mcp_hub.broadcast_event('agent_joined', {
            'agent_id': agent_id,
            'capabilities': capabilities
        })


        return agent_profile


    async def coordinate_task(self, task_description: str, required_capabilities: list):
        """协调多个 Agent 完成复杂任务"""


        # 1. 任务分解
        task_plan = await self.task_planner.decompose_task({
            'description': task_description,
            'required_capabilities': required_capabilities,
            'available_agents': self.agents
        })


        # 2. Agent 分配
        agent_assignments = await self.assign_agents_to_subtasks(task_plan)


        # 3. 建立共享上下文
        shared_context_id = await self.context_store.create_shared_context({
            'task_id': task_plan['id'],
            'participants': agent_assignments.keys(),
            'initial_context': task_plan['context']
        })


        # 4. 通过 MCP 协调执行
        results = {}
        for agent_id, subtasks in agent_assignments.items():
            results[agent_id] = await self.mcp_hub.delegate_task(
                agent_id, 
                subtasks, 
                shared_context_id
            )


        return await self.synthesize_results(results, task_plan)

class SharedContextManager:
    def __init__(self):
        self.context_store = MCPContextStore()
        self.context_prioritizer = ContextPrioritizer()


    async def share_context_between_agents(self, from_agent: str, to_agent: str, context_data: dict):
        """在 Agent 之间共享上下文"""


        # 1. 上下文标准化
        standardized_context = await self.standardize_context({
            'source_agent': from_agent,
            'target_agent': to_agent,
            'timestamp': datetime.now(),
            'context_data': context_data,
            'relevance_score': await self.calculate_relevance(context_data, to_agent)
        })


        # 2. 通过 MCP 协议传递
        await self.context_store.store_shared_context(
            context_id=standardized_context['id'],
            cnotallow=standardized_context
        )


        # 3. 通知目标 Agent
        await self.notify_agent_of_new_context(to_agent, standardized_context['id'])


        # 4. 更新上下文优先级
        await self.context_prioritizer.update_priorities(to_agent)


        return standardized_context['id']

class DynamicToolDiscovery:
    def __init__(self):
        self.tool_registry = MCPToolRegistry()
        self.capability_matcher = CapabilityMatcher()


    async def discover_available_tools(self, requesting_agent: str, task_requirements: dict):
        """为特定任务动态发现可用工具"""


        # 1. 查询 MCP 工具注册中心
        available_tools = await self.tool_registry.query_tools({
            'capabilities': task_requirements['required_capabilities'],
            'permissions': await self.get_agent_permissions(requesting_agent),
            'availability': 'active'
        })


        # 2. 能力匹配评分
        scored_tools = []
        for tool in available_tools:
            compatibility_score = await self.capability_matcher.calculate_compatibility(
                tool['capabilities'], 
                task_requirements
            )


            if compatibility_score > 0.7:  # 70% 兼容性阈值
                scored_tools.append({
                    'tool': tool,
                    'score': compatibility_score,
                    'owner_agent': tool['owner_agent']
                })


        # 3. 排序并推荐
        recommended_tools = sorted(scored_tools, key=lambda x: x['score'], reverse=True)


        return recommended_tools[:5]  # 返回前5个最匹配的工具

class IntelligentCustomerService:
    def __init__(self):
        self.mcp_coordinator = MCPCoordinator()
        self.agents = {
            'reception': ReceptionAgent(),
            'technical': TechnicalSupportAgent(),
            'billing': BillingAgent(),
            'recommendation': ProductRecommendationAgent(),
            'relationship': CustomerRelationshipAgent()
        }


    async def handle_customer_inquiry(self, customer_id: str, inquiry: str):
        """处理客户查询的完整流程"""


        # 1. 接收 Agent 初步分析
        initial_analysis = await self.agents['reception'].analyze_inquiry({
            'customer_id': customer_id,
            'inquiry': inquiry,
            'channel': 'chat'
        })


        # 2. 通过 MCP 建立共享上下文
        shared_context_id = await self.mcp_coordinator.create_shared_context({
            'customer_id': customer_id,
            'inquiry': inquiry,
            'initial_analysis': initial_analysis,
            'participants': []  # 将动态添加参与的 Agent
        })


        # 3. 根据分析结果动态组建 Agent 团队
        required_agents = self.determine_required_agents(initial_analysis)


        # 4. 并行处理不同面向
        async with TaskGroup() as tg:
            tasks = {}


            if 'technical_issue' in initial_analysis['categories']:
                tasks['technical'] = tg.create_task(
                    self.agents['technical'].investigate_technical_issue(
                        shared_context_id, initial_analysis['technical_indicators']
                    )
                )


            if 'billing_inquiry' in initial_analysis['categories']:
                tasks['billing'] = tg.create_task(
                    self.agents['billing'].check_billing_status(
                        shared_context_id, customer_id
                    )
                )


            if 'product_interest' in initial_analysis['categories']:
                tasks['recommendation'] = tg.create_task(
                    self.agents['recommendation'].generate_recommendations(
                        shared_context_id, customer_id, initial_analysis['interests']
                    )
                )


        # 5. 整合结果并生成回应
        integrated_response = await self.integrate_agent_responses(
            shared_context_id, tasks
        )


        # 6. 客户关系 Agent 进行后续追踪规划
        follow_up_plan = await self.agents['relationship'].plan_follow_up(
            shared_context_id, integrated_response
        )


        return {
            'response': integrated_response,
            'follow_up_plan': follow_up_plan,
            'context_id': shared_context_id
        }

class HierarchicalCoordination:
    def __init__(self):
        self.coordinator_agent = CoordinatorAgent()
        self.specialist_agents = {
            'data_analysis': DataAnalysisAgent(),
            'report_generation': ReportGenerationAgent(),
            'quality_assurance': QualityAssuranceAgent()
        }


    async def execute_hierarchical_task(self, task: dict):
        """阶层式任务执行"""


        # Coordinator 分解任务
        task_breakdown = await self.coordinator_agent.decompose_task(task)


        # 依序派发给予专业 Agent
        results = {}
        for phase in task_breakdown['phases']:
            agent_type = phase['assigned_agent']
            agent = self.specialist_agents[agent_type]


            # 通过 MCP 提供前一阶段的上下文
            phase_context = await self.get_phase_context(phase['dependencies'])


            results[phase['id']] = await agent.execute_phase(
                phase['instructions'], 
                phase_context
            )


        return await self.coordinator_agent.synthesize_results(results)

class ParallelCollaboration:
    def __init__(self):
        self.agents = [
            SpecialistAgent('market_analysis'),
            SpecialistAgent('competitor_research'),
            SpecialistAgent('customer_insights'),
            SpecialistAgent('financial_modeling')
        ]
        self.mcp_sync = MCPSynchronizer()


    async def parallel_business_analysis(self, company_data: dict):
        """平行业务分析"""


        # 建立共享工作空间
        workspace_id = await self.mcp_sync.create_shared_workspace({
            'participants': [agent.id for agent in self.agents],
            'initial_data': company_data
        })


        # 所有 Agent 并行开始工作
        async with TaskGroup() as tg:
            tasks = []
            for agent in self.agents:
                task = tg.create_task(
                    agent.analyze_with_shared_context(workspace_id)
                )
                tasks.append((agent.specialty, task))


        # 收集并整合所有分析结果
        analysis_results = {}
        for specialty, task in tasks:
            analysis_results[specialty] = await task


        return await self.synthesize_parallel_analysis(analysis_results)

class SelfOrganizingNetwork:
    def __init__(self):
        self.agent_network = AgentNetwork()
        self.reputation_system = ReputationSystem()
        self.task_marketplace = TaskMarketplace()


    async def self_organize_for_task(self, complex_task: dict):
        """自组织完成复杂任务"""


        # 1. 任务分解并发布到市场
        subtasks = await self.decompose_task(complex_task)


        for subtask in subtasks:
            await self.task_marketplace.publish_subtask(subtask)


        # 2. Agent 根据能力和声誉竞标
        bids = await self.collect_bids_from_agents(subtasks)


        # 3. 优化分配（考虑能力、声誉、成本）
        optimal_allocation = await self.optimize_task_allocation(bids)


        # 4. 动态形成工作团队
        working_group = await self.form_dynamic_team(optimal_allocation)


        # 5. 团队协作执行
        return await working_group.collaborative_execution()

class AgentDiscoveryService:
    def __init__(self):
        self.service_registry = MCPServiceRegistry()
        self.capability_ontology = CapabilityOntology()


    async def discover_agents_by_capability(self, required_capabilities: list):
        """根据能力需求发现合适的 Agent"""


        # 1. 语义匹配
        semantic_matches = await self.capability_ontology.find_semantic_matches(
            required_capabilities
        )


        # 2. 服务注册查询
        available_agents = await self.service_registry.query_agents({
            'capabilities': semantic_matches,
            'status': 'available',
            'load_threshold': 0.8  # 负载低于80%
        })


        # 3. 能力评分
        scored_agents = []
        for agent in available_agents:
            capability_score = await self.calculate_capability_match(
                agent['capabilities'], 
                required_capabilities
            )


            performance_score = await self.get_historical_performance(agent['id'])


            combined_score = (capability_score * 0.7) + (performance_score * 0.3)


            scored_agents.append({
                'agent': agent,
                'score': combined_score
            })


        return sorted(scored_agents, key=lambda x: x['score'], reverse=True)

class FaultTolerantCoordination:
    def __init__(self):
        self.health_monitor = AgentHealthMonitor()
        self.backup_registry = BackupAgentRegistry()
        self.recovery_planner = RecoveryPlanner()


    async def handle_agent_failure(self, failed_agent_id: str, current_tasks: list):
        """处理 Agent 故障"""


        # 1. 检测故障类型
        failure_analysis = await self.analyze_failure(failed_agent_id)


        # 2. 保存当前任务状态
        task_states = await self.save_task_states(current_tasks)


        # 3. 寻找替代 Agent
        replacement_candidates = await self.backup_registry.find_replacement_agents(
            failed_agent_capabilities=failure_analysis['capabilities'],
            workload_requirements=failure_analysis['workload']
        )


        # 4. 选择最佳替代方案
        best_replacement = await self.select_best_replacement(
            replacement_candidates, 
            task_states
        )


        # 5. 执行无缝切换
        if best_replacement:
            await self.seamless_handover(
                failed_agent_id, 
                best_replacement['agent_id'], 
                task_states
            )
        else:
            # 如果没有直接替代，则重新分配任务
            await self.redistribute_tasks(current_tasks)


        return {
            'recovery_strategy': 'replacement' if best_replacement else 'redistribution',
            'estimated_delay': await self.estimate_recovery_time(failure_analysis),
            'affected_tasks': len(current_tasks)
        }

class MultiAgentPerformanceAnalyzer:
    def __init__(self):
        self.metrics_collector = MCPMetricsCollector()  # MCP指标收集器
        self.performance_analyzer = PerformanceAnalyzer()  # 性能分析器
        self.optimizer = SystemOptimizer()  # 系统优化器


    async def analyze_system_performance(self, time_window: str = '24h'):
        """分析多Agent系统性能"""


        # 1. 收集性能指标
        metrics = await self.metrics_collector.collect_metrics({
            'time_window': time_window,  # 时间窗口，默认24小时
            'metrics_types': [
                'task_completion_time',  # 任务完成时间
                'agent_utilization',  # 代理利用率
                'context_sharing_efficiency',  # 上下文共享效率
                'coordination_overhead',  # 协调开销
                'resource_usage'  # 资源使用率
            ]
        })


        # 2. 性能分析
        analysis = await self.performance_analyzer.analyze({
            'metrics': metrics,  # 指标数据
            'baseline_comparison': True,  # 与基准值比较
            'bottleneck_detection': True,  # 检测瓶颈
            'efficiency_assessment': True  # 效率评估
        })


        # 3. 生成优化建议
        optimization_recommendations = await self.optimizer.generate_recommendations({
            'current_performance': analysis,  # 当前性能分析结果
            'system_constraints': await self.get_system_constraints(),  # 系统约束条件
            'business_objectives': await self.get_business_objectives()  # 业务目标
        })


        return {
            'performance_summary': analysis['summary'],  # 性能摘要
            'identified_bottlenecks': analysis['bottlenecks'],  # 已识别的瓶颈
            'optimization_opportunities': optimization_recommendations,  # 优化机会
            'estimated_improvements': await self.estimate_improvement_potential(
                optimization_recommendations  # 预估改进空间
            )
        }


    async def get_system_constraints(self):
        """获取系统约束条件"""
        # 实际实现中会返回硬件资源限制、网络带宽等约束条件
        return await SystemConstraintsProvider.get_constraints()


    async def estimate_improvement_potential(self, recommendations):
        """预估优化建议的改进潜力"""
        # 根据建议内容计算可能的性能提升幅度
        return await ImprovementEstimator.calculate(recommendations)

class MultiAgentSecurityFramework:
    def __init__(self):
        self.auth_manager = AgentAuthenticationManager()  # 身份验证管理器
        self.permission_controller = AgentPermissionController()  # 权限控制器
        self.audit_logger = MultiAgentAuditLogger()  # 多代理审计日志器


    async def enforce_security_policies(self, agent_interaction: dict):
        """实施多 Agent 安全策略"""


        # 1. 身份验证
        auth_result = await self.auth_manager.authenticate_agents([
            agent_interaction['source_agent'],  # 源代理
            agent_interaction['target_agent']   # 目标代理
        ])


        if not auth_result['valid']:
            raise SecurityException("代理身份验证失败")


        # 2. 权限检查
        permission_check = await self.permission_controller.check_interaction_permissions({
            'source_agent': agent_interaction['source_agent'],
            'target_agent': agent_interaction['target_agent'],
            'interaction_type': agent_interaction['type'],  # 交互类型
            'requested_resources': agent_interaction.get('resources', [])  # 请求的资源
        })


        if not permission_check['allowed']:
            raise PermissionDeniedException(permission_check['reason'])


        # 3. 审计记录
        await self.audit_logger.log_interaction({
            'timestamp': datetime.now(),  # 时间戳
            'source_agent': agent_interaction['source_agent'],
            'target_agent': agent_interaction['target_agent'],
            'interaction_type': agent_interaction['type'],
            'permission_check': permission_check,
            'context_shared': agent_interaction.get('context_shared', False)  # 是否共享上下文
        })


        return True