AutoGPT Platform架构深度解析：分布式AI Agent执行引擎

graph TB
    subgraph "AutoGPT Platform 分布式架构"
        subgraph "前端层 - Frontend Layer"
            UI[React Web应用]
            FlowEditor[工作流编辑器]
            AgentGraph[Agent图形界面]
            RealTime[实时状态展示]
        end
        
        subgraph "API网关层 - API Gateway"
            RestAPI[REST API服务]
            WebSocketAPI[WebSocket API服务] 
            Auth[身份认证中间件]
            RateLimit[限流中间件]
        end
        
        subgraph "应用服务层 - Application Layer"
            AppEntry[应用入口 app.py]
            DatabaseManager[数据库管理器]
            NotificationManager[通知管理器]
            ScheduleManager[调度管理器]
        end
        
        subgraph "执行引擎层 - Execution Engine"
            ExecutionManager[执行管理器]
            ExecutionProcessor[执行处理器]
            NodeExecutor[节点执行器]
            BlockRegistry[Block注册表]
        end
        
        subgraph "Block生态系统 - Block Ecosystem"
            AgentBlock[Agent执行Block]
            CodeExecutor[代码执行Block]
            DataProcessor[数据处理Block]
            Integrations[第三方集成Block]
            CustomBlocks[自定义Block]
        end
        
        subgraph "共享服务层 - Shared Services"
            SharedLibs[autogpt_libs共享库]
            AuthService[身份认证服务]
            LoggingService[日志服务]
            CacheService[缓存服务]
            RateLimitService[限流服务]
        end
        
        subgraph "数据持久化层 - Data Layer"
            PostgresDB[(PostgreSQL数据库)]
            RedisCache[(Redis缓存)]
            RabbitMQ[消息队列]
            FileStorage[文件存储]
        end
        
        subgraph "外部系统 - External Systems"
            OpenAI[OpenAI API]
            Anthropic[Anthropic API]
            ThirdPartyAPIs[第三方API集成]
            E2BSandbox[代码执行沙箱]
        end
        
        subgraph "基础设施 - Infrastructure"
            Docker[Docker容器化]
            K8s[Kubernetes编排]
            Monitoring[监控告警]
            CloudProvider[云服务商]
        end
    end
    
    %% 连接关系
    UI --> RestAPI
    UI --> WebSocketAPI
    FlowEditor --> AgentGraph
    
    RestAPI --> Auth
    WebSocketAPI --> Auth
    Auth --> RateLimit
    
    RestAPI --> AppEntry
    WebSocketAPI --> AppEntry
    
    AppEntry --> DatabaseManager
    AppEntry --> NotificationManager
    AppEntry --> ScheduleManager
    AppEntry --> ExecutionManager
    
    ExecutionManager --> ExecutionProcessor
    ExecutionProcessor --> NodeExecutor
    NodeExecutor --> BlockRegistry
    
    BlockRegistry --> AgentBlock
    BlockRegistry --> CodeExecutor
    BlockRegistry --> DataProcessor
    BlockRegistry --> Integrations
    BlockRegistry --> CustomBlocks
    
    AppEntry --> SharedLibs
    SharedLibs --> AuthService
    SharedLibs --> LoggingService
    SharedLibs --> CacheService
    SharedLibs --> RateLimitService
    
    DatabaseManager --> PostgresDB
    ExecutionProcessor --> RedisCache
    NotificationManager --> RabbitMQ
    BlockRegistry --> FileStorage
    
    AgentBlock --> OpenAI
    AgentBlock --> Anthropic
    Integrations --> ThirdPartyAPIs
    CodeExecutor --> E2BSandbox
    
    AppEntry --> Docker
    Docker --> K8s
    K8s --> Monitoring
    K8s --> CloudProvider

sequenceDiagram
    participant FE as Frontend (UI)
    participant GW as API Gateway (REST/WS)
    participant RL as RateLimit MW
    participant AU as Auth Dep
    participant API as AgentServer
    participant EM as ExecutionManager
    participant TP as ThreadPool
    participant EP as ExecutionProcessor
    participant NE as NodeExecutor
    participant DB as PostgreSQL
    participant WS as WebSocket
    participant LG as Logging

    FE->>GW: HTTP POST /executions
    GW->>RL: rate_limit_middleware()
    RL-->>GW: allow or 429
    alt allowed
        GW->>AU: Security(get_jwt_payload)
        AU-->>GW: User
        GW->>API: create_graph_execution()
        API->>DB: INSERT execution
        API->>EM: enqueue(graph_exec_entry)
        EM->>EM: run_execution_loop()
        EM->>TP: submit(execute_graph)
        TP->>EP: on_graph_executor_start()
        TP->>EP: on_graph_execution(entry, cancel_event)
        loop nodes
            EP->>NE: execute_node()
            NE->>DB: save_output()
            NE->>WS: publish update
            EP->>EP: enqueue next
        end
        EP->>DB: update_graph_execution(COMPLETED)
        API-->>FE: 202 Accepted {id}
        WS-->>FE: streaming status
        API->>LG: logger.info/debug/error
    else limited
        GW-->>FE: 429 Too Many Requests
    end

sequenceDiagram
    participant U as 用户界面
    participant API as REST API
    participant EM as ExecutionManager
    participant EP as ExecutionProcessor  
    participant NE as NodeExecutor
    participant B as Block实例
    participant DB as 数据库
    participant MQ as 消息队列
    participant WS as WebSocket

    U->>API: 提交Agent执行请求
    API->>DB: 创建执行记录
    API->>EM: 加入执行队列
    
    EM->>EP: 分配执行任务
    EP->>DB: 获取图结构定义
    EP->>EP: 初始化节点队列
    
    loop 节点执行循环
        EP->>NE: 执行节点
        NE->>B: 调用Block.run()
        B->>B: 执行具体逻辑
        
        alt 外部API调用
            B->>ThirdPartyAPI: API请求
            ThirdPartyAPI-->>B: API响应
        end
        
        B-->>NE: 返回执行结果
        NE->>DB: 保存执行结果
        NE->>WS: 发送状态更新
        WS-->>U: 实时状态推送
        
        NE->>EP: 返回后续节点
        EP->>EP: 更新节点队列
    end
    
    EP->>DB: 更新执行状态
    EP->>WS: 发送完成通知
    WS-->>U: 执行完成反馈

stateDiagram-v2
    [*] --> 初始化
    初始化 --> 验证输入 : Block.validate()
    验证输入 --> 获取凭据 : input validation pass
    验证输入 --> 执行失败 : validation error
    
    获取凭据 --> 执行准备 : credentials acquired
    获取凭据 --> 执行失败 : credentials error
    
    执行准备 --> 执行中 : Block.run()
    执行中 --> 处理输出 : execution complete
    执行中 --> 执行失败 : execution error
    
    处理输出 --> 保存结果 : output processed  
    保存结果 --> 触发后续 : result saved
    触发后续 --> [*] : workflow continues
    
    执行失败 --> 错误处理 : handle exception
    错误处理 --> [*] : error logged

def main(**kwargs):
    """
    启动AutoGPT服务器的所有必需进程（REST和WebSocket API）
    """
    run_processes(
        DatabaseManager().set_log_level("warning"),  # 数据库管理
        Scheduler(),                                 # 任务调度器  
        NotificationManager(),                       # 通知管理器
        WebsocketServer(),                          # WebSocket服务
        AgentServer(),                              # REST API服务
        ExecutionManager(),                         # 执行管理器
        **kwargs,
    )

classDiagram
    class AppProcess { +start(**kwargs); +stop() }
    class DatabaseManager
    class Scheduler
    class NotificationManager
    class WebsocketServer
    class AgentServer
    class ExecutionManager

    AppProcess <|-- DatabaseManager
    AppProcess <|-- Scheduler
    AppProcess <|-- NotificationManager
    AppProcess <|-- WebsocketServer
    AppProcess <|-- AgentServer
    AppProcess <|-- ExecutionManager

    class ExecutionProcessor { +on_graph_executor_start(); +on_graph_execution(...); +on_node_execution(...) }
    class ExecutionQueue
    class GraphExecutionEntry
    class NodeExecutionEntry

    ExecutionManager --> ExecutionQueue : uses
    ExecutionManager --> ExecutionProcessor : threads own
    ExecutionProcessor --> GraphExecutionEntry : consumes
    ExecutionProcessor --> NodeExecutionEntry : produces

class Block:
    class Input(BaseModel):
        # 输入数据模型定义
        pass
    
    class Output(BaseModel): 
        # 输出数据模型定义
        pass
        
    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
        # 具体执行逻辑
        pass

// Agent图状态管理
const {
  nodes, setNodes,           // 节点状态
  edges, setEdges,           // 连线状态  
  saveAgent,                 // 保存Agent
  saveAndRun,               // 保存并运行
  isRunning,                // 运行状态
  executionUpdates          // 执行更新
} = useAgentGraph(flowID, flowVersion);

@requires_user
async def protected_endpoint(user: User = Depends()):
    # 需要认证的端点逻辑
    pass

flowchart LR
    subgraph "数据输入"
        UI[用户界面]
        API[API请求]
    end
    
    subgraph "数据处理"
        Validation[输入验证]
        GraphDB[(Graph定义)]
        ExecutionQueue[执行队列]
    end
    
    subgraph "执行层"
        NodeExec[节点执行]
        BlockRun[Block运行]
        ResultStore[结果存储]
    end
    
    subgraph "输出反馈"
        StatusUpdate[状态更新]
        ResultReturn[结果返回]
        Notification[通知推送]
    end
    
    UI --> Validation
    API --> Validation
    Validation --> GraphDB
    GraphDB --> ExecutionQueue
    ExecutionQueue --> NodeExec
    NodeExec --> BlockRun
    BlockRun --> ResultStore
    ResultStore --> StatusUpdate
    StatusUpdate --> Notification
    ResultReturn --> UI

services:
  api_srv:          # API服务
  exec_srv:         # 执行服务  
  frontend:         # 前端服务
  postgres:         # 数据库
  redis:           # 缓存
  rabbitmq:        # 消息队列

# 响应式执行引擎 - 参考ReactiveX模式
class ReactiveExecutionEngine:
    """
    响应式执行引擎
    
    通过...实现高效的事件驱动执行
    """
    
    def __init__(self):
        self.event_streams: Dict[str, asyncio.Queue] = {}
        self.observers: Dict[str, list[Callable]] = {}
        
    async def create_execution_stream(self, graph_id: str) -> 'ExecutionStream':
        """创建执行流"""
        
        stream = ExecutionStream(graph_id)
        
        # 订阅节点执行事件
        await stream.subscribe('node.start', self._on_node_start)
        await stream.subscribe('node.complete', self._on_node_complete)
        await stream.subscribe('node.error', self._on_node_error)
        
        return stream
    
    async def _on_node_start(self, event: dict):
        """节点开始执行事件处理"""
        node_id = event['node_id']
        logger.info(f"节点 {node_id} 开始执行")
        
        # 更新UI状态
        await self._update_ui_state(node_id, 'running')
        
        # 发送实时通知
        await self._send_realtime_update(event)
    
    async def _on_node_complete(self, event: dict):
        """节点完成执行事件处理"""
        node_id = event['node_id']
        output_data = event['output_data']
        
        logger.info(f"节点 {node_id} 执行完成")
        
        # 触发依赖节点
        await self._trigger_dependent_nodes(node_id, output_data)
        
        # 更新执行统计
        await self._update_execution_stats(event)

class ExecutionStream:
    """执行流"""
    
    def __init__(self, graph_id: str):
        self.graph_id = graph_id
        self.event_queue = asyncio.Queue()
        self.subscribers: Dict[str, list[Callable]] = {}
    
    async def subscribe(self, event_type: str, handler: Callable):
        """订阅事件"""
        if event_type not in self.subscribers:
            self.subscribers[event_type] = []
        self.subscribers[event_type].append(handler)
    
    async def publish(self, event_type: str, event_data: dict):
        """发布事件"""
        handlers = self.subscribers.get(event_type, [])
        
        for handler in handlers:
            try:
                await handler(event_data)
            except Exception as e:
                logger.error(f"事件处理器异常: {e}")

# 智能资源调度器 - 基于负载预测的资源分配
class IntelligentResourceScheduler:
    """
    智能资源调度器
    
    特性：
    1. 基于历史数据的负载预测
    2. 动态资源分配和回收
    3. 优先级队列管理
    4. 成本优化调度
    """
    
    def __init__(self):
        self.resource_pools = {
            'cpu_intensive': ResourcePool('cpu', max_size=4),
            'io_intensive': ResourcePool('io', max_size=10),
            'memory_intensive': ResourcePool('memory', max_size=2),
        }
        self.load_predictor = LoadPredictor()
        
    async def schedule_execution(self, execution_request: dict) -> str:
        """调度执行请求"""
        
        # 分析执行特征
        execution_profile = await self._analyze_execution_profile(execution_request)
        
        # 预测资源需求
        resource_demand = await self.load_predictor.predict_demand(execution_profile)
        
        # 选择最优资源池
        optimal_pool = self._select_optimal_pool(resource_demand)
        
        # 分配资源
        resource_allocation = await optimal_pool.allocate(execution_request)
        
        return resource_allocation['allocation_id']
    
    async def _analyze_execution_profile(self, request: dict) -> dict:
        """分析执行特征"""
        
        graph_complexity = self._calculate_graph_complexity(request['graph'])
        block_types = self._extract_block_types(request['graph'])
        
        return {
            'complexity_score': graph_complexity,
            'dominant_block_type': max(block_types, key=block_types.get),
            'estimated_duration': self._estimate_duration(request),
            'resource_requirements': self._analyze_resource_requirements(request),
        }

class LoadPredictor:
    """负载预测器"""
    
    def __init__(self):
        self.historical_data = {}
        self.prediction_model = None
        
    async def predict_demand(self, execution_profile: dict) -> dict:
        """预测资源需求"""
        
        # 简化的预测逻辑（实际可使用ML模型）
        complexity = execution_profile['complexity_score']
        block_type = execution_profile['dominant_block_type']
        
        # 基于历史数据预测
        base_demand = {
            'cpu_cores': min(4, max(1, complexity // 10)),
            'memory_mb': min(2048, max(256, complexity * 50)),
            'io_bandwidth': 100 if 'IO' in block_type else 50,
        }
        
        # 应用时间因素（高峰期增加需求）
        current_hour = datetime.now().hour
        if 9 <= current_hour <= 17:  # 工作时间
            base_demand = {k: int(v * 1.3) for k, v in base_demand.items()}
        
        return base_demand

# docker-compose.production.yml - 生产环境配置
version: '3.8'

services:
  api_srv:
    image: autogpt/platform-api:${API_VERSION}
    deploy:
      replicas: 3
      resources:
        limits:
          cpus: '2'
          memory: 4G
        reservations:
          cpus: '1'
          memory: 2G
    environment:
      - ENVIRONMENT=production
      - LOG_LEVEL=INFO
      - REDIS_CLUSTER_NODES=redis-1:6379,redis-2:6379,redis-3:6379
      - DATABASE_URL=postgresql://user:pass@postgres-primary:5432/autogpt
      - DATABASE_REPLICA_URL=postgresql://user:pass@postgres-replica:5432/autogpt
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

  execution_srv:
    image: autogpt/platform-executor:${EXECUTOR_VERSION}
    deploy:
      replicas: 5
      resources:
        limits:
          cpus: '4'
          memory: 8G
    environment:
      - WORKER_CONCURRENCY=50
      - MAX_EXECUTION_TIME=3600
      - ENABLE_PERFORMANCE_MONITORING=true

  frontend:
    image: autogpt/platform-frontend:${FRONTEND_VERSION}
    deploy:
      replicas: 2
    environment:
      - NEXT_PUBLIC_API_URL=https://api.autogpt.com
      - NEXT_PUBLIC_WS_URL=wss://api.autogpt.com/ws
      - NEXT_PUBLIC_SENTRY_DSN=${SENTRY_DSN}

  postgres_primary:
    image: postgres:15-alpine
    environment:
      - POSTGRES_DB=autogpt
      - POSTGRES_USER=autogpt_user  
      - POSTGRES_PASSWORD=${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
      - ./sql/init.sql:/docker-entrypoint-initdb.d/init.sql

  redis_cluster:
    image: redis:7-alpine
    command: redis-server --cluster-enabled yes --cluster-config-file nodes.conf
    deploy:
      replicas: 6
    
  monitoring:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.console.libraries=/etc/prometheus/console_libraries'
      - '--web.console.templates=/etc/prometheus/consoles'
      - '--storage.tsdb.retention.time=200h'
      - '--web.enable-lifecycle'

volumes:
  postgres_data:
  prometheus_data:

networks:
  autogpt_network:
    driver: overlay
    attachable: true

# k8s/autogpt-platform.yaml - Kubernetes部署配置
apiVersion: apps/v1
kind: Deployment
metadata:
  name: autogpt-api
  labels:
    app: autogpt-api
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: autogpt-api
  template:
    metadata:
      labels:
        app: autogpt-api
    spec:
      containers:
      - name: api
        image: autogpt/platform-api:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: autogpt-secrets
              key: database-url
        - name: REDIS_URL
          valueFrom:
            configMapKeyRef:
              name: autogpt-config
              key: redis-url
        resources:
          requests:
            cpu: 500m
            memory: 1Gi
          limits:
            cpu: 2
            memory: 4Gi
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 10

---
apiVersion: v1
kind: Service
metadata:
  name: autogpt-api-service
spec:
  selector:
    app: autogpt-api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: autogpt-api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: autogpt-api
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

# 综合性能监控系统
class ComprehensiveMonitoringSystem:
    """
    综合性能监控系统 - 整合业界最佳实践
    
    监控维度：
    1. 基础设施指标：CPU、内存、磁盘、网络
    2. 应用性能指标：响应时间、吞吐量、错误率
    3. 业务指标：执行成功率、用户活跃度、成本效率
    4. 用户体验指标：页面加载时间、交互响应时间
    """
    
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_manager = AlertManager()
        self.dashboard_generator = DashboardGenerator()
        
    async def start_monitoring(self):
        """启动综合监控"""
        
        # 启动各类监控协程
        monitoring_tasks = [
            self._monitor_infrastructure(),
            self._monitor_application_performance(),
            self._monitor_business_metrics(),
            self._monitor_user_experience(),
            self._generate_intelligent_alerts(),
        ]
        
        await asyncio.gather(*monitoring_tasks)
    
    async def _monitor_infrastructure(self):
        """基础设施监控"""
        while True:
            try:
                # 收集系统指标
                cpu_usage = psutil.cpu_percent(interval=1)
                memory_info = psutil.virtual_memory()
                disk_info = psutil.disk_usage('/')
                
                # 数据库连接状态
                db_connections = await self._check_database_connections()
                
                # Redis集群状态
                redis_cluster_info = await self._check_redis_cluster()
                
                # 发送指标到Prometheus
                await self.metrics_collector.send_metrics({
                    'cpu_usage_percent': cpu_usage,
                    'memory_usage_percent': memory_info.percent,
                    'disk_usage_percent': disk_info.percent,
                    'db_active_connections': db_connections['active'],
                    'redis_cluster_nodes': redis_cluster_info['active_nodes'],
                })
                
                await asyncio.sleep(30)  # 30秒间隔
                
            except Exception as e:
                logger.error(f"基础设施监控异常: {e}")
                await asyncio.sleep(60)
    
    async def _monitor_business_metrics(self):
        """业务指标监控"""
        while True:
            try:
                # 统计时间窗口（过去1小时）
                end_time = datetime.utcnow()
                start_time = end_time - timedelta(hours=1)
                
                # 执行成功率
                execution_stats = await self._get_execution_stats(start_time, end_time)
                success_rate = execution_stats['success'] / execution_stats['total'] if execution_stats['total'] > 0 else 0
                
                # 用户活跃度
                active_users = await self._count_active_users(start_time, end_time)
                
                # 成本效率
                cost_stats = await self._calculate_cost_efficiency(start_time, end_time)
                
                # Block使用统计
                block_usage = await self._get_block_usage_stats(start_time, end_time)
                
                # 发送业务指标
                await self.metrics_collector.send_metrics({
                    'execution_success_rate': success_rate,
                    'active_users_count': active_users,
                    'cost_per_execution': cost_stats['avg_cost'],
                    'most_used_block': block_usage['top_block'],
                })
                
                await asyncio.sleep(300)  # 5分钟间隔
                
            except Exception as e:
                logger.error(f"业务指标监控异常: {e}")
                await asyncio.sleep(600)