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

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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,
    )

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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

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// 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);

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

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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

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# 响应式执行引擎 - 参考ReactiveX模式
class ReactiveExecutionEngine:
    """
    响应式执行引擎
    
    基于观察者模式和流式处理，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}")

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# 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

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# 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

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# 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

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# 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)

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# 安全审计系统 - 参考网上安全最佳实践
class SecurityAuditSystem:
    """
    安全审计系统
    
    功能：
    1. 用户行为审计
    2. 数据访问审计  
    3. 系统变更审计
    4. 合规性检查
    """
    
    def __init__(self):
        self.audit_logger = logging.getLogger('security.audit')
        self.compliance_rules = self._load_compliance_rules()
        
    async def audit_user_action(self, user_id: str, action: str, context: dict):
        """审计用户操作"""
        
        audit_record = {
            'timestamp': datetime.utcnow().isoformat(),
            'user_id': user_id,
            'action': action,
            'context': context,
            'ip_address': context.get('client_ip'),
            'user_agent': context.get('user_agent'),
            'session_id': context.get('session_id'),
        }
        
        # 敏感操作检查
        if self._is_sensitive_action(action):
            audit_record['sensitivity_level'] = 'HIGH'
            
            # 额外验证
            verification_result = await self._verify_sensitive_action(user_id, action, context)
            audit_record['verification'] = verification_result
        
        # 记录审计日志
        self.audit_logger.info(json.dumps(audit_record))
        
        # 实时风险评估
        risk_score = await self._calculate_risk_score(audit_record)
        if risk_score > 0.8:
            await self._trigger_security_alert(audit_record, risk_score)
    
    async def _verify_sensitive_action(self, user_id: str, action: str, context: dict) -> dict:
        """验证敏感操作"""
        
        verification_result = {
            'multi_factor_auth': False,
            'admin_approval': False,
            'rate_limit_check': True,
        }
        
        # 多因素认证检查
        if action in ['delete_agent', 'modify_user_permissions']:
            mfa_status = await self._check_mfa_status(user_id)
            verification_result['multi_factor_auth'] = mfa_status
        
        # 管理员审批检查
        if action in ['system_configuration_change']:
            approval_status = await self._check_admin_approval(user_id, action)
            verification_result['admin_approval'] = approval_status
        
        return verification_result
    
    def _is_sensitive_action(self, action: str) -> bool:
        """判断是否为敏感操作"""
        sensitive_actions = [
            'delete_agent',
            'modify_user_permissions', 
            'access_sensitive_data',
            'system_configuration_change',
            'bulk_data_export',
        ]
        return action in sensitive_actions