feat: 多智能体竞标调度系统 v1.0.0

核心组件：
- Orchestrator: 意图理解、任务拆分、竞标管理、结果验证
- Worker: 竞标任务、执行交付
- TaskBoard: 状态管理、信息存储
- BidEvaluator: 竞标评估算法
- ExecutionMonitor: 执行监控、超时处理
- LLMClient: 大模型接口调用

功能特性：
- 竞标机制：Agent主动竞争任务
- 动态调度：串行/并行任务智能调度
- 智能容错：超时切换、验证重试
- 质量保证：结果验证、历史追踪

Web界面：首页、请求列表、任务列表、Agent管理
API接口：请求/任务/Agent管理、测试接口
端口：19015

app/worker.py

@@ -0,0 +1,202 @@
+"""
+执行Agent (Worker) - 竞标任务、执行交付
+"""
+
+import time
+import json
+from typing import Dict, List, Optional, Any
+from .models import Task, Bid, Execution, AgentProfile
+from .task_board import TaskBoard
+from .llm_client import LLMClient, default_client
+
+
+class Worker:
+    """执行Agent"""
+    
+    def __init__(
+        self,
+        profile: AgentProfile,
+        task_board: TaskBoard,
+        llm_client: LLMClient = None
+    ):
+        self.profile = profile
+        self.task_board = task_board
+        self.llm = llm_client or default_client
+        
+        # 注册到任务公告板
+        self.task_board.register_agent(profile)
+        
+        # 当前任务
+        self.current_tasks: Dict[str, Execution] = {}
+    
+    # === 竞标 ===
+    
+    def bid_for_task(self, task: Task) -> Optional[Bid]:
+        """
+        为任务竞标
+        
+        Args:
+            task: 任务
+        
+        Returns:
+            竞标书或None
+        """
+        try:
+            # 使用LLM评估任务并生成竞标
+            bid_data = self.llm.generate_bid(task.to_dict(), self.profile.to_dict())
+            
+            bid = Bid(
+                task_id=task.id,
+                agent_id=self.profile.id,
+                capability_match=float(bid_data.get('capability_match', 0.5)),
+                estimated_time=int(bid_data.get('estimated_time', 60)),
+                confidence=float(bid_data.get('confidence', 0.5)),
+                approach=bid_data.get('approach', ''),
+                prerequisites=bid_data.get('prerequisites', []),
+                alternative_approaches=bid_data.get('alternative_approaches', [])
+            )
+            
+            return bid
+        
+        except Exception as e:
+            return None
+    
+    # === 执行 ===
+    
+    def execute_task(self, task: Task, bid: Bid) -> Execution:
+        """
+        执行任务
+        
+        Args:
+            task: 任务
+            bid: 竞标书
+        
+        Returns:
+            Execution对象
+        """
+        execution = Execution(
+            task_id=task.id,
+            agent_id=self.profile.id,
+            bid_id=bid.id,
+            status='running'
+        )
+        
+        self.current_tasks[task.id] = execution
+        self.task_board.add_execution(execution)
+        
+        try:
+            # 使用LLM执行任务
+            result = self.llm.execute_task(task.to_dict(), bid.approach)
+            
+            execution.status = 'completed'
+            execution.end_time = time.time()
+            execution.result = result.get('result') or result
+            
+            self.task_board.update_execution(execution)
+            
+            # 更新统计
+            duration = execution.end_time - execution.start_time
+            self.task_board.update_agent_stats(
+                self.profile.id,
+                success=True,
+                duration=duration
+            )
+        
+        except Exception as e:
+            execution.status = 'failed'
+            execution.end_time = time.time()
+            execution.error = str(e)
+            
+            self.task_board.update_execution(execution)
+            
+            # 更新统计
+            self.task_board.update_agent_stats(
+                self.profile.id,
+                success=False,
+                duration=time.time() - execution.start_time
+            )
+        
+        finally:
+            self.current_tasks.pop(task.id, None)
+        
+        return execution
+    
+    def get_status(self) -> Dict:
+        """获取Worker状态"""
+        return {
+            'profile': self.profile.to_dict(),
+            'current_tasks': len(self.current_tasks),
+            'is_busy': len(self.current_tasks) >= self.profile.max_concurrent_tasks
+        }
+
+
+class WorkerPool:
+    """Worker池"""
+    
+    def __init__(self, task_board: TaskBoard):
+        self.task_board = task_board
+        self.workers: Dict[str, Worker] = {}
+    
+    def create_worker(self, profile: AgentProfile, llm_client: LLMClient = None) -> Worker:
+        """创建Worker"""
+        worker = Worker(profile, self.task_board, llm_client)
+        self.workers[profile.id] = worker
+        return worker
+    
+    def get_worker(self, agent_id: str) -> Optional[Worker]:
+        """获取Worker"""
+        return self.workers.get(agent_id)
+    
+    def list_workers(self) -> List[Worker]:
+        """列出所有Worker"""
+        return list(self.workers.values())
+    
+    def find_available_workers(self) -> List[Worker]:
+        """找到空闲的Worker"""
+        return [w for w in self.workers.values() if not w.get_status()['is_busy']]
+
+
+def create_default_workers(task_board: TaskBoard, llm_client: LLMClient = None) -> WorkerPool:
+    """创建默认的Worker池"""
+    pool = WorkerPool(task_board)
+    
+    # 创建几个默认的Agent
+    default_profiles = [
+        AgentProfile(
+            id="coder_agent",
+            name="代码专家",
+            description="擅长代码编写、调试、优化",
+            capabilities=["coding", "debugging", "optimization"],
+            max_concurrent_tasks=2,
+            preferred_task_types=["coding"]
+        ),
+        AgentProfile(
+            id="search_agent",
+            name="搜索专家",
+            description="擅长信息搜索、资料收集",
+            capabilities=["search", "research", "summarization"],
+            max_concurrent_tasks=3,
+            preferred_task_types=["search"]
+        ),
+        AgentProfile(
+            id="writer_agent",
+            name="写作专家",
+            description="擅长文档撰写、内容创作",
+            capabilities=["writing", "documentation", "translation"],
+            max_concurrent_tasks=2,
+            preferred_task_types=["writing"]
+        ),
+        AgentProfile(
+            id="analyst_agent",
+            name="分析专家",
+            description="擅长数据分析、报告生成",
+            capabilities=["analysis", "reporting", "visualization"],
+            max_concurrent_tasks=2,
+            preferred_task_types=["analysis"]
+        )
+    ]
+    
+    for profile in default_profiles:
+        pool.create_worker(profile, llm_client)
+    
+    return pool