dc398d7c7b
- 新增 CLIPluginAdapter 统一接口 (backend/app/core/agent_adapter.py) - 新增 LLM 服务层，支持 Anthropic/OpenAI/DeepSeek/Ollama (backend/app/services/llm_service.py) - 新增 Agent 执行引擎，支持文件锁自动管理 (backend/app/services/agent_executor.py) - 新增 NativeLLMAgent 原生 LLM 适配器 (backend/app/adapters/native_llm_agent.py) - 新增进程管理器 (backend/app/services/process_manager.py) - 新增 Agent 控制 API (backend/app/routers/agents_control.py) - 新增 WebSocket 实时通信 (backend/app/routers/websocket.py) - 更新前端 AgentsPage，支持启动/停止 Agent - 测试通过：Agent 启动、批量操作、栅栏同步 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
487 lines
15 KiB
Python
487 lines
15 KiB
Python
"""
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Agent 执行引擎
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负责协调 LLM 调用和资源管理，提供声明式的任务执行接口。
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自动管理文件锁、心跳更新等生命周期。
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"""
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import asyncio
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import logging
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import re
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import time
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from typing import Dict, List, Optional, Any, Callable
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from dataclasses import dataclass, field
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from pathlib import Path
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from .llm_service import ModelRouter, LLMMessage, TaskType
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from .storage import get_storage
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from .file_lock import get_file_lock_service
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from .heartbeat import get_heartbeat_service
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from .agent_registry import get_agent_registry, AgentInfo
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from ..core.agent_adapter import Task, Result
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logger = logging.getLogger(__name__)
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@dataclass
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class ExecutionPlan:
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"""任务执行计划"""
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steps: List[str] = field(default_factory=list)
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required_files: List[str] = field(default_factory=list)
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estimated_duration: str = ""
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complexity: str = "medium"
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requires_code_execution: bool = False
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subtasks: List[str] = field(default_factory=list)
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@dataclass
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class ExecutionContext:
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"""执行上下文"""
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agent_id: str
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agent_role: str
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agent_model: str
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task_id: str
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acquired_locks: List[str] = field(default_factory=list)
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start_time: float = 0
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messages: List[LLMMessage] = field(default_factory=list)
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metadata: Dict[str, Any] = field(default_factory=dict)
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class AgentExecutor:
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"""
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Agent 任务执行引擎
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功能：
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1. 任务分析 - 解析任务描述，识别需要的文件
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2. 计划生成 - 调用 LLM 生成执行计划
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3. 资源管理 - 自动获取和释放文件锁
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4. 任务执行 - 调用 LLM 执行任务
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5. 结果处理 - 格式化输出，更新状态
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"""
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def __init__(
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self,
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llm_service: ModelRouter = None,
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storage=None,
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lock_service=None,
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heartbeat_service=None,
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registry=None
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):
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self.llm = llm_service
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self.storage = storage or get_storage()
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self.locks = lock_service or get_file_lock_service()
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self.heartbeat = heartbeat_service or get_heartbeat_service()
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self.registry = registry or get_agent_registry()
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# 工作目录
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self.work_dir = Path.cwd()
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async def execute_task(
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self,
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agent: AgentInfo,
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task: Task,
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context: Dict[str, Any] = None
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) -> Result:
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"""
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执行任务的主入口
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自动管理：
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1. 文件锁获取和释放
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2. 心跳更新
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3. 任务进度跟踪
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4. 错误处理和恢复
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"""
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execution_context = ExecutionContext(
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agent_id=agent.agent_id,
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agent_role=agent.role,
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agent_model=agent.model,
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task_id=task.task_id,
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start_time=time.time(),
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metadata=context or {}
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)
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try:
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# 1. 更新心跳 - 开始执行
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await self.heartbeat.update_heartbeat(
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agent.agent_id,
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"working",
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task.description[:100], # 截断过长描述
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0
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)
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# 2. 分析任务，识别需要的文件
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execution_context.required_files = await self._analyze_required_files(
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task.description
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)
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# 3. 获取文件锁
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await self._acquire_locks(execution_context)
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# 4. 构建执行上下文消息
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execution_context.messages = await self._build_messages(
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agent, task, execution_context
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)
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# 5. 调用 LLM 执行任务
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llm_response = await self._call_llm(execution_context)
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# 6. 处理结果
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result = await self._process_result(
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llm_response, execution_context
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)
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# 7. 更新心跳 - 完成
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await self.heartbeat.update_heartbeat(
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agent.agent_id,
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"idle",
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"",
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100
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)
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return result
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except Exception as e:
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logger.error(f"任务执行失败: {e}", exc_info=True)
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# 更新心跳为错误状态
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await self.heartbeat.update_heartbeat(
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agent.agent_id,
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"error",
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str(e),
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0
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)
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return Result(
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success=False,
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output="",
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error=str(e),
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execution_time=time.time() - execution_context.start_time
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)
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finally:
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# 8. 释放所有锁
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await self._release_locks(execution_context)
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async def _analyze_required_files(self, task_description: str) -> List[str]:
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"""
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分析任务描述，识别需要的文件
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使用 LLM 分析任务中提到的文件路径
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"""
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# 使用正则表达式快速匹配文件路径
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file_patterns = [
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r'[a-zA-Z_/\\][a-zA-Z0-9_/\\]*\.(?:py|js|ts|tsx|jsx|java|go|rs|c|h|cpp|hpp|css|html|md|json|yaml|yml)',
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r'[a-zA-Z_/\\][a-zA-Z0-9_/\\]*\.(?:py|js|ts|tsx|jsx)',
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r'src/[a-zA-Z0-9_/\\]*',
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r'app/[a-zA-Z0-9_/\\]*',
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r'components/[a-zA-Z0-9_/\\]*',
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r'pages/[a-zA-Z0-9_/\\]*',
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r'services/[a-zA-Z0-9_/\\]*',
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r'utils/[a-zA-Z0-9_/\\]*',
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]
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files = set()
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for pattern in file_patterns:
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matches = re.findall(pattern, task_description)
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files.update(matches)
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# 规范化路径
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normalized_files = []
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for f in files:
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# 转换反斜杠
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f = f.replace('\\', '/')
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# 移除重复的斜杠
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f = re.sub(r'/+', '/', f)
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if f not in normalized_files:
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normalized_files.append(f)
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logger.debug(f"识别到文件: {normalized_files}")
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return normalized_files
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async def _acquire_locks(self, context: ExecutionContext) -> None:
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"""获取所有需要的文件锁"""
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for file_path in context.required_files:
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success = await self.locks.acquire_lock(
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file_path,
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context.agent_id,
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context.agent_role.upper()
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)
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if success:
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context.acquired_locks.append(file_path)
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logger.debug(f"获取锁成功: {file_path}")
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else:
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logger.warning(f"获取锁失败: {file_path} (可能被其他 Agent 占用)")
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async def _release_locks(self, context: ExecutionContext) -> None:
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"""释放所有获取的文件锁"""
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for file_path in context.acquired_locks:
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try:
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await self.locks.release_lock(file_path, context.agent_id)
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logger.debug(f"释放锁: {file_path}")
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except Exception as e:
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logger.warning(f"释放锁失败: {file_path}: {e}")
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async def _build_messages(
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self,
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agent: AgentInfo,
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task: Task,
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context: ExecutionContext
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) -> List[LLMMessage]:
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"""构建 LLM 消息列表"""
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messages = []
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# 系统提示词
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system_prompt = self._build_system_prompt(agent, context)
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messages.append(LLMMessage(role="system", content=system_prompt))
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# 添加上下文信息
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if context.required_files:
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context_info = f"\n相关文件: {', '.join(context.required_files)}\n"
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# 尝试读取文件内容
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file_contents = await self._read_file_contents(context.required_files)
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if file_contents:
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context_info += f"\n文件内容:\n{file_contents}\n"
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messages.append(LLMMessage(role="user", content=context_info))
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# 任务描述
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messages.append(LLMMessage(role="user", content=task.description))
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# 添加额外上下文
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if task.context:
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context_str = f"\n额外上下文:\n{json.dumps(task.context, ensure_ascii=False, indent=2)}\n"
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messages.append(LLMMessage(role="user", content=context_str))
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return messages
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def _build_system_prompt(self, agent: AgentInfo, context: ExecutionContext) -> str:
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"""构建系统提示词"""
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role_prompts = {
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"architect": """
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你是一个系统架构师。你擅长：
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- 系统设计和模块划分
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- 技术选型和架构决策
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- 接口设计和数据流规划
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- 性能优化和扩展性考虑
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请给出清晰、完整的架构方案。
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""",
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"pm": """
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你是一个产品经理。你擅长：
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- 需求分析和用户故事
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- 功能优先级排序
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- 产品规划
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- 用户体验考虑
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请从用户角度分析需求。
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""",
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"developer": """
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你是一个高级开发工程师。你擅长：
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- 编写高质量、可维护的代码
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- 遵循最佳实践和编码规范
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- 考虑边界情况和错误处理
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- 编写清晰的注释和文档
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请给出可以直接使用的代码实现。
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""",
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"reviewer": """
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你是一个代码审查专家。你擅长：
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- 发现代码中的潜在问题
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- 安全漏洞检测
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- 性能问题识别
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- 代码风格和可读性改进
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请给出详细的审查意见。
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""",
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"qa": """
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你是一个测试工程师。你擅长：
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- 编写全面的测试用例
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- 边界条件测试
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- 自动化测试
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- 测试策略制定
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请给出完整的测试方案。
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"""
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}
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base_prompt = f"""你是 {agent.name}，一个 AI 编程助手。
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当前任务 ID: {context.task_id}
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你的角色: {agent.role}
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使用的模型: {agent.model}
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工作原则：
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1. 仔细理解任务需求
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2. 给出清晰、具体的回答
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3. 如果涉及代码，确保代码正确且可运行
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4. 考虑边界情况和错误处理
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5. 必要时给出解释和说明
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"""
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role_prompt = role_prompts.get(agent.role, "")
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return base_prompt + role_prompt
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async def _read_file_contents(self, file_paths: List[str]) -> str:
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"""读取文件内容（如果存在）"""
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contents = []
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for file_path in file_paths[:3]: # 限制读取文件数量
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full_path = self.work_dir / file_path
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if full_path.exists():
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try:
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with open(full_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 限制每个文件的内容长度
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if len(content) > 2000:
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content = content[:2000] + "\n... (文件过长，已截断)"
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contents.append(f"### {file_path}\n```\n{content}\n```")
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except Exception as e:
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logger.warning(f"读取文件失败: {file_path}: {e}")
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return "\n\n".join(contents)
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async def _call_llm(self, context: ExecutionContext) -> str:
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"""调用 LLM 执行任务"""
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if not self.llm:
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# 如果没有配置 LLM 服务，使用模拟响应
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return await self._mock_llm_response(context)
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response = await self.llm.route_task(
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task=context.messages[-1].content,
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messages=context.messages,
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preferred_model=context.agent_model
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)
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logger.info(f"LLM 调用完成: {response.provider}/{response.model}, "
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f"tokens: {response.tokens_used}, latency: {response.latency:.2f}s")
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return response.content
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async def _mock_llm_response(self, context: ExecutionContext) -> str:
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"""模拟 LLM 响应（用于测试）"""
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await asyncio.sleep(0.5) # 模拟延迟
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return f"""[模拟响应]
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作为 {context.agent_role}，我对任务的分析如下：
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任务需要处理的文件: {', '.join(context.required_files) or '无'}
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## 分析
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这是一个模拟响应，表示系统正在正常工作。
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## 建议
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1. 配置 LLM API 密钥以启用真实 AI 能力
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2. 在环境变量中设置 ANTHROPIC_API_KEY 或 DEEPSEEK_API_KEY
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3. 重启服务后即可使用完整功能
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---
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*Agent ID: {context.agent_id}*
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*任务 ID: {context.task_id}*
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"""
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async def _process_result(
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self,
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llm_output: str,
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context: ExecutionContext
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) -> Result:
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"""处理 LLM 输出，返回格式化结果"""
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execution_time = time.time() - context.start_time
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return Result(
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success=True,
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output=llm_output,
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metadata={
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"agent_id": context.agent_id,
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"agent_role": context.agent_role,
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"agent_model": context.agent_model,
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"task_id": context.task_id,
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"required_files": context.required_files,
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"acquired_locks": context.acquired_locks,
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"execution_time": execution_time
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},
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execution_time=execution_time
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)
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async def create_execution_plan(
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self,
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agent: AgentInfo,
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task: str
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) -> ExecutionPlan:
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"""
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创建任务执行计划
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使用 LLM 分析任务，生成详细的执行步骤
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"""
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if not self.llm:
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return self._create_mock_plan(task)
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plan_prompt = f"""
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请分析以下任务，生成执行计划。
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任务: {task}
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请返回 JSON 格式的执行计划，包含:
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{{
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"steps": ["步骤1", "步骤2", ...],
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"required_files": ["file1.py", "file2.js", ...],
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"estimated_duration": "预计时间",
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"complexity": "simple|medium|complex",
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"requires_code_execution": true/false,
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"subtasks": ["子任务1", "子任务2", ...]
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}}
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"""
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try:
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response = await self.llm.route_task(
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task=plan_prompt,
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messages=[LLMMessage(role="user", content=plan_prompt)]
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)
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# 尝试解析 JSON
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import json
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plan_data = json.loads(response)
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return ExecutionPlan(
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steps=plan_data.get("steps", []),
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required_files=plan_data.get("required_files", []),
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estimated_duration=plan_data.get("estimated_duration", ""),
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complexity=plan_data.get("complexity", "medium"),
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requires_code_execution=plan_data.get("requires_code_execution", False),
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subtasks=plan_data.get("subtasks", [])
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)
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except Exception as e:
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logger.warning(f"解析执行计划失败: {e}")
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return self._create_mock_plan(task)
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def _create_mock_plan(self, task: str) -> ExecutionPlan:
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"""创建模拟执行计划"""
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return ExecutionPlan(
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steps=[
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"1. 分析任务需求",
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"2. 查看相关文件",
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"3. 制定实现方案",
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"4. 执行实现"
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],
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estimated_duration="5-10 分钟",
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complexity="medium"
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)
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# 单例获取函数
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_executor: Optional[AgentExecutor] = None
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def get_agent_executor(llm_service: ModelRouter = None) -> AgentExecutor:
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"""获取 Agent 执行引擎单例"""
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global _executor
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if _executor is None:
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_executor = AgentExecutor(llm_service=llm_service)
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return _executor
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def reset_agent_executor():
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"""重置执行引擎（主要用于测试）"""
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global _executor
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_executor = None
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