""" Agent Runner - Agent 执行引擎 核心职责： 1. 执行 Agent 任务（循环调用 LLM + 工具） 2. 记录执行轨迹（Trace + Messages + GoalTree） 3. 检索和注入记忆（Experience + Skill） 4. 管理执行计划（GoalTree） 5. 收集反馈，提取经验 """ import logging from datetime import datetime from typing import AsyncIterator, Optional, Dict, Any, List, Callable, Literal, Union from agent.core.config import AgentConfig, CallResult from agent.execution.models import Trace, Message from agent.execution.protocols import TraceStore from agent.goal.models import GoalTree from agent.goal.tool import goal_tool from agent.memory.models import Experience, Skill from agent.memory.protocols import MemoryStore, StateStore from agent.memory.skill_loader import load_skills_from_dir from agent.tools import ToolRegistry, get_tool_registry logger = logging.getLogger(__name__) # 内置工具列表（始终自动加载） BUILTIN_TOOLS = [ "read_file", "edit_file", "write_file", "glob_files", "grep_content", "bash_command", "skill", "list_skills", "goal", ] class AgentRunner: """ Agent 执行引擎 支持两种模式： 1. call(): 单次 LLM 调用（简洁 API） 2. run(): Agent 模式（循环 + 记忆 + 追踪） """ def __init__( self, trace_store: Optional[TraceStore] = None, memory_store: Optional[MemoryStore] = None, state_store: Optional[StateStore] = None, tool_registry: Optional[ToolRegistry] = None, llm_call: Optional[Callable] = None, config: Optional[AgentConfig] = None, skills_dir: Optional[str] = None, goal_tree: Optional[GoalTree] = None, debug: bool = False, ): """ 初始化 AgentRunner Args: trace_store: Trace 存储（可选，不提供则不记录） memory_store: Memory 存储（可选，不提供则不使用记忆） state_store: State 存储（可选，用于任务状态） tool_registry: 工具注册表（可选，默认使用全局注册表） llm_call: LLM 调用函数（必须提供，用于实际调用 LLM） config: Agent 配置 skills_dir: Skills 目录路径（可选，不提供则不加载 skills） goal_tree: 执行计划（可选，不提供则在运行时按需创建） debug: 保留参数（已废弃，请使用 API Server 可视化） """ self.trace_store = trace_store self.memory_store = memory_store self.state_store = state_store self.tools = tool_registry or get_tool_registry() self.llm_call = llm_call self.config = config or AgentConfig() self.skills_dir = skills_dir self.goal_tree = goal_tree self.debug = debug def _generate_id(self) -> str: """生成唯一 ID""" import uuid return str(uuid.uuid4()) # ===== 单次调用 ===== async def call( self, messages: List[Dict], model: str = "gpt-4o", tools: Optional[List[str]] = None, uid: Optional[str] = None, trace: bool = True, **kwargs ) -> CallResult: """ 单次 LLM 调用 Args: messages: 消息列表 model: 模型名称 tools: 工具名称列表 uid: 用户 ID trace: 是否记录 Trace **kwargs: 其他参数传递给 LLM Returns: CallResult """ if not self.llm_call: raise ValueError("llm_call function not provided") trace_id = None message_id = None # 创建 Trace if trace and self.trace_store: trace_obj = Trace.create( mode="call", uid=uid, context={"model": model} ) trace_id = await self.trace_store.create_trace(trace_obj) # 准备工具 Schema tool_names = BUILTIN_TOOLS.copy() if tools: for tool in tools: if tool not in tool_names: tool_names.append(tool) tool_schemas = self.tools.get_schemas(tool_names) # 调用 LLM result = await self.llm_call( messages=messages, model=model, tools=tool_schemas, **kwargs ) # 记录 Message（单次调用模式不使用 GoalTree） if trace and self.trace_store and trace_id: msg = Message.create( trace_id=trace_id, role="assistant", sequence=1, goal_id="0", # 单次调用没有 goal，使用占位符 content={"text": result.get("content", ""), "tool_calls": result.get("tool_calls")}, tokens=result.get("prompt_tokens", 0) + result.get("completion_tokens", 0), cost=result.get("cost", 0), ) message_id = await self.trace_store.add_message(msg) # 完成 Trace await self.trace_store.update_trace( trace_id, status="completed", completed_at=datetime.now(), ) return CallResult( reply=result.get("content", ""), tool_calls=result.get("tool_calls"), trace_id=trace_id, step_id=message_id, # 兼容字段名 tokens={ "prompt": result.get("prompt_tokens", 0), "completion": result.get("completion_tokens", 0), }, cost=result.get("cost", 0) ) # ===== Agent 模式 ===== async def run( self, task: str, messages: Optional[List[Dict]] = None, system_prompt: Optional[str] = None, model: str = "gpt-4o", tools: Optional[List[str]] = None, agent_type: Optional[str] = None, uid: Optional[str] = None, max_iterations: Optional[int] = None, enable_memory: Optional[bool] = None, auto_execute_tools: Optional[bool] = None, **kwargs ) -> AsyncIterator[Union[Trace, Message]]: """ Agent 模式执行 Args: task: 任务描述 messages: 初始消息（可选） system_prompt: 系统提示（可选） model: 模型名称 tools: 工具名称列表 agent_type: Agent 类型 uid: 用户 ID max_iterations: 最大迭代次数 enable_memory: 是否启用记忆 auto_execute_tools: 是否自动执行工具 **kwargs: 其他参数 Yields: Union[Trace, Message]: Trace 对象（状态变化）或 Message 对象（执行过程） """ if not self.llm_call: raise ValueError("llm_call function not provided") # 使用配置默认值 agent_type = agent_type or self.config.agent_type max_iterations = max_iterations or self.config.max_iterations enable_memory = enable_memory if enable_memory is not None else self.config.enable_memory auto_execute_tools = auto_execute_tools if auto_execute_tools is not None else self.config.auto_execute_tools # 创建 Trace trace_id = self._generate_id() trace_obj = Trace( trace_id=trace_id, mode="agent", task=task, agent_type=agent_type, uid=uid, context={"model": model, **kwargs}, status="running" ) if self.trace_store: await self.trace_store.create_trace(trace_obj) # 初始化 GoalTree goal_tree = self.goal_tree or GoalTree(mission=task) await self.trace_store.update_goal_tree(trace_id, goal_tree) # 返回 Trace（表示开始） yield trace_obj try: # 加载记忆（Experience 和 Skill） experiences_text = "" skills_text = "" if enable_memory and self.memory_store: scope = f"agent:{agent_type}" experiences = await self.memory_store.search_experiences(scope, task) experiences_text = self._format_experiences(experiences) logger.info(f"加载 {len(experiences)} 条经验") # 加载 Skills（内置 + 用户自定义） skills = load_skills_from_dir(self.skills_dir) if skills: skills_text = self._format_skills(skills) if self.skills_dir: logger.info(f"加载 {len(skills)} 个 skills (内置 + 自定义: {self.skills_dir})") else: logger.info(f"加载 {len(skills)} 个内置 skills") # 构建初始消息 if messages is None: messages = [] if system_prompt: # 注入记忆和 skills 到 system prompt full_system = system_prompt if skills_text: full_system += f"\n\n## Skills\n{skills_text}" if experiences_text: full_system += f"\n\n## 相关经验\n{experiences_text}" messages = [{"role": "system", "content": full_system}] + messages # 添加任务描述 messages.append({"role": "user", "content": task}) # 获取 GoalTree goal_tree = None if self.trace_store: goal_tree = await self.trace_store.get_goal_tree(trace_id) # 设置 goal_tree 到 goal 工具（供 LLM 调用） from agent.tools.builtin.goal import set_goal_tree set_goal_tree(goal_tree) # 准备工具 Schema tool_names = BUILTIN_TOOLS.copy() if tools: for tool in tools: if tool not in tool_names: tool_names.append(tool) tool_schemas = self.tools.get_schemas(tool_names) # 执行循环 sequence = 1 for iteration in range(max_iterations): # 注入当前计划到 messages（如果有 goals） llm_messages = list(messages) if goal_tree and goal_tree.goals: plan_text = f"\n## Current Plan\n\n{goal_tree.to_prompt()}" # 在最后一条 system 消息之后注入 llm_messages.append({"role": "system", "content": plan_text}) # 调用 LLM result = await self.llm_call( messages=llm_messages, model=model, tools=tool_schemas, **kwargs ) response_content = result.get("content", "") tool_calls = result.get("tool_calls") step_tokens = result.get("prompt_tokens", 0) + result.get("completion_tokens", 0) step_cost = result.get("cost", 0) # 获取当前 goal_id current_goal_id = goal_tree.current_id if goal_tree else "0" # 记录 assistant Message assistant_msg = Message.create( trace_id=trace_id, role="assistant", sequence=sequence, goal_id=current_goal_id, content={"text": response_content, "tool_calls": tool_calls}, tokens=step_tokens, cost=step_cost, ) if self.trace_store: await self.trace_store.add_message(assistant_msg) # WebSocket 广播由 add_message 内部的 append_event 触发 yield assistant_msg sequence += 1 # 处理工具调用 if tool_calls and auto_execute_tools: # 添加 assistant 消息到对话历史 messages.append({ "role": "assistant", "content": response_content, "tool_calls": tool_calls, }) for tc in tool_calls: tool_name = tc["function"]["name"] tool_args = tc["function"]["arguments"] if isinstance(tool_args, str): import json tool_args = json.loads(tool_args) # 拦截 goal 工具调用（需要保存更新后的 GoalTree） if tool_name == "goal": # 执行 goal 工具 tool_result = await self.tools.execute( tool_name, tool_args, uid=uid or "" ) # 保存更新后的 GoalTree if self.trace_store and goal_tree: await self.trace_store.update_goal_tree(trace_id, goal_tree) # TODO: 广播 goal 更新事件 else: # 执行普通工具 tool_result = await self.tools.execute( tool_name, tool_args, uid=uid or "" ) # 记录 tool Message tool_msg = Message.create( trace_id=trace_id, role="tool", sequence=sequence, goal_id=current_goal_id, tool_call_id=tc["id"], content={"tool_name": tool_name, "result": tool_result}, ) if self.trace_store: await self.trace_store.add_message(tool_msg) yield tool_msg sequence += 1 # 添加到消息历史 messages.append({ "role": "tool", "tool_call_id": tc["id"], "name": tool_name, "content": str(tool_result), }) continue # 继续循环 # 无工具调用，任务完成 break # 完成 Trace if self.trace_store: trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj: await self.trace_store.update_trace( trace_id, status="completed", completed_at=datetime.now(), ) # 重新获取更新后的 Trace 并返回 trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj: yield trace_obj except Exception as e: logger.error(f"Agent run failed: {e}") if self.trace_store: await self.trace_store.update_trace( trace_id, status="failed", completed_at=datetime.now() ) trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj: yield trace_obj raise # ===== 辅助方法 ===== def _format_skills(self, skills: List[Skill]) -> str: """格式化技能为 Prompt 文本""" if not skills: return "" return "\n\n".join(s.to_prompt_text() for s in skills) def _format_experiences(self, experiences: List[Experience]) -> str: """格式化经验为 Prompt 文本""" if not experiences: return "" return "\n".join(f"- {e.to_prompt_text()}" for e in experiences)