""" 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. 支持续跑（continue）和回溯重跑（rewind） 参数分层： - Infrastructure: AgentRunner 构造时设置（trace_store, llm_call 等） - RunConfig: 每次 run 时指定（model, trace_id, insert_after 等） - Messages: OpenAI SDK 格式的任务消息 """ import asyncio import json import logging import os import uuid from dataclasses import dataclass, field from datetime import datetime from typing import AsyncIterator, Optional, Dict, Any, List, Callable, Literal, Tuple, Union from agent.trace.models import Trace, Message from agent.trace.protocols import TraceStore from agent.trace.goal_models import GoalTree from agent.memory.models import 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__) # ===== 运行配置 ===== @dataclass class RunConfig: """ 运行参数 — 控制 Agent 如何执行 分为模型层参数（由上游 agent 或用户决定）和框架层参数（由系统注入）。 """ # --- 模型层参数 --- model: str = "gpt-4o" temperature: float = 0.3 max_iterations: int = 200 tools: Optional[List[str]] = None # None = 全部内置工具 # --- 框架层参数 --- agent_type: str = "default" uid: Optional[str] = None system_prompt: Optional[str] = None # None = 从 skills 自动构建 enable_memory: bool = True auto_execute_tools: bool = True name: Optional[str] = None # 显示名称（空则由 utility_llm 自动生成） # --- Trace 控制 --- trace_id: Optional[str] = None # None = 新建 parent_trace_id: Optional[str] = None # 子 Agent 专用 parent_goal_id: Optional[str] = None # --- 续跑控制 --- insert_after: Optional[int] = None # 回溯插入点（message sequence） # --- 额外 LLM 参数（传给 llm_call 的 **kwargs）--- extra_llm_params: Dict[str, Any] = field(default_factory=dict) # 内置工具列表（始终自动加载） BUILTIN_TOOLS = [ # 文件操作工具 "read_file", "edit_file", "write_file", "glob_files", "grep_content", # 系统工具 "bash_command", # 技能和目标管理 "skill", "list_skills", "goal", "agent", "evaluate", # 搜索工具 "search_posts", "get_search_suggestions", # 沙箱工具 "sandbox_create_environment", "sandbox_run_shell", "sandbox_rebuild_with_ports", "sandbox_destroy_environment", # 浏览器工具 "browser_navigate_to_url", "browser_search_web", "browser_go_back", "browser_wait", "browser_click_element", "browser_input_text", "browser_send_keys", "browser_upload_file", "browser_scroll_page", "browser_find_text", "browser_screenshot", "browser_switch_tab", "browser_close_tab", "browser_get_dropdown_options", "browser_select_dropdown_option", "browser_extract_content", "browser_read_long_content", "browser_get_page_html", "browser_get_selector_map", "browser_evaluate", "browser_ensure_login_with_cookies", "browser_wait_for_user_action", "browser_done", ] # ===== 向后兼容 ===== @dataclass class AgentConfig: """[向后兼容] Agent 配置，新代码请使用 RunConfig""" agent_type: str = "default" max_iterations: int = 200 enable_memory: bool = True auto_execute_tools: bool = True @dataclass class CallResult: """单次调用结果""" reply: str tool_calls: Optional[List[Dict]] = None trace_id: Optional[str] = None step_id: Optional[str] = None tokens: Optional[Dict[str, int]] = None cost: float = 0.0 # ===== 执行引擎 ===== CONTEXT_INJECTION_INTERVAL = 10 # 每 N 轮注入一次 GoalTree + Collaborators class AgentRunner: """ Agent 执行引擎 支持三种运行模式（通过 RunConfig 区分）： 1. 新建：trace_id=None 2. 续跑：trace_id=已有ID, insert_after=None 3. 回溯：trace_id=已有ID, insert_after=N """ 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, utility_llm_call: Optional[Callable] = None, config: Optional[AgentConfig] = None, skills_dir: Optional[str] = None, experiences_path: Optional[str] = "./cache/experiences.md", goal_tree: Optional[GoalTree] = None, debug: bool = False, ): """ 初始化 AgentRunner Args: trace_store: Trace 存储 memory_store: Memory 存储（可选） state_store: State 存储（可选） tool_registry: 工具注册表（默认使用全局注册表） llm_call: 主 LLM 调用函数 utility_llm_call: 轻量 LLM（用于生成任务标题等），可选 config: [向后兼容] AgentConfig skills_dir: Skills 目录路径 experiences_path: 经验文件路径（默认 ./cache/experiences.md） goal_tree: 初始 GoalTree（可选） debug: 保留参数（已废弃） """ 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.utility_llm_call = utility_llm_call self.config = config or AgentConfig() self.skills_dir = skills_dir self.experiences_path = experiences_path self.goal_tree = goal_tree self.debug = debug self._cancel_events: Dict[str, asyncio.Event] = {} # trace_id → cancel event # ===== 核心公开方法 ===== async def run( self, messages: List[Dict], config: Optional[RunConfig] = None, ) -> AsyncIterator[Union[Trace, Message]]: """ Agent 模式执行（核心方法） Args: messages: OpenAI SDK 格式的输入消息 新建: 初始任务消息 [{"role": "user", "content": "..."}] 续跑: 追加的新消息 回溯: 在插入点之后追加的消息 config: 运行配置 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") config = config or RunConfig() trace = None try: # Phase 1: PREPARE TRACE trace, goal_tree, sequence = await self._prepare_trace(messages, config) # 注册取消事件 self._cancel_events[trace.trace_id] = asyncio.Event() yield trace # Phase 2: BUILD HISTORY history, sequence, created_messages, head_seq = await self._build_history( trace.trace_id, messages, goal_tree, config, sequence ) # Update trace's head_sequence in memory trace.head_sequence = head_seq for msg in created_messages: yield msg # Phase 3: AGENT LOOP async for event in self._agent_loop(trace, history, goal_tree, config, sequence): yield event except Exception as e: logger.error(f"Agent run failed: {e}") tid = config.trace_id or (trace.trace_id if trace else None) if self.trace_store and tid: await self.trace_store.update_trace( tid, status="failed", error_message=str(e), completed_at=datetime.now() ) trace_obj = await self.trace_store.get_trace(tid) if trace_obj: yield trace_obj raise finally: # 清理取消事件 if trace: self._cancel_events.pop(trace.trace_id, None) async def run_result( self, messages: List[Dict], config: Optional[RunConfig] = None, ) -> Dict[str, Any]: """ 结果模式 — 消费 run()，返回结构化结果。 主要用于 agent/evaluate 工具内部。 """ last_assistant_text = "" final_trace: Optional[Trace] = None async for item in self.run(messages=messages, config=config): if isinstance(item, Message) and item.role == "assistant": content = item.content text = "" if isinstance(content, dict): text = content.get("text", "") or "" elif isinstance(content, str): text = content if text and text.strip(): last_assistant_text = text elif isinstance(item, Trace): final_trace = item config = config or RunConfig() if not final_trace and config.trace_id and self.trace_store: final_trace = await self.trace_store.get_trace(config.trace_id) status = final_trace.status if final_trace else "unknown" error = final_trace.error_message if final_trace else None summary = last_assistant_text if not summary: status = "failed" error = error or "Agent 没有产生 assistant 文本结果" return { "status": status, "summary": summary, "trace_id": final_trace.trace_id if final_trace else config.trace_id, "error": error, "stats": { "total_messages": final_trace.total_messages if final_trace else 0, "total_tokens": final_trace.total_tokens if final_trace else 0, "total_cost": final_trace.total_cost if final_trace else 0.0, }, } async def stop(self, trace_id: str) -> bool: """ 停止运行中的 Trace 设置取消信号，agent loop 在下一个 LLM 调用前检查并退出。 Trace 状态置为 "stopped"。 Returns: True 如果成功发送停止信号，False 如果该 trace 不在运行中 """ cancel_event = self._cancel_events.get(trace_id) if cancel_event is None: return False cancel_event.set() return True # ===== 单次调用（保留）===== 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（无 Agent Loop） """ if not self.llm_call: raise ValueError("llm_call function not provided") trace_id = None message_id = None 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) if trace and self.trace_store: trace_obj = Trace.create(mode="call", uid=uid, model=model, tools=tool_schemas, llm_params=kwargs) trace_id = await self.trace_store.create_trace(trace_obj) result = await self.llm_call(messages=messages, model=model, tools=tool_schemas, **kwargs) if trace and self.trace_store and trace_id: msg = Message.create( trace_id=trace_id, role="assistant", sequence=1, goal_id=None, content={"text": result.get("content", ""), "tool_calls": result.get("tool_calls")}, prompt_tokens=result.get("prompt_tokens", 0), completion_tokens=result.get("completion_tokens", 0), finish_reason=result.get("finish_reason"), cost=result.get("cost", 0), ) message_id = await self.trace_store.add_message(msg) 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, tokens={"prompt": result.get("prompt_tokens", 0), "completion": result.get("completion_tokens", 0)}, cost=result.get("cost", 0) ) # ===== Phase 1: PREPARE TRACE ===== async def _prepare_trace( self, messages: List[Dict], config: RunConfig, ) -> Tuple[Trace, Optional[GoalTree], int]: """ 准备 Trace：创建新的或加载已有的 Returns: (trace, goal_tree, next_sequence) """ if config.trace_id: return await self._prepare_existing_trace(config) else: return await self._prepare_new_trace(messages, config) async def _prepare_new_trace( self, messages: List[Dict], config: RunConfig, ) -> Tuple[Trace, Optional[GoalTree], int]: """创建新 Trace""" trace_id = str(uuid.uuid4()) # 生成任务名称 task_name = config.name or await self._generate_task_name(messages) # 准备工具 Schema tool_schemas = self._get_tool_schemas(config.tools) trace_obj = Trace( trace_id=trace_id, mode="agent", task=task_name, agent_type=config.agent_type, parent_trace_id=config.parent_trace_id, parent_goal_id=config.parent_goal_id, uid=config.uid, model=config.model, tools=tool_schemas, llm_params={"temperature": config.temperature, **config.extra_llm_params}, status="running", ) goal_tree = self.goal_tree or GoalTree(mission=task_name) if self.trace_store: await self.trace_store.create_trace(trace_obj) await self.trace_store.update_goal_tree(trace_id, goal_tree) return trace_obj, goal_tree, 1 async def _prepare_existing_trace( self, config: RunConfig, ) -> Tuple[Trace, Optional[GoalTree], int]: """加载已有 Trace（续跑或回溯）""" if not self.trace_store: raise ValueError("trace_store required for continue/rewind") trace_obj = await self.trace_store.get_trace(config.trace_id) if not trace_obj: raise ValueError(f"Trace not found: {config.trace_id}") goal_tree = await self.trace_store.get_goal_tree(config.trace_id) if config.insert_after is not None: # 回溯模式 sequence = await self._rewind(config.trace_id, config.insert_after, goal_tree) else: # 续跑模式：从 last_sequence + 1 开始 sequence = trace_obj.last_sequence + 1 # 状态置为 running await self.trace_store.update_trace( config.trace_id, status="running", completed_at=None, ) trace_obj.status = "running" return trace_obj, goal_tree, sequence # ===== Phase 2: BUILD HISTORY ===== async def _build_history( self, trace_id: str, new_messages: List[Dict], goal_tree: Optional[GoalTree], config: RunConfig, sequence: int, ) -> Tuple[List[Dict], int, List[Message]]: """ 构建完整的 LLM 消息历史 1. 从 head_sequence 沿 parent chain 加载主路径消息（续跑/回溯场景） 2. 构建 system prompt（新建时注入 skills） 3. 新建时：在第一条 user message 末尾注入当前经验 4. 追加 input messages（设置 parent_sequence 链接到当前 head） Returns: (history, next_sequence, created_messages, head_sequence) created_messages: 本次新创建并持久化的 Message 列表，供 run() yield 给调用方 head_sequence: 当前主路径头节点的 sequence """ history: List[Dict] = [] created_messages: List[Message] = [] head_seq: Optional[int] = None # 当前主路径的头节点 sequence # 1. 加载已有 messages（通过主路径遍历） if config.trace_id and self.trace_store: trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj and trace_obj.head_sequence > 0: main_path = await self.trace_store.get_main_path_messages( trace_id, trace_obj.head_sequence ) history = [msg.to_llm_dict() for msg in main_path] if main_path: head_seq = main_path[-1].sequence # 2. 构建 system prompt（如果历史中没有 system message） has_system = any(m.get("role") == "system" for m in history) has_system_in_new = any(m.get("role") == "system" for m in new_messages) if not has_system and not has_system_in_new: system_prompt = await self._build_system_prompt(config) if system_prompt: history = [{"role": "system", "content": system_prompt}] + history if self.trace_store: system_msg = Message.create( trace_id=trace_id, role="system", sequence=sequence, goal_id=None, content=system_prompt, parent_sequence=None, # system message 是 root ) await self.trace_store.add_message(system_msg) created_messages.append(system_msg) head_seq = sequence sequence += 1 # 3. 新建时：在第一条 user message 末尾注入当前经验 if not config.trace_id: # 新建模式 experiences_text = self._load_experiences() if experiences_text: for msg in new_messages: if msg.get("role") == "user" and isinstance(msg.get("content"), str): msg["content"] += f"\n\n## 参考经验\n\n{experiences_text}" break # 4. 追加新 messages（设置 parent_sequence 链接到当前 head） for msg_dict in new_messages: history.append(msg_dict) if self.trace_store: stored_msg = Message.from_llm_dict( msg_dict, trace_id=trace_id, sequence=sequence, goal_id=None, parent_sequence=head_seq, ) await self.trace_store.add_message(stored_msg) created_messages.append(stored_msg) head_seq = sequence sequence += 1 # 5. 更新 trace 的 head_sequence if self.trace_store and head_seq is not None: await self.trace_store.update_trace(trace_id, head_sequence=head_seq) return history, sequence, created_messages, head_seq or 0 # ===== Phase 3: AGENT LOOP ===== async def _agent_loop( self, trace: Trace, history: List[Dict], goal_tree: Optional[GoalTree], config: RunConfig, sequence: int, ) -> AsyncIterator[Union[Trace, Message]]: """ReAct 循环""" trace_id = trace.trace_id tool_schemas = self._get_tool_schemas(config.tools) # 当前主路径头节点的 sequence（用于设置 parent_sequence） head_seq = trace.head_sequence # 设置 goal_tree 到 goal 工具 if goal_tree and self.trace_store: from agent.trace.goal_tool import set_goal_tree set_goal_tree(goal_tree) for iteration in range(config.max_iterations): # 检查取消信号 cancel_event = self._cancel_events.get(trace_id) if cancel_event and cancel_event.is_set(): logger.info(f"Trace {trace_id} stopped by user") if self.trace_store: await self.trace_store.update_trace( trace_id, status="stopped", completed_at=datetime.now(), ) trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj: yield trace_obj return # 构建 LLM messages（注入上下文） llm_messages = list(history) # 周期性注入 GoalTree + Collaborators if iteration % CONTEXT_INJECTION_INTERVAL == 0: context_injection = self._build_context_injection(trace, goal_tree) if context_injection: llm_messages.append({"role": "system", "content": context_injection}) # 调用 LLM result = await self.llm_call( messages=llm_messages, model=config.model, tools=tool_schemas, temperature=config.temperature, **config.extra_llm_params, ) response_content = result.get("content", "") tool_calls = result.get("tool_calls") finish_reason = result.get("finish_reason") prompt_tokens = result.get("prompt_tokens", 0) completion_tokens = result.get("completion_tokens", 0) step_cost = result.get("cost", 0) # 按需自动创建 root goal if goal_tree and not goal_tree.goals and tool_calls: has_goal_call = any( tc.get("function", {}).get("name") == "goal" for tc in tool_calls ) if not has_goal_call: mission = goal_tree.mission root_desc = mission[:200] if len(mission) > 200 else mission goal_tree.add_goals( descriptions=[root_desc], reasons=["系统自动创建：Agent 未显式创建目标"], parent_id=None ) goal_tree.focus(goal_tree.goals[0].id) if self.trace_store: await self.trace_store.update_goal_tree(trace_id, goal_tree) await self.trace_store.add_goal(trace_id, goal_tree.goals[0]) logger.info(f"自动创建 root goal: {goal_tree.goals[0].id}") # 获取当前 goal_id current_goal_id = goal_tree.current_id if (goal_tree and goal_tree.current_id) else None # 记录 assistant Message（parent_sequence 指向当前 head） assistant_msg = Message.create( trace_id=trace_id, role="assistant", sequence=sequence, goal_id=current_goal_id, parent_sequence=head_seq if head_seq > 0 else None, content={"text": response_content, "tool_calls": tool_calls}, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, finish_reason=finish_reason, cost=step_cost, ) if self.trace_store: await self.trace_store.add_message(assistant_msg) yield assistant_msg head_seq = sequence sequence += 1 # 处理工具调用 if tool_calls and config.auto_execute_tools: history.append({ "role": "assistant", "content": response_content, "tool_calls": tool_calls, }) for tc in tool_calls: current_goal_id = goal_tree.current_id if (goal_tree and goal_tree.current_id) else None tool_name = tc["function"]["name"] tool_args = tc["function"]["arguments"] if isinstance(tool_args, str): tool_args = json.loads(tool_args) if tool_args.strip() else {} elif tool_args is None: tool_args = {} tool_result = await self.tools.execute( tool_name, tool_args, uid=config.uid or "", context={ "store": self.trace_store, "trace_id": trace_id, "goal_id": current_goal_id, "runner": self, } ) tool_msg = Message.create( trace_id=trace_id, role="tool", sequence=sequence, goal_id=current_goal_id, parent_sequence=head_seq, 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 head_seq = sequence sequence += 1 history.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 # 更新 head_sequence 并完成 Trace if self.trace_store: await self.trace_store.update_trace( trace_id, status="completed", head_sequence=head_seq, completed_at=datetime.now(), ) trace_obj = await self.trace_store.get_trace(trace_id) if trace_obj: yield trace_obj # ===== 回溯（Rewind）===== async def _rewind( self, trace_id: str, insert_after: int, goal_tree: Optional[GoalTree], ) -> int: """ 执行回溯：快照 GoalTree，重建干净树，设置 head_sequence 新消息的 parent_sequence 将指向 rewind 点，旧消息通过树结构自然脱离主路径。 Returns: 下一个可用的 sequence 号 """ if not self.trace_store: raise ValueError("trace_store required for rewind") # 1. 加载所有 messages all_messages = await self.trace_store.get_trace_messages( trace_id, include_abandoned=True ) if not all_messages: return 1 # 2. 找到安全截断点（确保不截断在 tool_call 和 tool response 之间） cutoff = self._find_safe_cutoff(all_messages, insert_after) # 3. 快照并重建 GoalTree if goal_tree: # 找出 rewind 点之前已完成的 goal IDs # 通过主路径消息来判断：cutoff 之前的消息引用的 completed goals messages_before = [m for m in all_messages if m.sequence <= cutoff] completed_goal_ids = set() for goal in goal_tree.goals: if goal.status == "completed": # 检查该 goal 是否在 rewind 点之前就已完成（有关联消息在 cutoff 之前） goal_msgs = [m for m in messages_before if m.goal_id == goal.id] if goal_msgs: completed_goal_ids.add(goal.id) # 快照到 events await self.trace_store.append_event(trace_id, "rewind", { "insert_after_sequence": cutoff, "goal_tree_snapshot": goal_tree.to_dict(), }) # 重建干净的 GoalTree new_tree = goal_tree.rebuild_for_rewind(completed_goal_ids) await self.trace_store.update_goal_tree(trace_id, new_tree) # 更新内存中的引用 goal_tree.goals = new_tree.goals goal_tree.current_id = new_tree.current_id # 4. 更新 head_sequence 到 rewind 点 await self.trace_store.update_trace(trace_id, head_sequence=cutoff) # 5. 返回 next sequence（全局递增，不复用） max_seq = max((m.sequence for m in all_messages), default=0) return max_seq + 1 def _find_safe_cutoff(self, messages: List[Message], insert_after: int) -> int: """ 找到安全的截断点。 如果 insert_after 指向一条带 tool_calls 的 assistant message， 则自动扩展到其所有对应的 tool response 之后。 """ cutoff = insert_after # 找到 insert_after 对应的 message target_msg = None for msg in messages: if msg.sequence == insert_after: target_msg = msg break if not target_msg: return cutoff # 如果是 assistant 且有 tool_calls，找到所有对应的 tool responses if target_msg.role == "assistant": content = target_msg.content if isinstance(content, dict) and content.get("tool_calls"): tool_call_ids = set() for tc in content["tool_calls"]: if isinstance(tc, dict) and tc.get("id"): tool_call_ids.add(tc["id"]) # 找到这些 tool_call 对应的 tool messages for msg in messages: if (msg.role == "tool" and msg.tool_call_id and msg.tool_call_id in tool_call_ids): cutoff = max(cutoff, msg.sequence) return cutoff # ===== 上下文注入 ===== def _build_context_injection( self, trace: Trace, goal_tree: Optional[GoalTree], ) -> str: """构建周期性注入的上下文（GoalTree + Active Collaborators）""" parts = [] # GoalTree if goal_tree and goal_tree.goals: parts.append(f"## Current Plan\n\n{goal_tree.to_prompt()}") # Active Collaborators collaborators = trace.context.get("collaborators", []) if collaborators: lines = ["## Active Collaborators"] for c in collaborators: status_str = c.get("status", "unknown") ctype = c.get("type", "agent") summary = c.get("summary", "") name = c.get("name", "unnamed") lines.append(f"- {name} [{ctype}, {status_str}]: {summary}") parts.append("\n".join(lines)) return "\n\n".join(parts) # ===== 辅助方法 ===== def _get_tool_schemas(self, tools: Optional[List[str]]) -> List[Dict]: """获取工具 Schema""" tool_names = BUILTIN_TOOLS.copy() if tools: for tool in tools: if tool not in tool_names: tool_names.append(tool) return self.tools.get_schemas(tool_names) async def _build_system_prompt(self, config: RunConfig) -> Optional[str]: """构建 system prompt（注入 skills）""" system_prompt = config.system_prompt # 加载 Skills skills_text = "" skills = load_skills_from_dir(self.skills_dir) if skills: skills_text = self._format_skills(skills) # 拼装 if system_prompt: if skills_text: system_prompt += f"\n\n## Skills\n{skills_text}" elif skills_text: system_prompt = f"## Skills\n{skills_text}" return system_prompt async def _generate_task_name(self, messages: List[Dict]) -> str: """生成任务名称：优先使用 utility_llm，fallback 到文本截取""" # 提取 messages 中的文本内容 text_parts = [] for msg in messages: content = msg.get("content", "") if isinstance(content, str): text_parts.append(content) elif isinstance(content, list): for part in content: if isinstance(part, dict) and part.get("type") == "text": text_parts.append(part.get("text", "")) raw_text = " ".join(text_parts).strip() if not raw_text: return "未命名任务" # 尝试使用 utility_llm 生成标题 if self.utility_llm_call: try: result = await self.utility_llm_call( messages=[ {"role": "system", "content": "用中文为以下任务生成一个简短标题（10-30字），只输出标题本身："}, {"role": "user", "content": raw_text[:2000]}, ], model="gpt-4o-mini", # 使用便宜模型 ) title = result.get("content", "").strip() if title and len(title) < 100: return title except Exception: pass # Fallback: 截取前 50 字符 return raw_text[:50] + ("..." if len(raw_text) > 50 else "") def _format_skills(self, skills: List[Skill]) -> str: if not skills: return "" return "\n\n".join(s.to_prompt_text() for s in skills) def _load_experiences(self) -> str: """从文件加载经验（./cache/experiences.md）""" if not self.experiences_path: return "" try: if os.path.exists(self.experiences_path): with open(self.experiences_path, "r", encoding="utf-8") as f: return f.read().strip() except Exception as e: logger.warning(f"Failed to load experiences from {self.experiences_path}: {e}") return ""