""" OASIS模拟运行器 在后台运行模拟并记录每个Agent的动作，支持实时状态监控 """ import os import sys import json import time import asyncio import threading import subprocess import signal import atexit from typing import Dict, Any, List, Optional, Union from dataclasses import dataclass, field from datetime import datetime from enum import Enum from queue import Queue from ..config import Config from ..utils.logger import get_logger from .zep_graph_memory_updater import ZepGraphMemoryManager from .simulation_ipc import SimulationIPCClient, CommandType, IPCResponse logger = get_logger('mirofish.simulation_runner') # 标记是否已注册清理函数 _cleanup_registered = False class RunnerStatus(str, Enum): """运行器状态""" IDLE = "idle" STARTING = "starting" RUNNING = "running" PAUSED = "paused" STOPPING = "stopping" STOPPED = "stopped" COMPLETED = "completed" FAILED = "failed" @dataclass class AgentAction: """Agent动作记录""" round_num: int timestamp: str platform: str # twitter / reddit agent_id: int agent_name: str action_type: str # CREATE_POST, LIKE_POST, etc. action_args: Dict[str, Any] = field(default_factory=dict) result: Optional[str] = None success: bool = True def to_dict(self) -> Dict[str, Any]: return { "round_num": self.round_num, "timestamp": self.timestamp, "platform": self.platform, "agent_id": self.agent_id, "agent_name": self.agent_name, "action_type": self.action_type, "action_args": self.action_args, "result": self.result, "success": self.success, } @dataclass class RoundSummary: """每轮摘要""" round_num: int start_time: str end_time: Optional[str] = None simulated_hour: int = 0 twitter_actions: int = 0 reddit_actions: int = 0 active_agents: List[int] = field(default_factory=list) actions: List[AgentAction] = field(default_factory=list) def to_dict(self) -> Dict[str, Any]: return { "round_num": self.round_num, "start_time": self.start_time, "end_time": self.end_time, "simulated_hour": self.simulated_hour, "twitter_actions": self.twitter_actions, "reddit_actions": self.reddit_actions, "active_agents": self.active_agents, "actions_count": len(self.actions), "actions": [a.to_dict() for a in self.actions], } @dataclass class SimulationRunState: """模拟运行状态（实时）""" simulation_id: str runner_status: RunnerStatus = RunnerStatus.IDLE # 进度信息 current_round: int = 0 total_rounds: int = 0 simulated_hours: int = 0 total_simulation_hours: int = 0 # 平台状态 twitter_running: bool = False reddit_running: bool = False twitter_actions_count: int = 0 reddit_actions_count: int = 0 # 每轮摘要 rounds: List[RoundSummary] = field(default_factory=list) # 最近动作（用于前端实时展示） recent_actions: List[AgentAction] = field(default_factory=list) max_recent_actions: int = 50 # 时间戳 started_at: Optional[str] = None updated_at: str = field(default_factory=lambda: datetime.now().isoformat()) completed_at: Optional[str] = None # 错误信息 error: Optional[str] = None # 进程ID（用于停止） process_pid: Optional[int] = None def add_action(self, action: AgentAction): """添加动作到最近动作列表""" self.recent_actions.insert(0, action) if len(self.recent_actions) > self.max_recent_actions: self.recent_actions = self.recent_actions[:self.max_recent_actions] if action.platform == "twitter": self.twitter_actions_count += 1 else: self.reddit_actions_count += 1 self.updated_at = datetime.now().isoformat() def to_dict(self) -> Dict[str, Any]: return { "simulation_id": self.simulation_id, "runner_status": self.runner_status.value, "current_round": self.current_round, "total_rounds": self.total_rounds, "simulated_hours": self.simulated_hours, "total_simulation_hours": self.total_simulation_hours, "progress_percent": round(self.current_round / max(self.total_rounds, 1) * 100, 1), "twitter_running": self.twitter_running, "reddit_running": self.reddit_running, "twitter_actions_count": self.twitter_actions_count, "reddit_actions_count": self.reddit_actions_count, "total_actions_count": self.twitter_actions_count + self.reddit_actions_count, "started_at": self.started_at, "updated_at": self.updated_at, "completed_at": self.completed_at, "error": self.error, "process_pid": self.process_pid, } def to_detail_dict(self) -> Dict[str, Any]: """包含最近动作的详细信息""" result = self.to_dict() result["recent_actions"] = [a.to_dict() for a in self.recent_actions] result["rounds_count"] = len(self.rounds) return result class SimulationRunner: """ 模拟运行器 负责： 1. 在后台进程中运行OASIS模拟 2. 解析运行日志，记录每个Agent的动作 3. 提供实时状态查询接口 4. 支持暂停/停止/恢复操作 """ # 运行状态存储目录 RUN_STATE_DIR = os.path.join( os.path.dirname(__file__), '../../uploads/simulations' ) # 脚本目录 SCRIPTS_DIR = os.path.join( os.path.dirname(__file__), '../../scripts' ) # 内存中的运行状态 _run_states: Dict[str, SimulationRunState] = {} _processes: Dict[str, subprocess.Popen] = {} _action_queues: Dict[str, Queue] = {} _monitor_threads: Dict[str, threading.Thread] = {} _stdout_files: Dict[str, Any] = {} # 存储 stdout 文件句柄 _stderr_files: Dict[str, Any] = {} # 存储 stderr 文件句柄 # 图谱记忆更新配置 _graph_memory_enabled: Dict[str, bool] = {} # simulation_id -> enabled @classmethod def get_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: """获取运行状态""" if simulation_id in cls._run_states: return cls._run_states[simulation_id] # 尝试从文件加载 state = cls._load_run_state(simulation_id) if state: cls._run_states[simulation_id] = state return state @classmethod def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: """从文件加载运行状态""" state_file = os.path.join(cls.RUN_STATE_DIR, simulation_id, "run_state.json") if not os.path.exists(state_file): return None try: with open(state_file, 'r', encoding='utf-8') as f: data = json.load(f) state = SimulationRunState( simulation_id=simulation_id, runner_status=RunnerStatus(data.get("runner_status", "idle")), current_round=data.get("current_round", 0), total_rounds=data.get("total_rounds", 0), simulated_hours=data.get("simulated_hours", 0), total_simulation_hours=data.get("total_simulation_hours", 0), twitter_running=data.get("twitter_running", False), reddit_running=data.get("reddit_running", False), twitter_actions_count=data.get("twitter_actions_count", 0), reddit_actions_count=data.get("reddit_actions_count", 0), started_at=data.get("started_at"), updated_at=data.get("updated_at", datetime.now().isoformat()), completed_at=data.get("completed_at"), error=data.get("error"), process_pid=data.get("process_pid"), ) # 加载最近动作 actions_data = data.get("recent_actions", []) for a in actions_data: state.recent_actions.append(AgentAction( round_num=a.get("round_num", 0), timestamp=a.get("timestamp", ""), platform=a.get("platform", ""), agent_id=a.get("agent_id", 0), agent_name=a.get("agent_name", ""), action_type=a.get("action_type", ""), action_args=a.get("action_args", {}), result=a.get("result"), success=a.get("success", True), )) return state except Exception as e: logger.error(f"加载运行状态失败: {str(e)}") return None @classmethod def _save_run_state(cls, state: SimulationRunState): """保存运行状态到文件""" sim_dir = os.path.join(cls.RUN_STATE_DIR, state.simulation_id) os.makedirs(sim_dir, exist_ok=True) state_file = os.path.join(sim_dir, "run_state.json") data = state.to_detail_dict() with open(state_file, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=2) cls._run_states[state.simulation_id] = state @classmethod def start_simulation( cls, simulation_id: str, platform: str = "parallel", # twitter / reddit / parallel max_rounds: int = None, # 最大模拟轮数（可选，用于截断过长的模拟） enable_graph_memory_update: bool = False, # 是否将活动更新到Zep图谱 graph_id: str = None # Zep图谱ID（启用图谱更新时必需） ) -> SimulationRunState: """ 启动模拟 Args: simulation_id: 模拟ID platform: 运行平台 (twitter/reddit/parallel) max_rounds: 最大模拟轮数（可选，用于截断过长的模拟） enable_graph_memory_update: 是否将Agent活动动态更新到Zep图谱 graph_id: Zep图谱ID（启用图谱更新时必需） Returns: SimulationRunState """ # 检查是否已在运行 existing = cls.get_run_state(simulation_id) if existing and existing.runner_status in [RunnerStatus.RUNNING, RunnerStatus.STARTING]: raise ValueError(f"模拟已在运行中: {simulation_id}") # 加载模拟配置 sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) config_path = os.path.join(sim_dir, "simulation_config.json") if not os.path.exists(config_path): raise ValueError(f"模拟配置不存在，请先调用 /prepare 接口") with open(config_path, 'r', encoding='utf-8') as f: config = json.load(f) # 初始化运行状态 time_config = config.get("time_config", {}) total_hours = time_config.get("total_simulation_hours", 72) minutes_per_round = time_config.get("minutes_per_round", 30) total_rounds = int(total_hours * 60 / minutes_per_round) # 如果指定了最大轮数，则截断 if max_rounds is not None and max_rounds > 0: original_rounds = total_rounds total_rounds = min(total_rounds, max_rounds) if total_rounds < original_rounds: logger.info(f"轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})") state = SimulationRunState( simulation_id=simulation_id, runner_status=RunnerStatus.STARTING, total_rounds=total_rounds, total_simulation_hours=total_hours, started_at=datetime.now().isoformat(), ) cls._save_run_state(state) # 如果启用图谱记忆更新，创建更新器 if enable_graph_memory_update: if not graph_id: raise ValueError("启用图谱记忆更新时必须提供 graph_id") try: ZepGraphMemoryManager.create_updater(simulation_id, graph_id) cls._graph_memory_enabled[simulation_id] = True logger.info(f"已启用图谱记忆更新: simulation_id={simulation_id}, graph_id={graph_id}") except Exception as e: logger.error(f"创建图谱记忆更新器失败: {e}") cls._graph_memory_enabled[simulation_id] = False else: cls._graph_memory_enabled[simulation_id] = False # 确定运行哪个脚本（脚本位于 backend/scripts/ 目录） if platform == "twitter": script_name = "run_twitter_simulation.py" state.twitter_running = True elif platform == "reddit": script_name = "run_reddit_simulation.py" state.reddit_running = True else: script_name = "run_parallel_simulation.py" state.twitter_running = True state.reddit_running = True script_path = os.path.join(cls.SCRIPTS_DIR, script_name) if not os.path.exists(script_path): raise ValueError(f"脚本不存在: {script_path}") # 创建动作队列 action_queue = Queue() cls._action_queues[simulation_id] = action_queue # 启动模拟进程 try: # 构建运行命令，使用完整路径 # 新的日志结构： # twitter/actions.jsonl - Twitter 动作日志 # reddit/actions.jsonl - Reddit 动作日志 # simulation.log - 主进程日志 cmd = [ sys.executable, # Python解释器 script_path, "--config", config_path, # 使用完整配置文件路径 ] # 如果指定了最大轮数，添加到命令行参数 if max_rounds is not None and max_rounds > 0: cmd.extend(["--max-rounds", str(max_rounds)]) # 创建主日志文件，避免 stdout/stderr 管道缓冲区满导致进程阻塞 main_log_path = os.path.join(sim_dir, "simulation.log") main_log_file = open(main_log_path, 'w', encoding='utf-8') # 设置工作目录为模拟目录（数据库等文件会生成在此） # 使用 start_new_session=True 创建新的进程组，确保可以通过 os.killpg 终止所有子进程 process = subprocess.Popen( cmd, cwd=sim_dir, stdout=main_log_file, stderr=subprocess.STDOUT, # stderr 也写入同一个文件 text=True, bufsize=1, start_new_session=True, # 创建新进程组，确保服务器关闭时能终止所有相关进程 ) # 保存文件句柄以便后续关闭 cls._stdout_files[simulation_id] = main_log_file cls._stderr_files[simulation_id] = None # 不再需要单独的 stderr state.process_pid = process.pid state.runner_status = RunnerStatus.RUNNING cls._processes[simulation_id] = process cls._save_run_state(state) # 启动监控线程 monitor_thread = threading.Thread( target=cls._monitor_simulation, args=(simulation_id,), daemon=True ) monitor_thread.start() cls._monitor_threads[simulation_id] = monitor_thread logger.info(f"模拟启动成功: {simulation_id}, pid={process.pid}, platform={platform}") except Exception as e: state.runner_status = RunnerStatus.FAILED state.error = str(e) cls._save_run_state(state) raise return state @classmethod def _monitor_simulation(cls, simulation_id: str): """监控模拟进程，解析动作日志""" sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) # 新的日志结构：分平台的动作日志 twitter_actions_log = os.path.join(sim_dir, "twitter", "actions.jsonl") reddit_actions_log = os.path.join(sim_dir, "reddit", "actions.jsonl") process = cls._processes.get(simulation_id) state = cls.get_run_state(simulation_id) if not process or not state: return twitter_position = 0 reddit_position = 0 try: while process.poll() is None: # 进程仍在运行 # 读取 Twitter 动作日志 if os.path.exists(twitter_actions_log): twitter_position = cls._read_action_log( twitter_actions_log, twitter_position, state, "twitter" ) # 读取 Reddit 动作日志 if os.path.exists(reddit_actions_log): reddit_position = cls._read_action_log( reddit_actions_log, reddit_position, state, "reddit" ) # 更新状态 cls._save_run_state(state) time.sleep(2) # 进程结束后，最后读取一次日志 if os.path.exists(twitter_actions_log): cls._read_action_log(twitter_actions_log, twitter_position, state, "twitter") if os.path.exists(reddit_actions_log): cls._read_action_log(reddit_actions_log, reddit_position, state, "reddit") # 进程结束 exit_code = process.returncode if exit_code == 0: state.runner_status = RunnerStatus.COMPLETED state.completed_at = datetime.now().isoformat() logger.info(f"模拟完成: {simulation_id}") else: state.runner_status = RunnerStatus.FAILED # 从主日志文件读取错误信息 main_log_path = os.path.join(sim_dir, "simulation.log") error_info = "" try: if os.path.exists(main_log_path): with open(main_log_path, 'r', encoding='utf-8') as f: error_info = f.read()[-2000:] # 取最后2000字符 except Exception: pass state.error = f"进程退出码: {exit_code}, 错误: {error_info}" logger.error(f"模拟失败: {simulation_id}, error={state.error}") state.twitter_running = False state.reddit_running = False cls._save_run_state(state) except Exception as e: logger.error(f"监控线程异常: {simulation_id}, error={str(e)}") state.runner_status = RunnerStatus.FAILED state.error = str(e) cls._save_run_state(state) finally: # 停止图谱记忆更新器 if cls._graph_memory_enabled.get(simulation_id, False): try: ZepGraphMemoryManager.stop_updater(simulation_id) logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}") except Exception as e: logger.error(f"停止图谱记忆更新器失败: {e}") cls._graph_memory_enabled.pop(simulation_id, None) # 清理进程资源 cls._processes.pop(simulation_id, None) cls._action_queues.pop(simulation_id, None) # 关闭日志文件句柄 if simulation_id in cls._stdout_files: try: cls._stdout_files[simulation_id].close() except Exception: pass cls._stdout_files.pop(simulation_id, None) if simulation_id in cls._stderr_files and cls._stderr_files[simulation_id]: try: cls._stderr_files[simulation_id].close() except Exception: pass cls._stderr_files.pop(simulation_id, None) @classmethod def _read_action_log( cls, log_path: str, position: int, state: SimulationRunState, platform: str ) -> int: """ 读取动作日志文件 Args: log_path: 日志文件路径 position: 上次读取位置 state: 运行状态对象 platform: 平台名称 (twitter/reddit) Returns: 新的读取位置 """ # 检查是否启用了图谱记忆更新 graph_memory_enabled = cls._graph_memory_enabled.get(state.simulation_id, False) graph_updater = None if graph_memory_enabled: graph_updater = ZepGraphMemoryManager.get_updater(state.simulation_id) try: with open(log_path, 'r', encoding='utf-8') as f: f.seek(position) for line in f: line = line.strip() if line: try: action_data = json.loads(line) # 跳过事件类型的条目（如 simulation_start, round_start 等） if "event_type" in action_data: continue action = AgentAction( round_num=action_data.get("round", 0), timestamp=action_data.get("timestamp", datetime.now().isoformat()), platform=platform, agent_id=action_data.get("agent_id", 0), agent_name=action_data.get("agent_name", ""), action_type=action_data.get("action_type", ""), action_args=action_data.get("action_args", {}), result=action_data.get("result"), success=action_data.get("success", True), ) state.add_action(action) # 更新轮次 if action.round_num and action.round_num > state.current_round: state.current_round = action.round_num # 如果启用了图谱记忆更新，将活动发送到Zep if graph_updater: graph_updater.add_activity_from_dict(action_data, platform) except json.JSONDecodeError: pass return f.tell() except Exception as e: logger.warning(f"读取动作日志失败: {log_path}, error={e}") return position @classmethod def stop_simulation(cls, simulation_id: str) -> SimulationRunState: """停止模拟""" state = cls.get_run_state(simulation_id) if not state: raise ValueError(f"模拟不存在: {simulation_id}") if state.runner_status not in [RunnerStatus.RUNNING, RunnerStatus.PAUSED]: raise ValueError(f"模拟未在运行: {simulation_id}, status={state.runner_status}") state.runner_status = RunnerStatus.STOPPING cls._save_run_state(state) # 终止进程 process = cls._processes.get(simulation_id) if process and process.poll() is None: try: # 使用进程组 ID 终止整个进程组（包括所有子进程） # 由于使用了 start_new_session=True，进程组 ID 等于主进程 PID pgid = os.getpgid(process.pid) logger.info(f"终止进程组: simulation={simulation_id}, pgid={pgid}") # 先发送 SIGTERM 给整个进程组 os.killpg(pgid, signal.SIGTERM) try: process.wait(timeout=10) except subprocess.TimeoutExpired: # 如果 10 秒后还没结束，强制发送 SIGKILL logger.warning(f"进程组未响应 SIGTERM，强制终止: {simulation_id}") os.killpg(pgid, signal.SIGKILL) process.wait(timeout=5) except ProcessLookupError: # 进程已经不存在 pass except Exception as e: logger.error(f"终止进程组失败: {simulation_id}, error={e}") # 回退到直接终止进程 try: process.terminate() process.wait(timeout=5) except Exception: process.kill() state.runner_status = RunnerStatus.STOPPED state.twitter_running = False state.reddit_running = False state.completed_at = datetime.now().isoformat() cls._save_run_state(state) # 停止图谱记忆更新器 if cls._graph_memory_enabled.get(simulation_id, False): try: ZepGraphMemoryManager.stop_updater(simulation_id) logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}") except Exception as e: logger.error(f"停止图谱记忆更新器失败: {e}") cls._graph_memory_enabled.pop(simulation_id, None) logger.info(f"模拟已停止: {simulation_id}") return state @classmethod def get_actions( cls, simulation_id: str, limit: int = 100, offset: int = 0, platform: Optional[str] = None, agent_id: Optional[int] = None, round_num: Optional[int] = None ) -> List[AgentAction]: """ 获取动作历史 Args: simulation_id: 模拟ID limit: 返回数量限制 offset: 偏移量 platform: 过滤平台 agent_id: 过滤Agent round_num: 过滤轮次 Returns: 动作列表 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) actions_log = os.path.join(sim_dir, "actions.jsonl") if not os.path.exists(actions_log): return [] actions = [] with open(actions_log, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if not line: continue try: data = json.loads(line) # 过滤 if platform and data.get("platform") != platform: continue if agent_id is not None and data.get("agent_id") != agent_id: continue if round_num is not None and data.get("round") != round_num: continue actions.append(AgentAction( round_num=data.get("round", 0), timestamp=data.get("timestamp", ""), platform=data.get("platform", ""), agent_id=data.get("agent_id", 0), agent_name=data.get("agent_name", ""), action_type=data.get("action_type", ""), action_args=data.get("action_args", {}), result=data.get("result"), success=data.get("success", True), )) except json.JSONDecodeError: continue # 按时间倒序排列 actions.reverse() # 分页 return actions[offset:offset + limit] @classmethod def get_timeline( cls, simulation_id: str, start_round: int = 0, end_round: Optional[int] = None ) -> List[Dict[str, Any]]: """ 获取模拟时间线（按轮次汇总） Args: simulation_id: 模拟ID start_round: 起始轮次 end_round: 结束轮次 Returns: 每轮的汇总信息 """ actions = cls.get_actions(simulation_id, limit=10000) # 按轮次分组 rounds: Dict[int, Dict[str, Any]] = {} for action in actions: round_num = action.round_num if round_num < start_round: continue if end_round is not None and round_num > end_round: continue if round_num not in rounds: rounds[round_num] = { "round_num": round_num, "twitter_actions": 0, "reddit_actions": 0, "active_agents": set(), "action_types": {}, "first_action_time": action.timestamp, "last_action_time": action.timestamp, } r = rounds[round_num] if action.platform == "twitter": r["twitter_actions"] += 1 else: r["reddit_actions"] += 1 r["active_agents"].add(action.agent_id) r["action_types"][action.action_type] = r["action_types"].get(action.action_type, 0) + 1 r["last_action_time"] = action.timestamp # 转换为列表 result = [] for round_num in sorted(rounds.keys()): r = rounds[round_num] result.append({ "round_num": round_num, "twitter_actions": r["twitter_actions"], "reddit_actions": r["reddit_actions"], "total_actions": r["twitter_actions"] + r["reddit_actions"], "active_agents_count": len(r["active_agents"]), "active_agents": list(r["active_agents"]), "action_types": r["action_types"], "first_action_time": r["first_action_time"], "last_action_time": r["last_action_time"], }) return result @classmethod def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]: """ 获取每个Agent的统计信息 Returns: Agent统计列表 """ actions = cls.get_actions(simulation_id, limit=10000) agent_stats: Dict[int, Dict[str, Any]] = {} for action in actions: agent_id = action.agent_id if agent_id not in agent_stats: agent_stats[agent_id] = { "agent_id": agent_id, "agent_name": action.agent_name, "total_actions": 0, "twitter_actions": 0, "reddit_actions": 0, "action_types": {}, "first_action_time": action.timestamp, "last_action_time": action.timestamp, } stats = agent_stats[agent_id] stats["total_actions"] += 1 if action.platform == "twitter": stats["twitter_actions"] += 1 else: stats["reddit_actions"] += 1 stats["action_types"][action.action_type] = stats["action_types"].get(action.action_type, 0) + 1 stats["last_action_time"] = action.timestamp # 按总动作数排序 result = sorted(agent_stats.values(), key=lambda x: x["total_actions"], reverse=True) return result # 防止重复清理的标志 _cleanup_done = False @classmethod def cleanup_all_simulations(cls): """ 清理所有运行中的模拟进程 在服务器关闭时调用，确保所有子进程被终止 """ # 防止重复清理 if cls._cleanup_done: return cls._cleanup_done = True # 检查是否有内容需要清理（避免空进程的进程打印无用日志） has_processes = bool(cls._processes) has_updaters = bool(cls._graph_memory_enabled) if not has_processes and not has_updaters: return # 没有需要清理的内容，静默返回 logger.info("正在清理所有模拟进程...") # 首先停止所有图谱记忆更新器（stop_all 内部会打印日志） try: ZepGraphMemoryManager.stop_all() except Exception as e: logger.error(f"停止图谱记忆更新器失败: {e}") cls._graph_memory_enabled.clear() # 复制字典以避免在迭代时修改 processes = list(cls._processes.items()) for simulation_id, process in processes: try: if process.poll() is None: # 进程仍在运行 logger.info(f"终止模拟进程: {simulation_id}, pid={process.pid}") try: # 使用进程组终止（包括所有子进程） pgid = os.getpgid(process.pid) os.killpg(pgid, signal.SIGTERM) try: process.wait(timeout=5) except subprocess.TimeoutExpired: logger.warning(f"进程组未响应 SIGTERM，强制终止: {simulation_id}") os.killpg(pgid, signal.SIGKILL) process.wait(timeout=5) except (ProcessLookupError, OSError): # 进程可能已经不存在，尝试直接终止 try: process.terminate() process.wait(timeout=3) except Exception: process.kill() # 更新 run_state.json state = cls.get_run_state(simulation_id) if state: state.runner_status = RunnerStatus.STOPPED state.twitter_running = False state.reddit_running = False state.completed_at = datetime.now().isoformat() state.error = "服务器关闭，模拟被终止" cls._save_run_state(state) # 同时更新 state.json，将状态设为 stopped try: sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) state_file = os.path.join(sim_dir, "state.json") logger.info(f"尝试更新 state.json: {state_file}") if os.path.exists(state_file): with open(state_file, 'r', encoding='utf-8') as f: state_data = json.load(f) state_data['status'] = 'stopped' state_data['updated_at'] = datetime.now().isoformat() with open(state_file, 'w', encoding='utf-8') as f: json.dump(state_data, f, indent=2, ensure_ascii=False) logger.info(f"已更新 state.json 状态为 stopped: {simulation_id}") else: logger.warning(f"state.json 不存在: {state_file}") except Exception as state_err: logger.warning(f"更新 state.json 失败: {simulation_id}, error={state_err}") except Exception as e: logger.error(f"清理进程失败: {simulation_id}, error={e}") # 清理文件句柄 for simulation_id, file_handle in list(cls._stdout_files.items()): try: if file_handle: file_handle.close() except Exception: pass cls._stdout_files.clear() for simulation_id, file_handle in list(cls._stderr_files.items()): try: if file_handle: file_handle.close() except Exception: pass cls._stderr_files.clear() # 清理内存中的状态 cls._processes.clear() cls._action_queues.clear() logger.info("模拟进程清理完成") @classmethod def register_cleanup(cls): """ 注册清理函数 在 Flask 应用启动时调用，确保服务器关闭时清理所有模拟进程 """ global _cleanup_registered if _cleanup_registered: return # Flask debug 模式下，只在 reloader 子进程中注册清理（实际运行应用的进程） # WERKZEUG_RUN_MAIN=true 表示是 reloader 子进程 # 如果不是 debug 模式，则没有这个环境变量，也需要注册 is_reloader_process = os.environ.get('WERKZEUG_RUN_MAIN') == 'true' is_debug_mode = os.environ.get('FLASK_DEBUG') == '1' or os.environ.get('WERKZEUG_RUN_MAIN') is not None # 在 debug 模式下，只在 reloader 子进程中注册；非 debug 模式下始终注册 if is_debug_mode and not is_reloader_process: _cleanup_registered = True # 标记已注册，防止子进程再次尝试 return # 保存原有的信号处理器 original_sigint = signal.getsignal(signal.SIGINT) original_sigterm = signal.getsignal(signal.SIGTERM) def cleanup_handler(signum=None, frame=None): """信号处理器：先清理模拟进程，再调用原处理器""" # 只有在有进程需要清理时才打印日志 if cls._processes or cls._graph_memory_enabled: logger.info(f"收到信号 {signum}，开始清理...") cls.cleanup_all_simulations() # 调用原有的信号处理器，让 Flask 正常退出 if signum == signal.SIGINT and callable(original_sigint): original_sigint(signum, frame) elif signum == signal.SIGTERM and callable(original_sigterm): original_sigterm(signum, frame) else: # 如果原处理器不可调用（如 SIG_DFL），则使用默认行为 raise KeyboardInterrupt # 注册 atexit 处理器（作为备用） atexit.register(cls.cleanup_all_simulations) # 注册信号处理器（仅在主线程中） try: # SIGTERM: kill 命令默认信号 signal.signal(signal.SIGTERM, cleanup_handler) # SIGINT: Ctrl+C signal.signal(signal.SIGINT, cleanup_handler) except ValueError: # 不在主线程中，只能使用 atexit logger.warning("无法注册信号处理器（不在主线程），仅使用 atexit") _cleanup_registered = True @classmethod def get_running_simulations(cls) -> List[str]: """ 获取所有正在运行的模拟ID列表 """ running = [] for sim_id, process in cls._processes.items(): if process.poll() is None: running.append(sim_id) return running # ============== Interview 功能 ============== @classmethod def check_env_alive(cls, simulation_id: str) -> bool: """ 检查模拟环境是否存活（可以接收Interview命令） Args: simulation_id: 模拟ID Returns: True 表示环境存活，False 表示环境已关闭 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): return False ipc_client = SimulationIPCClient(sim_dir) return ipc_client.check_env_alive() @classmethod def get_env_status_detail(cls, simulation_id: str) -> Dict[str, Any]: """ 获取模拟环境的详细状态信息 Args: simulation_id: 模拟ID Returns: 状态详情字典，包含 status, twitter_available, reddit_available, timestamp """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) status_file = os.path.join(sim_dir, "env_status.json") default_status = { "status": "stopped", "twitter_available": False, "reddit_available": False, "timestamp": None } if not os.path.exists(status_file): return default_status try: with open(status_file, 'r', encoding='utf-8') as f: status = json.load(f) return { "status": status.get("status", "stopped"), "twitter_available": status.get("twitter_available", False), "reddit_available": status.get("reddit_available", False), "timestamp": status.get("timestamp") } except (json.JSONDecodeError, OSError): return default_status @classmethod def interview_agent( cls, simulation_id: str, agent_id: int, prompt: str, platform: str = None, timeout: float = 60.0 ) -> Dict[str, Any]: """ 采访单个Agent Args: simulation_id: 模拟ID agent_id: Agent ID prompt: 采访问题 platform: 指定平台（可选） - "twitter": 只采访Twitter平台 - "reddit": 只采访Reddit平台 - None: 双平台模拟时同时采访两个平台，返回整合结果 timeout: 超时时间（秒） Returns: 采访结果字典 Raises: ValueError: 模拟不存在或环境未运行 TimeoutError: 等待响应超时 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): raise ValueError(f"模拟不存在: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}") logger.info(f"发送Interview命令: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}") response = ipc_client.send_interview( agent_id=agent_id, prompt=prompt, platform=platform, timeout=timeout ) if response.status.value == "completed": return { "success": True, "agent_id": agent_id, "prompt": prompt, "result": response.result, "timestamp": response.timestamp } else: return { "success": False, "agent_id": agent_id, "prompt": prompt, "error": response.error, "timestamp": response.timestamp } @classmethod def interview_agents_batch( cls, simulation_id: str, interviews: List[Dict[str, Any]], platform: str = None, timeout: float = 120.0 ) -> Dict[str, Any]: """ 批量采访多个Agent Args: simulation_id: 模拟ID interviews: 采访列表，每个元素包含 {"agent_id": int, "prompt": str, "platform": str(可选)} platform: 默认平台（可选，会被每个采访项的platform覆盖） - "twitter": 默认只采访Twitter平台 - "reddit": 默认只采访Reddit平台 - None: 双平台模拟时每个Agent同时采访两个平台 timeout: 超时时间（秒） Returns: 批量采访结果字典 Raises: ValueError: 模拟不存在或环境未运行 TimeoutError: 等待响应超时 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): raise ValueError(f"模拟不存在: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}") logger.info(f"发送批量Interview命令: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}") response = ipc_client.send_batch_interview( interviews=interviews, platform=platform, timeout=timeout ) if response.status.value == "completed": return { "success": True, "interviews_count": len(interviews), "result": response.result, "timestamp": response.timestamp } else: return { "success": False, "interviews_count": len(interviews), "error": response.error, "timestamp": response.timestamp } @classmethod def interview_all_agents( cls, simulation_id: str, prompt: str, platform: str = None, timeout: float = 180.0 ) -> Dict[str, Any]: """ 采访所有Agent（全局采访） 使用相同的问题采访模拟中的所有Agent Args: simulation_id: 模拟ID prompt: 采访问题（所有Agent使用相同问题） platform: 指定平台（可选） - "twitter": 只采访Twitter平台 - "reddit": 只采访Reddit平台 - None: 双平台模拟时每个Agent同时采访两个平台 timeout: 超时时间（秒） Returns: 全局采访结果字典 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): raise ValueError(f"模拟不存在: {simulation_id}") # 从配置文件获取所有Agent信息 config_path = os.path.join(sim_dir, "simulation_config.json") if not os.path.exists(config_path): raise ValueError(f"模拟配置不存在: {simulation_id}") with open(config_path, 'r', encoding='utf-8') as f: config = json.load(f) agent_configs = config.get("agent_configs", []) if not agent_configs: raise ValueError(f"模拟配置中没有Agent: {simulation_id}") # 构建批量采访列表 interviews = [] for agent_config in agent_configs: agent_id = agent_config.get("agent_id") if agent_id is not None: interviews.append({ "agent_id": agent_id, "prompt": prompt }) logger.info(f"发送全局Interview命令: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}") return cls.interview_agents_batch( simulation_id=simulation_id, interviews=interviews, platform=platform, timeout=timeout ) @classmethod def close_simulation_env( cls, simulation_id: str, timeout: float = 30.0 ) -> Dict[str, Any]: """ 关闭模拟环境（而不是停止模拟进程） 向模拟发送关闭环境命令，使其优雅退出等待命令模式 Args: simulation_id: 模拟ID timeout: 超时时间（秒） Returns: 操作结果字典 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): raise ValueError(f"模拟不存在: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): return { "success": True, "message": "环境已经关闭" } logger.info(f"发送关闭环境命令: simulation_id={simulation_id}") try: response = ipc_client.send_close_env(timeout=timeout) return { "success": response.status.value == "completed", "message": "环境关闭命令已发送", "result": response.result, "timestamp": response.timestamp } except TimeoutError: # 超时可能是因为环境正在关闭 return { "success": True, "message": "环境关闭命令已发送（等待响应超时，环境可能正在关闭）" } @classmethod def _get_interview_history_from_db( cls, db_path: str, platform_name: str, agent_id: Optional[int] = None, limit: int = 100 ) -> List[Dict[str, Any]]: """从单个数据库获取Interview历史""" import sqlite3 if not os.path.exists(db_path): return [] results = [] try: conn = sqlite3.connect(db_path) cursor = conn.cursor() if agent_id is not None: cursor.execute(""" SELECT user_id, info, created_at FROM trace WHERE action = 'interview' AND user_id = ? ORDER BY created_at DESC LIMIT ? """, (agent_id, limit)) else: cursor.execute(""" SELECT user_id, info, created_at FROM trace WHERE action = 'interview' ORDER BY created_at DESC LIMIT ? """, (limit,)) for user_id, info_json, created_at in cursor.fetchall(): try: info = json.loads(info_json) if info_json else {} except json.JSONDecodeError: info = {"raw": info_json} results.append({ "agent_id": user_id, "response": info.get("response", info), "prompt": info.get("prompt", ""), "timestamp": created_at, "platform": platform_name }) conn.close() except Exception as e: logger.error(f"读取Interview历史失败 ({platform_name}): {e}") return results @classmethod def get_interview_history( cls, simulation_id: str, platform: str = None, agent_id: Optional[int] = None, limit: int = 100 ) -> List[Dict[str, Any]]: """ 获取Interview历史记录（从数据库读取） Args: simulation_id: 模拟ID platform: 平台类型（reddit/twitter/None） - "reddit": 只获取Reddit平台的历史 - "twitter": 只获取Twitter平台的历史 - None: 获取两个平台的所有历史 agent_id: 指定Agent ID（可选，只获取该Agent的历史） limit: 每个平台返回数量限制 Returns: Interview历史记录列表 """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) results = [] # 确定要查询的平台 if platform in ("reddit", "twitter"): platforms = [platform] else: # 不指定platform时，查询两个平台 platforms = ["twitter", "reddit"] for p in platforms: db_path = os.path.join(sim_dir, f"{p}_simulation.db") platform_results = cls._get_interview_history_from_db( db_path=db_path, platform_name=p, agent_id=agent_id, limit=limit ) results.extend(platform_results) # 按时间降序排序 results.sort(key=lambda x: x.get("timestamp", ""), reverse=True) # 如果查询了多个平台，限制总数 if len(platforms) > 1 and len(results) > limit: results = results[:limit] return results