""" OASIS 双平台并行模拟预设脚本 同时运行Twitter和Reddit模拟，读取相同的配置文件 使用方式: python run_parallel_simulation.py --config simulation_config.json 日志结构: sim_xxx/ ├── twitter/ │ └── actions.jsonl # Twitter 平台动作日志 ├── reddit/ │ └── actions.jsonl # Reddit 平台动作日志 ├── simulation.log # 主模拟进程日志 └── run_state.json # 运行状态（API 查询用） """ import argparse import asyncio import json import logging import os import random import sqlite3 import sys from datetime import datetime from typing import Dict, Any, List, Optional, Tuple # 添加 backend 目录到路径 # 脚本固定位于 backend/scripts/ 目录 _scripts_dir = os.path.dirname(os.path.abspath(__file__)) _backend_dir = os.path.abspath(os.path.join(_scripts_dir, '..')) _project_root = os.path.abspath(os.path.join(_backend_dir, '..')) sys.path.insert(0, _scripts_dir) sys.path.insert(0, _backend_dir) # 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置） from dotenv import load_dotenv _env_file = os.path.join(_project_root, '.env') if os.path.exists(_env_file): load_dotenv(_env_file) print(f"已加载环境配置: {_env_file}") else: # 尝试加载 backend/.env _backend_env = os.path.join(_backend_dir, '.env') if os.path.exists(_backend_env): load_dotenv(_backend_env) print(f"已加载环境配置: {_backend_env}") class MaxTokensWarningFilter(logging.Filter): """过滤掉 camel-ai 关于 max_tokens 的警告（我们故意不设置 max_tokens，让模型自行决定）""" def filter(self, record): # 过滤掉包含 max_tokens 警告的日志 if "max_tokens" in record.getMessage() and "Invalid or missing" in record.getMessage(): return False return True # 在模块加载时立即添加过滤器，确保在 camel 代码执行前生效 logging.getLogger().addFilter(MaxTokensWarningFilter()) def disable_oasis_logging(): """ 禁用 OASIS 库的详细日志输出 OASIS 的日志太冗余（记录每个 agent 的观察和动作），我们使用自己的 action_logger """ # 禁用 OASIS 的所有日志器 oasis_loggers = [ "social.agent", "social.twitter", "social.rec", "oasis.env", "table", ] for logger_name in oasis_loggers: logger = logging.getLogger(logger_name) logger.setLevel(logging.CRITICAL) # 只记录严重错误 logger.handlers.clear() logger.propagate = False def init_logging_for_simulation(simulation_dir: str): """ 初始化模拟的日志配置 Args: simulation_dir: 模拟目录路径 """ # 禁用 OASIS 的详细日志 disable_oasis_logging() # 清理旧的 log 目录（如果存在） old_log_dir = os.path.join(simulation_dir, "log") if os.path.exists(old_log_dir): import shutil shutil.rmtree(old_log_dir, ignore_errors=True) from action_logger import SimulationLogManager, PlatformActionLogger try: from camel.models import ModelFactory from camel.types import ModelPlatformType import oasis from oasis import ( ActionType, LLMAction, ManualAction, generate_twitter_agent_graph, generate_reddit_agent_graph ) except ImportError as e: print(f"错误: 缺少依赖 {e}") print("请先安装: pip install oasis-ai camel-ai") sys.exit(1) # Twitter可用动作 TWITTER_ACTIONS = [ ActionType.CREATE_POST, ActionType.LIKE_POST, ActionType.REPOST, ActionType.FOLLOW, ActionType.DO_NOTHING, ActionType.QUOTE_POST, ] # Reddit可用动作 REDDIT_ACTIONS = [ ActionType.LIKE_POST, ActionType.DISLIKE_POST, ActionType.CREATE_POST, ActionType.CREATE_COMMENT, ActionType.LIKE_COMMENT, ActionType.DISLIKE_COMMENT, ActionType.SEARCH_POSTS, ActionType.SEARCH_USER, ActionType.TREND, ActionType.REFRESH, ActionType.DO_NOTHING, ActionType.FOLLOW, ActionType.MUTE, ] def load_config(config_path: str) -> Dict[str, Any]: """加载配置文件""" with open(config_path, 'r', encoding='utf-8') as f: return json.load(f) # 需要过滤掉的非核心动作类型（这些动作对分析价值较低） FILTERED_ACTIONS = {'refresh', 'sign_up'} # 动作类型映射表（数据库中的名称 -> 标准名称） ACTION_TYPE_MAP = { 'create_post': 'CREATE_POST', 'like_post': 'LIKE_POST', 'dislike_post': 'DISLIKE_POST', 'repost': 'REPOST', 'quote_post': 'QUOTE_POST', 'follow': 'FOLLOW', 'mute': 'MUTE', 'create_comment': 'CREATE_COMMENT', 'like_comment': 'LIKE_COMMENT', 'dislike_comment': 'DISLIKE_COMMENT', 'search_posts': 'SEARCH_POSTS', 'search_user': 'SEARCH_USER', 'trend': 'TREND', 'do_nothing': 'DO_NOTHING', 'interview': 'INTERVIEW', } def get_agent_names_from_config(config: Dict[str, Any]) -> Dict[int, str]: """ 从 simulation_config 中获取 agent_id -> entity_name 的映射 这样可以在 actions.jsonl 中显示真实的实体名称，而不是 "Agent_0" 这样的代号 Args: config: simulation_config.json 的内容 Returns: agent_id -> entity_name 的映射字典 """ agent_names = {} agent_configs = config.get("agent_configs", []) for agent_config in agent_configs: agent_id = agent_config.get("agent_id") entity_name = agent_config.get("entity_name", f"Agent_{agent_id}") if agent_id is not None: agent_names[agent_id] = entity_name return agent_names def fetch_new_actions_from_db( db_path: str, last_rowid: int, agent_names: Dict[int, str] ) -> Tuple[List[Dict[str, Any]], int]: """ 从数据库中获取新的动作记录 Args: db_path: 数据库文件路径 last_rowid: 上次读取的最大 rowid 值（使用 rowid 而不是 created_at，因为不同平台的 created_at 格式不同） agent_names: agent_id -> agent_name 映射 Returns: (actions_list, new_last_rowid) - actions_list: 动作列表，每个元素包含 agent_id, agent_name, action_type, action_args - new_last_rowid: 新的最大 rowid 值 """ actions = [] new_last_rowid = last_rowid if not os.path.exists(db_path): return actions, new_last_rowid try: conn = sqlite3.connect(db_path) cursor = conn.cursor() # 使用 rowid 来追踪已处理的记录（rowid 是 SQLite 的内置自增字段） # 这样可以避免 created_at 格式差异问题（Twitter 用整数，Reddit 用日期时间字符串） cursor.execute(""" SELECT rowid, user_id, action, info FROM trace WHERE rowid > ? ORDER BY rowid ASC """, (last_rowid,)) for rowid, user_id, action, info_json in cursor.fetchall(): # 更新最大 rowid new_last_rowid = rowid # 过滤非核心动作 if action in FILTERED_ACTIONS: continue # 解析动作参数 try: action_args = json.loads(info_json) if info_json else {} except json.JSONDecodeError: action_args = {} # 精简 action_args，只保留关键字段 simplified_args = {} if 'content' in action_args: content = action_args['content'] # 截断过长的内容 simplified_args['content'] = content[:200] + '...' if len(content) > 200 else content if 'post_id' in action_args: simplified_args['post_id'] = action_args['post_id'] if 'comment_id' in action_args: simplified_args['comment_id'] = action_args['comment_id'] if 'quoted_id' in action_args: simplified_args['quoted_id'] = action_args['quoted_id'] if 'new_post_id' in action_args: simplified_args['new_post_id'] = action_args['new_post_id'] if 'follow_id' in action_args: simplified_args['follow_id'] = action_args['follow_id'] if 'query' in action_args: simplified_args['query'] = action_args['query'] if 'like_id' in action_args: simplified_args['like_id'] = action_args['like_id'] if 'dislike_id' in action_args: simplified_args['dislike_id'] = action_args['dislike_id'] # 转换动作类型名称 action_type = ACTION_TYPE_MAP.get(action, action.upper()) actions.append({ 'agent_id': user_id, 'agent_name': agent_names.get(user_id, f'Agent_{user_id}'), 'action_type': action_type, 'action_args': simplified_args, }) conn.close() except Exception as e: print(f"读取数据库动作失败: {e}") return actions, new_last_rowid def create_model(config: Dict[str, Any], use_boost: bool = False): """ 创建LLM模型 支持双 LLM 配置，用于并行模拟时提速： - 通用配置：LLM_API_KEY, LLM_BASE_URL, LLM_MODEL_NAME - 加速配置（可选）：LLM_BOOST_API_KEY, LLM_BOOST_BASE_URL, LLM_BOOST_MODEL_NAME 如果配置了加速 LLM，并行模拟时可以让不同平台使用不同的 API 服务商，提高并发能力。 Args: config: 模拟配置字典 use_boost: 是否使用加速 LLM 配置（如果可用） """ # 检查是否有加速配置 boost_api_key = os.environ.get("LLM_BOOST_API_KEY", "") boost_base_url = os.environ.get("LLM_BOOST_BASE_URL", "") boost_model = os.environ.get("LLM_BOOST_MODEL_NAME", "") has_boost_config = bool(boost_api_key) # 根据参数和配置情况选择使用哪个 LLM if use_boost and has_boost_config: # 使用加速配置 llm_api_key = boost_api_key llm_base_url = boost_base_url llm_model = boost_model or os.environ.get("LLM_MODEL_NAME", "") config_label = "[加速LLM]" else: # 使用通用配置 llm_api_key = os.environ.get("LLM_API_KEY", "") llm_base_url = os.environ.get("LLM_BASE_URL", "") llm_model = os.environ.get("LLM_MODEL_NAME", "") config_label = "[通用LLM]" # 如果 .env 中没有模型名，则使用 config 作为备用 if not llm_model: llm_model = config.get("llm_model", "gpt-4o-mini") # 设置 camel-ai 所需的环境变量 if llm_api_key: os.environ["OPENAI_API_KEY"] = llm_api_key if not os.environ.get("OPENAI_API_KEY"): raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY") if llm_base_url: os.environ["OPENAI_API_BASE_URL"] = llm_base_url print(f"{config_label} model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...") return ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=llm_model, ) def get_active_agents_for_round( env, config: Dict[str, Any], current_hour: int, round_num: int ) -> List: """根据时间和配置决定本轮激活哪些Agent""" time_config = config.get("time_config", {}) agent_configs = config.get("agent_configs", []) base_min = time_config.get("agents_per_hour_min", 5) base_max = time_config.get("agents_per_hour_max", 20) peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22]) off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5]) if current_hour in peak_hours: multiplier = time_config.get("peak_activity_multiplier", 1.5) elif current_hour in off_peak_hours: multiplier = time_config.get("off_peak_activity_multiplier", 0.3) else: multiplier = 1.0 target_count = int(random.uniform(base_min, base_max) * multiplier) candidates = [] for cfg in agent_configs: agent_id = cfg.get("agent_id", 0) active_hours = cfg.get("active_hours", list(range(8, 23))) activity_level = cfg.get("activity_level", 0.5) if current_hour not in active_hours: continue if random.random() < activity_level: candidates.append(agent_id) selected_ids = random.sample( candidates, min(target_count, len(candidates)) ) if candidates else [] active_agents = [] for agent_id in selected_ids: try: agent = env.agent_graph.get_agent(agent_id) active_agents.append((agent_id, agent)) except Exception: pass return active_agents async def run_twitter_simulation( config: Dict[str, Any], simulation_dir: str, action_logger: Optional[PlatformActionLogger] = None, main_logger: Optional[SimulationLogManager] = None, max_rounds: Optional[int] = None ): """运行Twitter模拟 Args: config: 模拟配置 simulation_dir: 模拟目录 action_logger: 动作日志记录器 main_logger: 主日志管理器 max_rounds: 最大模拟轮数（可选，用于截断过长的模拟） """ def log_info(msg): if main_logger: main_logger.info(f"[Twitter] {msg}") print(f"[Twitter] {msg}") log_info("初始化...") # Twitter 使用通用 LLM 配置 model = create_model(config, use_boost=False) # OASIS Twitter使用CSV格式 profile_path = os.path.join(simulation_dir, "twitter_profiles.csv") if not os.path.exists(profile_path): log_info(f"错误: Profile文件不存在: {profile_path}") return agent_graph = await generate_twitter_agent_graph( profile_path=profile_path, model=model, available_actions=TWITTER_ACTIONS, ) # 从配置文件获取 Agent 真实名称映射（使用 entity_name 而非默认的 Agent_X） agent_names = get_agent_names_from_config(config) # 如果配置中没有某个 agent，则使用 OASIS 的默认名称 for agent_id, agent in agent_graph.get_agents(): if agent_id not in agent_names: agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}') db_path = os.path.join(simulation_dir, "twitter_simulation.db") if os.path.exists(db_path): os.remove(db_path) env = oasis.make( agent_graph=agent_graph, platform=oasis.DefaultPlatformType.TWITTER, database_path=db_path, semaphore=30, # 限制最大并发 LLM 请求数，防止 API 过载 ) await env.reset() log_info("环境已启动") if action_logger: action_logger.log_simulation_start(config) total_actions = 0 last_rowid = 0 # 跟踪数据库中最后处理的行号（使用 rowid 避免 created_at 格式差异） # 执行初始事件 event_config = config.get("event_config", {}) initial_posts = event_config.get("initial_posts", []) # 记录 round 0 开始（初始事件阶段） if action_logger: action_logger.log_round_start(0, 0) # round 0, simulated_hour 0 initial_action_count = 0 if initial_posts: initial_actions = {} for post in initial_posts: agent_id = post.get("poster_agent_id", 0) content = post.get("content", "") try: agent = env.agent_graph.get_agent(agent_id) initial_actions[agent] = ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} ) if action_logger: action_logger.log_action( round_num=0, agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="CREATE_POST", action_args={"content": content[:100] + "..." if len(content) > 100 else content} ) total_actions += 1 initial_action_count += 1 except Exception: pass if initial_actions: await env.step(initial_actions) log_info(f"已发布 {len(initial_actions)} 条初始帖子") # 记录 round 0 结束 if action_logger: action_logger.log_round_end(0, initial_action_count) # 主模拟循环 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 = (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: log_info(f"轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})") start_time = datetime.now() for round_num in range(total_rounds): simulated_minutes = round_num * minutes_per_round simulated_hour = (simulated_minutes // 60) % 24 simulated_day = simulated_minutes // (60 * 24) + 1 active_agents = get_active_agents_for_round( env, config, simulated_hour, round_num ) # 无论是否有活跃agent，都记录round开始 if action_logger: action_logger.log_round_start(round_num + 1, simulated_hour) if not active_agents: # 没有活跃agent时也记录round结束（actions_count=0） if action_logger: action_logger.log_round_end(round_num + 1, 0) continue actions = {agent: LLMAction() for _, agent in active_agents} await env.step(actions) # 从数据库获取实际执行的动作并记录 actual_actions, last_rowid = fetch_new_actions_from_db( db_path, last_rowid, agent_names ) round_action_count = 0 for action_data in actual_actions: if action_logger: action_logger.log_action( round_num=round_num + 1, agent_id=action_data['agent_id'], agent_name=action_data['agent_name'], action_type=action_data['action_type'], action_args=action_data['action_args'] ) total_actions += 1 round_action_count += 1 if action_logger: action_logger.log_round_end(round_num + 1, round_action_count) if (round_num + 1) % 20 == 0: progress = (round_num + 1) / total_rounds * 100 log_info(f"Day {simulated_day}, {simulated_hour:02d}:00 - Round {round_num + 1}/{total_rounds} ({progress:.1f}%)") await env.close() if action_logger: action_logger.log_simulation_end(total_rounds, total_actions) elapsed = (datetime.now() - start_time).total_seconds() log_info(f"模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}") async def run_reddit_simulation( config: Dict[str, Any], simulation_dir: str, action_logger: Optional[PlatformActionLogger] = None, main_logger: Optional[SimulationLogManager] = None, max_rounds: Optional[int] = None ): """运行Reddit模拟 Args: config: 模拟配置 simulation_dir: 模拟目录 action_logger: 动作日志记录器 main_logger: 主日志管理器 max_rounds: 最大模拟轮数（可选，用于截断过长的模拟） """ def log_info(msg): if main_logger: main_logger.info(f"[Reddit] {msg}") print(f"[Reddit] {msg}") log_info("初始化...") # Reddit 使用加速 LLM 配置（如果有的话，否则回退到通用配置） model = create_model(config, use_boost=True) profile_path = os.path.join(simulation_dir, "reddit_profiles.json") if not os.path.exists(profile_path): log_info(f"错误: Profile文件不存在: {profile_path}") return agent_graph = await generate_reddit_agent_graph( profile_path=profile_path, model=model, available_actions=REDDIT_ACTIONS, ) # 从配置文件获取 Agent 真实名称映射（使用 entity_name 而非默认的 Agent_X） agent_names = get_agent_names_from_config(config) # 如果配置中没有某个 agent，则使用 OASIS 的默认名称 for agent_id, agent in agent_graph.get_agents(): if agent_id not in agent_names: agent_names[agent_id] = getattr(agent, 'name', f'Agent_{agent_id}') db_path = os.path.join(simulation_dir, "reddit_simulation.db") if os.path.exists(db_path): os.remove(db_path) env = oasis.make( agent_graph=agent_graph, platform=oasis.DefaultPlatformType.REDDIT, database_path=db_path, semaphore=30, # 限制最大并发 LLM 请求数，防止 API 过载 ) await env.reset() log_info("环境已启动") if action_logger: action_logger.log_simulation_start(config) total_actions = 0 last_rowid = 0 # 跟踪数据库中最后处理的行号（使用 rowid 避免 created_at 格式差异） # 执行初始事件 event_config = config.get("event_config", {}) initial_posts = event_config.get("initial_posts", []) # 记录 round 0 开始（初始事件阶段） if action_logger: action_logger.log_round_start(0, 0) # round 0, simulated_hour 0 initial_action_count = 0 if initial_posts: initial_actions = {} for post in initial_posts: agent_id = post.get("poster_agent_id", 0) content = post.get("content", "") try: agent = env.agent_graph.get_agent(agent_id) if agent in initial_actions: if not isinstance(initial_actions[agent], list): initial_actions[agent] = [initial_actions[agent]] initial_actions[agent].append(ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} )) else: initial_actions[agent] = ManualAction( action_type=ActionType.CREATE_POST, action_args={"content": content} ) if action_logger: action_logger.log_action( round_num=0, agent_id=agent_id, agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"), action_type="CREATE_POST", action_args={"content": content[:100] + "..." if len(content) > 100 else content} ) total_actions += 1 initial_action_count += 1 except Exception: pass if initial_actions: await env.step(initial_actions) log_info(f"已发布 {len(initial_actions)} 条初始帖子") # 记录 round 0 结束 if action_logger: action_logger.log_round_end(0, initial_action_count) # 主模拟循环 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 = (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: log_info(f"轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})") start_time = datetime.now() for round_num in range(total_rounds): simulated_minutes = round_num * minutes_per_round simulated_hour = (simulated_minutes // 60) % 24 simulated_day = simulated_minutes // (60 * 24) + 1 active_agents = get_active_agents_for_round( env, config, simulated_hour, round_num ) # 无论是否有活跃agent，都记录round开始 if action_logger: action_logger.log_round_start(round_num + 1, simulated_hour) if not active_agents: # 没有活跃agent时也记录round结束（actions_count=0） if action_logger: action_logger.log_round_end(round_num + 1, 0) continue actions = {agent: LLMAction() for _, agent in active_agents} await env.step(actions) # 从数据库获取实际执行的动作并记录 actual_actions, last_rowid = fetch_new_actions_from_db( db_path, last_rowid, agent_names ) round_action_count = 0 for action_data in actual_actions: if action_logger: action_logger.log_action( round_num=round_num + 1, agent_id=action_data['agent_id'], agent_name=action_data['agent_name'], action_type=action_data['action_type'], action_args=action_data['action_args'] ) total_actions += 1 round_action_count += 1 if action_logger: action_logger.log_round_end(round_num + 1, round_action_count) if (round_num + 1) % 20 == 0: progress = (round_num + 1) / total_rounds * 100 log_info(f"Day {simulated_day}, {simulated_hour:02d}:00 - Round {round_num + 1}/{total_rounds} ({progress:.1f}%)") await env.close() if action_logger: action_logger.log_simulation_end(total_rounds, total_actions) elapsed = (datetime.now() - start_time).total_seconds() log_info(f"模拟完成! 耗时: {elapsed:.1f}秒, 总动作: {total_actions}") async def main(): parser = argparse.ArgumentParser(description='OASIS双平台并行模拟') parser.add_argument( '--config', type=str, required=True, help='配置文件路径 (simulation_config.json)' ) parser.add_argument( '--twitter-only', action='store_true', help='只运行Twitter模拟' ) parser.add_argument( '--reddit-only', action='store_true', help='只运行Reddit模拟' ) parser.add_argument( '--max-rounds', type=int, default=None, help='最大模拟轮数（可选，用于截断过长的模拟）' ) args = parser.parse_args() if not os.path.exists(args.config): print(f"错误: 配置文件不存在: {args.config}") sys.exit(1) config = load_config(args.config) simulation_dir = os.path.dirname(args.config) or "." # 初始化日志配置（禁用 OASIS 日志，清理旧文件） init_logging_for_simulation(simulation_dir) # 创建日志管理器 log_manager = SimulationLogManager(simulation_dir) twitter_logger = log_manager.get_twitter_logger() reddit_logger = log_manager.get_reddit_logger() log_manager.info("=" * 60) log_manager.info("OASIS 双平台并行模拟") log_manager.info(f"配置文件: {args.config}") log_manager.info(f"模拟ID: {config.get('simulation_id', 'unknown')}") log_manager.info("=" * 60) 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) config_total_rounds = (total_hours * 60) // minutes_per_round log_manager.info(f"模拟参数:") log_manager.info(f" - 总模拟时长: {total_hours}小时") log_manager.info(f" - 每轮时间: {minutes_per_round}分钟") log_manager.info(f" - 配置总轮数: {config_total_rounds}") if args.max_rounds: log_manager.info(f" - 最大轮数限制: {args.max_rounds}") if args.max_rounds < config_total_rounds: log_manager.info(f" - 实际执行轮数: {args.max_rounds} (已截断)") log_manager.info(f" - Agent数量: {len(config.get('agent_configs', []))}") log_manager.info("日志结构:") log_manager.info(f" - 主日志: simulation.log") log_manager.info(f" - Twitter动作: twitter/actions.jsonl") log_manager.info(f" - Reddit动作: reddit/actions.jsonl") log_manager.info("=" * 60) start_time = datetime.now() if args.twitter_only: await run_twitter_simulation(config, simulation_dir, twitter_logger, log_manager, args.max_rounds) elif args.reddit_only: await run_reddit_simulation(config, simulation_dir, reddit_logger, log_manager, args.max_rounds) else: # 并行运行（每个平台使用独立的日志记录器） await asyncio.gather( run_twitter_simulation(config, simulation_dir, twitter_logger, log_manager, args.max_rounds), run_reddit_simulation(config, simulation_dir, reddit_logger, log_manager, args.max_rounds), ) total_elapsed = (datetime.now() - start_time).total_seconds() log_manager.info("=" * 60) log_manager.info(f"全部模拟完成! 总耗时: {total_elapsed:.1f}秒") log_manager.info(f"日志文件:") log_manager.info(f" - {os.path.join(simulation_dir, 'simulation.log')}") log_manager.info(f" - {os.path.join(simulation_dir, 'twitter', 'actions.jsonl')}") log_manager.info(f" - {os.path.join(simulation_dir, 'reddit', 'actions.jsonl')}") log_manager.info("=" * 60) if __name__ == "__main__": asyncio.run(main())