Enhance backend functionality with OASIS simulation features

- Updated README.md to include new simulation scripts and configuration details for OASIS, including API retry mechanisms and environment variable settings.
- Added simulation management and configuration generation services to streamline the simulation process across Twitter and Reddit platforms.
- Introduced new API routes for simulation-related operations, including entity retrieval and simulation status management.
- Implemented a robust retry mechanism for external API calls to improve system stability.
- Enhanced task management model to include detailed progress tracking.
- Added logging capabilities for action tracking during simulations.
- Included new scripts for running parallel simulations and testing profile formats.

backend/scripts/action_logger.py

@@ -0,0 +1,138 @@
+"""
+动作日志记录器
+用于记录OASIS模拟中每个Agent的动作，供后端监控使用
+"""
+
+import json
+import os
+from datetime import datetime
+from typing import Dict, Any, Optional
+
+
+class ActionLogger:
+    """动作日志记录器"""
+    
+    def __init__(self, log_path: str):
+        """
+        初始化日志记录器
+        
+        Args:
+            log_path: 日志文件路径（.jsonl格式）
+        """
+        self.log_path = log_path
+        self._ensure_dir()
+    
+    def _ensure_dir(self):
+        """确保目录存在"""
+        log_dir = os.path.dirname(self.log_path)
+        if log_dir:
+            os.makedirs(log_dir, exist_ok=True)
+    
+    def log_action(
+        self,
+        round_num: int,
+        platform: str,
+        agent_id: int,
+        agent_name: str,
+        action_type: str,
+        action_args: Optional[Dict[str, Any]] = None,
+        result: Optional[str] = None,
+        success: bool = True
+    ):
+        """
+        记录一个动作
+        
+        Args:
+            round_num: 轮次
+            platform: 平台 (twitter/reddit)
+            agent_id: Agent ID
+            agent_name: Agent名称
+            action_type: 动作类型
+            action_args: 动作参数
+            result: 执行结果
+            success: 是否成功
+        """
+        entry = {
+            "round": round_num,
+            "timestamp": datetime.now().isoformat(),
+            "platform": platform,
+            "agent_id": agent_id,
+            "agent_name": agent_name,
+            "action_type": action_type,
+            "action_args": action_args or {},
+            "result": result,
+            "success": success,
+        }
+        
+        with open(self.log_path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+    
+    def log_round_start(self, round_num: int, simulated_hour: int, platform: str):
+        """记录轮次开始"""
+        entry = {
+            "round": round_num,
+            "timestamp": datetime.now().isoformat(),
+            "platform": platform,
+            "event_type": "round_start",
+            "simulated_hour": simulated_hour,
+        }
+        
+        with open(self.log_path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+    
+    def log_round_end(self, round_num: int, actions_count: int, platform: str):
+        """记录轮次结束"""
+        entry = {
+            "round": round_num,
+            "timestamp": datetime.now().isoformat(),
+            "platform": platform,
+            "event_type": "round_end",
+            "actions_count": actions_count,
+        }
+        
+        with open(self.log_path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+    
+    def log_simulation_start(self, platform: str, config: Dict[str, Any]):
+        """记录模拟开始"""
+        entry = {
+            "timestamp": datetime.now().isoformat(),
+            "platform": platform,
+            "event_type": "simulation_start",
+            "total_rounds": config.get("time_config", {}).get("total_simulation_hours", 72) * 2,
+            "agents_count": len(config.get("agent_configs", [])),
+        }
+        
+        with open(self.log_path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+    
+    def log_simulation_end(self, platform: str, total_rounds: int, total_actions: int):
+        """记录模拟结束"""
+        entry = {
+            "timestamp": datetime.now().isoformat(),
+            "platform": platform,
+            "event_type": "simulation_end",
+            "total_rounds": total_rounds,
+            "total_actions": total_actions,
+        }
+        
+        with open(self.log_path, 'a', encoding='utf-8') as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+
+
+# 全局日志实例（可选）
+_global_logger: Optional[ActionLogger] = None
+
+
+def get_logger(log_path: Optional[str] = None) -> ActionLogger:
+    """获取全局日志实例"""
+    global _global_logger
+    
+    if log_path:
+        _global_logger = ActionLogger(log_path)
+    
+    if _global_logger is None:
+        _global_logger = ActionLogger("actions.jsonl")
+    
+    return _global_logger
+

backend/scripts/run_parallel_simulation.py

@@ -0,0 +1,503 @@
+"""
+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 [--action-log actions.jsonl]
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import random
+import sys
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from action_logger import ActionLogger
+
+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)
+
+
+def create_model(config: Dict[str, Any]):
+    """
+    创建LLM模型
+    
+    OASIS使用camel-ai的ModelFactory，配置方式：
+    - 标准OpenAI: 只需设置 OPENAI_API_KEY 环境变量
+    - 自定义API: 设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
+    """
+    llm_model = config.get("llm_model", "gpt-4o-mini")
+    llm_base_url = config.get("llm_base_url", "")
+    
+    # 如果配置了base_url，设置环境变量
+    if llm_base_url:
+        os.environ["OPENAI_API_BASE_URL"] = llm_base_url
+    
+    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[ActionLogger] = None
+):
+    """运行Twitter模拟"""
+    print("[Twitter] 初始化...")
+    
+    model = create_model(config)
+    
+    # OASIS Twitter使用CSV格式
+    profile_path = os.path.join(simulation_dir, "twitter_profiles.csv")
+    if not os.path.exists(profile_path):
+        print(f"[Twitter] 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,
+    )
+    
+    # 获取Agent名称映射
+    agent_names = {}
+    for agent_id, agent in agent_graph.get_agents():
+        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,
+    )
+    
+    await env.reset()
+    print("[Twitter] 环境已启动")
+    
+    if action_logger:
+        action_logger.log_simulation_start("twitter", config)
+    
+    total_actions = 0
+    
+    # 执行初始事件
+    event_config = config.get("event_config", {})
+    initial_posts = event_config.get("initial_posts", [])
+    
+    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,
+                        platform="twitter",
+                        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
+            except Exception:
+                pass
+        
+        if initial_actions:
+            await env.step(initial_actions)
+            print(f"[Twitter] 已发布 {len(initial_actions)} 条初始帖子")
+    
+    # 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
+    
+    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
+        )
+        
+        if not active_agents:
+            continue
+        
+        if action_logger:
+            action_logger.log_round_start(round_num + 1, simulated_hour, "twitter")
+        
+        actions = {agent: LLMAction() for _, agent in active_agents}
+        await env.step(actions)
+        
+        # 记录动作
+        for agent_id, agent in active_agents:
+            if action_logger:
+                action_logger.log_action(
+                    round_num=round_num + 1,
+                    platform="twitter",
+                    agent_id=agent_id,
+                    agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
+                    action_type="LLM_ACTION",
+                    action_args={}
+                )
+                total_actions += 1
+        
+        if action_logger:
+            action_logger.log_round_end(round_num + 1, len(active_agents), "twitter")
+        
+        if (round_num + 1) % 20 == 0:
+            progress = (round_num + 1) / total_rounds * 100
+            print(f"[Twitter] Day {simulated_day}, {simulated_hour:02d}:00 "
+                  f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)")
+    
+    await env.close()
+    
+    if action_logger:
+        action_logger.log_simulation_end("twitter", total_rounds, total_actions)
+    
+    elapsed = (datetime.now() - start_time).total_seconds()
+    print(f"[Twitter] 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[ActionLogger] = None
+):
+    """运行Reddit模拟"""
+    print("[Reddit] 初始化...")
+    
+    model = create_model(config)
+    
+    profile_path = os.path.join(simulation_dir, "reddit_profiles.json")
+    if not os.path.exists(profile_path):
+        print(f"[Reddit] 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,
+    )
+    
+    # 获取Agent名称映射
+    agent_names = {}
+    for agent_id, agent in agent_graph.get_agents():
+        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,
+    )
+    
+    await env.reset()
+    print("[Reddit] 环境已启动")
+    
+    if action_logger:
+        action_logger.log_simulation_start("reddit", config)
+    
+    total_actions = 0
+    
+    # 执行初始事件
+    event_config = config.get("event_config", {})
+    initial_posts = event_config.get("initial_posts", [])
+    
+    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,
+                        platform="reddit",
+                        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
+            except Exception:
+                pass
+        
+        if initial_actions:
+            await env.step(initial_actions)
+            print(f"[Reddit] 已发布 {len(initial_actions)} 条初始帖子")
+    
+    # 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
+    
+    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
+        )
+        
+        if not active_agents:
+            continue
+        
+        if action_logger:
+            action_logger.log_round_start(round_num + 1, simulated_hour, "reddit")
+        
+        actions = {agent: LLMAction() for _, agent in active_agents}
+        await env.step(actions)
+        
+        # 记录动作
+        for agent_id, agent in active_agents:
+            if action_logger:
+                action_logger.log_action(
+                    round_num=round_num + 1,
+                    platform="reddit",
+                    agent_id=agent_id,
+                    agent_name=agent_names.get(agent_id, f"Agent_{agent_id}"),
+                    action_type="LLM_ACTION",
+                    action_args={}
+                )
+                total_actions += 1
+        
+        if action_logger:
+            action_logger.log_round_end(round_num + 1, len(active_agents), "reddit")
+        
+        if (round_num + 1) % 20 == 0:
+            progress = (round_num + 1) / total_rounds * 100
+            print(f"[Reddit] Day {simulated_day}, {simulated_hour:02d}:00 "
+                  f"- Round {round_num + 1}/{total_rounds} ({progress:.1f}%)")
+    
+    await env.close()
+    
+    if action_logger:
+        action_logger.log_simulation_end("reddit", total_rounds, total_actions)
+    
+    elapsed = (datetime.now() - start_time).total_seconds()
+    print(f"[Reddit] 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(
+        '--action-log',
+        type=str,
+        default='actions.jsonl',
+        help='动作日志文件路径 (默认: actions.jsonl)'
+    )
+    
+    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 "."
+    
+    # 创建动作日志记录器
+    action_log_path = os.path.join(simulation_dir, args.action_log)
+    action_logger = ActionLogger(action_log_path)
+    
+    print("=" * 60)
+    print("OASIS 双平台并行模拟")
+    print(f"配置文件: {args.config}")
+    print(f"模拟ID: {config.get('simulation_id', 'unknown')}")
+    print(f"动作日志: {action_log_path}")
+    print("=" * 60)
+    
+    time_config = config.get("time_config", {})
+    print(f"\n模拟参数:")
+    print(f"  - 总模拟时长: {time_config.get('total_simulation_hours', 72)}小时")
+    print(f"  - 每轮时间: {time_config.get('minutes_per_round', 30)}分钟")
+    print(f"  - Agent数量: {len(config.get('agent_configs', []))}")
+    
+    # LLM推理说明
+    reasoning = config.get("generation_reasoning", "")
+    if reasoning:
+        print(f"\nLLM配置推理:")
+        print(f"  {reasoning[:500]}..." if len(reasoning) > 500 else f"  {reasoning}")
+    
+    print("\n" + "=" * 60)
+    
+    start_time = datetime.now()
+    
+    if args.twitter_only:
+        await run_twitter_simulation(config, simulation_dir, action_logger)
+    elif args.reddit_only:
+        await run_reddit_simulation(config, simulation_dir, action_logger)
+    else:
+        # 并行运行（共享同一个action_logger）
+        await asyncio.gather(
+            run_twitter_simulation(config, simulation_dir, action_logger),
+            run_reddit_simulation(config, simulation_dir, action_logger),
+        )
+    
+    total_elapsed = (datetime.now() - start_time).total_seconds()
+    print("\n" + "=" * 60)
+    print(f"全部模拟完成! 总耗时: {total_elapsed:.1f}秒")
+    print(f"动作日志已保存到: {action_log_path}")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

backend/scripts/run_reddit_simulation.py

@@ -0,0 +1,298 @@
+"""
+OASIS Reddit模拟预设脚本
+此脚本读取配置文件中的参数来执行模拟，实现全程自动化
+
+使用方式:
+    python run_reddit_simulation.py --config /path/to/simulation_config.json
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import random
+import sys
+from datetime import datetime
+from typing import Dict, Any, List
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+try:
+    from camel.models import ModelFactory
+    from camel.types import ModelPlatformType
+    import oasis
+    from oasis import (
+        ActionType,
+        LLMAction,
+        ManualAction,
+        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)
+
+
+class RedditSimulationRunner:
+    """Reddit模拟运行器"""
+    
+    # Reddit可用动作
+    AVAILABLE_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 __init__(self, config_path: str):
+        """
+        初始化模拟运行器
+        
+        Args:
+            config_path: 配置文件路径 (simulation_config.json)
+        """
+        self.config_path = config_path
+        self.config = self._load_config()
+        self.simulation_dir = os.path.dirname(config_path)
+        
+    def _load_config(self) -> Dict[str, Any]:
+        """加载配置文件"""
+        with open(self.config_path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    
+    def _get_profile_path(self) -> str:
+        """获取Profile文件路径"""
+        return os.path.join(self.simulation_dir, "reddit_profiles.json")
+    
+    def _get_db_path(self) -> str:
+        """获取数据库路径"""
+        return os.path.join(self.simulation_dir, "reddit_simulation.db")
+    
+    def _create_model(self):
+        """
+        创建LLM模型
+        
+        OASIS使用camel-ai的ModelFactory，配置方式：
+        - 标准OpenAI: 只需设置 OPENAI_API_KEY 环境变量
+        - 自定义API: 设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
+        """
+        import os
+        
+        llm_model = self.config.get("llm_model", "gpt-4o-mini")
+        llm_base_url = self.config.get("llm_base_url", "")
+        
+        # 如果配置了base_url，设置环境变量
+        if llm_base_url:
+            os.environ["OPENAI_API_BASE_URL"] = llm_base_url
+        
+        return ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=llm_model,
+        )
+    
+    def _get_active_agents_for_round(
+        self, 
+        env, 
+        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 = self.config.get("time_config", {})
+        agent_configs = self.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(self):
+        """运行Reddit模拟"""
+        print("=" * 60)
+        print("OASIS Reddit模拟")
+        print(f"配置文件: {self.config_path}")
+        print(f"模拟ID: {self.config.get('simulation_id', 'unknown')}")
+        print("=" * 60)
+        
+        time_config = self.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
+        
+        print(f"\n模拟参数:")
+        print(f"  - 总模拟时长: {total_hours}小时")
+        print(f"  - 每轮时间: {minutes_per_round}分钟")
+        print(f"  - 总轮数: {total_rounds}")
+        print(f"  - Agent数量: {len(self.config.get('agent_configs', []))}")
+        
+        print("\n初始化LLM模型...")
+        model = self._create_model()
+        
+        print("加载Agent Profile...")
+        profile_path = self._get_profile_path()
+        if not os.path.exists(profile_path):
+            print(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=self.AVAILABLE_ACTIONS,
+        )
+        
+        db_path = self._get_db_path()
+        if os.path.exists(db_path):
+            os.remove(db_path)
+            print(f"已删除旧数据库: {db_path}")
+        
+        print("创建OASIS环境...")
+        env = oasis.make(
+            agent_graph=agent_graph,
+            platform=oasis.DefaultPlatformType.REDDIT,
+            database_path=db_path,
+        )
+        
+        await env.reset()
+        print("环境初始化完成\n")
+        
+        # 执行初始事件
+        event_config = self.config.get("event_config", {})
+        initial_posts = event_config.get("initial_posts", [])
+        
+        if initial_posts:
+            print(f"执行初始事件 ({len(initial_posts)}条初始帖子)...")
+            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}
+                        )
+                except Exception as e:
+                    print(f"  警告: 无法为Agent {agent_id}创建初始帖子: {e}")
+            
+            if initial_actions:
+                await env.step(initial_actions)
+                print(f"  已发布 {len(initial_actions)} 条初始帖子")
+        
+        # 主模拟循环
+        print("\n开始模拟循环...")
+        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 = self._get_active_agents_for_round(
+                env, simulated_hour, round_num
+            )
+            
+            if not active_agents:
+                continue
+            
+            actions = {
+                agent: LLMAction()
+                for _, agent in active_agents
+            }
+            
+            await env.step(actions)
+            
+            if (round_num + 1) % 10 == 0 or round_num == 0:
+                elapsed = (datetime.now() - start_time).total_seconds()
+                progress = (round_num + 1) / total_rounds * 100
+                print(f"  [Day {simulated_day}, {simulated_hour:02d}:00] "
+                      f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
+                      f"- {len(active_agents)} agents active "
+                      f"- elapsed: {elapsed:.1f}s")
+        
+        await env.close()
+        
+        total_elapsed = (datetime.now() - start_time).total_seconds()
+        print(f"\n模拟完成!")
+        print(f"  - 总耗时: {total_elapsed:.1f}秒")
+        print(f"  - 数据库: {db_path}")
+        print("=" * 60)
+
+
+async def main():
+    parser = argparse.ArgumentParser(description='OASIS Reddit模拟')
+    parser.add_argument(
+        '--config', 
+        type=str, 
+        required=True,
+        help='配置文件路径 (simulation_config.json)'
+    )
+    
+    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)
+    
+    runner = RedditSimulationRunner(args.config)
+    await runner.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

backend/scripts/run_twitter_simulation.py

@@ -0,0 +1,313 @@
+"""
+OASIS Twitter模拟预设脚本
+此脚本读取配置文件中的参数来执行模拟，实现全程自动化
+
+使用方式:
+    python run_twitter_simulation.py --config /path/to/simulation_config.json
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import random
+import sys
+from datetime import datetime
+from typing import Dict, Any, List
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+try:
+    from camel.models import ModelFactory
+    from camel.types import ModelPlatformType
+    import oasis
+    from oasis import (
+        ActionType,
+        LLMAction,
+        ManualAction,
+        generate_twitter_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)
+
+
+class TwitterSimulationRunner:
+    """Twitter模拟运行器"""
+    
+    # Twitter可用动作
+    AVAILABLE_ACTIONS = [
+        ActionType.CREATE_POST,
+        ActionType.LIKE_POST,
+        ActionType.REPOST,
+        ActionType.FOLLOW,
+        ActionType.DO_NOTHING,
+        ActionType.QUOTE_POST,
+    ]
+    
+    def __init__(self, config_path: str):
+        """
+        初始化模拟运行器
+        
+        Args:
+            config_path: 配置文件路径 (simulation_config.json)
+        """
+        self.config_path = config_path
+        self.config = self._load_config()
+        self.simulation_dir = os.path.dirname(config_path)
+        
+    def _load_config(self) -> Dict[str, Any]:
+        """加载配置文件"""
+        with open(self.config_path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    
+    def _get_profile_path(self) -> str:
+        """获取Profile文件路径（OASIS Twitter使用CSV格式）"""
+        return os.path.join(self.simulation_dir, "twitter_profiles.csv")
+    
+    def _get_db_path(self) -> str:
+        """获取数据库路径"""
+        return os.path.join(self.simulation_dir, "twitter_simulation.db")
+    
+    def _create_model(self):
+        """
+        创建LLM模型
+        
+        OASIS使用camel-ai的ModelFactory，配置方式：
+        - 标准OpenAI: 只需设置 OPENAI_API_KEY 环境变量
+        - 自定义API: 设置 OPENAI_API_KEY 和 OPENAI_API_BASE_URL 环境变量
+        
+        配置文件中的 llm_model 对应 model_type
+        """
+        import os
+        
+        llm_model = self.config.get("llm_model", "gpt-4o-mini")
+        llm_base_url = self.config.get("llm_base_url", "")
+        
+        # 如果配置了base_url，设置环境变量（OASIS通过环境变量读取）
+        if llm_base_url:
+            os.environ["OPENAI_API_BASE_URL"] = llm_base_url
+        
+        return ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=llm_model,
+        )
+    
+    def _get_active_agents_for_round(
+        self, 
+        env, 
+        current_hour: int,
+        round_num: int
+    ) -> List:
+        """
+        根据时间和配置决定本轮激活哪些Agent
+        
+        Args:
+            env: OASIS环境
+            current_hour: 当前模拟小时（0-23）
+            round_num: 当前轮数
+            
+        Returns:
+            激活的Agent列表
+        """
+        time_config = self.config.get("time_config", {})
+        agent_configs = self.config.get("agent_configs", [])
+        
+        # 基础激活数量
+        base_min = time_config.get("agents_per_hour_min", 5)
+        base_max = time_config.get("agents_per_hour_max", 20)
+        
+        # 根据时段调整
+        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)
+        
+        # 根据每个Agent的配置计算激活概率
+        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)
+            
+            # 检查是否在活跃时间
+            if current_hour not in active_hours:
+                continue
+            
+            # 根据活跃度计算概率
+            if random.random() < activity_level:
+                candidates.append(agent_id)
+        
+        # 随机选择
+        selected_ids = random.sample(
+            candidates, 
+            min(target_count, len(candidates))
+        ) if candidates else []
+        
+        # 转换为Agent对象
+        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(self):
+        """运行Twitter模拟"""
+        print("=" * 60)
+        print("OASIS Twitter模拟")
+        print(f"配置文件: {self.config_path}")
+        print(f"模拟ID: {self.config.get('simulation_id', 'unknown')}")
+        print("=" * 60)
+        
+        # 加载时间配置
+        time_config = self.config.get("time_config", {})
+        total_hours = time_config.get("total_simulation_hours", 72)
+        minutes_per_round = time_config.get("minutes_per_round", 30)
+        
+        # 计算总轮数
+        total_rounds = (total_hours * 60) // minutes_per_round
+        
+        print(f"\n模拟参数:")
+        print(f"  - 总模拟时长: {total_hours}小时")
+        print(f"  - 每轮时间: {minutes_per_round}分钟")
+        print(f"  - 总轮数: {total_rounds}")
+        print(f"  - Agent数量: {len(self.config.get('agent_configs', []))}")
+        
+        # 创建模型
+        print("\n初始化LLM模型...")
+        model = self._create_model()
+        
+        # 加载Agent图
+        print("加载Agent Profile...")
+        profile_path = self._get_profile_path()
+        if not os.path.exists(profile_path):
+            print(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=self.AVAILABLE_ACTIONS,
+        )
+        
+        # 数据库路径
+        db_path = self._get_db_path()
+        if os.path.exists(db_path):
+            os.remove(db_path)
+            print(f"已删除旧数据库: {db_path}")
+        
+        # 创建环境
+        print("创建OASIS环境...")
+        env = oasis.make(
+            agent_graph=agent_graph,
+            platform=oasis.DefaultPlatformType.TWITTER,
+            database_path=db_path,
+        )
+        
+        await env.reset()
+        print("环境初始化完成\n")
+        
+        # 执行初始事件
+        event_config = self.config.get("event_config", {})
+        initial_posts = event_config.get("initial_posts", [])
+        
+        if initial_posts:
+            print(f"执行初始事件 ({len(initial_posts)}条初始帖子)...")
+            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}
+                    )
+                except Exception as e:
+                    print(f"  警告: 无法为Agent {agent_id}创建初始帖子: {e}")
+            
+            if initial_actions:
+                await env.step(initial_actions)
+                print(f"  已发布 {len(initial_actions)} 条初始帖子")
+        
+        # 主模拟循环
+        print("\n开始模拟循环...")
+        start_time = datetime.now()
+        
+        for round_num in range(total_rounds):
+            # 计算当前模拟时间
+            simulated_minutes = round_num * minutes_per_round
+            simulated_hour = (simulated_minutes // 60) % 24
+            simulated_day = simulated_minutes // (60 * 24) + 1
+            
+            # 获取本轮激活的Agent
+            active_agents = self._get_active_agents_for_round(
+                env, simulated_hour, round_num
+            )
+            
+            if not active_agents:
+                continue
+            
+            # 构建动作
+            actions = {
+                agent: LLMAction()
+                for _, agent in active_agents
+            }
+            
+            # 执行动作
+            await env.step(actions)
+            
+            # 打印进度
+            if (round_num + 1) % 10 == 0 or round_num == 0:
+                elapsed = (datetime.now() - start_time).total_seconds()
+                progress = (round_num + 1) / total_rounds * 100
+                print(f"  [Day {simulated_day}, {simulated_hour:02d}:00] "
+                      f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
+                      f"- {len(active_agents)} agents active "
+                      f"- elapsed: {elapsed:.1f}s")
+        
+        # 关闭环境
+        await env.close()
+        
+        total_elapsed = (datetime.now() - start_time).total_seconds()
+        print(f"\n模拟完成!")
+        print(f"  - 总耗时: {total_elapsed:.1f}秒")
+        print(f"  - 数据库: {db_path}")
+        print("=" * 60)
+
+
+async def main():
+    parser = argparse.ArgumentParser(description='OASIS Twitter模拟')
+    parser.add_argument(
+        '--config', 
+        type=str, 
+        required=True,
+        help='配置文件路径 (simulation_config.json)'
+    )
+    
+    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)
+    
+    runner = TwitterSimulationRunner(args.config)
+    await runner.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

backend/scripts/test_profile_format.py

@@ -0,0 +1,166 @@
+"""
+测试Profile格式生成是否符合OASIS要求
+验证：
+1. Twitter Profile生成CSV格式
+2. Reddit Profile生成JSON详细格式
+"""
+
+import os
+import sys
+import json
+import csv
+import tempfile
+
+# 添加项目路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.services.oasis_profile_generator import OasisProfileGenerator, OasisAgentProfile
+
+
+def test_profile_formats():
+    """测试Profile格式"""
+    print("=" * 60)
+    print("OASIS Profile格式测试")
+    print("=" * 60)
+    
+    # 创建测试Profile数据
+    test_profiles = [
+        OasisAgentProfile(
+            user_id=0,
+            user_name="test_user_123",
+            name="Test User",
+            bio="A test user for validation",
+            persona="Test User is an enthusiastic participant in social discussions.",
+            karma=1500,
+            friend_count=100,
+            follower_count=200,
+            statuses_count=500,
+            age=25,
+            gender="male",
+            mbti="INTJ",
+            country="China",
+            profession="Student",
+            interested_topics=["Technology", "Education"],
+            source_entity_uuid="test-uuid-123",
+            source_entity_type="Student",
+        ),
+        OasisAgentProfile(
+            user_id=1,
+            user_name="org_official_456",
+            name="Official Organization",
+            bio="Official account for Organization",
+            persona="This is an official institutional account that communicates official positions.",
+            karma=5000,
+            friend_count=50,
+            follower_count=10000,
+            statuses_count=200,
+            profession="Organization",
+            interested_topics=["Public Policy", "Announcements"],
+            source_entity_uuid="test-uuid-456",
+            source_entity_type="University",
+        ),
+    ]
+    
+    generator = OasisProfileGenerator.__new__(OasisProfileGenerator)
+    
+    # 使用临时目录
+    with tempfile.TemporaryDirectory() as temp_dir:
+        twitter_path = os.path.join(temp_dir, "twitter_profiles.csv")
+        reddit_path = os.path.join(temp_dir, "reddit_profiles.json")
+        
+        # 测试Twitter CSV格式
+        print("\n1. 测试Twitter Profile (CSV格式)")
+        print("-" * 40)
+        generator._save_twitter_csv(test_profiles, twitter_path)
+        
+        # 读取并验证CSV
+        with open(twitter_path, 'r', encoding='utf-8') as f:
+            reader = csv.DictReader(f)
+            rows = list(reader)
+            
+        print(f"   文件: {twitter_path}")
+        print(f"   行数: {len(rows)}")
+        print(f"   表头: {list(rows[0].keys())}")
+        print(f"\n   示例数据 (第1行):")
+        for key, value in rows[0].items():
+            print(f"     {key}: {value}")
+        
+        # 验证必需字段
+        required_twitter_fields = ['user_id', 'user_name', 'name', 'bio', 
+                                   'friend_count', 'follower_count', 'statuses_count', 'created_at']
+        missing = set(required_twitter_fields) - set(rows[0].keys())
+        if missing:
+            print(f"\n   [错误] 缺少字段: {missing}")
+        else:
+            print(f"\n   [通过] 所有必需字段都存在")
+        
+        # 测试Reddit JSON格式
+        print("\n2. 测试Reddit Profile (JSON详细格式)")
+        print("-" * 40)
+        generator._save_reddit_json(test_profiles, reddit_path)
+        
+        # 读取并验证JSON
+        with open(reddit_path, 'r', encoding='utf-8') as f:
+            reddit_data = json.load(f)
+        
+        print(f"   文件: {reddit_path}")
+        print(f"   条目数: {len(reddit_data)}")
+        print(f"   字段: {list(reddit_data[0].keys())}")
+        print(f"\n   示例数据 (第1条):")
+        print(json.dumps(reddit_data[0], ensure_ascii=False, indent=4))
+        
+        # 验证详细格式字段
+        required_reddit_fields = ['realname', 'username', 'bio', 'persona']
+        optional_reddit_fields = ['age', 'gender', 'mbti', 'country', 'profession', 'interested_topics']
+        
+        missing = set(required_reddit_fields) - set(reddit_data[0].keys())
+        if missing:
+            print(f"\n   [错误] 缺少必需字段: {missing}")
+        else:
+            print(f"\n   [通过] 所有必需字段都存在")
+        
+        present_optional = set(optional_reddit_fields) & set(reddit_data[0].keys())
+        print(f"   [信息] 可选字段: {present_optional}")
+    
+    print("\n" + "=" * 60)
+    print("测试完成!")
+    print("=" * 60)
+
+
+def show_expected_formats():
+    """显示OASIS期望的格式"""
+    print("\n" + "=" * 60)
+    print("OASIS 期望的Profile格式参考")
+    print("=" * 60)
+    
+    print("\n1. Twitter Profile (CSV格式)")
+    print("-" * 40)
+    twitter_example = """user_id,user_name,name,bio,friend_count,follower_count,statuses_count,created_at
+0,user0,User Zero,I am user zero with interests in technology.,100,150,500,2023-01-01
+1,user1,User One,Tech enthusiast and coffee lover.,200,250,1000,2023-01-02"""
+    print(twitter_example)
+    
+    print("\n2. Reddit Profile (JSON详细格式)")
+    print("-" * 40)
+    reddit_example = [
+        {
+            "realname": "James Miller",
+            "username": "millerhospitality",
+            "bio": "Passionate about hospitality & tourism.",
+            "persona": "James is a seasoned professional in the Hospitality & Tourism industry...",
+            "age": 40,
+            "gender": "male",
+            "mbti": "ESTJ",
+            "country": "UK",
+            "profession": "Hospitality & Tourism",
+            "interested_topics": ["Economics", "Business"]
+        }
+    ]
+    print(json.dumps(reddit_example, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    test_profile_formats()
+    show_expected_formats()
+
+