refactor(api, chat): 改进异步处理并解决并发问题

内存可视化器 API 端点之前在异步路由中执行同步的阻塞操作（文件 I/O、数据处理）。在处理大型图文件时，这可能导致服务器冻结。现在，这些任务已被移至 ThreadPoolExecutor，从而使 API 非阻塞并显著提高响应速度。

在聊天消息管理器中，竞争条件可能导致消息处理重叠或中断后数据流停滞。此提交引入了：
- 并发锁（`is_chatter_processing`）以防止流循环同时运行多个 chatter 实例。
- 故障保护机制，在中断时重置处理状态，确保数据流能够恢复并正确继续。

scripts/memory_cleaner.py

@@ -0,0 +1,680 @@
+"""
+记忆清理脚本
+
+功能：
+1. 遍历所有长期记忆
+2. 使用 LLM 评估每条记忆的价值
+3. 删除无效/低价值记忆
+4. 合并/精简相似记忆
+
+使用方式：
+cd Bot
+python scripts/memory_cleaner.py [--dry-run] [--batch-size 10]
+"""
+
+import argparse
+import asyncio
+import json
+import sys
+from datetime import datetime
+from pathlib import Path
+
+# 添加项目根目录到 Python 路径
+project_root = Path(__file__).parent.parent
+sys.path.insert(0, str(project_root))
+
+from src.config.config import model_config
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config
+
+
+# ==================== 配置 ====================
+
+# LLM 评估提示词
+EVALUATION_PROMPT = """你是一个非常严格的记忆价值评估专家。你的任务是大幅清理低质量记忆，只保留真正有价值的信息。
+
+## 核心原则：宁缺毋滥！
+- 默认态度是 DELETE（删除）
+- 只有非常明确、有具体信息的记忆才能保留
+- 有任何疑虑就删除
+
+## 必须删除的记忆（直接 delete）：
+
+1. **无意义内容**：
+   - 单字/短语回复："？"、"1"、"好"、"哦"、"啊"、"嗯"、"哈哈"、"呜呜"
+   - 表情包、颜文字、emoji 刷屏
+   - "某人发了图片/表情/语音"等无实质内容
+   - 乱码、无法理解的内容
+
+2. **模糊/缺乏上下文的信息**：
+   - "用户说了什么" 但没有具体内容
+   - "某人和某人聊天" 但不知道聊什么
+   - 泛泛的描述如"用户很开心"但不知道原因
+   - 指代不明的内容（"那个"、"这个"、"它"）
+
+3. **水群/无营养聊天**：
+   - 群内的日常寒暄、问好
+   - 闲聊、灌水、抖机灵
+   - 无实际信息的互动
+   - 复读、玩梗、接龙
+   - 讨论与用户个人无关的话题
+
+4. **临时/过时信息**：
+   - 游戏状态、在线状态
+   - 已过期的活动、事件
+   - 天气、时间等即时信息
+   - "刚才"、"现在"等时效性表述
+
+5. **重复/冗余**：
+   - 相同内容的多条记录
+   - 可以合并的相似信息
+
+6. **AI自身的记忆**：
+   - AI说了什么话
+   - AI的回复内容
+   - AI的想法/计划
+
+## 可以保留的记忆（必须同时满足）：
+
+1. **有明确的主体**：知道是谁（具体的用户名/昵称/ID）
+2. **有具体的内容**：知道具体说了什么、做了什么、是什么
+3. **有长期价值**：这个信息在一个月后仍然有参考意义
+
+**保留示例**：
+- "用户张三说他是程序员，在杭州工作" ✅
+- "李四说他喜欢打篮球，每周三都会去" ✅  
+- "小明说他女朋友叫小红，在一起2年了" ✅
+- "用户A的生日是3月15日" ✅
+
+**删除示例**：
+- "用户发了个表情" ❌
+- "群里在聊天" ❌
+- "某人说了什么" ❌
+- "今天天气很好" ❌
+- "哈哈哈太好笑了" ❌
+
+## 待评估记忆
+
+{memories}
+
+## 输出要求
+
+严格按以下 JSON 格式输出，不要有任何其他内容：
+
+```json
+{{
+    "evaluations": [
+        {{
+            "memory_id": "记忆的ID（从上面复制）",
+            "action": "delete",
+            "reason": "删除原因"
+        }},
+        {{
+            "memory_id": "另一个ID",
+            "action": "keep", 
+            "reason": "保留原因"
+        }}
+    ]
+}}
+```
+
+action 只能是：
+- "delete": 删除（应该是大多数）
+- "keep": 保留（只有高价值记忆）
+- "summarize": 精简（很少用，只有内容过长但有价值时）
+
+如果 action 是 summarize，需要加 "new_content": "精简后的内容"
+
+直接输出 JSON，不要任何解释。"""
+
+
+class MemoryCleaner:
+    """记忆清理器"""
+
+    def __init__(self, dry_run: bool = True, batch_size: int = 10, concurrency: int = 5):
+        """
+        初始化清理器
+        
+        Args:
+            dry_run: 是否为模拟运行（不实际修改数据）
+            batch_size: 每批处理的记忆数量
+            concurrency: 并发请求数
+        """
+        self.dry_run = dry_run
+        self.batch_size = batch_size
+        self.concurrency = concurrency
+        self.data_dir = project_root / "data" / "memory_graph"
+        self.memory_file = self.data_dir / "memory_graph.json"
+        self.backup_dir = self.data_dir / "backups"
+        
+        # 并发控制
+        self.semaphore: asyncio.Semaphore | None = None
+        
+        # 统计信息
+        self.stats = {
+            "total": 0,
+            "kept": 0,
+            "deleted": 0,
+            "summarized": 0,
+            "errors": 0,
+            "deleted_nodes": 0,
+            "deleted_edges": 0,
+        }
+        
+        # 日志文件
+        self.log_file = self.data_dir / f"cleanup_log_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        self.cleanup_log = []
+
+    def load_memories(self) -> dict:
+        """加载记忆数据"""
+        print(f"📂 加载记忆文件: {self.memory_file}")
+        
+        if not self.memory_file.exists():
+            raise FileNotFoundError(f"记忆文件不存在: {self.memory_file}")
+        
+        with open(self.memory_file, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        
+        return data
+
+    def extract_memory_text(self, memory_dict: dict) -> str:
+        """从记忆字典中提取可读文本"""
+        parts = []
+        
+        # 提取基本信息
+        memory_id = memory_dict.get("id", "unknown")
+        parts.append(f"ID: {memory_id}")
+        
+        # 提取节点内容
+        nodes = memory_dict.get("nodes", [])
+        for node in nodes:
+            node_type = node.get("node_type", "")
+            content = node.get("content", "")
+            if content:
+                parts.append(f"[{node_type}] {content}")
+        
+        # 提取边关系
+        edges = memory_dict.get("edges", [])
+        for edge in edges:
+            relation = edge.get("relation", "")
+            if relation:
+                parts.append(f"关系: {relation}")
+        
+        # 提取元数据
+        metadata = memory_dict.get("metadata", {})
+        if metadata:
+            if "context" in metadata:
+                parts.append(f"上下文: {metadata['context']}")
+            if "emotion" in metadata:
+                parts.append(f"情感: {metadata['emotion']}")
+        
+        # 提取重要性和状态
+        importance = memory_dict.get("importance", 0)
+        status = memory_dict.get("status", "unknown")
+        created_at = memory_dict.get("created_at", "unknown")
+        
+        parts.append(f"重要性: {importance}, 状态: {status}, 创建时间: {created_at}")
+        
+        return "\n".join(parts)
+
+    async def evaluate_batch(self, memories: list[dict], batch_id: int = 0) -> tuple[int, list[dict]]:
+        """
+        使用 LLM 评估一批记忆（带并发控制）
+        
+        Args:
+            memories: 记忆字典列表
+            batch_id: 批次编号
+            
+        Returns:
+            (批次ID, 评估结果列表)
+        """
+        async with self.semaphore:
+            # 构建记忆文本
+            memory_texts = []
+            for i, mem in enumerate(memories):
+                text = self.extract_memory_text(mem)
+                memory_texts.append(f"=== 记忆 {i+1} ===\n{text}")
+            
+            combined_text = "\n\n".join(memory_texts)
+            prompt = EVALUATION_PROMPT.format(memories=combined_text)
+            
+            try:
+                # 使用 LLMRequest 调用模型
+                if model_config is None:
+                    raise RuntimeError("model_config 未初始化，请确保已加载配置")
+                task_config = model_config.model_task_config.utils
+                llm = LLMRequest(task_config, request_type="memory_cleanup")
+                response_text, (reasoning, model_name, _) = await llm.generate_response_async(
+                    prompt=prompt,
+                    temperature=0.2,
+                    max_tokens=4000,
+                )
+                
+                print(f"   ✅ 批次 {batch_id} 完成 (模型: {model_name})")
+                
+                # 解析 JSON 响应
+                response_text = response_text.strip()
+                
+                # 尝试提取 JSON
+                if "```json" in response_text:
+                    json_start = response_text.find("```json") + 7
+                    json_end = response_text.find("```", json_start)
+                    response_text = response_text[json_start:json_end].strip()
+                elif "```" in response_text:
+                    json_start = response_text.find("```") + 3
+                    json_end = response_text.find("```", json_start)
+                    response_text = response_text[json_start:json_end].strip()
+                
+                result = json.loads(response_text)
+                evaluations = result.get("evaluations", [])
+                
+                # 为评估结果添加实际的 memory_id
+                for j, eval_result in enumerate(evaluations):
+                    if j < len(memories):
+                        eval_result["memory_id"] = memories[j].get("id", f"unknown_{batch_id}_{j}")
+                
+                return (batch_id, evaluations)
+                
+            except json.JSONDecodeError as e:
+                print(f"   ❌ 批次 {batch_id} JSON 解析失败: {e}")
+                return (batch_id, [])
+            except Exception as e:
+                print(f"   ❌ 批次 {batch_id} LLM 调用失败: {e}")
+                return (batch_id, [])
+
+    async def initialize(self):
+        """初始化（创建信号量）"""
+        self.semaphore = asyncio.Semaphore(self.concurrency)
+        print(f"🔧 初始化完成 (并发数: {self.concurrency})")
+
+    def create_backup(self, data: dict):
+        """创建数据备份"""
+        self.backup_dir.mkdir(parents=True, exist_ok=True)
+        backup_file = self.backup_dir / f"memory_graph_backup_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        
+        print(f"💾 创建备份: {backup_file}")
+        with open(backup_file, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+        
+        return backup_file
+
+    def apply_changes(self, data: dict, evaluations: list[dict]) -> dict:
+        """
+        应用评估结果到数据
+        
+        Args:
+            data: 原始数据
+            evaluations: 评估结果列表
+            
+        Returns:
+            修改后的数据
+        """
+        # 创建评估结果索引
+        eval_map = {e["memory_id"]: e for e in evaluations if "memory_id" in e}
+        
+        # 需要删除的记忆 ID
+        to_delete = set()
+        # 需要更新的记忆
+        to_update = {}
+        
+        for eval_result in evaluations:
+            memory_id = eval_result.get("memory_id")
+            action = eval_result.get("action")
+            
+            if action == "delete":
+                to_delete.add(memory_id)
+                self.stats["deleted"] += 1
+                self.cleanup_log.append({
+                    "memory_id": memory_id,
+                    "action": "delete",
+                    "reason": eval_result.get("reason", ""),
+                    "timestamp": datetime.now().isoformat()
+                })
+            elif action == "summarize":
+                to_update[memory_id] = eval_result.get("new_content")
+                self.stats["summarized"] += 1
+                self.cleanup_log.append({
+                    "memory_id": memory_id,
+                    "action": "summarize",
+                    "reason": eval_result.get("reason", ""),
+                    "new_content": eval_result.get("new_content"),
+                    "timestamp": datetime.now().isoformat()
+                })
+            else:
+                self.stats["kept"] += 1
+        
+        if self.dry_run:
+            print("🔍 [DRY RUN] 不实际修改数据")
+            return data
+        
+        # 实际修改数据
+        # 1. 删除记忆
+        memories = data.get("memories", {})
+        for mem_id in to_delete:
+            if mem_id in memories:
+                del memories[mem_id]
+        
+        # 2. 更新记忆内容
+        for mem_id, new_content in to_update.items():
+            if mem_id in memories:
+                # 更新主题节点的内容
+                memory = memories[mem_id]
+                for node in memory.get("nodes", []):
+                    if node.get("node_type") in ["主题", "topic", "TOPIC"]:
+                        node["content"] = new_content
+                        break
+        
+        # 3. 清理孤立节点和边
+        data = self.cleanup_orphaned_nodes_and_edges(data)
+        
+        return data
+    
+    def cleanup_orphaned_nodes_and_edges(self, data: dict) -> dict:
+        """
+        清理孤立的节点和边
+        
+        孤立节点：其 metadata.memory_ids 中的所有记忆都已被删除
+        孤立边：其 source 或 target 节点已被删除
+        """
+        print("\n🔗 清理孤立节点和边...")
+        
+        # 获取当前所有有效的记忆 ID
+        valid_memory_ids = set(data.get("memories", {}).keys())
+        print(f"   有效记忆数: {len(valid_memory_ids)}")
+        
+        # 清理节点
+        nodes = data.get("nodes", [])
+        original_node_count = len(nodes)
+        
+        valid_nodes = []
+        valid_node_ids = set()
+        
+        for node in nodes:
+            node_id = node.get("id")
+            metadata = node.get("metadata", {})
+            memory_ids = metadata.get("memory_ids", [])
+            
+            # 检查节点关联的记忆是否还存在
+            if memory_ids:
+                # 过滤掉已删除的记忆 ID
+                remaining_memory_ids = [mid for mid in memory_ids if mid in valid_memory_ids]
+                
+                if remaining_memory_ids:
+                    # 更新 metadata 中的 memory_ids
+                    metadata["memory_ids"] = remaining_memory_ids
+                    valid_nodes.append(node)
+                    valid_node_ids.add(node_id)
+                # 如果没有剩余的有效记忆 ID，节点被丢弃
+            else:
+                # 没有 memory_ids 的节点（可能是其他方式创建的），检查是否被某个记忆引用
+                # 保守处理：保留这些节点
+                valid_nodes.append(node)
+                valid_node_ids.add(node_id)
+        
+        deleted_nodes = original_node_count - len(valid_nodes)
+        data["nodes"] = valid_nodes
+        print(f"   ✅ 节点: {original_node_count} → {len(valid_nodes)} (删除 {deleted_nodes})")
+        
+        # 清理边
+        edges = data.get("edges", [])
+        original_edge_count = len(edges)
+        
+        valid_edges = []
+        for edge in edges:
+            source = edge.get("source")
+            target = edge.get("target")
+            
+            # 只保留两端节点都存在的边
+            if source in valid_node_ids and target in valid_node_ids:
+                valid_edges.append(edge)
+        
+        deleted_edges = original_edge_count - len(valid_edges)
+        data["edges"] = valid_edges
+        print(f"   ✅ 边: {original_edge_count} → {len(valid_edges)} (删除 {deleted_edges})")
+        
+        # 更新统计
+        self.stats["deleted_nodes"] = deleted_nodes
+        self.stats["deleted_edges"] = deleted_edges
+        
+        return data
+
+    def save_data(self, data: dict):
+        """保存修改后的数据"""
+        if self.dry_run:
+            print("🔍 [DRY RUN] 跳过保存")
+            return
+        
+        print(f"💾 保存数据到: {self.memory_file}")
+        with open(self.memory_file, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+
+    def save_log(self):
+        """保存清理日志"""
+        print(f"📝 保存清理日志到: {self.log_file}")
+        with open(self.log_file, "w", encoding="utf-8") as f:
+            json.dump({
+                "stats": self.stats,
+                "dry_run": self.dry_run,
+                "timestamp": datetime.now().isoformat(),
+                "log": self.cleanup_log
+            }, f, ensure_ascii=False, indent=2)
+
+    async def run(self):
+        """运行清理流程"""
+        print("=" * 60)
+        print("🧹 记忆清理脚本 (高并发版)")
+        print("=" * 60)
+        print(f"模式: {'模拟运行 (DRY RUN)' if self.dry_run else '实际执行'}")
+        print(f"批次大小: {self.batch_size}")
+        print(f"并发数: {self.concurrency}")
+        print("=" * 60)
+        
+        # 初始化
+        await self.initialize()
+        
+        # 加载数据
+        data = self.load_memories()
+        
+        # 获取所有记忆
+        memories = data.get("memories", {})
+        memory_list = list(memories.values())
+        self.stats["total"] = len(memory_list)
+        
+        print(f"📊 总记忆数: {self.stats['total']}")
+        
+        if not memory_list:
+            print("⚠️ 没有记忆需要处理")
+            return
+        
+        # 创建备份
+        if not self.dry_run:
+            self.create_backup(data)
+        
+        # 分批
+        batches = []
+        for i in range(0, len(memory_list), self.batch_size):
+            batch = memory_list[i:i + self.batch_size]
+            batches.append(batch)
+        
+        total_batches = len(batches)
+        print(f"📦 共 {total_batches} 个批次，开始并发处理...\n")
+        
+        # 并发处理所有批次
+        start_time = datetime.now()
+        tasks = [
+            self.evaluate_batch(batch, batch_id=idx)
+            for idx, batch in enumerate(batches)
+        ]
+        
+        # 使用 asyncio.gather 并发执行
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        end_time = datetime.now()
+        elapsed = (end_time - start_time).total_seconds()
+        
+        # 收集所有评估结果
+        all_evaluations = []
+        success_count = 0
+        error_count = 0
+        
+        for result in results:
+            if isinstance(result, Exception):
+                print(f"   ❌ 批次异常: {result}")
+                error_count += 1
+            elif isinstance(result, tuple):
+                batch_id, evaluations = result
+                if evaluations:
+                    all_evaluations.extend(evaluations)
+                    success_count += 1
+                else:
+                    error_count += 1
+        
+        print(f"\n⏱️ 并发处理完成，耗时 {elapsed:.1f} 秒")
+        print(f"   成功批次: {success_count}/{total_batches}, 失败: {error_count}")
+        
+        # 统计评估结果
+        delete_count = sum(1 for e in all_evaluations if e.get("action") == "delete")
+        keep_count = sum(1 for e in all_evaluations if e.get("action") == "keep")
+        summarize_count = sum(1 for e in all_evaluations if e.get("action") == "summarize")
+        
+        print(f"   📊 评估结果: 保留 {keep_count}, 删除 {delete_count}, 精简 {summarize_count}")
+        
+        # 应用更改
+        print("\n" + "=" * 60)
+        print("📊 应用更改...")
+        data = self.apply_changes(data, all_evaluations)
+        
+        # 保存数据
+        self.save_data(data)
+        
+        # 保存日志
+        self.save_log()
+        
+        # 打印统计
+        print("\n" + "=" * 60)
+        print("📊 清理统计")
+        print("=" * 60)
+        print(f"总记忆数: {self.stats['total']}")
+        print(f"保留: {self.stats['kept']}")
+        print(f"删除: {self.stats['deleted']}")
+        print(f"精简: {self.stats['summarized']}")
+        print(f"删除节点: {self.stats['deleted_nodes']}")
+        print(f"删除边: {self.stats['deleted_edges']}")
+        print(f"错误: {self.stats['errors']}")
+        print(f"处理速度: {self.stats['total'] / elapsed:.1f} 条/秒")
+        print("=" * 60)
+        
+        if self.dry_run:
+            print("\n⚠️ 这是模拟运行，实际数据未被修改")
+            print("如要实际执行，请移除 --dry-run 参数")
+
+    async def run_cleanup_only(self):
+        """只清理孤立节点和边，不重新评估记忆"""
+        print("=" * 60)
+        print("🔗 孤立节点/边清理模式")
+        print("=" * 60)
+        print(f"模式: {'模拟运行 (DRY RUN)' if self.dry_run else '实际执行'}")
+        print("=" * 60)
+        
+        # 加载数据
+        data = self.load_memories()
+        
+        # 统计原始数据
+        memories = data.get("memories", {})
+        nodes = data.get("nodes", [])
+        edges = data.get("edges", [])
+        
+        print(f"📊 当前状态: {len(memories)} 条记忆, {len(nodes)} 个节点, {len(edges)} 条边")
+        
+        if not self.dry_run:
+            self.create_backup(data)
+        
+        # 清理孤立节点和边
+        if self.dry_run:
+            # 模拟运行：统计但不修改
+            valid_memory_ids = set(memories.keys())
+            
+            # 统计要删除的节点
+            nodes_to_keep = 0
+            for node in nodes:
+                metadata = node.get("metadata", {})
+                memory_ids = metadata.get("memory_ids", [])
+                if memory_ids:
+                    remaining = [mid for mid in memory_ids if mid in valid_memory_ids]
+                    if remaining:
+                        nodes_to_keep += 1
+                else:
+                    nodes_to_keep += 1
+            
+            nodes_to_delete = len(nodes) - nodes_to_keep
+            
+            # 统计要删除的边（需要先确定哪些节点会被保留）
+            valid_node_ids = set()
+            for node in nodes:
+                metadata = node.get("metadata", {})
+                memory_ids = metadata.get("memory_ids", [])
+                if memory_ids:
+                    remaining = [mid for mid in memory_ids if mid in valid_memory_ids]
+                    if remaining:
+                        valid_node_ids.add(node.get("id"))
+                else:
+                    valid_node_ids.add(node.get("id"))
+            
+            edges_to_keep = sum(1 for e in edges if e.get("source") in valid_node_ids and e.get("target") in valid_node_ids)
+            edges_to_delete = len(edges) - edges_to_keep
+            
+            print(f"\n🔍 [DRY RUN] 预计清理:")
+            print(f"   节点: {len(nodes)} → {nodes_to_keep} (删除 {nodes_to_delete})")
+            print(f"   边: {len(edges)} → {edges_to_keep} (删除 {edges_to_delete})")
+            print("\n⚠️ 这是模拟运行，实际数据未被修改")
+            print("如要实际执行，请移除 --dry-run 参数")
+        else:
+            data = self.cleanup_orphaned_nodes_and_edges(data)
+            self.save_data(data)
+            
+            print(f"\n✅ 清理完成!")
+            print(f"   删除节点: {self.stats['deleted_nodes']}")
+            print(f"   删除边: {self.stats['deleted_edges']}")
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="记忆清理脚本 (高并发版)")
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="模拟运行，不实际修改数据"
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=10,
+        help="每批处理的记忆数量 (默认: 10)"
+    )
+    parser.add_argument(
+        "--concurrency",
+        type=int,
+        default=10,
+        help="并发请求数 (默认: 10)"
+    )
+    parser.add_argument(
+        "--cleanup-only",
+        action="store_true",
+        help="只清理孤立节点和边，不重新评估记忆"
+    )
+    
+    args = parser.parse_args()
+    
+    cleaner = MemoryCleaner(
+        dry_run=args.dry_run,
+        batch_size=args.batch_size,
+        concurrency=args.concurrency,
+    )
+    
+    if args.cleanup_only:
+        await cleaner.run_cleanup_only()
+    else:
+        await cleaner.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/api/memory_visualizer_router.py

@@ -4,7 +4,9 @@
 提供 Web API 用于可视化记忆图数据
 """
 
+import asyncio
 from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
 from pathlib import Path
 from typing import Any
@@ -23,6 +25,9 @@ data_dir = project_root / "data" / "memory_graph"
 graph_data_cache = None
 current_data_file = None
 
+# 线程池用于异步文件读取
+_executor = ThreadPoolExecutor(max_workers=2)
+
 # FastAPI 路由
 router = APIRouter()
 
@@ -64,7 +69,7 @@ def find_available_data_files() -> list[Path]:
 
 
 async def load_graph_data_from_file(file_path: Path | None = None) -> dict[str, Any]:
-    """从磁盘加载图数据"""
+    """从磁盘加载图数据（异步，不阻塞主线程）"""
     global graph_data_cache, current_data_file
 
     if file_path and file_path != current_data_file:
@@ -86,65 +91,81 @@ async def load_graph_data_from_file(file_path: Path | None = None) -> dict[str,
         if not graph_file.exists():
             return {"error": f"文件不存在: {graph_file}", "nodes": [], "edges": [], "stats": {}}
 
-        with open(graph_file, encoding="utf-8") as f:
-            data = orjson.loads(f.read())
+        # 在线程池中异步读取文件，避免阻塞主事件循环
+        loop = asyncio.get_event_loop()
+        data = await loop.run_in_executor(_executor, _sync_load_json_file, graph_file)
 
         nodes = data.get("nodes", [])
         edges = data.get("edges", [])
         metadata = data.get("metadata", {})
 
-        nodes_dict = {
-            node["id"]: {
-                **node,
-                "label": node.get("content", ""),
-                "group": node.get("node_type", ""),
-                "title": f"{node.get('node_type', '')}: {node.get('content', '')}",
-            }
-            for node in nodes
-            if node.get("id")
-        }
-
-        edges_list = []
-        seen_edge_ids = set()
-        for edge in edges:
-            edge_id = edge.get("id")
-            if edge_id and edge_id not in seen_edge_ids:
-                edges_list.append(
-                    {
-                        **edge,
-                        "from": edge.get("source", edge.get("source_id")),
-                        "to": edge.get("target", edge.get("target_id")),
-                        "label": edge.get("relation", ""),
-                        "arrows": "to",
-                    }
-                )
-                seen_edge_ids.add(edge_id)
-
-        stats = metadata.get("statistics", {})
-        total_memories = stats.get("total_memories", 0)
-
-        graph_data_cache = {
-            "nodes": list(nodes_dict.values()),
-            "edges": edges_list,
-            "memories": [],
-            "stats": {
-                "total_nodes": len(nodes_dict),
-                "total_edges": len(edges_list),
-                "total_memories": total_memories,
-            },
-            "current_file": str(graph_file),
-            "file_size": graph_file.stat().st_size,
-            "file_modified": datetime.fromtimestamp(graph_file.stat().st_mtime).isoformat(),
-        }
+        # 在线程池中处理数据转换
+        processed = await loop.run_in_executor(
+            _executor, _process_graph_data, nodes, edges, metadata, graph_file
+        )
+        
+        graph_data_cache = processed
         return graph_data_cache
 
     except Exception as e:
         import traceback
-
         traceback.print_exc()
         raise HTTPException(status_code=500, detail=f"加载图数据失败: {e}")
 
 
+def _sync_load_json_file(file_path: Path) -> dict:
+    """同步加载 JSON 文件（在线程池中执行）"""
+    with open(file_path, encoding="utf-8") as f:
+        return orjson.loads(f.read())
+
+
+def _process_graph_data(nodes: list, edges: list, metadata: dict, graph_file: Path) -> dict:
+    """处理图数据（在线程池中执行）"""
+    nodes_dict = {
+        node["id"]: {
+            **node,
+            "label": node.get("content", ""),
+            "group": node.get("node_type", ""),
+            "title": f"{node.get('node_type', '')}: {node.get('content', '')}",
+        }
+        for node in nodes
+        if node.get("id")
+    }
+
+    edges_list = []
+    seen_edge_ids = set()
+    for edge in edges:
+        edge_id = edge.get("id")
+        if edge_id and edge_id not in seen_edge_ids:
+            edges_list.append(
+                {
+                    **edge,
+                    "from": edge.get("source", edge.get("source_id")),
+                    "to": edge.get("target", edge.get("target_id")),
+                    "label": edge.get("relation", ""),
+                    "arrows": "to",
+                }
+            )
+            seen_edge_ids.add(edge_id)
+
+    stats = metadata.get("statistics", {})
+    total_memories = stats.get("total_memories", 0)
+
+    return {
+        "nodes": list(nodes_dict.values()),
+        "edges": edges_list,
+        "memories": [],
+        "stats": {
+            "total_nodes": len(nodes_dict),
+            "total_edges": len(edges_list),
+            "total_memories": total_memories,
+        },
+        "current_file": str(graph_file),
+        "file_size": graph_file.stat().st_size,
+        "file_modified": datetime.fromtimestamp(graph_file.stat().st_mtime).isoformat(),
+    }
+
+
 @router.get("/", response_class=HTMLResponse)
 async def index(request: Request):
     """主页面"""
@@ -152,7 +173,7 @@ async def index(request: Request):
 
 
 def _format_graph_data_from_manager(memory_manager) -> dict[str, Any]:
-    """从 MemoryManager 提取并格式化图数据"""
+    """从 MemoryManager 提取并格式化图数据（同步版本，需在线程池中调用）"""
     if not memory_manager.graph_store:
         return {"nodes": [], "edges": [], "memories": [], "stats": {}}
 
@@ -216,7 +237,9 @@ async def get_full_graph():
 
         data = {}
         if memory_manager and memory_manager._initialized:
-            data = _format_graph_data_from_manager(memory_manager)
+            # 在线程池中执行，避免阻塞主事件循环
+            loop = asyncio.get_event_loop()
+            data = await loop.run_in_executor(_executor, _format_graph_data_from_manager, memory_manager)
         else:
             # 如果内存管理器不可用，则从文件加载
             data = await load_graph_data_from_file()
@@ -270,71 +293,93 @@ async def get_paginated_graph(
 
         memory_manager = get_memory_manager()
 
-        # 获取完整数据
+        # 获取完整数据（已经是异步的）
         if memory_manager and memory_manager._initialized:
-            full_data = _format_graph_data_from_manager(memory_manager)
+            loop = asyncio.get_event_loop()
+            full_data = await loop.run_in_executor(_executor, _format_graph_data_from_manager, memory_manager)
         else:
             full_data = await load_graph_data_from_file()
 
-        nodes = full_data.get("nodes", [])
-        edges = full_data.get("edges", [])
+        # 在线程池中处理分页逻辑
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(
+            _executor, 
+            _process_pagination, 
+            full_data, page, page_size, min_importance, node_types
+        )
 
-        # 过滤节点类型
-        if node_types:
-            allowed_types = set(node_types.split(","))
-            nodes = [n for n in nodes if n.get("group") in allowed_types]
-
-        # 按重要性排序（如果有importance字段）
-        nodes_with_importance = []
-        for node in nodes:
-            # 计算节点重要性（连接的边数）
-            edge_count = sum(1 for e in edges if e.get("from") == node["id"] or e.get("to") == node["id"])
-            importance = edge_count / max(len(edges), 1)
-            if importance >= min_importance:
-                node["importance"] = importance
-                nodes_with_importance.append(node)
-
-        # 按重要性降序排序
-        nodes_with_importance.sort(key=lambda x: x.get("importance", 0), reverse=True)
-
-        # 分页
-        total_nodes = len(nodes_with_importance)
-        total_pages = (total_nodes + page_size - 1) // page_size
-        start_idx = (page - 1) * page_size
-        end_idx = min(start_idx + page_size, total_nodes)
-
-        paginated_nodes = nodes_with_importance[start_idx:end_idx]
-        node_ids = set(n["id"] for n in paginated_nodes)
-
-        # 只保留连接分页节点的边
-        paginated_edges = [
-            e for e in edges
-            if e.get("from") in node_ids and e.get("to") in node_ids
-        ]
-
-        return JSONResponse(content={"success": True, "data": {
-            "nodes": paginated_nodes,
-            "edges": paginated_edges,
-            "pagination": {
-                "page": page,
-                "page_size": page_size,
-                "total_nodes": total_nodes,
-                "total_pages": total_pages,
-                "has_next": page < total_pages,
-                "has_prev": page > 1,
-            },
-            "stats": {
-                "total_nodes": total_nodes,
-                "total_edges": len(paginated_edges),
-                "total_memories": full_data.get("stats", {}).get("total_memories", 0),
-            },
-        }})
+        return JSONResponse(content={"success": True, "data": result})
     except Exception as e:
         import traceback
         traceback.print_exc()
         return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
 
 
+def _process_pagination(full_data: dict, page: int, page_size: int, min_importance: float, node_types: str | None) -> dict:
+    """处理分页逻辑（在线程池中执行）"""
+    nodes = full_data.get("nodes", [])
+    edges = full_data.get("edges", [])
+
+    # 过滤节点类型
+    if node_types:
+        allowed_types = set(node_types.split(","))
+        nodes = [n for n in nodes if n.get("group") in allowed_types]
+
+    # 构建边的索引以加速查找
+    edge_count_map = {}
+    for e in edges:
+        from_id = e.get("from")
+        to_id = e.get("to")
+        edge_count_map[from_id] = edge_count_map.get(from_id, 0) + 1
+        edge_count_map[to_id] = edge_count_map.get(to_id, 0) + 1
+
+    # 按重要性排序
+    nodes_with_importance = []
+    total_edges = max(len(edges), 1)
+    for node in nodes:
+        edge_count = edge_count_map.get(node["id"], 0)
+        importance = edge_count / total_edges
+        if importance >= min_importance:
+            node["importance"] = importance
+            nodes_with_importance.append(node)
+
+    # 按重要性降序排序
+    nodes_with_importance.sort(key=lambda x: x.get("importance", 0), reverse=True)
+
+    # 分页
+    total_nodes = len(nodes_with_importance)
+    total_pages = (total_nodes + page_size - 1) // page_size
+    start_idx = (page - 1) * page_size
+    end_idx = min(start_idx + page_size, total_nodes)
+
+    paginated_nodes = nodes_with_importance[start_idx:end_idx]
+    node_ids = set(n["id"] for n in paginated_nodes)
+
+    # 只保留连接分页节点的边
+    paginated_edges = [
+        e for e in edges
+        if e.get("from") in node_ids and e.get("to") in node_ids
+    ]
+
+    return {
+        "nodes": paginated_nodes,
+        "edges": paginated_edges,
+        "pagination": {
+            "page": page,
+            "page_size": page_size,
+            "total_nodes": total_nodes,
+            "total_pages": total_pages,
+            "has_next": page < total_pages,
+            "has_prev": page > 1,
+        },
+        "stats": {
+            "total_nodes": total_nodes,
+            "total_edges": len(paginated_edges),
+            "total_memories": full_data.get("stats", {}).get("total_memories", 0),
+        },
+    }
+
+
 @router.get("/api/graph/clustered")
 async def get_clustered_graph(
     max_nodes: int = Query(300, ge=50, le=1000, description="最大节点数"),
@@ -346,9 +391,10 @@ async def get_clustered_graph(
 
         memory_manager = get_memory_manager()
 
-        # 获取完整数据
+        # 获取完整数据（异步）
         if memory_manager and memory_manager._initialized:
-            full_data = _format_graph_data_from_manager(memory_manager)
+            loop = asyncio.get_event_loop()
+            full_data = await loop.run_in_executor(_executor, _format_graph_data_from_manager, memory_manager)
         else:
             full_data = await load_graph_data_from_file()
 
@@ -364,8 +410,11 @@ async def get_clustered_graph(
                 "clustered": False,
             }})
 
-        # 执行聚类
-        clustered_data = _cluster_graph_data(nodes, edges, max_nodes, cluster_threshold)
+        # 在线程池中执行聚类
+        loop = asyncio.get_event_loop()
+        clustered_data = await loop.run_in_executor(
+            _executor, _cluster_graph_data, nodes, edges, max_nodes, cluster_threshold
+        )
 
         return JSONResponse(content={"success": True, "data": {
             **clustered_data,

src/chat/message_manager/distribution_manager.py

@@ -318,6 +318,15 @@ class StreamLoopManager:
                     has_messages = force_dispatch or await self._has_messages_to_process(context)
 
                     if has_messages:
+                        # 🔒 并发保护：如果 Chatter 正在处理中，跳过本轮
+                        # 这可能发生在：1) 打断后重启循环 2) 处理时间超过轮询间隔
+                        if context.is_chatter_processing:
+                            logger.debug(f"🔒 [流工作器] stream={stream_id[:8]}, Chatter正在处理中，跳过本轮")
+                            # 不打印"开始处理"日志，直接进入下一轮等待
+                            # 使用较短的等待时间，等待当前处理完成
+                            await asyncio.sleep(1.0)
+                            continue
+                        
                         if force_dispatch:
                             logger.info(f"⚡ [流工作器] stream={stream_id[:8]}, 任务ID={task_id}, 未读消息 {unread_count} 条，触发强制分发")
                         else:
@@ -477,10 +486,11 @@ class StreamLoopManager:
             logger.warning(f"Chatter管理器未设置: {stream_id}")
             return False
 
-        # 🔒 防止并发处理：如果已经在处理中，直接返回
+        # 🔒 二次并发保护（防御性检查）
+        # 正常情况下不应该触发，如果触发说明有竞态条件
         if context.is_chatter_processing:
-            logger.debug(f"🔒 [并发保护] stream={stream_id[:8]}, Chatter 正在处理中，跳过本次处理请求")
-            return True  # 返回 True，这是正常的保护机制，不是失败
+            logger.warning(f"🔒 [并发保护] stream={stream_id[:8]}, Chatter正在处理中（二次检查触发，可能存在竞态）")
+            return False
 
         # 设置处理状态为正在处理
         self._set_stream_processing_status(stream_id, True)
@@ -720,8 +730,8 @@ class StreamLoopManager:
             chat_manager = get_chat_manager()
             chat_stream = await chat_manager.get_stream(stream_id)
             if chat_stream and not chat_stream.group_info:
-                # 私聊：有消息时几乎立即响应，空转时稍微等待
-                min_interval = 0.1 if has_messages else 3.0
+                # 私聊：有消息时快速响应，空转时稍微等待
+                min_interval = 0.5 if has_messages else 5.0
                 logger.debug(f"流 {stream_id} 私聊模式，使用最小间隔: {min_interval:.2f}s")
                 return min_interval
         except Exception as e:

src/chat/message_manager/message_manager.py

@@ -370,12 +370,18 @@ class MessageManager:
 
             logger.info(f"🚀 打断后重新创建流循环任务: {stream_id}")
 
-            # 等待一小段时间确保当前消息已经添加到未读消息中
-            await asyncio.sleep(0.1)
-
             # 获取当前的stream context
             context = chat_stream.context
 
+            # 🔒 重要：确保 is_chatter_processing 被重置
+            # 被取消的任务的 finally 块可能还没执行完，这里强制重置
+            if context.is_chatter_processing:
+                logger.debug(f"打断后强制重置 is_chatter_processing: {stream_id}")
+                context.is_chatter_processing = False
+
+            # 等待一小段时间确保当前消息已经添加到未读消息中
+            await asyncio.sleep(0.1)
+
             # 确保有未读消息需要处理
             unread_messages = context.get_unread_messages()
             if not unread_messages: