diff --git a/src/memory_graph/__init__.py b/src/memory_graph/__init__.py
index 08a8012ec..f2070de74 100644
--- a/src/memory_graph/__init__.py
+++ b/src/memory_graph/__init__.py
@@ -4,6 +4,7 @@
 基于知识图谱 + 语义向量的混合记忆架构
 """
 
+from src.memory_graph.manager import MemoryManager
 from src.memory_graph.models import (
     Memory,
     MemoryEdge,
@@ -15,6 +16,7 @@ from src.memory_graph.models import (
 )
 
 __all__ = [
+    "MemoryManager",
     "Memory",
     "MemoryNode",
     "MemoryEdge",
diff --git a/src/memory_graph/manager.py b/src/memory_graph/manager.py
new file mode 100644
index 000000000..79f1c2457
--- /dev/null
+++ b/src/memory_graph/manager.py
@@ -0,0 +1,692 @@
+"""
+记忆管理器 - Phase 3
+
+统一的记忆系统管理接口，整合所有组件：
+- 记忆创建、检索、更新、删除
+- 记忆生命周期管理（激活、遗忘）
+- 记忆整合与维护
+- 多策略检索优化
+"""
+
+import asyncio
+import logging
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from src.memory_graph.config import MemoryGraphConfig
+from src.memory_graph.core.builder import MemoryBuilder
+from src.memory_graph.core.extractor import MemoryExtractor
+from src.memory_graph.models import Memory, MemoryNode, MemoryType, NodeType
+from src.memory_graph.storage.graph_store import GraphStore
+from src.memory_graph.storage.persistence import PersistenceManager
+from src.memory_graph.storage.vector_store import VectorStore
+from src.memory_graph.tools.memory_tools import MemoryTools
+from src.memory_graph.utils.embeddings import EmbeddingGenerator
+
+logger = logging.getLogger(__name__)
+
+
+class MemoryManager:
+    """
+    记忆管理器
+    
+    核心管理类，提供记忆系统的统一接口：
+    - 记忆 CRUD 操作
+    - 记忆生命周期管理
+    - 智能检索与推荐
+    - 记忆维护与优化
+    """
+
+    def __init__(
+        self,
+        config: Optional[MemoryGraphConfig] = None,
+        data_dir: Optional[Path] = None,
+    ):
+        """
+        初始化记忆管理器
+        
+        Args:
+            config: 记忆图配置
+            data_dir: 数据目录
+        """
+        self.config = config or MemoryGraphConfig()
+        self.data_dir = data_dir or Path("data/memory_graph")
+        
+        # 存储组件
+        self.vector_store: Optional[VectorStore] = None
+        self.graph_store: Optional[GraphStore] = None
+        self.persistence: Optional[PersistenceManager] = None
+        
+        # 核心组件
+        self.embedding_generator: Optional[EmbeddingGenerator] = None
+        self.extractor: Optional[MemoryExtractor] = None
+        self.builder: Optional[MemoryBuilder] = None
+        self.tools: Optional[MemoryTools] = None
+        
+        # 状态
+        self._initialized = False
+        self._last_maintenance = datetime.now()
+        
+        logger.info(f"记忆管理器已创建 (data_dir={data_dir})")
+
+    async def initialize(self) -> None:
+        """
+        初始化所有组件
+        
+        按照依赖顺序初始化：
+        1. 存储层（向量存储、图存储、持久化）
+        2. 工具层（嵌入生成器、提取器）
+        3. 管理层（构建器、工具接口）
+        """
+        if self._initialized:
+            logger.warning("记忆管理器已经初始化")
+            return
+
+        try:
+            logger.info("开始初始化记忆管理器...")
+            
+            # 1. 初始化存储层
+            self.data_dir.mkdir(parents=True, exist_ok=True)
+            
+            self.vector_store = VectorStore(
+                collection_name=self.config.storage.vector_collection_name,
+                data_dir=self.data_dir,
+            )
+            await self.vector_store.initialize()
+            
+            self.persistence = PersistenceManager(data_dir=self.data_dir)
+            
+            # 尝试加载现有图数据
+            self.graph_store = await self.persistence.load_graph_store()
+            if not self.graph_store:
+                logger.info("未找到现有图数据，创建新的图存储")
+                self.graph_store = GraphStore()
+            else:
+                stats = self.graph_store.get_statistics()
+                logger.info(
+                    f"加载图数据: {stats['total_memories']} 条记忆, "
+                    f"{stats['total_nodes']} 个节点, {stats['total_edges']} 条边"
+                )
+            
+            # 2. 初始化工具层
+            self.embedding_generator = EmbeddingGenerator()
+            # EmbeddingGenerator 使用延迟初始化，在第一次调用时自动初始化
+            
+            self.extractor = MemoryExtractor()
+            
+            # 3. 初始化管理层
+            self.builder = MemoryBuilder(
+                vector_store=self.vector_store,
+                graph_store=self.graph_store,
+                embedding_generator=self.embedding_generator,
+            )
+            
+            self.tools = MemoryTools(
+                vector_store=self.vector_store,
+                graph_store=self.graph_store,
+                persistence_manager=self.persistence,
+                embedding_generator=self.embedding_generator,
+            )
+            
+            self._initialized = True
+            logger.info("✅ 记忆管理器初始化完成")
+            
+        except Exception as e:
+            logger.error(f"记忆管理器初始化失败: {e}", exc_info=True)
+            raise
+
+    async def shutdown(self) -> None:
+        """
+        关闭记忆管理器，保存所有数据
+        """
+        if not self._initialized:
+            return
+
+        try:
+            logger.info("正在关闭记忆管理器...")
+            
+            # 保存图数据
+            if self.graph_store and self.persistence:
+                await self.persistence.save_graph_store(self.graph_store)
+                logger.info("图数据已保存")
+            
+            self._initialized = False
+            logger.info("✅ 记忆管理器已关闭")
+            
+        except Exception as e:
+            logger.error(f"关闭记忆管理器失败: {e}", exc_info=True)
+
+    # ==================== 记忆 CRUD 操作 ====================
+
+    async def create_memory(
+        self,
+        subject: str,
+        memory_type: str,
+        topic: str,
+        object: Optional[str] = None,
+        attributes: Optional[Dict[str, str]] = None,
+        importance: float = 0.5,
+        **kwargs,
+    ) -> Optional[Memory]:
+        """
+        创建新记忆
+        
+        Args:
+            subject: 主体（谁）
+            memory_type: 记忆类型（事件/观点/事实/关系）
+            topic: 主题（做什么/想什么）
+            object: 客体（对谁/对什么）
+            attributes: 属性字典（时间、地点、原因等）
+            importance: 重要性 (0.0-1.0)
+            **kwargs: 其他参数
+            
+        Returns:
+            创建的记忆对象，失败返回 None
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            result = await self.tools.create_memory(
+                subject=subject,
+                memory_type=memory_type,
+                topic=topic,
+                object=object,
+                attributes=attributes,
+                importance=importance,
+                **kwargs,
+            )
+            
+            if result["success"]:
+                memory_id = result["memory_id"]
+                memory = self.graph_store.get_memory_by_id(memory_id)
+                logger.info(f"记忆创建成功: {memory_id}")
+                return memory
+            else:
+                logger.error(f"记忆创建失败: {result.get('error', 'Unknown error')}")
+                return None
+                
+        except Exception as e:
+            logger.error(f"创建记忆时发生异常: {e}", exc_info=True)
+            return None
+
+    async def get_memory(self, memory_id: str) -> Optional[Memory]:
+        """
+        根据 ID 获取记忆
+        
+        Args:
+            memory_id: 记忆 ID
+            
+        Returns:
+            记忆对象，不存在返回 None
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        return self.graph_store.get_memory_by_id(memory_id)
+
+    async def update_memory(
+        self,
+        memory_id: str,
+        **updates,
+    ) -> bool:
+        """
+        更新记忆
+        
+        Args:
+            memory_id: 记忆 ID
+            **updates: 要更新的字段
+            
+        Returns:
+            是否更新成功
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            memory = self.graph_store.get_memory_by_id(memory_id)
+            if not memory:
+                logger.warning(f"记忆不存在: {memory_id}")
+                return False
+            
+            # 更新元数据
+            if "importance" in updates:
+                memory.importance = updates["importance"]
+            
+            if "metadata" in updates:
+                memory.metadata.update(updates["metadata"])
+            
+            memory.updated_at = datetime.now()
+            
+            # 保存更新
+            await self.persistence.save_graph_store(self.graph_store)
+            logger.info(f"记忆更新成功: {memory_id}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"更新记忆失败: {e}", exc_info=True)
+            return False
+
+    async def delete_memory(self, memory_id: str) -> bool:
+        """
+        删除记忆
+        
+        Args:
+            memory_id: 记忆 ID
+            
+        Returns:
+            是否删除成功
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            memory = self.graph_store.get_memory_by_id(memory_id)
+            if not memory:
+                logger.warning(f"记忆不存在: {memory_id}")
+                return False
+            
+            # 从向量存储删除节点
+            for node in memory.nodes:
+                if node.embedding is not None:
+                    await self.vector_store.delete_node(node.id)
+            
+            # 从图存储删除记忆
+            self.graph_store.remove_memory(memory_id)
+            
+            # 保存更新
+            await self.persistence.save_graph_store(self.graph_store)
+            logger.info(f"记忆删除成功: {memory_id}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"删除记忆失败: {e}", exc_info=True)
+            return False
+
+    # ==================== 记忆检索操作 ====================
+
+    async def search_memories(
+        self,
+        query: str,
+        top_k: int = 10,
+        memory_types: Optional[List[str]] = None,
+        time_range: Optional[Tuple[datetime, datetime]] = None,
+        min_importance: float = 0.0,
+        include_forgotten: bool = False,
+    ) -> List[Memory]:
+        """
+        搜索记忆
+        
+        Args:
+            query: 搜索查询
+            top_k: 返回结果数
+            memory_types: 记忆类型过滤
+            time_range: 时间范围过滤 (start, end)
+            min_importance: 最小重要性
+            include_forgotten: 是否包含已遗忘的记忆
+            
+        Returns:
+            记忆列表
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            params = {
+                "query": query,
+                "top_k": top_k,
+            }
+            
+            if memory_types:
+                params["memory_types"] = memory_types
+            
+            result = await self.tools.search_memories(**params)
+            
+            if not result["success"]:
+                logger.error(f"搜索失败: {result.get('error', 'Unknown error')}")
+                return []
+            
+            memories = result.get("results", [])
+            
+            # 后处理过滤
+            filtered_memories = []
+            for mem_dict in memories:
+                # 从字典重建 Memory 对象
+                memory_id = mem_dict.get("memory_id", "")
+                if not memory_id:
+                    continue
+                    
+                memory = self.graph_store.get_memory_by_id(memory_id)
+                if not memory:
+                    continue
+                
+                # 重要性过滤
+                if memory.importance < min_importance:
+                    continue
+                
+                # 遗忘状态过滤
+                if not include_forgotten and memory.metadata.get("forgotten", False):
+                    continue
+                
+                # 时间范围过滤
+                if time_range:
+                    mem_time = memory.created_at
+                    if not (time_range[0] <= mem_time <= time_range[1]):
+                        continue
+                
+                filtered_memories.append(memory)
+            
+            logger.info(f"搜索完成: 找到 {len(filtered_memories)} 条记忆")
+            return filtered_memories[:top_k]
+            
+        except Exception as e:
+            logger.error(f"搜索记忆失败: {e}", exc_info=True)
+            return []
+
+    async def link_memories(
+        self,
+        source_description: str,
+        target_description: str,
+        relation_type: str,
+        importance: float = 0.5,
+    ) -> bool:
+        """
+        关联两条记忆
+        
+        Args:
+            source_description: 源记忆描述
+            target_description: 目标记忆描述
+            relation_type: 关系类型（导致/引用/相似/相反）
+            importance: 关系重要性
+            
+        Returns:
+            是否关联成功
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            result = await self.tools.link_memories(
+                source_memory_description=source_description,
+                target_memory_description=target_description,
+                relation_type=relation_type,
+                importance=importance,
+            )
+            
+            if result["success"]:
+                logger.info(
+                    f"记忆关联成功: {result['source_memory_id']} -> "
+                    f"{result['target_memory_id']} ({relation_type})"
+                )
+                return True
+            else:
+                logger.error(f"记忆关联失败: {result.get('error', 'Unknown error')}")
+                return False
+                
+        except Exception as e:
+            logger.error(f"关联记忆失败: {e}", exc_info=True)
+            return False
+
+    # ==================== 记忆生命周期管理 ====================
+
+    async def activate_memory(self, memory_id: str, strength: float = 1.0) -> bool:
+        """
+        激活记忆
+        
+        更新记忆的激活度，并传播到相关记忆
+        
+        Args:
+            memory_id: 记忆 ID
+            strength: 激活强度 (0.0-1.0)
+            
+        Returns:
+            是否激活成功
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            memory = self.graph_store.get_memory_by_id(memory_id)
+            if not memory:
+                logger.warning(f"记忆不存在: {memory_id}")
+                return False
+            
+            # 更新激活信息
+            now = datetime.now()
+            activation_info = memory.metadata.get("activation", {})
+            
+            # 更新激活度（考虑时间衰减）
+            last_access = activation_info.get("last_access")
+            if last_access:
+                # 计算时间衰减
+                last_access_dt = datetime.fromisoformat(last_access)
+                hours_passed = (now - last_access_dt).total_seconds() / 3600
+                decay_factor = 0.9 ** (hours_passed / 24)  # 每天衰减 10%
+                current_activation = activation_info.get("level", 0.0) * decay_factor
+            else:
+                current_activation = 0.0
+            
+            # 新的激活度 = 当前激活度 + 激活强度
+            new_activation = min(1.0, current_activation + strength)
+            
+            activation_info.update({
+                "level": new_activation,
+                "last_access": now.isoformat(),
+                "access_count": activation_info.get("access_count", 0) + 1,
+            })
+            
+            memory.metadata["activation"] = activation_info
+            memory.last_accessed = now
+            
+            # 激活传播：激活相关记忆（强度减半）
+            if strength > 0.1:  # 只有足够强的激活才传播
+                related_memories = self._get_related_memories(memory_id)
+                propagation_strength = strength * 0.5
+                
+                for related_id in related_memories[:5]:  # 最多传播到 5 个相关记忆
+                    await self.activate_memory(related_id, propagation_strength)
+            
+            # 保存更新
+            await self.persistence.save_graph_store(self.graph_store)
+            logger.debug(f"记忆已激活: {memory_id} (level={new_activation:.3f})")
+            return True
+            
+        except Exception as e:
+            logger.error(f"激活记忆失败: {e}", exc_info=True)
+            return False
+
+    def _get_related_memories(self, memory_id: str, max_depth: int = 1) -> List[str]:
+        """
+        获取相关记忆 ID 列表
+        
+        Args:
+            memory_id: 记忆 ID
+            max_depth: 最大遍历深度
+            
+        Returns:
+            相关记忆 ID 列表
+        """
+        memory = self.graph_store.get_memory_by_id(memory_id)
+        if not memory:
+            return []
+        
+        related_ids = set()
+        
+        # 遍历记忆的节点
+        for node in memory.nodes:
+            # 获取节点的邻居
+            neighbors = list(self.graph_store.graph.neighbors(node.id))
+            
+            for neighbor_id in neighbors:
+                # 获取邻居节点所属的记忆
+                neighbor_node = self.graph_store.graph.nodes.get(neighbor_id)
+                if neighbor_node:
+                    neighbor_memory_ids = neighbor_node.get("memory_ids", [])
+                    for mem_id in neighbor_memory_ids:
+                        if mem_id != memory_id:
+                            related_ids.add(mem_id)
+        
+        return list(related_ids)
+
+    async def forget_memory(self, memory_id: str) -> bool:
+        """
+        遗忘记忆（标记为已遗忘，不删除）
+        
+        Args:
+            memory_id: 记忆 ID
+            
+        Returns:
+            是否遗忘成功
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            memory = self.graph_store.get_memory_by_id(memory_id)
+            if not memory:
+                logger.warning(f"记忆不存在: {memory_id}")
+                return False
+            
+            memory.metadata["forgotten"] = True
+            memory.metadata["forgotten_at"] = datetime.now().isoformat()
+            
+            # 保存更新
+            await self.persistence.save_graph_store(self.graph_store)
+            logger.info(f"记忆已遗忘: {memory_id}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"遗忘记忆失败: {e}", exc_info=True)
+            return False
+
+    async def auto_forget_memories(self, threshold: float = 0.1) -> int:
+        """
+        自动遗忘低激活度的记忆
+        
+        Args:
+            threshold: 激活度阈值
+            
+        Returns:
+            遗忘的记忆数量
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            forgotten_count = 0
+            all_memories = self.graph_store.get_all_memories()
+            
+            for memory in all_memories:
+                # 跳过已遗忘的记忆
+                if memory.metadata.get("forgotten", False):
+                    continue
+                
+                # 跳过高重要性记忆
+                if memory.importance >= 0.8:
+                    continue
+                
+                # 计算当前激活度
+                activation_info = memory.metadata.get("activation", {})
+                last_access = activation_info.get("last_access")
+                
+                if last_access:
+                    last_access_dt = datetime.fromisoformat(last_access)
+                    days_passed = (datetime.now() - last_access_dt).days
+                    
+                    # 长时间未访问的记忆，应用时间衰减
+                    decay_factor = 0.9 ** days_passed
+                    current_activation = activation_info.get("level", 0.0) * decay_factor
+                    
+                    # 低于阈值则遗忘
+                    if current_activation < threshold:
+                        await self.forget_memory(memory.id)
+                        forgotten_count += 1
+            
+            logger.info(f"自动遗忘完成: 遗忘了 {forgotten_count} 条记忆")
+            return forgotten_count
+            
+        except Exception as e:
+            logger.error(f"自动遗忘失败: {e}", exc_info=True)
+            return 0
+
+    # ==================== 统计与维护 ====================
+
+    def get_statistics(self) -> Dict[str, Any]:
+        """
+        获取记忆系统统计信息
+        
+        Returns:
+            统计信息字典
+        """
+        if not self._initialized or not self.graph_store:
+            return {}
+
+        stats = self.graph_store.get_statistics()
+        
+        # 添加激活度统计
+        all_memories = self.graph_store.get_all_memories()
+        activation_levels = []
+        forgotten_count = 0
+        
+        for memory in all_memories:
+            if memory.metadata.get("forgotten", False):
+                forgotten_count += 1
+            else:
+                activation_info = memory.metadata.get("activation", {})
+                activation_levels.append(activation_info.get("level", 0.0))
+        
+        if activation_levels:
+            stats["avg_activation"] = sum(activation_levels) / len(activation_levels)
+            stats["max_activation"] = max(activation_levels)
+        else:
+            stats["avg_activation"] = 0.0
+            stats["max_activation"] = 0.0
+        
+        stats["forgotten_memories"] = forgotten_count
+        stats["active_memories"] = stats["total_memories"] - forgotten_count
+        
+        return stats
+
+    async def maintenance(self) -> Dict[str, Any]:
+        """
+        执行维护任务
+        
+        包括：
+        - 清理过期记忆
+        - 自动遗忘低激活度记忆
+        - 保存数据
+        
+        Returns:
+            维护结果
+        """
+        if not self._initialized:
+            await self.initialize()
+
+        try:
+            logger.info("开始执行记忆系统维护...")
+            
+            result = {
+                "forgotten": 0,
+                "deleted": 0,
+                "saved": False,
+            }
+            
+            # 1. 自动遗忘
+            forgotten_count = await self.auto_forget_memories(threshold=0.1)
+            result["forgotten"] = forgotten_count
+            
+            # 2. 清理非常旧的已遗忘记忆（可选）
+            # TODO: 实现清理逻辑
+            
+            # 3. 保存数据
+            await self.persistence.save_graph_store(self.graph_store)
+            result["saved"] = True
+            
+            self._last_maintenance = datetime.now()
+            logger.info(f"维护完成: {result}")
+            return result
+            
+        except Exception as e:
+            logger.error(f"维护失败: {e}", exc_info=True)
+            return {"error": str(e)}
diff --git a/src/memory_graph/storage/graph_store.py b/src/memory_graph/storage/graph_store.py
index a0d3e3c50..a99a55282 100644
--- a/src/memory_graph/storage/graph_store.py
+++ b/src/memory_graph/storage/graph_store.py
@@ -96,6 +96,15 @@ class GraphStore:
         """
         return self.memory_index.get(memory_id)
 
+    def get_all_memories(self) -> List[Memory]:
+        """
+        获取所有记忆
+        
+        Returns:
+            所有记忆的列表
+        """
+        return list(self.memory_index.values())
+
     def get_memories_by_node(self, node_id: str) -> List[Memory]:
         """
         获取包含指定节点的所有记忆
diff --git a/src/memory_graph/tools/memory_tools.py b/src/memory_graph/tools/memory_tools.py
index 1a1910d43..f933eda9f 100644
--- a/src/memory_graph/tools/memory_tools.py
+++ b/src/memory_graph/tools/memory_tools.py
@@ -387,7 +387,16 @@ class MemoryTools:
             memory_ids = set()
             for node_id, similarity, metadata in similar_nodes:
                 if "memory_ids" in metadata:
-                    memory_ids.update(metadata["memory_ids"])
+                    ids = metadata["memory_ids"]
+                    # 确保是列表
+                    if isinstance(ids, str):
+                        import json
+                        try:
+                            ids = json.loads(ids)
+                        except:
+                            ids = [ids]
+                    if isinstance(ids, list):
+                        memory_ids.update(ids)
 
             # 4. 获取完整记忆
             memories = []
@@ -463,10 +472,25 @@ class MemoryTools:
 
         # 获取最相似节点关联的记忆
         node_id, similarity, metadata = similar_nodes[0]
-        if "memory_ids" in metadata and metadata["memory_ids"]:
-            memory_id = metadata["memory_ids"][0]
+        
+        if "memory_ids" not in metadata or not metadata["memory_ids"]:
+            return None
+        
+        ids = metadata["memory_ids"]
+        
+        # 确保是列表
+        if isinstance(ids, str):
+            import json
+            try:
+                ids = json.loads(ids)
+            except Exception as e:
+                logger.warning(f"JSON 解析失败: {e}")
+                ids = [ids]
+        
+        if isinstance(ids, list) and ids:
+            memory_id = ids[0]
             return self.graph_store.get_memory_by_id(memory_id)
-
+        
         return None
 
     def _summarize_memory(self, memory: Memory) -> str: