diff --git a/packages/powermem-langchain/src/powermem_langchain/middleware.py b/packages/powermem-langchain/src/powermem_langchain/middleware.py
index 59c43c99f5..aa3ee64b6c 100644
--- a/packages/powermem-langchain/src/powermem_langchain/middleware.py
+++ b/packages/powermem-langchain/src/powermem_langchain/middleware.py
@@ -1,31 +1,117 @@
-"""LangChain middleware entry point for PowerMem.
-
-The VLDB 2026 summer school branch intentionally provides only the public entry
-point. Students are expected to replace this placeholder with a LangChain
-middleware implementation that satisfies the package contract tests.
-"""
+"""为LangChain agent提供PowerMem长期记忆能力的中间件"""
 
 from __future__ import annotations
 
+import asyncio
+import inspect
+import logging
+from collections.abc import Awaitable, Callable, Mapping, Sequence
 from typing import Any, NotRequired, TypedDict
 
-from langchain.agents.middleware import AgentMiddleware, AgentState
+from langchain.agents.middleware import (
+    AgentMiddleware,
+    AgentState,
+    ModelRequest,
+    ModelResponse,
+)
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
+
+logger = logging.getLogger(__name__)
+
+_CONTEXT_HEADER = "Relevant long-term memory for this user:"
+_CONTEXT_GUIDANCE = "Use this memory only when relevant to the current request."
 
 
 class PowerMemState(AgentState):
-    """State schema reserved for the PowerMem middleware implementation."""
+    """扩展agent状态，用于保存从PowerMem检索出的上下文"""
 
     powermem_context: NotRequired[str]
 
 
 class PowerMemStateUpdate(TypedDict):
-    """State update returned by memory-loading middleware hooks."""
+    """记忆加载hook返回的状态更新。"""
 
     powermem_context: str
 
 
+def _content_text(content: Any) -> str:
+    """从LangChain消息内容中提取文本部分"""
+    if isinstance(content, str):
+        return content.strip()
+
+    if isinstance(content, Sequence) and not isinstance(content, (str, bytes)):
+        parts: list[str] = []
+        for block in content:
+            if isinstance(block, str): # 普通字符串
+                parts.append(block)
+            elif isinstance(block, Mapping): # 列表
+                text = block.get("text")
+                if isinstance(text, str):
+                    parts.append(text)
+        return "\n".join(part.strip() for part in parts if part.strip())
+
+    return ""
+
+
+def _latest_message_text(
+    messages: Sequence[Any],
+    message_type: type[BaseMessage],
+    role: str,
+) -> str:
+    """从标准消息对象或字典消息中查找最新的指定角色消息"""
+    for message in reversed(messages):
+        if isinstance(message, message_type):
+            return _content_text(message.content)
+        if isinstance(message, Mapping) and message.get("role") == role:
+            return _content_text(message.get("content"))
+    return ""
+
+
+def _format_search_result(result: Any) -> str:
+    """把PowerMem检索结果格式化为模型可见的上下文"""
+    if isinstance(result, Mapping):
+        items = result.get("results", [])
+    elif isinstance(result, Sequence) and not isinstance(result, (str, bytes)):
+        items = result
+    else:
+        return ""
+
+    memories: list[str] = []
+    seen_memories: set[str] = set()
+    for item in items:
+        if isinstance(item, Mapping):
+            value = item.get("memory") or item.get("content")
+            if isinstance(value, str) and value.strip():
+                normalized = value.strip()
+                # set仅用于判断是否重复，list负责保留检索结果的原始顺序
+                if normalized not in seen_memories:
+                    seen_memories.add(normalized)
+                    memories.append(normalized)
+
+    if not memories:
+        return ""
+
+    bullets = "\n".join(f"- {memory}" for memory in memories)
+    return f"{_CONTEXT_HEADER}\n{bullets}\n\n{_CONTEXT_GUIDANCE}"
+
+
+async def _call_async(method: Callable[..., Any], /, **kwargs: Any) -> Any:
+    """在异步路径中安全调用PowerMem的异步或同步方法"""
+    if inspect.iscoroutinefunction(method):
+        return await method(**kwargs)
+
+    result = await asyncio.to_thread(method, **kwargs)
+    if inspect.isawaitable(result):
+        return await result
+    return result
+
+
 class PowerMemMiddleware(AgentMiddleware[PowerMemState, Any, Any]):
-    """Placeholder for the summer school implementation."""
+    """为agent增加PowerMem记忆检索和交互写回能力
+
+    记忆检索采用fail-open策略：记忆服务不可用时不阻止agent回答
+    写回发生在agent成功运行之后，因此同样采用fail-open策略
+    """
 
     state_schema = PowerMemState
 
@@ -33,22 +119,146 @@ def __init__(
         self,
         *,
         memory: Any,
-        user_id: str | None = None,
+        user_id: str,
         search_limit: int = 5,
         save_interactions: bool = True,
-        **kwargs: Any,
     ) -> None:
-        pass
+        if not user_id or not user_id.strip():
+            raise ValueError("user_id must be a non-empty string")
+        if search_limit < 1:
+            raise ValueError("search_limit must be at least 1")
 
-    def before_agent(self, state: PowerMemState, runtime) -> PowerMemStateUpdate | None:
-        pass
+        self.memory = memory
+        self.user_id = user_id
+        self.search_limit = search_limit
+        self.save_interactions = save_interactions
+
+    def before_agent(
+        self,
+        state: PowerMemState,
+        runtime: Any,
+    ) -> PowerMemStateUpdate:
+        """在同步agent开始运行前检索一次相关记忆"""
+        query = _latest_message_text(state["messages"], HumanMessage, "user")
+        if not query:
+            return {"powermem_context": ""}
+
+        try:
+            result = self.memory.search(
+                query,
+                user_id=self.user_id,
+                limit=self.search_limit,
+            )
+            if inspect.isawaitable(result):
+                result = asyncio.run(result)
+            return {"powermem_context": _format_search_result(result)}
+        except Exception:
+            logger.warning("PowerMem search failed; continuing without memory", exc_info=True)
+            return {"powermem_context": ""}
 
     async def abefore_agent(
         self,
         state: PowerMemState,
-        runtime,
-    ) -> PowerMemStateUpdate | None:
-        pass
+        runtime: Any,
+    ) -> PowerMemStateUpdate:
+        """在异步agent开始运行前检索一次相关记忆"""
+        query = _latest_message_text(state["messages"], HumanMessage, "user")
+        if not query:
+            return {"powermem_context": ""}
+
+        try:
+            result = await _call_async(
+                self.memory.search,
+                query=query,
+                user_id=self.user_id,
+                limit=self.search_limit,
+            )
+            return {"powermem_context": _format_search_result(result)}
+        except Exception:
+            logger.warning("PowerMem search failed; continuing without memory", exc_info=True)
+            return {"powermem_context": ""}
+
+    @staticmethod
+    def _request_with_memory(request: ModelRequest) -> ModelRequest:
+        """把检索到的记忆追加到请求原有的系统消息中"""
+        context = request.state.get("powermem_context", "")
+        if not context:
+            return request
+
+        # 调用方可能没有为agent配置系统消息，此时从空内容开始构造
+        # 如果已有系统消息，则保留原内容，只在末尾追加PowerMem上下文
+        if request.system_message is None:
+            content_blocks = []
+        else:
+            content_blocks = list(request.system_message.content_blocks)
+        content_blocks.append({"type": "text", "text": context})
+        return request.override(system_message=SystemMessage(content=content_blocks))
+
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse:
+        """为agent运行期间的每次同步模型调用注入记忆"""
+        return handler(self._request_with_memory(request))
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse:
+        """为agent运行期间的每次异步模型调用注入记忆"""
+        return await handler(self._request_with_memory(request))
+
+    def _interaction(self, state: PowerMemState) -> list[dict[str, str]] | None:
+        """提取最新用户请求和最终助手回复"""
+        messages = state["messages"]
+        user_text = _latest_message_text(messages, HumanMessage, "user")
+        assistant_text = _latest_message_text(messages, AIMessage, "assistant")
+        if not user_text or not assistant_text:
+            return None
+        return [
+            {"role": "user", "content": user_text},
+            {"role": "assistant", "content": assistant_text},
+        ]
+
+    def after_agent(self, state: PowerMemState, runtime: Any) -> None:
+        """启用写回时，保存已完成的同步交互"""
+        if not self.save_interactions:
+            return None
+
+        interaction = self._interaction(state)
+        if interaction is None:
+            return None
+
+        try:
+            result = self.memory.add(
+                interaction,
+                user_id=self.user_id,
+                infer=False,
+            )
+            if inspect.isawaitable(result):
+                asyncio.run(result)
+        except Exception:
+            logger.warning("PowerMem write-back failed; agent result is unchanged", exc_info=True)
+        return None
+
+    async def aafter_agent(self, state: PowerMemState, runtime: Any) -> None:
+        """启用写回时，保存已完成的异步交互"""
+        if not self.save_interactions:
+            return None
+
+        interaction = self._interaction(state)
+        if interaction is None:
+            return None
 
-    def after_agent(self, state: PowerMemState, runtime) -> None:
-        pass
+        try:
+            await _call_async(
+                self.memory.add,
+                messages=interaction,
+                user_id=self.user_id,
+                infer=False,
+            )
+        except Exception:
+            logger.warning("PowerMem write-back failed; agent result is unchanged", exc_info=True)
+        return None