diff --git a/app/agent/__init__.py b/app/agent/__init__.py
index 5cd76360..bcaf6d73 100644
--- a/app/agent/__init__.py
+++ b/app/agent/__init__.py
@@ -18,6 +18,7 @@ from langgraph.checkpoint.memory import InMemorySaver
 
 from app.agent.callback import StreamingHandler
 from app.agent.memory import memory_manager
+from app.agent.middleware.activity_log import ActivityLogMiddleware
 from app.agent.middleware.jobs import JobsMiddleware
 from app.agent.middleware.memory import MemoryMiddleware
 from app.agent.middleware.patch_tool_calls import PatchToolCallsMiddleware
@@ -115,6 +116,10 @@ class MoviePilotAgent:
                 MemoryMiddleware(
                     sources=[str(settings.CONFIG_PATH / "agent" / "MEMORY.md")]
                 ),
+                # 活动日志
+                ActivityLogMiddleware(
+                    activity_dir=str(settings.CONFIG_PATH / "agent" / "activity"),
+                ),
                 # 上下文压缩
                 SummarizationMiddleware(model=llm, trigger=("fraction", 0.85)),
                 # 错误工具调用修复
diff --git a/app/agent/middleware/activity_log.py b/app/agent/middleware/activity_log.py
new file mode 100644
index 00000000..ea3e3d6d
--- /dev/null
+++ b/app/agent/middleware/activity_log.py
@@ -0,0 +1,350 @@
+"""
+活动日志中间件 - 自动记录 Agent 每次交互的操作摘要。
+
+按日期存储在 CONFIG_PATH/agent/activity/YYYY-MM-DD.md 中，
+每次 Agent 执行完毕后自动追加一条活动记录，
+并在每次 Agent 启动时加载近几天的活动日志注入系统提示词。
+"""
+
+import re
+from collections.abc import Awaitable, Callable
+from datetime import datetime, timedelta
+from typing import Annotated, Any, NotRequired, TypedDict
+
+from anyio import Path as AsyncPath
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    AgentState,
+    ContextT,
+    ModelRequest,
+    ModelResponse,
+    PrivateStateAttr,  # noqa
+    ResponseT,
+)
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.runtime import Runtime
+
+from app.agent.middleware.utils import append_to_system_message
+from app.log import logger
+
+# 活动日志保留天数
+DEFAULT_RETENTION_DAYS = 7
+
+# 单次活动记录的最大长度
+MAX_ENTRY_LENGTH = 300
+
+# 注入系统提示词时加载的天数
+PROMPT_LOAD_DAYS = 3
+
+# 每日日志文件最大大小 (256KB)
+MAX_LOG_FILE_SIZE = 256 * 1024
+
+
+class ActivityLogState(AgentState):
+    """ActivityLogMiddleware 的状态模型。"""
+
+    activity_log_contents: NotRequired[Annotated[dict[str, str], PrivateStateAttr]]
+    """将日期字符串映射到日志内容的字典。标记为私有，不包含在最终代理状态中。"""
+
+
+class ActivityLogStateUpdate(TypedDict):
+    """ActivityLogMiddleware 的状态更新。"""
+
+    activity_log_contents: dict[str, str]
+
+
+def _extract_activity_summary(messages: list) -> str | None:
+    """从本次对话的消息列表中提取活动摘要。
+
+    只关注最后一轮交互（从最后一条用户消息到结尾），
+    分析用户问题、Agent 使用的工具、最终回复，生成一行简洁的活动描述。
+
+    参数：
+        messages: Agent 执行后的完整消息列表。
+
+    返回：
+        活动摘要字符串，如果没有有意义的活动则返回 None。
+    """
+    if not messages:
+        return None
+
+    # 找到最后一条用户消息的索引，从此处开始截取本轮交互
+    last_human_idx = None
+    for i in range(len(messages) - 1, -1, -1):
+        if isinstance(messages[i], HumanMessage) and messages[i].content:
+            last_human_idx = i
+            break
+
+    if last_human_idx is None:
+        return None
+
+    # 本轮交互的消息
+    round_messages = messages[last_human_idx:]
+
+    # 提取用户问题
+    user_msg = round_messages[0]
+    user_content = (
+        user_msg.content if isinstance(user_msg.content, str) else str(user_msg.content)
+    )
+
+    # 跳过系统心跳消息
+    if user_content.strip().startswith("[System Heartbeat]"):
+        return None
+
+    user_query = user_content.strip()[:100]
+
+    # 收集本轮交互中使用的工具（仅限本轮）
+    tool_names = set()
+    for msg in round_messages:
+        if isinstance(msg, AIMessage) and hasattr(msg, "tool_calls") and msg.tool_calls:
+            for tc in msg.tool_calls:
+                if isinstance(tc, dict) and "name" in tc:
+                    tool_names.add(tc["name"])
+
+    # 提取本轮最后一条 AI 回复的摘要
+    ai_reply = None
+    for msg in reversed(round_messages):
+        if (
+            isinstance(msg, AIMessage)
+            and msg.content
+            and not (hasattr(msg, "tool_calls") and msg.tool_calls)
+        ):
+            content = msg.content if isinstance(msg.content, str) else str(msg.content)
+            ai_reply = content.strip()[:120]
+            break
+
+    # 组装摘要
+    parts = [f"用户: {user_query}"]
+    if tool_names:
+        parts.append(f"工具: {', '.join(sorted(tool_names))}")
+    if ai_reply:
+        parts.append(f"结果: {ai_reply}")
+
+    summary = " | ".join(parts)
+    if len(summary) > MAX_ENTRY_LENGTH:
+        summary = summary[: MAX_ENTRY_LENGTH - 3] + "..."
+    return summary
+
+
+ACTIVITY_LOG_SYSTEM_PROMPT = """<activity_log>
+{activity_log}
+</activity_log>
+
+<activity_log_guidelines>
+    The above <activity_log> contains a record of your recent interactions with the user, automatically maintained by the system.
+    
+    **How to use this information:**
+    - Reference past activities when relevant to provide continuity (e.g., "之前帮你订阅了《XXX》，现在有更新了")
+    - Use activity history to understand ongoing tasks and user patterns
+    - When the user asks "你之前帮我做了什么" or similar questions, refer to this log
+    - Activity logs are automatically recorded after each interaction - you do NOT need to manually update them
+    
+    **What is automatically logged:**
+    - Each user interaction: what was asked, which tools were used, and the outcome
+    - Timestamps for all activities
+    - The log is organized by date for easy reference
+    
+    **Important:**
+    - Activity logs are READ-ONLY from your perspective - the system manages them automatically
+    - Do not attempt to edit or write to activity log files
+    - For long-term preferences and knowledge, continue to use MEMORY.md
+    - Activity logs are retained for {retention_days} days and then automatically cleaned up
+</activity_log_guidelines>
+"""
+
+
+class ActivityLogMiddleware(AgentMiddleware[ActivityLogState, ContextT, ResponseT]):  # noqa
+    """自动记录和加载 Agent 活动日志的中间件。
+
+    - abefore_agent: 加载近几天的活动日志
+    - awrap_model_call: 将活动日志注入系统提示词
+    - aafter_agent: 从本次对话中提取摘要并追加到当日日志文件
+
+    参数：
+        activity_dir: 活动日志存储目录路径。
+        retention_days: 日志保留天数（默认 7 天）。
+        prompt_load_days: 注入系统提示词时加载的天数（默认 3 天）。
+    """
+
+    state_schema = ActivityLogState
+
+    def __init__(
+        self,
+        *,
+        activity_dir: str,
+        retention_days: int = DEFAULT_RETENTION_DAYS,
+        prompt_load_days: int = PROMPT_LOAD_DAYS,
+    ) -> None:
+        self.activity_dir = activity_dir
+        self.retention_days = retention_days
+        self.prompt_load_days = prompt_load_days
+
+    def _get_log_path(self, date_str: str) -> AsyncPath:
+        """获取指定日期的日志文件路径。"""
+        return AsyncPath(self.activity_dir) / f"{date_str}.md"
+
+    def _format_activity_log(self, contents: dict[str, str]) -> str:
+        """格式化活动日志用于系统提示词注入。"""
+        if not contents:
+            return ACTIVITY_LOG_SYSTEM_PROMPT.format(
+                activity_log="(暂无活动记录)",
+                retention_days=self.retention_days,
+            )
+
+        # 按日期排序（最近的在前）
+        sorted_dates = sorted(contents.keys(), reverse=True)
+        sections = []
+        for date_str in sorted_dates:
+            content = contents[date_str].strip()
+            if content:
+                sections.append(f"### {date_str}\n{content}")
+
+        if not sections:
+            return ACTIVITY_LOG_SYSTEM_PROMPT.format(
+                activity_log="(暂无活动记录)",
+                retention_days=self.retention_days,
+            )
+
+        log_body = "\n\n".join(sections)
+        return ACTIVITY_LOG_SYSTEM_PROMPT.format(
+            activity_log=log_body,
+            retention_days=self.retention_days,
+        )
+
+    async def _load_recent_logs(self) -> dict[str, str]:
+        """加载近几天的活动日志。"""
+        contents: dict[str, str] = {}
+        today = datetime.now().date()
+
+        for i in range(self.prompt_load_days):
+            date = today - timedelta(days=i)
+            date_str = date.strftime("%Y-%m-%d")
+            log_path = self._get_log_path(date_str)
+
+            if await log_path.exists():
+                try:
+                    content = await log_path.read_text(encoding="utf-8")
+                    contents[date_str] = content
+                    logger.debug("Loaded activity log for %s", date_str)
+                except Exception as e:
+                    logger.warning("Failed to load activity log %s: %s", date_str, e)
+
+        return contents
+
+    async def _append_activity(self, summary: str) -> None:
+        """将一条活动记录追加到当日日志文件。"""
+        today_str = datetime.now().strftime("%Y-%m-%d")
+        now_str = datetime.now().strftime("%H:%M")
+        log_path = self._get_log_path(today_str)
+
+        # 确保目录存在
+        dir_path = AsyncPath(self.activity_dir)
+        if not await dir_path.exists():
+            await dir_path.mkdir(parents=True, exist_ok=True)
+
+        # 检查文件大小
+        if await log_path.exists():
+            stat = await log_path.stat()
+            if stat.st_size >= MAX_LOG_FILE_SIZE:
+                logger.warning(
+                    "Activity log %s exceeds size limit (%d bytes), skipping append",
+                    today_str,
+                    stat.st_size,
+                )
+                return
+
+        # 追加记录
+        entry = f"- **{now_str}** {summary}\n"
+        try:
+            if await log_path.exists():
+                existing = await log_path.read_text(encoding="utf-8")
+                await log_path.write_text(existing + entry, encoding="utf-8")
+            else:
+                header = f"# {today_str} 活动日志\n\n"
+                await log_path.write_text(header + entry, encoding="utf-8")
+            logger.debug("Activity logged: %s", summary[:80])
+        except Exception as e:
+            logger.warning("Failed to append activity log: %s", e)
+
+    async def _cleanup_old_logs(self) -> None:
+        """清理超过保留天数的旧日志文件。"""
+        dir_path = AsyncPath(self.activity_dir)
+        if not await dir_path.exists():
+            return
+
+        cutoff_date = datetime.now().date() - timedelta(days=self.retention_days)
+        date_pattern = re.compile(r"^(\d{4}-\d{2}-\d{2})\.md$")
+
+        try:
+            async for path in dir_path.iterdir():
+                if not await path.is_file():
+                    continue
+                match = date_pattern.match(path.name)
+                if not match:
+                    continue
+                try:
+                    file_date = datetime.strptime(match.group(1), "%Y-%m-%d").date()
+                    if file_date < cutoff_date:
+                        await path.unlink()
+                        logger.debug("Cleaned up old activity log: %s", path.name)
+                except ValueError:
+                    continue
+        except Exception as e:
+            logger.warning("Failed to cleanup old activity logs: %s", e)
+
+    async def abefore_agent(
+        self, state: ActivityLogState, runtime: Runtime
+    ) -> ActivityLogStateUpdate | None:
+        """在 Agent 执行前加载近期活动日志。"""
+        # 如果已经加载则跳过
+        if "activity_log_contents" in state:
+            return None
+
+        contents = await self._load_recent_logs()
+
+        # 趁机清理旧日志（低频操作，不影响性能）
+        await self._cleanup_old_logs()
+
+        return ActivityLogStateUpdate(activity_log_contents=contents)
+
+    def modify_request(self, request: ModelRequest[ContextT]) -> ModelRequest[ContextT]:
+        """将活动日志注入系统消息。"""
+        contents = request.state.get("activity_log_contents", {})
+        activity_log_prompt = self._format_activity_log(contents)
+
+        new_system_message = append_to_system_message(
+            request.system_message, activity_log_prompt
+        )
+        return request.override(system_message=new_system_message)
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest[ContextT],
+        handler: Callable[
+            [ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
+        ],
+    ) -> ModelResponse[ResponseT]:
+        """异步包装模型调用，注入活动日志到系统提示词。"""
+        modified_request = self.modify_request(request)
+        return await handler(modified_request)
+
+    async def aafter_agent(
+        self, state: ActivityLogState, runtime: Runtime
+    ) -> dict[str, Any] | None:
+        """Agent 执行完毕后，从对话中提取摘要并追加到当日活动日志。"""
+        try:
+            messages = state.get("messages", [])
+            if not messages:
+                return None
+
+            # 提取活动摘要
+            summary = _extract_activity_summary(messages)
+            if summary:
+                await self._append_activity(summary)
+        except Exception as e:
+            logger.warning("Failed to record activity: %s", e)
+
+        return None
+
+
+__all__ = ["ActivityLogMiddleware"]
diff --git a/app/agent/middleware/memory.py b/app/agent/middleware/memory.py
index cd828ccf..9b7ee55b 100644
--- a/app/agent/middleware/memory.py
+++ b/app/agent/middleware/memory.py
@@ -72,6 +72,7 @@ MEMORY_SYSTEM_PROMPT = """<agent_memory>
     - When the information is stale or irrelevant in future conversations
     - Never store API keys, access tokens, passwords, or any other credentials in any file, memory, or system prompt.
     - If the user asks where to put API keys or provides an API key, do NOT echo or save it.
+    - Do NOT record daily activities or task execution history in MEMORY.md - these are automatically tracked in the activity log system (see <activity_log>). MEMORY.md is only for long-term knowledge, preferences, and patterns.
 
     **Examples:**
     Example 1 (remembering user information):
@@ -111,9 +112,9 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
     state_schema = MemoryState
 
     def __init__(
-            self,
-            *,
-            sources: list[str],
+        self,
+        *,
+        sources: list[str],
     ) -> None:
         """初始化记忆中间件。
 
@@ -138,9 +139,12 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
         """
         if not contents:
             return MEMORY_SYSTEM_PROMPT.format(
-                agent_memory=f"(No memory loaded), but you can add some by calling the `write_file` tool to the file: {self.sources[0]}.")
+                agent_memory=f"(No memory loaded), but you can add some by calling the `write_file` tool to the file: {self.sources[0]}."
+            )
 
-        sections = [f"{path}\n{contents[path]}" for path in self.sources if contents.get(path)]
+        sections = [
+            f"{path}\n{contents[path]}" for path in self.sources if contents.get(path)
+        ]
 
         if not sections:
             return MEMORY_SYSTEM_PROMPT.format(agent_memory="(No memory loaded)")
@@ -148,8 +152,12 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
         memory_body = "\n\n".join(sections)
         return MEMORY_SYSTEM_PROMPT.format(agent_memory=memory_body)
 
-    async def abefore_agent(self, state: MemoryState, runtime: Runtime,  # noqa
-                            config: RunnableConfig) -> MemoryStateUpdate | None:
+    async def abefore_agent(
+        self,
+        state: MemoryState,
+        runtime: Runtime,  # noqa
+        config: RunnableConfig,
+    ) -> MemoryStateUpdate | None:
         """在代理执行前加载记忆内容。
 
         从所有配置的源加载记忆并存储在状态中。
@@ -188,14 +196,18 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
         contents = request.state.get("memory_contents", {})  # noqa
         agent_memory = self._format_agent_memory(contents)
 
-        new_system_message = append_to_system_message(request.system_message, agent_memory)
+        new_system_message = append_to_system_message(
+            request.system_message, agent_memory
+        )
 
         return request.override(system_message=new_system_message)
 
     async def awrap_model_call(
-            self,
-            request: ModelRequest[ContextT],
-            handler: Callable[[ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]],
+        self,
+        request: ModelRequest[ContextT],
+        handler: Callable[
+            [ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
+        ],
     ) -> ModelResponse[ResponseT]:
         """异步包装模型调用，将记忆注入系统提示词。