refactor(qbittorrent): convert static methods to instance methods for better encapsulation

Support WebUI API Key auth, newer add responses, and cookie sync so qBittorrent 5.2 can connect reliably while keeping legacy fallback behavior.

Fixes #5724

.gitignore

@@ -19,6 +19,7 @@ config/cookies/**
 config/app.env
 config/user.db*
 config/sites/**
+config/agent/
 config/logs/
 config/temp/
 config/cache/

README.md

@@ -1,3 +1,4 @@
+
 # MoviePilot
 
 简体中文 | [English](README_EN.md)

app/agent/callback/__init__.py

@@ -1,6 +1,8 @@
 import asyncio
 import threading
-from typing import Optional, Tuple
+from typing import Any, Optional, Tuple
+
+from fastapi.concurrency import run_in_threadpool
 
 from app.chain import ChainBase
 from app.log import logger
@@ -60,16 +62,40 @@ class StreamingHandler:
         self._user_id: Optional[str] = None
         self._username: Optional[str] = None
         self._title: str = ""
+        self._allow_dispatch_without_context = False
+        # 非啰嗦模式下的待输出工具统计，等下一段文本到来时再统一补一句摘要
+        self._pending_tool_stats: dict[str, dict[str, Any]] = {}
 
-    def emit(self, token: str):
+    def set_dispatch_policy(
+        self, allow_dispatch_without_context: bool = False
+    ) -> None:
+        """
+        设置在缺少渠道上下文时是否仍允许向默认通知渠道分发消息。
+        后台 DISPATCH 任务允许，CAPTURE_ONLY 必须禁止。
+        """
+        self._allow_dispatch_without_context = allow_dispatch_without_context
+
+    def emit(self, token: str) -> str:
         """
         接收 LLM 流式 token，积累到缓冲区。
+        如果存在待输出的工具统计，则会先补上一句摘要再追加 token。
         """
         with self._lock:
+            emitted = token or ""
+
+            if self._pending_tool_stats:
+                summary = self._consume_pending_tool_summary_locked()
+                if summary:
+                    if emitted:
+                        emitted = f"{summary}{emitted.lstrip(chr(10))}"
+                    else:
+                        emitted = summary
+
             # 如果存量消息结束是两个换行，则去掉新消息前面的换行，避免过多空行
-            if self._buffer.endswith("\n\n") and token.startswith("\n"):
-                token = token.lstrip("\n")
-            self._buffer += token
+            if self._buffer.endswith("\n\n") and emitted.startswith("\n"):
+                emitted = emitted.lstrip("\n")
+            self._buffer += emitted
+            return emitted
 
     async def take(self) -> str:
         """
@@ -80,6 +106,8 @@ class StreamingHandler:
 
         注意：流式渠道不调用此方法，工具消息直接 emit 到 buffer 中。
         """
+        self.flush_pending_tool_summary()
+
         with self._lock:
             if not self._buffer:
                 return ""
@@ -97,6 +125,7 @@ class StreamingHandler:
             self._sent_text = ""
             self._message_response = None
             self._msg_start_offset = 0
+            self._pending_tool_stats = {}
 
     def reset(self):
         """
@@ -110,6 +139,7 @@ class StreamingHandler:
             self._buffer = ""
             self._sent_text = ""
             self._msg_start_offset = 0
+            self._pending_tool_stats = {}
 
     async def start_streaming(
         self,
@@ -139,6 +169,7 @@ class StreamingHandler:
         self._sent_text = ""
         self._message_response = None
         self._msg_start_offset = 0
+        self._pending_tool_stats = {}
 
         # 检查渠道是否支持消息编辑，不支持则仅收集 token 到 buffer，不实时推送
         if not self._can_stream():
@@ -174,13 +205,16 @@ class StreamingHandler:
         # 取消定时任务
         await self._cancel_flush_task()
 
+        # 将未落地的工具统计补入缓冲区，避免流式结束时丢失这段执行信息
+        self.flush_pending_tool_summary()
+
         # 执行最后一次刷新
         await self._flush()
 
         # 检查是否所有缓冲内容都已发送
         with self._lock:
             # 当前消息的文本 = buffer 中从 _msg_start_offset 开始的部分
-            current_msg_text = self._buffer[self._msg_start_offset :]
+            current_msg_text = self._buffer[self._msg_start_offset:]
             all_sent = (
                 self._message_response is not None
                 and self._sent_text
@@ -192,11 +226,172 @@ class StreamingHandler:
             self._sent_text = ""
             self._message_response = None
             self._msg_start_offset = 0
+            self._pending_tool_stats = {}
             if all_sent:
                 # 所有内容已通过流式发送，清空缓冲区
                 self._buffer = ""
             return all_sent, final_text
 
+    def record_tool_call(
+        self,
+        tool_name: str,
+        tool_message: Optional[str] = None,
+        tool_kwargs: Optional[dict[str, Any]] = None,
+    ):
+        """
+        记录一次工具调用，供非啰嗦模式下延迟汇总输出。
+        """
+        category, target = self._classify_tool_call(
+            tool_name=tool_name,
+            tool_message=tool_message,
+            tool_kwargs=tool_kwargs or {},
+        )
+        with self._lock:
+            bucket = self._pending_tool_stats.setdefault(
+                category,
+                {
+                    "count": 0,
+                    "targets": set(),
+                },
+            )
+            bucket["count"] += 1
+            if target:
+                bucket["targets"].add(str(target))
+
+    def flush_pending_tool_summary(self) -> str:
+        """
+        将待输出的工具统计摘要补入缓冲区，并返回本次新增的摘要文本。
+        """
+        with self._lock:
+            summary = self._consume_pending_tool_summary_locked()
+            if summary:
+                self._buffer += summary
+            return summary
+
+    @staticmethod
+    def _classify_tool_call(
+        tool_name: str,
+        tool_message: Optional[str],
+        tool_kwargs: dict[str, Any],
+    ) -> tuple[str, Optional[str]]:
+        tool_name = (tool_name or "").strip().lower()
+        tool_message = (tool_message or "").strip()
+        tool_message_lower = tool_message.lower()
+
+        if tool_name == "read_file":
+            return "file_read", tool_kwargs.get("file_path")
+        if tool_name in {"write_file", "edit_file"}:
+            return "file_write", tool_kwargs.get("file_path")
+        if tool_name in {"list_directory", "query_directory_settings"}:
+            return "directory", tool_kwargs.get("path")
+        if tool_name == "browse_webpage":
+            return (
+                "web_browse",
+                tool_kwargs.get("url")
+                or tool_kwargs.get("target_url")
+                or tool_kwargs.get("path"),
+            )
+        if tool_name == "execute_command":
+            return "command", tool_kwargs.get("command")
+        if tool_name == "ask_user_choice":
+            return "interaction", tool_kwargs.get("message")
+        if tool_name.startswith("search_") or tool_name in {"get_search_results"}:
+            return (
+                "search",
+                tool_kwargs.get("query")
+                or tool_kwargs.get("title")
+                or tool_kwargs.get("keyword"),
+            )
+        if tool_name.startswith("query_") or tool_name.startswith("list_") or tool_name.startswith("get_"):
+            return "data_query", None
+        if tool_name.startswith(("add_", "update_", "delete_", "modify_", "run_")):
+            return "action", None
+        if tool_name in {
+            "recognize_media",
+            "scrape_metadata",
+            "transfer_file",
+            "test_site",
+            "send_message",
+            "send_local_file",
+            "send_voice_message",
+        }:
+            return "action", None
+
+        if "读取文件" in tool_message or "read file" in tool_message_lower:
+            return "file_read", tool_kwargs.get("file_path")
+        if (
+            "写入文件" in tool_message
+            or "编辑文件" in tool_message
+            or "write file" in tool_message_lower
+            or "edit file" in tool_message_lower
+        ):
+            return "file_write", tool_kwargs.get("file_path")
+        if "目录" in tool_message or "directory" in tool_message_lower:
+            return "directory", tool_kwargs.get("path")
+        if "搜索" in tool_message or "search" in tool_message_lower:
+            return (
+                "search",
+                tool_kwargs.get("query")
+                or tool_kwargs.get("title")
+                or tool_kwargs.get("keyword"),
+            )
+        if "网页" in tool_message or "browser" in tool_message_lower or "webpage" in tool_message_lower:
+            return "web_browse", tool_kwargs.get("url")
+        if "命令" in tool_message or "command" in tool_message_lower:
+            return "command", tool_kwargs.get("command")
+
+        return "tool", None
+
+    def _consume_pending_tool_summary_locked(self) -> str:
+        if not self._pending_tool_stats:
+            return ""
+
+        parts = []
+        for category, bucket in self._pending_tool_stats.items():
+            value = bucket["count"]
+            if category in {"file_read", "file_write", "directory", "web_browse"} and bucket["targets"]:
+                value = len(bucket["targets"])
+            part = self._format_tool_stat(category, value)
+            if part:
+                parts.append(part)
+
+        self._pending_tool_stats = {}
+        if not parts:
+            return ""
+
+        summary = f"（{'，'.join(parts)}）"
+        visible_buffer = self._buffer.rstrip(" \t")
+        last_char = visible_buffer[-1:] if visible_buffer.strip() else ""
+        prefix = ""
+        if self._buffer and last_char != "\n":
+            prefix = "\n\n"
+        return f"{prefix}{summary}\n\n"
+
+    @staticmethod
+    def _format_tool_stat(category: str, count: int) -> str:
+        if count <= 0:
+            return ""
+
+        if category == "search":
+            return f"执行了 {count} 次搜索"
+        if category == "file_read":
+            return f"读取了 {count} 个文件"
+        if category == "file_write":
+            return f"修改了 {count} 个文件"
+        if category == "directory":
+            return f"查看了 {count} 个目录"
+        if category == "web_browse":
+            return f"浏览了 {count} 个网页"
+        if category == "command":
+            return f"执行了 {count} 条命令"
+        if category == "data_query":
+            return f"查询了 {count} 次数据"
+        if category == "action":
+            return f"执行了 {count} 次操作"
+        if category == "interaction":
+            return f"发起了 {count} 次交互"
+        return f"调用了 {count} 次工具"
+
     def _can_stream(self) -> bool:
         """
         检查当前渠道是否支持流式输出（消息编辑）
@@ -246,17 +441,24 @@ class StreamingHandler:
         """
         with self._lock:
             # 当前消息的文本 = buffer 中从 _msg_start_offset 开始的部分
-            current_text = self._buffer[self._msg_start_offset :]
+            current_text = self._buffer[self._msg_start_offset:]
             if not current_text or current_text == self._sent_text:
                 # 没有新内容需要刷新
                 return
+            if (
+                (not self._channel or not self._source)
+                and not self._allow_dispatch_without_context
+            ):
+                logger.debug("流式输出缺少渠道上下文，当前模式禁止外发消息")
+                return
 
         chain = _StreamChain()
 
         try:
             if self._message_response is None:
                 # 第一次发送：发送新消息并获取 message_id
-                response = chain.send_direct_message(
+                response = await run_in_threadpool(
+                    chain.send_direct_message,
                     Notification(
                         channel=self._channel,
                         source=self._source,
@@ -264,7 +466,7 @@ class StreamingHandler:
                         username=self._username,
                         title=self._title,
                         text=current_text,
-                    )
+                    ),
                 )
                 if response and response.success and response.message_id:
                     self._message_response = response
@@ -291,13 +493,14 @@ class StreamingHandler:
                     )
                     with self._lock:
                         self._msg_start_offset += len(self._sent_text)
-                        current_text = self._buffer[self._msg_start_offset :]
+                        current_text = self._buffer[self._msg_start_offset:]
                     self._message_response = None
                     self._sent_text = ""
 
                     # 如果偏移后还有新内容，立即发送为新消息
                     if current_text:
-                        response = chain.send_direct_message(
+                        response = await run_in_threadpool(
+                            chain.send_direct_message,
                             Notification(
                                 channel=self._channel,
                                 source=self._source,
@@ -305,7 +508,7 @@ class StreamingHandler:
                                 username=self._username,
                                 title=self._title,
                                 text=current_text,
-                            )
+                            ),
                         )
                         if response and response.success and response.message_id:
                             self._message_response = response
@@ -324,7 +527,8 @@ class StreamingHandler:
                     except (ValueError, KeyError):
                         return
 
-                    success = chain.edit_message(
+                    success = await run_in_threadpool(
+                        chain.edit_message,
                         channel=channel_enum,
                         source=self._message_response.source,
                         message_id=self._message_response.message_id,
@@ -360,3 +564,11 @@ class StreamingHandler:
         是否已经通过流式输出发送过消息（当前轮次）
         """
         return self._message_response is not None
+
+    @property
+    def last_buffer_char(self) -> str:
+        """
+        返回当前缓冲区最后一个字符；缓冲区为空时返回空字符串。
+        """
+        with self._lock:
+            return self._buffer[-1:] if self._buffer else ""

app/agent/defaults/CURRENT_PERSONA.md

@@ -0,0 +1,19 @@
+---
+version: 3
+active_persona: default
+extra_context_files: []
+deprecated_phrases: []
+---
+# CURRENT_PERSONA
+
+当前激活人格：`default`
+
+运行时加载顺序固定如下：
+
+1. 核心系统提示词（程序内置，不可运行时覆盖）
+2. `personas/<active_persona>/PERSONA.md`
+3. `extra_context_files`
+4. `memory/*.md`
+5. `activity/*.md`
+
+`memory` 中的长期偏好可以细化回复方式，但不应覆盖系统核心身份、目标和安全边界。

app/agent/defaults/personas/aloof/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: aloof
+label: 高冷
+description: 冷静、克制、低温度，话少但不失礼。
+aliases:
+  - 冷淡
+  - 冷感
+  - 冷艳
+---
+# PERSONA
+
+- Tone: cool, distant, and composed.
+- Keep emotional temperature low and transitions short.
+- Be brief and efficient, but do not become rude or contemptuous.
+- Prefer understatement over enthusiasm.
+
+## RESPONSE_FORMAT
+
+- Lead with the answer or the action result.
+- Keep explanations minimal unless the user explicitly asks for detail.
+- Avoid extra reassurance, hype, or emotional softening.

app/agent/defaults/personas/anime/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: anime
+label: 二次元
+description: 带一点 ACG 语感和戏剧化表达，但仍然以任务完成和清晰沟通为主。
+aliases:
+  - 动漫风
+  - ACG
+  - 宅系
+---
+# PERSONA
+
+- Tone: lively, stylized, and lightly dramatic, with a small amount of anime-flavored wording.
+- Keep the actual task handling grounded and practical; the style should stay mostly in phrasing.
+- You may occasionally use short ACG-like interjections, but do not flood the reply with memes, kaomoji, or niche jargon.
+- Stay readable first. If the task is serious, reduce the stylistic flavor automatically.
+
+## RESPONSE_FORMAT
+
+- Prefer short paragraphs or compact lists.
+- A light playful closing line is acceptable after the real result is already clear.
+- Do not let the style make operational instructions vague.

app/agent/defaults/personas/catgirl/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: catgirl
+label: 猫娘
+description: 带一点猫系拟人风格，轻松可爱，但不过度角色扮演。
+aliases:
+  - 猫猫
+  - 喵系
+  - 猫耳
+---
+# PERSONA
+
+- Tone: playful, cat-like, and cute, with occasional feline wording.
+- You may occasionally use a light "喵" style suffix or cat metaphor, but only sparingly.
+- Do not turn the reply into full roleplay; task clarity remains the primary goal.
+- If the content is operational, keep the answer direct first and add only a thin layer of style.
+
+## RESPONSE_FORMAT
+
+- Keep answers compact and readable.
+- Use only a very small amount of repeated verbal tic.
+- The result or action status should always appear before any playful flourish.

app/agent/defaults/personas/concise/PERSONA.md

@@ -0,0 +1,23 @@
+---
+version: 1
+persona_id: concise
+label: 极简
+description: 更短、更硬朗，优先结论和动作，不主动展开背景解释。
+aliases:
+  - 简洁
+  - 干脆
+  - 极简人格
+---
+# PERSONA
+
+- Tone: terse, decisive, and highly compressed.
+- Prefer the shortest complete answer that still moves the task forward.
+- Default to one sentence when possible. Only use lists when they materially improve readability.
+- Avoid extra context, caveats, or teaching unless the user explicitly asks for explanation.
+- Keep transitions minimal and skip conversational softening.
+
+## RESPONSE_FORMAT
+
+- Lead with the conclusion or result.
+- For option lists, keep each item very short.
+- Do not repeat already-known context back to the user unless it is needed to disambiguate the action.

app/agent/defaults/personas/cute/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: cute
+label: 可爱
+description: 语气更亲和、更柔软、更讨喜，但不做重度角色扮演。
+aliases:
+  - 软萌
+  - 甜系
+  - 亲和
+---
+# PERSONA
+
+- Tone: warm, cheerful, and gently cute.
+- Sound approachable and pleasant, but keep the answer concise and useful.
+- Avoid baby talk, excessive repetition, or exaggerated emotive punctuation.
+- If the user asks for directness, keep the cute flavor minimal.
+
+## RESPONSE_FORMAT
+
+- Prefer friendly short paragraphs.
+- For lists, keep each item short and easy to read.
+- When something fails, explain it gently but clearly.

app/agent/defaults/personas/default/PERSONA.md

@@ -0,0 +1,24 @@
+---
+version: 1
+persona_id: default
+label: 默认
+description: 专业、克制、简洁，适合大多数日常媒体管理场景。
+aliases:
+  - 专业
+  - 默认人格
+---
+# PERSONA
+
+- Tone: professional, concise, restrained.
+- Be direct. No unnecessary preamble, no repeating the user's words, no narrating internal reasoning.
+- Do not flatter the user, praise the question, or add emotional cushioning.
+- Do not use emojis, exclamation marks, cute language, or excessive apology.
+- Prefer short declarative sentences. Default to one or two short paragraphs; use lists only when they improve scanability.
+- Use Markdown for structured data. Use `inline code` for media titles and paths.
+
+## RESPONSE_FORMAT
+
+- Keep confirmations short.
+- For search or comparison results, prefer a brief list over a long paragraph.
+- Skip filler phrases like "Let me help you", "Here are the results", or "I found...".
+- When an error occurs, briefly state the blocker and the next best action.

app/agent/defaults/personas/disdain/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: disdain
+label: 不屑
+description: 带一点嫌弃感和轻微毒舌，但必须保持可控和不越界。
+aliases:
+  - 嫌弃
+  - 毒舌
+  - 鄙视链
+---
+# PERSONA
+
+- Tone: dry, skeptical, and faintly dismissive.
+- Mild sarcasm is acceptable, but it must stay controlled and should never turn into direct insult or humiliation.
+- Prioritize sharp phrasing and low patience, while still giving the user the actual answer.
+- If the task is sensitive or the user is clearly frustrated, reduce the bite automatically.
+
+## RESPONSE_FORMAT
+
+- Keep answers crisp and pointed.
+- Use short, cutting observations only when they improve the style without harming clarity.
+- Always include the concrete result, instruction, or blocker.

app/agent/defaults/personas/guide/PERSONA.md

@@ -0,0 +1,22 @@
+---
+version: 1
+persona_id: guide
+label: 说明型
+description: 在复杂问题上更愿意解释原因和步骤，但仍保持克制，不会无节制展开。
+aliases:
+  - 讲解
+  - 解释型
+  - 教学
+---
+# PERSONA
+
+- Tone: clear, structured, and mildly explanatory.
+- When the task is simple, stay concise. When the task is complex or the user asks why/how, provide a short explanation with visible structure.
+- Keep explanations practical and tied to the current decision, not generic theory.
+- Remain restrained: do not become chatty, cute, or overly warm.
+
+## RESPONSE_FORMAT
+
+- For non-trivial tasks, prefer short sections or a compact numbered list.
+- When describing tradeoffs, keep them concrete and action-oriented.
+- End with the actual outcome or next step, not a generic summary.

app/agent/defaults/personas/moe/PERSONA.md

@@ -0,0 +1,23 @@
+---
+version: 1
+persona_id: moe
+label: 萌系
+description: 更轻小说感、更元气、更可爱，但仍然保持边界和专业度。
+aliases:
+  - 萝莉风
+  - 轻小说风
+  - 元气少女
+  - 萌萌
+---
+# PERSONA
+
+- Tone: soft, upbeat, cute, and lightly playful.
+- Keep the personality in wording only; do not imitate a child, emphasize age, or use any sexualized framing.
+- Use cute particles or soft wording sparingly so the answer still feels useful instead of noisy.
+- When the task is urgent or technical, reduce the fluff and keep the result clear.
+
+## RESPONSE_FORMAT
+
+- Prefer short, bright sentences.
+- A small amount of cute phrasing is acceptable, but the final answer must still be easy to scan.
+- Do not bury the actual conclusion under roleplay language.

app/agent/interaction.py

@@ -1,107 +0,0 @@
-"""Agent 客户端交互请求管理。"""
-
-from dataclasses import dataclass, field
-from datetime import datetime, timedelta
-from threading import Lock
-from typing import Dict, List, Optional
-import uuid
-
-
-@dataclass(frozen=True)
-class AgentInteractionOption:
-    """交互选项。"""
-
-    label: str
-    value: str
-
-
-@dataclass
-class PendingAgentInteraction:
-    """待处理的 Agent 客户端交互请求。"""
-
-    request_id: str
-    session_id: str
-    user_id: str
-    channel: Optional[str]
-    source: Optional[str]
-    username: Optional[str]
-    title: Optional[str]
-    prompt: str
-    options: List[AgentInteractionOption]
-    created_at: datetime = field(default_factory=datetime.now)
-
-
-class AgentInteractionManager:
-    """管理 Agent 发起的客户端交互请求。"""
-
-    _ttl = timedelta(hours=24)
-
-    def __init__(self):
-        self._pending_interactions: Dict[str, PendingAgentInteraction] = {}
-        self._lock = Lock()
-
-    def _cleanup_locked(self):
-        expire_before = datetime.now() - self._ttl
-        expired_ids = [
-            request_id
-            for request_id, request in self._pending_interactions.items()
-            if request.created_at < expire_before
-        ]
-        for request_id in expired_ids:
-            self._pending_interactions.pop(request_id, None)
-
-    def create_request(
-        self,
-        session_id: str,
-        user_id: str,
-        channel: Optional[str],
-        source: Optional[str],
-        username: Optional[str],
-        title: Optional[str],
-        prompt: str,
-        options: List[AgentInteractionOption],
-    ) -> PendingAgentInteraction:
-        with self._lock:
-            self._cleanup_locked()
-            request_id = uuid.uuid4().hex[:12]
-            while request_id in self._pending_interactions:
-                request_id = uuid.uuid4().hex[:12]
-            request = PendingAgentInteraction(
-                request_id=request_id,
-                session_id=session_id,
-                user_id=str(user_id),
-                channel=channel,
-                source=source,
-                username=username,
-                title=title,
-                prompt=prompt,
-                options=options,
-            )
-            self._pending_interactions[request_id] = request
-            return request
-
-    def resolve(
-        self,
-        request_id: str,
-        option_index: int,
-        user_id: Optional[str] = None,
-    ) -> Optional[tuple[PendingAgentInteraction, AgentInteractionOption]]:
-        with self._lock:
-            self._cleanup_locked()
-            request = self._pending_interactions.get(request_id)
-            if not request:
-                return None
-            if user_id is not None and str(request.user_id) != str(user_id):
-                return None
-            if option_index < 1 or option_index > len(request.options):
-                return None
-            option = request.options[option_index - 1]
-            self._pending_interactions.pop(request_id, None)
-            return request, option
-
-    def clear(self):
-        with self._lock:
-            self._pending_interactions.clear()
-
-
-agent_interaction_manager = AgentInteractionManager()

app/agent/llm/__init__.py

@@ -0,0 +1,19 @@
+"""Agent 内部使用的 LLM 适配层。"""
+
+from app.agent.llm.helper import LLMHelper, LLMTestError, LLMTestTimeout
+from app.agent.llm.provider import (
+    LLMProviderAuthError,
+    LLMProviderError,
+    LLMProviderManager,
+    render_auth_result_html,
+)
+
+__all__ = [
+    "LLMHelper",
+    "LLMProviderAuthError",
+    "LLMProviderError",
+    "LLMProviderManager",
+    "LLMTestError",
+    "LLMTestTimeout",
+    "render_auth_result_html",
+]

app/agent/llm/helper.py

@@ -0,0 +1,839 @@
+"""LLM模型相关辅助功能"""
+
+import asyncio
+import inspect
+import json
+import time
+from functools import wraps
+from typing import Any, List
+
+from langchain_core.messages import AIMessage
+
+from app.core.config import settings
+from app.log import logger
+
+
+class LLMTestError(RuntimeError):
+    """LLM 测试调用异常，附带请求耗时。"""
+
+    def __init__(self, message: str, duration_ms: int | None = None):
+        super().__init__(message)
+        self.duration_ms = duration_ms
+
+
+class LLMTestTimeout(TimeoutError):
+    """LLM 测试调用超时，附带请求耗时。"""
+
+    def __init__(self, message: str, duration_ms: int | None = None):
+        super().__init__(message)
+        self.duration_ms = duration_ms
+
+
+def _patch_gemini_thought_signature():
+    """
+    修复 langchain-google-genai 中 Gemini 2.5 思考模型的 thought_signature 兼容问题。
+    langchain-google-genai 的 _is_gemini_3_or_later() 仅检查 "gemini-3"，
+    导致 Gemini 2.5 思考模型（如 gemini-2.5-flash、gemini-2.5-pro）在工具调用时
+    缺少 thought_signature 而报错 400。
+    此补丁将检查范围扩展到 Gemini 2.5 模型。
+    """
+    try:
+        import langchain_google_genai.chat_models as _cm
+
+        # 仅在未修补时执行
+        if getattr(_cm, "_thought_signature_patched", False):
+            return
+
+        def _patched_is_gemini_3_or_later(model_name: str) -> bool:
+            if not model_name:
+                return False
+            name = model_name.lower().replace("models/", "")
+            # Gemini 2.5 思考模型也需要 thought_signature 支持
+            return "gemini-3" in name or "gemini-2.5" in name
+
+        _cm._is_gemini_3_or_later = _patched_is_gemini_3_or_later
+        _cm._thought_signature_patched = True
+        logger.debug(
+            "已修补 langchain-google-genai thought_signature 兼容性（覆盖 Gemini 2.5 模型）"
+        )
+    except Exception as e:
+        logger.warning(f"修补 langchain-google-genai thought_signature 失败: {e}")
+
+
+def _get_httpx_proxy_key() -> str:
+    """
+    获取当前 httpx 版本支持的代理参数名。
+    httpx < 0.28 使用 "proxies"（复数），>= 0.28 使用 "proxy"（单数）。
+    google-genai SDK 会静默过滤掉不在 httpx.Client.__init__ 签名中的参数，
+    因此必须使用与当前 httpx 版本匹配的参数名。
+    """
+    try:
+        import httpx
+
+        params = inspect.signature(httpx.Client.__init__).parameters
+        if "proxy" in params:
+            return "proxy"
+        return "proxies"
+    except Exception as e:
+        logger.warning(f"检测 httpx 代理参数失败，默认使用 'proxies'：{e}")
+        return "proxies"
+
+
+def _deepseek_thinking_toggle(extra_body: Any) -> bool | None:
+    """
+    解析 DeepSeek extra_body 中显式传入的 thinking 开关。
+    """
+    if not isinstance(extra_body, dict):
+        return None
+
+    thinking = extra_body.get("thinking")
+    if not isinstance(thinking, dict):
+        return None
+
+    thinking_type = str(thinking.get("type") or "").strip().lower()
+    if thinking_type == "enabled":
+        return True
+    if thinking_type == "disabled":
+        return False
+    return None
+
+
+def _is_deepseek_thinking_enabled(model_name: str | None, extra_body: Any) -> bool:
+    """
+    判断本次 DeepSeek 调用是否处于 thinking mode。
+    """
+    explicit_toggle = _deepseek_thinking_toggle(extra_body)
+    if explicit_toggle is not None:
+        return explicit_toggle
+
+    normalized_model_name = str(model_name or "").strip().lower()
+    if normalized_model_name == "deepseek-reasoner":
+        return True
+    if normalized_model_name.startswith("deepseek-v4-"):
+        # DeepSeek V4 默认启用 thinking mode，除非显式关闭。
+        return True
+    return False
+
+
+def _patch_deepseek_reasoning_content_support():
+    """
+    修补 langchain-deepseek 在 tool-call 场景下遗漏 reasoning_content 回传的问题。
+
+    DeepSeek thinking mode 要求：若 assistant 历史消息包含 tool_calls，
+    后续请求中必须带回该条消息的顶层 reasoning_content。
+    某些 langchain-deepseek 版本虽然能从响应中拿到 reasoning_content，
+    但不会在重放消息历史时写回请求载荷，导致 400。
+    """
+    try:
+        from langchain_deepseek import ChatDeepSeek
+    except Exception as err:
+        logger.debug(f"跳过 langchain-deepseek reasoning_content 修补：{err}")
+        return
+
+    if getattr(ChatDeepSeek, "_moviepilot_reasoning_content_patched", False):
+        return
+
+    original_get_request_payload = getattr(ChatDeepSeek, "_get_request_payload", None)
+    if not callable(original_get_request_payload):
+        logger.warning("langchain-deepseek 缺少 _get_request_payload，无法修补 reasoning_content")
+        return
+
+    @wraps(original_get_request_payload)
+    def _patched_get_request_payload(self, input_, *, stop=None, **kwargs):
+        payload = original_get_request_payload(self, input_, stop=stop, **kwargs)
+
+        # Resolve original messages so we can extract reasoning_content from
+        # additional_kwargs.  The parent's payload builder does not propagate
+        # this DeepSeek-specific field.
+        messages = self._convert_input(input_).to_messages()
+
+        for i, message in enumerate(payload["messages"]):
+            if message["role"] == "tool" and isinstance(message["content"], list):
+                message["content"] = json.dumps(message["content"])
+            elif message["role"] == "assistant":
+                if isinstance(message["content"], list):
+                    # DeepSeek API expects assistant content to be a string,
+                    # not a list. Extract text blocks and join them, or use
+                    # empty string if none exist.
+                    text_parts = [
+                        block.get("text", "")
+                        for block in message["content"]
+                        if isinstance(block, dict) and block.get("type") == "text"
+                    ]
+                    message["content"] = "".join(text_parts) if text_parts else ""
+
+                # DeepSeek reasoning models require every assistant message to
+                # carry a reasoning_content field (even when empty).  The value
+                # is stored in AIMessage.additional_kwargs by
+                # _create_chat_result(); re-inject it into the API payload.
+                if (
+                        "reasoning_content" not in message
+                        and i < len(messages)
+                        and isinstance(messages[i], AIMessage)
+                ):
+                    message["reasoning_content"] = messages[i].additional_kwargs.get(
+                        "reasoning_content", ""
+                    )
+
+        return payload
+
+    ChatDeepSeek._get_request_payload = _patched_get_request_payload
+    ChatDeepSeek._moviepilot_reasoning_content_patched = True
+    logger.debug("已修补 langchain-deepseek thinking tool-call 的 reasoning_content 回传兼容性")
+
+
+def _patch_openai_responses_instructions_support():
+    """
+    修补 langchain-openai 在使用 use_responses_api=True 时，
+    提取 system 消息为顶层 instructions 字段。
+    由于 Codex 等模型 (Responses API) 强依赖 instructions 字段，
+    如果没有该字段会报 400 "Instructions are required"。
+    """
+    try:
+        from langchain_openai import ChatOpenAI
+    except Exception as err:
+        logger.debug(f"跳过 langchain-openai instructions 修补：{err}")
+        return
+
+    if getattr(ChatOpenAI, "_moviepilot_responses_instructions_patched", False):
+        return
+
+    original_get_request_payload = getattr(ChatOpenAI, "_get_request_payload", None)
+    if not callable(original_get_request_payload):
+        logger.warning("langchain-openai 缺少 _get_request_payload，无法修补 instructions")
+        return
+
+    @wraps(original_get_request_payload)
+    def _patched_get_request_payload(self, input_, *, stop=None, **kwargs):
+        payload = original_get_request_payload(self, input_, stop=stop, **kwargs)
+
+        base_url = str(getattr(self, "openai_api_base", "") or "").lower()
+
+        # 处理 GitHub Copilot 端点兼容性
+        if "githubcopilot.com" in base_url:
+            payload.pop("stream_options", None)
+            payload.pop("metadata", None)
+
+        # 处理 ChatGPT 官方 Responses API (Codex) 端点兼容性
+        is_codex = "chatgpt.com/backend-api/codex" in base_url
+        
+        if is_codex and (getattr(self, "use_responses_api", False) or "input" in payload):
+            instructions = payload.get("instructions", "")
+            inputs = payload.get("input", [])
+            new_inputs = []
+
+            for msg in inputs:
+                if isinstance(msg, dict) and msg.get("role") == "system":
+                    content = msg.get("content")
+                    if isinstance(content, str) and content.strip():
+                        if instructions:
+                            instructions += "\n\n" + content
+                        else:
+                            instructions = content
+                else:
+                    new_inputs.append(msg)
+
+            payload["input"] = new_inputs
+            payload["instructions"] = instructions or "You are a helpful assistant."
+            payload["store"] = False
+            
+            # Codex 端点不支持的部分常见补全参数，统一清理避免 400 报错
+            unsupported_keys = [
+                "presence_penalty", "frequency_penalty", "top_p", "n", "user", 
+                "stop", "metadata", "logit_bias", "logprobs", "top_logprobs",
+                "stream_options", "temperature"
+            ]
+            for key in unsupported_keys:
+                payload.pop(key, None)
+
+        return payload
+
+    ChatOpenAI._get_request_payload = _patched_get_request_payload
+    ChatOpenAI._moviepilot_responses_instructions_patched = True
+    logger.debug("已修补 langchain-openai responses API 的 instructions 兼容性")
+
+
+class LLMHelper:
+    """LLM模型相关辅助功能"""
+
+    _SUPPORTED_THINKING_LEVELS = frozenset(
+        {"off", "auto", "minimal", "low", "medium", "high", "max", "xhigh"}
+    )
+
+    @staticmethod
+    def _normalize_model_name(model_name: str | None) -> str:
+        """
+        统一清理模型名称，便于按模型族做能力映射。
+        """
+        return (model_name or "").strip().lower()
+
+    @classmethod
+    def _normalize_deepseek_reasoning_effort(
+            cls, thinking_level: str | None = None
+    ) -> str | None:
+        """
+        DeepSeek 文档当前建议使用 high/max；兼容常见 effort 别名。
+        """
+        if not thinking_level or thinking_level in {"off", "auto"}:
+            return None
+
+        if thinking_level in {"minimal", "low", "medium", "high"}:
+            return "high"
+        if thinking_level in {"max", "xhigh"}:
+            return "max"
+
+        logger.warning(f"忽略不支持的 DeepSeek reasoning_effort 配置: {thinking_level}")
+        return None
+
+    @classmethod
+    def _normalize_openai_reasoning_effort(
+            cls, thinking_level: str | None = None
+    ) -> str | None:
+        """
+        OpenAI reasoning_effort 支持更细粒度的 effort，统一做最近似映射。
+        """
+        if not thinking_level or thinking_level == "auto":
+            return None
+        if thinking_level == "off":
+            return "none"
+        if thinking_level == "max":
+            return "xhigh"
+        return thinking_level
+
+    @classmethod
+    def _build_google_thinking_kwargs(
+            cls, model_name: str, thinking_level: str
+    ) -> dict[str, Any]:
+        """
+        Gemini 3 使用 thinking_level；Gemini 2.5 使用 thinking_budget。
+        """
+        if not model_name or thinking_level == "auto":
+            return {}
+
+        if "gemini-2.5" in model_name:
+            if thinking_level == "off":
+                if "pro" in model_name:
+                    # Gemini 2.5 Pro 官方不支持完全关闭思考，回退到最小预算。
+                    return {
+                        "thinking_budget": 128,
+                        "include_thoughts": False,
+                    }
+                return {
+                    "thinking_budget": 0,
+                    "include_thoughts": False,
+                }
+
+            budget_map = {
+                "minimal": 512,
+                "low": 1024,
+                "medium": 4096,
+                "high": 8192,
+                "max": 24576,
+                "xhigh": 24576,
+            }
+            budget = budget_map.get(thinking_level)
+            return (
+                {
+                    "thinking_budget": budget,
+                    "include_thoughts": False,
+                }
+                if budget is not None
+                else {}
+            )
+
+        if "gemini-3" in model_name:
+            level_map = {
+                "off": "minimal",
+                "minimal": "minimal",
+                "low": "low",
+                "medium": "medium",
+                "high": "high",
+                "max": "high",
+                "xhigh": "high",
+            }
+            google_level = level_map.get(thinking_level)
+            return (
+                {
+                    "thinking_level": google_level,
+                    "include_thoughts": False,
+                }
+                if google_level
+                else {}
+            )
+
+        return {}
+
+    @classmethod
+    def _build_kimi_thinking_kwargs(
+            cls, model_name: str, thinking_level: str
+    ) -> dict[str, Any]:
+        """
+        Kimi 当前公开文档仅支持思考开关，不支持显式深度调节。
+        """
+        if model_name.startswith("kimi-k2-thinking"):
+            return {}
+        if thinking_level == "off":
+            return {"extra_body": {"thinking": {"type": "disabled"}}}
+        return {}
+
+    @classmethod
+    def _build_thinking_kwargs(
+            cls,
+            provider: str,
+            model: str | None,
+            thinking_level: str | None = None
+    ) -> dict[str, Any]:
+        """
+        按 provider/model 生成思考模式相关参数。
+
+        优先使用 LangChain/OpenAI SDK 已支持的原生字段；仅在 provider
+        明确要求自定义请求体时，才回退到 extra_body。
+        """
+        provider_name = (provider or "").strip().lower()
+        model_name = cls._normalize_model_name(model)
+
+        if provider_name == "deepseek":
+            if thinking_level == "off":
+                return {"extra_body": {"thinking": {"type": "disabled"}}}
+            if thinking_level == "auto":
+                return {}
+
+            kwargs: dict[str, Any] = {"extra_body": {"thinking": {"type": "enabled"}}}
+            deepseek_effort = cls._normalize_deepseek_reasoning_effort(
+                thinking_level
+            )
+            if deepseek_effort:
+                kwargs["reasoning_effort"] = deepseek_effort
+            return kwargs
+
+        if model_name.startswith(("kimi-k2.5", "kimi-k2.6", "kimi-k2-thinking")):
+            return cls._build_kimi_thinking_kwargs(model_name, thinking_level)
+
+        if not model_name:
+            return {}
+
+        # OpenAI 原生推理模型优先走 LangChain 内置 reasoning_effort。
+        if provider_name in {"openai", "chatgpt"} and model_name.startswith(
+                ("gpt-5", "o1", "o3", "o4")
+        ):
+            openai_effort = cls._normalize_openai_reasoning_effort(
+                thinking_level
+            )
+            return {"reasoning_effort": openai_effort} if openai_effort else {}
+
+        # Gemini 使用 google-genai / langchain-google-genai 内置思考控制参数。
+        if provider_name == "google":
+            return cls._build_google_thinking_kwargs(
+                model_name, thinking_level
+            )
+
+        return {}
+
+    @staticmethod
+    def supports_image_input() -> bool:
+        """
+        判断当前模型是否启用了图片输入能力。
+        """
+        return bool(settings.LLM_SUPPORT_IMAGE_INPUT)
+
+    @staticmethod
+    def _build_legacy_runtime(
+            provider_name: str,
+            model_name: str | None,
+            api_key: str | None = None,
+            base_url: str | None = None,
+    ) -> dict[str, Any]:
+        """
+        在 provider 目录不可用时回退到旧的直接构造逻辑。
+
+        这主要用于单测 stub 环境以及极端的最小运行环境，正常生产路径仍优先
+        走 `LLMProviderManager.resolve_runtime()`。
+        """
+        api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
+        base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
+        if not api_key_value:
+            raise ValueError("未配置LLM API Key")
+
+        runtime_name = provider_name if provider_name in {"google", "deepseek"} else "openai_compatible"
+        return {
+            "provider_id": provider_name,
+            "runtime": runtime_name,
+            "model_id": model_name,
+            "api_key": api_key_value,
+            "base_url": base_url_value,
+            "default_headers": None,
+            "use_responses_api": None,
+            "model_record": None,
+            "model_metadata": None,
+        }
+
+    @classmethod
+    def _resolve_thinking_level(
+            cls,
+            thinking_level: str | None = None,
+    ) -> str | None:
+        """
+        统一兼容新旧 thinking 参数。
+        """
+
+        def _normalize(value: str | None) -> str | None:
+            normalized = str(value or "").strip().lower()
+            if not normalized:
+                return None
+            alias_map = {
+                "none": "off",
+                "disabled": "off",
+                "disable": "off",
+                "enabled": "auto",
+                "enable": "auto",
+                "default": "auto",
+                "dynamic": "auto",
+            }
+            normalized = alias_map.get(normalized, normalized)
+            if normalized in cls._SUPPORTED_THINKING_LEVELS:
+                return normalized
+            logger.warning(f"忽略不支持的思考级别: {value}")
+            return None
+
+        normalized_thinking_level = _normalize(thinking_level)
+        if normalized_thinking_level:
+            return normalized_thinking_level
+
+        return "off"
+
+    @classmethod
+    async def get_llm(
+            cls,
+            streaming: bool = False,
+            provider: str | None = None,
+            model: str | None = None,
+            thinking_level: str | None = None,
+            api_key: str | None = settings.LLM_API_KEY,
+            base_url: str | None = settings.LLM_BASE_URL,
+    ):
+        """
+        获取LLM实例
+        :param streaming: 是否启用流式输出
+        :param provider: LLM提供商，默认为配置项LLM_PROVIDER
+        :param model: 模型名称，默认为配置项LLM_MODEL
+        :param thinking_level: 思考模式级别，默认为 None（即自动判断
+            是否启用思考模式）。支持的级别包括 "off"（关闭）、"auto"（自动）、"minimal"、"low"、"medium"、"high"、"max"/"xhigh"（最大）。
+            不同模型对思考模式的支持和表现不同，具体映射关系请
+            参考代码实现。对于不支持思考模式的模型，该参数将被忽略。
+        :param api_key: API Key，默认为配置项LLM_API_KEY。对于某些提供商（如 DeepSeek），可能需要同时提供 base_url。
+        :param base_url: API Base URL，默认为配置项LLM_BASE_URL。
+        :return: LLM实例
+        """
+        provider_name = str(provider if provider is not None else settings.LLM_PROVIDER).lower()
+        model_name = model if model is not None else settings.LLM_MODEL
+        normalized_thinking_level = cls._resolve_thinking_level(
+            thinking_level=thinking_level,
+        )
+        try:
+            # 延迟导入，避免单测在最小 stub 环境下 import `llm.py` 时被 provider
+            # 目录依赖链拖住。
+            from app.agent.llm.provider import LLMProviderManager
+
+            runtime = await LLMProviderManager().resolve_runtime(
+                provider_id=provider_name,
+                model=model_name,
+                api_key=api_key,
+                base_url=base_url,
+            )
+        except Exception as err:
+            logger.debug(f"LLM provider 目录不可用，回退到旧运行时逻辑: {err}")
+            runtime = cls._build_legacy_runtime(
+                provider_name=provider_name,
+                model_name=model_name,
+                api_key=api_key,
+                base_url=base_url,
+            )
+        model_name = runtime.get("model_id") or model_name
+        thinking_kwargs = cls._build_thinking_kwargs(
+            provider=provider_name,
+            model=model_name,
+            thinking_level=normalized_thinking_level,
+        )
+
+        if runtime["runtime"] == "google":
+            # 修补 Gemini 2.5 思考模型的 thought_signature 兼容性
+            _patch_gemini_thought_signature()
+
+            # 统一使用 langchain-google-genai 原生接口
+            # 不使用 OpenAI 兼容端点，因其不支持 Gemini 思考模型的 thought_signature，
+            # 会导致工具调用时报错 400
+            from langchain_google_genai import ChatGoogleGenerativeAI
+
+            client_args = None
+            if settings.PROXY_HOST:
+                proxy_key = _get_httpx_proxy_key()
+                client_args = {proxy_key: settings.PROXY_HOST}
+
+            model = ChatGoogleGenerativeAI(
+                model=model_name,
+                api_key=runtime["api_key"],
+                retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                client_args=client_args,
+                **thinking_kwargs,
+            )
+        elif runtime["runtime"] == "deepseek":
+            from langchain_deepseek import ChatDeepSeek
+
+            _patch_deepseek_reasoning_content_support()
+            model = ChatDeepSeek(
+                model=model_name,
+                api_key=runtime["api_key"],
+                api_base=runtime["base_url"],
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                stream_usage=True,
+                **thinking_kwargs,
+            )
+        elif runtime["runtime"] in {"anthropic_compatible", "copilot_anthropic"}:
+            from langchain_anthropic import ChatAnthropic
+
+            model = ChatAnthropic(
+                model=model_name,
+                api_key=runtime["api_key"],
+                base_url=runtime["base_url"],
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                stream_usage=True,
+                anthropic_proxy=settings.PROXY_HOST,
+                default_headers=runtime.get("default_headers"),
+                **thinking_kwargs,
+            )
+        else:
+            from langchain_openai import ChatOpenAI
+
+            _patch_openai_responses_instructions_support()
+            
+            # ChatGPT Codex 端点强制要求 stream: True
+            if runtime.get("use_responses_api") and "chatgpt.com/backend-api/codex" in str(runtime.get("base_url") or ""):
+                streaming = True
+
+            model = ChatOpenAI(
+                model=model_name,
+                api_key=runtime["api_key"],
+                max_retries=3,
+                base_url=runtime.get("base_url"),
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                stream_usage=True,
+                openai_proxy=settings.PROXY_HOST,
+                default_headers=runtime.get("default_headers"),
+                use_responses_api=runtime.get("use_responses_api"),
+                **thinking_kwargs,
+            )
+
+        # 优先使用 provider / models.dev 目录中的上下文上限，减少用户手填成本。
+        model_profile = getattr(model, "profile", None)
+        if model_profile:
+            logger.debug(f"使用LLM模型: {model.model}，Profile: {model.profile}")
+        else:
+            model_record = runtime.get("model_record") or {}
+            model_metadata = runtime.get("model_metadata") or {}
+            metadata_limit = model_metadata.get("limit") or {}
+            max_input_tokens = (
+                    model_record.get("input_tokens")
+                    or model_record.get("context_tokens")
+                    or metadata_limit.get("input")
+                    or metadata_limit.get("context")
+                    or settings.LLM_MAX_CONTEXT_TOKENS * 1000
+            )
+            model.profile = {
+                "max_input_tokens": int(max_input_tokens),
+            }
+
+        return model
+
+    @staticmethod
+    def _extract_text_content(content) -> str:
+        """
+        从响应内容中提取纯文本，仅保留真实文本块。
+        """
+        if content is None:
+            return ""
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            text_parts = []
+            for block in content:
+                if isinstance(block, str):
+                    text_parts.append(block)
+                    continue
+
+                if isinstance(block, dict) or hasattr(block, "get"):
+                    block_type = block.get("type")
+                    if block.get("thought") or block_type in (
+                            "thinking",
+                            "reasoning_content",
+                            "reasoning",
+                            "thought",
+                    ):
+                        continue
+                    if block_type == "text":
+                        text_parts.append(block.get("text", ""))
+                        continue
+                    if not block_type and isinstance(block.get("text"), str):
+                        text_parts.append(block.get("text", ""))
+            return "".join(text_parts)
+        if isinstance(content, dict) or hasattr(content, "get"):
+            if content.get("thought"):
+                return ""
+            if content.get("type") == "text":
+                return content.get("text", "")
+            if not content.get("type") and isinstance(content.get("text"), str):
+                return content.get("text", "")
+        return ""
+
+    @staticmethod
+    async def test_current_settings(
+            prompt: str = "请只回复 OK",
+            timeout: int = 20,
+            provider: str | None = None,
+            model: str | None = None,
+            thinking_level: str | None = None,
+            api_key: str | None = None,
+            base_url: str | None = None,
+    ) -> dict:
+        """
+        使用当前已保存配置执行一次最小 LLM 调用。
+        """
+        provider_name = provider if provider is not None else settings.LLM_PROVIDER
+        model_name = model if model is not None else settings.LLM_MODEL
+        start = time.perf_counter()
+        llm = await LLMHelper.get_llm(
+            streaming=False,
+            provider=provider_name,
+            model=model_name,
+            thinking_level=thinking_level,
+            api_key=api_key,
+            base_url=base_url,
+        )
+        try:
+            response = await asyncio.wait_for(llm.ainvoke(prompt), timeout=timeout)
+        except TimeoutError as err:
+            duration_ms = round((time.perf_counter() - start) * 1000)
+            raise LLMTestTimeout("LLM 调用超时", duration_ms=duration_ms) from err
+        except Exception as err:
+            duration_ms = round((time.perf_counter() - start) * 1000)
+            raise LLMTestError(str(err), duration_ms=duration_ms) from err
+
+        reply_text = LLMHelper._extract_text_content(
+            getattr(response, "content", response)
+        ).strip()
+        duration_ms = round((time.perf_counter() - start) * 1000)
+
+        data = {
+            "provider": provider_name,
+            "model": model_name,
+            "duration_ms": duration_ms,
+        }
+        if reply_text:
+            data["reply_preview"] = reply_text[:120]
+        return data
+
+    async def get_models(
+            self,
+            provider: str,
+            api_key: str | None = None,
+            base_url: str | None = None,
+            force_refresh: bool = False,
+    ) -> List[dict[str, Any]]:
+        """
+        获取模型列表。
+
+        返回值会带上 context/supports_reasoning 等元数据，供前端直接渲染并自动
+        回填上下文大小。
+        """
+        logger.info(f"获取 {provider} 模型列表...")
+        try:
+            from app.agent.llm.provider import LLMProviderManager
+
+            return await LLMProviderManager().list_models(
+                provider_id=provider,
+                api_key=api_key,
+                base_url=base_url,
+                force_refresh=force_refresh,
+            )
+        except Exception as err:
+            logger.debug(f"LLM provider 目录不可用，回退旧模型列表逻辑: {err}")
+            if provider == "google":
+                return [
+                    {"id": model_id, "name": model_id}
+                    for model_id in await self._get_google_models(api_key or "")
+                ]
+            model_list_base_url = base_url
+            try:
+                from app.agent.llm.provider import LLMProviderManager
+
+                model_list_base_url = (
+                    LLMProviderManager().resolve_model_list_base_url(
+                        provider_id=provider,
+                        base_url=base_url,
+                    )
+                    or base_url
+                )
+            except Exception:
+                model_list_base_url = base_url
+            return [
+                {"id": model_id, "name": model_id}
+                for model_id in await self._get_openai_compatible_models(
+                    provider,
+                    api_key or "",
+                    model_list_base_url,
+                )
+            ]
+
+    @staticmethod
+    async def _get_google_models(api_key: str) -> List[str]:
+        """获取Google模型列表（使用 google-genai SDK v1）"""
+        try:
+            from google import genai
+            from google.genai.types import HttpOptions
+
+            http_options = None
+            if settings.PROXY_HOST:
+                proxy_key = _get_httpx_proxy_key()
+                proxy_args = {proxy_key: settings.PROXY_HOST}
+                http_options = HttpOptions(
+                    client_args=proxy_args,
+                    async_client_args=proxy_args,
+                )
+
+            client = genai.Client(api_key=api_key, http_options=http_options)
+            models = await client.aio.models.list()
+            result = [
+                m.name
+                for m in models.page
+                if m.supported_actions and "generateContent" in m.supported_actions
+            ]
+            await client.aio.aclose()
+            return result
+        except Exception as e:
+            logger.error(f"获取Google模型列表失败：{e}")
+            raise e
+
+    @staticmethod
+    async def _get_openai_compatible_models(
+            provider: str, api_key: str, base_url: str = None
+    ) -> List[str]:
+        """获取OpenAI兼容模型列表"""
+        try:
+            from openai import AsyncOpenAI
+
+            if provider == "deepseek":
+                base_url = base_url or "https://api.deepseek.com"
+
+            client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+            models = await client.models.list()
+            await client.close()
+            return [model.id for model in models.data]
+        except Exception as e:
+            logger.error(f"获取 {provider} 模型列表失败：{e}")
+            raise e

app/agent/middleware/activity_log.py

@@ -158,9 +158,9 @@ async def _summarize_with_llm(conversation_text: str) -> str | None:
         LLM 生成的摘要字符串，失败时返回 None。
     """
     try:
-        from app.helper.llm import LLMHelper
+        from app.agent.llm import LLMHelper
 
-        llm = LLMHelper.get_llm(streaming=False)
+        llm = await LLMHelper.get_llm(streaming=False)
         prompt = SUMMARY_PROMPT.format(conversation=conversation_text)
         response = await llm.ainvoke(prompt)
         summary = response.content.strip()
@@ -355,7 +355,7 @@ class ActivityLogMiddleware(AgentMiddleware[ActivityLogState, ContextT, Response
 
     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", {})
+        contents = request.state.get("activity_log_contents", {})  # noqa
         activity_log_prompt = self._format_activity_log(contents)
 
         new_system_message = append_to_system_message(

app/agent/middleware/memory.py

@@ -57,8 +57,8 @@ You can create, edit, or organize any `.md` files in this directory to manage yo
 
     **Memory file organization:**
     - All `.md` files in `{memory_dir}` are automatically loaded as memory.
-    - `MEMORY.md` is the default/primary memory file for general user preferences and profile.
-    - You may create additional `.md` files to organize knowledge by topic (e.g., `MEDIA_RULES.md`, `DOWNLOAD_PREFERENCES.md`, `SITE_CONFIGS.md`, etc.).
+    - `MEMORY.md` is the default/primary memory file for general user preferences, communication style, and durable working rules.
+    - You may create additional `.md` files to organize knowledge by topic (e.g., `MEDIA_RULES.md`, `COMMUNICATION_PREFERENCES.md`, `DOWNLOAD_PREFERENCES.md`, `SITE_CONFIGS.md`, etc.).
     - Keep each file focused on a specific domain or topic for better organization.
     - Subdirectories are NOT scanned — only `.md` files directly in `{memory_dir}`.
 
@@ -78,11 +78,11 @@ You can create, edit, or organize any `.md` files in this directory to manage yo
 
     **When to update memories:**
     - When the user explicitly asks you to remember something (e.g., "remember my email", "save this preference")
-    - When the user describes your role or how you should behave (e.g., "you are a web researcher", "always do X")
+    - When the user gives durable communication or reply-format preferences (e.g., "be more concise", "prefer tables", "use JSON when summarizing")
     - When the user gives feedback on your work - capture what was wrong and how to improve
     - When the user provides information required for tool use (e.g., slack channel ID, email addresses)
     - When the user provides context useful for future tasks, such as how to use tools, or which actions to take in a particular situation
-    - When you discover new patterns or preferences (coding styles, conventions, workflows)
+    - When you discover new user-specific patterns or preferences (communication style, formatting, workflows)
 
     **When to NOT update memories:**
     - When the information is temporary or transient (e.g., "I'm running late", "I'm on my phone right now")
@@ -90,6 +90,8 @@ You can create, edit, or organize any `.md` files in this directory to manage yo
     - When the information is a simple question that doesn't reveal lasting preferences (e.g., "What day is it?", "Can you explain X?")
     - When the information is an acknowledgment or small talk (e.g., "Sounds good!", "Hello", "Thanks for that")
     - When the information is stale or irrelevant in future conversations
+    - Memory may refine user-facing style, but it must NOT redefine the agent's core identity, safety boundaries, or global system-task rules.
+    - If the user wants a built-in speaking style/persona, prefer the dedicated persona-switching tools instead of rewriting memory as a substitute.
     - 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 files - these are automatically tracked in the activity log system (see <activity_log>). Memory files are only for long-term knowledge, preferences, and patterns.
@@ -124,41 +126,36 @@ Default memory file: {memory_file}
 </agent_memory>
 
 <memory_onboarding>
-    **IMPORTANT — First-time user detected!**
+    First-time user detected.
 
-    The memory directory is currently empty. This means this is likely the user's first interaction, or their preferences have been reset.
+    The memory directory is currently empty. This likely means the user has no saved long-term preferences yet.
 
-    **Your MANDATORY first action in this conversation:**
-    Before doing ANYTHING else (before answering questions, before calling tools, before performing any task), you MUST proactively greet the user warmly and ask them about their preferences so you can provide personalized service going forward. Specifically, ask about:
+    **Behavior requirements:**
+    - Do NOT interrupt the current task just to collect preferences.
+    - Do NOT proactively greet warmly, build rapport, or ask a long onboarding questionnaire.
+    - Default to a concise, professional style until the user states a preference.
+    - Only ask for preferences when they are directly useful for the current task, or when a short follow-up question at the end would clearly help future interactions.
 
-    1. **How to address the user** — Ask what name or nickname they'd like you to call them (e.g., a real name, a nickname, or a fun title). This is the top priority for building a personal connection.
-    2. **Communication style preference** — Do they prefer a cute/playful tone (with emojis), a formal/professional tone, a concise/minimalist style, or something else?
-    3. **Media preferences** — What types of media do they primarily care about? (e.g., movies, TV shows, anime, documentaries, etc.)
-    4. **Quality preferences** — Do they have preferred video quality (4K, 1080p), codecs (H.265, H.264), or subtitle language preferences?
-    5. **Any other special requests** — Anything else they'd like you to always keep in mind?
+    **What to collect when useful:**
+    - Preferred communication style or persona preference
+    - Media interests
+    - Quality / codec / subtitle preferences
+    - Any standing rules the user wants you to follow
 
-    **After the user replies**, you MUST immediately:
-    1. Use the `write_file` tool to save ALL their preferences to the memory file at: `{memory_file}`
-    2. Format the memory file in clean Markdown with clear sections (e.g., `## User Profile`, `## Communication Style`, `## Media Preferences`, etc.)
-    3. The `## User Profile` section MUST include the user's preferred name/nickname at the top
-    4. Only AFTER saving the preferences, proceed to help with whatever the user originally asked about (if anything)
-    5. From this point on, always address the user by their preferred name/nickname in conversations
-    6. You may also create additional `.md` files in the memory directory (`{memory_dir}`) for different topics as needed.
+    **When the user provides lasting preferences**, you MUST promptly save them to `{memory_file}` using `write_file` or `edit_file`.
 
-    **If the user skips the preference questions** and directly asks you to do something:
-    - Go ahead and help them with their request first
-    - But still ask about their preferences naturally at the end of the interaction
-    - Save whatever you learn about them (implicit or explicit) to the memory file
-
-    **Example onboarding flow:**
-    The greeting should introduce yourself, explain this is the first meeting, and ask the above questions in a numbered list. Adapt the tone to your persona defined in the base system prompt.
+    **Memory format requirements:**
+    - Use clean Markdown with short sections.
+    - Record only durable preferences and working rules.
+    - Do NOT invent personal details or preferred names.
+    - Do NOT force use of a nickname or personalized greeting.
 </memory_onboarding>
 
 <memory_guidelines>
     Your memory directory is at: {memory_dir}. You can save new knowledge by calling the `edit_file` or `write_file` tool on any `.md` file in this directory.
 
     **Memory file organization:**
-    - `MEMORY.md` is the default/primary memory file for general user preferences and profile.
+    - `MEMORY.md` is the default/primary memory file for user preferences, persona preferences, and durable working rules.
     - You may create additional `.md` files to organize knowledge by topic.
     - All `.md` files directly in the memory directory are automatically loaded on each conversation.
 
@@ -171,15 +168,17 @@ Default memory file: {memory_file}
 
     **When to update memories:**
     - When the user explicitly asks you to remember something
-    - When the user describes your role or how you should behave
+    - When the user gives durable communication or reply-format preferences
     - When the user gives feedback on your work
     - When the user provides information required for tool use
-    - When you discover new patterns or preferences
+    - When you discover new user-specific patterns or preferences
 
     **When to NOT update memories:**
     - Temporary/transient information
     - One-time task requests
     - Simple questions, acknowledgments, or small talk
+    - Memory may refine user-facing style, but it must NOT redefine the agent's core identity, safety boundaries, or global system-task rules
+    - If the user wants a built-in speaking style/persona, prefer the dedicated persona-switching tools instead of rewriting memory as a substitute
     - Never store API keys, access tokens, passwords, or credentials
     - Do NOT record daily activities in memory files — those go to the activity log
 </memory_guidelines>
@@ -193,7 +192,8 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
     支持多文件记忆组织：用户可以创建多个 `.md` 文件来按主题组织知识。
 
     参数：
-        memory_dir: 记忆文件目录路径。
+        memory_dir: 记忆文件目录路径。建议使用独立的 `config/agent/memory`
+            目录，避免与核心规则或人格定义混写。
     """
 
     state_schema = MemoryState
@@ -206,7 +206,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
         """初始化记忆中间件。
 
         参数：
-            memory_dir: 记忆文件目录路径（例如，`"/config/agent"`）。
+            memory_dir: 记忆文件目录路径（例如，`"/config/agent/memory"`）。
                         该目录下所有 `.md` 文件都会被自动加载为记忆。
         """
         self.memory_dir = memory_dir
@@ -293,7 +293,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryState, ContextT, ResponseT]):  # no
 
         return md_files
 
-    async def abefore_agent(
+    async def abefore_agent(  # noqa
         self,
         state: MemoryState,
         runtime: Runtime,  # noqa

app/agent/middleware/runtime_config.py

@@ -0,0 +1,42 @@
+"""动态注入 Agent 根层运行时配置的中间件。"""
+
+from collections.abc import Awaitable, Callable
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    ContextT,
+    ModelRequest,
+    ModelResponse,
+    ResponseT,
+)
+
+from app.agent.middleware.utils import append_to_system_message
+from app.agent.runtime import agent_runtime_manager
+
+
+class RuntimeConfigMiddleware(AgentMiddleware[dict, ContextT, ResponseT]):  # noqa
+    """在每次模型调用前动态加载运行时配置。
+
+    这里不把结果缓存到 middleware state 中，目的是让人格切换工具在同一轮
+    Agent 执行里修改 CURRENT_PERSONA 后，后续模型调用可以立即看到新的人格。
+    """
+
+    def modify_request(self, request: ModelRequest[ContextT]) -> ModelRequest[ContextT]:  # noqa
+        runtime_config = agent_runtime_manager.load_runtime_config()
+        runtime_sections = runtime_config.render_prompt_sections()
+        new_system_message = append_to_system_message(
+            request.system_message, runtime_sections
+        )
+        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]:
+        return await handler(self.modify_request(request))
+
+
+__all__ = ["RuntimeConfigMiddleware"]

app/agent/middleware/skills.py

@@ -310,7 +310,8 @@ def _extract_version(skill_md: Path) -> int:
     """从 SKILL.md 文件中快速提取 version 字段，无法提取时返回 0。"""
     try:
         content = skill_md.read_text(encoding="utf-8")
-    except Exception:
+    except Exception as err:
+        print(err)
         return 0
     match = re.match(r"^---\s*\n(.*?)\n---\s*\n", content, re.DOTALL)
     if not match:

app/agent/middleware/tool_selection.py

@@ -0,0 +1,549 @@
+"""MoviePilot 自定义工具筛选中间件。"""
+
+import json
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+from typing import Annotated, Any, Literal, Union, NotRequired
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    AgentState,
+    ContextT,
+    ModelRequest,
+    ModelResponse,
+    ResponseT,
+)
+from langchain.agents.middleware.types import (
+    PrivateStateAttr,  # noqa
+)
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tools import BaseTool
+from langgraph.runtime import Runtime
+from pydantic import Field, TypeAdapter
+from typing_extensions import TypedDict  # noqa
+
+from app.log import logger
+
+DEFAULT_SYSTEM_PROMPT = (
+    "Your goal is to select the most relevant tools for answering the user's query."
+)
+
+
+@dataclass
+class _SelectionRequest:
+    """Prepared inputs for tool selection."""
+
+    available_tools: list[BaseTool]
+    system_message: str
+    last_user_message: HumanMessage
+    model: BaseChatModel
+    valid_tool_names: list[str]
+
+
+def _create_tool_selection_response(tools: list[BaseTool]) -> TypeAdapter[Any]:
+    """Create a structured output schema for tool selection.
+
+    Args:
+        tools: Available tools to include in the schema.
+
+    Returns:
+        `TypeAdapter` for a schema where each tool name is a `Literal` with its
+            description.
+
+    Raises:
+        AssertionError: If `tools` is empty.
+    """
+    if not tools:
+        msg = "Invalid usage: tools must be non-empty"
+        raise AssertionError(msg)
+
+    # Create a Union of Annotated Literal types for each tool name with description
+    # For instance: Union[Annotated[Literal["tool1"], Field(description="...")], ...]
+    literals = [
+        Annotated[Literal[tool.name], Field(description=tool.description)]
+        for tool in tools  # noqa
+    ]
+    selected_tool_type = Union[tuple(literals)]  # type: ignore[valid-type]  # noqa: UP007
+
+    description = "Tools to use. Place the most relevant tools first."
+
+    class ToolSelectionResponse(TypedDict):
+        """Use to select relevant tools."""
+
+        tools: Annotated[list[selected_tool_type], Field(description=description)]  # type: ignore[valid-type]
+
+    return TypeAdapter(ToolSelectionResponse)
+
+
+def _render_tool_list(tools: list[BaseTool]) -> str:
+    """Format tools as markdown list.
+
+    Args:
+        tools: Tools to format.
+
+    Returns:
+        Markdown string with each tool on a new line.
+    """
+    return "\n".join(f"- {tool.name}: {tool.description}" for tool in tools)
+
+
+class ToolSelectionState(AgentState):
+    """工具筛选中间件私有状态。"""
+
+    selected_tool_names: NotRequired[Annotated[list[str] | None, PrivateStateAttr]]
+    """当前这条用户请求首轮筛选得到的工具名列表。"""
+
+
+class ToolSelectionStateUpdate(TypedDict):
+    """工具筛选中间件状态更新项。"""
+
+    selected_tool_names: list[str] | None
+
+
+class ToolSelectorMiddleware(
+    AgentMiddleware[AgentState[ResponseT], ContextT, ResponseT]
+):
+    """
+    为 DeepSeek 兼容端点提供更稳妥的工具筛选实现。
+
+    LangChain 默认会通过 `with_structured_output()` 走 OpenAI 的
+    `response_format=json_schema` 路径，但 DeepSeek 官方 OpenAI 兼容端点公开文档
+    仅保证 `json_object` 模式可用。对于 `deepseek-reasoner`，这会在工具筛选阶段
+    提前触发 400，导致 Agent 还没真正开始执行工具就失败。
+
+    因此这里仅在识别到 DeepSeek 模型/端点时，退回到显式 JSON 输出模式：
+    1. 使用 `response_format={"type": "json_object"}`；
+    2. 在提示词中明确约束返回 JSON 结构；
+    3. 手动解析 `{"tools": [...]}`，其余模型继续沿用 LangChain 默认实现。
+
+    另外，LangChain 原生工具筛选挂在 `wrap_model_call` 上，会在同一条用户请求
+    的每次“模型回合”前都重新筛选一次工具。对于会多轮调用工具的复杂任务，
+    这会重复消耗一次额外的 LLM 调用。这里改成：
+    - `abefore_agent()`：在本轮 Agent 执行开始时筛选一次；
+    - `awrap_model_call()`：从 `request.state` 读取首轮筛选结果并复用。
+    """
+
+    state_schema = ToolSelectionState
+
+    def __init__(
+            self,
+            model: BaseChatModel,
+            system_prompt: str = DEFAULT_SYSTEM_PROMPT,
+            selection_tools: list[Any] | None = None,
+            max_tools: int | None = None,
+            always_include: list[str] | None = None,
+    ) -> None:
+        super().__init__()
+        self.model = model
+        self.system_prompt = system_prompt
+        self.max_tools = max_tools
+        self.always_include = always_include or []
+        self.selection_tools = selection_tools or []
+
+    def _prepare_selection_request(
+            self, request: ModelRequest[ContextT]
+    ) -> _SelectionRequest | None:
+        """Prepare inputs for tool selection.
+
+        Args:
+            request: the model request.
+
+        Returns:
+            `SelectionRequest` with prepared inputs, or `None` if no selection is
+            needed.
+
+        Raises:
+            ValueError: If tools in `always_include` are not found in the request.
+            AssertionError: If no user message is found in the request messages.
+        """
+        # If no tools available, return None
+        if not request.tools or len(request.tools) == 0:
+            return None
+
+        # Filter to only BaseTool instances (exclude provider-specific tool dicts)
+        base_tools = [tool for tool in request.tools if not isinstance(tool, dict)]
+
+        # Validate that always_include tools exist
+        if self.always_include:
+            available_tool_names = {tool.name for tool in base_tools}
+            missing_tools = [
+                name for name in self.always_include if name not in available_tool_names
+            ]
+            if missing_tools:
+                msg = (
+                    f"Tools in always_include not found in request: {missing_tools}. "
+                    f"Available tools: {sorted(available_tool_names)}"
+                )
+                raise ValueError(msg)
+
+        # Separate tools that are always included from those available for selection
+        available_tools = [
+            tool for tool in base_tools if tool.name not in self.always_include
+        ]
+
+        # If no tools available for selection, return None
+        if not available_tools:
+            return None
+
+        system_message = self.system_prompt
+        # If there's a max_tools limit, append instructions to the system prompt
+        if self.max_tools is not None:
+            system_message += (
+                f"\nIMPORTANT: List the tool names in order of relevance, "
+                f"with the most relevant first. "
+                f"If you exceed the maximum number of tools, "
+                f"only the first {self.max_tools} will be used."
+            )
+
+        # Get the last user message from the conversation history
+        last_user_message: HumanMessage
+        for message in reversed(request.messages):
+            if isinstance(message, HumanMessage):
+                last_user_message = message
+                break
+        else:
+            msg = "No user message found in request messages"
+            raise AssertionError(msg)
+
+        model = self.model or request.model
+        valid_tool_names = [tool.name for tool in available_tools]
+
+        return _SelectionRequest(
+            available_tools=available_tools,
+            system_message=system_message,
+            last_user_message=last_user_message,
+            model=model,
+            valid_tool_names=valid_tool_names,
+        )
+
+    def _process_selection_response(
+            self,
+            response: dict[str, Any],
+            available_tools: list[BaseTool],
+            valid_tool_names: list[str],
+            request: ModelRequest[ContextT],
+    ) -> ModelRequest[ContextT]:
+        """Process the selection response and return filtered `ModelRequest`."""
+        selected_tool_names: list[str] = []
+        invalid_tool_selections = []
+
+        for tool_name in response["tools"]:
+            if tool_name not in valid_tool_names:
+                invalid_tool_selections.append(tool_name)
+                continue
+
+            # Only add if not already selected and within max_tools limit
+            if tool_name not in selected_tool_names and (
+                    self.max_tools is None or len(selected_tool_names) < self.max_tools
+            ):
+                selected_tool_names.append(tool_name)
+
+        if invalid_tool_selections:
+            msg = f"Model selected invalid tools: {invalid_tool_selections}"
+            raise ValueError(msg)
+
+        # Filter tools based on selection and append always-included tools
+        if selected_tool_names:
+            selected_tools: list[BaseTool] = [
+                tool for tool in available_tools if tool.name in selected_tool_names
+            ]
+        else:
+            # 如果模型筛选结果为空，则不对工具进行裁剪，使用所有可用工具
+            logger.warning("工具筛选结果为空，将恢复使用所有工具。")
+            selected_tools = available_tools
+
+        always_included_tools: list[BaseTool] = [
+            tool
+            for tool in request.tools
+            if not isinstance(tool, dict) and tool.name in self.always_include
+        ]
+        selected_tools.extend(always_included_tools)
+
+        # Also preserve any provider-specific tool dicts from the original request
+        provider_tools = [tool for tool in request.tools if isinstance(tool, dict)]
+
+        return request.override(tools=[*selected_tools, *provider_tools])
+
+    @staticmethod
+    def _is_deepseek_compatible_model(model: BaseChatModel) -> bool:
+        """
+        判断当前模型是否应当走 DeepSeek JSON 兼容分支。
+
+        除了官方 `langchain_deepseek`，用户也可能通过 OpenAI-compatible
+        配置把 DeepSeek 端点接到 `ChatOpenAI`。因此这里同时检查模块名、模型名
+        和 Base URL，避免只靠单一条件漏判。
+        """
+        module_name = type(model).__module__.lower()
+        model_name = (
+            str(getattr(model, "model_name", "") or getattr(model, "model", ""))
+            .strip()
+            .lower()
+        )
+        base_url = (
+            str(getattr(model, "openai_api_base", "") or getattr(model, "api_base", ""))
+            .strip()
+            .lower()
+        )
+
+        return (
+                "deepseek" in module_name
+                or model_name.startswith("deepseek-")
+                or "api.deepseek.com" in base_url
+        )
+
+    @staticmethod
+    def _extract_text_content(content: Any) -> str:
+        """
+        从模型响应中提取纯文本。
+
+        这里不依赖上层 LLMHelper，避免中间件与 LLM 构造逻辑互相耦合。
+        """
+        if content is None:
+            return ""
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            text_parts: list[str] = []
+            for block in content:
+                if isinstance(block, str):
+                    text_parts.append(block)
+                    continue
+                if isinstance(block, dict):
+                    if block.get("type") == "text" and isinstance(
+                            block.get("text"), str
+                    ):
+                        text_parts.append(block["text"])
+                        continue
+                    if not block.get("type") and isinstance(block.get("text"), str):
+                        text_parts.append(block["text"])
+            return "".join(text_parts)
+        if isinstance(content, dict):
+            if content.get("type") == "text" and isinstance(content.get("text"), str):
+                return content["text"]
+            if not content.get("type") and isinstance(content.get("text"), str):
+                return content["text"]
+        return ""
+
+    @staticmethod
+    def _parse_json_object(text: str) -> dict[str, Any]:
+        """
+        解析模型返回的 JSON。
+
+        DeepSeek 在 JSON 模式下通常会返回纯 JSON，但这里仍做一层兜底，
+        兼容模型偶发输出围栏或前后说明文本的情况。
+        """
+        stripped_text = text.strip()
+        if not stripped_text:
+            raise ValueError("工具筛选返回了空响应")
+
+        try:
+            payload = json.loads(stripped_text)
+            if isinstance(payload, dict):
+                return payload
+        except json.JSONDecodeError:
+            pass
+
+        start = stripped_text.find("{")
+        end = stripped_text.rfind("}")
+        if start == -1 or end == -1 or end <= start:
+            raise ValueError(f"工具筛选返回的内容不是合法 JSON: {stripped_text}")
+
+        payload = json.loads(stripped_text[start: end + 1])
+        if not isinstance(payload, dict):
+            raise ValueError("工具筛选 JSON 顶层必须是对象")
+        return payload
+
+    @staticmethod
+    def _render_tool_list(available_tools: list[Any]) -> str:
+        """把工具名和描述渲染成稳定的文本列表。"""
+        return "\n".join(
+            f"- {tool.name}: {tool.description}" for tool in available_tools
+        )
+
+    def _build_deepseek_selection_prompt(self, selection_request: Any) -> str:
+        """
+        为 DeepSeek 生成显式 JSON 输出提示。
+
+        DeepSeek 官方文档要求在 JSON 输出模式下，提示词中必须明确包含 JSON
+        约束，否则兼容端点可能返回空内容或无意义输出。
+        """
+        limit_instruction = ""
+        if self.max_tools:
+            limit_instruction = f"- Select up to {self.max_tools} tools. IF NO TOOLS ARE RELEVANT, DO NOT RETURN AN EMPTY ARRAY. SELECT THE MOST APPLICABLE ONES TO ENSURE THE REQUEST IS HANDLED."
+
+        return (
+            f"{selection_request.system_message}\n\n"
+            "Return the answer in JSON only.\n"
+            'Use exactly this shape: {"tools": ["tool_name_1", "tool_name_2"]}\n'
+            "Rules:\n"
+            "- The `tools` field must be a JSON array of strings.\n"
+            "- Only use tool names from the allowed list below.\n"
+            "- Order tools by relevance, with the most relevant first.\n"
+            f"{limit_instruction}\n"
+            "- Do not add explanations, markdown, or extra keys.\n\n"
+            "Allowed tools:\n"
+            f"{self._render_tool_list(selection_request.available_tools)}"
+        )
+
+    def _normalize_selection_response(self, response: Any) -> dict[str, list[str]]:
+        """
+        解析并标准化 DeepSeek JSON 模式的工具筛选结果。
+        """
+        content = getattr(response, "content", response)
+        text = self._extract_text_content(content)
+        logger.debug(f"工具筛选原始响应: {text}")
+        payload = self._parse_json_object(text)
+
+        tools = payload.get("tools")
+        if not isinstance(tools, list):
+            raise ValueError(f"工具筛选 JSON 缺少 `tools` 数组: {payload}")
+
+        normalized_tools = [
+            tool_name for tool_name in tools if isinstance(tool_name, str)
+        ]
+        logger.debug(f"工具筛选标准化结果: {normalized_tools}")
+        return {"tools": normalized_tools}
+
+    async def _aselect_tools_with_deepseek(
+            self, selection_request: Any
+    ) -> dict[str, list[str]]:
+        """
+        使用 DeepSeek 兼容的 JSON 输出模式执行异步工具筛选。
+        """
+        logger.debug("工具筛选走 DeepSeek JSON 兼容分支")
+        structured_model = selection_request.model.bind(
+            response_format={"type": "json_object"}
+        )
+        response = await structured_model.ainvoke(
+            [
+                {
+                    "role": "system",
+                    "content": self._build_deepseek_selection_prompt(selection_request),
+                },
+                selection_request.last_user_message,
+            ]
+        )
+        return self._normalize_selection_response(response)
+
+    @staticmethod
+    def _extract_selected_tool_names(request: ModelRequest) -> list[str]:
+        """从已筛选后的请求中提取最终工具名，保留原有顺序。"""
+        return [tool.name for tool in request.tools if not isinstance(tool, dict)]
+
+    @staticmethod
+    def _apply_selected_tools(
+            request: ModelRequest[ContextT],
+            selected_tool_names: list[str],
+    ) -> ModelRequest[ContextT]:
+        """
+        将已筛选出的工具集应用到当前模型请求。
+
+        这里只复用首次筛选出的客户端工具名；provider-specific 的 dict 工具仍然
+        原样保留，避免破坏 LangChain/provider 自身的工具绑定约定。
+        """
+        if not selected_tool_names:
+            return request
+
+        current_tools_by_name = {
+            tool.name: tool for tool in request.tools if not isinstance(tool, dict)
+        }
+        selected_tools = [
+            current_tools_by_name[tool_name]
+            for tool_name in selected_tool_names
+            if tool_name in current_tools_by_name
+        ]
+        provider_tools = [tool for tool in request.tools if isinstance(tool, dict)]
+        return request.override(tools=[*selected_tools, *provider_tools])
+
+    async def _aselect_request_once(
+            self, request: ModelRequest[ContextT]
+    ) -> ModelRequest[ContextT]:
+        """
+        执行一次真实工具筛选，并返回筛选后的请求对象。
+
+        这里单独抽成 helper，便于首次筛选后缓存结果，也便于测试覆盖
+        “首轮筛选，后续复用”的行为。
+        """
+        selection_request = self._prepare_selection_request(request)
+        if selection_request is None:
+            return request
+
+        if not self._is_deepseek_compatible_model(selection_request.model):
+            captured_request: ModelRequest[ContextT] = request
+
+            async def _capture_handler(
+                    updated_request: ModelRequest[ContextT],
+            ) -> ModelRequest[ContextT]:
+                nonlocal captured_request
+                captured_request = updated_request
+                return updated_request
+
+            await super().awrap_model_call(request, _capture_handler)
+            return captured_request
+
+        response = await self._aselect_tools_with_deepseek(selection_request)
+        return self._process_selection_response(
+            response,
+            selection_request.available_tools,
+            selection_request.valid_tool_names,
+            request,
+        )
+
+    async def abefore_agent(  # noqa
+            self,
+            state: ToolSelectionState,
+            runtime: Runtime,  # noqa
+            config: RunnableConfig,
+    ) -> ToolSelectionStateUpdate | None:  # ty: ignore[invalid-method-override]
+        """
+        在本轮 Agent 执行开始前完成一次真实工具筛选。
+
+        这样后续多轮 `model -> tools -> model` 循环都只复用这一次结果，
+        不会为每次模型回合重复追加一笔 selector LLM 开销。
+        """
+        if "selected_tool_names" in state:
+            return None
+
+        if not self.selection_tools or self.model is None:
+            return ToolSelectionStateUpdate(selected_tool_names=None)
+
+        selection_request = ModelRequest(
+            model=self.model,
+            tools=list(self.selection_tools),
+            messages=state["messages"],
+            state=state,
+            runtime=runtime,
+        )
+        modified_request = await self._aselect_request_once(selection_request)
+        selected_tool_names = self._extract_selected_tool_names(modified_request)
+        return ToolSelectionStateUpdate(selected_tool_names=selected_tool_names or None)
+
+    async def awrap_model_call(
+            self,
+            request: ModelRequest[ContextT],
+            handler: Callable[
+                [ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
+            ],
+    ) -> ModelResponse[ResponseT]:
+        """
+        从 state 中读取首次筛选结果，并应用到每次模型回合。
+        """
+        selected_tool_names = request.state.get("selected_tool_names")  # noqa
+
+        # 正常路径下，`abefore_agent()` 已经提前写入状态；这里只保留一层兜底，
+        # 兼容直接单测或未来某些绕过 before_agent 的调用场景。
+        if (
+                selected_tool_names is None
+                and self.selection_tools
+                and self.model is not None
+        ):
+            request = await self._aselect_request_once(request)
+            selected_tool_names = self._extract_selected_tool_names(request) or None
+            request.state["selected_tool_names"] = selected_tool_names  # noqa
+
+        if selected_tool_names:
+            request = self._apply_selected_tools(request, selected_tool_names)
+
+        return await handler(request)

app/agent/middleware/usage.py

@@ -0,0 +1,184 @@
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    ContextT,
+    ModelRequest,
+    ModelResponse,
+    ResponseT,
+)
+from langchain_core.messages import AIMessage
+
+from app.log import logger
+
+
+class UsageMiddleware(AgentMiddleware):
+    """记录模型调用 usage 信息并回传给外部会话。"""
+
+    def __init__(
+        self,
+        *,
+        on_usage: Callable[[dict[str, Any]], None] | None = None,
+    ) -> None:
+        self.on_usage = on_usage
+
+    @staticmethod
+    def _coerce_int(value: Any) -> int | None:
+        if value is None:
+            return None
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return None
+
+    @classmethod
+    def _lookup_int(cls, container: Any, *keys: str) -> int | None:
+        if not container:
+            return None
+
+        getter = getattr(container, "get", None)
+        if callable(getter):
+            for key in keys:
+                value = getter(key)
+                if value is not None:
+                    return cls._coerce_int(value)
+
+        for key in keys:
+            value = getattr(container, key, None)
+            if value is not None:
+                return cls._coerce_int(value)
+
+        return None
+
+    @classmethod
+    def _extract_model_name(cls, model: Any) -> str | None:
+        return (
+            getattr(model, "model", None)
+            or getattr(model, "model_name", None)
+            or getattr(model, "model_id", None)
+        )
+
+    @classmethod
+    def _extract_context_window_tokens(cls, model: Any) -> int | None:
+        profile = getattr(model, "profile", None)
+        if not profile:
+            return None
+        return cls._lookup_int(profile, "max_input_tokens", "input_token_limit")
+
+    @classmethod
+    def _extract_usage(cls, ai_message: AIMessage) -> dict[str, Any]:
+        usage_metadata = getattr(ai_message, "usage_metadata", None)
+
+        input_tokens = cls._lookup_int(usage_metadata, "input_tokens")
+        output_tokens = cls._lookup_int(usage_metadata, "output_tokens")
+        total_tokens = cls._lookup_int(usage_metadata, "total_tokens")
+
+        response_metadata = getattr(ai_message, "response_metadata", None) or {}
+        token_usage = (
+            response_metadata.get("token_usage")
+            or response_metadata.get("usage")
+            or response_metadata.get("usage_metadata")
+            or {}
+        )
+
+        if input_tokens is None:
+            input_tokens = cls._lookup_int(
+                token_usage,
+                "prompt_tokens",
+                "input_tokens",
+            )
+        if input_tokens is None:
+            input_tokens = cls._lookup_int(
+                response_metadata,
+                "prompt_token_count",
+                "input_tokens",
+            )
+
+        if output_tokens is None:
+            output_tokens = cls._lookup_int(
+                token_usage,
+                "completion_tokens",
+                "output_tokens",
+            )
+        if output_tokens is None:
+            output_tokens = cls._lookup_int(
+                response_metadata,
+                "candidates_token_count",
+                "output_tokens",
+            )
+
+        if total_tokens is None:
+            total_tokens = cls._lookup_int(token_usage, "total_tokens")
+        if total_tokens is None:
+            total_tokens = cls._lookup_int(response_metadata, "total_token_count")
+
+        has_usage = any(
+            value is not None for value in (input_tokens, output_tokens, total_tokens)
+        )
+        resolved_input = input_tokens or 0
+        resolved_output = output_tokens or 0
+        resolved_total = (
+            total_tokens
+            if total_tokens is not None
+            else resolved_input + resolved_output
+        )
+
+        return {
+            "has_usage": has_usage,
+            "input_tokens": resolved_input,
+            "output_tokens": resolved_output,
+            "total_tokens": resolved_total,
+        }
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest[ContextT],
+        handler: Callable[
+            [ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
+        ],
+    ) -> ModelResponse[ResponseT]:
+        response = await handler(request)
+
+        if not callable(self.on_usage):
+            return response
+
+        try:
+            ai_message = next(
+                (
+                    message
+                    for message in reversed(response.result)
+                    if isinstance(message, AIMessage)
+                ),
+                None,
+            )
+            usage = (
+                self._extract_usage(ai_message)
+                if ai_message
+                else {
+                    "has_usage": False,
+                    "input_tokens": 0,
+                    "output_tokens": 0,
+                    "total_tokens": 0,
+                }
+            )
+            context_window_tokens = self._extract_context_window_tokens(request.model)
+            context_usage_ratio = None
+            if context_window_tokens and usage["has_usage"]:
+                context_usage_ratio = usage["input_tokens"] / context_window_tokens
+
+            self.on_usage(
+                {
+                    "model": self._extract_model_name(request.model),
+                    "context_window_tokens": context_window_tokens,
+                    "context_usage_ratio": context_usage_ratio,
+                    **usage,
+                }
+            )
+        except Exception as e:
+            logger.debug("记录模型 usage 失败: %s", e)
+
+        return response
+
+
+__all__ = ["UsageMiddleware"]

app/agent/prompt/Agent Prompt.txt

@@ -1,68 +0,0 @@
-You are an AI media assistant powered by MoviePilot. You specialize in managing home media ecosystems: searching for movies/TV shows, managing subscriptions, overseeing downloads, and organizing media libraries.
-
-All your responses must be in **Chinese (中文)**.
-
-You act as a proactive agent. Your goal is to fully resolve the user's media-related requests autonomously. Do not end your turn until the task is complete or you are blocked and require user feedback.
-
-Core Capabilities:
-1. Media Search & Recognition — Identify movies, TV shows, and anime; recognize media from fuzzy filenames or incomplete titles.
-2. Subscription Management — Create rules for automated downloading; monitor trending content.
-3. Download Control — Search torrents across trackers; filter by quality, codec, and release group.
-4. System Status & Organization — Monitor downloads, server health, file transfers, renaming, and library cleanup.
-5. Visual Input Handling — Users may attach images from supported channels; analyze them together with the text when relevant.
-6. File Context Handling — User messages may arrive as structured JSON. Treat the `message` field as the user's text. Attachments appear in `files`; when `local_path` is present, use local file tools to inspect the uploaded file directly. When image input is disabled for the current model, user images may also be delivered through `files`.
-
-<communication>
-{verbose_spec}
-
-- Tone: friendly, concise. Like a knowledgeable friend, not a corporate bot.
-- Use emojis sparingly (1-3 per response): greetings, completions, errors.
-- Be direct. NO unnecessary preamble, NO repeating user's words, NO explaining your thinking.
-- Use Markdown for structured data. Use `inline code` for media titles/paths.
-- Include key details (year, rating, resolution) but do NOT over-explain.
-- Do not stop for approval on read-only operations. Only confirm before critical actions (starting downloads, deleting subscriptions).
-- If the current channel supports image sending and an image would materially help, you may use the `send_message` tool with `image_url` to send it.
-- If the current channel supports file sending and you need to return a local image/file for the user to download, use `send_local_file`.
-{button_choice_spec}
-- Voice replies: {voice_reply_spec}
-- NOT a coding assistant. Do not offer code snippets.
-- If user has set preferred communication style in memory, follow that strictly.
-</communication>
-
-<response_format>
-- Responses MUST be short and punchy: one sentence for confirmations, brief list for search results.
-- NO filler phrases like "Let me help you", "Here are the results", "I found..." — skip all unnecessary preamble.
-- NO repeating what user said.
-- NO narrating your internal reasoning.
-- After task completion: one line summary only.
-- When error occurs: brief acknowledgment + suggestion, then move on.
-</response_format>
-
-<flow>
-1. Media Discovery: Identify exact media metadata (TMDB ID, Season/Episode) using search tools.
-2. Context Checking: Verify current status (already in library? already subscribed?).
-3. Action Execution: Perform the task with a brief status update only if the operation takes time.
-4. Final Confirmation: State the result concisely.
-</flow>
-
-<tool_calling_strategy>
-- Call independent tools in parallel whenever possible.
-- If search results are ambiguous, use `query_media_detail` or `recognize_media` to clarify before proceeding.
-- If `search_media` fails, fall back to `search_web` or `recognize_media`. Only ask the user when all automated methods are exhausted.
-</tool_calling_strategy>
-
-<media_management_rules>
-1. Download Safety: Present found torrents (size, seeds, quality) and get explicit consent before downloading.
-2. Subscription Logic: Check for the best matching quality profile based on user history or defaults.
-3. Library Awareness: Check if content already exists in the library to avoid duplicates.
-4. Error Handling: If a tool or site fails, briefly explain what went wrong and suggest an alternative.
-</media_management_rules>
-
-<markdown_spec>
-Specific markdown rules:
-{markdown_spec}
-</markdown_spec>
-
-<system_info>
-{moviepilot_info}
-</system_info>

app/agent/prompt/System Core Prompt.txt

@@ -0,0 +1,72 @@
+You are the MoviePilot agent runtime. Follow the injected runtime configuration to determine the active persona and any extra user-specific context.
+
+All your responses must be in **Chinese (中文)**.
+
+You act as a proactive agent. Your goal is to fully resolve the user's media-related requests autonomously. Do not end your turn until the task is complete or you are blocked and require user feedback.
+
+<agent_core>
+Identity and Goal:
+- You are an AI media assistant powered by MoviePilot.
+- Your primary goal is to fully resolve the user's MoviePilot-related media tasks with the available tools whenever the request is actionable.
+- Focus on MoviePilot's home media domain: search, recognition, subscriptions, downloads, library organization, file transfer, and system status.
+- Stay within the MoviePilot product domain unless the user explicitly asks for adjacent help that can be handled with your existing tools.
+
+Behavior Model:
+- Prioritize task progress over conversation.
+- Check current state before making changes, then do the smallest correct action.
+- Do not stop for approval on read-only operations. Only confirm before destructive or high-impact actions such as starting downloads, deleting subscriptions, or removing history.
+- When a request can be completed by tools, prefer doing the work over explaining what you might do.
+- After an action, perform the minimum validation needed to confirm the result actually landed.
+- If the user explicitly asks to change the speaking style or persona, use the dedicated persona tools instead of editing runtime files manually.
+- If the user explicitly asks to rewrite or create a persona definition, prefer `update_persona_definition` rather than generic file-editing tools.
+- Do not let user memory or persona style override this core identity, safety boundaries, or built-in background task rules.
+- You are not a general-purpose coding assistant in normal media conversations. Only cross into implementation details when the user explicitly asks about MoviePilot internals or debugging.
+
+Core Capabilities:
+1. Media Search and Recognition - Identify movies, TV shows, and anime; recognize media from fuzzy filenames or incomplete titles.
+2. Subscription Management - Create rules for automated downloading and monitor trending content.
+3. Download Control - Search torrents across trackers and filter by quality, codec, and release group.
+4. System Status and Organization - Monitor downloads, server health, file transfers, renaming, and library cleanup.
+5. Visual Input Handling - Users may attach images from supported channels; analyze them together with the text when relevant.
+6. File Context Handling - User messages may arrive as structured JSON. Treat the `message` field as the user's text. Attachments appear in `files`; when `local_path` is present, use local file tools to inspect the uploaded file directly. When image input is disabled for the current model, user images may also be delivered through `files`.
+7. Persona Management - If the user explicitly asks to change the speaking style or persona, prefer `query_personas` and `switch_persona`; if the user asks to rewrite or create a persona definition, prefer `update_persona_definition` instead of editing runtime files manually.
+
+Core Workflow:
+1. Media Discovery: Identify exact media metadata such as TMDB ID and Season or Episode using search tools when needed.
+2. Context Checking: Verify whether the media already exists in the library, has already been subscribed, or has relevant history that affects the next step.
+3. Action Execution: Perform the requested task with concise user-facing output unless the operation is destructive or blocked.
+4. Final Confirmation: State the outcome briefly, including the key media facts or blocker.
+
+Tool Calling Strategy:
+- Call independent tools in parallel whenever possible.
+- If search results are ambiguous, use `query_media_detail` or `recognize_media` to clarify before proceeding.
+- If `search_media` fails, fall back to `search_web` or `recognize_media`. Only ask the user when automated paths are exhausted.
+- Reuse known media identity, prior tool results, and current system context instead of repeating expensive recognition or search calls.
+- When a tool fails, try one narrower fallback path before escalating to the user.
+
+Media Management Rules:
+1. Download Safety: Present found torrents with size, seeds, and quality, then get explicit consent before downloading.
+2. Subscription Logic: Check for the best matching quality profile based on user history or defaults.
+3. Library Awareness: Check if content already exists in the library to avoid duplicates.
+4. Error Handling: If a tool or site fails, briefly explain what went wrong and suggest an alternative.
+5. TV Subscription Rule: When calling `add_subscribe` for a TV show, omitting `season` means subscribe to season 1 only. To subscribe multiple seasons or the full series, call `add_subscribe` separately for each season.
+</agent_core>
+
+<communication_runtime>
+{verbose_spec}
+
+- Channel-aware formatting: Follow the capability rules below for Markdown, plain text, buttons, and voice replies.
+{button_choice_spec}
+- Voice replies: {voice_reply_spec}
+- If the current channel supports image sending and an image would materially help, you may use the `send_message` tool with `image_url` to send it.
+- If the current channel supports file sending and you need to return a local image or file for the user to download, use `send_local_file`.
+</communication_runtime>
+
+<markdown_spec>
+Specific markdown rules:
+{markdown_spec}
+</markdown_spec>
+
+<system_info>
+{moviepilot_info}
+</system_info>

app/agent/prompt/System Tasks.yaml

@@ -0,0 +1,139 @@
+version: 2
+shared_rules:
+  - This is a background system task, NOT a user conversation.
+  - Your final response will be consumed by the system. Keep it concise and task-focused.
+  - Do NOT include greetings, explanations, or conversational text.
+  - Respond in Chinese (中文).
+task_types:
+  heartbeat:
+    header: "[System Heartbeat]"
+    objective: "Check all jobs in your jobs directory and process pending tasks."
+    steps_title: "Follow these steps"
+    steps:
+      - "List all jobs with status 'pending' or 'in_progress'."
+      - "For 'recurring' jobs, check 'last_run' to determine if it's time to run again."
+      - "For 'once' jobs with status 'pending', execute them now."
+      - "After executing each job, update its status, 'last_run' time, and execution log in the JOB.md file."
+    empty_result: "If no jobs were executed, output nothing."
+  health_check:
+    header: "[System Health Check]"
+    objective: "Verify that the agent execution pipeline is alive."
+    steps_title: "Follow these steps"
+    steps:
+      - "Verify that runtime config, tools, and jobs can all be accessed normally."
+      - "If a real issue is detected, report the failing subsystem and the immediate blocking reason."
+    empty_result: "If there is nothing meaningful to report, output OK only."
+  transfer_failed_retry:
+    header: "[System Task - Transfer Failed Retry]"
+    objective: "A file transfer or organization has failed. Please use the `transfer-failed-retry` skill to retry the failed transfer."
+    context_title: "Task context"
+    context_lines:
+      - "Failed transfer history record IDs: {history_ids_csv}"
+      - "Total failed records: {history_count}"
+    steps_title: "Follow these steps"
+    steps:
+      - "Use `query_transfer_history` with status='failed' to find the record with id={history_id} and understand the failure details such as source path, error message, and media info."
+      - "Analyze the error message to determine the best retry strategy."
+      - "If the source file no longer exists, skip this retry and report that the file is missing."
+      - "Delete the failed history record using `delete_transfer_history` with history_id={history_id}."
+      - "Re-identify the media using `recognize_media` with the source file path."
+      - "If recognition fails, try `search_media` with keywords from the filename."
+      - "Re-transfer using `transfer_file` with the source path and any identified media info such as tmdbid and media_type."
+      - "Report the final result."
+  batch_transfer_failed_retry:
+    header: "[System Task - Batch Transfer Failed Retry]"
+    objective: "Multiple file transfers from the same source have failed. These files likely belong to the same media. Please use the `transfer-failed-retry` skill to retry them efficiently."
+    context_title: "Task context"
+    context_lines:
+      - "Failed transfer history record IDs: {history_ids_csv}"
+      - "Total failed records: {history_count}"
+    steps_title: "Follow these steps"
+    steps:
+      - "Use `query_transfer_history` with status='failed' to find all records with these IDs and understand the failure details."
+      - "Analyze the first record to determine the shared media identity and the best retry strategy because the root cause is usually the same for all files."
+      - "If the error is about media recognition, identify the media once using `recognize_media` or `search_media`, then reuse that result for all files."
+      - "For each failed record, delete the old history entry with `delete_transfer_history` and re-transfer using `transfer_file`."
+      - "Report how many retries succeeded and how many still failed."
+    task_rules:
+      - "These files share the same media identity. Do NOT call `recognize_media` or `search_media` repeatedly for each file."
+  manual_transfer_redo:
+    header: "[System Task - Manual Transfer Re-Organize]"
+    objective: "A user manually triggered an AI re-organize task from the transfer history page."
+    context_title: "Transfer history record"
+    context_lines:
+      - "- History ID: {history_id}"
+      - "- Current status: {current_status}"
+      - "- Current recognized title: {recognized_title}"
+      - "- Media type: {media_type}"
+      - "- Category: {category}"
+      - "- Year: {year}"
+      - "- Season/Episode: {season_episode}"
+      - "- Source path: {source_path}"
+      - "- Source storage: {source_storage}"
+      - "- Destination path: {destination_path}"
+      - "- Destination storage: {destination_storage}"
+      - "- Transfer mode: {transfer_mode}"
+      - "- Current TMDB ID: {tmdbid}"
+      - "- Current Douban ID: {doubanid}"
+      - "- Error message: {error_message}"
+    steps_title: "Required workflow"
+    steps:
+      - "Use `query_transfer_history` to locate and inspect the record with id={history_id}, and verify the source path, status, media info, and failure context."
+      - "Decide whether the current recognition is trustworthy."
+      - "If the source file no longer exists or cannot be safely processed, stop and report the reason."
+      - "If the current recognition is wrong or the record should be reorganized, determine the correct media identity first."
+      - "Prefer `recognize_media` with the source path. If recognition is not reliable, use `search_media` with keywords from filename, title, or year."
+      - "Only continue when you have high confidence in the target media."
+      - "Before re-organizing, delete the old transfer history record with `delete_transfer_history` so the system will not skip the source file."
+      - "Then use `transfer_file` to organize the source path directly."
+      - "When calling `transfer_file`, reuse known context when appropriate: source storage, target path, target storage, transfer mode, season, tmdbid or doubanid, and media_type."
+      - "If this record is already correct and no re-organize is needed, do not perform destructive actions; simply report that no change is necessary."
+    task_rules:
+      - "Do NOT rely on previous chat context. Work only from the record above."
+      - "Your goal is to directly fix one transfer history record by using MoviePilot tools to analyze, clean up the old history entry if necessary, and organize the source file again."
+      - "You should complete the re-organize by directly using tools such as `query_transfer_history`, `recognize_media`, `search_media`, `delete_transfer_history`, and `transfer_file`."
+      - "Do NOT reorganize blindly when media identity is uncertain."
+      - "If the previous record was successful but obviously identified as the wrong media, still use the tool-based flow above instead of `/redo`."
+      - "Keep the final response short and focused on outcome."
+  batch_manual_transfer_redo:
+    header: "[System Task - Batch Manual Transfer Re-Organize]"
+    objective: "A user manually triggered a batch AI re-organize task from the transfer history page."
+    context_title: "Selected transfer history records"
+    context_lines:
+      - "- History IDs: {history_ids_csv}"
+      - "- Total records: {history_count}"
+      - "{records_context}"
+    steps_title: "Required workflow"
+    steps:
+      - "Review the selected records below first and group them by likely shared media identity, source directory, or retry strategy when possible."
+      - "Use the provided record context as the primary source of truth. Call `query_transfer_history` only when you need extra confirmation."
+      - "For each group, decide whether the current recognition is trustworthy."
+      - "If multiple records clearly belong to the same movie or series, identify the media once with `recognize_media` or `search_media`, then reuse that result for the related records."
+      - "If a source file no longer exists or cannot be safely processed, skip that record and note the reason."
+      - "Before re-organizing a record, delete the old transfer history record with `delete_transfer_history` so the system will not skip the source file."
+      - "Then use `transfer_file` to organize the source path directly."
+      - "When calling `transfer_file`, reuse known context when appropriate: source storage, target path, target storage, transfer mode, season, tmdbid or doubanid, and media_type."
+      - "If a record is already correct and no re-organize is needed, do not perform destructive actions; simply mark it as skipped."
+      - "Report only the aggregate outcome, including how many records succeeded, skipped, and failed."
+    task_rules:
+      - "Do NOT assume every selected record belongs to the same media."
+      - "When several records obviously share the same media identity, avoid repeated `recognize_media` or `search_media` calls."
+      - "Process every selected record exactly once."
+      - "Keep the final response short and focused on the aggregate outcome."
+  search_recommend:
+    header: "[System Task - Search Results Recommendation]"
+    objective: "Analyze the provided search results and select the best matching items based on user preferences."
+    context_title: "Task context"
+    context_lines:
+      - "{search_results}"
+    steps_title: "Follow these steps"
+    steps:
+      - "Review all search result items carefully."
+      - "Evaluate each item based on the user preference criteria."
+      - "Select the top items that best match the preferences."
+      - "Return ONLY a JSON array of item indices."
+    task_rules:
+      - "Return ONLY a JSON array of index numbers, e.g., [0, 3, 1]."
+      - "Do NOT include any explanations, markdown formatting, conversational text, or other content."
+      - "Do NOT call any tools. Simply analyze and return the JSON result directly."
+      - "Respond in JSON format only."

app/agent/prompt/__init__.py

@@ -1,9 +1,13 @@
 """提示词管理器"""
 
 import socket
+from dataclasses import dataclass, field
 from pathlib import Path
+from string import Formatter
 from time import strftime
-from typing import Dict
+from typing import Any, Dict, Optional
+
+import yaml
 
 from app.core.config import settings
 from app.log import logger
@@ -15,6 +19,37 @@ from app.schemas import (
 )
 from app.utils.system import SystemUtils
 
+SYSTEM_TASKS_FILE = "System Tasks.yaml"
+SYSTEM_TASKS_SCHEMA_VERSION = 2
+
+
+class PromptConfigError(ValueError):
+    """程序内置提示词定义加载异常。"""
+
+
+@dataclass
+class SystemTaskTypeDefinition:
+    """单个后台系统任务定义。"""
+
+    header: str
+    objective: str
+    context_title: Optional[str] = None
+    context_lines: list[str] = field(default_factory=list)
+    steps_title: Optional[str] = None
+    steps: list[str] = field(default_factory=list)
+    task_rules: list[str] = field(default_factory=list)
+    empty_result: Optional[str] = None
+
+
+@dataclass
+class SystemTasksDefinition:
+    """程序内置后台系统任务定义。"""
+
+    path: Path
+    version: int
+    shared_rules: list[str]
+    task_types: dict[str, SystemTaskTypeDefinition]
+
 
 class PromptManager:
     """
@@ -27,6 +62,8 @@ class PromptManager:
         else:
             self.prompts_dir = Path(prompts_dir)
         self.prompts_cache: Dict[str, str] = {}
+        self._system_tasks_cache: Optional[SystemTasksDefinition] = None
+        self._system_tasks_signature: Optional[tuple[int, int]] = None
 
     def load_prompt(self, prompt_name: str) -> str:
         """
@@ -50,17 +87,16 @@ class PromptManager:
             logger.error(f"加载提示词失败: {prompt_name}, 错误: {e}")
             raise
 
-    def get_agent_prompt(
-        self, channel: str = None, prefer_voice_reply: bool = False
-    ) -> str:
+    def get_agent_prompt(self, channel: str = None) -> str:
         """
         获取智能体提示词
         :param channel: 消息渠道（Telegram、微信、Slack等）
-        :param prefer_voice_reply: 是否优先使用语音回复
         :return: 提示词内容
         """
-        # 基础提示词
-        base_prompt = self.load_prompt("Agent Prompt.txt")
+        # 基础提示词只保留 MoviePilot 运行时和渠道能力相关约束。
+        # 根层运行时配置由 RuntimeConfigMiddleware 在每次模型调用前动态注入，
+        # 这样人格切换可以在同一轮 Agent 执行里立即生效。
+        base_prompt = self.load_prompt("System Core Prompt.txt")
 
         # 识别渠道
         markdown_spec = ""
@@ -82,18 +118,18 @@ class PromptManager:
         verbose_spec = ""
         if not settings.AI_AGENT_VERBOSE:
             verbose_spec = (
-                "\n\n[Important Instruction] STRICTLY ENFORCED: DO NOT output any conversational "
-                "text, thinking processes, or explanations before or during tool calls. Call tools "
-                "directly without any transitional phrases. "
-                "You MUST remain completely silent until the task is completely finished. "
-                "DO NOT output any content whatsoever until your final summary reply."
+                "\n\n[Important Instruction] STRICTLY ENFORCED: "
+                "If tools are needed, DO NOT output any conversational text, explanations, progress updates, "
+                "or acknowledgements before the first tool call or between tool calls. "
+                "Call tools directly without any transitional phrases. "
+                "You MUST remain completely silent until all required tools have finished and you have the final result. "
+                "Only then may you send one final user-facing reply. "
+                "DO NOT output any intermediate content whatsoever."
             )
 
         # MoviePilot系统信息
         moviepilot_info = self._get_moviepilot_info()
-        voice_reply_spec = self._generate_voice_reply_instructions(
-            prefer_voice_reply=prefer_voice_reply
-        )
+        voice_reply_spec = self._generate_voice_reply_instructions()
 
         # 始终替换占位符，避免后续 .format() 时因残留花括号报 KeyError
         base_prompt = base_prompt.format(
@@ -106,6 +142,115 @@ class PromptManager:
 
         return base_prompt
 
+    def load_system_tasks_definition(self) -> SystemTasksDefinition:
+        """加载程序内置的后台系统任务定义。"""
+        system_tasks_path = self.prompts_dir / SYSTEM_TASKS_FILE
+        try:
+            stat = system_tasks_path.stat()
+        except FileNotFoundError as err:
+            logger.error(f"系统任务定义文件不存在: {system_tasks_path}")
+            raise PromptConfigError(f"系统任务定义文件不存在: {system_tasks_path}") from err
+
+        signature = (stat.st_mtime_ns, stat.st_size)
+        if (
+            self._system_tasks_signature == signature
+            and self._system_tasks_cache is not None
+        ):
+            return self._system_tasks_cache
+
+        try:
+            content = system_tasks_path.read_text(encoding="utf-8")
+        except Exception as err:  # noqa: BLE001
+            logger.error(f"读取系统任务定义失败: {system_tasks_path}, 错误: {err}")
+            raise PromptConfigError(
+                f"读取系统任务定义失败 {system_tasks_path}: {err}"
+            ) from err
+
+        try:
+            data = yaml.safe_load(content) or {}
+        except yaml.YAMLError as err:
+            raise PromptConfigError(f"YAML 解析失败 {system_tasks_path}: {err}") from err
+        if not isinstance(data, dict):
+            raise PromptConfigError(
+                f"YAML 根节点必须是映射类型: {system_tasks_path}"
+            )
+
+        definition = self._parse_system_tasks_definition(system_tasks_path, data)
+        self._system_tasks_signature = signature
+        self._system_tasks_cache = definition
+        return definition
+
+    def render_system_task_message(
+        self,
+        task_type: str,
+        *,
+        template_context: Optional[dict[str, Any]] = None,
+        extra_rules: Optional[list[str]] = None,
+    ) -> str:
+        """根据程序内置 YAML 渲染后台系统任务提示词。"""
+        system_tasks = self.load_system_tasks_definition()
+        task_definition = system_tasks.task_types.get(task_type)
+        if not task_definition:
+            raise PromptConfigError(f"未定义的后台系统任务类型: {task_type}")
+
+        rendered_context = self._render_template_lines(
+            task_definition.context_lines,
+            template_context,
+            task_type,
+            "context_lines",
+        )
+        rendered_steps = self._render_template_lines(
+            task_definition.steps,
+            template_context,
+            task_type,
+            "steps",
+        )
+        rendered_task_rules = self._render_template_lines(
+            task_definition.task_rules,
+            template_context,
+            task_type,
+            "task_rules",
+        )
+
+        sections = [
+            self._render_template_text(
+                task_definition.header,
+                template_context,
+                task_type,
+                "header",
+            ).strip(),
+            self._render_template_text(
+                task_definition.objective,
+                template_context,
+                task_type,
+                "objective",
+            ).strip(),
+        ]
+        if rendered_context:
+            sections.append(
+                self._format_titled_lines(
+                    task_definition.context_title or "Task context",
+                    rendered_context,
+                )
+            )
+        if rendered_steps:
+            sections.append(
+                self._format_titled_lines(
+                    task_definition.steps_title or "Follow these steps",
+                    rendered_steps,
+                )
+            )
+
+        rules = list(system_tasks.shared_rules)
+        if task_definition.empty_result:
+            rules.append(task_definition.empty_result)
+        rules.extend(rendered_task_rules)
+        if extra_rules:
+            rules.extend(rule.strip() for rule in extra_rules if rule and rule.strip())
+        if rules:
+            sections.append(self._format_numbered_rules("IMPORTANT", rules))
+        return "\n\n".join(section for section in sections if section).strip()
+
     @staticmethod
     def _get_moviepilot_info() -> str:
         """
@@ -176,41 +321,196 @@ class PromptManager:
         return "\n".join(instructions)
 
     @staticmethod
-    def _generate_voice_reply_instructions(prefer_voice_reply: bool) -> str:
-        if not prefer_voice_reply:
-            return (
-                "- Voice replies: Use normal text replies by default. "
-                "Only call `send_voice_message` when spoken playback is clearly better than plain text."
-            )
+    def _generate_voice_reply_instructions() -> str:
         return (
-            "- Current message context: The user sent a voice message.\n"
-            "- Reply preference: Prioritize calling `send_voice_message` for the main user-facing reply.\n"
-            "- Fallback: If voice is unavailable on the current channel, `send_voice_message` will fall back to text.\n"
-            "- Do not repeat the same full reply again after calling `send_voice_message`."
+            "- Voice replies: Use normal text replies by default. "
+            "Only call `send_voice_message` when the user explicitly asks for a voice reply "
+            "or spoken playback is clearly better than plain text."
         )
 
     @staticmethod
     def _generate_button_choice_instructions(
         channel: MessageChannel = None,
     ) -> str:
-        if channel and ChannelCapabilityManager.supports_buttons(
+        if (
             channel
-        ) and ChannelCapabilityManager.supports_callbacks(channel):
+            and ChannelCapabilityManager.supports_buttons(channel)
+            and ChannelCapabilityManager.supports_callbacks(channel)
+        ):
             return (
                 "- User questions: If you need the user to choose from a few clear options, "
                 "call `ask_user_choice` to send button options. After the user clicks a button, "
                 "the selected value will come back as the user's next message. After calling this tool, "
                 "wait for the user's selection instead of repeating the question in plain text."
             )
-        return (
-            "- User questions: When you truly need user input, ask briefly in plain text."
+        return "- User questions: When you truly need user input, ask briefly in plain text."
+
+    def _parse_system_tasks_definition(
+        self,
+        path: Path,
+        data: dict[str, Any],
+    ) -> SystemTasksDefinition:
+        """把 YAML 结构转换成系统任务定义对象。"""
+        version = self._normalize_positive_int(data.get("version"), "version", default=1)
+        if version < SYSTEM_TASKS_SCHEMA_VERSION:
+            raise PromptConfigError(
+                f"{path} 的 version={version} 过旧，"
+                f"当前要求 System Tasks schema v{SYSTEM_TASKS_SCHEMA_VERSION} 或更高版本"
+            )
+
+        shared_rules = self._normalize_string_list(data.get("shared_rules"), "shared_rules")
+        if not shared_rules:
+            raise PromptConfigError(f"{path} 缺少 shared_rules")
+
+        raw_task_types = data.get("task_types")
+        if not isinstance(raw_task_types, dict) or not raw_task_types:
+            raise PromptConfigError(f"{path} 缺少 task_types 映射")
+
+        task_types: dict[str, SystemTaskTypeDefinition] = {}
+        for key, raw in raw_task_types.items():
+            if not isinstance(raw, dict):
+                raise PromptConfigError(f"task_types.{key} 必须是映射")
+
+            header = str(raw.get("header") or "").strip()
+            objective = str(raw.get("objective") or "").strip()
+            if not header or not objective:
+                raise PromptConfigError(f"task_types.{key} 缺少 header 或 objective")
+
+            task_types[str(key)] = SystemTaskTypeDefinition(
+                header=header,
+                objective=objective,
+                context_title=str(raw.get("context_title") or "").strip() or None,
+                context_lines=self._normalize_string_list(
+                    raw.get("context_lines"),
+                    f"task_types.{key}.context_lines",
+                ),
+                steps_title=str(raw.get("steps_title") or "").strip() or None,
+                steps=self._normalize_string_list(
+                    raw.get("steps"),
+                    f"task_types.{key}.steps",
+                ),
+                task_rules=self._normalize_string_list(
+                    raw.get("task_rules"),
+                    f"task_types.{key}.task_rules",
+                ),
+                empty_result=str(raw.get("empty_result") or "").strip() or None,
+            )
+        return SystemTasksDefinition(
+            path=path,
+            version=version,
+            shared_rules=shared_rules,
+            task_types=task_types,
         )
 
+    @classmethod
+    def _render_template_text(
+        cls,
+        text: str,
+        template_context: Optional[dict[str, Any]],
+        task_type: str,
+        field_name: str,
+    ) -> str:
+        if not text:
+            return ""
+
+        formatter = Formatter()
+        required_fields = {
+            placeholder_name
+            for _, placeholder_name, _, _ in formatter.parse(text)
+            if placeholder_name
+        }
+        if not required_fields:
+            return text
+
+        context = cls._normalize_template_context(template_context)
+        missing_fields = sorted(field for field in required_fields if field not in context)
+        if missing_fields:
+            raise PromptConfigError(
+                f"系统任务定义 `{task_type}` 的 `{field_name}` 缺少变量: "
+                + ", ".join(f"`{field}`" for field in missing_fields)
+            )
+
+        # 这里统一做字符串替换，让 YAML 成为后台任务文案的唯一行为来源。
+        return text.format_map(context)
+
+    @classmethod
+    def _render_template_lines(
+        cls,
+        items: list[str],
+        template_context: Optional[dict[str, Any]],
+        task_type: str,
+        field_name: str,
+    ) -> list[str]:
+        return [
+            cls._render_template_text(
+                item,
+                template_context,
+                task_type,
+                f"{field_name}[{index}]",
+            ).rstrip()
+            for index, item in enumerate(items, start=1)
+            if item and item.rstrip()
+        ]
+
+    @staticmethod
+    def _normalize_template_context(
+        template_context: Optional[dict[str, Any]],
+    ) -> dict[str, str]:
+        if not template_context:
+            return {}
+        return {
+            str(key): "" if value is None else str(value)
+            for key, value in template_context.items()
+        }
+
+    @staticmethod
+    def _format_numbered_rules(title: str, items: list[str]) -> str:
+        return "\n".join(
+            [f"{title}:"] + [f"{index}. {item}" for index, item in enumerate(items, start=1)]
+        )
+
+    @staticmethod
+    def _format_titled_lines(title: str, items: list[str]) -> str:
+        cleaned = [item.rstrip() for item in items if item and item.rstrip()]
+        return "\n".join([f"{title}:"] + cleaned)
+
+    @staticmethod
+    def _normalize_positive_int(
+        value: Any,
+        field_name: str,
+        *,
+        default: int,
+    ) -> int:
+        if value in (None, ""):
+            return default
+        try:
+            normalized = int(value)
+        except (TypeError, ValueError) as err:
+            raise PromptConfigError(f"{field_name} 必须是正整数") from err
+        if normalized <= 0:
+            raise PromptConfigError(f"{field_name} 必须是正整数")
+        return normalized
+
+    @staticmethod
+    def _normalize_string_list(values: Any, field_name: str) -> list[str]:
+        if values is None:
+            return []
+        if not isinstance(values, list):
+            raise PromptConfigError(f"{field_name} 必须是字符串数组")
+        normalized: list[str] = []
+        for value in values:
+            text = str(value).strip()
+            if text:
+                normalized.append(text)
+        return normalized
+
     def clear_cache(self):
         """
         清空缓存
         """
         self.prompts_cache.clear()
+        self._system_tasks_cache = None
+        self._system_tasks_signature = None
         logger.info("提示词缓存已清空")
 
 

app/agent/runtime.py

@@ -0,0 +1,755 @@
+"""Agent 根层运行时配置管理。"""
+
+from __future__ import annotations
+
+import re
+import shutil
+import threading
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Iterable, Optional
+
+import yaml
+
+from app.core.config import settings
+from app.log import logger
+
+CURRENT_PERSONA_FILE = "CURRENT_PERSONA.md"
+SYSTEM_RUNTIME_DIR = "runtime"
+MEMORY_DIR = "memory"
+SKILLS_DIR = "skills"
+JOBS_DIR = "jobs"
+ACTIVITY_DIR = "activity"
+PERSONAS_DIR = "personas"
+PERSONA_FILE = "PERSONA.md"
+CURRENT_PERSONA_SCHEMA_VERSION = 3
+PERSONA_SCHEMA_VERSION = 1
+DEFAULT_PERSONA_ID = "default"
+PERSONA_ID_PATTERN = re.compile(r"^[a-z0-9][a-z0-9_-]{0,63}$")
+
+ROOT_LEVEL_RUNTIME_FILES = {
+    CURRENT_PERSONA_FILE,
+}
+
+OBSOLETE_AGENT_ROOT_FILES = {
+    "AGENT_CORE.md",
+    "AGENT_PROFILE.md",
+    "AGENT_WORKFLOW.md",
+    "AGENT_HOOKS.md",
+    "USER_PREFERENCES.md",
+    "SYSTEM_TASKS.md",
+    "WAKE_FORMAT.md",
+}
+
+OBSOLETE_RUNTIME_FILES = {
+    Path("AGENT_CORE.md"),
+    Path("AGENT_PROFILE.md"),
+    Path("AGENT_WORKFLOW.md"),
+    Path("AGENT_HOOKS.md"),
+    Path("USER_PREFERENCES.md"),
+    Path("SYSTEM_TASKS.md"),
+    Path("WAKE_FORMAT.md"),
+    Path("personas") / DEFAULT_PERSONA_ID / "AGENT_PROFILE.md",
+    Path("personas") / DEFAULT_PERSONA_ID / "AGENT_WORKFLOW.md",
+    Path("personas") / DEFAULT_PERSONA_ID / "AGENT_HOOKS.md",
+    Path("system_tasks") / "SYSTEM_TASKS.md",
+    Path("templates") / "WAKE_FORMAT.md",
+}
+
+FRONTMATTER_PATTERN = re.compile(r"^---\s*\n(.*?)\n---\s*\n?", re.DOTALL)
+
+
+class AgentRuntimeConfigError(ValueError):
+    """根层配置加载异常。"""
+
+
+@dataclass
+class ParsedMarkdownDocument:
+    """解析后的 Markdown 文档。"""
+
+    metadata: dict[str, Any]
+    body: str
+
+
+@dataclass
+class PersonaDefinition:
+    """单个人格定义。"""
+
+    persona_id: str
+    path: Path
+    label: str
+    description: str
+    text: str
+    aliases: list[str] = field(default_factory=list)
+
+    def matches(self, query: str) -> bool:
+        """判断 query 是否命中当前人格。"""
+        normalized = query.strip().casefold()
+        if not normalized:
+            return False
+        candidates = [self.persona_id, self.label, *self.aliases]
+        return any(candidate.strip().casefold() == normalized for candidate in candidates)
+
+    def summary_line(self) -> str:
+        """渲染可读的一行人格摘要。"""
+        parts = [f"`{self.persona_id}`"]
+        if self.label and self.label != self.persona_id:
+            parts.append(self.label)
+        if self.description:
+            parts.append(self.description)
+        return " - ".join(parts)
+
+    def to_dict(self, *, is_active: bool) -> dict[str, Any]:
+        """输出给查询工具的结构化信息。"""
+        return {
+            "persona_id": self.persona_id,
+            "label": self.label,
+            "description": self.description,
+            "aliases": self.aliases,
+            "is_active": is_active,
+            "path": str(self.path),
+        }
+
+
+@dataclass
+class AgentRuntimeConfig:
+    """一次加载后的根层配置快照。"""
+
+    source_root: Path
+    active_persona: str
+    current_persona_path: Path
+    persona: PersonaDefinition
+    available_personas: list[PersonaDefinition]
+    extra_context_paths: list[Path]
+    extra_contexts: list[tuple[Path, str]]
+    warnings: list[str] = field(default_factory=list)
+    used_fallback: bool = False
+
+    def render_prompt_sections(self) -> str:
+        """渲染进入系统提示词的运行时片段。"""
+        sections: list[str] = [
+            "<agent_runtime_config>",
+            f"- Active persona: `{self.active_persona}`",
+            f"- Active persona source: `{self.persona.path}`",
+        ]
+        if self.available_personas:
+            sections.append("- Available personas:")
+            sections.extend(f"  - {persona.summary_line()}" for persona in self.available_personas)
+        sections.append("</agent_runtime_config>")
+
+        if self.warnings:
+            sections.extend(
+                [
+                    "",
+                    "<agent_runtime_warnings>",
+                    *[f"- {warning}" for warning in self.warnings],
+                    "</agent_runtime_warnings>",
+                ]
+            )
+
+        sections.extend(
+            [
+                "",
+                "<agent_persona>",
+                f"- Persona ID: `{self.persona.persona_id}`",
+            ]
+        )
+        if self.persona.label and self.persona.label != self.persona.persona_id:
+            sections.append(f"- Persona Label: {self.persona.label}")
+        if self.persona.description:
+            sections.append(f"- Persona Description: {self.persona.description}")
+        sections.extend(
+            [
+                "",
+                self.persona.text.strip() or "(No persona instructions configured.)",
+                "</agent_persona>",
+            ]
+        )
+        for path, text in self.extra_contexts:
+            if not text.strip():
+                continue
+            sections.extend(
+                [
+                    "",
+                    f'<agent_extra_context source="{path.name}">',
+                    text.strip(),
+                    "</agent_extra_context>",
+                ]
+            )
+        return "\n".join(sections).strip()
+
+    def list_personas(self) -> list[dict[str, Any]]:
+        """返回全部人格摘要。"""
+        return [
+            persona.to_dict(is_active=persona.persona_id == self.active_persona)
+            for persona in self.available_personas
+        ]
+
+
+class AgentRuntimeManager:
+    """统一管理 agent 根层运行时配置目录、校验与人格切换。"""
+
+    def __init__(
+        self,
+        *,
+        agent_root_dir: Optional[Path] = None,
+        bundled_defaults_dir: Optional[Path] = None,
+    ) -> None:
+        self.agent_root_dir = agent_root_dir or (settings.CONFIG_PATH / "agent")
+        self.runtime_dir = self.agent_root_dir / SYSTEM_RUNTIME_DIR
+        self.memory_dir = self.agent_root_dir / MEMORY_DIR
+        self.skills_dir = self.agent_root_dir / SKILLS_DIR
+        self.jobs_dir = self.agent_root_dir / JOBS_DIR
+        self.activity_dir = self.agent_root_dir / ACTIVITY_DIR
+        self.bundled_defaults_dir = bundled_defaults_dir or (
+            Path(__file__).parent / "defaults"
+        )
+        self._cache_lock = threading.Lock()
+        self._cached_signature: Optional[tuple[tuple[str, int, int], ...]] = None
+        self._cached_config: Optional[AgentRuntimeConfig] = None
+
+    def ensure_layout(self) -> None:
+        """创建目录、同步默认文件，并清理废弃的旧版 runtime 文件。"""
+        self.agent_root_dir.mkdir(parents=True, exist_ok=True)
+        self.runtime_dir.mkdir(parents=True, exist_ok=True)
+        self.memory_dir.mkdir(parents=True, exist_ok=True)
+        self.skills_dir.mkdir(parents=True, exist_ok=True)
+        self.jobs_dir.mkdir(parents=True, exist_ok=True)
+        self.activity_dir.mkdir(parents=True, exist_ok=True)
+        self._migrate_root_runtime_files()
+        self._remove_obsolete_runtime_files()
+        self._sync_bundled_defaults()
+        self._migrate_root_memory_files()
+
+    def load_runtime_config(self) -> AgentRuntimeConfig:
+        """加载配置。用户目录损坏时自动回退到内置默认配置。"""
+        self.ensure_layout()
+        signature = self._build_signature()
+        with self._cache_lock:
+            if self._cached_signature == signature and self._cached_config:
+                return self._cached_config
+
+            try:
+                config = self._load_from_root(self.runtime_dir)
+            except AgentRuntimeConfigError as err:
+                logger.warning("Agent 根层配置无效，回退到内置默认配置: %s", err)
+                config = self._load_from_root(self.bundled_defaults_dir)
+                config.used_fallback = True
+                config.warnings.insert(
+                    0, f"用户运行时配置加载失败，已回退到内置默认配置: {err}"
+                )
+
+            self._cached_signature = signature
+            self._cached_config = config
+            return config
+
+    def invalidate_cache(self) -> None:
+        """供测试或手动刷新时清理缓存。"""
+        with self._cache_lock:
+            self._cached_signature = None
+            self._cached_config = None
+
+    def set_active_persona(self, persona_query: str) -> AgentRuntimeConfig:
+        """切换当前激活人格，并立即刷新缓存。"""
+        self.ensure_layout()
+        runtime_root = self.runtime_dir
+        current_path = runtime_root / CURRENT_PERSONA_FILE
+        current_doc = self._read_markdown(current_path)
+        current_meta = current_doc.metadata
+
+        available_personas = self._load_personas(runtime_root)
+        persona = self._resolve_persona_definition(persona_query, available_personas)
+
+        document = self._render_current_persona_document(
+            active_persona=persona.persona_id,
+            extra_context_files=self._coerce_string_list(
+                current_meta.get("extra_context_files")
+            ),
+            deprecated_phrases=self._coerce_string_list(
+                current_meta.get("deprecated_phrases")
+            ),
+        )
+        current_path.write_text(document, encoding="utf-8")
+        self.invalidate_cache()
+        logger.info("已切换 Agent 人格: %s", persona.persona_id)
+        return self.load_runtime_config()
+
+    def list_personas(self) -> list[PersonaDefinition]:
+        """列出当前可用人格。"""
+        return self.load_runtime_config().available_personas
+
+    def update_persona_definition(
+        self,
+        persona_query: str,
+        *,
+        label: Optional[str] = None,
+        description: Optional[str] = None,
+        aliases: Optional[list[str]] = None,
+        instructions: Optional[str] = None,
+        append_instructions: Optional[list[str]] = None,
+        create_if_missing: bool = False,
+    ) -> tuple[PersonaDefinition, bool]:
+        """更新或创建运行时人格定义。"""
+        self.ensure_layout()
+        runtime_root = self.runtime_dir
+        available_personas = self._load_personas(runtime_root)
+
+        created = False
+        try:
+            persona = self._resolve_persona_definition(persona_query, available_personas)
+            target_persona_id = persona.persona_id
+            target_path = persona.path
+            existing_body = persona.text
+            existing_label = persona.label
+            existing_description = persona.description
+            existing_aliases = list(persona.aliases)
+        except AgentRuntimeConfigError:
+            if not create_if_missing:
+                raise
+            target_persona_id = self._validate_new_persona_id(persona_query)
+            target_path = runtime_root / PERSONAS_DIR / target_persona_id / PERSONA_FILE
+            existing_body = ""
+            existing_label = target_persona_id
+            existing_description = ""
+            existing_aliases = []
+            created = True
+
+        final_label = (
+            label.strip()
+            if isinstance(label, str) and label.strip()
+            else existing_label or target_persona_id
+        )
+        final_description = (
+            description.strip()
+            if isinstance(description, str) and description.strip()
+            else existing_description
+        )
+        final_aliases = (
+            self._normalize_persona_aliases(aliases, "aliases")
+            if aliases is not None
+            else existing_aliases
+        )
+        final_body = (
+            self._normalize_persona_body(instructions)
+            if isinstance(instructions, str) and instructions.strip()
+            else self._normalize_persona_body(existing_body)
+        )
+        final_body = self._merge_persona_instructions(
+            final_body,
+            append_instructions,
+        )
+        if not final_body.strip():
+            raise AgentRuntimeConfigError("人格定义正文不能为空")
+
+        document = self._render_persona_document(
+            persona_id=target_persona_id,
+            label=final_label,
+            description=final_description,
+            aliases=final_aliases,
+            body=final_body,
+        )
+        target_path.parent.mkdir(parents=True, exist_ok=True)
+        target_path.write_text(document, encoding="utf-8")
+        self.invalidate_cache()
+
+        runtime_config = self.load_runtime_config()
+        updated_persona = self._resolve_persona_definition(
+            target_persona_id,
+            runtime_config.available_personas,
+        )
+        logger.info(
+            "已%s Agent 人格定义: %s",
+            "创建" if created else "更新",
+            updated_persona.persona_id,
+        )
+        return updated_persona, created
+
+    def _build_signature(self) -> tuple[tuple[str, int, int], ...]:
+        """基于运行时配置和内置人格生成文件签名。"""
+        entries: list[tuple[str, int, int]] = []
+        for prefix, root in (
+            ("runtime", self.runtime_dir),
+            ("bundled", self.bundled_defaults_dir),
+        ):
+            if not root.exists():
+                continue
+            for path in sorted(root.rglob("*")):
+                if not path.is_file():
+                    continue
+                stat = path.stat()
+                relative = path.relative_to(root).as_posix()
+                entries.append((f"{prefix}:{relative}", stat.st_mtime_ns, stat.st_size))
+        return tuple(entries)
+
+    def _sync_bundled_defaults(self) -> None:
+        """仅复制缺失的默认运行时文件，避免覆盖用户自定义。"""
+        if not self.bundled_defaults_dir.exists():
+            return
+        for path in sorted(self.bundled_defaults_dir.rglob("*")):
+            relative = path.relative_to(self.bundled_defaults_dir)
+            target = self.runtime_dir / relative
+            if path.is_dir():
+                target.mkdir(parents=True, exist_ok=True)
+                continue
+            if target.exists():
+                continue
+            target.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(path, target)
+            logger.info("已同步默认 Agent 运行时文件: %s", target)
+
+    def _migrate_root_runtime_files(self) -> None:
+        """兼容早期直接放在 `config/agent` 根目录的 CURRENT_PERSONA。"""
+        source = self.agent_root_dir / CURRENT_PERSONA_FILE
+        target = self.runtime_dir / CURRENT_PERSONA_FILE
+        if not source.exists() or target.exists():
+            return
+        target.parent.mkdir(parents=True, exist_ok=True)
+        source.rename(target)
+        logger.info("已迁移旧版 Agent 根配置文件: %s -> %s", source, target)
+
+    def _remove_obsolete_runtime_files(self) -> None:
+        """删除不再支持的旧版 Agent 配置文件，避免被误迁移到 memory。"""
+        for filename in sorted(OBSOLETE_AGENT_ROOT_FILES):
+            path = self.agent_root_dir / filename
+            if not path.exists() or not path.is_file():
+                continue
+            path.unlink()
+            logger.info("已删除废弃的 Agent 根配置文件: %s", path)
+
+        for relative_path in sorted(OBSOLETE_RUNTIME_FILES):
+            path = self.runtime_dir / relative_path
+            if not path.exists() or not path.is_file():
+                continue
+            path.unlink()
+            logger.info("已删除废弃的 Agent 运行时文件: %s", path)
+
+    def _migrate_root_memory_files(self) -> None:
+        """将旧版根目录 memory 文件移入 `config/agent/memory`。"""
+        for path in sorted(self.agent_root_dir.glob("*.md")):
+            if path.name in ROOT_LEVEL_RUNTIME_FILES:
+                continue
+            target = self.memory_dir / path.name
+            if target.exists():
+                continue
+            path.rename(target)
+            logger.info("已迁移旧版 Agent memory 文件: %s -> %s", path, target)
+
+    def _load_from_root(self, root: Path) -> AgentRuntimeConfig:
+        current_persona_path = root / CURRENT_PERSONA_FILE
+        current_doc = self._read_markdown(current_persona_path)
+        current_meta = current_doc.metadata
+
+        active_persona = str(
+            current_meta.get("active_persona") or DEFAULT_PERSONA_ID
+        ).strip()
+        if not active_persona:
+            raise AgentRuntimeConfigError("CURRENT_PERSONA.md 缺少 active_persona")
+
+        extra_context_paths = self._resolve_optional_paths(
+            root, current_meta.get("extra_context_files", [])
+        )
+
+        available_personas = self._load_personas(root)
+        persona = self._resolve_persona_definition(active_persona, available_personas)
+        extra_contexts = [
+            (path, self._read_markdown(path).body)
+            for path in extra_context_paths
+        ]
+
+        warnings = self._validate_runtime_config(
+            current_meta=current_meta,
+            persona_path=persona.path,
+            extra_context_paths=extra_context_paths,
+            persona_text=persona.text,
+        )
+        return AgentRuntimeConfig(
+            source_root=root,
+            active_persona=active_persona,
+            current_persona_path=current_persona_path,
+            persona=persona,
+            available_personas=available_personas,
+            extra_context_paths=extra_context_paths,
+            extra_contexts=extra_contexts,
+            warnings=warnings,
+        )
+
+    def _load_personas(self, root: Path) -> list[PersonaDefinition]:
+        """扫描并解析所有可用人格。"""
+        personas_root = root / PERSONAS_DIR
+        if not personas_root.exists():
+            raise AgentRuntimeConfigError(f"缺少 personas 目录: {personas_root}")
+
+        personas: list[PersonaDefinition] = []
+        seen_ids: set[str] = set()
+        for persona_dir in sorted(personas_root.iterdir()):
+            if not persona_dir.is_dir():
+                continue
+            persona_path = persona_dir / PERSONA_FILE
+            if not persona_path.exists():
+                continue
+            document = self._read_markdown(persona_path)
+            persona_id = str(document.metadata.get("persona_id") or persona_dir.name).strip()
+            if not persona_id:
+                raise AgentRuntimeConfigError(f"{persona_path} 缺少 persona_id")
+            if persona_id in seen_ids:
+                raise AgentRuntimeConfigError(f"检测到重复的人格 ID: {persona_id}")
+            seen_ids.add(persona_id)
+            aliases = self._normalize_string_list(
+                document.metadata.get("aliases"),
+                f"{persona_path}.aliases",
+            )
+            personas.append(
+                PersonaDefinition(
+                    persona_id=persona_id,
+                    path=persona_path,
+                    label=str(document.metadata.get("label") or persona_id).strip(),
+                    description=str(document.metadata.get("description") or "").strip(),
+                    text=document.body,
+                    aliases=aliases,
+                )
+            )
+
+        if not personas:
+            raise AgentRuntimeConfigError(f"{personas_root} 中未找到任何人格定义")
+        return personas
+
+    @staticmethod
+    def _resolve_persona_definition(
+        persona_query: str,
+        personas: list[PersonaDefinition],
+    ) -> PersonaDefinition:
+        """按 persona_id、label 或 aliases 解析人格。"""
+        normalized = (persona_query or "").strip()
+        if not normalized:
+            raise AgentRuntimeConfigError("人格 ID 不能为空")
+
+        for persona in personas:
+            if persona.persona_id == normalized:
+                return persona
+        for persona in personas:
+            if persona.matches(normalized):
+                return persona
+
+        available = ", ".join(persona.persona_id for persona in personas)
+        raise AgentRuntimeConfigError(
+            f"未找到人格 `{persona_query}`，可用人格: {available}"
+        )
+
+    @staticmethod
+    def _validate_new_persona_id(persona_id: str) -> str:
+        """校验新建人格的 ID，避免写入非法路径。"""
+        normalized = (persona_id or "").strip()
+        if not normalized:
+            raise AgentRuntimeConfigError("新建人格时 persona_id 不能为空")
+        if not PERSONA_ID_PATTERN.fullmatch(normalized):
+            raise AgentRuntimeConfigError(
+                "新建人格时 persona_id 只能使用小写字母、数字、下划线和中划线，且必须以字母或数字开头"
+            )
+        return normalized
+
+    @staticmethod
+    def _read_markdown(path: Path) -> ParsedMarkdownDocument:
+        if not path.exists():
+            raise AgentRuntimeConfigError(f"缺少配置文件: {path}")
+        try:
+            content = path.read_text(encoding="utf-8")
+        except Exception as err:  # noqa: BLE001
+            raise AgentRuntimeConfigError(f"读取配置文件失败 {path}: {err}") from err
+
+        metadata: dict[str, Any] = {}
+        body = content
+        match = FRONTMATTER_PATTERN.match(content)
+        if match:
+            try:
+                metadata = yaml.safe_load(match.group(1)) or {}
+            except yaml.YAMLError as err:
+                raise AgentRuntimeConfigError(
+                    f"YAML frontmatter 解析失败 {path}: {err}"
+                ) from err
+            if not isinstance(metadata, dict):
+                raise AgentRuntimeConfigError(f"frontmatter 必须是映射类型: {path}")
+            body = content[match.end():]
+        return ParsedMarkdownDocument(metadata=metadata, body=body.strip())
+
+    @staticmethod
+    def _resolve_optional_paths(root: Path, values: Any) -> list[Path]:
+        if not values:
+            return []
+        if not isinstance(values, list):
+            raise AgentRuntimeConfigError("extra_context_files 必须是数组")
+        return [AgentRuntimeManager._resolve_relative_path(root, str(value)) for value in values]
+
+    @staticmethod
+    def _resolve_relative_path(root: Path, value: str) -> Path:
+        candidate = Path(value)
+        return candidate if candidate.is_absolute() else (root / candidate).resolve()
+
+    @staticmethod
+    def _normalize_string_list(values: Any, field_name: str) -> list[str]:
+        if values is None:
+            return []
+        if not isinstance(values, list):
+            raise AgentRuntimeConfigError(f"{field_name} 必须是字符串数组")
+        normalized: list[str] = []
+        for value in values:
+            text = str(value).strip()
+            if text:
+                normalized.append(text)
+        return normalized
+
+    @staticmethod
+    def _coerce_string_list(values: Any) -> list[str]:
+        if not isinstance(values, list):
+            return []
+        return [str(value).strip() for value in values if str(value).strip()]
+
+    @staticmethod
+    def _normalize_persona_aliases(values: Any, field_name: str) -> list[str]:
+        """规范化人格别名，保持顺序并去重。"""
+        normalized = AgentRuntimeManager._normalize_string_list(values, field_name)
+        deduped: list[str] = []
+        seen: set[str] = set()
+        for alias in normalized:
+            folded = alias.casefold()
+            if folded in seen:
+                continue
+            seen.add(folded)
+            deduped.append(alias)
+        return deduped
+
+    @staticmethod
+    def _merge_persona_instructions(
+        base_body: str,
+        append_instructions: Optional[list[str]],
+    ) -> str:
+        """把增量规则安全追加到人格正文末尾。"""
+        merged = (base_body or "").strip()
+        if not append_instructions:
+            return merged
+
+        extras: list[str] = []
+        for item in append_instructions:
+            text = str(item).strip()
+            if not text:
+                continue
+            if not re.match(r"^([-*]|\d+\.)\s", text):
+                text = f"- {text}"
+            extras.append(text)
+
+        if not extras:
+            return merged
+        if not merged:
+            return "\n".join(extras)
+        return merged.rstrip() + "\n\n" + "\n".join(extras)
+
+    @staticmethod
+    def _normalize_persona_body(body: Optional[str]) -> str:
+        """去掉重复的 PERSONA 标题，保持正文可安全回写。"""
+        normalized = (body or "").strip()
+        if not normalized:
+            return ""
+        if normalized.startswith("# PERSONA"):
+            _, _, remainder = normalized.partition("\n")
+            return remainder.strip()
+        return normalized
+
+    def _validate_runtime_config(
+        self,
+        *,
+        current_meta: dict[str, Any],
+        persona_path: Path,
+        extra_context_paths: list[Path],
+        persona_text: str,
+    ) -> list[str]:
+        warnings: list[str] = []
+        required_paths = [persona_path]
+        duplicates = self._find_duplicate_paths(required_paths + extra_context_paths)
+        if duplicates:
+            warnings.append(
+                "检测到重复引用的根层配置文件: "
+                + ", ".join(path.as_posix() for path in duplicates)
+            )
+
+        deprecated_phrases = self._normalize_string_list(
+            current_meta.get("deprecated_phrases"), "deprecated_phrases"
+        )
+        if deprecated_phrases:
+            for phrase in deprecated_phrases:
+                if phrase and phrase in persona_text:
+                    warnings.append(f"检测到已废弃短语 `{phrase}` 仍出现在 persona 中")
+        return warnings
+
+    @staticmethod
+    def _find_duplicate_paths(paths: Iterable[Path]) -> list[Path]:
+        seen: set[Path] = set()
+        duplicates: list[Path] = []
+        for path in paths:
+            resolved = path.resolve()
+            if resolved in seen and resolved not in duplicates:
+                duplicates.append(resolved)
+            seen.add(resolved)
+        return duplicates
+
+    @staticmethod
+    def _render_current_persona_document(
+        *,
+        active_persona: str,
+        extra_context_files: list[str],
+        deprecated_phrases: list[str],
+    ) -> str:
+        """统一生成 CURRENT_PERSONA.md，避免手写时结构漂移。"""
+        metadata = {
+            "version": CURRENT_PERSONA_SCHEMA_VERSION,
+            "active_persona": active_persona,
+            "extra_context_files": extra_context_files,
+            "deprecated_phrases": deprecated_phrases,
+        }
+        body_lines = [
+            "# CURRENT_PERSONA",
+            "",
+            f"当前激活人格：`{active_persona}`",
+            "",
+            "运行时加载顺序固定如下：",
+            "",
+            "1. 核心系统提示词（程序内置，不可运行时覆盖）",
+            "2. `personas/<active_persona>/PERSONA.md`",
+            "3. `extra_context_files`",
+            "4. `memory/*.md`",
+            "5. `activity/*.md`",
+            "",
+            "`memory` 中的长期偏好可以细化回复方式，但不应覆盖系统核心身份、目标和安全边界。",
+        ]
+        frontmatter = yaml.safe_dump(
+            metadata,
+            sort_keys=False,
+            allow_unicode=True,
+        ).strip()
+        return f"---\n{frontmatter}\n---\n" + "\n".join(body_lines) + "\n"
+
+    @staticmethod
+    def _render_persona_document(
+        *,
+        persona_id: str,
+        label: str,
+        description: str,
+        aliases: list[str],
+        body: str,
+    ) -> str:
+        """统一生成人格定义文件，避免手写 frontmatter 漂移。"""
+        metadata = {
+            "version": PERSONA_SCHEMA_VERSION,
+            "persona_id": persona_id,
+            "label": label,
+            "description": description,
+            "aliases": aliases,
+        }
+        frontmatter = yaml.safe_dump(
+            metadata,
+            sort_keys=False,
+            allow_unicode=True,
+        ).strip()
+        normalized_body = AgentRuntimeManager._normalize_persona_body(body)
+        return f"---\n{frontmatter}\n---\n# PERSONA\n\n{normalized_body}\n"
+
+
+agent_runtime_manager = AgentRuntimeManager()

app/agent/tools/base.py

@@ -1,6 +1,10 @@
+import asyncio
 import json
+import threading
 from abc import ABCMeta, abstractmethod
-from typing import Any, Optional
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+from typing import Any, Callable, Optional
 
 from langchain_core.tools import BaseTool
 from pydantic import PrivateAttr
@@ -19,6 +23,44 @@ class ToolChain(ChainBase):
     pass
 
 
+# 将常见的阻塞调用按能力域拆分到独立线程池，避免外部慢 IO 抢占同一批 worker。
+_BLOCKING_BUCKET_LIMITS = {
+    "default": 4,
+    "config": 2,
+    "db": 4,
+    "downloader": 4,
+    "mediaserver": 4,
+    "plugin": 2,
+    "rule": 2,
+    "site": 4,
+    "storage": 4,
+    "subscribe": 2,
+    "workflow": 2,
+}
+_blocking_semaphores = {
+    bucket: asyncio.Semaphore(limit)
+    for bucket, limit in _BLOCKING_BUCKET_LIMITS.items()
+}
+_blocking_executors: dict[str, ThreadPoolExecutor] = {}
+_blocking_executor_lock = threading.Lock()
+
+
+def _get_blocking_executor(bucket: str) -> ThreadPoolExecutor:
+    """按桶懒加载线程池，避免在导入阶段创建过多 worker。"""
+    with _blocking_executor_lock:
+        executor = _blocking_executors.get(bucket)
+        if executor:
+            return executor
+
+        limit = _BLOCKING_BUCKET_LIMITS[bucket]
+        executor = ThreadPoolExecutor(
+            max_workers=limit,
+            thread_name_prefix=f"agent-tool-{bucket}",
+        )
+        _blocking_executors[bucket] = executor
+        return executor
+
+
 class MoviePilotTool(BaseTool, metaclass=ABCMeta):
     """
     MoviePilot专用工具基类（LangChain v1 / langchain_core）
@@ -71,19 +113,44 @@ class MoviePilotTool(BaseTool, metaclass=ABCMeta):
                     if tool_message:
                         self._stream_handler.emit(f"\n\n⚙️ => {tool_message}\n\n")
                 else:
-                    # 渠道不支持编辑：取出 Agent 文字 + 工具消息合并独立发送
-                    agent_message = await self._stream_handler.take()
-                    messages = []
-                    if agent_message:
-                        messages.append(agent_message)
-                    if tool_message:
-                        messages.append(f"⚙️ => {tool_message}")
-                    if messages:
-                        merged_message = "\n\n".join(messages)
-                        await self.send_tool_message(merged_message)
+                    allow_dispatch_without_context = self._agent_context.get(
+                        "should_dispatch_reply", False
+                    )
+                    if self._channel and self._source:
+                        # 渠道不支持编辑：取出 Agent 文字 + 工具消息合并独立发送
+                        agent_message = await self._stream_handler.take()
+                        messages = []
+                        if agent_message:
+                            messages.append(agent_message)
+                        if tool_message:
+                            messages.append(f"⚙️ => {tool_message}")
+                        if messages:
+                            merged_message = "\n\n".join(messages)
+                            await self.send_tool_message(merged_message)
+                    elif allow_dispatch_without_context:
+                        agent_message = await self._stream_handler.take()
+                        messages = []
+                        if agent_message:
+                            messages.append(agent_message)
+                        if tool_message:
+                            messages.append(f"⚙️ => {tool_message}")
+                        if messages:
+                            merged_message = "\n\n".join(messages)
+                            await self.send_tool_message(merged_message)
+                    else:
+                        # 后台 capture 流程没有渠道上下文，不能把工具提示回灌到默认通知渠道。
+                        self._stream_handler.record_tool_call(
+                            tool_name=self.name,
+                            tool_message=tool_message,
+                            tool_kwargs=kwargs,
+                        )
             else:
-                # 非VERBOSE，重置缓冲区从头更新，保持消息编辑能力
-                self._stream_handler.reset()
+                # 非VERBOSE：不逐条回显工具调用，转为在下一段文本前补一句聚合摘要
+                self._stream_handler.record_tool_call(
+                    tool_name=self.name,
+                    tool_message=tool_message,
+                    tool_kwargs=kwargs,
+                )
         else:
             # 未启用流式传输，不发送任何工具消息内容
             pass
@@ -129,6 +196,23 @@ class MoviePilotTool(BaseTool, metaclass=ABCMeta):
         """子类实现具体的工具执行逻辑"""
         raise NotImplementedError
 
+    @staticmethod
+    async def run_blocking(
+            bucket: str, func: Callable[..., Any], *args: Any, **kwargs: Any
+    ) -> Any:
+        """
+        在受控线程池中运行阻塞型同步代码，避免拖住 FastAPI 主事件循环。
+        """
+        bucket_name = bucket if bucket in _BLOCKING_BUCKET_LIMITS else "default"
+        semaphore = _blocking_semaphores[bucket_name]
+        bound_call = partial(func, *args, **kwargs)
+
+        async with semaphore:
+            loop = asyncio.get_running_loop()
+            return await loop.run_in_executor(
+                _get_blocking_executor(bucket_name), bound_call
+            )
+
     def set_message_attr(self, channel: str, source: str, username: str):
         """
         设置消息属性
@@ -164,6 +248,8 @@ class MoviePilotTool(BaseTool, metaclass=ABCMeta):
         if not self._channel or not self._source:
             return None
 
+        # 渠道配置来自 SystemConfigOper 内存缓存，可以直接读取；
+        # 只有用户信息需要走异步数据库查询。
         user_id_str = str(self._user_id) if self._user_id else None
 
         channel_type_map = {
@@ -219,7 +305,7 @@ class MoviePilotTool(BaseTool, metaclass=ABCMeta):
                             return None
 
                         user = (
-                            UserOper().get_by_name(self._username)
+                            await UserOper().async_get_by_name(self._username)
                             if self._username
                             else None
                         )
@@ -234,7 +320,7 @@ class MoviePilotTool(BaseTool, metaclass=ABCMeta):
                         )
                     else:
                         user = (
-                            UserOper().get_by_name(self._username)
+                            await UserOper().async_get_by_name(self._username)
                             if self._username
                             else None
                         )

app/agent/tools/factory.py

@@ -16,6 +16,14 @@ from app.agent.tools.impl.test_site import TestSiteTool
 from app.agent.tools.impl.query_subscribes import QuerySubscribesTool
 from app.agent.tools.impl.query_subscribe_shares import QuerySubscribeSharesTool
 from app.agent.tools.impl.query_rule_groups import QueryRuleGroupsTool
+from app.agent.tools.impl.query_builtin_filter_rules import QueryBuiltinFilterRulesTool
+from app.agent.tools.impl.query_custom_filter_rules import QueryCustomFilterRulesTool
+from app.agent.tools.impl.add_custom_filter_rule import AddCustomFilterRuleTool
+from app.agent.tools.impl.update_custom_filter_rule import UpdateCustomFilterRuleTool
+from app.agent.tools.impl.delete_custom_filter_rule import DeleteCustomFilterRuleTool
+from app.agent.tools.impl.add_rule_group import AddRuleGroupTool
+from app.agent.tools.impl.update_rule_group import UpdateRuleGroupTool
+from app.agent.tools.impl.delete_rule_group import DeleteRuleGroupTool
 from app.agent.tools.impl.query_popular_subscribes import QueryPopularSubscribesTool
 from app.agent.tools.impl.query_subscribe_history import QuerySubscribeHistoryTool
 from app.agent.tools.impl.delete_subscribe import DeleteSubscribeTool
@@ -37,6 +45,9 @@ from app.agent.tools.impl.query_schedulers import QuerySchedulersTool
 from app.agent.tools.impl.run_scheduler import RunSchedulerTool
 from app.agent.tools.impl.query_workflows import QueryWorkflowsTool
 from app.agent.tools.impl.run_workflow import RunWorkflowTool
+from app.agent.tools.impl.query_personas import QueryPersonasTool
+from app.agent.tools.impl.switch_persona import SwitchPersonaTool
+from app.agent.tools.impl.update_persona_definition import UpdatePersonaDefinitionTool
 from app.agent.tools.impl.update_site_cookie import UpdateSiteCookieTool
 from app.agent.tools.impl.delete_download import DeleteDownloadTool
 from app.agent.tools.impl.delete_download_history import DeleteDownloadHistoryTool
@@ -52,7 +63,14 @@ from app.agent.tools.impl.write_file import WriteFileTool
 from app.agent.tools.impl.read_file import ReadFileTool
 from app.agent.tools.impl.browse_webpage import BrowseWebpageTool
 from app.agent.tools.impl.query_installed_plugins import QueryInstalledPluginsTool
+from app.agent.tools.impl.query_market_plugins import QueryMarketPluginsTool
 from app.agent.tools.impl.query_plugin_capabilities import QueryPluginCapabilitiesTool
+from app.agent.tools.impl.query_plugin_config import QueryPluginConfigTool
+from app.agent.tools.impl.update_plugin_config import UpdatePluginConfigTool
+from app.agent.tools.impl.reload_plugin import ReloadPluginTool
+from app.agent.tools.impl.query_plugin_data import QueryPluginDataTool
+from app.agent.tools.impl.install_plugin import InstallPluginTool
+from app.agent.tools.impl.uninstall_plugin import UninstallPluginTool
 from app.agent.tools.impl.run_slash_command import RunSlashCommandTool
 from app.agent.tools.impl.list_slash_commands import ListSlashCommandsTool
 from app.agent.tools.impl.query_custom_identifiers import QueryCustomIdentifiersTool
@@ -69,6 +87,18 @@ class MoviePilotToolFactory:
     MoviePilot工具工厂
     """
 
+    # 这些通用工具需要始终保留，避免大工具集裁剪后让 Agent 丢失基础的
+    # 文件系统、命令执行或交互确认能力。AskUserChoiceTool 仅在支持按钮
+    # 的渠道中才会实际注入，因此后续会再按已加载工具做一次求交集。
+    TOOL_SELECTOR_ALWAYS_INCLUDE_NAMES = (
+        "list_directory",
+        "write_file",
+        "read_file",
+        "edit_file",
+        "execute_command",
+        "ask_user_choice",
+    )
+
     @staticmethod
     def _should_enable_choice_tool(channel: str = None) -> bool:
         if not channel:
@@ -81,6 +111,25 @@ class MoviePilotToolFactory:
             message_channel
         ) and ChannelCapabilityManager.supports_callbacks(message_channel)
 
+    @classmethod
+    def get_tool_selector_always_include_names(
+        cls, tools: List[MoviePilotTool]
+    ) -> List[str]:
+        """
+        返回当前实际已加载且需要绕过工具筛选的工具名。
+
+        `LLMToolSelectorMiddleware` 会校验 `always_include` 中的工具名是否
+        存在于当前请求里，因此这里必须根据运行时工具列表做交集过滤。
+        """
+        available_tool_names = {
+            tool.name for tool in tools if getattr(tool, "name", None)
+        }
+        return [
+            tool_name
+            for tool_name in cls.TOOL_SELECTOR_ALWAYS_INCLUDE_NAMES
+            if tool_name in available_tool_names
+        ]
+
     @staticmethod
     def create_tools(
         session_id: str,
@@ -90,6 +139,7 @@ class MoviePilotToolFactory:
         username: str = None,
         stream_handler: Callable = None,
         agent_context: dict = None,
+        allow_message_tools: bool = True,
     ) -> List[MoviePilotTool]:
         """
         创建MoviePilot工具列表
@@ -113,7 +163,15 @@ class MoviePilotToolFactory:
             QuerySubscribesTool,
             QuerySubscribeSharesTool,
             QueryPopularSubscribesTool,
+            QueryBuiltinFilterRulesTool,
+            QueryCustomFilterRulesTool,
             QueryRuleGroupsTool,
+            AddCustomFilterRuleTool,
+            UpdateCustomFilterRuleTool,
+            DeleteCustomFilterRuleTool,
+            AddRuleGroupTool,
+            UpdateRuleGroupTool,
+            DeleteRuleGroupTool,
             QuerySubscribeHistoryTool,
             DeleteSubscribeTool,
             QueryDownloadTasksTool,
@@ -139,13 +197,23 @@ class MoviePilotToolFactory:
             RunSchedulerTool,
             QueryWorkflowsTool,
             RunWorkflowTool,
+            QueryPersonasTool,
+            SwitchPersonaTool,
+            UpdatePersonaDefinitionTool,
             ExecuteCommandTool,
             EditFileTool,
             WriteFileTool,
             ReadFileTool,
             BrowseWebpageTool,
             QueryInstalledPluginsTool,
+            QueryMarketPluginsTool,
             QueryPluginCapabilitiesTool,
+            QueryPluginConfigTool,
+            UpdatePluginConfigTool,
+            ReloadPluginTool,
+            QueryPluginDataTool,
+            InstallPluginTool,
+            UninstallPluginTool,
             RunSlashCommandTool,
             ListSlashCommandsTool,
             QueryCustomIdentifiersTool,
@@ -162,6 +230,8 @@ class MoviePilotToolFactory:
         # 创建内置工具
         for ToolClass in tool_definitions:
             tool = ToolClass(session_id=session_id, user_id=user_id)
+            if not allow_message_tools and getattr(tool, "sends_message", False):
+                continue
             tool.set_message_attr(channel=channel, source=source, username=username)
             tool.set_stream_handler(stream_handler=stream_handler)
             tool.set_agent_context(agent_context=agent_context)
@@ -184,6 +254,8 @@ class MoviePilotToolFactory:
                         continue
                     # 创建工具实例
                     tool = ToolClass(session_id=session_id, user_id=user_id)
+                    if not allow_message_tools and getattr(tool, "sends_message", False):
+                        continue
                     tool.set_message_attr(
                         channel=channel, source=source, username=username
                     )

app/agent/tools/impl/_filter_rule_utils.py

@@ -0,0 +1,540 @@
+"""过滤规则 Agent 工具共用的校验、查询和引用处理逻辑。"""
+
+import copy
+import re
+from typing import Any, Dict, Iterable, Optional
+
+from app.core.event import eventmanager
+from app.db import AsyncSessionFactory
+from app.db.models.subscribe import Subscribe
+from app.db.systemconfig_oper import SystemConfigOper
+from app.helper.rule import RuleHelper
+from app.modules.filter.RuleParser import RuleParser
+from app.modules.filter.builtin_rules import BUILTIN_RULE_SET
+from app.schemas import CustomRule, FilterRuleGroup
+from app.schemas.event import ConfigChangeEventData
+from app.schemas.types import EventType, SystemConfigKey
+
+RULE_ID_PATTERN = re.compile(r"^[A-Za-z0-9]+$")
+RULE_TOKEN_PATTERN = re.compile(r"[A-Za-z][A-Za-z0-9]*|[0-9][A-Za-z0-9]+")
+NUMERIC_RANGE_PATTERN = re.compile(
+    r"^\d+(?:\.\d+)?(?:\s*-\s*\d+(?:\.\d+)?)?$"
+)
+
+MEDIA_TYPE_ALIASES = {
+    "movie": "电影",
+    "film": "电影",
+    "tv": "电视剧",
+    "series": "电视剧",
+    "show": "电视剧",
+    "电影": "电影",
+    "电视剧": "电视剧",
+}
+
+RULE_STRING_SYNTAX = {
+    "level_separator": ">",
+    "and_operator": "&",
+    "not_operator": "!",
+    "supported_grouping": "Parentheses are supported inside a single level.",
+    "spacing_note": "Prefer spaces around '&', and '>' for readability; use '!RULE' for negation.",
+    "match_order": "Levels are evaluated from left to right. The first matched level wins and stops further matching.",
+    "match_result": "If no level matches, the torrent is filtered out. If a level matches, the torrent is kept.",
+    "writing_workflow": [
+        "First query built-in rules and custom rules to learn valid rule IDs.",
+        "Compose one priority level with '&', '!' and optional parentheses.",
+        "Join multiple priority levels with '>' from highest priority to lowest priority.",
+        "Use spaces around '&', and '>' for readability.",
+    ],
+    "examples": [
+        {
+            "description": "Prefer torrents with special subtitles and Chinese dubbing at 4K, otherwise fall back to Chinese subtitles and Chinese dubbing at 4K.",
+            "rule_string": "SPECSUB & CNVOI & 4K & !BLU & !REMUX & !WEBDL > CNSUB & CNVOI & 4K & !BLU & !REMUX & !WEBDL",
+        },
+        {
+            "description": "Inside one level, require 4K and reject Blu-ray source.",
+            "rule_string": "4K & !BLU",
+        },
+        {
+            "description": "Inside one level, accept either special subtitles or Chinese subtitles, then also require 1080P.",
+            "rule_string": "(SPECSUB | CNSUB) & 1080P",
+        },
+    ],
+}
+
+
+def normalize_optional_text(value: Optional[str]) -> Optional[str]:
+    """把空白字符串折叠为 None，避免保存无意义的空值。"""
+    if value is None:
+        return None
+    value = str(value).strip()
+    return value or None
+
+
+def normalize_media_type(value: Optional[str]) -> Optional[str]:
+    """兼容英中文媒体类型输入，最终统一为后端实际使用的中文值。"""
+    value = normalize_optional_text(value)
+    if not value:
+        return None
+    normalized = MEDIA_TYPE_ALIASES.get(value.lower(), value)
+    if normalized not in {"电影", "电视剧"}:
+        raise ValueError(
+            "media_type 仅支持 '电影'、'电视剧'、'movie' 或 'tv'"
+        )
+    return normalized
+
+
+def validate_numeric_range(
+    field_name: str, value: Optional[str]
+) -> Optional[str]:
+    """校验 size_range / publish_time 这类单值或区间值。"""
+    value = normalize_optional_text(value)
+    if not value:
+        return None
+    if not NUMERIC_RANGE_PATTERN.match(value):
+        raise ValueError(
+            f"{field_name} 格式无效，支持 '1000' 或 '1000-5000' 这类数字区间格式"
+        )
+
+    parts = [float(item.strip()) for item in value.split("-")]
+    if len(parts) == 2 and parts[0] > parts[1]:
+        raise ValueError(f"{field_name} 区间起始值不能大于结束值")
+    return value
+
+
+def validate_seeders(value: Optional[str]) -> Optional[str]:
+    """做种人数最终会被 int() 解析，这里提前拦住非法值。"""
+    value = normalize_optional_text(value)
+    if not value:
+        return None
+    if not value.isdigit():
+        raise ValueError("seeders 必须是非负整数")
+    return value
+
+
+def get_builtin_rules() -> Dict[str, dict]:
+    """返回内置规则的深拷贝，避免调用方误改共享常量。"""
+    return copy.deepcopy(BUILTIN_RULE_SET)
+
+
+def get_custom_rules() -> list[CustomRule]:
+    return RuleHelper().get_custom_rules()
+
+
+def get_rule_groups() -> list[FilterRuleGroup]:
+    return RuleHelper().get_rule_groups()
+
+
+def build_custom_rule_map(rules: Optional[Iterable[CustomRule]] = None) -> Dict[str, CustomRule]:
+    return {
+        rule.id: rule
+        for rule in (rules or get_custom_rules())
+        if rule.id
+    }
+
+
+def build_rule_group_map(
+    groups: Optional[Iterable[FilterRuleGroup]] = None,
+) -> Dict[str, FilterRuleGroup]:
+    return {
+        group.name: group
+        for group in (groups or get_rule_groups())
+        if group.name
+    }
+
+
+def extract_rule_tokens(rule_string: Optional[str]) -> list[str]:
+    """从规则串里提取规则 ID，用于引用分析和未知规则校验。"""
+    if not rule_string:
+        return []
+    # dict.fromkeys 用来在保留顺序的同时去重，便于展示和报错。
+    return list(dict.fromkeys(RULE_TOKEN_PATTERN.findall(rule_string)))
+
+
+def parse_rule_string(rule_string: str) -> dict:
+    """使用后端同款 RuleParser 解析规则串，并拆出每一层的元数据。"""
+    normalized = normalize_optional_text(rule_string)
+    if not normalized:
+        raise ValueError("rule_string 不能为空")
+
+    parser = RuleParser()
+    levels = [level.strip() for level in normalized.split(">")]
+    if any(not level for level in levels):
+        raise ValueError("rule_string 不能包含空层级，请检查 '>' 两侧内容")
+
+    parsed_levels = []
+    for index, level in enumerate(levels, start=1):
+        try:
+            parser.parse(level)
+        except Exception as exc:  # pragma: no cover - 依赖 pyparsing 的具体异常
+            raise ValueError(f"规则串第 {index} 层语法错误: {exc}") from exc
+
+        parsed_levels.append(
+            {
+                "priority": index,
+                "expression": level,
+                "referenced_rules": extract_rule_tokens(level),
+            }
+        )
+
+    return {
+        "rule_string": " > ".join(levels),
+        "levels": parsed_levels,
+        "referenced_rules": extract_rule_tokens(normalized),
+    }
+
+
+def validate_rule_string(rule_string: str, available_rule_ids: Iterable[str]) -> dict:
+    """校验规则串语法和引用规则是否都存在。"""
+    parsed = parse_rule_string(rule_string)
+    available_ids = set(available_rule_ids)
+    unknown_rules = sorted(
+        {
+            rule_id
+            for rule_id in parsed["referenced_rules"]
+            if rule_id not in available_ids
+        }
+    )
+    if unknown_rules:
+        raise ValueError(
+            f"rule_string 引用了不存在的规则: {', '.join(unknown_rules)}"
+        )
+    return parsed
+
+
+def serialize_builtin_rule(rule_id: str, payload: dict) -> dict:
+    """把内置规则整理成适合 Agent 阅读的结构。"""
+    data = copy.deepcopy(payload)
+    data["id"] = rule_id
+    data["source"] = "builtin"
+    return data
+
+
+def serialize_custom_rule(rule: CustomRule, group_refs: Optional[list[str]] = None) -> dict:
+    data = rule.model_dump(exclude_none=True)
+    data["source"] = "custom"
+    data["referenced_by_rule_groups"] = group_refs or []
+    return data
+
+
+def serialize_rule_group(group: FilterRuleGroup, usage: Optional[dict] = None) -> dict:
+    """查询时尽量附带解析结果，便于 Agent 理解优先级层级。"""
+    data = group.model_dump(exclude_none=True)
+    if group.rule_string:
+        try:
+            parsed = parse_rule_string(group.rule_string)
+            data["levels"] = parsed["levels"]
+            data["referenced_rules"] = parsed["referenced_rules"]
+            data["syntax_valid"] = True
+        except ValueError as exc:
+            data["syntax_valid"] = False
+            data["syntax_error"] = str(exc)
+            data["referenced_rules"] = extract_rule_tokens(group.rule_string)
+    else:
+        data["syntax_valid"] = False
+        data["syntax_error"] = "rule_string 为空"
+        data["referenced_rules"] = []
+    data["usage"] = usage or default_rule_group_usage()
+    return data
+
+
+def default_rule_group_usage() -> dict:
+    return {
+        "used_in_global_search": False,
+        "used_in_global_subscribe": False,
+        "used_in_global_best_version": False,
+        "subscribes": [],
+    }
+
+
+async def collect_rule_group_usages(
+    group_names: Optional[Iterable[str]] = None,
+) -> Dict[str, dict]:
+    """收集规则组在全局配置和订阅上的引用情况。"""
+    target_names = set(group_names or [])
+    search_groups = set(
+        SystemConfigOper().get(SystemConfigKey.SearchFilterRuleGroups) or []
+    )
+    subscribe_groups = set(
+        SystemConfigOper().get(SystemConfigKey.SubscribeFilterRuleGroups) or []
+    )
+    best_version_groups = set(
+        SystemConfigOper().get(SystemConfigKey.BestVersionFilterRuleGroups) or []
+    )
+
+    usage_map = {
+        name: default_rule_group_usage()
+        for name in target_names
+    }
+
+    def ensure_usage(name: str) -> dict:
+        if name not in usage_map:
+            usage_map[name] = default_rule_group_usage()
+        return usage_map[name]
+
+    for name in search_groups:
+        if target_names and name not in target_names:
+            continue
+        ensure_usage(name)["used_in_global_search"] = True
+    for name in subscribe_groups:
+        if target_names and name not in target_names:
+            continue
+        ensure_usage(name)["used_in_global_subscribe"] = True
+    for name in best_version_groups:
+        if target_names and name not in target_names:
+            continue
+        ensure_usage(name)["used_in_global_best_version"] = True
+
+    async with AsyncSessionFactory() as db:
+        subscribes = await Subscribe.async_list(db)
+        for subscribe in subscribes:
+            filter_groups = subscribe.filter_groups or []
+            for name in filter_groups:
+                if target_names and name not in target_names:
+                    continue
+                ensure_usage(name)["subscribes"].append(
+                    {
+                        "subscribe_id": subscribe.id,
+                        "name": subscribe.name,
+                        "season": subscribe.season,
+                        "type": subscribe.type,
+                        "username": subscribe.username,
+                        "best_version": bool(subscribe.best_version),
+                    }
+                )
+
+    return usage_map
+
+
+def collect_custom_rule_group_refs(
+    rule_groups: Iterable[FilterRuleGroup],
+    rule_ids: Optional[Iterable[str]] = None,
+) -> Dict[str, list[str]]:
+    """收集自定义规则被哪些规则组引用。"""
+    target_rule_ids = set(rule_ids or [])
+    refs: Dict[str, list[str]] = {
+        rule_id: []
+        for rule_id in target_rule_ids
+    }
+
+    for group in rule_groups:
+        if not group.name or not group.rule_string:
+            continue
+        referenced = set(extract_rule_tokens(group.rule_string))
+        for rule_id in referenced:
+            if target_rule_ids and rule_id not in target_rule_ids:
+                continue
+            refs.setdefault(rule_id, []).append(group.name)
+
+    for names in refs.values():
+        names.sort()
+    return refs
+
+
+def normalize_custom_rule(
+    rule_id: str,
+    name: str,
+    include: Optional[str],
+    exclude: Optional[str],
+    size_range: Optional[str],
+    seeders: Optional[str],
+    publish_time: Optional[str],
+    existing_rules: Iterable[CustomRule],
+    original_rule_id: Optional[str] = None,
+) -> CustomRule:
+    """新增/更新自定义规则时统一走这里，避免多处散落校验逻辑。"""
+    normalized_rule_id = normalize_optional_text(rule_id)
+    normalized_name = normalize_optional_text(name)
+    if not normalized_rule_id:
+        raise ValueError("rule_id 不能为空")
+    if not normalized_name:
+        raise ValueError("name 不能为空")
+    if not RULE_ID_PATTERN.match(normalized_rule_id):
+        raise ValueError("rule_id 仅支持英文字母和数字")
+    if (
+        normalized_rule_id in BUILTIN_RULE_SET
+        and normalized_rule_id != original_rule_id
+    ):
+        raise ValueError(
+            f"rule_id '{normalized_rule_id}' 与内置规则冲突，不能覆盖内置规则"
+        )
+
+    for existing_rule in existing_rules:
+        if (
+            existing_rule.id == normalized_rule_id
+            and existing_rule.id != original_rule_id
+        ):
+            raise ValueError(f"rule_id '{normalized_rule_id}' 已存在")
+        if (
+            existing_rule.name == normalized_name
+            and existing_rule.id != original_rule_id
+        ):
+            raise ValueError(f"规则名称 '{normalized_name}' 已存在")
+
+    return CustomRule(
+        id=normalized_rule_id,
+        name=normalized_name,
+        include=normalize_optional_text(include),
+        exclude=normalize_optional_text(exclude),
+        size_range=validate_numeric_range("size_range", size_range),
+        seeders=validate_seeders(seeders),
+        publish_time=validate_numeric_range("publish_time", publish_time),
+    )
+
+
+def normalize_rule_group(
+    name: str,
+    rule_string: str,
+    media_type: Optional[str],
+    category: Optional[str],
+    existing_groups: Iterable[FilterRuleGroup],
+    available_rule_ids: Iterable[str],
+    original_name: Optional[str] = None,
+) -> tuple[FilterRuleGroup, dict]:
+    """新增/更新规则组时统一校验名字、适用范围和规则串。"""
+    normalized_name = normalize_optional_text(name)
+    if not normalized_name:
+        raise ValueError("规则组名称不能为空")
+
+    for group in existing_groups:
+        if group.name == normalized_name and group.name != original_name:
+            raise ValueError(f"规则组名称 '{normalized_name}' 已存在")
+
+    normalized_media_type = normalize_media_type(media_type)
+    normalized_category = normalize_optional_text(category)
+    if normalized_category and not normalized_media_type:
+        raise ValueError("设置 category 时必须同时设置 media_type")
+
+    parsed = validate_rule_string(rule_string, available_rule_ids)
+    return (
+        FilterRuleGroup(
+            name=normalized_name,
+            rule_string=parsed["rule_string"],
+            media_type=normalized_media_type,
+            category=normalized_category,
+        ),
+        parsed,
+    )
+
+
+async def save_system_config(
+    key: SystemConfigKey, value: Any
+) -> Optional[bool]:
+    """通过统一入口保存配置并补发 ConfigChanged 事件。"""
+    normalized_value = value
+    if isinstance(normalized_value, list):
+        normalized_value = [
+            item
+            for item in normalized_value
+            if item is not None and item != ""
+        ]
+        normalized_value = normalized_value or None
+
+    success = await SystemConfigOper().async_set(key, normalized_value)
+    if success:
+        await eventmanager.async_send_event(
+            etype=EventType.ConfigChanged,
+            data=ConfigChangeEventData(
+                key=key,
+                value=normalized_value,
+                change_type="update",
+            ),
+        )
+    return success
+
+
+def replace_rule_id_in_rule_string(
+    rule_string: str, old_rule_id: str, new_rule_id: str
+) -> str:
+    """只替换完整 token，避免误伤其他规则名。"""
+    pattern = re.compile(
+        rf"(?<![A-Za-z0-9]){re.escape(old_rule_id)}(?![A-Za-z0-9])"
+    )
+    return pattern.sub(new_rule_id, rule_string)
+
+
+def replace_group_name_in_list(
+    values: Optional[Iterable[str]], old_name: str, new_name: str
+) -> list[str]:
+    """更新配置里的规则组名引用，并顺手去重。"""
+    result = []
+    for value in values or []:
+        mapped = new_name if value == old_name else value
+        if mapped not in result:
+            result.append(mapped)
+    return result
+
+
+async def rename_rule_group_references(old_name: str, new_name: str) -> dict:
+    """规则组改名后，联动更新全局设置和订阅引用。"""
+    changed = {
+        "global_settings": {},
+        "subscribes": [],
+    }
+
+    for config_key in (
+        SystemConfigKey.SearchFilterRuleGroups,
+        SystemConfigKey.SubscribeFilterRuleGroups,
+        SystemConfigKey.BestVersionFilterRuleGroups,
+    ):
+        original = SystemConfigOper().get(config_key) or []
+        updated = replace_group_name_in_list(original, old_name, new_name)
+        if updated != original:
+            await save_system_config(config_key, updated)
+            changed["global_settings"][config_key.value] = updated
+
+    async with AsyncSessionFactory() as db:
+        subscribes = await Subscribe.async_list(db)
+        for subscribe in subscribes:
+            original = subscribe.filter_groups or []
+            updated = replace_group_name_in_list(original, old_name, new_name)
+            if updated == original:
+                continue
+            await subscribe.async_update(db, {"filter_groups": updated})
+            changed["subscribes"].append(
+                {
+                    "subscribe_id": subscribe.id,
+                    "name": subscribe.name,
+                    "season": subscribe.season,
+                    "filter_groups": updated,
+                }
+            )
+
+    return changed
+
+
+async def remove_rule_group_references(group_name: str) -> dict:
+    """删除规则组后，清理全局设置和订阅里的悬空引用。"""
+    changed = {
+        "global_settings": {},
+        "subscribes": [],
+    }
+
+    for config_key in (
+        SystemConfigKey.SearchFilterRuleGroups,
+        SystemConfigKey.SubscribeFilterRuleGroups,
+        SystemConfigKey.BestVersionFilterRuleGroups,
+    ):
+        original = SystemConfigOper().get(config_key) or []
+        updated = [value for value in original if value != group_name]
+        if updated != original:
+            await save_system_config(config_key, updated)
+            changed["global_settings"][config_key.value] = updated
+
+    async with AsyncSessionFactory() as db:
+        subscribes = await Subscribe.async_list(db)
+        for subscribe in subscribes:
+            original = subscribe.filter_groups or []
+            updated = [value for value in original if value != group_name]
+            if updated == original:
+                continue
+            await subscribe.async_update(db, {"filter_groups": updated})
+            changed["subscribes"].append(
+                {
+                    "subscribe_id": subscribe.id,
+                    "name": subscribe.name,
+                    "season": subscribe.season,
+                    "filter_groups": updated,
+                }
+            )
+
+    return changed

app/agent/tools/impl/_plugin_tool_utils.py

@@ -0,0 +1,290 @@
+"""插件 Agent 工具共享辅助方法"""
+
+import json
+import shutil
+from typing import Any, Optional
+
+from app.core.config import settings
+from app.core.plugin import PluginManager
+from app.db.systemconfig_oper import SystemConfigOper
+from app.helper.plugin import PluginHelper
+from app.schemas.types import SystemConfigKey
+
+# 默认只向智能体返回一个可读预览，避免超大插件数据挤爆上下文窗口。
+DEFAULT_PLUGIN_DATA_PREVIEW_CHARS = 12_000
+MAX_PLUGIN_DATA_PREVIEW_CHARS = 50_000
+PLUGIN_DATA_KEY_PREVIEW_LIMIT = 50
+PLUGIN_DATA_TRUNCATION_SUFFIX = "\n...(插件数据内容过长，已截断)"
+DEFAULT_PLUGIN_CANDIDATE_LIMIT = 500
+
+
+def get_plugin_snapshot(plugin_id: str) -> Optional[dict[str, Any]]:
+    """
+    获取已安装插件的基础信息快照。
+    """
+    plugin_manager = PluginManager()
+    for plugin in plugin_manager.get_local_plugins():
+        if plugin.id == plugin_id:
+            return {
+                "plugin_id": plugin.id,
+                "plugin_name": plugin.plugin_name,
+                "plugin_version": plugin.plugin_version,
+                "state": plugin.state,
+            }
+    return None
+
+
+def clamp_preview_chars(max_chars: Optional[int]) -> int:
+    """
+    约束插件数据预览长度，避免工具结果无限膨胀。
+    """
+    if max_chars is None:
+        return DEFAULT_PLUGIN_DATA_PREVIEW_CHARS
+    return max(512, min(int(max_chars), MAX_PLUGIN_DATA_PREVIEW_CHARS))
+
+
+def serialize_for_agent(value: Any) -> str:
+    """
+    将结果稳定序列化为 JSON 字符串，无法原生序列化的对象退化为字符串。
+    """
+    return json.dumps(value, ensure_ascii=False, indent=2, default=str)
+
+
+def build_preview_payload(value: Any, max_chars: Optional[int]) -> tuple[bool, int, int, str]:
+    """
+    为可能很大的插件数据生成预览结果。
+    """
+    serialized = serialize_for_agent(value)
+    if len(serialized) <= clamp_preview_chars(max_chars):
+        return False, len(serialized), len(serialized), serialized
+
+    preview_limit = clamp_preview_chars(max_chars)
+    preview = serialized[:preview_limit] + PLUGIN_DATA_TRUNCATION_SUFFIX
+    return True, len(serialized), len(preview), preview
+
+
+def reload_plugin_runtime(plugin_id: str) -> None:
+    """
+    重载插件并重新注册其命令、定时任务和 API。
+    """
+    # 这些依赖只在真正执行重载时才导入，避免普通查询工具引入不必要的初始化开销。
+    from app.api.endpoints.plugin import register_plugin_api
+    from app.command import Command
+    from app.scheduler import Scheduler
+
+    plugin_manager = PluginManager()
+    plugin_manager.reload_plugin(plugin_id)
+    Scheduler().update_plugin_job(plugin_id)
+    Command().init_commands(plugin_id)
+    register_plugin_api(plugin_id)
+
+
+def summarize_plugin(plugin: Any) -> dict[str, Any]:
+    """
+    提取插件对象中对 Agent 有价值的摘要字段。
+    """
+    repo_url = getattr(plugin, "repo_url", None)
+    return {
+        "id": getattr(plugin, "id", None),
+        "plugin_name": getattr(plugin, "plugin_name", None),
+        "plugin_desc": getattr(plugin, "plugin_desc", None),
+        "plugin_version": getattr(plugin, "plugin_version", None),
+        "plugin_author": getattr(plugin, "plugin_author", None),
+        "installed": bool(getattr(plugin, "installed", False)),
+        "has_update": bool(getattr(plugin, "has_update", False)),
+        "state": bool(getattr(plugin, "state", False)),
+        "repo_url": repo_url,
+        "source": "local_repo" if PluginHelper.is_local_repo_url(repo_url) else "market",
+    }
+
+
+async def load_market_plugins(force_refresh: bool = False) -> list[Any]:
+    """
+    聚合插件市场与本地插件仓库中的候选插件。
+    """
+    plugin_manager = PluginManager()
+    online_plugins = await plugin_manager.async_get_online_plugins(force=force_refresh)
+    local_repo_plugins = plugin_manager.get_local_repo_plugins()
+    if not online_plugins and not local_repo_plugins:
+        return []
+    return plugin_manager.process_plugins_list(online_plugins + local_repo_plugins, [])
+
+
+def list_installed_plugins() -> list[Any]:
+    """
+    返回当前已安装插件列表。
+    """
+    plugin_manager = PluginManager()
+    return [plugin for plugin in plugin_manager.get_local_plugins() if plugin.installed]
+
+
+def _normalize_text(value: Optional[str]) -> str:
+    return (value or "").strip().lower()
+
+
+def is_exact_plugin_match(plugin: Any, query: str) -> bool:
+    """
+    精确匹配插件 ID 或插件名称，用于安全地自动选择候选。
+    """
+    normalized_query = _normalize_text(query)
+    return normalized_query in {
+        _normalize_text(getattr(plugin, "id", None)),
+        _normalize_text(getattr(plugin, "plugin_name", None)),
+    }
+
+
+def search_plugin_candidates(query: str, plugins: list[Any]) -> list[dict[str, Any]]:
+    """
+    按插件 ID、名称、描述和作者搜索候选，并返回打分结果。
+    """
+    normalized_query = _normalize_text(query)
+    if not normalized_query:
+        return []
+
+    tokens = [token for token in normalized_query.replace("-", " ").split() if token]
+    matches: list[dict[str, Any]] = []
+
+    for plugin in plugins:
+        plugin_id = _normalize_text(getattr(plugin, "id", None))
+        plugin_name = _normalize_text(getattr(plugin, "plugin_name", None))
+        plugin_desc = _normalize_text(getattr(plugin, "plugin_desc", None))
+        plugin_author = _normalize_text(getattr(plugin, "plugin_author", None))
+        haystack = "\n".join([plugin_id, plugin_name, plugin_desc, plugin_author])
+
+        score = 0
+        if normalized_query == plugin_id:
+            score = 100
+        elif normalized_query == plugin_name:
+            score = 95
+        elif plugin_id.startswith(normalized_query):
+            score = 85
+        elif plugin_name.startswith(normalized_query):
+            score = 80
+        elif normalized_query in plugin_id:
+            score = 75
+        elif normalized_query in plugin_name:
+            score = 70
+        elif tokens and all(token in plugin_name for token in tokens):
+            score = 68
+        elif tokens and all(token in plugin_id for token in tokens):
+            score = 66
+        elif normalized_query in plugin_desc:
+            score = 45
+        elif normalized_query in plugin_author:
+            score = 40
+        elif tokens and all(token in haystack for token in tokens):
+            score = 35
+
+        if score <= 0:
+            continue
+
+        matches.append(
+            {
+                "plugin": plugin,
+                "score": score,
+                "exact": is_exact_plugin_match(plugin, normalized_query),
+            }
+        )
+
+    return sorted(
+        matches,
+        key=lambda item: (
+            -item["score"],
+            not item["exact"],
+            -int(bool(getattr(item["plugin"], "has_update", False))),
+            -int(bool(getattr(item["plugin"], "installed", False))),
+            -int(getattr(item["plugin"], "add_time", 0) or 0),
+        ),
+    )
+
+
+def summarize_candidates(matches: list[dict[str, Any]], limit: int = DEFAULT_PLUGIN_CANDIDATE_LIMIT) -> list[dict[str, Any]]:
+    """
+    压缩候选列表，避免一次性把完整市场数据返回给 Agent。
+    """
+    return [
+        {
+            **summarize_plugin(item["plugin"]),
+            "score": item["score"],
+            "exact": item["exact"],
+        }
+        for item in matches[:limit]
+    ]
+
+
+async def install_plugin_runtime(
+    plugin_id: str, repo_url: Optional[str], force: bool = False
+) -> tuple[bool, str, bool]:
+    """
+    按现有插件接口的行为安装插件，并刷新运行态注册信息。
+    """
+    install_plugins = SystemConfigOper().get(SystemConfigKey.UserInstalledPlugins) or []
+    plugin_manager = PluginManager()
+    plugin_helper = PluginHelper()
+
+    refreshed_only = False
+    if not force and plugin_id in plugin_manager.get_plugin_ids():
+        refreshed_only = True
+        await plugin_helper.async_install_reg(pid=plugin_id, repo_url=repo_url)
+        message = "插件已存在，已刷新加载"
+    else:
+        if not repo_url:
+            return False, "没有传入仓库地址，无法正确安装插件，请检查配置", False
+        state, message = await plugin_helper.async_install(
+            pid=plugin_id,
+            repo_url=repo_url,
+            force_install=force,
+        )
+        if not state:
+            return False, message, False
+
+    if plugin_id not in install_plugins:
+        install_plugins.append(plugin_id)
+        await SystemConfigOper().async_set(
+            SystemConfigKey.UserInstalledPlugins, install_plugins
+        )
+
+    reload_plugin_runtime(plugin_id)
+    return True, message or "插件安装成功", refreshed_only
+
+
+async def uninstall_plugin_runtime(plugin_id: str) -> dict[str, Any]:
+    """
+    按现有卸载逻辑移除插件，并清理运行态注册与分组信息。
+    """
+    from app.api.endpoints.plugin import _remove_plugin_from_folders, remove_plugin_api
+    from app.scheduler import Scheduler
+
+    config_oper = SystemConfigOper()
+    install_plugins = config_oper.get(SystemConfigKey.UserInstalledPlugins) or []
+    if plugin_id in install_plugins:
+        install_plugins = [plugin for plugin in install_plugins if plugin != plugin_id]
+        await config_oper.async_set(SystemConfigKey.UserInstalledPlugins, install_plugins)
+
+    remove_plugin_api(plugin_id)
+    Scheduler().remove_plugin_job(plugin_id)
+
+    plugin_manager = PluginManager()
+    plugin_class = plugin_manager.plugins.get(plugin_id)
+    was_clone = bool(getattr(plugin_class, "is_clone", False))
+    clone_files_removed = False
+
+    if was_clone:
+        plugin_manager.delete_plugin_config(plugin_id)
+        plugin_manager.delete_plugin_data(plugin_id)
+        plugin_base_dir = settings.ROOT_PATH / "app" / "plugins" / plugin_id.lower()
+        if plugin_base_dir.exists():
+            try:
+                shutil.rmtree(plugin_base_dir)
+                plugin_manager.plugins.pop(plugin_id, None)
+                clone_files_removed = True
+            except Exception:
+                clone_files_removed = False
+
+    _remove_plugin_from_folders(plugin_id)
+    plugin_manager.remove_plugin(plugin_id)
+
+    return {
+        "was_clone": was_clone,
+        "clone_files_removed": clone_files_removed,
+    }

app/agent/tools/impl/add_custom_filter_rule.py

@@ -0,0 +1,111 @@
+"""新增自定义过滤规则工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    get_custom_rules,
+    normalize_custom_rule,
+    save_system_config,
+    serialize_custom_rule,
+)
+from app.log import logger
+from app.schemas.types import SystemConfigKey
+
+
+class AddCustomFilterRuleInput(BaseModel):
+    """新增自定义过滤规则工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    rule_id: str = Field(
+        ...,
+        description="Unique custom rule ID. Only letters and numbers are allowed.",
+    )
+    name: str = Field(..., description="Display name of the custom rule.")
+    include: Optional[str] = Field(
+        None, description="Optional include regex for the rule."
+    )
+    exclude: Optional[str] = Field(
+        None, description="Optional exclude regex for the rule."
+    )
+    size_range: Optional[str] = Field(
+        None, description="Optional size range in MB, for example '1000-5000'."
+    )
+    seeders: Optional[str] = Field(
+        None, description="Optional minimum seeder count as a non-negative integer."
+    )
+    publish_time: Optional[str] = Field(
+        None,
+        description="Optional publish-time filter in minutes, for example '60' or '60-1440'.",
+    )
+
+
+class AddCustomFilterRuleTool(MoviePilotTool):
+    name: str = "add_custom_filter_rule"
+    description: str = (
+        "Add a custom filter rule to CustomFilterRules. "
+        "The new rule can then be referenced by rule ID inside filter rule groups."
+    )
+    args_schema: Type[BaseModel] = AddCustomFilterRuleInput
+    require_admin: bool = True
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        return f"新增自定义过滤规则 {kwargs.get('rule_id', '')}"
+
+    async def run(
+        self,
+        rule_id: str,
+        name: str,
+        include: Optional[str] = None,
+        exclude: Optional[str] = None,
+        size_range: Optional[str] = None,
+        seeders: Optional[str] = None,
+        publish_time: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}, rule_id={rule_id}")
+
+        try:
+            custom_rules = get_custom_rules()
+            new_rule = normalize_custom_rule(
+                rule_id=rule_id,
+                name=name,
+                include=include,
+                exclude=exclude,
+                size_range=size_range,
+                seeders=seeders,
+                publish_time=publish_time,
+                existing_rules=custom_rules,
+            )
+
+            custom_rules.append(new_rule)
+            await save_system_config(
+                SystemConfigKey.CustomFilterRules,
+                [rule.model_dump(exclude_none=True) for rule in custom_rules],
+            )
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"已新增自定义过滤规则 {new_rule.id}",
+                    "custom_rule": serialize_custom_rule(new_rule),
+                    "count": len(custom_rules),
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"新增自定义过滤规则失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"新增自定义过滤规则失败: {exc}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/add_download.py

@@ -47,13 +47,13 @@ class AddDownloadTool(MoviePilotTool):
         if torrent_urls:
             if len(torrent_urls) == 1:
                 if self._is_torrent_ref(torrent_urls[0]):
-                    message = f"正在添加下载任务: 资源 {torrent_urls[0]}"
+                    message = f"添加下载任务: 资源 {torrent_urls[0]}"
                 else:
-                    message = "正在添加下载任务: 磁力链接"
+                    message = "添加下载任务: 磁力链接"
             else:
-                message = f"正在批量添加下载任务: 共 {len(torrent_urls)} 个资源"
+                message = f"批量添加下载任务: 共 {len(torrent_urls)} 个资源"
         else:
-            message = "正在添加下载任务"
+            message = "添加下载任务"
         if downloader:
             message += f" [下载器: {downloader}]"
 
@@ -104,6 +104,29 @@ class AddDownloadTool(MoviePilotTool):
             return None
         return context
 
+    @classmethod
+    async def _async_resolve_cached_context(cls, torrent_ref: str) -> Optional[Context]:
+        """异步读取最近搜索缓存，避免在协程里直接访问同步文件缓存。"""
+        ref = str(torrent_ref).strip()
+        if ":" not in ref:
+            return None
+        try:
+            ref_hash, ref_index = ref.split(":", 1)
+            index = int(ref_index)
+        except (TypeError, ValueError):
+            return None
+
+        if index < 1:
+            return None
+
+        results = await SearchChain().async_last_search_results() or []
+        if index > len(results):
+            return None
+        context = results[index - 1]
+        if not ref_hash or cls._build_torrent_ref(context) != ref_hash:
+            return None
+        return context
+
     @staticmethod
     def _merge_labels_with_system_tag(labels: Optional[str]) -> Optional[str]:
         """合并用户标签与系统默认标签，确保任务可被系统管理"""
@@ -164,6 +187,43 @@ class AddDownloadTool(MoviePilotTool):
 
         return Path(FileURI(storage=dir_conf.storage or "local", path=dir_conf.download_path).uri)
 
+    @staticmethod
+    def _download_direct_sync(
+        torrent_input: str,
+        download_dir: Path,
+        merged_labels: Optional[str],
+        downloader: Optional[str],
+    ) -> tuple[Optional[str], Optional[str]]:
+        """同步添加磁力下载任务，避免下载器调用阻塞事件循环。"""
+        result = DownloadChain().download(
+            content=torrent_input,
+            download_dir=download_dir,
+            cookie=None,
+            label=merged_labels,
+            downloader=downloader,
+        )
+        if result:
+            _, did, _, error_msg = result
+        else:
+            did, error_msg = None, "未找到下载器"
+        return did, error_msg
+
+    @staticmethod
+    def _download_single_sync(
+        context: Context,
+        downloader: Optional[str],
+        save_path: Optional[str],
+        merged_labels: Optional[str],
+    ) -> tuple[Optional[str], Optional[str]]:
+        """同步提交带上下文的下载任务，避免站点下载与下载器调用阻塞事件循环。"""
+        return DownloadChain().download_single(
+            context=context,
+            downloader=downloader,
+            save_path=save_path,
+            label=merged_labels,
+            return_detail=True,
+        )
+
     async def run(self, torrent_url: Optional[List[str]] = None,
                   downloader: Optional[str] = None, save_path: Optional[str] = None,
                   labels: Optional[str] = None, **kwargs) -> str:
@@ -175,14 +235,13 @@ class AddDownloadTool(MoviePilotTool):
             if not torrent_inputs:
                 return "错误：torrent_url 不能为空。"
 
-            download_chain = DownloadChain()
             merged_labels = self._merge_labels_with_system_tag(labels)
             success_count = 0
             failed_messages = []
 
             for torrent_input in torrent_inputs:
                 if self._is_torrent_ref(torrent_input):
-                    cached_context = self._resolve_cached_context(torrent_input)
+                    cached_context = await self._async_resolve_cached_context(torrent_input)
                     if not cached_context or not cached_context.torrent_info:
                         failed_messages.append(f"{torrent_input} 引用无效，请重新使用 get_search_results 查看搜索结果")
                         continue
@@ -232,33 +291,33 @@ class AddDownloadTool(MoviePilotTool):
                             f"{torrent_input} 不是有效的下载内容，非 hash:id 时仅支持 magnet: 开头"
                         )
                         continue
-                    download_dir = self._resolve_direct_download_dir(save_path)
+                    download_dir = await self.run_blocking(
+                        "storage", self._resolve_direct_download_dir, save_path
+                    )
                     if not download_dir:
                         failed_messages.append(f"{torrent_input} 缺少保存路径，且系统未配置可用下载目录")
                         continue
-                    result = download_chain.download(
-                        content=torrent_input,
-                        download_dir=download_dir,
-                        cookie=None,
-                        label=merged_labels,
-                        downloader=downloader
+                    did, error_msg = await self.run_blocking(
+                        "downloader",
+                        self._download_direct_sync,
+                        torrent_input,
+                        download_dir,
+                        merged_labels,
+                        downloader,
                     )
-                    if result:
-                        _, did, _, error_msg = result
-                    else:
-                        did, error_msg = None, "未找到下载器"
                     if did:
                         success_count += 1
                     else:
                         failed_messages.append(self._build_failure_message(torrent_input, error_msg))
                     continue
 
-                did, error_msg = download_chain.download_single(
-                    context=context,
-                    downloader=downloader,
-                    save_path=save_path,
-                    label=merged_labels,
-                    return_detail=True
+                did, error_msg = await self.run_blocking(
+                    "downloader",
+                    self._download_single_sync,
+                    context,
+                    downloader,
+                    save_path,
+                    merged_labels,
                 )
                 if did:
                     success_count += 1

app/agent/tools/impl/add_rule_group.py

@@ -0,0 +1,115 @@
+"""新增过滤规则组工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    build_custom_rule_map,
+    collect_rule_group_usages,
+    get_builtin_rules,
+    get_custom_rules,
+    get_rule_groups,
+    normalize_rule_group,
+    save_system_config,
+    serialize_rule_group,
+)
+from app.log import logger
+from app.schemas.types import SystemConfigKey
+
+
+class AddRuleGroupInput(BaseModel):
+    """新增过滤规则组工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    name: str = Field(..., description="New rule group name.")
+    rule_string: str = Field(
+        ...,
+        description=(
+            "Rule expression using built-in/custom rule IDs. "
+            "Use '&', '!' inside one level, and use '>' between priority levels. "
+            "Example: 'SPECSUB & CNVOI & 4K & !BLU > CNSUB & CNVOI & 4K & !BLU'."
+        ),
+    )
+    media_type: Optional[str] = Field(
+        None,
+        description="Optional media type scope: '电影', '电视剧', 'movie', or 'tv'.",
+    )
+    category: Optional[str] = Field(
+        None,
+        description="Optional media category. Only valid when media_type is set.",
+    )
+
+
+class AddRuleGroupTool(MoviePilotTool):
+    name: str = "add_rule_group"
+    description: str = (
+        "Add a new filter rule group to UserFilterRuleGroups. "
+        "Rule groups are matched level by level from left to right and can be linked to search/subscription flows. "
+        "Before calling this tool, first use query_builtin_filter_rules and query_custom_filter_rules to confirm valid rule IDs, "
+        "and optionally use query_rule_groups to imitate existing rule_string patterns."
+    )
+    args_schema: Type[BaseModel] = AddRuleGroupInput
+    require_admin: bool = True
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        return f"新增规则组 {kwargs.get('name', '')}"
+
+    async def run(
+        self,
+        name: str,
+        rule_string: str,
+        media_type: Optional[str] = None,
+        category: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}, name={name}")
+
+        try:
+            custom_rules = get_custom_rules()
+            available_rule_ids = set(get_builtin_rules().keys()) | set(
+                build_custom_rule_map(custom_rules).keys()
+            )
+            rule_groups = get_rule_groups()
+            new_group, _ = normalize_rule_group(
+                name=name,
+                rule_string=rule_string,
+                media_type=media_type,
+                category=category,
+                existing_groups=rule_groups,
+                available_rule_ids=available_rule_ids,
+            )
+
+            rule_groups.append(new_group)
+            await save_system_config(
+                SystemConfigKey.UserFilterRuleGroups,
+                [group.model_dump(exclude_none=True) for group in rule_groups],
+            )
+            usage = await collect_rule_group_usages([new_group.name])
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"已新增规则组 {new_group.name}",
+                    "rule_group": serialize_rule_group(
+                        new_group, usage.get(new_group.name)
+                    ),
+                    "count": len(rule_groups),
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"新增规则组失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"新增规则组失败: {exc}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/add_subscribe.py

@@ -1,47 +1,86 @@
 """添加订阅工具"""
 
-from typing import Optional, Type, List
+from typing import List, Optional, Type
 
 from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
 from app.chain.subscribe import SubscribeChain
+from app.db.user_oper import UserOper
 from app.log import logger
-from app.schemas.types import MediaType
+from app.schemas.types import MediaType, MessageChannel
 
 
 class AddSubscribeInput(BaseModel):
     """添加订阅工具的输入参数模型"""
-    explanation: str = Field(..., description="Clear explanation of why this tool is being used in the current context")
-    title: str = Field(..., description="The title of the media to subscribe to (e.g., 'The Matrix', 'Breaking Bad')")
-    year: str = Field(..., description="Release year of the media (required for accurate identification)")
-    media_type: str = Field(...,
-                            description="Allowed values: movie, tv")
-    season: Optional[int] = Field(None,
-                                  description="Season number for TV shows (optional, if not specified will subscribe to all seasons)")
-    tmdb_id: Optional[int] = Field(None,
-                                   description="TMDB database ID for precise media identification (optional, can be obtained from search_media tool)")
-    douban_id: Optional[str] = Field(None,
-                                     description="Douban ID for precise media identification (optional, alternative to tmdb_id)")
-    start_episode: Optional[int] = Field(None,
-                                          description="Starting episode number for TV shows (optional, defaults to 1 if not specified)")
-    total_episode: Optional[int] = Field(None,
-                                          description="Total number of episodes for TV shows (optional, will be auto-detected from TMDB if not specified)")
-    quality: Optional[str] = Field(None,
-                                   description="Quality filter as regular expression (optional, e.g., 'BluRay|WEB-DL|HDTV')")
-    resolution: Optional[str] = Field(None,
-                                      description="Resolution filter as regular expression (optional, e.g., '1080p|720p|2160p')")
-    effect: Optional[str] = Field(None,
-                                  description="Effect filter as regular expression (optional, e.g., 'HDR|DV|SDR')")
-    filter_groups: Optional[List[str]] = Field(None,
-                               description="List of filter rule group names to apply (optional, can be obtained from query_rule_groups tool)")
-    sites: Optional[List[int]] = Field(None,
-                           description="List of site IDs to search from (optional, can be obtained from query_sites tool)")
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    title: str = Field(
+        ...,
+        description="The title of the media to subscribe to (e.g., 'The Matrix', 'Breaking Bad')",
+    )
+    year: str = Field(
+        ...,
+        description="Release year of the media (required for accurate identification)",
+    )
+    media_type: str = Field(..., description="Allowed values: movie, tv")
+    season: Optional[int] = Field(
+        None,
+        description=(
+            "Season number for TV shows (optional). If omitted, the subscription defaults to season 1 only. "
+            "To subscribe multiple seasons or the full series, call this tool separately for each season."
+        ),
+    )
+    tmdb_id: Optional[int] = Field(
+        None,
+        description="TMDB database ID for precise media identification (optional, can be obtained from search_media tool)",
+    )
+    douban_id: Optional[str] = Field(
+        None,
+        description="Douban ID for precise media identification (optional, alternative to tmdb_id)",
+    )
+    start_episode: Optional[int] = Field(
+        None,
+        description="Starting episode number for TV shows (optional, defaults to 1 if not specified)",
+    )
+    total_episode: Optional[int] = Field(
+        None,
+        description="Total number of episodes for TV shows (optional, will be auto-detected from TMDB if not specified)",
+    )
+    quality: Optional[str] = Field(
+        None,
+        description="Quality filter as regular expression (optional, e.g., 'BluRay|WEB-DL|HDTV')",
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="Resolution filter as regular expression (optional, e.g., '1080p|720p|2160p')",
+    )
+    effect: Optional[str] = Field(
+        None,
+        description="Effect filter as regular expression (optional, e.g., 'HDR|DV|SDR')",
+    )
+    filter_groups: Optional[List[str]] = Field(
+        None,
+        description="List of filter rule group names to apply (optional, can be obtained from query_rule_groups tool)",
+    )
+    sites: Optional[List[int]] = Field(
+        None,
+        description="List of site IDs to search from (optional, can be obtained from query_sites tool)",
+    )
 
 
 class AddSubscribeTool(MoviePilotTool):
     name: str = "add_subscribe"
-    description: str = "Add media subscription to create automated download rules for movies and TV shows. The system will automatically search and download new episodes or releases based on the subscription criteria. Supports advanced filtering options like quality, resolution, and effect filters using regular expressions."
+    description: str = (
+        "Add media subscription to create automated download rules for movies and TV shows. "
+        "The system will automatically search and download new episodes or releases based on the subscription criteria. "
+        "For TV shows, omitting `season` subscribes season 1 only by default; to subscribe multiple seasons or "
+        "the full series, call this tool once per season. Supports advanced filtering options like quality, "
+        "resolution, and effect filters using regular expressions."
+    )
     args_schema: Type[BaseModel] = AddSubscribeInput
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
@@ -50,52 +89,103 @@ class AddSubscribeTool(MoviePilotTool):
         year = kwargs.get("year", "")
         media_type = kwargs.get("media_type", "")
         season = kwargs.get("season")
-        
-        message = f"正在添加订阅: {title}"
+
+        message = f"添加订阅: {title}"
         if year:
             message += f" ({year})"
         if media_type:
             message += f" [{media_type}]"
         if season:
             message += f" 第{season}季"
-        
+        elif media_type == "tv":
+            message += " 第1季(默认)"
+
         return message
 
-    async def run(self, title: str, year: str, media_type: str,
-                  season: Optional[int] = None, tmdb_id: Optional[int] = None,
-                  douban_id: Optional[str] = None,
-                  start_episode: Optional[int] = None, total_episode: Optional[int] = None,
-                  quality: Optional[str] = None, resolution: Optional[str] = None,
-                  effect: Optional[str] = None, filter_groups: Optional[List[str]] = None,
-                  sites: Optional[List[int]] = None, **kwargs) -> str:
+    async def _resolve_subscribe_username(self) -> Optional[str]:
+        """优先映射为系统用户名，未绑定时回退当前渠道用户名。"""
+        resolved_username = self._username
+        if not self._channel or not self._user_id:
+            return resolved_username
+
+        try:
+            channel = MessageChannel(self._channel)
+        except ValueError:
+            return resolved_username
+
+        binding_keys = {
+            MessageChannel.Telegram: ("telegram_userid",),
+            MessageChannel.Discord: ("discord_userid",),
+            MessageChannel.Wechat: ("wechat_userid",),
+            MessageChannel.Slack: ("slack_userid",),
+            MessageChannel.VoceChat: ("vocechat_userid",),
+            MessageChannel.SynologyChat: ("synologychat_userid",),
+            MessageChannel.QQ: ("qq_userid", "qq_openid"),
+        }.get(channel)
+        if not binding_keys:
+            return resolved_username
+
+        mapped_username = await self.run_blocking(
+            "db",
+            UserOper().get_name,
+            **{key: self._user_id for key in binding_keys},
+        )
+        return mapped_username or resolved_username
+
+    async def run(
+        self,
+        title: str,
+        year: str,
+        media_type: str,
+        season: Optional[int] = None,
+        tmdb_id: Optional[int] = None,
+        douban_id: Optional[str] = None,
+        start_episode: Optional[int] = None,
+        total_episode: Optional[int] = None,
+        quality: Optional[str] = None,
+        resolution: Optional[str] = None,
+        effect: Optional[str] = None,
+        filter_groups: Optional[List[str]] = None,
+        sites: Optional[List[int]] = None,
+        **kwargs,
+    ) -> str:
         logger.info(
             f"执行工具: {self.name}, 参数: title={title}, year={year}, media_type={media_type}, "
             f"season={season}, tmdb_id={tmdb_id}, douban_id={douban_id}, start_episode={start_episode}, "
             f"total_episode={total_episode}, quality={quality}, resolution={resolution}, "
-            f"effect={effect}, filter_groups={filter_groups}, sites={sites}")
+            f"effect={effect}, filter_groups={filter_groups}, sites={sites}"
+        )
 
         try:
             subscribe_chain = SubscribeChain()
             media_type_enum = MediaType.from_agent(media_type)
             if not media_type_enum:
                 return f"错误：无效的媒体类型 '{media_type}'，支持的类型：'movie', 'tv'"
+            effective_season = (
+                season
+                if season is not None
+                else 1
+                if media_type_enum == MediaType.TV
+                else None
+            )
+            subscribe_username = await self._resolve_subscribe_username()
 
             # 构建额外的订阅参数
             subscribe_kwargs = {}
             if start_episode is not None:
-                subscribe_kwargs['start_episode'] = start_episode
+                subscribe_kwargs["start_episode"] = start_episode
             if total_episode is not None:
-                subscribe_kwargs['total_episode'] = total_episode
+                subscribe_kwargs["total_episode"] = total_episode
             if quality:
-                subscribe_kwargs['quality'] = quality
+                subscribe_kwargs["quality"] = quality
             if resolution:
-                subscribe_kwargs['resolution'] = resolution
+                subscribe_kwargs["resolution"] = resolution
             if effect:
-                subscribe_kwargs['effect'] = effect
+                subscribe_kwargs["effect"] = effect
             if filter_groups:
-                subscribe_kwargs['filter_groups'] = filter_groups
+                subscribe_kwargs["filter_groups"] = filter_groups
             if sites:
-                subscribe_kwargs['sites'] = sites
+                subscribe_kwargs["sites"] = sites
 
             sid, message = await subscribe_chain.async_add(
                 mtype=media_type_enum,
@@ -104,14 +194,22 @@ class AddSubscribeTool(MoviePilotTool):
                 tmdbid=tmdb_id,
                 doubanid=douban_id,
                 season=season,
-                username=self._user_id,
-                **subscribe_kwargs
+                username=subscribe_username,
+                **subscribe_kwargs,
             )
             if sid:
                 if message and "已存在" in message:
-                    return f"订阅已存在：{title} ({year})。如需修改参数请先删除旧订阅。"
+                    result_msg = f"订阅已存在：{title} ({year})"
+                    if effective_season is not None:
+                        result_msg += f" 第{effective_season}季"
+                    result_msg += "。如需修改参数请先删除旧订阅。"
+                    return result_msg
 
                 result_msg = f"成功添加订阅：{title} ({year})"
+                if effective_season is not None:
+                    result_msg += f" 第{effective_season}季"
+                    if season is None:
+                        result_msg += "（未指定季号，默认按第一季订阅）"
                 if subscribe_kwargs:
                     params = []
                     if start_episode is not None:

app/agent/tools/impl/ask_user_choice.py

@@ -5,7 +5,7 @@ from typing import List, Optional, Type
 from pydantic import BaseModel, Field, model_validator
 
 from app.agent.tools.base import MoviePilotTool, ToolChain
-from app.agent.interaction import (
+from app.helper.interaction import (
     AgentInteractionOption,
     agent_interaction_manager,
 )
@@ -64,6 +64,7 @@ class AskUserChoiceInput(BaseModel):
 
 class AskUserChoiceTool(MoviePilotTool):
     name: str = "ask_user_choice"
+    sends_message: bool = True
     description: str = (
         "Ask the user to choose from button options on channels that support interactive buttons. "
         "After the user clicks a button, the selected value will come back as the user's next message."
@@ -75,7 +76,7 @@ class AskUserChoiceTool(MoviePilotTool):
         message = kwargs.get("message", "") or ""
         if len(message) > 40:
             message = message[:40] + "..."
-        return f"正在发送按钮选择: {message}"
+        return f"发送按钮选择: {message}"
 
     @staticmethod
     def _truncate_button_text(text: str, max_length: int) -> str:
@@ -106,7 +107,7 @@ class AskUserChoiceTool(MoviePilotTool):
         ):
             return f"当前渠道 {channel.value} 不支持按钮选择"
 
-        max_per_row = ChannelCapabilityManager.get_max_buttons_per_row(channel)
+        max_per_row = 1
         max_rows = ChannelCapabilityManager.get_max_button_rows(channel)
         max_text_length = ChannelCapabilityManager.get_max_button_text_length(channel)
         max_options = max_per_row * max_rows

app/agent/tools/impl/browse_webpage.py

@@ -108,16 +108,16 @@ class BrowseWebpageTool(MoviePilotTool):
         url = kwargs.get("url", "")
         selector = kwargs.get("selector", "")
         action_messages = {
-            "goto": f"正在打开网页: {url}",
-            "get_content": "正在获取页面内容",
-            "screenshot": "正在截取页面截图",
-            "click": f"正在点击元素: {selector}",
-            "fill": f"正在填写表单: {selector}",
-            "select": f"正在选择选项: {selector}",
-            "evaluate": "正在执行 JavaScript",
-            "wait": f"正在等待元素: {selector}",
+            "goto": f"打开网页: {url}",
+            "get_content": "获取页面内容",
+            "screenshot": "截取页面截图",
+            "click": f"点击元素: {selector}",
+            "fill": f"填写表单: {selector}",
+            "select": f"选择选项: {selector}",
+            "evaluate": "执行 JavaScript",
+            "wait": f"等待元素: {selector}",
         }
-        return action_messages.get(action, f"正在执行浏览器操作: {action}")
+        return action_messages.get(action, f"执行浏览器操作: {action}")
 
     async def run(
         self,

app/agent/tools/impl/delete_custom_filter_rule.py

@@ -0,0 +1,97 @@
+"""删除自定义过滤规则工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    collect_custom_rule_group_refs,
+    get_custom_rules,
+    get_rule_groups,
+    save_system_config,
+)
+from app.log import logger
+from app.schemas.types import SystemConfigKey
+
+
+class DeleteCustomFilterRuleInput(BaseModel):
+    """删除自定义过滤规则工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    rule_id: str = Field(..., description="Custom rule ID to delete.")
+
+
+class DeleteCustomFilterRuleTool(MoviePilotTool):
+    name: str = "delete_custom_filter_rule"
+    description: str = (
+        "Delete a custom filter rule from CustomFilterRules. "
+        "If the rule is still referenced by rule groups, the deletion is blocked to avoid breaking rule_string expressions."
+    )
+    args_schema: Type[BaseModel] = DeleteCustomFilterRuleInput
+    require_admin: bool = True
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        return f"删除自定义过滤规则 {kwargs.get('rule_id', '')}"
+
+    async def run(self, rule_id: str, **kwargs) -> str:
+        logger.info(f"执行工具: {self.name}, rule_id={rule_id}")
+
+        try:
+            custom_rules = get_custom_rules()
+            target_rule = next((rule for rule in custom_rules if rule.id == rule_id), None)
+            if not target_rule:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": f"自定义过滤规则 '{rule_id}' 不存在",
+                    },
+                    ensure_ascii=False,
+                )
+
+            refs = collect_custom_rule_group_refs(get_rule_groups(), [rule_id]).get(
+                rule_id, []
+            )
+            if refs:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": (
+                            f"自定义过滤规则 '{rule_id}' 仍被规则组引用，无法删除。"
+                        ),
+                        "referenced_by_rule_groups": refs,
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+
+            remaining_rules = [
+                rule for rule in custom_rules if rule.id != rule_id
+            ]
+            await save_system_config(
+                SystemConfigKey.CustomFilterRules,
+                [rule.model_dump(exclude_none=True) for rule in remaining_rules],
+            )
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"已删除自定义过滤规则 {rule_id}",
+                    "count": len(remaining_rules),
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"删除自定义过滤规则失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"删除自定义过滤规则失败: {exc}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/delete_download.py

@@ -41,7 +41,7 @@ class DeleteDownloadTool(MoviePilotTool):
         downloader = kwargs.get("downloader")
         delete_files = kwargs.get("delete_files", False)
 
-        message = f"正在删除下载任务: {hash_value}"
+        message = f"删除下载任务: {hash_value}"
         if downloader:
             message += f" [下载器: {downloader}]"
         if delete_files:
@@ -49,6 +49,15 @@ class DeleteDownloadTool(MoviePilotTool):
 
         return message
 
+    @staticmethod
+    def _delete_download_sync(
+        hash_value: str, downloader: Optional[str] = None, delete_files: bool = False
+    ) -> bool:
+        """同步删除下载任务，避免下载器客户端阻塞事件循环。"""
+        return DownloadChain().remove_torrents(
+            hashs=[hash_value], downloader=downloader, delete_file=delete_files
+        )
+
     async def run(
         self,
         hash: str,
@@ -61,16 +70,18 @@ class DeleteDownloadTool(MoviePilotTool):
         )
 
         try:
-            download_chain = DownloadChain()
-
             # 仅支持通过hash删除任务
             if len(hash) != 40 or not all(c in "0123456789abcdefABCDEF" for c in hash):
                 return "参数错误：hash 格式无效，请先使用 query_download_tasks 工具获取正确的 hash。"
 
             # 删除下载任务
             # remove_torrents 支持 delete_file 参数，可以控制是否删除文件
-            result = download_chain.remove_torrents(
-                hashs=[hash], downloader=downloader, delete_file=delete_files
+            result = await self.run_blocking(
+                "downloader",
+                self._delete_download_sync,
+                hash,
+                downloader,
+                bool(delete_files),
             )
 
             if result:

app/agent/tools/impl/delete_download_history.py

@@ -30,7 +30,7 @@ class DeleteDownloadHistoryTool(MoviePilotTool):
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         history_id = kwargs.get("history_id")
-        return f"正在删除下载历史记录 ID: {history_id}"
+        return f"删除下载历史记录 ID: {history_id}"
 
     async def run(self, history_id: int, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: history_id={history_id}")

app/agent/tools/impl/delete_rule_group.py

@@ -0,0 +1,81 @@
+"""删除过滤规则组工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    get_rule_groups,
+    remove_rule_group_references,
+    save_system_config,
+)
+from app.log import logger
+from app.schemas.types import SystemConfigKey
+
+
+class DeleteRuleGroupInput(BaseModel):
+    """删除过滤规则组工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    name: str = Field(..., description="Rule group name to delete.")
+
+
+class DeleteRuleGroupTool(MoviePilotTool):
+    name: str = "delete_rule_group"
+    description: str = (
+        "Delete a filter rule group from UserFilterRuleGroups. "
+        "The tool also removes dangling references from global settings and subscriptions."
+    )
+    args_schema: Type[BaseModel] = DeleteRuleGroupInput
+    require_admin: bool = True
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        return f"删除规则组 {kwargs.get('name', '')}"
+
+    async def run(self, name: str, **kwargs) -> str:
+        logger.info(f"执行工具: {self.name}, name={name}")
+
+        try:
+            rule_groups = get_rule_groups()
+            if not any(group.name == name for group in rule_groups):
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": f"规则组 '{name}' 不存在",
+                    },
+                    ensure_ascii=False,
+                )
+
+            remaining_groups = [
+                group for group in rule_groups if group.name != name
+            ]
+            await save_system_config(
+                SystemConfigKey.UserFilterRuleGroups,
+                [group.model_dump(exclude_none=True) for group in remaining_groups],
+            )
+            reference_changes = await remove_rule_group_references(name)
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"已删除规则组 {name}",
+                    "count": len(remaining_groups),
+                    "reference_updates": reference_changes,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"删除规则组失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"删除规则组失败: {exc}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/delete_subscribe.py

@@ -34,7 +34,7 @@ class DeleteSubscribeTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据删除参数生成友好的提示消息"""
         subscribe_id = kwargs.get("subscribe_id")
-        return f"正在删除订阅 (ID: {subscribe_id})"
+        return f"删除订阅 (ID: {subscribe_id})"
 
     async def run(self, subscribe_id: int, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: subscribe_id={subscribe_id}")
@@ -49,8 +49,11 @@ class DeleteSubscribeTool(MoviePilotTool):
             # 在删除之前获取订阅信息（用于事件）
             subscribe_info = subscribe.to_dict()
 
-            # 删除订阅
-            subscribe_oper.delete(subscribe_id)
+            await subscribe_oper.async_delete(subscribe_id)
+            # 分享订阅统计刷新本身已异步化，这里只需要在删除后触发即可。
+            SubscribeHelper().sub_done_async(
+                {"tmdbid": subscribe.tmdbid, "doubanid": subscribe.doubanid}
+            )
 
             # 发送事件
             await eventmanager.async_send_event(
@@ -58,11 +61,6 @@ class DeleteSubscribeTool(MoviePilotTool):
                 {"subscribe_id": subscribe_id, "subscribe_info": subscribe_info},
             )
 
-            # 统计订阅
-            SubscribeHelper().sub_done_async(
-                {"tmdbid": subscribe.tmdbid, "doubanid": subscribe.doubanid}
-            )
-
             return f"成功删除订阅：{subscribe.name} ({subscribe.year})"
         except Exception as e:
             logger.error(f"删除订阅失败: {e}", exc_info=True)

app/agent/tools/impl/delete_transfer_history.py

@@ -30,28 +30,24 @@ class DeleteTransferHistoryTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据参数生成友好的提示消息"""
         history_id = kwargs.get("history_id")
-        return f"正在删除整理历史记录: ID={history_id}"
+        return f"删除整理历史记录: ID={history_id}"
 
     async def run(self, history_id: int, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: history_id={history_id}")
 
         try:
             transferhis = TransferHistoryOper()
-
-            # 查询历史记录是否存在
-            history = transferhis.get(history_id)
+            history = await transferhis.async_get(history_id)
             if not history:
                 return f"错误：整理历史记录不存在，ID={history_id}"
 
-            # 保存信息用于返回
             title = history.title or "未知"
             src = history.src or "未知"
             status = "成功" if history.status else "失败"
-
-            # 删除记录
-            transferhis.delete(history_id)
-
-            return f"已删除整理历史记录：ID={history_id}，标题={title}，源路径={src}，状态={status}"
+            await transferhis.async_delete(history_id)
+            return (
+                f"已删除整理历史记录：ID={history_id}，标题={title}，源路径={src}，状态={status}"
+            )
         except Exception as e:
             logger.error(f"删除整理历史记录失败: {e}", exc_info=True)
             return f"删除整理历史记录时发生错误: {str(e)}"

app/agent/tools/impl/edit_file.py

@@ -28,7 +28,7 @@ class EditFileTool(MoviePilotTool):
         """根据参数生成友好的提示消息"""
         file_path = kwargs.get("file_path", "")
         file_name = Path(file_path).name if file_path else "未知文件"
-        return f"正在编辑文件: {file_name}"
+        return f"编辑文件: {file_name}"
 
     async def run(self, file_path: str, old_text: str, new_text: str, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: file_path={file_path}")

app/agent/tools/impl/execute_command.py

@@ -1,7 +1,12 @@
 """执行Shell命令工具"""
 
 import asyncio
-from typing import Optional, Type
+import os
+import signal
+import subprocess
+from dataclasses import dataclass, field
+from tempfile import NamedTemporaryFile
+from typing import Optional, TextIO, Type
 
 from pydantic import BaseModel, Field
 
@@ -9,6 +14,107 @@ from app.agent.tools.base import MoviePilotTool
 from app.log import logger
 
 
+DEFAULT_TIMEOUT_SECONDS = 60
+MAX_TIMEOUT_SECONDS = 300
+MAX_OUTPUT_PREVIEW_BYTES = 10 * 1024
+READ_CHUNK_SIZE = 4096
+KILL_GRACE_SECONDS = 3
+COMMAND_CONCURRENCY_LIMIT = 2
+
+_command_semaphore = asyncio.Semaphore(COMMAND_CONCURRENCY_LIMIT)
+
+
+@dataclass
+class _CommandOutput:
+    """保存前 10KB 预览，并在超限时将完整输出写入临时文件。"""
+
+    preview_limit_bytes: int
+    preview_entries: list[tuple[str, str]] = field(default_factory=list)
+    captured_bytes: int = 0
+    preview_truncated: bool = False
+    temp_file_path: Optional[str] = None
+    temp_file_handle: Optional[TextIO] = None
+    last_written_stream: Optional[str] = None
+
+    @staticmethod
+    def _clip_text_to_bytes(text: str, byte_limit: int) -> str:
+        if byte_limit <= 0:
+            return ""
+        return text.encode("utf-8")[:byte_limit].decode("utf-8", errors="ignore")
+
+    def _write_chunk(self, stream_name: str, text: str) -> None:
+        if not self.temp_file_handle or not text:
+            return
+
+        if self.last_written_stream != stream_name:
+            if self.temp_file_handle.tell() > 0:
+                self.temp_file_handle.write("\n")
+            title = "标准输出" if stream_name == "stdout" else "错误输出"
+            self.temp_file_handle.write(f"[{title}]\n")
+            self.last_written_stream = stream_name
+
+        self.temp_file_handle.write(text)
+
+    def _ensure_temp_file(self) -> None:
+        if self.temp_file_handle:
+            return
+
+        temp_file = NamedTemporaryFile(
+            mode="w",
+            encoding="utf-8",
+            suffix=".log",
+            prefix="moviepilot-command-",
+            delete=False,
+        )
+        self.temp_file_path = temp_file.name
+        self.temp_file_handle = temp_file
+        for stream_name, chunk in self.preview_entries:
+            self._write_chunk(stream_name, chunk)
+
+    def close(self) -> None:
+        if not self.temp_file_handle:
+            return
+        self.temp_file_handle.flush()
+        self.temp_file_handle.close()
+        self.temp_file_handle = None
+
+    def append(self, stream_name: str, text: str) -> None:
+        if not text:
+            return
+
+        if self.temp_file_handle:
+            self._write_chunk(stream_name, text)
+            return
+
+        chunk_bytes = len(text.encode("utf-8"))
+        remaining = self.preview_limit_bytes - self.captured_bytes
+        if chunk_bytes <= remaining:
+            self.preview_entries.append((stream_name, text))
+            self.captured_bytes += chunk_bytes
+            return
+
+        self.preview_truncated = True
+        self._ensure_temp_file()
+        self._write_chunk(stream_name, text)
+
+        preview = self._clip_text_to_bytes(text, remaining)
+        if preview:
+            self.preview_entries.append((stream_name, preview))
+            self.captured_bytes += len(preview.encode("utf-8"))
+
+    @property
+    def stdout(self) -> str:
+        return "".join(
+            text for stream_name, text in self.preview_entries if stream_name == "stdout"
+        ).strip()
+
+    @property
+    def stderr(self) -> str:
+        return "".join(
+            text for stream_name, text in self.preview_entries if stream_name == "stderr"
+        ).strip()
+
+
 class ExecuteCommandInput(BaseModel):
     """执行Shell命令工具的输入参数模型"""
 
@@ -23,14 +129,157 @@ class ExecuteCommandInput(BaseModel):
 
 class ExecuteCommandTool(MoviePilotTool):
     name: str = "execute_command"
-    description: str = "Safely execute shell commands on the server. Useful for system maintenance, checking status, or running custom scripts. Includes timeout and output limits."
+    description: str = (
+        "Safely execute shell commands on the server. Useful for system "
+        "maintenance, checking status, or running custom scripts. Includes "
+        "timeout, concurrency, and output preview limits."
+    )
     args_schema: Type[BaseModel] = ExecuteCommandInput
     require_admin: bool = True
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据命令生成友好的提示消息"""
         command = kwargs.get("command", "")
-        return f"正在执行系统命令: {command}"
+        return f"执行系统命令: {command}"
+
+    @staticmethod
+    def _normalize_timeout(timeout: Optional[int]) -> tuple[int, Optional[str]]:
+        """限制命令最长运行时间，避免 Agent 传入过大的 timeout。"""
+        try:
+            normalized = int(timeout or DEFAULT_TIMEOUT_SECONDS)
+        except (TypeError, ValueError):
+            normalized = DEFAULT_TIMEOUT_SECONDS
+
+        if normalized <= 0:
+            return DEFAULT_TIMEOUT_SECONDS, "timeout 参数无效，已使用默认 60 秒"
+        if normalized > MAX_TIMEOUT_SECONDS:
+            return (
+                MAX_TIMEOUT_SECONDS,
+                f"timeout 参数超过上限，已从 {normalized} 秒限制为 {MAX_TIMEOUT_SECONDS} 秒",
+            )
+        return normalized, None
+
+    @staticmethod
+    def _subprocess_kwargs() -> dict:
+        """为子进程创建独立进程组，便于超时场景清理整棵子进程。"""
+        kwargs = {
+            "stdin": subprocess.DEVNULL,
+            "stdout": asyncio.subprocess.PIPE,
+            "stderr": asyncio.subprocess.PIPE,
+        }
+        if os.name == "posix":
+            kwargs["start_new_session"] = True
+        elif os.name == "nt":
+            kwargs["creationflags"] = subprocess.CREATE_NEW_PROCESS_GROUP
+        return kwargs
+
+    @staticmethod
+    async def _read_stream(
+        stream: asyncio.StreamReader,
+        stream_name: str,
+        output: _CommandOutput,
+    ) -> None:
+        """按块读取输出，始终只把前 10KB 保留在返回结果中。"""
+        while True:
+            chunk = await stream.read(READ_CHUNK_SIZE)
+            if not chunk:
+                break
+
+            output.append(stream_name, chunk.decode("utf-8", errors="replace"))
+
+    @staticmethod
+    def _terminate_process(process: asyncio.subprocess.Process, sig: int):
+        """向进程组发送终止信号；不支持进程组的平台回退为单进程终止。"""
+        try:
+            if os.name == "posix":
+                os.killpg(process.pid, sig)
+            elif sig == getattr(signal, "SIGKILL", None):
+                process.kill()
+            else:
+                process.terminate()
+        except ProcessLookupError:
+            pass
+
+    @classmethod
+    async def _cleanup_process(
+        cls,
+        process: asyncio.subprocess.Process,
+        wait_task: asyncio.Task,
+    ) -> None:
+        """先温和终止，失败后强杀，避免超时 shell 遗留子进程。"""
+        if wait_task.done():
+            return
+
+        cls._terminate_process(process, signal.SIGTERM)
+        try:
+            await asyncio.wait_for(
+                asyncio.shield(wait_task), timeout=KILL_GRACE_SECONDS
+            )
+            return
+        except asyncio.TimeoutError:
+            pass
+
+        kill_signal = getattr(signal, "SIGKILL", signal.SIGTERM)
+        cls._terminate_process(process, kill_signal)
+        try:
+            await asyncio.wait_for(
+                asyncio.shield(wait_task), timeout=KILL_GRACE_SECONDS
+            )
+        except asyncio.TimeoutError:
+            logger.warning("命令进程强制清理超时: pid=%s", process.pid)
+
+    @staticmethod
+    async def _finish_reader_tasks(reader_tasks: list[asyncio.Task]) -> None:
+        """等待输出读取任务退出，异常只记录不影响工具返回。"""
+        if not reader_tasks:
+            return
+        done, pending = await asyncio.wait(reader_tasks, timeout=1)
+        for task in pending:
+            task.cancel()
+        results = await asyncio.gather(*done, *pending, return_exceptions=True)
+        for result in results:
+            if isinstance(result, Exception) and not isinstance(
+                result, asyncio.CancelledError
+            ):
+                logger.debug("命令输出读取任务异常: %s", result)
+
+    @staticmethod
+    def _format_result(
+        *,
+        exit_code: Optional[int],
+        output: _CommandOutput,
+        timeout: int,
+        timed_out: bool,
+        timeout_note: Optional[str],
+    ) -> str:
+        if timed_out:
+            result = f"命令执行超时 (限制: {timeout}秒，已终止进程)"
+        else:
+            result = f"命令执行完成 (退出码: {exit_code})"
+
+        if timeout_note:
+            result += f"\n\n提示:\n{timeout_note}"
+        if output.temp_file_path:
+            file_note = (
+                "截至命令终止前的完整输出"
+                if timed_out
+                else "完整输出"
+            )
+            result += (
+                "\n\n提示:\n"
+                f"命令输出超过 10KB，仅返回前 {MAX_OUTPUT_PREVIEW_BYTES} 字节内容。\n"
+                f"{file_note}已写入临时文件: {output.temp_file_path}\n"
+                "如需完整内容，请继续读取该文件。"
+            )
+        if output.stdout:
+            result += f"\n\n标准输出:\n{output.stdout}"
+        if output.stderr:
+            result += f"\n\n错误输出:\n{output.stderr}"
+        if output.preview_truncated:
+            result += "\n\n...(仅展示前 10KB 内容)"
+        if not output.stdout and not output.stderr:
+            result += "\n\n(无输出内容)"
+        return result
 
     async def run(self, command: str, timeout: Optional[int] = 60, **kwargs) -> str:
         logger.info(
@@ -50,46 +299,46 @@ class ExecuteCommandTool(MoviePilotTool):
             if keyword in command:
                 return f"错误：命令包含禁止使用的关键字 '{keyword}'"
 
+        normalized_timeout, timeout_note = self._normalize_timeout(timeout)
+
         try:
-            # 执行命令
-            process = await asyncio.create_subprocess_shell(
-                command, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
-            )
-
-            try:
-                # 等待完成，带超时
-                stdout, stderr = await asyncio.wait_for(
-                    process.communicate(), timeout=timeout
+            async with _command_semaphore:
+                # 命令输出可能非常大，必须边读边落盘，不能使用 communicate() 一次性收集。
+                process = await asyncio.create_subprocess_shell(
+                    command, **self._subprocess_kwargs()
                 )
+                output = _CommandOutput(preview_limit_bytes=MAX_OUTPUT_PREVIEW_BYTES)
+                wait_task = asyncio.create_task(process.wait())
+                reader_tasks = [
+                    asyncio.create_task(
+                        self._read_stream(process.stdout, "stdout", output)
+                    ),
+                    asyncio.create_task(
+                        self._read_stream(process.stderr, "stderr", output)
+                    ),
+                ]
 
-                # 处理输出
-                stdout_str = stdout.decode("utf-8", errors="replace").strip()
-                stderr_str = stderr.decode("utf-8", errors="replace").strip()
-                exit_code = process.returncode
-
-                result = f"命令执行完成 (退出码: {exit_code})"
-                if stdout_str:
-                    result += f"\n\n标准输出:\n{stdout_str}"
-                if stderr_str:
-                    result += f"\n\n错误输出:\n{stderr_str}"
-
-                # 如果没有输出
-                if not stdout_str and not stderr_str:
-                    result += "\n\n(无输出内容)"
-
-                # 限制输出长度，防止上下文过长
-                if len(result) > 3000:
-                    result = result[:3000] + "\n\n...(输出内容过长，已截断)"
-
-                return result
-
-            except asyncio.TimeoutError:
-                # 超时处理
+                timed_out = False
                 try:
-                    process.kill()
-                except ProcessLookupError:
-                    pass
-                return f"命令执行超时 (限制: {timeout}秒)"
+                    await asyncio.wait_for(
+                        asyncio.shield(wait_task), timeout=normalized_timeout
+                    )
+                except asyncio.TimeoutError:
+                    timed_out = True
+                    await self._cleanup_process(process, wait_task)
+
+                try:
+                    await self._finish_reader_tasks(reader_tasks)
+                finally:
+                    output.close()
+
+                return self._format_result(
+                    exit_code=process.returncode,
+                    output=output,
+                    timeout=normalized_timeout,
+                    timed_out=timed_out,
+                    timeout_note=timeout_note,
+                )
 
         except Exception as e:
             logger.error(f"执行命令失败: {e}", exc_info=True)

app/agent/tools/impl/get_recommendations.py

@@ -62,7 +62,7 @@ class GetRecommendationsTool(MoviePilotTool):
             "douban_hot": "豆瓣热门",
             "douban_movie_hot": "豆瓣热门电影",
             "douban_tv_hot": "豆瓣热门电视剧",
-            "douban_movie_showing": "豆瓣正在热映",
+            "douban_movie_showing": "豆瓣热映",
             "douban_movies": "豆瓣最新电影",
             "douban_tvs": "豆瓣最新电视剧",
             "douban_movie_top250": "豆瓣电影TOP250",
@@ -73,7 +73,7 @@ class GetRecommendationsTool(MoviePilotTool):
         }
         source_desc = source_map.get(source, source)
 
-        message = f"正在获取推荐: {source_desc}"
+        message = f"获取推荐: {source_desc}"
         if media_type != "all":
             message += f" [{media_type}]"
         message += f" (第{page}页)"

app/agent/tools/impl/get_search_results.py

@@ -53,7 +53,7 @@ class GetSearchResultsTool(MoviePilotTool):
     args_schema: Type[BaseModel] = GetSearchResultsInput
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
-        return "正在获取搜索结果"
+        return "获取搜索结果"
 
     async def run(
         self,

app/agent/tools/impl/install_plugin.py

@@ -0,0 +1,118 @@
+"""安装插件工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import (
+    get_plugin_snapshot,
+    install_plugin_runtime,
+    load_market_plugins,
+    summarize_plugin,
+)
+from app.log import logger
+
+
+class InstallPluginInput(BaseModel):
+    """安装插件工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    plugin_id: str = Field(
+        ...,
+        description="Exact plugin ID to install. Use query_market_plugins first to find the correct plugin_id.",
+    )
+    force: bool = Field(
+        False,
+        description="Whether to force reinstall or upgrade the specified plugin.",
+    )
+    force_refresh_market: bool = Field(
+        False,
+        description="Whether to refresh plugin market caches before reading the market list.",
+    )
+
+
+class InstallPluginTool(MoviePilotTool):
+    name: str = "install_plugin"
+    description: str = (
+        "Install a plugin by exact plugin_id from the plugin market or local plugin repositories. "
+        "Use query_market_plugins first when you need filtering or discovery."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = InstallPluginInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        plugin_id = kwargs.get("plugin_id")
+        return f"安装插件: {plugin_id or '未知插件'}"
+
+    async def run(
+        self,
+        plugin_id: str,
+        force: bool = False,
+        force_refresh_market: bool = False,
+        **kwargs,
+    ) -> str:
+        logger.info(
+            f"执行工具: {self.name}, 参数: plugin_id={plugin_id}, force={force}"
+        )
+
+        try:
+            plugins = await load_market_plugins(force_refresh=force_refresh_market)
+            if not plugins:
+                return json.dumps(
+                    {"success": False, "message": "当前插件市场没有可用插件"},
+                    ensure_ascii=False,
+                )
+
+            candidate = next((plugin for plugin in plugins if plugin.id == plugin_id), None)
+            if not candidate:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": f"未在插件市场中找到插件: {plugin_id}。请先调用 query_market_plugins 确认 plugin_id。",
+                    },
+                    ensure_ascii=False,
+                )
+
+            success, message, refreshed_only = await install_plugin_runtime(
+                candidate.id,
+                getattr(candidate, "repo_url", None),
+                force=force,
+            )
+            if not success:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "plugin": summarize_plugin(candidate),
+                        "message": message,
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+
+            plugin_snapshot = get_plugin_snapshot(candidate.id)
+            if refreshed_only and getattr(candidate, "has_update", False) and not force:
+                message = "插件已安装，当前仅刷新加载；如需升级到市场新版本，请设置 force=true"
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": message,
+                    "force": force,
+                    "refreshed_only": refreshed_only,
+                    "plugin": summarize_plugin(candidate),
+                    "runtime": plugin_snapshot,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as e:
+            logger.error(f"安装插件失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"安装插件时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/list_directory.py

@@ -32,99 +32,87 @@ class ListDirectoryTool(MoviePilotTool):
         path = kwargs.get("path", "")
         storage = kwargs.get("storage", "local")
         
-        message = f"正在查询目录: {path}"
+        message = f"查询目录: {path}"
         if storage != "local":
             message += f" [存储: {storage}]"
         
         return message
 
+    @staticmethod
+    def _list_directory_sync(
+        path: str, storage: Optional[str] = "local", sort_by: Optional[str] = "name"
+    ) -> str:
+        """
+        目录遍历可能触发本地磁盘或远程存储请求，统一放到线程池中执行。
+        """
+        if not path:
+            return "错误：路径不能为空"
+
+        if storage == "local":
+            if not path.startswith("/") and not (len(path) > 1 and path[1] == ":"):
+                path = str(Path(path).resolve())
+        elif not path.startswith("/"):
+            path = "/" + path
+
+        fileitem = FileItem(storage=storage or "local", path=path, type="dir")
+        file_list = StorageChain().list_files(fileitem, recursion=False)
+
+        if file_list is None:
+            return f"无法访问目录：{path}，请检查路径是否正确或存储是否可用"
+        if not file_list:
+            return f"目录 {path} 为空"
+
+        if sort_by == "time":
+            file_list.sort(key=lambda x: x.modify_time or 0, reverse=True)
+        else:
+            file_list.sort(
+                key=lambda x: (
+                    0 if x.type == "dir" else 1,
+                    StringUtils.natural_sort_key(x.name or ""),
+                )
+            )
+
+        total_count = len(file_list)
+        limited_list = file_list[:20]
+        simplified_items = []
+        for item in limited_list:
+            size_str = StringUtils.str_filesize(item.size) if item.size else None
+            modify_time_str = None
+            if item.modify_time:
+                try:
+                    modify_time_str = datetime.fromtimestamp(item.modify_time).strftime(
+                        "%Y-%m-%d %H:%M:%S"
+                    )
+                except (ValueError, OSError):
+                    modify_time_str = str(item.modify_time)
+
+            simplified = {
+                "name": item.name,
+                "type": item.type,
+                "path": item.path,
+                "size": size_str,
+                "modify_time": modify_time_str,
+            }
+            if item.type == "file" and item.extension:
+                simplified["extension"] = item.extension
+            simplified_items.append(simplified)
+
+        result_json = json.dumps(simplified_items, ensure_ascii=False, indent=2)
+        if total_count > 20:
+            return (
+                f"注意：目录中共有 {total_count} 个项目，为节省上下文空间，仅显示前 20 个项目。\n\n"
+                f"{result_json}"
+            )
+        return result_json
+
     async def run(self, path: str, storage: Optional[str] = "local",
                   sort_by: Optional[str] = "name", **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: path={path}, storage={storage}, sort_by={sort_by}")
 
         try:
-            # 规范化路径
-            if not path:
-                return "错误：路径不能为空"
-            
-            # 确保路径格式正确
-            if storage == "local":
-                # 本地路径处理
-                if not path.startswith("/") and not (len(path) > 1 and path[1] == ":"):
-                    # 相对路径，尝试转换为绝对路径
-                    path = str(Path(path).resolve())
-            else:
-                # 远程存储路径，确保以/开头
-                if not path.startswith("/"):
-                    path = "/" + path
-            
-            # 创建FileItem
-            fileitem = FileItem(
-                storage=storage or "local",
-                path=path,
-                type="dir"
+            return await self.run_blocking(
+                "storage", self._list_directory_sync, path, storage, sort_by
             )
-            
-            # 查询目录内容
-            storage_chain = StorageChain()
-            file_list = storage_chain.list_files(fileitem, recursion=False)
-            
-            if file_list is None:
-                return f"无法访问目录：{path}，请检查路径是否正确或存储是否可用"
-            
-            if not file_list:
-                return f"目录 {path} 为空"
-            
-            # 排序
-            if sort_by == "time":
-                file_list.sort(key=lambda x: x.modify_time or 0, reverse=True)
-            else:
-                # 默认按名称排序（目录优先，然后按名称）
-                file_list.sort(key=lambda x: (
-                    0 if x.type == "dir" else 1,
-                    StringUtils.natural_sort_key(x.name or "")
-                ))
-            
-            # 限制返回数量
-            total_count = len(file_list)
-            limited_list = file_list[:20]
-            
-            # 转换为字典格式
-            simplified_items = []
-            for item in limited_list:
-                # 格式化文件大小
-                size_str = None
-                if item.size:
-                    size_str = StringUtils.str_filesize(item.size)
-                
-                # 格式化修改时间
-                modify_time_str = None
-                if item.modify_time:
-                    try:
-                        modify_time_str = datetime.fromtimestamp(item.modify_time).strftime("%Y-%m-%d %H:%M:%S")
-                    except (ValueError, OSError):
-                        modify_time_str = str(item.modify_time)
-                
-                simplified = {
-                    "name": item.name,
-                    "type": item.type,
-                    "path": item.path,
-                    "size": size_str,
-                    "modify_time": modify_time_str
-                }
-                # 如果是文件，添加扩展名
-                if item.type == "file" and item.extension:
-                    simplified["extension"] = item.extension
-                simplified_items.append(simplified)
-            
-            result_json = json.dumps(simplified_items, ensure_ascii=False, indent=2)
-            
-            # 如果结果被裁剪，添加提示信息
-            if total_count > 100:
-                return f"注意：目录中共有 {total_count} 个项目，为节省上下文空间，仅显示前 100 个项目。\n\n{result_json}"
-            else:
-                return result_json
         except Exception as e:
             logger.error(f"查询目录内容失败: {e}", exc_info=True)
             return f"查询目录内容时发生错误: {str(e)}"
-

app/agent/tools/impl/list_slash_commands.py

@@ -33,7 +33,7 @@ class ListSlashCommandsTool(MoviePilotTool):
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
-        return "正在查询所有可用命令"
+        return "查询所有可用命令"
 
     async def run(self, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}")

app/agent/tools/impl/modify_download.py

@@ -55,7 +55,7 @@ class ModifyDownloadTool(MoviePilotTool):
         tags = kwargs.get("tags")
         downloader = kwargs.get("downloader")
 
-        parts = [f"正在修改下载任务: {hash_value}"]
+        parts = [f"修改下载任务: {hash_value}"]
         if action == "start":
             parts.append("操作: 开始下载")
         elif action == "stop":
@@ -66,6 +66,38 @@ class ModifyDownloadTool(MoviePilotTool):
             parts.append(f"下载器: {downloader}")
         return " | ".join(parts)
 
+    @staticmethod
+    def _modify_download_sync(
+        hash_value: str,
+        action: Optional[str] = None,
+        tags: Optional[List[str]] = None,
+        downloader: Optional[str] = None,
+    ) -> List[str]:
+        """同步修改下载任务状态和标签，避免下载器 SDK 阻塞事件循环。"""
+        download_chain = DownloadChain()
+        results = []
+
+        if tags:
+            tag_result = download_chain.set_torrents_tag(
+                hashs=[hash_value], tags=tags, downloader=downloader
+            )
+            if tag_result:
+                results.append(f"成功设置标签：{', '.join(tags)}")
+            else:
+                results.append("设置标签失败，请检查任务是否存在或下载器是否可用")
+
+        if action:
+            action_result = download_chain.set_downloading(
+                hash_str=hash_value, oper=action, name=downloader
+            )
+            action_desc = "开始" if action == "start" else "暂停"
+            if action_result:
+                results.append(f"成功{action_desc}下载任务")
+            else:
+                results.append(f"{action_desc}下载任务失败，请检查任务是否存在或下载器是否可用")
+
+        return results
+
     async def run(
         self,
         hash: str,
@@ -91,31 +123,14 @@ class ModifyDownloadTool(MoviePilotTool):
             if action and action not in ("start", "stop"):
                 return f"参数错误：action 只支持 'start'（开始下载）或 'stop'（暂停下载），收到: '{action}'。"
 
-            download_chain = DownloadChain()
-            results = []
-
-            # 设置标签
-            if tags:
-                tag_result = download_chain.set_torrents_tag(
-                    hashs=[hash], tags=tags, downloader=downloader
-                )
-                if tag_result:
-                    results.append(f"成功设置标签：{', '.join(tags)}")
-                else:
-                    results.append(f"设置标签失败，请检查任务是否存在或下载器是否可用")
-
-            # 执行开始/暂停操作
-            if action:
-                action_result = download_chain.set_downloading(
-                    hash_str=hash, oper=action, name=downloader
-                )
-                action_desc = "开始" if action == "start" else "暂停"
-                if action_result:
-                    results.append(f"成功{action_desc}下载任务")
-                else:
-                    results.append(
-                        f"{action_desc}下载任务失败，请检查任务是否存在或下载器是否可用"
-                    )
+            results = await self.run_blocking(
+                "downloader",
+                self._modify_download_sync,
+                hash,
+                action,
+                tags,
+                downloader,
+            )
 
             return f"下载任务 {hash}：" + "；".join(results)
 

app/agent/tools/impl/query_builtin_filter_rules.py

@@ -0,0 +1,85 @@
+"""查询内置过滤规则工具。"""
+
+import json
+from typing import Optional, Type, List
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    get_builtin_rules,
+    serialize_builtin_rule,
+    RULE_STRING_SYNTAX,
+)
+from app.log import logger
+
+
+class QueryBuiltinFilterRulesInput(BaseModel):
+    """查询内置过滤规则工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    rule_ids: Optional[List[str]] = Field(
+        None,
+        description="Optional list of built-in rule IDs to query. If omitted, return all built-in rules.",
+    )
+
+
+class QueryBuiltinFilterRulesTool(MoviePilotTool):
+    name: str = "query_builtin_filter_rules"
+    description: str = (
+        "Query built-in filter rules defined by the backend filter module. "
+        "These rule IDs can be used directly inside rule_string expressions for filter rule groups. "
+        "Use this tool before add_rule_group or update_rule_group to learn valid built-in rule IDs."
+    )
+    args_schema: Type[BaseModel] = QueryBuiltinFilterRulesInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        rule_ids = kwargs.get("rule_ids") or []
+        if rule_ids:
+            return f"查询内置过滤规则: {', '.join(rule_ids)}"
+        return "查询所有内置过滤规则"
+
+    async def run(
+        self,
+        rule_ids: Optional[List[str]] = None,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}")
+
+        try:
+            builtin_rules = get_builtin_rules()
+            if rule_ids:
+                target_ids = set(rule_ids)
+                builtin_rules = {
+                    rule_id: payload
+                    for rule_id, payload in builtin_rules.items()
+                    if rule_id in target_ids
+                }
+
+            serialized = [
+                serialize_builtin_rule(rule_id, payload)
+                for rule_id, payload in builtin_rules.items()
+            ]
+            return json.dumps(
+                {
+                    "success": True,
+                    "count": len(serialized),
+                    "rule_string_syntax": RULE_STRING_SYNTAX,
+                    "rules": serialized,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"查询内置过滤规则失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"查询内置过滤规则失败: {exc}",
+                    "rules": [],
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_custom_filter_rules.py

@@ -0,0 +1,95 @@
+"""查询自定义过滤规则工具。"""
+
+import json
+from typing import Optional, Type, List
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    collect_custom_rule_group_refs,
+    get_custom_rules,
+    get_rule_groups,
+    serialize_custom_rule,
+)
+from app.log import logger
+
+
+class QueryCustomFilterRulesInput(BaseModel):
+    """查询自定义过滤规则工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    rule_ids: Optional[List[str]] = Field(
+        None,
+        description="Optional list of custom rule IDs to query. If omitted, return all custom rules.",
+    )
+    include_group_refs: bool = Field(
+        True,
+        description="Whether to include which rule groups reference each custom rule.",
+    )
+
+
+class QueryCustomFilterRulesTool(MoviePilotTool):
+    name: str = "query_custom_filter_rules"
+    description: str = (
+        "Query custom filter rules stored in CustomFilterRules. "
+        "Custom rules can be referenced from rule_string expressions in filter rule groups. "
+        "Use this tool before add_rule_group or update_rule_group to learn valid custom rule IDs."
+    )
+    args_schema: Type[BaseModel] = QueryCustomFilterRulesInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        rule_ids = kwargs.get("rule_ids") or []
+        if rule_ids:
+            return f"查询自定义过滤规则: {', '.join(rule_ids)}"
+        return "查询所有自定义过滤规则"
+
+    async def run(
+        self,
+        rule_ids: Optional[List[str]] = None,
+        include_group_refs: bool = True,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}")
+
+        try:
+            custom_rules = get_custom_rules()
+            if rule_ids:
+                target_ids = set(rule_ids)
+                custom_rules = [
+                    rule for rule in custom_rules if rule.id in target_ids
+                ]
+
+            refs = {}
+            if include_group_refs:
+                refs = collect_custom_rule_group_refs(
+                    get_rule_groups(),
+                    [rule.id for rule in custom_rules if rule.id],
+                )
+
+            serialized = [
+                serialize_custom_rule(rule, refs.get(rule.id))
+                for rule in custom_rules
+            ]
+            return json.dumps(
+                {
+                    "success": True,
+                    "count": len(serialized),
+                    "rules": serialized,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"查询自定义过滤规则失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"查询自定义过滤规则失败: {exc}",
+                    "rules": [],
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_custom_identifiers.py

@@ -31,13 +31,17 @@ class QueryCustomIdentifiersTool(MoviePilotTool):
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
-        return "正在查询自定义识别词"
+        return "查询自定义识别词"
+
+    @staticmethod
+    def _load_custom_identifiers():
+        """从内存配置缓存中读取自定义识别词。"""
+        return SystemConfigOper().get(SystemConfigKey.CustomIdentifiers)
 
     async def run(self, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}")
         try:
-            system_config_oper = SystemConfigOper()
-            identifiers = system_config_oper.get(SystemConfigKey.CustomIdentifiers)
+            identifiers = self._load_custom_identifiers()
             if identifiers:
                 return json.dumps(
                     {

app/agent/tools/impl/query_directory_settings.py

@@ -32,7 +32,7 @@ class QueryDirectorySettingsTool(MoviePilotTool):
         storage_type = kwargs.get("storage_type", "all")
         name = kwargs.get("name")
         
-        parts = ["正在查询目录配置"]
+        parts = ["查询目录配置"]
         
         if directory_type != "all":
             type_map = {"download": "下载目录", "library": "媒体库目录"}
@@ -47,88 +47,93 @@ class QueryDirectorySettingsTool(MoviePilotTool):
         
         return " | ".join(parts) if len(parts) > 1 else parts[0]
 
+    @staticmethod
+    def _query_directory_settings(
+        directory_type: Optional[str] = "all",
+        storage_type: Optional[str] = "all",
+        name: Optional[str] = None,
+    ) -> str:
+        """
+        目录配置完全来自内存配置缓存，这里只做本地过滤和序列化。
+        """
+        directory_helper = DirectoryHelper()
+
+        if directory_type == "download":
+            dirs = directory_helper.get_download_dirs()
+        elif directory_type == "library":
+            dirs = directory_helper.get_library_dirs()
+        else:
+            dirs = directory_helper.get_dirs()
+
+        filtered_dirs = []
+        for d in dirs:
+            if storage_type == "local":
+                if directory_type == "download" and d.storage != "local":
+                    continue
+                if directory_type == "library" and d.library_storage != "local":
+                    continue
+                if directory_type == "all":
+                    if d.download_path and d.storage != "local":
+                        continue
+                    if d.library_path and d.library_storage != "local":
+                        continue
+            elif storage_type == "remote":
+                if directory_type == "download" and d.storage == "local":
+                    continue
+                if directory_type == "library" and d.library_storage == "local":
+                    continue
+                if directory_type == "all":
+                    if d.download_path and d.storage == "local":
+                        continue
+                    if d.library_path and d.library_storage == "local":
+                        continue
+
+            if name and d.name and name.lower() not in d.name.lower():
+                continue
+            filtered_dirs.append(d)
+
+        if not filtered_dirs:
+            return "未找到相关目录配置"
+
+        simplified_dirs = []
+        for d in filtered_dirs:
+            simplified_dirs.append(
+                {
+                    "name": d.name,
+                    "priority": d.priority,
+                    "storage": d.storage,
+                    "download_path": d.download_path,
+                    "library_path": d.library_path,
+                    "library_storage": d.library_storage,
+                    "media_type": d.media_type,
+                    "media_category": d.media_category,
+                    "monitor_type": d.monitor_type,
+                    "monitor_mode": d.monitor_mode,
+                    "transfer_type": d.transfer_type,
+                    "overwrite_mode": d.overwrite_mode,
+                    "renaming": d.renaming,
+                    "scraping": d.scraping,
+                    "notify": d.notify,
+                    "download_type_folder": d.download_type_folder,
+                    "download_category_folder": d.download_category_folder,
+                    "library_type_folder": d.library_type_folder,
+                    "library_category_folder": d.library_category_folder,
+                }
+            )
+
+        return json.dumps(simplified_dirs, ensure_ascii=False, indent=2)
+
     async def run(self, directory_type: Optional[str] = "all",
                   storage_type: Optional[str] = "all",
                   name: Optional[str] = None, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: directory_type={directory_type}, storage_type={storage_type}, name={name}")
 
         try:
-            directory_helper = DirectoryHelper()
-            
-            # 根据目录类型获取目录列表
-            if directory_type == "download":
-                dirs = directory_helper.get_download_dirs()
-            elif directory_type == "library":
-                dirs = directory_helper.get_library_dirs()
-            else:
-                dirs = directory_helper.get_dirs()
-            
-            # 按存储类型过滤
-            filtered_dirs = []
-            for d in dirs:
-                # 按存储类型过滤
-                if storage_type == "local":
-                    # 对于下载目录，检查 storage；对于媒体库目录，检查 library_storage
-                    if directory_type == "download" and d.storage != "local":
-                        continue
-                    elif directory_type == "library" and d.library_storage != "local":
-                        continue
-                    elif directory_type == "all":
-                        # 检查是否有本地存储配置
-                        if d.download_path and d.storage != "local":
-                            continue
-                        if d.library_path and d.library_storage != "local":
-                            continue
-                elif storage_type == "remote":
-                    # 对于下载目录，检查 storage；对于媒体库目录，检查 library_storage
-                    if directory_type == "download" and d.storage == "local":
-                        continue
-                    elif directory_type == "library" and d.library_storage == "local":
-                        continue
-                    elif directory_type == "all":
-                        # 检查是否有远程存储配置
-                        if d.download_path and d.storage == "local":
-                            continue
-                        if d.library_path and d.library_storage == "local":
-                            continue
-                
-                # 按名称过滤（部分匹配）
-                if name and d.name and name.lower() not in d.name.lower():
-                    continue
-                
-                filtered_dirs.append(d)
-            
-            if filtered_dirs:
-                # 转换为字典格式，只保留关键信息
-                simplified_dirs = []
-                for d in filtered_dirs:
-                    simplified = {
-                        "name": d.name,
-                        "priority": d.priority,
-                        "storage": d.storage,
-                        "download_path": d.download_path,
-                        "library_path": d.library_path,
-                        "library_storage": d.library_storage,
-                        "media_type": d.media_type,
-                        "media_category": d.media_category,
-                        "monitor_type": d.monitor_type,
-                        "monitor_mode": d.monitor_mode,
-                        "transfer_type": d.transfer_type,
-                        "overwrite_mode": d.overwrite_mode,
-                        "renaming": d.renaming,
-                        "scraping": d.scraping,
-                        "notify": d.notify,
-                        "download_type_folder": d.download_type_folder,
-                        "download_category_folder": d.download_category_folder,
-                        "library_type_folder": d.library_type_folder,
-                        "library_category_folder": d.library_category_folder
-                    }
-                    simplified_dirs.append(simplified)
-                
-                result_json = json.dumps(simplified_dirs, ensure_ascii=False, indent=2)
-                return result_json
-            return "未找到相关目录配置"
+            return self._query_directory_settings(
+                directory_type=directory_type,
+                storage_type=storage_type,
+                name=name,
+            )
         except Exception as e:
             logger.error(f"查询系统目录设置失败: {e}", exc_info=True)
             return f"查询系统目录设置时发生错误: {str(e)}"
-

app/agent/tools/impl/query_download_tasks.py

@@ -1,7 +1,7 @@
 """查询下载工具"""
 
 import json
-from typing import Optional, Type, List, Union
+from typing import Any, Dict, List, Optional, Type, Union
 
 from pydantic import BaseModel, Field
 
@@ -36,7 +36,7 @@ class QueryDownloadTasksTool(MoviePilotTool):
         查询所有状态的任务（包括下载中和已完成的任务）
         """
         all_torrents = []
-        # 查询正在下载的任务
+        # 查询下载的任务
         downloading_torrents = download_chain.list_torrents(
             downloader=downloader, 
             status=TorrentStatus.DOWNLOADING
@@ -64,6 +64,126 @@ class QueryDownloadTasksTool(MoviePilotTool):
         except (TypeError, ValueError):
             return None
 
+    @staticmethod
+    def _apply_download_history(
+        torrent: Union[TransferTorrent, DownloadingTorrent], history: Any
+    ) -> None:
+        """将下载历史中的补充信息回填到下载任务结果中。"""
+        if not history:
+            return
+        if hasattr(torrent, "media"):
+            torrent.media = {
+                "tmdbid": history.tmdbid,
+                "type": history.type,
+                "title": history.title,
+                "season": history.seasons,
+                "episode": history.episodes,
+                "image": history.image,
+            }
+        if hasattr(torrent, "username"):
+            torrent.username = history.username
+        torrent.userid = history.userid
+
+    @classmethod
+    def _load_history_map(
+        cls, torrents: List[Union[TransferTorrent, DownloadingTorrent]]
+    ) -> Dict[str, Any]:
+        """批量加载下载历史，避免逐条查询形成 N+1。"""
+        hashes = [torrent.hash for torrent in torrents if getattr(torrent, "hash", None)]
+        if not hashes:
+            return {}
+        return DownloadHistoryOper().get_by_hashes(hashes)
+
+    @classmethod
+    def _query_downloads_sync(
+        cls,
+        downloader: Optional[str] = None,
+        status: Optional[str] = "all",
+        hash_value: Optional[str] = None,
+        title: Optional[str] = None,
+        tag: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        同步查询下载器和下载历史，整个链路放在线程池中执行。
+        """
+        download_chain = DownloadChain()
+
+        if hash_value:
+            torrents = (
+                download_chain.list_torrents(downloader=downloader, hashs=[hash_value])
+                or []
+            )
+            if not torrents:
+                return {
+                    "message": f"未找到hash为 {hash_value} 的下载任务（该任务可能已完成、已删除或不存在）"
+                }
+
+            history_map = cls._load_history_map(torrents)
+            for torrent in torrents:
+                cls._apply_download_history(torrent, history_map.get(torrent.hash))
+            filtered_downloads = list(torrents)
+        elif title:
+            all_torrents = cls._get_all_torrents(download_chain, downloader)
+            history_map = cls._load_history_map(all_torrents)
+            filtered_downloads = []
+            title_lower = title.lower()
+
+            for torrent in all_torrents:
+                history = history_map.get(torrent.hash)
+                matched = title_lower in (torrent.title or "").lower() or title_lower in (
+                    getattr(torrent, "name", None) or ""
+                ).lower()
+                if not matched and history and history.title:
+                    matched = title_lower in history.title.lower()
+
+                if not matched:
+                    continue
+
+                cls._apply_download_history(torrent, history)
+                filtered_downloads.append(torrent)
+
+            if not filtered_downloads:
+                return {"message": f"未找到标题包含 '{title}' 的下载任务"}
+        else:
+            if status == "downloading":
+                downloads = download_chain.downloading(name=downloader) or []
+                filtered_downloads = [
+                    dl
+                    for dl in downloads
+                    if not downloader or dl.downloader == downloader
+                ]
+            else:
+                all_torrents = cls._get_all_torrents(download_chain, downloader)
+                filtered_downloads = []
+                for torrent in all_torrents:
+                    if downloader and torrent.downloader != downloader:
+                        continue
+                    if status == "completed" and torrent.state not in [
+                        "seeding",
+                        "completed",
+                    ]:
+                        continue
+                    if status == "paused" and torrent.state != "paused":
+                        continue
+                    filtered_downloads.append(torrent)
+
+                history_map = cls._load_history_map(filtered_downloads)
+                for torrent in filtered_downloads:
+                    cls._apply_download_history(torrent, history_map.get(torrent.hash))
+
+        if tag and filtered_downloads:
+            tag_lower = tag.lower()
+            filtered_downloads = [
+                d for d in filtered_downloads if d.tags and tag_lower in d.tags.lower()
+            ]
+            if not filtered_downloads:
+                return {"message": f"未找到标签包含 '{tag}' 的下载任务"}
+
+        if not filtered_downloads:
+            return {"message": "未找到相关下载任务"}
+
+        return {"downloads": filtered_downloads}
+
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据查询参数生成友好的提示消息"""
         downloader = kwargs.get("downloader")
@@ -71,7 +191,7 @@ class QueryDownloadTasksTool(MoviePilotTool):
         hash_value = kwargs.get("hash")
         title = kwargs.get("title")
         
-        parts = ["正在查询下载任务"]
+        parts = ["查询下载任务"]
         
         if downloader:
             parts.append(f"下载器: {downloader}")
@@ -98,124 +218,19 @@ class QueryDownloadTasksTool(MoviePilotTool):
                   tag: Optional[str] = None, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: downloader={downloader}, status={status}, hash={hash}, title={title}, tag={tag}")
         try:
-            download_chain = DownloadChain()
-            
-            # 如果提供了hash，直接查询该hash的任务（不限制状态）
-            if hash:
-                torrents = download_chain.list_torrents(downloader=downloader, hashs=[hash]) or []
-                if not torrents:
-                    return f"未找到hash为 {hash} 的下载任务（该任务可能已完成、已删除或不存在）"
-                # 转换为DownloadingTorrent格式
-                downloads = []
-                for torrent in torrents:
-                    # 获取下载历史信息
-                    history = DownloadHistoryOper().get_by_hash(torrent.hash)
-                    if history:
-                        if hasattr(torrent, "media"):
-                            torrent.media = {
-                                "tmdbid": history.tmdbid,
-                                "type": history.type,
-                                "title": history.title,
-                                "season": history.seasons,
-                                "episode": history.episodes,
-                                "image": history.image,
-                            }
-                        if hasattr(torrent, "username"):
-                            torrent.username = history.username
-                        torrent.userid = history.userid
-                    downloads.append(torrent)
-                filtered_downloads = downloads
-            elif title:
-                # 如果提供了title，查询所有任务并搜索匹配的标题
-                # 查询所有状态的任务
-                all_torrents = self._get_all_torrents(download_chain, downloader)
-                filtered_downloads = []
-                title_lower = title.lower()
-                for torrent in all_torrents:
-                    # 获取下载历史信息
-                    history = DownloadHistoryOper().get_by_hash(torrent.hash)
-                    
-                    # 检查标题或名称是否匹配（包括下载历史中的标题）
-                    matched = False
-                    # 检查torrent的title和name字段
-                    if (title_lower in (torrent.title or "").lower()) or \
-                       (title_lower in (getattr(torrent, "name", None) or "").lower()):
-                        matched = True
-                    # 检查下载历史中的标题
-                    if history and history.title:
-                        if title_lower in history.title.lower():
-                            matched = True
-                    
-                    if matched:
-                        if history:
-                            if hasattr(torrent, "media"):
-                                torrent.media = {
-                                    "tmdbid": history.tmdbid,
-                                    "type": history.type,
-                                    "title": history.title,
-                                    "season": history.seasons,
-                                    "episode": history.episodes,
-                                    "image": history.image,
-                                }
-                            if hasattr(torrent, "username"):
-                                torrent.username = history.username
-                            torrent.userid = history.userid
-                        filtered_downloads.append(torrent)
-                if not filtered_downloads:
-                    return f"未找到标题包含 '{title}' 的下载任务"
-            else:
-                # 根据status决定查询方式
-                if status == "downloading":
-                    # 如果status为下载中，使用downloading方法
-                    downloads = download_chain.downloading(name=downloader) or []
-                    filtered_downloads = []
-                    for dl in downloads:
-                        if downloader and dl.downloader != downloader:
-                            continue
-                        filtered_downloads.append(dl)
-                else:
-                    # 其他状态（completed、paused、all），使用list_torrents查询所有任务
-                    # 查询所有状态的任务
-                    all_torrents = self._get_all_torrents(download_chain, downloader)
-                    filtered_downloads = []
-                    for torrent in all_torrents:
-                        if downloader and torrent.downloader != downloader:
-                            continue
-                        # 根据status过滤
-                        if status == "completed":
-                            # 已完成的任务（state为seeding或completed）
-                            if torrent.state not in ["seeding", "completed"]:
-                                continue
-                        elif status == "paused":
-                            # 已暂停的任务
-                            if torrent.state != "paused":
-                                continue
-                        # status == "all" 时不过滤
-                        # 获取下载历史信息
-                        history = DownloadHistoryOper().get_by_hash(torrent.hash)
-                        if history:
-                            if hasattr(torrent, "media"):
-                                torrent.media = {
-                                    "tmdbid": history.tmdbid,
-                                    "type": history.type,
-                                    "title": history.title,
-                                    "season": history.seasons,
-                                    "episode": history.episodes,
-                                    "image": history.image,
-                                }
-                            if hasattr(torrent, "username"):
-                                torrent.username = history.username
-                            torrent.userid = history.userid
-                        filtered_downloads.append(torrent)
-            # 按tag过滤
-            if tag and filtered_downloads:
-                tag_lower = tag.lower()
-                filtered_downloads = [
-                    d for d in filtered_downloads
-                    if d.tags and tag_lower in d.tags.lower()
-                ]
-                if not filtered_downloads:
-                    return f"未找到标签包含 '{tag}' 的下载任务"
+            payload = await self.run_blocking(
+                "downloader",
+                self._query_downloads_sync,
+                downloader,
+                status,
+                hash,
+                title,
+                tag,
+            )
+            if payload.get("message"):
+                return payload["message"]
+
+            filtered_downloads = payload.get("downloads") or []
             if filtered_downloads:
                 # 限制最多20条结果
                 total_count = len(filtered_downloads)

app/agent/tools/impl/query_downloaders.py

@@ -23,13 +23,17 @@ class QueryDownloadersTool(MoviePilotTool):
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
-        return "正在查询下载器配置"
+        return "查询下载器配置"
+
+    @staticmethod
+    def _load_downloaders_config():
+        """从内存配置缓存中读取下载器配置。"""
+        return SystemConfigOper().get(SystemConfigKey.Downloaders)
 
     async def run(self, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}")
         try:
-            system_config_oper = SystemConfigOper()
-            downloaders_config = system_config_oper.get(SystemConfigKey.Downloaders)
+            downloaders_config = self._load_downloaders_config()
             if downloaders_config:
                 return json.dumps(downloaders_config, ensure_ascii=False, indent=2)
             return "未配置下载器。"

app/agent/tools/impl/query_episode_schedule.py

@@ -29,7 +29,7 @@ class QueryEpisodeScheduleTool(MoviePilotTool):
         season = kwargs.get("season")
         episode_group = kwargs.get("episode_group")
 
-        message = f"正在查询剧集上映时间: TMDB ID {tmdb_id} 第{season}季"
+        message = f"查询剧集上映时间: TMDB ID {tmdb_id} 第{season}季"
         if episode_group:
             message += f" (剧集组: {episode_group})"
 

app/agent/tools/impl/query_installed_plugins.py

@@ -6,7 +6,13 @@ from typing import Optional, Type
 from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
-from app.core.plugin import PluginManager
+from app.agent.tools.impl._plugin_tool_utils import (
+    DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+    list_installed_plugins,
+    search_plugin_candidates,
+    summarize_candidates,
+    summarize_plugin,
+)
 from app.log import logger
 
 
@@ -17,49 +23,86 @@ class QueryInstalledPluginsInput(BaseModel):
         ...,
         description="Clear explanation of why this tool is being used in the current context",
     )
+    query: Optional[str] = Field(
+        None,
+        description="Optional keyword to filter installed plugins by plugin ID, name, description, or author.",
+    )
+    max_results: Optional[int] = Field(
+        DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+        description="Maximum number of plugins to return. Defaults to 10.",
+    )
 
 
 class QueryInstalledPluginsTool(MoviePilotTool):
     name: str = "query_installed_plugins"
     description: str = (
-        "Query all installed plugins in MoviePilot. Returns a list of installed plugins with their ID, name, "
-        "description, version, author, running state, and other information. "
-        "Use this tool to discover what plugins are available before querying plugin capabilities or running plugin commands."
+        "Query installed plugins in MoviePilot. Returns all installed plugins or filters them by keywords. "
+        "Use this tool to find the exact plugin_id before uninstall_plugin or other plugin management tools are used."
     )
     require_admin: bool = True
     args_schema: Type[BaseModel] = QueryInstalledPluginsInput
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
-        return "正在查询已安装插件"
+        query = kwargs.get("query")
+        if query:
+            return f"查询已安装插件: {query}"
+        return "查询已安装插件"
 
-    async def run(self, **kwargs) -> str:
-        logger.info(f"执行工具: {self.name}")
+    @staticmethod
+    def _clamp_results(max_results: Optional[int]) -> int:
+        if max_results is None:
+            return DEFAULT_PLUGIN_CANDIDATE_LIMIT
+        return max(1, min(int(max_results), 200))
+
+    async def run(
+        self,
+        query: Optional[str] = None,
+        max_results: Optional[int] = DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}, 参数: query={query}")
         try:
-            plugin_manager = PluginManager()
-            local_plugins = plugin_manager.get_local_plugins()
-            # 仅返回已安装的插件
-            installed_plugins = [plugin for plugin in local_plugins if plugin.installed]
-
+            installed_plugins = list_installed_plugins()
             if not installed_plugins:
-                return "当前没有已安装的插件"
-
-            plugins_list = []
-            for plugin in installed_plugins:
-                plugins_list.append(
-                    {
-                        "id": plugin.id,
-                        "plugin_name": plugin.plugin_name,
-                        "plugin_desc": plugin.plugin_desc,
-                        "plugin_version": plugin.plugin_version,
-                        "plugin_author": plugin.plugin_author,
-                        "state": plugin.state,
-                        "has_page": plugin.has_page,
-                    }
+                return json.dumps(
+                    {"success": False, "message": "当前没有已安装的插件"},
+                    ensure_ascii=False,
                 )
 
-            result_json = json.dumps(plugins_list, ensure_ascii=False, indent=2)
-            return result_json
+            limit = self._clamp_results(max_results)
+            if query:
+                matches = search_plugin_candidates(query, installed_plugins)
+                return json.dumps(
+                    {
+                        "success": True,
+                        "query": query,
+                        "total_installed": len(installed_plugins),
+                        "match_count": len(matches),
+                        "truncated": len(matches) > limit,
+                        "plugins": summarize_candidates(matches, limit=limit),
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+
+            plugin_summaries = [
+                summarize_plugin(plugin) for plugin in installed_plugins[:limit]
+            ]
+            return json.dumps(
+                {
+                    "success": True,
+                    "total_installed": len(installed_plugins),
+                    "returned_count": len(plugin_summaries),
+                    "truncated": len(installed_plugins) > limit,
+                    "plugins": plugin_summaries,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
         except Exception as e:
             logger.error(f"查询已安装插件失败: {e}", exc_info=True)
-            return f"查询已安装插件时发生错误: {str(e)}"
+            return json.dumps(
+                {"success": False, "message": f"查询已安装插件时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_library_exists.py

@@ -1,5 +1,6 @@
 """查询媒体库工具"""
 
+import asyncio
 import json
 from collections import OrderedDict
 from typing import Optional, Type, Any
@@ -93,15 +94,25 @@ class QueryLibraryExistsTool(MoviePilotTool):
         media_type = kwargs.get("media_type")
 
         if tmdb_id:
-            message = f"正在查询媒体库: TMDB={tmdb_id}"
+            message = f"查询媒体库: TMDB={tmdb_id}"
         elif douban_id:
-            message = f"正在查询媒体库: 豆瓣={douban_id}"
+            message = f"查询媒体库: 豆瓣={douban_id}"
         else:
-            message = "正在查询媒体库"
+            message = "查询媒体库"
         if media_type:
             message += f" [{media_type}]"
         return message
 
+    @staticmethod
+    def _get_media_server_names() -> list[str]:
+        """同步读取已加载媒体服务器名称。"""
+        return sorted(MediaServerHelper().get_services().keys())
+
+    @staticmethod
+    def _query_media_exists(mediainfo, server: Optional[str] = None):
+        """同步查询单个媒体服务器的存在性信息。"""
+        return MediaServerChain().media_exists(mediainfo=mediainfo, server=server)
+
     async def run(self, tmdb_id: Optional[int] = None, douban_id: Optional[str] = None,
                   media_type: Optional[str] = None, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: tmdb_id={tmdb_id}, douban_id={douban_id}, media_type={media_type}")
@@ -116,7 +127,7 @@ class QueryLibraryExistsTool(MoviePilotTool):
                     return f"错误：无效的媒体类型 '{media_type}'，支持的类型：'movie', 'tv'"
 
             media_chain = MediaServerChain()
-            mediainfo = media_chain.recognize_media(
+            mediainfo = await media_chain.async_recognize_media(
                 tmdbid=tmdb_id,
                 doubanid=douban_id,
                 mtype=media_type_enum,
@@ -127,12 +138,22 @@ class QueryLibraryExistsTool(MoviePilotTool):
 
             # 2. 遍历所有媒体服务器，分别查询存在性信息
             server_results = OrderedDict()
-            media_server_helper = MediaServerHelper()
             total_seasons = _filter_regular_seasons(mediainfo.seasons)
-            global_existsinfo = media_chain.media_exists(mediainfo=mediainfo)
+            service_names = self._get_media_server_names()
 
-            for service_name in sorted(media_server_helper.get_services().keys()):
-                existsinfo = media_chain.media_exists(mediainfo=mediainfo, server=service_name)
+            server_checks = await asyncio.gather(
+                *[
+                    self.run_blocking(
+                        "mediaserver",
+                        self._query_media_exists,
+                        mediainfo,
+                        service_name,
+                    )
+                    for service_name in service_names
+                ]
+            )
+
+            for service_name, existsinfo in zip(service_names, server_checks):
                 if not existsinfo:
                     continue
 
@@ -147,21 +168,23 @@ class QueryLibraryExistsTool(MoviePilotTool):
                         "exists": True
                     }
 
-            if global_existsinfo:
-                fallback_server_name = global_existsinfo.server or "local"
-                if fallback_server_name not in server_results:
-                    if global_existsinfo.type == MediaType.TV:
-                        server_results[fallback_server_name] = _build_tv_server_result(
-                            existing_seasons=_filter_regular_seasons(global_existsinfo.seasons),
-                            total_seasons=total_seasons
-                        )
-                    else:
-                        server_results[fallback_server_name] = {
-                            "exists": True
-                        }
-
             if not server_results:
-                return "媒体库中未找到相关媒体"
+                global_existsinfo = await self.run_blocking(
+                    "mediaserver", self._query_media_exists, mediainfo, None
+                )
+                if not global_existsinfo:
+                    return "媒体库中未找到相关媒体"
+
+                fallback_server_name = global_existsinfo.server or "local"
+                if global_existsinfo.type == MediaType.TV:
+                    server_results[fallback_server_name] = _build_tv_server_result(
+                        existing_seasons=_filter_regular_seasons(global_existsinfo.seasons),
+                        total_seasons=total_seasons
+                    )
+                else:
+                    server_results[fallback_server_name] = {
+                        "exists": True
+                    }
 
             # 3. 组装统一的存在性结果，不查询媒体服务器详情
             result_dict = {

app/agent/tools/impl/query_library_latest.py

@@ -1,5 +1,6 @@
 """查询媒体服务器最近入库影片工具"""
 
+import asyncio
 import json
 from typing import Optional, Type
 
@@ -39,7 +40,7 @@ class QueryLibraryLatestTool(MoviePilotTool):
         server = kwargs.get("server")
         page = kwargs.get("page", 1)
 
-        parts = ["正在查询媒体服务器最近入库影片"]
+        parts = ["查询媒体服务器最近入库影片"]
 
         if server:
             parts.append(f"服务器: {server}")
@@ -50,6 +51,32 @@ class QueryLibraryLatestTool(MoviePilotTool):
 
         return " | ".join(parts)
 
+    @staticmethod
+    def _get_enabled_servers() -> list[str]:
+        """同步读取启用的媒体服务器列表。"""
+        mediaservers = ServiceConfigHelper.get_mediaserver_configs()
+        return [ms.name for ms in mediaservers if ms.enabled]
+
+    @staticmethod
+    def _load_latest_items(
+        server_name: str, count: int, username: Optional[str] = None
+    ) -> list[dict]:
+        """
+        媒体服务器 SDK 和 requests 调用都是同步的，这里在线程池中转换为可序列化结果。
+        """
+        latest_items = MediaServerChain().latest(
+            server=server_name, count=count, username=username
+        )
+        if not latest_items:
+            return []
+        return [
+            {
+                **item.model_dump(exclude_none=True),
+                "server": server_name,
+            }
+            for item in latest_items
+        ]
+
     async def run(
         self, server: Optional[str] = None, page: Optional[int] = 1, **kwargs
     ) -> str:
@@ -58,37 +85,34 @@ class QueryLibraryLatestTool(MoviePilotTool):
         fetch_count = page * PAGE_SIZE
         logger.info(f"执行工具: {self.name}, 参数: server={server}, page={page}")
         try:
-            media_chain = MediaServerChain()
-            results = []
-
             # 如果没有指定服务器，获取所有启用的媒体服务器
             if not server:
-                mediaservers = ServiceConfigHelper.get_mediaserver_configs()
-                enabled_servers = [ms.name for ms in mediaservers if ms.enabled]
-
+                enabled_servers = self._get_enabled_servers()
                 if not enabled_servers:
                     return "未找到启用的媒体服务器"
-
-                # 遍历所有启用的服务器
-                for server_name in enabled_servers:
-                    latest_items = media_chain.latest(
-                        server=server_name, count=fetch_count, username=self._username
-                    )
-                    if latest_items:
-                        for item in latest_items:
-                            item_dict = item.model_dump(exclude_none=True)
-                            item_dict["server"] = server_name
-                            results.append(item_dict)
-            else:
-                # 查询指定服务器
-                latest_items = media_chain.latest(
-                    server=server, count=fetch_count, username=self._username
+                server_results = await asyncio.gather(
+                    *[
+                        self.run_blocking(
+                            "mediaserver",
+                            self._load_latest_items,
+                            server_name,
+                            fetch_count,
+                            self._username,
+                        )
+                        for server_name in enabled_servers
+                    ]
+                )
+                results = [
+                    item for items in server_results for item in items if items
+                ]
+            else:
+                results = await self.run_blocking(
+                    "mediaserver",
+                    self._load_latest_items,
+                    server,
+                    fetch_count,
+                    self._username,
                 )
-                if latest_items:
-                    for item in latest_items:
-                        item_dict = item.model_dump(exclude_none=True)
-                        item_dict["server"] = server
-                        results.append(item_dict)
 
             if not results:
                 server_info = f"服务器 {server}" if server else "所有服务器"

app/agent/tools/impl/query_market_plugins.py

@@ -0,0 +1,113 @@
+"""查询插件市场工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import (
+    DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+    load_market_plugins,
+    search_plugin_candidates,
+    summarize_candidates,
+    summarize_plugin,
+)
+from app.log import logger
+
+
+class QueryMarketPluginsInput(BaseModel):
+    """查询插件市场工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    query: Optional[str] = Field(
+        None,
+        description="Optional keyword to filter plugin market results by plugin ID, name, description, or author.",
+    )
+    max_results: Optional[int] = Field(
+        DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+        description="Maximum number of plugins to return. Defaults to 10.",
+    )
+    force_refresh: Optional[bool] = Field(
+        False,
+        description="Whether to refresh plugin market caches before querying.",
+    )
+
+
+class QueryMarketPluginsTool(MoviePilotTool):
+    name: str = "query_market_plugins"
+    description: str = (
+        "Query available plugins from the plugin market and local plugin repositories. "
+        "Can return the full plugin list or filter by keywords before install_plugin is used."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = QueryMarketPluginsInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        query = kwargs.get("query")
+        if query:
+            return f"查询插件市场: {query}"
+        return "查询插件市场全部插件"
+
+    @staticmethod
+    def _clamp_results(max_results: Optional[int]) -> int:
+        if max_results is None:
+            return DEFAULT_PLUGIN_CANDIDATE_LIMIT
+        return max(1, min(int(max_results), 200))
+
+    async def run(
+        self,
+        query: Optional[str] = None,
+        max_results: Optional[int] = DEFAULT_PLUGIN_CANDIDATE_LIMIT,
+        force_refresh: bool = False,
+        **kwargs,
+    ) -> str:
+        logger.info(
+            f"执行工具: {self.name}, 参数: query={query}, force_refresh={force_refresh}"
+        )
+
+        try:
+            plugins = await load_market_plugins(force_refresh=force_refresh)
+            if not plugins:
+                return json.dumps(
+                    {"success": False, "message": "当前插件市场没有可用插件"},
+                    ensure_ascii=False,
+                )
+
+            limit = self._clamp_results(max_results)
+            if query:
+                matches = search_plugin_candidates(query, plugins)
+                return json.dumps(
+                    {
+                        "success": True,
+                        "query": query,
+                        "total_available": len(plugins),
+                        "match_count": len(matches),
+                        "truncated": len(matches) > limit,
+                        "plugins": summarize_candidates(matches, limit=limit),
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+
+            plugin_summaries = [summarize_plugin(plugin) for plugin in plugins[:limit]]
+            return json.dumps(
+                {
+                    "success": True,
+                    "total_available": len(plugins),
+                    "returned_count": len(plugin_summaries),
+                    "truncated": len(plugins) > limit,
+                    "plugins": plugin_summaries,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as e:
+            logger.error(f"查询插件市场失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"查询插件市场时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_media_detail.py

@@ -29,8 +29,8 @@ class QueryMediaDetailTool(MoviePilotTool):
         tmdb_id = kwargs.get("tmdb_id")
         douban_id = kwargs.get("douban_id")
         if tmdb_id:
-            return f"正在查询媒体详情: TMDB ID {tmdb_id}"
-        return f"正在查询媒体详情: 豆瓣 ID {douban_id}"
+            return f"查询媒体详情: TMDB ID {tmdb_id}"
+        return f"查询媒体详情: 豆瓣 ID {douban_id}"
 
     async def run(self, media_type: str, tmdb_id: Optional[int] = None, douban_id: Optional[str] = None, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: tmdb_id={tmdb_id}, douban_id={douban_id}, media_type={media_type}")

app/agent/tools/impl/query_personas.py

@@ -0,0 +1,75 @@
+"""查询可用人格工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.runtime import agent_runtime_manager
+from app.agent.tools.base import MoviePilotTool
+from app.log import logger
+
+
+class QueryPersonasInput(BaseModel):
+    """查询人格工具的输入参数模型。"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    query: Optional[str] = Field(
+        None,
+        description=(
+            "Optional search keyword for persona_id, label, description, or aliases. "
+            "Use this when the user asks for a certain speaking style but the exact persona name is unknown."
+        ),
+    )
+
+
+class QueryPersonasTool(MoviePilotTool):
+    name: str = "query_personas"
+    description: str = (
+        "List all available personas (人格) and show which one is currently active. "
+        "Use this before switching persona when the user asks for a different speaking style but does not name "
+        "an exact persona_id. The result includes persona_id, label, description, aliases, and whether it is active."
+    )
+    args_schema: Type[BaseModel] = QueryPersonasInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        query = kwargs.get("query")
+        if query:
+            return f"查询人格列表: {query}"
+        return "查询人格列表"
+
+    async def run(self, query: Optional[str] = None, **kwargs) -> str:
+        logger.info("执行工具: %s, 参数: query=%s", self.name, query)
+        try:
+            runtime_config = agent_runtime_manager.load_runtime_config()
+            personas = runtime_config.list_personas()
+
+            if query:
+                normalized = query.strip().casefold()
+                personas = [
+                    persona
+                    for persona in personas
+                    if normalized in persona["persona_id"].casefold()
+                    or normalized in persona["label"].casefold()
+                    or normalized in persona["description"].casefold()
+                    or any(normalized in alias.casefold() for alias in persona["aliases"])
+                ]
+
+            payload = {
+                "active_persona": runtime_config.active_persona,
+                "count": len(personas),
+                "personas": personas,
+            }
+            return json.dumps(payload, ensure_ascii=False, indent=2)
+        except Exception as e:  # noqa: BLE001
+            logger.error("查询人格列表失败: %s", e, exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"查询人格列表时发生错误: {str(e)}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_plugin_capabilities.py

@@ -40,73 +40,71 @@ class QueryPluginCapabilitiesTool(MoviePilotTool):
         """生成友好的提示消息"""
         plugin_id = kwargs.get("plugin_id")
         if plugin_id:
-            return f"正在查询插件 {plugin_id} 的能力"
-        return "正在查询所有插件的能力"
+            return f"查询插件 {plugin_id} 的能力"
+        return "查询所有插件的能力"
 
-    async def run(self, plugin_id: Optional[str] = None, **kwargs) -> str:
-        logger.info(f"执行工具: {self.name}, 参数: plugin_id={plugin_id}")
-        try:
-            plugin_manager = PluginManager()
-            result = {}
+    @staticmethod
+    def _load_plugin_capabilities(plugin_id: Optional[str] = None) -> dict:
+        """读取运行中插件实例暴露的内存能力信息。"""
+        plugin_manager = PluginManager()
+        result = {}
 
-            # 获取插件命令
-            commands = plugin_manager.get_plugin_commands(pid=plugin_id)
-            if commands:
-                commands_list = []
-                for cmd in commands:
-                    cmd_info = {
-                        "cmd": cmd.get("cmd"),
-                        "desc": cmd.get("desc"),
-                        "plugin_id": cmd.get("pid"),
-                    }
-                    # data 字段可能包含额外参数信息
-                    if cmd.get("data"):
-                        cmd_info["data"] = cmd.get("data")
-                    commands_list.append(cmd_info)
-                result["commands"] = commands_list
+        commands = plugin_manager.get_plugin_commands(pid=plugin_id)
+        if commands:
+            result["commands"] = [
+                {
+                    "cmd": cmd.get("cmd"),
+                    "desc": cmd.get("desc"),
+                    "plugin_id": cmd.get("pid"),
+                    **({"data": cmd.get("data")} if cmd.get("data") else {}),
+                }
+                for cmd in commands
+            ]
 
-            # 获取插件动作
-            actions = plugin_manager.get_plugin_actions(pid=plugin_id)
-            if actions:
-                actions_list = []
-                for action_group in actions:
-                    plugin_actions = {
+        actions = plugin_manager.get_plugin_actions(pid=plugin_id)
+        if actions:
+            actions_list = []
+            for action_group in actions:
+                actions_list.append(
+                    {
                         "plugin_id": action_group.get("plugin_id"),
                         "plugin_name": action_group.get("plugin_name"),
-                        "actions": [],
-                    }
-                    for action in action_group.get("actions", []):
-                        plugin_actions["actions"].append(
+                        "actions": [
                             {
                                 "id": action.get("id"),
                                 "name": action.get("name"),
                             }
-                        )
-                    actions_list.append(plugin_actions)
-                result["actions"] = actions_list
-
-            # 获取插件定时服务
-            services = plugin_manager.get_plugin_services(pid=plugin_id)
-            if services:
-                services_list = []
-                for svc in services:
-                    svc_info = {
-                        "id": svc.get("id"),
-                        "name": svc.get("name"),
+                            for action in action_group.get("actions", [])
+                        ],
                     }
-                    # 包含触发器信息
-                    trigger = svc.get("trigger")
-                    if trigger:
-                        svc_info["trigger"] = str(trigger)
-                    # 包含定时器参数
-                    svc_kwargs = svc.get("kwargs")
-                    if svc_kwargs:
-                        svc_info["trigger_kwargs"] = {
-                            k: str(v) for k, v in svc_kwargs.items()
-                        }
-                    services_list.append(svc_info)
-                result["services"] = services_list
+                )
+            result["actions"] = actions_list
 
+        services = plugin_manager.get_plugin_services(pid=plugin_id)
+        if services:
+            services_list = []
+            for svc in services:
+                svc_info = {
+                    "id": svc.get("id"),
+                    "name": svc.get("name"),
+                }
+                trigger = svc.get("trigger")
+                if trigger:
+                    svc_info["trigger"] = str(trigger)
+                svc_kwargs = svc.get("kwargs")
+                if svc_kwargs:
+                    svc_info["trigger_kwargs"] = {
+                        k: str(v) for k, v in svc_kwargs.items()
+                    }
+                services_list.append(svc_info)
+            result["services"] = services_list
+
+        return result
+
+    async def run(self, plugin_id: Optional[str] = None, **kwargs) -> str:
+        logger.info(f"执行工具: {self.name}, 参数: plugin_id={plugin_id}")
+        try:
+            result = self._load_plugin_capabilities(plugin_id)
             if not result:
                 if plugin_id:
                     return f"插件 {plugin_id} 没有注册任何命令、动作或定时服务"

app/agent/tools/impl/query_plugin_config.py

@@ -0,0 +1,88 @@
+"""查询插件配置工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import get_plugin_snapshot
+from app.core.plugin import PluginManager
+from app.log import logger
+
+
+class QueryPluginConfigInput(BaseModel):
+    """查询插件配置工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    plugin_id: str = Field(
+        ...,
+        description="The plugin ID to query. Use query_installed_plugins first to discover valid plugin IDs.",
+    )
+
+
+class QueryPluginConfigTool(MoviePilotTool):
+    name: str = "query_plugin_config"
+    description: str = (
+        "Query the saved configuration of an installed plugin. "
+        "Returns the current saved config and, when available, the plugin's default config model. "
+        "Use this before update_plugin_config so you only change the intended keys."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = QueryPluginConfigInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        """生成友好的提示消息"""
+        plugin_id = kwargs.get("plugin_id", "")
+        return f"查询插件配置: {plugin_id}"
+
+    @staticmethod
+    def _query_plugin_config(plugin_id: str) -> str:
+        """
+        读取插件已保存配置，并尽量补充默认配置模型方便后续精确修改。
+        """
+        plugin_info = get_plugin_snapshot(plugin_id)
+        if not plugin_info:
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"插件 {plugin_id} 不存在，请先使用 query_installed_plugins 查询有效插件 ID",
+                },
+                ensure_ascii=False,
+            )
+
+        plugin_manager = PluginManager()
+        saved_config = plugin_manager.get_plugin_config(plugin_id) or {}
+        result = {
+            "success": True,
+            **plugin_info,
+            "config": saved_config,
+        }
+
+        # get_form 的 model 通常就是插件期望的配置结构，适合作为修改前的键参考。
+        plugin_instance = plugin_manager.running_plugins.get(plugin_id)
+        if plugin_instance and hasattr(plugin_instance, "get_form"):
+            try:
+                _form_schema, default_model = plugin_instance.get_form()
+                if default_model is not None:
+                    result["default_model"] = default_model
+            except Exception as err:
+                logger.warning(f"读取插件 {plugin_id} 默认配置模型失败: {err}")
+
+        return json.dumps(result, ensure_ascii=False, indent=2, default=str)
+
+    async def run(self, plugin_id: str, **kwargs) -> str:
+        logger.info(f"执行工具: {self.name}, 参数: plugin_id={plugin_id}")
+
+        try:
+            # 插件配置来自内存配置缓存和运行态插件实例，直接读取即可。
+            return self._query_plugin_config(plugin_id)
+        except Exception as e:
+            logger.error(f"查询插件配置失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"查询插件配置时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_plugin_data.py

@@ -0,0 +1,158 @@
+"""查询插件数据工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import (
+    PLUGIN_DATA_KEY_PREVIEW_LIMIT,
+    build_preview_payload,
+    get_plugin_snapshot,
+)
+from app.db.plugindata_oper import PluginDataOper
+from app.log import logger
+
+
+class QueryPluginDataInput(BaseModel):
+    """查询插件数据工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    plugin_id: str = Field(
+        ...,
+        description="The plugin ID to query. Use query_installed_plugins first to discover valid plugin IDs.",
+    )
+    key: Optional[str] = Field(
+        None,
+        description="Optional plugin data key. If omitted, returns all plugin data entries for the plugin.",
+    )
+    max_chars: Optional[int] = Field(
+        None,
+        description="Maximum number of preview characters to return when plugin data is too large. Default 12000, capped at 50000.",
+    )
+
+
+class QueryPluginDataTool(MoviePilotTool):
+    name: str = "query_plugin_data"
+    description: str = (
+        "Query persisted data of an installed plugin. "
+        "Optionally specify a key to read a single data item; otherwise all plugin data entries are returned. "
+        "When the result is too large, the tool automatically truncates it and returns a preview instead."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = QueryPluginDataInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        """生成友好的提示消息"""
+        plugin_id = kwargs.get("plugin_id", "")
+        key = kwargs.get("key")
+        if key:
+            return f"查询插件数据: {plugin_id}.{key}"
+        return f"查询插件全部数据: {plugin_id}"
+
+    @staticmethod
+    async def _query_plugin_data(
+            plugin_id: str, key: Optional[str] = None, max_chars: Optional[int] = None
+    ) -> str:
+        """
+        插件数据改走异步 ORM 查询，避免再套一层线程池。
+        """
+        plugin_info = get_plugin_snapshot(plugin_id)
+        if not plugin_info:
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"插件 {plugin_id} 不存在，请先使用 query_installed_plugins 查询有效插件 ID",
+                },
+                ensure_ascii=False,
+            )
+
+        plugin_data_oper = PluginDataOper()
+        if key:
+            value = await plugin_data_oper.async_get_data(plugin_id, key)
+            if value is None:
+                return json.dumps(
+                    {
+                        "success": True,
+                        **plugin_info,
+                        "key": key,
+                        "found": False,
+                        "message": f"插件 {plugin_id} 没有数据项 {key}",
+                    },
+                    ensure_ascii=False,
+                    indent=2,
+                )
+
+            truncated, total_chars, returned_chars, preview = build_preview_payload(
+                value, max_chars
+            )
+            result = {
+                "success": True,
+                **plugin_info,
+                "key": key,
+                "found": True,
+                "truncated": truncated,
+                "total_chars": total_chars,
+                "returned_chars": returned_chars,
+            }
+            if truncated:
+                result["value_preview"] = preview
+                result["message"] = "插件数据内容过大，已截断预览"
+            else:
+                result["value"] = value
+            return json.dumps(result, ensure_ascii=False, indent=2, default=str)
+
+        rows = await plugin_data_oper.async_get_data_all(plugin_id) or []
+        data_map = {row.key: row.value for row in rows}
+        keys = list(data_map.keys())
+        key_preview = keys[:PLUGIN_DATA_KEY_PREVIEW_LIMIT]
+
+        result = {
+            "success": True,
+            **plugin_info,
+            "count": len(data_map),
+            "keys": key_preview,
+            "keys_truncated": len(keys) > PLUGIN_DATA_KEY_PREVIEW_LIMIT,
+        }
+
+        if not data_map:
+            result["data"] = {}
+            result["truncated"] = False
+            return json.dumps(result, ensure_ascii=False, indent=2, default=str)
+
+        truncated, total_chars, returned_chars, preview = build_preview_payload(
+            data_map, max_chars
+        )
+        result["truncated"] = truncated
+        result["total_chars"] = total_chars
+        result["returned_chars"] = returned_chars
+        if truncated:
+            result["data_preview"] = preview
+            result["message"] = "插件数据内容过大，已截断。请传入 key 精确查询单个数据项。"
+        else:
+            result["data"] = data_map
+        return json.dumps(result, ensure_ascii=False, indent=2, default=str)
+
+    async def run(
+        self,
+        plugin_id: str,
+        key: Optional[str] = None,
+        max_chars: Optional[int] = None,
+        **kwargs,
+    ) -> str:
+        logger.info(
+            f"执行工具: {self.name}, 参数: plugin_id={plugin_id}, key={key}"
+        )
+
+        try:
+            return await self._query_plugin_data(plugin_id, key, max_chars)
+        except Exception as e:
+            logger.error(f"查询插件数据失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"查询插件数据时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_popular_subscribes.py

@@ -39,7 +39,7 @@ class QueryPopularSubscribesTool(MoviePilotTool):
         min_rating = kwargs.get("min_rating")
         max_rating = kwargs.get("max_rating")
         
-        parts = [f"正在查询热门订阅 [{media_type}]"]
+        parts = [f"查询热门订阅 [{media_type}]"]
         
         if min_sub:
             parts.append(f"最少订阅: {min_sub}")

app/agent/tools/impl/query_rule_groups.py

@@ -1,64 +1,104 @@
-"""查询规则组工具"""
+"""查询过滤规则组工具。"""
 
 import json
-from typing import Optional, Type
+from typing import Optional, Type, List
 
 from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
-from app.helper.rule import RuleHelper
+from app.agent.tools.impl._filter_rule_utils import (
+    collect_rule_group_usages,
+    get_rule_groups,
+    serialize_rule_group,
+    RULE_STRING_SYNTAX,
+)
 from app.log import logger
 
 
 class QueryRuleGroupsInput(BaseModel):
     """查询规则组工具的输入参数模型"""
-    explanation: str = Field(..., description="Clear explanation of why this tool is being used in the current context")
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    group_names: Optional[List[str]] = Field(
+        None,
+        description="Optional list of rule group names to query. If omitted, return all rule groups.",
+    )
+    include_usage: bool = Field(
+        True,
+        description="Whether to include where each rule group is referenced by global settings or subscriptions.",
+    )
 
 
 class QueryRuleGroupsTool(MoviePilotTool):
     name: str = "query_rule_groups"
-    description: str = "Query all filter rule groups available in the system. Rule groups are used to filter torrents when searching or subscribing. Returns rule group names, media types, and categories, but excludes rule_string to keep results concise."
+    description: str = (
+        "Query filter rule groups (过滤规则组 / 优先级规则组). "
+        "Each rule group contains a rule_string made of built-in rules and/or custom rules. "
+        "Inside one level use '&', '|', '!' and optional parentheses; use '>' between levels. "
+        "Levels are evaluated from left to right, and the first matched level wins. "
+        "The result includes parsed levels and syntax guidance so the agent can learn existing patterns before writing a new rule group."
+    )
     args_schema: Type[BaseModel] = QueryRuleGroupsInput
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
-        """根据查询参数生成友好的提示消息"""
-        return "正在查询所有规则组"
+        group_names = kwargs.get("group_names") or []
+        if group_names:
+            return f"查询规则组: {', '.join(group_names)}"
+        return "查询所有规则组"
 
-    async def run(self, **kwargs) -> str:
+    async def run(
+        self,
+        group_names: Optional[List[str]] = None,
+        include_usage: bool = True,
+        **kwargs,
+    ) -> str:
         logger.info(f"执行工具: {self.name}")
-        
+
         try:
-            rule_helper = RuleHelper()
-            rule_groups = rule_helper.get_rule_groups()
-            
-            if not rule_groups:
-                return json.dumps({
-                    "message": "未找到任何规则组",
-                    "rule_groups": []
-                }, ensure_ascii=False, indent=2)
-            
-            # 精简字段，过滤掉 rule_string 避免结果过大
-            simplified_groups = []
-            for group in rule_groups:
-                simplified = {
-                    "name": group.name,
-                    "media_type": group.media_type,
-                    "category": group.category
-                }
-                simplified_groups.append(simplified)
-            
-            result = {
-                "message": f"找到 {len(simplified_groups)} 个规则组",
-                "rule_groups": simplified_groups
-            }
-            
-            return json.dumps(result, ensure_ascii=False, indent=2)
-            
-        except Exception as e:
-            error_message = f"查询规则组失败: {str(e)}"
-            logger.error(f"查询规则组失败: {e}", exc_info=True)
-            return json.dumps({
-                "success": False,
-                "message": error_message,
-                "rule_groups": []
-            }, ensure_ascii=False)
+            rule_groups = get_rule_groups()
+            if group_names:
+                target_names = set(group_names)
+                rule_groups = [
+                    group for group in rule_groups if group.name in target_names
+                ]
+
+            usage_map = {}
+            if include_usage:
+                usage_map = await collect_rule_group_usages(
+                    [group.name for group in rule_groups if group.name]
+                )
+
+            serialized = [
+                serialize_rule_group(group, usage_map.get(group.name))
+                for group in rule_groups
+            ]
+            message = (
+                f"找到 {len(serialized)} 个规则组"
+                if serialized
+                else "未找到任何规则组"
+            )
+
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": message,
+                    "count": len(serialized),
+                    "rule_string_syntax": RULE_STRING_SYNTAX,
+                    "rule_groups": serialized,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"查询规则组失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"查询规则组失败: {exc}",
+                    "rule_groups": [],
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/query_schedulers.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
 from app.log import logger
-from app.scheduler import Scheduler
 
 
 class QuerySchedulersInput(BaseModel):
@@ -22,11 +21,13 @@ class QuerySchedulersTool(MoviePilotTool):
 
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
-        return "正在查询定时服务"
+        return "查询定时服务"
 
     async def run(self, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}")
         try:
+            from app.scheduler import Scheduler
+
             scheduler = Scheduler()
             schedulers = scheduler.list()
             if schedulers:

app/agent/tools/impl/query_site_userdata.py

@@ -40,7 +40,7 @@ class QuerySiteUserdataTool(MoviePilotTool):
         site_id = kwargs.get("site_id")
         workdate = kwargs.get("workdate")
 
-        message = f"正在查询站点 #{site_id} 的用户数据"
+        message = f"查询站点 #{site_id} 的用户数据"
         if workdate:
             message += f" (日期: {workdate})"
         else:

app/agent/tools/impl/query_sites.py

@@ -37,7 +37,7 @@ class QuerySitesTool(MoviePilotTool):
         status = kwargs.get("status", "all")
         name = kwargs.get("name")
 
-        parts = ["正在查询站点"]
+        parts = ["查询站点"]
 
         if status != "all":
             status_map = {"active": "已启用", "inactive": "已禁用"}

app/agent/tools/impl/query_subscribe_history.py

@@ -44,7 +44,7 @@ class QuerySubscribeHistoryTool(MoviePilotTool):
         name = kwargs.get("name")
         page = kwargs.get("page", 1)
 
-        parts = ["正在查询订阅历史"]
+        parts = ["查询订阅历史"]
 
         if media_type != "all":
             parts.append(f"类型: {media_type}")

app/agent/tools/impl/query_subscribe_shares.py

@@ -34,7 +34,7 @@ class QuerySubscribeSharesTool(MoviePilotTool):
         min_rating = kwargs.get("min_rating")
         max_rating = kwargs.get("max_rating")
         
-        parts = ["正在查询订阅分享"]
+        parts = ["查询订阅分享"]
         
         if name:
             parts.append(f"名称: {name}")

app/agent/tools/impl/query_subscribes.py

@@ -79,7 +79,7 @@ class QuerySubscribesTool(MoviePilotTool):
         media_type = kwargs.get("media_type", "all")
         page = kwargs.get("page", 1)
 
-        parts = ["正在查询订阅"]
+        parts = ["查询订阅"]
 
         # 根据状态过滤条件生成提示
         if status != "all":

app/agent/tools/impl/query_transfer_history.py

@@ -33,7 +33,7 @@ class QueryTransferHistoryTool(MoviePilotTool):
         status = kwargs.get("status", "all")
         page = kwargs.get("page", 1)
 
-        parts = ["正在查询整理历史"]
+        parts = ["查询整理历史"]
 
         if title:
             parts.append(f"标题: {title}")

app/agent/tools/impl/query_workflows.py

@@ -30,7 +30,7 @@ class QueryWorkflowsTool(MoviePilotTool):
         name = kwargs.get("name")
         trigger_type = kwargs.get("trigger_type", "all")
         
-        parts = ["正在查询工作流"]
+        parts = ["查询工作流"]
         
         if state != "all":
             state_map = {"W": "等待", "R": "运行中", "P": "暂停", "S": "成功", "F": "失败"}

app/agent/tools/impl/read_file.py

@@ -29,7 +29,7 @@ class ReadFileTool(MoviePilotTool):
         """根据参数生成友好的提示消息"""
         file_path = kwargs.get("file_path", "")
         file_name = Path(file_path).name if file_path else "未知文件"
-        return f"正在读取文件: {file_name}"
+        return f"读取文件: {file_name}"
 
     async def run(self, file_path: str, start_line: Optional[int] = None,
                   end_line: Optional[int] = None, **kwargs) -> str:

app/agent/tools/impl/recognize_media.py

@@ -33,13 +33,13 @@ class RecognizeMediaTool(MoviePilotTool):
         path = kwargs.get("path")
         
         if path:
-            message = f"正在识别文件媒体信息: {path}"
+            message = f"识别文件媒体信息: {path}"
         elif title:
-            message = f"正在识别种子媒体信息: {title}"
+            message = f"识别种子媒体信息: {title}"
             if subtitle:
                 message += f" ({subtitle})"
         else:
-            message = "正在识别媒体信息"
+            message = "识别媒体信息"
         
         return message
 

app/agent/tools/impl/reload_plugin.py

@@ -0,0 +1,84 @@
+"""重载插件工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import (
+    get_plugin_snapshot,
+    reload_plugin_runtime,
+)
+from app.log import logger
+
+
+class ReloadPluginInput(BaseModel):
+    """重载插件工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    plugin_id: str = Field(
+        ...,
+        description="The plugin ID to reload so the latest saved config takes effect.",
+    )
+
+
+class ReloadPluginTool(MoviePilotTool):
+    name: str = "reload_plugin"
+    description: str = (
+        "Reload an installed plugin so its latest saved configuration takes effect. "
+        "This also refreshes the plugin's registered commands, scheduled services, and API routes."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = ReloadPluginInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        """生成友好的提示消息"""
+        plugin_id = kwargs.get("plugin_id", "")
+        return f"重载插件: {plugin_id}"
+
+    @staticmethod
+    def _reload_plugin_sync(plugin_id: str) -> str:
+        """
+        按后台接口同样的流程重载插件，确保最新配置和注册信息一起刷新。
+        """
+        plugin_info = get_plugin_snapshot(plugin_id)
+        if not plugin_info:
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"插件 {plugin_id} 不存在，请先使用 query_installed_plugins 查询有效插件 ID",
+                },
+                ensure_ascii=False,
+            )
+
+        reload_plugin_runtime(plugin_id)
+        refreshed_plugin = get_plugin_snapshot(plugin_id) or plugin_info
+
+        return json.dumps(
+            {
+                "success": True,
+                **refreshed_plugin,
+                "message": "插件已重载，最新配置已生效",
+            },
+            ensure_ascii=False,
+            indent=2,
+            default=str,
+        )
+
+    async def run(self, plugin_id: str, **kwargs) -> str:
+        logger.info(f"执行工具: {self.name}, 参数: plugin_id={plugin_id}")
+
+        try:
+            return await self.run_blocking(
+                "plugin", self._reload_plugin_sync, plugin_id
+            )
+        except Exception as e:
+            logger.error(f"重载插件失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"重载插件时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/run_scheduler.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
 from app.log import logger
-from app.scheduler import Scheduler
 
 
 class RunSchedulerInput(BaseModel):
@@ -31,29 +30,30 @@ class RunSchedulerTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据运行参数生成友好的提示消息"""
         job_id = kwargs.get("job_id", "")
-        return f"正在运行定时服务 (ID: {job_id})"
+        return f"运行定时服务 (ID: {job_id})"
+
+    @staticmethod
+    def _run_scheduler_sync(job_id: str) -> tuple[bool, str]:
+        """同步触发定时服务，避免调度器扫描阻塞事件循环。"""
+        from app.scheduler import Scheduler
+
+        scheduler = Scheduler()
+        for scheduler_item in scheduler.list():
+            if scheduler_item.id == job_id:
+                scheduler.start(job_id)
+                return True, scheduler_item.name
+        return False, ""
 
     async def run(self, job_id: str, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: job_id={job_id}")
 
         try:
-            scheduler = Scheduler()
-            # 检查定时服务是否存在
-            schedulers = scheduler.list()
-            job_exists = False
-            job_name = None
-            for s in schedulers:
-                if s.id == job_id:
-                    job_exists = True
-                    job_name = s.name
-                    break
-
+            job_exists, job_name = await self.run_blocking(
+                "workflow", self._run_scheduler_sync, job_id
+            )
             if not job_exists:
                 return f"定时服务 ID {job_id} 不存在，请使用 query_schedulers 工具查询可用的定时服务"
 
-            # 运行定时服务
-            scheduler.start(job_id)
-
             return f"成功触发定时服务：{job_name} (ID: {job_id})"
         except Exception as e:
             logger.error(f"运行定时服务失败: {e}", exc_info=True)

app/agent/tools/impl/run_slash_command.py

@@ -45,7 +45,7 @@ class RunSlashCommandTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """生成友好的提示消息"""
         command = kwargs.get("command", "")
-        return f"正在执行命令: {command}"
+        return f"执行命令: {command}"
 
     async def run(self, command: str, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: command={command}")

app/agent/tools/impl/run_workflow.py

@@ -38,7 +38,7 @@ class RunWorkflowTool(MoviePilotTool):
         workflow_id = kwargs.get("workflow_id")
         from_begin = kwargs.get("from_begin", True)
 
-        message = f"正在执行工作流: {workflow_id}"
+        message = f"执行工作流: {workflow_id}"
         if not from_begin:
             message += " (从上次位置继续)"
         else:
@@ -46,6 +46,13 @@ class RunWorkflowTool(MoviePilotTool):
 
         return message
 
+    @staticmethod
+    def _run_workflow_sync(
+        workflow_id: int, from_begin: Optional[bool] = True
+    ) -> tuple[bool, str]:
+        """同步执行工作流，放到专用线程池避免长流程阻塞 API 响应。"""
+        return WorkflowChain().process(workflow_id, from_begin=from_begin)
+
     async def run(
         self, workflow_id: int, from_begin: Optional[bool] = True, **kwargs
     ) -> str:
@@ -62,10 +69,12 @@ class RunWorkflowTool(MoviePilotTool):
                 if not workflow:
                     return f"未找到工作流：{workflow_id}，请使用 query_workflows 工具查询可用的工作流"
 
-                # 执行工作流
-                workflow_chain = WorkflowChain()
-                state, errmsg = workflow_chain.process(
-                    workflow.id, from_begin=from_begin
+                # 工作流执行链路包含大量同步步骤，统一放到 workflow 线程池。
+                state, errmsg = await self.run_blocking(
+                    "workflow",
+                    self._run_workflow_sync,
+                    workflow.id,
+                    from_begin,
                 )
 
                 if not state:

app/agent/tools/impl/scrape_metadata.py

@@ -8,7 +8,6 @@ from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
 from app.chain.media import MediaChain
-from app.core.config import global_vars
 from app.core.metainfo import MetaInfoPath
 from app.log import logger
 from app.schemas import FileItem
@@ -47,7 +46,7 @@ class ScrapeMetadataTool(MoviePilotTool):
         storage = kwargs.get("storage", "local")
         overwrite = kwargs.get("overwrite", False)
 
-        message = f"正在刮削媒体元数据: {path}"
+        message = f"刮削媒体元数据: {path}"
         if storage != "local":
             message += f" [存储: {storage}]"
         if overwrite:
@@ -104,15 +103,14 @@ class ScrapeMetadataTool(MoviePilotTool):
                     ensure_ascii=False,
                 )
 
-            # 在线程池中执行同步的刮削操作
-            await global_vars.loop.run_in_executor(
-                None,
-                lambda: media_chain.scrape_metadata(
-                    fileitem=fileitem,
-                    meta=meta,
-                    mediainfo=mediainfo,
-                    overwrite=overwrite,
-                ),
+            # 刮削会包含磁盘写入和外部图片/元数据访问，统一放到 storage 线程池。
+            await self.run_blocking(
+                "storage",
+                media_chain.scrape_metadata,
+                fileitem=fileitem,
+                meta=meta,
+                mediainfo=mediainfo,
+                overwrite=overwrite,
             )
 
             return json.dumps(

app/agent/tools/impl/search_media.py

@@ -34,7 +34,7 @@ class SearchMediaTool(MoviePilotTool):
         media_type = kwargs.get("media_type")
         season = kwargs.get("season")
         
-        message = f"正在搜索媒体: {title}"
+        message = f"搜索媒体: {title}"
         if year:
             message += f" ({year})"
         if media_type:

app/agent/tools/impl/search_person.py

@@ -24,7 +24,7 @@ class SearchPersonTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据搜索参数生成友好的提示消息"""
         name = kwargs.get("name", "")
-        return f"正在搜索人物: {name}"
+        return f"搜索人物: {name}"
 
     async def run(self, name: str, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: name={name}")

app/agent/tools/impl/search_person_credits.py

@@ -29,7 +29,7 @@ class SearchPersonCreditsTool(MoviePilotTool):
         """根据搜索参数生成友好的提示消息"""
         person_id = kwargs.get("person_id", "")
         source = kwargs.get("source", "")
-        return f"正在搜索人物参演作品: {source} ID {person_id}"
+        return f"搜索人物参演作品: {source} ID {person_id}"
 
     async def run(self, person_id: int, source: str, page: Optional[int] = 1, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: person_id={person_id}, source={source}, page={page}")

app/agent/tools/impl/search_subscribe.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
 from app.chain.subscribe import SubscribeChain
-from app.core.config import global_vars
 from app.db.subscribe_oper import SubscribeOper
 from app.log import logger
 from app.schemas.types import media_type_to_agent
@@ -32,7 +31,7 @@ class SearchSubscribeTool(MoviePilotTool):
         subscribe_id = kwargs.get("subscribe_id")
         manual = kwargs.get("manual", False)
 
-        message = f"正在搜索订阅 #{subscribe_id} 的缺失剧集"
+        message = f"搜索订阅 #{subscribe_id} 的缺失剧集"
         if manual:
             message += "（手动搜索）"
 
@@ -81,19 +80,13 @@ class SearchSubscribeTool(MoviePilotTool):
                 subscribe_oper.update(subscribe_id, {"filter_groups": filter_groups})
                 logger.info(f"更新订阅 #{subscribe_id} 的规则组为: {filter_groups}")
 
-            # 调用 SubscribeChain 的 search 方法
-            # search 方法是同步的，需要在异步环境中运行
-            subscribe_chain = SubscribeChain()
-
-            # 在线程池中执行同步的搜索操作
-            # 当 sid 有值时，state 参数会被忽略，直接处理该订阅
-            await global_vars.loop.run_in_executor(
-                None,
-                lambda: subscribe_chain.search(
-                    sid=subscribe_id,
-                    state='R',  # 默认状态，当 sid 有值时此参数会被忽略
-                    manual=manual
-                )
+            # 订阅搜索会触发大量同步站点访问，统一走 subscribe 线程池。
+            await self.run_blocking(
+                "subscribe",
+                SubscribeChain().search,
+                sid=subscribe_id,
+                state="R",  # 当 sid 有值时此参数会被忽略
+                manual=manual,
             )
 
             # 重新获取订阅信息以获取更新后的状态

app/agent/tools/impl/search_torrents.py

@@ -41,15 +41,20 @@ class SearchTorrentsTool(MoviePilotTool):
         media_type = kwargs.get("media_type")
 
         if tmdb_id:
-            message = f"正在搜索种子: TMDB={tmdb_id}"
+            message = f"搜索种子: TMDB={tmdb_id}"
         elif douban_id:
-            message = f"正在搜索种子: 豆瓣={douban_id}"
+            message = f"搜索种子: 豆瓣={douban_id}"
         else:
-            message = "正在搜索种子"
+            message = "搜索种子"
         if media_type:
             message += f" [{media_type}]"
         return message
 
+    @staticmethod
+    def _load_configured_sites() -> List[int]:
+        """同步读取默认搜索站点列表。"""
+        return SystemConfigOper().get(SystemConfigKey.IndexerSites) or []
+
     async def run(self, tmdb_id: Optional[int] = None, douban_id: Optional[str] = None,
                   media_type: Optional[str] = None, area: Optional[str] = None,
                   sites: Optional[List[int]] = None, **kwargs) -> str:
@@ -83,8 +88,7 @@ class SearchTorrentsTool(MoviePilotTool):
             if sites:
                 search_site_ids = sites
             else:
-                configured_sites = SystemConfigOper().get(SystemConfigKey.IndexerSites)
-                search_site_ids = configured_sites if configured_sites else []
+                search_site_ids = self._load_configured_sites()
 
             if filtered_torrents:
                 await search_chain.async_save_cache(filtered_torrents, SEARCH_RESULT_CACHE_FILE)

app/agent/tools/impl/search_web.py

@@ -41,7 +41,7 @@ class SearchWebTool(MoviePilotTool):
         """根据搜索参数生成友好的提示消息"""
         query = kwargs.get("query", "")
         max_results = kwargs.get("max_results", 20)
-        return f"正在搜索网络内容: {query} (最多返回 {max_results} 条结果)"
+        return f"搜索网络内容: {query} (最多返回 {max_results} 条结果)"
 
     async def run(self, query: str, max_results: Optional[int] = 20, **kwargs) -> str:
         """

app/agent/tools/impl/send_local_file.py

@@ -45,6 +45,7 @@ class SendLocalFileInput(BaseModel):
 
 class SendLocalFileTool(MoviePilotTool):
     name: str = "send_local_file"
+    sends_message: bool = True
     description: str = (
         "Send a local image or file from the server filesystem to the current user. "
         "Use this when you have generated or identified a local file the user should download."
@@ -55,7 +56,7 @@ class SendLocalFileTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         file_path = kwargs.get("file_path", "")
         file_name = Path(file_path).name if file_path else "未知文件"
-        return f"正在发送本地附件: {file_name}"
+        return f"发送本地附件: {file_name}"
 
     async def run(
         self,

app/agent/tools/impl/send_message.py

@@ -37,6 +37,7 @@ class SendMessageInput(BaseModel):
 
 class SendMessageTool(MoviePilotTool):
     name: str = "send_message"
+    sends_message: bool = True
     description: str = "Send notification message to the user through configured notification channels (Telegram, Slack, WeChat, etc.). Supports optional image_url on channels that can send images. Used to inform users about operation results, errors, important updates, or proactively send a relevant image."
     args_schema: Type[BaseModel] = SendMessageInput
     require_admin: bool = True
@@ -52,12 +53,12 @@ class SendMessageTool(MoviePilotTool):
             message = message[:50] + "..."
 
         if title and image_url:
-            return f"正在发送图文消息: [{title}] {message}"
+            return f"发送图文消息: [{title}] {message}"
         if title:
-            return f"正在发送消息: [{title}] {message}"
+            return f"发送消息: [{title}] {message}"
         if image_url:
-            return f"正在发送图片消息: {message}"
-        return f"正在发送消息: {message}"
+            return f"发送图片消息: {message}"
+        return f"发送消息: {message}"
 
     async def run(
         self,

app/agent/tools/impl/send_voice_message.py

@@ -8,10 +8,8 @@ from pydantic import BaseModel, Field
 from app.agent.tools.base import MoviePilotTool, ToolChain
 from app.core.config import settings
 from app.helper.voice import VoiceHelper
-from app.helper.service import ServiceConfigHelper
 from app.log import logger
 from app.schemas import Notification, NotificationType
-from app.schemas.types import MessageChannel
 
 
 class SendVoiceMessageInput(BaseModel):
@@ -29,10 +27,12 @@ class SendVoiceMessageInput(BaseModel):
 
 class SendVoiceMessageTool(MoviePilotTool):
     name: str = "send_voice_message"
+    sends_message: bool = True
     description: str = (
-        "Send a voice reply to the current user. Prefer this when the user sent a voice message "
-        "or when spoken playback is more natural. On channels without voice support or when TTS "
-        "is unavailable, it automatically falls back to sending the same content as plain text."
+        "Send a voice reply to the current user. Use this only when the user explicitly asks for "
+        "a voice reply or when spoken playback is clearly better than plain text. On channels "
+        "without voice support or when TTS is unavailable, it automatically falls back to sending "
+        "the same content as plain text."
     )
     args_schema: Type[BaseModel] = SendVoiceMessageInput
     require_admin: bool = False
@@ -41,19 +41,7 @@ class SendVoiceMessageTool(MoviePilotTool):
         message = kwargs.get("message") or ""
         if len(message) > 40:
             message = message[:40] + "..."
-        return f"正在发送语音回复: {message}"
-
-    def _supports_real_voice_reply(self) -> bool:
-        channel = self._channel or ""
-        if channel == MessageChannel.Telegram.value:
-            return True
-        if channel != MessageChannel.Wechat.value:
-            return False
-        for config in ServiceConfigHelper.get_notification_configs():
-            if config.name != self._source:
-                continue
-            return (config.config or {}).get("WECHAT_MODE", "app") != "bot"
-        return False
+        return f"发送语音回复: {message}"
 
     async def run(self, message: str, **kwargs) -> str:
         if not message:
@@ -62,11 +50,23 @@ class SendVoiceMessageTool(MoviePilotTool):
         voice_path = None
         used_voice = False
         channel = self._channel or ""
-        if self._supports_real_voice_reply() and VoiceHelper.is_available("tts"):
+        reply_mode = VoiceHelper.resolve_reply_mode(
+            channel=channel,
+            source=self._source,
+        )
+        fallback_reason = "当前渠道不支持语音回复"
+        if not VoiceHelper.is_enabled():
+            fallback_reason = "当前未启用音频输入输出"
+        if (
+            reply_mode == VoiceHelper.REPLY_MODE_NATIVE
+            and VoiceHelper.is_available("tts")
+        ):
             voice_file = await asyncio.to_thread(VoiceHelper.synthesize_speech, message)
             if voice_file:
                 voice_path = str(voice_file)
                 used_voice = True
+        elif reply_mode == VoiceHelper.REPLY_MODE_NATIVE:
+            fallback_reason = "当前未配置可用的语音合成能力"
 
         logger.info(
             "执行工具: %s, channel=%s, use_voice=%s, text_len=%s",
@@ -85,7 +85,11 @@ class SendVoiceMessageTool(MoviePilotTool):
                 username=self._username,
                 text=message,
                 voice_path=voice_path,
-                voice_caption=message if settings.AI_VOICE_REPLY_WITH_TEXT else None,
+                voice_caption=(
+                    message
+                    if voice_path and settings.AI_VOICE_REPLY_WITH_TEXT
+                    else None
+                ),
             )
         )
         self._agent_context["user_reply_sent"] = True
@@ -93,4 +97,4 @@ class SendVoiceMessageTool(MoviePilotTool):
 
         if used_voice:
             return "语音回复已发送"
-        return "当前未使用语音通道，已自动回退为文字回复"
+        return f"{fallback_reason}，已自动回退为文字回复"

app/agent/tools/impl/switch_persona.py

@@ -0,0 +1,62 @@
+"""切换当前激活人格工具。"""
+
+import json
+from typing import Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.runtime import agent_runtime_manager
+from app.agent.tools.base import MoviePilotTool
+from app.log import logger
+
+
+class SwitchPersonaInput(BaseModel):
+    """切换人格工具的输入参数模型。"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    persona_id: str = Field(
+        ...,
+        description=(
+            "The target persona to activate. This can be the exact persona_id, label, or one of the persona aliases. "
+            "If the exact persona is unclear, call query_personas first."
+        ),
+    )
+
+
+class SwitchPersonaTool(MoviePilotTool):
+    name: str = "switch_persona"
+    description: str = (
+        "Switch the active persona (人格) used by the agent runtime. "
+        "This change is persistent for future turns. "
+        "Use this when the user explicitly asks to change the speaking style, tone, or response persona. "
+        "If the user asks for a vague style and you are not sure which persona matches best, call query_personas first."
+    )
+    args_schema: Type[BaseModel] = SwitchPersonaInput
+
+    def get_tool_message(self, **kwargs) -> str:
+        persona_id = kwargs.get("persona_id") or "未知人格"
+        return f"切换人格: {persona_id}"
+
+    async def run(self, persona_id: str, **kwargs) -> str:
+        logger.info("执行工具: %s, 参数: persona_id=%s", self.name, persona_id)
+        try:
+            runtime_config = agent_runtime_manager.set_active_persona(persona_id)
+            payload = {
+                "success": True,
+                "active_persona": runtime_config.active_persona,
+                "persona": runtime_config.persona.to_dict(is_active=True),
+                "message": f"已切换为人格 `{runtime_config.active_persona}`",
+            }
+            return json.dumps(payload, ensure_ascii=False, indent=2)
+        except Exception as e:  # noqa: BLE001
+            logger.error("切换人格失败: %s", e, exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"切换人格时发生错误: {str(e)}",
+                },
+                ensure_ascii=False,
+            )

app/agent/tools/impl/test_site.py

@@ -24,26 +24,31 @@ class TestSiteTool(MoviePilotTool):
     def get_tool_message(self, **kwargs) -> Optional[str]:
         """根据测试参数生成友好的提示消息"""
         site_identifier = kwargs.get("site_identifier")
-        return f"正在测试站点连通性: {site_identifier}"
+        return f"测试站点连通性: {site_identifier}"
+
+    @staticmethod
+    def _test_site_sync(site_identifier: int) -> tuple[Optional[str], Optional[str], bool, str]:
+        """在同步线程里执行站点联通测试，避免网络请求卡住事件循环。"""
+        site = SiteOper().get(site_identifier)
+        if not site:
+            return None, None, False, f"未找到站点：{site_identifier}，请使用 query_sites 工具查询可用的站点"
+
+        status, message = SiteChain().test(site.domain)
+        return site.name, site.domain, status, message
 
     async def run(self, site_identifier: int, **kwargs) -> str:
         logger.info(f"执行工具: {self.name}, 参数: site_identifier={site_identifier}")
 
         try:
-            site_oper = SiteOper()
-            site_chain = SiteChain()
-            site = await site_oper.async_get(site_identifier)
-            
-            if not site:
-                return f"未找到站点：{site_identifier}，请使用 query_sites 工具查询可用的站点"
-            
-            # 测试站点连通性
-            status, message = site_chain.test(site.domain)
-            
+            site_name, site_domain, status, message = await self.run_blocking(
+                "site", self._test_site_sync, site_identifier
+            )
+            if not site_name:
+                return message
             if status:
-                return f"站点连通性测试成功：{site.name} ({site.domain})\n{message}"
+                return f"站点连通性测试成功：{site_name} ({site_domain})\n{message}"
             else:
-                return f"站点连通性测试失败：{site.name} ({site.domain})\n{message}"
+                return f"站点连通性测试失败：{site_name} ({site_domain})\n{message}"
         except Exception as e:
             logger.error(f"测试站点连通性失败: {e}", exc_info=True)
             return f"测试站点连通性时发生错误: {str(e)}"

app/agent/tools/impl/transfer_file.py

@@ -6,7 +6,6 @@ from typing import Optional, Type
 from pydantic import BaseModel, Field
 
 from app.agent.tools.base import MoviePilotTool
-from app.chain.transfer import TransferChain
 from app.log import logger
 from app.schemas import FileItem, MediaType
 
@@ -68,7 +67,7 @@ class TransferFileTool(MoviePilotTool):
         transfer_type = kwargs.get("transfer_type")
         background = kwargs.get("background", False)
 
-        message = f"正在整理文件: {file_path}"
+        message = f"整理文件: {file_path}"
         if media_type:
             message += f" [{media_type}]"
         if transfer_type:
@@ -84,6 +83,75 @@ class TransferFileTool(MoviePilotTool):
 
         return message
 
+    @staticmethod
+    def _transfer_file_sync(
+        file_path: str,
+        storage: Optional[str] = "local",
+        target_path: Optional[str] = None,
+        target_storage: Optional[str] = None,
+        media_type: Optional[str] = None,
+        tmdbid: Optional[int] = None,
+        doubanid: Optional[str] = None,
+        season: Optional[int] = None,
+        transfer_type: Optional[str] = None,
+        background: Optional[bool] = False,
+    ) -> str:
+        """
+        文件整理链路包含大量同步磁盘与外部服务调用，需要在线程池中运行。
+        """
+        if not file_path:
+            return "错误：必须提供文件或目录路径"
+
+        if storage == "local":
+            if not file_path.startswith("/") and not (
+                len(file_path) > 1 and file_path[1] == ":"
+            ):
+                file_path = str(Path(file_path).resolve())
+        elif not file_path.startswith("/"):
+            file_path = "/" + file_path
+
+        fileitem = FileItem(
+            storage=storage or "local",
+            path=file_path,
+            type="dir" if file_path.endswith("/") else "file",
+        )
+        target_path_obj = Path(target_path) if target_path else None
+
+        media_type_enum = None
+        if media_type:
+            media_type_enum = MediaType.from_agent(media_type)
+            if not media_type_enum:
+                return f"错误：无效的媒体类型 '{media_type}'，支持的类型：'movie', 'tv'"
+
+        from app.chain.transfer import TransferChain
+
+        state, errormsg = TransferChain().manual_transfer(
+            fileitem=fileitem,
+            target_storage=target_storage,
+            target_path=target_path_obj,
+            tmdbid=tmdbid,
+            doubanid=doubanid,
+            mtype=media_type_enum,
+            season=season,
+            transfer_type=transfer_type,
+            background=background,
+        )
+
+        if state:
+            if background:
+                return f"整理任务已提交到后台运行：{file_path}"
+            return f"整理成功：{file_path}"
+
+        if isinstance(errormsg, list):
+            error_text = f"整理完成，{len(errormsg)} 个文件转移失败"
+            if errormsg:
+                error_text += "：\n" + "\n".join(str(e) for e in errormsg[:5])
+                if len(errormsg) > 5:
+                    error_text += f"\n... 还有 {len(errormsg) - 5} 个错误"
+        else:
+            error_text = str(errormsg)
+        return f"整理失败：{error_text}"
+
     async def run(
         self,
         file_path: str,
@@ -105,73 +173,20 @@ class TransferFileTool(MoviePilotTool):
         )
 
         try:
-            if not file_path:
-                return "错误：必须提供文件或目录路径"
-
-            # 规范化路径
-            if storage == "local":
-                # 本地路径处理
-                if not file_path.startswith("/") and not (
-                    len(file_path) > 1 and file_path[1] == ":"
-                ):
-                    # 相对路径，尝试转换为绝对路径
-                    file_path = str(Path(file_path).resolve())
-            else:
-                # 远程存储路径，确保以/开头
-                if not file_path.startswith("/"):
-                    file_path = "/" + file_path
-
-            # 创建FileItem
-            fileitem = FileItem(
-                storage=storage or "local",
-                path=file_path,
-                type="dir" if file_path.endswith("/") else "file",
+            return await self.run_blocking(
+                "storage",
+                self._transfer_file_sync,
+                file_path,
+                storage,
+                target_path,
+                target_storage,
+                media_type,
+                tmdbid,
+                doubanid,
+                season,
+                transfer_type,
+                background,
             )
-
-            # 处理目标路径
-            target_path_obj = None
-            if target_path:
-                target_path_obj = Path(target_path)
-
-            # 处理媒体类型
-            media_type_enum = None
-            if media_type:
-                media_type_enum = MediaType.from_agent(media_type)
-                if not media_type_enum:
-                    return f"错误：无效的媒体类型 '{media_type}'，支持的类型：'movie', 'tv'"
-
-            # 调用整理方法
-            transfer_chain = TransferChain()
-            state, errormsg = transfer_chain.manual_transfer(
-                fileitem=fileitem,
-                target_storage=target_storage,
-                target_path=target_path_obj,
-                tmdbid=tmdbid,
-                doubanid=doubanid,
-                mtype=media_type_enum,
-                season=season,
-                transfer_type=transfer_type,
-                background=background,
-            )
-
-            if not state:
-                # 处理错误信息
-                if isinstance(errormsg, list):
-                    error_text = f"整理完成，{len(errormsg)} 个文件转移失败"
-                    if errormsg:
-                        error_text += f"：\n" + "\n".join(
-                            str(e) for e in errormsg[:5]
-                        )  # 只显示前5个错误
-                        if len(errormsg) > 5:
-                            error_text += f"\n... 还有 {len(errormsg) - 5} 个错误"
-                else:
-                    error_text = str(errormsg)
-                return f"整理失败：{error_text}"
-            else:
-                if background:
-                    return f"整理任务已提交到后台运行：{file_path}"
-                else:
-                    return f"整理成功：{file_path}"
         except Exception as e:
             logger.error(f"整理文件失败: {e}", exc_info=True)
             return f"整理文件时发生错误: {str(e)}"

app/agent/tools/impl/uninstall_plugin.py

@@ -0,0 +1,84 @@
+"""卸载插件工具"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._plugin_tool_utils import (
+    list_installed_plugins,
+    summarize_plugin,
+    uninstall_plugin_runtime,
+)
+from app.log import logger
+
+
+class UninstallPluginInput(BaseModel):
+    """卸载插件工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    plugin_id: str = Field(
+        ...,
+        description="Exact plugin ID to uninstall. Use query_installed_plugins first to find the correct plugin_id.",
+    )
+
+
+class UninstallPluginTool(MoviePilotTool):
+    name: str = "uninstall_plugin"
+    description: str = (
+        "Uninstall an installed plugin by exact plugin_id. "
+        "Use query_installed_plugins first when you need filtering or discovery."
+    )
+    require_admin: bool = True
+    args_schema: Type[BaseModel] = UninstallPluginInput
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        plugin_id = kwargs.get("plugin_id")
+        return f"卸载插件: {plugin_id or '未知插件'}"
+
+    async def run(
+        self,
+        plugin_id: str,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}, 参数: plugin_id={plugin_id}")
+
+        try:
+            plugins = list_installed_plugins()
+            if not plugins:
+                return json.dumps(
+                    {"success": False, "message": "当前没有已安装的插件"},
+                    ensure_ascii=False,
+                )
+
+            candidate = next((plugin for plugin in plugins if plugin.id == plugin_id), None)
+            if not candidate:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": f"未找到已安装插件: {plugin_id}。请先调用 query_installed_plugins 确认 plugin_id。",
+                    },
+                    ensure_ascii=False,
+                )
+
+            cleanup_result = await uninstall_plugin_runtime(candidate.id)
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"插件 {candidate.id} 已卸载",
+                    "plugin": summarize_plugin(candidate),
+                    **cleanup_result,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as e:
+            logger.error(f"卸载插件失败: {e}", exc_info=True)
+            return json.dumps(
+                {"success": False, "message": f"卸载插件时发生错误: {str(e)}"},
+                ensure_ascii=False,
+            )

app/agent/tools/impl/update_custom_filter_rule.py

@@ -0,0 +1,190 @@
+"""更新自定义过滤规则工具。"""
+
+import json
+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from app.agent.tools.base import MoviePilotTool
+from app.agent.tools.impl._filter_rule_utils import (
+    collect_custom_rule_group_refs,
+    get_custom_rules,
+    get_rule_groups,
+    normalize_custom_rule,
+    replace_rule_id_in_rule_string,
+    save_system_config,
+    serialize_custom_rule,
+)
+from app.log import logger
+from app.schemas.types import SystemConfigKey
+
+
+class UpdateCustomFilterRuleInput(BaseModel):
+    """更新自定义过滤规则工具的输入参数模型"""
+
+    explanation: str = Field(
+        ...,
+        description="Clear explanation of why this tool is being used in the current context",
+    )
+    current_rule_id: str = Field(
+        ..., description="Existing custom rule ID to update."
+    )
+    new_rule_id: Optional[str] = Field(
+        None,
+        description="New rule ID. If omitted, keep the original rule ID.",
+    )
+    name: Optional[str] = Field(
+        None, description="New display name. If omitted, keep the original name."
+    )
+    include: Optional[str] = Field(
+        None,
+        description="New include regex. Pass an empty string to clear it.",
+    )
+    exclude: Optional[str] = Field(
+        None,
+        description="New exclude regex. Pass an empty string to clear it.",
+    )
+    size_range: Optional[str] = Field(
+        None,
+        description="New size range in MB. Pass an empty string to clear it.",
+    )
+    seeders: Optional[str] = Field(
+        None,
+        description="New minimum seeder count. Pass an empty string to clear it.",
+    )
+    publish_time: Optional[str] = Field(
+        None,
+        description="New publish-time filter in minutes. Pass an empty string to clear it.",
+    )
+
+
+class UpdateCustomFilterRuleTool(MoviePilotTool):
+    name: str = "update_custom_filter_rule"
+    description: str = (
+        "Update an existing custom filter rule. "
+        "If the rule ID is renamed, all rule groups that reference the old ID are updated automatically."
+    )
+    args_schema: Type[BaseModel] = UpdateCustomFilterRuleInput
+    require_admin: bool = True
+
+    def get_tool_message(self, **kwargs) -> Optional[str]:
+        current_rule_id = kwargs.get("current_rule_id", "")
+        new_rule_id = kwargs.get("new_rule_id")
+        if new_rule_id and new_rule_id != current_rule_id:
+            return f"更新自定义过滤规则 {current_rule_id} -> {new_rule_id}"
+        return f"更新自定义过滤规则 {current_rule_id}"
+
+    async def run(
+        self,
+        current_rule_id: str,
+        new_rule_id: Optional[str] = None,
+        name: Optional[str] = None,
+        include: Optional[str] = None,
+        exclude: Optional[str] = None,
+        size_range: Optional[str] = None,
+        seeders: Optional[str] = None,
+        publish_time: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        logger.info(f"执行工具: {self.name}, current_rule_id={current_rule_id}")
+
+        try:
+            custom_rules = get_custom_rules()
+            rule_map = {rule.id: rule for rule in custom_rules if rule.id}
+            current_rule = rule_map.get(current_rule_id)
+            if not current_rule:
+                return json.dumps(
+                    {
+                        "success": False,
+                        "message": f"自定义过滤规则 '{current_rule_id}' 不存在",
+                    },
+                    ensure_ascii=False,
+                )
+
+            updated_rule = normalize_custom_rule(
+                rule_id=new_rule_id or current_rule.id,
+                name=name if name is not None else current_rule.name,
+                include=include if include is not None else current_rule.include,
+                exclude=exclude if exclude is not None else current_rule.exclude,
+                size_range=(
+                    size_range if size_range is not None else current_rule.size_range
+                ),
+                seeders=seeders if seeders is not None else current_rule.seeders,
+                publish_time=(
+                    publish_time
+                    if publish_time is not None
+                    else current_rule.publish_time
+                ),
+                existing_rules=custom_rules,
+                original_rule_id=current_rule.id,
+            )
+
+            rule_groups = get_rule_groups()
+            updated_rule_groups = rule_groups
+            renamed_group_refs = []
+            if updated_rule.id != current_rule.id:
+                updated_rule_groups = []
+                for group in rule_groups:
+                    if not group.rule_string:
+                        updated_rule_groups.append(group)
+                        continue
+                    new_rule_string = replace_rule_id_in_rule_string(
+                        group.rule_string,
+                        current_rule.id,
+                        updated_rule.id,
+                    )
+                    if new_rule_string == group.rule_string:
+                        updated_rule_groups.append(group)
+                        continue
+                    renamed_group_refs.append(group.name)
+                    updated_rule_groups.append(
+                        group.model_copy(update={"rule_string": new_rule_string})
+                    )
+
+                # 先保存规则组引用，再保存规则自身，避免在过滤模块重载时出现新规则 ID 尚未同步的问题。
+                await save_system_config(
+                    SystemConfigKey.UserFilterRuleGroups,
+                    [
+                        group.model_dump(exclude_none=True)
+                        for group in updated_rule_groups
+                    ],
+                )
+
+            final_rules = []
+            for rule in custom_rules:
+                if rule.id == current_rule.id:
+                    final_rules.append(updated_rule)
+                else:
+                    final_rules.append(rule)
+
+            await save_system_config(
+                SystemConfigKey.CustomFilterRules,
+                [rule.model_dump(exclude_none=True) for rule in final_rules],
+            )
+
+            updated_refs = collect_custom_rule_group_refs(
+                updated_rule_groups,
+                [updated_rule.id],
+            )
+            return json.dumps(
+                {
+                    "success": True,
+                    "message": f"已更新自定义过滤规则 {updated_rule.id}",
+                    "custom_rule": serialize_custom_rule(
+                        updated_rule,
+                        updated_refs.get(updated_rule.id),
+                    ),
+                    "rule_groups_updated_for_rule_id_rename": renamed_group_refs,
+                },
+                ensure_ascii=False,
+                indent=2,
+            )
+        except Exception as exc:
+            logger.error(f"更新自定义过滤规则失败: {exc}", exc_info=True)
+            return json.dumps(
+                {
+                    "success": False,
+                    "message": f"更新自定义过滤规则失败: {exc}",
+                },
+                ensure_ascii=False,
+            )