Initial plan

* fix(#4188): 从文件安装插件时先终止并解绑已存在的同名插件

* feat(star): 优化从文件安装插件的处理同名冲突逻辑，增加边缘检查

.github/copilot-instructions.md

@@ -15,7 +15,6 @@ Always reference these instructions first and fallback to search or bash command
 ### Running the Application
 - Run main application: `uv run main.py` -- starts in ~3 seconds
 - Application creates WebUI on http://localhost:6185 (default credentials: `astrbot`/`astrbot`)
-- Application loads plugins automatically from `packages/` and `data/plugins/` directories
 
 ### Dashboard Build (Vue.js/Node.js)
 - **Prerequisites**: Node.js 20+ and npm 10+ required
@@ -35,7 +34,7 @@ Always reference these instructions first and fallback to search or bash command
 - **ALWAYS** run `uv run ruff check .` and `uv run ruff format .` before committing changes
 
 ### Plugin Development
-- Plugins load from `packages/` (built-in) and `data/plugins/` (user-installed)
+- Plugins load from `astrbot/builtin_stars/` (built-in) and `data/plugins/` (user-installed)
 - Plugin system supports function tools and message handlers
 - Key plugins: python_interpreter, web_searcher, astrbot, reminder, session_controller
 

.github/workflows/dashboard_ci.yml

@@ -36,7 +36,7 @@ jobs:
           zip -r dist.zip dist
 
       - name: Archive production artifacts
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: dist-without-markdown
           path: |

.github/workflows/stale.yml

@@ -1,27 +1,63 @@
-# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+# 本工作流用于标记并关闭长期不活跃的 Issue。
+# 目前仅针对带 `bug` 标签的 Issue 生效，不会处理 PR。
 #
-# You can adjust the behavior by modifying this file.
-# For more information, see:
-# https://github.com/actions/stale
-name: Mark stale issues and pull requests
+# 文档: https://github.com/actions/stale
+name: Mark stale bug issues
 
 on:
   schedule:
-  - cron: '21 23 * * *'
+    # 每天 UTC 08:30 执行 (北京时间 16:30)
+    - cron: '30 8 * * *'
+  workflow_dispatch:
+    inputs:
+      dry-run:
+        description: '仅预览, 不实际执行 (Dry run mode)'
+        required: false
+        default: true
+        type: boolean
 
 jobs:
   stale:
-
     runs-on: ubuntu-latest
     permissions:
       issues: write
-      pull-requests: write
 
     steps:
-    - uses: actions/stale@v10
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        stale-issue-message: 'Stale issue message'
-        stale-pr-message: 'Stale pull request message'
-        stale-issue-label: 'no-issue-activity'
-        stale-pr-label: 'no-pr-activity'
+      - uses: actions/stale@v10
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+          # 只处理带 bug 标签的 Issue
+          any-of-labels: 'bug'
+
+          # 不处理 PR
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+
+          # 不活跃判定与关闭策略: 先标记 stale, 再延迟关闭
+          days-before-issue-stale: 60
+          days-before-issue-close: 30
+
+          stale-issue-label: 'stale'
+          stale-issue-message: |
+            This issue has been automatically marked as **stale** because it has not had any activity.
+            It will be closed in a certain period of time if no further activity occurs.
+            If this issue is still relevant, please leave a comment.
+
+            ---
+
+            该 Issue 已较长时间无活动, 已被标记为 `stale`。
+            如无后续活动, 将在一段时间后自动关闭。
+            如仍需跟进, 请回复评论。
+          close-issue-message: |
+            This issue has been automatically closed due to inactivity.
+            If the problem still exists, feel free to reopen or create a new issue with updated information.
+
+            ---
+
+            该 Issue 因长期无活动已自动关闭。
+            如问题仍存在, 欢迎补充复现信息并重新打开或新建 Issue。
+
+          remove-stale-when-updated: true
+
+          debug-only: ${{ github.event_name == 'workflow_dispatch' && inputs.dry-run }}

.gitignore

@@ -24,9 +24,9 @@ configs/session
 configs/config.yaml
 cmd_config.json
 
-# Plugins and packages
+# Plugins
 addons/plugins
-packages/python_interpreter/workplace
+astrbot/builtin_stars/python_interpreter/workplace
 tests/astrbot_plugin_openai
 
 # Dashboard

CONTRIBUTING.md

@@ -33,6 +33,20 @@
 - 请使用英文描述您的 PR。
 - 标题请使用 `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` 等语义化前缀，并简要描述更改内容。如：`fix: correct login page typo`。
 
+#### 代码规范
+
+##### Core
+
+我们使用 Ruff 作为代码格式化和静态分析工具。在提交代码之前，请运行以下命令以确保代码符合规范：
+
+```bash
+ruff format .
+ruff check .
+```
+
+如果您使用 VSCode，可以安装 `Ruff` 插件。
+
+
 ## Contributing Guide
 
 First off, thanks for taking the time to contribute! ❤️
@@ -62,4 +76,15 @@ We use the `fix/` prefix for bug fixes and the `feat/` prefix for new features.
 
 #### PR Description
 - Please use English to describe your PR.
-- Use semantic prefixes like `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` in the title, followed by a brief description of the changes, e.g., `fix: correct login page typo`.
+- Use semantic prefixes like `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` in the title, followed by a brief description of the changes, e.g., `fix: correct login page typo`.
+
+#### Code Style
+
+##### Core
+
+We use Ruff as our code formatter and static analysis tool. Before submitting your code, please run the following commands to ensure your code adheres to the style guidelines:
+
+```bash
+ruff format .
+ruff check .
+```

README.md

@@ -243,4 +243,10 @@ pre-commit install
 
 </details>
 
+<div align="center">
+
 _私は、高性能ですから!_
+
+<img src="https://files.astrbot.app/watashiwa-koseino-desukara.gif" width="100"/>
+</div
+

astrbot/builtin_stars/astrbot/main.py

@@ -100,16 +100,8 @@ class Main(star.Star):
                 logger.error(f"ltm: {e}")
 
     @filter.on_llm_response()
-    async def inject_reasoning(self, event: AstrMessageEvent, resp: LLMResponse):
-        """在 LLM 响应后基于配置注入思考过程文本 / 在 LLM 响应后记录对话"""
-        umo = event.unified_msg_origin
-        cfg = self.context.get_config(umo).get("provider_settings", {})
-        show_reasoning = cfg.get("display_reasoning_text", False)
-        if show_reasoning and resp.reasoning_content:
-            resp.completion_text = (
-                f"🤔 思考: {resp.reasoning_content}\n\n{resp.completion_text}"
-            )
-
+    async def record_llm_resp_to_ltm(self, event: AstrMessageEvent, resp: LLMResponse):
+        """在 LLM 响应后记录对话"""
         if self.ltm and self.ltm_enabled(event):
             try:
                 await self.ltm.after_req_llm(event, resp)

astrbot/builtin_stars/astrbot/process_llm_request.py

@@ -7,6 +7,7 @@ from astrbot.api import logger, sp, star
 from astrbot.api.event import AstrMessageEvent
 from astrbot.api.message_components import Image, Reply
 from astrbot.api.provider import Provider, ProviderRequest
+from astrbot.core.agent.message import TextPart
 from astrbot.core.provider.func_tool_manager import ToolSet
 
 
@@ -85,7 +86,9 @@ class ProcessLLMRequest:
                 req.image_urls,
             )
             if caption:
-                req.prompt = f"(Image Caption: {caption})\n\n{req.prompt}"
+                req.extra_user_content_parts.append(
+                    TextPart(text=f"<image_caption>{caption}</image_caption>")
+                )
                 req.image_urls = []
         except Exception as e:
             logger.error(f"处理图片描述失败: {e}")
@@ -129,13 +132,14 @@ class ProcessLLMRequest:
             else:
                 req.prompt = prefix + req.prompt
 
+        # 收集系统提醒信息
+        system_parts = []
+
         # user identifier
         if cfg.get("identifier"):
             user_id = event.message_obj.sender.user_id
             user_nickname = event.message_obj.sender.nickname
-            req.prompt = (
-                f"\n[User ID: {user_id}, Nickname: {user_nickname}]\n{req.prompt}"
-            )
+            system_parts.append(f"User ID: {user_id}, Nickname: {user_nickname}")
 
         # group name identifier
         if cfg.get("group_name_display") and event.message_obj.group_id:
@@ -146,7 +150,7 @@ class ProcessLLMRequest:
                 return
             group_name = event.message_obj.group.group_name
             if group_name:
-                req.system_prompt += f"\nGroup name: {group_name}\n"
+                system_parts.append(f"Group name: {group_name}")
 
         # time info
         if cfg.get("datetime_system_prompt"):
@@ -162,7 +166,7 @@ class ProcessLLMRequest:
                 current_time = (
                     datetime.datetime.now().astimezone().strftime("%Y-%m-%d %H:%M (%Z)")
                 )
-            req.system_prompt += f"\nCurrent datetime: {current_time}\n"
+            system_parts.append(f"Current datetime: {current_time}")
 
         img_cap_prov_id: str = cfg.get("default_image_caption_provider_id") or ""
         if req.conversation:
@@ -181,37 +185,61 @@ class ProcessLLMRequest:
                 quote = comp
                 break
         if quote:
-            sender_info = ""
-            if quote.sender_nickname:
-                sender_info = f"(Sent by {quote.sender_nickname})"
-            message_str = quote.message_str or "[Empty Text]"
-            req.system_prompt += (
-                f"\nUser is quoting a message{sender_info}.\n"
-                f"Here are the information of the quoted message: Text Content: {message_str}.\n"
+            content_parts = []
+
+            # 1. 处理引用的文本
+            sender_info = (
+                f"({quote.sender_nickname}): " if quote.sender_nickname else ""
             )
+            message_str = quote.message_str or "[Empty Text]"
+            content_parts.append(f"{sender_info}{message_str}")
+
+            # 2. 处理引用的图片 (保留原有逻辑，但改变输出目标)
             image_seg = None
             if quote.chain:
                 for comp in quote.chain:
                     if isinstance(comp, Image):
                         image_seg = comp
                         break
+
             if image_seg:
                 try:
+                    # 找到可以生成图片描述的 provider
                     prov = None
                     if img_cap_prov_id:
                         prov = self.ctx.get_provider_by_id(img_cap_prov_id)
                     if prov is None:
                         prov = self.ctx.get_using_provider(event.unified_msg_origin)
+
+                    # 调用 provider 生成图片描述
                     if prov and isinstance(prov, Provider):
                         llm_resp = await prov.text_chat(
                             prompt="Please describe the image content.",
                             image_urls=[await image_seg.convert_to_file_path()],
                         )
                         if llm_resp.completion_text:
-                            req.system_prompt += (
-                                f"Image Caption: {llm_resp.completion_text}\n"
+                            # 将图片描述作为文本添加到 content_parts
+                            content_parts.append(
+                                f"[Image Caption in quoted message]: {llm_resp.completion_text}"
                             )
                     else:
-                        logger.warning("No provider found for image captioning.")
+                        logger.warning(
+                            "No provider found for image captioning in quote."
+                        )
                 except BaseException as e:
                     logger.error(f"处理引用图片失败: {e}")
+
+            # 3. 将所有部分组合成文本并添加到 extra_user_content_parts 中
+            # 确保引用内容被正确的标签包裹
+            quoted_content = "\n".join(content_parts)
+            # 确保所有内容都在<Quoted Message>标签内
+            quoted_text = f"<Quoted Message>\n{quoted_content}\n</Quoted Message>"
+
+            req.extra_user_content_parts.append(TextPart(text=quoted_text))
+
+        # 统一包裹所有系统提醒
+        if system_parts:
+            system_content = (
+                "<system_reminder>" + "\n".join(system_parts) + "</system_reminder>"
+            )
+            req.extra_user_content_parts.append(TextPart(text=system_content))

astrbot/builtin_stars/builtin_commands/commands/admin.py

@@ -71,6 +71,7 @@ class AdminCommands:
             event.set_result(MessageEventResult().message("此 SID 不在白名单内。"))
 
     async def update_dashboard(self, event: AstrMessageEvent):
+        """更新管理面板"""
         await event.send(MessageChain().message("正在尝试更新管理面板..."))
         await download_dashboard(version=f"v{VERSION}", latest=False)
         await event.send(MessageChain().message("管理面板更新完成。"))

astrbot/builtin_stars/builtin_commands/commands/help.py

@@ -0,0 +1,88 @@
+import aiohttp
+
+from astrbot.api import star
+from astrbot.api.event import AstrMessageEvent, MessageEventResult
+from astrbot.core.config.default import VERSION
+from astrbot.core.star import command_management
+from astrbot.core.utils.io import get_dashboard_version
+
+
+class HelpCommand:
+    def __init__(self, context: star.Context):
+        self.context = context
+
+    async def _query_astrbot_notice(self):
+        try:
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.get(
+                    "https://astrbot.app/notice.json",
+                    timeout=2,
+                ) as resp:
+                    return (await resp.json())["notice"]
+        except BaseException:
+            return ""
+
+    async def _build_reserved_command_lines(self) -> list[str]:
+        """
+        使用实时指令配置生成内置指令清单，确保重命名/禁用后与实际生效状态保持一致。
+        """
+        try:
+            commands = await command_management.list_commands()
+        except BaseException:
+            return []
+
+        lines: list[str] = []
+        hidden_commands = {"set", "unset", "websearch"}
+
+        def walk(items: list[dict], indent: int = 0):
+            for item in items:
+                if not item.get("reserved") or not item.get("enabled"):
+                    continue
+                # 仅展示顶级指令或指令组
+                if item.get("type") == "sub_command":
+                    continue
+                if item.get("parent_signature"):
+                    continue
+
+                effective = (
+                    item.get("effective_command")
+                    or item.get("original_command")
+                    or item.get("handler_name")
+                )
+                if not effective:
+                    continue
+                if effective in hidden_commands:
+                    continue
+
+                description = item.get("description") or ""
+                desc_text = f" - {description}" if description else ""
+                indent_prefix = "  " * indent
+                lines.append(f"{indent_prefix}/{effective}{desc_text}")
+
+        walk(commands)
+        return lines
+
+    async def help(self, event: AstrMessageEvent):
+        """查看帮助"""
+        notice = ""
+        try:
+            notice = await self._query_astrbot_notice()
+        except BaseException:
+            pass
+
+        dashboard_version = await get_dashboard_version()
+        command_lines = await self._build_reserved_command_lines()
+        commands_section = (
+            "\n".join(command_lines) if command_lines else "暂无启用的内置指令"
+        )
+
+        msg_parts = [
+            f"AstrBot v{VERSION}(WebUI: {dashboard_version})",
+            "内置指令:",
+            commands_section,
+        ]
+        if notice:
+            msg_parts.append(notice)
+        msg = "\n".join(msg_parts)
+
+        event.set_result(MessageEventResult().message(msg).use_t2i(False))

astrbot/builtin_stars/builtin_commands/commands/provider.py

@@ -184,7 +184,8 @@ class ProviderCommands:
                 event.set_result(MessageEventResult().message("请输入序号。"))
                 return
             if idx2 > len(self.context.get_all_tts_providers()) or idx2 < 1:
-                event.set_result(MessageEventResult().message("无效的序号。"))
+                event.set_result(MessageEventResult().message("无效的提供商序号。"))
+                return
             provider = self.context.get_all_tts_providers()[idx2 - 1]
             id_ = provider.meta().id
             await self.context.provider_manager.set_provider(
@@ -198,7 +199,8 @@ class ProviderCommands:
                 event.set_result(MessageEventResult().message("请输入序号。"))
                 return
             if idx2 > len(self.context.get_all_stt_providers()) or idx2 < 1:
-                event.set_result(MessageEventResult().message("无效的序号。"))
+                event.set_result(MessageEventResult().message("无效的提供商序号。"))
+                return
             provider = self.context.get_all_stt_providers()[idx2 - 1]
             id_ = provider.meta().id
             await self.context.provider_manager.set_provider(
@@ -209,8 +211,8 @@ class ProviderCommands:
             event.set_result(MessageEventResult().message(f"成功切换到 {id_}。"))
         elif isinstance(idx, int):
             if idx > len(self.context.get_all_providers()) or idx < 1:
-                event.set_result(MessageEventResult().message("无效的序号。"))
-
+                event.set_result(MessageEventResult().message("无效的提供商序号。"))
+                return
             provider = self.context.get_all_providers()[idx - 1]
             id_ = provider.meta().id
             await self.context.provider_manager.set_provider(

astrbot/builtin_stars/builtin_commands/main.py

@@ -49,7 +49,7 @@ class Main(star.Star):
 
     @filter.command_group("tool")
     def tool(self):
-        pass
+        """函数工具管理"""
 
     @tool.command("ls")
     async def tool_ls(self, event: AstrMessageEvent):
@@ -73,7 +73,7 @@ class Main(star.Star):
 
     @filter.command_group("plugin")
     def plugin(self):
-        pass
+        """插件管理"""
 
     @plugin.command("ls")
     async def plugin_ls(self, event: AstrMessageEvent):
@@ -219,6 +219,7 @@ class Main(star.Star):
     @filter.permission_type(filter.PermissionType.ADMIN)
     @filter.command("dashboard_update")
     async def update_dashboard(self, event: AstrMessageEvent):
+        """更新管理面板"""
         await self.admin_c.update_dashboard(event)
 
     @filter.command("set")

astrbot/builtin_stars/python_interpreter/main.py

@@ -157,9 +157,8 @@ class Main(star.Star):
     async def is_docker_available(self) -> bool:
         """Check if docker is available"""
         try:
-            docker = aiodocker.Docker()
-            await docker.version()
-            await docker.close()
+            async with aiodocker.Docker() as docker:
+                await docker.version()
             return True
         except BaseException as e:
             logger.info(f"检查 Docker 可用性: {e}")
@@ -249,7 +248,7 @@ class Main(star.Star):
 
     @filter.command_group("pi")
     def pi(self):
-        pass
+        """代码执行器配置"""
 
     @pi.command("absdir")
     async def pi_absdir(self, event: AstrMessageEvent, path: str = ""):
@@ -279,14 +278,14 @@ class Main(star.Star):
     @pi.command("repull")
     async def pi_repull(self, event: AstrMessageEvent):
         """重新拉取沙箱镜像"""
-        docker = aiodocker.Docker()
-        image_name = await self.get_image_name()
-        try:
-            await docker.images.get(image_name)
-            await docker.images.delete(image_name, force=True)
-        except aiodocker.exceptions.DockerError:
-            pass
-        await docker.images.pull(image_name)
+        async with aiodocker.Docker() as docker:
+            image_name = await self.get_image_name()
+            try:
+                await docker.images.get(image_name)
+                await docker.images.delete(image_name, force=True)
+            except aiodocker.exceptions.DockerError:
+                pass
+            await docker.images.pull(image_name)
         yield event.plain_result("重新拉取沙箱镜像成功。")
 
     @pi.command("file")
@@ -371,137 +370,137 @@ class Main(star.Star):
         obs = ""
         n = 5
 
-        for i in range(n):
-            if i > 0:
-                logger.info(f"Try {i + 1}/{n}")
+        async with aiodocker.Docker() as docker:
+            for i in range(n):
+                if i > 0:
+                    logger.info(f"Try {i + 1}/{n}")
 
-            PROMPT_ = PROMPT.format(
-                prompt=plain_text,
-                extra_input=extra_inputs,
-                extra_prompt=obs,
-            )
-            provider = self.context.get_using_provider()
-            llm_response = await provider.text_chat(
-                prompt=PROMPT_,
-                session_id=f"{event.session_id}_{magic_code}_{i!s}",
-            )
-
-            logger.debug(
-                "code interpreter llm gened code:" + llm_response.completion_text,
-            )
-
-            # 整理代码并保存
-            code_clean = await self.tidy_code(llm_response.completion_text)
-            with open(os.path.join(workplace_path, "exec.py"), "w") as f:
-                f.write(code_clean)
-
-            # 启动容器
-            docker = aiodocker.Docker()
-
-            # 检查有没有image
-            image_name = await self.get_image_name()
-            try:
-                await docker.images.get(image_name)
-            except aiodocker.exceptions.DockerError:
-                # 拉取镜像
-                logger.info(f"未找到沙箱镜像，正在尝试拉取 {image_name}...")
-                await docker.images.pull(image_name)
-
-            yield event.plain_result(
-                f"使用沙箱执行代码中，请稍等...(尝试次数: {i + 1}/{n})",
-            )
-
-            self.docker_host_astrbot_abs_path = self.config.get(
-                "docker_host_astrbot_abs_path",
-                "",
-            )
-            if self.docker_host_astrbot_abs_path:
-                host_shared = os.path.join(
-                    self.docker_host_astrbot_abs_path,
-                    self.shared_path,
+                PROMPT_ = PROMPT.format(
+                    prompt=plain_text,
+                    extra_input=extra_inputs,
+                    extra_prompt=obs,
                 )
-                host_output = os.path.join(
-                    self.docker_host_astrbot_abs_path,
-                    output_path,
-                )
-                host_workplace = os.path.join(
-                    self.docker_host_astrbot_abs_path,
-                    workplace_path,
+                provider = self.context.get_using_provider()
+                llm_response = await provider.text_chat(
+                    prompt=PROMPT_,
+                    session_id=f"{event.session_id}_{magic_code}_{i!s}",
                 )
 
-            else:
-                host_shared = os.path.abspath(self.shared_path)
-                host_output = os.path.abspath(output_path)
-                host_workplace = os.path.abspath(workplace_path)
+                logger.debug(
+                    "code interpreter llm gened code:" + llm_response.completion_text,
+                )
 
-            logger.debug(
-                f"host_shared: {host_shared}, host_output: {host_output}, host_workplace: {host_workplace}",
-            )
+                # 整理代码并保存
+                code_clean = await self.tidy_code(llm_response.completion_text)
+                with open(os.path.join(workplace_path, "exec.py"), "w") as f:
+                    f.write(code_clean)
 
-            container = await docker.containers.run(
-                {
-                    "Image": image_name,
-                    "Cmd": ["python", "exec.py"],
-                    "Memory": 512 * 1024 * 1024,
-                    "NanoCPUs": 1000000000,
-                    "HostConfig": {
-                        "Binds": [
-                            f"{host_shared}:/astrbot_sandbox/shared:ro",
-                            f"{host_output}:/astrbot_sandbox/output:rw",
-                            f"{host_workplace}:/astrbot_sandbox:rw",
-                        ],
-                    },
-                    "Env": [f"MAGIC_CODE={magic_code}"],
-                    "AutoRemove": True,
-                },
-            )
+                # 检查有没有image
+                image_name = await self.get_image_name()
+                try:
+                    await docker.images.get(image_name)
+                except aiodocker.exceptions.DockerError:
+                    # 拉取镜像
+                    logger.info(f"未找到沙箱镜像，正在尝试拉取 {image_name}...")
+                    await docker.images.pull(image_name)
 
-            logger.debug(f"Container {container.id} created.")
-            logs = await self.run_container(container)
+                yield event.plain_result(
+                    f"使用沙箱执行代码中，请稍等...(尝试次数: {i + 1}/{n})",
+                )
 
-            logger.debug(f"Container {container.id} finished.")
-            logger.debug(f"Container {container.id} logs: {logs}")
-
-            # 发送结果
-            pattern = r"\[ASTRBOT_(TEXT|IMAGE|FILE)_OUTPUT#\w+\]: (.*)"
-            ok = False
-            traceback = ""
-            for idx, log in enumerate(logs):
-                match = re.match(pattern, log)
-                if match:
-                    ok = True
-                    if match.group(1) == "TEXT":
-                        yield event.plain_result(match.group(2))
-                    elif match.group(1) == "IMAGE":
-                        image_path = os.path.join(workplace_path, match.group(2))
-                        logger.debug(f"Sending image: {image_path}")
-                        yield event.image_result(image_path)
-                    elif match.group(1) == "FILE":
-                        file_path = os.path.join(workplace_path, match.group(2))
-                        # logger.debug(f"Sending file: {file_path}")
-                        # file_s3_url = await self.file_upload(file_path)
-                        # logger.info(f"文件上传到 AstrBot 云节点: {file_s3_url}")
-                        file_name = os.path.basename(file_path)
-                        chain: list[BaseMessageComponent] = [
-                            File(name=file_name, file=file_path)
-                        ]
-                        yield event.set_result(MessageEventResult(chain=chain))
-
-                elif "Traceback (most recent call last)" in log or "[Error]: " in log:
-                    traceback = "\n".join(logs[idx:])
-
-            if not ok:
-                if traceback:
-                    obs = f"## Observation \n When execute the code: ```python\n{code_clean}\n```\n\n Error occurred:\n\n{traceback}\n Need to improve/fix the code."
-                else:
-                    logger.warning(
-                        f"未从沙箱输出中捕获到合法的输出。沙箱输出日志: {logs}",
+                self.docker_host_astrbot_abs_path = self.config.get(
+                    "docker_host_astrbot_abs_path",
+                    "",
+                )
+                if self.docker_host_astrbot_abs_path:
+                    host_shared = os.path.join(
+                        self.docker_host_astrbot_abs_path,
+                        self.shared_path,
                     )
-                    break
-            else:
-                # 成功了
-                self.user_file_msg_buffer.pop(event.get_session_id())
-                return
+                    host_output = os.path.join(
+                        self.docker_host_astrbot_abs_path,
+                        output_path,
+                    )
+                    host_workplace = os.path.join(
+                        self.docker_host_astrbot_abs_path,
+                        workplace_path,
+                    )
+
+                else:
+                    host_shared = os.path.abspath(self.shared_path)
+                    host_output = os.path.abspath(output_path)
+                    host_workplace = os.path.abspath(workplace_path)
+
+                logger.debug(
+                    f"host_shared: {host_shared}, host_output: {host_output}, host_workplace: {host_workplace}",
+                )
+
+                container = await docker.containers.run(
+                    {
+                        "Image": image_name,
+                        "Cmd": ["python", "exec.py"],
+                        "Memory": 512 * 1024 * 1024,
+                        "NanoCPUs": 1000000000,
+                        "HostConfig": {
+                            "Binds": [
+                                f"{host_shared}:/astrbot_sandbox/shared:ro",
+                                f"{host_output}:/astrbot_sandbox/output:rw",
+                                f"{host_workplace}:/astrbot_sandbox:rw",
+                            ],
+                        },
+                        "Env": [f"MAGIC_CODE={magic_code}"],
+                        "AutoRemove": True,
+                    },
+                )
+
+                logger.debug(f"Container {container.id} created.")
+                logs = await self.run_container(container)
+
+                logger.debug(f"Container {container.id} finished.")
+                logger.debug(f"Container {container.id} logs: {logs}")
+
+                # 发送结果
+                pattern = r"\[ASTRBOT_(TEXT|IMAGE|FILE)_OUTPUT#\w+\]: (.*)"
+                ok = False
+                traceback = ""
+                for idx, log in enumerate(logs):
+                    match = re.match(pattern, log)
+                    if match:
+                        ok = True
+                        if match.group(1) == "TEXT":
+                            yield event.plain_result(match.group(2))
+                        elif match.group(1) == "IMAGE":
+                            image_path = os.path.join(workplace_path, match.group(2))
+                            logger.debug(f"Sending image: {image_path}")
+                            yield event.image_result(image_path)
+                        elif match.group(1) == "FILE":
+                            file_path = os.path.join(workplace_path, match.group(2))
+                            # logger.debug(f"Sending file: {file_path}")
+                            # file_s3_url = await self.file_upload(file_path)
+                            # logger.info(f"文件上传到 AstrBot 云节点: {file_s3_url}")
+                            file_name = os.path.basename(file_path)
+                            chain: list[BaseMessageComponent] = [
+                                File(name=file_name, file=file_path)
+                            ]
+                            yield event.set_result(MessageEventResult(chain=chain))
+
+                    elif (
+                        "Traceback (most recent call last)" in log or "[Error]: " in log
+                    ):
+                        traceback = "\n".join(logs[idx:])
+
+                if not ok:
+                    if traceback:
+                        obs = f"## Observation \n When execute the code: ```python\n{code_clean}\n```\n\n Error occurred:\n\n{traceback}\n Need to improve/fix the code."
+                    else:
+                        logger.warning(
+                            f"未从沙箱输出中捕获到合法的输出。沙箱输出日志: {logs}",
+                        )
+                        break
+                else:
+                    # 成功了
+                    self.user_file_msg_buffer.pop(event.get_session_id())
+                    return
 
         yield event.plain_result(
             "经过多次尝试后，未从沙箱输出中捕获到合法的输出，请更换问法或者查看日志。",

astrbot/builtin_stars/reminder/main.py

@@ -179,7 +179,7 @@ class Main(star.Star):
 
     @filter.command_group("reminder")
     def reminder(self):
-        """The command group of the reminder."""
+        """待办提醒"""
 
     async def get_upcoming_reminders(self, unified_msg_origin: str):
         """Get upcoming reminders."""

astrbot/builtin_stars/web_searcher/main.py

@@ -185,6 +185,7 @@ class Main(star.Star):
 
     @filter.command("websearch")
     async def websearch(self, event: AstrMessageEvent, oper: str | None = None):
+        """网页搜索指令（已废弃）"""
         event.set_result(
             MessageEventResult().message(
                 "此指令已经被废弃，请在 WebUI 中开启或关闭网页搜索功能。",

astrbot/cli/__init__.py

@@ -1 +1 @@
-__version__ = "4.9.0"
+__version__ = "4.10.3"

astrbot/core/agent/message.py

@@ -3,7 +3,7 @@
 
 from typing import Any, ClassVar, Literal, cast
 
-from pydantic import BaseModel, GetCoreSchemaHandler, model_validator
+from pydantic import BaseModel, GetCoreSchemaHandler, model_serializer, model_validator
 from pydantic_core import core_schema
 
 
@@ -12,7 +12,7 @@ class ContentPart(BaseModel):
 
     __content_part_registry: ClassVar[dict[str, type["ContentPart"]]] = {}
 
-    type: str
+    type: Literal["text", "think", "image_url", "audio_url"]
 
     def __init_subclass__(cls, **kwargs: Any) -> None:
         super().__init_subclass__(**kwargs)
@@ -63,6 +63,28 @@ class TextPart(ContentPart):
     text: str
 
 
+class ThinkPart(ContentPart):
+    """
+    >>> ThinkPart(think="I think I need to think about this.").model_dump()
+    {'type': 'think', 'think': 'I think I need to think about this.', 'encrypted': None}
+    """
+
+    type: str = "think"
+    think: str
+    encrypted: str | None = None
+    """Encrypted thinking content, or signature."""
+
+    def merge_in_place(self, other: Any) -> bool:
+        if not isinstance(other, ThinkPart):
+            return False
+        if self.encrypted:
+            return False
+        self.think += other.think
+        if other.encrypted:
+            self.encrypted = other.encrypted
+        return True
+
+
 class ImageURLPart(ContentPart):
     """
     >>> ImageURLPart(image_url="http://example.com/image.jpg").model_dump()
@@ -122,10 +144,12 @@ class ToolCall(BaseModel):
     extra_content: dict[str, Any] | None = None
     """Extra metadata for the tool call."""
 
-    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
+    @model_serializer(mode="wrap")
+    def serialize(self, handler):
+        data = handler(self)
         if self.extra_content is None:
-            kwargs.setdefault("exclude", set()).add("extra_content")
-        return super().model_dump(**kwargs)
+            data.pop("extra_content", None)
+        return data
 
 
 class ToolCallPart(BaseModel):
@@ -167,6 +191,15 @@ class Message(BaseModel):
             )
         return self
 
+    @model_serializer(mode="wrap")
+    def serialize(self, handler):
+        data = handler(self)
+        if self.tool_calls is None:
+            data.pop("tool_calls", None)
+        if self.tool_call_id is None:
+            data.pop("tool_call_id", None)
+        return data
+
 
 class AssistantMessageSegment(Message):
     """A message segment from the assistant."""

astrbot/core/agent/response.py

@@ -1,7 +1,8 @@
 import typing as T
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import TokenUsage
 
 
 class AgentResponseData(T.TypedDict):
@@ -12,3 +13,23 @@ class AgentResponseData(T.TypedDict):
 class AgentResponse:
     type: str
     data: AgentResponseData
+
+
+@dataclass
+class AgentStats:
+    token_usage: TokenUsage = field(default_factory=TokenUsage)
+    start_time: float = 0.0
+    end_time: float = 0.0
+    time_to_first_token: float = 0.0
+
+    @property
+    def duration(self) -> float:
+        return self.end_time - self.start_time
+
+    def to_dict(self) -> dict:
+        return {
+            "token_usage": self.token_usage.__dict__,
+            "start_time": self.start_time,
+            "end_time": self.end_time,
+            "time_to_first_token": self.time_to_first_token,
+        }

astrbot/core/agent/run_context.py

@@ -9,7 +9,7 @@ from .message import Message
 TContext = TypeVar("TContext", default=Any)
 
 
-@dataclass(config={"arbitrary_types_allowed": True})
+@dataclass
 class ContextWrapper(Generic[TContext]):
     """A context for running an agent, which can be used to pass additional data or state."""
 

astrbot/core/agent/runners/tool_loop_agent_runner.py

@@ -1,4 +1,5 @@
 import sys
+import time
 import traceback
 import typing as T
 
@@ -12,6 +13,8 @@ from mcp.types import (
 )
 
 from astrbot import logger
+from astrbot.core.agent.message import TextPart, ThinkPart
+from astrbot.core.message.components import Json
 from astrbot.core.message.message_event_result import (
     MessageChain,
 )
@@ -24,7 +27,7 @@ from astrbot.core.provider.provider import Provider
 
 from ..hooks import BaseAgentRunHooks
 from ..message import AssistantMessageSegment, Message, ToolCallMessageSegment
-from ..response import AgentResponseData
+from ..response import AgentResponseData, AgentStats
 from ..run_context import ContextWrapper, TContext
 from ..tool_executor import BaseFunctionToolExecutor
 from .base import AgentResponse, AgentState, BaseAgentRunner
@@ -69,14 +72,25 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             )
         self.run_context.messages = messages
 
+        self.stats = AgentStats()
+        self.stats.start_time = time.time()
+
     async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
         """Yields chunks *and* a final LLMResponse."""
+        payload = {
+            "contexts": self.run_context.messages,  # list[Message]
+            "func_tool": self.req.func_tool,
+            "model": self.req.model,  # NOTE: in fact, this arg is None in most cases
+            "session_id": self.req.session_id,
+            "extra_user_content_parts": self.req.extra_user_content_parts,  # list[ContentPart]
+        }
+
         if self.streaming:
-            stream = self.provider.text_chat_stream(**self.req.__dict__)
+            stream = self.provider.text_chat_stream(**payload)
             async for resp in stream:  # type: ignore
                 yield resp
         else:
-            yield await self.provider.text_chat(**self.req.__dict__)
+            yield await self.provider.text_chat(**payload)
 
     @override
     async def step(self):
@@ -98,6 +112,10 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
 
         async for llm_response in self._iter_llm_responses():
             if llm_response.is_chunk:
+                # update ttft
+                if self.stats.time_to_first_token == 0:
+                    self.stats.time_to_first_token = time.time() - self.stats.start_time
+
                 if llm_response.result_chain:
                     yield AgentResponse(
                         type="streaming_delta",
@@ -121,6 +139,10 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                     )
                 continue
             llm_resp_result = llm_response
+
+            if not llm_response.is_chunk and llm_response.usage:
+                # only count the token usage of the final response for computation purpose
+                self.stats.token_usage += llm_response.usage
             break  # got final response
 
         if not llm_resp_result:
@@ -132,6 +154,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
         if llm_resp.role == "err":
             # 如果 LLM 响应错误，转换到错误状态
             self.final_llm_resp = llm_resp
+            self.stats.end_time = time.time()
             self._transition_state(AgentState.ERROR)
             yield AgentResponse(
                 type="err",
@@ -146,13 +169,21 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             # 如果没有工具调用，转换到完成状态
             self.final_llm_resp = llm_resp
             self._transition_state(AgentState.DONE)
+            self.stats.end_time = time.time()
+
             # record the final assistant message
-            self.run_context.messages.append(
-                Message(
-                    role="assistant",
-                    content=llm_resp.completion_text or "",
-                ),
-            )
+            parts = []
+            if llm_resp.reasoning_content or llm_resp.reasoning_signature:
+                parts.append(
+                    ThinkPart(
+                        think=llm_resp.reasoning_content,
+                        encrypted=llm_resp.reasoning_signature,
+                    )
+                )
+            parts.append(TextPart(text=llm_resp.completion_text or "*No response*"))
+            self.run_context.messages.append(Message(role="assistant", content=parts))
+
+            # call the on_agent_done hook
             try:
                 await self.agent_hooks.on_agent_done(self.run_context, llm_resp)
             except Exception as e:
@@ -175,29 +206,35 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
         # 如果有工具调用，还需处理工具调用
         if llm_resp.tools_call_name:
             tool_call_result_blocks = []
-            for tool_call_name in llm_resp.tools_call_name:
-                yield AgentResponse(
-                    type="tool_call",
-                    data=AgentResponseData(
-                        chain=MessageChain(type="tool_call").message(
-                            f"🔨 调用工具: {tool_call_name}"
-                        ),
-                    ),
-                )
             async for result in self._handle_function_tools(self.req, llm_resp):
                 if isinstance(result, list):
                     tool_call_result_blocks = result
                 elif isinstance(result, MessageChain):
-                    result.type = "tool_call_result"
+                    if result.type is None:
+                        # should not happen
+                        continue
+                    if result.type == "tool_direct_result":
+                        ar_type = "tool_call_result"
+                    else:
+                        ar_type = result.type
                     yield AgentResponse(
-                        type="tool_call_result",
+                        type=ar_type,
                         data=AgentResponseData(chain=result),
                     )
             # 将结果添加到上下文中
+            parts = []
+            if llm_resp.reasoning_content or llm_resp.reasoning_signature:
+                parts.append(
+                    ThinkPart(
+                        think=llm_resp.reasoning_content,
+                        encrypted=llm_resp.reasoning_signature,
+                    )
+                )
+            parts.append(TextPart(text=llm_resp.completion_text or "*No response*"))
             tool_calls_result = ToolCallsResult(
                 tool_calls_info=AssistantMessageSegment(
                     tool_calls=llm_resp.to_openai_to_calls_model(),
-                    content=llm_resp.completion_text,
+                    content=parts,
                 ),
                 tool_calls_result=tool_call_result_blocks,
             )
@@ -218,6 +255,25 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             async for resp in self.step():
                 yield resp
 
+        #  如果循环结束了但是 agent 还没有完成，说明是达到了 max_step
+        if not self.done():
+            logger.warning(
+                f"Agent reached max steps ({max_step}), forcing a final response."
+            )
+            # 拔掉所有工具
+            if self.req:
+                self.req.func_tool = None
+            # 注入提示词
+            self.run_context.messages.append(
+                Message(
+                    role="user",
+                    content="工具调用次数已达到上限，请停止使用工具，并根据已经收集到的信息，对你的任务和发现进行总结，然后直接回复用户。",
+                )
+            )
+            # 再执行最后一步
+            async for resp in self.step():
+                yield resp
+
     async def _handle_function_tools(
         self,
         req: ProviderRequest,
@@ -233,6 +289,19 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             llm_response.tools_call_args,
             llm_response.tools_call_ids,
         ):
+            yield MessageChain(
+                type="tool_call",
+                chain=[
+                    Json(
+                        data={
+                            "id": func_tool_id,
+                            "name": func_tool_name,
+                            "args": func_tool_args,
+                            "ts": time.time(),
+                        }
+                    )
+                ],
+            )
             try:
                 if not req.func_tool:
                     return
@@ -306,7 +375,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                     content=res.content[0].text,
                                 ),
                             )
-                            yield MessageChain().message(res.content[0].text)
                         elif isinstance(res.content[0], ImageContent):
                             tool_call_result_blocks.append(
                                 ToolCallMessageSegment(
@@ -328,7 +396,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                         content=resource.text,
                                     ),
                                 )
-                                yield MessageChain().message(resource.text)
                             elif (
                                 isinstance(resource, BlobResourceContents)
                                 and resource.mimeType
@@ -352,20 +419,34 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                         content="返回的数据类型不受支持",
                                     ),
                                 )
-                                yield MessageChain().message("返回的数据类型不受支持。")
 
                     elif resp is None:
                         # Tool 直接请求发送消息给用户
                         # 这里我们将直接结束 Agent Loop。
                         # 发送消息逻辑在 ToolExecutor 中处理了。
                         logger.warning(
-                            f"{func_tool_name} 没有没有返回值或者将结果直接发送给用户，此工具调用不会被记录到历史中。"
+                            f"{func_tool_name} 没有没有返回值或者将结果直接发送给用户。"
                         )
                         self._transition_state(AgentState.DONE)
+                        self.stats.end_time = time.time()
+                        tool_call_result_blocks.append(
+                            ToolCallMessageSegment(
+                                role="tool",
+                                tool_call_id=func_tool_id,
+                                content="*工具没有返回值或者将结果直接发送给了用户*",
+                            ),
+                        )
                     else:
                         # 不应该出现其他类型
                         logger.warning(
-                            f"Tool 返回了不支持的类型: {type(resp)}，将忽略。",
+                            f"Tool 返回了不支持的类型: {type(resp)}。",
+                        )
+                        tool_call_result_blocks.append(
+                            ToolCallMessageSegment(
+                                role="tool",
+                                tool_call_id=func_tool_id,
+                                content="*工具返回了不支持的类型，请告诉用户检查这个工具的定义和实现。*",
+                            ),
                         )
 
                 try:
@@ -387,6 +468,22 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                     ),
                 )
 
+        # yield the last tool call result
+        if tool_call_result_blocks:
+            last_tcr_content = str(tool_call_result_blocks[-1].content)
+            yield MessageChain(
+                type="tool_call_result",
+                chain=[
+                    Json(
+                        data={
+                            "id": func_tool_id,
+                            "ts": time.time(),
+                            "result": last_tcr_content,
+                        }
+                    )
+                ],
+            )
+
         # 处理函数调用响应
         if tool_call_result_blocks:
             yield tool_call_result_blocks

astrbot/core/astr_agent_context.py

@@ -6,8 +6,10 @@ from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.context import Context
 
 
-@dataclass(config={"arbitrary_types_allowed": True})
+@dataclass
 class AstrAgentContext:
+    __pydantic_config__ = {"arbitrary_types_allowed": True}
+
     context: Context
     """The star context instance"""
     event: AstrMessageEvent

astrbot/core/astr_agent_hooks.py

@@ -13,6 +13,12 @@ from astrbot.core.star.star_handler import EventType
 class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
     async def on_agent_done(self, run_context, llm_response):
         # 执行事件钩子
+        if llm_response and llm_response.reasoning_content:
+            # we will use this in result_decorate stage to inject reasoning content to chain
+            run_context.context.event.set_extra(
+                "_llm_reasoning_content", llm_response.reasoning_content
+            )
+
         await call_event_hook(
             run_context.context.event,
             EventType.OnLLMResponseEvent,

astrbot/core/astr_agent_run_util.py

@@ -2,8 +2,10 @@ import traceback
 from collections.abc import AsyncGenerator
 
 from astrbot.core import logger
+from astrbot.core.agent.message import Message
 from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
 from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.message.components import Json
 from astrbot.core.message.message_event_result import (
     MessageChain,
     MessageEventResult,
@@ -23,8 +25,25 @@ async def run_agent(
 ) -> AsyncGenerator[MessageChain | None, None]:
     step_idx = 0
     astr_event = agent_runner.run_context.context.event
-    while step_idx < max_step:
+    while step_idx < max_step + 1:
         step_idx += 1
+
+        if step_idx == max_step + 1:
+            logger.warning(
+                f"Agent reached max steps ({max_step}), forcing a final response."
+            )
+            if not agent_runner.done():
+                # 拔掉所有工具
+                if agent_runner.req:
+                    agent_runner.req.func_tool = None
+                # 注入提示词
+                agent_runner.run_context.messages.append(
+                    Message(
+                        role="user",
+                        content="工具调用次数已达到上限，请停止使用工具，并根据已经收集到的信息，对你的任务和发现进行总结，然后直接回复用户。",
+                    )
+                )
+
         try:
             async for resp in agent_runner.step():
                 if astr_event.is_stopped():
@@ -33,16 +52,27 @@ async def run_agent(
                     msg_chain = resp.data["chain"]
                     if msg_chain.type == "tool_direct_result":
                         # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
-                        await astr_event.send(resp.data["chain"])
+                        await astr_event.send(msg_chain)
                         continue
+                    if astr_event.get_platform_id() == "webchat":
+                        await astr_event.send(msg_chain)
                     # 对于其他情况，暂时先不处理
                     continue
                 elif resp.type == "tool_call":
                     if agent_runner.streaming:
                         # 用来标记流式响应需要分节
                         yield MessageChain(chain=[], type="break")
-                    if show_tool_use:
+
+                    if astr_event.get_platform_name() == "webchat":
                         await astr_event.send(resp.data["chain"])
+                    elif show_tool_use:
+                        json_comp = resp.data["chain"].chain[0]
+                        if isinstance(json_comp, Json):
+                            m = f"🔨 调用工具: {json_comp.data.get('name')}"
+                        else:
+                            m = "🔨 调用工具..."
+                        chain = MessageChain(type="tool_call").message(m)
+                        await astr_event.send(chain)
                     continue
 
                 if stream_to_general and resp.type == "streaming_delta":
@@ -69,6 +99,15 @@ async def run_agent(
                         continue
                     yield resp.data["chain"]  # MessageChain
             if agent_runner.done():
+                # send agent stats to webchat
+                if astr_event.get_platform_name() == "webchat":
+                    await astr_event.send(
+                        MessageChain(
+                            type="agent_stats",
+                            chain=[Json(data=agent_runner.stats.to_dict())],
+                        )
+                    )
+
                 break
 
         except Exception as e:

astrbot/core/astr_agent_tool_exec.py

@@ -209,12 +209,42 @@ async def call_local_llm_tool(
         else:
             raise ValueError(f"未知的方法名: {method_name}")
     except ValueError as e:
-        logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
-    except TypeError:
-        logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
+        raise Exception(f"Tool execution ValueError: {e}") from e
+    except TypeError as e:
+        # 获取函数的签名（包括类型），除了第一个 event/context 参数。
+        try:
+            sig = inspect.signature(handler)
+            params = list(sig.parameters.values())
+            # 跳过第一个参数（event 或 context）
+            if params:
+                params = params[1:]
+
+            param_strs = []
+            for param in params:
+                param_str = param.name
+                if param.annotation != inspect.Parameter.empty:
+                    # 获取类型注解的字符串表示
+                    if isinstance(param.annotation, type):
+                        type_str = param.annotation.__name__
+                    else:
+                        type_str = str(param.annotation)
+                    param_str += f": {type_str}"
+                if param.default != inspect.Parameter.empty:
+                    param_str += f" = {param.default!r}"
+                param_strs.append(param_str)
+
+            handler_param_str = (
+                ", ".join(param_strs) if param_strs else "(no additional parameters)"
+            )
+        except Exception:
+            handler_param_str = "(unable to inspect signature)"
+
+        raise Exception(
+            f"Tool handler parameter mismatch, please check the handler definition. Handler parameters: {handler_param_str}"
+        ) from e
     except Exception as e:
         trace_ = traceback.format_exc()
-        logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
+        raise Exception(f"Tool execution error: {e}. Traceback: {trace_}") from e
 
     if not ready_to_call:
         return

astrbot/core/backup/__init__.py

@@ -0,0 +1,26 @@
+"""AstrBot 备份与恢复模块
+
+提供数据导出和导入功能，支持用户在服务器迁移时一键备份和恢复所有数据。
+"""
+
+# 从 constants 模块导入共享常量
+from .constants import (
+    BACKUP_MANIFEST_VERSION,
+    KB_METADATA_MODELS,
+    MAIN_DB_MODELS,
+    get_backup_directories,
+)
+
+# 导入导出器和导入器
+from .exporter import AstrBotExporter
+from .importer import AstrBotImporter, ImportPreCheckResult
+
+__all__ = [
+    "AstrBotExporter",
+    "AstrBotImporter",
+    "ImportPreCheckResult",
+    "MAIN_DB_MODELS",
+    "KB_METADATA_MODELS",
+    "get_backup_directories",
+    "BACKUP_MANIFEST_VERSION",
+]

astrbot/core/backup/constants.py

@@ -0,0 +1,77 @@
+"""AstrBot 备份模块共享常量
+
+此文件定义了导出器和导入器共享的常量，确保两端配置一致。
+"""
+
+from sqlmodel import SQLModel
+
+from astrbot.core.db.po import (
+    Attachment,
+    CommandConfig,
+    CommandConflict,
+    ConversationV2,
+    Persona,
+    PlatformMessageHistory,
+    PlatformSession,
+    PlatformStat,
+    Preference,
+)
+from astrbot.core.knowledge_base.models import (
+    KBDocument,
+    KBMedia,
+    KnowledgeBase,
+)
+from astrbot.core.utils.astrbot_path import (
+    get_astrbot_config_path,
+    get_astrbot_plugin_data_path,
+    get_astrbot_plugin_path,
+    get_astrbot_t2i_templates_path,
+    get_astrbot_temp_path,
+    get_astrbot_webchat_path,
+)
+
+# ============================================================
+# 共享常量 - 确保导出和导入端配置一致
+# ============================================================
+
+# 主数据库模型类映射
+MAIN_DB_MODELS: dict[str, type[SQLModel]] = {
+    "platform_stats": PlatformStat,
+    "conversations": ConversationV2,
+    "personas": Persona,
+    "preferences": Preference,
+    "platform_message_history": PlatformMessageHistory,
+    "platform_sessions": PlatformSession,
+    "attachments": Attachment,
+    "command_configs": CommandConfig,
+    "command_conflicts": CommandConflict,
+}
+
+# 知识库元数据模型类映射
+KB_METADATA_MODELS: dict[str, type[SQLModel]] = {
+    "knowledge_bases": KnowledgeBase,
+    "kb_documents": KBDocument,
+    "kb_media": KBMedia,
+}
+
+
+def get_backup_directories() -> dict[str, str]:
+    """获取需要备份的目录列表
+
+    使用 astrbot_path 模块动态获取路径，支持通过环境变量 ASTRBOT_ROOT 自定义根目录。
+
+    Returns:
+        dict: 键为备份文件中的目录名称，值为目录的绝对路径
+    """
+    return {
+        "plugins": get_astrbot_plugin_path(),  # 插件本体
+        "plugin_data": get_astrbot_plugin_data_path(),  # 插件数据
+        "config": get_astrbot_config_path(),  # 配置目录
+        "t2i_templates": get_astrbot_t2i_templates_path(),  # T2I 模板
+        "webchat": get_astrbot_webchat_path(),  # WebChat 数据
+        "temp": get_astrbot_temp_path(),  # 临时文件
+    }
+
+
+# 备份清单版本号
+BACKUP_MANIFEST_VERSION = "1.1"

astrbot/core/backup/exporter.py

@@ -0,0 +1,477 @@
+"""AstrBot 数据导出器
+
+负责将所有数据导出为 ZIP 备份文件。
+导出格式为 JSON，这是数据库无关的方案，支持未来向 MySQL/PostgreSQL 迁移。
+"""
+
+import hashlib
+import json
+import os
+import zipfile
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from sqlalchemy import select
+
+from astrbot.core import logger
+from astrbot.core.config.default import VERSION
+from astrbot.core.db import BaseDatabase
+from astrbot.core.utils.astrbot_path import (
+    get_astrbot_backups_path,
+    get_astrbot_data_path,
+)
+
+# 从共享常量模块导入
+from .constants import (
+    BACKUP_MANIFEST_VERSION,
+    KB_METADATA_MODELS,
+    MAIN_DB_MODELS,
+    get_backup_directories,
+)
+
+if TYPE_CHECKING:
+    from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
+
+CMD_CONFIG_FILE_PATH = os.path.join(get_astrbot_data_path(), "cmd_config.json")
+
+
+class AstrBotExporter:
+    """AstrBot 数据导出器
+
+    导出内容：
+    - 主数据库所有表（data/data_v4.db）
+    - 知识库元数据（data/knowledge_base/kb.db）
+    - 每个知识库的向量文档数据
+    - 配置文件（data/cmd_config.json）
+    - 附件文件
+    - 知识库多媒体文件
+    - 插件目录（data/plugins）
+    - 插件数据目录（data/plugin_data）
+    - 配置目录（data/config）
+    - T2I 模板目录（data/t2i_templates）
+    - WebChat 数据目录（data/webchat）
+    - 临时文件目录（data/temp）
+    """
+
+    def __init__(
+        self,
+        main_db: BaseDatabase,
+        kb_manager: "KnowledgeBaseManager | None" = None,
+        config_path: str = CMD_CONFIG_FILE_PATH,
+    ):
+        self.main_db = main_db
+        self.kb_manager = kb_manager
+        self.config_path = config_path
+        self._checksums: dict[str, str] = {}
+
+    async def export_all(
+        self,
+        output_dir: str | None = None,
+        progress_callback: Any | None = None,
+    ) -> str:
+        """导出所有数据到 ZIP 文件
+
+        Args:
+            output_dir: 输出目录
+            progress_callback: 进度回调函数，接收参数 (stage, current, total, message)
+
+        Returns:
+            str: 生成的 ZIP 文件路径
+        """
+        if output_dir is None:
+            output_dir = get_astrbot_backups_path()
+
+        # 确保输出目录存在
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        zip_filename = f"astrbot_backup_{timestamp}.zip"
+        zip_path = os.path.join(output_dir, zip_filename)
+
+        logger.info(f"开始导出备份到 {zip_path}")
+
+        try:
+            with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
+                # 1. 导出主数据库
+                if progress_callback:
+                    await progress_callback("main_db", 0, 100, "正在导出主数据库...")
+                main_data = await self._export_main_database()
+                main_db_json = json.dumps(
+                    main_data, ensure_ascii=False, indent=2, default=str
+                )
+                zf.writestr("databases/main_db.json", main_db_json)
+                self._add_checksum("databases/main_db.json", main_db_json)
+                if progress_callback:
+                    await progress_callback("main_db", 100, 100, "主数据库导出完成")
+
+                # 2. 导出知识库数据
+                kb_meta_data: dict[str, Any] = {
+                    "knowledge_bases": [],
+                    "kb_documents": [],
+                    "kb_media": [],
+                }
+                if self.kb_manager:
+                    if progress_callback:
+                        await progress_callback(
+                            "kb_metadata", 0, 100, "正在导出知识库元数据..."
+                        )
+                    kb_meta_data = await self._export_kb_metadata()
+                    kb_meta_json = json.dumps(
+                        kb_meta_data, ensure_ascii=False, indent=2, default=str
+                    )
+                    zf.writestr("databases/kb_metadata.json", kb_meta_json)
+                    self._add_checksum("databases/kb_metadata.json", kb_meta_json)
+                    if progress_callback:
+                        await progress_callback(
+                            "kb_metadata", 100, 100, "知识库元数据导出完成"
+                        )
+
+                    # 导出每个知识库的文档数据
+                    kb_insts = self.kb_manager.kb_insts
+                    total_kbs = len(kb_insts)
+                    for idx, (kb_id, kb_helper) in enumerate(kb_insts.items()):
+                        if progress_callback:
+                            await progress_callback(
+                                "kb_documents",
+                                idx,
+                                total_kbs,
+                                f"正在导出知识库 {kb_helper.kb.kb_name} 的文档数据...",
+                            )
+                        doc_data = await self._export_kb_documents(kb_helper)
+                        doc_json = json.dumps(
+                            doc_data, ensure_ascii=False, indent=2, default=str
+                        )
+                        doc_path = f"databases/kb_{kb_id}/documents.json"
+                        zf.writestr(doc_path, doc_json)
+                        self._add_checksum(doc_path, doc_json)
+
+                        # 导出 FAISS 索引文件
+                        await self._export_faiss_index(zf, kb_helper, kb_id)
+
+                        # 导出知识库多媒体文件
+                        await self._export_kb_media_files(zf, kb_helper, kb_id)
+
+                    if progress_callback:
+                        await progress_callback(
+                            "kb_documents", total_kbs, total_kbs, "知识库文档导出完成"
+                        )
+
+                # 3. 导出配置文件
+                if progress_callback:
+                    await progress_callback("config", 0, 100, "正在导出配置文件...")
+                if os.path.exists(self.config_path):
+                    with open(self.config_path, encoding="utf-8") as f:
+                        config_content = f.read()
+                    zf.writestr("config/cmd_config.json", config_content)
+                    self._add_checksum("config/cmd_config.json", config_content)
+                if progress_callback:
+                    await progress_callback("config", 100, 100, "配置文件导出完成")
+
+                # 4. 导出附件文件
+                if progress_callback:
+                    await progress_callback("attachments", 0, 100, "正在导出附件...")
+                await self._export_attachments(zf, main_data.get("attachments", []))
+                if progress_callback:
+                    await progress_callback("attachments", 100, 100, "附件导出完成")
+
+                # 5. 导出插件和其他目录
+                if progress_callback:
+                    await progress_callback(
+                        "directories", 0, 100, "正在导出插件和数据目录..."
+                    )
+                dir_stats = await self._export_directories(zf)
+                if progress_callback:
+                    await progress_callback("directories", 100, 100, "目录导出完成")
+
+                # 6. 生成 manifest
+                if progress_callback:
+                    await progress_callback("manifest", 0, 100, "正在生成清单...")
+                manifest = self._generate_manifest(main_data, kb_meta_data, dir_stats)
+                manifest_json = json.dumps(manifest, ensure_ascii=False, indent=2)
+                zf.writestr("manifest.json", manifest_json)
+                if progress_callback:
+                    await progress_callback("manifest", 100, 100, "清单生成完成")
+
+            logger.info(f"备份导出完成: {zip_path}")
+            return zip_path
+
+        except Exception as e:
+            logger.error(f"备份导出失败: {e}")
+            # 清理失败的文件
+            if os.path.exists(zip_path):
+                os.remove(zip_path)
+            raise
+
+    async def _export_main_database(self) -> dict[str, list[dict]]:
+        """导出主数据库所有表"""
+        export_data: dict[str, list[dict]] = {}
+
+        async with self.main_db.get_db() as session:
+            for table_name, model_class in MAIN_DB_MODELS.items():
+                try:
+                    result = await session.execute(select(model_class))
+                    records = result.scalars().all()
+                    export_data[table_name] = [
+                        self._model_to_dict(record) for record in records
+                    ]
+                    logger.debug(
+                        f"导出表 {table_name}: {len(export_data[table_name])} 条记录"
+                    )
+                except Exception as e:
+                    logger.warning(f"导出表 {table_name} 失败: {e}")
+                    export_data[table_name] = []
+
+        return export_data
+
+    async def _export_kb_metadata(self) -> dict[str, list[dict]]:
+        """导出知识库元数据库"""
+        if not self.kb_manager:
+            return {"knowledge_bases": [], "kb_documents": [], "kb_media": []}
+
+        export_data: dict[str, list[dict]] = {}
+
+        async with self.kb_manager.kb_db.get_db() as session:
+            for table_name, model_class in KB_METADATA_MODELS.items():
+                try:
+                    result = await session.execute(select(model_class))
+                    records = result.scalars().all()
+                    export_data[table_name] = [
+                        self._model_to_dict(record) for record in records
+                    ]
+                    logger.debug(
+                        f"导出知识库表 {table_name}: {len(export_data[table_name])} 条记录"
+                    )
+                except Exception as e:
+                    logger.warning(f"导出知识库表 {table_name} 失败: {e}")
+                    export_data[table_name] = []
+
+        return export_data
+
+    async def _export_kb_documents(self, kb_helper: Any) -> dict[str, Any]:
+        """导出知识库的文档块数据"""
+        try:
+            from astrbot.core.db.vec_db.faiss_impl.vec_db import FaissVecDB
+
+            vec_db: FaissVecDB = kb_helper.vec_db
+            if not vec_db or not vec_db.document_storage:
+                return {"documents": []}
+
+            # 获取所有文档
+            docs = await vec_db.document_storage.get_documents(
+                metadata_filters={},
+                offset=0,
+                limit=None,  # 获取全部
+            )
+
+            return {"documents": docs}
+        except Exception as e:
+            logger.warning(f"导出知识库文档失败: {e}")
+            return {"documents": []}
+
+    async def _export_faiss_index(
+        self,
+        zf: zipfile.ZipFile,
+        kb_helper: Any,
+        kb_id: str,
+    ) -> None:
+        """导出 FAISS 索引文件"""
+        try:
+            index_path = kb_helper.kb_dir / "index.faiss"
+            if index_path.exists():
+                archive_path = f"databases/kb_{kb_id}/index.faiss"
+                zf.write(str(index_path), archive_path)
+                logger.debug(f"导出 FAISS 索引: {archive_path}")
+        except Exception as e:
+            logger.warning(f"导出 FAISS 索引失败: {e}")
+
+    async def _export_kb_media_files(
+        self, zf: zipfile.ZipFile, kb_helper: Any, kb_id: str
+    ) -> None:
+        """导出知识库的多媒体文件"""
+        try:
+            media_dir = kb_helper.kb_medias_dir
+            if not media_dir.exists():
+                return
+
+            for root, _, files in os.walk(media_dir):
+                for file in files:
+                    file_path = Path(root) / file
+                    # 计算相对路径
+                    rel_path = file_path.relative_to(kb_helper.kb_dir)
+                    archive_path = f"files/kb_media/{kb_id}/{rel_path}"
+                    zf.write(str(file_path), archive_path)
+        except Exception as e:
+            logger.warning(f"导出知识库媒体文件失败: {e}")
+
+    async def _export_directories(
+        self, zf: zipfile.ZipFile
+    ) -> dict[str, dict[str, int]]:
+        """导出插件和其他数据目录
+
+        Returns:
+            dict: 每个目录的统计信息 {dir_name: {"files": count, "size": bytes}}
+        """
+        stats: dict[str, dict[str, int]] = {}
+        backup_directories = get_backup_directories()
+
+        for dir_name, dir_path in backup_directories.items():
+            full_path = Path(dir_path)
+            if not full_path.exists():
+                logger.debug(f"目录不存在，跳过: {full_path}")
+                continue
+
+            file_count = 0
+            total_size = 0
+
+            try:
+                for root, dirs, files in os.walk(full_path):
+                    # 跳过 __pycache__ 目录
+                    dirs[:] = [d for d in dirs if d != "__pycache__"]
+
+                    for file in files:
+                        # 跳过 .pyc 文件
+                        if file.endswith(".pyc"):
+                            continue
+
+                        file_path = Path(root) / file
+                        try:
+                            # 计算相对路径
+                            rel_path = file_path.relative_to(full_path)
+                            archive_path = f"directories/{dir_name}/{rel_path}"
+                            zf.write(str(file_path), archive_path)
+                            file_count += 1
+                            total_size += file_path.stat().st_size
+                        except Exception as e:
+                            logger.warning(f"导出文件 {file_path} 失败: {e}")
+
+                stats[dir_name] = {"files": file_count, "size": total_size}
+                logger.debug(
+                    f"导出目录 {dir_name}: {file_count} 个文件, {total_size} 字节"
+                )
+            except Exception as e:
+                logger.warning(f"导出目录 {dir_path} 失败: {e}")
+                stats[dir_name] = {"files": 0, "size": 0}
+
+        return stats
+
+    async def _export_attachments(
+        self, zf: zipfile.ZipFile, attachments: list[dict]
+    ) -> None:
+        """导出附件文件"""
+        for attachment in attachments:
+            try:
+                file_path = attachment.get("path", "")
+                if file_path and os.path.exists(file_path):
+                    # 使用 attachment_id 作为文件名
+                    attachment_id = attachment.get("attachment_id", "")
+                    ext = os.path.splitext(file_path)[1]
+                    archive_path = f"files/attachments/{attachment_id}{ext}"
+                    zf.write(file_path, archive_path)
+            except Exception as e:
+                logger.warning(f"导出附件失败: {e}")
+
+    def _model_to_dict(self, record: Any) -> dict:
+        """将 SQLModel 实例转换为字典
+
+        这是数据库无关的序列化方式，支持未来迁移到其他数据库。
+        """
+        # 使用 SQLModel 内置的 model_dump 方法（如果可用）
+        if hasattr(record, "model_dump"):
+            data = record.model_dump(mode="python")
+            # 处理 datetime 类型
+            for key, value in data.items():
+                if isinstance(value, datetime):
+                    data[key] = value.isoformat()
+            return data
+
+        # 回退到手动提取
+        data = {}
+        # 使用 inspect 获取表信息
+        from sqlalchemy import inspect as sa_inspect
+
+        mapper = sa_inspect(record.__class__)
+        for column in mapper.columns:
+            value = getattr(record, column.name)
+            # 处理 datetime 类型 - 统一转为 ISO 格式字符串
+            if isinstance(value, datetime):
+                value = value.isoformat()
+            data[column.name] = value
+        return data
+
+    def _add_checksum(self, path: str, content: str | bytes) -> None:
+        """计算并添加文件校验和"""
+        if isinstance(content, str):
+            content = content.encode("utf-8")
+        checksum = hashlib.sha256(content).hexdigest()
+        self._checksums[path] = f"sha256:{checksum}"
+
+    def _generate_manifest(
+        self,
+        main_data: dict[str, list[dict]],
+        kb_meta_data: dict[str, list[dict]],
+        dir_stats: dict[str, dict[str, int]] | None = None,
+    ) -> dict:
+        """生成备份清单"""
+        if dir_stats is None:
+            dir_stats = {}
+        # 收集知识库 ID
+        kb_document_tables = {}
+        if self.kb_manager:
+            for kb_id in self.kb_manager.kb_insts.keys():
+                kb_document_tables[kb_id] = "documents"
+
+        # 收集附件文件列表
+        attachment_files = []
+        for attachment in main_data.get("attachments", []):
+            attachment_id = attachment.get("attachment_id", "")
+            path = attachment.get("path", "")
+            if attachment_id and path:
+                ext = os.path.splitext(path)[1]
+                attachment_files.append(f"{attachment_id}{ext}")
+
+        # 收集知识库媒体文件
+        kb_media_files: dict[str, list[str]] = {}
+        if self.kb_manager:
+            for kb_id, kb_helper in self.kb_manager.kb_insts.items():
+                media_files: list[str] = []
+                media_dir = kb_helper.kb_medias_dir
+                if media_dir.exists():
+                    for root, _, files in os.walk(media_dir):
+                        for file in files:
+                            media_files.append(file)
+                if media_files:
+                    kb_media_files[kb_id] = media_files
+
+        manifest = {
+            "version": BACKUP_MANIFEST_VERSION,
+            "astrbot_version": VERSION,
+            "exported_at": datetime.now(timezone.utc).isoformat(),
+            "origin": "exported",  # 标记备份来源：exported=本实例导出, uploaded=用户上传
+            "schema_version": {
+                "main_db": "v4",
+                "kb_db": "v1",
+            },
+            "tables": {
+                "main_db": list(main_data.keys()),
+                "kb_metadata": list(kb_meta_data.keys()),
+                "kb_documents": kb_document_tables,
+            },
+            "files": {
+                "attachments": attachment_files,
+                "kb_media": kb_media_files,
+            },
+            "directories": list(dir_stats.keys()),
+            "checksums": self._checksums,
+            "statistics": {
+                "main_db": {
+                    table: len(records) for table, records in main_data.items()
+                },
+                "kb_metadata": {
+                    table: len(records) for table, records in kb_meta_data.items()
+                },
+                "directories": dir_stats,
+            },
+        }
+
+        return manifest

astrbot/core/backup/importer.py

@@ -0,0 +1,761 @@
+"""AstrBot 数据导入器
+
+负责从 ZIP 备份文件恢复所有数据。
+导入时进行版本校验：
+- 主版本（前两位）不同时直接拒绝导入
+- 小版本（第三位）不同时提示警告，用户可选择强制导入
+- 版本匹配时也需要用户确认
+"""
+
+import json
+import os
+import shutil
+import zipfile
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from sqlalchemy import delete
+
+from astrbot.core import logger
+from astrbot.core.config.default import VERSION
+from astrbot.core.db import BaseDatabase
+from astrbot.core.utils.astrbot_path import (
+    get_astrbot_data_path,
+    get_astrbot_knowledge_base_path,
+)
+from astrbot.core.utils.version_comparator import VersionComparator
+
+# 从共享常量模块导入
+from .constants import (
+    KB_METADATA_MODELS,
+    MAIN_DB_MODELS,
+    get_backup_directories,
+)
+
+if TYPE_CHECKING:
+    from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
+
+
+def _get_major_version(version_str: str) -> str:
+    """提取版本的主版本部分（前两位）
+
+    Args:
+        version_str: 版本字符串，如 "4.9.1", "4.10.0-beta"
+
+    Returns:
+        主版本字符串，如 "4.9", "4.10"
+    """
+    if not version_str:
+        return "0.0"
+    # 移除 v 前缀和预发布标签
+    version = version_str.lower().replace("v", "").split("-")[0].split("+")[0]
+    parts = [p for p in version.split(".") if p]  # 过滤空字符串
+    if len(parts) >= 2:
+        return f"{parts[0]}.{parts[1]}"
+    elif len(parts) == 1 and parts[0]:
+        return f"{parts[0]}.0"
+    return "0.0"
+
+
+CMD_CONFIG_FILE_PATH = os.path.join(get_astrbot_data_path(), "cmd_config.json")
+KB_PATH = get_astrbot_knowledge_base_path()
+
+
+@dataclass
+class ImportPreCheckResult:
+    """导入预检查结果
+
+    用于在实际导入前检查备份文件的版本兼容性，
+    并返回确认信息让用户决定是否继续导入。
+    """
+
+    # 检查是否通过（文件有效且版本可导入）
+    valid: bool = False
+    # 是否可以导入（版本兼容）
+    can_import: bool = False
+    # 版本状态: match（完全匹配）, minor_diff（小版本差异）, major_diff（主版本不同，拒绝）
+    version_status: str = ""
+    # 备份文件中的 AstrBot 版本
+    backup_version: str = ""
+    # 当前运行的 AstrBot 版本
+    current_version: str = VERSION
+    # 备份创建时间
+    backup_time: str = ""
+    # 确认消息（显示给用户）
+    confirm_message: str = ""
+    # 警告消息列表
+    warnings: list[str] = field(default_factory=list)
+    # 错误消息（如果检查失败）
+    error: str = ""
+    # 备份包含的内容摘要
+    backup_summary: dict = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        return {
+            "valid": self.valid,
+            "can_import": self.can_import,
+            "version_status": self.version_status,
+            "backup_version": self.backup_version,
+            "current_version": self.current_version,
+            "backup_time": self.backup_time,
+            "confirm_message": self.confirm_message,
+            "warnings": self.warnings,
+            "error": self.error,
+            "backup_summary": self.backup_summary,
+        }
+
+
+class ImportResult:
+    """导入结果"""
+
+    def __init__(self):
+        self.success = True
+        self.imported_tables: dict[str, int] = {}
+        self.imported_files: dict[str, int] = {}
+        self.imported_directories: dict[str, int] = {}
+        self.warnings: list[str] = []
+        self.errors: list[str] = []
+
+    def add_warning(self, msg: str) -> None:
+        self.warnings.append(msg)
+        logger.warning(msg)
+
+    def add_error(self, msg: str) -> None:
+        self.errors.append(msg)
+        self.success = False
+        logger.error(msg)
+
+    def to_dict(self) -> dict:
+        return {
+            "success": self.success,
+            "imported_tables": self.imported_tables,
+            "imported_files": self.imported_files,
+            "imported_directories": self.imported_directories,
+            "warnings": self.warnings,
+            "errors": self.errors,
+        }
+
+
+class AstrBotImporter:
+    """AstrBot 数据导入器
+
+    导入备份文件中的所有数据，包括：
+    - 主数据库所有表
+    - 知识库元数据和文档
+    - 配置文件
+    - 附件文件
+    - 知识库多媒体文件
+    - 插件目录（data/plugins）
+    - 插件数据目录（data/plugin_data）
+    - 配置目录（data/config）
+    - T2I 模板目录（data/t2i_templates）
+    - WebChat 数据目录（data/webchat）
+    - 临时文件目录（data/temp）
+    """
+
+    def __init__(
+        self,
+        main_db: BaseDatabase,
+        kb_manager: "KnowledgeBaseManager | None" = None,
+        config_path: str = CMD_CONFIG_FILE_PATH,
+        kb_root_dir: str = KB_PATH,
+    ):
+        self.main_db = main_db
+        self.kb_manager = kb_manager
+        self.config_path = config_path
+        self.kb_root_dir = kb_root_dir
+
+    def pre_check(self, zip_path: str) -> ImportPreCheckResult:
+        """预检查备份文件
+
+        在实际导入前检查备份文件的有效性和版本兼容性。
+        返回检查结果供前端显示确认对话框。
+
+        Args:
+            zip_path: ZIP 备份文件路径
+
+        Returns:
+            ImportPreCheckResult: 预检查结果
+        """
+        result = ImportPreCheckResult()
+        result.current_version = VERSION
+
+        if not os.path.exists(zip_path):
+            result.error = f"备份文件不存在: {zip_path}"
+            return result
+
+        try:
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                # 读取 manifest
+                try:
+                    manifest_data = zf.read("manifest.json")
+                    manifest = json.loads(manifest_data)
+                except KeyError:
+                    result.error = "备份文件缺少 manifest.json，不是有效的 AstrBot 备份"
+                    return result
+                except json.JSONDecodeError as e:
+                    result.error = f"manifest.json 格式错误: {e}"
+                    return result
+
+                # 提取基本信息
+                result.backup_version = manifest.get("astrbot_version", "未知")
+                result.backup_time = manifest.get("exported_at", "未知")
+                result.valid = True
+
+                # 构建备份摘要
+                result.backup_summary = {
+                    "tables": list(manifest.get("tables", {}).keys()),
+                    "has_knowledge_bases": manifest.get("has_knowledge_bases", False),
+                    "has_config": manifest.get("has_config", False),
+                    "directories": manifest.get("directories", []),
+                }
+
+                # 检查版本兼容性
+                version_check = self._check_version_compatibility(result.backup_version)
+                result.version_status = version_check["status"]
+                result.can_import = version_check["can_import"]
+
+                # 版本信息由前端根据 version_status 和 i18n 生成显示
+                # 不再将版本消息添加到 warnings 列表中，避免中文硬编码
+                # warnings 列表保留用于其他非版本相关的警告
+
+                return result
+
+        except zipfile.BadZipFile:
+            result.error = "无效的 ZIP 文件"
+            return result
+        except Exception as e:
+            result.error = f"检查备份文件失败: {e}"
+            return result
+
+    def _check_version_compatibility(self, backup_version: str) -> dict:
+        """检查版本兼容性
+
+        规则：
+        - 主版本（前两位，如 4.9）必须一致，否则拒绝
+        - 小版本（第三位，如 4.9.1 vs 4.9.2）不同时，警告但允许导入
+
+        Returns:
+            dict: {status, can_import, message}
+        """
+        if not backup_version:
+            return {
+                "status": "major_diff",
+                "can_import": False,
+                "message": "备份文件缺少版本信息",
+            }
+
+        # 提取主版本（前两位）进行比较
+        backup_major = _get_major_version(backup_version)
+        current_major = _get_major_version(VERSION)
+
+        # 比较主版本
+        if VersionComparator.compare_version(backup_major, current_major) != 0:
+            return {
+                "status": "major_diff",
+                "can_import": False,
+                "message": (
+                    f"主版本不兼容: 备份版本 {backup_version}, 当前版本 {VERSION}。"
+                    f"跨主版本导入可能导致数据损坏，请使用相同主版本的 AstrBot。"
+                ),
+            }
+
+        # 比较完整版本
+        version_cmp = VersionComparator.compare_version(backup_version, VERSION)
+        if version_cmp != 0:
+            return {
+                "status": "minor_diff",
+                "can_import": True,
+                "message": (
+                    f"小版本差异: 备份版本 {backup_version}, 当前版本 {VERSION}。"
+                ),
+            }
+
+        return {
+            "status": "match",
+            "can_import": True,
+            "message": "版本匹配",
+        }
+
+    async def import_all(
+        self,
+        zip_path: str,
+        mode: str = "replace",  # "replace" 清空后导入
+        progress_callback: Any | None = None,
+    ) -> ImportResult:
+        """从 ZIP 文件导入所有数据
+
+        Args:
+            zip_path: ZIP 备份文件路径
+            mode: 导入模式，目前仅支持 "replace"（清空后导入）
+            progress_callback: 进度回调函数，接收参数 (stage, current, total, message)
+
+        Returns:
+            ImportResult: 导入结果
+        """
+        result = ImportResult()
+
+        if not os.path.exists(zip_path):
+            result.add_error(f"备份文件不存在: {zip_path}")
+            return result
+
+        logger.info(f"开始从 {zip_path} 导入备份")
+
+        try:
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                # 1. 读取并验证 manifest
+                if progress_callback:
+                    await progress_callback("validate", 0, 100, "正在验证备份文件...")
+
+                try:
+                    manifest_data = zf.read("manifest.json")
+                    manifest = json.loads(manifest_data)
+                except KeyError:
+                    result.add_error("备份文件缺少 manifest.json")
+                    return result
+                except json.JSONDecodeError as e:
+                    result.add_error(f"manifest.json 格式错误: {e}")
+                    return result
+
+                # 版本校验
+                try:
+                    self._validate_version(manifest)
+                except ValueError as e:
+                    result.add_error(str(e))
+                    return result
+
+                if progress_callback:
+                    await progress_callback("validate", 100, 100, "验证完成")
+
+                # 2. 导入主数据库
+                if progress_callback:
+                    await progress_callback("main_db", 0, 100, "正在导入主数据库...")
+
+                try:
+                    main_data_content = zf.read("databases/main_db.json")
+                    main_data = json.loads(main_data_content)
+
+                    if mode == "replace":
+                        await self._clear_main_db()
+
+                    imported = await self._import_main_database(main_data)
+                    result.imported_tables.update(imported)
+                except Exception as e:
+                    result.add_error(f"导入主数据库失败: {e}")
+                    return result
+
+                if progress_callback:
+                    await progress_callback("main_db", 100, 100, "主数据库导入完成")
+
+                # 3. 导入知识库
+                if self.kb_manager and "databases/kb_metadata.json" in zf.namelist():
+                    if progress_callback:
+                        await progress_callback("kb", 0, 100, "正在导入知识库...")
+
+                    try:
+                        kb_meta_content = zf.read("databases/kb_metadata.json")
+                        kb_meta_data = json.loads(kb_meta_content)
+
+                        if mode == "replace":
+                            await self._clear_kb_data()
+
+                        await self._import_knowledge_bases(zf, kb_meta_data, result)
+                    except Exception as e:
+                        result.add_warning(f"导入知识库失败: {e}")
+
+                    if progress_callback:
+                        await progress_callback("kb", 100, 100, "知识库导入完成")
+
+                # 4. 导入配置文件
+                if progress_callback:
+                    await progress_callback("config", 0, 100, "正在导入配置文件...")
+
+                if "config/cmd_config.json" in zf.namelist():
+                    try:
+                        config_content = zf.read("config/cmd_config.json")
+                        # 备份现有配置
+                        if os.path.exists(self.config_path):
+                            backup_path = f"{self.config_path}.bak"
+                            shutil.copy2(self.config_path, backup_path)
+
+                        with open(self.config_path, "wb") as f:
+                            f.write(config_content)
+                        result.imported_files["config"] = 1
+                    except Exception as e:
+                        result.add_warning(f"导入配置文件失败: {e}")
+
+                if progress_callback:
+                    await progress_callback("config", 100, 100, "配置文件导入完成")
+
+                # 5. 导入附件文件
+                if progress_callback:
+                    await progress_callback("attachments", 0, 100, "正在导入附件...")
+
+                attachment_count = await self._import_attachments(
+                    zf, main_data.get("attachments", [])
+                )
+                result.imported_files["attachments"] = attachment_count
+
+                if progress_callback:
+                    await progress_callback("attachments", 100, 100, "附件导入完成")
+
+                # 6. 导入插件和其他目录
+                if progress_callback:
+                    await progress_callback(
+                        "directories", 0, 100, "正在导入插件和数据目录..."
+                    )
+
+                dir_stats = await self._import_directories(zf, manifest, result)
+                result.imported_directories = dir_stats
+
+                if progress_callback:
+                    await progress_callback("directories", 100, 100, "目录导入完成")
+
+            logger.info(f"备份导入完成: {result.to_dict()}")
+            return result
+
+        except zipfile.BadZipFile:
+            result.add_error("无效的 ZIP 文件")
+            return result
+        except Exception as e:
+            result.add_error(f"导入失败: {e}")
+            return result
+
+    def _validate_version(self, manifest: dict) -> None:
+        """验证版本兼容性 - 仅允许相同主版本导入
+
+        注意：此方法仅在 import_all 中调用，用于双重校验。
+        前端应先调用 pre_check 获取详细的版本信息并让用户确认。
+        """
+        backup_version = manifest.get("astrbot_version")
+        if not backup_version:
+            raise ValueError("备份文件缺少版本信息")
+
+        # 使用新的版本兼容性检查
+        version_check = self._check_version_compatibility(backup_version)
+
+        if version_check["status"] == "major_diff":
+            raise ValueError(version_check["message"])
+
+        # minor_diff 和 match 都允许导入
+        if version_check["status"] == "minor_diff":
+            logger.warning(f"版本差异警告: {version_check['message']}")
+
+    async def _clear_main_db(self) -> None:
+        """清空主数据库所有表"""
+        async with self.main_db.get_db() as session:
+            async with session.begin():
+                for table_name, model_class in MAIN_DB_MODELS.items():
+                    try:
+                        await session.execute(delete(model_class))
+                        logger.debug(f"已清空表 {table_name}")
+                    except Exception as e:
+                        logger.warning(f"清空表 {table_name} 失败: {e}")
+
+    async def _clear_kb_data(self) -> None:
+        """清空知识库数据"""
+        if not self.kb_manager:
+            return
+
+        # 清空知识库元数据表
+        async with self.kb_manager.kb_db.get_db() as session:
+            async with session.begin():
+                for table_name, model_class in KB_METADATA_MODELS.items():
+                    try:
+                        await session.execute(delete(model_class))
+                        logger.debug(f"已清空知识库表 {table_name}")
+                    except Exception as e:
+                        logger.warning(f"清空知识库表 {table_name} 失败: {e}")
+
+        # 删除知识库文件目录
+        for kb_id in list(self.kb_manager.kb_insts.keys()):
+            try:
+                kb_helper = self.kb_manager.kb_insts[kb_id]
+                await kb_helper.terminate()
+                if kb_helper.kb_dir.exists():
+                    shutil.rmtree(kb_helper.kb_dir)
+            except Exception as e:
+                logger.warning(f"清理知识库 {kb_id} 失败: {e}")
+
+        self.kb_manager.kb_insts.clear()
+
+    async def _import_main_database(
+        self, data: dict[str, list[dict]]
+    ) -> dict[str, int]:
+        """导入主数据库数据"""
+        imported: dict[str, int] = {}
+
+        async with self.main_db.get_db() as session:
+            async with session.begin():
+                for table_name, rows in data.items():
+                    model_class = MAIN_DB_MODELS.get(table_name)
+                    if not model_class:
+                        logger.warning(f"未知的表: {table_name}")
+                        continue
+
+                    count = 0
+                    for row in rows:
+                        try:
+                            # 转换 datetime 字符串为 datetime 对象
+                            row = self._convert_datetime_fields(row, model_class)
+                            obj = model_class(**row)
+                            session.add(obj)
+                            count += 1
+                        except Exception as e:
+                            logger.warning(f"导入记录到 {table_name} 失败: {e}")
+
+                    imported[table_name] = count
+                    logger.debug(f"导入表 {table_name}: {count} 条记录")
+
+        return imported
+
+    async def _import_knowledge_bases(
+        self,
+        zf: zipfile.ZipFile,
+        kb_meta_data: dict[str, list[dict]],
+        result: ImportResult,
+    ) -> None:
+        """导入知识库数据"""
+        if not self.kb_manager:
+            return
+
+        # 1. 导入知识库元数据
+        async with self.kb_manager.kb_db.get_db() as session:
+            async with session.begin():
+                for table_name, rows in kb_meta_data.items():
+                    model_class = KB_METADATA_MODELS.get(table_name)
+                    if not model_class:
+                        continue
+
+                    count = 0
+                    for row in rows:
+                        try:
+                            row = self._convert_datetime_fields(row, model_class)
+                            obj = model_class(**row)
+                            session.add(obj)
+                            count += 1
+                        except Exception as e:
+                            logger.warning(f"导入知识库记录到 {table_name} 失败: {e}")
+
+                    result.imported_tables[f"kb_{table_name}"] = count
+
+        # 2. 导入每个知识库的文档和文件
+        for kb_data in kb_meta_data.get("knowledge_bases", []):
+            kb_id = kb_data.get("kb_id")
+            if not kb_id:
+                continue
+
+            # 创建知识库目录
+            kb_dir = Path(self.kb_root_dir) / kb_id
+            kb_dir.mkdir(parents=True, exist_ok=True)
+
+            # 导入文档数据
+            doc_path = f"databases/kb_{kb_id}/documents.json"
+            if doc_path in zf.namelist():
+                try:
+                    doc_content = zf.read(doc_path)
+                    doc_data = json.loads(doc_content)
+
+                    # 导入到文档存储数据库
+                    await self._import_kb_documents(kb_id, doc_data)
+                except Exception as e:
+                    result.add_warning(f"导入知识库 {kb_id} 的文档失败: {e}")
+
+            # 导入 FAISS 索引
+            faiss_path = f"databases/kb_{kb_id}/index.faiss"
+            if faiss_path in zf.namelist():
+                try:
+                    target_path = kb_dir / "index.faiss"
+                    with zf.open(faiss_path) as src, open(target_path, "wb") as dst:
+                        dst.write(src.read())
+                except Exception as e:
+                    result.add_warning(f"导入知识库 {kb_id} 的 FAISS 索引失败: {e}")
+
+            # 导入媒体文件
+            media_prefix = f"files/kb_media/{kb_id}/"
+            for name in zf.namelist():
+                if name.startswith(media_prefix):
+                    try:
+                        rel_path = name[len(media_prefix) :]
+                        target_path = kb_dir / rel_path
+                        target_path.parent.mkdir(parents=True, exist_ok=True)
+                        with zf.open(name) as src, open(target_path, "wb") as dst:
+                            dst.write(src.read())
+                    except Exception as e:
+                        result.add_warning(f"导入媒体文件 {name} 失败: {e}")
+
+        # 3. 重新加载知识库实例
+        await self.kb_manager.load_kbs()
+
+    async def _import_kb_documents(self, kb_id: str, doc_data: dict) -> None:
+        """导入知识库文档到向量数据库"""
+        from astrbot.core.db.vec_db.faiss_impl.document_storage import DocumentStorage
+
+        kb_dir = Path(self.kb_root_dir) / kb_id
+        doc_db_path = kb_dir / "doc.db"
+
+        # 初始化文档存储
+        doc_storage = DocumentStorage(str(doc_db_path))
+        await doc_storage.initialize()
+
+        try:
+            documents = doc_data.get("documents", [])
+            for doc in documents:
+                try:
+                    await doc_storage.insert_document(
+                        doc_id=doc.get("doc_id", ""),
+                        text=doc.get("text", ""),
+                        metadata=json.loads(doc.get("metadata", "{}")),
+                    )
+                except Exception as e:
+                    logger.warning(f"导入文档块失败: {e}")
+        finally:
+            await doc_storage.close()
+
+    async def _import_attachments(
+        self,
+        zf: zipfile.ZipFile,
+        attachments: list[dict],
+    ) -> int:
+        """导入附件文件"""
+        count = 0
+
+        attachments_dir = Path(self.config_path).parent / "attachments"
+        attachments_dir.mkdir(parents=True, exist_ok=True)
+
+        attachment_prefix = "files/attachments/"
+        for name in zf.namelist():
+            if name.startswith(attachment_prefix) and name != attachment_prefix:
+                try:
+                    # 从附件记录中找到原始路径
+                    attachment_id = os.path.splitext(os.path.basename(name))[0]
+                    original_path = None
+                    for att in attachments:
+                        if att.get("attachment_id") == attachment_id:
+                            original_path = att.get("path")
+                            break
+
+                    if original_path:
+                        target_path = Path(original_path)
+                    else:
+                        target_path = attachments_dir / os.path.basename(name)
+
+                    target_path.parent.mkdir(parents=True, exist_ok=True)
+                    with zf.open(name) as src, open(target_path, "wb") as dst:
+                        dst.write(src.read())
+                    count += 1
+                except Exception as e:
+                    logger.warning(f"导入附件 {name} 失败: {e}")
+
+        return count
+
+    async def _import_directories(
+        self,
+        zf: zipfile.ZipFile,
+        manifest: dict,
+        result: ImportResult,
+    ) -> dict[str, int]:
+        """导入插件和其他数据目录
+
+        Args:
+            zf: ZIP 文件对象
+            manifest: 备份清单
+            result: 导入结果对象
+
+        Returns:
+            dict: 每个目录导入的文件数量
+        """
+        dir_stats: dict[str, int] = {}
+
+        # 检查备份版本是否支持目录备份（需要版本 >= 1.1）
+        backup_version = manifest.get("version", "1.0")
+        if VersionComparator.compare_version(backup_version, "1.1") < 0:
+            logger.info("备份版本不支持目录备份，跳过目录导入")
+            return dir_stats
+
+        backed_up_dirs = manifest.get("directories", [])
+        backup_directories = get_backup_directories()
+
+        for dir_name in backed_up_dirs:
+            if dir_name not in backup_directories:
+                result.add_warning(f"未知的目录类型: {dir_name}")
+                continue
+
+            target_dir = Path(backup_directories[dir_name])
+            archive_prefix = f"directories/{dir_name}/"
+
+            file_count = 0
+
+            try:
+                # 获取该目录下的所有文件
+                dir_files = [
+                    name
+                    for name in zf.namelist()
+                    if name.startswith(archive_prefix) and name != archive_prefix
+                ]
+
+                if not dir_files:
+                    continue
+
+                # 备份现有目录（如果存在）
+                if target_dir.exists():
+                    backup_path = Path(f"{target_dir}.bak")
+                    if backup_path.exists():
+                        shutil.rmtree(backup_path)
+                    shutil.move(str(target_dir), str(backup_path))
+                    logger.debug(f"已备份现有目录 {target_dir} 到 {backup_path}")
+
+                # 创建目标目录
+                target_dir.mkdir(parents=True, exist_ok=True)
+
+                # 解压文件
+                for name in dir_files:
+                    try:
+                        # 计算相对路径
+                        rel_path = name[len(archive_prefix) :]
+                        if not rel_path:  # 跳过目录条目
+                            continue
+
+                        target_path = target_dir / rel_path
+                        target_path.parent.mkdir(parents=True, exist_ok=True)
+
+                        with zf.open(name) as src, open(target_path, "wb") as dst:
+                            dst.write(src.read())
+                        file_count += 1
+                    except Exception as e:
+                        result.add_warning(f"导入文件 {name} 失败: {e}")
+
+                dir_stats[dir_name] = file_count
+                logger.debug(f"导入目录 {dir_name}: {file_count} 个文件")
+
+            except Exception as e:
+                result.add_warning(f"导入目录 {dir_name} 失败: {e}")
+                dir_stats[dir_name] = 0
+
+        return dir_stats
+
+    def _convert_datetime_fields(self, row: dict, model_class: type) -> dict:
+        """转换 datetime 字符串字段为 datetime 对象"""
+        result = row.copy()
+
+        # 获取模型的 datetime 字段
+        from sqlalchemy import inspect as sa_inspect
+
+        try:
+            mapper = sa_inspect(model_class)
+            for column in mapper.columns:
+                if column.name in result and result[column.name] is not None:
+                    # 检查是否是 datetime 类型的列
+                    from sqlalchemy import DateTime
+
+                    if isinstance(column.type, DateTime):
+                        value = result[column.name]
+                        if isinstance(value, str):
+                            # 解析 ISO 格式的日期时间字符串
+                            result[column.name] = datetime.fromisoformat(value)
+        except Exception:
+            pass
+
+        return result

astrbot/core/config/astrbot_config.py

@@ -80,6 +80,8 @@ class AstrBotConfig(dict):
                 if v["type"] == "object":
                     conf[k] = {}
                     _parse_schema(v["items"], conf[k])
+                elif v["type"] == "template_list":
+                    conf[k] = default
                 else:
                     conf[k] = default
 

astrbot/core/config/default.py

@@ -1,10 +1,11 @@
 """如需修改配置，请在 `data/cmd_config.json` 中修改或者在管理面板中可视化修改。"""
 
 import os
+from typing import Any, TypedDict
 
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
-VERSION = "4.9.0"
+VERSION = "4.10.3"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")
 
 WEBHOOK_SUPPORTED_PLATFORMS = [
@@ -61,7 +62,8 @@ DEFAULT_CONFIG = {
         "ignore_bot_self_message": False,
         "ignore_at_all": False,
     },
-    "provider": [],
+    "provider_sources": [],  # provider sources
+    "provider": [],  # models from provider_sources
     "provider_settings": {
         "enable": True,
         "default_provider_id": "",
@@ -108,6 +110,7 @@ DEFAULT_CONFIG = {
         "provider_id": "",
         "dual_output": False,
         "use_file_service": False,
+        "trigger_probability": 1.0,
     },
     "provider_ltm_settings": {
         "group_icl_enable": False,
@@ -170,6 +173,22 @@ DEFAULT_CONFIG = {
 }
 
 
+class ChatProviderTemplate(TypedDict):
+    id: str
+    provider_source_id: str
+    model: str
+    modalities: list
+    custom_extra_body: dict[str, Any]
+
+
+CHAT_PROVIDER_TEMPLATE = {
+    "id": "",
+    "provide_source_id": "",
+    "model": "",
+    "modalities": [],
+    "custom_extra_body": {},
+}
+
 """
 AstrBot v3 时代的配置元数据，目前仅承担以下功能：
 
@@ -208,7 +227,7 @@ CONFIG_METADATA_2 = {
                         "callback_server_host": "0.0.0.0",
                         "port": 6196,
                     },
-                    "QQ 个人号(OneBot v11)": {
+                    "OneBot v11": {
                         "id": "default",
                         "type": "aiocqhttp",
                         "enable": False,
@@ -843,6 +862,7 @@ CONFIG_METADATA_2 = {
         "metadata": {
             "provider": {
                 "type": "list",
+                # provider sources templates
                 "config_template": {
                     "OpenAI": {
                         "id": "openai",
@@ -853,107 +873,10 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.openai.com/v1",
                         "timeout": 120,
-                        "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                        "hint": "也兼容所有与 OpenAI API 兼容的服务。",
                     },
-                    "Azure OpenAI": {
-                        "id": "azure",
-                        "provider": "azure",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "api_version": "2024-05-01-preview",
-                        "key": [],
-                        "api_base": "",
-                        "timeout": 120,
-                        "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "xAI": {
-                        "id": "xai",
-                        "provider": "xai",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "api_base": "https://api.x.ai/v1",
-                        "timeout": 120,
-                        "model_config": {"model": "grok-2-latest", "temperature": 0.4},
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "xai_native_search": False,
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "Anthropic": {
-                        "hint": "注意Claude系列模型的温度调节范围为0到1.0，超出可能导致报错",
-                        "id": "claude",
-                        "provider": "anthropic",
-                        "type": "anthropic_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "api_base": "https://api.anthropic.com/v1",
-                        "timeout": 120,
-                        "model_config": {
-                            "model": "claude-3-5-sonnet-latest",
-                            "max_tokens": 4096,
-                            "temperature": 0.2,
-                        },
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "Ollama": {
-                        "hint": "启用前请确保已正确安装并运行 Ollama 服务端，Ollama默认不带鉴权，无需修改key",
-                        "id": "ollama_default",
-                        "provider": "ollama",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": ["ollama"],  # ollama 的 key 默认是 ollama
-                        "api_base": "http://localhost:11434/v1",
-                        "model_config": {"model": "llama3.1-8b", "temperature": 0.4},
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "LM Studio": {
-                        "id": "lm_studio",
-                        "provider": "lm_studio",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": ["lmstudio"],
-                        "api_base": "http://localhost:1234/v1",
-                        "model_config": {
-                            "model": "llama-3.1-8b",
-                        },
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "Gemini(OpenAI兼容)": {
-                        "id": "gemini_default",
-                        "provider": "google",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
-                        "timeout": 120,
-                        "model_config": {
-                            "model": "gemini-1.5-flash",
-                            "temperature": 0.4,
-                        },
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "Gemini": {
-                        "id": "gemini_default",
+                    "Google Gemini": {
+                        "id": "google_gemini",
                         "provider": "google",
                         "type": "googlegenai_chat_completion",
                         "provider_type": "chat_completion",
@@ -961,10 +884,6 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://generativelanguage.googleapis.com/",
                         "timeout": 120,
-                        "model_config": {
-                            "model": "gemini-2.0-flash-exp",
-                            "temperature": 0.4,
-                        },
                         "gm_resp_image_modal": False,
                         "gm_native_search": False,
                         "gm_native_coderunner": False,
@@ -975,13 +894,44 @@ CONFIG_METADATA_2 = {
                             "sexually_explicit": "BLOCK_MEDIUM_AND_ABOVE",
                             "dangerous_content": "BLOCK_MEDIUM_AND_ABOVE",
                         },
-                        "gm_thinking_config": {
-                            "budget": 0,
-                        },
-                        "modalities": ["text", "image", "tool_use"],
+                        "gm_thinking_config": {"budget": 0, "level": "HIGH"},
+                    },
+                    "Anthropic": {
+                        "id": "anthropic",
+                        "provider": "anthropic",
+                        "type": "anthropic_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "api_base": "https://api.anthropic.com/v1",
+                        "timeout": 120,
+                        "anth_thinking_config": {"budget": 0},
+                    },
+                    "Moonshot": {
+                        "id": "moonshot",
+                        "provider": "moonshot",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "timeout": 120,
+                        "api_base": "https://api.moonshot.cn/v1",
+                        "custom_headers": {},
+                    },
+                    "xAI": {
+                        "id": "xai",
+                        "provider": "xai",
+                        "type": "xai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "api_base": "https://api.x.ai/v1",
+                        "timeout": 120,
+                        "custom_headers": {},
+                        "xai_native_search": False,
                     },
                     "DeepSeek": {
-                        "id": "deepseek_default",
+                        "id": "deepseek",
                         "provider": "deepseek",
                         "type": "openai_chat_completion",
                         "provider_type": "chat_completion",
@@ -989,13 +939,75 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.deepseek.com/v1",
                         "timeout": 120,
-                        "model_config": {"model": "deepseek-chat", "temperature": 0.4},
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "tool_use"],
+                    },
+                    "Zhipu": {
+                        "id": "zhipu",
+                        "provider": "zhipu",
+                        "type": "zhipu_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "timeout": 120,
+                        "api_base": "https://open.bigmodel.cn/api/paas/v4/",
+                        "custom_headers": {},
+                    },
+                    "Azure OpenAI": {
+                        "id": "azure_openai",
+                        "provider": "azure",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "api_version": "2024-05-01-preview",
+                        "key": [],
+                        "api_base": "",
+                        "timeout": 120,
+                        "custom_headers": {},
+                    },
+                    "Ollama": {
+                        "id": "ollama",
+                        "provider": "ollama",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": ["ollama"],  # ollama 的 key 默认是 ollama
+                        "api_base": "http://127.0.0.1:11434/v1",
+                        "custom_headers": {},
+                    },
+                    "LM Studio": {
+                        "id": "lm_studio",
+                        "provider": "lm_studio",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": ["lmstudio"],
+                        "api_base": "http://127.0.0.1:1234/v1",
+                        "custom_headers": {},
+                    },
+                    "ModelStack": {
+                        "id": "modelstack",
+                        "provider": "modelstack",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "api_base": "https://modelstack.app/v1",
+                        "timeout": 120,
+                        "custom_headers": {},
+                    },
+                    "Gemini_OpenAI_API": {
+                        "id": "google_gemini_openai",
+                        "provider": "google",
+                        "type": "openai_chat_completion",
+                        "provider_type": "chat_completion",
+                        "enable": True,
+                        "key": [],
+                        "api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
+                        "timeout": 120,
+                        "custom_headers": {},
                     },
                     "Groq": {
-                        "id": "groq_default",
+                        "id": "groq",
                         "provider": "groq",
                         "type": "groq_chat_completion",
                         "provider_type": "chat_completion",
@@ -1003,13 +1015,7 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.groq.com/openai/v1",
                         "timeout": 120,
-                        "model_config": {
-                            "model": "openai/gpt-oss-20b",
-                            "temperature": 0.4,
-                        },
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "tool_use"],
                     },
                     "302.AI": {
                         "id": "302ai",
@@ -1020,12 +1026,9 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.302.ai/v1",
                         "timeout": 120,
-                        "model_config": {"model": "gpt-4.1-mini", "temperature": 0.4},
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
                     },
-                    "硅基流动": {
+                    "SiliconFlow": {
                         "id": "siliconflow",
                         "provider": "siliconflow",
                         "type": "openai_chat_completion",
@@ -1034,15 +1037,9 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "timeout": 120,
                         "api_base": "https://api.siliconflow.cn/v1",
-                        "model_config": {
-                            "model": "deepseek-ai/DeepSeek-V3",
-                            "temperature": 0.4,
-                        },
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
                     },
-                    "PPIO派欧云": {
+                    "PPIO": {
                         "id": "ppio",
                         "provider": "ppio",
                         "type": "openai_chat_completion",
@@ -1051,14 +1048,9 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.ppinfra.com/v3/openai",
                         "timeout": 120,
-                        "model_config": {
-                            "model": "deepseek/deepseek-r1",
-                            "temperature": 0.4,
-                        },
                         "custom_headers": {},
-                        "custom_extra_body": {},
                     },
-                    "小马算力": {
+                    "TokenPony": {
                         "id": "tokenpony",
                         "provider": "tokenpony",
                         "type": "openai_chat_completion",
@@ -1067,14 +1059,9 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.tokenpony.cn/v1",
                         "timeout": 120,
-                        "model_config": {
-                            "model": "kimi-k2-instruct-0905",
-                            "temperature": 0.7,
-                        },
                         "custom_headers": {},
-                        "custom_extra_body": {},
                     },
-                    "优云智算": {
+                    "Compshare": {
                         "id": "compshare",
                         "provider": "compshare",
                         "type": "openai_chat_completion",
@@ -1083,42 +1070,18 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.modelverse.cn/v1",
                         "timeout": 120,
-                        "model_config": {
-                            "model": "moonshotai/Kimi-K2-Instruct",
-                        },
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
                     },
-                    "Kimi": {
-                        "id": "moonshot",
-                        "provider": "moonshot",
+                    "ModelScope": {
+                        "id": "modelscope",
+                        "provider": "modelscope",
                         "type": "openai_chat_completion",
                         "provider_type": "chat_completion",
                         "enable": True,
                         "key": [],
                         "timeout": 120,
-                        "api_base": "https://api.moonshot.cn/v1",
-                        "model_config": {"model": "moonshot-v1-8k", "temperature": 0.4},
+                        "api_base": "https://api-inference.modelscope.cn/v1",
                         "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
-                    "智谱 AI": {
-                        "id": "zhipu_default",
-                        "provider": "zhipu",
-                        "type": "zhipu_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "timeout": 120,
-                        "api_base": "https://open.bigmodel.cn/api/paas/v4/",
-                        "model_config": {
-                            "model": "glm-4-flash",
-                        },
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
                     },
                     "Dify": {
                         "id": "dify_app_default",
@@ -1133,7 +1096,6 @@ CONFIG_METADATA_2 = {
                         "dify_query_input_key": "astrbot_text_query",
                         "variables": {},
                         "timeout": 60,
-                        "hint": "请确保你在 AstrBot 里设置的 APP 类型和 Dify 里面创建的应用的类型一致！",
                     },
                     "Coze": {
                         "id": "coze",
@@ -1164,20 +1126,6 @@ CONFIG_METADATA_2 = {
                         "variables": {},
                         "timeout": 60,
                     },
-                    "ModelScope": {
-                        "id": "modelscope",
-                        "provider": "modelscope",
-                        "type": "openai_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "timeout": 120,
-                        "api_base": "https://api-inference.modelscope.cn/v1",
-                        "model_config": {"model": "Qwen/Qwen3-32B", "temperature": 0.4},
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "image", "tool_use"],
-                    },
                     "FastGPT": {
                         "id": "fastgpt",
                         "provider": "fastgpt",
@@ -1201,7 +1149,6 @@ CONFIG_METADATA_2 = {
                         "model": "whisper-1",
                     },
                     "Whisper(Local)": {
-                        "hint": "启用前请 pip 安装 openai-whisper 库（N卡用户大约下载 2GB，主要是 torch 和 cuda，CPU 用户大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                         "provider": "openai",
                         "type": "openai_whisper_selfhost",
                         "provider_type": "speech_to_text",
@@ -1210,7 +1157,6 @@ CONFIG_METADATA_2 = {
                         "model": "tiny",
                     },
                     "SenseVoice(Local)": {
-                        "hint": "启用前请 pip 安装 funasr、funasr_onnx、torchaudio、torch、modelscope、jieba 库（默认使用CPU，大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                         "type": "sensevoice_stt_selfhost",
                         "provider": "sensevoice",
                         "provider_type": "speech_to_text",
@@ -1232,7 +1178,6 @@ CONFIG_METADATA_2 = {
                         "timeout": "20",
                     },
                     "Edge TTS": {
-                        "hint": "提示：使用这个服务前需要安装有 ffmpeg，并且可以直接在终端调用 ffmpeg 指令。",
                         "id": "edge_tts",
                         "provider": "microsoft",
                         "type": "edge_tts",
@@ -1342,7 +1287,7 @@ CONFIG_METADATA_2 = {
                         "minimax-is-timber-weight": False,
                         "minimax-voice-id": "female-shaonv",
                         "minimax-timber-weight": '[\n    {\n        "voice_id": "Chinese (Mandarin)_Warm_Girl",\n        "weight": 25\n    },\n    {\n        "voice_id": "Chinese (Mandarin)_BashfulGirl",\n        "weight": 50\n    }\n]',
-                        "minimax-voice-emotion": "neutral",
+                        "minimax-voice-emotion": "auto",
                         "minimax-voice-latex": False,
                         "minimax-voice-english-normalization": False,
                         "timeout": 20,
@@ -1448,6 +1393,10 @@ CONFIG_METADATA_2 = {
                     },
                 },
                 "items": {
+                    "provider_source_id": {
+                        "invisible": True,
+                        "type": "string",
+                    },
                     "xai_native_search": {
                         "description": "启用原生搜索功能",
                         "type": "bool",
@@ -1818,13 +1767,35 @@ CONFIG_METADATA_2 = {
                         },
                     },
                     "gm_thinking_config": {
-                        "description": "Gemini思考设置",
+                        "description": "Thinking Config",
                         "type": "object",
                         "items": {
                             "budget": {
-                                "description": "思考预算",
+                                "description": "Thinking Budget",
                                 "type": "int",
-                                "hint": "模型应该生成的思考Token的数量，设为0关闭思考。除gemini-2.5-flash外的模型会静默忽略此参数。",
+                                "hint": "Guides the model on the specific number of thinking tokens to use for reasoning. See: https://ai.google.dev/gemini-api/docs/thinking#set-budget",
+                            },
+                            "level": {
+                                "description": "Thinking Level",
+                                "type": "string",
+                                "hint": "Recommended for Gemini 3 models and onwards, lets you control reasoning behavior.See: https://ai.google.dev/gemini-api/docs/thinking#thinking-levels",
+                                "options": [
+                                    "MINIMAL",
+                                    "LOW",
+                                    "MEDIUM",
+                                    "HIGH",
+                                ],
+                            },
+                        },
+                    },
+                    "anth_thinking_config": {
+                        "description": "Thinking Config",
+                        "type": "object",
+                        "items": {
+                            "budget": {
+                                "description": "Thinking Budget",
+                                "type": "int",
+                                "hint": "Anthropic thinking.budget_tokens param. Must >= 1024. See: https://platform.claude.com/docs/en/build-with-claude/extended-thinking",
                             },
                         },
                     },
@@ -1899,15 +1870,18 @@ CONFIG_METADATA_2 = {
                     "minimax-voice-emotion": {
                         "type": "string",
                         "description": "情绪",
-                        "hint": "控制合成语音的情绪",
+                        "hint": "控制合成语音的情绪。当为 auto 时，将根据文本内容自动选择情绪。",
                         "options": [
+                            "auto",
                             "happy",
                             "sad",
                             "angry",
                             "fearful",
                             "disgusted",
                             "surprised",
-                            "neutral",
+                            "calm",
+                            "fluent",
+                            "whisper",
                         ],
                     },
                     "minimax-voice-latex": {
@@ -2005,7 +1979,6 @@ CONFIG_METADATA_2 = {
                     "id": {
                         "description": "ID",
                         "type": "string",
-                        "hint": "模型提供商名字。",
                     },
                     "type": {
                         "description": "模型提供商种类",
@@ -2025,29 +1998,15 @@ CONFIG_METADATA_2 = {
                         "description": "API Key",
                         "type": "list",
                         "items": {"type": "string"},
-                        "hint": "提供商 API Key。",
                     },
                     "api_base": {
                         "description": "API Base URL",
                         "type": "string",
-                        "hint": "API Base URL 请在模型提供商处获得。如出现 404 报错，尝试在地址末尾加上 /v1",
                     },
-                    "model_config": {
-                        "description": "模型配置",
-                        "type": "object",
-                        "items": {
-                            "model": {
-                                "description": "模型名称",
-                                "type": "string",
-                                "hint": "模型名称，如 gpt-4o-mini, deepseek-chat。",
-                            },
-                            "max_tokens": {
-                                "description": "模型最大输出长度（tokens）",
-                                "type": "int",
-                            },
-                            "temperature": {"description": "温度", "type": "float"},
-                            "top_p": {"description": "Top P值", "type": "float"},
-                        },
+                    "model": {
+                        "description": "模型 ID",
+                        "type": "string",
+                        "hint": "模型名称，如 gpt-4o-mini, deepseek-chat。",
                     },
                     "dify_api_key": {
                         "description": "API Key",
@@ -2209,6 +2168,9 @@ CONFIG_METADATA_2 = {
                     "use_file_service": {
                         "type": "bool",
                     },
+                    "trigger_probability": {
+                        "type": "float",
+                    },
                 },
             },
             "provider_ltm_settings": {
@@ -2419,6 +2381,14 @@ CONFIG_METADATA_3 = {
                             "provider_tts_settings.enable": True,
                         },
                     },
+                    "provider_tts_settings.trigger_probability": {
+                        "description": "TTS 触发概率",
+                        "type": "float",
+                        "slider": {"min": 0, "max": 1, "step": 0.05},
+                        "condition": {
+                            "provider_tts_settings.enable": True,
+                        },
+                    },
                     "provider_settings.image_caption_prompt": {
                         "description": "图片转述提示词",
                         "type": "text",
@@ -2986,6 +2956,7 @@ CONFIG_METADATA_3 = {
                         "description": "回复概率",
                         "type": "float",
                         "hint": "0.0-1.0 之间的数值",
+                        "slider": {"min": 0, "max": 1, "step": 0.05},
                         "condition": {
                             "provider_ltm_settings.active_reply.enable": True,
                         },
@@ -3093,4 +3064,5 @@ DEFAULT_VALUE_MAP = {
     "text": "",
     "list": [],
     "object": {},
+    "template_list": [],
 }

astrbot/core/config/i18n_utils.py

@@ -79,6 +79,7 @@ class ConfigMetadataI18n:
                             "_special",
                             "invisible",
                             "options",
+                            "slider",
                         ]:
                             if attr in field_data:
                                 field_result[attr] = field_data[attr]

astrbot/core/core_lifecycle.py

@@ -33,6 +33,7 @@ from astrbot.core.star.context import Context
 from astrbot.core.star.star_handler import EventType, star_handlers_registry, star_map
 from astrbot.core.umop_config_router import UmopConfigRouter
 from astrbot.core.updator import AstrBotUpdator
+from astrbot.core.utils.llm_metadata import update_llm_metadata
 from astrbot.core.utils.migra_helper import migra
 
 from . import astrbot_config, html_renderer
@@ -185,6 +186,8 @@ class AstrBotCoreLifecycle:
         # 初始化关闭控制面板的事件
         self.dashboard_shutdown_event = asyncio.Event()
 
+        asyncio.create_task(update_llm_metadata())
+
     def _load(self) -> None:
         """加载事件总线和任务并初始化."""
         # 创建一个异步任务来执行事件总线的 dispatch() 方法

astrbot/core/db/__init__.py

@@ -9,6 +9,8 @@ from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_asyn
 
 from astrbot.core.db.po import (
     Attachment,
+    CommandConfig,
+    CommandConflict,
     ConversationV2,
     Persona,
     PlatformMessageHistory,
@@ -314,6 +316,76 @@ class BaseDatabase(abc.ABC):
         """Clear all preferences for a specific scope ID."""
         ...
 
+    @abc.abstractmethod
+    async def get_command_configs(self) -> list[CommandConfig]:
+        """Get all stored command configurations."""
+        ...
+
+    @abc.abstractmethod
+    async def get_command_config(self, handler_full_name: str) -> CommandConfig | None:
+        """Fetch a single command configuration by handler."""
+        ...
+
+    @abc.abstractmethod
+    async def upsert_command_config(
+        self,
+        handler_full_name: str,
+        plugin_name: str,
+        module_path: str,
+        original_command: str,
+        *,
+        resolved_command: str | None = None,
+        enabled: bool | None = None,
+        keep_original_alias: bool | None = None,
+        conflict_key: str | None = None,
+        resolution_strategy: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_managed: bool | None = None,
+    ) -> CommandConfig:
+        """Create or update a command configuration."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_command_config(self, handler_full_name: str) -> None:
+        """Delete a single command configuration."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_command_configs(self, handler_full_names: list[str]) -> None:
+        """Bulk delete command configurations."""
+        ...
+
+    @abc.abstractmethod
+    async def list_command_conflicts(
+        self,
+        status: str | None = None,
+    ) -> list[CommandConflict]:
+        """List recorded command conflict entries."""
+        ...
+
+    @abc.abstractmethod
+    async def upsert_command_conflict(
+        self,
+        conflict_key: str,
+        handler_full_name: str,
+        plugin_name: str,
+        *,
+        status: str | None = None,
+        resolution: str | None = None,
+        resolved_command: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_generated: bool | None = None,
+    ) -> CommandConflict:
+        """Create or update a conflict record."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_command_conflicts(self, ids: list[int]) -> None:
+        """Delete conflict records."""
+        ...
+
     # @abc.abstractmethod
     # async def insert_llm_message(
     #     self,

astrbot/core/db/po.py

@@ -234,6 +234,65 @@ class Attachment(SQLModel, table=True):
     )
 
 
+class CommandConfig(SQLModel, table=True):
+    """Per-command configuration overrides for dashboard management."""
+
+    __tablename__ = "command_configs"  # type: ignore
+
+    handler_full_name: str = Field(
+        primary_key=True,
+        max_length=512,
+    )
+    plugin_name: str = Field(nullable=False, max_length=255)
+    module_path: str = Field(nullable=False, max_length=255)
+    original_command: str = Field(nullable=False, max_length=255)
+    resolved_command: str | None = Field(default=None, max_length=255)
+    enabled: bool = Field(default=True, nullable=False)
+    keep_original_alias: bool = Field(default=False, nullable=False)
+    conflict_key: str | None = Field(default=None, max_length=255)
+    resolution_strategy: str | None = Field(default=None, max_length=64)
+    note: str | None = Field(default=None, sa_type=Text)
+    extra_data: dict | None = Field(default=None, sa_type=JSON)
+    auto_managed: bool = Field(default=False, nullable=False)
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        sa_column_kwargs={"onupdate": datetime.now(timezone.utc)},
+    )
+
+
+class CommandConflict(SQLModel, table=True):
+    """Conflict tracking for duplicated command names."""
+
+    __tablename__ = "command_conflicts"  # type: ignore
+
+    id: int | None = Field(
+        default=None, primary_key=True, sa_column_kwargs={"autoincrement": True}
+    )
+    conflict_key: str = Field(nullable=False, max_length=255)
+    handler_full_name: str = Field(nullable=False, max_length=512)
+    plugin_name: str = Field(nullable=False, max_length=255)
+    status: str = Field(default="pending", max_length=32)
+    resolution: str | None = Field(default=None, max_length=64)
+    resolved_command: str | None = Field(default=None, max_length=255)
+    note: str | None = Field(default=None, sa_type=Text)
+    extra_data: dict | None = Field(default=None, sa_type=JSON)
+    auto_generated: bool = Field(default=False, nullable=False)
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        sa_column_kwargs={"onupdate": datetime.now(timezone.utc)},
+    )
+
+    __table_args__ = (
+        UniqueConstraint(
+            "conflict_key",
+            "handler_full_name",
+            name="uix_conflict_handler",
+        ),
+    )
+
+
 @dataclass
 class Conversation:
     """LLM 对话类

astrbot/core/db/sqlite.py

@@ -1,6 +1,7 @@
 import asyncio
 import threading
 import typing as T
+from collections.abc import Awaitable, Callable
 from datetime import datetime, timedelta, timezone
 
 from sqlalchemy import CursorResult
@@ -10,6 +11,8 @@ from sqlmodel import col, delete, desc, func, or_, select, text, update
 from astrbot.core.db import BaseDatabase
 from astrbot.core.db.po import (
     Attachment,
+    CommandConfig,
+    CommandConflict,
     ConversationV2,
     Persona,
     PlatformMessageHistory,
@@ -26,6 +29,7 @@ from astrbot.core.db.po import (
 )
 
 NOT_GIVEN = T.TypeVar("NOT_GIVEN")
+TxResult = T.TypeVar("TxResult")
 
 
 class SQLiteDatabase(BaseDatabase):
@@ -670,6 +674,242 @@ class SQLiteDatabase(BaseDatabase):
                 )
             await session.commit()
 
+    # ====
+    # Command Configuration & Conflict Tracking
+    # ====
+
+    async def _run_in_tx(
+        self,
+        fn: Callable[[AsyncSession], Awaitable[TxResult]],
+    ) -> TxResult:
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                return await fn(session)
+
+    @staticmethod
+    def _apply_updates(model, **updates) -> None:
+        for field, value in updates.items():
+            if value is not None:
+                setattr(model, field, value)
+
+    @staticmethod
+    def _new_command_config(
+        handler_full_name: str,
+        plugin_name: str,
+        module_path: str,
+        original_command: str,
+        *,
+        resolved_command: str | None = None,
+        enabled: bool | None = None,
+        keep_original_alias: bool | None = None,
+        conflict_key: str | None = None,
+        resolution_strategy: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_managed: bool | None = None,
+    ) -> CommandConfig:
+        return CommandConfig(
+            handler_full_name=handler_full_name,
+            plugin_name=plugin_name,
+            module_path=module_path,
+            original_command=original_command,
+            resolved_command=resolved_command,
+            enabled=True if enabled is None else enabled,
+            keep_original_alias=False
+            if keep_original_alias is None
+            else keep_original_alias,
+            conflict_key=conflict_key or original_command,
+            resolution_strategy=resolution_strategy,
+            note=note,
+            extra_data=extra_data,
+            auto_managed=bool(auto_managed),
+        )
+
+    @staticmethod
+    def _new_command_conflict(
+        conflict_key: str,
+        handler_full_name: str,
+        plugin_name: str,
+        *,
+        status: str | None = None,
+        resolution: str | None = None,
+        resolved_command: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_generated: bool | None = None,
+    ) -> CommandConflict:
+        return CommandConflict(
+            conflict_key=conflict_key,
+            handler_full_name=handler_full_name,
+            plugin_name=plugin_name,
+            status=status or "pending",
+            resolution=resolution,
+            resolved_command=resolved_command,
+            note=note,
+            extra_data=extra_data,
+            auto_generated=bool(auto_generated),
+        )
+
+    async def get_command_configs(self) -> list[CommandConfig]:
+        async with self.get_db() as session:
+            session: AsyncSession
+            result = await session.execute(select(CommandConfig))
+            return list(result.scalars().all())
+
+    async def get_command_config(
+        self,
+        handler_full_name: str,
+    ) -> CommandConfig | None:
+        async with self.get_db() as session:
+            session: AsyncSession
+            return await session.get(CommandConfig, handler_full_name)
+
+    async def upsert_command_config(
+        self,
+        handler_full_name: str,
+        plugin_name: str,
+        module_path: str,
+        original_command: str,
+        *,
+        resolved_command: str | None = None,
+        enabled: bool | None = None,
+        keep_original_alias: bool | None = None,
+        conflict_key: str | None = None,
+        resolution_strategy: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_managed: bool | None = None,
+    ) -> CommandConfig:
+        async def _op(session: AsyncSession) -> CommandConfig:
+            config = await session.get(CommandConfig, handler_full_name)
+            if not config:
+                config = self._new_command_config(
+                    handler_full_name,
+                    plugin_name,
+                    module_path,
+                    original_command,
+                    resolved_command=resolved_command,
+                    enabled=enabled,
+                    keep_original_alias=keep_original_alias,
+                    conflict_key=conflict_key,
+                    resolution_strategy=resolution_strategy,
+                    note=note,
+                    extra_data=extra_data,
+                    auto_managed=auto_managed,
+                )
+                session.add(config)
+            else:
+                self._apply_updates(
+                    config,
+                    plugin_name=plugin_name,
+                    module_path=module_path,
+                    original_command=original_command,
+                    resolved_command=resolved_command,
+                    enabled=enabled,
+                    keep_original_alias=keep_original_alias,
+                    conflict_key=conflict_key,
+                    resolution_strategy=resolution_strategy,
+                    note=note,
+                    extra_data=extra_data,
+                    auto_managed=auto_managed,
+                )
+            await session.flush()
+            await session.refresh(config)
+            return config
+
+        return await self._run_in_tx(_op)
+
+    async def delete_command_config(self, handler_full_name: str) -> None:
+        await self.delete_command_configs([handler_full_name])
+
+    async def delete_command_configs(self, handler_full_names: list[str]) -> None:
+        if not handler_full_names:
+            return
+
+        async def _op(session: AsyncSession) -> None:
+            await session.execute(
+                delete(CommandConfig).where(
+                    col(CommandConfig.handler_full_name).in_(handler_full_names),
+                ),
+            )
+
+        await self._run_in_tx(_op)
+
+    async def list_command_conflicts(
+        self,
+        status: str | None = None,
+    ) -> list[CommandConflict]:
+        async with self.get_db() as session:
+            session: AsyncSession
+            query = select(CommandConflict)
+            if status:
+                query = query.where(CommandConflict.status == status)
+            result = await session.execute(query)
+            return list(result.scalars().all())
+
+    async def upsert_command_conflict(
+        self,
+        conflict_key: str,
+        handler_full_name: str,
+        plugin_name: str,
+        *,
+        status: str | None = None,
+        resolution: str | None = None,
+        resolved_command: str | None = None,
+        note: str | None = None,
+        extra_data: dict | None = None,
+        auto_generated: bool | None = None,
+    ) -> CommandConflict:
+        async def _op(session: AsyncSession) -> CommandConflict:
+            result = await session.execute(
+                select(CommandConflict).where(
+                    CommandConflict.conflict_key == conflict_key,
+                    CommandConflict.handler_full_name == handler_full_name,
+                ),
+            )
+            record = result.scalar_one_or_none()
+            if not record:
+                record = self._new_command_conflict(
+                    conflict_key,
+                    handler_full_name,
+                    plugin_name,
+                    status=status,
+                    resolution=resolution,
+                    resolved_command=resolved_command,
+                    note=note,
+                    extra_data=extra_data,
+                    auto_generated=auto_generated,
+                )
+                session.add(record)
+            else:
+                self._apply_updates(
+                    record,
+                    plugin_name=plugin_name,
+                    status=status,
+                    resolution=resolution,
+                    resolved_command=resolved_command,
+                    note=note,
+                    extra_data=extra_data,
+                    auto_generated=auto_generated,
+                )
+            await session.flush()
+            await session.refresh(record)
+            return record
+
+        return await self._run_in_tx(_op)
+
+    async def delete_command_conflicts(self, ids: list[int]) -> None:
+        if not ids:
+            return
+
+        async def _op(session: AsyncSession) -> None:
+            await session.execute(
+                delete(CommandConflict).where(col(CommandConflict.id).in_(ids)),
+            )
+
+        await self._run_in_tx(_op)
+
     # ====
     # Deprecated Methods
     # ====

astrbot/core/log.py

@@ -58,7 +58,7 @@ def is_plugin_path(pathname):
         return False
 
     norm_path = os.path.normpath(pathname)
-    return ("data/plugins" in norm_path) or ("packages/" in norm_path)
+    return ("data/plugins" in norm_path) or ("astrbot/builtin_stars/" in norm_path)
 
 
 def get_short_level_name(level_name):

astrbot/core/message/components.py

@@ -629,12 +629,11 @@ class Nodes(BaseMessageComponent):
 
 class Json(BaseMessageComponent):
     type = ComponentType.Json
-    data: str | dict
-    resid: int | None = 0
+    data: dict
 
-    def __init__(self, data, **_):
-        if isinstance(data, dict):
-            data = json.dumps(data)
+    def __init__(self, data: str | dict, **_):
+        if isinstance(data, str):
+            data = json.loads(data)
         super().__init__(data=data, **_)
 
 

astrbot/core/pipeline/process_stage/method/agent_sub_stages/internal.py

@@ -6,6 +6,7 @@ import json
 from collections.abc import AsyncGenerator
 
 from astrbot.core import logger
+from astrbot.core.agent.message import Message
 from astrbot.core.agent.tool import ToolSet
 from astrbot.core.astr_agent_context import AstrAgentContext
 from astrbot.core.conversation_mgr import Conversation
@@ -294,6 +295,7 @@ class InternalAgentSubStage(Stage):
         event: AstrMessageEvent,
         req: ProviderRequest,
         llm_response: LLMResponse | None,
+        all_messages: list[Message],
     ):
         if (
             not req
@@ -307,26 +309,23 @@ class InternalAgentSubStage(Stage):
             logger.debug("LLM 响应为空，不保存记录。")
             return
 
-        if req.contexts is None:
-            req.contexts = []
+        # using agent context messages to save to history
+        message_to_save = []
+        for message in all_messages:
+            if message.role == "system":
+                # we do not save system messages to history
+                continue
+            if message.role in ["assistant", "user"] and getattr(
+                message, "_no_save", None
+            ):
+                # we do not save user and assistant messages that are marked as _no_save
+                continue
+            message_to_save.append(message.model_dump())
 
-        # 历史上下文
-        messages = copy.deepcopy(req.contexts)
-        # 这一轮对话请求的用户输入
-        messages.append(await req.assemble_context())
-        # 这一轮对话的 LLM 响应
-        if req.tool_calls_result:
-            if not isinstance(req.tool_calls_result, list):
-                messages.extend(req.tool_calls_result.to_openai_messages())
-            elif isinstance(req.tool_calls_result, list):
-                for tcr in req.tool_calls_result:
-                    messages.extend(tcr.to_openai_messages())
-        messages.append({"role": "assistant", "content": llm_response.completion_text})
-        messages = list(filter(lambda item: "_no_save" not in item, messages))
         await self.conv_manager.update_conversation(
             event.unified_msg_origin,
             req.conversation.cid,
-            history=messages,
+            history=message_to_save,
         )
 
     def _fix_messages(self, messages: list[dict]) -> list[dict]:
@@ -350,174 +349,190 @@ class InternalAgentSubStage(Stage):
     ) -> AsyncGenerator[None, None]:
         req: ProviderRequest | None = None
 
-        provider = self._select_provider(event)
-        if provider is None:
-            return
-        if not isinstance(provider, Provider):
-            logger.error(f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。")
-            return
-
-        streaming_response = self.streaming_response
-        if (enable_streaming := event.get_extra("enable_streaming")) is not None:
-            streaming_response = bool(enable_streaming)
-
-        logger.debug("ready to request llm provider")
-        async with session_lock_manager.acquire_lock(event.unified_msg_origin):
-            logger.debug("acquired session lock for llm request")
-            if event.get_extra("provider_request"):
-                req = event.get_extra("provider_request")
-                assert isinstance(req, ProviderRequest), (
-                    "provider_request 必须是 ProviderRequest 类型。"
+        try:
+            provider = self._select_provider(event)
+            if provider is None:
+                return
+            if not isinstance(provider, Provider):
+                logger.error(
+                    f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。"
                 )
+                return
 
-                if req.conversation:
-                    req.contexts = json.loads(req.conversation.history)
+            streaming_response = self.streaming_response
+            if (enable_streaming := event.get_extra("enable_streaming")) is not None:
+                streaming_response = bool(enable_streaming)
 
-            else:
-                req = ProviderRequest()
-                req.prompt = ""
-                req.image_urls = []
-                if sel_model := event.get_extra("selected_model"):
-                    req.model = sel_model
-                if provider_wake_prefix and not event.message_str.startswith(
-                    provider_wake_prefix
-                ):
+            logger.debug("ready to request llm provider")
+            async with session_lock_manager.acquire_lock(event.unified_msg_origin):
+                logger.debug("acquired session lock for llm request")
+                if event.get_extra("provider_request"):
+                    req = event.get_extra("provider_request")
+                    assert isinstance(req, ProviderRequest), (
+                        "provider_request 必须是 ProviderRequest 类型。"
+                    )
+
+                    if req.conversation:
+                        req.contexts = json.loads(req.conversation.history)
+
+                else:
+                    req = ProviderRequest()
+                    req.prompt = ""
+                    req.image_urls = []
+                    if sel_model := event.get_extra("selected_model"):
+                        req.model = sel_model
+                    if provider_wake_prefix and not event.message_str.startswith(
+                        provider_wake_prefix
+                    ):
+                        return
+
+                    req.prompt = event.message_str[len(provider_wake_prefix) :]
+                    # func_tool selection 现在已经转移到 astrbot/builtin_stars/astrbot 插件中进行选择。
+                    # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
+                    for comp in event.message_obj.message:
+                        if isinstance(comp, Image):
+                            image_path = await comp.convert_to_file_path()
+                            req.image_urls.append(image_path)
+
+                    conversation = await self._get_session_conv(event)
+                    req.conversation = conversation
+                    req.contexts = json.loads(conversation.history)
+
+                    event.set_extra("provider_request", req)
+
+                # fix contexts json str
+                if isinstance(req.contexts, str):
+                    req.contexts = json.loads(req.contexts)
+
+                # apply file extract
+                if self.file_extract_enabled:
+                    try:
+                        await self._apply_file_extract(event, req)
+                    except Exception as e:
+                        logger.error(f"Error occurred while applying file extract: {e}")
+
+                if not req.prompt and not req.image_urls:
                     return
 
-                req.prompt = event.message_str[len(provider_wake_prefix) :]
-                # func_tool selection 现在已经转移到 packages/astrbot 插件中进行选择。
-                # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
-                for comp in event.message_obj.message:
-                    if isinstance(comp, Image):
-                        image_path = await comp.convert_to_file_path()
-                        req.image_urls.append(image_path)
+                # call event hook
+                if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
+                    return
 
-                conversation = await self._get_session_conv(event)
-                req.conversation = conversation
-                req.contexts = json.loads(conversation.history)
+                # apply knowledge base feature
+                await self._apply_kb(event, req)
 
-                event.set_extra("provider_request", req)
+                # truncate contexts to fit max length
+                if req.contexts:
+                    req.contexts = self._truncate_contexts(req.contexts)
+                    self._fix_messages(req.contexts)
 
-            # fix contexts json str
-            if isinstance(req.contexts, str):
-                req.contexts = json.loads(req.contexts)
+                # session_id
+                if not req.session_id:
+                    req.session_id = event.unified_msg_origin
 
-            # apply file extract
-            if self.file_extract_enabled:
-                try:
-                    await self._apply_file_extract(event, req)
-                except Exception as e:
-                    logger.error(f"Error occurred while applying file extract: {e}")
+                # check provider modalities, if provider does not support image/tool_use, clear them in request.
+                self._modalities_fix(provider, req)
 
-            if not req.prompt and not req.image_urls:
-                return
+                # filter tools, only keep tools from this pipeline's selected plugins
+                self._plugin_tool_fix(event, req)
 
-            # call event hook
-            if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
-                return
-
-            # apply knowledge base feature
-            await self._apply_kb(event, req)
-
-            # truncate contexts to fit max length
-            if req.contexts:
-                req.contexts = self._truncate_contexts(req.contexts)
-                self._fix_messages(req.contexts)
-
-            # session_id
-            if not req.session_id:
-                req.session_id = event.unified_msg_origin
-
-            # check provider modalities, if provider does not support image/tool_use, clear them in request.
-            self._modalities_fix(provider, req)
-
-            # filter tools, only keep tools from this pipeline's selected plugins
-            self._plugin_tool_fix(event, req)
-
-            stream_to_general = (
-                self.unsupported_streaming_strategy == "turn_off"
-                and not event.platform_meta.support_streaming_message
-            )
-            # 备份 req.contexts
-            backup_contexts = copy.deepcopy(req.contexts)
-
-            # run agent
-            agent_runner = AgentRunner()
-            logger.debug(
-                f"handle provider[id: {provider.provider_config['id']}] request: {req}",
-            )
-            astr_agent_ctx = AstrAgentContext(
-                context=self.ctx.plugin_manager.context,
-                event=event,
-            )
-            await agent_runner.reset(
-                provider=provider,
-                request=req,
-                run_context=AgentContextWrapper(
-                    context=astr_agent_ctx,
-                    tool_call_timeout=self.tool_call_timeout,
-                ),
-                tool_executor=FunctionToolExecutor(),
-                agent_hooks=MAIN_AGENT_HOOKS,
-                streaming=streaming_response,
-            )
-
-            if streaming_response and not stream_to_general:
-                # 流式响应
-                event.set_result(
-                    MessageEventResult()
-                    .set_result_content_type(ResultContentType.STREAMING_RESULT)
-                    .set_async_stream(
-                        run_agent(
-                            agent_runner,
-                            self.max_step,
-                            self.show_tool_use,
-                            show_reasoning=self.show_reasoning,
-                        ),
-                    ),
+                stream_to_general = (
+                    self.unsupported_streaming_strategy == "turn_off"
+                    and not event.platform_meta.support_streaming_message
                 )
-                yield
-                if agent_runner.done():
-                    if final_llm_resp := agent_runner.get_final_llm_resp():
-                        if final_llm_resp.completion_text:
-                            chain = (
-                                MessageChain()
-                                .message(final_llm_resp.completion_text)
-                                .chain
-                            )
-                        elif final_llm_resp.result_chain:
-                            chain = final_llm_resp.result_chain.chain
-                        else:
-                            chain = MessageChain().chain
-                        event.set_result(
-                            MessageEventResult(
-                                chain=chain,
-                                result_content_type=ResultContentType.STREAMING_FINISH,
+                # 备份 req.contexts
+                backup_contexts = copy.deepcopy(req.contexts)
+
+                # run agent
+                agent_runner = AgentRunner()
+                logger.debug(
+                    f"handle provider[id: {provider.provider_config['id']}] request: {req}",
+                )
+                astr_agent_ctx = AstrAgentContext(
+                    context=self.ctx.plugin_manager.context,
+                    event=event,
+                )
+                await agent_runner.reset(
+                    provider=provider,
+                    request=req,
+                    run_context=AgentContextWrapper(
+                        context=astr_agent_ctx,
+                        tool_call_timeout=self.tool_call_timeout,
+                    ),
+                    tool_executor=FunctionToolExecutor(),
+                    agent_hooks=MAIN_AGENT_HOOKS,
+                    streaming=streaming_response,
+                )
+
+                if streaming_response and not stream_to_general:
+                    # 流式响应
+                    event.set_result(
+                        MessageEventResult()
+                        .set_result_content_type(ResultContentType.STREAMING_RESULT)
+                        .set_async_stream(
+                            run_agent(
+                                agent_runner,
+                                self.max_step,
+                                self.show_tool_use,
+                                show_reasoning=self.show_reasoning,
                             ),
-                        )
-            else:
-                async for _ in run_agent(
-                    agent_runner,
-                    self.max_step,
-                    self.show_tool_use,
-                    stream_to_general,
-                    show_reasoning=self.show_reasoning,
-                ):
+                        ),
+                    )
                     yield
+                    if agent_runner.done():
+                        if final_llm_resp := agent_runner.get_final_llm_resp():
+                            if final_llm_resp.completion_text:
+                                chain = (
+                                    MessageChain()
+                                    .message(final_llm_resp.completion_text)
+                                    .chain
+                                )
+                            elif final_llm_resp.result_chain:
+                                chain = final_llm_resp.result_chain.chain
+                            else:
+                                chain = MessageChain().chain
+                            event.set_result(
+                                MessageEventResult(
+                                    chain=chain,
+                                    result_content_type=ResultContentType.STREAMING_FINISH,
+                                ),
+                            )
+                else:
+                    async for _ in run_agent(
+                        agent_runner,
+                        self.max_step,
+                        self.show_tool_use,
+                        stream_to_general,
+                        show_reasoning=self.show_reasoning,
+                    ):
+                        yield
 
-            # 恢复备份的 contexts
-            req.contexts = backup_contexts
+                # 恢复备份的 contexts
+                req.contexts = backup_contexts
 
-            await self._save_to_history(event, req, agent_runner.get_final_llm_resp())
+                await self._save_to_history(
+                    event,
+                    req,
+                    agent_runner.get_final_llm_resp(),
+                    agent_runner.run_context.messages,
+                )
 
-        # 异步处理 WebChat 特殊情况
-        if event.get_platform_name() == "webchat":
-            asyncio.create_task(self._handle_webchat(event, req, provider))
+            # 异步处理 WebChat 特殊情况
+            if event.get_platform_name() == "webchat":
+                asyncio.create_task(self._handle_webchat(event, req, provider))
 
-        asyncio.create_task(
-            Metric.upload(
-                llm_tick=1,
-                model_name=agent_runner.provider.get_model(),
-                provider_type=agent_runner.provider.meta().type,
-            ),
-        )
+            asyncio.create_task(
+                Metric.upload(
+                    llm_tick=1,
+                    model_name=agent_runner.provider.get_model(),
+                    provider_type=agent_runner.provider.meta().type,
+                ),
+            )
+
+        except Exception as e:
+            logger.error(f"Error occurred while processing agent: {e}")
+            await event.send(
+                MessageChain().message(
+                    f"Error occurred while processing agent request: {e}"
+                )
+            )

astrbot/core/pipeline/respond/stage.py

@@ -119,7 +119,7 @@ class RespondStage(Stage):
 
         if (result := event.get_result()) is None:
             return False
-        if self.only_llm_result and result.is_llm_result():
+        if self.only_llm_result and not result.is_llm_result():
             return False
 
         if event.get_platform_name() in [
@@ -158,7 +158,11 @@ class RespondStage(Stage):
         result = event.get_result()
         if result is None:
             return
+        if event.get_extra("_streaming_finished", False):
+            # prevent some plugin make result content type to LLM_RESULT after streaming finished, lead to send again
+            return
         if result.result_content_type == ResultContentType.STREAMING_FINISH:
+            event.set_extra("_streaming_finished", True)
             return
 
         logger.info(

astrbot/core/pipeline/result_decorate/stage.py

@@ -1,3 +1,4 @@
+import random
 import re
 import time
 import traceback
@@ -42,6 +43,18 @@ class ResultDecorateStage(Stage):
             "forward_threshold"
         ]
 
+        trigger_probability = ctx.astrbot_config["provider_tts_settings"].get(
+            "trigger_probability",
+            1,
+        )
+        try:
+            self.tts_trigger_probability = max(
+                0.0,
+                min(float(trigger_probability), 1.0),
+            )
+        except (TypeError, ValueError):
+            self.tts_trigger_probability = 1.0
+
         # 分段回复
         self.words_count_threshold = int(
             ctx.astrbot_config["platform_settings"]["segmented_reply"][
@@ -85,6 +98,9 @@ class ResultDecorateStage(Stage):
                     self.content_safe_check_stage = stage_cls()
                     await self.content_safe_check_stage.initialize(ctx)
 
+        provider_cfg = ctx.astrbot_config.get("provider_settings", {})
+        self.show_reasoning = provider_cfg.get("display_reasoning_text", False)
+
     def _split_text_by_words(self, text: str) -> list[str]:
         """使用分段词列表分段文本"""
         if not self.split_words_pattern:
@@ -241,63 +257,75 @@ class ResultDecorateStage(Stage):
                 event.unified_msg_origin,
             )
 
-            if (
-                self.ctx.astrbot_config["provider_tts_settings"]["enable"]
+            should_tts = (
+                bool(self.ctx.astrbot_config["provider_tts_settings"]["enable"])
                 and result.is_llm_result()
                 and SessionServiceManager.should_process_tts_request(event)
+                and random.random() <= self.tts_trigger_probability
+                and tts_provider
+            )
+            if should_tts and not tts_provider:
+                logger.warning(
+                    f"会话 {event.unified_msg_origin} 未配置文本转语音模型。",
+                )
+
+            if (
+                not should_tts
+                and self.show_reasoning
+                and event.get_extra("_llm_reasoning_content")
             ):
-                if not tts_provider:
-                    logger.warning(
-                        f"会话 {event.unified_msg_origin} 未配置文本转语音模型。",
-                    )
-                else:
-                    new_chain = []
-                    for comp in result.chain:
-                        if isinstance(comp, Plain) and len(comp.text) > 1:
-                            try:
-                                logger.info(f"TTS 请求: {comp.text}")
-                                audio_path = await tts_provider.get_audio(comp.text)
-                                logger.info(f"TTS 结果: {audio_path}")
-                                if not audio_path:
-                                    logger.error(
-                                        f"由于 TTS 音频文件未找到，消息段转语音失败: {comp.text}",
-                                    )
-                                    new_chain.append(comp)
-                                    continue
+                # inject reasoning content to chain
+                reasoning_content = event.get_extra("_llm_reasoning_content")
+                result.chain.insert(0, Plain(f"🤔 思考: {reasoning_content}\n"))
 
-                                use_file_service = self.ctx.astrbot_config[
-                                    "provider_tts_settings"
-                                ]["use_file_service"]
-                                callback_api_base = self.ctx.astrbot_config[
-                                    "callback_api_base"
-                                ]
-                                dual_output = self.ctx.astrbot_config[
-                                    "provider_tts_settings"
-                                ]["dual_output"]
-
-                                url = None
-                                if use_file_service and callback_api_base:
-                                    token = await file_token_service.register_file(
-                                        audio_path,
-                                    )
-                                    url = f"{callback_api_base}/api/file/{token}"
-                                    logger.debug(f"已注册：{url}")
-
-                                new_chain.append(
-                                    Record(
-                                        file=url or audio_path,
-                                        url=url or audio_path,
-                                    ),
+            if should_tts and tts_provider:
+                new_chain = []
+                for comp in result.chain:
+                    if isinstance(comp, Plain) and len(comp.text) > 1:
+                        try:
+                            logger.info(f"TTS 请求: {comp.text}")
+                            audio_path = await tts_provider.get_audio(comp.text)
+                            logger.info(f"TTS 结果: {audio_path}")
+                            if not audio_path:
+                                logger.error(
+                                    f"由于 TTS 音频文件未找到，消息段转语音失败: {comp.text}",
                                 )
-                                if dual_output:
-                                    new_chain.append(comp)
-                            except Exception:
-                                logger.error(traceback.format_exc())
-                                logger.error("TTS 失败，使用文本发送。")
                                 new_chain.append(comp)
-                        else:
+                                continue
+
+                            use_file_service = self.ctx.astrbot_config[
+                                "provider_tts_settings"
+                            ]["use_file_service"]
+                            callback_api_base = self.ctx.astrbot_config[
+                                "callback_api_base"
+                            ]
+                            dual_output = self.ctx.astrbot_config[
+                                "provider_tts_settings"
+                            ]["dual_output"]
+
+                            url = None
+                            if use_file_service and callback_api_base:
+                                token = await file_token_service.register_file(
+                                    audio_path,
+                                )
+                                url = f"{callback_api_base}/api/file/{token}"
+                                logger.debug(f"已注册：{url}")
+
+                            new_chain.append(
+                                Record(
+                                    file=url or audio_path,
+                                    url=url or audio_path,
+                                ),
+                            )
+                            if dual_output:
+                                new_chain.append(comp)
+                        except Exception:
+                            logger.error(traceback.format_exc())
+                            logger.error("TTS 失败，使用文本发送。")
                             new_chain.append(comp)
-                    result.chain = new_chain
+                    else:
+                        new_chain.append(comp)
+                result.chain = new_chain
 
             # 文本转图片
             elif (

astrbot/core/pipeline/waking_check/stage.py

@@ -1,9 +1,10 @@
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncGenerator, Callable
 
 from astrbot import logger
 from astrbot.core.message.components import At, AtAll, Reply
 from astrbot.core.message.message_event_result import MessageChain, MessageEventResult
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.platform.message_type import MessageType
 from astrbot.core.star.filter.command_group import CommandGroupFilter
 from astrbot.core.star.filter.permission import PermissionTypeFilter
 from astrbot.core.star.session_plugin_manager import SessionPluginManager
@@ -13,6 +14,23 @@ from astrbot.core.star.star_handler import EventType, star_handlers_registry
 from ..context import PipelineContext
 from ..stage import Stage, register_stage
 
+UNIQUE_SESSION_ID_BUILDERS: dict[str, Callable[[AstrMessageEvent], str | None]] = {
+    "aiocqhttp": lambda e: f"{e.get_sender_id()}_{e.get_group_id()}",
+    "slack": lambda e: f"{e.get_sender_id()}_{e.get_group_id()}",
+    "dingtalk": lambda e: e.get_sender_id(),
+    "qq_official": lambda e: e.get_sender_id(),
+    "qq_official_webhook": lambda e: e.get_sender_id(),
+    "lark": lambda e: f"{e.get_sender_id()}%{e.get_group_id()}",
+    "misskey": lambda e: f"{e.get_session_id()}_{e.get_sender_id()}",
+    "wechatpadpro": lambda e: f"{e.get_group_id()}#{e.get_sender_id()}",
+}
+
+
+def build_unique_session_id(event: AstrMessageEvent) -> str | None:
+    platform = event.get_platform_name()
+    builder = UNIQUE_SESSION_ID_BUILDERS.get(platform)
+    return builder(event) if builder else None
+
 
 @register_stage
 class WakingCheckStage(Stage):
@@ -53,18 +71,27 @@ class WakingCheckStage(Stage):
         self.disable_builtin_commands = self.ctx.astrbot_config.get(
             "disable_builtin_commands", False
         )
+        platform_settings = self.ctx.astrbot_config.get("platform_settings", {})
+        self.unique_session = platform_settings.get("unique_session", False)
 
     async def process(
         self,
         event: AstrMessageEvent,
     ) -> None | AsyncGenerator[None, None]:
+        # apply unique session
+        if self.unique_session and event.message_obj.type == MessageType.GROUP_MESSAGE:
+            sid = build_unique_session_id(event)
+            if sid:
+                event.session_id = sid
+
+        # ignore bot self message
         if (
             self.ignore_bot_self_message
             and event.get_self_id() == event.get_sender_id()
         ):
-            # 忽略机器人自己发送的消息
             event.stop_event()
             return
+
         # 设置 sender 身份
         event.message_str = event.message_str.strip()
         for admin_id in self.ctx.astrbot_config["admins_id"]:
@@ -136,7 +163,8 @@ class WakingCheckStage(Stage):
         ):
             if (
                 self.disable_builtin_commands
-                and handler.handler_module_path == "packages.builtin_commands.main"
+                and handler.handler_module_path
+                == "astrbot.builtin_stars.builtin_commands.main"
             ):
                 logger.debug("skipping builtin command")
                 continue

astrbot/core/platform/sources/aiocqhttp/aiocqhttp_platform_adapter.py

@@ -41,7 +41,6 @@ class AiocqhttpAdapter(Platform):
         super().__init__(platform_config, event_queue)
 
         self.settings = platform_settings
-        self.unique_session = platform_settings["unique_session"]
         self.host = platform_config["ws_reverse_host"]
         self.port = platform_config["ws_reverse_port"]
 
@@ -136,14 +135,11 @@ class AiocqhttpAdapter(Platform):
             abm.group_id = str(event.group_id)
         else:
             abm.type = MessageType.FRIEND_MESSAGE
-        if self.unique_session and abm.type == MessageType.GROUP_MESSAGE:
-            abm.session_id = str(abm.sender.user_id) + "_" + str(event.group_id)
-        else:
-            abm.session_id = (
-                str(event.group_id)
-                if abm.type == MessageType.GROUP_MESSAGE
-                else abm.sender.user_id
-            )
+        abm.session_id = (
+            str(event.group_id)
+            if abm.type == MessageType.GROUP_MESSAGE
+            else abm.sender.user_id
+        )
         abm.message_str = ""
         abm.message = []
         abm.timestamp = int(time.time())
@@ -164,16 +160,11 @@ class AiocqhttpAdapter(Platform):
             abm.type = MessageType.GROUP_MESSAGE
         else:
             abm.type = MessageType.FRIEND_MESSAGE
-        if self.unique_session and abm.type == MessageType.GROUP_MESSAGE:
-            abm.session_id = (
-                str(abm.sender.user_id) + "_" + str(event.group_id)
-            )  # 也保留群组 id
-        else:
-            abm.session_id = (
-                str(event.group_id)
-                if abm.type == MessageType.GROUP_MESSAGE
-                else abm.sender.user_id
-            )
+        abm.session_id = (
+            str(event.group_id)
+            if abm.type == MessageType.GROUP_MESSAGE
+            else abm.sender.user_id
+        )
         abm.message_str = ""
         abm.message = []
         abm.raw_message = event
@@ -210,16 +201,11 @@ class AiocqhttpAdapter(Platform):
             abm.group.group_name = event.get("group_name", "N/A")
         elif event["message_type"] == "private":
             abm.type = MessageType.FRIEND_MESSAGE
-        if self.unique_session and abm.type == MessageType.GROUP_MESSAGE:
-            abm.session_id = (
-                abm.sender.user_id + "_" + str(event.group_id)
-            )  # 也保留群组 id
-        else:
-            abm.session_id = (
-                str(event.group_id)
-                if abm.type == MessageType.GROUP_MESSAGE
-                else abm.sender.user_id
-            )
+        abm.session_id = (
+            str(event.group_id)
+            if abm.type == MessageType.GROUP_MESSAGE
+            else abm.sender.user_id
+        )
 
         abm.message_id = str(event.message_id)
         abm.message = []
@@ -385,10 +371,25 @@ class AiocqhttpAdapter(Platform):
                         logger.error(f"获取 @ 用户信息失败: {e}，此消息段将被忽略。")
 
                 message_str += "".join(at_parts)
+            elif t == "markdown":
+                text = m["data"].get("markdown") or m["data"].get("content", "")
+                abm.message.append(Plain(text=text))
+                message_str += text
             else:
                 for m in m_group:
-                    a = ComponentTypes[t](**m["data"])
-                    abm.message.append(a)
+                    try:
+                        if t not in ComponentTypes:
+                            logger.warning(
+                                f"不支持的消息段类型，已忽略: {t}, data={m['data']}"
+                            )
+                            continue
+                        a = ComponentTypes[t](**m["data"])
+                        abm.message.append(a)
+                    except Exception as e:
+                        logger.exception(
+                            f"消息段解析失败: type={t}, data={m['data']}. {e}"
+                        )
+                        continue
 
         abm.timestamp = int(time.time())
         abm.message_str = message_str

astrbot/core/platform/sources/dingtalk/dingtalk_adapter.py

@@ -50,8 +50,6 @@ class DingtalkPlatformAdapter(Platform):
     ) -> None:
         super().__init__(platform_config, event_queue)
 
-        self.unique_session = platform_settings["unique_session"]
-
         self.client_id = platform_config["client_id"]
         self.client_secret = platform_config["client_secret"]
 
@@ -129,10 +127,7 @@ class DingtalkPlatformAdapter(Platform):
                     if id := self._id_to_sid(user.dingtalk_id):
                         abm.message.append(At(qq=id))
             abm.group_id = message.conversation_id
-            if self.unique_session:
-                abm.session_id = abm.sender.user_id
-            else:
-                abm.session_id = abm.group_id
+            abm.session_id = abm.group_id
         else:
             abm.session_id = abm.sender.user_id
 

astrbot/core/platform/sources/dingtalk/dingtalk_event.py

@@ -25,6 +25,20 @@ class DingtalkMessageEvent(AstrMessageEvent):
         client: dingtalk_stream.ChatbotHandler,
         message: MessageChain,
     ):
+        icm = cast(dingtalk_stream.ChatbotMessage, self.message_obj.raw_message)
+        ats = []
+        # fixes: #4218
+        # 钉钉 at 机器人需要使用 sender_staff_id 而不是 sender_id
+        for i in message.chain:
+            if isinstance(i, Comp.At):
+                print(i.qq, icm.sender_id, icm.sender_staff_id)
+                if str(i.qq) in str(icm.sender_id or ""):
+                    # 适配器会将开头的 $:LWCP_v1:$ 去掉，因此我们用 in 判断
+                    ats.append(f"@{icm.sender_staff_id}")
+                else:
+                    ats.append(f"@{i.qq}")
+        at_str = " ".join(ats)
+
         for segment in message.chain:
             if isinstance(segment, Comp.Plain):
                 segment.text = segment.text.strip()
@@ -32,7 +46,7 @@ class DingtalkMessageEvent(AstrMessageEvent):
                     None,
                     client.reply_markdown,
                     segment.text,
-                    segment.text,
+                    f"{at_str} {segment.text}".strip(),
                     cast(dingtalk_stream.ChatbotMessage, self.message_obj.raw_message),
                 )
             elif isinstance(segment, Comp.Image):

astrbot/core/platform/sources/lark/lark_adapter.py

@@ -44,8 +44,6 @@ class LarkPlatformAdapter(Platform):
     ) -> None:
         super().__init__(platform_config, event_queue)
 
-        self.unique_session = platform_settings["unique_session"]
-
         self.appid = platform_config["app_id"]
         self.appsecret = platform_config["app_secret"]
         self.domain = platform_config.get("domain", lark.FEISHU_DOMAIN)
@@ -81,7 +79,12 @@ class LarkPlatformAdapter(Platform):
         )
 
         self.lark_api = (
-            lark.Client.builder().app_id(self.appid).app_secret(self.appsecret).build()
+            lark.Client.builder()
+            .app_id(self.appid)
+            .app_secret(self.appsecret)
+            .log_level(lark.LogLevel.ERROR)
+            .domain(self.domain)
+            .build()
         )
 
         self.webhook_server = None
@@ -312,14 +315,8 @@ class LarkPlatformAdapter(Platform):
             user_id=event.event.sender.sender_id.open_id,
             nickname=event.event.sender.sender_id.open_id[:8],
         )
-        # 独立会话
-        if not self.unique_session:
-            if abm.type == MessageType.GROUP_MESSAGE:
-                abm.session_id = abm.group_id
-            else:
-                abm.session_id = abm.sender.user_id
-        elif abm.type == MessageType.GROUP_MESSAGE:
-            abm.session_id = f"{abm.sender.user_id}%{abm.group_id}"  # 也保留群组id
+        if abm.type == MessageType.GROUP_MESSAGE:
+            abm.session_id = abm.group_id
         else:
             abm.session_id = abm.sender.user_id
 

astrbot/core/platform/sources/misskey/misskey_adapter.py

@@ -91,8 +91,6 @@ class MisskeyPlatformAdapter(Platform):
         except Exception:
             self.max_download_bytes = None
 
-        self.unique_session = platform_settings["unique_session"]
-
         self.api: MisskeyAPI | None = None
         self._running = False
         self.client_self_id = ""
@@ -641,7 +639,6 @@ class MisskeyPlatformAdapter(Platform):
             sender_info,
             self.client_self_id,
             is_chat=False,
-            unique_session=self.unique_session,
         )
         cache_user_info(
             self._user_cache,
@@ -690,7 +687,6 @@ class MisskeyPlatformAdapter(Platform):
             sender_info,
             self.client_self_id,
             is_chat=True,
-            unique_session=self.unique_session,
         )
         cache_user_info(
             self._user_cache,
@@ -720,7 +716,6 @@ class MisskeyPlatformAdapter(Platform):
             self.client_self_id,
             is_chat=False,
             room_id=room_id,
-            unique_session=self.unique_session,
         )
 
         cache_user_info(

astrbot/core/platform/sources/misskey/misskey_utils.py

@@ -338,7 +338,6 @@ def create_base_message(
     client_self_id: str,
     is_chat: bool = False,
     room_id: str | None = None,
-    unique_session: bool = False,
 ) -> AstrBotMessage:
     """创建基础消息对象"""
     message = AstrBotMessage()
@@ -353,8 +352,6 @@ def create_base_message(
     if room_id:
         session_prefix = "room"
         session_id = f"{session_prefix}%{room_id}"
-        if unique_session:
-            session_id += f"_{sender_info['sender_id']}"
         message.type = MessageType.GROUP_MESSAGE
         message.group_id = room_id
     elif is_chat:

astrbot/core/platform/sources/qqofficial/qqofficial_platform_adapter.py

@@ -44,11 +44,8 @@ class botClient(Client):
             message,
             MessageType.GROUP_MESSAGE,
         )
-        abm.session_id = (
-            abm.sender.user_id
-            if self.platform.unique_session
-            else cast(str, message.group_openid)
-        )
+        abm.group_id = cast(str, message.group_openid)
+        abm.session_id = abm.group_id
         self._commit(abm)
 
     # 收到频道消息
@@ -57,9 +54,8 @@ class botClient(Client):
             message,
             MessageType.GROUP_MESSAGE,
         )
-        abm.session_id = (
-            abm.sender.user_id if self.platform.unique_session else message.channel_id
-        )
+        abm.group_id = message.channel_id
+        abm.session_id = abm.group_id
         self._commit(abm)
 
     # 收到私聊消息
@@ -104,7 +100,6 @@ class QQOfficialPlatformAdapter(Platform):
 
         self.appid = platform_config["appid"]
         self.secret = platform_config["secret"]
-        self.unique_session: bool = platform_settings["unique_session"]
         qq_group = platform_config["enable_group_c2c"]
         guild_dm = platform_config["enable_guild_direct_message"]
 

astrbot/core/platform/sources/qqofficial_webhook/qo_webhook_adapter.py

@@ -35,11 +35,8 @@ class botClient(Client):
             message,
             MessageType.GROUP_MESSAGE,
         )
-        abm.session_id = (
-            abm.sender.user_id
-            if self.platform.unique_session
-            else cast(str, message.group_openid)
-        )
+        abm.group_id = cast(str, message.group_openid)
+        abm.session_id = abm.group_id
         self._commit(abm)
 
     # 收到频道消息
@@ -48,9 +45,8 @@ class botClient(Client):
             message,
             MessageType.GROUP_MESSAGE,
         )
-        abm.session_id = (
-            abm.sender.user_id if self.platform.unique_session else message.channel_id
-        )
+        abm.group_id = message.channel_id
+        abm.session_id = abm.group_id
         self._commit(abm)
 
     # 收到私聊消息
@@ -95,7 +91,6 @@ class QQOfficialWebhookPlatformAdapter(Platform):
 
         self.appid = platform_config["appid"]
         self.secret = platform_config["secret"]
-        self.unique_session = platform_settings["unique_session"]
         self.unified_webhook_mode = platform_config.get("unified_webhook_mode", False)
 
         intents = botpy.Intents(

astrbot/core/platform/sources/satori/satori_adapter.py

@@ -142,7 +142,12 @@ class SatoriPlatformAdapter(Platform):
             raise ValueError(f"WebSocket URL必须以ws://或wss://开头: {self.endpoint}")
 
         try:
-            websocket = await connect(self.endpoint, additional_headers={})
+            websocket = await connect(
+                self.endpoint,
+                additional_headers={},
+                max_size=10 * 1024 * 1024,  # 10MB
+            )
+
             self.ws = websocket
 
             await asyncio.sleep(0.1)

astrbot/core/platform/sources/slack/slack_adapter.py

@@ -41,7 +41,6 @@ class SlackAdapter(Platform):
     ) -> None:
         super().__init__(platform_config, event_queue)
         self.settings = platform_settings
-        self.unique_session = platform_settings.get("unique_session", False)
 
         self.bot_token = platform_config.get("bot_token")
         self.app_token = platform_config.get("app_token")
@@ -147,12 +146,10 @@ class SlackAdapter(Platform):
             abm.group_id = channel_id
 
         # 设置会话ID
-        if self.unique_session and abm.type == MessageType.GROUP_MESSAGE:
-            abm.session_id = f"{user_id}_{channel_id}"
+        if abm.type == MessageType.GROUP_MESSAGE:
+            abm.session_id = abm.group_id
         else:
-            abm.session_id = (
-                channel_id if abm.type == MessageType.GROUP_MESSAGE else user_id
-            )
+            abm.session_id = user_id
 
         abm.message_id = event.get("client_msg_id", uuid.uuid4().hex)
         abm.timestamp = int(float(event.get("ts", time.time())))

astrbot/core/platform/sources/telegram/tg_event.py

@@ -200,6 +200,15 @@ class TelegramPlatformEvent(AstrMessageEvent):
             if isinstance(chain, MessageChain):
                 if chain.type == "break":
                     # 分割符
+                    if message_id:
+                        try:
+                            await self.client.edit_message_text(
+                                text=delta,
+                                chat_id=payload["chat_id"],
+                                message_id=message_id,
+                            )
+                        except Exception as e:
+                            logger.warning(f"编辑消息失败(streaming-break): {e!s}")
                     message_id = None  # 重置消息 ID
                     delta = ""  # 重置 delta
                     continue

astrbot/core/platform/sources/webchat/webchat_adapter.py

@@ -79,7 +79,6 @@ class WebChatAdapter(Platform):
         super().__init__(platform_config, event_queue)
 
         self.settings = platform_settings
-        self.unique_session = platform_settings["unique_session"]
         self.imgs_dir = os.path.join(get_astrbot_data_path(), "webchat", "imgs")
         os.makedirs(self.imgs_dir, exist_ok=True)
 

astrbot/core/platform/sources/webchat/webchat_event.py

@@ -1,11 +1,12 @@
 import base64
+import json
 import os
 import shutil
 import uuid
 
 from astrbot.api import logger
 from astrbot.api.event import AstrMessageEvent, MessageChain
-from astrbot.api.message_components import File, Image, Plain, Record
+from astrbot.api.message_components import File, Image, Json, Plain, Record
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
 from .webchat_queue_mgr import webchat_queue_mgr
@@ -41,12 +42,20 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "plain",
-                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                         "chain_type": message.type,
                     },
                 )
+            elif isinstance(comp, Json):
+                await web_chat_back_queue.put(
+                    {
+                        "type": "plain",
+                        "data": json.dumps(comp.data, ensure_ascii=False),
+                        "streaming": streaming,
+                        "chain_type": message.type,
+                    },
+                )
             elif isinstance(comp, Image):
                 # save image to local
                 filename = f"{str(uuid.uuid4())}.jpg"
@@ -58,7 +67,6 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "image",
-                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -74,7 +82,6 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "record",
-                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -91,7 +98,6 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "file",
-                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -111,18 +117,17 @@ class WebChatMessageEvent(AstrMessageEvent):
         cid = self.session_id.split("!")[-1]
         web_chat_back_queue = webchat_queue_mgr.get_or_create_back_queue(cid)
         async for chain in generator:
-            if chain.type == "break" and final_data:
-                # 分割符
-                await web_chat_back_queue.put(
-                    {
-                        "type": "break",  # break means a segment end
-                        "data": final_data,
-                        "streaming": True,
-                        "cid": cid,
-                    },
-                )
-                final_data = ""
-                continue
+            # if chain.type == "break" and final_data:
+            #     # 分割符
+            #     await web_chat_back_queue.put(
+            #         {
+            #             "type": "break",  # break means a segment end
+            #             "data": final_data,
+            #             "streaming": True,
+            #         },
+            #     )
+            #     final_data = ""
+            #     continue
 
             r = await WebChatMessageEvent._send(
                 chain,
@@ -142,7 +147,6 @@ class WebChatMessageEvent(AstrMessageEvent):
                 "data": final_data,
                 "reasoning": reasoning_content,
                 "streaming": True,
-                "cid": cid,
             },
         )
         await super().send_streaming(generator, use_fallback)

astrbot/core/platform/sources/wechatpadpro/wechatpadpro_adapter.py

@@ -47,7 +47,6 @@ class WeChatPadProAdapter(Platform):
         self._shutdown_event = None
         self.wxnewpass = None
         self.settings = platform_settings
-        self.unique_session = platform_settings.get("unique_session", False)
 
         self.metadata = PlatformMetadata(
             name="wechatpadpro",
@@ -509,11 +508,10 @@ class WeChatPadProAdapter(Platform):
                 if accurate_nickname:
                     abm.sender.nickname = accurate_nickname
 
-            # 对于群聊，session_id 可以是群聊 ID 或发送者 ID + 群聊 ID (如果 unique_session 为 True)
-            if self.unique_session:
-                abm.session_id = f"{from_user_name}#{abm.sender.user_id}"
+            if abm.type == MessageType.GROUP_MESSAGE:
+                abm.session_id = abm.group_id
             else:
-                abm.session_id = from_user_name
+                abm.session_id = abm.sender.user_id
 
             msg_source = raw_message.get("msg_source", "")
             if self.wxid in msg_source:

astrbot/core/provider/entities.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import base64
 import enum
 import json
@@ -12,6 +14,7 @@ import astrbot.core.message.components as Comp
 from astrbot import logger
 from astrbot.core.agent.message import (
     AssistantMessageSegment,
+    ContentPart,
     ToolCall,
     ToolCallMessageSegment,
 )
@@ -90,6 +93,8 @@ class ProviderRequest:
     """会话 ID"""
     image_urls: list[str] = field(default_factory=list)
     """图片 URL 列表"""
+    extra_user_content_parts: list[ContentPart] = field(default_factory=list)
+    """额外的用户消息内容部分列表，用于在用户消息后添加额外的内容块（如系统提醒、指令等）。支持 dict 或 ContentPart 对象"""
     func_tool: ToolSet | None = None
     """可用的函数工具"""
     contexts: list[dict] = field(default_factory=list)
@@ -164,13 +169,23 @@ class ProviderRequest:
 
     async def assemble_context(self) -> dict:
         """将请求(prompt 和 image_urls)包装成 OpenAI 的消息格式。"""
+        # 构建内容块列表
+        content_blocks = []
+
+        # 1. 用户原始发言（OpenAI 建议：用户发言在前）
+        if self.prompt and self.prompt.strip():
+            content_blocks.append({"type": "text", "text": self.prompt})
+        elif self.image_urls:
+            # 如果没有文本但有图片，添加占位文本
+            content_blocks.append({"type": "text", "text": "[图片]"})
+
+        # 2. 额外的内容块（系统提醒、指令等）
+        if self.extra_user_content_parts:
+            for part in self.extra_user_content_parts:
+                content_blocks.append(part.model_dump())
+
+        # 3. 图片内容
         if self.image_urls:
-            user_content = {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": self.prompt if self.prompt else "[图片]"},
-                ],
-            }
             for image_url in self.image_urls:
                 if image_url.startswith("http"):
                     image_path = await download_image_by_url(image_url)
@@ -183,11 +198,21 @@ class ProviderRequest:
                 if not image_data:
                     logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
                     continue
-                user_content["content"].append(
+                content_blocks.append(
                     {"type": "image_url", "image_url": {"url": image_data}},
                 )
-            return user_content
-        return {"role": "user", "content": self.prompt}
+
+        # 只有当只有一个来自 prompt 的文本块且没有额外内容块时，才降级为简单格式以保持向后兼容
+        if (
+            len(content_blocks) == 1
+            and content_blocks[0]["type"] == "text"
+            and not self.extra_user_content_parts
+            and not self.image_urls
+        ):
+            return {"role": "user", "content": content_blocks[0]["text"]}
+
+        # 否则返回多模态格式
+        return {"role": "user", "content": content_blocks}
 
     async def _encode_image_bs64(self, image_url: str) -> str:
         """将图片转换为 base64"""
@@ -199,6 +224,38 @@ class ProviderRequest:
         return ""
 
 
+@dataclass
+class TokenUsage:
+    input_other: int = 0
+    """The number of input tokens, excluding cached tokens."""
+    input_cached: int = 0
+    """The number of input cached tokens."""
+    output: int = 0
+    """The number of output tokens."""
+
+    @property
+    def total(self) -> int:
+        return self.input_other + self.input_cached + self.output
+
+    @property
+    def input(self) -> int:
+        return self.input_other + self.input_cached
+
+    def __add__(self, other: TokenUsage) -> TokenUsage:
+        return TokenUsage(
+            input_other=self.input_other + other.input_other,
+            input_cached=self.input_cached + other.input_cached,
+            output=self.output + other.output,
+        )
+
+    def __sub__(self, other: TokenUsage) -> TokenUsage:
+        return TokenUsage(
+            input_other=self.input_other - other.input_other,
+            input_cached=self.input_cached - other.input_cached,
+            output=self.output - other.output,
+        )
+
+
 @dataclass
 class LLMResponse:
     role: str
@@ -215,6 +272,8 @@ class LLMResponse:
     """Tool call extra content. tool_call_id -> extra_content dict"""
     reasoning_content: str = ""
     """The reasoning content extracted from the LLM, if any."""
+    reasoning_signature: str | None = None
+    """The signature of the reasoning content, if any."""
 
     raw_completion: (
         ChatCompletion | GenerateContentResponse | AnthropicMessage | None
@@ -227,20 +286,29 @@ class LLMResponse:
     is_chunk: bool = False
     """Indicates if the response is a chunked response."""
 
+    id: str | None = None
+    """The ID of the response. For chunked responses, it's the ID of the chunk; for non-chunked responses, it's the ID of the response."""
+    usage: TokenUsage | None = None
+    """The usage of the response. For chunked responses, it's the usage of the chunk; for non-chunked responses, it's the usage of the response."""
+
     def __init__(
         self,
         role: str,
-        completion_text: str = "",
+        completion_text: str | None = None,
         result_chain: MessageChain | None = None,
         tools_call_args: list[dict[str, Any]] | None = None,
         tools_call_name: list[str] | None = None,
         tools_call_ids: list[str] | None = None,
         tools_call_extra_content: dict[str, dict[str, Any]] | None = None,
+        reasoning_content: str | None = None,
+        reasoning_signature: str | None = None,
         raw_completion: ChatCompletion
         | GenerateContentResponse
         | AnthropicMessage
         | None = None,
         is_chunk: bool = False,
+        id: str | None = None,
+        usage: TokenUsage | None = None,
     ):
         """初始化 LLMResponse
 
@@ -253,6 +321,8 @@ class LLMResponse:
             raw_completion (ChatCompletion, optional): 原始响应, OpenAI 格式. Defaults to None.
 
         """
+        if reasoning_content is None:
+            reasoning_content = ""
         if tools_call_args is None:
             tools_call_args = []
         if tools_call_name is None:
@@ -269,6 +339,8 @@ class LLMResponse:
         self.tools_call_name = tools_call_name
         self.tools_call_ids = tools_call_ids
         self.tools_call_extra_content = tools_call_extra_content
+        self.reasoning_content = reasoning_content
+        self.reasoning_signature = reasoning_signature
         self.raw_completion = raw_completion
         self.is_chunk = is_chunk
 

astrbot/core/provider/manager.py

@@ -1,4 +1,5 @@
 import asyncio
+import copy
 import traceback
 from typing import Protocol, runtime_checkable
 
@@ -32,10 +33,12 @@ class ProviderManager:
         persona_mgr: PersonaManager,
     ):
         self.reload_lock = asyncio.Lock()
+        self.resource_lock = asyncio.Lock()
         self.persona_mgr = persona_mgr
         self.acm = acm
         config = acm.confs["default"]
         self.providers_config: list = config["provider"]
+        self.provider_sources_config: list = config.get("provider_sources", [])
         self.provider_settings: dict = config["provider_settings"]
         self.provider_stt_settings: dict = config.get("provider_stt_settings", {})
         self.provider_tts_settings: dict = config.get("provider_tts_settings", {})
@@ -148,6 +151,7 @@ class ProviderManager:
 
         """
         provider = None
+        provider_id = None
         if umo:
             provider_id = sp.get(
                 f"provider_perf_{provider_type.value}",
@@ -185,6 +189,12 @@ class ProviderManager:
                     )
             else:
                 raise ValueError(f"Unknown provider type: {provider_type}")
+
+        if not provider and provider_id:
+            logger.warning(
+                f"没有找到 ID 为 {provider_id} 的提供商，这可能是由于您修改了提供商（模型）ID 导致的。"
+            )
+
         return provider
 
     async def initialize(self):
@@ -251,7 +261,136 @@ class ProviderManager:
         # 初始化 MCP Client 连接
         asyncio.create_task(self.llm_tools.init_mcp_clients(), name="init_mcp_clients")
 
+    def dynamic_import_provider(self, type: str):
+        """动态导入提供商适配器模块
+
+        Args:
+            type (str): 提供商请求类型。
+
+        Raises:
+            ImportError: 如果提供商类型未知或无法导入对应模块，则抛出异常。
+        """
+        match type:
+            case "openai_chat_completion":
+                from .sources.openai_source import (
+                    ProviderOpenAIOfficial as ProviderOpenAIOfficial,
+                )
+            case "zhipu_chat_completion":
+                from .sources.zhipu_source import ProviderZhipu as ProviderZhipu
+            case "groq_chat_completion":
+                from .sources.groq_source import ProviderGroq as ProviderGroq
+            case "anthropic_chat_completion":
+                from .sources.anthropic_source import (
+                    ProviderAnthropic as ProviderAnthropic,
+                )
+            case "googlegenai_chat_completion":
+                from .sources.gemini_source import (
+                    ProviderGoogleGenAI as ProviderGoogleGenAI,
+                )
+            case "sensevoice_stt_selfhost":
+                from .sources.sensevoice_selfhosted_source import (
+                    ProviderSenseVoiceSTTSelfHost as ProviderSenseVoiceSTTSelfHost,
+                )
+            case "openai_whisper_api":
+                from .sources.whisper_api_source import (
+                    ProviderOpenAIWhisperAPI as ProviderOpenAIWhisperAPI,
+                )
+            case "openai_whisper_selfhost":
+                from .sources.whisper_selfhosted_source import (
+                    ProviderOpenAIWhisperSelfHost as ProviderOpenAIWhisperSelfHost,
+                )
+            case "xinference_stt":
+                from .sources.xinference_stt_provider import (
+                    ProviderXinferenceSTT as ProviderXinferenceSTT,
+                )
+            case "openai_tts_api":
+                from .sources.openai_tts_api_source import (
+                    ProviderOpenAITTSAPI as ProviderOpenAITTSAPI,
+                )
+            case "edge_tts":
+                from .sources.edge_tts_source import (
+                    ProviderEdgeTTS as ProviderEdgeTTS,
+                )
+            case "gsv_tts_selfhost":
+                from .sources.gsv_selfhosted_source import (
+                    ProviderGSVTTS as ProviderGSVTTS,
+                )
+            case "gsvi_tts_api":
+                from .sources.gsvi_tts_source import (
+                    ProviderGSVITTS as ProviderGSVITTS,
+                )
+            case "fishaudio_tts_api":
+                from .sources.fishaudio_tts_api_source import (
+                    ProviderFishAudioTTSAPI as ProviderFishAudioTTSAPI,
+                )
+            case "dashscope_tts":
+                from .sources.dashscope_tts import (
+                    ProviderDashscopeTTSAPI as ProviderDashscopeTTSAPI,
+                )
+            case "azure_tts":
+                from .sources.azure_tts_source import (
+                    AzureTTSProvider as AzureTTSProvider,
+                )
+            case "minimax_tts_api":
+                from .sources.minimax_tts_api_source import (
+                    ProviderMiniMaxTTSAPI as ProviderMiniMaxTTSAPI,
+                )
+            case "volcengine_tts":
+                from .sources.volcengine_tts import (
+                    ProviderVolcengineTTS as ProviderVolcengineTTS,
+                )
+            case "gemini_tts":
+                from .sources.gemini_tts_source import (
+                    ProviderGeminiTTSAPI as ProviderGeminiTTSAPI,
+                )
+            case "openai_embedding":
+                from .sources.openai_embedding_source import (
+                    OpenAIEmbeddingProvider as OpenAIEmbeddingProvider,
+                )
+            case "gemini_embedding":
+                from .sources.gemini_embedding_source import (
+                    GeminiEmbeddingProvider as GeminiEmbeddingProvider,
+                )
+            case "vllm_rerank":
+                from .sources.vllm_rerank_source import (
+                    VLLMRerankProvider as VLLMRerankProvider,
+                )
+            case "xinference_rerank":
+                from .sources.xinference_rerank_source import (
+                    XinferenceRerankProvider as XinferenceRerankProvider,
+                )
+            case "bailian_rerank":
+                from .sources.bailian_rerank_source import (
+                    BailianRerankProvider as BailianRerankProvider,
+                )
+
+    def get_merged_provider_config(self, provider_config: dict) -> dict:
+        """获取 provider 配置和 provider_source 配置合并后的结果
+
+        Returns:
+            dict: 合并后的 provider 配置，key 为 provider id，value 为合并后的配置字典
+        """
+        pc = copy.deepcopy(provider_config)
+        provider_source_id = pc.get("provider_source_id", "")
+        if provider_source_id:
+            provider_source = None
+            for ps in self.provider_sources_config:
+                if ps.get("id") == provider_source_id:
+                    provider_source = ps
+                    break
+
+            if provider_source:
+                # 合并配置，provider 的配置优先级更高
+                merged_config = {**provider_source, **pc}
+                # 保持 id 为 provider 的 id，而不是 source 的 id
+                merged_config["id"] = pc["id"]
+                pc = merged_config
+        return pc
+
     async def load_provider(self, provider_config: dict):
+        # 如果 provider_source_id 存在且不为空，则从 provider_sources 中找到对应的配置并合并
+        provider_config = self.get_merged_provider_config(provider_config)
+
         if not provider_config["enable"]:
             logger.info(f"Provider {provider_config['id']} is disabled, skipping")
             return
@@ -264,99 +403,7 @@ class ProviderManager:
 
         # 动态导入
         try:
-            match provider_config["type"]:
-                case "openai_chat_completion":
-                    from .sources.openai_source import (
-                        ProviderOpenAIOfficial as ProviderOpenAIOfficial,
-                    )
-                case "zhipu_chat_completion":
-                    from .sources.zhipu_source import ProviderZhipu as ProviderZhipu
-                case "groq_chat_completion":
-                    from .sources.groq_source import ProviderGroq as ProviderGroq
-                case "anthropic_chat_completion":
-                    from .sources.anthropic_source import (
-                        ProviderAnthropic as ProviderAnthropic,
-                    )
-                case "googlegenai_chat_completion":
-                    from .sources.gemini_source import (
-                        ProviderGoogleGenAI as ProviderGoogleGenAI,
-                    )
-                case "sensevoice_stt_selfhost":
-                    from .sources.sensevoice_selfhosted_source import (
-                        ProviderSenseVoiceSTTSelfHost as ProviderSenseVoiceSTTSelfHost,
-                    )
-                case "openai_whisper_api":
-                    from .sources.whisper_api_source import (
-                        ProviderOpenAIWhisperAPI as ProviderOpenAIWhisperAPI,
-                    )
-                case "openai_whisper_selfhost":
-                    from .sources.whisper_selfhosted_source import (
-                        ProviderOpenAIWhisperSelfHost as ProviderOpenAIWhisperSelfHost,
-                    )
-                case "xinference_stt":
-                    from .sources.xinference_stt_provider import (
-                        ProviderXinferenceSTT as ProviderXinferenceSTT,
-                    )
-                case "openai_tts_api":
-                    from .sources.openai_tts_api_source import (
-                        ProviderOpenAITTSAPI as ProviderOpenAITTSAPI,
-                    )
-                case "edge_tts":
-                    from .sources.edge_tts_source import (
-                        ProviderEdgeTTS as ProviderEdgeTTS,
-                    )
-                case "gsv_tts_selfhost":
-                    from .sources.gsv_selfhosted_source import (
-                        ProviderGSVTTS as ProviderGSVTTS,
-                    )
-                case "gsvi_tts_api":
-                    from .sources.gsvi_tts_source import (
-                        ProviderGSVITTS as ProviderGSVITTS,
-                    )
-                case "fishaudio_tts_api":
-                    from .sources.fishaudio_tts_api_source import (
-                        ProviderFishAudioTTSAPI as ProviderFishAudioTTSAPI,
-                    )
-                case "dashscope_tts":
-                    from .sources.dashscope_tts import (
-                        ProviderDashscopeTTSAPI as ProviderDashscopeTTSAPI,
-                    )
-                case "azure_tts":
-                    from .sources.azure_tts_source import (
-                        AzureTTSProvider as AzureTTSProvider,
-                    )
-                case "minimax_tts_api":
-                    from .sources.minimax_tts_api_source import (
-                        ProviderMiniMaxTTSAPI as ProviderMiniMaxTTSAPI,
-                    )
-                case "volcengine_tts":
-                    from .sources.volcengine_tts import (
-                        ProviderVolcengineTTS as ProviderVolcengineTTS,
-                    )
-                case "gemini_tts":
-                    from .sources.gemini_tts_source import (
-                        ProviderGeminiTTSAPI as ProviderGeminiTTSAPI,
-                    )
-                case "openai_embedding":
-                    from .sources.openai_embedding_source import (
-                        OpenAIEmbeddingProvider as OpenAIEmbeddingProvider,
-                    )
-                case "gemini_embedding":
-                    from .sources.gemini_embedding_source import (
-                        GeminiEmbeddingProvider as GeminiEmbeddingProvider,
-                    )
-                case "vllm_rerank":
-                    from .sources.vllm_rerank_source import (
-                        VLLMRerankProvider as VLLMRerankProvider,
-                    )
-                case "xinference_rerank":
-                    from .sources.xinference_rerank_source import (
-                        XinferenceRerankProvider as XinferenceRerankProvider,
-                    )
-                case "bailian_rerank":
-                    from .sources.bailian_rerank_source import (
-                        BailianRerankProvider as BailianRerankProvider,
-                    )
+            self.dynamic_import_provider(provider_config["type"])
         except (ImportError, ModuleNotFoundError) as e:
             logger.critical(
                 f"加载 {provider_config['type']}({provider_config['id']}) 提供商适配器失败：{e}。可能是因为有未安装的依赖。",
@@ -499,6 +546,7 @@ class ProviderManager:
 
             # 和配置文件保持同步
             self.providers_config = astrbot_config["provider"]
+            self.provider_sources_config = astrbot_config.get("provider_sources", [])
             config_ids = [provider["id"] for provider in self.providers_config]
             logger.info(f"providers in user's config: {config_ids}")
             for key in list(self.inst_map.keys()):
@@ -570,6 +618,68 @@ class ProviderManager:
             )
             del self.inst_map[provider_id]
 
+    async def delete_provider(
+        self, provider_id: str | None = None, provider_source_id: str | None = None
+    ):
+        """Delete provider and/or provider source from config and terminate the instances. Config will be saved after deletion."""
+        async with self.resource_lock:
+            # delete from config
+            target_prov_ids = []
+            if provider_id:
+                target_prov_ids.append(provider_id)
+            else:
+                for prov in self.providers_config:
+                    if prov.get("provider_source_id") == provider_source_id:
+                        target_prov_ids.append(prov.get("id"))
+            config = self.acm.default_conf
+            for tpid in target_prov_ids:
+                await self.terminate_provider(tpid)
+                config["provider"] = [
+                    prov for prov in config["provider"] if prov.get("id") != tpid
+                ]
+            config.save_config()
+            logger.info(f"Provider {target_prov_ids} 已从配置中删除。")
+
+    async def update_provider(self, origin_provider_id: str, new_config: dict):
+        """Update provider config and reload the instance. Config will be saved after update."""
+        async with self.resource_lock:
+            npid = new_config.get("id", None)
+            if not npid:
+                raise ValueError("New provider config must have an 'id' field")
+            config = self.acm.default_conf
+            for provider in config["provider"]:
+                if (
+                    provider.get("id", None) == npid
+                    and provider.get("id", None) != origin_provider_id
+                ):
+                    raise ValueError(f"Provider ID {npid} already exists")
+            # update config
+            for idx, provider in enumerate(config["provider"]):
+                if provider.get("id", None) == origin_provider_id:
+                    config["provider"][idx] = new_config
+                    break
+            else:
+                raise ValueError(f"Provider ID {origin_provider_id} not found")
+            config.save_config()
+            # reload instance
+            await self.reload(new_config)
+
+    async def create_provider(self, new_config: dict):
+        """Add new provider config and load the instance. Config will be saved after addition."""
+        async with self.resource_lock:
+            npid = new_config.get("id", None)
+            if not npid:
+                raise ValueError("New provider config must have an 'id' field")
+            config = self.acm.default_conf
+            for provider in config["provider"]:
+                if provider.get("id", None) == npid:
+                    raise ValueError(f"Provider ID {npid} already exists")
+            # add to config
+            config["provider"].append(new_config)
+            config.save_config()
+            # load instance
+            await self.load_provider(new_config)
+
     async def terminate(self):
         for provider_inst in self.provider_insts:
             if hasattr(provider_inst, "terminate"):

astrbot/core/provider/provider.py

@@ -4,7 +4,7 @@ import os
 from collections.abc import AsyncGenerator
 from typing import TypeAlias, Union
 
-from astrbot.core.agent.message import Message
+from astrbot.core.agent.message import ContentPart, Message
 from astrbot.core.agent.tool import ToolSet
 from astrbot.core.provider.entities import (
     LLMResponse,
@@ -103,6 +103,7 @@ class Provider(AbstractProvider):
         system_prompt: str | None = None,
         tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
         model: str | None = None,
+        extra_user_content_parts: list[ContentPart] | None = None,
         **kwargs,
     ) -> LLMResponse:
         """获得 LLM 的文本对话结果。会使用当前的模型进行对话。
@@ -114,6 +115,7 @@ class Provider(AbstractProvider):
             tools: tool set
             contexts: 上下文，和 prompt 二选一使用
             tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
+            extra_user_content_parts: 额外的内容块列表，用于在用户消息后添加额外的文本块（如系统提醒、指令等）
             kwargs: 其他参数
 
         Notes:

astrbot/core/provider/sources/anthropic_source.py

@@ -6,10 +6,13 @@ from mimetypes import guess_type
 import anthropic
 from anthropic import AsyncAnthropic
 from anthropic.types import Message
+from anthropic.types.message_delta_usage import MessageDeltaUsage
+from anthropic.types.usage import Usage
 
 from astrbot import logger
 from astrbot.api.provider import Provider
-from astrbot.core.provider.entities import LLMResponse
+from astrbot.core.agent.message import ContentPart, ImageURLPart, TextPart
+from astrbot.core.provider.entities import LLMResponse, TokenUsage
 from astrbot.core.provider.func_tool_manager import ToolSet
 from astrbot.core.utils.io import download_image_by_url
 
@@ -45,7 +48,9 @@ class ProviderAnthropic(Provider):
             base_url=self.base_url,
         )
 
-        self.set_model(provider_config["model_config"]["model"])
+        self.thinking_config = provider_config.get("anth_thinking_config", {})
+
+        self.set_model(provider_config.get("model", "unknown"))
 
     def _prepare_payload(self, messages: list[dict]):
         """准备 Anthropic API 的请求 payload
@@ -61,12 +66,33 @@ class ProviderAnthropic(Provider):
         new_messages = []
         for message in messages:
             if message["role"] == "system":
-                system_prompt = message["content"]
+                system_prompt = message["content"] or "<empty system prompt>"
             elif message["role"] == "assistant":
                 blocks = []
-                if isinstance(message["content"], str):
+                reasoning_content = ""
+                thinking_signature = ""
+                if isinstance(message["content"], str) and message["content"].strip():
                     blocks.append({"type": "text", "text": message["content"]})
-                if "tool_calls" in message:
+                elif isinstance(message["content"], list):
+                    for part in message["content"]:
+                        if part.get("type") == "think":
+                            # only pick the last think part for now
+                            reasoning_content = part.get("think")
+                            thinking_signature = part.get("encrypted")
+                        else:
+                            blocks.append(part)
+
+                if reasoning_content and thinking_signature:
+                    blocks.insert(
+                        0,
+                        {
+                            "type": "thinking",
+                            "thinking": reasoning_content,
+                            "signature": thinking_signature,
+                        },
+                    )
+
+                if "tool_calls" in message and isinstance(message["tool_calls"], list):
                     for tool_call in message["tool_calls"]:
                         blocks.append(  # noqa: PERF401
                             {
@@ -97,7 +123,7 @@ class ProviderAnthropic(Provider):
                             {
                                 "type": "tool_result",
                                 "tool_use_id": message["tool_call_id"],
-                                "content": message["content"],
+                                "content": message["content"] or "<empty response>",
                             },
                         ],
                     },
@@ -107,12 +133,40 @@ class ProviderAnthropic(Provider):
 
         return system_prompt, new_messages
 
+    def _extract_usage(self, usage: Usage) -> TokenUsage:
+        # https://docs.claude.com/en/docs/build-with-claude/prompt-caching#tracking-cache-performance
+        return TokenUsage(
+            input_other=usage.input_tokens or 0,
+            input_cached=usage.cache_read_input_tokens or 0,
+            output=usage.output_tokens,
+        )
+
+    def _update_usage(self, token_usage: TokenUsage, usage: MessageDeltaUsage) -> None:
+        if usage.input_tokens is not None:
+            token_usage.input_other = usage.input_tokens
+        if usage.cache_read_input_tokens is not None:
+            token_usage.input_cached = usage.cache_read_input_tokens
+        if usage.output_tokens is not None:
+            token_usage.output = usage.output_tokens
+
     async def _query(self, payloads: dict, tools: ToolSet | None) -> LLMResponse:
         if tools:
             if tool_list := tools.get_func_desc_anthropic_style():
                 payloads["tools"] = tool_list
 
-        completion = await self.client.messages.create(**payloads, stream=False)
+        extra_body = self.provider_config.get("custom_extra_body", {})
+
+        if "max_tokens" not in payloads:
+            payloads["max_tokens"] = 1024
+        if self.thinking_config.get("budget"):
+            payloads["thinking"] = {
+                "budget_tokens": self.thinking_config.get("budget"),
+                "type": "enabled",
+            }
+
+        completion = await self.client.messages.create(
+            **payloads, stream=False, extra_body=extra_body
+        )
 
         assert isinstance(completion, Message)
         logger.debug(f"completion: {completion}")
@@ -127,10 +181,19 @@ class ProviderAnthropic(Provider):
                 completion_text = str(content_block.text).strip()
                 llm_response.completion_text = completion_text
 
+            if content_block.type == "thinking":
+                reasoning_content = str(content_block.thinking).strip()
+                llm_response.reasoning_content = reasoning_content
+                llm_response.reasoning_signature = content_block.signature
+
             if content_block.type == "tool_use":
                 llm_response.tools_call_args.append(content_block.input)
                 llm_response.tools_call_name.append(content_block.name)
                 llm_response.tools_call_ids.append(content_block.id)
+
+        llm_response.id = completion.id
+        llm_response.usage = self._extract_usage(completion.usage)
+
         # TODO(Soulter): 处理 end_turn 情况
         if not llm_response.completion_text and not llm_response.tools_call_args:
             raise Exception(f"Anthropic API 返回的 completion 无法解析：{completion}。")
@@ -151,10 +214,29 @@ class ProviderAnthropic(Provider):
         # 用于累积最终结果
         final_text = ""
         final_tool_calls = []
+        id = None
+        usage = TokenUsage()
+        extra_body = self.provider_config.get("custom_extra_body", {})
+        reasoning_content = ""
+        reasoning_signature = ""
 
-        async with self.client.messages.stream(**payloads) as stream:
+        if "max_tokens" not in payloads:
+            payloads["max_tokens"] = 1024
+        if self.thinking_config.get("budget"):
+            payloads["thinking"] = {
+                "budget_tokens": self.thinking_config.get("budget"),
+                "type": "enabled",
+            }
+
+        async with self.client.messages.stream(
+            **payloads, extra_body=extra_body
+        ) as stream:
             assert isinstance(stream, anthropic.AsyncMessageStream)
             async for event in stream:
+                if event.type == "message_start":
+                    # the usage contains input token usage
+                    id = event.message.id
+                    usage = self._extract_usage(event.message.usage)
                 if event.type == "content_block_start":
                     if event.content_block.type == "text":
                         # 文本块开始
@@ -162,6 +244,8 @@ class ProviderAnthropic(Provider):
                             role="assistant",
                             completion_text="",
                             is_chunk=True,
+                            usage=usage,
+                            id=id,
                         )
                     elif event.content_block.type == "tool_use":
                         # 工具使用块开始，初始化缓冲区
@@ -179,7 +263,24 @@ class ProviderAnthropic(Provider):
                             role="assistant",
                             completion_text=event.delta.text,
                             is_chunk=True,
+                            usage=usage,
+                            id=id,
                         )
+                    elif event.delta.type == "thinking_delta":
+                        # 思考增量
+                        reasoning = event.delta.thinking
+                        if reasoning:
+                            yield LLMResponse(
+                                role="assistant",
+                                reasoning_content=reasoning,
+                                is_chunk=True,
+                                usage=usage,
+                                id=id,
+                                reasoning_signature=reasoning_signature or None,
+                            )
+                            reasoning_content += reasoning
+                    elif event.delta.type == "signature_delta":
+                        reasoning_signature = event.delta.signature
                     elif event.delta.type == "input_json_delta":
                         # 工具调用参数增量
                         if event.index in tool_use_buffer:
@@ -215,6 +316,8 @@ class ProviderAnthropic(Provider):
                                 tools_call_name=[tool_info["name"]],
                                 tools_call_ids=[tool_info["id"]],
                                 is_chunk=True,
+                                usage=usage,
+                                id=id,
                             )
                         except json.JSONDecodeError:
                             # JSON 解析失败，跳过这个工具调用
@@ -223,11 +326,19 @@ class ProviderAnthropic(Provider):
                         # 清理缓冲区
                         del tool_use_buffer[event.index]
 
+                elif event.type == "message_delta":
+                    if event.usage:
+                        self._update_usage(usage, event.usage)
+
         # 返回最终的完整结果
         final_response = LLMResponse(
             role="assistant",
             completion_text=final_text,
             is_chunk=False,
+            usage=usage,
+            id=id,
+            reasoning_content=reasoning_content,
+            reasoning_signature=reasoning_signature or None,
         )
 
         if final_tool_calls:
@@ -249,13 +360,16 @@ class ProviderAnthropic(Provider):
         system_prompt=None,
         tool_calls_result=None,
         model=None,
+        extra_user_content_parts=None,
         **kwargs,
     ) -> LLMResponse:
         if contexts is None:
             contexts = []
         new_record = None
         if prompt is not None:
-            new_record = await self.assemble_context(prompt, image_urls)
+            new_record = await self.assemble_context(
+                prompt, image_urls, extra_user_content_parts
+            )
         context_query = self._ensure_message_to_dicts(contexts)
         if new_record:
             context_query.append(new_record)
@@ -277,10 +391,9 @@ class ProviderAnthropic(Provider):
 
         system_prompt, new_messages = self._prepare_payload(context_query)
 
-        model_config = self.provider_config.get("model_config", {})
-        model_config["model"] = model or self.get_model()
+        model = model or self.get_model()
 
-        payloads = {"messages": new_messages, **model_config}
+        payloads = {"messages": new_messages, "model": model}
 
         # Anthropic has a different way of handling system prompts
         if system_prompt:
@@ -290,28 +403,30 @@ class ProviderAnthropic(Provider):
         try:
             llm_response = await self._query(payloads, func_tool)
         except Exception as e:
-            # logger.error(f"发生了错误。Provider 配置如下: {model_config}")
             raise e
 
         return llm_response
 
     async def text_chat_stream(
         self,
-        prompt,
+        prompt=None,
         session_id=None,
-        image_urls=...,
+        image_urls=None,
         func_tool=None,
-        contexts=...,
+        contexts=None,
         system_prompt=None,
         tool_calls_result=None,
         model=None,
+        extra_user_content_parts=None,
         **kwargs,
     ):
         if contexts is None:
             contexts = []
         new_record = None
         if prompt is not None:
-            new_record = await self.assemble_context(prompt, image_urls)
+            new_record = await self.assemble_context(
+                prompt, image_urls, extra_user_content_parts
+            )
         context_query = self._ensure_message_to_dicts(contexts)
         if new_record:
             context_query.append(new_record)
@@ -332,10 +447,9 @@ class ProviderAnthropic(Provider):
 
         system_prompt, new_messages = self._prepare_payload(context_query)
 
-        model_config = self.provider_config.get("model_config", {})
-        model_config["model"] = model or self.get_model()
+        model = model or self.get_model()
 
-        payloads = {"messages": new_messages, **model_config}
+        payloads = {"messages": new_messages, "model": model}
 
         # Anthropic has a different way of handling system prompts
         if system_prompt:
@@ -344,15 +458,15 @@ class ProviderAnthropic(Provider):
         async for llm_response in self._query_stream(payloads, func_tool):
             yield llm_response
 
-    async def assemble_context(self, text: str, image_urls: list[str] | None = None):
+    async def assemble_context(
+        self,
+        text: str,
+        image_urls: list[str] | None = None,
+        extra_user_content_parts: list[ContentPart] | None = None,
+    ):
         """组装上下文，支持文本和图片"""
-        if not image_urls:
-            return {"role": "user", "content": text}
 
-        content = []
-        content.append({"type": "text", "text": text})
-
-        for image_url in image_urls:
+        async def resolve_image_url(image_url: str) -> dict | None:
             if image_url.startswith("http"):
                 image_path = await download_image_by_url(image_url)
                 image_data = await self.encode_image_bs64(image_path)
@@ -364,28 +478,68 @@ class ProviderAnthropic(Provider):
 
             if not image_data:
                 logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
-                continue
+                return None
 
             # Get mime type for the image
             mime_type, _ = guess_type(image_url)
             if not mime_type:
                 mime_type = "image/jpeg"  # Default to JPEG if can't determine
 
-            content.append(
-                {
-                    "type": "image",
-                    "source": {
-                        "type": "base64",
-                        "media_type": mime_type,
-                        "data": (
-                            image_data.split("base64,")[1]
-                            if "base64," in image_data
-                            else image_data
-                        ),
-                    },
+            return {
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": mime_type,
+                    "data": (
+                        image_data.split("base64,")[1]
+                        if "base64," in image_data
+                        else image_data
+                    ),
                 },
-            )
+            }
 
+        content = []
+
+        # 1. 用户原始发言（OpenAI 建议：用户发言在前）
+        if text:
+            content.append({"type": "text", "text": text})
+        elif image_urls:
+            # 如果没有文本但有图片，添加占位文本
+            content.append({"type": "text", "text": "[图片]"})
+        elif extra_user_content_parts:
+            # 如果只有额外内容块，也需要添加占位文本
+            content.append({"type": "text", "text": " "})
+
+        # 2. 额外的内容块（系统提醒、指令等）
+        if extra_user_content_parts:
+            for block in extra_user_content_parts:
+                if isinstance(block, TextPart):
+                    content.append({"type": "text", "text": block.text})
+                elif isinstance(block, ImageURLPart):
+                    image_dict = await resolve_image_url(block.image_url.url)
+                    if image_dict:
+                        content.append(image_dict)
+                else:
+                    raise ValueError(f"不支持的额外内容块类型: {type(block)}")
+
+        # 3. 图片内容
+        if image_urls:
+            for image_url in image_urls:
+                image_dict = await resolve_image_url(image_url)
+                if image_dict:
+                    content.append(image_dict)
+
+        # 如果只有主文本且没有额外内容块和图片，返回简单格式以保持向后兼容
+        if (
+            text
+            and not extra_user_content_parts
+            and not image_urls
+            and len(content) == 1
+            and content[0]["type"] == "text"
+        ):
+            return {"role": "user", "content": content[0]["text"]}
+
+        # 否则返回多模态格式
         return {"role": "user", "content": content}
 
     async def encode_image_bs64(self, image_url: str) -> str:

astrbot/core/provider/sources/fishaudio_tts_api_source.py

@@ -56,10 +56,14 @@ class ProviderFishAudioTTSAPI(TTSProvider):
             "api_base",
             "https://api.fish-audio.cn/v1",
         )
+        try:
+            self.timeout: int = int(provider_config.get("timeout", 20))
+        except ValueError:
+            self.timeout = 20
         self.headers = {
             "Authorization": f"Bearer {self.chosen_api_key}",
         }
-        self.set_model(provider_config["model"])
+        self.set_model(provider_config.get("model", None))
 
     async def _get_reference_id_by_character(self, character: str) -> str | None:
         """获取角色的reference_id
@@ -135,17 +139,21 @@ class ProviderFishAudioTTSAPI(TTSProvider):
         path = os.path.join(temp_dir, f"fishaudio_tts_api_{uuid.uuid4()}.wav")
         self.headers["content-type"] = "application/msgpack"
         request = await self._generate_request(text)
-        async with AsyncClient(base_url=self.api_base).stream(
+        async with AsyncClient(base_url=self.api_base, timeout=self.timeout).stream(
             "POST",
             "/tts",
             headers=self.headers,
             content=ormsgpack.packb(request, option=ormsgpack.OPT_SERIALIZE_PYDANTIC),
         ) as response:
-            if response.headers["content-type"] == "audio/wav":
+            if response.status_code == 200 and response.headers.get(
+                "content-type", ""
+            ).startswith("audio/"):
                 with open(path, "wb") as f:
                     async for chunk in response.aiter_bytes():
                         f.write(chunk)
                 return path
-            body = await response.aread()
-            text = body.decode("utf-8", errors="replace")
-            raise Exception(f"Fish Audio API请求失败: {text}")
+            error_bytes = await response.aread()
+            error_text = error_bytes.decode("utf-8", errors="replace")[:1024]
+            raise Exception(
+                f"Fish Audio API请求失败: 状态码 {response.status_code}, 响应内容: {error_text}"
+            )

astrbot/core/provider/sources/gemini_source.py

@@ -13,8 +13,9 @@ from google.genai.errors import APIError
 import astrbot.core.message.components as Comp
 from astrbot import logger
 from astrbot.api.provider import Provider
+from astrbot.core.agent.message import ContentPart, ImageURLPart, TextPart
 from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import LLMResponse
+from astrbot.core.provider.entities import LLMResponse, TokenUsage
 from astrbot.core.provider.func_tool_manager import ToolSet
 from astrbot.core.utils.io import download_image_by_url
 
@@ -68,7 +69,7 @@ class ProviderGoogleGenAI(Provider):
             self.api_base = self.api_base[:-1]
 
         self._init_client()
-        self.set_model(provider_config["model_config"]["model"])
+        self.set_model(provider_config.get("model", "unknown"))
         self._init_safety_settings()
 
     def _init_client(self) -> None:
@@ -138,7 +139,7 @@ class ProviderGoogleGenAI(Provider):
             modalities = ["TEXT"]
 
         tool_list: list[types.Tool] | None = []
-        model_name = self.get_model()
+        model_name = cast(str, payloads.get("model", self.get_model()))
         native_coderunner = self.provider_config.get("gm_native_coderunner", False)
         native_search = self.provider_config.get("gm_native_search", False)
         url_context = self.provider_config.get("gm_url_context", False)
@@ -197,6 +198,53 @@ class ProviderGoogleGenAI(Provider):
                 types.Tool(function_declarations=func_desc["function_declarations"]),
             ]
 
+        # oper thinking config
+        thinking_config = None
+        if model_name in [
+            "gemini-2.5-pro",
+            "gemini-2.5-pro-preview",
+            "gemini-2.5-flash",
+            "gemini-2.5-flash-preview",
+            "gemini-2.5-flash-lite",
+            "gemini-2.5-flash-lite-preview",
+            "gemini-robotics-er-1.5-preview",
+            "gemini-live-2.5-flash-preview-native-audio-09-2025",
+        ]:
+            # The thinkingBudget parameter, introduced with the Gemini 2.5 series
+            thinking_budget = self.provider_config.get("gm_thinking_config", {}).get(
+                "budget", 0
+            )
+            if thinking_budget is not None:
+                thinking_config = types.ThinkingConfig(
+                    thinking_budget=thinking_budget,
+                )
+        elif model_name in [
+            "gemini-3-pro",
+            "gemini-3-pro-preview",
+            "gemini-3-flash",
+            "gemini-3-flash-preview",
+            "gemini-3-flash-lite",
+            "gemini-3-flash-lite-preview",
+        ]:
+            # The thinkingLevel parameter, recommended for Gemini 3 models and onwards
+            # Gemini 2.5 series models don't support thinkingLevel; use thinkingBudget instead.
+            thinking_level = self.provider_config.get("gm_thinking_config", {}).get(
+                "level", "HIGH"
+            )
+            if thinking_level and isinstance(thinking_level, str):
+                thinking_level = thinking_level.upper()
+                if thinking_level not in ["MINIMAL", "LOW", "MEDIUM", "HIGH"]:
+                    logger.warning(
+                        f"Invalid thinking level: {thinking_level}, using HIGH"
+                    )
+                    thinking_level = "HIGH"
+                level = types.ThinkingLevel(thinking_level)
+                thinking_config = types.ThinkingConfig()
+                if not hasattr(types.ThinkingConfig, "thinking_level"):
+                    setattr(types.ThinkingConfig, "thinking_level", level)
+                else:
+                    thinking_config.thinking_level = level
+
         return types.GenerateContentConfig(
             system_instruction=system_instruction,
             temperature=temperature,
@@ -216,22 +264,7 @@ class ProviderGoogleGenAI(Provider):
             response_modalities=modalities,
             tools=cast(types.ToolListUnion | None, tool_list),
             safety_settings=self.safety_settings if self.safety_settings else None,
-            thinking_config=(
-                types.ThinkingConfig(
-                    thinking_budget=min(
-                        int(
-                            self.provider_config.get("gm_thinking_config", {}).get(
-                                "budget",
-                                0,
-                            ),
-                        ),
-                        24576,
-                    ),
-                )
-                if "gemini-2.5-flash" in self.get_model()
-                and hasattr(types.ThinkingConfig, "thinking_budget")
-                else None
-            ),
+            thinking_config=thinking_config,
             automatic_function_calling=types.AutomaticFunctionCallingConfig(
                 disable=True,
             ),
@@ -288,9 +321,37 @@ class ProviderGoogleGenAI(Provider):
                 append_or_extend(gemini_contents, parts, types.UserContent)
 
             elif role == "assistant":
-                if content:
+                if isinstance(content, str):
                     parts = [types.Part.from_text(text=content)]
                     append_or_extend(gemini_contents, parts, types.ModelContent)
+                elif isinstance(content, list):
+                    parts = []
+                    thinking_signature = None
+                    text = ""
+                    for part in content:
+                        # for most cases, assistant content only contains two parts: think and text
+                        if part.get("type") == "think":
+                            thinking_signature = part.get("encrypted") or None
+                        else:
+                            text += str(part.get("text"))
+
+                    if thinking_signature and isinstance(thinking_signature, str):
+                        try:
+                            thinking_signature = base64.b64decode(thinking_signature)
+                        except Exception as e:
+                            logger.warning(
+                                f"Failed to decode google gemini thinking signature: {e}",
+                                exc_info=True,
+                            )
+                            thinking_signature = None
+                    parts.append(
+                        types.Part(
+                            text=text,
+                            thought_signature=thinking_signature,
+                        )
+                    )
+                    append_or_extend(gemini_contents, parts, types.ModelContent)
+
                 elif not native_tool_enabled and "tool_calls" in message:
                     parts = []
                     for tool in message["tool_calls"]:
@@ -347,6 +408,16 @@ class ProviderGoogleGenAI(Provider):
         ]
         return "".join(thought_buf).strip()
 
+    def _extract_usage(
+        self, usage_metadata: types.GenerateContentResponseUsageMetadata
+    ) -> TokenUsage:
+        """Extract usage from candidate"""
+        return TokenUsage(
+            input_other=usage_metadata.prompt_token_count or 0,
+            input_cached=usage_metadata.cached_content_token_count or 0,
+            output=usage_metadata.candidates_token_count or 0,
+        )
+
     def _process_content_parts(
         self,
         candidate: types.Candidate,
@@ -398,7 +469,8 @@ class ProviderGoogleGenAI(Provider):
         for part in result_parts:
             if part.text:
                 chain.append(Comp.Plain(part.text))
-            elif (
+
+            if (
                 part.function_call
                 and part.function_call.name is not None
                 and part.function_call.args is not None
@@ -415,13 +487,18 @@ class ProviderGoogleGenAI(Provider):
                     llm_response.tools_call_extra_content[tool_call_id] = {
                         "google": {"thought_signature": ts_bs64}
                     }
-            elif (
+
+            if (
                 part.inline_data
                 and part.inline_data.mime_type
                 and part.inline_data.mime_type.startswith("image/")
                 and part.inline_data.data
             ):
                 chain.append(Comp.Image.fromBytes(part.inline_data.data))
+
+            if ts := part.thought_signature:
+                # only keep the last thinking signature
+                llm_response.reasoning_signature = base64.b64encode(ts).decode("utf-8")
         return MessageChain(chain=chain)
 
     async def _query(self, payloads: dict, tools: ToolSet | None) -> LLMResponse:
@@ -431,6 +508,8 @@ class ProviderGoogleGenAI(Provider):
             None,
         )
 
+        model = payloads.get("model", self.get_model())
+
         modalities = ["TEXT"]
         if self.provider_config.get("gm_resp_image_modal", False):
             modalities.append("IMAGE")
@@ -449,7 +528,7 @@ class ProviderGoogleGenAI(Provider):
                     temperature,
                 )
                 result = await self.client.models.generate_content(
-                    model=self.get_model(),
+                    model=model,
                     contents=cast(types.ContentListUnion, conversation),
                     config=config,
                 )
@@ -475,11 +554,11 @@ class ProviderGoogleGenAI(Provider):
                     e.message = ""
                 if "Developer instruction is not enabled" in e.message:
                     logger.warning(
-                        f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)",
+                        f"{model} 不支持 system prompt，已自动去除(影响人格设置)",
                     )
                     system_instruction = None
                 elif "Function calling is not enabled" in e.message:
-                    logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
+                    logger.warning(f"{model} 不支持函数调用，已自动去除")
                     tools = None
                 elif (
                     "Multi-modal output is not supported" in e.message
@@ -488,7 +567,7 @@ class ProviderGoogleGenAI(Provider):
                     or "only supports text output" in e.message
                 ):
                     logger.warning(
-                        f"{self.get_model()} 不支持多模态输出，降级为文本模态",
+                        f"{model} 不支持多模态输出，降级为文本模态",
                     )
                     modalities = ["TEXT"]
                 else:
@@ -501,6 +580,9 @@ class ProviderGoogleGenAI(Provider):
             result.candidates[0],
             llm_response,
         )
+        llm_response.id = result.response_id
+        if result.usage_metadata:
+            llm_response.usage = self._extract_usage(result.usage_metadata)
         return llm_response
 
     async def _query_stream(
@@ -513,7 +595,7 @@ class ProviderGoogleGenAI(Provider):
             (msg["content"] for msg in payloads["messages"] if msg["role"] == "system"),
             None,
         )
-
+        model = payloads.get("model", self.get_model())
         conversation = self._prepare_conversation(payloads)
 
         result = None
@@ -525,7 +607,7 @@ class ProviderGoogleGenAI(Provider):
                     system_instruction,
                 )
                 result = await self.client.models.generate_content_stream(
-                    model=self.get_model(),
+                    model=model,
                     contents=cast(types.ContentListUnion, conversation),
                     config=config,
                 )
@@ -535,11 +617,11 @@ class ProviderGoogleGenAI(Provider):
                     e.message = ""
                 if "Developer instruction is not enabled" in e.message:
                     logger.warning(
-                        f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)",
+                        f"{model} 不支持 system prompt，已自动去除(影响人格设置)",
                     )
                     system_instruction = None
                 elif "Function calling is not enabled" in e.message:
-                    logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
+                    logger.warning(f"{model} 不支持函数调用，已自动去除")
                     tools = None
                 else:
                     raise
@@ -569,6 +651,9 @@ class ProviderGoogleGenAI(Provider):
                     chunk.candidates[0],
                     llm_response,
                 )
+                llm_response.id = chunk.response_id
+                if chunk.usage_metadata:
+                    llm_response.usage = self._extract_usage(chunk.usage_metadata)
                 yield llm_response
                 return
 
@@ -596,6 +681,9 @@ class ProviderGoogleGenAI(Provider):
                         chunk.candidates[0],
                         final_response,
                     )
+                    final_response.id = chunk.response_id
+                    if chunk.usage_metadata:
+                        final_response.usage = self._extract_usage(chunk.usage_metadata)
                 break
 
         # Yield final complete response with accumulated text
@@ -627,13 +715,16 @@ class ProviderGoogleGenAI(Provider):
         system_prompt=None,
         tool_calls_result=None,
         model=None,
+        extra_user_content_parts=None,
         **kwargs,
     ) -> LLMResponse:
         if contexts is None:
             contexts = []
         new_record = None
         if prompt is not None:
-            new_record = await self.assemble_context(prompt, image_urls)
+            new_record = await self.assemble_context(
+                prompt, image_urls, extra_user_content_parts
+            )
         context_query = self._ensure_message_to_dicts(contexts)
         if new_record:
             context_query.append(new_record)
@@ -652,10 +743,9 @@ class ProviderGoogleGenAI(Provider):
                 for tcr in tool_calls_result:
                     context_query.extend(tcr.to_openai_messages())
 
-        model_config = self.provider_config.get("model_config", {})
-        model_config["model"] = model or self.get_model()
+        model = model or self.get_model()
 
-        payloads = {"messages": context_query, **model_config}
+        payloads = {"messages": context_query, "model": model}
 
         retry = 10
         keys = self.api_keys.copy()
@@ -680,13 +770,16 @@ class ProviderGoogleGenAI(Provider):
         system_prompt=None,
         tool_calls_result=None,
         model=None,
+        extra_user_content_parts=None,
         **kwargs,
     ) -> AsyncGenerator[LLMResponse, None]:
         if contexts is None:
             contexts = []
         new_record = None
         if prompt is not None:
-            new_record = await self.assemble_context(prompt, image_urls)
+            new_record = await self.assemble_context(
+                prompt, image_urls, extra_user_content_parts
+            )
         context_query = self._ensure_message_to_dicts(contexts)
         if new_record:
             context_query.append(new_record)
@@ -705,10 +798,9 @@ class ProviderGoogleGenAI(Provider):
                 for tcr in tool_calls_result:
                     context_query.extend(tcr.to_openai_messages())
 
-        model_config = self.provider_config.get("model_config", {})
-        model_config["model"] = model or self.get_model()
+        model = model or self.get_model()
 
-        payloads = {"messages": context_query, **model_config}
+        payloads = {"messages": context_query, "model": model}
 
         retry = 10
         keys = self.api_keys.copy()
@@ -746,33 +838,75 @@ class ProviderGoogleGenAI(Provider):
         self.chosen_api_key = key
         self._init_client()
 
-    async def assemble_context(self, text: str, image_urls: list[str] | None = None):
+    async def assemble_context(
+        self,
+        text: str,
+        image_urls: list[str] | None = None,
+        extra_user_content_parts: list[ContentPart] | None = None,
+    ):
         """组装上下文。"""
-        if image_urls:
-            user_content = {
-                "role": "user",
-                "content": [{"type": "text", "text": text if text else "[图片]"}],
+
+        async def resolve_image_part(image_url: str) -> dict | None:
+            if image_url.startswith("http"):
+                image_path = await download_image_by_url(image_url)
+                image_data = await self.encode_image_bs64(image_path)
+            elif image_url.startswith("file:///"):
+                image_path = image_url.replace("file:///", "")
+                image_data = await self.encode_image_bs64(image_path)
+            else:
+                image_data = await self.encode_image_bs64(image_url)
+            if not image_data:
+                logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
+                return None
+            return {
+                "type": "image_url",
+                "image_url": {"url": image_data},
             }
-            for image_url in image_urls:
-                if image_url.startswith("http"):
-                    image_path = await download_image_by_url(image_url)
-                    image_data = await self.encode_image_bs64(image_path)
-                elif image_url.startswith("file:///"):
-                    image_path = image_url.replace("file:///", "")
-                    image_data = await self.encode_image_bs64(image_path)
+
+        # 构建内容块列表
+        content_blocks = []
+
+        # 1. 用户原始发言（OpenAI 建议：用户发言在前）
+        if text:
+            content_blocks.append({"type": "text", "text": text})
+        elif image_urls:
+            # 如果没有文本但有图片，添加占位文本
+            content_blocks.append({"type": "text", "text": "[图片]"})
+        elif extra_user_content_parts:
+            # 如果只有额外内容块，也需要添加占位文本
+            content_blocks.append({"type": "text", "text": " "})
+
+        # 2. 额外的内容块（系统提醒、指令等）
+        if extra_user_content_parts:
+            for part in extra_user_content_parts:
+                if isinstance(part, TextPart):
+                    content_blocks.append({"type": "text", "text": part.text})
+                elif isinstance(part, ImageURLPart):
+                    image_part = await resolve_image_part(part.image_url.url)
+                    if image_part:
+                        content_blocks.append(image_part)
                 else:
-                    image_data = await self.encode_image_bs64(image_url)
-                if not image_data:
-                    logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
-                    continue
-                user_content["content"].append(
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": image_data},
-                    },
-                )
-            return user_content
-        return {"role": "user", "content": text}
+                    raise ValueError(f"不支持的额外内容块类型: {type(part)}")
+
+        # 3. 图片内容
+        if image_urls:
+            for image_url in image_urls:
+                image_part = await resolve_image_part(image_url)
+                if image_part:
+                    content_blocks.append(image_part)
+
+        # 如果只有主文本且没有额外内容块和图片，返回简单格式以保持向后兼容
+        if (
+            text
+            and not extra_user_content_parts
+            and not image_urls
+            and len(content_blocks) == 1
+            and content_blocks[0]["type"] == "text"
+        ):
+            return {"role": "user", "content": content_blocks[0]["text"]}
+
+        # 否则返回多模态格式
+        return {"role": "user", "content": content_blocks}
 
     async def encode_image_bs64(self, image_url: str) -> str:
         """将图片转换为 base64"""

astrbot/core/provider/sources/minimax_tts_api_source.py

@@ -51,7 +51,7 @@ class ProviderMiniMaxTTSAPI(TTSProvider):
             "voice_id": ""
             if self.is_timber_weight
             else provider_config.get("minimax-voice-id", ""),
-            "emotion": provider_config.get("minimax-voice-emotion", "neutral"),
+            "emotion": provider_config.get("minimax-voice-emotion", "auto"),
             "latex_read": provider_config.get("minimax-voice-latex", False),
             "english_normalization": provider_config.get(
                 "minimax-voice-english-normalization",
@@ -59,6 +59,9 @@ class ProviderMiniMaxTTSAPI(TTSProvider):
             ),
         }
 
+        if self.voice_setting["emotion"] == "auto":
+            self.voice_setting.pop("emotion", None)
+
         self.audio_setting: dict = {
             "sample_rate": 32000,
             "bitrate": 128000,

astrbot/core/provider/sources/openai_source.py

@@ -12,14 +12,15 @@ from openai._exceptions import NotFoundError
 from openai.lib.streaming.chat._completions import ChatCompletionStreamState
 from openai.types.chat.chat_completion import ChatCompletion
 from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
+from openai.types.completion_usage import CompletionUsage
 
 import astrbot.core.message.components as Comp
 from astrbot import logger
 from astrbot.api.provider import Provider
-from astrbot.core.agent.message import Message
+from astrbot.core.agent.message import ContentPart, ImageURLPart, Message, TextPart
 from astrbot.core.agent.tool import ToolSet
 from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
+from astrbot.core.provider.entities import LLMResponse, TokenUsage, ToolCallsResult
 from astrbot.core.utils.io import download_image_by_url
 
 from ..register import register_provider_adapter
@@ -68,34 +69,11 @@ class ProviderOpenAIOfficial(Provider):
             self.client.chat.completions.create,
         ).parameters.keys()
 
-        model_config = provider_config.get("model_config", {})
-        model = model_config.get("model", "unknown")
+        model = provider_config.get("model", "unknown")
         self.set_model(model)
 
         self.reasoning_key = "reasoning_content"
 
-    def _maybe_inject_xai_search(self, payloads: dict, **kwargs):
-        """当开启 xAI 原生搜索时，向请求体注入 Live Search 参数。
-
-        - 仅在 provider_config.xai_native_search 为 True 时生效
-        - 默认注入 {"mode": "auto"}
-        - 允许通过 kwargs 使用 xai_search_mode 覆盖（on/auto/off）
-        """
-        if not bool(self.provider_config.get("xai_native_search", False)):
-            return
-
-        mode = kwargs.get("xai_search_mode", "auto")
-        mode = str(mode).lower()
-        if mode not in ("auto", "on", "off"):
-            mode = "auto"
-
-        # off 时不注入，保持与未开启一致
-        if mode == "off":
-            return
-
-        # OpenAI SDK 不识别的字段会在 _query/_query_stream 中放入 extra_body
-        payloads["search_parameters"] = {"mode": mode}
-
     async def get_models(self):
         try:
             models_str = []
@@ -134,10 +112,6 @@ class ProviderOpenAIOfficial(Provider):
 
         model = payloads.get("model", "").lower()
 
-        # 针对 deepseek 模型的特殊处理：deepseek-reasoner调用必须移除 tools ，否则将被切换至 deepseek-chat
-        if model == "deepseek-reasoner" and "tools" in payloads:
-            del payloads["tools"]
-
         completion = await self.client.chat.completions.create(
             **payloads,
             stream=False,
@@ -208,6 +182,7 @@ class ProviderOpenAIOfficial(Provider):
             # handle the content delta
             reasoning = self._extract_reasoning_content(chunk)
             _y = False
+            llm_response.id = chunk.id
             if reasoning:
                 llm_response.reasoning_content = reasoning
                 _y = True
@@ -217,6 +192,8 @@ class ProviderOpenAIOfficial(Provider):
                     chain=[Comp.Plain(completion_text)],
                 )
                 _y = True
+            if chunk.usage:
+                llm_response.usage = self._extract_usage(chunk.usage)
             if _y:
                 yield llm_response
 
@@ -245,6 +222,19 @@ class ProviderOpenAIOfficial(Provider):
                 reasoning_text = str(reasoning_attr)
         return reasoning_text
 
+    def _extract_usage(self, usage: CompletionUsage) -> TokenUsage:
+        ptd = usage.prompt_tokens_details
+        cached = ptd.cached_tokens if ptd and ptd.cached_tokens else 0
+        prompt_tokens = 0 if usage.prompt_tokens is None else usage.prompt_tokens
+        completion_tokens = (
+            0 if usage.completion_tokens is None else usage.completion_tokens
+        )
+        return TokenUsage(
+            input_other=prompt_tokens - cached,
+            input_cached=cached,
+            output=completion_tokens,
+        )
+
     async def _parse_openai_completion(
         self, completion: ChatCompletion, tools: ToolSet | None
     ) -> LLMResponse:
@@ -321,6 +311,10 @@ class ProviderOpenAIOfficial(Provider):
             raise Exception(f"API 返回的 completion 无法解析：{completion}。")
 
         llm_response.raw_completion = completion
+        llm_response.id = completion.id
+
+        if completion.usage:
+            llm_response.usage = self._extract_usage(completion.usage)
 
         return llm_response
 
@@ -332,6 +326,7 @@ class ProviderOpenAIOfficial(Provider):
         system_prompt: str | None = None,
         tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
         model: str | None = None,
+        extra_user_content_parts: list[ContentPart] | None = None,
         **kwargs,
     ) -> tuple:
         """准备聊天所需的有效载荷和上下文"""
@@ -339,7 +334,9 @@ class ProviderOpenAIOfficial(Provider):
             contexts = []
         new_record = None
         if prompt is not None:
-            new_record = await self.assemble_context(prompt, image_urls)
+            new_record = await self.assemble_context(
+                prompt, image_urls, extra_user_content_parts
+            )
         context_query = self._ensure_message_to_dicts(contexts)
         if new_record:
             context_query.append(new_record)
@@ -358,16 +355,31 @@ class ProviderOpenAIOfficial(Provider):
                 for tcr in tool_calls_result:
                     context_query.extend(tcr.to_openai_messages())
 
-        model_config = self.provider_config.get("model_config", {})
-        model_config["model"] = model or self.get_model()
+        model = model or self.get_model()
 
-        payloads = {"messages": context_query, **model_config}
+        payloads = {"messages": context_query, "model": model}
 
-        # xAI origin search tool inject
-        self._maybe_inject_xai_search(payloads, **kwargs)
+        self._finally_convert_payload(payloads)
 
         return payloads, context_query
 
+    def _finally_convert_payload(self, payloads: dict):
+        """Finally convert the payload. Such as think part conversion, tool inject."""
+        for message in payloads.get("messages", []):
+            if message.get("role") == "assistant" and isinstance(
+                message.get("content"), list
+            ):
+                reasoning_content = ""
+                new_content = []  # not including think part
+                for part in message["content"]:
+                    if part.get("type") == "think":
+                        reasoning_content += str(part.get("think"))
+                    else:
+                        new_content.append(part)
+                message["content"] = new_content
+                # reasoning key is "reasoning_content"
+                message["reasoning_content"] = reasoning_content
+
     async def _handle_api_error(
         self,
         e: Exception,
@@ -461,6 +473,7 @@ class ProviderOpenAIOfficial(Provider):
         system_prompt=None,
         tool_calls_result=None,
         model=None,
+        extra_user_content_parts=None,
         **kwargs,
     ) -> LLMResponse:
         payloads, context_query = await self._prepare_chat_payload(
@@ -470,6 +483,7 @@ class ProviderOpenAIOfficial(Provider):
             system_prompt,
             tool_calls_result,
             model=model,
+            extra_user_content_parts=extra_user_content_parts,
             **kwargs,
         )
 
@@ -609,33 +623,71 @@ class ProviderOpenAIOfficial(Provider):
         self,
         text: str,
         image_urls: list[str] | None = None,
+        extra_user_content_parts: list[ContentPart] | None = None,
     ) -> dict:
         """组装成符合 OpenAI 格式的 role 为 user 的消息段"""
-        if image_urls:
-            user_content = {
-                "role": "user",
-                "content": [{"type": "text", "text": text if text else "[图片]"}],
+
+        async def resolve_image_part(image_url: str) -> dict | None:
+            if image_url.startswith("http"):
+                image_path = await download_image_by_url(image_url)
+                image_data = await self.encode_image_bs64(image_path)
+            elif image_url.startswith("file:///"):
+                image_path = image_url.replace("file:///", "")
+                image_data = await self.encode_image_bs64(image_path)
+            else:
+                image_data = await self.encode_image_bs64(image_url)
+            if not image_data:
+                logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
+                return None
+            return {
+                "type": "image_url",
+                "image_url": {"url": image_data},
             }
-            for image_url in image_urls:
-                if image_url.startswith("http"):
-                    image_path = await download_image_by_url(image_url)
-                    image_data = await self.encode_image_bs64(image_path)
-                elif image_url.startswith("file:///"):
-                    image_path = image_url.replace("file:///", "")
-                    image_data = await self.encode_image_bs64(image_path)
+
+        # 构建内容块列表
+        content_blocks = []
+
+        # 1. 用户原始发言（OpenAI 建议：用户发言在前）
+        if text:
+            content_blocks.append({"type": "text", "text": text})
+        elif image_urls:
+            # 如果没有文本但有图片，添加占位文本
+            content_blocks.append({"type": "text", "text": "[图片]"})
+        elif extra_user_content_parts:
+            # 如果只有额外内容块，也需要添加占位文本
+            content_blocks.append({"type": "text", "text": " "})
+
+        # 2. 额外的内容块（系统提醒、指令等）
+        if extra_user_content_parts:
+            for part in extra_user_content_parts:
+                if isinstance(part, TextPart):
+                    content_blocks.append({"type": "text", "text": part.text})
+                elif isinstance(part, ImageURLPart):
+                    image_part = await resolve_image_part(part.image_url.url)
+                    if image_part:
+                        content_blocks.append(image_part)
                 else:
-                    image_data = await self.encode_image_bs64(image_url)
-                if not image_data:
-                    logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
-                    continue
-                user_content["content"].append(
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": image_data},
-                    },
-                )
-            return user_content
-        return {"role": "user", "content": text}
+                    raise ValueError(f"不支持的额外内容块类型: {type(part)}")
+
+        # 3. 图片内容
+        if image_urls:
+            for image_url in image_urls:
+                image_part = await resolve_image_part(image_url)
+                if image_part:
+                    content_blocks.append(image_part)
+
+        # 如果只有主文本且没有额外内容块和图片，返回简单格式以保持向后兼容
+        if (
+            text
+            and not extra_user_content_parts
+            and not image_urls
+            and len(content_blocks) == 1
+            and content_blocks[0]["type"] == "text"
+        ):
+            return {"role": "user", "content": content_blocks[0]["text"]}
+
+        # 否则返回多模态格式
+        return {"role": "user", "content": content_blocks}
 
     async def encode_image_bs64(self, image_url: str) -> str:
         """将图片转换为 base64"""

astrbot/core/provider/sources/xai_source.py

@@ -0,0 +1,29 @@
+from ..register import register_provider_adapter
+from .openai_source import ProviderOpenAIOfficial
+
+
+@register_provider_adapter(
+    "xai_chat_completion", "xAI Chat Completion Provider Adapter"
+)
+class ProviderXAI(ProviderOpenAIOfficial):
+    def __init__(
+        self,
+        provider_config: dict,
+        provider_settings: dict,
+    ) -> None:
+        super().__init__(provider_config, provider_settings)
+
+    def _maybe_inject_xai_search(self, payloads: dict):
+        """当开启 xAI 原生搜索时，向请求体注入 Live Search 参数。
+
+        - 仅在 provider_config.xai_native_search 为 True 时生效
+        - 默认注入 {"mode": "auto"}
+        """
+        if not bool(self.provider_config.get("xai_native_search", False)):
+            return
+        # OpenAI SDK 不识别的字段会在 _query/_query_stream 中放入 extra_body
+        payloads["search_parameters"] = {"mode": "auto"}
+
+    def _finally_convert_payload(self, payloads: dict):
+        self._maybe_inject_xai_search(payloads)
+        super()._finally_convert_payload(payloads)

astrbot/core/provider/sources/xinference_stt_provider.py

@@ -8,7 +8,10 @@ from xinference_client.client.restful.async_restful_client import (
 
 from astrbot.core import logger
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
-from astrbot.core.utils.tencent_record_helper import tencent_silk_to_wav
+from astrbot.core.utils.tencent_record_helper import (
+    convert_to_pcm_wav,
+    tencent_silk_to_wav,
+)
 
 from ..entities import ProviderType
 from ..provider import STTProvider
@@ -111,17 +114,22 @@ class ProviderXinferenceSTT(STTProvider):
                 return ""
 
             # 2. Check for conversion
-            needs_conversion = False
-            if (
-                audio_url.endswith((".amr", ".silk"))
-                or is_tencent
-                or b"SILK" in audio_bytes[:8]
-            ):
-                needs_conversion = True
+            conversion_type = None
+
+            if b"SILK" in audio_bytes[:8]:
+                conversion_type = "silk"
+            elif b"#!AMR" in audio_bytes[:6]:
+                conversion_type = "amr"
+            elif audio_url.endswith(".silk") or is_tencent:
+                conversion_type = "silk"
+            elif audio_url.endswith(".amr"):
+                conversion_type = "amr"
 
             # 3. Perform conversion if needed
-            if needs_conversion:
-                logger.info("Audio requires conversion, using temporary files...")
+            if conversion_type:
+                logger.info(
+                    f"Audio requires conversion ({conversion_type}), using temporary files..."
+                )
                 temp_dir = os.path.join(get_astrbot_data_path(), "temp")
                 os.makedirs(temp_dir, exist_ok=True)
 
@@ -132,8 +140,12 @@ class ProviderXinferenceSTT(STTProvider):
                 with open(input_path, "wb") as f:
                     f.write(audio_bytes)
 
-                logger.info("Converting silk/amr file to wav ...")
-                await tencent_silk_to_wav(input_path, output_path)
+                if conversion_type == "silk":
+                    logger.info("Converting silk to wav ...")
+                    await tencent_silk_to_wav(input_path, output_path)
+                elif conversion_type == "amr":
+                    logger.info("Converting amr to wav ...")
+                    await convert_to_pcm_wav(input_path, output_path)
 
                 with open(output_path, "rb") as f:
                     audio_bytes = f.read()

astrbot/core/star/__init__.py

@@ -2,15 +2,19 @@ from astrbot.core import html_renderer
 from astrbot.core.provider import Provider
 from astrbot.core.star.star_tools import StarTools
 from astrbot.core.utils.command_parser import CommandParserMixin
+from astrbot.core.utils.plugin_kv_store import PluginKVStoreMixin
 
 from .context import Context
 from .star import StarMetadata, star_map, star_registry
 from .star_manager import PluginManager
 
 
-class Star(CommandParserMixin):
+class Star(CommandParserMixin, PluginKVStoreMixin):
     """所有插件（Star）的父类，所有插件都应该继承于这个类"""
 
+    author: str
+    name: str
+
     def __init__(self, context: Context, config: dict | None = None):
         StarTools.initialize(context)
         self.context = context

astrbot/core/star/command_management.py

@@ -0,0 +1,496 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Any
+
+from astrbot.core import db_helper, logger
+from astrbot.core.db.po import CommandConfig
+from astrbot.core.star.filter.command import CommandFilter
+from astrbot.core.star.filter.command_group import CommandGroupFilter
+from astrbot.core.star.filter.permission import PermissionType, PermissionTypeFilter
+from astrbot.core.star.star import star_map
+from astrbot.core.star.star_handler import StarHandlerMetadata, star_handlers_registry
+
+
+@dataclass
+class CommandDescriptor:
+    handler: StarHandlerMetadata = field(repr=False)
+    filter_ref: CommandFilter | CommandGroupFilter | None = field(
+        default=None,
+        repr=False,
+    )
+    handler_full_name: str = ""
+    handler_name: str = ""
+    plugin_name: str = ""
+    plugin_display_name: str | None = None
+    module_path: str = ""
+    description: str = ""
+    command_type: str = "command"  # "command" | "group" | "sub_command"
+    raw_command_name: str | None = None
+    current_fragment: str | None = None
+    parent_signature: str = ""
+    parent_group_handler: str = ""
+    original_command: str | None = None
+    effective_command: str | None = None
+    aliases: list[str] = field(default_factory=list)
+    permission: str = "everyone"
+    enabled: bool = True
+    is_group: bool = False
+    is_sub_command: bool = False
+    reserved: bool = False
+    config: CommandConfig | None = None
+    has_conflict: bool = False
+    sub_commands: list[CommandDescriptor] = field(default_factory=list)
+
+
+async def sync_command_configs() -> None:
+    """同步指令配置，清理过期配置。"""
+    descriptors = _collect_descriptors(include_sub_commands=False)
+    config_records = await db_helper.get_command_configs()
+    config_map = _bind_configs_to_descriptors(descriptors, config_records)
+    live_handlers = {desc.handler_full_name for desc in descriptors}
+
+    stale_configs = [key for key in config_map if key not in live_handlers]
+    if stale_configs:
+        await db_helper.delete_command_configs(stale_configs)
+
+
+async def toggle_command(handler_full_name: str, enabled: bool) -> CommandDescriptor:
+    descriptor = _build_descriptor_by_full_name(handler_full_name)
+    if not descriptor:
+        raise ValueError("指定的处理函数不存在或不是指令。")
+
+    existing_cfg = await db_helper.get_command_config(handler_full_name)
+    config = await db_helper.upsert_command_config(
+        handler_full_name=handler_full_name,
+        plugin_name=descriptor.plugin_name or "",
+        module_path=descriptor.module_path,
+        original_command=descriptor.original_command or descriptor.handler_name,
+        resolved_command=(
+            existing_cfg.resolved_command
+            if existing_cfg
+            else descriptor.current_fragment
+        ),
+        enabled=enabled,
+        keep_original_alias=False,
+        conflict_key=existing_cfg.conflict_key
+        if existing_cfg and existing_cfg.conflict_key
+        else descriptor.original_command,
+        resolution_strategy=existing_cfg.resolution_strategy if existing_cfg else None,
+        note=existing_cfg.note if existing_cfg else None,
+        extra_data=existing_cfg.extra_data if existing_cfg else None,
+        auto_managed=False,
+    )
+    _bind_descriptor_with_config(descriptor, config)
+    await sync_command_configs()
+    return descriptor
+
+
+async def rename_command(
+    handler_full_name: str,
+    new_fragment: str,
+    aliases: list[str] | None = None,
+) -> CommandDescriptor:
+    descriptor = _build_descriptor_by_full_name(handler_full_name)
+    if not descriptor:
+        raise ValueError("指定的处理函数不存在或不是指令。")
+
+    new_fragment = new_fragment.strip()
+    if not new_fragment:
+        raise ValueError("指令名不能为空。")
+
+    # 校验主指令名
+    candidate_full = _compose_command(descriptor.parent_signature, new_fragment)
+    if _is_command_in_use(handler_full_name, candidate_full):
+        raise ValueError(f"指令名 '{candidate_full}' 已被其他指令占用。")
+
+    # 校验别名
+    if aliases:
+        for alias in aliases:
+            alias = alias.strip()
+            if not alias:
+                continue
+            alias_full = _compose_command(descriptor.parent_signature, alias)
+            if _is_command_in_use(handler_full_name, alias_full):
+                raise ValueError(f"别名 '{alias_full}' 已被其他指令占用。")
+
+    existing_cfg = await db_helper.get_command_config(handler_full_name)
+    merged_extra = dict(existing_cfg.extra_data or {}) if existing_cfg else {}
+    merged_extra["resolved_aliases"] = aliases or []
+
+    config = await db_helper.upsert_command_config(
+        handler_full_name=handler_full_name,
+        plugin_name=descriptor.plugin_name or "",
+        module_path=descriptor.module_path,
+        original_command=descriptor.original_command or descriptor.handler_name,
+        resolved_command=new_fragment,
+        enabled=True if descriptor.enabled else False,
+        keep_original_alias=False,
+        conflict_key=descriptor.original_command,
+        resolution_strategy="manual_rename",
+        note=None,
+        extra_data=merged_extra,
+        auto_managed=False,
+    )
+    _bind_descriptor_with_config(descriptor, config)
+
+    await sync_command_configs()
+    return descriptor
+
+
+async def list_commands() -> list[dict[str, Any]]:
+    descriptors = _collect_descriptors(include_sub_commands=True)
+    config_records = await db_helper.get_command_configs()
+    _bind_configs_to_descriptors(descriptors, config_records)
+
+    conflict_groups = _group_conflicts(descriptors)
+    conflict_handler_names: set[str] = {
+        d.handler_full_name for group in conflict_groups.values() for d in group
+    }
+
+    # 分类，设置冲突标志，将子指令挂载到父指令组
+    group_map: dict[str, CommandDescriptor] = {}
+    sub_commands: list[CommandDescriptor] = []
+    root_commands: list[CommandDescriptor] = []
+
+    for desc in descriptors:
+        desc.has_conflict = desc.handler_full_name in conflict_handler_names
+        if desc.is_group:
+            group_map[desc.handler_full_name] = desc
+        elif desc.is_sub_command:
+            sub_commands.append(desc)
+        else:
+            root_commands.append(desc)
+
+    for sub in sub_commands:
+        if sub.parent_group_handler and sub.parent_group_handler in group_map:
+            group_map[sub.parent_group_handler].sub_commands.append(sub)
+        else:
+            root_commands.append(sub)
+
+    # 指令组 + 普通指令，按 effective_command 字母排序
+    all_commands = list(group_map.values()) + root_commands
+    all_commands.sort(key=lambda d: (d.effective_command or "").lower())
+
+    result = [_descriptor_to_dict(desc) for desc in all_commands]
+    return result
+
+
+async def list_command_conflicts() -> list[dict[str, Any]]:
+    """列出所有冲突的指令组。"""
+    descriptors = _collect_descriptors(include_sub_commands=False)
+    config_records = await db_helper.get_command_configs()
+    _bind_configs_to_descriptors(descriptors, config_records)
+
+    conflict_groups = _group_conflicts(descriptors)
+    details = [
+        {
+            "conflict_key": key,
+            "handlers": [
+                {
+                    "handler_full_name": item.handler_full_name,
+                    "plugin": item.plugin_name,
+                    "current_name": item.effective_command,
+                }
+                for item in group
+            ],
+        }
+        for key, group in conflict_groups.items()
+    ]
+    return details
+
+
+# Internal helpers ----------------------------------------------------------
+
+
+def _collect_descriptors(include_sub_commands: bool) -> list[CommandDescriptor]:
+    """收集指令，按需包含子指令。"""
+    descriptors: list[CommandDescriptor] = []
+    for handler in star_handlers_registry:
+        try:
+            desc = _build_descriptor(handler)
+            if not desc:
+                continue
+            if not include_sub_commands and desc.is_sub_command:
+                continue
+            descriptors.append(desc)
+        except Exception as e:
+            logger.warning(
+                f"解析指令处理函数 {handler.handler_full_name} 失败，跳过该指令。原因: {e!s}"
+            )
+            continue
+    return descriptors
+
+
+def _build_descriptor(handler: StarHandlerMetadata) -> CommandDescriptor | None:
+    filter_ref = _locate_primary_filter(handler)
+    if filter_ref is None:
+        return None
+
+    plugin_meta = star_map.get(handler.handler_module_path)
+    plugin_name = (
+        plugin_meta.name if plugin_meta else None
+    ) or handler.handler_module_path
+    plugin_display = plugin_meta.display_name if plugin_meta else None
+
+    is_sub_command = bool(handler.extras_configs.get("sub_command"))
+    parent_group_handler = ""
+
+    if isinstance(filter_ref, CommandFilter):
+        raw_fragment = getattr(
+            filter_ref, "_original_command_name", filter_ref.command_name
+        )
+        current_fragment = filter_ref.command_name
+        parent_signature = (filter_ref.parent_command_names or [""])[0].strip()
+        # 如果是子指令，尝试找到父指令组的 handler_full_name
+        if is_sub_command and parent_signature:
+            parent_group_handler = _find_parent_group_handler(
+                handler.handler_module_path, parent_signature
+            )
+    else:
+        raw_fragment = getattr(
+            filter_ref, "_original_group_name", filter_ref.group_name
+        )
+        current_fragment = filter_ref.group_name
+        parent_signature = _resolve_group_parent_signature(filter_ref)
+
+    original_command = _compose_command(parent_signature, raw_fragment)
+    effective_command = _compose_command(parent_signature, current_fragment)
+
+    # 确定 command_type
+    if isinstance(filter_ref, CommandGroupFilter):
+        command_type = "group"
+    elif is_sub_command:
+        command_type = "sub_command"
+    else:
+        command_type = "command"
+
+    descriptor = CommandDescriptor(
+        handler=handler,
+        filter_ref=filter_ref,
+        handler_full_name=handler.handler_full_name,
+        handler_name=handler.handler_name,
+        plugin_name=plugin_name,
+        plugin_display_name=plugin_display,
+        module_path=handler.handler_module_path,
+        description=handler.desc or "",
+        command_type=command_type,
+        raw_command_name=raw_fragment,
+        current_fragment=current_fragment,
+        parent_signature=parent_signature,
+        parent_group_handler=parent_group_handler,
+        original_command=original_command,
+        effective_command=effective_command,
+        aliases=sorted(getattr(filter_ref, "alias", set())),
+        permission=_determine_permission(handler),
+        enabled=handler.enabled,
+        is_group=isinstance(filter_ref, CommandGroupFilter),
+        is_sub_command=is_sub_command,
+        reserved=plugin_meta.reserved if plugin_meta else False,
+    )
+    return descriptor
+
+
+def _build_descriptor_by_full_name(full_name: str) -> CommandDescriptor | None:
+    handler = star_handlers_registry.get_handler_by_full_name(full_name)
+    if not handler:
+        return None
+    return _build_descriptor(handler)
+
+
+def _locate_primary_filter(
+    handler: StarHandlerMetadata,
+) -> CommandFilter | CommandGroupFilter | None:
+    for filter_ref in handler.event_filters:
+        if isinstance(filter_ref, (CommandFilter, CommandGroupFilter)):
+            return filter_ref
+    return None
+
+
+def _determine_permission(handler: StarHandlerMetadata) -> str:
+    for filter_ref in handler.event_filters:
+        if isinstance(filter_ref, PermissionTypeFilter):
+            return (
+                "admin"
+                if filter_ref.permission_type == PermissionType.ADMIN
+                else "member"
+            )
+    return "everyone"
+
+
+def _resolve_group_parent_signature(group_filter: CommandGroupFilter) -> str:
+    signatures: list[str] = []
+    parent = group_filter.parent_group
+    while parent:
+        signatures.append(getattr(parent, "_original_group_name", parent.group_name))
+        parent = parent.parent_group
+    return " ".join(reversed(signatures)).strip()
+
+
+def _find_parent_group_handler(module_path: str, parent_signature: str) -> str:
+    """根据模块路径和父级签名，找到对应的指令组 handler_full_name。"""
+    parent_sig_normalized = parent_signature.strip()
+    for handler in star_handlers_registry:
+        if handler.handler_module_path != module_path:
+            continue
+        filter_ref = _locate_primary_filter(handler)
+        if not isinstance(filter_ref, CommandGroupFilter):
+            continue
+        # 检查该指令组的完整指令名是否匹配 parent_signature
+        group_names = filter_ref.get_complete_command_names()
+        if parent_sig_normalized in group_names:
+            return handler.handler_full_name
+    return ""
+
+
+def _compose_command(parent_signature: str, fragment: str | None) -> str:
+    fragment = (fragment or "").strip()
+    parent_signature = parent_signature.strip()
+    if not parent_signature:
+        return fragment
+    if not fragment:
+        return parent_signature
+    return f"{parent_signature} {fragment}"
+
+
+def _bind_descriptor_with_config(
+    descriptor: CommandDescriptor,
+    config: CommandConfig,
+) -> None:
+    _apply_config_to_descriptor(descriptor, config)
+    _apply_config_to_runtime(descriptor, config)
+
+
+def _apply_config_to_descriptor(
+    descriptor: CommandDescriptor,
+    config: CommandConfig,
+) -> None:
+    descriptor.config = config
+    descriptor.enabled = config.enabled
+
+    if config.original_command:
+        descriptor.original_command = config.original_command
+
+    new_fragment = config.resolved_command or descriptor.current_fragment
+    descriptor.current_fragment = new_fragment
+    descriptor.effective_command = _compose_command(
+        descriptor.parent_signature,
+        new_fragment,
+    )
+
+    extra = config.extra_data or {}
+    resolved_aliases = extra.get("resolved_aliases")
+    if isinstance(resolved_aliases, list):
+        descriptor.aliases = [str(x) for x in resolved_aliases if str(x).strip()]
+
+
+def _apply_config_to_runtime(
+    descriptor: CommandDescriptor,
+    config: CommandConfig,
+) -> None:
+    descriptor.handler.enabled = config.enabled
+    if descriptor.filter_ref:
+        if descriptor.current_fragment:
+            _set_filter_fragment(descriptor.filter_ref, descriptor.current_fragment)
+        extra = config.extra_data or {}
+        resolved_aliases = extra.get("resolved_aliases")
+        if isinstance(resolved_aliases, list):
+            _set_filter_aliases(
+                descriptor.filter_ref,
+                [str(x) for x in resolved_aliases if str(x).strip()],
+            )
+
+
+def _bind_configs_to_descriptors(
+    descriptors: list[CommandDescriptor],
+    config_records: list[CommandConfig],
+) -> dict[str, CommandConfig]:
+    config_map = {cfg.handler_full_name: cfg for cfg in config_records}
+    for desc in descriptors:
+        if cfg := config_map.get(desc.handler_full_name):
+            _bind_descriptor_with_config(desc, cfg)
+    return config_map
+
+
+def _group_conflicts(
+    descriptors: list[CommandDescriptor],
+) -> dict[str, list[CommandDescriptor]]:
+    conflicts: dict[str, list[CommandDescriptor]] = defaultdict(list)
+    for desc in descriptors:
+        if desc.effective_command and desc.enabled:
+            conflicts[desc.effective_command].append(desc)
+    return {k: v for k, v in conflicts.items() if len(v) > 1}
+
+
+def _set_filter_fragment(
+    filter_ref: CommandFilter | CommandGroupFilter,
+    fragment: str,
+) -> None:
+    attr = (
+        "group_name" if isinstance(filter_ref, CommandGroupFilter) else "command_name"
+    )
+    current_value = getattr(filter_ref, attr)
+    if fragment == current_value:
+        return
+    setattr(filter_ref, attr, fragment)
+    if hasattr(filter_ref, "_cmpl_cmd_names"):
+        filter_ref._cmpl_cmd_names = None
+
+
+def _set_filter_aliases(
+    filter_ref: CommandFilter | CommandGroupFilter,
+    aliases: list[str],
+) -> None:
+    current_aliases = getattr(filter_ref, "alias", set())
+    if set(aliases) == current_aliases:
+        return
+    setattr(filter_ref, "alias", set(aliases))
+    if hasattr(filter_ref, "_cmpl_cmd_names"):
+        filter_ref._cmpl_cmd_names = None
+
+
+def _is_command_in_use(
+    target_handler_full_name: str,
+    candidate_full_command: str,
+) -> bool:
+    candidate = candidate_full_command.strip()
+    for handler in star_handlers_registry:
+        if handler.handler_full_name == target_handler_full_name:
+            continue
+        filter_ref = _locate_primary_filter(handler)
+        if not filter_ref:
+            continue
+        names = {name.strip() for name in filter_ref.get_complete_command_names()}
+        if candidate in names:
+            return True
+    return False
+
+
+def _descriptor_to_dict(desc: CommandDescriptor) -> dict[str, Any]:
+    result = {
+        "handler_full_name": desc.handler_full_name,
+        "handler_name": desc.handler_name,
+        "plugin": desc.plugin_name,
+        "plugin_display_name": desc.plugin_display_name,
+        "module_path": desc.module_path,
+        "description": desc.description,
+        "type": desc.command_type,
+        "parent_signature": desc.parent_signature,
+        "parent_group_handler": desc.parent_group_handler,
+        "original_command": desc.original_command,
+        "current_fragment": desc.current_fragment,
+        "effective_command": desc.effective_command,
+        "aliases": desc.aliases,
+        "permission": desc.permission,
+        "enabled": desc.enabled,
+        "is_group": desc.is_group,
+        "has_conflict": desc.has_conflict,
+        "reserved": desc.reserved,
+    }
+    # 如果是指令组，包含子指令列表
+    if desc.is_group and desc.sub_commands:
+        result["sub_commands"] = [_descriptor_to_dict(sub) for sub in desc.sub_commands]
+    else:
+        result["sub_commands"] = []
+    return result

astrbot/core/star/context.py

@@ -267,6 +267,10 @@ class Context:
     ):
         """通过 ID 获取对应的 LLM Provider。"""
         prov = self.provider_manager.inst_map.get(provider_id)
+        if provider_id and not prov:
+            logger.warning(
+                f"没有找到 ID 为 {provider_id} 的提供商，这可能是由于您修改了提供商（模型）ID 导致的。"
+            )
         return prov
 
     def get_all_providers(self) -> list[Provider]:
@@ -373,7 +377,7 @@ class Context:
             if not module_path:
                 _parts = []
                 module_part = tool.__module__.split(".")
-                flags = ["packages", "plugins"]
+                flags = ["builtin_stars", "plugins"]
                 for i, part in enumerate(module_part):
                     _parts.append(part)
                     if part in flags and i + 1 < len(module_part):

astrbot/core/star/filter/command.py

@@ -40,6 +40,7 @@ class CommandFilter(HandlerFilter):
     ):
         self.command_name = command_name
         self.alias = alias if alias else set()
+        self._original_command_name = command_name
         self.parent_command_names = (
             parent_command_names if parent_command_names is not None else [""]
         )

astrbot/core/star/filter/command_group.py

@@ -18,6 +18,7 @@ class CommandGroupFilter(HandlerFilter):
     ):
         self.group_name = group_name
         self.alias = alias if alias else set()
+        self._original_group_name = group_name
         self.sub_command_filters: list[CommandFilter | CommandGroupFilter] = []
         self.custom_filter_list: list[CustomFilter] = []
         self.parent_group = parent_group

astrbot/core/star/star_handler.py

@@ -118,6 +118,8 @@ class StarHandlerRegistry(Generic[T]):
             # 过滤事件类型
             if handler.event_type != event_type:
                 continue
+            if not handler.enabled:
+                continue
             # 过滤启用状态
             if only_activated:
                 plugin = star_map.get(handler.handler_module_path)
@@ -220,6 +222,8 @@ class StarHandlerMetadata(Generic[H]):
     extras_configs: dict = field(default_factory=dict)
     """插件注册的一些其他的信息, 如 priority 等"""
 
+    enabled: bool = True
+
     def __lt__(self, other: StarHandlerMetadata):
         """定义小于运算符以支持优先队列"""
         return self.extras_configs.get("priority", 0) < other.extras_configs.get(

astrbot/core/star/star_manager.py

@@ -18,11 +18,13 @@ from astrbot.core.config.astrbot_config import AstrBotConfig
 from astrbot.core.provider.register import llm_tools
 from astrbot.core.utils.astrbot_path import (
     get_astrbot_config_path,
+    get_astrbot_path,
     get_astrbot_plugin_path,
 )
 from astrbot.core.utils.io import remove_dir
 
 from . import StarMetadata
+from .command_management import sync_command_configs
 from .context import Context
 from .filter.permission import PermissionType, PermissionTypeFilter
 from .star import star_map, star_registry
@@ -48,13 +50,10 @@ class PluginManager:
         """存储插件的路径。即 data/plugins"""
         self.plugin_config_path = get_astrbot_config_path()
         """存储插件配置的路径。data/config"""
-        self.reserved_plugin_path = os.path.abspath(
-            os.path.join(
-                os.path.dirname(os.path.abspath(__file__)),
-                "../../../packages",
-            ),
+        self.reserved_plugin_path = os.path.join(
+            get_astrbot_path(), "astrbot", "builtin_stars"
         )
-        """保留插件的路径。在 packages 目录下"""
+        """保留插件的路径。在 astrbot/builtin_stars 目录下"""
         self.conf_schema_fname = "_conf_schema.json"
         self.logo_fname = "logo.png"
         """插件配置 Schema 文件名"""
@@ -251,7 +250,7 @@ class PluginManager:
             list[str]: 与该插件相关的模块名列表
 
         """
-        prefix = "packages." if is_reserved else "data.plugins."
+        prefix = "astrbot.builtin_stars." if is_reserved else "data.plugins."
         return [
             key
             for key in list(sys.modules.keys())
@@ -269,7 +268,7 @@ class PluginManager:
         可以基于模块名模式或插件目录名移除模块，用于清理插件相关的模块缓存
 
         Args:
-            module_patterns: 要移除的模块名模式列表（例如 ["data.plugins", "packages"]）
+            module_patterns: 要移除的模块名模式列表（例如 ["data.plugins", "astrbot.builtin_stars"]）
             root_dir_name: 插件根目录名，用于移除与该插件相关的所有模块
             is_reserved: 插件是否为保留插件（影响模块路径前缀）
 
@@ -381,9 +380,9 @@ class PluginManager:
                 reserved = plugin_module.get(
                     "reserved",
                     False,
-                )  # 是否是保留插件。目前在 packages/ 目录下的都是保留插件。保留插件不可以卸载。
+                )  # 是否是保留插件。目前在 astrbot/builtin_stars 目录下的都是保留插件。保留插件不可以卸载。
 
-                path = "data.plugins." if not reserved else "packages."
+                path = "data.plugins." if not reserved else "astrbot.builtin_stars."
                 path += root_dir_name + "." + module_str
 
                 # 检查是否需要载入指定的插件
@@ -467,6 +466,18 @@ class PluginManager:
                             metadata.star_cls = metadata.star_cls_type(
                                 context=self.context,
                             )
+
+                        p_name = (metadata.name or "unknown").lower().replace("/", "_")
+                        p_author = (
+                            (metadata.author or "unknown").lower().replace("/", "_")
+                        )
+                        setattr(metadata.star_cls, "name", p_name)
+                        setattr(metadata.star_cls, "author", p_author)
+                        setattr(
+                            metadata.star_cls,
+                            "plugin_id",
+                            f"{p_author}/{p_name}",
+                        )
                     else:
                         logger.info(f"插件 {metadata.name} 已被禁用。")
 
@@ -618,6 +629,11 @@ class PluginManager:
         # 清除 pip.main 导致的多余的 logging handlers
         for handler in logging.root.handlers[:]:
             logging.root.removeHandler(handler)
+        try:
+            await sync_command_configs()
+        except Exception as e:
+            logger.error(f"同步指令配置失败: {e!s}")
+            logger.error(traceback.format_exc())
 
         if not fail_rec:
             return True, None
@@ -811,7 +827,7 @@ class PluginManager:
             if (
                 mp
                 and mp.startswith(plugin_module_path)
-                and not mp.endswith(("packages", "data.plugins"))
+                and not mp.endswith(("astrbot.builtin_stars", "data.plugins"))
             ):
                 to_remove.append(func_tool)
         for func_tool in to_remove:
@@ -866,7 +882,7 @@ class PluginManager:
                     plugin.module_path
                     and mp
                     and plugin.module_path.startswith(mp)
-                    and not mp.endswith(("packages", "data.plugins"))
+                    and not mp.endswith(("astrbot.builtin_stars", "data.plugins"))
                 ):
                     func_tool.active = False
                     if func_tool.name not in inactivated_llm_tools:
@@ -915,7 +931,7 @@ class PluginManager:
                 plugin.module_path
                 and mp
                 and plugin.module_path.startswith(mp)
-                and not mp.endswith(("packages", "data.plugins"))
+                and not mp.endswith(("astrbot.builtin_stars", "data.plugins"))
                 and func_tool.name in inactivated_llm_tools
             ):
                 inactivated_llm_tools.remove(func_tool.name)
@@ -928,8 +944,49 @@ class PluginManager:
         dir_name = os.path.basename(zip_file_path).replace(".zip", "")
         dir_name = dir_name.removesuffix("-master").removesuffix("-main").lower()
         desti_dir = os.path.join(self.plugin_store_path, dir_name)
+
+        # 第一步：检查是否已安装同目录名的插件，先终止旧插件
+        existing_plugin = None
+        for star in self.context.get_all_stars():
+            if star.root_dir_name == dir_name:
+                existing_plugin = star
+                break
+
+        if existing_plugin:
+            logger.info(f"检测到插件 {existing_plugin.name} 已安装，正在终止旧插件...")
+            try:
+                await self._terminate_plugin(existing_plugin)
+            except Exception:
+                logger.warning(traceback.format_exc())
+            if existing_plugin.name and existing_plugin.module_path:
+                await self._unbind_plugin(
+                    existing_plugin.name, existing_plugin.module_path
+                )
+
         self.updator.unzip_file(zip_file_path, desti_dir)
 
+        # 第二步：解压后，读取新插件的 metadata.yaml，检查是否存在同名但不同目录的插件
+        try:
+            new_metadata = self._load_plugin_metadata(desti_dir)
+            if new_metadata and new_metadata.name:
+                for star in self.context.get_all_stars():
+                    if (
+                        star.name == new_metadata.name
+                        and star.root_dir_name != dir_name
+                    ):
+                        logger.warning(
+                            f"检测到同名插件 {star.name} 存在于不同目录 {star.root_dir_name}，正在终止..."
+                        )
+                        try:
+                            await self._terminate_plugin(star)
+                        except Exception:
+                            logger.warning(traceback.format_exc())
+                        if star.name and star.module_path:
+                            await self._unbind_plugin(star.name, star.module_path)
+                        break  # 只处理第一个匹配的
+        except Exception as e:
+            logger.debug(f"读取新插件 metadata.yaml 失败，跳过同名检查: {e!s}")
+
         # remove the zip
         try:
             os.remove(zip_file_path)

astrbot/core/utils/astrbot_path.py

@@ -5,6 +5,10 @@
 数据目录路径：固定为根目录下的 data 目录
 配置文件路径：固定为数据目录下的 config 目录
 插件目录路径：固定为数据目录下的 plugins 目录
+插件数据目录路径：固定为数据目录下的 plugin_data 目录
+T2I 模板目录路径：固定为数据目录下的 t2i_templates 目录
+WebChat 数据目录路径：固定为数据目录下的 webchat 目录
+临时文件目录路径：固定为数据目录下的 temp 目录
 """
 
 import os
@@ -37,3 +41,33 @@ def get_astrbot_config_path() -> str:
 def get_astrbot_plugin_path() -> str:
     """获取Astrbot插件目录路径"""
     return os.path.realpath(os.path.join(get_astrbot_data_path(), "plugins"))
+
+
+def get_astrbot_plugin_data_path() -> str:
+    """获取Astrbot插件数据目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "plugin_data"))
+
+
+def get_astrbot_t2i_templates_path() -> str:
+    """获取Astrbot T2I 模板目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "t2i_templates"))
+
+
+def get_astrbot_webchat_path() -> str:
+    """获取Astrbot WebChat 数据目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "webchat"))
+
+
+def get_astrbot_temp_path() -> str:
+    """获取Astrbot临时文件目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "temp"))
+
+
+def get_astrbot_knowledge_base_path() -> str:
+    """获取Astrbot知识库根目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "knowledge_base"))
+
+
+def get_astrbot_backups_path() -> str:
+    """获取Astrbot备份目录路径"""
+    return os.path.realpath(os.path.join(get_astrbot_data_path(), "backups"))

astrbot/core/utils/llm_metadata.py

@@ -0,0 +1,63 @@
+from typing import Literal, TypedDict
+
+import aiohttp
+
+from astrbot.core import logger
+
+
+class LLMModalities(TypedDict):
+    input: list[Literal["text", "image", "audio", "video"]]
+    output: list[Literal["text", "image", "audio", "video"]]
+
+
+class LLMLimit(TypedDict):
+    context: int
+    output: int
+
+
+class LLMMetadata(TypedDict):
+    id: str
+    reasoning: bool
+    tool_call: bool
+    knowledge: str
+    release_date: str
+    modalities: LLMModalities
+    open_weights: bool
+    limit: LLMLimit
+
+
+LLM_METADATAS: dict[str, LLMMetadata] = {}
+
+
+async def update_llm_metadata():
+    url = "https://models.dev/api.json"
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as response:
+                data = await response.json()
+                global LLM_METADATAS
+                models = {}
+                for info in data.values():
+                    for model in info.get("models", {}).values():
+                        model_id = model.get("id")
+                        if not model_id:
+                            continue
+                        models[model_id] = LLMMetadata(
+                            id=model_id,
+                            reasoning=model.get("reasoning", False),
+                            tool_call=model.get("tool_call", False),
+                            knowledge=model.get("knowledge", "none"),
+                            release_date=model.get("release_date", ""),
+                            modalities=model.get(
+                                "modalities", {"input": [], "output": []}
+                            ),
+                            open_weights=model.get("open_weights", False),
+                            limit=model.get("limit", {"context": 0, "output": 0}),
+                        )
+                # Replace the global cache in-place so references remain valid
+                LLM_METADATAS.clear()
+                LLM_METADATAS.update(models)
+                logger.info(f"Successfully fetched metadata for {len(models)} LLMs.")
+    except Exception as e:
+        logger.error(f"Failed to fetch LLM metadata: {e}")
+        return

astrbot/core/utils/migra_helper.py

@@ -32,6 +32,92 @@ def _migra_agent_runner_configs(conf: AstrBotConfig, ids_map: dict) -> None:
         logger.error(traceback.format_exc())
 
 
+def _migra_provider_to_source_structure(conf: AstrBotConfig) -> None:
+    """
+    Migrate old provider structure to new provider-source separation.
+    Provider only keeps: id, provider_source_id, model, modalities, custom_extra_body
+    All other fields move to provider_sources.
+    """
+    providers = conf.get("provider", [])
+    provider_sources = conf.get("provider_sources", [])
+
+    # Track if any migration happened
+    migrated = False
+
+    # Provider-only fields that should stay in provider
+    provider_only_fields = {
+        "id",
+        "provider_source_id",
+        "model",
+        "modalities",
+        "custom_extra_body",
+        "enable",
+    }
+
+    # Fields that should not go to source
+    source_exclude_fields = provider_only_fields | {"model_config"}
+
+    for provider in providers:
+        # Skip if already has provider_source_id
+        if provider.get("provider_source_id"):
+            continue
+
+        # Skip non-chat-completion types (they don't need source separation)
+        provider_type = provider.get("provider_type", "")
+        if provider_type != "chat_completion":
+            # For old types without provider_type, check type field
+            old_type = provider.get("type", "")
+            if "chat_completion" not in old_type:
+                continue
+
+        migrated = True
+        logger.info(f"Migrating provider {provider.get('id')} to new structure")
+
+        # Extract source fields from provider
+        source_fields = {}
+        for key, value in list(provider.items()):
+            if key not in source_exclude_fields:
+                source_fields[key] = value
+
+        # Create new provider_source
+        source_id = provider.get("id", "") + "_source"
+        new_source = {"id": source_id, **source_fields}
+
+        # Update provider to only keep necessary fields
+        provider["provider_source_id"] = source_id
+
+        # Extract model from model_config if exists
+        if "model_config" in provider and isinstance(provider["model_config"], dict):
+            model_config = provider["model_config"]
+            provider["model"] = model_config.get("model", "")
+
+            # Put other model_config fields into custom_extra_body
+            extra_body_fields = {k: v for k, v in model_config.items() if k != "model"}
+            if extra_body_fields:
+                if "custom_extra_body" not in provider:
+                    provider["custom_extra_body"] = {}
+                provider["custom_extra_body"].update(extra_body_fields)
+
+        # Initialize new fields if not present
+        if "modalities" not in provider:
+            provider["modalities"] = []
+        if "custom_extra_body" not in provider:
+            provider["custom_extra_body"] = {}
+
+        # Remove fields that should be in source
+        keys_to_remove = [k for k in provider.keys() if k not in provider_only_fields]
+        for key in keys_to_remove:
+            del provider[key]
+
+        # Add source to provider_sources
+        provider_sources.append(new_source)
+
+    if migrated:
+        conf["provider_sources"] = provider_sources
+        conf.save_config()
+        logger.info("Provider-source structure migration completed")
+
+
 async def migra(
     db, astrbot_config_mgr, umop_config_router, acm: AstrBotConfigManager
 ) -> None:
@@ -71,3 +157,10 @@ async def migra(
 
     for conf in acm.confs.values():
         _migra_agent_runner_configs(conf, ids_map)
+
+    # Migrate providers to new structure: extract source fields to provider_sources
+    try:
+        _migra_provider_to_source_structure(astrbot_config)
+    except Exception as e:
+        logger.error(f"Migration for provider-source structure failed: {e!s}")
+        logger.error(traceback.format_exc())