Merge remote-tracking branch 'origin/master' into feat/tauri-app

* feat: supports thinking level of google gemini

- Updated google-genai version to >=1.56.0 in pyproject.toml and requirements.txt.
- Changed model configuration from "gemini-1.5-flash" to "gemini-3-flash-preview" in default.py.
- Enhanced thinking configuration handling in gemini_source.py to support new parameters for Gemini 3 models.

* fix: standardize thinking level configuration in default.py and gemini_source.py

- Updated the thinking level values in default.py to uppercase for consistency.
- Enhanced gemini_source.py to validate the thinking level and default to "HIGH" if an invalid value is provided.

.github/workflows/build-app.yml

@@ -0,0 +1,79 @@
+name: Build Desktop App
+
+on:
+  push:
+    tags:
+      - 'v*'
+  workflow_dispatch:
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        platform: [macos-latest, ubuntu-latest, windows-latest]
+
+    runs-on: ${{ matrix.platform }}
+    
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Install Rust
+        uses: dtolnay/rust-toolchain@stable
+
+      - name: Install dependencies (Ubuntu)
+        if: matrix.platform == 'ubuntu-latest'
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev libappindicator3-dev librsvg2-dev patchelf
+
+      - name: Install Python dependencies
+        run: |
+          pip install uv
+          uv sync
+
+      - name: Build Python backend with Nuitka
+        run: |
+          pip install nuitka
+          python build_nuitka.py
+
+      - name: Install Node dependencies
+        working-directory: ./dashboard
+        run: npm install
+
+      - name: Build Tauri app
+        working-directory: ./dashboard
+        run: npm run tauri:build
+
+      - name: Upload artifacts (macOS)
+        if: matrix.platform == 'macos-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: astrbot-macos
+          path: dashboard/src-tauri/target/release/bundle/dmg/*.dmg
+
+      - name: Upload artifacts (Windows)
+        if: matrix.platform == 'windows-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: astrbot-windows
+          path: dashboard/src-tauri/target/release/bundle/msi/*.msi
+
+      - name: Upload artifacts (Linux)
+        if: matrix.platform == 'ubuntu-latest'
+        uses: actions/upload-artifact@v4
+        with:
+          name: astrbot-linux
+          path: |
+            dashboard/src-tauri/target/release/bundle/deb/*.deb
+            dashboard/src-tauri/target/release/bundle/appimage/*.AppImage

.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: |

.gitignore

@@ -32,6 +32,7 @@ tests/astrbot_plugin_openai
 # Dashboard
 dashboard/node_modules/
 dashboard/dist/
+dashboard/src-tauri/target
 package-lock.json
 package.json
 yarn.lock
@@ -48,5 +49,6 @@ astrbot.lock
 chroma
 venv/*
 pytest.ini
+build/
 AGENTS.md
 IFLOW.md

BUILD_INSTRUCTIONS.md

@@ -0,0 +1,287 @@
+# AstrBot 桌面应用构建指南
+
+本指南介绍如何使用 Nuitka 将 Python 后端打包并集成到 Tauri 桌面应用中。
+
+## 前置要求
+
+### 系统要求
+- Python 3.10+
+- Node.js 20+
+- Rust (通过 rustup 安装)
+- UV 包管理器
+
+### macOS 额外要求
+- Xcode Command Line Tools: `xcode-select --install`
+
+### Linux 额外要求
+```bash
+sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev \
+  libappindicator3-dev librsvg2-dev patchelf
+```
+
+### Windows 额外要求
+- Visual Studio 2019+ with C++ build tools
+- Windows 10 SDK
+
+## 构建步骤
+
+### 1. 安装 Python 依赖
+```bash
+pip install uv
+uv sync
+```
+
+### 2. 安装 Nuitka
+```bash
+pip install nuitka
+```
+
+### 3. 构建 Python 后端
+```bash
+python build_nuitka.py
+```
+
+这会使用 Nuitka 将 `main.py` 编译为独立可执行文件，输出到 `build/nuitka/` 目录。
+
+**注意**: Nuitka 编译过程可能需要 10-30 分钟，取决于您的系统性能。
+
+### 4. 安装前端依赖
+```bash
+cd dashboard
+npm install
+```
+
+### 5. 构建 Tauri 应用
+```bash
+npm run tauri:build
+```
+
+构建脚本会自动:
+1. 运行 `build_nuitka.py` 编译 Python 后端
+2. 将编译好的可执行文件复制到 `src-tauri/resources/` 目录
+3. 构建 Tauri 应用并打包所有资源
+
+### 6. 查找构建产物
+
+构建完成后，您可以在以下位置找到安装包:
+
+- **macOS**: `dashboard/src-tauri/target/release/bundle/dmg/AstrBot_*.dmg`
+- **Windows**: `dashboard/src-tauri/target/release/bundle/msi/AstrBot_*.msi`
+- **Linux**: 
+  - `dashboard/src-tauri/target/release/bundle/deb/astrbot_*.deb`
+  - `dashboard/src-tauri/target/release/bundle/appimage/astrbot_*.AppImage`
+
+## 开发模式
+
+在开发时，您可能不想每次都完整编译 Python 后端。
+
+### 仅开发 Tauri + Vue
+```bash
+cd dashboard
+npm run tauri:dev
+```
+
+这会启动开发服务器，但不会自动启动 Python 后端。您需要手动运行:
+```bash
+uv run main.py
+```
+
+### 测试完整集成
+如果您想测试 Tauri 自动启动 Python 后端的功能:
+
+1. 先编译一次 Python 后端:
+```bash
+python build_nuitka.py
+```
+
+2. 手动复制到资源目录:
+```bash
+# macOS
+cp -r build/nuitka/main.app dashboard/src-tauri/resources/astrbot-backend.app
+
+# Windows
+copy build\nuitka\main.exe dashboard\src-tauri\resources\astrbot-backend.exe
+
+# Linux
+cp build/nuitka/main.bin dashboard/src-tauri/resources/astrbot-backend
+```
+
+3. 运行开发模式:
+```bash
+cd dashboard
+npm run tauri:dev
+```
+
+## Nuitka 构建选项说明
+
+`build_nuitka.py` 脚本使用以下关键选项:
+
+- `--standalone`: 创建包含所有依赖的独立目录
+- `--onefile`: 将所有内容打包到单个可执行文件
+- `--follow-imports`: 自动跟踪所有 Python 导入
+- `--include-package`: 明确包含特定包
+- `--include-data-dir`: 包含数据目录（插件、配置等）
+
+### 自定义构建
+
+如果您需要修改构建选项，编辑 `build_nuitka.py`:
+
+```python
+# 添加更多要包含的包
+include_packages = [
+    "astrbot",
+    "your_custom_package",
+    # ...
+]
+
+# 添加更多数据目录
+data_includes = [
+    "data/config",
+    "your_custom_data",
+    # ...
+]
+```
+
+## 常见问题
+
+### 1. Nuitka 编译失败
+**问题**: 编译时出现 "module not found" 错误
+
+**解决方案**: 在 `build_nuitka.py` 中添加缺失的包到 `include_packages` 列表
+
+### 2. 运行时找不到资源文件
+**问题**: 应用启动后提示找不到配置文件或插件
+
+**解决方案**: 确保在 `build_nuitka.py` 中使用 `--include-data-dir` 包含了所有必要的数据目录
+
+### 3. macOS 安全警告
+**问题**: macOS 提示"应用来自未知开发者"
+
+**解决方案**: 
+```bash
+# 临时解除限制
+sudo spctl --master-disable
+
+# 或者为特定应用授权
+xattr -cr /Applications/AstrBot.app
+```
+
+对于生产发布，您需要:
+1. 注册 Apple Developer 账号
+2. 对应用进行代码签名
+3. 提交公证 (Notarization)
+
+### 4. Windows Defender 报毒
+**问题**: Windows Defender 或其他杀毒软件报毒
+
+**解决方案**: 
+- 这是 Nuitka 打包程序的常见问题
+- 可以使用 `--windows-company-name` 和 `--windows-product-name` 添加元数据
+- 对于生产发布，需要购买代码签名证书
+
+### 5. Linux 依赖问题
+**问题**: 在某些 Linux 发行版上缺少共享库
+
+**解决方案**: 使用 AppImage 格式，它包含所有依赖:
+```bash
+# 构建时会自动生成 AppImage
+npm run tauri:build
+```
+
+## 优化构建大小
+
+默认的 `--onefile` 模式会生成较大的可执行文件。如果需要减小体积:
+
+1. 移除不需要的包
+2. 使用 `--standalone` 而不是 `--onefile`
+3. 排除不必要的数据文件
+
+修改 `build_nuitka.py`:
+```python
+# 移除 --onefile，使用 --standalone
+nuitka_cmd = [
+    sys.executable,
+    "-m", "nuitka",
+    "--standalone",  # 只使用 standalone
+    # "--onefile",   # 注释掉 onefile
+    # ...
+]
+```
+
+## CI/CD 集成
+
+项目已配置 GitHub Actions 工作流 (`.github/workflows/build-app.yml`)，可以自动为所有平台构建应用。
+
+推送标签时自动触发:
+```bash
+git tag v4.5.7
+git push origin v4.5.7
+```
+
+或手动触发:
+在 GitHub Actions 页面选择 "Build Desktop App" 工作流并点击 "Run workflow"
+
+## 发布清单
+
+在发布新版本前:
+
+- [ ] 更新版本号
+  - `pyproject.toml` - Python 项目版本
+  - `dashboard/package.json` - Node 项目版本
+  - `dashboard/src-tauri/Cargo.toml` - Rust 项目版本
+  - `dashboard/src-tauri/tauri.conf.json` - Tauri 配置版本
+
+- [ ] 运行代码检查
+  ```bash
+  uv run ruff check .
+  uv run ruff format .
+  ```
+
+- [ ] 本地测试构建
+  ```bash
+  python build_nuitka.py
+  cd dashboard && npm run tauri:build
+  ```
+
+- [ ] 测试安装包
+  - 安装生成的安装包
+  - 验证应用启动
+  - 验证 Python 后端自动启动
+  - 测试核心功能
+
+- [ ] 创建发布标签
+  ```bash
+  git tag -a v4.5.7 -m "Release v4.5.7"
+  git push origin v4.5.7
+  ```
+
+## 技术架构
+
+```
+┌─────────────────────────────────────┐
+│         Tauri Desktop App           │
+│  (Rust + WebView)                   │
+│                                     │
+│  ┌─────────────────────────────┐   │
+│  │   Vue.js Dashboard          │   │
+│  │   (Frontend UI)             │   │
+│  └─────────────────────────────┘   │
+│                                     │
+│  ┌─────────────────────────────┐   │
+│  │   Python Backend            │   │
+│  │   (Nuitka Compiled)         │   │
+│  │   - AstrBot Core            │   │
+│  │   - Plugins                 │   │
+│  │   - API Server              │   │
+│  └─────────────────────────────┘   │
+│                                     │
+│         HTTP/WebSocket              │
+│      localhost:6185                 │
+└─────────────────────────────────────┘
+```
+
+## 参考资源
+
+- [Nuitka 文档](https://nuitka.net/doc/user-manual.html)
+- [Tauri 文档](https://tauri.app/v1/guides/)
+- [AstrBot 文档](https://astrbot.fun)

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/cli/__init__.py

@@ -1 +1 @@
-__version__ = "4.9.0"
+__version__ = "4.9.2"

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
 
 
@@ -122,10 +122,12 @@ 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):

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 +13,7 @@ from mcp.types import (
 )
 
 from astrbot import logger
+from astrbot.core.message.components import Json
 from astrbot.core.message.message_event_result import (
     MessageChain,
 )
@@ -24,7 +26,7 @@ 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
@@ -69,6 +71,9 @@ 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."""
         if self.streaming:
@@ -98,6 +103,10 @@ 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 +130,10 @@ 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 +145,7 @@ 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",
@@ -146,6 +160,7 @@ 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(
@@ -175,22 +190,19 @@ 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
@@ -233,6 +245,19 @@ 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 +331,6 @@ 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 +352,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                         content=resource.text,
                                     ),
                                 )
-                                yield MessageChain().message(resource.text)
                             elif (
                                 isinstance(resource, BlobResourceContents)
                                 and resource.mimeType
@@ -352,7 +375,22 @@ 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。")
+
+                        # 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,
+                                        }
+                                    )
+                                ],
+                            )
 
                     elif resp is None:
                         # Tool 直接请求发送消息给用户
@@ -362,6 +400,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                             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。"
                         )
                         self._transition_state(AgentState.DONE)
+                        self.stats.end_time = time.time()
                     else:
                         # 不应该出现其他类型
                         logger.warning(

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_run_util.py

@@ -4,6 +4,7 @@ from collections.abc import AsyncGenerator
 from astrbot.core import logger
 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,
@@ -33,16 +34,27 @@ 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 +81,15 @@ 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/config/default.py

@@ -4,7 +4,7 @@ import os
 
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
-VERSION = "4.9.0"
+VERSION = "4.9.2"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")
 
 WEBHOOK_SUPPORTED_PLATFORMS = [
@@ -108,6 +108,7 @@ DEFAULT_CONFIG = {
         "provider_id": "",
         "dual_output": False,
         "use_file_service": False,
+        "trigger_probability": 1.0,
     },
     "provider_ltm_settings": {
         "group_icl_enable": False,
@@ -208,7 +209,7 @@ 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,
@@ -945,7 +946,7 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
                         "timeout": 120,
                         "model_config": {
-                            "model": "gemini-1.5-flash",
+                            "model": "gemini-3-flash-preview",
                             "temperature": 0.4,
                         },
                         "custom_headers": {},
@@ -962,7 +963,7 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://generativelanguage.googleapis.com/",
                         "timeout": 120,
                         "model_config": {
-                            "model": "gemini-2.0-flash-exp",
+                            "model": "gemini-3-flash-preview",
                             "temperature": 0.4,
                         },
                         "gm_resp_image_modal": False,
@@ -975,9 +976,7 @@ CONFIG_METADATA_2 = {
                             "sexually_explicit": "BLOCK_MEDIUM_AND_ABOVE",
                             "dangerous_content": "BLOCK_MEDIUM_AND_ABOVE",
                         },
-                        "gm_thinking_config": {
-                            "budget": 0,
-                        },
+                        "gm_thinking_config": {"budget": 0, "level": "HIGH"},
                         "modalities": ["text", "image", "tool_use"],
                     },
                     "DeepSeek": {
@@ -1818,13 +1817,24 @@ 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",
+                                ],
                             },
                         },
                     },
@@ -2209,6 +2219,9 @@ CONFIG_METADATA_2 = {
                     "use_file_service": {
                         "type": "bool",
                     },
+                    "trigger_probability": {
+                        "type": "float",
+                    },
                 },
             },
             "provider_ltm_settings": {
@@ -2419,6 +2432,14 @@ 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 +3007,7 @@ 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,
                         },

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/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/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"][
@@ -246,7 +259,14 @@ class ResultDecorateStage(Stage):
                 and result.is_llm_result()
                 and SessionServiceManager.should_process_tts_request(event)
             ):
-                if not tts_provider:
+                should_tts = self.tts_trigger_probability >= 1.0 or (
+                    self.tts_trigger_probability > 0.0
+                    and random.random() <= self.tts_trigger_probability
+                )
+
+                if not should_tts:
+                    logger.debug("跳过 TTS：触发概率未命中。")
+                elif not tts_provider:
                     logger.warning(
                         f"会话 {event.unified_msg_origin} 未配置文本转语音模型。",
                     )

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_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/provider/entities.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import base64
 import enum
 import json
@@ -199,6 +201,38 @@ 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
@@ -227,6 +261,11 @@ 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,
@@ -241,6 +280,8 @@ class LLMResponse:
         | AnthropicMessage
         | None = None,
         is_chunk: bool = False,
+        id: str | None = None,
+        usage: TokenUsage | None = None,
     ):
         """初始化 LLMResponse
 

astrbot/core/provider/sources/anthropic_source.py

@@ -6,10 +6,12 @@ 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.provider.entities import LLMResponse, TokenUsage
 from astrbot.core.provider.func_tool_manager import ToolSet
 from astrbot.core.utils.io import download_image_by_url
 
@@ -107,6 +109,22 @@ 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():
@@ -131,6 +149,10 @@ class ProviderAnthropic(Provider):
                 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}。")
@@ -152,9 +174,16 @@ class ProviderAnthropic(Provider):
         final_text = ""
         final_tool_calls = []
 
+        id = None
+        usage = TokenUsage()
+
         async with self.client.messages.stream(**payloads) 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 +191,8 @@ 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
@@ -179,6 +210,8 @@ class ProviderAnthropic(Provider):
                             role="assistant",
                             completion_text=event.delta.text,
                             is_chunk=True,
+                            usage=usage,
+                            id=id,
                         )
                     elif event.delta.type == "input_json_delta":
                         # 工具调用参数增量
@@ -215,6 +248,8 @@ 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 +258,17 @@ 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,
         )
 
         if final_tool_calls:

astrbot/core/provider/sources/gemini_source.py

@@ -14,7 +14,7 @@ import astrbot.core.message.components as Comp
 from astrbot import logger
 from astrbot.api.provider import Provider
 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
 
@@ -138,7 +138,7 @@ class ProviderGoogleGenAI(Provider):
             modalities = ["TEXT"]
 
         tool_list: list[types.Tool] | None = []
-        model_name = self.get_model()
+        model_name = 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 +197,37 @@ class ProviderGoogleGenAI(Provider):
                 types.Tool(function_declarations=func_desc["function_declarations"]),
             ]
 
+        # oper thinking config
+        thinking_config = None
+        if model_name.startswith("gemini-2.5"):
+            # 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.startswith("gemini-3"):
+            # 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 +247,7 @@ 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,
             ),
@@ -347,6 +363,16 @@ 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,
@@ -431,6 +457,8 @@ 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 +477,7 @@ 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 +503,11 @@ 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 +516,7 @@ 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 +529,9 @@ 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 +544,7 @@ 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 +556,7 @@ 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 +566,11 @@ 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 +600,9 @@ 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 +630,9 @@ 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

astrbot/core/provider/sources/openai_source.py

@@ -12,6 +12,7 @@ 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
@@ -19,7 +20,7 @@ from astrbot.api.provider import Provider
 from astrbot.core.agent.message import Message
 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
@@ -208,6 +209,7 @@ 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 +219,8 @@ 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 +249,15 @@ 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
+        return TokenUsage(
+            input_other=usage.prompt_tokens - cached,
+            input_cached=ptd.cached_tokens if ptd and ptd.cached_tokens else 0,
+            output=usage.completion_tokens,
+        )
+
     async def _parse_openai_completion(
         self, completion: ChatCompletion, tools: ToolSet | None
     ) -> LLMResponse:
@@ -321,6 +334,10 @@ 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
 

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/context.py

@@ -296,6 +296,10 @@ class Context:
             provider_type=ProviderType.CHAT_COMPLETION,
             umo=umo,
         )
+        if prov is None:
+            raise ProviderNotFoundError(
+                "provider not found, please choose provider first"
+            )
         if not isinstance(prov, Provider):
             raise ValueError("返回的 Provider 不是 Provider 类型")
         return prov

astrbot/core/star/star_manager.py

@@ -468,6 +468,18 @@ 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。")
 

astrbot/core/utils/plugin_kv_store.py

@@ -0,0 +1,28 @@
+from typing import TypeVar
+
+from astrbot.core import sp
+
+SUPPORTED_VALUE_TYPES = int | float | str | bytes | bool | dict | list | None
+_VT = TypeVar("_VT")
+
+
+class PluginKVStoreMixin:
+    """为插件提供键值存储功能的 Mixin 类"""
+
+    plugin_id: str
+
+    async def put_kv_data(
+        self,
+        key: str,
+        value: SUPPORTED_VALUE_TYPES,
+    ) -> None:
+        """为指定插件存储一个键值对"""
+        await sp.put_async("plugin", self.plugin_id, key, value)
+
+    async def get_kv_data(self, key: str, default: _VT) -> _VT | None:
+        """获取指定插件存储的键值对"""
+        return await sp.get_async("plugin", self.plugin_id, key, default)
+
+    async def delete_kv_data(self, key: str) -> None:
+        """删除指定插件存储的键值对"""
+        await sp.remove_async("plugin", self.plugin_id, key)

astrbot/dashboard/routes/chat.py

@@ -227,16 +227,19 @@ class ChatRoute(Route):
         text: str,
         media_parts: list,
         reasoning: str,
+        agent_stats: dict,
     ):
         """保存 bot 消息到历史记录，返回保存的记录"""
         bot_message_parts = []
+        bot_message_parts.extend(media_parts)
         if text:
             bot_message_parts.append({"type": "plain", "text": text})
-        bot_message_parts.extend(media_parts)
 
         new_his = {"type": "bot", "message": bot_message_parts}
         if reasoning:
             new_his["reasoning"] = reasoning
+        if agent_stats:
+            new_his["agent_stats"] = agent_stats
 
         record = await self.platform_history_mgr.insert(
             platform_id="webchat",
@@ -294,7 +297,8 @@ class ChatRoute(Route):
             accumulated_parts = []
             accumulated_text = ""
             accumulated_reasoning = ""
-
+            tool_calls = {}
+            agent_stats = {}
             try:
                 async with track_conversation(self.running_convs, webchat_conv_id):
                     while True:
@@ -314,6 +318,16 @@ class ChatRoute(Route):
                         result_text = result["data"]
                         msg_type = result.get("type")
                         streaming = result.get("streaming", False)
+                        chain_type = result.get("chain_type")
+
+                        if chain_type == "agent_stats":
+                            stats_info = {
+                                "type": "agent_stats",
+                                "data": json.loads(result_text),
+                            }
+                            yield f"data: {json.dumps(stats_info, ensure_ascii=False)}\n\n"
+                            agent_stats = stats_info["data"]
+                            continue
 
                         # 发送 SSE 数据
                         try:
@@ -335,11 +349,35 @@ class ChatRoute(Route):
 
                         # 累积消息部分
                         if msg_type == "plain":
-                            chain_type = result.get("chain_type", "normal")
-                            if chain_type == "reasoning":
+                            chain_type = result.get("chain_type")
+                            if chain_type == "tool_call":
+                                tool_call = json.loads(result_text)
+                                tool_calls[tool_call.get("id")] = tool_call
+                                if accumulated_text:
+                                    # 如果累积了文本，则先保存文本
+                                    accumulated_parts.append(
+                                        {"type": "plain", "text": accumulated_text}
+                                    )
+                                    accumulated_text = ""
+                            elif chain_type == "tool_call_result":
+                                tcr = json.loads(result_text)
+                                tc_id = tcr.get("id")
+                                if tc_id in tool_calls:
+                                    tool_calls[tc_id]["result"] = tcr.get("result")
+                                    tool_calls[tc_id]["finished_ts"] = tcr.get("ts")
+                                accumulated_parts.append(
+                                    {
+                                        "type": "tool_call",
+                                        "tool_calls": [tool_calls[tc_id]],
+                                    }
+                                )
+                                tool_calls.pop(tc_id, None)
+                            elif chain_type == "reasoning":
                                 accumulated_reasoning += result_text
-                            else:
+                            elif streaming:
                                 accumulated_text += result_text
+                            else:
+                                accumulated_text = result_text
                         elif msg_type == "image":
                             filename = result_text.replace("[IMAGE]", "")
                             part = await self._create_attachment_from_file(
@@ -367,15 +405,20 @@ class ChatRoute(Route):
                         if msg_type == "end":
                             break
                         elif (
-                            (streaming and msg_type == "complete")
-                            or not streaming
-                            or msg_type == "break"
+                            (streaming and msg_type == "complete") or not streaming
+                            # or msg_type == "break"
                         ):
+                            if (
+                                chain_type == "tool_call"
+                                or chain_type == "tool_call_result"
+                            ):
+                                continue
                             saved_record = await self._save_bot_message(
                                 webchat_conv_id,
                                 accumulated_text,
                                 accumulated_parts,
                                 accumulated_reasoning,
+                                agent_stats,
                             )
                             # 发送保存的消息信息给前端
                             if saved_record and not client_disconnected:
@@ -390,11 +433,11 @@ class ChatRoute(Route):
                                     yield f"data: {json.dumps(saved_info, ensure_ascii=False)}\n\n"
                                 except Exception:
                                     pass
-                            # 重置累积变量 (对于 break 后的下一段消息)
-                            if msg_type == "break":
-                                accumulated_parts = []
-                                accumulated_text = ""
-                                accumulated_reasoning = ""
+                            accumulated_parts = []
+                            accumulated_text = ""
+                            accumulated_reasoning = ""
+                            tool_calls = {}
+                            agent_stats = {}
             except BaseException as e:
                 logger.exception(f"WebChat stream unexpected error: {e}", exc_info=True)
 

build_nuitka.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+"""
+Use Nuitka to build the AstrBot project into standalone executables
+"""
+
+import os
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+
+def get_platform_info():
+    """fetch the current platform information"""
+    system = platform.system()
+    machine = platform.machine()
+    return system, machine
+
+
+def build_with_nuitka():
+    """use Nuitka to build the project"""
+    system, machine = get_platform_info()
+
+    print(f"🚀 Starting build for {system} ({machine}) platform...")
+
+    # Output directory
+    output_dir = Path("build/nuitka")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Base Nuitka command
+    nuitka_cmd = [
+        sys.executable,
+        "-m",
+        "nuitka",
+        "--standalone",  # Create standalone directory
+        "--onefile",  # Single file mode
+        "--follow-imports",  # Follow all imports
+        "--enable-plugin=multiprocessing",  # Enable multiprocessing support
+        "--output-dir=build/nuitka",  # Output directory
+        "--quiet",  # Reduce output verbosity
+        "--assume-yes-for-downloads",  # Automatically download dependencies
+        "--jobs=4",  # Use multiple CPU cores
+    ]
+
+    # include specific packages
+    include_packages = [
+        "astrbot",
+    ]
+
+    for pkg in include_packages:
+        nuitka_cmd.extend([f"--include-package={pkg}"])
+
+    # include data directories
+    # data_includes = [
+    #     "data/config",
+    #     "data/plugins",
+    #     "data/temp",
+    # ]
+
+    # for data_dir in data_includes:
+    #     if os.path.exists(data_dir):
+    #         nuitka_cmd.extend([f"--include-data-dir={data_dir}={data_dir}"])
+
+    # include packages directory (built-in plugins)
+    # if os.path.exists("packages"):
+    #     nuitka_cmd.extend(["--include-data-dir=packages=packages"])
+
+    # Platform specific settings
+    if system == "Darwin":  # macOS
+        nuitka_cmd.extend(
+            [
+                "--macos-create-app-bundle",  # Create .app bundle
+                "--macos-app-name=AstrBot",
+            ]
+        )
+        # macOS icon (if exists)
+        icon_path = "dashboard/src-tauri/icons/icon.icns"
+        if os.path.exists(icon_path):
+            nuitka_cmd.extend([f"--macos-app-icon={icon_path}"])
+    elif system == "Windows":
+        nuitka_cmd.extend(
+            [
+                "--windows-console-mode=disable",  # 无控制台窗口
+            ]
+        )
+        # Windows icon (if exists)
+        icon_path = "dashboard/src-tauri/icons/icon.ico"
+        if os.path.exists(icon_path):
+            nuitka_cmd.extend([f"--windows-icon-from-ico={icon_path}"])
+
+    # Main file to compile
+    nuitka_cmd.append("main.py")
+
+    print(f"📦 Executing command: {' '.join(nuitka_cmd)}")
+
+    try:
+        subprocess.run(nuitka_cmd, check=True)
+        print("✅ Nuitka build successful!")
+
+        # Find the generated executable
+        if system == "Darwin":
+            built_file = list(output_dir.glob("*.app"))
+            if built_file:
+                print(f"Generated macOS app: {built_file[0]}")
+        elif system == "Windows":
+            built_file = list(output_dir.glob("*.exe"))
+            if built_file:
+                print(f"Generated Windows executable: {built_file[0]}")
+        else:  # Linux
+            built_file = list(output_dir.glob("main.bin"))
+            if built_file:
+                print(f"Generated Linux executable: {built_file[0]}")
+
+        return True
+    except subprocess.CalledProcessError as e:
+        print(f"❌ Nuitka build failed: {e}")
+        return False
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("AstrBot Nuitka Builder")
+    print("=" * 60)
+
+    # 构建
+    if build_with_nuitka():
+        print("\n" + "=" * 60)
+        print("🎉 Build Complete!")
+        print("=" * 60)
+    else:
+        print("\n" + "=" * 60)
+        print("❌ Build Failed")
+        print("=" * 60)
+        sys.exit(1)

build_pyinstaller.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+"""
+Use PyInstaller to build the AstrBot project into standalone executables
+"""
+
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+
+def get_platform_info():
+    """fetch the current platform information"""
+    system = platform.system()
+    machine = platform.machine()
+    return system, machine
+
+
+def build_with_pyinstaller():
+    """use PyInstaller to build the project"""
+    system, machine = get_platform_info()
+
+    print(f"🚀 Starting build for {system} ({machine}) platform...")
+
+    # Output directory
+    output_dir = Path("build/pyinstaller")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Base PyInstaller command
+    pyinstaller_cmd = [
+        sys.executable,
+        "-m",
+        "PyInstaller",
+        "--clean",  # Clean cache before build
+        "--noconfirm",  # Replace output directory without asking
+        "--onefile",  # Single file mode
+        "--distpath=build/pyinstaller/dist",  # Distribution directory
+        "--workpath=build/pyinstaller/build",  # Work directory
+        "--specpath=build/pyinstaller",  # Spec file directory
+        "--name=AstrBot",  # Output executable name
+    ]
+    # Platform specific settings
+    # if system == "Darwin":  # macOS
+    #     # macOS icon (if exists)
+    #     icon_path = "dashboard/src-tauri/icons/icon.icns"
+    #     if os.path.exists(icon_path):
+    #         pyinstaller_cmd.extend([f"--icon={icon_path}"])
+    #     # Create .app bundle
+    #     pyinstaller_cmd.extend(["--windowed"])
+    # elif system == "Windows":
+    #     # Windows icon (if exists)
+    #     icon_path = "dashboard/src-tauri/icons/icon.ico"
+    #     if os.path.exists(icon_path):
+    #         pyinstaller_cmd.extend([f"--icon={icon_path}"])
+    #     # No console window
+    #     pyinstaller_cmd.extend(["--windowed"])
+    # else:  # Linux
+    #     pyinstaller_cmd.extend(["--console"])
+
+    # Main file to compile
+    pyinstaller_cmd.append("main.py")
+
+    print(f"📦 Executing command: {' '.join(pyinstaller_cmd)}")
+
+    try:
+        subprocess.run(pyinstaller_cmd, check=True)
+        print("✅ PyInstaller build successful!")
+
+        # Find the generated executable
+        dist_dir = output_dir / "dist"
+        if system == "Darwin":
+            built_file = list(dist_dir.glob("AstrBot.app"))
+            if not built_file:
+                built_file = list(dist_dir.glob("AstrBot"))
+            if built_file:
+                print(f"📱 Generated macOS app: {built_file[0]}")
+        elif system == "Windows":
+            built_file = list(dist_dir.glob("AstrBot.exe"))
+            if built_file:
+                print(f"💻 Generated Windows executable: {built_file[0]}")
+        else:  # Linux
+            built_file = list(dist_dir.glob("AstrBot"))
+            if built_file:
+                print(f"🐧 Generated Linux executable: {built_file[0]}")
+
+        print(f"\n📁 Output directory: {dist_dir.absolute()}")
+        return True
+    except subprocess.CalledProcessError as e:
+        print(f"❌ PyInstaller build failed: {e}")
+        return False
+    except Exception as e:
+        print(f"❌ Unexpected error: {e}")
+        return False
+
+
+def install_pyinstaller():
+    """Install PyInstaller if not already installed"""
+    try:
+        import PyInstaller
+
+        print(f"✅ PyInstaller already installed (version {PyInstaller.__version__})")
+        return True
+    except ImportError:
+        print("📥 PyInstaller not found, installing...")
+        try:
+            subprocess.run(
+                [sys.executable, "-m", "pip", "install", "pyinstaller"], check=True
+            )
+            print("✅ PyInstaller installed successfully!")
+            return True
+        except subprocess.CalledProcessError as e:
+            print(f"❌ Failed to install PyInstaller: {e}")
+            return False
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("AstrBot PyInstaller Builder")
+    print("=" * 60)
+
+    # Check and install PyInstaller
+    if not install_pyinstaller():
+        sys.exit(1)
+
+    # Build
+    if build_with_pyinstaller():
+        print("\n" + "=" * 60)
+        print("🎉 Build Complete!")
+        print("=" * 60)
+    else:
+        print("\n" + "=" * 60)
+        print("❌ Build Failed")
+        print("=" * 60)
+        sys.exit(1)

changelogs/v4.9.1.md

@@ -0,0 +1,3 @@
+## What's Changed
+
+-

changelogs/v4.9.2.md

@@ -0,0 +1,17 @@
+## What's Changed
+
+### 修复
+
+- 企业自部署飞书（自定义 domain）可以接收消息但无法发送消息的问题。
+- 安装插件 Dialog 的深色样式问题。
+
+### 优化
+
+- 避免某些插件在流式响应结束后重d复发送消息的问题。
+
+### 新增
+
+- 支持在对话管理批量导出对话轨迹数据为 `jsonl` 格式文件。入口：WebUI -> 对话管理 -> 批量选中 -> 导出。
+- 支持对 TTS（文本转语音）设置概率触发。
+- （插件开发）支持在 schema 中对 float 和 int 类型设置 `slider` 滑块控件。例如 `slider: {min: 0, max: 1, step: 0.1}`。
+- （插件开发）支持 key-value 存储功能。例如使用 `await self.put_kv_data("key", value)`, `await self.get_kv_data("key", default_value)` 和 `await self.delete_kv_data("key")`。

dashboard/TAURI_README.md

@@ -0,0 +1,225 @@
+# AstrBot Dashboard - Tauri 桌面应用
+
+本项目现已支持通过 Tauri 构建为桌面应用，同时保持与 Web 版本的兼容性。
+
+## 环境要求
+
+### 系统依赖
+
+**macOS:**
+```bash
+# 安装 Xcode Command Line Tools
+xcode-select --install
+```
+
+**Windows:**
+- 安装 [Microsoft Visual Studio C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+- 安装 [WebView2](https://developer.microsoft.com/en-us/microsoft-edge/webview2/)
+
+**Linux (Ubuntu/Debian):**
+```bash
+sudo apt update
+sudo apt install libwebkit2gtk-4.0-dev \
+    build-essential \
+    curl \
+    wget \
+    file \
+    libssl-dev \
+    libgtk-3-dev \
+    libayatana-appindicator3-dev \
+    librsvg2-dev
+```
+
+### Rust 环境
+
+```bash
+# 安装 Rust
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+
+# 验证安装
+rustc --version
+cargo --version
+```
+
+## 安装依赖
+
+```bash
+cd dashboard
+npm install
+```
+
+## 开发模式
+
+### Web 端开发（不变）
+
+```bash
+npm run dev
+```
+
+访问 http://localhost:3000
+
+### 桌面端开发
+
+```bash
+npm run tauri:dev
+```
+
+这会同时启动：
+1. Vite 开发服务器（端口 3000）
+2. Tauri 桌面应用窗口
+
+热重载功能正常工作，修改代码后会自动刷新。
+
+## 构建
+
+### Web 端构建（不变）
+
+```bash
+npm run build
+```
+
+输出目录：`dist/`
+
+### 桌面端构建
+
+```bash
+npm run tauri:build
+```
+
+构建产物位置：
+- **macOS**: `src-tauri/target/release/bundle/dmg/`
+- **Windows**: `src-tauri/target/release/bundle/msi/`
+- **Linux**: `src-tauri/target/release/bundle/deb/` 或 `appimage/`
+
+## 图标设置
+
+### 自动生成图标
+
+准备一个至少 512x512 像素的 PNG 图标，然后运行：
+
+```bash
+npm run tauri icon path/to/your/icon.png
+```
+
+### 手动设置图标
+
+将以下图标放入 `src-tauri/icons/` 目录：
+- `32x32.png`
+- `128x128.png`
+- `128x128@2x.png`
+- `icon.icns` (macOS)
+- `icon.ico` (Windows)
+
+## 代码兼容性
+
+项目已配置为同时支持 Web 和桌面端，使用相同的代码库。
+
+### 环境检测工具
+
+在 `src/utils/tauri.ts` 中提供了环境检测工具：
+
+```typescript
+import { isTauri, isWeb, PlatformAPI } from '@/utils/tauri';
+
+// 检测运行环境
+if (isTauri()) {
+  console.log('运行在桌面应用中');
+} else {
+  console.log('运行在浏览器中');
+}
+
+// 获取正确的 API 端点
+const baseURL = PlatformAPI.getBaseURL();
+```
+
+### API 调用注意事项
+
+- **Web 端**: 使用 Vite 代理，API 路径为 `/api/*`
+- **桌面端**: 直接连接到 `http://127.0.0.1:6185`
+
+已在 `PlatformAPI.getBaseURL()` 中处理，使用 axios 时：
+
+```typescript
+import axios from 'axios';
+import { PlatformAPI } from '@/utils/tauri';
+
+const api = axios.create({
+  baseURL: PlatformAPI.getBaseURL()
+});
+```
+
+## 配置说明
+
+### tauri.conf.json
+
+主要配置项：
+- `build.devPath`: 开发服务器地址（http://localhost:3000）
+- `build.distDir`: 构建输出目录（../dist）
+- `tauri.allowlist`: API 权限配置
+- `tauri.windows`: 窗口配置（大小、标题等）
+
+### 安全性
+
+默认配置已启用必要的权限：
+- 文件系统访问（限定在 APPDATA 目录）
+- HTTP 请求（限定到本地后端）
+- 窗口控制
+- 对话框（打开/保存文件）
+
+可在 `tauri.conf.json` 的 `allowlist` 部分调整权限。
+
+## 后端连接
+
+桌面应用需要后端服务运行在 `http://127.0.0.1:6185`。
+
+### 启动流程
+
+1. 启动 AstrBot 后端:
+   ```bash
+   cd /path/to/AstrBot
+   uv run main.py
+   ```
+
+2. 启动桌面应用:
+   ```bash
+   cd dashboard
+   npm run tauri:dev
+   ```
+
+或直接运行打包后的应用（后端需要已启动）。
+
+## 常见问题
+
+### Q: 桌面应用无法连接到后端？
+
+确保：
+1. AstrBot 后端正在运行（`uv run main.py`）
+2. 后端监听在 `127.0.0.1:6185`
+3. 防火墙未阻止连接
+
+### Q: 图标未显示？
+
+检查 `src-tauri/icons/` 目录中是否有所需的图标文件，或使用 `npm run tauri icon` 命令生成。
+
+### Q: 构建失败？
+
+- 确保已安装 Rust 和系统依赖
+- 运行 `cargo clean` 清理缓存后重试
+- 检查 Rust 版本（需要 1.60+）
+
+### Q: Web 端功能是否受影响？
+
+不受影响。`npm run dev` 和 `npm run build` 的行为完全不变。
+
+## 开发建议
+
+1. **优先使用 Web 端开发**: 更快的热重载，更好的调试体验
+2. **定期测试桌面端**: 确保跨平台兼容性
+3. **使用环境检测**: 针对不同平台提供最佳体验
+4. **注意 API 差异**: Web 和桌面端的某些 API 可能有差异
+
+## 更多资源
+
+- [Tauri 官方文档](https://tauri.app/)
+- [Tauri API 参考](https://tauri.app/v1/api/js/)
+- [Tauri Discord 社区](https://discord.com/invite/tauri)

dashboard/package.json

@@ -10,10 +10,14 @@
     "build-prod": "vue-tsc --noEmit && vite build --base=/vue/free/",
     "preview": "vite preview --port 5050",
     "typecheck": "vue-tsc --noEmit",
-    "lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore"
+    "lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore",
+    "tauri": "tauri",
+    "tauri:dev": "tauri dev",
+    "tauri:build": "tauri build"
   },
   "dependencies": {
     "@guolao/vue-monaco-editor": "^1.5.4",
+    "@tauri-apps/api": "^2.9.0",
     "@tiptap/starter-kit": "2.1.7",
     "@tiptap/vue-3": "2.1.7",
     "apexcharts": "3.42.0",
@@ -43,6 +47,7 @@
   "devDependencies": {
     "@mdi/font": "7.2.96",
     "@rushstack/eslint-patch": "1.3.3",
+    "@tauri-apps/cli": "^2.9.4",
     "@types/chance": "1.1.3",
     "@types/markdown-it": "^14.1.2",
     "@types/node": "^20.5.7",

dashboard/src-tauri/.gitignore

@@ -0,0 +1,3 @@
+# Tauri specific
+src-tauri/target/
+src-tauri/WixTools/

dashboard/src-tauri/Cargo.toml

@@ -0,0 +1,27 @@
+[package]
+name = "astrbot-dashboard"
+version = "4.5.6"
+description = "AstrBot"
+authors = ["AstrBot Team"]
+license = "AGPL-3.0"
+repository = "https://github.com/AstrBotDevs/AstrBot"
+default-run = "astrbot-dashboard"
+edition = "2021"
+rust-version = "1.91.0"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[build-dependencies]
+tauri-build = { version = "2", features = [] }
+
+[dependencies]
+serde_json = "1.0"
+serde = { version = "1.0", features = ["derive"] }
+tauri = { version = "2.9.2", features = ["macos-private-api", "protocol-asset"] }
+tauri-plugin-opener = "2"
+
+[features]
+# this feature is used for production builds or when `devPath` points to the filesystem and the built-in dev server is disabled.
+# If you use cargo directly instead of tauri's cli you can use this feature flag to switch between tauri's `dev` and `build` modes.
+# DO NOT REMOVE!!
+custom-protocol = [ "tauri/custom-protocol" ]

dashboard/src-tauri/build.rs

@@ -0,0 +1,3 @@
+fn main() {
+    tauri_build::build()
+}

dashboard/src-tauri/gen/schemas/capabilities.json

@@ -0,0 +1 @@
+{}

dashboard/src-tauri/icons/android/mipmap-anydpi-v26/ic_launcher.xml

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+  <foreground android:drawable="@mipmap/ic_launcher_foreground"/>
+  <background android:drawable="@color/ic_launcher_background"/>
+</adaptive-icon>

dashboard/src-tauri/icons/android/values/ic_launcher_background.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+  <color name="ic_launcher_background">#fff</color>
+</resources>

dashboard/src-tauri/src/main.rs

@@ -0,0 +1,104 @@
+// Prevents additional console window on Windows in release, DO NOT REMOVE!!
+#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
+
+use std::process::{Child, Command};
+use std::sync::Mutex;
+use tauri::{AppHandle, Emitter, Listener, Manager, State};
+
+struct BackendProcess(Mutex<Option<Child>>);
+
+fn start_backend_process(app_handle: &AppHandle) -> Option<Child> {
+    #[cfg(target_os = "macos")]
+    let backend_path = "astrbot-backend.app/Contents/MacOS/main";
+
+    #[cfg(target_os = "windows")]
+    let backend_path = "astrbot-backend.exe";
+
+    #[cfg(target_os = "linux")]
+    let backend_path = "astrbot-backend";
+
+    // 获取资源目录
+    let resource_dir = match app_handle
+        .path()
+        .resource_dir()
+    {
+        Ok(dir) => dir,
+        Err(e) => {
+            eprintln!("Failed to get resource directory: {}", e);
+            return None;
+        }
+    };
+
+    let full_backend_path = resource_dir.join(backend_path);
+
+    println!("Starting backend process at: {:?}", full_backend_path);
+
+    match Command::new(&full_backend_path).spawn() {
+        Ok(child) => {
+            println!(
+                "Backend process started successfully with PID: {}",
+                child.id()
+            );
+            Some(child)
+        }
+        Err(e) => {
+            eprintln!("Failed to start backend process: {}", e);
+            None
+        }
+    }
+}
+
+#[tauri::command]
+fn restart_backend(
+    app_handle: AppHandle,
+    backend_state: State<BackendProcess>,
+) -> Result<String, String> {
+    let mut backend = backend_state.0.lock().unwrap();
+
+    // 停止现有进程
+    if let Some(mut child) = backend.take() {
+        let _ = child.kill();
+        let _ = child.wait();
+    }
+
+    // 启动新进程
+    *backend = start_backend_process(&app_handle);
+
+    if backend.is_some() {
+        Ok("Backend restarted successfully".to_string())
+    } else {
+        Err("Failed to restart backend".to_string())
+    }
+}
+
+#[cfg_attr(mobile, tauri::mobile_entry_point)]
+pub fn run() {
+    tauri::Builder::default()
+        .setup(|app| {
+            // 启动后端进程
+            let backend_process = start_backend_process(app.handle());
+            app.manage(BackendProcess(Mutex::new(backend_process)));
+            Ok(())
+        })
+        .plugin(tauri_plugin_opener::init())
+        .invoke_handler(tauri::generate_handler![restart_backend])
+        .on_window_event(|window, event| {
+            if let tauri::WindowEvent::CloseRequested { .. } = event {
+                // 关闭窗口时清理后端进程
+                if let Some(backend_state) = window.app_handle().try_state::<BackendProcess>() {
+                    let mut backend = backend_state.0.lock().unwrap();
+                    if let Some(mut child) = backend.take() {
+                        let _ = child.kill();
+                        let _ = child.wait();
+                    }
+                }
+            }
+        })
+        .run(tauri::generate_context!())
+        .expect("error while running tauri application");
+}
+
+fn main() {
+    run();
+}
+

dashboard/src-tauri/tauri.conf.json

@@ -0,0 +1,53 @@
+{
+  "$schema": "https://schema.tauri.app/config/2",
+  "productName": "AstrBot",
+  "version": "4.5.6",
+  "identifier": "com.astrbot.app",
+  "build": {
+    "beforeDevCommand": "pnpm dev",
+    "devUrl": "http://localhost:3000",
+    "beforeBuildCommand": "pnpm build",
+    "frontendDist": "../dist"
+  },
+  "app": {
+    "withGlobalTauri": true,
+    "macOSPrivateApi": true,
+    "windows": [
+      {
+        "title": "AstrBot",
+        "label": "main",
+        "url": "/",
+        "width": 1400,
+        "height": 900
+      }
+    ],
+    "security": {
+      "csp": null,
+      "assetProtocol": {
+        "enable": true,
+        "scope": [
+          "$APPDATA/**"
+        ]
+      }
+    }
+  },
+  "bundle": {
+    "active": true,
+    "targets": "all",
+    "icon": [
+      "icons/32x32.png",
+      "icons/128x128.png",
+      "icons/128x128@2x.png",
+      "icons/icon.icns",
+      "icons/icon.ico"
+    ],
+    "resources": [
+      "resources/*"
+    ]
+  },
+  "plugins": {
+    "fs": {
+      "requireLiteralLeadingDot": false
+    }
+  }
+}

dashboard/src/components/chat/Chat.vue

@@ -575,5 +575,9 @@ onBeforeUnmount(() => {
     .chat-page-container {
         padding: 0 !important;
     }
+
+    .conversation-header {
+        padding: 2px;
+    }
 }
 </style>