chore: bump beta version

CI on feat/sandbox failed across Unit Tests, Lint and Build Dev Image.
Root causes and fixes:

- pyproject.toml had a [tool.uv.sources] editable override pinning
  langbot-plugin to ../langbot-plugin-sdk. That path only exists in a
  paired local checkout, so `uv sync` failed on every CI runner
  ("Distribution not found"). Remove the override and regenerate uv.lock
  so langbot-plugin==0.4.0b1 resolves from PyPI, matching master.

- tests/integration/api/test_pipelines.py: the pipeline extensions
  endpoint now calls ap.skill_service.list_skills(); add the missing
  skill_service mock to the fake_pipeline_app fixture (the test came
  from master, the endpoint change from feat/sandbox).

- Apply ruff format to three src files and prettier to three web files
  that had committed formatting drift, failing `ruff format --check`
  and `pnpm lint`.

.dockerignore

@@ -0,0 +1,8 @@
+.github
+.venv
+.vscode
+.data
+.temp
+web/.next
+web/node_modules
+web/.env

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: 漏洞反馈
-description: 【供中文用户】报错或漏洞请使用这个模板创建，不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决，请勿提 issue。容器间网络连接问题，参考文档 https://docs.langbot.app/zh/workshop/network-details.html  
+description: 【供中文用户】报错或漏洞请使用这个模板创建，不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决，请勿提 issue。容器间网络连接问题，参考文档 https://link.langbot.app/zh/docs/network  
 title: "[Bug]: "
 labels: ["bug?"]
 body:
@@ -19,7 +19,7 @@ body:
   - type: textarea
     attributes:
       label: 复现步骤
-      description: 提供越多信息，我们会越快解决问题，建议多提供配置截图；**如果你不认真填写（只一两句话概括），我们会很生气并且立即关闭 issue 或两年后才回复你**
+      description: 提供越多信息，我们会越快解决问题，建议多提供配置截图；**如果涉及 Dify、n8n、Langflow 等外部平台，请提供应用的导出文件（如 Dify 应用的 DSL），我们将更快回复您。**
     validations:
       required: false
   - type: textarea

.github/ISSUE_TEMPLATE/bug-report_en.yml

@@ -1,5 +1,5 @@
 name: Bug report
-description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://docs.langbot.app/en/workshop/network-details.html
+description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://link.langbot.app/en/docs/network
 title: "[Bug]: "
 labels: ["bug?"]
 body:
@@ -19,7 +19,7 @@ body:
   - type: textarea
     attributes:
       label: Reproduction steps
-      description: How to reproduce this problem, the more detailed the better; the more information you provide, the faster we will solve the problem. 【注意】请务必认真填写此部分，若不提供完整信息（如只有一两句话的概括），我们将不会回复！
+      description: How to reproduce this problem, the more detailed the better; the more information you provide, the faster we will solve the problem.
     validations:
       required: false
   - type: textarea

.github/pull_request_template.md

@@ -2,6 +2,17 @@
 
 > 请在此部分填写你实现/解决/优化的内容:  
 > Summary of what you implemented/solved/optimized:
+> 
+
+### 更改前后对比截图 / Screenshots
+
+> 请在此部分粘贴更改前后对比截图（可以是界面截图、控制台输出、对话截图等）:  
+> Please paste the screenshots of changes before and after here (can be interface screenshots, console output, conversation screenshots, etc.):
+> 
+> 修改前 / Before:
+> 
+> 修改后 / After:
+> 
 
 ## 检查清单 / Checklist
 

.github/workflows/build-docker-image.yml

@@ -3,7 +3,6 @@ on:
   ## 发布release的时候会自动构建
   release:
     types: [published]
-  workflow_dispatch:
 jobs:
   publish-docker-image:
     runs-on: ubuntu-latest
@@ -42,7 +41,7 @@ jobs:
         run: docker buildx create --name mybuilder --use
       - name: Build for Release # only relase, exlude pre-release
         if: ${{ github.event.release.prerelease == false }}
-        run: docker buildx build --platform linux/amd64 -t rockchin/langbot:${{ steps.check_version.outputs.version }} -t rockchin/langbot:latest . --push
+        run: docker buildx build --platform linux/arm64,linux/amd64 -t rockchin/langbot:${{ steps.check_version.outputs.version }} -t rockchin/langbot:latest . --push
       - name: Build for Pre-release # no update for latest tag
         if: ${{ github.event.release.prerelease == true }}
-        run: docker buildx build --platform linux/amd64 -t rockchin/langbot:${{ steps.check_version.outputs.version }} . --push
+        run: docker buildx build --platform linux/arm64,linux/amd64 -t rockchin/langbot:${{ steps.check_version.outputs.version }} . --push

.github/workflows/build-release-artifacts.yaml

@@ -43,10 +43,10 @@ jobs:
         run: |
           cd /tmp/langbot_build_web/web
           npm install
-          npm run build
+          npx vite build
       - name: Package Output
         run: |
-          cp -r /tmp/langbot_build_web/web/out ./web
+          cp -r /tmp/langbot_build_web/web/dist ./web
       - name: Upload Artifact
         uses: actions/upload-artifact@v4
         with:

.github/workflows/check-i18n.yml

@@ -0,0 +1,25 @@
+name: Check i18n Keys
+
+on:
+  push:
+    branches:
+      - main
+      - master
+
+jobs:
+  check-i18n:
+    name: Check i18n Key Consistency
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '20'
+
+      - name: Check i18n keys against en-US reference
+        run: node web/scripts/check-i18n.mjs

.github/workflows/lint.yml

@@ -0,0 +1,60 @@
+name: Lint
+
+on:
+  push:
+    branches:
+      - main
+      - master
+      - dev
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+
+jobs:
+  ruff:
+    name: Ruff Lint & Format
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run ruff check
+        run: uv run ruff check src
+
+      - name: Run ruff format
+        run: uv run ruff format src --check
+
+  frontend:
+    name: Frontend Lint
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '25'
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Install dependencies
+        working-directory: web
+        run: pnpm install
+
+      - name: Run lint
+        working-directory: web
+        run: pnpm lint

.github/workflows/publish-to-pypi.yml

@@ -0,0 +1,46 @@
+name: Build and Publish to PyPI
+
+on:
+  workflow_dispatch:
+  release:
+    types: [published]
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      id-token: write  # Required for trusted publishing to PyPI
+    
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+
+      - name: Build frontend
+        run: |
+          cd web
+          npm install -g pnpm
+          pnpm install
+          pnpm build
+          mkdir -p ../src/langbot/web/dist
+          cp -r dist ../src/langbot/web/
+
+      - name: Install the latest version of uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "latest"
+
+      - name: Build package
+        run: |
+          uv build
+
+      - name: Publish to PyPI
+        run: |
+          uv publish --token ${{ secrets.PYPI_TOKEN }}

.github/workflows/run-tests.yml

@@ -4,29 +4,29 @@ on:
   pull_request:
     types: [opened, ready_for_review, synchronize]
     paths:
-      - 'pkg/**'
+      - 'src/langbot/**'
       - 'tests/**'
       - '.github/workflows/run-tests.yml'
       - 'pyproject.toml'
+      - 'uv.lock'
       - 'run_tests.sh'
+      - 'scripts/test-*.sh'
   push:
     branches:
       - master
       - develop
-    paths:
-      - 'pkg/**'
-      - 'tests/**'
-      - '.github/workflows/run-tests.yml'
-      - 'pyproject.toml'
-      - 'run_tests.sh'
+      - 'feat/**'
+    # No path filter on push: every push to the branches above runs the
+    # full unit-test suite. feat/** branches in particular must be tested
+    # on every push (they accumulate large changes before a PR exists).
 
 jobs:
   test:
-    name: Run Unit Tests
+    name: Unit Tests
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12']
+        python-version: ['3.11', '3.12', '3.13']
       fail-fast: false
 
     steps:
@@ -39,28 +39,13 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install uv
-        run: |
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+        uses: astral-sh/setup-uv@v4
 
       - name: Install dependencies
-        run: |
-          uv sync --dev
+        run: uv sync --dev
 
-      - name: Run unit tests
-        run: |
-          bash run_tests.sh
-
-      - name: Upload coverage to Codecov
-        if: matrix.python-version == '3.12'
-        uses: codecov/codecov-action@v5
-        with:
-          files: ./coverage.xml
-          flags: unit-tests
-          name: unit-tests-coverage
-          fail_ci_if_error: false
-        env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      - name: Run unit + smoke tests
+        run: uv run pytest tests/unit_tests/ tests/smoke/ -q --tb=short
 
       - name: Test Summary
         if: always()
@@ -69,3 +54,79 @@ jobs:
           echo "" >> $GITHUB_STEP_SUMMARY
           echo "Python Version: ${{ matrix.python-version }}" >> $GITHUB_STEP_SUMMARY
           echo "Test Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY
+
+  integration:
+    name: Fast Integration Tests
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run fast integration tests
+        run: uv run pytest tests/integration/ -m "not slow" -q --tb=short
+
+      - name: Integration Test Summary
+        if: always()
+        run: |
+          echo "## Integration Tests Results" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "Test Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY
+
+  coverage:
+    name: Coverage Gate
+    runs-on: ubuntu-latest
+    needs: [test, integration]
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run coverage (unit + smoke)
+        run: |
+          uv run pytest tests/unit_tests/ tests/smoke/ \
+            --cov=langbot \
+            --cov-report=xml \
+            --cov-report=term-missing \
+            --cov-fail-under=18 \
+            -q --tb=short
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          files: ./coverage.xml
+          flags: unit-tests
+          name: coverage-report
+          fail_ci_if_error: false
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+      - name: Coverage Summary
+        if: always()
+        run: |
+          echo "## Coverage Results" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "Threshold: 18%" >> $GITHUB_STEP_SUMMARY
+          echo "Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY

.github/workflows/test-migrations.yml

@@ -0,0 +1,78 @@
+name: Test Migrations
+
+on:
+  push:
+    branches:
+      - main
+      - master
+      - dev
+    paths:
+      - 'src/langbot/pkg/persistence/**'
+      - 'src/langbot/pkg/entity/persistence/**'
+      - 'tests/integration/persistence/**'
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+    paths:
+      - 'src/langbot/pkg/persistence/**'
+      - 'src/langbot/pkg/entity/persistence/**'
+      - 'tests/integration/persistence/**'
+
+jobs:
+  test-migrations-sqlite:
+    name: Migrations (SQLite)
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run SQLite migration tests
+        run: uv run pytest tests/integration/persistence/test_migrations.py -q --tb=short
+
+  test-migrations-postgres:
+    name: Migrations (PostgreSQL)
+    runs-on: ubuntu-latest
+    services:
+      postgres:
+        image: postgres:16
+        env:
+          POSTGRES_USER: langbot
+          POSTGRES_PASSWORD: langbot
+          POSTGRES_DB: langbot_test
+        ports:
+          - 5432:5432
+        options: >-
+          --health-cmd="pg_isready -U langbot"
+          --health-interval=5s
+          --health-timeout=5s
+          --health-retries=5
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run PostgreSQL migration tests
+        env:
+          TEST_POSTGRES_URL: postgresql+asyncpg://langbot:langbot@localhost:5432/langbot_test
+        run: uv run pytest tests/integration/persistence/test_migrations_postgres.py -q --tb=short

.gitignore

@@ -42,8 +42,17 @@ botpy.log*
 test.py
 /web_ui
 .venv/
-uv.lock
 /test
 plugins.bak
 coverage.xml
 .coverage
+src/langbot/web/
+testsdk/
+
+# Build artifacts
+/dist
+/build
+*.egg-info
+
+# Next.js build cache (legacy)
+web/.next/

.mcp.json

@@ -0,0 +1,37 @@
+{
+  "mcpServers": {
+    "shadcn": {
+      "command": "npx",
+      "args": [
+        "shadcn@latest",
+        "mcp"
+      ]
+    },
+    "sequential-thinking": {
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-sequential-thinking"],
+      "env": {}
+    },
+    "github": {
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-github"],
+      "env": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_PERSONAL_ACCESS_TOKEN}"
+      }
+    },
+    "fetch": {
+      "type": "stdio",
+      "command": "uvx",
+      "args": ["mcp-server-fetch"],
+      "env": {}
+    },
+    "playwright": {
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@playwright/mcp@latest"],
+      "env": {}
+    }
+  }
+}

.pre-commit-config.yaml

@@ -9,16 +9,14 @@ repos:
       # Run the formatter of backend.
       - id: ruff-format
 
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.1.0
-    hooks:
-      - id: prettier
-        types_or: [javascript, jsx, ts, tsx, css, scss]
-        additional_dependencies:
-          - prettier@3.1.0
-
   - repo: local
     hooks:
+      - id: prettier
+        name: prettier
+        entry: npx --prefix web prettier --write --ignore-unknown
+        language: system
+        types_or: [javascript, jsx, ts, tsx, css, scss]
+
       - id: lint-staged
         name: lint-staged
         entry: cd web && pnpm lint-staged

AGENTS.md

@@ -8,16 +8,17 @@ LangBot is a open-source LLM native instant messaging bot development platform,
 
 LangBot has a comprehensive frontend, all operations can be performed through the frontend. The project splited into these major parts:
 
-- `./pkg`: The core python package of the project backend.
-    - `./pkg/platform`: The platform module of the project, containing the logic of message platform adapters, bot managers, message session managers, etc.
-    - `./pkg/provider`: The provider module of the project, containing the logic of LLM providers, tool providers, etc.
-    - `./pkg/pipeline`: The pipeline module of the project, containing the logic of pipelines, stages, query pool, etc.
-    - `./pkg/api`: The api module of the project, containing the http api controllers and services.
-    - `./pkg/plugin`: LangBot bridge for connecting with plugin system.
-- `./libs`: Some SDKs we previously developed for the project, such as `qq_official_api`, `wecom_api`, etc.
-- `./templates`: Templates of config files, components, etc.
-- `./web`: Frontend codebase, built with Next.js + **shadcn** + **Tailwind CSS**.
-- `./docker`: docker-compose deployment files.
+- `./src/langbot`: The main python package of the project, below are the main modules in this package:
+    - `./pkg`: The core python package of the project backend.
+        - `./pkg/platform`: The platform module of the project, containing the logic of message platform adapters, bot managers, message session managers, etc.
+        - `./pkg/provider`: The provider module of the project, containing the logic of LLM providers, tool providers, etc.
+        - `./pkg/pipeline`: The pipeline module of the project, containing the logic of pipelines, stages, query pool, etc.
+        - `./pkg/api`: The api module of the project, containing the http api controllers and services.
+        - `./pkg/plugin`: LangBot bridge for connecting with plugin system.
+    - `./libs`: Some SDKs we previously developed for the project, such as `qq_official_api`, `wecom_api`, etc.
+    - `./templates`: Templates of config files, components, etc.
+    - `./web`: Frontend codebase, built with Next.js + **shadcn** + **Tailwind CSS**.
+    - `./docker`: docker-compose deployment files.
 
 ## Backend Development
 
@@ -69,6 +70,7 @@ Plugin Runtime automatically starts each installed plugin and interacts through
     - type: must be a specific type, such as feat (new feature), fix (bug fix), docs (documentation), style (code style), refactor (refactoring), perf (performance optimization), etc.
     - scope: the scope of the commit, such as the package name, the file name, the function name, the class name, the module name, etc.
     - subject: the subject of the commit, such as the description of the commit, the reason for the commit, the impact of the commit, etc.
+- LangBot uses [Alembic](https://alembic.sqlalchemy.org/) to manage database migrations, supporting both SQLite and PostgreSQL. Migration files are located in `src/langbot/pkg/persistence/alembic/versions/`. If you changed the definition of database entities (ORM models), generate a new migration script by running `uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "description of your change"` in the project root (requires `data/config.yaml` to exist). Review and edit the generated script before committing. Migrations are executed automatically on LangBot startup. For data migrations (e.g. modifying JSON field content), you need to manually add the migration code in the generated script.
 
 ## Some Principles
 

Dockerfile

@@ -4,7 +4,7 @@ WORKDIR /app
 
 COPY web ./web
 
-RUN cd web && npm install && npm run build
+RUN cd web && npm install && npx vite build
 
 FROM python:3.12.7-slim
 
@@ -12,7 +12,7 @@ WORKDIR /app
 
 COPY . .
 
-COPY --from=node /app/web/out ./web/out
+COPY --from=node /app/web/dist ./web/dist
 
 RUN apt update \
     && apt install gcc -y \
@@ -20,4 +20,4 @@ RUN apt update \
     && uv sync \
     && touch /.dockerenv
 
-CMD [ "uv", "run", "main.py" ]
+CMD [ "uv", "run", "--no-sync", "main.py" ]

Makefile

@@ -0,0 +1,36 @@
+# LangBot Makefile
+# Quick developer commands
+
+.PHONY: test test-quick test-integration-fast test-coverage test-all-local lint
+
+# Run all tests (full suite with coverage)
+test:
+	bash run_tests.sh
+
+# Quick self-test for developers (lint + unit + smoke, no real credentials needed)
+test-quick:
+	bash scripts/test-quick.sh
+
+# Fast integration tests (SQLite/API/Pipeline, no external services)
+test-integration-fast:
+	bash scripts/test-integration-fast.sh
+
+# Coverage gate (all tests, enforces minimum threshold)
+test-coverage:
+	bash scripts/test-coverage.sh
+
+# Full local quality gate (quick + integration + coverage)
+test-all-local:
+	bash scripts/test-quick.sh
+	bash scripts/test-integration-fast.sh
+	bash scripts/test-coverage.sh
+
+# Run linting only
+lint:
+	ruff check src/langbot/ tests/
+	ruff format --check src/langbot/ tests/
+
+# Fix linting issues
+lint-fix:
+	ruff check --fix src/langbot/ tests/
+	ruff format src/langbot/ tests/

README.md

@@ -1,37 +1,71 @@
-
 <p align="center">
 <a href="https://langbot.app">
-<img src="https://docs.langbot.app/social_zh.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>
 
 <div align="center">
 
-<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 
-[English](README_EN.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / (PR for your language)
+<h3>Production-grade platform for building agentic IM bots.</h3>
+<h4>Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.</h4>
+
+English / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
-[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
-[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
-
-<a href="https://langbot.app">项目主页</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">部署文档</a> ｜
-<a href="https://docs.langbot.app/zh/plugin/plugin-intro.html">插件介绍</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">提交插件</a>
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
+<a href="https://langbot.app">Website</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Features</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Docs</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app/cloud">Cloud</a> ｜
+<a href="https://space.langbot.app">Plugin Market</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Roadmap</a>
 
 </div>
 
 </p>
 
-LangBot 是一个开源的大语言模型原生即时通信机器人开发平台，旨在提供开箱即用的 IM 机器人开发体验，具有 Agent、RAG、MCP 等多种 LLM 应用功能，适配全球主流即时通信平台，并提供丰富的 API 接口，支持自定义开发。
+---
 
-## 📦 开始使用
+## What is LangBot?
 
-#### Docker Compose 部署
+LangBot is an **open-source, production-grade platform** for building AI-powered instant messaging bots. It connects Large Language Models (LLMs) to any chat platform, enabling you to create intelligent agents that can converse, execute tasks, and integrate with your existing workflows.
+
+### Key Capabilities
+
+- **AI Conversations & Agents** — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Universal IM Platform Support** — One codebase for Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Production-Ready** — Access control, rate limiting, sensitive word filtering, comprehensive monitoring, and exception handling. Trusted by enterprises.
+- **Plugin Ecosystem** — Hundreds of plugins, event-driven architecture, component extensions, and [MCP protocol](https://modelcontextprotocol.io/) support.
+- **Web Management Panel** — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
+- **Multi-Pipeline Architecture** — Different bots for different scenarios, with comprehensive monitoring and exception handling.
+
+[→ Learn more about all features](https://link.langbot.app/en/docs/features)
+
+📍 Practical guides: [deploy a multi-platform AI bot in 5 minutes](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [connect DeepSeek to WeChat, Discord, and Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [run a Dify Agent in Discord, Telegram, and Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/), and [build an n8n-powered chatbot](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## Quick Start
+
+### ☁️ LangBot Cloud (Recommended)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Zero deployment, ready to use.
+
+### One-Line Launch
+
+```bash
+uvx langbot
+```
+
+> Requires [uv](https://docs.astral.sh/uv/getting-started/installation/). Visit http://localhost:5300 — done.
+
+### Docker Compose
 
 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -39,122 +73,106 @@ cd LangBot/docker
 docker compose up -d
 ```
 
-访问 http://localhost:5300 即可开始使用。
-
-详细文档[Docker 部署](https://docs.langbot.app/zh/deploy/langbot/docker.html)。
-
-#### 宝塔面板部署
-
-已上架宝塔面板，若您已安装宝塔面板，可以根据[文档](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html)使用。
-
-#### Zeabur 云部署
-
-社区贡献的 Zeabur 模板。
-
-[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
-
-#### Railway 云部署
+### One-Click Cloud Deploy
 
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-#### 手动部署
+**More options:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
-直接使用发行版运行，查看文档[手动部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
+---
 
-#### Kubernetes 部署
+## Supported Platforms
 
-参考 [Kubernetes 部署](./docker/README_K8S.md) 文档。
+| Platform | Status | Notes |
+|----------|--------|-------|
+| Discord | ✅ | Official |
+| Telegram | ✅ | Official |
+| Slack | ✅ | Official |
+| LINE | ✅ | Official |
+| QQ | ✅ | Personal & Official API (Channel, DM, Group) |
+| WeCom | ✅ | Enterprise WeChat, External CS, AI Bot |
+| WeChat | ✅ | Personal & Official Account |
+| Lark | ✅ | Official |
+| DingTalk | ✅ | Official |
+| KOOK | ✅ | Official |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Supports multiple bridged platforms such as Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip, and more |
 
-## 😎 保持更新
+---
 
-点击仓库右上角 Star 和 Watch 按钮，获取最新动态。
+## Supported LLMs & Integrations
 
-![star gif](https://docs.langbot.app/star.gif)
+| Provider                                                                                                          | Type         | Status |
+| ----------------------------------------------------------------------------------------------------------------- | ------------ | ------ |
+| [OpenAI](https://platform.openai.com/)                                                                            | LLM          | ✅     |
+| [Anthropic](https://www.anthropic.com/)                                                                           | LLM          | ✅     |
+| [DeepSeek](https://www.deepseek.com/)                                                                             | LLM          | ✅     |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat)                                                     | LLM          | ✅     |
+| [xAI](https://x.ai/)                                                                                              | LLM          | ✅     |
+| [Moonshot](https://www.moonshot.cn/)                                                                              | LLM          | ✅     |
+| [Zhipu AI](https://open.bigmodel.cn/)                                                                             | LLM          | ✅     |
+| [Ollama](https://ollama.com/)                                                                                     | Local LLM    | ✅     |
+| [LM Studio](https://lmstudio.ai/)                                                                                 | Local LLM    | ✅     |
+| [Dify](https://dify.ai)                                                                                           | LLMOps       | ✅     |
+| [MCP](https://modelcontextprotocol.io/)                                                                           | Protocol     | ✅     |
+| [SiliconFlow](https://siliconflow.cn/)                                                                            | Gateway      | ✅     |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/)                                                             | Gateway      | ✅     |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Gateway      | ✅     |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro)                                        | Gateway      | ✅     |
+| [GiteeAI](https://ai.gitee.com/)                                                                                  | Gateway      | ✅     |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot)                                                     | GPU Platform | ✅     |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot)                             | GPU Platform | ✅     |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758)                                                    | GPU Platform | ✅     |
+| [接口 AI](https://jiekou.ai/)                                                                                     | Gateway      | ✅     |
+| [302.AI](https://share.302.ai/SuTG99)                                                                             | Gateway      | ✅     |
+| [Qiniu](https://www.qiniu.com/ai/agent)                                                                           | Gateway      | ✅     |
 
-## ✨ 特性
+[→ View all integrations](https://link.langbot.app/en/docs/features)
 
-- 💬 大模型对话、Agent：支持多种大模型，适配群聊和私聊；具有多轮对话、工具调用、多模态、流式输出能力，自带 RAG（知识库）实现，并深度适配 [Dify](https://dify.ai)。
-- 🤖 多平台支持：目前支持 QQ、QQ频道、企业微信、个人微信、飞书、Discord、Telegram 等平台。
-- 🛠️ 高稳定性、功能完备：原生支持访问控制、限速、敏感词过滤等机制；配置简单，支持多种部署方式。支持多流水线配置，不同机器人用于不同应用场景。
-- 🧩 插件扩展、活跃社区：支持事件驱动、组件扩展等插件机制；适配 Anthropic [MCP 协议](https://modelcontextprotocol.io/)；目前已有数百个插件。
-- 😻 Web 管理面板：支持通过浏览器管理 LangBot 实例，不再需要手动编写配置文件。
+---
 
-详细规格特性请访问[文档](https://docs.langbot.app/zh/insight/features.html)。
+## Why LangBot?
 
-或访问 demo 环境：https://demo.langbot.dev/  
-  - 登录信息：邮箱：`demo@langbot.app` 密码：`langbot123456`
-  - 注意：仅展示 WebUI 效果，公开环境，请不要在其中填入您的任何敏感信息。
+| Use Case                    | How LangBot Helps                                                                          |
+| --------------------------- | ------------------------------------------------------------------------------------------ |
+| **Customer Support**        | Deploy AI agents to Slack/Discord/Telegram that answer questions using your knowledge base |
+| **Internal Tools**          | Connect n8n/Dify workflows to WeCom/DingTalk for automated business processes              |
+| **Community Management**    | Moderate QQ/Discord groups with AI-powered content filtering and interaction               |
+| **Multi-Platform Presence** | One bot, all platforms. Manage from a single dashboard                                     |
 
-### 消息平台
+---
 
-| 平台 | 状态 | 备注 |
-| --- | --- | --- |
-| QQ 个人号 | ✅ | QQ 个人号私聊、群聊 |
-| QQ 官方机器人 | ✅ | QQ 官方机器人，支持频道、私聊、群聊 |
-| 企业微信 | ✅ |  |
-| 企微对外客服 | ✅ |  |
-| 企微智能机器人 | ✅ |  |
-| 个人微信 | ✅ |  |
-| 微信公众号 | ✅ |  |
-| 飞书 | ✅ |  |
-| 钉钉 | ✅ |  |
-| Discord | ✅ |  |
-| Telegram | ✅ |  |
-| Slack | ✅ |  |
-| LINE | ✅ |  |
+## Live Demo
 
-### 大模型能力
+**Try it now:** https://demo.langbot.dev/
 
-| 模型 | 状态 | 备注 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 可接入任何 OpenAI 接口格式模型 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [智谱AI](https://open.bigmodel.cn/) | ✅ |  |
-| [胜算云](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | 全球大模型都可调用（友情推荐） |
-| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型和 GPU 资源平台 |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型和 GPU 资源平台 |
-| [接口 AI](https://jiekou.ai/) | ✅ | 大模型聚合平台，专注全球大模型接入 |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | 大模型聚合平台 |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOps 平台 |
-| [Ollama](https://ollama.com/) | ✅ | 本地大模型运行平台 |
-| [LMStudio](https://lmstudio.ai/) | ✅ | 本地大模型运行平台 |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | 大模型接口聚合平台 |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
-| [小马算力](https://www.tokenpony.cn/453z1) | ✅ | 大模型聚合平台 |
-| [阿里云百炼](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | 支持通过 MCP 协议获取工具 |
-| [百宝箱Tbox](https://www.tbox.cn/open) | ✅ | 蚂蚁百宝箱智能体平台，每月免费10亿大模型Token |
+- Email: `demo@langbot.app`
+- Password: `langbot123456`
 
-### TTS
+_Note: Public demo environment. Do not enter sensitive information._
 
-| 平台/模型 | 备注 |
-| --- | --- |
-| [FishAudio](https://fish.audio/zh-CN/discovery/) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
-| [海豚 AI](https://www.ttson.cn/?source=thelazy) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
-| [AzureTTS](https://portal.azure.com/) | [插件](https://github.com/Ingnaryk/LangBot_AzureTTS) |
+---
 
-### 文生图
+## Community
 
-| 平台/模型 | 备注 |
-| --- | --- |
-| 阿里云百炼 | [插件](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin)
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
 
-## 😘 社区贡献
+- [Discord Community](https://discord.gg/wdNEHETs87)
 
-感谢以下[代码贡献者](https://github.com/langbot-app/LangBot/graphs/contributors)和社区里其他成员对 LangBot 的贡献：
+---
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Contributors
+
+Thanks to all [contributors](https://github.com/langbot-app/LangBot/graphs/contributors) who have helped make LangBot better:
 
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
 </a>
-
-<!--
-## For Code Agents
-
-If you're a code agent based on LLM, you should read [AGENTS.md](AGENTS.md) to understand the project structure and development process before you start any development.
--->

README_CN.md

@@ -0,0 +1,201 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>生产级 AI 即时通信机器人开发平台。</h3>
+<h4>快速构建、调试和部署 AI 机器人到微信、QQ、飞书、Slack、Discord、Telegram 等平台。</h4>
+
+[English](README.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/DxZZcNxM1W)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
+
+<a href="https://langbot.app">官网</a> ｜
+<a href="https://link.langbot.app/zh/docs/features">特性</a> ｜
+<a href="https://link.langbot.app/zh/docs/guide">文档</a> ｜
+<a href="https://link.langbot.app/zh/docs/api">API</a> ｜
+<a href="https://space.langbot.app/cloud">Cloud</a> ｜
+<a href="https://space.langbot.app">扩展市场</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">路线图</a>
+
+</div>
+
+</p>
+
+---
+
+LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时通信机器人。它将大语言模型（LLM）连接到各种聊天平台，帮助你创建能够对话、执行任务、并集成到现有工作流程中的智能 Agent。
+
+### 核心能力
+
+- **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **全平台支持** — 一套代码，覆盖 QQ、微信、企业微信、飞书、钉钉、Discord、Telegram、Slack、LINE、KOOK 等平台。
+- **生产就绪** — 访问控制、限速、敏感词过滤、全面监控与异常处理，已被多家企业采用。
+- **插件生态** — 数百个插件，跨进程的事件驱动架构，组件扩展，适配 [MCP 协议](https://modelcontextprotocol.io/)。
+- **Web 管理面板** — 通过浏览器直观地配置、管理和监控机器人，无需手动编辑配置文件。
+- **多流水线架构** — 不同机器人用于不同场景，具备全面的监控和异常处理能力。
+
+[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
+
+📍 实践指南：[5 分钟部署多平台 AI 机器人](https://blog.langbot.app/zh/blog/deploy-ai-bot-in-5-minutes/)、[将 DeepSeek 接入微信、企业微信与 Discord](https://blog.langbot.app/zh/blog/connect-deepseek-to-wechat/)、[让 Dify Agent 跑在 Discord、Telegram 和 Slack 上](https://blog.langbot.app/zh/blog/dify-agent-discord-telegram-slack/)，以及[用 n8n 构建多平台 AI 聊天机器人](https://blog.langbot.app/zh/blog/n8n-multi-platform-ai-chatbot/)。
+
+---
+
+## 快速开始
+
+### ☁️ LangBot Cloud（推荐）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，开箱即用。
+
+### 一键启动
+
+```bash
+uvx langbot
+```
+
+> 需要安装 [uv](https://docs.astral.sh/uv/getting-started/installation/)。访问 http://localhost:5300 即可使用。
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### 一键云部署
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手动部署](https://link.langbot.app/zh/docs/manual-deploy) · [宝塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## 支持的平台
+
+| 平台 | 状态 | 备注 |
+|------|------|------|
+| QQ | ✅ | 个人号、官方机器人（频道、私聊、群聊） |
+| 微信 | ✅ | 个人微信、微信公众号 |
+| 企业微信 | ✅ | 应用消息、对外客服、智能机器人 |
+| 飞书 | ✅ | 官方 |
+| 钉钉 | ✅ | 官方 |
+| Satori | ✅ |  |
+| Discord | ✅ | 官方 |
+| Telegram | ✅ | 官方 |
+| Slack | ✅ | 官方 |
+| LINE | ✅ | 官方 |
+| KOOK | ✅ | 官方 |
+| Email | ✅ | 只 Matrix、Satori |
+| Matrix | ✅ | 支持多种桥接平台，如 Signal、WhatsApp、Messenger、iMessage、Mattermost、Google Chat、IRC、XMPP、Zulip 等 |
+
+---
+
+## 支持的大模型与集成
+
+| 提供商 | 类型 | 状态 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [智谱AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 本地 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 本地 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 协议 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 聚合平台 | ✅ |
+| [阿里云百炼](https://bailian.console.aliyun.com/) | 聚合平台 | ✅ |
+| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 聚合平台 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 聚合平台 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 聚合平台 | ✅ |
+| [胜算云](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 平台 | ✅ |
+| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+| [小马算力](https://www.tokenpony.cn/453z1) | 聚合平台 | ✅ |
+| [百宝箱Tbox](https://www.tbox.cn/open) | 智能体平台 | ✅ |
+| [七牛云Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |
+
+[→ 查看完整集成列表](https://link.langbot.app/zh/docs/features)
+
+### TTS（语音合成）
+
+| 平台/模型 | 备注 |
+|-----------|------|
+| [FishAudio](https://fish.audio/zh-CN/discovery/) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
+| [海豚 AI](https://www.ttson.cn/?source=thelazy) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
+| [AzureTTS](https://portal.azure.com/) | [插件](https://github.com/Ingnaryk/LangBot_AzureTTS) |
+
+### 文生图
+
+| 平台/模型 | 备注 |
+|-----------|------|
+| 阿里云百炼 | [插件](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |
+
+---
+
+## 为什么选择 LangBot？
+
+| 使用场景 | LangBot 如何帮助 |
+|----------|------------------|
+| **客户服务** | 将 AI Agent 部署到微信/企微/钉钉/飞书，基于知识库自动回答用户问题 |
+| **内部工具** | 将 n8n/Dify 工作流接入企微/钉钉，实现业务流程自动化 |
+| **社群运营** | 在 QQ/Discord 群中使用 AI 驱动的内容审核与智能互动 |
+| **多平台触达** | 一个机器人，覆盖所有平台。通过统一面板集中管理 |
+
+---
+
+## 在线演示
+
+**立即体验：** https://demo.langbot.dev/
+- 邮箱：`demo@langbot.app`
+- 密码：`langbot123456`
+
+*注意：公开演示环境，请不要在其中填入任何敏感信息。*
+
+---
+
+## 社区
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/DxZZcNxM1W)
+
+- [Discord 社区](https://discord.gg/wdNEHETs87)
+- [QQ 社区群](https://qm.qq.com/q/DxZZcNxM1W)
+
+---
+
+## Star 趋势
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 贡献者
+
+感谢所有[贡献者](https://github.com/langbot-app/LangBot/graphs/contributors)对 LangBot 的帮助：
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>
+
+<!--
+## For Code Agents
+
+If you're a code agent based on LLM, you should read [AGENTS.md](AGENTS.md) to understand the project structure and development process before you start any development.
+-->

README_EN.md

@@ -1,131 +0,0 @@
-<p align="center">
-<a href="https://langbot.app">
-<img src="https://docs.langbot.app/social_en.png" alt="LangBot"/>
-</a>
-
-<div align="center">
-
-English / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / (PR for your language)
-
-[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
-[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
-
-<a href="https://langbot.app">Home</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Deployment</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Plugin</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Submit Plugin</a>
-
-</div>
-
-</p>
-
-LangBot is an open-source LLM native instant messaging robot development platform, aiming to provide out-of-the-box IM robot development experience, with Agent, RAG, MCP and other LLM application functions, adapting to global instant messaging platforms, and providing rich API interfaces, supporting custom development.
-
-## 📦 Getting Started
-
-#### Docker Compose Deployment
-
-```bash
-git clone https://github.com/langbot-app/LangBot
-cd LangBot/docker
-docker compose up -d
-```
-
-Visit http://localhost:5300 to start using it.
-
-Detailed documentation [Docker Deployment](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### One-click Deployment on BTPanel
-
-LangBot has been listed on the BTPanel, if you have installed the BTPanel, you can use the [document](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) to use it.
-
-#### Zeabur Cloud Deployment
-
-Community contributed Zeabur template.
-
-[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Railway Cloud Deployment
-
-[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
-
-#### Other Deployment Methods
-
-Directly use the released version to run, see the [Manual Deployment](https://docs.langbot.app/en/deploy/langbot/manual.html) documentation.
-
-#### Kubernetes Deployment
-
-Refer to the [Kubernetes Deployment](./docker/README_K8S.md) documentation.
-
-## 😎 Stay Ahead
-
-Click the Star and Watch button in the upper right corner of the repository to get the latest updates.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Features
-
-- 💬 Chat with LLM / Agent: Supports multiple LLMs, adapt to group chats and private chats; Supports multi-round conversations, tool calls, multi-modal, and streaming output capabilities. Built-in RAG (knowledge base) implementation, and deeply integrates with [Dify](https://dify.ai).
-- 🤖 Multi-platform Support: Currently supports QQ, QQ Channel, WeCom, personal WeChat, Lark, DingTalk, Discord, Telegram, etc.
-- 🛠️ High Stability, Feature-rich: Native access control, rate limiting, sensitive word filtering, etc. mechanisms; Easy to use, supports multiple deployment methods. Supports multiple pipeline configurations, different bots can be used for different scenarios.
-- 🧩 Plugin Extension, Active Community: Support event-driven, component extension, etc. plugin mechanisms; Integrate Anthropic [MCP protocol](https://modelcontextprotocol.io/); Currently has hundreds of plugins.
-- 😻 Web UI: Support management LangBot instance through the browser. No need to manually write configuration files.
-
-For more detailed specifications, please refer to the [documentation](https://docs.langbot.app/en/insight/features.html).
-
-Or visit the demo environment: https://demo.langbot.dev/
-  - Login information: Email: `demo@langbot.app` Password: `langbot123456`
-  - Note: For WebUI demo only, please do not fill in any sensitive information in the public environment.
-
-### Message Platform
-
-| Platform | Status | Remarks |
-| --- | --- | --- |
-| Discord | ✅ |  |
-| Telegram | ✅ |  |
-| Slack | ✅ |  |
-| LINE | ✅ |  |
-| Personal QQ | ✅ |  |
-| QQ Official API | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| Personal WeChat | ✅ |  |
-| Lark | ✅ |  |
-| DingTalk | ✅ |  |
-
-### LLMs
-
-| LLM | Status | Remarks |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Available for any OpenAI interface format model |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | LLM and GPU resource platform |
-| [Dify](https://dify.ai) | ✅ | LLMOps platform |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | LLM and GPU resource platform |
-| [接口 AI](https://jiekou.ai/) | ✅ | LLM aggregation platform, dedicated to global LLMs |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | LLM and GPU resource platform |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | LLM gateway(MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Ollama](https://ollama.com/) | ✅ | Local LLM running platform |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Local LLM running platform |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | LLM interface gateway(MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | LLM gateway(MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLM gateway(MaaS), LLMOps platform |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLM gateway(MaaS), LLMOps platform |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLM gateway(MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Support tool access through MCP protocol |
-
-## 🤝 Community Contribution
-
-Thank you for the following [code contributors](https://github.com/langbot-app/LangBot/graphs/contributors) and other members in the community for their contributions to LangBot:
-
-<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
-</a>

README_ES.md

@@ -0,0 +1,176 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>Plataforma de grado de producción para construir bots de mensajería instantánea con agentes de IA.</h3>
+<h4>Construya, depure y despliegue bots de IA rápidamente en Slack, Discord, Telegram, WeChat y más.</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / Español / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+
+<a href="https://langbot.app">Inicio</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Características</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Documentación</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app">Mercado de Plugins</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Hoja de Ruta</a>
+
+</div>
+
+</p>
+
+---
+
+## ¿Qué es LangBot?
+
+LangBot es una **plataforma de código abierto y grado de producción** para construir bots de mensajería instantánea impulsados por IA. Conecta modelos de lenguaje de gran escala (LLMs) con cualquier plataforma de chat, permitiéndole crear agentes inteligentes que pueden conversar, ejecutar tareas e integrarse con sus flujos de trabajo existentes.
+
+### Capacidades Clave
+
+- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Soporte Universal de Plataformas de MI** — Un solo código base para Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Listo para Producción** — Control de acceso, limitación de velocidad, filtrado de palabras sensibles, monitoreo completo y manejo de excepciones. De confianza para empresas.
+- **Ecosistema de Plugins** — Cientos de plugins, arquitectura basada en eventos, extensiones de componentes y soporte del [protocolo MCP](https://modelcontextprotocol.io/).
+- **Panel de Gestión Web** — Configure, gestione y monitoree sus bots a través de una interfaz de navegador intuitiva. Sin necesidad de editar YAML.
+- **Arquitectura Multi-Pipeline** — Diferentes bots para diferentes escenarios, con monitoreo completo y manejo de excepciones.
+
+[→ Conocer más sobre todas las funcionalidades](https://link.langbot.app/en/docs/features)
+
+📍 Guías prácticas: [desplegar un bot de IA multiplataforma en 5 minutos](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [conectar DeepSeek a WeChat, Discord y Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [ejecutar un Dify Agent en Discord, Telegram y Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) y [crear un chatbot con n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## Inicio Rápido
+
+### ☁️ LangBot Cloud (Recomendado)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Sin despliegue, listo para usar.
+
+### Lanzamiento en una línea
+
+```bash
+uvx langbot
+```
+
+> Requiere [uv](https://docs.astral.sh/uv/getting-started/installation/). Visite http://localhost:5300 — listo.
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### Despliegue en la Nube con un Clic
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**Más opciones:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## Plataformas Soportadas
+
+| Plataforma | Estado | Notas |
+|----------|--------|-------|
+| Discord | ✅ | Oficial |
+| Telegram | ✅ | Oficial |
+| Slack | ✅ | Oficial |
+| LINE | ✅ | Oficial |
+| QQ | ✅ | Personal y API Oficial (Canal, DM, Grupo) |
+| WeCom | ✅ | WeChat Empresarial, CS Externo, AI Bot |
+| WeChat | ✅ | Personal y Cuenta Oficial |
+| Lark | ✅ | Oficial |
+| DingTalk | ✅ | Oficial |
+| KOOK | ✅ | Oficial |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Admite varias plataformas puenteadas como Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip y más |
+
+---
+
+## LLMs e Integraciones Soportadas
+
+| Proveedor | Tipo | Estado |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM Local | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM Local | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Protocolo | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Pasarela | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Pasarela | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Pasarela | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Pasarela | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Pasarela | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plataforma GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plataforma GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plataforma GPU | ✅ |
+| [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | Pasarela | ✅ |
+
+[→ Ver todas las integraciones](https://link.langbot.app/en/docs/features)
+
+---
+
+## ¿Por qué LangBot?
+
+| Caso de Uso | Cómo Ayuda LangBot |
+|----------|-------------------|
+| **Atención al cliente** | Despliegue agentes de IA en Slack/Discord/Telegram que respondan preguntas usando su base de conocimientos |
+| **Herramientas internas** | Conecte flujos de trabajo de n8n/Dify a WeCom/DingTalk para procesos empresariales automatizados |
+| **Gestión de comunidades** | Modere grupos de QQ/Discord con filtrado de contenido e interacción impulsados por IA |
+| **Presencia multiplataforma** | Un solo bot, todas las plataformas. Gestione desde un único panel de control |
+
+---
+
+## Demo en Vivo
+
+**Pruébelo ahora:** https://demo.langbot.dev/
+- Correo electrónico: `demo@langbot.app`
+- Contraseña: `langbot123456`
+
+*Nota: Entorno de demostración público. No ingrese información confidencial.*
+
+---
+
+## Comunidad
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Comunidad de Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Historial de Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Colaboradores
+
+Gracias a todos los [colaboradores](https://github.com/langbot-app/LangBot/graphs/contributors) que han ayudado a mejorar LangBot:
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>

README_FR.md

@@ -0,0 +1,176 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>Plateforme de niveau production pour construire des bots de messagerie instantanée avec agents IA.</h3>
+<h4>Créez, déboguez et déployez rapidement des bots IA sur Slack, Discord, Telegram, WeChat et plus.</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / Français / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+
+<a href="https://langbot.app">Accueil</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Fonctionnalités</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Documentation</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app">Marché des Plugins</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Feuille de Route</a>
+
+</div>
+
+</p>
+
+---
+
+## Qu'est-ce que LangBot ?
+
+LangBot est une **plateforme open-source de niveau production** pour créer des bots de messagerie instantanée alimentés par l'IA. Elle connecte les grands modèles de langage (LLMs) à n'importe quelle plateforme de chat, vous permettant de créer des agents intelligents capables de converser, d'exécuter des tâches et de s'intégrer à vos workflows existants.
+
+### Capacités Clés
+
+- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Support Universel des Plateformes de MI** — Un seul code pour Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Prêt pour la Production** — Contrôle d'accès, limitation de débit, filtrage de mots sensibles, surveillance complète et gestion des exceptions. Approuvé par les entreprises.
+- **Écosystème de Plugins** — Des centaines de plugins, architecture événementielle, extensions de composants, et support du [protocole MCP](https://modelcontextprotocol.io/).
+- **Panneau de Gestion Web** — Configurez, gérez et surveillez vos bots via une interface navigateur intuitive. Aucune édition de YAML requise.
+- **Architecture Multi-Pipeline** — Différents bots pour différents scénarios, avec surveillance complète et gestion des exceptions.
+
+[→ En savoir plus sur toutes les fonctionnalités](https://link.langbot.app/en/docs/features)
+
+📍 Guides pratiques : [déployer un bot IA multiplateforme en 5 minutes](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [connecter DeepSeek à WeChat, Discord et Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [exécuter un Dify Agent dans Discord, Telegram et Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) et [créer un chatbot avec n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## Démarrage Rapide
+
+### ☁️ LangBot Cloud (Recommandé)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Sans déploiement, prêt à utiliser.
+
+### Lancement en une ligne
+
+```bash
+uvx langbot
+```
+
+> Nécessite [uv](https://docs.astral.sh/uv/getting-started/installation/). Visitez http://localhost:5300 — c'est prêt.
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### Déploiement Cloud en un Clic
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**Plus d'options :** [Docker](https://link.langbot.app/en/docs/docker) · [Manuel](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## Plateformes Supportées
+
+| Plateforme | Statut | Notes |
+|----------|--------|-------|
+| Discord | ✅ | Officiel |
+| Telegram | ✅ | Officiel |
+| Slack | ✅ | Officiel |
+| LINE | ✅ | Officiel |
+| QQ | ✅ | Personnel & API Officielle (Canal, DM, Groupe) |
+| WeCom | ✅ | WeChat Entreprise, CS Externe, AI Bot |
+| WeChat | ✅ | Personnel & Compte Officiel |
+| Lark | ✅ | Officiel |
+| DingTalk | ✅ | Officiel |
+| KOOK | ✅ | Officiel |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Prend en charge plusieurs plateformes via ponts, comme Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip, etc. |
+
+---
+
+## LLMs et Intégrations Supportés
+
+| Fournisseur | Type | Statut |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM Local | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM Local | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Protocole | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Passerelle | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Passerelle | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Passerelle | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Passerelle | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Passerelle | ✅ |
+| [接口 AI](https://jiekou.ai/) | Passerelle | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Passerelle | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plateforme GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plateforme GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plateforme GPU | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | Passerelle | ✅ |
+
+[→ Voir toutes les intégrations](https://link.langbot.app/en/docs/features)
+
+---
+
+## Pourquoi LangBot ?
+
+| Cas d'Usage | Comment LangBot Aide |
+|----------|-------------------|
+| **Support Client** | Déployez des agents IA sur Slack/Discord/Telegram qui répondent aux questions en utilisant votre base de connaissances |
+| **Outils Internes** | Connectez les workflows n8n/Dify à WeCom/DingTalk pour automatiser vos processus métier |
+| **Gestion de Communauté** | Modérez les groupes QQ/Discord avec un filtrage de contenu et des interactions alimentés par l'IA |
+| **Présence Multi-plateforme** | Un seul bot, toutes les plateformes. Gérez tout depuis un tableau de bord unique |
+
+---
+
+## Démo en Ligne
+
+**Essayez maintenant :** https://demo.langbot.dev/
+- Email : `demo@langbot.app`
+- Mot de passe : `langbot123456`
+
+*Note : Environnement de démonstration public. Ne saisissez pas d'informations sensibles.*
+
+---
+
+## Communauté
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Communauté Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Historique des Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Contributeurs
+
+Merci à tous les [contributeurs](https://github.com/langbot-app/LangBot/graphs/contributors) qui ont aidé à améliorer LangBot :
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>

README_JP.md

@@ -1,31 +1,70 @@
 <p align="center">
 <a href="https://langbot.app">
-<img src="https://docs.langbot.app/social_en.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>
 
 <div align="center">
 
-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / 日本語 / (PR for your language)
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>AIエージェント搭載IMボットを構築するための本番グレードプラットフォーム。</h3>
+<h4>Slack、Discord、Telegram、WeChat などに AI ボットを素早く構築、デバッグ、デプロイ。</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / 日本語 / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">ホーム</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">デプロイ</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">プラグイン</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">プラグインの提出</a>
+<a href="https://link.langbot.app/ja/docs/features">機能</a> ｜
+<a href="https://link.langbot.app/ja/docs/guide">ドキュメント</a> ｜
+<a href="https://link.langbot.app/ja/docs/api">API</a> ｜
+<a href="https://space.langbot.app">プラグインマーケット</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">ロードマップ</a>
 
 </div>
 
 </p>
 
-LangBot は、エージェント、RAG、MCP などの LLM アプリケーション機能を備えた、オープンソースの LLM ネイティブのインスタントメッセージングロボット開発プラットフォームです。世界中のインスタントメッセージングプラットフォームに適応し、豊富な API インターフェースを提供し、カスタム開発をサポートします。
+---
 
-## 📦 始め方
+## LangBot とは？
 
-#### Docker Compose デプロイ
+LangBot は、AI搭載のインスタントメッセージングボットを構築するための**オープンソースの本番グレードプラットフォーム**です。大規模言語モデル（LLM）をあらゆるチャットプラットフォームに接続し、会話、タスク実行、既存のワークフローとの統合が可能なインテリジェントエージェントを作成できます。
+
+### 主な機能
+
+- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) と深く統合。
+- **ユニバーサルIMプラットフォーム対応** — 単一のコードベースで Discord、Telegram、Slack、LINE、QQ、WeChat、WeCom、Lark、DingTalk、KOOK に対応。
+- **本番環境対応** — アクセス制御、レート制限、センシティブワードフィルタリング、包括的な監視、例外処理を搭載。エンタープライズの信頼に応える品質。
+- **プラグインエコシステム** — 数百のプラグイン、イベント駆動アーキテクチャ、コンポーネント拡張、[MCPプロトコル](https://modelcontextprotocol.io/)対応。
+- **Web管理パネル** — 直感的なブラウザインターフェースからボットの設定、管理、監視が可能。YAML編集は不要。
+- **マルチパイプラインアーキテクチャ** — 異なるシナリオに異なるボットを配置し、包括的な監視と例外処理を実現。
+
+[→ すべての機能について詳しく見る](https://link.langbot.app/ja/docs/features)
+
+📍 実践ガイド: [5分でマルチプラットフォームAIボットをデプロイ](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/)、[DeepSeekをWeChat・Discord・Telegramに接続](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/)、[Dify AgentをDiscord・Telegram・Slackで動かす](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/)、[n8n連携チャットボットを構築](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/)。
+
+---
+
+## クイックスタート
+
+### ☁️ LangBot Cloud（推奨）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — デプロイ不要、すぐに使えます。
+
+### ワンライン起動
+
+```bash
+uvx langbot
+```
+
+> [uv](https://docs.astral.sh/uv/getting-started/installation/) が必要です。http://localhost:5300 にアクセスして完了。
+
+### Docker Compose
 
 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -33,98 +72,104 @@ cd LangBot/docker
 docker compose up -d
 ```
 
-http://localhost:5300 にアクセスして使用を開始します。
-
-詳細なドキュメントは[Dockerデプロイ](https://docs.langbot.app/en/deploy/langbot/docker.html)を参照してください。
-
-#### Panelでのワンクリックデプロイ
-
-LangBotはBTPanelにリストされています。BTPanelをインストールしている場合は、[ドキュメント](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html)を使用して使用できます。
-
-#### Zeaburクラウドデプロイ
-
-コミュニティが提供するZeaburテンプレート。
+### ワンクリッククラウドデプロイ
 
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Railwayクラウドデプロイ
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-#### その他のデプロイ方法
+**その他:** [Docker](https://link.langbot.app/en/docs/docker) · [手動デプロイ](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
-リリースバージョンを直接使用して実行します。[手動デプロイ](https://docs.langbot.app/en/deploy/langbot/manual.html)のドキュメントを参照してください。
+---
 
-#### Kubernetes デプロイ
-
-[Kubernetes デプロイ](./docker/README_K8S.md) ドキュメントを参照してください。
-
-## 😎 最新情報を入手
-
-リポジトリの右上にある Star と Watch ボタンをクリックして、最新の更新を取得してください。
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 機能
-
-- 💬 LLM / エージェントとのチャット: 複数のLLMをサポートし、グループチャットとプライベートチャットに対応。マルチラウンドの会話、ツールの呼び出し、マルチモーダル、ストリーミング出力機能をサポート、RAG（知識ベース）を組み込み、[Dify](https://dify.ai) と深く統合。
-- 🤖 多プラットフォーム対応: 現在、QQ、QQ チャンネル、WeChat、個人 WeChat、Lark、DingTalk、Discord、Telegram など、複数のプラットフォームをサポートしています。
-- 🛠️ 高い安定性、豊富な機能: ネイティブのアクセス制御、レート制限、敏感な単語のフィルタリングなどのメカニズムをサポート。使いやすく、複数のデプロイ方法をサポート。複数のパイプライン設定をサポートし、異なるボットを異なる用途に使用できます。
-- 🧩 プラグイン拡張、活発なコミュニティ: イベント駆動、コンポーネント拡張などのプラグインメカニズムをサポート。適配 Anthropic [MCP プロトコル](https://modelcontextprotocol.io/)；豊富なエコシステム、現在数百のプラグインが存在。
-- 😻 Web UI: ブラウザを通じてLangBotインスタンスを管理することをサポート。
-
-詳細な仕様については、[ドキュメント](https://docs.langbot.app/en/insight/features.html)を参照してください。
-
-または、デモ環境にアクセスしてください: https://demo.langbot.dev/
-  - ログイン情報: メール: `demo@langbot.app` パスワード: `langbot123456`
-  - 注意: WebUI のデモンストレーションのみの場合、公開環境では機密情報を入力しないでください。
-
-### メッセージプラットフォーム
+## 対応プラットフォーム
 
 | プラットフォーム | ステータス | 備考 |
-| --- | --- | --- |
-| Discord | ✅ |  |
-| Telegram | ✅ |  |
-| Slack | ✅ |  |
-| LINE | ✅ |  |
-| 個人QQ | ✅ |  |
-| QQ公式API | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| 個人WeChat | ✅ | |
-| Lark | ✅ |  |
-| DingTalk | ✅ |  |
+|----------|--------|-------|
+| Discord | ✅ | 公式 |
+| Telegram | ✅ | 公式 |
+| Slack | ✅ | 公式 |
+| LINE | ✅ | 公式 |
+| QQ | ✅ | 個人・公式API（チャンネル・DM・グループ） |
+| WeCom | ✅ | 企業WeChat、外部CS、AIボット |
+| WeChat | ✅ | 個人・公式アカウント |
+| Lark | ✅ | 公式 |
+| DingTalk | ✅ | 公式 |
+| KOOK | ✅ | 公式 |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix、Satori |
+| Matrix | ✅ | Signal、WhatsApp、Messenger、iMessage、Mattermost、Google Chat、IRC、XMPP、Zulip など複数のブリッジ先プラットフォームに対応 |
 
-### LLMs
+---
 
-| LLM | ステータス | 備考 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 任意のOpenAIインターフェース形式モデルに対応 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型とGPUリソースプラットフォーム |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型とGPUリソースプラットフォーム |
-| [接口 AI](https://jiekou.ai/) | ✅ | LLMゲートウェイ(MaaS) |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | LLMとGPUリソースプラットフォーム |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | LLMゲートウェイ(MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOpsプラットフォーム |
-| [Ollama](https://ollama.com/) | ✅ | ローカルLLM実行プラットフォーム |
-| [LMStudio](https://lmstudio.ai/) | ✅ | ローカルLLM実行プラットフォーム |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | LLMインターフェースゲートウェイ(MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | LLMゲートウェイ(MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLMゲートウェイ(MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | MCPプロトコルをサポート |
+## 対応LLMと統合
 
-## 🤝 コミュニティ貢献
+| プロバイダー | タイプ | ステータス |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | ローカルLLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | ローカルLLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | プロトコル | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | ゲートウェイ | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ゲートウェイ | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ゲートウェイ | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ゲートウェイ | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | ゲートウェイ | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPUプラットフォーム | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPUプラットフォーム | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPUプラットフォーム | ✅ |
+| [接口 AI](https://jiekou.ai/) | ゲートウェイ | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | ゲートウェイ | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | ゲートウェイ | ✅ |
 
-LangBot への貢献に対して、以下の [コード貢献者](https://github.com/langbot-app/LangBot/graphs/contributors) とコミュニティの他のメンバーに感謝します。
+[→ すべての統合を表示](https://link.langbot.app/en/docs/features)
+
+---
+
+## なぜ LangBot？
+
+| ユースケース | LangBot の活用方法 |
+|----------|-------------------|
+| **カスタマーサポート** | ナレッジベースを活用して質問に回答するAIエージェントをSlack/Discord/Telegramにデプロイ |
+| **社内ツール** | n8n/Difyのワークフローを WeCom/DingTalk に接続し、業務プロセスを自動化 |
+| **コミュニティ管理** | AI搭載のコンテンツフィルタリングとインタラクションでQQ/Discordグループをモデレーション |
+| **マルチプラットフォーム展開** | 1つのボットで全プラットフォームに対応。単一のダッシュボードから管理 |
+
+---
+
+## ライブデモ
+
+**今すぐ試す:** https://demo.langbot.dev/
+- メール: `demo@langbot.app`
+- パスワード: `langbot123456`
+
+*注意: 公開デモ環境です。機密情報を入力しないでください。*
+
+---
+
+## コミュニティ
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Discord コミュニティ](https://discord.gg/wdNEHETs87)
+
+---
+
+## Star 推移
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## コントリビューター
+
+LangBot をより良くするために貢献してくださったすべての[コントリビューター](https://github.com/langbot-app/LangBot/graphs/contributors)に感謝します:
 
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />

README_KO.md

@@ -0,0 +1,176 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>AI 에이전트 IM 봇 구축을 위한 프로덕션 등급 플랫폼.</h3>
+<h4>Slack, Discord, Telegram, WeChat 등에 AI 봇을 빠르게 구축, 디버그 및 배포.</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / 한국어 / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+
+<a href="https://langbot.app">홈</a> ｜
+<a href="https://link.langbot.app/en/docs/features">기능</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">문서</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app">플러그인 마켓</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">로드맵</a>
+
+</div>
+
+</p>
+
+---
+
+## LangBot이란?
+
+LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈소스 프로덕션 등급 플랫폼**입니다. 대규모 언어 모델(LLM)을 모든 채팅 플랫폼에 연결하여 대화, 작업 실행, 기존 워크플로우와의 통합이 가능한 지능형 에이전트를 만들 수 있습니다.
+
+### 핵심 기능
+
+- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org) 심층 통합.
+- **유니버설 IM 플랫폼 지원** — 단일 코드베이스로 Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK 지원.
+- **프로덕션 레디** — 접근 제어, 속도 제한, 민감어 필터링, 종합 모니터링 및 예외 처리. 기업 환경에서 검증됨.
+- **플러그인 생태계** — 수백 개의 플러그인, 이벤트 기반 아키텍처, 컴포넌트 확장, [MCP 프로토콜](https://modelcontextprotocol.io/) 지원.
+- **웹 관리 패널** — 직관적인 브라우저 인터페이스로 봇을 구성, 관리 및 모니터링. YAML 편집 불필요.
+- **멀티 파이프라인 아키텍처** — 다양한 시나리오에 맞는 다양한 봇 구성, 종합 모니터링 및 예외 처리.
+
+[→ 모든 기능 자세히 보기](https://link.langbot.app/en/docs/features)
+
+📍 실전 가이드: [5분 만에 멀티 플랫폼 AI 봇 배포하기](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [DeepSeek를 WeChat, Discord, Telegram에 연결하기](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [Dify Agent를 Discord, Telegram, Slack에서 실행하기](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/), [n8n 기반 챗봇 만들기](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## 빠른 시작
+
+### ☁️ LangBot Cloud (추천)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 배포 없이 바로 사용.
+
+### 원라인 실행
+
+```bash
+uvx langbot
+```
+
+> [uv](https://docs.astral.sh/uv/getting-started/installation/) 설치 필요. http://localhost:5300 방문 — 완료.
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### 원클릭 클라우드 배포
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**더 많은 옵션:** [Docker](https://link.langbot.app/en/docs/docker) · [수동 배포](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## 지원 플랫폼
+
+| 플랫폼 | 상태 | 비고 |
+|--------|------|------|
+| Discord | ✅ | 공식 |
+| Telegram | ✅ | 공식 |
+| Slack | ✅ | 공식 |
+| LINE | ✅ | 공식 |
+| QQ | ✅ | 개인 및 공식 API (채널, DM, 그룹) |
+| WeCom | ✅ | 기업 WeChat, 외부 CS, AI Bot |
+| WeChat | ✅ | 개인 및 공식 계정 |
+| Lark | ✅ | 공식 |
+| DingTalk | ✅ | 공식 |
+| KOOK | ✅ | 공식 |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip 등 여러 브리지 플랫폼 지원 |
+
+---
+
+## 지원 LLM 및 통합
+
+| 제공자 | 유형 | 상태 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 로컬 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 로컬 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 프로토콜 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 게이트웨이 | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | 게이트웨이 | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 게이트웨이 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 게이트웨이 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 게이트웨이 | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 플랫폼 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 플랫폼 | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 플랫폼 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | 게이트웨이 | ✅ |
+
+[→ 모든 통합 보기](https://link.langbot.app/en/docs/features)
+
+---
+
+## 왜 LangBot인가?
+
+| 사용 사례 | LangBot 활용 방법 |
+|-----------|-------------------|
+| **고객 지원** | 지식 베이스를 활용하여 질문에 답변하는 AI 에이전트를 Slack/Discord/Telegram에 배포 |
+| **내부 도구** | n8n/Dify 워크플로우를 WeCom/DingTalk에 연결하여 비즈니스 프로세스 자동화 |
+| **커뮤니티 관리** | AI 기반 콘텐츠 필터링 및 상호작용으로 QQ/Discord 그룹 관리 |
+| **멀티 플랫폼** | 하나의 봇으로 모든 플랫폼 지원. 단일 대시보드에서 관리 |
+
+---
+
+## 라이브 데모
+
+**지금 체험:** https://demo.langbot.dev/
+- 이메일: `demo@langbot.app`
+- 비밀번호: `langbot123456`
+
+*참고: 공개 데모 환경입니다. 민감한 정보를 입력하지 마세요.*
+
+---
+
+## 커뮤니티
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Discord 커뮤니티](https://discord.gg/wdNEHETs87)
+
+---
+
+## Star 추이
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 기여자
+
+LangBot을 더 나은 프로젝트로 만들어 주신 모든 [기여자](https://github.com/langbot-app/LangBot/graphs/contributors)분들께 감사드립니다:
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>

README_RU.md

@@ -0,0 +1,176 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>Платформа производственного уровня для создания агентных IM-ботов.</h3>
+<h4>Быстро создавайте, отлаживайте и развертывайте ИИ-ботов в Slack, Discord, Telegram, WeChat и других платформах.</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / Русский / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+
+<a href="https://langbot.app">Главная</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Возможности</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Документация</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app">Магазин плагинов</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Дорожная карта</a>
+
+</div>
+
+</p>
+
+---
+
+## Что такое LangBot?
+
+LangBot — это **платформа с открытым исходным кодом производственного уровня** для создания ИИ-ботов в мессенджерах. Она связывает большие языковые модели (LLM) с любой чат-платформой, позволяя создавать интеллектуальных агентов, которые могут вести диалоги, выполнять задачи и интегрироваться с вашими существующими рабочими процессами.
+
+### Ключевые возможности
+
+- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Универсальная поддержка IM-платформ** — Единая кодовая база для Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Готовность к продакшену** — Контроль доступа, ограничение скорости, фильтрация чувствительных слов, комплексный мониторинг и обработка исключений. Проверено в корпоративной среде.
+- **Экосистема плагинов** — Сотни плагинов, событийно-ориентированная архитектура, расширения компонентов и поддержка [протокола MCP](https://modelcontextprotocol.io/).
+- **Веб-панель управления** — Настраивайте, управляйте и мониторьте ваших ботов через интуитивный браузерный интерфейс. Ручное редактирование YAML не требуется.
+- **Мультиконвейерная архитектура** — Разные боты для разных сценариев с комплексным мониторингом и обработкой исключений.
+
+[→ Подробнее обо всех возможностях](https://link.langbot.app/en/docs/features)
+
+📍 Практические руководства: [развернуть мультиплатформенного ИИ-бота за 5 минут](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [подключить DeepSeek к WeChat, Discord и Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [запустить Dify Agent в Discord, Telegram и Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) и [создать чат-бота на n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## Быстрый старт
+
+### ☁️ LangBot Cloud (Рекомендуется)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Без развёртывания, готово к использованию.
+
+### Запуск одной командой
+
+```bash
+uvx langbot
+```
+
+> Требуется [uv](https://docs.astral.sh/uv/getting-started/installation/). Откройте http://localhost:5300 — готово.
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### Облачное развертывание одним кликом
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**Другие варианты:** [Docker](https://link.langbot.app/en/docs/docker) · [Ручная установка](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## Поддерживаемые платформы
+
+| Платформа | Статус | Примечания |
+|-----------|--------|------------|
+| Discord | ✅ | Официальный |
+| Telegram | ✅ | Официальный |
+| Slack | ✅ | Официальный |
+| LINE | ✅ | Официальный |
+| QQ | ✅ | Личный и официальный API (Канал, ЛС, Группа) |
+| WeCom | ✅ | Корпоративный WeChat, внешний CS, AI-бот |
+| WeChat | ✅ | Личный и официальный аккаунт |
+| Lark | ✅ | Официальный |
+| DingTalk | ✅ | Официальный |
+| KOOK | ✅ | Официальный |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Поддерживает несколько платформ через мосты, включая Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip и другие |
+
+---
+
+## Поддерживаемые LLM и интеграции
+
+| Провайдер | Тип | Статус |
+|-----------|-----|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | Локальный LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | Локальный LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Протокол | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Шлюз | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Шлюз | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Шлюз | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Шлюз | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Шлюз | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Шлюз | ✅ |
+| [接口 AI](https://jiekou.ai/) | Шлюз | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Платформа GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Платформа GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Платформа GPU | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | Шлюз | ✅ |
+
+[→ Смотреть все интеграции](https://link.langbot.app/en/docs/features)
+
+---
+
+## Почему LangBot?
+
+| Сценарий использования | Как помогает LangBot |
+|------------------------|----------------------|
+| **Поддержка клиентов** | Разверните ИИ-агентов в Slack/Discord/Telegram, которые отвечают на вопросы, используя вашу базу знаний |
+| **Внутренние инструменты** | Подключите рабочие процессы n8n/Dify к WeCom/DingTalk для автоматизации бизнес-процессов |
+| **Управление сообществом** | Модерируйте группы QQ/Discord с помощью ИИ-фильтрации контента и взаимодействия |
+| **Мультиплатформенное присутствие** | Один бот — все платформы. Управляйте из единой панели |
+
+---
+
+## Демо
+
+**Попробуйте прямо сейчас:** https://demo.langbot.dev/
+- Email: `demo@langbot.app`
+- Пароль: `langbot123456`
+
+*Примечание: Публичная демо-среда. Не вводите конфиденциальную информацию.*
+
+---
+
+## Сообщество
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Сообщество Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## История Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Участники
+
+Спасибо всем [участникам](https://github.com/langbot-app/LangBot/graphs/contributors), которые помогли сделать LangBot лучше:
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>

README_TW.md

@@ -1,33 +1,72 @@
 <p align="center">
 <a href="https://langbot.app">
-<img src="https://docs.langbot.app/social_zh.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>
 
-<div align="center"><a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<div align="center">
 
-[English](README_EN.md) / [简体中文](README.md) / 繁體中文 / [日本語](README_JP.md) / (PR for your language)
+<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>生產級 AI 即時通訊機器人開發平台。</h3>
+<h4>快速建構、除錯和部署 AI 機器人到微信、QQ、飛書、Slack、Discord、Telegram 等平台。</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / 繁體中文 / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 [![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
 
-<a href="https://langbot.app">主頁</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">部署文件</a> ｜
-<a href="https://docs.langbot.app/zh/plugin/plugin-intro.html">外掛介紹</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">提交外掛</a>
+<a href="https://langbot.app">官網</a> ｜
+<a href="https://link.langbot.app/zh/docs/features">特性</a> ｜
+<a href="https://link.langbot.app/zh/docs/guide">文件</a> ｜
+<a href="https://link.langbot.app/zh/docs/api">API</a> ｜
+<a href="https://space.langbot.app">外掛市場</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">路線圖</a>
 
 </div>
 
 </p>
 
-LangBot 是一個開源的大語言模型原生即時通訊機器人開發平台，旨在提供開箱即用的 IM 機器人開發體驗，具有 Agent、RAG、MCP 等多種 LLM 應用功能，適配全球主流即時通訊平台，並提供豐富的 API 介面，支援自定義開發。
+---
 
-## 📦 開始使用
+## 什麼是 LangBot？
 
-#### Docker Compose 部署
+LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時通訊機器人。它將大語言模型（LLM）連接到各種聊天平台，幫助你創建能夠對話、執行任務、並整合到現有工作流程中的智能 Agent。
+
+### 核心能力
+
+- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **全平台支援** — 一套程式碼，覆蓋 QQ、微信、企業微信、飛書、釘釘、Discord、Telegram、Slack、LINE、KOOK 等平台。
+- **生產就緒** — 存取控制、限速、敏感詞過濾、全面監控與異常處理，已被多家企業採用。
+- **外掛生態** — 數百個外掛，事件驅動架構，組件擴展，適配 [MCP 協議](https://modelcontextprotocol.io/)。
+- **Web 管理面板** — 透過瀏覽器直觀地配置、管理和監控機器人，無需手動編輯設定檔。
+- **多流水線架構** — 不同機器人用於不同場景，具備全面的監控和異常處理能力。
+
+[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
+
+📍 實踐指南：[5 分鐘部署多平台 AI 機器人](https://blog.langbot.app/zh/blog/deploy-ai-bot-in-5-minutes/)、[將 DeepSeek 接入微信、企業微信與 Discord](https://blog.langbot.app/zh/blog/connect-deepseek-to-wechat/)、[讓 Dify Agent 跑在 Discord、Telegram 和 Slack 上](https://blog.langbot.app/zh/blog/dify-agent-discord-telegram-slack/)，以及[用 n8n 建構多平台 AI 聊天機器人](https://blog.langbot.app/zh/blog/n8n-multi-platform-ai-chatbot/)。
+
+---
+
+## 快速開始
+
+### ☁️ LangBot Cloud（推薦）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，開箱即用。
+
+### 一鍵啟動
+
+```bash
+uvx langbot
+```
+
+> 需要安裝 [uv](https://docs.astral.sh/uv/getting-started/installation/)。訪問 http://localhost:5300 即可使用。
+
+### Docker Compose
 
 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -35,99 +74,66 @@ cd LangBot/docker
 docker compose up -d
 ```
 
-訪問 http://localhost:5300 即可開始使用。
-
-詳細文件[Docker 部署](https://docs.langbot.app/zh/deploy/langbot/docker.html)。
-
-#### 寶塔面板部署
-
-已上架寶塔面板，若您已安裝寶塔面板，可以根據[文件](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html)使用。
-
-#### Zeabur 雲端部署
-
-社群貢獻的 Zeabur 模板。
+### 一鍵雲端部署
 
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
-
-#### Railway 雲端部署
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-#### 手動部署
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手動部署](https://link.langbot.app/zh/docs/manual-deploy) · [寶塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
-直接使用發行版運行，查看文件[手動部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
+---
 
-#### Kubernetes 部署
-
-參考 [Kubernetes 部署](./docker/README_K8S.md) 文件。
-
-## 😎 保持更新
-
-點擊倉庫右上角 Star 和 Watch 按鈕，獲取最新動態。
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 特性
-
-- 💬 大模型對話、Agent：支援多種大模型，適配群聊和私聊；具有多輪對話、工具調用、多模態、流式輸出能力，自帶 RAG（知識庫）實現，並深度適配 [Dify](https://dify.ai)。
-- 🤖 多平台支援：目前支援 QQ、QQ頻道、企業微信、個人微信、飛書、Discord、Telegram 等平台。
-- 🛠️ 高穩定性、功能完備：原生支援訪問控制、限速、敏感詞過濾等機制；配置簡單，支援多種部署方式。支援多流水線配置，不同機器人用於不同應用場景。
-- 🧩 外掛擴展、活躍社群：支援事件驅動、組件擴展等外掛機制；適配 Anthropic [MCP 協議](https://modelcontextprotocol.io/)；目前已有數百個外掛。
-- 😻 Web 管理面板：支援通過瀏覽器管理 LangBot 實例，不再需要手動編寫配置文件。
-
-詳細規格特性請訪問[文件](https://docs.langbot.app/zh/insight/features.html)。
-
-或訪問 demo 環境：https://demo.langbot.dev/  
-  - 登入資訊：郵箱：`demo@langbot.app` 密碼：`langbot123456`
-  - 注意：僅展示 WebUI 效果，公開環境，請不要在其中填入您的任何敏感資訊。
-
-### 訊息平台
+## 支援的平台
 
 | 平台 | 狀態 | 備註 |
-| --- | --- | --- |
-| Discord | ✅ |  |
-| Telegram | ✅ |  |
-| Slack | ✅ |  |
-| LINE | ✅ |  |
-| QQ 個人號 | ✅ | QQ 個人號私聊、群聊 |
-| QQ 官方機器人 | ✅ | QQ 官方機器人，支援頻道、私聊、群聊 |
-| 微信 | ✅ |  |
-| 企微對外客服 | ✅ |  |
-| 企微智能機器人 | ✅ |  |
-| 微信公眾號 | ✅ |  |
-| Lark | ✅ |  |
-| DingTalk | ✅ |  |
+|------|------|------|
+| Discord | ✅ | 官方 |
+| Telegram | ✅ | 官方 |
+| Slack | ✅ | 官方 |
+| LINE | ✅ | 官方 |
+| QQ | ✅ | 個人號、官方機器人（頻道、私聊、群聊） |
+| 企業微信 | ✅ | 應用訊息、對外客服、智能機器人 |
+| 微信 | ✅ | 個人微信、微信公眾號 |
+| 飛書 | ✅ | 官方 |
+| 釘釘 | ✅ | 官方 |
+| KOOK | ✅ | 官方 |
+| Satori | ✅ |  |
+| Email | ✅ | 只 Matrix、Satori |
+| Matrix | ✅ | 支援多種橋接平台，如 Signal、WhatsApp、Messenger、iMessage、Mattermost、Google Chat、IRC、XMPP、Zulip 等 |
 
-### 大模型能力
+---
 
-| 模型 | 狀態 | 備註 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 可接入任何 OpenAI 介面格式模型 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [智譜AI](https://open.bigmodel.cn/) | ✅ |  |
-| [勝算雲](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | 大模型和 GPU 資源平台 |
-| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型和 GPU 資源平台 |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型和 GPU 資源平台 |
-| [接口 AI](https://jiekou.ai/) | ✅ | 大模型聚合平台，專注全球大模型接入 |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | 大模型聚合平台 |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOps 平台 |
-| [Ollama](https://ollama.com/) | ✅ | 本地大模型運行平台 |
-| [LMStudio](https://lmstudio.ai/) | ✅ | 本地大模型運行平台 |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | 大模型介面聚合平台 |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
-| [阿里雲百煉](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | 支援通過 MCP 協議獲取工具 |
+## 支援的大模型與整合
 
-### TTS
+| 提供商 | 類型 | 狀態 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [智譜AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 本地 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 本地 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 協議 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 聚合平台 | ✅ |
+| [阿里雲百煉](https://bailian.console.aliyun.com/) | 聚合平台 | ✅ |
+| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 聚合平台 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 聚合平台 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 聚合平台 | ✅ |
+| [勝算雲](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 平台 | ✅ |
+| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |
+
+### TTS（語音合成）
 
 | 平台/模型 | 備註 |
-| --- | --- |
+|-----------|------|
 | [FishAudio](https://fish.audio/zh-CN/discovery/) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
 | [海豚 AI](https://www.ttson.cn/?source=thelazy) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
 | [AzureTTS](https://portal.azure.com/) | [外掛](https://github.com/Ingnaryk/LangBot_AzureTTS) |
@@ -135,13 +141,54 @@ docker compose up -d
 ### 文生圖
 
 | 平台/模型 | 備註 |
-| --- | --- |
-| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin)
+|-----------|------|
+| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |
 
-## 😘 社群貢獻
+[→ 查看完整整合列表](https://link.langbot.app/zh/docs/features)
 
-感謝以下[程式碼貢獻者](https://github.com/langbot-app/LangBot/graphs/contributors)和社群裡其他成員對 LangBot 的貢獻：
+---
+
+## 為什麼選擇 LangBot？
+
+| 使用場景 | LangBot 如何幫助 |
+|----------|------------------|
+| **客戶服務** | 將 AI Agent 部署到微信/企微/釘釘/飛書，基於知識庫自動回答使用者問題 |
+| **內部工具** | 將 n8n/Dify 工作流接入企微/釘釘，實現業務流程自動化 |
+| **社群運營** | 在 QQ/Discord 群中使用 AI 驅動的內容審核與智能互動 |
+| **多平台觸達** | 一個機器人，覆蓋所有平台。透過統一面板集中管理 |
+
+---
+
+## 線上演示
+
+**立即體驗：** https://demo.langbot.dev/
+- 信箱：`demo@langbot.app`
+- 密碼：`langbot123456`
+
+*注意：公開演示環境，請不要在其中填入任何敏感資訊。*
+
+---
+
+## 社群
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
+
+- [Discord 社群](https://discord.gg/wdNEHETs87)
+- [QQ 社群群](https://qm.qq.com/q/JLi38whHum)
+
+---
+
+## Star 趨勢
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 貢獻者
+
+感謝所有[貢獻者](https://github.com/langbot-app/LangBot/graphs/contributors)對 LangBot 的幫助：
 
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
-</a> 
+</a>

README_VI.md

@@ -0,0 +1,176 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>Nền tảng cấp sản xuất để xây dựng bot IM với AI agent.</h3>
+<h4>Xây dựng, gỡ lỗi và triển khai bot AI nhanh chóng trên Slack, Discord, Telegram, WeChat và nhiều nền tảng khác.</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / Tiếng Việt
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+
+<a href="https://langbot.app">Trang chủ</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Tính năng</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Tài liệu</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
+<a href="https://space.langbot.app">Chợ Plugin</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Lộ trình</a>
+
+</div>
+
+</p>
+
+---
+
+## LangBot là gì?
+
+LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để xây dựng bot nhắn tin tức thời được hỗ trợ bởi AI. Nó kết nối các Mô hình Ngôn ngữ Lớn (LLM) với bất kỳ nền tảng chat nào, cho phép bạn tạo các agent thông minh có thể trò chuyện, thực hiện tác vụ và tích hợp với quy trình làm việc hiện có của bạn.
+
+### Khả năng chính
+
+- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Hỗ trợ đa nền tảng IM** — Một mã nguồn cho Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Sẵn sàng cho sản xuất** — Kiểm soát truy cập, giới hạn tốc độ, lọc từ nhạy cảm, giám sát toàn diện và xử lý ngoại lệ. Được doanh nghiệp tin dùng.
+- **Hệ sinh thái Plugin** — Hàng trăm plugin, kiến trúc hướng sự kiện, mở rộng thành phần, và hỗ trợ [giao thức MCP](https://modelcontextprotocol.io/).
+- **Bảng quản lý Web** — Cấu hình, quản lý và giám sát bot thông qua giao diện trình duyệt trực quan. Không cần chỉnh sửa YAML.
+- **Kiến trúc đa Pipeline** — Các bot khác nhau cho các kịch bản khác nhau, với giám sát toàn diện và xử lý ngoại lệ.
+
+[→ Tìm hiểu thêm về tất cả tính năng](https://link.langbot.app/en/docs/features)
+
+📍 Hướng dẫn thực hành: [triển khai bot AI đa nền tảng trong 5 phút](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [kết nối DeepSeek với WeChat, Discord và Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [chạy Dify Agent trên Discord, Telegram và Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) và [xây dựng chatbot với n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
+
+---
+
+## Bắt đầu nhanh
+
+### ☁️ LangBot Cloud (Khuyên dùng)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Không cần triển khai, sẵn sàng sử dụng.
+
+### Khởi chạy một dòng
+
+```bash
+uvx langbot
+```
+
+> Yêu cầu [uv](https://docs.astral.sh/uv/getting-started/installation/). Truy cập http://localhost:5300 — xong.
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### Triển khai đám mây một cú nhấp
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+**Thêm tùy chọn:** [Docker](https://link.langbot.app/en/docs/docker) · [Thủ công](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+
+---
+
+## Nền tảng được hỗ trợ
+
+| Nền tảng | Trạng thái | Ghi chú |
+|----------|--------|-------|
+| Discord | ✅ | Chính thức |
+| Telegram | ✅ | Chính thức |
+| Slack | ✅ | Chính thức |
+| LINE | ✅ | Chính thức |
+| QQ | ✅ | Cá nhân & API chính thức (Kênh, DM, Nhóm) |
+| WeCom | ✅ | WeChat doanh nghiệp, CS bên ngoài, AI Bot |
+| WeChat | ✅ | Cá nhân & Tài khoản công khai |
+| Lark | ✅ | Chính thức |
+| DingTalk | ✅ | Chính thức |
+| KOOK | ✅ | Chính thức |
+| Satori | ✅ |  |
+| Email | ✅ | Matrix, Satori |
+| Matrix | ✅ | Hỗ trợ nhiều nền tảng qua bridge như Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip và hơn thế nữa |
+
+---
+
+## LLM và tích hợp được hỗ trợ
+
+| Nhà cung cấp | Loại | Trạng thái |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM cục bộ | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM cục bộ | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Giao thức | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Cổng | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Cổng | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Cổng | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Cổng | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Cổng | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Nền tảng GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Nền tảng GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Nền tảng GPU | ✅ |
+| [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |
+| [Qiniu](https://www.qiniu.com/ai/agent) | Cổng | ✅ |
+
+[→ Xem tất cả tích hợp](https://link.langbot.app/en/docs/features)
+
+---
+
+## Tại sao chọn LangBot?
+
+| Trường hợp sử dụng | LangBot giúp như thế nào |
+|----------|-------------------|
+| **Hỗ trợ khách hàng** | Triển khai agent AI trên Slack/Discord/Telegram để trả lời câu hỏi bằng cơ sở kiến thức của bạn |
+| **Công cụ nội bộ** | Kết nối quy trình n8n/Dify với WeCom/DingTalk để tự động hóa quy trình kinh doanh |
+| **Quản lý cộng đồng** | Quản lý nhóm QQ/Discord với tính năng lọc nội dung và tương tác được hỗ trợ bởi AI |
+| **Đa nền tảng** | Một bot, tất cả nền tảng. Quản lý từ một bảng điều khiển duy nhất |
+
+---
+
+## Demo trực tuyến
+
+**Thử ngay:** https://demo.langbot.dev/
+- Email: `demo@langbot.app`
+- Mật khẩu: `langbot123456`
+
+*Lưu ý: Môi trường demo công khai. Không nhập thông tin nhạy cảm.*
+
+---
+
+## Cộng đồng
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Cộng đồng Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Lịch sử Star
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Người đóng góp
+
+Cảm ơn tất cả [người đóng góp](https://github.com/langbot-app/LangBot/graphs/contributors) đã giúp LangBot trở nên tốt hơn:
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>

docker/README_K8S.md

@@ -312,7 +312,7 @@ spec:
 ### 参考资源
 
 - [LangBot 官方文档](https://docs.langbot.app)
-- [Docker 部署文档](https://docs.langbot.app/zh/deploy/langbot/docker.html)
+- [Docker 部署文档](https://link.langbot.app/zh/docs/docker)
 - [Kubernetes 官方文档](https://kubernetes.io/docs/)
 
 ---
@@ -625,5 +625,5 @@ spec:
 ### References
 
 - [LangBot Official Documentation](https://docs.langbot.app)
-- [Docker Deployment Guide](https://docs.langbot.app/zh/deploy/langbot/docker.html)
+- [Docker Deployment Guide](https://link.langbot.app/zh/docs/docker)
 - [Kubernetes Official Documentation](https://kubernetes.io/docs/)

docker/docker-compose.yaml

@@ -14,7 +14,41 @@ services:
     restart: on-failure
     environment:
       - TZ=Asia/Shanghai
-    command: ["uv", "run", "-m", "langbot_plugin.cli.__init__", "rt"]
+    command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "rt"]
+    networks:
+      - langbot_network
+
+  # The Box sandbox runtime is optional. It is only started when you run
+  # ``docker compose --profile box up`` (or ``docker compose --profile all
+  # up``). With Box off, LangBot keeps the dashboard / skills list visible
+  # (read-only) but disables sandbox tools, skill add/edit and stdio MCP —
+  # set ``box.enabled: false`` in ``data/config.yaml`` (or
+  # ``BOX__ENABLED=false`` in the langbot service env below) to match.
+  langbot_box:
+    image: rockchin/langbot:latest
+    container_name: langbot_box
+    profiles: ["box", "all"]
+    volumes:
+      # Keep the source and target path identical because langbot_box uses the
+      # host Docker socket to create sandbox containers. Override
+      # LANGBOT_BOX_ROOT with an absolute path if you do not want the default.
+      - ${LANGBOT_BOX_ROOT:-${PWD}/data/box}:${LANGBOT_BOX_ROOT:-${PWD}/data/box}
+      # Mount container runtime socket for Box sandbox backend.
+      # Uncomment the one that matches your container runtime:
+      # - /var/run/podman/podman.sock:/var/run/podman/podman.sock   # Podman
+      - /var/run/docker.sock:/var/run/docker.sock                   # Docker
+    restart: on-failure
+    environment:
+      - TZ=Asia/Shanghai
+      # The Box runtime does NOT read box.local.* from config.yaml or env; it
+      # receives its configuration from LangBot via the INIT RPC action.
+      # Do not add LANGBOT_BOX_* / BOX__* here — they would be silently ignored.
+    # Launched through the same CLI entry point as the plugin runtime
+    # (`langbot_plugin.cli.__init__ <subcommand>`). WebSocket is the default
+    # control transport — mirrors `rt`, which also runs with no flag. Pass
+    # `-s` / `--stdio-control` only for the stdio mode LangBot uses outside
+    # containers.
+    command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "box"]
     networks:
       - langbot_network
 
@@ -23,13 +57,19 @@ services:
     container_name: langbot
     volumes:
       - ./data:/app/data
-      - ./plugins:/app/plugins
     restart: on-failure
     environment:
       - TZ=Asia/Shanghai
+      # Unified env-override convention: SECTION__SUBSECTION__KEY overrides the
+      # matching config.yaml field (see LoadConfigStage). These map onto
+      # box.local.* and are forwarded to the Box runtime via INIT RPC.
+      - BOX__LOCAL__HOST_ROOT=${LANGBOT_BOX_ROOT:-${PWD}/data/box}
+      - BOX__LOCAL__DEFAULT_WORKSPACE=default
+      - BOX__LOCAL__SKILLS_ROOT=skills
+      - BOX__LOCAL__ALLOWED_MOUNT_ROOTS=${LANGBOT_BOX_ROOT:-${PWD}/data/box}
     ports:
-      - 5300:5300  # For web ui
-      - 2280-2290:2280-2290  # For platform webhook
+      - 5300:5300  # For web ui and webhook callback
+      - 2280-2285:2280-2285  # For platform reverse connection
     networks:
       - langbot_network
 

docs/MIGRATION_SUMMARY.md

@@ -0,0 +1,412 @@
+# WebChat 到 WebSocket 迁移总结
+
+## 概述
+
+已完全移除旧的基于SSE的WebChat系统，并替换为基于WebSocket的双向实时通信系统。这是一个内置在LangBot中的完整IM系统，支持流式输出。
+
+## 已删除的文件
+
+### 后端
+- ❌ `src/langbot/pkg/api/http/controller/groups/pipelines/webchat.py` - 旧的SSE路由
+- ❌ `src/langbot/pkg/platform/sources/webchat.py` - 旧的WebChat适配器
+- ❌ `src/langbot/pkg/platform/sources/webchat.yaml` - 旧的配置文件
+
+### 前端
+- ❌ BackendClient中所有SSE相关代码已完全移除
+- ❌ DebugDialog中所有SSE相关逻辑已完全替换
+
+## 新增的文件
+
+### 后端核心文件
+
+**1. WebSocket连接管理器**
+```
+src/langbot/pkg/platform/sources/websocket_manager.py
+```
+- 管理所有并发WebSocket连接
+- 线程安全的连接池
+- 按流水线、会话类型分组
+- 广播和单播消息功能
+- 连接统计和监控
+
+**2. WebSocket适配器**
+```
+src/langbot/pkg/platform/sources/websocket_adapter.py
+```
+- 实现平台适配器接口
+- **完整流式支持** (`reply_message_chunk` 方法)
+- 双向消息流处理
+- 消息历史管理
+- 会话管理
+
+**3. WebSocket路由控制器**
+```
+src/langbot/pkg/api/http/controller/groups/pipelines/websocket_chat.py
+```
+- WebSocket端点处理
+- REST API接口
+- 心跳机制
+- 连接生命周期管理
+
+**4. 配置文件**
+```
+src/langbot/pkg/platform/sources/websocket.yaml
+```
+- WebSocket适配器元数据
+
+### 前端核心文件
+
+**1. WebSocket客户端**
+```
+web/src/app/infra/websocket/WebSocketClient.ts
+```
+- WebSocket连接管理
+- 自动重连（最多5次）
+- 心跳机制（30秒）
+- 事件回调系统
+
+**2. 更新的组件**
+```
+web/src/app/home/pipelines/components/debug-dialog/DebugDialog.tsx
+```
+- 完全重写，使用WebSocket
+- 实时连接状态显示
+- 流式消息支持
+- 自动重连
+
+**3. HTTP客户端更新**
+```
+web/src/app/infra/http/BackendClient.ts
+```
+- 移除所有旧的WebChat API
+- 仅保留WebSocket API
+
+### 测试工具
+
+**Python测试客户端**
+```
+test_websocket_client.py
+```
+- 单连接交互测试
+- 多连接并发测试
+- 命令行工具
+
+### 文档
+
+**使用文档**
+```
+WEBSOCKET_README.md
+```
+- 完整的API文档
+- 架构说明
+- 使用示例
+- 故障排查
+
+## 核心变更
+
+### 后端变更
+
+**1. botmgr.py**
+- ❌ 移除 `webchat_proxy_bot`
+- ✅ 仅保留 `websocket_proxy_bot`
+- ✅ 更新适配器过滤逻辑（排除`websocket`而非`webchat`）
+
+**2. 适配器注册**
+```python
+# 旧代码（已删除）
+webchat_adapter_class = self.adapter_dict['webchat']
+self.webchat_proxy_bot = RuntimeBot(...)
+
+# 新代码
+websocket_adapter_class = self.adapter_dict['websocket']
+self.websocket_proxy_bot = RuntimeBot(
+    uuid='websocket-proxy-bot',
+    name='WebSocket',
+    adapter='websocket',
+    ...
+)
+```
+
+### 前端变更
+
+**1. API调用完全更换**
+
+旧代码（已删除）:
+```typescript
+// SSE流式请求
+await fetch(url, {
+  method: 'POST',
+  body: JSON.stringify({ is_stream: true })
+})
+// 手动解析 text/event-stream
+```
+
+新代码:
+```typescript
+// WebSocket实时通信
+const wsClient = new WebSocketClient(pipelineId, sessionType);
+await wsClient.connect();
+
+wsClient.onMessage((message) => {
+  // 流式消息自动处理
+  setMessages(prev => [...prev, message]);
+});
+
+wsClient.sendMessage(messageChain);
+```
+
+**2. 连接状态管理**
+
+新增功能:
+- ✅ 实时连接状态指示器（绿色/红色圆点）
+- ✅ 连接/断开toast提示
+- ✅ 自动重连逻辑
+- ✅ 心跳保活
+
+**3. 流式支持**
+
+完整的流式消息处理:
+```typescript
+wsClient.onMessage((message) => {
+  if (message.is_final) {
+    // 最终消息
+    finalizeBotMessage(message);
+  } else {
+    // 中间消息块，实时更新UI
+    updateBotMessage(message);
+  }
+});
+```
+
+## API对比
+
+### WebSocket端点
+
+**连接**
+```
+ws://localhost:8000/api/v1/pipelines/<pipeline_uuid>/ws/connect?session_type=<person|group>
+```
+
+**消息格式**
+
+客户端发送:
+```json
+{
+  "type": "message",
+  "message": [
+    {"type": "Plain", "text": "你好"}
+  ]
+}
+```
+
+服务器响应（流式）:
+```json
+{
+  "type": "response",
+  "data": {
+    "id": 1,
+    "role": "assistant",
+    "content": "你好，我是...",
+    "is_final": false,
+    "timestamp": "2025-01-28T..."
+  }
+}
+```
+
+### REST API
+
+| 端点 | 方法 | 说明 |
+|------|------|------|
+| `/api/v1/pipelines/<uuid>/ws/messages/<type>` | GET | 获取消息历史 |
+| `/api/v1/pipelines/<uuid>/ws/reset/<type>` | POST | 重置会话 |
+| `/api/v1/pipelines/<uuid>/ws/connections` | GET | 获取连接统计 |
+| `/api/v1/pipelines/<uuid>/ws/broadcast` | POST | 广播消息 |
+
+## 流式支持详解
+
+### 后端流式实现
+
+**WebSocket Adapter**
+```python
+async def reply_message_chunk(
+    self,
+    message_source: platform_events.MessageEvent,
+    bot_message,
+    message: platform_message.MessageChain,
+    quote_origin: bool = False,
+    is_final: bool = False,
+) -> dict:
+    """回复消息块 - 流式"""
+    message_data = WebSocketMessage(
+        id=-1,
+        role='assistant',
+        content=str(message),
+        message_chain=[component.__dict__ for component in message],
+        timestamp=datetime.now().isoformat(),
+        is_final=is_final and bot_message.tool_calls is None,
+    )
+
+    # 发送到队列，由WebSocket连接处理发送
+    await session.resp_queues[message_id].put(message_data)
+    return message_data.model_dump()
+
+async def is_stream_output_supported(self) -> bool:
+    """WebSocket始终支持流式输出"""
+    return True
+```
+
+### 前端流式处理
+
+**DebugDialog组件**
+```typescript
+wsClient.onMessage((message) => {
+  setMessages((prevMessages) => {
+    const existingIndex = prevMessages.findIndex(
+      (msg) => msg.role === 'assistant' && msg.content === 'Generating...'
+    );
+
+    if (existingIndex !== -1) {
+      // 更新正在生成的消息
+      const updatedMessages = [...prevMessages];
+      updatedMessages[existingIndex] = message;
+      return updatedMessages;
+    } else {
+      // 添加新消息
+      return [...prevMessages, message];
+    }
+  });
+});
+```
+
+## 兼容性说明
+
+### ⚠️ 不兼容旧版本
+
+此次迁移**完全不兼容**旧的WebChat系统：
+
+1. **API端点变更**
+   - 旧: `/api/v1/pipelines/<uuid>/chat/send`
+   - 新: `ws://.../<uuid>/ws/connect`
+
+2. **通信协议变更**
+   - 旧: HTTP + SSE (Server-Sent Events)
+   - 新: WebSocket (双向)
+
+3. **流式实现变更**
+   - 旧: `text/event-stream` 格式
+   - 新: WebSocket JSON消息
+
+### 迁移要求
+
+使用新系统需要:
+1. ✅ 前端必须支持WebSocket
+2. ✅ 后端必须运行新的WebSocket适配器
+3. ✅ 清除旧的WebChat相关配置
+
+## 优势对比
+
+| 特性 | 旧WebChat (SSE) | 新WebSocket |
+|------|----------------|-------------|
+| 双向通信 | ❌ 单向（服务器→客户端） | ✅ 双向 |
+| 主动推送 | ❌ 不支持 | ✅ 支持 |
+| 连接管理 | ❌ 无状态 | ✅ 有状态，完整生命周期 |
+| 流式输出 | ✅ 支持 | ✅ 支持（更优） |
+| 心跳机制 | ❌ 无 | ✅ 30秒心跳 |
+| 自动重连 | ❌ 无 | ✅ 最多5次 |
+| 多连接 | ⚠️ 难以管理 | ✅ 完整支持 |
+| 连接状态 | ❌ 不可见 | ✅ 实时显示 |
+| 广播功能 | ❌ 不支持 | ✅ 支持 |
+
+## 测试方式
+
+### 1. Python测试客户端
+
+```bash
+# 单连接测试
+python test_websocket_client.py <pipeline_uuid>
+
+# 指定会话类型
+python test_websocket_client.py <pipeline_uuid> --session-type group
+
+# 多连接并发测试（5个连接）
+python test_websocket_client.py <pipeline_uuid> --multi 5
+```
+
+### 2. 前端测试
+
+1. 启动LangBot服务器
+2. 访问前端界面
+3. 打开流水线调试对话框
+4. 观察连接状态指示器（左下角圆点）
+5. 发送消息测试流式响应
+
+### 3. 浏览器控制台测试
+
+```javascript
+const ws = new WebSocket('ws://localhost:8000/api/v1/pipelines/<uuid>/ws/connect?session_type=person');
+
+ws.onopen = () => {
+  console.log('已连接');
+  ws.send(JSON.stringify({
+    type: 'message',
+    message: [{type: 'Plain', text: '你好'}]
+  }));
+};
+
+ws.onmessage = (event) => {
+  console.log('收到:', JSON.parse(event.data));
+};
+```
+
+## 常见问题
+
+### Q: 为什么完全删除旧代码而不保留兼容性？
+A: 根据需求，不需要考虑任何对老版本的兼容性，彻底迁移可以避免代码冗余和维护负担。
+
+### Q: 流式输出如何工作？
+A:
+1. 后端通过`reply_message_chunk`发送消息块
+2. 消息块放入队列
+3. WebSocket连接从队列取出并发送
+4. 前端实时更新UI
+5. `is_final=true`表示最后一块
+
+### Q: 如何确保连接不断开？
+A:
+1. 客户端每30秒发送心跳（ping）
+2. 服务器响应pong
+3. 连接断开时自动重连（最多5次）
+
+### Q: 如何实现后端主动推送？
+A:
+1. 调用 `/api/v1/pipelines/<uuid>/ws/broadcast` API
+2. 消息会被推送到该流水线的所有连接
+3. 前端通过`onBroadcast`回调接收
+
+## 总结
+
+✅ **完成的工作**
+- 完全移除旧的WebChat/SSE系统
+- 实现完整的WebSocket双向通信系统
+- 支持流式输出
+- 支持多连接并发
+- 实现自动重连和心跳机制
+- 提供完整的测试工具和文档
+
+✅ **核心特性**
+- 双向实时通信
+- 流式消息支持
+- 多连接管理
+- 自动重连
+- 心跳保活
+- 连接状态可视化
+- 广播消息
+
+✅ **技术亮点**
+- 异步架构（asyncio）
+- 线程安全的连接管理
+- 独立的消息队列
+- 完整的错误处理
+- 模块化设计
+
+🎉 系统已完全迁移到WebSocket，无任何旧代码遗留！

docs/PYPI_INSTALLATION.md

@@ -0,0 +1,117 @@
+# LangBot PyPI Package Installation
+
+## Quick Start with uvx
+
+The easiest way to run LangBot is using `uvx` (recommended for quick testing):
+
+```bash
+uvx langbot
+```
+
+This will automatically download and run the latest version of LangBot.
+
+## Install with pip/uv
+
+You can also install LangBot as a regular Python package:
+
+```bash
+# Using pip
+pip install langbot
+
+# Using uv
+uv pip install langbot
+```
+
+Then run it:
+
+```bash
+langbot
+```
+
+Or using Python module syntax:
+
+```bash
+python -m langbot
+```
+
+## Installation with Frontend
+
+When published to PyPI, the LangBot package includes the pre-built frontend files. You don't need to build the frontend separately.
+
+## Data Directory
+
+When running LangBot as a package, it will create a `data/` directory in your current working directory to store configuration, logs, and other runtime data. You can run LangBot from any directory, and it will set up its data directory there.
+
+## Command Line Options
+
+LangBot supports the following command line options:
+
+- `--standalone-runtime`: Use standalone plugin runtime
+- `--debug`: Enable debug mode
+
+Example:
+
+```bash
+langbot --debug
+```
+
+## Comparison with Other Installation Methods
+
+### PyPI Package (uvx/pip)
+- **Pros**: Easy to install and update, no need to clone repository or build frontend
+- **Cons**: Less flexible for development/customization
+
+### Docker
+- **Pros**: Isolated environment, easy deployment
+- **Cons**: Requires Docker
+
+### Manual Source Installation
+- **Pros**: Full control, easy to customize and develop
+- **Cons**: Requires building frontend, managing dependencies manually
+
+## Development
+
+If you want to contribute or customize LangBot, you should still use the manual installation method by cloning the repository:
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot
+uv sync
+cd web
+npm install
+npm run build
+cd ..
+uv run main.py
+```
+
+## Updating
+
+To update to the latest version:
+
+```bash
+# With pip
+pip install --upgrade langbot
+
+# With uv
+uv pip install --upgrade langbot
+
+# With uvx (automatically uses latest)
+uvx langbot
+```
+
+## System Requirements
+
+- Python 3.10.1 or higher
+- Operating System: Linux, macOS, or Windows
+
+## Differences from Source Installation
+
+When running LangBot from the PyPI package (via uvx or pip), there are a few behavioral differences compared to running from source:
+
+1. **Version Check**: The package version does not prompt for user input when the Python version is incompatible. It simply prints an error message and exits. This makes it compatible with non-interactive environments like containers and CI/CD.
+
+2. **Working Directory**: The package version does not require being run from the LangBot project root. You can run `langbot` from any directory, and it will create a `data/` directory in your current working directory.
+
+3. **Frontend Files**: The frontend is pre-built and included in the package, so you don't need to run `npm build` separately.
+
+These differences are intentional to make the package more user-friendly and suitable for various deployment scenarios.

docs/SEEKDB_INTEGRATION.md

@@ -0,0 +1,259 @@
+# SeekDB Vector Database Integration
+
+This document describes how to use OceanBase SeekDB as the vector database backend for LangBot's knowledge base feature.
+
+## What is SeekDB?
+
+**OceanBase SeekDB** is an AI-native search database that unifies relational, vector, text, JSON and GIS in a single engine, enabling hybrid search and in-database AI workflows. It's developed by OceanBase and released under Apache 2.0 license.
+
+### Key Features
+
+- **Hybrid Search**: Combine vector search, full-text search and relational query in a single statement
+- **Multi-Model Support**: Support relational, vector, text, JSON and GIS in a single engine
+- **Lightweight**: Requires as little as 1 CPU core and 2 GB of memory
+- **Multiple Deployment Modes**: Supports both embedded mode and client/server mode
+- **MySQL Compatible**: Powered by OceanBase engine with full ACID compliance and MySQL compatibility
+
+## Installation
+
+SeekDB support is automatically included when you install LangBot. The required dependency `pyseekdb` is listed in `pyproject.toml`.
+
+If you need to install it manually:
+
+```bash
+pip install pyseekdb
+```
+
+## ⚠️ Platform Compatibility
+
+### Embedded Mode
+
+| Platform | Status | Notes |
+|----------|--------|-------|
+| Linux | ✅ Supported | Full embedded mode support via `pylibseekdb` |
+| macOS | ❌ Not Supported | `pylibseekdb` is Linux-only; use server mode instead |
+| Windows | ❌ Not Supported | `pylibseekdb` is Linux-only; use server mode instead |
+
+**Important**: Embedded mode requires the `pylibseekdb` library, which is only available on Linux. If you're on macOS or Windows, you must use server mode.
+
+### Server Mode (Docker)
+
+| Platform | Status | Notes |
+|----------|--------|-------|
+| Linux | ✅ Supported | Full Docker support |
+| macOS | ⚠️ Known Issue | Docker container initialization failure - [See Issue #36](https://github.com/oceanbase/seekdb/issues/36) |
+| Windows | ⚠️ Untested | Should work but not yet tested |
+
+**macOS Users**: Currently, SeekDB Docker containers have an initialization issue on macOS ([oceanbase/seekdb#36](https://github.com/oceanbase/seekdb/issues/36)). Until this is resolved, we recommend:
+- Using ChromaDB or Qdrant as alternatives
+- Connecting to a remote SeekDB server on Linux if available
+
+### Server Mode (Remote Connection)
+
+| Platform | Status | Notes |
+|----------|--------|-------|
+| All Platforms | ✅ Supported | Connect to SeekDB running on a remote Linux server |
+
+**Recommendation for macOS/Windows users**: Deploy SeekDB on a Linux server and connect via server mode configuration.
+
+## Configuration
+
+### Embedded Mode (Recommended for Development)
+
+Embedded mode runs SeekDB directly within the LangBot process, storing data locally. This is the simplest setup and requires no external services.
+
+Edit your `config.yaml`:
+
+```yaml
+vdb:
+  use: seekdb
+  seekdb:
+    mode: embedded
+    path: './data/seekdb'  # Path to store SeekDB data
+    database: 'langbot'    # Database name
+```
+
+### Server Mode (For Production)
+
+Server mode connects to a remote SeekDB server or OceanBase server. This is recommended for production deployments.
+
+#### SeekDB Server
+
+```yaml
+vdb:
+  use: seekdb
+  seekdb:
+    mode: server
+    host: 'localhost'
+    port: 2881
+    database: 'langbot'
+    user: 'root'
+    password: ''  # Can also use SEEKDB_PASSWORD env var
+```
+
+#### OceanBase Server
+
+If you're using OceanBase with seekdb capabilities:
+
+```yaml
+vdb:
+  use: seekdb
+  seekdb:
+    mode: server
+    host: 'localhost'
+    port: 2881
+    tenant: 'sys'        # OceanBase tenant name
+    database: 'langbot'
+    user: 'root'
+    password: ''
+```
+
+## Configuration Parameters
+
+| Parameter  | Required | Default      | Description |
+|-----------|----------|--------------|-------------|
+| `mode`    | No       | `embedded`   | Deployment mode: `embedded` or `server` |
+| `path`    | No       | `./data/seekdb` | Data directory for embedded mode |
+| `database` | No      | `langbot`    | Database name |
+| `host`    | No       | `localhost`  | Server host (server mode only) |
+| `port`    | No       | `2881`       | Server port (server mode only) |
+| `user`    | No       | `root`       | Username (server mode only) |
+| `password` | No      | `''`         | Password (server mode only) |
+| `tenant`  | No       | None         | OceanBase tenant (optional, server mode only) |
+
+## Usage
+
+Once configured, SeekDB will be used automatically for all knowledge base operations in LangBot:
+
+1. **Creating Knowledge Bases**: Vectors will be stored in SeekDB collections
+2. **Adding Documents**: Document embeddings will be indexed in SeekDB
+3. **Searching**: Vector similarity search will use SeekDB's efficient indexing
+4. **Deleting**: Document removal will delete vectors from SeekDB
+
+No code changes are required - just update your configuration!
+
+## Architecture Details
+
+### Implementation
+
+The SeekDB adapter is implemented in `src/langbot/pkg/vector/vdbs/seekdb.py` and follows the same `VectorDatabase` interface as Chroma and Qdrant adapters.
+
+Key methods:
+- `add_embeddings()`: Add vectors with metadata to a collection
+- `search()`: Perform vector similarity search
+- `delete_by_file_id()`: Delete vectors by file ID metadata
+- `get_or_create_collection()`: Manage collections
+- `delete_collection()`: Remove entire collections
+
+### Vector Storage
+
+- Collections are created with HNSW (Hierarchical Navigable Small World) index
+- Default distance metric: Cosine similarity
+- Default vector dimension: 384 (adjusts automatically based on embeddings)
+- Metadata is stored alongside vectors for filtering
+
+## Advantages Over Other Vector Databases
+
+### vs. ChromaDB
+- ✅ Better MySQL compatibility
+- ✅ Hybrid search capabilities (vector + full-text + SQL)
+- ✅ Production-grade distributed mode support
+- ✅ Lightweight embedded mode
+
+### vs. Qdrant
+- ✅ SQL query support
+- ✅ MySQL ecosystem integration
+- ✅ Simpler deployment (no Docker required for embedded mode)
+- ✅ Multi-model data support (not just vectors)
+
+## Troubleshooting
+
+### Import Error
+
+If you see: `ImportError: pyseekdb is not installed`
+
+Solution:
+```bash
+pip install pyseekdb
+```
+
+### Embedded Mode Error on macOS/Windows
+
+**Error**:
+```
+RuntimeError: Embedded Client is not available because pylibseekdb is not available.
+Please install pylibseekdb (Linux only) or use RemoteServerClient (host/port) instead.
+```
+
+**Cause**: `pylibseekdb` is only available on Linux platforms.
+
+**Solution**: Use server mode instead:
+1. Deploy SeekDB on a Linux server or VM
+2. Configure LangBot to use server mode:
+```yaml
+vdb:
+  use: seekdb
+  seekdb:
+    mode: server
+    host: 'your-seekdb-server-ip'
+    port: 2881
+    database: 'langbot'
+    user: 'root'
+    password: ''
+```
+
+**Alternative**: Use ChromaDB or Qdrant, which work on all platforms:
+```yaml
+vdb:
+  use: chroma  # or qdrant
+```
+
+### Docker Container Fails on macOS
+
+**Symptoms**:
+```bash
+docker run -d -p 2881:2881 oceanbase/seekdb:latest
+# Container exits immediately with code 30
+```
+
+**Error in logs**:
+```
+[ERROR] Code: Agent.SeekDB.Not.Exists
+Message: initialize failed: init agent failed: SeekDB not exists in current directory.
+```
+
+**Cause**: This is a known issue with SeekDB Docker containers on macOS. See [oceanbase/seekdb#36](https://github.com/oceanbase/seekdb/issues/36).
+
+**Status**: Under investigation by OceanBase team.
+
+**Workaround Options**:
+1. **Use alternatives**: ChromaDB or Qdrant work perfectly on macOS
+2. **Remote server**: Deploy SeekDB on a Linux server and connect remotely
+3. **Wait for fix**: Monitor the GitHub issue for updates
+
+### Connection Error (Server Mode)
+
+If SeekDB server is not reachable, check:
+1. Server is running: `ps aux | grep observer`
+2. Port is accessible: `nc -zv localhost 2881`
+3. Credentials are correct in config
+4. Firewall allows connections on port 2881
+
+### Performance Issues
+
+For large datasets:
+- Use server mode instead of embedded mode
+- Ensure adequate memory allocation
+- Consider using OceanBase distributed mode for very large scale
+- Adjust HNSW index parameters if needed
+
+## Resources
+
+- SeekDB GitHub: https://github.com/oceanbase/seekdb
+- pyseekdb SDK: https://github.com/oceanbase/pyseekdb
+- OceanBase Documentation: https://oceanbase.ai
+- LangBot Documentation: https://docs.langbot.app
+
+## License
+
+SeekDB is licensed under Apache License 2.0.

docs/WEBSOCKET_README.md

@@ -0,0 +1,394 @@
+# LangBot WebSocket 双向通信系统
+
+## 概述
+
+这是一个内置在 LangBot 中的完整 IM (即时通讯) 系统，支持：
+
+- ✅ WebSocket 双向实时通信
+- ✅ 多个客户端并发连接
+- ✅ 前端到后端的消息发送
+- ✅ 后端到前端的主动推送
+- ✅ 流式响应支持
+- ✅ 连接管理和会话隔离
+- ✅ 心跳机制
+- ✅ 广播消息功能
+
+## 架构设计
+
+### 核心组件
+
+1. **WebSocketConnectionManager** (`websocket_manager.py`)
+   - 管理所有活跃的 WebSocket 连接
+   - 支持按流水线、会话类型查询连接
+   - 提供广播和单播功能
+   - 线程安全的并发访问控制
+
+2. **WebSocketAdapter** (`websocket_adapter.py`)
+   - 实现平台适配器接口
+   - 处理消息的接收和发送
+   - 支持流式输出
+   - 管理消息历史
+
+3. **WebSocketChatRouterGroup** (`websocket_chat.py`)
+   - WebSocket 路由控制器
+   - 处理连接建立、消息收发
+   - 实现心跳机制
+   - 提供 REST API 接口
+
+## API 接口
+
+### WebSocket 连接
+
+#### 建立连接
+
+```
+ws://localhost:8000/api/v1/pipelines/<pipeline_uuid>/ws/connect?session_type=<person|group>
+```
+
+**参数:**
+- `pipeline_uuid`: 流水线 UUID (必需)
+- `session_type`: 会话类型，可选 `person` 或 `group` (默认: `person`)
+
+**连接成功响应:**
+```json
+{
+  "type": "connected",
+  "connection_id": "550e8400-e29b-41d4-a716-446655440000",
+  "pipeline_uuid": "your-pipeline-uuid",
+  "session_type": "person",
+  "timestamp": "2025-01-28T12:00:00"
+}
+```
+
+### 消息格式
+
+#### 客户端发送消息
+
+**发送聊天消息:**
+```json
+{
+  "type": "message",
+  "message": [
+    {
+      "type": "Plain",
+      "text": "你好，这是一条测试消息"
+    }
+  ]
+}
+```
+
+**发送心跳:**
+```json
+{
+  "type": "ping"
+}
+```
+
+**主动断开连接:**
+```json
+{
+  "type": "disconnect"
+}
+```
+
+#### 服务器响应消息
+
+**聊天响应 (流式):**
+```json
+{
+  "type": "response",
+  "data": {
+    "id": 1,
+    "role": "assistant",
+    "content": "这是机器人的回复",
+    "message_chain": [...],
+    "timestamp": "2025-01-28T12:00:00",
+    "is_final": false,
+    "connection_id": "..."
+  }
+}
+```
+
+**心跳响应:**
+```json
+{
+  "type": "pong",
+  "timestamp": "2025-01-28T12:00:00"
+}
+```
+
+**广播消息:**
+```json
+{
+  "type": "broadcast",
+  "message": "这是一条广播消息",
+  "timestamp": "2025-01-28T12:00:00"
+}
+```
+
+**错误消息:**
+```json
+{
+  "type": "error",
+  "message": "错误描述"
+}
+```
+
+### REST API 接口
+
+#### 1. 获取消息历史
+
+```http
+GET /api/v1/pipelines/<pipeline_uuid>/ws/messages/<session_type>
+```
+
+**响应:**
+```json
+{
+  "code": 0,
+  "msg": "ok",
+  "data": {
+    "messages": [...]
+  }
+}
+```
+
+#### 2. 重置会话
+
+```http
+POST /api/v1/pipelines/<pipeline_uuid>/ws/reset/<session_type>
+```
+
+**响应:**
+```json
+{
+  "code": 0,
+  "msg": "ok",
+  "data": {
+    "message": "Session reset successfully"
+  }
+}
+```
+
+#### 3. 获取连接统计
+
+```http
+GET /api/v1/pipelines/<pipeline_uuid>/ws/connections
+```
+
+**响应:**
+```json
+{
+  "code": 0,
+  "msg": "ok",
+  "data": {
+    "stats": {
+      "total_connections": 5,
+      "pipelines": 2,
+      "connections_by_pipeline": {
+        "pipeline-1": 3,
+        "pipeline-2": 2
+      },
+      "connections_by_session_type": {
+        "person": 4,
+        "group": 1
+      }
+    },
+    "connections": [
+      {
+        "connection_id": "...",
+        "session_type": "person",
+        "created_at": "2025-01-28T12:00:00",
+        "last_active": "2025-01-28T12:05:00",
+        "is_active": true
+      }
+    ]
+  }
+}
+```
+
+#### 4. 广播消息 (后端主动推送)
+
+```http
+POST /api/v1/pipelines/<pipeline_uuid>/ws/broadcast
+Content-Type: application/json
+
+{
+  "message": "这是一条广播消息"
+}
+```
+
+**响应:**
+```json
+{
+  "code": 0,
+  "msg": "ok",
+  "data": {
+    "message": "Broadcast sent successfully"
+  }
+}
+```
+
+## 使用示例
+
+### Python 客户端示例
+
+使用提供的测试客户端：
+
+```bash
+# 安装依赖
+pip install websockets
+
+# 单个连接测试
+python test_websocket_client.py <pipeline_uuid>
+
+# 指定会话类型
+python test_websocket_client.py <pipeline_uuid> --session-type group
+
+# 多连接并发测试
+python test_websocket_client.py <pipeline_uuid> --multi 5
+```
+
+### JavaScript 客户端示例
+
+```javascript
+// 建立 WebSocket 连接
+const ws = new WebSocket('ws://localhost:8000/api/v1/pipelines/your-pipeline-uuid/ws/connect?session_type=person');
+
+// 连接建立
+ws.onopen = () => {
+  console.log('WebSocket 连接已建立');
+
+  // 发送消息
+  ws.send(JSON.stringify({
+    type: 'message',
+    message: [
+      {
+        type: 'Plain',
+        text: '你好'
+      }
+    ]
+  }));
+};
+
+// 接收消息
+ws.onmessage = (event) => {
+  const data = JSON.parse(event.data);
+
+  if (data.type === 'connected') {
+    console.log('连接成功:', data.connection_id);
+  } else if (data.type === 'response') {
+    console.log('机器人回复:', data.data.content);
+    if (data.data.is_final) {
+      console.log('响应完成');
+    }
+  } else if (data.type === 'broadcast') {
+    console.log('收到广播:', data.message);
+  }
+};
+
+// 连接关闭
+ws.onclose = () => {
+  console.log('WebSocket 连接已关闭');
+};
+
+// 错误处理
+ws.onerror = (error) => {
+  console.error('WebSocket 错误:', error);
+};
+
+// 发送心跳
+setInterval(() => {
+  if (ws.readyState === WebSocket.OPEN) {
+    ws.send(JSON.stringify({ type: 'ping' }));
+  }
+}, 30000); // 每 30 秒发送一次心跳
+```
+
+## 特性说明
+
+### 1. 多连接支持
+
+系统支持同时建立多个 WebSocket 连接，每个连接都有唯一的 `connection_id`。连接按照流水线和会话类型进行分组管理。
+
+### 2. 双向通信
+
+- **前端 → 后端**: 客户端可以主动发送消息给服务器
+- **后端 → 前端**: 服务器可以通过广播 API 主动推送消息给客户端
+
+### 3. 流式响应
+
+支持流式输出，机器人的响应会分块发送，客户端可以实时显示部分响应内容。
+
+### 4. 会话隔离
+
+支持 `person` 和 `group` 两种会话类型，不同类型的会话消息历史互不影响。
+
+### 5. 连接管理
+
+- 自动追踪连接状态
+- 记录最后活跃时间
+- 支持连接统计查询
+- 连接断开时自动清理资源
+
+### 6. 心跳机制
+
+客户端可以定期发送 `ping` 消息，服务器会响应 `pong`，用于保持连接活跃和检测连接状态。
+
+## 架构优势
+
+1. **高并发**: 使用 asyncio 异步架构，支持大量并发连接
+2. **可扩展**: 模块化设计，易于扩展新功能
+3. **线程安全**: 连接管理器使用锁机制保证并发安全
+4. **消息队列**: 每个连接独立的发送队列，避免消息混乱
+5. **灵活路由**: 支持按流水线、会话类型灵活路由消息
+
+## 注意事项
+
+1. **认证**: 当前 WebSocket 连接不需要认证，生产环境建议添加认证机制
+2. **心跳**: 建议客户端实现心跳机制，避免连接超时
+3. **重连**: 客户端应实现断线重连逻辑
+4. **消息大小**: 注意控制单条消息大小，避免内存溢出
+5. **连接数限制**: 生产环境建议设置最大连接数限制
+
+## 故障排查
+
+### 连接失败
+
+1. 检查流水线 UUID 是否正确
+2. 检查服务器是否正常运行
+3. 检查防火墙设置
+
+### 消息发送失败
+
+1. 检查消息格式是否正确
+2. 检查连接是否仍然活跃
+3. 查看服务器日志获取详细错误信息
+
+### 性能问题
+
+1. 检查并发连接数是否过多
+2. 检查消息处理速度
+3. 考虑使用连接池或负载均衡
+
+## 开发调试
+
+启用详细日志：
+
+```python
+import logging
+logging.getLogger('langbot.pkg.platform.sources.websocket_adapter').setLevel(logging.DEBUG)
+logging.getLogger('langbot.pkg.platform.sources.websocket_manager').setLevel(logging.DEBUG)
+logging.getLogger('langbot.pkg.api.http.controller.groups.pipelines.websocket_chat').setLevel(logging.DEBUG)
+```
+
+## 后续改进建议
+
+1. 添加用户认证和授权机制
+2. 实现消息持久化
+3. 添加消息加密
+4. 实现更丰富的消息类型 (图片、文件等)
+5. 添加消息已读/未读状态
+6. 实现群组聊天功能
+7. 添加在线状态显示
+8. 实现消息撤回功能

docs/review/box-architecture.md

@@ -0,0 +1,594 @@
+# Box 系统架构深度分析
+
+> 更新日期: 2026-05-19
+> 分支: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+> 相关文档: [问题清单](./box-issues.md) | [Session 作用域](./box-session-scope.md) | [Runtime 对比](./box-vs-plugin-runtime.md) | [测试覆盖](./box-test-coverage.md) | [toB 分析](./box-tob-analysis.md)
+
+---
+
+## 1. 全局架构
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│                       LangBot 主进程                              │
+│                                                                   │
+│  LocalAgentRunner ──> ToolManager ──> NativeToolLoader            │
+│       │                    │              │                       │
+│       │                    │      exec / read / write / edit      │
+│       │                    │              glob / grep             │
+│       │                    │                                      │
+│       │                    ├──> MCPLoader ──> BoxStdioSession     │
+│       │                    │       (shared 容器, 多 process)       │
+│       │                    │                                      │
+│       │                    ├──> SkillToolLoader (activate 工具)    │
+│       │                    │                                      │
+│       │                    ├──> SkillAuthoringToolLoader          │
+│       │                    │                                      │
+│       │                    └──> PluginToolLoader                  │
+│       │                                                           │
+│  BoxService (门面)                                                 │
+│    ├─ Profile 管理 (locked 字段)                                   │
+│    ├─ Host mount 校验 (allowed_mount_roots)                        │
+│    ├─ Workspace quota 检查                                         │
+│    ├─ 输出截断 (head+tail)                                         │
+│    ├─ Session ID 模板解析 (resolve_box_session_id)                 │
+│    ├─ 技能挂载组装 (build_skill_extra_mounts)                      │
+│    ├─ 重连循环 (_reconnect_loop, 指数退避)                          │
+│    └─ BoxRuntimeConnector                                          │
+│         ├─ 心跳 loop (20s ping)                                    │
+│         └─ ActionRPCBoxClient                                      │
+│              │  Action RPC (stdio 或 WebSocket)                    │
+│                                                                    │
+│  SkillManager (skill_mgr)                                          │
+│    └─ 从 Box runtime 拉取 skills, 不可用时回落 data/skills          │
+└──────────────────────────────────────────────────────────────────┘
+               │
+               ▼
+┌──────────────────────────────────────────────────────────────────┐
+│              Box Runtime 进程 (SDK 侧)                            │
+│                                                                   │
+│  BoxServerHandler (Action RPC 处理, INIT 配置注入)                  │
+│       │                                                           │
+│  BoxRuntime (session 管理 / 进程生命周期 / TTL reaper)              │
+│       │       └─ session.managed_processes: dict[pid, _ManagedProcess]
+│       │                                                           │
+│  Backend (启动时根据 box.backend 配置选择):                          │
+│    DockerBackend ──┐                                              │
+│    PodmanBackend ──┤── CLISandboxBackend                          │
+│    NsjailBackend ──┘  (本地 CLI 或 fallback 到容器内 CLI)            │
+│    E2BBackend         (云沙箱, 需要 E2B_API_KEY)                    │
+│                                                                   │
+│  BoxSkillStore                                                    │
+│    ├─ list / get / create / update / delete                       │
+│    ├─ scan_skill_directory / read_skill_file / write_skill_file   │
+│    └─ preview_skill_zip / install_skill_zip (zip 或 GitHub)        │
+│                                                                   │
+│  aiohttp 单端口服务 (默认 :5410):                                    │
+│    /rpc/ws                                       — Action RPC      │
+│    /v1/sessions/{id}/managed-process/ws          — 默认 process     │
+│    /v1/sessions/{id}/managed-process/{pid}/ws    — 指定 process     │
+└──────────────────────────────────────────────────────────────────┘
+               │
+               ▼
+┌──────────────────────────────────────────────────────────────────┐
+│  容器 / 沙箱 (Docker/Podman 容器, nsjail sandbox, 或 E2B 远程沙箱)  │
+│  - 隔离文件系统 / 网络 / PID 命名空间                                │
+│  - 资源限制 (CPU, 内存, PID 数, 可选 workspace 配额)                 │
+│  - 主挂载 (host_path → mount_path) + 任意条 extra_mounts             │
+│      └─ Skills 通过 extra_mounts 挂在 /workspace/.skills/<name>     │
+│  - exec: 用户命令在此执行                                            │
+│  - managed process: 多个长驻进程并存 (MCP Server / 自定义服务)        │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+**核心设计原则**:
+- Box Runtime 作为独立进程运行，通过 Action RPC 与 LangBot 主进程通信，两者复用 SDK 的 IO 层（Handler → Connection → Controller）
+- 一个 session_id 对应一个容器/沙箱实例。同一 session 内可并存多条 mount 与多个 managed process
+- Skill / 默认 exec / MCP Server 共享同一个 session 容器（详见 [box-session-scope.md](./box-session-scope.md)）
+
+---
+
+## 2. LangBot 侧模块
+
+### 2.1 BoxService (`pkg/box/service.py`, 722 行)
+
+应用层门面，协调 Profile、安全校验、配额、连接、Skill 挂载与 Session 模板：
+
+主要公开方法（按定义顺序）：
+
+```
+BoxService
+  ├─ initialize()                              连接 Box Runtime + 默认 workspace 准备
+  ├─ _on_runtime_disconnect(connector)         触发重连
+  ├─ _reconnect_loop(connector)                指数退避重连
+  ├─ available (property)                      连接状态
+  │
+  ├─ resolve_box_session_id(query)             从 pipeline 模板解析 session_id
+  ├─ build_skill_extra_mounts(query)           组装 pipeline-bound skill 的挂载列表
+  │
+  ├─ execute_tool(parameters, query)           Agent 调用 exec 时的入口
+  │    ├─ _apply_profile / build_spec
+  │    ├─ _validate_host_mount
+  │    ├─ _enforce_workspace_quota (phase=pre)
+  │    ├─ client.execute(spec)
+  │    ├─ _enforce_workspace_quota (phase=post)
+  │    └─ _truncate (stdout/stderr)
+  │
+  ├─ execute_spec_payload(spec_payload, ...)   内部入口（其他 loader 调用）
+  ├─ create_session(spec_payload, ...)         显式创建 session
+  ├─ start_managed_process(session_id, ...)    启动 managed process
+  ├─ get_managed_process(session_id, pid)      查询进程状态（pid 默认 'default'）
+  ├─ stop_managed_process(session_id, pid)     单独停止某个 managed process
+  ├─ get_managed_process_websocket_url(...)    返回 WS attach URL
+  │
+  ├─ list_skills() / get_skill(name)           Skill 元数据
+  ├─ create_skill / update_skill / delete_skill  Skill CRUD
+  ├─ scan_skill_directory(path)                扫描目录
+  ├─ list_skill_files / read_skill_file / write_skill_file
+  ├─ preview_skill_zip / install_skill_zip     zip / GitHub 安装
+  │
+  ├─ shutdown() / dispose()                    清理：RPC SHUTDOWN + 进程终止
+  ├─ get_status() / get_sessions() / get_recent_errors()
+  └─ get_system_guidance()                     LLM 系统提示
+```
+
+**Profile 系统**: 4 个内置 Profile（`default` / `offline_readonly` / `network_basic` / `network_extended`），`locked` frozenset 字段不可被 LLM 覆盖。参数合并顺序：Profile defaults → LLM 请求参数 → locked 强制值。
+
+**输出截断**: 默认 4000 字符上限，保留前 60% + 后 40%，中间插入 `[...truncated...]`。
+
+**Skill 挂载合并**: `execute_tool()` 调用时，`build_skill_extra_mounts(query)` 会把当前 pipeline-bound 的所有 skill 的 `package_root` 作为 `extra_mounts` 加入 BoxSpec，挂在 `/workspace/.skills/<name>`。LLM 通过 `activate` 工具显式激活某个 skill 后，工具调用才允许引用这个 skill 的虚拟路径。
+
+### 2.2 BoxRuntimeConnector (`pkg/box/connector.py`, 357 行)
+
+管理与 Box Runtime 的通信连接：
+
+- **本地 stdio**: Unix/macOS 默认路径，fork `python -m langbot_plugin.cli.__init__ box -s --ws-control-port {port}` 子进程（与 plugin runtime 统一走 `lbp` CLI 入口）
+- **本地 subprocess + WS**: Windows 本地（asyncio ProactorEventLoop 不支持 stdio pipe）
+- **远程 WebSocket**: Docker 部署 / `box.runtime.endpoint` 显式配置时，连接 `ws://{host}:{port}/rpc/ws`
+- **同步等待**: `asyncio.Event` + `wait_for(timeout=30s)` 模式确认连接
+- **心跳**: `_heartbeat_loop()` 每 20s 调用 `ping()`，失败仅 DEBUG 日志（断开检测靠 connection close）
+- **重连**: `runtime_disconnect_callback` 由 BoxService 提供，触发 `_reconnect_loop`
+- **INIT 注入**: 连接建立后立即下发当前 `box.*` 配置子树（剔除 `runtime` 私有字段），Runtime 据此初始化 backend
+
+> **历史改进**: 2026-04-16 版本本文档曾列 P0 「Box 无心跳 / 无重连」，已修复（commit `2dfd9d5d`、`c6882cf`、`5029d9c` 等）。
+
+### 2.3 BoxWorkspaceSession 工具 (`pkg/box/workspace.py`, 413 行)
+
+此文件目前提供两类能力：
+
+1. **路径与命令重写工具函数** — `normalize_host_path` / `rewrite_mounted_path` / `unwrap_venv_path` / `rewrite_venv_command` / `infer_workspace_host_path`，被 MCP loader 与 Skill 路径解析共用。
+2. **`BoxWorkspaceSession`** — 围绕 BoxService 的轻量包装，专供 MCP-in-Box 场景使用（管理一个共享 session 的 session_id、构建挂载 payload、stage host 文件到共享 workspace）。
+
+**变化点**: 早期 Skill exec 会为每个 skill 创建独立 BoxWorkspaceSession（独占 session）；当前实现已转为 `extra_mounts` 模式，Skill 不再独占容器，只追加挂载。这部分 wrapping 逻辑已从 native loader 移除。
+
+### 2.4 policy.py (`pkg/box/policy.py`, 98 行) — 仍是死代码
+
+三层安全策略设计（`SandboxPolicy` / `ToolPolicy` / `ElevatedPolicy`），全项目无任何导入或调用。详见 [问题清单 #1](./box-issues.md)。
+
+### 2.5 SkillManager (`pkg/skill/manager.py`, 186 行)
+
+```
+SkillManager
+  ├─ initialize()                  调用 reload_skills()
+  ├─ reload_skills()               先从 Box runtime list_skills()，
+  │                                 不可用则回落 data/skills/ 扫描
+  ├─ refresh_skill_from_disk()     单 skill 重新加载
+  ├─ get_skill_by_name(name)
+  └─ get_managed_skills_root()     返回 Box 视角的 skills_root 路径
+```
+
+skill 元数据通过 `parse_frontmatter` 解析 `SKILL.md` 头部（`name` / `description` / `instructions`），不再做整体扫描的代价（典型 < 50 个）。
+
+### 2.6 Skill activation (`pkg/skill/activation.py`, 33 行) + Skill loader 辅助
+
+历史上 skill 通过 LLM 在文本中输出 `[ACTIVATE_SKILL:name]` 标记激活；当前已改为 **Tool Call 机制**：
+
+- `SkillToolLoader` (`pkg/provider/tools/loaders/skill.py`, 157 行) 暴露 `activate` 工具，参数为 skill 名
+- 工具实现调用 `register_activated_skill(query, skill_data)`，将激活态写入 `query.variables['_activated_skills']`
+- 这种 KV-cache-friendly 模式对齐 Claude Code 设计；详见 [box-session-scope.md §4.3](./box-session-scope.md) 的 Tool Call 描述
+
+`activation.py` 现仅保留对外辅助函数（pipeline 层调用 loader 的 `register_activated_skill`）。
+
+---
+
+## 3. SDK 侧模块
+
+### 3.1 BoxRuntime (`box/runtime.py`, 599 行)
+
+核心编排器，管理 session 生命周期与 backend 调度：
+
+```
+Session 生命周期:
+
+  Client EXEC / CREATE_SESSION
+       │
+       ▼
+  _get_or_create_session(spec)
+    ├─ _reap_expired_sessions_locked()   清理 TTL 过期 session
+    ├─ 已存在? → _assert_session_compatible() → 复用
+    ├─ Backend session 失踪? → 重建 (commit c6882cf)
+    └─ 新建? → backend.start_session(spec) → 创建容器
+       │       └─ 应用 spec.extra_mounts （多挂载）
+       ▼
+  execute(spec)
+    ├─ 获取 session lock (每 session 独立)
+    ├─ backend.exec(session, spec)       在容器中执行命令
+    ├─ 更新 last_used_at
+    └─ 超时? → 销毁 session
+       │
+       ▼
+  Session 保持存活直到:
+    ├─ TTL 过期 (默认 300s，下次操作时清理)
+    ├─ 执行超时 (自动销毁)
+    ├─ 客户端 DELETE_SESSION
+    └─ SHUTDOWN
+```
+
+**关键设计**:
+- 每 session 有独立 `asyncio.Lock`，同一 session 内的命令串行执行
+- 每 session 维护 `managed_processes: dict[process_id, _ManagedProcess]`，支持多个长驻进程并存（MCP / 自定义）
+- 全局 `_lock` 保护 `_sessions` dict 的读写
+- 兼容性检查：比较核心 spec 字段，`image` 字段对不支持自定义镜像的 backend（nsjail/E2B）会跳过
+
+**Backend 选择 (`_select_backend`)**: 优先级
+1. 显式 `box.backend` 配置（`docker` / `nsjail` / `e2b`）
+2. `local` (默认) → Docker / Podman / nsjail CLI 顺序探测
+3. `get_status` 调用时若当前 backend 不可用，会尝试重新选择 (commit `e5617c7`)
+
+### 3.2 Backend 系统
+
+#### CLISandboxBackend (`box/backend.py`, 411 行)
+
+Docker / Podman 公共基类：
+
+```
+start_session(spec):
+  1. validate_sandbox_security(spec)
+  2. docker/podman run -d --rm --name <name>
+     --network none (可选)
+     --cpus/--memory/--pids-limit
+     --read-only + --tmpfs /tmp
+     -v <host>:<mount>:<mode>          主挂载
+     -v <extra.host>:<extra.mount>:..  额外挂载 (extra_mounts)
+     <image> sh -lc 'while true; do sleep 3600; done'
+  3. 返回 BoxSessionInfo
+
+exec(session, spec):
+  docker/podman exec -e KEY=VAL <container>
+    sh -lc 'mkdir -p <workdir> && cd <workdir> && <cmd>'
+
+start_managed_process(session, spec):
+  docker/podman exec -i <container>
+    sh -lc 'mkdir -p <cwd> && cd <cwd> && exec <command> <args>'
+  返回 asyncio.subprocess.Process (stdin/stdout PIPE)
+```
+
+容器以 idle 进程启动，实际命令通过 `docker exec` 执行。`--rm` 确保容器退出时自动清理。
+
+**Windows 支持**: backend 内对 Windows 路径处理与 subprocess 调用做了适配（commit `120817a`）。
+
+**孤儿清理**: 启动时枚举 `langbot.box=true` 标签的容器，instance_id 不匹配的强制删除。
+
+#### NsjailBackend (`box/nsjail_backend.py`, 552 行)
+
+轻量级 Linux 沙箱（无容器引擎依赖）：
+
+- 使用 namespace 隔离（user/mount/pid/ipc/uts/cgroup/net）
+- 挂载宿主 `/usr`/`/lib`/`/bin`/`/sbin` 只读 + 选定 `/etc` 条目
+- 每 session 创建独立目录（workspace/tmp/home）
+- 资源限制: cgroup v2 优先，fallback 到 rlimit
+- **CLI 兼容**: 通过 `shutil.which(self._nsjail_bin)` 检测系统安装版 nsjail；不存在时再尝试容器内 nsjail（commit `686fcc0`、`feed530`）
+- **无自定义镜像**: 使用宿主 OS，`image` 字段固定为 `'host'`，兼容性检查跳过 image
+
+#### E2BBackend (`box/e2b_backend.py`, 429 行)
+
+云沙箱后端（commit `75b547f` 引入）：
+
+- 通过 `e2b` SDK 与 E2B 平台通信
+- 配置：`box.e2b.api_key` / `api_url` / `template`
+- 支持 `extra_mounts`（commit `0fea9b1` 同步上传文件）
+- 无本地容器引擎依赖，适合无 Docker 的部署或 SaaS 多租户场景
+- 不支持自定义 image 字段，由 template 控制
+
+### 3.3 Server (`box/server.py`, 508 行)
+
+单端口 aiohttp 服务（默认 5410），通过路径区分（commit `8c71ec5` 合并端口）：
+
+1. **Action RPC** (`/rpc/ws`): `BoxServerHandler` 处理所有 action，包括 `INIT` 配置注入、skill store 操作等
+2. **WS Relay** (`/v1/sessions/{id}/managed-process/ws` 与 `/v1/sessions/{id}/managed-process/{pid}/ws`): 双向桥接 WebSocket ↔ 指定 managed process stdin/stdout
+
+stdio 模式同样会在 5410 启动 aiohttp，专门承担 managed process attach；Action RPC 走 stdin/stdout。
+
+### 3.4 Client (`box/client.py`, 377 行)
+
+`ActionRPCBoxClient` 封装 `Handler.call_action()` 调用：
+
+- 25+ 方法对应 25+ 个 RPC action（exec / session / managed-process / skill / status / shutdown）
+- 错误还原: `_translate_action_error()` 通过字符串前缀匹配还原 SDK 侧异常类型
+- `execute()` timeout = 300s，其他默认 15s
+- `BoxRuntimeClient` 是 ABC，供后续可能的非 RPC 实现复用
+
+包级别 `__init__.py` 显式导出：`BoxRuntimeClient`、`ActionRPCBoxClient`（commit `df9c722`）。
+
+### 3.5 Actions (`box/actions.py`, 34 行)
+
+`LangBotToBoxAction` 枚举共定义 **25 个** action：
+
+| 类别 | Actions |
+|------|---------|
+| 控制 | `INIT`、`HEALTH`、`STATUS`、`GET_BACKEND_INFO`、`SHUTDOWN` |
+| 执行 | `EXEC` |
+| Session | `CREATE_SESSION` / `GET_SESSION` / `GET_SESSIONS` / `DELETE_SESSION` |
+| Managed Process | `START_MANAGED_PROCESS` / `GET_MANAGED_PROCESS` / `STOP_MANAGED_PROCESS` |
+| Skill | `LIST_SKILLS` / `GET_SKILL` / `CREATE_SKILL` / `UPDATE_SKILL` / `DELETE_SKILL` / `SCAN_SKILL_DIRECTORY` / `LIST_SKILL_FILES` / `READ_SKILL_FILE` / `WRITE_SKILL_FILE` / `PREVIEW_SKILL_ZIP` / `INSTALL_SKILL_ZIP` |
+
+### 3.6 Models (`box/models.py`, 331 行)
+
+核心数据模型：
+
+| 模型 | 用途 |
+|------|------|
+| `BoxNetworkMode` | `OFF` / `ON` |
+| `BoxExecutionStatus` | `COMPLETED` / `TIMED_OUT` |
+| `BoxHostMountMode` | `NONE` / `READ_ONLY` / `READ_WRITE` |
+| `BoxManagedProcessStatus` | `RUNNING` / `EXITED` |
+| `BoxMountSpec` | 单条挂载（host_path/mount_path/mode）— **新增** |
+| `BoxSpec` | 执行请求；新增 `extra_mounts: list[BoxMountSpec]`、`persistent`、`workspace_quota_mb` |
+| `BoxProfile` | 4 个内置 Profile + `locked` frozenset |
+| `BoxSessionInfo` | Session 状态（含 backend_name/created_at/last_used_at） |
+| `BoxManagedProcessSpec` | 长驻进程参数（process_id/command/args/env/cwd） |
+| `BoxManagedProcessInfo` | 进程状态（status/exit_code/stderr_preview/attached） |
+| `BoxExecutionResult` | 执行结果（status/exit_code/stdout/stderr/duration_ms） |
+
+`BoxSpec` 校验器: `workdir` 默认继承 `mount_path`；`host_path` 支持 POSIX 和 Windows 路径；设置 `host_path` 时 `workdir` 必须在 `mount_path` 下。
+
+### 3.7 BoxSkillStore (`box/skill_store.py`, 647 行)
+
+新增模块（commit `4ab3502`），把 skill 持久化收归 Box runtime：
+
+```
+BoxSkillStore
+  ├─ list_skills() / get_skill(name)
+  ├─ create_skill(data) / update_skill(name, data) / delete_skill(name)
+  ├─ scan_skill_directory(path)            扫描目录返回候选 skill 包列表
+  ├─ list_skill_files(name, path)          浏览 skill 内文件树
+  ├─ read_skill_file(name, path) / write_skill_file(name, path, content)
+  ├─ preview_skill_zip(zip_bytes, ...)     不落盘预览 zip 内容
+  └─ install_skill_zip(zip_bytes, ...)     解压、校验、复制到 skills_root
+     └─ 支持 source_subdir / target_suffix（commit 1aa043f）
+```
+
+GitHub 安装路径：HTTP 层（`api/http/service/skill.py`）先 `git clone` 拉取，再走 `install_skill_zip` 或 directory 路径。Skill 文件存放于 `box.local.skills_root`（默认 `skills`，相对 `host_root`），容器内对应 `/workspace/.skills/`。
+
+### 3.8 Security (`box/security.py`, 52 行)
+
+`validate_sandbox_security()`: 黑名单校验 host_path，阻止挂载 `/etc`/`/proc`/`/sys`/`/dev`/`/root`/`/boot` 及 Docker/Podman socket。
+
+**已知缺陷**: 根路径 `/` 未拦截，用户 home 目录未拦截，是 denylist 而非 allowlist 策略。详见 [问题清单 #5](./box-issues.md)。
+
+### 3.9 Errors (`box/errors.py`, 33 行)
+
+| 异常类型 | 含义 |
+|----------|------|
+| `BoxError` | 基类 |
+| `BoxValidationError` | spec/参数校验失败 |
+| `BoxBackendUnavailableError` | 无可用 backend |
+| `BoxRuntimeUnavailableError` | Runtime 服务不可用 |
+| `BoxSessionConflictError` | session 已存在但 spec 不兼容 |
+| `BoxSessionNotFoundError` | session 不存在 |
+| `BoxManagedProcessConflictError` | session 已有同名 process |
+| `BoxManagedProcessNotFoundError` | process 不存在 |
+
+---
+
+## 4. 工具系统集成
+
+### 4.1 ToolManager 编排 (`toolmgr.py`)
+
+```
+ToolManager.initialize()
+  ├─ NativeToolLoader      (exec / read / write / edit / glob / grep)
+  ├─ PluginToolLoader      (插件工具)
+  ├─ MCPLoader             (MCP Server 工具)
+  ├─ SkillToolLoader       (activate 工具 — Tool Call 激活)
+  └─ SkillAuthoringToolLoader  (Skill CRUD)
+
+工具调用优先级: native → plugin → mcp → skill → skill_authoring
+```
+
+### 4.2 Native Tools (`native.py`, 846 行)
+
+| 工具 | 是否在 Box 中执行 | 是否访问宿主文件系统 |
+|------|:---:|:---:|
+| `exec`  | 是 | 否 |
+| `read`  | **否** | **是** — 直接 `open()` 宿主文件 |
+| `write` | **否** | **是** — 直接 `open()` 宿主文件 |
+| `edit`  | **否** | **是** — 直接 `open()` 宿主文件 |
+| `glob`  | **否** | **是** — 直接遍历宿主目录 |
+| `grep`  | **否** | **是** — 直接读宿主文件 |
+
+**沙箱边界不对称**: 这是刻意的设计权衡 — `read`/`write`/`edit`/`glob`/`grep` 绕过沙箱以获得性能（避免容器 I/O 开销与跨进程拷贝），但意味着 LLM 可以直接读写 `allowed_mount_roots` 下任何文件。Skill 路径经 `_resolve_host_path()` 重写，禁止穿越 `package_root`。
+
+**exec 的 Skill 分支**: 命令中引用 `/workspace/.skills/<name>` 的 skill 时：
+1. 验证 skill 已激活
+2. 单次 exec 只能引用一个 skill 包
+3. 若 skill 是 Python 项目（有 `requirements.txt` 或 `pyproject.toml`），命令会被 venv bootstrap 包裹（在 skill 挂载点内创建 `.venv`）
+4. 调用 `box_service.execute_tool()` → 走默认 session_id 与已组装好的 `extra_mounts`，**不再为每 skill 起独立 session**
+
+### 4.3 MCP-in-Box (`mcp_stdio.py`, 354 行)
+
+`BoxStdioSessionRuntime` 让 MCP stdio 服务器在 Box 容器中运行，**共享 session、多 process**模式（commit `529088e`）：
+
+```
+initialize()
+  1. 复用/创建共享 session (session_id = _build_box_session_id())
+     - persistent=True，长期保持
+  2. workspace.execute_raw(install_cmd) 安装依赖 (可选)
+  3. 将每个 MCP server 文件 stage 到 /workspace/.mcp/<process_id>/
+  4. workspace.start_managed_process(process_id=<server>)
+  5. websocket_client(ws_url) 通过 WS relay 连接
+  6. ClientSession.initialize() MCP 协议握手
+```
+
+配置 (`MCPServerBoxConfig`): `network='on'` (MCP 服务器通常需要网络)，`host_path_mode='ro'` (默认只读)，`startup_timeout_sec=120` (留时间给 pip install)。
+
+每条 MCP server 是同一 session 中的一个 managed process，独立的 `process_id`、独立 attach URL，互不阻塞。
+
+---
+
+## 5. 启动与生命周期
+
+### 5.1 启动顺序 (`build_app.py`)
+
+```
+BuildAppStage.run(ap)
+  ├─ ... (persistence, models, sessions) ...
+  │
+  ├─ BoxService(ap)
+  ├─ box_service.initialize()
+  │    └─ connector.initialize()
+  │         ├─ [stdio] fork box subprocess
+  │         ├─ [subprocess+WS] Windows 本地
+  │         └─ [remote WS] connect URL
+  │    └─ 启动心跳 _heartbeat_task
+  ├─ ap.box_service = box_service
+  │
+  ├─ ToolManager(ap)
+  ├─ tool_mgr.initialize()
+  │    ├─ NativeToolLoader   (检查 box_service.available)
+  │    ├─ PluginToolLoader
+  │    ├─ MCPLoader          (Box 可用时，stdio MCP 走沙箱)
+  │    └─ SkillAuthoringToolLoader
+  ├─ ap.tool_mgr = tool_mgr
+  │
+  ├─ ... (platform, pipeline) ...
+  ├─ SkillManager.initialize()    (从 Box runtime 加载 skill 列表)
+  └─ ... (RAG, HTTP, plugins) ...
+```
+
+BoxService 在 ToolManager **之前**初始化。ToolManager 创建 loader 时检查 `box_service.available`。
+
+### 5.2 初始化失败处理
+
+```python
+try:
+    await self._runtime_connector.initialize()
+    self._available = True
+except Exception as e:
+    self._available = False
+    logger.warning(f"Box runtime unavailable: {e}")
+```
+
+**静默降级**: Box 初始化失败不会阻止应用启动，仅导致 6 个 native tool、所有 Skill 工具和 MCP-in-Box 工具不暴露给 LLM。与 Plugin 的行为不同（Plugin 失败会抛异常）。
+
+### 5.3 销毁流程
+
+```
+app.dispose()
+  └─ box_service.dispose()
+       ├─ connector.dispose()
+       │    ├─ cancel _heartbeat_task
+       │    ├─ cancel _handler_task / _ctrl_task
+       │    └─ terminate subprocess (SIGTERM)
+       └─ loop.create_task(client.shutdown())
+            └─ RPC SHUTDOWN → Box Runtime 清理所有容器
+```
+
+Box 额外做了 RPC SHUTDOWN 通知 Runtime 主动清理容器，比 Plugin 的直接杀进程更安全。
+
+---
+
+## 6. 配置
+
+### config.yaml (重构后)
+
+```yaml
+box:
+    enabled: true         # 整个 Box 子系统的总开关。设为 false 时：
+                          #  - 不连接远程 Box runtime，不 fork 本地 stdio 子进程
+                          #  - sandbox 工具 (exec/read/write/edit/glob/grep) 不暴露给 LLM
+                          #  - skill 添加/编辑 / GitHub 安装 / 文件写入全部拒绝
+                          #  - stdio 模式的 MCP server 启动时报错（http/sse 模式不受影响）
+                          #  - skill 列表/读取保持只读可用
+                          # BOX__ENABLED 环境变量可覆盖（统一约定）
+    backend: 'local'      # 'local' (探测) / 'docker' / 'nsjail' / 'e2b'
+                          # BOX_BACKEND 环境变量优先级更高
+    runtime:
+        endpoint: ''      # 外部 Runtime 的 WS 基地址 'ws://host:5410'
+                          # 留空 = 本地自管 Runtime
+    local:
+        profile: 'default'
+        image: ''                       # 覆盖 profile 默认 image
+        host_root: './data/box'         # 工作区挂载根，Docker 部署需绝对路径
+        default_workspace: ''           # 默认 '<host_root>/default'
+        skills_root: 'skills'           # Box 管理的 skill 包目录（相对 host_root）
+        allowed_mount_roots:            # 默认 ['<host_root>']
+            - './data/box'
+            - '/tmp'
+        workspace_quota_mb: null        # 配额覆盖，null = 走 profile
+    e2b:
+        api_key: ''                     # 也可走 E2B_API_KEY 环境变量
+        api_url: ''                     # 自托管 E2B 时填写
+        template: ''                    # 默认 template ID
+```
+
+> **重大变更**: 较 2026-04-16 文档，配置结构完全重组（commit `eefdea4`）。原字段 `box.profile` / `box.runtime_url` / `box.shared_host_root` / `box.allowed_host_mount_roots` 全部迁入 `box.local.*` 子表，新增 `box.backend` 与 `box.e2b.*` 配置组。
+
+### docker-compose.yaml
+
+`langbot_box` 服务受 compose profile 控制,默认 `docker compose up` **不会**启动它。需要 sandbox 时:
+
+```bash
+docker compose --profile box up        # 启动 langbot + langbot_box + plugin runtime
+docker compose --profile all up        # 同上
+docker compose up                       # 只起 langbot + plugin runtime (box 关闭)
+```
+
+若不起 `langbot_box`,需要同步在 `data/config.yaml` 中设 `box.enabled: false`(或 langbot 容器 env 加 `BOX__ENABLED=false`),否则 LangBot 会一直尝试连接不存在的 Box runtime 并报错。
+
+```yaml
+# langbot_box 的关键 volume
+volumes:
+  - ${LANGBOT_BOX_ROOT}:${LANGBOT_BOX_ROOT}         # 工作区挂载(源/目标同路径)
+  - /var/run/docker.sock:/var/run/docker.sock       # Docker backend 复用宿主 docker
+```
+
+### 关闭/连接失败时的行为矩阵
+
+`box.enabled = false` 与"启用但连接失败"在用户可观察行为上**完全一致**——都通过 `BoxService.available = False` 表达,只是 `get_status` 多返回 `enabled` 字段供前端区分文案。
+
+| 消费方 | Box 可用 | Box 不可用(disabled 或 failed) |
+|---|---|---|
+| native exec/read/write/edit/glob/grep 工具 | 暴露给 LLM | **不暴露** |
+| `activate` / `register_skill` 工具 | 暴露给 LLM | **不暴露** |
+| stdio MCP server | 在 Box 内启动 | **`_init_stdio_python_server` 抛 RuntimeError** 拒绝;不退化到宿主 stdio |
+| http/sse MCP server | 正常 | 正常(不依赖 Box) |
+| Skill 列表/读取 (`list_skills`/`get_skill`/`read_skill_file`) | 走 Box runtime | 走 LangBot 本地 `data/skills/` 只读 fallback |
+| Skill 创建/编辑/安装/写文件 | 走 Box runtime | **HTTP 400** + 明确错误信息(`_require_box_for_write`) |
+| Pipeline AI 配置中 `box-session-id-template` | 正常生效 | **前端 banner** 提示字段无效 |
+| Pipeline 扩展页 `enable_all_skills` / 绑定 skill | 可编辑 | **前端禁用** + banner |
+| 仪表盘 Box 状态卡片 | 绿点 / "已连接" | 灰点 / "已禁用"(disabled) 或 红点 / "已断开"(failed) |
+
+> 后端拒写的边界条件:如果 `ap.box_service` **完全没装**(老式 dev mode,没经过 BuildAppStage),`_require_box_for_write` 视作 no-op,保留 `data/skills/` 本地路径——以兼容历史测试与最小化设置。生产环境总会装 `ap.box_service`,因此该 fallback 不会被触发。
+
+### Pipeline 配置 (templates/metadata/pipeline/ai.yaml)
+
+`local-agent.config.box-session-id-template` 控制 session 作用域，预设：
+
+- `{launcher_type}_{launcher_id}` — 每个会话 (推荐，默认)
+- `{launcher_type}_{launcher_id}_{sender_id}` — 群聊每个用户
+- `{launcher_type}_{launcher_id}_{conversation_id}` — 每个对话上下文
+- `{query_id}` — 每条消息（完全隔离）
+
+详见 [box-session-scope.md](./box-session-scope.md)。
+
+### REST API
+
+| 端点 | 方法 | 说明 | 前端 |
+|------|------|------|:---:|
+| `/api/v1/box/status` | GET | 可用性、Profile、后端信息 | ✅ 监控页 |
+| `/api/v1/box/sessions` | GET | 活跃 session 列表 | ❌ |
+| `/api/v1/box/errors` | GET | 最近 50 条错误 | ❌ |
+| `/api/v1/skills` 等 | GET/POST/PUT/DELETE | Skill CRUD、文件浏览、zip/GitHub 安装、preview | ✅ Skill 管理页 |
+
+前端 `web/src/app/home/monitoring/components/overview-cards/SystemStatusCards.tsx` 已接入 `/api/v1/box/status`，展示 backend 名称、profile 与活跃 session 数。Sessions 与 errors API 仍未接入。

docs/review/box-issues.md

@@ -0,0 +1,157 @@
+# Box 系统架构问题清单
+
+> 更新日期: 2026-05-19
+> 分支: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+
+---
+
+## 已解决（自上一轮 review）
+
+下列原 P0/P1 项在最新分支已被修复，仅作记录：
+
+| 原编号 | 问题 | 处理 commit / 说明 |
+|--------|------|---------------------|
+| #3 | Box 无重连机制 | `_make_connection_callback` 已接入 `runtime_disconnect_callback`；`BoxService._reconnect_loop()` 实现指数退避重连 (`2dfd9d5d`、`c6882cf`) |
+| #4 | Box 无心跳 | `BoxRuntimeConnector._heartbeat_loop()`，间隔 20s（沿用 Plugin 模式） |
+| #10 | Windows 兼容 | connector 增加 Windows 分支 (subprocess + WS)，backend 适配 Windows Docker (`120817a`、`fafb7a4`) |
+| #12 | nsjail image 字段冲突 | `_assert_session_compatible()` 在不支持自定义镜像的 backend 跳过 image 字段 |
+| #22 | 前端无 Box UI | 监控页 `SystemStatusCards.tsx` 已接入 `/api/v1/box/status`；Skill 管理页接入了全部 skill API（sessions/errors API 仍未接入） |
+
+---
+
+## P0 — 合并前建议修复
+
+### 1. policy.py 是死代码
+
+- **位置**: `pkg/box/policy.py` (98 行)
+- **现状**: `SandboxPolicy`、`ToolPolicy`、`ElevatedPolicy` 三个类已定义，但全项目无任何导入或调用
+- **影响**: 三层安全策略（沙箱模式 / 工具白名单 / 权限提升）完全未生效。当前实际策略仍是"Box 可用就暴露全部 6 个 native tool，不可用就全部隐藏"
+- **建议**: 要么删除死代码，要么接入 NativeToolLoader 的工具暴露 / exec 调用链。如果短期不会接入，至少在 `pkg/box/__init__.py` 显式标注其状态
+
+### 2. WebSocket relay 无认证
+
+- **位置**: SDK `box/server.py` — Action RPC 路径 `/rpc/ws` 与 managed-process relay `/v1/sessions/{id}/managed-process/{pid}/ws`
+- **现状**: 任何能访问 5410 端口的客户端都可以连接，attach 任意 session 的 managed process stdin/stdout，或直接发起 EXEC
+- **影响**: 容器化 / Docker compose 部署中，若 Box runtime 端口外暴露，网络内的攻击者可直接控制沙箱
+- **建议**: 至少加 token 认证（INIT 时下发，WS 连接 query string 或 header 校验）；多 process 后 attach 面更大，更不能裸奔
+
+### 3. security.py 根路径未拦截
+
+- **位置**: SDK `box/security.py` `BLOCKED_HOST_PATHS_POSIX`
+- **现状**: 黑名单中没有 `/`，`host_path="/"` 可通过校验并挂载整个主机文件系统；用户 home 目录、`/var` 等也未拦截
+- **建议**: 将 `/` 加入黑名单，或改用白名单策略与 LangBot 侧 `allowed_mount_roots` 二次拦截
+
+### 4. INIT 与 backend 初始化的竞态
+
+- **位置**: SDK `box/runtime.py` `init()` 在握手后才下发实际配置；`backend` 在 INIT 之前可能已经按默认值实例化
+- **现状**: commit `5029d9c` 修复了 "init config before backend reuse" 的部分场景，但 backend 重新实例化时若有正在执行的 session，可能命中旧 backend
+- **建议**: 整理 init/handshake 顺序——要么 INIT 完成前不接受任何业务 action，要么允许 backend 配置变更时显式清理现有 session
+
+---
+
+## P1 — 合并后优先跟进
+
+### 5. Session 数量无上限
+
+- **位置**: SDK `box/runtime.py` `_get_or_create_session()`
+- **现状**: `_sessions` dict 无容量限制，恶意或异常调用可创建无限 session
+- **建议**: 加 `max_sessions` 配置项，达到上限时拒绝新建或按 LRU 清理
+
+### 6. Quota 检查存在 TOCTOU
+
+- **位置**: `pkg/box/service.py` `_enforce_workspace_quota()`
+- **现状**: 应用层先读磁盘大小再执行命令，两步之间有竞态窗口
+- **建议**: 短期用 Docker `--storage-opt size=` 做内核级限制；长期用 Redis 原子计数器做预留式配额
+
+### 7. 全局锁持有期间执行慢操作
+
+- **位置**: SDK `box/runtime.py` `_get_or_create_session()` — `self._lock` 下调用 `backend.start_session()` (即 `docker run` / `nsjail` 进程启动 / E2B `Sandbox.create`)
+- **影响**: `docker run` 可能耗时数秒（含镜像拉取）、E2B 冷启动通常 > 1s，期间阻塞所有并发请求
+- **建议**: 在 `_lock` 下仅做状态检查和 session 注册，容器创建在锁外执行
+
+### 8. Session 清理是机会性的
+
+- **位置**: SDK `box/runtime.py` `_reap_expired_sessions_locked()` — 仅在 `_get_or_create_session()` 时调用
+- **影响**: 如果长时间无新 session 请求，过期 session（含容器）不会被清理
+- **建议**: 加一个独立的 `asyncio.create_task` 定时清理（如每 60s 一次）
+
+### 9. server.py 直接访问 runtime 私有字段
+
+- **位置**: SDK `box/server.py` — managed-process WS handler 直接读 `runtime._sessions`
+- **影响**: 绕过锁和封装，在并发场景下可能读到不一致状态
+- **建议**: 在 BoxRuntime 上增加公共方法（如 `get_session_managed_process(session_id, process_id)`）
+
+### 10. workspace quota 检查阻塞事件循环
+
+- **位置**: `pkg/box/service.py` `_get_workspace_size_bytes()` — 使用同步 `os.scandir` 递归遍历
+- **影响**: 大工作区可能阻塞 asyncio event loop
+- **建议**: 用 `asyncio.to_thread()` 包装，或用 `aiofiles` 异步扫描
+
+### 11. extra_mounts 一旦容器创建即固定
+
+- **位置**: SDK `box/runtime.py` 的兼容性检查；`pkg/box/service.py:build_skill_extra_mounts()`
+- **现状**: Skill 挂载在容器创建时一次性写入；同一 session 后续 pipeline 切换 skill 列表时，新挂载不会生效（除非销毁重建）
+- **影响**: 用户长时间共享 session 的场景下，新激活的 skill 可能挂不上
+- **建议**: 要么在创建时把 pipeline 绑定的所有 skill 都挂上（实际现状）+ 写入文档；要么变更挂载时强制销毁 session 重建（已被 commit `5029d9c` 部分覆盖，需校验）
+
+---
+
+## P2 — 后续迭代
+
+### 12. 重复的 `_is_path_under` 函数
+
+- **位置**: `pkg/box/service.py` 行 30 附近 — 同名函数定义两次
+- **建议**: 删除重复定义
+
+### 13. localagent.py 工具循环无迭代上限
+
+- **位置**: `pkg/provider/runners/localagent.py` `while pending_tool_calls` 循环
+- **影响**: 恶意或混乱的 LLM 可无限产生 tool call，消耗资源
+- **建议**: 加 `max_tool_iterations` 配置项（如默认 50 次）
+
+### 14. localagent.py 中的死代码
+
+- **位置**: `pkg/provider/runners/localagent.py:29-35` 附近 — 旧命名 `SANDBOX_EXEC_TOOL_NAME` 和 `SANDBOX_EXEC_SYSTEM_GUIDANCE`
+- **现状**: 旧命名方案的遗留常量，从未被引用（实际使用 `EXEC_TOOL_NAME` from native.py）
+- **建议**: 删除
+
+### 15. @loader_class 装饰器未使用
+
+- **位置**: `pkg/provider/tools/loader.py` — `preregistered_loaders` 列表和 `@loader_class` 装饰器
+- **现状**: 各 loader 的 `@loader_class` 多数被注释掉，ToolManager 手动实例化所有 loader
+- **建议**: 要么启用装饰器自动注册，要么删除未用的机制
+
+### 16. 工具名冲突风险
+
+- **位置**: `pkg/provider/tools/toolmgr.py` `execute_func_call()` — 按优先级 native → plugin → mcp → skill → skill_authoring 分发
+- **影响**: 如果 plugin 或 MCP 有名为 `exec`/`read`/`write`/`edit`/`glob`/`grep`/`activate` 的工具，会被前序 loader 静默遮蔽
+- **建议**: 加命名空间前缀或冲突检测告警
+
+### 17. client.py 反序列化不一致
+
+- **位置**: SDK `box/client.py` — `execute()` 与其他方法对返回值的反序列化方式不统一（部分手动构造 model，部分用 `model_validate`）
+- **建议**: 统一使用 `model_validate`
+
+### 18. 错误类型还原基于字符串前缀匹配
+
+- **位置**: SDK `box/client.py` `_translate_action_error()`
+- **影响**: 如果 server 端错误消息格式变化，client 会回退到通用 `BoxError`，丢失类型信息
+- **建议**: 在 ActionResponse 中增加结构化的错误类型字段（如 `error_code` 枚举）
+
+### 19. 前端只用到了 status
+
+- **位置**: `web/src/app/home/monitoring/...` 已接入 `/api/v1/box/status`
+- **现状**: `/api/v1/box/sessions` 与 `/api/v1/box/errors` 后端可用、前端未消费
+- **建议**: 在监控页或独立 Box 详情页展示活跃 session 列表与最近错误，提升运维体感
+
+### 20. skill_store 测试覆盖偏薄
+
+- **位置**: SDK `tests/box/test_skill_store.py` 仅 88 行
+- **现状**: 相对 `skill_store.py` 的 647 行实现，单测覆盖度不够；GitHub 安装路径、`source_subdir` / `target_suffix` 组合、损坏 zip 的错误处理等场景未覆盖
+- **建议**: 至少补到核心 path 覆盖（preview/install/list/file CRUD 各 2~3 个 case）
+
+### 21. 集成测试未进 CI
+
+- **位置**: LangBot `tests/integration_tests/box/test_box_integration.py`、`test_box_mcp_integration.py`，SDK 端的 E2B 真机测试
+- **现状**: 容器实际执行、E2B 真实 sandbox、Managed process WS attach 均仅本地能跑
+- **建议**: 加一个可选的 Docker-in-Docker CI stage，或在合并前手动跑 checklist

docs/review/box-session-scope.md

@@ -0,0 +1,401 @@
+# Box Session Scope Design
+
+> Date: 2026-04-18 (last reviewed 2026-05-19)
+> Branch: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+> Related: [Box Architecture](./box-architecture.md) | [Box vs Plugin Runtime](./box-vs-plugin-runtime.md)
+
+---
+
+## 0. Implementation Status (2026-05-19)
+
+This document was authored as a design proposal. The current `feat/sandbox` branch
+has shipped the design largely as written:
+
+| Item | Status | Notes |
+|------|--------|-------|
+| `BoxMountSpec` + `BoxSpec.extra_mounts` | ✅ Shipped | SDK `box/models.py` |
+| Docker / nsjail / E2B backends apply extra mounts | ✅ Shipped | Last gap closed by SDK commit `0fea9b1` (E2B) |
+| `box-session-id-template` in `local-agent` pipeline config | ✅ Shipped | `templates/metadata/pipeline/ai.yaml`, default `{launcher_type}_{launcher_id}` |
+| `BoxService.resolve_box_session_id(query)` | ✅ Shipped | `pkg/box/service.py:166` |
+| `BoxService.build_skill_extra_mounts(query)` | ✅ Shipped | `pkg/box/service.py:189` |
+| Skill exec uses unified container + extra mounts | ✅ Shipped | `pkg/provider/tools/loaders/native.py` skill branch |
+| MCP-in-Box uses shared persistent session, multi-process | ✅ Shipped (earlier than originally scoped) | SDK commit `529088e`, LangBot `mcp_stdio.py:_build_box_session_id` |
+| `BoxManagedProcessSpec.process_id` + multi-process per session | ✅ Shipped | `BoxRuntime` keeps `managed_processes: dict[pid, _ManagedProcess]` |
+| Per-tenant / quota integration with templates | ❌ Not started | See [box-tob-analysis.md](./box-tob-analysis.md) |
+
+The "Phase 2 deferred" note in §10 is **out of date** — MCP unification went in on
+the same line. Pipeline-scoped (not user-scoped) MCP container is the realized
+behavior: each pipeline's MCP servers share one `mcp-<pipeline>` session, and
+user exec sessions use the template-derived id.
+
+The remaining open work is multi-tenant overlays (tenant_id in session_id,
+quota counters keyed by tenant), tracked in the toB analysis doc rather than here.
+
+---
+
+## 1. Problems
+
+### 1.1 Default exec: per-message containers
+
+Currently, `BoxService.execute_tool()` sets `session_id = str(query.query_id)` — an
+auto-incrementing integer per incoming message. Every user message creates a new sandbox
+container. Dependencies installed and in-container state are lost between messages.
+
+### 1.2 Three isolated container pools
+
+Default exec, skills, and MCP servers each manage their own containers with
+independent session IDs:
+
+| Path         | Session ID                                    | Container   |
+|--------------|-----------------------------------------------|-------------|
+| Default exec | `str(query_id)` (per message)                 | Ephemeral   |
+| Skill exec   | `skill-{launcher}_{id}-{skill_name}`          | Per skill   |
+| MCP stdio    | `mcp-{server_uuid}`                           | Per server  |
+
+This means a single logical user interaction can spawn 3+ containers that cannot
+share state, see each other's files, or reuse installed dependencies.
+
+### 1.3 Single bind mount limitation
+
+`BoxSpec` currently supports only **one** `host_path` → `mount_path` bind mount.
+This prevents mounting both a default workspace and skill directories into the
+same container.
+
+---
+
+## 2. Concept Model
+
+```
+Platform Message
+  → Query (query_id: int, auto-increment, per message)
+    → Session (launcher_type + launcher_id, per chat window)
+      → Conversation (uuid, per dialogue context within a Session)
+```
+
+| Concept       | Key                                 | Example                    | Scope                        |
+|---------------|-------------------------------------|----------------------------|------------------------------|
+| Query         | `query_id`                          | `42`                       | Single message               |
+| Session       | `launcher_type` + `launcher_id`     | `group_123456`             | Chat window (group or PM)    |
+| Conversation  | `conversation_id` (UUID)            | `a1b2c3d4-...`             | Dialogue context within a Session |
+| Sender        | `sender_id`                         | `789`                      | Individual user              |
+
+Note: in a **group chat**, all users share the same Session (keyed by `group_id`). The
+individual sender is tracked as `sender_id` but does not affect Session/Conversation routing.
+
+---
+
+## 3. Target Scenarios
+
+| #  | Scenario                       | Box Granularity                          | Desired `session_id`                                   |
+|----|--------------------------------|------------------------------------------|---------------------------------------------------------|
+| 1  | Personal assistant             | 1 Box per user, long-lived               | `{launcher_type}_{launcher_id}`                          |
+| 2  | Customer service               | 1 Box per customer, cross-pipeline       | `{launcher_type}_{launcher_id}`                          |
+| 3  | Internal employee tool         | 1 Box per employee                       | `{launcher_type}_{launcher_id}`                          |
+| 4  | Group chat shared assistant    | 1 Box per group                          | `{launcher_type}_{launcher_id}`                          |
+| 5  | Group chat isolated per user   | 1 Box per user within a group            | `{launcher_type}_{launcher_id}_{sender_id}`              |
+| 6  | Teaching (cross-channel)       | 1 Box per student across groups/PMs      | `{sender_id}`                                           |
+| 7  | One-off execution              | 1 Box per message (current behavior)     | `{query_id}`                                            |
+| 8  | Multi-project development      | 1 Box per conversation context           | `{launcher_type}_{launcher_id}_{conversation_id}`        |
+
+No single fixed granularity covers all scenarios. A template-based approach is needed.
+
+---
+
+## 4. Design Overview
+
+Two key changes:
+
+1. **Unified container**: exec, skills, and MCP all share the same container per
+   session scope. No more separate container pools.
+2. **Configurable session scope**: `session_id` is generated from a template with
+   pipeline variables, configurable per pipeline.
+
+### 4.1 Unified Container with Multiple Mounts
+
+A single container per session scope is created on first use. It has:
+
+- **Primary mount**: default workspace at `/workspace` (from `default_host_workspace`)
+- **Skill mounts**: each pipeline-bound skill's `package_root` mounted at
+  `/workspace/.skills/{skill_name}/`
+- **MCP servers**: run as managed processes inside the same container
+
+```
+Container (session_id = "group_123456")
+  /workspace/                          ← default workspace (bind mount, rw)
+  /workspace/.skills/web-search/       ← skill package (bind mount, rw)
+  /workspace/.skills/data-analysis/    ← skill package (bind mount, rw)
+  [managed process: mcp-server-a]      ← MCP server running inside
+  [managed process: mcp-server-b]      ← MCP server running inside
+```
+
+This requires extending `BoxSpec` to support multiple mounts (see §5).
+
+### 4.2 Session ID Template
+
+A new field `box-session-id-template` in the `local-agent` pipeline runner config
+controls the session scope:
+
+```yaml
+# templates/metadata/pipeline/ai.yaml (under local-agent.config)
+- name: box-session-id-template
+  label:
+    en_US: Sandbox Scope
+    zh_Hans: 沙箱作用域
+  description:
+    en_US: >-
+      Determines how sandbox environments are shared. Use variables to
+      control isolation granularity.
+    zh_Hans: >-
+      决定沙箱环境的共享方式。使用变量控制隔离粒度。
+  type: select
+  required: false
+  default: "{launcher_type}_{launcher_id}"
+  options:
+    - value: "{launcher_type}_{launcher_id}"
+      label:
+        en_US: Per chat (Recommended)
+        zh_Hans: 每个会话（推荐）
+    - value: "{launcher_type}_{launcher_id}_{sender_id}"
+      label:
+        en_US: Per user in chat
+        zh_Hans: 会话中每个用户
+    - value: "{launcher_type}_{launcher_id}_{conversation_id}"
+      label:
+        en_US: Per conversation context
+        zh_Hans: 每个对话上下文
+    - value: "{query_id}"
+      label:
+        en_US: Per message (isolated)
+        zh_Hans: 每条消息（完全隔离）
+```
+
+Available template variables (populated by PreProcessor in `query.variables`):
+
+| Variable            | Source                          | Example              |
+|---------------------|---------------------------------|----------------------|
+| `{launcher_type}`   | `query.session.launcher_type`   | `person` / `group`   |
+| `{launcher_id}`     | `query.session.launcher_id`     | `123456`             |
+| `{sender_id}`       | `query.sender_id`               | `789`                |
+| `{conversation_id}` | `conversation.uuid`             | `a1b2c3d4-...`       |
+| `{query_id}`        | `query.query_id`                | `42`                 |
+
+Default `{launcher_type}_{launcher_id}` covers scenarios 1–4 out of the box.
+
+---
+
+## 5. SDK Changes: Multi-Mount BoxSpec
+
+### 5.1 Model Extension
+
+```python
+# box/models.py
+
+class BoxMountSpec(pydantic.BaseModel):
+    """A single bind mount specification."""
+    host_path: str
+    mount_path: str
+    mode: BoxHostMountMode = BoxHostMountMode.READ_WRITE
+
+class BoxSpec(pydantic.BaseModel):
+    # ... existing fields ...
+    host_path: str | None = None              # Primary mount (backward compat)
+    host_path_mode: BoxHostMountMode = BoxHostMountMode.READ_WRITE
+    mount_path: str = DEFAULT_BOX_MOUNT_PATH
+    extra_mounts: list[BoxMountSpec] = []     # NEW: additional mounts
+```
+
+`extra_mounts` is additive — the existing `host_path` / `mount_path` pair remains
+the primary mount for backward compatibility.
+
+### 5.2 Backend: Apply Extra Mounts
+
+```python
+# box/backend.py — CLISandboxBackend.start_session()
+
+# Primary mount (unchanged)
+if spec.host_path is not None and spec.host_path_mode != BoxHostMountMode.NONE:
+    args.extend(['-v', f'{spec.host_path}:{spec.mount_path}:{spec.host_path_mode.value}'])
+
+# Extra mounts (NEW)
+for mount in spec.extra_mounts:
+    if mount.mode != BoxHostMountMode.NONE:
+        args.extend(['-v', f'{mount.host_path}:{mount.mount_path}:{mount.mode.value}'])
+```
+
+Same pattern for nsjail backend.
+
+---
+
+## 6. LangBot Changes
+
+### 6.1 Session ID Resolution
+
+In `BoxService.execute_tool()`:
+
+```python
+# Before:
+spec_payload.setdefault('session_id', str(query.query_id))
+
+# After:
+template = (query.pipeline_config or {}).get('ai', {}) \
+    .get('local-agent', {}).get('box-session-id-template',
+         '{launcher_type}_{launcher_id}')
+variables = query.variables or {}
+session_id = template.format_map(collections.defaultdict(
+    lambda: 'unknown', variables
+))
+spec_payload.setdefault('session_id', session_id)
+```
+
+### 6.2 Skill Exec: Use Same Container
+
+Currently `native.py:_invoke_exec` creates a separate `BoxWorkspaceSession` per
+skill with `host_path=package_root`. Instead:
+
+1. Use the **same session_id** as default exec (from the template).
+2. Pass the skill's `package_root` as an **extra mount** at
+   `/workspace/.skills/{skill_name}/` instead of replacing `/workspace`.
+3. The container already has the default workspace at `/workspace`.
+
+```python
+# native.py — _invoke_exec, skill branch (REVISED)
+
+# Same session_id as default exec
+session_id = resolve_box_session_id(query)
+
+spec_payload = {
+    'cmd': rewritten_command,
+    'workdir': rewritten_workdir,
+    'session_id': session_id,
+    'extra_mounts': [{
+        'host_path': package_root,
+        'mount_path': f'/workspace/.skills/{selected_skill_name}',
+        'mode': 'rw',
+    }],
+}
+result = await self.ap.box_service.execute_spec_payload(spec_payload, query)
+```
+
+The virtual path `/workspace/.skills/{name}` no longer needs rewriting at the
+command level — it maps directly to the bind mount path inside the container.
+
+### 6.3 MCP: Use Same Container
+
+MCP servers should run inside the same container as exec and skills. Changes:
+
+1. `BoxStdioSessionRuntime` uses the pipeline's session_id template instead of
+   `mcp-{server_uuid}`.
+2. MCP server's working directory is a subdirectory (e.g. `/workspace/.mcp/{name}/`).
+3. MCP server's dependencies are mounted or installed into that subdirectory.
+4. The MCP server runs as a managed process inside the shared container.
+
+Since MCP servers start at LangBot boot (not per-query), the session must be
+created eagerly. The container will be kept alive by the managed process
+exemption in TTL reaping (`runtime.py:259`).
+
+**Note**: MCP sessions are pipeline-scoped (not per-launcher), so their session_id
+should be a **fixed identifier per pipeline** rather than the user-facing template.
+This means one shared MCP container per pipeline, with user exec sessions separate.
+
+Alternatively, in a future iteration, MCP managed processes could be launched
+lazily into the user's container on first MCP tool call. This is more complex
+but maximizes sharing. For V1, keeping MCP containers at pipeline scope is
+simpler and more predictable.
+
+---
+
+## 7. Mount Layout Summary
+
+### Default exec (no skills activated)
+
+```
+Container (session_id from template)
+  /workspace/          ← default_host_workspace (rw)
+```
+
+### Exec with activated skills
+
+```
+Container (same session_id)
+  /workspace/                          ← default_host_workspace (rw)
+  /workspace/.skills/web-search/       ← skill package_root (rw)
+  /workspace/.skills/data-analysis/    ← skill package_root (rw)
+```
+
+Extra mounts are **additive** — they are added when the container is first
+created (or on the first exec that references a skill). Since Docker bind
+mounts are specified at container creation time, skills must be known at
+creation time.
+
+**Resolution**: When creating a container, inject `extra_mounts` for **all
+pipeline-bound skills** (from `extensions_preferences`), not just the
+currently activated one. This way any skill can be activated later without
+recreating the container.
+
+### MCP servers (V1: pipeline-scoped)
+
+```
+Container (session_id = "mcp-pipeline-{pipeline_uuid}")
+  /workspace/                    ← MCP shared workspace
+  /workspace/.mcp/server-a/      ← MCP server A files
+  /workspace/.mcp/server-b/      ← MCP server B files
+  [managed process: server-a]
+  [managed process: server-b]
+```
+
+---
+
+## 8. Data Migration
+
+Existing pipelines do not have `box-session-id-template`. The backend uses
+`.get(..., default)` so missing keys fall back to `{launcher_type}_{launcher_id}`.
+This changes behavior from per-message to per-launcher for existing pipelines.
+
+Recommendation: **accept the behavior change** — per-launcher is the more
+intuitive default, and the old per-message behavior was rarely desired.
+
+---
+
+## 9. Cloud Quota Implications
+
+| Scope                                         | Typical concurrent containers |
+|-----------------------------------------------|-------------------------------|
+| `{query_id}` (per message)                    | Many, short-lived             |
+| `{launcher_type}_{launcher_id}` (per chat)    | = active chat count           |
+| `{sender_id}` (per user)                      | = active user count           |
+| `{conversation_id}` (per conversation)        | Between per-chat and per-msg  |
+
+With the unified container model, each scope value maps to exactly **one**
+container (instead of potentially 3+ per-message). This significantly reduces
+resource usage.
+
+Quota enforcement point: `BoxRuntime._get_or_create_session()` in the SDK.
+
+---
+
+## 10. Implementation Phases
+
+### Phase 1: Session scope + skill unification (this PR)
+
+1. **SDK**: Extend `BoxSpec` with `extra_mounts: list[BoxMountSpec]`.
+2. **SDK**: Update Docker/nsjail backends to apply extra mounts.
+3. **LangBot**: Add `box-session-id-template` to `local-agent` YAML metadata
+   and default pipeline config JSON.
+4. **LangBot**: Update `BoxService.execute_tool()` to use template interpolation.
+5. **LangBot**: Update `native.py:_invoke_exec` skill branch to use same
+   session_id + extra mounts instead of separate `BoxWorkspaceSession`.
+6. **LangBot**: On container creation, inject extra mounts for all
+   pipeline-bound skills.
+7. **Frontend**: No code change — `DynamicFormComponent` renders `select` fields.
+8. **Tests**: Unit tests for template interpolation and multi-mount specs.
+
+### Phase 2: MCP unification (future)
+
+1. Refactor `BoxStdioSessionRuntime` to use pipeline-scoped shared container.
+2. MCP servers become managed processes in the shared container.
+3. Support multiple concurrent managed processes per container.
+
+MCP unification is deferred because it requires changes to the managed process
+model (currently 1 managed process per session) and has startup ordering
+concerns (MCP servers start at boot, before any user query determines
+a session_id).

docs/review/box-test-coverage.md

@@ -0,0 +1,121 @@
+# Box 系统测试覆盖分析
+
+> 更新日期: 2026-05-19
+> 分支: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+
+---
+
+## 1. 测试文件清单
+
+### LangBot 仓库
+
+| 文件 | 行数 | CI 运行 | 覆盖范围 |
+|------|------|---------|---------|
+| `tests/unit_tests/box/test_box_connector.py` | 106 | 是 | Connector 传输决策、WS relay URL、dispose、心跳/重连 |
+| `tests/unit_tests/box/test_box_service.py` | 1224 | 是 | Service 核心逻辑（最全面） |
+| `tests/unit_tests/box/test_workspace.py` | 147 | 是 | WorkspaceSession 路径重写、payload 构建 |
+| `tests/unit_tests/provider/test_mcp_box_integration.py` | 707 | 是 | MCP Box 配置、路径重写、payload、shared-session/multi-process、runtime info |
+| `tests/unit_tests/provider/test_localagent_sandbox_exec.py` | 444 | 是 | LocalAgent exec 流程、流式、Skill 激活 (Tool Call) |
+| `tests/unit_tests/provider/test_tool_manager_native.py` | 249 | 是 | ToolManager 路由、native tool CRUD、路径穿越、6 工具暴露 |
+| `tests/unit_tests/provider/test_skill_tools.py` | 582 | 是 | Skill 管理、Tool Call 激活、路径、authoring CRUD |
+| `tests/unit_tests/test_skill_service.py` | 396 | 是 | HTTP service：skill CRUD、zip/GitHub install、文件浏览 |
+| `tests/unit_tests/test_paths.py` | 23 | 是 | paths 工具 |
+| `tests/unit_tests/test_preproc.py` | 134 | 是 | PreProcessor 注入 session 变量、bound skill 解析 |
+| `tests/unit_tests/pipeline/test_chat_handler_logging.py` | 78 | 是 | Chat handler 日志相关回归 |
+| `tests/integration_tests/box/test_box_integration.py` | 329 | **否** | 真实容器执行、超时、网络隔离 |
+| `tests/integration_tests/box/test_box_mcp_integration.py` | 368 | **否** | Managed process、WS attach、shared-session 清理 |
+
+### SDK 仓库
+
+| 文件 | 行数 | CI 运行 | 覆盖范围 |
+|------|------|---------|---------|
+| `tests/box/test_backend_selection.py` | 255 | 是 | 显式 backend / local 模式探测顺序 / 配置变更触发 reselect |
+| `tests/box/test_nsjail_backend.py` | 452 | 是 | nsjail 可用性、安装版 CLI vs 容器内 CLI、session、arg 构建、资源限制 |
+| `tests/box/test_e2b_backend.py` | 482 | 是 | E2B SDK mock、session 生命周期、extra_mounts 同步 |
+| `tests/box/test_skill_store.py` | 88 | 是 | zip preview/install、基础 file CRUD |
+
+**总计**: 17 个测试文件, ~6,500 行测试代码; 其中 2 个集成测试（约 700 行）在 CI 中不运行。
+
+> 较 2026-04-16 版增加：`test_skill_service.py`、`test_paths.py`、`test_preproc.py`、`test_chat_handler_logging.py` (LangBot)，`test_backend_selection.py`、`test_e2b_backend.py`、`test_skill_store.py` (SDK)。`test_nsjail_backend.py` 增加 CLI 兼容性 case (commit `feed530`)。
+
+---
+
+## 2. 覆盖良好的区域
+
+| 区域 | 质量 | 说明 |
+|------|------|------|
+| BoxRuntime session 管理 | 优秀 | session 复用、冲突检测、TTL 配置、消失 session 重建 |
+| BoxService Profile 系统 | 优秀 | 4 个内置 Profile、locked/unlocked 字段、timeout clamp |
+| BoxService host mount 安全 | 优秀 | allowed_mount_roots、disallowed_roots、shared host root |
+| BoxService workspace quota | 优秀 | 前置/后置配额检查、超额清理 |
+| BoxService 输出截断 | 优秀 | 短/精确边界/长输出、独立 stderr |
+| BoxService 可观测性 | 优秀 | 状态报告、error ring buffer、buffer 上限 |
+| BoxService session 模板 | 良好 | `resolve_box_session_id` + `build_skill_extra_mounts` 在 service / native / mcp 三处都有覆盖 |
+| RPC client/server 协议 | 优秀 | execute/get_sessions/delete/create/conflict error |
+| BoxRuntimeConnector | 良好 | local/remote 模式、Docker 平台、relay URL、心跳与重连回调 |
+| BoxWorkspaceSession | 良好 | payload 构建、managed process 路径重写、stage host file |
+| BoxHostMountMode.NONE | 良好 | 枚举校验、workdir 约束 |
+| NsjailBackend | 良好 | 可用性、安装版 vs 容器内、session 生命周期、arg 构建、资源限制 |
+| E2BBackend | 良好 | mock SDK、session/extra_mounts 同步 |
+| Backend selection | 良好 | 显式 backend 优先级、local 探测顺序、配置变更触发 reselect |
+| MCP Box 集成 | 良好 | config model、路径重写、payload、shared-session 多 process |
+| Native tool loader | 良好 | 6 工具（exec/read/write/edit/glob/grep）、路径穿越拦截 |
+| LocalAgent exec 流程 | 良好 | 完整 tool call 循环、流式、system prompt 注入、Tool Call 激活 |
+| Skill 系统 | 良好 | 加载、Tool Call 激活、marker、路径解析、authoring CRUD、HTTP service |
+
+---
+
+## 3. 覆盖缺失的区域
+
+### 3.1 零测试 / 严重不足
+
+| 区域 | 源文件 | 影响 |
+|------|--------|------|
+| **`security.py`** | SDK `box/security.py` (52 行) | `validate_sandbox_security()` 无任何测试。阻止 `/etc`/`/proc`/Docker socket 等危险挂载的安全函数从未被验证 |
+| **`policy.py`** | `pkg/box/policy.py` (98 行) | 三层安全策略无测试（也是死代码） |
+| **`skill_store.py` 边缘场景** | SDK `box/skill_store.py` (647 行) vs 测试 88 行 | GitHub 安装路径、`source_subdir` / `target_suffix` 组合、损坏 zip、文件冲突等场景未覆盖 |
+
+### 3.2 未测试的关键路径
+
+| 区域 | 说明 |
+|------|------|
+| **Session TTL 过期** | 测试配置了 `session_ttl_sec` 但从未推进时间验证过期清理 |
+| **并发 session 访问** | 无并发 exec / 并发创建 / race condition 测试 |
+| **Container backend (Docker)** | 仅通过集成测试覆盖（CI 不运行），单元测试全用 FakeBackend |
+| **E2B 真实 sandbox** | 单测全是 mock，未对接真实 E2B API |
+| **BoxRuntime shutdown()** | 在 test cleanup 中调用但未验证行为 |
+| **BoxServerHandler 错误路径** | 畸形请求、未知 action 类型 |
+| **WS relay** | 仅在集成测试中覆盖（CI 不运行） |
+| **NsjailBackend managed process** | 完全未测试 |
+| **MCP stdio 完整生命周期** | 依赖安装 → 进程启动 → 健康检查 → 多 process 并发 → 重试 |
+| **BoxService start/stop_managed_process** | 单 process 流转有单测，多 process 互不阻塞主要靠集成测试 |
+| **重连指数退避** | connector 单测覆盖回调接线，未实际跑完整重连周期 |
+
+### 3.3 边缘情况缺失
+
+| 区域 | 说明 |
+|------|------|
+| BoxSpec 校验 | 无效 session_id 格式、超长命令、env 特殊字符 |
+| BoxSpec.extra_mounts | 重复 mount_path、与 host_path 冲突、绝对 vs 相对路径 |
+| BoxExecutionResult | 仅 COMPLETED 和 TIMED_OUT，无 ERROR 状态测试 |
+| 多后端 fallback | local 模式探测顺序仅靠 mock，无真实 Docker 不可用 → nsjail 真机 fallback 测试 |
+| Profile YAML 加载 | 测试用硬编码字符串，未从真实 config.yaml 加载 |
+| INIT 配置变更触发 backend 重建 | 单测仅在初始化场景验证 |
+
+---
+
+## 4. 集成测试 vs CI 的差距
+
+CI 仅运行 `tests/unit_tests/`，以下场景**从未在自动化中验证**:
+
+- 真实容器的创建/执行/销毁
+- 容器网络隔离（`--network none`）
+- 容器资源限制生效（cpus/memory/pids_limit）
+- Managed process 的 WS 双向 I/O
+- 多 process 同 session 并发 I/O
+- 孤儿容器清理
+- Session 删除清理容器
+- 进程退出检测
+- E2B 真实 sandbox 行为
+
+**建议**: 在 CI 中加一个可选的 Docker-in-Docker 集成测试 stage，至少覆盖核心执行路径（exec / MCP attach / session 销毁）。

docs/review/box-tob-analysis.md

@@ -0,0 +1,166 @@
+# Box 系统 toB 商业化分析
+
+> 更新日期: 2026-05-19
+> 分支: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+
+---
+
+## 1. 现有优势
+
+| 能力 | toB 价值 | 代码位置 |
+|------|---------|---------|
+| **沙箱隔离执行** | 企业安全运行不受信代码的基础能力 | SDK `box/backend.py` |
+| **多后端支持** | 适配不同企业容器基础设施 (Podman/Docker/nsjail/E2B) | SDK `box/runtime.py` `_select_backend()` |
+| **E2B 云沙箱** | SaaS / 无 Docker 部署的兜底执行环境 | SDK `box/e2b_backend.py` |
+| **连接自愈** | 心跳 + 自动重连，单点 Box runtime 故障可恢复 | `pkg/box/connector.py` `_heartbeat_loop`, `pkg/box/service.py` `_reconnect_loop` |
+| **Profile + locked 字段** | 运维锁定安全边界，LLM/用户无法绕过 | `pkg/box/service.py`, SDK `box/models.py` |
+| **资源限制** | CPU/内存/PID 数限制防止资源滥用 | SDK `backend.py` `--cpus/--memory/--pids-limit` |
+| **Workspace quota** | 磁盘用量控制 | `pkg/box/service.py` `_enforce_workspace_quota` |
+| **静默降级** | Box 不可用不影响其他功能，降低部署门槛 | `pkg/box/service.py:78` `_available=False` |
+| **孤儿容器清理** | 防止泄漏的容器持续占用资源 | SDK `backend.py` `cleanup_orphaned_containers` |
+| **网络隔离** | `--network none` 防止数据外泄 | SDK `backend.py` start_session |
+| **只读根文件系统** | `--read-only` 防止容器被持久篡改 | SDK `backend.py` start_session |
+| **Host path 白名单** | `allowed_host_mount_roots` 限制可挂载目录 | `pkg/box/service.py` `_validate_host_mount` |
+
+---
+
+## 2. toB 差距分析
+
+### 2.1 安全与合规
+
+| 维度 | 现状 | toB 要求 | 优先级 |
+|------|------|---------|--------|
+| **WS relay 认证** | 无认证，任何人可 attach | 至少 token 认证 | **P0** |
+| **安全策略** | policy.py 是死代码，实际无细粒度控制 | 工具级 allow/deny、沙箱模式控制 | **P0** |
+| **审计日志** | 仅内存中 50 条 `_recent_errors` | 持久化审计：谁何时执行了什么、结果如何 | **P0** |
+| **Host path 校验** | 黑名单策略，`/` 未拦截 | 白名单策略，默认拒绝 | **P1** |
+| **数据驻留** | 无控制 | GDPR / 等保要求的数据隔离 | **P2** |
+
+### 2.2 多租户
+
+| 维度 | 现状 | toB 要求 | 优先级 |
+|------|------|---------|--------|
+| **租户隔离** | 无租户概念 | BoxSpec/Profile 绑定 tenant_id | **P0** |
+| **RBAC** | 仅 token 认证 | admin/operator/viewer 角色权限 | **P0** |
+| **资源配额** | 单一 workspace quota | 每租户 CPU 时间/内存/并发/执行次数配额 | **P1** |
+| **Session 隔离** | 所有 session 共享 dict | 按租户分区，互不可见 | **P1** |
+
+### 2.3 可靠性
+
+| 维度 | 现状 | toB 要求 | 优先级 |
+|------|------|---------|--------|
+| **连接恢复** | 已实现：20s 心跳 + `_reconnect_loop` 指数退避 | 已满足基本要求 | 已有 |
+| **Session 清理** | 机会性（仅新建时触发） | 定时清理 + 独立 reaper | **P1** |
+| **水平扩展** | 单 Box Runtime 实例 | 多实例负载均衡（按 tenant 路由） | **P1** |
+| **优雅降级** | 已有（_available=False） | 已满足基本要求 | 已有 |
+| **Backend 自愈** | 已实现：`get_status` 时若 backend 不可用会重新选择 | 已满足基本要求 | 已有 |
+
+### 2.4 可观测性
+
+| 维度 | 现状 | toB 要求 | 优先级 |
+|------|------|---------|--------|
+| **监控指标** | 无 Prometheus metrics | session 数/执行延迟/资源用量/错误率 | **P1** |
+| **结构化日志** | Python logging, 无结构化 | JSON 格式日志，含 trace_id/tenant_id | **P1** |
+| **前端面板** | 监控页接入 `/api/v1/box/status`（backend 名 + 活跃 session 数）；`sessions` / `errors` 仍未接入 | 完整状态面板 + 历史错误/审计列表 | **P2** |
+
+---
+
+## 3. SaaS 部署架构建议
+
+### 3.1 方案 A: 共享 Box Runtime Pool (快速上线)
+
+```
+LangBot Instance ──> Box Runtime (共享)
+                       ├─ tenant_id 标签隔离
+                       ├─ Redis 配额计数器
+                       └─ Container labels: langbot.tenant_id=xxx
+```
+
+- **优点**: 改动最小，加 tenant_id 到 BoxSpec/labels 即可
+- **缺点**: 容器引擎共享，安全隔离弱
+
+### 3.2 方案 B: 每租户 K8s Namespace + gVisor (推荐中期)
+
+```
+LangBot ──> K8s API
+              ├─ namespace: tenant-xxx
+              │    ├─ RuntimeClass: gVisor (runsc)
+              │    ├─ ResourceQuota
+              │    └─ NetworkPolicy
+              └─ namespace: tenant-yyy
+                   └─ ...
+```
+
+- **优点**: 强隔离（namespace + gVisor），原生 K8s 配额
+- **缺点**: 需要重写 backend 为 K8s Job，部署复杂度高
+
+### 3.3 方案 C: K8s Job 直接编排 (长期)
+
+```
+LangBot ──> K8s Job per execution
+              ├─ 每次执行创建 Job
+              ├─ Pod Security Standards
+              ├─ 自动调度和资源分配
+              └─ Job TTL Controller 自动清理
+```
+
+- **优点**: 最强隔离，天然水平扩展
+- **缺点**: 冷启动延迟，架构重写
+
+**推荐演进路径**: A → B → C
+
+---
+
+## 4. 配额体系建议
+
+### 三层配额
+
+| 层 | 实现 | 作用 |
+|----|------|------|
+| **内核层** | Docker `--cpus`/`--memory`/`--storage-opt` | 硬性资源上限，不可绕过 |
+| **应用层** | Redis 原子计数器 | 并发 session 数/执行次数/CPU 时间预算 |
+| **计费层** | 月度聚合 | 按租户计费（session-hours/execution-count） |
+
+### Profile 与套餐映射
+
+| 套餐 | Profile | locked 字段 | 配额 |
+|------|---------|------------|------|
+| Free | `offline_readonly` | network, host_path_mode, rootfs | 10 exec/天, 0.5 CPU, 256MB |
+| Pro | `default` | (无) | 100 exec/天, 1 CPU, 512MB |
+| Enterprise | `network_extended` | (按需) | 无限, 2 CPU, 1GB, 自定义镜像 |
+
+### TOCTOU 配额修复
+
+当前 `_enforce_workspace_quota` 的 TOCTOU 问题可通过两种方式解决:
+
+1. **预留式配额** (应用层): Redis `INCRBY` 预扣额度 → 执行 → 成功则扣减，失败则回滚
+2. **内核级限制** (Docker): `--storage-opt size=500m` 直接限制容器可写层大小
+
+---
+
+## 5. 优先实施路线
+
+### Phase 1 (2-4 周): 安全基线
+
+- [ ] WS relay 加 token 认证
+- [ ] 接入或删除 policy.py
+- [x] ~~Box 加重连和心跳~~（已完成，见 [box-issues.md 已解决](./box-issues.md)）
+- [ ] 审计日志持久化（至少写文件/数据库）
+- [ ] `security.py` 加 `/` 拦截，考虑白名单
+- [ ] INIT 与 backend 初始化顺序整理（避免 backend 在配置到达前实例化）
+
+### Phase 2 (4-8 周): 多租户基础
+
+- [ ] BoxSpec 加 `tenant_id` 字段
+- [ ] 容器 labels 加 tenant 标识
+- [ ] Redis 配额计数器（并发/执行次数/时间）
+- [ ] RBAC 基础框架
+- [ ] 定时 session reaper
+
+### Phase 3 (8-16 周): 生产就绪
+
+- [ ] Prometheus metrics exporter
+- [ ] 前端 Box 状态面板
+- [ ] K8s backend 支持 (方案 B)
+- [ ] 结构化日志 (JSON, trace_id)
+- [ ] 水平扩展支持

docs/review/box-vs-plugin-runtime.md

@@ -0,0 +1,221 @@
+# Box Runtime vs Plugin Runtime: 连接架构对比
+
+> 更新日期: 2026-05-19
+> 分支: `feat/sandbox` (LangBot + langbot-plugin-sdk)
+
+---
+
+## 1. 总体差异
+
+| 维度 | Plugin Runtime | Box Runtime |
+|------|---------------|-------------|
+| **继承关系** | `PluginRuntimeConnector(ManagedRuntimeConnector)` | `BoxRuntimeConnector`（独立类） |
+| **传输分支** | 3 条 (Docker/WS, Win32/subprocess+WS, Unix/stdio) | 3 条 (本地 stdio, Win32/subprocess+WS, 远程 WS) |
+| **心跳** | 20s ping loop | 20s ping loop（`_heartbeat_loop`） |
+| **重连** | WS 模式: sleep 3s → re-initialize | 由 BoxService `_reconnect_loop` 处理，指数退避 |
+| **Handler 类型** | `RuntimeConnectionHandler` (1132 行, 25+ action) | 基础 `Handler` + `BoxServerHandler`（SDK 端 25 action） |
+| **Client 抽象** | Handler 即 API | 独立 `ActionRPCBoxClient` 封装 Handler |
+| **启用/禁用** | `is_enable_plugin` 开关 | 无开关（可用/不可用由初始化结果决定） |
+| **初始化失败** | 异常上抛 | 静默降级 `_available=False` |
+| **Shutdown** | 直接杀进程 | RPC SHUTDOWN → 清理容器 → 再杀进程 |
+
+---
+
+## 2. 传输决策
+
+### Plugin: 3-路决策
+
+```python
+# pkg/plugin/connector.py:106-165
+if get_platform() == 'docker' or use_websocket_to_connect_plugin_runtime():
+    # Docker/WS → ws://langbot_plugin_runtime:5400/control/ws
+elif get_platform() == 'win32':
+    # Windows → 起子进程(无 pipe) + ws://localhost:5400/control/ws
+else:
+    # Unix/Mac → StdioClientController(python -m langbot_plugin.cli rt -s)
+```
+
+### Box: 3-路决策
+
+```python
+# pkg/box/connector.py
+if self._uses_websocket():
+    if platform.get_platform() == 'win32' and not self.configured_runtime_url:
+        await self._start_subprocess_then_ws()  # subprocess + ws://localhost:5410/rpc/ws
+    else:
+        await self._connect_remote_ws()         # ws://{host}:5410/rpc/ws
+else:
+    await self._start_local_stdio()             # StdioClientController
+```
+
+> 历史：2026-04-16 版本本文档曾把 Box 描述为 2 路决策（缺 Windows 分支）。现已对齐 Plugin 的 3 路设计。
+
+### 决策矩阵
+
+| 环境 | Plugin | Box |
+|------|--------|-----|
+| Docker | WS → `:5400` | WS → `:5410/rpc/ws` |
+| `--standalone-box` | N/A | WS → `localhost:5410/rpc/ws` |
+| Windows 非 Docker | subprocess + WS (`:5400`) | subprocess + WS (`localhost:5410/rpc/ws`) |
+| Unix/Mac 非 Docker | stdio | stdio |
+| 手动配置 URL | 通过配置项 | WS → 用户配置的 URL |
+
+---
+
+## 3. 连接建立
+
+### 同步模式差异
+
+**Plugin**: `new_connection_callback` 内直接 ping + await handler_task，`initialize()` 通过 `create_task()` 异步启动，不阻塞等待连接。
+
+**Box**: 使用 `asyncio.Event` + `wait_for(timeout=30s)` 模式，`initialize()` 同步等待连接成功或超时。
+
+### Box stdio 路径
+
+```
+connector._start_local_stdio()
+  ├─ connected = asyncio.Event()
+  ├─ ctrl = StdioClientController(python, ['-m', 'langbot_plugin.cli.__init__', 'box', '-s', '--ws-control-port', N])
+  ├─ _ctrl_task = create_task(ctrl.run(callback))
+  │    callback:
+  │      handler = Handler(connection)          ← 基础 Handler, 无 disconnect_callback
+  │      client.set_handler(handler)
+  │      _handler_task = create_task(handler.run())
+  │      call_action(PING, {})                  ← 握手, timeout=15s
+  │      connected.set()                        ← 通知外层
+  │      await _handler_task                    ← 阻塞直到断开
+  └─ await wait_for(connected.wait(), 30s)      ← 同步等待
+```
+
+### Plugin stdio 路径
+
+```
+connector.initialize()
+  ├─ ctrl = StdioClientController(python, ['-m', 'langbot_plugin.cli', 'rt', '-s'])
+  ├─ task = ctrl.run(callback)
+  │    callback:
+  │      disconnect_callback:
+  │        [WS] → runtime_disconnect_callback → 重连
+  │        [stdio] → 仅日志, 不重连
+  │      handler = RuntimeConnectionHandler(conn, disconnect_cb, ap)
+  │      create_task(handler.run())
+  │      handler.ping()                         ← 握手, timeout=10s
+  │      await handler_task                     ← 阻塞直到断开
+  ├─ create_task(heartbeat_loop())              ← 20s ping loop
+  └─ create_task(task)                          ← 不等待连接
+```
+
+---
+
+## 4. 心跳与重连
+
+### 心跳
+
+| 维度 | Plugin | Box |
+|------|--------|-----|
+| 有心跳? | 是 | 是（`connector.py` `_heartbeat_loop`） |
+| 间隔 | 20s | 20s |
+| 失败处理 | 仅 DEBUG 日志，不触发重连 | 仅 DEBUG 日志，依赖 connection close 触发重连 |
+| 生命周期 | 整个应用生命周期 | 连接建立后启动；`dispose()` 时 cancel |
+
+### 重连
+
+| 维度 | Plugin | Box |
+|------|--------|-----|
+| Docker/WS 断开 | `runtime_disconnect_callback` → sleep 3s → re-initialize | `runtime_disconnect_callback` → `BoxService._reconnect_loop()`（指数退避） |
+| WS 连接失败 | 同上 | 同上；初次失败时 `_available=False`，重连成功后恢复 |
+| stdio 断开 | 仅日志，不重连 | 接同样回调；stdio 重连需重新 fork 子进程 |
+| 重连退避 | 固定 3s，无 backoff | 指数退避 |
+
+> 历史：2026-04-16 版本本文档曾把心跳与重连标记为 Box 缺失。这两项已在 commit `2dfd9d5d` / `c6882cf` / `5029d9c` 等修复（详见 [box-issues.md 已解决](./box-issues.md)）。
+
+---
+
+## 5. 共享 IO 层
+
+两者复用同一套 SDK IO 基础设施：
+
+```
+Handler ← ABC                              (runtime/io/handler.py)
+  ├── RuntimeConnectionHandler              (Plugin 用, LangBot 侧)
+  ├── ControlConnectionHandler              (Plugin 用, SDK 侧)
+  ├── BoxServerHandler                      (Box 用, SDK 侧)
+  └── 匿名 Handler 实例                     (Box 用, LangBot 侧)
+
+Connection ← ABC
+  ├── StdioConnection    (stdio: 16KB chunks, 应用层分帧协议)
+  └── WebSocketConnection (WS: 64KB chunks, 原生 WS 分帧)
+
+Controller ← ABC
+  ├── StdioClientController    (fork 子进程, pipe stdin/stdout)
+  ├── StdioServerController    (接管当前进程 stdin/stdout)
+  ├── WebSocketClientController (连接 WS 服务端)
+  └── WebSocketServerController (监听 WS 端口)
+```
+
+共享的核心机制：
+- `call_action()` / `call_action_generator()` — RPC 调用/流式调用
+- `ActionRequest` / `ActionResponse` — 请求/响应协议
+- `seq_id` 关联 — 并发请求复用单连接
+- `CommonAction.PING` — 两者都用于初始握手
+- 文件传输 (`send_file`) — Plugin 用，Box 不用
+
+---
+
+## 6. 端口方案
+
+| 服务 | Plugin | Box |
+|------|--------|-----|
+| Action RPC (stdio) | stdin/stdout | stdin/stdout |
+| Action RPC (WS) | `:5400` | `:5410/rpc/ws` |
+| 辅助服务 | debug WS `:5401` | managed process WS relay `:5410/v1/sessions/{id}/managed-process/ws` |
+
+**Box 特点**: 单端口 aiohttp 服务（默认 5410），通过路径区分 Action RPC 和 managed process relay。即使在 stdio 模式，也在 `:5410` 启动 aiohttp 用于 managed process attach。Plugin 在 stdio 模式不开额外端口。
+
+---
+
+## 7. 销毁对比
+
+### Plugin
+
+```python
+dispose():
+  if stdio: ctrl.process.terminate()
+  _dispose_subprocess()         # Windows 子进程
+  heartbeat_task.cancel()
+```
+
+### Box
+
+```python
+connector.dispose():
+  _handler_task.cancel()
+  _ctrl_task.cancel()
+  _subprocess.terminate()
+
+service.dispose():
+  connector.dispose()
+  loop.create_task(client.shutdown())   # RPC SHUTDOWN → 清理所有容器
+```
+
+Box 的 RPC SHUTDOWN 确保容器被正确停止，不会成为孤儿。Plugin 直接杀进程。
+
+---
+
+## 8. 改进建议
+
+### P0
+
+1. **两者都加 WS 认证**: 至少 token 认证（INIT 时下发，连接时校验）
+
+### P1
+
+2. **考虑 Box 继承 ManagedRuntimeConnector**: 复用 `_start_runtime_subprocess` / `_wait_until_ready` / `_dispose_subprocess`，减少重复代码
+3. **Plugin 重连加退避**: 固定 3s 无 backoff 可能造成日志洪水，建议向 Box 的指数退避看齐
+4. **统一连接管理模式**: Event-based (Box) vs direct-await (Plugin)，考虑收敛为一种
+
+### 已完成（自上一轮）
+
+- ~~Box 加重连~~（commit `2dfd9d5d`）
+- ~~Box 加心跳~~（20s loop 与 Plugin 一致）
+- ~~Box 加 Windows 支持~~（commit `120817a` / `fafb7a4`）

docs/service-api-openapi.json

@@ -9,7 +9,7 @@
       "url": "https://langbot.app"
     },
     "license": {
-      "name": "AGPL-3.0",
+      "name": "Apache-2.0",
       "url": "https://github.com/langbot-app/LangBot/blob/master/LICENSE"
     }
   },

libs/dify_service_api/test.py

@@ -1,45 +0,0 @@
-from v1 import client  # type: ignore
-
-import asyncio
-
-import os
-import json
-
-
-class TestDifyClient:
-    async def test_chat_messages(self):
-        cln = client.AsyncDifyServiceClient(api_key=os.getenv('DIFY_API_KEY'), base_url=os.getenv('DIFY_BASE_URL'))
-
-        async for chunk in cln.chat_messages(inputs={}, query='调用工具查看现在几点？', user='test'):
-            print(json.dumps(chunk, ensure_ascii=False, indent=4))
-
-    async def test_upload_file(self):
-        cln = client.AsyncDifyServiceClient(api_key=os.getenv('DIFY_API_KEY'), base_url=os.getenv('DIFY_BASE_URL'))
-
-        file_bytes = open('img.png', 'rb').read()
-
-        print(type(file_bytes))
-
-        file = ('img2.png', file_bytes, 'image/png')
-
-        resp = await cln.upload_file(file=file, user='test')
-        print(json.dumps(resp, ensure_ascii=False, indent=4))
-
-    async def test_workflow_run(self):
-        cln = client.AsyncDifyServiceClient(api_key=os.getenv('DIFY_API_KEY'), base_url=os.getenv('DIFY_BASE_URL'))
-
-        # resp = await cln.workflow_run(inputs={}, user="test")
-        # # print(json.dumps(resp, ensure_ascii=False, indent=4))
-        # print(resp)
-        chunks = []
-
-        ignored_events = ['text_chunk']
-        async for chunk in cln.workflow_run(inputs={}, user='test'):
-            if chunk['event'] in ignored_events:
-                continue
-            chunks.append(chunk)
-        print(json.dumps(chunks, ensure_ascii=False, indent=4))
-
-
-if __name__ == '__main__':
-    asyncio.run(TestDifyClient().test_chat_messages())

libs/qq_official_api/api.py

@@ -1,263 +0,0 @@
-import time
-from quart import request
-import httpx
-from quart import Quart
-from typing import Callable, Dict, Any
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-from .qqofficialevent import QQOfficialEvent
-import json
-import traceback
-from cryptography.hazmat.primitives.asymmetric import ed25519
-
-
-def handle_validation(body: dict, bot_secret: str):
-    # bot正确的secert是32位的，此处仅为了适配演示demo
-    while len(bot_secret) < 32:
-        bot_secret = bot_secret * 2
-    bot_secret = bot_secret[:32]
-    # 实际使用场景中以上三行内容可清除
-
-    seed_bytes = bot_secret.encode()
-
-    signing_key = ed25519.Ed25519PrivateKey.from_private_bytes(seed_bytes)
-
-    msg = body['d']['event_ts'] + body['d']['plain_token']
-    msg_bytes = msg.encode()
-
-    signature = signing_key.sign(msg_bytes)
-
-    signature_hex = signature.hex()
-
-    response = {'plain_token': body['d']['plain_token'], 'signature': signature_hex}
-
-    return response
-
-
-class QQOfficialClient:
-    def __init__(self, secret: str, token: str, app_id: str, logger: None):
-        self.app = Quart(__name__)
-        self.app.add_url_rule(
-            '/callback/command',
-            'handle_callback',
-            self.handle_callback_request,
-            methods=['GET', 'POST'],
-        )
-        self.secret = secret
-        self.token = token
-        self.app_id = app_id
-        self._message_handlers = {}
-        self.base_url = 'https://api.sgroup.qq.com'
-        self.access_token = ''
-        self.access_token_expiry_time = None
-        self.logger = logger
-
-    async def check_access_token(self):
-        """检查access_token是否存在"""
-        if not self.access_token or await self.is_token_expired():
-            return False
-        return bool(self.access_token and self.access_token.strip())
-
-    async def get_access_token(self):
-        """获取access_token"""
-        url = 'https://bots.qq.com/app/getAppAccessToken'
-        async with httpx.AsyncClient() as client:
-            params = {
-                'appId': self.app_id,
-                'clientSecret': self.secret,
-            }
-            headers = {
-                'content-type': 'application/json',
-            }
-            try:
-                response = await client.post(url, json=params, headers=headers)
-                if response.status_code == 200:
-                    response_data = response.json()
-                access_token = response_data.get('access_token')
-                expires_in = int(response_data.get('expires_in', 7200))
-                self.access_token_expiry_time = time.time() + expires_in - 60
-                if access_token:
-                    self.access_token = access_token
-            except Exception as e:
-                await self.logger.error(f'获取access_token失败: {response_data}')
-                raise Exception(f'获取access_token失败: {e}')
-
-    async def handle_callback_request(self):
-        """处理回调请求"""
-        try:
-            # 读取请求数据
-            body = await request.get_data()
-            payload = json.loads(body)
-
-            # 验证是否为回调验证请求
-            if payload.get('op') == 13:
-                # 生成签名
-                response = handle_validation(payload, self.secret)
-
-                return response
-
-            if payload.get('op') == 0:
-                message_data = await self.get_message(payload)
-                if message_data:
-                    event = QQOfficialEvent.from_payload(message_data)
-                    await self._handle_message(event)
-
-            return {'code': 0, 'message': 'success'}
-
-        except Exception as e:
-            await self.logger.error(f'Error in handle_callback_request: {traceback.format_exc()}')
-            return {'error': str(e)}, 400
-
-    async def run_task(self, host: str, port: int, *args, **kwargs):
-        """启动 Quart 应用"""
-        await self.app.run_task(host=host, port=port, *args, **kwargs)
-
-    def on_message(self, msg_type: str):
-        """注册消息类型处理器"""
-
-        def decorator(func: Callable[[platform_events.Event], None]):
-            if msg_type not in self._message_handlers:
-                self._message_handlers[msg_type] = []
-            self._message_handlers[msg_type].append(func)
-            return func
-
-        return decorator
-
-    async def _handle_message(self, event: QQOfficialEvent):
-        """处理消息事件"""
-        msg_type = event.t
-        if msg_type in self._message_handlers:
-            for handler in self._message_handlers[msg_type]:
-                await handler(event)
-
-    async def get_message(self, msg: dict) -> Dict[str, Any]:
-        """获取消息"""
-        message_data = {
-            't': msg.get('t', {}),
-            'user_openid': msg.get('d', {}).get('author', {}).get('user_openid', {}),
-            'timestamp': msg.get('d', {}).get('timestamp', {}),
-            'd_author_id': msg.get('d', {}).get('author', {}).get('id', {}),
-            'content': msg.get('d', {}).get('content', {}),
-            'd_id': msg.get('d', {}).get('id', {}),
-            'id': msg.get('id', {}),
-            'channel_id': msg.get('d', {}).get('channel_id', {}),
-            'username': msg.get('d', {}).get('author', {}).get('username', {}),
-            'guild_id': msg.get('d', {}).get('guild_id', {}),
-            'member_openid': msg.get('d', {}).get('author', {}).get('openid', {}),
-            'group_openid': msg.get('d', {}).get('group_openid', {}),
-        }
-        attachments = msg.get('d', {}).get('attachments', [])
-        image_attachments = [attachment['url'] for attachment in attachments if await self.is_image(attachment)]
-        image_attachments_type = [
-            attachment['content_type'] for attachment in attachments if await self.is_image(attachment)
-        ]
-        if image_attachments:
-            message_data['image_attachments'] = image_attachments[0]
-            message_data['content_type'] = image_attachments_type[0]
-        else:
-            message_data['image_attachments'] = None
-
-        return message_data
-
-    async def is_image(self, attachment: dict) -> bool:
-        """判断是否为图片附件"""
-        content_type = attachment.get('content_type', '')
-        return content_type.startswith('image/')
-
-    async def send_private_text_msg(self, user_openid: str, content: str, msg_id: str):
-        """发送私聊消息"""
-        if not await self.check_access_token():
-            await self.get_access_token()
-
-        url = self.base_url + '/v2/users/' + user_openid + '/messages'
-        async with httpx.AsyncClient() as client:
-            headers = {
-                'Authorization': f'QQBot {self.access_token}',
-                'Content-Type': 'application/json',
-            }
-            data = {
-                'content': content,
-                'msg_type': 0,
-                'msg_id': msg_id,
-            }
-            response = await client.post(url, headers=headers, json=data)
-            response_data = response.json()
-            if response.status_code == 200:
-                return
-            else:
-                await self.logger.error(f'发送私聊消息失败: {response_data}')
-                raise ValueError(response)
-
-    async def send_group_text_msg(self, group_openid: str, content: str, msg_id: str):
-        """发送群聊消息"""
-        if not await self.check_access_token():
-            await self.get_access_token()
-
-        url = self.base_url + '/v2/groups/' + group_openid + '/messages'
-        async with httpx.AsyncClient() as client:
-            headers = {
-                'Authorization': f'QQBot {self.access_token}',
-                'Content-Type': 'application/json',
-            }
-            data = {
-                'content': content,
-                'msg_type': 0,
-                'msg_id': msg_id,
-            }
-            response = await client.post(url, headers=headers, json=data)
-            if response.status_code == 200:
-                return
-            else:
-                await self.logger.error(f'发送群聊消息失败:{response.json()}')
-                raise Exception(response.read().decode())
-
-    async def send_channle_group_text_msg(self, channel_id: str, content: str, msg_id: str):
-        """发送频道群聊消息"""
-        if not await self.check_access_token():
-            await self.get_access_token()
-
-        url = self.base_url + '/channels/' + channel_id + '/messages'
-        async with httpx.AsyncClient() as client:
-            headers = {
-                'Authorization': f'QQBot {self.access_token}',
-                'Content-Type': 'application/json',
-            }
-            params = {
-                'content': content,
-                'msg_type': 0,
-                'msg_id': msg_id,
-            }
-            response = await client.post(url, headers=headers, json=params)
-            if response.status_code == 200:
-                return True
-            else:
-                await self.logger.error(f'发送频道群聊消息失败: {response.json()}')
-                raise Exception(response)
-
-    async def send_channle_private_text_msg(self, guild_id: str, content: str, msg_id: str):
-        """发送频道私聊消息"""
-        if not await self.check_access_token():
-            await self.get_access_token()
-
-        url = self.base_url + '/dms/' + guild_id + '/messages'
-        async with httpx.AsyncClient() as client:
-            headers = {
-                'Authorization': f'QQBot {self.access_token}',
-                'Content-Type': 'application/json',
-            }
-            params = {
-                'content': content,
-                'msg_type': 0,
-                'msg_id': msg_id,
-            }
-            response = await client.post(url, headers=headers, json=params)
-            if response.status_code == 200:
-                return True
-            else:
-                await self.logger.error(f'发送频道私聊消息失败: {response.json()}')
-                raise Exception(response)
-
-    async def is_token_expired(self):
-        """检查token是否过期"""
-        if self.access_token_expiry_time is None:
-            return True
-        return time.time() > self.access_token_expiry_time

libs/wecom_ai_bot_api/api.py

@@ -1,588 +0,0 @@
-import asyncio
-import base64
-import json
-import time
-import traceback
-import uuid
-import xml.etree.ElementTree as ET
-from dataclasses import dataclass, field
-from typing import Any, Callable, Optional
-from urllib.parse import unquote
-
-import httpx
-from Crypto.Cipher import AES
-from quart import Quart, request, Response, jsonify
-
-from libs.wecom_ai_bot_api import wecombotevent
-from libs.wecom_ai_bot_api.WXBizMsgCrypt3 import WXBizMsgCrypt
-from pkg.platform.logger import EventLogger
-
-
-@dataclass
-class StreamChunk:
-    """描述单次推送给企业微信的流式片段。"""
-
-    # 需要返回给企业微信的文本内容
-    content: str
-
-    # 标记是否为最终片段，对应企业微信协议里的 finish 字段
-    is_final: bool = False
-
-    # 预留额外元信息，未来支持多模态扩展时可使用
-    meta: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class StreamSession:
-    """维护一次企业微信流式会话的上下文。"""
-
-    # 企业微信要求的 stream_id，用于标识后续刷新请求
-    stream_id: str
-
-    # 原始消息的 msgid，便于与流水线消息对应
-    msg_id: str
-
-    # 群聊会话标识（单聊时为空）
-    chat_id: Optional[str]
-
-    # 触发消息的发送者
-    user_id: Optional[str]
-
-    # 会话创建时间
-    created_at: float = field(default_factory=time.time)
-
-    # 最近一次被访问的时间，cleanup 依据该值判断过期
-    last_access: float = field(default_factory=time.time)
-
-    # 将流水线增量结果缓存到队列，刷新请求逐条消费
-    queue: asyncio.Queue = field(default_factory=asyncio.Queue)
-
-    # 是否已经完成（收到最终片段）
-    finished: bool = False
-
-    # 缓存最近一次片段，处理重试或超时兜底
-    last_chunk: Optional[StreamChunk] = None
-
-
-class StreamSessionManager:
-    """管理 stream 会话的生命周期，并负责队列的生产消费。"""
-
-    def __init__(self, logger: EventLogger, ttl: int = 60) -> None:
-        self.logger = logger
-
-        self.ttl = ttl  # 超时时间（秒），超过该时间未被访问的会话会被清理由 cleanup
-        self._sessions: dict[str, StreamSession] = {}  # stream_id -> StreamSession 映射
-        self._msg_index: dict[str, str] = {}  # msgid -> stream_id 映射，便于流水线根据消息 ID 找到会话
-
-    def get_stream_id_by_msg(self, msg_id: str) -> Optional[str]:
-        if not msg_id:
-            return None
-        return self._msg_index.get(msg_id)
-
-    def get_session(self, stream_id: str) -> Optional[StreamSession]:
-        return self._sessions.get(stream_id)
-
-    def create_or_get(self, msg_json: dict[str, Any]) -> tuple[StreamSession, bool]:
-        """根据企业微信回调创建或获取会话。
-
-        Args:
-            msg_json: 企业微信解密后的回调 JSON。
-
-        Returns:
-            Tuple[StreamSession, bool]: `StreamSession` 为会话实例，`bool` 指示是否为新建会话。
-
-        Example:
-            在首次回调中调用，得到 `is_new=True` 后再触发流水线。
-        """
-        msg_id = msg_json.get('msgid', '')
-        if msg_id and msg_id in self._msg_index:
-            stream_id = self._msg_index[msg_id]
-            session = self._sessions.get(stream_id)
-            if session:
-                session.last_access = time.time()
-                return session, False
-
-        stream_id = str(uuid.uuid4())
-        session = StreamSession(
-            stream_id=stream_id,
-            msg_id=msg_id,
-            chat_id=msg_json.get('chatid'),
-            user_id=msg_json.get('from', {}).get('userid'),
-        )
-
-        if msg_id:
-            self._msg_index[msg_id] = stream_id
-        self._sessions[stream_id] = session
-        return session, True
-
-    async def publish(self, stream_id: str, chunk: StreamChunk) -> bool:
-        """向 stream 队列写入新的增量片段。
-
-        Args:
-            stream_id: 企业微信分配的流式会话 ID。
-            chunk: 待发送的增量片段。
-
-        Returns:
-            bool: 当流式队列存在并成功入队时返回 True。
-
-        Example:
-            在收到模型增量后调用 `await manager.publish('sid', StreamChunk('hello'))`。
-        """
-        session = self._sessions.get(stream_id)
-        if not session:
-            return False
-
-        session.last_access = time.time()
-        session.last_chunk = chunk
-
-        try:
-            session.queue.put_nowait(chunk)
-        except asyncio.QueueFull:
-            # 默认无界队列，此处兜底防御
-            await session.queue.put(chunk)
-
-        if chunk.is_final:
-            session.finished = True
-
-        return True
-
-    async def consume(self, stream_id: str, timeout: float = 0.5) -> Optional[StreamChunk]:
-        """从队列中取出一个片段，若超时返回 None。
-
-        Args:
-            stream_id: 企业微信流式会话 ID。
-            timeout: 取片段的最长等待时间（秒）。
-
-        Returns:
-            Optional[StreamChunk]: 成功时返回片段，超时或会话不存在时返回 None。
-
-        Example:
-            企业微信刷新到达时调用，若队列有数据则立即返回 `StreamChunk`。
-        """
-        session = self._sessions.get(stream_id)
-        if not session:
-            return None
-
-        session.last_access = time.time()
-
-        try:
-            chunk = await asyncio.wait_for(session.queue.get(), timeout)
-            session.last_access = time.time()
-            if chunk.is_final:
-                session.finished = True
-            return chunk
-        except asyncio.TimeoutError:
-            if session.finished and session.last_chunk:
-                return session.last_chunk
-            return None
-
-    def mark_finished(self, stream_id: str) -> None:
-        session = self._sessions.get(stream_id)
-        if session:
-            session.finished = True
-            session.last_access = time.time()
-
-    def cleanup(self) -> None:
-        """定期清理过期会话，防止队列与映射无上限累积。"""
-        now = time.time()
-        expired: list[str] = []
-        for stream_id, session in self._sessions.items():
-            if now - session.last_access > self.ttl:
-                expired.append(stream_id)
-
-        for stream_id in expired:
-            session = self._sessions.pop(stream_id, None)
-            if not session:
-                continue
-            msg_id = session.msg_id
-            if msg_id and self._msg_index.get(msg_id) == stream_id:
-                self._msg_index.pop(msg_id, None)
-
-
-class WecomBotClient:
-    def __init__(self, Token: str, EnCodingAESKey: str, Corpid: str, logger: EventLogger):
-        """企业微信智能机器人客户端。
-
-        Args:
-            Token: 企业微信回调验证使用的 token。
-            EnCodingAESKey: 企业微信消息加解密密钥。
-            Corpid: 企业 ID。
-            logger: 日志记录器。
-
-        Example:
-            >>> client = WecomBotClient(Token='token', EnCodingAESKey='aeskey', Corpid='corp', logger=logger)
-        """
-
-        self.Token = Token
-        self.EnCodingAESKey = EnCodingAESKey
-        self.Corpid = Corpid
-        self.ReceiveId = ''
-        self.app = Quart(__name__)
-        self.app.add_url_rule(
-            '/callback/command',
-            'handle_callback',
-            self.handle_callback_request,
-            methods=['POST', 'GET']
-        )
-        self._message_handlers = {
-            'example': [],
-        }
-        self.logger = logger
-        self.generated_content: dict[str, str] = {}
-        self.msg_id_map: dict[str, int] = {}
-        self.stream_sessions = StreamSessionManager(logger=logger)
-        self.stream_poll_timeout = 0.5
-
-    @staticmethod
-    def _build_stream_payload(stream_id: str, content: str, finish: bool) -> dict[str, Any]:
-        """按照企业微信协议拼装返回报文。
-
-        Args:
-            stream_id: 企业微信会话 ID。
-            content: 推送的文本内容。
-            finish: 是否为最终片段。
-
-        Returns:
-            dict[str, Any]: 可直接加密返回的 payload。
-
-        Example:
-            组装 `{'msgtype': 'stream', 'stream': {'id': 'sid', ...}}` 结构。
-        """
-        return {
-            'msgtype': 'stream',
-            'stream': {
-                'id': stream_id,
-                'finish': finish,
-                'content': content,
-            },
-        }
-
-    async def _encrypt_and_reply(self, payload: dict[str, Any], nonce: str) -> tuple[Response, int]:
-        """对响应进行加密封装并返回给企业微信。
-
-        Args:
-            payload: 待加密的响应内容。
-            nonce: 企业微信回调参数中的 nonce。
-
-        Returns:
-            Tuple[Response, int]: Quart Response 对象及状态码。
-
-        Example:
-            在首包或刷新场景中调用以生成加密响应。
-        """
-        reply_plain_str = json.dumps(payload, ensure_ascii=False)
-        reply_timestamp = str(int(time.time()))
-        ret, encrypt_text = self.wxcpt.EncryptMsg(reply_plain_str, nonce, reply_timestamp)
-        if ret != 0:
-            await self.logger.error(f'加密失败: {ret}')
-            return jsonify({'error': 'encrypt_failed'}), 500
-
-        root = ET.fromstring(encrypt_text)
-        encrypt = root.find('Encrypt').text
-        resp = {
-            'encrypt': encrypt,
-        }
-        return jsonify(resp), 200
-
-    async def _dispatch_event(self, event: wecombotevent.WecomBotEvent) -> None:
-        """异步触发流水线处理，避免阻塞首包响应。
-
-        Args:
-            event: 由企业微信消息转换的内部事件对象。
-        """
-        try:
-            await self._handle_message(event)
-        except Exception:
-            await self.logger.error(traceback.format_exc())
-
-    async def _handle_post_initial_response(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
-        """处理企业微信首次推送的消息，返回 stream_id 并开启流水线。
-
-        Args:
-            msg_json: 解密后的企业微信消息 JSON。
-            nonce: 企业微信回调参数 nonce。
-
-        Returns:
-            Tuple[Response, int]: Quart Response 及状态码。
-
-        Example:
-            首次回调时调用，立即返回带 `stream_id` 的响应。
-        """
-        session, is_new = self.stream_sessions.create_or_get(msg_json)
-
-        message_data = await self.get_message(msg_json)
-        if message_data:
-            message_data['stream_id'] = session.stream_id
-            try:
-                event = wecombotevent.WecomBotEvent(message_data)
-            except Exception:
-                await self.logger.error(traceback.format_exc())
-            else:
-                if is_new:
-                    asyncio.create_task(self._dispatch_event(event))
-
-        payload = self._build_stream_payload(session.stream_id, '', False)
-        return await self._encrypt_and_reply(payload, nonce)
-
-    async def _handle_post_followup_response(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
-        """处理企业微信的流式刷新请求，按需返回增量片段。
-
-        Args:
-            msg_json: 解密后的企业微信刷新请求。
-            nonce: 企业微信回调参数 nonce。
-
-        Returns:
-            Tuple[Response, int]: Quart Response 及状态码。
-
-        Example:
-            在刷新请求中调用，按需返回增量片段。
-        """
-        stream_info = msg_json.get('stream', {})
-        stream_id = stream_info.get('id', '')
-        if not stream_id:
-            await self.logger.error('刷新请求缺少 stream.id')
-            return await self._encrypt_and_reply(self._build_stream_payload('', '', True), nonce)
-
-        session = self.stream_sessions.get_session(stream_id)
-        chunk = await self.stream_sessions.consume(stream_id, timeout=self.stream_poll_timeout)
-
-        if not chunk:
-            cached_content = None
-            if session and session.msg_id:
-                cached_content = self.generated_content.pop(session.msg_id, None)
-            if cached_content is not None:
-                chunk = StreamChunk(content=cached_content, is_final=True)
-            else:
-                payload = self._build_stream_payload(stream_id, '', False)
-                return await self._encrypt_and_reply(payload, nonce)
-
-        payload = self._build_stream_payload(stream_id, chunk.content, chunk.is_final)
-        if chunk.is_final:
-            self.stream_sessions.mark_finished(stream_id)
-        return await self._encrypt_and_reply(payload, nonce)
-
-    async def handle_callback_request(self):
-        """企业微信回调入口。
-
-        Returns:
-            Quart Response: 根据请求类型返回验证、首包或刷新结果。
-
-        Example:
-            作为 Quart 路由处理函数直接注册并使用。
-        """
-        try:
-            self.wxcpt = WXBizMsgCrypt(self.Token, self.EnCodingAESKey, '')
-            await self.logger.info(f'{request.method} {request.url} {str(request.args)}')
-
-            if request.method == 'GET':
-                return await self._handle_get_callback()
-
-            if request.method == 'POST':
-                return await self._handle_post_callback()
-
-            return Response('', status=405)
-
-        except Exception:
-            await self.logger.error(traceback.format_exc())
-            return Response('Internal Server Error', status=500)
-
-    async def _handle_get_callback(self) -> tuple[Response, int] | Response:
-        """处理企业微信的 GET 验证请求。"""
-
-        msg_signature = unquote(request.args.get('msg_signature', ''))
-        timestamp = unquote(request.args.get('timestamp', ''))
-        nonce = unquote(request.args.get('nonce', ''))
-        echostr = unquote(request.args.get('echostr', ''))
-
-        if not all([msg_signature, timestamp, nonce, echostr]):
-            await self.logger.error('请求参数缺失')
-            return Response('缺少参数', status=400)
-
-        ret, decrypted_str = self.wxcpt.VerifyURL(msg_signature, timestamp, nonce, echostr)
-        if ret != 0:
-            await self.logger.error('验证URL失败')
-            return Response('验证失败', status=403)
-
-        return Response(decrypted_str, mimetype='text/plain')
-
-    async def _handle_post_callback(self) -> tuple[Response, int] | Response:
-        """处理企业微信的 POST 回调请求。"""
-
-        self.stream_sessions.cleanup()
-
-        msg_signature = unquote(request.args.get('msg_signature', ''))
-        timestamp = unquote(request.args.get('timestamp', ''))
-        nonce = unquote(request.args.get('nonce', ''))
-
-        encrypted_json = await request.get_json()
-        encrypted_msg = (encrypted_json or {}).get('encrypt', '')
-        if not encrypted_msg:
-            await self.logger.error("请求体中缺少 'encrypt' 字段")
-            return Response('Bad Request', status=400)
-
-        xml_post_data = f"<xml><Encrypt><![CDATA[{encrypted_msg}]]></Encrypt></xml>"
-        ret, decrypted_xml = self.wxcpt.DecryptMsg(xml_post_data, msg_signature, timestamp, nonce)
-        if ret != 0:
-            await self.logger.error('解密失败')
-            return Response('解密失败', status=400)
-
-        msg_json = json.loads(decrypted_xml)
-
-        if msg_json.get('msgtype') == 'stream':
-            return await self._handle_post_followup_response(msg_json, nonce)
-
-        return await self._handle_post_initial_response(msg_json, nonce)
-
-    async def get_message(self, msg_json):
-        message_data = {}
-
-        if msg_json.get('chattype', '') == 'single':
-            message_data['type'] = 'single'
-        elif msg_json.get('chattype', '') == 'group':
-            message_data['type'] = 'group'
-
-        if msg_json.get('msgtype') == 'text':
-            message_data['content'] = msg_json.get('text', {}).get('content')
-        elif msg_json.get('msgtype') == 'image':
-            picurl = msg_json.get('image', {}).get('url', '')
-            base64 = await self.download_url_to_base64(picurl, self.EnCodingAESKey)
-            message_data['picurl'] = base64
-        elif msg_json.get('msgtype') == 'mixed':
-            items = msg_json.get('mixed', {}).get('msg_item', [])
-            texts = []
-            picurl = None
-            for item in items:
-                if item.get('msgtype') == 'text':
-                    texts.append(item.get('text', {}).get('content', ''))
-                elif item.get('msgtype') == 'image' and picurl is None:
-                    picurl = item.get('image', {}).get('url')
-
-            if texts:
-                message_data['content'] = "".join(texts)  # 拼接所有 text
-            if picurl:
-                base64 = await self.download_url_to_base64(picurl, self.EnCodingAESKey)
-                message_data['picurl'] = base64  # 只保留第一个 image
-
-        # Extract user information
-        from_info = msg_json.get('from', {})
-        message_data['userid'] = from_info.get('userid', '')
-        message_data['username'] = from_info.get('alias', '') or from_info.get('name', '') or from_info.get('userid', '')
-
-        # Extract chat/group information
-        if msg_json.get('chattype', '') == 'group':
-            message_data['chatid'] = msg_json.get('chatid', '')
-            # Try to get group name if available
-            message_data['chatname'] = msg_json.get('chatname', '') or msg_json.get('chatid', '')
-
-        message_data['msgid'] = msg_json.get('msgid', '')
-
-        if msg_json.get('aibotid'):
-            message_data['aibotid'] = msg_json.get('aibotid', '')
-
-        return message_data
-
-    async def _handle_message(self, event: wecombotevent.WecomBotEvent):
-        """
-        处理消息事件。
-        """
-        try:
-            message_id = event.message_id
-            if message_id in self.msg_id_map.keys():
-                self.msg_id_map[message_id] += 1
-                return
-            self.msg_id_map[message_id] = 1
-            msg_type = event.type
-            if msg_type in self._message_handlers:
-                for handler in self._message_handlers[msg_type]:
-                    await handler(event)
-        except Exception:
-            print(traceback.format_exc())
-
-    async def push_stream_chunk(self, msg_id: str, content: str, is_final: bool = False) -> bool:
-        """将流水线片段推送到 stream 会话。
-
-        Args:
-            msg_id: 原始企业微信消息 ID。
-            content: 模型产生的片段内容。
-            is_final: 是否为最终片段。
-
-        Returns:
-            bool: 当成功写入流式队列时返回 True。
-
-        Example:
-            在流水线 `reply_message_chunk` 中调用，将增量推送至企业微信。
-        """
-        # 根据 msg_id 找到对应 stream 会话，如果不存在说明当前消息非流式
-        stream_id = self.stream_sessions.get_stream_id_by_msg(msg_id)
-        if not stream_id:
-            return False
-
-        chunk = StreamChunk(content=content, is_final=is_final)
-        await self.stream_sessions.publish(stream_id, chunk)
-        if is_final:
-            self.stream_sessions.mark_finished(stream_id)
-        return True
-
-    async def set_message(self, msg_id: str, content: str):
-        """兼容旧逻辑：若无法流式返回则缓存最终结果。
-
-        Args:
-            msg_id: 企业微信消息 ID。
-            content: 最终回复的文本内容。
-
-        Example:
-            在非流式场景下缓存最终结果以备刷新时返回。
-        """
-        handled = await self.push_stream_chunk(msg_id, content, is_final=True)
-        if not handled:
-            self.generated_content[msg_id] = content
-
-    def on_message(self, msg_type: str):
-        def decorator(func: Callable[[wecombotevent.WecomBotEvent], None]):
-            if msg_type not in self._message_handlers:
-                self._message_handlers[msg_type] = []
-            self._message_handlers[msg_type].append(func)
-            return func
-
-        return decorator
-
-    async def download_url_to_base64(self, download_url, encoding_aes_key):
-        async with httpx.AsyncClient() as client:
-            response = await client.get(download_url)
-            if response.status_code != 200:
-                await self.logger.error(f'failed to get file: {response.text}')
-                return None
-
-            encrypted_bytes = response.content
-
-        aes_key = base64.b64decode(encoding_aes_key + "=")  # base64 补齐
-        iv = aes_key[:16]
-
-        cipher = AES.new(aes_key, AES.MODE_CBC, iv)
-        decrypted = cipher.decrypt(encrypted_bytes)
-
-        pad_len = decrypted[-1]
-        decrypted = decrypted[:-pad_len]
-
-        if decrypted.startswith(b"\xff\xd8"):  # JPEG
-            mime_type = "image/jpeg"
-        elif decrypted.startswith(b"\x89PNG"):  # PNG
-            mime_type = "image/png"
-        elif decrypted.startswith((b"GIF87a", b"GIF89a")):  # GIF
-            mime_type = "image/gif"
-        elif decrypted.startswith(b"BM"):  # BMP
-            mime_type = "image/bmp"
-        elif decrypted.startswith(b"II*\x00") or decrypted.startswith(b"MM\x00*"):  # TIFF
-            mime_type = "image/tiff"
-        else:
-            mime_type = "application/octet-stream"
-
-        # 转 base64
-        base64_str = base64.b64encode(decrypted).decode("utf-8")
-        return f"data:{mime_type};base64,{base64_str}"
-
-    async def run_task(self, host: str, port: int, *args, **kwargs):
-        """
-        启动 Quart 应用。
-        """
-        await self.app.run_task(host=host, port=port, *args, **kwargs)

libs/wecom_ai_bot_api/wecombotevent.py

@@ -1,74 +0,0 @@
-from typing import Dict, Any, Optional
-
-
-class WecomBotEvent(dict):
-    @staticmethod
-    def from_payload(payload: Dict[str, Any]) -> Optional['WecomBotEvent']:
-        try:
-            event = WecomBotEvent(payload)
-            return event
-        except KeyError:
-            return None
-
-    @property
-    def type(self) -> str:
-        """
-        事件类型
-        """
-        return self.get('type', '')
-
-    @property
-    def userid(self) -> str:
-        """
-        用户id
-        """
-        return self.get('from', {}).get('userid', '') or self.get('userid', '')
-
-    @property
-    def username(self) -> str:
-        """
-        用户名称
-        """
-        return self.get('username', '') or self.get('from', {}).get('alias', '') or self.get('from', {}).get('name', '') or self.userid
-
-    @property
-    def chatname(self) -> str:
-        """
-        群组名称
-        """
-        return self.get('chatname', '') or str(self.chatid)
-
-    @property
-    def content(self) -> str:
-        """
-        内容
-        """
-        return self.get('content', '')
-
-    @property
-    def picurl(self) -> str:
-        """
-        图片url
-        """
-        return self.get('picurl', '')
-
-    @property
-    def chatid(self) -> str:
-        """
-        群组id
-        """
-        return self.get('chatid', {})
-
-    @property
-    def message_id(self) -> str:
-        """
-        消息id
-        """
-        return self.get('msgid', '')
-    
-    @property
-    def ai_bot_id(self) -> str:
-        """
-        AI Bot ID
-        """
-        return self.get('aibotid', '')

main.py

@@ -1,117 +1,3 @@
-import asyncio
-import argparse
-# LangBot 终端启动入口
-# 在此层级解决依赖项检查。
-# LangBot/main.py
+import langbot.__main__
 
-asciiart = r"""
- _                   ___      _   
-| |   __ _ _ _  __ _| _ ) ___| |_ 
-| |__/ _` | ' \/ _` | _ \/ _ \  _|
-|____\__,_|_||_\__, |___/\___/\__|
-               |___/              
-
-⭐️ Open Source 开源地址: https://github.com/langbot-app/LangBot
-📖 Documentation 文档地址: https://docs.langbot.app
-"""
-
-
-async def main_entry(loop: asyncio.AbstractEventLoop):
-    parser = argparse.ArgumentParser(description='LangBot')
-    parser.add_argument(
-        '--standalone-runtime',
-        action='store_true',
-        help='Use standalone plugin runtime / 使用独立插件运行时',
-        default=False,
-    )
-    parser.add_argument('--debug', action='store_true', help='Debug mode / 调试模式', default=False)
-    args = parser.parse_args()
-
-    if args.standalone_runtime:
-        from pkg.utils import platform
-
-        platform.standalone_runtime = True
-
-    if args.debug:
-        from pkg.utils import constants
-
-        constants.debug_mode = True
-
-    print(asciiart)
-
-    import sys
-
-    # 检查依赖
-
-    from pkg.core.bootutils import deps
-
-    missing_deps = await deps.check_deps()
-
-    if missing_deps:
-        print('以下依赖包未安装，将自动安装，请完成后重启程序：')
-        print(
-            'These dependencies are missing, they will be installed automatically, please restart the program after completion:'
-        )
-        for dep in missing_deps:
-            print('-', dep)
-        await deps.install_deps(missing_deps)
-        print('已自动安装缺失的依赖包，请重启程序。')
-        print('The missing dependencies have been installed automatically, please restart the program.')
-        sys.exit(0)
-
-    # # 检查pydantic版本，如果没有 pydantic.v1，则把 pydantic 映射为 v1
-    # import pydantic.version
-
-    # if pydantic.version.VERSION < '2.0':
-    #     import pydantic
-
-    #     sys.modules['pydantic.v1'] = pydantic
-
-    # 检查配置文件
-
-    from pkg.core.bootutils import files
-
-    generated_files = await files.generate_files()
-
-    if generated_files:
-        print('以下文件不存在，已自动生成：')
-        print('Following files do not exist and have been automatically generated:')
-        for file in generated_files:
-            print('-', file)
-
-    from pkg.core import boot
-
-    await boot.main(loop)
-
-
-if __name__ == '__main__':
-    import os
-    import sys
-
-    # 必须大于 3.10.1
-    if sys.version_info < (3, 10, 1):
-        print('需要 Python 3.10.1 及以上版本，当前 Python 版本为：', sys.version)
-        input('按任意键退出...')
-        print('Your Python version is not supported. Please exit the program by pressing any key.')
-        exit(1)
-
-    # Check if the current directory is the LangBot project root directory
-    invalid_pwd = False
-
-    if not os.path.exists('main.py'):
-        invalid_pwd = True
-    else:
-        with open('main.py', 'r', encoding='utf-8') as f:
-            content = f.read()
-            if 'LangBot/main.py' not in content:
-                invalid_pwd = True
-    if invalid_pwd:
-        print('请在 LangBot 项目根目录下以命令形式运行此程序。')
-        input('按任意键退出...')
-        print('Please run this program in the LangBot project root directory in command form.')
-        print('Press any key to exit...')
-        exit(1)
-
-    loop = asyncio.new_event_loop()
-
-    loop.run_until_complete(main_entry(loop))
+langbot.__main__.main()

pkg/api/http/controller/groups/pipelines/webchat.py

@@ -1,109 +0,0 @@
-import json
-
-import quart
-
-from ... import group
-
-
-@group.group_class('webchat', '/api/v1/pipelines/<pipeline_uuid>/chat')
-class WebChatDebugRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('/send', methods=['POST'])
-        async def send_message(pipeline_uuid: str) -> str:
-            """Send a message to the pipeline for debugging"""
-
-            async def stream_generator(generator):
-                yield 'data: {"type": "start"}\n\n'
-                async for message in generator:
-                    yield f'data: {json.dumps({"message": message})}\n\n'
-                yield 'data: {"type": "end"}\n\n'
-
-            try:
-                data = await quart.request.get_json()
-                session_type = data.get('session_type', 'person')
-                message_chain_obj = data.get('message', [])
-                is_stream = data.get('is_stream', False)
-
-                if not message_chain_obj:
-                    return self.http_status(400, -1, 'message is required')
-
-                if session_type not in ['person', 'group']:
-                    return self.http_status(400, -1, 'session_type must be person or group')
-
-                webchat_adapter = self.ap.platform_mgr.webchat_proxy_bot.adapter
-
-                if not webchat_adapter:
-                    return self.http_status(404, -1, 'WebChat adapter not found')
-
-                if is_stream:
-                    generator = webchat_adapter.send_webchat_message(
-                        pipeline_uuid, session_type, message_chain_obj, is_stream
-                    )
-                    # 设置正确的响应头
-                    headers = {
-                        'Content-Type': 'text/event-stream',
-                        'Transfer-Encoding': 'chunked',
-                        'Cache-Control': 'no-cache',
-                        'Connection': 'keep-alive',
-                    }
-                    return quart.Response(stream_generator(generator), mimetype='text/event-stream', headers=headers)
-
-                else:  # non-stream
-                    result = None
-                    async for message in webchat_adapter.send_webchat_message(
-                        pipeline_uuid, session_type, message_chain_obj
-                    ):
-                        result = message
-                    if result is not None:
-                        return self.success(
-                            data={
-                                'message': result,
-                            }
-                        )
-                    else:
-                        return self.http_status(400, -1, 'message is required')
-
-            except Exception as e:
-                return self.http_status(500, -1, f'Internal server error: {str(e)}')
-
-        @self.route('/messages/<session_type>', methods=['GET'])
-        async def get_messages(pipeline_uuid: str, session_type: str) -> str:
-            """Get the message history of the pipeline for debugging"""
-            try:
-                if session_type not in ['person', 'group']:
-                    return self.http_status(400, -1, 'session_type must be person or group')
-
-                webchat_adapter = self.ap.platform_mgr.webchat_proxy_bot.adapter
-
-                if not webchat_adapter:
-                    return self.http_status(404, -1, 'WebChat adapter not found')
-
-                messages = webchat_adapter.get_webchat_messages(pipeline_uuid, session_type)
-
-                return self.success(data={'messages': messages})
-
-            except Exception as e:
-                return self.http_status(500, -1, f'Internal server error: {str(e)}')
-
-        @self.route('/reset/<session_type>', methods=['POST'])
-        async def reset_session(session_type: str) -> str:
-            """Reset the debug session"""
-            try:
-                if session_type not in ['person', 'group']:
-                    return self.http_status(400, -1, 'session_type must be person or group')
-
-                webchat_adapter = None
-                for bot in self.ap.platform_mgr.bots:
-                    if hasattr(bot.adapter, '__class__') and bot.adapter.__class__.__name__ == 'WebChatAdapter':
-                        webchat_adapter = bot.adapter
-                        break
-
-                if not webchat_adapter:
-                    return self.http_status(404, -1, 'WebChat adapter not found')
-
-                webchat_adapter.reset_debug_session(session_type)
-
-                return self.success(data={'message': 'Session reset successfully'})
-
-            except Exception as e:
-                return self.http_status(500, -1, f'Internal server error: {str(e)}')

pkg/api/http/controller/groups/platform/adapters.py

@@ -1,34 +0,0 @@
-import quart
-
-from ... import group
-
-
-@group.group_class('adapters', '/api/v1/platform/adapters')
-class AdaptersRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('', methods=['GET'])
-        async def _() -> str:
-            return self.success(data={'adapters': self.ap.platform_mgr.get_available_adapters_info()})
-
-        @self.route('/<adapter_name>', methods=['GET'])
-        async def _(adapter_name: str) -> str:
-            adapter_info = self.ap.platform_mgr.get_available_adapter_info_by_name(adapter_name)
-
-            if adapter_info is None:
-                return self.http_status(404, -1, 'adapter not found')
-
-            return self.success(data={'adapter': adapter_info})
-
-        @self.route('/<adapter_name>/icon', methods=['GET'], auth_type=group.AuthType.NONE)
-        async def _(adapter_name: str) -> quart.Response:
-            adapter_manifest = self.ap.platform_mgr.get_available_adapter_manifest_by_name(adapter_name)
-
-            if adapter_manifest is None:
-                return self.http_status(404, -1, 'adapter not found')
-
-            icon_path = adapter_manifest.icon_rel_path
-
-            if icon_path is None:
-                return self.http_status(404, -1, 'icon not found')
-
-            return await quart.send_file(icon_path)

pkg/api/http/controller/groups/plugins.py

@@ -1,309 +0,0 @@
-from __future__ import annotations
-
-import base64
-import quart
-import re
-import httpx
-import uuid
-import os
-
-from .....core import taskmgr
-from .. import group
-from langbot_plugin.runtime.plugin.mgr import PluginInstallSource
-
-
-@group.group_class('plugins', '/api/v1/plugins')
-class PluginsRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            plugins = await self.ap.plugin_connector.list_plugins()
-
-            return self.success(data={'plugins': plugins})
-
-        @self.route(
-            '/<author>/<plugin_name>/upgrade',
-            methods=['POST'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _(author: str, plugin_name: str) -> str:
-            ctx = taskmgr.TaskContext.new()
-            wrapper = self.ap.task_mgr.create_user_task(
-                self.ap.plugin_connector.upgrade_plugin(author, plugin_name, task_context=ctx),
-                kind='plugin-operation',
-                name=f'plugin-upgrade-{plugin_name}',
-                label=f'Upgrading plugin {plugin_name}',
-                context=ctx,
-            )
-            return self.success(data={'task_id': wrapper.id})
-
-        @self.route(
-            '/<author>/<plugin_name>',
-            methods=['GET', 'DELETE'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _(author: str, plugin_name: str) -> str:
-            if quart.request.method == 'GET':
-                plugin = await self.ap.plugin_connector.get_plugin_info(author, plugin_name)
-                if plugin is None:
-                    return self.http_status(404, -1, 'plugin not found')
-                return self.success(data={'plugin': plugin})
-            elif quart.request.method == 'DELETE':
-                delete_data = quart.request.args.get('delete_data', 'false').lower() == 'true'
-                ctx = taskmgr.TaskContext.new()
-                wrapper = self.ap.task_mgr.create_user_task(
-                    self.ap.plugin_connector.delete_plugin(
-                        author, plugin_name, delete_data=delete_data, task_context=ctx
-                    ),
-                    kind='plugin-operation',
-                    name=f'plugin-remove-{plugin_name}',
-                    label=f'Removing plugin {plugin_name}',
-                    context=ctx,
-                )
-
-                return self.success(data={'task_id': wrapper.id})
-
-        @self.route(
-            '/<author>/<plugin_name>/config',
-            methods=['GET', 'PUT'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _(author: str, plugin_name: str) -> quart.Response:
-            plugin = await self.ap.plugin_connector.get_plugin_info(author, plugin_name)
-            if plugin is None:
-                return self.http_status(404, -1, 'plugin not found')
-
-            if quart.request.method == 'GET':
-                return self.success(data={'config': plugin['plugin_config']})
-            elif quart.request.method == 'PUT':
-                data = await quart.request.json
-
-                await self.ap.plugin_connector.set_plugin_config(author, plugin_name, data)
-
-                return self.success(data={})
-
-        @self.route(
-            '/<author>/<plugin_name>/icon',
-            methods=['GET'],
-            auth_type=group.AuthType.NONE,
-        )
-        async def _(author: str, plugin_name: str) -> quart.Response:
-            icon_data = await self.ap.plugin_connector.get_plugin_icon(author, plugin_name)
-            icon_base64 = icon_data['plugin_icon_base64']
-            mime_type = icon_data['mime_type']
-
-            icon_data = base64.b64decode(icon_base64)
-
-            return quart.Response(icon_data, mimetype=mime_type)
-
-        @self.route('/github/releases', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            """Get releases from a GitHub repository URL"""
-            data = await quart.request.json
-            repo_url = data.get('repo_url', '')
-
-            # Parse GitHub repository URL to extract owner and repo
-            # Supports: https://github.com/owner/repo or github.com/owner/repo
-            pattern = r'github\.com/([^/]+)/([^/]+?)(?:\.git)?(?:/.*)?$'
-            match = re.search(pattern, repo_url)
-
-            if not match:
-                return self.http_status(400, -1, 'Invalid GitHub repository URL')
-
-            owner, repo = match.groups()
-
-            try:
-                # Fetch releases from GitHub API
-                url = f'https://api.github.com/repos/{owner}/{repo}/releases'
-                async with httpx.AsyncClient(
-                    trust_env=True,
-                    follow_redirects=True,
-                    timeout=10,
-                ) as client:
-                    response = await client.get(url)
-                    response.raise_for_status()
-                    releases = response.json()
-
-                # Format releases data for frontend
-                formatted_releases = []
-                for release in releases:
-                    formatted_releases.append(
-                        {
-                            'id': release['id'],
-                            'tag_name': release['tag_name'],
-                            'name': release['name'],
-                            'published_at': release['published_at'],
-                            'prerelease': release['prerelease'],
-                            'draft': release['draft'],
-                        }
-                    )
-
-                return self.success(data={'releases': formatted_releases, 'owner': owner, 'repo': repo})
-            except httpx.RequestError as e:
-                return self.http_status(500, -1, f'Failed to fetch releases: {str(e)}')
-
-        @self.route(
-            '/github/release-assets',
-            methods=['POST'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _() -> str:
-            """Get assets from a specific GitHub release"""
-            data = await quart.request.json
-            owner = data.get('owner', '')
-            repo = data.get('repo', '')
-            release_id = data.get('release_id', '')
-
-            if not all([owner, repo, release_id]):
-                return self.http_status(400, -1, 'Missing required parameters')
-
-            try:
-                # Fetch release assets from GitHub API
-                url = f'https://api.github.com/repos/{owner}/{repo}/releases/{release_id}'
-                async with httpx.AsyncClient(
-                    trust_env=True,
-                    follow_redirects=True,
-                    timeout=10,
-                ) as client:
-                    response = await client.get(
-                        url,
-                    )
-                    response.raise_for_status()
-                    release = response.json()
-
-                # Format assets data for frontend
-                formatted_assets = []
-                for asset in release.get('assets', []):
-                    formatted_assets.append(
-                        {
-                            'id': asset['id'],
-                            'name': asset['name'],
-                            'size': asset['size'],
-                            'download_url': asset['browser_download_url'],
-                            'content_type': asset['content_type'],
-                        }
-                    )
-
-                # add zipball as a downloadable asset
-                # formatted_assets.append(
-                #     {
-                #         "id": 0,
-                #         "name": "Source code (zip)",
-                #         "size": -1,
-                #         "download_url": release["zipball_url"],
-                #         "content_type": "application/zip",
-                #     }
-                # )
-
-                return self.success(data={'assets': formatted_assets})
-            except httpx.RequestError as e:
-                return self.http_status(500, -1, f'Failed to fetch release assets: {str(e)}')
-
-        @self.route('/install/github', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            """Install plugin from GitHub release asset"""
-            data = await quart.request.json
-            asset_url = data.get('asset_url', '')
-            owner = data.get('owner', '')
-            repo = data.get('repo', '')
-            release_tag = data.get('release_tag', '')
-
-            if not asset_url:
-                return self.http_status(400, -1, 'Missing asset_url parameter')
-
-            ctx = taskmgr.TaskContext.new()
-            install_info = {
-                'asset_url': asset_url,
-                'owner': owner,
-                'repo': repo,
-                'release_tag': release_tag,
-                'github_url': f'https://github.com/{owner}/{repo}',
-            }
-
-            wrapper = self.ap.task_mgr.create_user_task(
-                self.ap.plugin_connector.install_plugin(PluginInstallSource.GITHUB, install_info, task_context=ctx),
-                kind='plugin-operation',
-                name='plugin-install-github',
-                label=f'Installing plugin from GitHub {owner}/{repo}@{release_tag}',
-                context=ctx,
-            )
-
-            return self.success(data={'task_id': wrapper.id})
-
-        @self.route(
-            '/install/marketplace',
-            methods=['POST'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _() -> str:
-            data = await quart.request.json
-
-            ctx = taskmgr.TaskContext.new()
-            wrapper = self.ap.task_mgr.create_user_task(
-                self.ap.plugin_connector.install_plugin(PluginInstallSource.MARKETPLACE, data, task_context=ctx),
-                kind='plugin-operation',
-                name='plugin-install-marketplace',
-                label=f'Installing plugin from marketplace ...{data}',
-                context=ctx,
-            )
-
-            return self.success(data={'task_id': wrapper.id})
-
-        @self.route('/install/local', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            file = (await quart.request.files).get('file')
-            if file is None:
-                return self.http_status(400, -1, 'file is required')
-
-            file_bytes = file.read()
-
-            data = {
-                'plugin_file': file_bytes,
-            }
-
-            ctx = taskmgr.TaskContext.new()
-            wrapper = self.ap.task_mgr.create_user_task(
-                self.ap.plugin_connector.install_plugin(PluginInstallSource.LOCAL, data, task_context=ctx),
-                kind='plugin-operation',
-                name='plugin-install-local',
-                label=f'Installing plugin from local ...{file.filename}',
-                context=ctx,
-            )
-
-            return self.success(data={'task_id': wrapper.id})
-
-        @self.route('/config-files', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            """Upload a file for plugin configuration"""
-            file = (await quart.request.files).get('file')
-            if file is None:
-                return self.http_status(400, -1, 'file is required')
-
-            # Check file size (10MB limit)
-            MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB
-            file_bytes = file.read()
-            if len(file_bytes) > MAX_FILE_SIZE:
-                return self.http_status(400, -1, 'file size exceeds 10MB limit')
-
-            # Generate unique file key with original extension
-            original_filename = file.filename
-            _, ext = os.path.splitext(original_filename)
-            file_key = f'plugin_config_{uuid.uuid4().hex}{ext}'
-
-            # Save file using storage manager
-            await self.ap.storage_mgr.storage_provider.save(file_key, file_bytes)
-
-            return self.success(data={'file_key': file_key})
-
-        @self.route('/config-files/<file_key>', methods=['DELETE'], auth_type=group.AuthType.USER_TOKEN)
-        async def _(file_key: str) -> str:
-            """Delete a plugin configuration file"""
-            # Only allow deletion of files with plugin_config_ prefix for security
-            if not file_key.startswith('plugin_config_'):
-                return self.http_status(400, -1, 'invalid file key')
-
-            try:
-                await self.ap.storage_mgr.storage_provider.delete(file_key)
-                return self.success(data={'deleted': True})
-            except Exception as e:
-                return self.http_status(500, -1, f'failed to delete file: {str(e)}')

pkg/api/http/controller/groups/system.py

@@ -1,115 +0,0 @@
-import quart
-
-from .. import group
-from .....utils import constants
-
-
-@group.group_class('system', '/api/v1/system')
-class SystemRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('/info', methods=['GET'], auth_type=group.AuthType.NONE)
-        async def _() -> str:
-            return self.success(
-                data={
-                    'version': constants.semantic_version,
-                    'debug': constants.debug_mode,
-                    'enable_marketplace': self.ap.instance_config.data.get('plugin', {}).get(
-                        'enable_marketplace', True
-                    ),
-                    'cloud_service_url': (
-                        self.ap.instance_config.data.get('plugin', {}).get(
-                            'cloud_service_url', 'https://space.langbot.app'
-                        )
-                        if 'cloud_service_url' in self.ap.instance_config.data.get('plugin', {})
-                        else 'https://space.langbot.app'
-                    ),
-                }
-            )
-
-        @self.route('/tasks', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            task_type = quart.request.args.get('type')
-
-            if task_type == '':
-                task_type = None
-
-            return self.success(data=self.ap.task_mgr.get_tasks_dict(task_type))
-
-        @self.route('/tasks/<task_id>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _(task_id: str) -> str:
-            task = self.ap.task_mgr.get_task_by_id(int(task_id))
-
-            if task is None:
-                return self.http_status(404, 404, 'Task not found')
-
-            return self.success(data=task.to_dict())
-
-        @self.route('/debug/exec', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            if not constants.debug_mode:
-                return self.http_status(403, 403, 'Forbidden')
-
-            py_code = await quart.request.data
-
-            ap = self.ap
-
-            return self.success(data=exec(py_code, {'ap': ap}))
-
-        @self.route('/debug/tools/call', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            if not constants.debug_mode:
-                return self.http_status(403, 403, 'Forbidden')
-
-            data = await quart.request.json
-
-            return self.success(
-                data=await self.ap.tool_mgr.execute_func_call(data['tool_name'], data['tool_parameters'])
-            )
-
-        @self.route(
-            '/debug/plugin/action',
-            methods=['POST'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _() -> str:
-            if not constants.debug_mode:
-                return self.http_status(403, 403, 'Forbidden')
-
-            data = await quart.request.json
-
-            class AnoymousAction:
-                value = 'anonymous_action'
-
-                def __init__(self, value: str):
-                    self.value = value
-
-            resp = await self.ap.plugin_connector.handler.call_action(
-                AnoymousAction(data['action']),
-                data['data'],
-                timeout=data.get('timeout', 10),
-            )
-
-            return self.success(data=resp)
-
-        @self.route(
-            '/status/plugin-system',
-            methods=['GET'],
-            auth_type=group.AuthType.USER_TOKEN,
-        )
-        async def _() -> str:
-            plugin_connector_error = 'ok'
-            is_connected = True
-
-            try:
-                await self.ap.plugin_connector.ping_plugin_runtime()
-            except Exception as e:
-                plugin_connector_error = str(e)
-                is_connected = False
-
-            return self.success(
-                data={
-                    'is_enable': self.ap.plugin_connector.is_enable_plugin,
-                    'is_connected': is_connected,
-                    'plugin_connector_error': plugin_connector_error,
-                }
-            )

pkg/api/http/controller/groups/user.py

@@ -1,85 +0,0 @@
-import quart
-import argon2
-import asyncio
-
-from .. import group
-
-
-@group.group_class('user', '/api/v1/user')
-class UserRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('/init', methods=['GET', 'POST'], auth_type=group.AuthType.NONE)
-        async def _() -> str:
-            if quart.request.method == 'GET':
-                return self.success(data={'initialized': await self.ap.user_service.is_initialized()})
-
-            if await self.ap.user_service.is_initialized():
-                return self.fail(1, 'System already initialized')
-
-            json_data = await quart.request.json
-
-            user_email = json_data['user']
-            password = json_data['password']
-
-            await self.ap.user_service.create_user(user_email, password)
-
-            return self.success()
-
-        @self.route('/auth', methods=['POST'], auth_type=group.AuthType.NONE)
-        async def _() -> str:
-            json_data = await quart.request.json
-
-            try:
-                token = await self.ap.user_service.authenticate(json_data['user'], json_data['password'])
-            except argon2.exceptions.VerifyMismatchError:
-                return self.fail(1, 'Invalid username or password')
-
-            return self.success(data={'token': token})
-
-        @self.route('/check-token', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _(user_email: str) -> str:
-            token = await self.ap.user_service.generate_jwt_token(user_email)
-
-            return self.success(data={'token': token})
-
-        @self.route('/reset-password', methods=['POST'], auth_type=group.AuthType.NONE)
-        async def _() -> str:
-            json_data = await quart.request.json
-
-            user_email = json_data['user']
-            recovery_key = json_data['recovery_key']
-            new_password = json_data['new_password']
-
-            # hard sleep 3s for security
-            await asyncio.sleep(3)
-
-            if not await self.ap.user_service.is_initialized():
-                return self.http_status(400, -1, 'System not initialized')
-
-            user_obj = await self.ap.user_service.get_user_by_email(user_email)
-
-            if user_obj is None:
-                return self.http_status(400, -1, 'User not found')
-
-            if recovery_key != self.ap.instance_config.data['system']['recovery_key']:
-                return self.http_status(403, -1, 'Invalid recovery key')
-
-            await self.ap.user_service.reset_password(user_email, new_password)
-
-            return self.success(data={'user': user_email})
-
-        @self.route('/change-password', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _(user_email: str) -> str:
-            json_data = await quart.request.json
-
-            current_password = json_data['current_password']
-            new_password = json_data['new_password']
-
-            try:
-                await self.ap.user_service.change_password(user_email, current_password, new_password)
-            except argon2.exceptions.VerifyMismatchError:
-                return self.http_status(400, -1, 'Current password is incorrect')
-            except ValueError as e:
-                return self.http_status(400, -1, str(e))
-
-            return self.success(data={'user': user_email})

pkg/api/http/service/knowledge.py

@@ -1,120 +0,0 @@
-from __future__ import annotations
-
-import uuid
-import sqlalchemy
-
-from ....core import app
-from ....entity.persistence import rag as persistence_rag
-
-
-class KnowledgeService:
-    """知识库服务"""
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application) -> None:
-        self.ap = ap
-
-    async def get_knowledge_bases(self) -> list[dict]:
-        """获取所有知识库"""
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
-        knowledge_bases = result.all()
-        return [
-            self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
-            for knowledge_base in knowledge_bases
-        ]
-
-    async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
-        """获取知识库"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
-        )
-        knowledge_base = result.first()
-        if knowledge_base is None:
-            return None
-        return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
-
-    async def create_knowledge_base(self, kb_data: dict) -> str:
-        """创建知识库"""
-        kb_data['uuid'] = str(uuid.uuid4())
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
-
-        kb = await self.get_knowledge_base(kb_data['uuid'])
-
-        await self.ap.rag_mgr.load_knowledge_base(kb)
-
-        return kb_data['uuid']
-
-    async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
-        """更新知识库"""
-        if 'uuid' in kb_data:
-            del kb_data['uuid']
-
-        if 'embedding_model_uuid' in kb_data:
-            del kb_data['embedding_model_uuid']
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(persistence_rag.KnowledgeBase)
-            .values(kb_data)
-            .where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
-        )
-        await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid)
-
-        kb = await self.get_knowledge_base(kb_uuid)
-
-        await self.ap.rag_mgr.load_knowledge_base(kb)
-
-    async def store_file(self, kb_uuid: str, file_id: str) -> int:
-        """存储文件"""
-        # await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(kb_id=kb_uuid, file_id=file_id))
-        # await self.ap.rag_mgr.store_file(file_id)
-        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-        if runtime_kb is None:
-            raise Exception('Knowledge base not found')
-        return await runtime_kb.store_file(file_id)
-
-    async def retrieve_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]:
-        """检索知识库"""
-        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-        if runtime_kb is None:
-            raise Exception('Knowledge base not found')
-        return [
-            result.model_dump() for result in await runtime_kb.retrieve(query, runtime_kb.knowledge_base_entity.top_k)
-        ]
-
-    async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]:
-        """获取知识库文件"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
-        )
-        files = result.all()
-        return [self.ap.persistence_mgr.serialize_model(persistence_rag.File, file) for file in files]
-
-    async def delete_file(self, kb_uuid: str, file_id: str) -> None:
-        """删除文件"""
-        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-        if runtime_kb is None:
-            raise Exception('Knowledge base not found')
-        await runtime_kb.delete_file(file_id)
-
-    async def delete_knowledge_base(self, kb_uuid: str) -> None:
-        """删除知识库"""
-        await self.ap.rag_mgr.delete_knowledge_base(kb_uuid)
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
-        )
-
-        # delete files
-        files = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
-        )
-        for file in files:
-            # delete chunks
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file.uuid)
-            )
-            # delete file
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file.uuid)
-            )

pkg/api/http/service/model.py

@@ -1,206 +0,0 @@
-from __future__ import annotations
-
-import uuid
-
-import sqlalchemy
-from langbot_plugin.api.entities.builtin.provider import message as provider_message
-
-from ....core import app
-from ....entity.persistence import model as persistence_model
-from ....entity.persistence import pipeline as persistence_pipeline
-from ....provider.modelmgr import requester as model_requester
-
-
-class LLMModelsService:
-    ap: app.Application
-
-    def __init__(self, ap: app.Application) -> None:
-        self.ap = ap
-
-    async def get_llm_models(self, include_secret: bool = True) -> list[dict]:
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
-
-        models = result.all()
-
-        masked_columns = []
-        if not include_secret:
-            masked_columns = ['api_keys']
-
-        return [
-            self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model, masked_columns)
-            for model in models
-        ]
-
-    async def create_llm_model(self, model_data: dict) -> str:
-        model_data['uuid'] = str(uuid.uuid4())
-
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_model.LLMModel).values(**model_data))
-
-        llm_model = await self.get_llm_model(model_data['uuid'])
-
-        await self.ap.model_mgr.load_llm_model(llm_model)
-
-        # check if default pipeline has no model bound
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
-                persistence_pipeline.LegacyPipeline.is_default == True
-            )
-        )
-        pipeline = result.first()
-        if pipeline is not None and pipeline.config['ai']['local-agent']['model'] == '':
-            pipeline_config = pipeline.config
-            pipeline_config['ai']['local-agent']['model'] = model_data['uuid']
-            pipeline_data = {'config': pipeline_config}
-            await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
-
-        return model_data['uuid']
-
-    async def get_llm_model(self, model_uuid: str) -> dict | None:
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
-        )
-
-        model = result.first()
-
-        if model is None:
-            return None
-
-        return self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model)
-
-    async def update_llm_model(self, model_uuid: str, model_data: dict) -> None:
-        if 'uuid' in model_data:
-            del model_data['uuid']
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(persistence_model.LLMModel)
-            .where(persistence_model.LLMModel.uuid == model_uuid)
-            .values(**model_data)
-        )
-
-        await self.ap.model_mgr.remove_llm_model(model_uuid)
-
-        llm_model = await self.get_llm_model(model_uuid)
-
-        await self.ap.model_mgr.load_llm_model(llm_model)
-
-    async def delete_llm_model(self, model_uuid: str) -> None:
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.delete(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
-        )
-
-        await self.ap.model_mgr.remove_llm_model(model_uuid)
-
-    async def test_llm_model(self, model_uuid: str, model_data: dict) -> None:
-        runtime_llm_model: model_requester.RuntimeLLMModel | None = None
-
-        if model_uuid != '_':
-            for model in self.ap.model_mgr.llm_models:
-                if model.model_entity.uuid == model_uuid:
-                    runtime_llm_model = model
-                    break
-
-            if runtime_llm_model is None:
-                raise Exception('model not found')
-
-        else:
-            runtime_llm_model = await self.ap.model_mgr.init_runtime_llm_model(model_data)
-
-        # Mon Nov 10 2025: Commented for some providers may not support thinking parameter
-        # # 有些模型厂商默认开启了思考功能，测试容易延迟
-        # extra_args = model_data.get('extra_args', {})
-        # if not extra_args or 'thinking' not in extra_args:
-        #     extra_args['thinking'] = {'type': 'disabled'}
-
-        await runtime_llm_model.requester.invoke_llm(
-            query=None,
-            model=runtime_llm_model,
-            messages=[provider_message.Message(role='user', content='Hello, world! Please just reply a "Hello".')],
-            funcs=[],
-            # extra_args=extra_args,
-        )
-
-
-class EmbeddingModelsService:
-    ap: app.Application
-
-    def __init__(self, ap: app.Application) -> None:
-        self.ap = ap
-
-    async def get_embedding_models(self) -> list[dict]:
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
-
-        models = result.all()
-        return [self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model) for model in models]
-
-    async def create_embedding_model(self, model_data: dict) -> str:
-        model_data['uuid'] = str(uuid.uuid4())
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
-        )
-
-        embedding_model = await self.get_embedding_model(model_data['uuid'])
-
-        await self.ap.model_mgr.load_embedding_model(embedding_model)
-
-        return model_data['uuid']
-
-    async def get_embedding_model(self, model_uuid: str) -> dict | None:
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_model.EmbeddingModel).where(
-                persistence_model.EmbeddingModel.uuid == model_uuid
-            )
-        )
-
-        model = result.first()
-
-        if model is None:
-            return None
-
-        return self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
-
-    async def update_embedding_model(self, model_uuid: str, model_data: dict) -> None:
-        if 'uuid' in model_data:
-            del model_data['uuid']
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(persistence_model.EmbeddingModel)
-            .where(persistence_model.EmbeddingModel.uuid == model_uuid)
-            .values(**model_data)
-        )
-
-        await self.ap.model_mgr.remove_embedding_model(model_uuid)
-
-        embedding_model = await self.get_embedding_model(model_uuid)
-
-        await self.ap.model_mgr.load_embedding_model(embedding_model)
-
-    async def delete_embedding_model(self, model_uuid: str) -> None:
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.delete(persistence_model.EmbeddingModel).where(
-                persistence_model.EmbeddingModel.uuid == model_uuid
-            )
-        )
-
-        await self.ap.model_mgr.remove_embedding_model(model_uuid)
-
-    async def test_embedding_model(self, model_uuid: str, model_data: dict) -> None:
-        runtime_embedding_model: model_requester.RuntimeEmbeddingModel | None = None
-
-        if model_uuid != '_':
-            for model in self.ap.model_mgr.embedding_models:
-                if model.model_entity.uuid == model_uuid:
-                    runtime_embedding_model = model
-                    break
-
-            if runtime_embedding_model is None:
-                raise Exception('model not found')
-
-        else:
-            runtime_embedding_model = await self.ap.model_mgr.init_runtime_embedding_model(model_data)
-
-        await runtime_embedding_model.requester.invoke_embedding(
-            model=runtime_embedding_model,
-            input_text=['Hello, world!'],
-            extra_args={},
-        )

pkg/api/http/service/user.py

@@ -1,99 +0,0 @@
-from __future__ import annotations
-
-import sqlalchemy
-import argon2
-import jwt
-import datetime
-
-from ....core import app
-from ....entity.persistence import user
-from ....utils import constants
-
-
-class UserService:
-    ap: app.Application
-
-    def __init__(self, ap: app.Application) -> None:
-        self.ap = ap
-
-    async def is_initialized(self) -> bool:
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(user.User).limit(1))
-
-        result_list = result.all()
-        return result_list is not None and len(result_list) > 0
-
-    async def create_user(self, user_email: str, password: str) -> None:
-        ph = argon2.PasswordHasher()
-
-        hashed_password = ph.hash(password)
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.insert(user.User).values(user=user_email, password=hashed_password)
-        )
-
-    async def get_user_by_email(self, user_email: str) -> user.User | None:
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(user.User).where(user.User.user == user_email)
-        )
-
-        result_list = result.all()
-        return result_list[0] if result_list is not None and len(result_list) > 0 else None
-
-    async def authenticate(self, user_email: str, password: str) -> str | None:
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(user.User).where(user.User.user == user_email)
-        )
-
-        result_list = result.all()
-
-        if result_list is None or len(result_list) == 0:
-            raise ValueError('用户不存在')
-
-        user_obj = result_list[0]
-
-        ph = argon2.PasswordHasher()
-
-        ph.verify(user_obj.password, password)
-
-        return await self.generate_jwt_token(user_email)
-
-    async def generate_jwt_token(self, user_email: str) -> str:
-        jwt_secret = self.ap.instance_config.data['system']['jwt']['secret']
-        jwt_expire = self.ap.instance_config.data['system']['jwt']['expire']
-
-        payload = {
-            'user': user_email,
-            'iss': 'LangBot-' + constants.edition,
-            'exp': datetime.datetime.now() + datetime.timedelta(seconds=jwt_expire),
-        }
-
-        return jwt.encode(payload, jwt_secret, algorithm='HS256')
-
-    async def verify_jwt_token(self, token: str) -> str:
-        jwt_secret = self.ap.instance_config.data['system']['jwt']['secret']
-
-        return jwt.decode(token, jwt_secret, algorithms=['HS256'])['user']
-
-    async def reset_password(self, user_email: str, new_password: str) -> None:
-        ph = argon2.PasswordHasher()
-
-        hashed_password = ph.hash(new_password)
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
-        )
-
-    async def change_password(self, user_email: str, current_password: str, new_password: str) -> None:
-        ph = argon2.PasswordHasher()
-
-        user_obj = await self.get_user_by_email(user_email)
-        if user_obj is None:
-            raise ValueError('User not found')
-
-        ph.verify(user_obj.password, current_password)
-
-        hashed_password = ph.hash(new_password)
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
-        )

pkg/core/stages/load_config.py

@@ -1,165 +0,0 @@
-from __future__ import annotations
-
-import os
-from typing import Any
-
-from .. import stage, app
-from ..bootutils import config
-
-
-def _apply_env_overrides_to_config(cfg: dict) -> dict:
-    """Apply environment variable overrides to data/config.yaml
-
-    Environment variables should be uppercase and use __ (double underscore)
-    to represent nested keys. For example:
-    - CONCURRENCY__PIPELINE overrides concurrency.pipeline
-    - PLUGIN__RUNTIME_WS_URL overrides plugin.runtime_ws_url
-
-    Arrays and dict types are ignored.
-
-    Args:
-        cfg: Configuration dictionary
-
-    Returns:
-        Updated configuration dictionary
-    """
-
-    def convert_value(value: str, original_value: Any) -> Any:
-        """Convert string value to appropriate type based on original value
-
-        Args:
-            value: String value from environment variable
-            original_value: Original value to infer type from
-
-        Returns:
-            Converted value (falls back to string if conversion fails)
-        """
-        if isinstance(original_value, bool):
-            return value.lower() in ('true', '1', 'yes', 'on')
-        elif isinstance(original_value, int):
-            try:
-                return int(value)
-            except ValueError:
-                # If conversion fails, keep as string (user error, but non-breaking)
-                return value
-        elif isinstance(original_value, float):
-            try:
-                return float(value)
-            except ValueError:
-                # If conversion fails, keep as string (user error, but non-breaking)
-                return value
-        else:
-            return value
-
-    # Process environment variables
-    for env_key, env_value in os.environ.items():
-        # Check if the environment variable is uppercase and contains __
-        if not env_key.isupper():
-            continue
-        if '__' not in env_key:
-            continue
-
-        print(f'apply env overrides to config: env_key: {env_key}, env_value: {env_value}')
-
-        # Convert environment variable name to config path
-        # e.g., CONCURRENCY__PIPELINE -> ['concurrency', 'pipeline']
-        keys = [key.lower() for key in env_key.split('__')]
-
-        # Navigate to the target value and validate the path
-        current = cfg
-
-        for i, key in enumerate(keys):
-            if not isinstance(current, dict) or key not in current:
-                break
-
-            if i == len(keys) - 1:
-                # At the final key - check if it's a scalar value
-                if isinstance(current[key], (dict, list)):
-                    # Skip dict and list types
-                    pass
-                else:
-                    # Valid scalar value - convert and set it
-                    converted_value = convert_value(env_value, current[key])
-                    current[key] = converted_value
-            else:
-                # Navigate deeper
-                current = current[key]
-
-    return cfg
-
-
-@stage.stage_class('LoadConfigStage')
-class LoadConfigStage(stage.BootingStage):
-    """Load config file stage"""
-
-    async def run(self, ap: app.Application):
-        """Load config file"""
-
-        # ======= deprecated =======
-        if os.path.exists('data/config/command.json'):
-            ap.command_cfg = await config.load_json_config(
-                'data/config/command.json',
-                'templates/legacy/command.json',
-                completion=False,
-            )
-
-        if os.path.exists('data/config/pipeline.json'):
-            ap.pipeline_cfg = await config.load_json_config(
-                'data/config/pipeline.json',
-                'templates/legacy/pipeline.json',
-                completion=False,
-            )
-
-        if os.path.exists('data/config/platform.json'):
-            ap.platform_cfg = await config.load_json_config(
-                'data/config/platform.json',
-                'templates/legacy/platform.json',
-                completion=False,
-            )
-
-        if os.path.exists('data/config/provider.json'):
-            ap.provider_cfg = await config.load_json_config(
-                'data/config/provider.json',
-                'templates/legacy/provider.json',
-                completion=False,
-            )
-
-        if os.path.exists('data/config/system.json'):
-            ap.system_cfg = await config.load_json_config(
-                'data/config/system.json',
-                'templates/legacy/system.json',
-                completion=False,
-            )
-
-        # ======= deprecated =======
-
-        ap.instance_config = await config.load_yaml_config(
-            'data/config.yaml', 'templates/config.yaml', completion=False
-        )
-
-        # Apply environment variable overrides to data/config.yaml
-        ap.instance_config.data = _apply_env_overrides_to_config(ap.instance_config.data)
-
-        await ap.instance_config.dump_config()
-
-        ap.sensitive_meta = await config.load_json_config(
-            'data/metadata/sensitive-words.json',
-            'templates/metadata/sensitive-words.json',
-        )
-        await ap.sensitive_meta.dump_config()
-
-        ap.pipeline_config_meta_trigger = await config.load_yaml_config(
-            'templates/metadata/pipeline/trigger.yaml',
-            'templates/metadata/pipeline/trigger.yaml',
-        )
-        ap.pipeline_config_meta_safety = await config.load_yaml_config(
-            'templates/metadata/pipeline/safety.yaml',
-            'templates/metadata/pipeline/safety.yaml',
-        )
-        ap.pipeline_config_meta_ai = await config.load_yaml_config(
-            'templates/metadata/pipeline/ai.yaml', 'templates/metadata/pipeline/ai.yaml'
-        )
-        ap.pipeline_config_meta_output = await config.load_yaml_config(
-            'templates/metadata/pipeline/output.yaml',
-            'templates/metadata/pipeline/output.yaml',
-        )

pkg/entity/persistence/model.py

@@ -1,46 +0,0 @@
-import sqlalchemy
-
-from .base import Base
-
-
-class LLMModel(Base):
-    """LLM model"""
-
-    __tablename__ = 'llm_models'
-
-    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
-    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
-    abilities = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default=[])
-    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
-    updated_at = sqlalchemy.Column(
-        sqlalchemy.DateTime,
-        nullable=False,
-        server_default=sqlalchemy.func.now(),
-        onupdate=sqlalchemy.func.now(),
-    )
-
-
-class EmbeddingModel(Base):
-    """Embedding 模型"""
-
-    __tablename__ = 'embedding_models'
-
-    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
-    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
-    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
-    updated_at = sqlalchemy.Column(
-        sqlalchemy.DateTime,
-        nullable=False,
-        server_default=sqlalchemy.func.now(),
-        onupdate=sqlalchemy.func.now(),
-    )

pkg/entity/persistence/user.py

@@ -1,18 +0,0 @@
-import sqlalchemy
-
-from .base import Base
-
-
-class User(Base):
-    __tablename__ = 'users'
-
-    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True)
-    user = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    password = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
-    updated_at = sqlalchemy.Column(
-        sqlalchemy.DateTime,
-        nullable=False,
-        server_default=sqlalchemy.func.now(),
-        onupdate=sqlalchemy.func.now(),
-    )

pkg/entity/rag/retriever.py

@@ -1,13 +0,0 @@
-from __future__ import annotations
-
-import pydantic
-
-from typing import Any
-
-
-class RetrieveResultEntry(pydantic.BaseModel):
-    id: str
-
-    metadata: dict[str, Any]
-
-    distance: float

pkg/persistence/migrations/dbm002_combine_quote_msg_config.py

@@ -1,41 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(2)
-class DBMigrateCombineQuoteMsgConfig(migration.DBMigration):
-    """Combine quote message config"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'misc' not in config['trigger']:
-                config['trigger']['misc'] = {}
-
-            if 'combine-quote-message' not in config['trigger']['misc']:
-                config['trigger']['misc']['combine-quote-message'] = False
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

pkg/persistence/migrations/dbm003_n8n_config.py

@@ -1,49 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(3)
-class DBMigrateN8nConfig(migration.DBMigration):
-    """N8n config"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'n8n-service-api' not in config['ai']:
-                config['ai']['n8n-service-api'] = {
-                    'webhook-url': 'http://your-n8n-webhook-url',
-                    'auth-type': 'none',
-                    'basic-username': '',
-                    'basic-password': '',
-                    'jwt-secret': '',
-                    'jwt-algorithm': 'HS256',
-                    'header-name': '',
-                    'header-value': '',
-                    'timeout': 120,
-                    'output-key': 'response',
-                }
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

pkg/persistence/migrations/dbm004_rag_kb_uuid.py

@@ -1,38 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(4)
-class DBMigrateRAGKBUUID(migration.DBMigration):
-    """RAG知识库UUID"""
-
-    async def upgrade(self):
-        """升级"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'knowledge-base' not in config['ai']['local-agent']:
-                config['ai']['local-agent']['knowledge-base'] = ''
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """降级"""
-        pass

pkg/persistence/migrations/dbm005_pipeline_remove_cot_config.py

@@ -1,38 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(5)
-class DBMigratePipelineRemoveCotConfig(migration.DBMigration):
-    """Pipeline remove cot config"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'remove-think' not in config['output']['misc']:
-                config['output']['misc']['remove-think'] = False
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

pkg/persistence/migrations/dbm006_langflow_api_config.py

@@ -1,45 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(6)
-class DBMigrateLangflowApiConfig(migration.DBMigration):
-    """Langflow API config"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'langflow-api' not in config['ai']:
-                config['ai']['langflow-api'] = {
-                    'base-url': 'http://localhost:7860',
-                    'api-key': 'your-api-key',
-                    'flow-id': 'your-flow-id',
-                    'input-type': 'chat',
-                    'output-type': 'chat',
-                    'tweaks': '{}',
-                }
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

pkg/persistence/migrations/dbm010_pipeline_multi_knowledge_base.py

@@ -1,88 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(10)
-class DBMigratePipelineMultiKnowledgeBase(migration.DBMigration):
-    """Pipeline support multiple knowledge base binding"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            # Convert knowledge-base from string to array
-            if 'local-agent' in config['ai']:
-                current_kb = config['ai']['local-agent'].get('knowledge-base', '')
-                
-                # If it's already a list, skip
-                if isinstance(current_kb, list):
-                    continue
-                
-                # Convert string to list
-                if current_kb and current_kb != '__none__':
-                    config['ai']['local-agent']['knowledge-bases'] = [current_kb]
-                else:
-                    config['ai']['local-agent']['knowledge-bases'] = []
-                
-                # Remove old field
-                if 'knowledge-base' in config['ai']['local-agent']:
-                    del config['ai']['local-agent']['knowledge-base']
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            # Convert knowledge-bases from array back to string
-            if 'local-agent' in config['ai']:
-                current_kbs = config['ai']['local-agent'].get('knowledge-bases', [])
-                
-                # If it's already a string, skip
-                if isinstance(current_kbs, str):
-                    continue
-                
-                # Convert list to string (take first one or empty)
-                if current_kbs and len(current_kbs) > 0:
-                    config['ai']['local-agent']['knowledge-base'] = current_kbs[0]
-                else:
-                    config['ai']['local-agent']['knowledge-base'] = ''
-                
-                # Remove new field
-                if 'knowledge-bases' in config['ai']['local-agent']:
-                    del config['ai']['local-agent']['knowledge-bases']
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )

pkg/persistence/migrations/dbm011_dify_base_prompt_config.py

@@ -1,40 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-
-from ...entity.persistence import pipeline as persistence_pipeline
-
-
-@migration.migration_class(11)
-class DBMigrateDifyApiConfig(migration.DBMigration):
-    """Langflow API config"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        # read all pipelines
-        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
-        for pipeline in pipelines:
-            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
-
-            config = serialized_pipeline['config']
-
-            if 'base-prompt' not in config['ai']['dify-service-api']:
-                config['ai']['dify-service-api']['base-prompt'] = (
-                    'When the file content is readable, please read the content of this file. When the file is an image, describe the content of this image.',
-                )
-
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
-                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
-                .values(
-                    {
-                        'config': config,
-                        'for_version': self.ap.ver_mgr.get_current_version(),
-                    }
-                )
-            )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

pkg/pipeline/cntfilter/filters/baiduexamine.py

@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-import aiohttp
-
-from .. import entities
-from .. import filter as filter_model
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-
-BAIDU_EXAMINE_URL = 'https://aip.baidubce.com/rest/2.0/solution/v1/text_censor/v2/user_defined?access_token={}'
-BAIDU_EXAMINE_TOKEN_URL = 'https://aip.baidubce.com/oauth/2.0/token'
-
-
-@filter_model.filter_class('baidu-cloud-examine')
-class BaiduCloudExamine(filter_model.ContentFilter):
-    """百度云内容审核"""
-
-    async def _get_token(self) -> str:
-        async with aiohttp.ClientSession() as session:
-            async with session.post(
-                BAIDU_EXAMINE_TOKEN_URL,
-                params={
-                    'grant_type': 'client_credentials',
-                    'client_id': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-key'],
-                    'client_secret': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-secret'],
-                },
-            ) as resp:
-                return (await resp.json())['access_token']
-
-    async def process(self, query: pipeline_query.Query, message: str) -> entities.FilterResult:
-        async with aiohttp.ClientSession() as session:
-            async with session.post(
-                BAIDU_EXAMINE_URL.format(await self._get_token()),
-                headers={
-                    'Content-Type': 'application/x-www-form-urlencoded',
-                    'Accept': 'application/json',
-                },
-                data=f'text={message}'.encode('utf-8'),
-            ) as resp:
-                result = await resp.json()
-
-                if 'error_code' in result:
-                    return entities.FilterResult(
-                        level=entities.ResultLevel.BLOCK,
-                        replacement=message,
-                        user_notice='',
-                        console_notice=f'百度云判定出错，错误信息：{result["error_msg"]}',
-                    )
-                else:
-                    conclusion = result['conclusion']
-
-                    if conclusion in ('合规'):
-                        return entities.FilterResult(
-                            level=entities.ResultLevel.PASS,
-                            replacement=message,
-                            user_notice='',
-                            console_notice=f'百度云判定结果：{conclusion}',
-                        )
-                    else:
-                        return entities.FilterResult(
-                            level=entities.ResultLevel.BLOCK,
-                            replacement=message,
-                            user_notice='消息中存在不合适的内容, 请修改',
-                            console_notice=f'百度云判定结果：{conclusion}',
-                        )

pkg/pipeline/preproc/preproc.py

@@ -1,147 +0,0 @@
-from __future__ import annotations
-
-import datetime
-
-from .. import stage, entities
-from langbot_plugin.api.entities.builtin.provider import message as provider_message
-import langbot_plugin.api.entities.events as events
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-
-
-@stage.stage_class('PreProcessor')
-class PreProcessor(stage.PipelineStage):
-    """Request pre-processing stage
-
-    Check out session, prompt, context, model, and content functions.
-
-    Rewrite:
-        - session
-        - prompt
-        - messages
-        - user_message
-        - use_model
-        - use_funcs
-    """
-
-    async def process(
-        self,
-        query: pipeline_query.Query,
-        stage_inst_name: str,
-    ) -> entities.StageProcessResult:
-        """Process"""
-        selected_runner = query.pipeline_config['ai']['runner']['runner']
-
-        session = await self.ap.sess_mgr.get_session(query)
-
-        # When not local-agent, llm_model is None
-        try:
-            llm_model = (
-                await self.ap.model_mgr.get_model_by_uuid(query.pipeline_config['ai']['local-agent']['model'])
-                if selected_runner == 'local-agent'
-                else None
-            )
-        except ValueError:
-            self.ap.logger.warning(
-                f'LLM model {query.pipeline_config["ai"]["local-agent"]["model"] + " "}not found or not configured'
-            )
-            llm_model = None
-
-        conversation = await self.ap.sess_mgr.get_conversation(
-            query,
-            session,
-            query.pipeline_config['ai']['local-agent']['prompt'],
-            query.pipeline_uuid,
-            query.bot_uuid,
-        )
-
-        # 设置query
-        query.session = session
-        query.prompt = conversation.prompt.copy()
-        query.messages = conversation.messages.copy()
-
-        if selected_runner == 'local-agent' and llm_model:
-            query.use_funcs = []
-            query.use_llm_model_uuid = llm_model.model_entity.uuid
-
-            if llm_model.model_entity.abilities.__contains__('func_call'):
-                # Get bound plugins and MCP servers for filtering tools
-                bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
-                bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
-                query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
-
-                self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
-                self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
-                self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
-
-        variables = {
-            'session_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
-            'conversation_id': conversation.uuid,
-            'msg_create_time': (
-                int(query.message_event.time) if query.message_event.time else int(datetime.datetime.now().timestamp())
-            ),
-        }
-        query.variables.update(variables)
-
-        # Check if this model supports vision, if not, remove all images
-        # TODO this checking should be performed in runner, and in this stage, the image should be reserved
-        if (
-            selected_runner == 'local-agent'
-            and llm_model
-            and not llm_model.model_entity.abilities.__contains__('vision')
-        ):
-            for msg in query.messages:
-                if isinstance(msg.content, list):
-                    for me in msg.content:
-                        if me.type == 'image_url':
-                            msg.content.remove(me)
-
-        content_list: list[provider_message.ContentElement] = []
-
-        plain_text = ''
-        qoute_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')
-
-        for me in query.message_chain:
-            if isinstance(me, platform_message.Plain):
-                content_list.append(provider_message.ContentElement.from_text(me.text))
-                plain_text += me.text
-            elif isinstance(me, platform_message.Image):
-                if selected_runner != 'local-agent' or (
-                    llm_model and llm_model.model_entity.abilities.__contains__('vision')
-                ):
-                    if me.base64 is not None:
-                        content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
-            elif isinstance(me, platform_message.File):
-                # if me.url is not None:
-                content_list.append(provider_message.ContentElement.from_file_url(me.url, me.name))
-            elif isinstance(me, platform_message.Quote) and qoute_msg:
-                for msg in me.origin:
-                    if isinstance(msg, platform_message.Plain):
-                        content_list.append(provider_message.ContentElement.from_text(msg.text))
-                    elif isinstance(msg, platform_message.Image):
-                        if selected_runner != 'local-agent' or (
-                            llm_model and llm_model.model_entity.abilities.__contains__('vision')
-                        ):
-                            if msg.base64 is not None:
-                                content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
-
-        query.variables['user_message_text'] = plain_text
-
-        query.user_message = provider_message.Message(role='user', content=content_list)
-        # =========== 触发事件 PromptPreProcessing
-
-        event = events.PromptPreProcessing(
-            session_name=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
-            default_prompt=query.prompt.messages,
-            prompt=query.messages,
-            query=query,
-        )
-
-        # Get bound plugins for filtering
-        bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
-        event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
-
-        query.prompt.messages = event_ctx.event.default_prompt
-        query.messages = event_ctx.event.prompt
-
-        return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

pkg/pipeline/process/handler.py

@@ -1,33 +0,0 @@
-from __future__ import annotations
-
-import abc
-
-from ...core import app
-from .. import entities
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-
-
-class MessageHandler(metaclass=abc.ABCMeta):
-    ap: app.Application
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def initialize(self):
-        pass
-
-    @abc.abstractmethod
-    async def handle(
-        self,
-        query: pipeline_query.Query,
-    ) -> entities.StageProcessResult:
-        raise NotImplementedError
-
-    def cut_str(self, s: str) -> str:
-        """
-        Take the first line of the string, up to 20 characters, if there are multiple lines, or more than 20 characters, add an ellipsis
-        """
-        s0 = s.split('\n')[0]
-        if len(s0) > 20 or '\n' in s:
-            s0 = s0[:20] + '...'
-        return s0

pkg/pipeline/process/handlers/chat.py

@@ -1,128 +0,0 @@
-from __future__ import annotations
-
-import uuid
-import typing
-import traceback
-
-
-from .. import handler
-from ... import entities
-from ....provider import runner as runner_module
-
-import langbot_plugin.api.entities.events as events
-from ....utils import importutil
-from ....provider import runners
-import langbot_plugin.api.entities.builtin.provider.session as provider_session
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-
-
-importutil.import_modules_in_pkg(runners)
-
-
-class ChatMessageHandler(handler.MessageHandler):
-    async def handle(
-        self,
-        query: pipeline_query.Query,
-    ) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
-        """处理"""
-        # 调API
-        #   生成器
-
-        # 触发插件事件
-        event_class = (
-            events.PersonNormalMessageReceived
-            if query.launcher_type == provider_session.LauncherTypes.PERSON
-            else events.GroupNormalMessageReceived
-        )
-
-        event = event_class(
-            launcher_type=query.launcher_type.value,
-            launcher_id=query.launcher_id,
-            sender_id=query.sender_id,
-            text_message=str(query.message_chain),
-            query=query,
-        )
-
-        # Get bound plugins for filtering
-        bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
-        event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
-
-        is_create_card = False  # 判断下是否需要创建流式卡片
-
-        if event_ctx.is_prevented_default():
-            if event_ctx.event.reply_message_chain is not None:
-                mc = event_ctx.event.reply_message_chain
-                query.resp_messages.append(mc)
-
-                yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
-            else:
-                yield entities.StageProcessResult(result_type=entities.ResultType.INTERRUPT, new_query=query)
-        else:
-            if event_ctx.event.user_message_alter is not None:
-                # if isinstance(event_ctx.event, str):  # 现在暂时不考虑多模态alter
-                query.user_message.content = event_ctx.event.user_message_alter
-
-            text_length = 0
-            try:
-                is_stream = await query.adapter.is_stream_output_supported()
-            except AttributeError:
-                is_stream = False
-
-            try:
-                for r in runner_module.preregistered_runners:
-                    if r.name == query.pipeline_config['ai']['runner']['runner']:
-                        runner = r(self.ap, query.pipeline_config)
-                        break
-                else:
-                    raise ValueError(f'未找到请求运行器: {query.pipeline_config["ai"]["runner"]["runner"]}')
-                if is_stream:
-                    resp_message_id = uuid.uuid4()
-
-                    async for result in runner.run(query):
-                        result.resp_message_id = str(resp_message_id)
-                        if query.resp_messages:
-                            query.resp_messages.pop()
-                        if query.resp_message_chain:
-                            query.resp_message_chain.pop()
-                        # 此时连接外部 AI 服务正常,创建卡片
-                        if not is_create_card:  # 只有不是第一次才创建卡片
-                            await query.adapter.create_message_card(str(resp_message_id), query.message_event)
-                            is_create_card = True
-                        query.resp_messages.append(result)
-                        self.ap.logger.info(f'对话({query.query_id})流式响应: {self.cut_str(result.readable_str())}')
-
-                        if result.content is not None:
-                            text_length += len(result.content)
-
-                        yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
-
-                else:
-                    async for result in runner.run(query):
-                        query.resp_messages.append(result)
-
-                        self.ap.logger.info(f'对话({query.query_id})响应: {self.cut_str(result.readable_str())}')
-
-                        if result.content is not None:
-                            text_length += len(result.content)
-
-                        yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
-
-                query.session.using_conversation.messages.append(query.user_message)
-
-                query.session.using_conversation.messages.extend(query.resp_messages)
-            except Exception as e:
-                self.ap.logger.error(f'对话({query.query_id})请求失败: {type(e).__name__} {str(e)}')
-                traceback.print_exc()
-
-                hide_exception_info = query.pipeline_config['output']['misc']['hide-exception']
-
-                yield entities.StageProcessResult(
-                    result_type=entities.ResultType.INTERRUPT,
-                    new_query=query,
-                    user_notice='请求失败' if hide_exception_info else f'{e}',
-                    error_notice=f'{e}',
-                    debug_notice=traceback.format_exc(),
-                )
-            finally:
-                # TODO statistics
-                pass

pkg/platform/botmgr.py

@@ -1,306 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import traceback
-import sqlalchemy
-
-from ..core import app, entities as core_entities, taskmgr
-
-from ..discover import engine
-
-from ..entity.persistence import bot as persistence_bot
-
-from ..entity.errors import platform as platform_errors
-
-from .logger import EventLogger
-from .webhook_pusher import WebhookPusher
-
-import langbot_plugin.api.entities.builtin.provider.session as provider_session
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-
-
-class RuntimeBot:
-    """运行时机器人"""
-
-    ap: app.Application
-
-    bot_entity: persistence_bot.Bot
-
-    enable: bool
-
-    adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter
-
-    task_wrapper: taskmgr.TaskWrapper
-
-    task_context: taskmgr.TaskContext
-
-    logger: EventLogger
-
-    def __init__(
-        self,
-        ap: app.Application,
-        bot_entity: persistence_bot.Bot,
-        adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
-        logger: EventLogger,
-    ):
-        self.ap = ap
-        self.bot_entity = bot_entity
-        self.enable = bot_entity.enable
-        self.adapter = adapter
-        self.task_context = taskmgr.TaskContext()
-        self.logger = logger
-
-    async def initialize(self):
-        async def on_friend_message(
-            event: platform_events.FriendMessage,
-            adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
-        ):
-            image_components = [
-                component for component in event.message_chain if isinstance(component, platform_message.Image)
-            ]
-
-            await self.logger.info(
-                f'{event.message_chain}',
-                images=image_components,
-                message_session_id=f'person_{event.sender.id}',
-            )
-
-            # Push to webhooks
-            if hasattr(self.ap, 'webhook_pusher') and self.ap.webhook_pusher:
-                asyncio.create_task(
-                    self.ap.webhook_pusher.push_person_message(
-                        event, self.bot_entity.uuid, adapter.__class__.__name__
-                    )
-                )
-
-            await self.ap.query_pool.add_query(
-                bot_uuid=self.bot_entity.uuid,
-                launcher_type=provider_session.LauncherTypes.PERSON,
-                launcher_id=event.sender.id,
-                sender_id=event.sender.id,
-                message_event=event,
-                message_chain=event.message_chain,
-                adapter=adapter,
-                pipeline_uuid=self.bot_entity.use_pipeline_uuid,
-            )
-
-        async def on_group_message(
-            event: platform_events.GroupMessage,
-            adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
-        ):
-            image_components = [
-                component for component in event.message_chain if isinstance(component, platform_message.Image)
-            ]
-
-            await self.logger.info(
-                f'{event.message_chain}',
-                images=image_components,
-                message_session_id=f'group_{event.group.id}',
-            )
-
-            # Push to webhooks
-            if hasattr(self.ap, 'webhook_pusher') and self.ap.webhook_pusher:
-                asyncio.create_task(
-                    self.ap.webhook_pusher.push_group_message(
-                        event, self.bot_entity.uuid, adapter.__class__.__name__
-                    )
-                )
-
-            await self.ap.query_pool.add_query(
-                bot_uuid=self.bot_entity.uuid,
-                launcher_type=provider_session.LauncherTypes.GROUP,
-                launcher_id=event.group.id,
-                sender_id=event.sender.id,
-                message_event=event,
-                message_chain=event.message_chain,
-                adapter=adapter,
-                pipeline_uuid=self.bot_entity.use_pipeline_uuid,
-            )
-
-        self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
-        self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
-
-    async def run(self):
-        async def exception_wrapper():
-            try:
-                self.task_context.set_current_action('Running...')
-                await self.adapter.run_async()
-                self.task_context.set_current_action('Exited.')
-            except Exception as e:
-                if isinstance(e, asyncio.CancelledError):
-                    self.task_context.set_current_action('Exited.')
-                    return
-
-                traceback_str = traceback.format_exc()
-                self.task_context.set_current_action('Exited with error.')
-                await self.logger.error(f'平台适配器运行出错:\n{e}\n{traceback_str}')
-
-        self.task_wrapper = self.ap.task_mgr.create_task(
-            exception_wrapper(),
-            kind='platform-adapter',
-            name=f'platform-adapter-{self.adapter.__class__.__name__}',
-            context=self.task_context,
-            scopes=[
-                core_entities.LifecycleControlScope.APPLICATION,
-                core_entities.LifecycleControlScope.PLATFORM,
-            ],
-        )
-
-    async def shutdown(self):
-        await self.adapter.kill()
-
-        self.ap.task_mgr.cancel_task(self.task_wrapper.id)
-
-
-# 控制QQ消息输入输出的类
-class PlatformManager:
-    # ====== 4.0 ======
-    ap: app.Application = None
-
-    bots: list[RuntimeBot]
-
-    webchat_proxy_bot: RuntimeBot
-
-    adapter_components: list[engine.Component]
-
-    adapter_dict: dict[str, type[abstract_platform_adapter.AbstractMessagePlatformAdapter]]
-
-    def __init__(self, ap: app.Application = None):
-        self.ap = ap
-        self.bots = []
-        self.adapter_components = []
-        self.adapter_dict = {}
-
-    async def initialize(self):
-        # delete all bot log images
-        await self.ap.storage_mgr.storage_provider.delete_dir_recursive('bot_log_images')
-
-        self.adapter_components = self.ap.discover.get_components_by_kind('MessagePlatformAdapter')
-        adapter_dict: dict[str, type[abstract_platform_adapter.AbstractMessagePlatformAdapter]] = {}
-        for component in self.adapter_components:
-            adapter_dict[component.metadata.name] = component.get_python_component_class()
-        self.adapter_dict = adapter_dict
-
-        webchat_adapter_class = self.adapter_dict['webchat']
-
-        # initialize webchat adapter
-        webchat_logger = EventLogger(name='webchat-adapter', ap=self.ap)
-        webchat_adapter_inst = webchat_adapter_class(
-            {},
-            webchat_logger,
-            ap=self.ap,
-            is_stream=False,
-        )
-
-        self.webchat_proxy_bot = RuntimeBot(
-            ap=self.ap,
-            bot_entity=persistence_bot.Bot(
-                uuid='webchat-proxy-bot',
-                name='WebChat',
-                description='',
-                adapter='webchat',
-                adapter_config={},
-                enable=True,
-            ),
-            adapter=webchat_adapter_inst,
-            logger=webchat_logger,
-        )
-        await self.webchat_proxy_bot.initialize()
-
-        await self.load_bots_from_db()
-
-    def get_running_adapters(self) -> list[abstract_platform_adapter.AbstractMessagePlatformAdapter]:
-        return [bot.adapter for bot in self.bots if bot.enable]
-
-    async def load_bots_from_db(self):
-        self.ap.logger.info('Loading bots from db...')
-
-        self.bots = []
-
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_bot.Bot))
-
-        bots = result.all()
-
-        for bot in bots:
-            # load all bots here, enable or disable will be handled in runtime
-            try:
-                await self.load_bot(bot)
-            except platform_errors.AdapterNotFoundError as e:
-                self.ap.logger.warning(f'Adapter {e.adapter_name} not found, skipping bot {bot.uuid}')
-            except Exception as e:
-                self.ap.logger.error(f'Failed to load bot {bot.uuid}: {e}\n{traceback.format_exc()}')
-
-    async def load_bot(
-        self,
-        bot_entity: persistence_bot.Bot | sqlalchemy.Row[persistence_bot.Bot] | dict,
-    ) -> RuntimeBot:
-        """加载机器人"""
-        if isinstance(bot_entity, sqlalchemy.Row):
-            bot_entity = persistence_bot.Bot(**bot_entity._mapping)
-        elif isinstance(bot_entity, dict):
-            bot_entity = persistence_bot.Bot(**bot_entity)
-
-        logger = EventLogger(name=f'platform-adapter-{bot_entity.name}', ap=self.ap)
-
-        if bot_entity.adapter not in self.adapter_dict:
-            raise platform_errors.AdapterNotFoundError(bot_entity.adapter)
-
-        adapter_inst = self.adapter_dict[bot_entity.adapter](
-            bot_entity.adapter_config,
-            logger,
-        )
-
-        runtime_bot = RuntimeBot(ap=self.ap, bot_entity=bot_entity, adapter=adapter_inst, logger=logger)
-
-        await runtime_bot.initialize()
-
-        self.bots.append(runtime_bot)
-
-        return runtime_bot
-
-    async def get_bot_by_uuid(self, bot_uuid: str) -> RuntimeBot | None:
-        for bot in self.bots:
-            if bot.bot_entity.uuid == bot_uuid:
-                return bot
-        return None
-
-    async def remove_bot(self, bot_uuid: str):
-        for bot in self.bots:
-            if bot.bot_entity.uuid == bot_uuid:
-                if bot.enable:
-                    await bot.shutdown()
-                self.bots.remove(bot)
-                return
-
-    def get_available_adapters_info(self) -> list[dict]:
-        return [
-            component.to_plain_dict() for component in self.adapter_components if component.metadata.name != 'webchat'
-        ]
-
-    def get_available_adapter_info_by_name(self, name: str) -> dict | None:
-        for component in self.adapter_components:
-            if component.metadata.name == name:
-                return component.to_plain_dict()
-        return None
-
-    def get_available_adapter_manifest_by_name(self, name: str) -> engine.Component | None:
-        for component in self.adapter_components:
-            if component.metadata.name == name:
-                return component
-        return None
-
-    async def run(self):
-        # This method will only be called when the application launching
-        await self.webchat_proxy_bot.run()
-
-        for bot in self.bots:
-            if bot.enable:
-                await bot.run()
-
-    async def shutdown(self):
-        for bot in self.bots:
-            if bot.enable:
-                await bot.shutdown()
-        self.ap.task_mgr.cancel_by_scope(core_entities.LifecycleControlScope.PLATFORM)

pkg/platform/sources/dingtalk.yaml

@@ -1,69 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: dingtalk
-  label:
-    en_US: DingTalk
-    zh_Hans: 钉钉
-  description:
-    en_US: DingTalk Adapter
-    zh_Hans: 钉钉适配器，请查看文档了解使用方式
-  icon: dingtalk.svg
-spec:
-  config:
-    - name: client_id
-      label:
-        en_US: Client ID
-        zh_Hans: 客户端ID
-      type: string
-      required: true
-      default: ""
-    - name: client_secret
-      label:
-        en_US: Client Secret
-        zh_Hans: 客户端密钥
-      type: string
-      required: true
-      default: ""
-    - name: robot_code
-      label:
-        en_US: Robot Code
-        zh_Hans: 机器人代码
-      type: string
-      required: true
-      default: ""
-    - name: robot_name
-      label:
-        en_US: Robot Name
-        zh_Hans: 机器人名称
-      type: string
-      required: true
-      default: ""
-    - name: markdown_card
-      label:
-        en_US: Markdown Card
-        zh_Hans: 是否使用 Markdown 卡片
-      type: boolean
-      required: false
-      default: true
-    - name: enable-stream-reply
-      label:
-        en_US: Enable Stream Reply Mode
-        zh_Hans: 启用钉钉卡片流式回复模式
-      description:
-        en_US: If enabled, the bot will use the stream of lark reply mode
-        zh_Hans: 如果启用，将使用钉钉卡片流式方式来回复内容
-      type: boolean
-      required: true
-      default: false
-    - name: card_template_id
-      label:
-        en_US: card template id
-        zh_Hans: 卡片模板ID
-      type: string
-      required: true
-      default: "填写你的卡片template_id"
-execution:
-  python:
-    path: ./dingtalk.py
-    attr: DingTalkAdapter

pkg/platform/sources/discord.yaml

@@ -1,31 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: discord
-  label:
-    en_US: Discord
-    zh_Hans: Discord
-  description:
-    en_US: Discord Adapter
-    zh_Hans: Discord 适配器，请查看文档了解使用方式
-  icon: discord.svg
-spec:
-  config:
-    - name: client_id
-      label:
-        en_US: Client ID
-        zh_Hans: 客户端ID
-      type: string
-      required: true
-      default: ""
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-execution:
-  python:
-    path: ./discord.py
-    attr: DiscordAdapter

pkg/platform/sources/lark.py

@@ -1,809 +0,0 @@
-from __future__ import annotations
-
-import lark_oapi
-from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody
-import traceback
-import typing
-import asyncio
-import re
-import base64
-import uuid
-import json
-import datetime
-import hashlib
-from Crypto.Cipher import AES
-
-import aiohttp
-import lark_oapi.ws.exception
-import quart
-from lark_oapi.api.im.v1 import *
-import pydantic
-from lark_oapi.api.cardkit.v1 import *
-
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
-import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_platform_logger
-
-
-class AESCipher(object):
-    def __init__(self, key):
-        self.bs = AES.block_size
-        self.key = hashlib.sha256(AESCipher.str_to_bytes(key)).digest()
-
-    @staticmethod
-    def str_to_bytes(data):
-        u_type = type(b''.decode('utf8'))
-        if isinstance(data, u_type):
-            return data.encode('utf8')
-        return data
-
-    @staticmethod
-    def _unpad(s):
-        return s[: -ord(s[len(s) - 1 :])]
-
-    def decrypt(self, enc):
-        iv = enc[: AES.block_size]
-        cipher = AES.new(self.key, AES.MODE_CBC, iv)
-        return self._unpad(cipher.decrypt(enc[AES.block_size :]))
-
-    def decrypt_string(self, enc):
-        enc = base64.b64decode(enc)
-        return self.decrypt(enc).decode('utf8')
-
-
-class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
-    @staticmethod
-    async def yiri2target(
-        message_chain: platform_message.MessageChain, api_client: lark_oapi.Client
-    ) -> typing.Tuple[list]:
-        message_elements = []
-        pending_paragraph = []
-        for msg in message_chain:
-            if isinstance(msg, platform_message.Plain):
-                # Ensure text is valid UTF-8
-                try:
-                    text = msg.text.encode('utf-8').decode('utf-8')
-                    pending_paragraph.append({'tag': 'md', 'text': text})
-                except UnicodeError:
-                    # If text is not valid UTF-8, try to decode with other encodings
-                    try:
-                        text = msg.text.encode('latin1').decode('utf-8')
-                        pending_paragraph.append({'tag': 'md', 'text': text})
-                    except UnicodeError:
-                        # If still fails, replace invalid characters
-                        text = msg.text.encode('utf-8', errors='replace').decode('utf-8')
-                        pending_paragraph.append({'tag': 'md', 'text': text})
-            elif isinstance(msg, platform_message.At):
-                pending_paragraph.append({'tag': 'at', 'user_id': msg.target, 'style': []})
-            elif isinstance(msg, platform_message.AtAll):
-                pending_paragraph.append({'tag': 'at', 'user_id': 'all', 'style': []})
-            elif isinstance(msg, platform_message.Image):
-                image_bytes = None
-
-                if msg.base64:
-                    try:
-                        # Remove data URL prefix if present
-                        if msg.base64.startswith('data:'):
-                            msg.base64 = msg.base64.split(',', 1)[1]
-                        image_bytes = base64.b64decode(msg.base64)
-                    except Exception:
-                        traceback.print_exc()
-                        continue
-                elif msg.url:
-                    try:
-                        async with aiohttp.ClientSession() as session:
-                            async with session.get(msg.url) as response:
-                                if response.status == 200:
-                                    image_bytes = await response.read()
-                                else:
-                                    traceback.print_exc()
-                                    continue
-                    except Exception:
-                        traceback.print_exc()
-                        continue
-                elif msg.path:
-                    try:
-                        with open(msg.path, 'rb') as f:
-                            image_bytes = f.read()
-                    except Exception:
-                        traceback.print_exc()
-                        continue
-
-                if image_bytes is None:
-                    continue
-
-                try:
-                    # Create a temporary file to store the image bytes
-                    import tempfile
-
-                    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-                        temp_file.write(image_bytes)
-                        temp_file.flush()
-
-                        # Create image request using the temporary file
-                        request = (
-                            CreateImageRequest.builder()
-                            .request_body(
-                                CreateImageRequestBody.builder()
-                                .image_type('message')
-                                .image(open(temp_file.name, 'rb'))
-                                .build()
-                            )
-                            .build()
-                        )
-
-                        response = await api_client.im.v1.image.acreate(request)
-
-                        if not response.success():
-                            raise Exception(
-                                f'client.im.v1.image.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-                            )
-
-                        image_key = response.data.image_key
-
-                        message_elements.append(pending_paragraph)
-                        message_elements.append(
-                            [
-                                {
-                                    'tag': 'img',
-                                    'image_key': image_key,
-                                }
-                            ]
-                        )
-                        pending_paragraph = []
-                except Exception:
-                    traceback.print_exc()
-                    continue
-                finally:
-                    # Clean up the temporary file
-                    import os
-
-                    if 'temp_file' in locals():
-                        os.unlink(temp_file.name)
-            elif isinstance(msg, platform_message.Forward):
-                for node in msg.node_list:
-                    message_elements.extend(await LarkMessageConverter.yiri2target(node.message_chain, api_client))
-
-        if pending_paragraph:
-            message_elements.append(pending_paragraph)
-
-        return message_elements
-
-    @staticmethod
-    async def target2yiri(
-        message: lark_oapi.api.im.v1.model.event_message.EventMessage,
-        api_client: lark_oapi.Client,
-    ) -> platform_message.MessageChain:
-        message_content = json.loads(message.content)
-
-        lb_msg_list = []
-
-        msg_create_time = datetime.datetime.fromtimestamp(int(message.create_time) / 1000)
-
-        lb_msg_list.append(platform_message.Source(id=message.message_id, time=msg_create_time))
-
-        if message.message_type == 'text':
-            element_list = []
-
-            def text_element_recur(text_ele: dict) -> list[dict]:
-                if text_ele['text'] == '':
-                    return []
-
-                at_pattern = re.compile(r'@_user_[\d]+')
-                at_matches = at_pattern.findall(text_ele['text'])
-
-                name_mapping = {}
-                for mathc in at_matches:
-                    for mention in message.mentions:
-                        if mention.key == mathc:
-                            name_mapping[mathc] = mention.name
-                            break
-
-                if len(name_mapping.keys()) == 0:
-                    return [text_ele]
-
-                # 只处理第一个，剩下的递归处理
-                text_split = text_ele['text'].split(list(name_mapping.keys())[0])
-
-                new_list = []
-
-                left_text = text_split[0]
-                right_text = text_split[1]
-
-                new_list.extend(text_element_recur({'tag': 'text', 'text': left_text, 'style': []}))
-
-                new_list.append(
-                    {
-                        'tag': 'at',
-                        'user_id': list(name_mapping.keys())[0],
-                        'user_name': name_mapping[list(name_mapping.keys())[0]],
-                        'style': [],
-                    }
-                )
-
-                new_list.extend(text_element_recur({'tag': 'text', 'text': right_text, 'style': []}))
-
-                return new_list
-
-            element_list = text_element_recur({'tag': 'text', 'text': message_content['text'], 'style': []})
-
-            message_content = {'title': '', 'content': element_list}
-
-        elif message.message_type == 'post':
-            new_list = []
-
-            for ele in message_content['content']:
-                if type(ele) is dict:
-                    new_list.append(ele)
-                elif type(ele) is list:
-                    new_list.extend(ele)
-
-            message_content['content'] = new_list
-        elif message.message_type == 'image':
-            message_content['content'] = [{'tag': 'img', 'image_key': message_content['image_key'], 'style': []}]
-
-        for ele in message_content['content']:
-            if ele['tag'] == 'text':
-                lb_msg_list.append(platform_message.Plain(text=ele['text']))
-            elif ele['tag'] == 'at':
-                lb_msg_list.append(platform_message.At(target=ele['user_name']))
-            elif ele['tag'] == 'img':
-                image_key = ele['image_key']
-
-                request: GetMessageResourceRequest = (
-                    GetMessageResourceRequest.builder()
-                    .message_id(message.message_id)
-                    .file_key(image_key)
-                    .type('image')
-                    .build()
-                )
-
-                response: GetMessageResourceResponse = await api_client.im.v1.message_resource.aget(request)
-
-                if not response.success():
-                    raise Exception(
-                        f'client.im.v1.message_resource.get failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-                    )
-
-                image_bytes = response.file.read()
-                image_base64 = base64.b64encode(image_bytes).decode()
-
-                image_format = response.raw.headers['content-type']
-
-                lb_msg_list.append(platform_message.Image(base64=f'data:{image_format};base64,{image_base64}'))
-
-        return platform_message.MessageChain(lb_msg_list)
-
-
-class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
-    @staticmethod
-    async def yiri2target(
-        event: platform_events.MessageEvent,
-    ) -> lark_oapi.im.v1.P2ImMessageReceiveV1:
-        pass
-
-    @staticmethod
-    async def target2yiri(
-        event: lark_oapi.im.v1.P2ImMessageReceiveV1, api_client: lark_oapi.Client
-    ) -> platform_events.Event:
-        message_chain = await LarkMessageConverter.target2yiri(event.event.message, api_client)
-
-        if event.event.message.chat_type == 'p2p':
-            return platform_events.FriendMessage(
-                sender=platform_entities.Friend(
-                    id=event.event.sender.sender_id.open_id,
-                    nickname=event.event.sender.sender_id.union_id,
-                    remark='',
-                ),
-                message_chain=message_chain,
-                time=event.event.message.create_time,
-            )
-        elif event.event.message.chat_type == 'group':
-            return platform_events.GroupMessage(
-                sender=platform_entities.GroupMember(
-                    id=event.event.sender.sender_id.open_id,
-                    member_name=event.event.sender.sender_id.union_id,
-                    permission=platform_entities.Permission.Member,
-                    group=platform_entities.Group(
-                        id=event.event.message.chat_id,
-                        name='',
-                        permission=platform_entities.Permission.Member,
-                    ),
-                    special_title='',
-                    join_timestamp=0,
-                    last_speak_timestamp=0,
-                    mute_time_remaining=0,
-                ),
-                message_chain=message_chain,
-                time=event.event.message.create_time,
-            )
-
-
-CARD_ID_CACHE_SIZE = 500
-CARD_ID_CACHE_MAX_LIFETIME = 20 * 60  # 20分钟
-
-
-class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    bot: lark_oapi.ws.Client = pydantic.Field(exclude=True)
-    api_client: lark_oapi.Client = pydantic.Field(exclude=True)
-
-    bot_account_id: str  # 用于在流水线中识别at是否是本bot，直接以bot_name作为标识
-    lark_tenant_key: str = pydantic.Field(exclude=True, default='')  # 飞书企业key
-
-    message_converter: LarkMessageConverter = LarkMessageConverter()
-    event_converter: LarkEventConverter = LarkEventConverter()
-
-    listeners: typing.Dict[
-        typing.Type[platform_events.Event],
-        typing.Callable[[platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None],
-    ]
-
-    quart_app: quart.Quart = pydantic.Field(exclude=True)
-
-    card_id_dict: dict[str, str]  # 消息id到卡片id的映射，便于创建卡片后的发送消息到指定卡片
-
-    seq: int  # 用于在发送卡片消息中识别消息顺序，直接以seq作为标识
-
-    def __init__(self, config: dict, logger: abstract_platform_logger.AbstractEventLogger, **kwargs):
-        quart_app = quart.Quart(__name__)
-
-        @quart_app.route('/lark/callback', methods=['POST'])
-        async def lark_callback():
-            try:
-                data = await quart.request.json
-
-                if 'encrypt' in data:
-                    cipher = AESCipher(config['encrypt-key'])
-                    data = cipher.decrypt_string(data['encrypt'])
-                    data = json.loads(data)
-
-                type = data.get('type')
-                if type is None:
-                    context = EventContext(data)
-                    type = context.header.event_type
-
-                if 'url_verification' == type:
-                    # todo 验证verification token
-                    return {'challenge': data.get('challenge')}
-                context = EventContext(data)
-                type = context.header.event_type
-                p2v1 = P2ImMessageReceiveV1()
-                p2v1.header = context.header
-                event = P2ImMessageReceiveV1Data()
-                event.message = EventMessage(context.event['message'])
-                event.sender = EventSender(context.event['sender'])
-                p2v1.event = event
-                p2v1.schema = context.schema
-                if 'im.message.receive_v1' == type:
-                    try:
-                        event = await self.event_converter.target2yiri(p2v1, self.api_client)
-                    except Exception:
-                        await self.logger.error(f'Error in lark callback: {traceback.format_exc()}')
-
-                    if event.__class__ in self.listeners:
-                        await self.listeners[event.__class__](event, self)
-
-                return {'code': 200, 'message': 'ok'}
-            except Exception:
-                await self.logger.error(f'Error in lark callback: {traceback.format_exc()}')
-                return {'code': 500, 'message': 'error'}
-
-        async def on_message(event: lark_oapi.im.v1.P2ImMessageReceiveV1):
-            lb_event = await self.event_converter.target2yiri(event, self.api_client)
-
-            await self.listeners[type(lb_event)](lb_event, self)
-
-        def sync_on_message(event: lark_oapi.im.v1.P2ImMessageReceiveV1):
-            asyncio.create_task(on_message(event))
-
-        event_handler = (
-            lark_oapi.EventDispatcherHandler.builder('', '').register_p2_im_message_receive_v1(sync_on_message).build()
-        )
-
-        bot_account_id = config['bot_name']
-
-        bot = lark_oapi.ws.Client(config['app_id'], config['app_secret'], event_handler=event_handler)
-        api_client = lark_oapi.Client.builder().app_id(config['app_id']).app_secret(config['app_secret']).build()
-
-        super().__init__(
-            config=config,
-            logger=logger,
-            lark_tenant_key=config.get('lark_tenant_key', ''),
-            card_id_dict={},
-            seq=1,
-            listeners={},
-            quart_app=quart_app,
-            bot=bot,
-            api_client=api_client,
-            bot_account_id=bot_account_id,
-            **kwargs,
-        )
-
-    async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
-        pass
-
-    async def is_stream_output_supported(self) -> bool:
-        is_stream = False
-        if self.config.get('enable-stream-reply', None):
-            is_stream = True
-        return is_stream
-
-    async def create_card_id(self, message_id):
-        try:
-            # self.logger.debug('飞书支持stream输出,创建卡片......')
-
-            card_data = {
-                'schema': '2.0',
-                'config': {
-                    'update_multi': True,
-                    'streaming_mode': True,
-                    'streaming_config': {
-                        'print_step': {'default': 1},
-                        'print_frequency_ms': {'default': 70},
-                        'print_strategy': 'fast',
-                    },
-                },
-                'body': {
-                    'direction': 'vertical',
-                    'padding': '12px 12px 12px 12px',
-                    'elements': [
-                        {
-                            'tag': 'div',
-                            'text': {
-                                'tag': 'plain_text',
-                                'content': 'LangBot',
-                                'text_size': 'normal',
-                                'text_align': 'left',
-                                'text_color': 'default',
-                            },
-                            'icon': {
-                                'tag': 'custom_icon',
-                                'img_key': 'img_v3_02p3_05c65d5d-9bad-440a-a2fb-c89571bfd5bg',
-                            },
-                        },
-                        {
-                            'tag': 'markdown',
-                            'content': '',
-                            'text_align': 'left',
-                            'text_size': 'normal',
-                            'margin': '0px 0px 0px 0px',
-                            'element_id': 'streaming_txt',
-                        },
-                        {
-                            'tag': 'markdown',
-                            'content': '',
-                            'text_align': 'left',
-                            'text_size': 'normal',
-                            'margin': '0px 0px 0px 0px',
-                        },
-                        {
-                            'tag': 'column_set',
-                            'horizontal_spacing': '8px',
-                            'horizontal_align': 'left',
-                            'columns': [
-                                {
-                                    'tag': 'column',
-                                    'width': 'weighted',
-                                    'elements': [
-                                        {
-                                            'tag': 'markdown',
-                                            'content': '',
-                                            'text_align': 'left',
-                                            'text_size': 'normal',
-                                            'margin': '0px 0px 0px 0px',
-                                        },
-                                        {
-                                            'tag': 'markdown',
-                                            'content': '',
-                                            'text_align': 'left',
-                                            'text_size': 'normal',
-                                            'margin': '0px 0px 0px 0px',
-                                        },
-                                        {
-                                            'tag': 'markdown',
-                                            'content': '',
-                                            'text_align': 'left',
-                                            'text_size': 'normal',
-                                            'margin': '0px 0px 0px 0px',
-                                        },
-                                    ],
-                                    'padding': '0px 0px 0px 0px',
-                                    'direction': 'vertical',
-                                    'horizontal_spacing': '8px',
-                                    'vertical_spacing': '2px',
-                                    'horizontal_align': 'left',
-                                    'vertical_align': 'top',
-                                    'margin': '0px 0px 0px 0px',
-                                    'weight': 1,
-                                }
-                            ],
-                            'margin': '0px 0px 0px 0px',
-                        },
-                        {'tag': 'hr', 'margin': '0px 0px 0px 0px'},
-                        {
-                            'tag': 'column_set',
-                            'horizontal_spacing': '12px',
-                            'horizontal_align': 'right',
-                            'columns': [
-                                {
-                                    'tag': 'column',
-                                    'width': 'weighted',
-                                    'elements': [
-                                        {
-                                            'tag': 'markdown',
-                                            'content': '<font color="grey-600">以上内容由 AI 生成，仅供参考。更多详细、准确信息可点击引用链接查看</font>',
-                                            'text_align': 'left',
-                                            'text_size': 'notation',
-                                            'margin': '4px 0px 0px 0px',
-                                            'icon': {
-                                                'tag': 'standard_icon',
-                                                'token': 'robot_outlined',
-                                                'color': 'grey',
-                                            },
-                                        }
-                                    ],
-                                    'padding': '0px 0px 0px 0px',
-                                    'direction': 'vertical',
-                                    'horizontal_spacing': '8px',
-                                    'vertical_spacing': '8px',
-                                    'horizontal_align': 'left',
-                                    'vertical_align': 'top',
-                                    'margin': '0px 0px 0px 0px',
-                                    'weight': 1,
-                                },
-                                {
-                                    'tag': 'column',
-                                    'width': '20px',
-                                    'elements': [
-                                        {
-                                            'tag': 'button',
-                                            'text': {'tag': 'plain_text', 'content': ''},
-                                            'type': 'text',
-                                            'width': 'fill',
-                                            'size': 'medium',
-                                            'icon': {'tag': 'standard_icon', 'token': 'thumbsup_outlined'},
-                                            'hover_tips': {'tag': 'plain_text', 'content': '有帮助'},
-                                            'margin': '0px 0px 0px 0px',
-                                        }
-                                    ],
-                                    'padding': '0px 0px 0px 0px',
-                                    'direction': 'vertical',
-                                    'horizontal_spacing': '8px',
-                                    'vertical_spacing': '8px',
-                                    'horizontal_align': 'left',
-                                    'vertical_align': 'top',
-                                    'margin': '0px 0px 0px 0px',
-                                },
-                                {
-                                    'tag': 'column',
-                                    'width': '30px',
-                                    'elements': [
-                                        {
-                                            'tag': 'button',
-                                            'text': {'tag': 'plain_text', 'content': ''},
-                                            'type': 'text',
-                                            'width': 'default',
-                                            'size': 'medium',
-                                            'icon': {'tag': 'standard_icon', 'token': 'thumbdown_outlined'},
-                                            'hover_tips': {'tag': 'plain_text', 'content': '无帮助'},
-                                            'margin': '0px 0px 0px 0px',
-                                        }
-                                    ],
-                                    'padding': '0px 0px 0px 0px',
-                                    'vertical_spacing': '8px',
-                                    'horizontal_align': 'left',
-                                    'vertical_align': 'top',
-                                    'margin': '0px 0px 0px 0px',
-                                },
-                            ],
-                            'margin': '0px 0px 4px 0px',
-                        },
-                    ],
-                },
-            }
-            # delay / fast 创建卡片模板，delay 延迟打印，fast 实时打印，可以自定义更好看的消息模板
-
-            request: CreateCardRequest = (
-                CreateCardRequest.builder()
-                .request_body(CreateCardRequestBody.builder().type('card_json').data(json.dumps(card_data)).build())
-                .build()
-            )
-
-            # 发起请求
-            response: CreateCardResponse = self.api_client.cardkit.v1.card.create(request)
-
-            # 处理失败返回
-            if not response.success():
-                raise Exception(
-                    f'client.cardkit.v1.card.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-                )
-
-            self.card_id_dict[message_id] = response.data.card_id
-
-            card_id = response.data.card_id
-            return card_id
-
-        except Exception as e:
-            raise e
-    async def create_message_card(self, message_id, event) -> str:
-        """
-        创建卡片消息。
-        使用卡片消息是因为普通消息更新次数有限制，而大模型流式返回结果可能很多而超过限制，而飞书卡片没有这个限制（api免费次数有限）
-        """
-        # message_id = event.message_chain.message_id
-
-        card_id = await self.create_card_id(message_id)
-        content = {
-            'type': 'card',
-            'data': {'card_id': card_id, 'template_variable': {'content': 'Thinking...'}},
-        }  # 当收到消息时发送消息模板，可添加模板变量，详情查看飞书中接口文档
-        request: ReplyMessageRequest = (
-            ReplyMessageRequest.builder()
-            .message_id(event.message_chain.message_id)
-            .request_body(
-                ReplyMessageRequestBody.builder().content(json.dumps(content)).msg_type('interactive').build()
-            )
-            .build()
-        )
-
-        # 发起请求
-        response: ReplyMessageResponse = await self.api_client.im.v1.message.areply(request)
-
-        # 处理失败返回
-        if not response.success():
-            raise Exception(
-                f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-            )
-        return True
-
-    async def reply_message(
-        self,
-        message_source: platform_events.MessageEvent,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-    ):
-        # 不再需要了，因为message_id已经被包含到message_chain中
-        # lark_event = await self.event_converter.yiri2target(message_source)
-        lark_message = await self.message_converter.yiri2target(message, self.api_client)
-
-        final_content = {
-            'zh_Hans': {
-                'title': '',
-                'content': lark_message,
-            },
-        }
-
-        request: ReplyMessageRequest = (
-            ReplyMessageRequest.builder()
-            .message_id(message_source.message_chain.message_id)
-            .request_body(
-                ReplyMessageRequestBody.builder()
-                .content(json.dumps(final_content))
-                .msg_type('post')
-                .reply_in_thread(False)
-                .uuid(str(uuid.uuid4()))
-                .build()
-            )
-            .build()
-        )
-
-        response: ReplyMessageResponse = await self.api_client.im.v1.message.areply(request)
-
-        if not response.success():
-            raise Exception(
-                f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-            )
-
-    async def reply_message_chunk(
-        self,
-        message_source: platform_events.MessageEvent,
-        bot_message,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-        is_final: bool = False,
-    ):
-        """
-        回复消息变成更新卡片消息
-        """
-        # self.seq += 1
-        message_id = bot_message.resp_message_id
-        msg_seq = bot_message.msg_sequence
-        if msg_seq % 8 == 0 or is_final:
-            lark_message = await self.message_converter.yiri2target(message, self.api_client)
-
-            text_message = ''
-            for ele in lark_message[0]:
-                if ele['tag'] == 'text':
-                    text_message += ele['text']
-                elif ele['tag'] == 'md':
-                    text_message += ele['text']
-
-            # content = {
-            #     'type': 'card_json',
-            #     'data': {'card_id': self.card_id_dict[message_id], 'elements': {'content': text_message}},
-            # }
-
-            request: ContentCardElementRequest = (
-                ContentCardElementRequest.builder()
-                .card_id(self.card_id_dict[message_id])
-                .element_id('streaming_txt')
-                .request_body(
-                    ContentCardElementRequestBody.builder()
-                    # .uuid("a0d69e20-1dd1-458b-k525-dfeca4015204")
-                    .content(text_message)
-                    .sequence(msg_seq)
-                    .build()
-                )
-                .build()
-            )
-
-            if is_final and bot_message.tool_calls is None:
-                # self.seq = 1  # 消息回复结束之后重置seq
-                self.card_id_dict.pop(message_id)  # 清理已经使用过的卡片
-            # 发起请求
-            response: ContentCardElementResponse = self.api_client.cardkit.v1.card_element.content(request)
-
-            # 处理失败返回
-            if not response.success():
-                raise Exception(
-                    f'client.im.v1.message.patch failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
-                )
-                return
-
-    async def is_muted(self, group_id: int) -> bool:
-        return False
-
-    def register_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        callback: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None
-        ],
-    ):
-        self.listeners[event_type] = callback
-
-    def unregister_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        callback: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None
-        ],
-    ):
-        self.listeners.pop(event_type)
-
-    async def run_async(self):
-        port = self.config['port']
-        enable_webhook = self.config['enable-webhook']
-
-        if not enable_webhook:
-            try:
-                await self.bot._connect()
-            except lark_oapi.ws.exception.ClientException as e:
-                raise e
-            except Exception as e:
-                await self.bot._disconnect()
-                if self.bot._auto_reconnect:
-                    await self.bot._reconnect()
-                else:
-                    raise e
-        else:
-
-            async def shutdown_trigger_placeholder():
-                while True:
-                    await asyncio.sleep(1)
-
-            await self.quart_app.run_task(
-                host='0.0.0.0',
-                port=port,
-                shutdown_trigger=shutdown_trigger_placeholder,
-            )
-
-    async def kill(self) -> bool:
-        # 需要断开连接，不然旧的连接会继续运行，导致飞书消息来时会随机选择一个连接
-        # 断开时lark.ws.Client的_receive_message_loop会打印error日志: receive message loop exit。然后进行重连，
-        # 所以要设置_auto_reconnect=False,让其不重连。
-        self.bot._auto_reconnect = False
-        await self.bot._disconnect()
-        return False

pkg/platform/sources/lark.yaml

@@ -1,81 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: lark
-  label:
-    en_US: Lark
-    zh_Hans: 飞书
-  description:
-    en_US: Lark Adapter
-    zh_Hans: 飞书适配器，请查看文档了解使用方式
-  icon: lark.svg
-spec:
-  config:
-    - name: app_id
-      label:
-        en_US: App ID
-        zh_Hans: 应用ID
-      type: string
-      required: true
-      default: ""
-    - name: app_secret
-      label:
-        en_US: App Secret
-        zh_Hans: 应用密钥
-      type: string
-      required: true
-      default: ""
-    - name: bot_name
-      label:
-        en_US: Bot Name
-        zh_Hans: 机器人名称
-      description:
-        en_US: Must be the same as the name of the bot in Lark, otherwise the bot will not be able to receive messages in the group
-        zh_Hans: 必须与飞书机器人名称一致，否则机器人将无法在群内正常接收消息
-      type: string
-      required: true
-      default: ""
-    - name: enable-webhook
-      label:
-        en_US: Enable Webhook Mode
-        zh_Hans: 启用Webhook模式
-      description:
-        en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
-        zh_Hans: 如果启用，机器人将使用 Webhook 模式接收消息。否则，将使用 WS 长连接模式
-      type: boolean
-      required: true
-      default: false
-    - name: port
-      label:
-        en_US: Webhook Port
-        zh_Hans: Webhook端口
-      description:
-        en_US: Only valid when webhook mode is enabled, please fill in the webhook port
-        zh_Hans: 仅在启用 Webhook 模式时有效，请填写 Webhook 端口
-      type: integer
-      required: true
-      default: 2285
-    - name: encrypt-key
-      label:
-        en_US: Encrypt Key
-        zh_Hans: 加密密钥
-      description:
-        en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
-        zh_Hans: 仅在启用 Webhook 模式时有效，请填写加密密钥
-      type: string
-      required: true
-      default: ""
-    - name: enable-stream-reply
-      label:
-        en_US: Enable Stream Reply Mode
-        zh_Hans: 启用飞书流式回复模式
-      description:
-        en_US: If enabled, the bot will use the stream of lark reply mode
-        zh_Hans: 如果启用，将使用飞书流式方式来回复内容
-      type: boolean
-      required: true
-      default: false
-execution:
-  python:
-    path: ./lark.py
-    attr: LarkAdapter

pkg/platform/sources/line.yaml

@@ -1,54 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: LINE
-  label:
-    en_US: LINE
-    zh_Hans: LINE
-  description:
-    en_US: LINE Adapter
-    zh_Hans: LINE适配器，请查看文档了解使用方式
-    ja_JP: LINEアダプター、ドキュメントを参照してください
-    zh_Hant: LINE適配器，請查看文檔了解使用方式
-  icon: line.png
-spec:
-  config:
-    - name: channel_access_token
-      label:
-        en_US: Channel access token
-        zh_Hans: 频道访问令牌
-        ja_JP: チャンネルアクセストークン
-        zh_Hant: 頻道訪問令牌
-      type: string
-      required: true
-      default: ""
-    - name: port
-      label:
-        en_US: Webhook Port
-        zh_Hans: Webhook端口
-      description:
-        en_US: Only valid when webhook mode is enabled, please fill in the webhook port
-        zh_Hans: 请填写 Webhook 端口
-        ja_JP: Webhookポートを入力してください
-        zh_Hant: 請填寫 Webhook 端口
-      type: integer
-      required: true
-      default: 2287
-    - name: channel_secret
-      label:
-        en_US: Channel secret
-        zh_Hans: 消息密钥
-        ja_JP: チャンネルシークレット
-        zh_Hant: 消息密钥
-      description:
-        en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
-        zh_Hans: 请填写加密密钥
-        ja_JP: Webhookモードが有効な場合にのみ、暗号化キーを入力してください
-        zh_Hant: 請填寫加密密钥
-      type: string
-      required: true
-      default: ""
-execution:
-  python:
-    path: ./line.py
-    attr: LINEAdapter

pkg/platform/sources/officialaccount.yaml

@@ -1,76 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: officialaccount
-  label:
-    en_US: Official Account
-    zh_Hans: 微信公众号
-  description:
-    en_US: Official Account Adapter
-    zh_Hans: 微信公众号适配器，请查看文档了解使用方式
-  icon: officialaccount.png
-spec:
-  config:
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-    - name: EncodingAESKey
-      label:
-        en_US: EncodingAESKey
-        zh_Hans: 消息加解密密钥
-      type: string
-      required: true
-      default: ""
-    - name: AppID
-      label:
-        en_US: App ID
-        zh_Hans: 应用ID
-      type: string
-      required: true
-      default: ""
-    - name: AppSecret
-      label:
-        en_US: App Secret
-        zh_Hans: 应用密钥
-      type: string
-      required: true
-      default: ""
-    - name: Mode
-      label:
-        en_US: Mode
-        zh_Hans: 接入模式
-      type: string
-      required: true
-      default: "drop"
-    - name: LoadingMessage
-      label:
-        en_US: Loading Message
-        zh_Hans: 加载消息
-      type: string
-      required: true
-      default: "AI正在思考中，请发送任意内容获取回复。"
-    - name: host
-      label:
-        en_US: Host
-        zh_Hans: 监听主机
-      description:
-        en_US: The host that Official Account listens on for Webhook connections.
-        zh_Hans: 微信公众号监听的主机，除非你知道自己在做什么，否则请写 0.0.0.0
-      type: string
-      required: true
-      default: 0.0.0.0
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: integer
-      required: true
-      default: 2287
-execution:
-  python:
-    path: ./officialaccount.py
-    attr: OfficialAccountAdapter

pkg/platform/sources/qqofficial.py

@@ -1,250 +0,0 @@
-from __future__ import annotations
-import typing
-import asyncio
-import traceback
-
-import datetime
-
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
-from langbot_plugin.api.entities.builtin.command import errors as command_errors
-from libs.qq_official_api.api import QQOfficialClient
-from libs.qq_official_api.qqofficialevent import QQOfficialEvent
-from ...utils import image
-from ..logger import EventLogger
-
-
-class QQOfficialMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
-    @staticmethod
-    async def yiri2target(message_chain: platform_message.MessageChain):
-        content_list = []
-        # 只实现了发文字
-        for msg in message_chain:
-            if type(msg) is platform_message.Plain:
-                content_list.append(
-                    {
-                        'type': 'text',
-                        'content': msg.text,
-                    }
-                )
-
-        return content_list
-
-    @staticmethod
-    async def target2yiri(message: str, message_id: str, pic_url: str, content_type):
-        yiri_msg_list = []
-        yiri_msg_list.append(platform_message.Source(id=message_id, time=datetime.datetime.now()))
-        if pic_url is not None:
-            base64_url = await image.get_qq_official_image_base64(pic_url=pic_url, content_type=content_type)
-            yiri_msg_list.append(platform_message.Image(base64=base64_url))
-
-        yiri_msg_list.append(platform_message.Plain(text=message))
-        chain = platform_message.MessageChain(yiri_msg_list)
-        return chain
-
-
-class QQOfficialEventConverter(abstract_platform_adapter.AbstractEventConverter):
-    @staticmethod
-    async def yiri2target(event: platform_events.MessageEvent) -> QQOfficialEvent:
-        return event.source_platform_object
-
-    @staticmethod
-    async def target2yiri(event: QQOfficialEvent):
-        """
-        QQ官方消息转换为LB对象
-        """
-        yiri_chain = await QQOfficialMessageConverter.target2yiri(
-            message=event.content,
-            message_id=event.d_id,
-            pic_url=event.attachments,
-            content_type=event.content_type,
-        )
-
-        if event.t == 'C2C_MESSAGE_CREATE':
-            friend = platform_entities.Friend(
-                id=event.user_openid,
-                nickname=event.t,
-                remark='',
-            )
-            return platform_events.FriendMessage(
-                sender=friend,
-                message_chain=yiri_chain,
-                time=int(datetime.datetime.strptime(event.timestamp, '%Y-%m-%dT%H:%M:%S%z').timestamp()),
-                source_platform_object=event,
-            )
-
-        if event.t == 'DIRECT_MESSAGE_CREATE':
-            friend = platform_entities.Friend(
-                id=event.guild_id,
-                nickname=event.t,
-                remark='',
-            )
-            return platform_events.FriendMessage(sender=friend, message_chain=yiri_chain, source_platform_object=event)
-        if event.t == 'GROUP_AT_MESSAGE_CREATE':
-            yiri_chain.insert(0, platform_message.At(target='justbot'))
-
-            sender = platform_entities.GroupMember(
-                id=event.group_openid,
-                member_name=event.t,
-                permission='MEMBER',
-                group=platform_entities.Group(
-                    id=event.group_openid,
-                    name='MEMBER',
-                    permission=platform_entities.Permission.Member,
-                ),
-                special_title='',
-                join_timestamp=0,
-                last_speak_timestamp=0,
-                mute_time_remaining=0,
-            )
-            time = int(datetime.datetime.strptime(event.timestamp, '%Y-%m-%dT%H:%M:%S%z').timestamp())
-            return platform_events.GroupMessage(
-                sender=sender,
-                message_chain=yiri_chain,
-                time=time,
-                source_platform_object=event,
-            )
-        if event.t == 'AT_MESSAGE_CREATE':
-            yiri_chain.insert(0, platform_message.At(target='justbot'))
-            sender = platform_entities.GroupMember(
-                id=event.channel_id,
-                member_name=event.t,
-                permission='MEMBER',
-                group=platform_entities.Group(
-                    id=event.channel_id,
-                    name='MEMBER',
-                    permission=platform_entities.Permission.Member,
-                ),
-                special_title='',
-                join_timestamp=0,
-                last_speak_timestamp=0,
-                mute_time_remaining=0,
-            )
-            time = int(datetime.datetime.strptime(event.timestamp, '%Y-%m-%dT%H:%M:%S%z').timestamp())
-            return platform_events.GroupMessage(
-                sender=sender,
-                message_chain=yiri_chain,
-                time=time,
-                source_platform_object=event,
-            )
-
-
-class QQOfficialAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    bot: QQOfficialClient
-    config: dict
-    bot_account_id: str
-    message_converter: QQOfficialMessageConverter = QQOfficialMessageConverter()
-    event_converter: QQOfficialEventConverter = QQOfficialEventConverter()
-
-    def __init__(self, config: dict, logger: EventLogger):
-        bot = QQOfficialClient(
-            app_id=config['appid'], secret=config['secret'], token=config['token'], logger=logger
-        )
-
-        super().__init__(
-            config=config,
-            logger=logger,
-            bot=bot,
-            bot_account_id=config['appid'],
-        )
-
-    async def reply_message(
-        self,
-        message_source: platform_events.MessageEvent,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-    ):
-        qq_official_event = await QQOfficialEventConverter.yiri2target(
-            message_source,
-        )
-
-        content_list = await QQOfficialMessageConverter.yiri2target(message)
-
-        # 私聊消息
-        if qq_official_event.t == 'C2C_MESSAGE_CREATE':
-            for content in content_list:
-                if content['type'] == 'text':
-                    await self.bot.send_private_text_msg(
-                        qq_official_event.user_openid,
-                        content['content'],
-                        qq_official_event.d_id,
-                    )
-
-        # 群聊消息
-        if qq_official_event.t == 'GROUP_AT_MESSAGE_CREATE':
-            for content in content_list:
-                if content['type'] == 'text':
-                    await self.bot.send_group_text_msg(
-                        qq_official_event.group_openid,
-                        content['content'],
-                        qq_official_event.d_id,
-                    )
-
-        # 频道群聊
-        if qq_official_event.t == 'AT_MESSAGE_CREATE':
-            for content in content_list:
-                if content['type'] == 'text':
-                    await self.bot.send_channle_group_text_msg(
-                        qq_official_event.channel_id,
-                        content['content'],
-                        qq_official_event.d_id,
-                    )
-
-        # 频道私聊
-        if qq_official_event.t == 'DIRECT_MESSAGE_CREATE':
-            for content in content_list:
-                if content['type'] == 'text':
-                    await self.bot.send_channle_private_text_msg(
-                        qq_official_event.guild_id,
-                        content['content'],
-                        qq_official_event.d_id,
-                    )
-
-    async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
-        pass
-
-    def register_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        callback: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None
-        ],
-    ):
-        async def on_message(event: QQOfficialEvent):
-            self.bot_account_id = 'justbot'
-            try:
-                return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception:
-                await self.logger.error(f'Error in qqofficial callback: {traceback.format_exc()}')
-
-        if event_type == platform_events.FriendMessage:
-            self.bot.on_message('DIRECT_MESSAGE_CREATE')(on_message)
-            self.bot.on_message('C2C_MESSAGE_CREATE')(on_message)
-        elif event_type == platform_events.GroupMessage:
-            self.bot.on_message('GROUP_AT_MESSAGE_CREATE')(on_message)
-            self.bot.on_message('AT_MESSAGE_CREATE')(on_message)
-
-    async def run_async(self):
-        async def shutdown_trigger_placeholder():
-            while True:
-                await asyncio.sleep(1)
-
-        await self.bot.run_task(
-            host='0.0.0.0',
-            port=self.config['port'],
-            shutdown_trigger=shutdown_trigger_placeholder,
-        )
-
-    async def kill(self) -> bool:
-        return False
-
-    def unregister_listener(
-        self,
-        event_type: type,
-        callback: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None
-        ],
-    ):
-        return super().unregister_listener(event_type, callback)

pkg/platform/sources/qqofficial.yaml

@@ -1,45 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: qqofficial
-  label:
-    en_US: QQ Official API
-    zh_Hans: QQ 官方 API
-  description:
-    en_US: QQ Official API (Webhook)
-    zh_Hans: QQ 官方 API (Webhook)，请查看文档了解使用方式
-  icon: qqofficial.svg
-spec:
-  config:
-    - name: appid
-      label:
-        en_US: App ID
-        zh_Hans: 应用ID
-      type: string
-      required: true
-      default: ""
-    - name: secret
-      label:
-        en_US: Secret
-        zh_Hans: 密钥
-      type: string
-      required: true
-      default: ""
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: integer
-      required: true
-      default: 2284
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-execution:
-  python:
-    path: ./qqofficial.py
-    attr: QQOfficialAdapter

pkg/platform/sources/slack.yaml

@@ -1,38 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: slack
-  label:
-    en_US: Slack
-    zh_Hans: Slack
-  description:
-    en_US: Slack Adapter
-    zh_Hans: Slack 适配器，请查看文档了解使用方式
-  icon: slack.png
-spec:
-  config:
-    - name: bot_token
-      label:
-        en_US: Bot Token
-        zh_Hans: 机器人令牌
-      type: string
-      required: true
-      default: ""
-    - name: signing_secret
-      label:
-        en_US: signing_secret
-        zh_Hans: 密钥
-      type: string
-      required: true
-      default: ""
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: int
-      required: true
-      default: 2288
-execution:
-  python:
-    path: ./slack.py
-    attr: SlackAdapter

pkg/platform/sources/telegram.yaml

@@ -1,41 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: telegram
-  label:
-    en_US: Telegram
-    zh_Hans: 电报
-  description:
-    en_US: Telegram Adapter
-    zh_Hans: 电报适配器，请查看文档了解使用方式
-  icon: telegram.svg
-spec:
-  config:
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-    - name: markdown_card
-      label:
-        en_US: Markdown Card
-        zh_Hans: 是否使用 Markdown 卡片
-      type: boolean
-      required: false
-      default: true
-    - name: enable-stream-reply
-      label:
-        en_US: Enable Stream Reply Mode
-        zh_Hans: 启用电报流式回复模式
-      description:
-        en_US: If enabled, the bot will use the stream of telegram reply mode
-        zh_Hans: 如果启用，将使用电报流式方式来回复内容
-      type: boolean
-      required: true
-      default: false
-execution:
-  python:
-    path: ./telegram.py
-    attr: TelegramAdapter

pkg/platform/sources/webchat.py

@@ -1,304 +0,0 @@
-import asyncio
-import logging
-import typing
-from datetime import datetime
-
-import pydantic
-
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
-import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_platform_logger
-from ...core import app
-
-logger = logging.getLogger(__name__)
-
-
-class WebChatMessage(pydantic.BaseModel):
-    id: int
-    role: str
-    content: str
-    message_chain: list[dict]
-    timestamp: str
-    is_final: bool = False
-
-
-class WebChatSession:
-    id: str
-    message_lists: dict[str, list[WebChatMessage]] = {}
-    resp_waiters: dict[int, asyncio.Future[WebChatMessage]]
-    resp_queues: dict[int, asyncio.Queue[WebChatMessage]]
-
-    def __init__(self, id: str):
-        self.id = id
-        self.message_lists = {}
-        self.resp_waiters = {}
-        self.resp_queues = {}
-
-    def get_message_list(self, pipeline_uuid: str) -> list[WebChatMessage]:
-        if pipeline_uuid not in self.message_lists:
-            self.message_lists[pipeline_uuid] = []
-
-        return self.message_lists[pipeline_uuid]
-
-
-class WebChatAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    """WebChat调试适配器，用于流水线调试"""
-
-    webchat_person_session: WebChatSession = pydantic.Field(exclude=True, default_factory=WebChatSession)
-    webchat_group_session: WebChatSession = pydantic.Field(exclude=True, default_factory=WebChatSession)
-
-    listeners: dict[
-        typing.Type[platform_events.Event],
-        typing.Callable[[platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None],
-    ] = pydantic.Field(default_factory=dict, exclude=True)
-
-    is_stream: bool = pydantic.Field(exclude=True)
-    debug_messages: dict[str, list[dict]] = pydantic.Field(default_factory=dict, exclude=True)
-
-    ap: app.Application = pydantic.Field(exclude=True)
-
-    def __init__(self, config: dict, logger: abstract_platform_logger.AbstractEventLogger, **kwargs):
-        super().__init__(
-            config=config,
-            logger=logger,
-            **kwargs,
-        )
-
-        self.webchat_person_session = WebChatSession(id='webchatperson')
-        self.webchat_group_session = WebChatSession(id='webchatgroup')
-
-        self.bot_account_id = 'webchatbot'
-
-        self.debug_messages = {}
-
-    async def send_message(
-        self,
-        target_type: str,
-        target_id: str,
-        message: platform_message.MessageChain,
-    ) -> dict:
-        """发送消息到调试会话"""
-        session_key = target_id
-
-        if session_key not in self.debug_messages:
-            self.debug_messages[session_key] = []
-
-        message_data = {
-            'id': len(self.debug_messages[session_key]) + 1,
-            'type': 'bot',
-            'content': str(message),
-            'timestamp': datetime.now().isoformat(),
-            'message_chain': [component.__dict__ for component in message],
-        }
-
-        self.debug_messages[session_key].append(message_data)
-
-        await self.logger.info(f'Send message to {session_key}: {message}')
-
-        return message_data
-
-    async def reply_message(
-        self,
-        message_source: platform_events.MessageEvent,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-    ) -> dict:
-        """回复消息"""
-        message_data = WebChatMessage(
-            id=-1,
-            role='assistant',
-            content=str(message),
-            message_chain=[component.__dict__ for component in message],
-            timestamp=datetime.now().isoformat(),
-        )
-
-        # notify waiter
-        if isinstance(message_source, platform_events.FriendMessage):
-            await self.webchat_person_session.resp_queues[message_source.message_chain.message_id].put(message_data)
-        elif isinstance(message_source, platform_events.GroupMessage):
-            await self.webchat_group_session.resp_queues[message_source.message_chain.message_id].put(message_data)
-
-        return message_data.model_dump()
-
-    async def reply_message_chunk(
-        self,
-        message_source: platform_events.MessageEvent,
-        bot_message,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-        is_final: bool = False,
-    ) -> dict:
-        """回复消息"""
-        message_data = WebChatMessage(
-            id=-1,
-            role='assistant',
-            content=str(message),
-            message_chain=[component.__dict__ for component in message],
-            timestamp=datetime.now().isoformat(),
-        )
-
-        # notify waiter
-        session = (
-            self.webchat_group_session
-            if isinstance(message_source, platform_events.GroupMessage)
-            else self.webchat_person_session
-        )
-        if message_source.message_chain.message_id not in session.resp_waiters:
-            # session.resp_waiters[message_source.message_chain.message_id] = asyncio.Queue()
-            queue = session.resp_queues[message_source.message_chain.message_id]
-
-        # if isinstance(message_source, platform_events.FriendMessage):
-        #     queue = self.webchat_person_session.resp_queues[message_source.message_chain.message_id]
-        # elif isinstance(message_source, platform_events.GroupMessage):
-        #     queue = self.webchat_group_session.resp_queues[message_source.message_chain.message_id]
-        if is_final and bot_message.tool_calls is None:
-            message_data.is_final = True
-        # print(message_data)
-        await queue.put(message_data)
-
-        return message_data.model_dump()
-
-    async def is_stream_output_supported(self) -> bool:
-        return self.is_stream
-
-    def register_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        func: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], typing.Awaitable[None]
-        ],
-    ):
-        """注册事件监听器"""
-        self.listeners[event_type] = func
-
-    def unregister_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        func: typing.Callable[
-            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], typing.Awaitable[None]
-        ],
-    ):
-        """取消注册事件监听器"""
-        del self.listeners[event_type]
-
-    async def is_muted(self, group_id: int) -> bool:
-        return False
-
-    async def run_async(self):
-        """运行适配器"""
-        await self.logger.info('WebChat调试适配器已启动')
-
-        try:
-            while True:
-                await asyncio.sleep(1)
-        except asyncio.CancelledError:
-            await self.logger.info('WebChat调试适配器已停止')
-            raise
-
-    async def kill(self):
-        """停止适配器"""
-        await self.logger.info('WebChat调试适配器正在停止')
-
-    async def send_webchat_message(
-        self,
-        pipeline_uuid: str,
-        session_type: str,
-        message_chain_obj: typing.List[dict],
-        is_stream: bool = False,
-    ) -> dict:
-        self.is_stream = is_stream
-        """发送调试消息到流水线"""
-        if session_type == 'person':
-            use_session = self.webchat_person_session
-        else:
-            use_session = self.webchat_group_session
-
-        message_chain = platform_message.MessageChain.parse_obj(message_chain_obj)
-
-        message_id = len(use_session.get_message_list(pipeline_uuid)) + 1
-
-        use_session.resp_queues[message_id] = asyncio.Queue()
-        logger.debug(f'Initialized queue for message_id: {message_id}')
-
-        use_session.get_message_list(pipeline_uuid).append(
-            WebChatMessage(
-                id=message_id,
-                role='user',
-                content=str(message_chain),
-                message_chain=message_chain_obj,
-                timestamp=datetime.now().isoformat(),
-            )
-        )
-
-        message_chain.insert(0, platform_message.Source(id=message_id, time=datetime.now().timestamp()))
-
-        if session_type == 'person':
-            sender = platform_entities.Friend(id='webchatperson', nickname='User', remark='User')
-            event = platform_events.FriendMessage(
-                sender=sender, message_chain=message_chain, time=datetime.now().timestamp()
-            )
-        else:
-            group = platform_entities.Group(
-                id='webchatgroup', name='Group', permission=platform_entities.Permission.Member
-            )
-            sender = platform_entities.GroupMember(
-                id='webchatperson',
-                member_name='User',
-                group=group,
-                permission=platform_entities.Permission.Member,
-            )
-            event = platform_events.GroupMessage(
-                sender=sender, message_chain=message_chain, time=datetime.now().timestamp()
-            )
-
-        self.ap.platform_mgr.webchat_proxy_bot.bot_entity.use_pipeline_uuid = pipeline_uuid
-
-        # trigger pipeline
-        if event.__class__ in self.listeners:
-            await self.listeners[event.__class__](event, self)
-
-        if is_stream:
-            queue = use_session.resp_queues[message_id]
-            msg_id = len(use_session.get_message_list(pipeline_uuid)) + 1
-            while True:
-                resp_message = await queue.get()
-                resp_message.id = msg_id
-                if resp_message.is_final:
-                    resp_message.id = msg_id
-                    use_session.get_message_list(pipeline_uuid).append(resp_message)
-                    yield resp_message.model_dump()
-                    break
-                yield resp_message.model_dump()
-            use_session.resp_queues.pop(message_id)
-
-        else:  # non-stream
-            # set waiter
-            # waiter = asyncio.Future[WebChatMessage]()
-            # use_session.resp_waiters[message_id] = waiter
-            # # waiter.add_done_callback(lambda future: use_session.resp_waiters.pop(message_id))
-            #
-            # resp_message = await waiter
-            #
-            # resp_message.id = len(use_session.get_message_list(pipeline_uuid)) + 1
-            #
-            # use_session.get_message_list(pipeline_uuid).append(resp_message)
-            #
-            # yield resp_message.model_dump()
-            msg_id = len(use_session.get_message_list(pipeline_uuid)) + 1
-
-            queue = use_session.resp_queues[message_id]
-            resp_message = await queue.get()
-            use_session.get_message_list(pipeline_uuid).append(resp_message)
-            resp_message.id = msg_id
-            resp_message.is_final = True
-
-            yield resp_message.model_dump()
-
-    def get_webchat_messages(self, pipeline_uuid: str, session_type: str) -> list[dict]:
-        """获取调试消息历史"""
-        if session_type == 'person':
-            return [message.model_dump() for message in self.webchat_person_session.get_message_list(pipeline_uuid)]
-        else:
-            return [message.model_dump() for message in self.webchat_group_session.get_message_list(pipeline_uuid)]

pkg/platform/sources/webchat.yaml

@@ -1,17 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: webchat
-  label:
-    en_US: "WebChat Debug"
-    zh_Hans: "网页聊天调试"
-  description:
-    en_US: "WebChat adapter for pipeline debugging"
-    zh_Hans: "用于流水线调试的网页聊天适配器"
-  icon: ""
-spec:
-  config: []
-execution:
-  python:
-    path: "webchat.py"
-    attr: "WebChatAdapter"

pkg/platform/sources/wecom.yaml

@@ -1,69 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: wecom
-  label:
-    en_US: WeCom
-    zh_Hans: 企业微信
-  description:
-    en_US: WeCom Adapter
-    zh_Hans: 企业微信适配器，请查看文档了解使用方式
-  icon: wecom.png
-spec:
-  config:
-    - name: host
-      label:
-        en_US: Host
-        zh_Hans: 监听主机
-      description:
-        en_US: Webhook host, unless you know what you're doing, please write 0.0.0.0
-        zh_Hans: Webhook 监听主机，除非你知道自己在做什么，否则请写 0.0.0.0
-      type: string
-      required: true
-      default: "0.0.0.0"
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: integer
-      required: true
-      default: 2290
-    - name: corpid
-      label:
-        en_US: Corpid
-        zh_Hans: 企业ID
-      type: string
-      required: true
-      default: ""
-    - name: secret
-      label:
-        en_US: Secret
-        zh_Hans: 密钥
-      type: string
-      required: true
-      default: ""
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-    - name: EncodingAESKey
-      label:
-        en_US: EncodingAESKey
-        zh_Hans: 消息加解密密钥
-      type: string
-      required: true
-      default: ""
-    - name: contacts_secret
-      label:
-        en_US: Contacts Secret
-        zh_Hans: 通讯录密钥
-      type: string
-      required: true
-      default: ""
-execution:
-  python:
-    path: ./wecom.py
-    attr: WecomAdapter

pkg/platform/sources/wecombot.py

@@ -1,211 +0,0 @@
-from __future__ import annotations
-import typing
-import asyncio
-import traceback
-
-import datetime
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
-import pydantic
-from ..logger import  EventLogger
-from libs.wecom_ai_bot_api.wecombotevent import WecomBotEvent
-from libs.wecom_ai_bot_api.api import WecomBotClient
-from ...core import app
-
-class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
-    @staticmethod
-    async def yiri2target(message_chain: platform_message.MessageChain):
-        content = ''
-        for msg in message_chain:
-            if type(msg) is platform_message.Plain:
-                content += msg.text
-        return content
-
-    @staticmethod
-    async def target2yiri(event: WecomBotEvent):
-        yiri_msg_list = []
-        if event.type == 'group':
-            yiri_msg_list.append(platform_message.At(target=event.ai_bot_id))
-        yiri_msg_list.append(platform_message.Source(id=event.message_id, time=datetime.datetime.now()))
-        yiri_msg_list.append(platform_message.Plain(text=event.content))
-        if event.picurl != '':
-            yiri_msg_list.append(platform_message.Image(base64=event.picurl))
-        chain = platform_message.MessageChain(yiri_msg_list)
-
-        return chain
-
-class WecomBotEventConverter(abstract_platform_adapter.AbstractEventConverter):
-
-    @staticmethod
-    async def yiri2target(event:platform_events.MessageEvent):
-        return event.source_platform_object
-    
-    @staticmethod
-    async def target2yiri(event:WecomBotEvent):
-        message_chain = await WecomBotMessageConverter.target2yiri(event)
-        if event.type == 'single':
-            return platform_events.FriendMessage(
-                sender=platform_entities.Friend(
-                    id=event.userid,
-                    nickname=event.username,
-                    remark='',
-                ),
-                message_chain=message_chain,
-                time=datetime.datetime.now().timestamp(),
-                source_platform_object=event,
-            )
-        elif event.type == 'group':
-            try:
-                sender = platform_entities.GroupMember(
-                    id=event.userid,
-                    permission='MEMBER',
-                    member_name=event.username,
-                    group=platform_entities.Group(
-                        id=str(event.chatid),
-                        name=event.chatname,
-                        permission=platform_entities.Permission.Member,
-                    ),
-                    special_title='',
-                    join_timestamp=0,
-                    last_speak_timestamp=0,
-                    mute_time_remaining=0,
-                )
-                time = datetime.datetime.now().timestamp()
-                return platform_events.GroupMessage(
-                    sender=sender,
-                    message_chain=message_chain,
-                    time=time,
-                    source_platform_object=event,
-                )
-            except Exception:
-                print(traceback.format_exc())
-
-class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    bot: WecomBotClient
-    bot_account_id: str
-    message_converter: WecomBotMessageConverter = WecomBotMessageConverter()
-    event_converter: WecomBotEventConverter = WecomBotEventConverter()
-    config: dict
-
-    def __init__(self, config: dict, logger: EventLogger):
-        required_keys = ['Token', 'EncodingAESKey', 'Corpid', 'BotId', 'port']
-        missing_keys = [key for key in required_keys if key not in config]
-        if missing_keys:
-            raise Exception(f'WecomBot 缺少配置项: {missing_keys}')
-
-        # 创建运行时 bot 对象
-        bot = WecomBotClient(
-            Token=config['Token'],
-            EnCodingAESKey=config['EncodingAESKey'],
-            Corpid=config['Corpid'],
-            logger=logger,
-        )
-        bot_account_id = config['BotId']
-
-        super().__init__(
-            config=config,
-            logger=logger,
-            bot=bot,
-            bot_account_id=bot_account_id,
-        )
-
-
-    async def reply_message(self, message_source:platform_events.MessageEvent, message:platform_message.MessageChain,quote_origin: bool = False):
-
-        content = await self.message_converter.yiri2target(message)
-        await self.bot.set_message(message_source.source_platform_object.message_id, content)
-
-    async def reply_message_chunk(
-        self,
-        message_source: platform_events.MessageEvent,
-        bot_message,
-        message: platform_message.MessageChain,
-        quote_origin: bool = False,
-        is_final: bool = False,
-    ):
-        """将流水线增量输出写入企业微信 stream 会话。
-
-        Args:
-            message_source: 流水线提供的原始消息事件。
-            bot_message: 当前片段对应的模型元信息（未使用）。
-            message: 需要回复的消息链。
-            quote_origin: 是否引用原消息（企业微信暂不支持）。
-            is_final: 标记当前片段是否为最终回复。
-
-        Returns:
-            dict: 包含 `stream` 键，标识写入是否成功。
-
-        Example:
-            在流水线 `reply_message_chunk` 调用中自动触发，无需手动调用。
-        """
-        # 转换为纯文本（智能机器人当前协议仅支持文本流）
-        content = await self.message_converter.yiri2target(message)
-        msg_id = message_source.source_platform_object.message_id
-
-        # 将片段推送到 WecomBotClient 中的队列，返回值用于判断是否走降级逻辑
-        success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
-        if not success and is_final:
-            # 未命中流式队列时使用旧有 set_message 兜底
-            await self.bot.set_message(msg_id, content)
-        return {'stream': success}
-
-    async def is_stream_output_supported(self) -> bool:
-        """智能机器人侧默认开启流式能力。
-
-        Returns:
-            bool: 恒定返回 True。
-
-        Example:
-            流水线执行阶段会调用此方法以确认是否启用流式。"""
-        return True
-
-    async def send_message(self, target_type, target_id, message):
-        pass
-
-    def register_listener(
-        self,
-        event_type: typing.Type[platform_events.Event],
-        callback: typing.Callable[[platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None],
-    ):
-        async def on_message(event: WecomBotEvent):
-            try:
-                return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception:
-                await self.logger.error(f'Error in wecombot callback: {traceback.format_exc()}')
-                print(traceback.format_exc())
-        try:
-            if event_type == platform_events.FriendMessage:
-                self.bot.on_message('single')(on_message)
-            elif event_type == platform_events.GroupMessage:
-                self.bot.on_message('group')(on_message)
-        except Exception:
-            print(traceback.format_exc())
-            
-
-    async def run_async(self):
-        async def shutdown_trigger_placeholder():
-            while True:
-                await asyncio.sleep(1)
-
-        await self.bot.run_task(
-            host='0.0.0.0',
-            port=self.config['port'],
-            shutdown_trigger=shutdown_trigger_placeholder,
-        )
-
-    async def kill(self) -> bool:
-        return False
-
-    async def unregister_listener(
-        self,
-        event_type: type,
-        callback: typing.Callable[[platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None],
-    ):
-        return super().unregister_listener(event_type, callback)
-    
-    async def is_muted(self, group_id: int) -> bool:
-        pass
-
-    

pkg/platform/sources/wecombot.yaml

@@ -1,52 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: wecombot
-  label:
-    en_US: WeComBot
-    zh_Hans: 企业微信智能机器人
-  description:
-    en_US: WeComBot Adapter
-    zh_Hans: 企业微信智能机器人适配器，请查看文档了解使用方式
-  icon: wecombot.png
-spec:
-  config:
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: integer
-      required: true
-      default: 2291
-    - name: Corpid
-      label:
-        en_US: Corpid
-        zh_Hans: 企业ID
-      type: string
-      required: true
-      default: ""
-    - name: Token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-    - name: EncodingAESKey
-      label:
-        en_US: EncodingAESKey
-        zh_Hans: 消息加解密密钥
-      type: string
-      required: true
-      default: ""
-    - name: BotId
-      label:
-        en_US: BotId
-        zh_Hans: 机器人ID
-      type: string
-      required: false
-      default: ""
-execution:
-  python:
-    path: ./wecombot.py
-    attr: WecomBotAdapter

pkg/platform/sources/wecomcs.yaml

@@ -1,52 +0,0 @@
-apiVersion: v1
-kind: MessagePlatformAdapter
-metadata:
-  name: wecomcs
-  label:
-    en_US: WeComCustomerService
-    zh_Hans: 企业微信客服
-  description:
-    en_US: WeComCSAdapter
-    zh_Hans: 企业微信客服适配器
-  icon: wecom.png
-spec:
-  config:
-    - name: port
-      label:
-        en_US: Port
-        zh_Hans: 监听端口
-      type: int
-      required: true
-      default: 2289
-    - name: corpid
-      label:
-        en_US: Corpid
-        zh_Hans: 企业ID
-      type: string
-      required: true
-      default: ""
-    - name: secret
-      label:
-        en_US: Secret
-        zh_Hans: 密钥
-      type: string
-      required: true
-      default: ""
-    - name: token
-      label:
-        en_US: Token
-        zh_Hans: 令牌
-      type: string
-      required: true
-      default: ""
-    - name: EncodingAESKey
-      label:
-        en_US: EncodingAESKey
-        zh_Hans: 消息加解密密钥
-      type: string
-      required: true
-      default: ""
-execution:
-  python:
-    path: ./wecomcs.py
-    attr: WecomCSAdapter

pkg/plugin/connector.py

@@ -1,364 +0,0 @@
-# For connect to plugin runtime.
-from __future__ import annotations
-
-import asyncio
-from typing import Any
-import typing
-import os
-import sys
-import httpx
-from async_lru import alru_cache
-
-from ..core import app
-from . import handler
-from ..utils import platform
-from langbot_plugin.runtime.io.controllers.stdio import (
-    client as stdio_client_controller,
-)
-from langbot_plugin.runtime.io.controllers.ws import client as ws_client_controller
-from langbot_plugin.api.entities import events
-from langbot_plugin.api.entities import context
-import langbot_plugin.runtime.io.connection as base_connection
-from langbot_plugin.api.definition.components.manifest import ComponentManifest
-from langbot_plugin.api.entities.builtin.command import (
-    context as command_context,
-    errors as command_errors,
-)
-from langbot_plugin.runtime.plugin.mgr import PluginInstallSource
-from ..core import taskmgr
-
-
-class PluginRuntimeConnector:
-    """Plugin runtime connector"""
-
-    ap: app.Application
-
-    handler: handler.RuntimeConnectionHandler
-
-    handler_task: asyncio.Task
-
-    heartbeat_task: asyncio.Task | None = None
-
-    stdio_client_controller: stdio_client_controller.StdioClientController
-
-    ctrl: stdio_client_controller.StdioClientController | ws_client_controller.WebSocketClientController
-
-    runtime_subprocess_on_windows: asyncio.subprocess.Process | None = None
-
-    runtime_subprocess_on_windows_task: asyncio.Task | None = None
-
-    runtime_disconnect_callback: typing.Callable[
-        [PluginRuntimeConnector], typing.Coroutine[typing.Any, typing.Any, None]
-    ]
-
-    is_enable_plugin: bool = True
-    """Mark if the plugin system is enabled"""
-
-    def __init__(
-        self,
-        ap: app.Application,
-        runtime_disconnect_callback: typing.Callable[
-            [PluginRuntimeConnector], typing.Coroutine[typing.Any, typing.Any, None]
-        ],
-    ):
-        self.ap = ap
-        self.runtime_disconnect_callback = runtime_disconnect_callback
-        self.is_enable_plugin = self.ap.instance_config.data.get('plugin', {}).get('enable', True)
-
-    async def heartbeat_loop(self):
-        while True:
-            await asyncio.sleep(20)
-            try:
-                await self.ping_plugin_runtime()
-                self.ap.logger.debug('Heartbeat to plugin runtime success.')
-            except Exception as e:
-                self.ap.logger.debug(f'Failed to heartbeat to plugin runtime: {e}')
-
-    async def initialize(self):
-        if not self.is_enable_plugin:
-            self.ap.logger.info('Plugin system is disabled.')
-            return
-
-        async def new_connection_callback(connection: base_connection.Connection):
-            async def disconnect_callback(
-                rchandler: handler.RuntimeConnectionHandler,
-            ) -> bool:
-                if platform.get_platform() == 'docker' or platform.use_websocket_to_connect_plugin_runtime():
-                    self.ap.logger.error('Disconnected from plugin runtime, trying to reconnect...')
-                    await self.runtime_disconnect_callback(self)
-                    return False
-                else:
-                    self.ap.logger.error(
-                        'Disconnected from plugin runtime, cannot automatically reconnect while LangBot connects to plugin runtime via stdio, please restart LangBot.'
-                    )
-                    return False
-
-            self.handler = handler.RuntimeConnectionHandler(connection, disconnect_callback, self.ap)
-
-            self.handler_task = asyncio.create_task(self.handler.run())
-            _ = await self.handler.ping()
-            self.ap.logger.info('Connected to plugin runtime.')
-            await self.handler_task
-
-        task: asyncio.Task | None = None
-
-        if platform.get_platform() == 'docker' or platform.use_websocket_to_connect_plugin_runtime():  # use websocket
-            self.ap.logger.info('use websocket to connect to plugin runtime')
-            ws_url = self.ap.instance_config.data.get('plugin', {}).get(
-                'runtime_ws_url', 'ws://langbot_plugin_runtime:5400/control/ws'
-            )
-
-            async def make_connection_failed_callback(
-                ctrl: ws_client_controller.WebSocketClientController,
-                exc: Exception = None,
-            ) -> None:
-                if exc is not None:
-                    self.ap.logger.error(f'Failed to connect to plugin runtime({ws_url}): {exc}')
-                else:
-                    self.ap.logger.error(f'Failed to connect to plugin runtime({ws_url}), trying to reconnect...')
-                await self.runtime_disconnect_callback(self)
-
-            self.ctrl = ws_client_controller.WebSocketClientController(
-                ws_url=ws_url,
-                make_connection_failed_callback=make_connection_failed_callback,
-            )
-            task = self.ctrl.run(new_connection_callback)
-        elif platform.get_platform() == 'win32':
-            # Due to Windows's lack of supports for both stdio and subprocess:
-            # See also: https://docs.python.org/zh-cn/3.13/library/asyncio-platforms.html
-            # We have to launch runtime via cmd but communicate via ws.
-            self.ap.logger.info('(windows) use cmd to launch plugin runtime and communicate via ws')
-            
-            if self.runtime_subprocess_on_windows is None:  # only launch once
-                python_path = sys.executable
-                env = os.environ.copy()
-                self.runtime_subprocess_on_windows = await asyncio.create_subprocess_exec(
-                    python_path,
-                    '-m', 'langbot_plugin.cli.__init__', 'rt',
-                    env=env,
-                )
-
-                # hold the process
-                self.runtime_subprocess_on_windows_task = asyncio.create_task(self.runtime_subprocess_on_windows.wait())
-
-            ws_url = 'ws://localhost:5400/control/ws'
-
-            async def make_connection_failed_callback(
-                ctrl: ws_client_controller.WebSocketClientController,
-                exc: Exception = None,
-            ) -> None:
-                if exc is not None:
-                    self.ap.logger.error(f'(windows) Failed to connect to plugin runtime({ws_url}): {exc}')
-                else:
-                    self.ap.logger.error(f'(windows) Failed to connect to plugin runtime({ws_url}), trying to reconnect...')
-                await self.runtime_disconnect_callback(self)
-            
-            self.ctrl = ws_client_controller.WebSocketClientController(
-                ws_url=ws_url,
-                make_connection_failed_callback=make_connection_failed_callback,
-            )
-            task = self.ctrl.run(new_connection_callback)
-
-        else:  # stdio
-            self.ap.logger.info('use stdio to connect to plugin runtime')
-            # cmd: lbp rt -s
-            python_path = sys.executable
-            env = os.environ.copy()
-            self.ctrl = stdio_client_controller.StdioClientController(
-                command=python_path,
-                args=['-m', 'langbot_plugin.cli.__init__', 'rt', '-s'],
-                env=env,
-            )
-            task = self.ctrl.run(new_connection_callback)
-
-        if self.heartbeat_task is None:
-            self.heartbeat_task = asyncio.create_task(self.heartbeat_loop())
-
-        asyncio.create_task(task)
-
-    async def initialize_plugins(self):
-        pass
-
-    async def ping_plugin_runtime(self):
-        if not hasattr(self, 'handler'):
-            raise Exception('Plugin runtime is not connected')
-
-        return await self.handler.ping()
-
-    async def install_plugin(
-        self,
-        install_source: PluginInstallSource,
-        install_info: dict[str, Any],
-        task_context: taskmgr.TaskContext | None = None,
-    ):
-        if install_source == PluginInstallSource.LOCAL:
-            # transfer file before install
-            file_bytes = install_info['plugin_file']
-            file_key = await self.handler.send_file(file_bytes, 'lbpkg')
-            install_info['plugin_file_key'] = file_key
-            del install_info['plugin_file']
-            self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
-        elif install_source == PluginInstallSource.GITHUB:
-            # download and transfer file
-            try:
-                async with httpx.AsyncClient(
-                    trust_env=True,
-                    follow_redirects=True,
-                    timeout=20,
-                ) as client:
-                    response = await client.get(
-                        install_info['asset_url'],
-                    )
-                    response.raise_for_status()
-                    file_bytes = response.content
-                    file_key = await self.handler.send_file(file_bytes, 'lbpkg')
-                    install_info['plugin_file_key'] = file_key
-                    self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
-            except Exception as e:
-                self.ap.logger.error(f'Failed to download file from GitHub: {e}')
-                raise Exception(f'Failed to download file from GitHub: {e}')
-
-        async for ret in self.handler.install_plugin(install_source.value, install_info):
-            current_action = ret.get('current_action', None)
-            if current_action is not None:
-                if task_context is not None:
-                    task_context.set_current_action(current_action)
-
-            trace = ret.get('trace', None)
-            if trace is not None:
-                if task_context is not None:
-                    task_context.trace(trace)
-
-    async def upgrade_plugin(
-        self,
-        plugin_author: str,
-        plugin_name: str,
-        task_context: taskmgr.TaskContext | None = None,
-    ) -> dict[str, Any]:
-        async for ret in self.handler.upgrade_plugin(plugin_author, plugin_name):
-            current_action = ret.get('current_action', None)
-            if current_action is not None:
-                if task_context is not None:
-                    task_context.set_current_action(current_action)
-
-            trace = ret.get('trace', None)
-            if trace is not None:
-                if task_context is not None:
-                    task_context.trace(trace)
-
-    async def delete_plugin(
-        self,
-        plugin_author: str,
-        plugin_name: str,
-        delete_data: bool = False,
-        task_context: taskmgr.TaskContext | None = None,
-    ) -> dict[str, Any]:
-        async for ret in self.handler.delete_plugin(plugin_author, plugin_name):
-            current_action = ret.get('current_action', None)
-            if current_action is not None:
-                if task_context is not None:
-                    task_context.set_current_action(current_action)
-
-            trace = ret.get('trace', None)
-            if trace is not None:
-                if task_context is not None:
-                    task_context.trace(trace)
-
-        # Clean up plugin settings and binary storage if requested
-        if delete_data:
-            if task_context is not None:
-                task_context.trace('Cleaning up plugin configuration and storage...')
-            await self.handler.cleanup_plugin_data(plugin_author, plugin_name)
-
-    async def list_plugins(self) -> list[dict[str, Any]]:
-        if not self.is_enable_plugin:
-            return []
-
-        return await self.handler.list_plugins()
-
-    async def get_plugin_info(self, author: str, plugin_name: str) -> dict[str, Any]:
-        return await self.handler.get_plugin_info(author, plugin_name)
-
-    async def set_plugin_config(self, plugin_author: str, plugin_name: str, config: dict[str, Any]) -> dict[str, Any]:
-        return await self.handler.set_plugin_config(plugin_author, plugin_name, config)
-
-    @alru_cache(ttl=5 * 60)  # 5 minutes
-    async def get_plugin_icon(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
-        return await self.handler.get_plugin_icon(plugin_author, plugin_name)
-
-    async def emit_event(
-        self,
-        event: events.BaseEventModel,
-        bound_plugins: list[str] | None = None,
-    ) -> context.EventContext:
-        event_ctx = context.EventContext.from_event(event)
-
-        if not self.is_enable_plugin:
-            return event_ctx
-
-        # Pass include_plugins to runtime for filtering
-        event_ctx_result = await self.handler.emit_event(
-            event_ctx.model_dump(serialize_as_any=False), include_plugins=bound_plugins
-        )
-
-        event_ctx = context.EventContext.model_validate(event_ctx_result['event_context'])
-
-        return event_ctx
-
-    async def list_tools(self, bound_plugins: list[str] | None = None) -> list[ComponentManifest]:
-        if not self.is_enable_plugin:
-            return []
-
-        # Pass include_plugins to runtime for filtering
-        list_tools_data = await self.handler.list_tools(include_plugins=bound_plugins)
-
-        tools = [ComponentManifest.model_validate(tool) for tool in list_tools_data]
-
-        return tools
-
-    async def call_tool(
-        self, tool_name: str, parameters: dict[str, Any], bound_plugins: list[str] | None = None
-    ) -> dict[str, Any]:
-        if not self.is_enable_plugin:
-            return {'error': 'Tool not found: plugin system is disabled'}
-
-        # Pass include_plugins to runtime for validation
-        return await self.handler.call_tool(tool_name, parameters, include_plugins=bound_plugins)
-
-    async def list_commands(self, bound_plugins: list[str] | None = None) -> list[ComponentManifest]:
-        if not self.is_enable_plugin:
-            return []
-
-        # Pass include_plugins to runtime for filtering
-        list_commands_data = await self.handler.list_commands(include_plugins=bound_plugins)
-
-        commands = [ComponentManifest.model_validate(command) for command in list_commands_data]
-
-        return commands
-
-    async def execute_command(
-        self, command_ctx: command_context.ExecuteContext, bound_plugins: list[str] | None = None
-    ) -> typing.AsyncGenerator[command_context.CommandReturn, None]:
-        if not self.is_enable_plugin:
-            yield command_context.CommandReturn(error=command_errors.CommandNotFoundError(command_ctx.command))
-            return
-
-        # Pass include_plugins to runtime for validation
-        gen = self.handler.execute_command(command_ctx.model_dump(serialize_as_any=True), include_plugins=bound_plugins)
-
-        async for ret in gen:
-            cmd_ret = command_context.CommandReturn.model_validate(ret)
-
-            yield cmd_ret
-
-    def dispose(self):
-        # No need to consider the shutdown on Windows
-        # for Windows can kill processes and subprocesses chainly
-
-        if self.is_enable_plugin and isinstance(self.ctrl, stdio_client_controller.StdioClientController):
-            self.ap.logger.info('Terminating plugin runtime process...')
-            self.ctrl.process.terminate()
-
-        if self.heartbeat_task is not None:
-            self.heartbeat_task.cancel()
-            self.heartbeat_task = None

pkg/provider/modelmgr/modelmgr.py

@@ -1,202 +0,0 @@
-from __future__ import annotations
-
-import sqlalchemy
-import traceback
-
-from . import requester
-from ...core import app
-from ...discover import engine
-from . import token
-from ...entity.persistence import model as persistence_model
-from ...entity.errors import provider as provider_errors
-
-FETCH_MODEL_LIST_URL = 'https://api.qchatgpt.rockchin.top/api/v2/fetch/model_list'
-
-
-class ModelManager:
-    """模型管理器"""
-
-    ap: app.Application
-
-    llm_models: list[requester.RuntimeLLMModel]
-
-    embedding_models: list[requester.RuntimeEmbeddingModel]
-
-    requester_components: list[engine.Component]
-
-    requester_dict: dict[str, type[requester.ProviderAPIRequester]]  # cache
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-        self.llm_models = []
-        self.embedding_models = []
-        self.requester_components = []
-        self.requester_dict = {}
-
-    async def initialize(self):
-        self.requester_components = self.ap.discover.get_components_by_kind('LLMAPIRequester')
-
-        # forge requester class dict
-        requester_dict: dict[str, type[requester.ProviderAPIRequester]] = {}
-        for component in self.requester_components:
-            requester_dict[component.metadata.name] = component.get_python_component_class()
-
-        self.requester_dict = requester_dict
-
-        await self.load_models_from_db()
-
-    async def load_models_from_db(self):
-        """从数据库加载模型"""
-        self.ap.logger.info('Loading models from db...')
-
-        self.llm_models = []
-        self.embedding_models = []
-
-        # llm models
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
-        llm_models = result.all()
-        for llm_model in llm_models:
-            try:
-                await self.load_llm_model(llm_model)
-            except provider_errors.RequesterNotFoundError as e:
-                self.ap.logger.warning(f'Requester {e.requester_name} not found, skipping llm model {llm_model.uuid}')
-            except Exception as e:
-                self.ap.logger.error(f'Failed to load model {llm_model.uuid}: {e}\n{traceback.format_exc()}')
-
-        # embedding models
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
-        embedding_models = result.all()
-        for embedding_model in embedding_models:
-            try:
-                await self.load_embedding_model(embedding_model)
-            except provider_errors.RequesterNotFoundError as e:
-                self.ap.logger.warning(
-                    f'Requester {e.requester_name} not found, skipping embedding model {embedding_model.uuid}'
-                )
-            except Exception as e:
-                self.ap.logger.error(f'Failed to load model {embedding_model.uuid}: {e}\n{traceback.format_exc()}')
-
-    async def init_runtime_llm_model(
-        self,
-        model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
-    ):
-        """初始化运行时 LLM 模型"""
-        if isinstance(model_info, sqlalchemy.Row):
-            model_info = persistence_model.LLMModel(**model_info._mapping)
-        elif isinstance(model_info, dict):
-            model_info = persistence_model.LLMModel(**model_info)
-
-        if model_info.requester not in self.requester_dict:
-            raise provider_errors.RequesterNotFoundError(model_info.requester)
-
-        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
-
-        await requester_inst.initialize()
-
-        runtime_llm_model = requester.RuntimeLLMModel(
-            model_entity=model_info,
-            token_mgr=token.TokenManager(
-                name=model_info.uuid,
-                tokens=model_info.api_keys,
-            ),
-            requester=requester_inst,
-        )
-
-        return runtime_llm_model
-
-    async def init_runtime_embedding_model(
-        self,
-        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
-    ):
-        """初始化运行时 Embedding 模型"""
-        if isinstance(model_info, sqlalchemy.Row):
-            model_info = persistence_model.EmbeddingModel(**model_info._mapping)
-        elif isinstance(model_info, dict):
-            model_info = persistence_model.EmbeddingModel(**model_info)
-
-        if model_info.requester not in self.requester_dict:
-            raise provider_errors.RequesterNotFoundError(model_info.requester)
-
-        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
-
-        await requester_inst.initialize()
-
-        runtime_embedding_model = requester.RuntimeEmbeddingModel(
-            model_entity=model_info,
-            token_mgr=token.TokenManager(
-                name=model_info.uuid,
-                tokens=model_info.api_keys,
-            ),
-            requester=requester_inst,
-        )
-
-        return runtime_embedding_model
-
-    async def load_llm_model(
-        self,
-        model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
-    ):
-        """加载 LLM 模型"""
-        runtime_llm_model = await self.init_runtime_llm_model(model_info)
-        self.llm_models.append(runtime_llm_model)
-
-    async def load_embedding_model(
-        self,
-        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
-    ):
-        """加载 Embedding 模型"""
-        runtime_embedding_model = await self.init_runtime_embedding_model(model_info)
-        self.embedding_models.append(runtime_embedding_model)
-
-    async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
-        """通过uuid获取 LLM 模型"""
-        for model in self.llm_models:
-            if model.model_entity.uuid == uuid:
-                return model
-        raise ValueError(f'LLM model {uuid} not found')
-
-    async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
-        """通过uuid获取 Embedding 模型"""
-        for model in self.embedding_models:
-            if model.model_entity.uuid == uuid:
-                return model
-        raise ValueError(f'Embedding model {uuid} not found')
-
-    async def remove_llm_model(self, model_uuid: str):
-        """移除 LLM 模型"""
-        for model in self.llm_models:
-            if model.model_entity.uuid == model_uuid:
-                self.llm_models.remove(model)
-                return
-
-    async def remove_embedding_model(self, model_uuid: str):
-        """移除 Embedding 模型"""
-        for model in self.embedding_models:
-            if model.model_entity.uuid == model_uuid:
-                self.embedding_models.remove(model)
-                return
-
-    def get_available_requesters_info(self, model_type: str) -> list[dict]:
-        """获取所有可用的请求器"""
-        if model_type != '':
-            return [
-                component.to_plain_dict()
-                for component in self.requester_components
-                if model_type in component.spec['support_type']
-            ]
-        else:
-            return [component.to_plain_dict() for component in self.requester_components]
-
-    def get_available_requester_info_by_name(self, name: str) -> dict | None:
-        """通过名称获取请求器信息"""
-        for component in self.requester_components:
-            if component.metadata.name == name:
-                return component.to_plain_dict()
-        return None
-
-    def get_available_requester_manifest_by_name(self, name: str) -> engine.Component | None:
-        """通过名称获取请求器清单"""
-        for component in self.requester_components:
-            if component.metadata.name == name:
-                return component
-        return None

pkg/provider/modelmgr/requester.py

@@ -1,142 +0,0 @@
-from __future__ import annotations
-
-import abc
-import typing
-
-from ...core import app
-from ...entity.persistence import model as persistence_model
-import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
-from . import token
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-
-
-class RuntimeLLMModel:
-    """运行时模型"""
-
-    model_entity: persistence_model.LLMModel
-    """模型数据"""
-
-    token_mgr: token.TokenManager
-    """api key管理器"""
-
-    requester: ProviderAPIRequester
-    """请求器实例"""
-
-    def __init__(
-        self,
-        model_entity: persistence_model.LLMModel,
-        token_mgr: token.TokenManager,
-        requester: ProviderAPIRequester,
-    ):
-        self.model_entity = model_entity
-        self.token_mgr = token_mgr
-        self.requester = requester
-
-
-class RuntimeEmbeddingModel:
-    """运行时 Embedding 模型"""
-
-    model_entity: persistence_model.EmbeddingModel
-    """模型数据"""
-
-    token_mgr: token.TokenManager
-    """api key管理器"""
-
-    requester: ProviderAPIRequester
-    """请求器实例"""
-
-    def __init__(
-        self,
-        model_entity: persistence_model.EmbeddingModel,
-        token_mgr: token.TokenManager,
-        requester: ProviderAPIRequester,
-    ):
-        self.model_entity = model_entity
-        self.token_mgr = token_mgr
-        self.requester = requester
-
-
-class ProviderAPIRequester(metaclass=abc.ABCMeta):
-    """Provider API请求器"""
-
-    name: str = None
-
-    ap: app.Application
-
-    default_config: dict[str, typing.Any] = {}
-
-    requester_cfg: dict[str, typing.Any] = {}
-
-    def __init__(self, ap: app.Application, config: dict[str, typing.Any]):
-        self.ap = ap
-        self.requester_cfg = {**self.default_config}
-        self.requester_cfg.update(config)
-
-    async def initialize(self):
-        pass
-
-    @abc.abstractmethod
-    async def invoke_llm(
-        self,
-        query: pipeline_query.Query,
-        model: RuntimeLLMModel,
-        messages: typing.List[provider_message.Message],
-        funcs: typing.List[resource_tool.LLMTool] = None,
-        extra_args: dict[str, typing.Any] = {},
-        remove_think: bool = False,
-    ) -> provider_message.Message:
-        """调用API
-
-        Args:
-            model (RuntimeLLMModel): 使用的模型信息
-            messages (typing.List[llm_entities.Message]): 消息对象列表
-            funcs (typing.List[tools_entities.LLMFunction], optional): 使用的工具函数列表. Defaults to None.
-            extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
-            remove_think (bool, optional): 是否移思考中的消息. Defaults to False.
-
-        Returns:
-            llm_entities.Message: 返回消息对象
-        """
-        pass
-
-    async def invoke_llm_stream(
-        self,
-        query: pipeline_query.Query,
-        model: RuntimeLLMModel,
-        messages: typing.List[provider_message.Message],
-        funcs: typing.List[resource_tool.LLMTool] = None,
-        extra_args: dict[str, typing.Any] = {},
-        remove_think: bool = False,
-    ) -> provider_message.MessageChunk:
-        """调用API
-
-        Args:
-            model (RuntimeLLMModel): 使用的模型信息
-            messages (typing.List[provider_message.Message]): 消息对象列表
-            funcs (typing.List[resource_tool.LLMTool], optional): 使用的工具函数列表. Defaults to None.
-            extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
-            remove_think (bool, optional): 是否移除思考中的消息. Defaults to False.
-
-        Returns:
-            typing.AsyncGenerator[provider_message.MessageChunk]: 返回消息对象
-        """
-        pass
-
-    async def invoke_embedding(
-        self,
-        model: RuntimeEmbeddingModel,
-        input_text: typing.List[str],
-        extra_args: dict[str, typing.Any] = {},
-    ) -> typing.List[typing.List[float]]:
-        """调用 Embedding API
-
-        Args:
-            model (RuntimeEmbeddingModel): 使用的模型信息
-            input_text (typing.List[str]): 输入文本
-            extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
-
-        Returns:
-            typing.List[typing.List[float]]: 返回的 embedding 向量
-        """
-        pass

pkg/provider/modelmgr/requesters/ollamachat.py

@@ -1,148 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import os
-import typing
-from typing import Union, Mapping, Any, AsyncIterator
-import uuid
-import json
-
-import ollama
-
-from .. import errors, requester
-import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-
-REQUESTER_NAME: str = 'ollama-chat'
-
-
-class OllamaChatCompletions(requester.ProviderAPIRequester):
-    """Ollama平台 ChatCompletion API请求器"""
-
-    client: ollama.AsyncClient
-
-    default_config: dict[str, typing.Any] = {
-        'base_url': 'http://127.0.0.1:11434',
-        'timeout': 120,
-    }
-
-    async def initialize(self):
-        os.environ['OLLAMA_HOST'] = self.requester_cfg['base_url']
-        self.client = ollama.AsyncClient(timeout=self.requester_cfg['timeout'])
-
-    async def _req(
-        self,
-        args: dict,
-    ) -> Union[Mapping[str, Any], AsyncIterator[Mapping[str, Any]]]:
-        return await self.client.chat(**args)
-
-    async def _closure(
-        self,
-        query: pipeline_query.Query,
-        req_messages: list[dict],
-        use_model: requester.RuntimeLLMModel,
-        use_funcs: list[resource_tool.LLMTool] = None,
-        extra_args: dict[str, typing.Any] = {},
-        remove_think: bool = False,
-    ) -> provider_message.Message:
-        args = extra_args.copy()
-        args['model'] = use_model.model_entity.name
-
-        messages: list[dict] = req_messages.copy()
-        for msg in messages:
-            if 'content' in msg and isinstance(msg['content'], list):
-                text_content: list = []
-                image_urls: list = []
-                for me in msg['content']:
-                    if me['type'] == 'text':
-                        text_content.append(me['text'])
-                    elif me['type'] == 'image_base64':
-                        image_urls.append(me['image_base64'])
-
-                msg['content'] = '\n'.join(text_content)
-                msg['images'] = [url.split(',')[1] for url in image_urls]
-            if 'tool_calls' in msg:  # LangBot 内部以 str 存储 tool_calls 的参数，这里需要转换为 dict
-                for tool_call in msg['tool_calls']:
-                    tool_call['function']['arguments'] = json.loads(tool_call['function']['arguments'])
-        args['messages'] = messages
-
-        args['tools'] = []
-        if use_funcs:
-            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
-            if tools:
-                args['tools'] = tools
-
-        resp = await self._req(args)
-        message: provider_message.Message = await self._make_msg(resp)
-        return message
-
-    async def _make_msg(self, chat_completions: ollama.ChatResponse) -> provider_message.Message:
-        message: ollama.Message = chat_completions.message
-        if message is None:
-            raise ValueError("chat_completions must contain a 'message' field")
-
-        ret_msg: provider_message.Message = None
-
-        if message.content is not None:
-            ret_msg = provider_message.Message(role='assistant', content=message.content)
-        if message.tool_calls is not None and len(message.tool_calls) > 0:
-            tool_calls: list[provider_message.ToolCall] = []
-
-            for tool_call in message.tool_calls:
-                tool_calls.append(
-                    provider_message.ToolCall(
-                        id=uuid.uuid4().hex,
-                        type='function',
-                        function=provider_message.FunctionCall(
-                            name=tool_call.function.name,
-                            arguments=json.dumps(tool_call.function.arguments),
-                        ),
-                    )
-                )
-            ret_msg.tool_calls = tool_calls
-
-        return ret_msg
-
-    async def invoke_llm(
-        self,
-        query: pipeline_query.Query,
-        model: requester.RuntimeLLMModel,
-        messages: typing.List[provider_message.Message],
-        funcs: typing.List[resource_tool.LLMTool] = None,
-        extra_args: dict[str, typing.Any] = {},
-        remove_think: bool = False,
-    ) -> provider_message.Message:
-        req_messages: list = []
-        for m in messages:
-            msg_dict: dict = m.dict(exclude_none=True)
-            content: Any = msg_dict.get('content')
-            if isinstance(content, list):
-                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
-                    msg_dict['content'] = '\n'.join(part['text'] for part in content)
-            req_messages.append(msg_dict)
-        try:
-            return await self._closure(
-                query=query,
-                req_messages=req_messages,
-                use_model=model,
-                use_funcs=funcs,
-                extra_args=extra_args,
-                remove_think=remove_think,
-            )
-        except asyncio.TimeoutError:
-            raise errors.RequesterError('请求超时')
-
-    async def invoke_embedding(
-        self,
-        model: requester.RuntimeEmbeddingModel,
-        input_text: list[str],
-        extra_args: dict[str, typing.Any] = {},
-    ) -> list[list[float]]:
-        return (
-            await self.client.embed(
-                model=model.model_entity.name,
-                input=input_text,
-                **extra_args,
-            )
-        ).embeddings

pkg/provider/runners/localagent.py

@@ -1,301 +0,0 @@
-from __future__ import annotations
-
-import json
-import copy
-import typing
-from .. import runner
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-
-
-rag_combined_prompt_template = """
-The following are relevant context entries retrieved from the knowledge base. 
-Please use them to answer the user's message. 
-Respond in the same language as the user's input.
-
-<context>
-{rag_context}
-</context>
-
-<user_message>
-{user_message}
-</user_message>
-"""
-
-
-@runner.runner_class('local-agent')
-class LocalAgentRunner(runner.RequestRunner):
-    """本地Agent请求运行器"""
-
-    class ToolCallTracker:
-        """工具调用追踪器"""
-
-        def __init__(self):
-            self.active_calls: dict[str, dict] = {}
-            self.completed_calls: list[provider_message.ToolCall] = []
-
-    async def run(
-        self, query: pipeline_query.Query
-    ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """运行请求"""
-        pending_tool_calls = []
-
-        # Get knowledge bases list (new field)
-        kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
-        
-        # Fallback to old field for backward compatibility
-        if not kb_uuids:
-            old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
-            if old_kb_uuid and old_kb_uuid != '__none__':
-                kb_uuids = [old_kb_uuid]
-
-        user_message = copy.deepcopy(query.user_message)
-
-        user_message_text = ''
-
-        if isinstance(user_message.content, str):
-            user_message_text = user_message.content
-        elif isinstance(user_message.content, list):
-            for ce in user_message.content:
-                if ce.type == 'text':
-                    user_message_text += ce.text
-                    break
-
-        if kb_uuids and user_message_text:
-            # only support text for now
-            all_results = []
-            
-            # Retrieve from each knowledge base
-            for kb_uuid in kb_uuids:
-                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-
-                if not kb:
-                    self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping')
-                    continue
-
-                result = await kb.retrieve(user_message_text, kb.knowledge_base_entity.top_k)
-                
-                if result:
-                    all_results.extend(result)
-
-            final_user_message_text = ''
-
-            if all_results:
-                rag_context = '\n\n'.join(
-                    f'[{i + 1}] {entry.metadata.get("text", "")}' for i, entry in enumerate(all_results)
-                )
-                final_user_message_text = rag_combined_prompt_template.format(
-                    rag_context=rag_context, user_message=user_message_text
-                )
-
-            else:
-                final_user_message_text = user_message_text
-
-            self.ap.logger.debug(f'Final user message text: {final_user_message_text}')
-
-            for ce in user_message.content:
-                if ce.type == 'text':
-                    ce.text = final_user_message_text
-                    break
-
-        req_messages = query.prompt.messages.copy() + query.messages.copy() + [user_message]
-
-        try:
-            is_stream = await query.adapter.is_stream_output_supported()
-        except AttributeError:
-            is_stream = False
-
-        remove_think = query.pipeline_config['output'].get('misc', '').get('remove-think')
-
-        use_llm_model = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
-
-        if not is_stream:
-            # 非流式输出，直接请求
-
-            msg = await use_llm_model.requester.invoke_llm(
-                query,
-                use_llm_model,
-                req_messages,
-                query.use_funcs,
-                extra_args=use_llm_model.model_entity.extra_args,
-                remove_think=remove_think,
-            )
-            yield msg
-            final_msg = msg
-        else:
-            # 流式输出，需要处理工具调用
-            tool_calls_map: dict[str, provider_message.ToolCall] = {}
-            msg_idx = 0
-            accumulated_content = ''  # 从开始累积的所有内容
-            last_role = 'assistant'
-            msg_sequence = 1
-            async for msg in use_llm_model.requester.invoke_llm_stream(
-                query,
-                use_llm_model,
-                req_messages,
-                query.use_funcs,
-                extra_args=use_llm_model.model_entity.extra_args,
-                remove_think=remove_think,
-            ):
-                msg_idx = msg_idx + 1
-
-                # 记录角色
-                if msg.role:
-                    last_role = msg.role
-
-                # 累积内容
-                if msg.content:
-                    accumulated_content += msg.content
-
-                # 处理工具调用
-                if msg.tool_calls:
-                    for tool_call in msg.tool_calls:
-                        if tool_call.id not in tool_calls_map:
-                            tool_calls_map[tool_call.id] = provider_message.ToolCall(
-                                id=tool_call.id,
-                                type=tool_call.type,
-                                function=provider_message.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '', arguments=''
-                                ),
-                            )
-                        if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-                # continue
-                # 每8个chunk或最后一个chunk时，输出所有累积的内容
-                if msg_idx % 8 == 0 or msg.is_final:
-                    msg_sequence += 1
-                    yield provider_message.MessageChunk(
-                        role=last_role,
-                        content=accumulated_content,  # 输出所有累积内容
-                        tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
-                        is_final=msg.is_final,
-                        msg_sequence=msg_sequence,
-                    )
-
-            # 创建最终消息用于后续处理
-            final_msg = provider_message.MessageChunk(
-                role=last_role,
-                content=accumulated_content,
-                tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
-                msg_sequence=msg_sequence,
-            )
-
-        pending_tool_calls = final_msg.tool_calls
-        first_content = final_msg.content
-        if isinstance(final_msg, provider_message.MessageChunk):
-            first_end_sequence = final_msg.msg_sequence
-
-        req_messages.append(final_msg)
-
-        # 持续请求，只要还有待处理的工具调用就继续处理调用
-        while pending_tool_calls:
-            for tool_call in pending_tool_calls:
-                try:
-                    func = tool_call.function
-
-                    parameters = json.loads(func.arguments)
-
-                    func_ret = await self.ap.tool_mgr.execute_func_call(func.name, parameters)
-                    if is_stream:
-                        msg = provider_message.MessageChunk(
-                            role='tool',
-                            content=json.dumps(func_ret, ensure_ascii=False),
-                            tool_call_id=tool_call.id,
-                        )
-                    else:
-                        msg = provider_message.Message(
-                            role='tool',
-                            content=json.dumps(func_ret, ensure_ascii=False),
-                            tool_call_id=tool_call.id,
-                        )
-
-                    yield msg
-
-                    req_messages.append(msg)
-                except Exception as e:
-                    # 工具调用出错，添加一个报错信息到 req_messages
-                    err_msg = provider_message.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
-
-                    yield err_msg
-
-                    req_messages.append(err_msg)
-
-            if is_stream:
-                tool_calls_map = {}
-                msg_idx = 0
-                accumulated_content = ''  # 从开始累积的所有内容
-                last_role = 'assistant'
-                msg_sequence = first_end_sequence
-
-                async for msg in use_llm_model.requester.invoke_llm_stream(
-                    query,
-                    use_llm_model,
-                    req_messages,
-                    query.use_funcs,
-                    extra_args=use_llm_model.model_entity.extra_args,
-                    remove_think=remove_think,
-                ):
-                    msg_idx += 1
-
-                    # 记录角色
-                    if msg.role:
-                        last_role = msg.role
-
-                    # 第一次请求工具调用时的内容
-                    if msg_idx == 1:
-                        accumulated_content = first_content if first_content is not None else accumulated_content
-
-                    # 累积内容
-                    if msg.content:
-                        accumulated_content += msg.content
-
-                    # 处理工具调用
-                    if msg.tool_calls:
-                        for tool_call in msg.tool_calls:
-                            if tool_call.id not in tool_calls_map:
-                                tool_calls_map[tool_call.id] = provider_message.ToolCall(
-                                    id=tool_call.id,
-                                    type=tool_call.type,
-                                    function=provider_message.FunctionCall(
-                                        name=tool_call.function.name if tool_call.function else '', arguments=''
-                                    ),
-                                )
-                            if tool_call.function and tool_call.function.arguments:
-                                # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                                tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-
-                    # 每8个chunk或最后一个chunk时，输出所有累积的内容
-                    if msg_idx % 8 == 0 or msg.is_final:
-                        msg_sequence += 1
-                        yield provider_message.MessageChunk(
-                            role=last_role,
-                            content=accumulated_content,  # 输出所有累积内容
-                            tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
-                            is_final=msg.is_final,
-                            msg_sequence=msg_sequence,
-                        )
-
-                final_msg = provider_message.MessageChunk(
-                    role=last_role,
-                    content=accumulated_content,
-                    tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
-                    msg_sequence=msg_sequence,
-                )
-            else:
-                # 处理完所有调用，再次请求
-                msg = await use_llm_model.requester.invoke_llm(
-                    query,
-                    use_llm_model,
-                    req_messages,
-                    query.use_funcs,
-                    extra_args=use_llm_model.model_entity.extra_args,
-                    remove_think=remove_think,
-                )
-
-                yield msg
-                final_msg = msg
-
-            pending_tool_calls = final_msg.tool_calls
-
-            req_messages.append(final_msg)

pkg/provider/runners/n8nsvapi.py

@@ -1,160 +0,0 @@
-from __future__ import annotations
-
-import typing
-import json
-import uuid
-import aiohttp
-
-from .. import runner
-from ...core import app
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.provider.message as provider_message
-
-
-class N8nAPIError(Exception):
-    """N8n API 请求失败"""
-
-    def __init__(self, message: str):
-        self.message = message
-        super().__init__(self.message)
-
-
-@runner.runner_class('n8n-service-api')
-class N8nServiceAPIRunner(runner.RequestRunner):
-    """N8n Service API 工作流请求器"""
-
-    def __init__(self, ap: app.Application, pipeline_config: dict):
-        self.ap = ap
-        self.pipeline_config = pipeline_config
-
-        # 获取webhook URL
-        self.webhook_url = self.pipeline_config['ai']['n8n-service-api']['webhook-url']
-
-        # 获取超时设置，默认为120秒
-        self.timeout = self.pipeline_config['ai']['n8n-service-api'].get('timeout', 120)
-
-        # 获取输出键名，默认为response
-        self.output_key = self.pipeline_config['ai']['n8n-service-api'].get('output-key', 'response')
-
-        # 获取认证类型，默认为none
-        self.auth_type = self.pipeline_config['ai']['n8n-service-api'].get('auth-type', 'none')
-
-        # 根据认证类型获取相应的认证信息
-        if self.auth_type == 'basic':
-            self.basic_username = self.pipeline_config['ai']['n8n-service-api'].get('basic-username', '')
-            self.basic_password = self.pipeline_config['ai']['n8n-service-api'].get('basic-password', '')
-        elif self.auth_type == 'jwt':
-            self.jwt_secret = self.pipeline_config['ai']['n8n-service-api'].get('jwt-secret', '')
-            self.jwt_algorithm = self.pipeline_config['ai']['n8n-service-api'].get('jwt-algorithm', 'HS256')
-        elif self.auth_type == 'header':
-            self.header_name = self.pipeline_config['ai']['n8n-service-api'].get('header-name', '')
-            self.header_value = self.pipeline_config['ai']['n8n-service-api'].get('header-value', '')
-
-    async def _preprocess_user_message(self, query: pipeline_query.Query) -> str:
-        """预处理用户消息，提取纯文本
-
-        Returns:
-            str: 纯文本消息
-        """
-        plain_text = ''
-
-        if isinstance(query.user_message.content, list):
-            for ce in query.user_message.content:
-                if ce.type == 'text':
-                    plain_text += ce.text
-                # 注意：n8n webhook目前不支持直接处理图片，如需支持可在此扩展
-        elif isinstance(query.user_message.content, str):
-            plain_text = query.user_message.content
-
-        return plain_text
-
-    async def _call_webhook(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """调用n8n webhook"""
-        # 生成会话ID（如果不存在）
-        if not query.session.using_conversation.uuid:
-            query.session.using_conversation.uuid = str(uuid.uuid4())
-
-        # 预处理用户消息
-        plain_text = await self._preprocess_user_message(query)
-
-        # 准备请求数据
-        payload = {
-            # 基本消息内容
-            'message': plain_text,
-            'user_message_text': plain_text,
-            'conversation_id': query.session.using_conversation.uuid,
-            'session_id': query.variables.get('session_id', ''),
-            'user_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
-            'msg_create_time': query.variables.get('msg_create_time', ''),
-        }
-
-        # 添加所有变量到payload
-        payload.update(query.variables)
-
-        try:
-            # 准备请求头和认证信息
-            headers = {}
-            auth = None
-
-            # 根据认证类型设置相应的认证信息
-            if self.auth_type == 'basic':
-                # 使用Basic认证
-                auth = aiohttp.BasicAuth(self.basic_username, self.basic_password)
-                self.ap.logger.debug(f'using basic auth: {self.basic_username}')
-            elif self.auth_type == 'jwt':
-                # 使用JWT认证
-                import jwt
-                import time
-
-                # 创建JWT令牌
-                payload_jwt = {
-                    'exp': int(time.time()) + 3600,  # 1小时过期
-                    'iat': int(time.time()),
-                    'sub': 'n8n-webhook',
-                }
-                token = jwt.encode(payload_jwt, self.jwt_secret, algorithm=self.jwt_algorithm)
-
-                # 添加到Authorization头
-                headers['Authorization'] = f'Bearer {token}'
-                self.ap.logger.debug('using jwt auth')
-            elif self.auth_type == 'header':
-                # 使用自定义请求头认证
-                headers[self.header_name] = self.header_value
-                self.ap.logger.debug(f'using header auth: {self.header_name}')
-            else:
-                self.ap.logger.debug('no auth')
-
-            # 调用webhook
-            async with aiohttp.ClientSession() as session:
-                async with session.post(
-                    self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
-                ) as response:
-                    if response.status != 200:
-                        error_text = await response.text()
-                        self.ap.logger.error(f'n8n webhook call failed: {response.status}, {error_text}')
-                        raise Exception(f'n8n webhook call failed: {response.status}, {error_text}')
-
-                    # 解析响应
-                    response_data = await response.json()
-                    self.ap.logger.debug(f'n8n webhook response: {response_data}')
-
-                    # 从响应中提取输出
-                    if self.output_key in response_data:
-                        output_content = response_data[self.output_key]
-                    else:
-                        # 如果没有指定的输出键，则使用整个响应
-                        output_content = json.dumps(response_data, ensure_ascii=False)
-
-                    # 返回消息
-                    yield provider_message.Message(
-                        role='assistant',
-                        content=output_content,
-                    )
-        except Exception as e:
-            self.ap.logger.error(f'n8n webhook call exception: {str(e)}')
-            raise N8nAPIError(f'n8n webhook call exception: {str(e)}')
-
-    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
-        """运行请求"""
-        async for msg in self._call_webhook(query):
-            yield msg

pkg/rag/knowledge/kbmgr.py

@@ -1,276 +0,0 @@
-from __future__ import annotations
-import traceback
-import uuid
-import zipfile
-import io
-from .services import parser, chunker
-from pkg.core import app
-from pkg.rag.knowledge.services.embedder import Embedder
-from pkg.rag.knowledge.services.retriever import Retriever
-import sqlalchemy
-from ...entity.persistence import rag as persistence_rag
-from pkg.core import taskmgr
-from ...entity.rag import retriever as retriever_entities
-
-
-class RuntimeKnowledgeBase:
-    ap: app.Application
-
-    knowledge_base_entity: persistence_rag.KnowledgeBase
-
-    parser: parser.FileParser
-
-    chunker: chunker.Chunker
-
-    embedder: Embedder
-
-    retriever: Retriever
-
-    def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
-        self.ap = ap
-        self.knowledge_base_entity = knowledge_base_entity
-        self.parser = parser.FileParser(ap=self.ap)
-        self.chunker = chunker.Chunker(ap=self.ap)
-        self.embedder = Embedder(ap=self.ap)
-        self.retriever = Retriever(ap=self.ap)
-        # 传递kb_id给retriever
-        self.retriever.kb_id = knowledge_base_entity.uuid
-
-    async def initialize(self):
-        pass
-
-    async def _store_file_task(self, file: persistence_rag.File, task_context: taskmgr.TaskContext):
-        try:
-            # set file status to processing
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_rag.File)
-                .where(persistence_rag.File.uuid == file.uuid)
-                .values(status='processing')
-            )
-
-            task_context.set_current_action('Parsing file')
-            # parse file
-            text = await self.parser.parse(file.file_name, file.extension)
-            if not text:
-                raise Exception(f'No text extracted from file {file.file_name}')
-
-            task_context.set_current_action('Chunking file')
-            # chunk file
-            chunks_texts = await self.chunker.chunk(text)
-            if not chunks_texts:
-                raise Exception(f'No chunks extracted from file {file.file_name}')
-
-            task_context.set_current_action('Embedding chunks')
-
-            embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
-                self.knowledge_base_entity.embedding_model_uuid
-            )
-            # embed chunks
-            await self.embedder.embed_and_store(
-                kb_id=self.knowledge_base_entity.uuid,
-                file_id=file.uuid,
-                chunks=chunks_texts,
-                embedding_model=embedding_model,
-            )
-
-            # set file status to completed
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_rag.File)
-                .where(persistence_rag.File.uuid == file.uuid)
-                .values(status='completed')
-            )
-
-        except Exception as e:
-            self.ap.logger.error(f'Error storing file {file.uuid}: {e}')
-            traceback.print_exc()
-            # set file status to failed
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.update(persistence_rag.File)
-                .where(persistence_rag.File.uuid == file.uuid)
-                .values(status='failed')
-            )
-
-            raise
-        finally:
-            # delete file from storage
-            await self.ap.storage_mgr.storage_provider.delete(file.file_name)
-
-    async def store_file(self, file_id: str) -> str:
-        # pre checking
-        if not await self.ap.storage_mgr.storage_provider.exists(file_id):
-            raise Exception(f'File {file_id} not found')
-
-        file_name = file_id
-        extension = file_name.split('.')[-1].lower()
-
-        if extension == 'zip':
-            return await self._store_zip_file(file_id)
-
-        file_uuid = str(uuid.uuid4())
-        kb_id = self.knowledge_base_entity.uuid
-
-        file_obj_data = {
-            'uuid': file_uuid,
-            'kb_id': kb_id,
-            'file_name': file_name,
-            'extension': extension,
-            'status': 'pending',
-        }
-
-        file_obj = persistence_rag.File(**file_obj_data)
-
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(file_obj_data))
-
-        # run background task asynchronously
-        ctx = taskmgr.TaskContext.new()
-        wrapper = self.ap.task_mgr.create_user_task(
-            self._store_file_task(file_obj, task_context=ctx),
-            kind='knowledge-operation',
-            name=f'knowledge-store-file-{file_id}',
-            label=f'Store file {file_id}',
-            context=ctx,
-        )
-        return wrapper.id
-
-    async def _store_zip_file(self, zip_file_id: str) -> str:
-        """Handle ZIP file by extracting each document and storing them separately."""
-        self.ap.logger.info(f'Processing ZIP file: {zip_file_id}')
-
-        zip_bytes = await self.ap.storage_mgr.storage_provider.load(zip_file_id)
-
-        supported_extensions = {'txt', 'pdf', 'docx', 'md', 'html'}
-        stored_file_tasks = []
-
-        # use utf-8 encoding
-        with zipfile.ZipFile(io.BytesIO(zip_bytes), 'r', metadata_encoding='utf-8') as zip_ref:
-            for file_info in zip_ref.filelist:
-                # skip directories and hidden files
-                if file_info.is_dir() or file_info.filename.startswith('.'):
-                    continue
-
-                file_extension = file_info.filename.split('.')[-1].lower()
-                if file_extension not in supported_extensions:
-                    self.ap.logger.debug(f'Skipping unsupported file in ZIP: {file_info.filename}')
-                    continue
-
-                try:
-                    file_content = zip_ref.read(file_info.filename)
-
-                    base_name = file_info.filename.replace('/', '_').replace('\\', '_')
-                    extension = base_name.split('.')[-1]
-                    file_name = base_name.split('.')[0]
-
-                    if file_name.startswith('__MACOSX'):
-                        continue
-
-                    extracted_file_id = file_name + '_' + str(uuid.uuid4())[:8] + '.' + extension
-                    # save file to storage
-
-                    await self.ap.storage_mgr.storage_provider.save(extracted_file_id, file_content)
-
-                    task_id = await self.store_file(extracted_file_id)
-                    stored_file_tasks.append(task_id)
-
-                    self.ap.logger.info(
-                        f'Extracted and stored file from ZIP: {file_info.filename} -> {extracted_file_id}'
-                    )
-
-                except Exception as e:
-                    self.ap.logger.warning(f'Failed to extract file {file_info.filename} from ZIP: {e}')
-                    continue
-
-        if not stored_file_tasks:
-            raise Exception('No supported files found in ZIP archive')
-
-        self.ap.logger.info(f'Successfully processed ZIP file {zip_file_id}, extracted {len(stored_file_tasks)} files')
-        await self.ap.storage_mgr.storage_provider.delete(zip_file_id)
-
-        return stored_file_tasks[0] if stored_file_tasks else ''
-
-    async def retrieve(self, query: str, top_k: int) -> list[retriever_entities.RetrieveResultEntry]:
-        embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
-            self.knowledge_base_entity.embedding_model_uuid
-        )
-        return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model, top_k)
-
-    async def delete_file(self, file_id: str):
-        # delete vector
-        await self.ap.vector_db_mgr.vector_db.delete_by_file_id(self.knowledge_base_entity.uuid, file_id)
-
-        # delete chunk
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file_id)
-        )
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
-        )
-
-    async def dispose(self):
-        await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
-
-
-class RAGManager:
-    ap: app.Application
-
-    knowledge_bases: list[RuntimeKnowledgeBase]
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-        self.knowledge_bases = []
-
-    async def initialize(self):
-        await self.load_knowledge_bases_from_db()
-
-    async def load_knowledge_bases_from_db(self):
-        self.ap.logger.info('Loading knowledge bases from db...')
-
-        self.knowledge_bases = []
-
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
-
-        knowledge_bases = result.all()
-
-        for knowledge_base in knowledge_bases:
-            try:
-                await self.load_knowledge_base(knowledge_base)
-            except Exception as e:
-                self.ap.logger.error(
-                    f'Error loading knowledge base {knowledge_base.uuid}: {e}\n{traceback.format_exc()}'
-                )
-
-    async def load_knowledge_base(
-        self,
-        knowledge_base_entity: persistence_rag.KnowledgeBase | sqlalchemy.Row | dict,
-    ) -> RuntimeKnowledgeBase:
-        if isinstance(knowledge_base_entity, sqlalchemy.Row):
-            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
-        elif isinstance(knowledge_base_entity, dict):
-            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
-
-        runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
-
-        await runtime_knowledge_base.initialize()
-
-        self.knowledge_bases.append(runtime_knowledge_base)
-
-        return runtime_knowledge_base
-
-    async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> RuntimeKnowledgeBase | None:
-        for kb in self.knowledge_bases:
-            if kb.knowledge_base_entity.uuid == kb_uuid:
-                return kb
-        return None
-
-    async def remove_knowledge_base_from_runtime(self, kb_uuid: str):
-        for kb in self.knowledge_bases:
-            if kb.knowledge_base_entity.uuid == kb_uuid:
-                self.knowledge_bases.remove(kb)
-                return
-
-    async def delete_knowledge_base(self, kb_uuid: str):
-        for kb in self.knowledge_bases:
-            if kb.knowledge_base_entity.uuid == kb_uuid:
-                await kb.dispose()
-                self.knowledge_bases.remove(kb)
-                return

pkg/rag/knowledge/services/base_service.py

@@ -1,15 +0,0 @@
-# 封装异步操作
-import asyncio
-
-
-class BaseService:
-    def __init__(self):
-        pass
-
-    async def _run_sync(self, func, *args, **kwargs):
-        """
-        在单独的线程中运行同步函数。
-        如果第一个参数是 session，则在 to_thread 中获取新的 session。
-        """
-
-        return await asyncio.to_thread(func, *args, **kwargs)

pkg/rag/knowledge/services/chunker.py

@@ -1,49 +0,0 @@
-from __future__ import annotations
-
-import json
-from typing import List
-from pkg.rag.knowledge.services import base_service
-from pkg.core import app
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-
-
-class Chunker(base_service.BaseService):
-    """
-    A class for splitting long texts into smaller, overlapping chunks.
-    """
-
-    def __init__(self, ap: app.Application, chunk_size: int = 500, chunk_overlap: int = 50):
-        self.ap = ap
-        self.chunk_size = chunk_size
-        self.chunk_overlap = chunk_overlap
-        if self.chunk_overlap >= self.chunk_size:
-            self.ap.logger.warning(
-                'Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.'
-            )
-
-    def _split_text_sync(self, text: str) -> List[str]:
-        """
-        Synchronously splits a long text into chunks with specified overlap.
-        This is a CPU-bound operation, intended to be run in a separate thread.
-        """
-        if not text:
-            return []
-
-        text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=self.chunk_size,
-            chunk_overlap=self.chunk_overlap,
-            length_function=len,
-            is_separator_regex=False,
-        )
-        return text_splitter.split_text(text)
-
-    async def chunk(self, text: str) -> List[str]:
-        """
-        Asynchronously chunks a given text into smaller pieces.
-        """
-        self.ap.logger.info(f'Chunking text (length: {len(text)})...')
-        # Run the synchronous splitting logic in a separate thread
-        chunks = await self._run_sync(self._split_text_sync, text)
-        self.ap.logger.info(f'Text chunked into {len(chunks)} pieces.')
-        self.ap.logger.debug(f'Chunks: {json.dumps(chunks, indent=4, ensure_ascii=False)}')
-        return chunks

pkg/rag/knowledge/services/embedder.py

@@ -1,47 +0,0 @@
-from __future__ import annotations
-import uuid
-from typing import List
-from pkg.rag.knowledge.services.base_service import BaseService
-from ....entity.persistence import rag as persistence_rag
-from ....core import app
-from ....provider.modelmgr.requester import RuntimeEmbeddingModel
-import sqlalchemy
-
-
-class Embedder(BaseService):
-    def __init__(self, ap: app.Application) -> None:
-        super().__init__()
-        self.ap = ap
-
-    async def embed_and_store(
-        self, kb_id: str, file_id: str, chunks: List[str], embedding_model: RuntimeEmbeddingModel
-    ) -> list[persistence_rag.Chunk]:
-        # save chunk to db
-        chunk_entities: list[persistence_rag.Chunk] = []
-        chunk_ids: list[str] = []
-
-        for chunk_text in chunks:
-            chunk_uuid = str(uuid.uuid4())
-            chunk_ids.append(chunk_uuid)
-            chunk_entity = persistence_rag.Chunk(uuid=chunk_uuid, file_id=file_id, text=chunk_text)
-            chunk_entities.append(chunk_entity)
-
-        chunk_dicts = [
-            self.ap.persistence_mgr.serialize_model(persistence_rag.Chunk, chunk) for chunk in chunk_entities
-        ]
-
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.Chunk).values(chunk_dicts))
-
-        # get embeddings
-        embeddings_list: list[list[float]] = await embedding_model.requester.invoke_embedding(
-            model=embedding_model,
-            input_text=chunks,
-            extra_args={},  # TODO: add extra args
-        )
-
-        # save embeddings to vdb
-        await self.ap.vector_db_mgr.vector_db.add_embeddings(kb_id, chunk_ids, embeddings_list, chunk_dicts)
-
-        self.ap.logger.info(f'Successfully saved {len(chunk_entities)} embeddings to Knowledge Base.')
-
-        return chunk_entities

pkg/rag/knowledge/services/parser.py

@@ -1,291 +0,0 @@
-from __future__ import annotations
-
-import PyPDF2
-import io
-from docx import Document
-import chardet
-from typing import Union, Callable, Any
-import markdown
-from bs4 import BeautifulSoup
-import re
-import asyncio  # Import asyncio for async operations
-from pkg.core import app
-
-
-class FileParser:
-    """
-    A robust file parser class to extract text content from various document formats.
-    It supports TXT, PDF, DOCX, XLSX, CSV, Markdown, HTML, and EPUB files.
-    All core file reading operations are designed to be run synchronously in a thread pool
-    to avoid blocking the asyncio event loop.
-    """
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def _run_sync(self, sync_func: Callable, *args: Any, **kwargs: Any) -> Any:
-        """
-        Runs a synchronous function in a separate thread to prevent blocking the event loop.
-        This is a general utility method for wrapping blocking I/O operations.
-        """
-        try:
-            return await asyncio.to_thread(sync_func, *args, **kwargs)
-        except Exception as e:
-            self.ap.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}')
-            raise
-
-    async def parse(self, file_name: str, extension: str) -> Union[str, None]:
-        """
-        Parses the file based on its extension and returns the extracted text content.
-        This is the main asynchronous entry point for parsing.
-
-        Args:
-            file_name (str): The name of the file to be parsed, get from ap.storage_mgr
-
-        Returns:
-            Union[str, None]: The extracted text content as a single string, or None if parsing fails.
-        """
-
-        file_extension = extension.lower()
-        parser_method = getattr(self, f'_parse_{file_extension}', None)
-
-        if parser_method is None:
-            self.ap.logger.error(f'Unsupported file format: {file_extension} for file {file_name}')
-            return None
-
-        try:
-            # Pass file_path to the specific parser methods
-            return await parser_method(file_name)
-        except Exception as e:
-            self.ap.logger.error(f'Failed to parse {file_extension} file {file_name}: {e}')
-            return None
-
-    # --- Helper for reading files with encoding detection ---
-    async def _read_file_content(self, file_name: str) -> Union[str, bytes]:
-        """
-        Reads a file with automatic encoding detection, ensuring the synchronous
-        file read operation runs in a separate thread.
-        """
-
-        # def _read_sync():
-        #     with open(file_path, 'rb') as file:
-        #         raw_data = file.read()
-        #         detected = chardet.detect(raw_data)
-        #         encoding = detected['encoding'] or 'utf-8'
-
-        #     if mode == 'r':
-        #         return raw_data.decode(encoding, errors='ignore')
-        #     return raw_data  # For binary mode
-
-        # return await self._run_sync(_read_sync)
-        file_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-        detected = chardet.detect(file_bytes)
-        encoding = detected['encoding'] or 'utf-8'
-
-        return file_bytes.decode(encoding, errors='ignore')
-
-    # --- Specific Parser Methods ---
-
-    async def _parse_txt(self, file_name: str) -> str:
-        """Parses a TXT file and returns its content."""
-        self.ap.logger.info(f'Parsing TXT file: {file_name}')
-        return await self._read_file_content(file_name)
-
-    async def _parse_pdf(self, file_name: str) -> str:
-        """Parses a PDF file and returns its text content."""
-        self.ap.logger.info(f'Parsing PDF file: {file_name}')
-
-        # def _parse_pdf_sync():
-        #     text_content = []
-        #     with open(file_name, 'rb') as file:
-        #         pdf_reader = PyPDF2.PdfReader(file)
-        #         for page in pdf_reader.pages:
-        #             text = page.extract_text()
-        #             if text:
-        #                 text_content.append(text)
-        #     return '\n'.join(text_content)
-
-        # return await self._run_sync(_parse_pdf_sync)
-
-        pdf_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-        def _parse_pdf_sync():
-            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes))
-            text_content = []
-            for page in pdf_reader.pages:
-                text = page.extract_text()
-                if text:
-                    text_content.append(text)
-            return '\n'.join(text_content)
-
-        return await self._run_sync(_parse_pdf_sync)
-
-    async def _parse_docx(self, file_name: str) -> str:
-        """Parses a DOCX file and returns its text content."""
-        self.ap.logger.info(f'Parsing DOCX file: {file_name}')
-
-        docx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-        def _parse_docx_sync():
-            doc = Document(io.BytesIO(docx_bytes))
-            text_content = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()]
-            return '\n'.join(text_content)
-
-        return await self._run_sync(_parse_docx_sync)
-
-    async def _parse_doc(self, file_name: str) -> str:
-        """Handles .doc files, explicitly stating lack of direct support."""
-        self.ap.logger.warning(f'Direct .doc parsing is not supported for {file_name}. Please convert to .docx first.')
-        raise NotImplementedError('Direct .doc parsing not supported. Please convert to .docx first.')
-
-    # async def _parse_xlsx(self, file_name: str) -> str:
-    #     """Parses an XLSX file, returning text from all sheets."""
-    #     self.ap.logger.info(f'Parsing XLSX file: {file_name}')
-
-    #     xlsx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-    #     def _parse_xlsx_sync():
-    #         excel_file = pd.ExcelFile(io.BytesIO(xlsx_bytes))
-    #         all_sheet_content = []
-    #         for sheet_name in excel_file.sheet_names:
-    #             df = pd.read_excel(io.BytesIO(xlsx_bytes), sheet_name=sheet_name)
-    #             sheet_text = f'--- Sheet: {sheet_name} ---\n{df.to_string(index=False)}\n'
-    #             all_sheet_content.append(sheet_text)
-    #         return '\n'.join(all_sheet_content)
-
-    #     return await self._run_sync(_parse_xlsx_sync)
-
-    # async def _parse_csv(self, file_name: str) -> str:
-    #     """Parses a CSV file and returns its content as a string."""
-    #     self.ap.logger.info(f'Parsing CSV file: {file_name}')
-
-    #     csv_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-    #     def _parse_csv_sync():
-    #         # pd.read_csv can often detect encoding, but explicit detection is safer
-    #         # raw_data = self._read_file_content(
-    #         #     file_name, mode='rb'
-    #         # )  # Note: this will need to be await outside this sync function
-    #         # _ = raw_data
-    #         # For simplicity, we'll let pandas handle encoding internally after a raw read.
-    #         # A more robust solution might pass encoding directly to pd.read_csv after detection.
-    #         detected = chardet.detect(io.BytesIO(csv_bytes))
-    #         encoding = detected['encoding'] or 'utf-8'
-    #         df = pd.read_csv(io.BytesIO(csv_bytes), encoding=encoding)
-    #         return df.to_string(index=False)
-
-    #     return await self._run_sync(_parse_csv_sync)
-
-    async def _parse_md(self, file_name: str) -> str:
-        """Parses a Markdown file, converting it to structured plain text."""
-        self.ap.logger.info(f'Parsing Markdown file: {file_name}')
-
-        md_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-        def _parse_markdown_sync():
-            md_content = io.BytesIO(md_bytes).read().decode('utf-8', errors='ignore')
-            html_content = markdown.markdown(
-                md_content, extensions=['extra', 'codehilite', 'tables', 'toc', 'fenced_code']
-            )
-            soup = BeautifulSoup(html_content, 'html.parser')
-            text_parts = []
-            for element in soup.children:
-                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
-                    level = int(element.name[1])
-                    text_parts.append('#' * level + ' ' + element.get_text().strip())
-                elif element.name == 'p':
-                    text = element.get_text().strip()
-                    if text:
-                        text_parts.append(text)
-                elif element.name in ['ul', 'ol']:
-                    for li in element.find_all('li'):
-                        text_parts.append(f'* {li.get_text().strip()}')
-                elif element.name == 'pre':
-                    code_block = element.get_text().strip()
-                    if code_block:
-                        text_parts.append(f'```\n{code_block}\n```')
-                elif element.name == 'table':
-                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
-                    if table_str:
-                        text_parts.append(table_str)
-                elif element.name:
-                    text = element.get_text(separator=' ', strip=True)
-                    if text:
-                        text_parts.append(text)
-            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
-            return cleaned_text.strip()
-
-        return await self._run_sync(_parse_markdown_sync)
-
-    async def _parse_html(self, file_name: str) -> str:
-        """Parses an HTML file, extracting structured plain text."""
-        self.ap.logger.info(f'Parsing HTML file: {file_name}')
-
-        html_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
-
-        def _parse_html_sync():
-            html_content = io.BytesIO(html_bytes).read().decode('utf-8', errors='ignore')
-            soup = BeautifulSoup(html_content, 'html.parser')
-            for script_or_style in soup(['script', 'style']):
-                script_or_style.decompose()
-            text_parts = []
-            for element in soup.body.children if soup.body else soup.children:
-                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
-                    level = int(element.name[1])
-                    text_parts.append('#' * level + ' ' + element.get_text().strip())
-                elif element.name == 'p':
-                    text = element.get_text().strip()
-                    if text:
-                        text_parts.append(text)
-                elif element.name in ['ul', 'ol']:
-                    for li in element.find_all('li'):
-                        text = li.get_text().strip()
-                        if text:
-                            text_parts.append(f'* {text}')
-                elif element.name == 'table':
-                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
-                    if table_str:
-                        text_parts.append(table_str)
-                elif element.name:
-                    text = element.get_text(separator=' ', strip=True)
-                    if text:
-                        text_parts.append(text)
-            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
-            return cleaned_text.strip()
-
-        return await self._run_sync(_parse_html_sync)
-
-    def _add_toc_items_sync(self, toc_list: list, text_content: list, level: int):
-        """Recursively adds TOC items to text_content (synchronous helper)."""
-        indent = '  ' * level
-        for item in toc_list:
-            if isinstance(item, tuple):
-                chapter, subchapters = item
-                text_content.append(f'{indent}- {chapter.title}')
-                self._add_toc_items_sync(subchapters, text_content, level + 1)
-            else:
-                text_content.append(f'{indent}- {item.title}')
-
-    def _extract_table_to_markdown_sync(self, table_element: BeautifulSoup) -> str:
-        """Helper to convert a BeautifulSoup table element into a Markdown table string (synchronous)."""
-        headers = [th.get_text().strip() for th in table_element.find_all('th')]
-        rows = []
-        for tr in table_element.find_all('tr'):
-            cells = [td.get_text().strip() for td in tr.find_all('td')]
-            if cells:
-                rows.append(cells)
-
-        if not headers and not rows:
-            return ''
-
-        table_lines = []
-        if headers:
-            table_lines.append(' | '.join(headers))
-            table_lines.append(' | '.join(['---'] * len(headers)))
-
-        for row_cells in rows:
-            padded_cells = row_cells + [''] * (len(headers) - len(row_cells)) if headers else row_cells
-            table_lines.append(' | '.join(padded_cells))
-
-        return '\n'.join(table_lines)

pkg/rag/knowledge/services/retriever.py

@@ -1,48 +0,0 @@
-from __future__ import annotations
-
-from . import base_service
-from ....core import app
-from ....provider.modelmgr.requester import RuntimeEmbeddingModel
-from ....entity.rag import retriever as retriever_entities
-
-
-class Retriever(base_service.BaseService):
-    def __init__(self, ap: app.Application):
-        super().__init__()
-        self.ap = ap
-
-    async def retrieve(
-        self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
-    ) -> list[retriever_entities.RetrieveResultEntry]:
-        self.ap.logger.info(
-            f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
-        )
-
-        query_embedding: list[float] = await embedding_model.requester.invoke_embedding(
-            model=embedding_model,
-            input_text=[query],
-            extra_args={},  # TODO: add extra args
-        )
-
-        vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)
-
-        # 'ids' shape mirrors the Chroma-style response contract for compatibility
-        matched_vector_ids = vector_results.get('ids', [[]])[0]
-        distances = vector_results.get('distances', [[]])[0]
-        vector_metadatas = vector_results.get('metadatas', [[]])[0]
-
-        if not matched_vector_ids:
-            self.ap.logger.info('No relevant chunks found in vector database.')
-            return []
-
-        result: list[retriever_entities.RetrieveResultEntry] = []
-
-        for i, id in enumerate(matched_vector_ids):
-            entry = retriever_entities.RetrieveResultEntry(
-                id=id,
-                metadata=vector_metadatas[i],
-                distance=distances[i],
-            )
-            result.append(entry)
-
-        return result

pkg/storage/providers/localstorage.py

@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-import os
-import aiofiles
-import shutil
-
-from ...core import app
-
-from .. import provider
-
-
-LOCAL_STORAGE_PATH = os.path.join('data', 'storage')
-
-
-class LocalStorageProvider(provider.StorageProvider):
-    def __init__(self, ap: app.Application):
-        super().__init__(ap)
-        if not os.path.exists(LOCAL_STORAGE_PATH):
-            os.makedirs(LOCAL_STORAGE_PATH)
-
-    async def save(
-        self,
-        key: str,
-        value: bytes,
-    ):
-        if not os.path.exists(os.path.join(LOCAL_STORAGE_PATH, os.path.dirname(key))):
-            os.makedirs(os.path.join(LOCAL_STORAGE_PATH, os.path.dirname(key)))
-        async with aiofiles.open(os.path.join(LOCAL_STORAGE_PATH, f'{key}'), 'wb') as f:
-            await f.write(value)
-
-    async def load(
-        self,
-        key: str,
-    ) -> bytes:
-        async with aiofiles.open(os.path.join(LOCAL_STORAGE_PATH, f'{key}'), 'rb') as f:
-            return await f.read()
-
-    async def exists(
-        self,
-        key: str,
-    ) -> bool:
-        return os.path.exists(os.path.join(LOCAL_STORAGE_PATH, f'{key}'))
-
-    async def delete(
-        self,
-        key: str,
-    ):
-        os.remove(os.path.join(LOCAL_STORAGE_PATH, f'{key}'))
-
-    async def delete_dir_recursive(
-        self,
-        dir_path: str,
-    ):
-        # 直接删除整个目录
-        if os.path.exists(os.path.join(LOCAL_STORAGE_PATH, dir_path)):
-            shutil.rmtree(os.path.join(LOCAL_STORAGE_PATH, dir_path))

pkg/utils/announce.py

@@ -1,115 +0,0 @@
-from __future__ import annotations
-
-import json
-import typing
-import os
-import base64
-import logging
-
-import pydantic
-import requests
-
-from ..core import app
-
-
-class Announcement(pydantic.BaseModel):
-    """公告"""
-
-    id: int
-
-    time: str
-
-    timestamp: int
-
-    content: str
-
-    enabled: typing.Optional[bool] = True
-
-    def to_dict(self) -> dict:
-        return {
-            'id': self.id,
-            'time': self.time,
-            'timestamp': self.timestamp,
-            'content': self.content,
-            'enabled': self.enabled,
-        }
-
-
-class AnnouncementManager:
-    """公告管理器"""
-
-    ap: app.Application = None
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def fetch_all(self) -> list[Announcement]:
-        """获取所有公告"""
-        try:
-            resp = requests.get(
-                url='https://api.github.com/repos/langbot-app/LangBot/contents/res/announcement.json',
-                proxies=self.ap.proxy_mgr.get_forward_proxies(),
-                timeout=5,
-            )
-            resp.raise_for_status()  # 检查请求是否成功
-            obj_json = resp.json()
-            b64_content = obj_json['content']
-            # 解码
-            content = base64.b64decode(b64_content).decode('utf-8')
-
-            return [Announcement(**item) for item in json.loads(content)]
-        except (requests.RequestException, json.JSONDecodeError, KeyError) as e:
-            self.ap.logger.warning(f'获取公告失败: {e}')
-            pass
-        return []  # 请求失败时返回空列表
-
-    async def fetch_saved(self) -> list[Announcement]:
-        if not os.path.exists('data/labels/announcement_saved.json'):
-            with open('data/labels/announcement_saved.json', 'w', encoding='utf-8') as f:
-                f.write('[]')
-
-        with open('data/labels/announcement_saved.json', 'r', encoding='utf-8') as f:
-            content = f.read()
-
-        if not content:
-            content = '[]'
-
-        return [Announcement(**item) for item in json.loads(content)]
-
-    async def write_saved(self, content: list[Announcement]):
-        with open('data/labels/announcement_saved.json', 'w', encoding='utf-8') as f:
-            f.write(json.dumps([item.to_dict() for item in content], indent=4, ensure_ascii=False))
-
-    async def fetch_new(self) -> list[Announcement]:
-        """获取新公告"""
-        all = await self.fetch_all()
-        saved = await self.fetch_saved()
-
-        to_show: list[Announcement] = []
-
-        for item in all:
-            # 遍历saved检查是否有相同id的公告
-            for saved_item in saved:
-                if saved_item.id == item.id:
-                    break
-            else:
-                if item.enabled:
-                    # 没有相同id的公告
-                    to_show.append(item)
-
-        await self.write_saved(all)
-        return to_show
-
-    async def show_announcements(self) -> typing.Tuple[str, int]:
-        """显示公告"""
-        try:
-            announcements = await self.fetch_new()
-            ann_text = ''
-            for ann in announcements:
-                ann_text += f'[公告] {ann.time}: {ann.content}\n'
-
-            # TODO statistics
-
-            return ann_text, logging.INFO
-        except Exception as e:
-            return f'获取公告时出错: {e}', logging.WARNING

pkg/utils/image.py

@@ -1,212 +0,0 @@
-import base64
-import typing
-import io
-from urllib.parse import urlparse, parse_qs
-import ssl
-
-import aiohttp
-import PIL.Image
-import httpx
-
-import asyncio
-
-
-async def get_gewechat_image_base64(
-    gewechat_url: str,
-    gewechat_file_url: str,
-    app_id: str,
-    xml_content: str,
-    token: str,
-    image_type: int = 2,
-) -> typing.Tuple[str, str]:
-    """从gewechat服务器获取图片并转换为base64格式
-
-    Args:
-        gewechat_url (str): gewechat服务器地址（用于获取图片URL）
-        gewechat_file_url (str): gewechat文件下载服务地址
-        app_id (str): gewechat应用ID
-        xml_content (str): 图片的XML内容
-        token (str): Gewechat API Token
-        image_type (int, optional): 图片类型. Defaults to 2.
-
-    Returns:
-        typing.Tuple[str, str]: (base64编码, 图片格式)
-
-    Raises:
-        aiohttp.ClientTimeout: 请求超时（15秒）或连接超时（2秒）
-        Exception: 其他错误
-    """
-    headers = {'X-GEWE-TOKEN': token, 'Content-Type': 'application/json'}
-
-    # 设置超时
-    timeout = aiohttp.ClientTimeout(
-        total=15.0,  # 总超时时间15秒
-        connect=2.0,  # 连接超时2秒
-        sock_connect=2.0,  # socket连接超时2秒
-        sock_read=15.0,  # socket读取超时15秒
-    )
-
-    try:
-        async with aiohttp.ClientSession(timeout=timeout) as session:
-            # 获取图片下载链接
-            try:
-                async with session.post(
-                    f'{gewechat_url}/v2/api/message/downloadImage',
-                    headers=headers,
-                    json={'appId': app_id, 'type': image_type, 'xml': xml_content},
-                ) as response:
-                    if response.status != 200:
-                        # print(response)
-                        raise Exception(f'获取gewechat图片下载失败: {await response.text()}')
-
-                    resp_data = await response.json()
-                    if resp_data.get('ret') != 200:
-                        raise Exception(f'获取gewechat图片下载链接失败: {resp_data}')
-
-                    file_url = resp_data['data']['fileUrl']
-            except asyncio.TimeoutError:
-                raise Exception('获取图片下载链接超时')
-            except aiohttp.ClientError as e:
-                raise Exception(f'获取图片下载链接网络错误: {str(e)}')
-
-            # 解析原始URL并替换端口
-            base_url = gewechat_file_url
-            download_url = f'{base_url}/download/{file_url}'
-
-            # 下载图片
-            try:
-                async with session.get(download_url) as img_response:
-                    if img_response.status != 200:
-                        raise Exception(f'下载图片失败: {await img_response.text()}, URL: {download_url}')
-
-                    image_data = await img_response.read()
-
-                    content_type = img_response.headers.get('Content-Type', '')
-                    if content_type:
-                        image_format = content_type.split('/')[-1]
-                    else:
-                        image_format = file_url.split('.')[-1]
-
-                    base64_str = base64.b64encode(image_data).decode('utf-8')
-
-                    return base64_str, image_format
-            except asyncio.TimeoutError:
-                raise Exception(f'下载图片超时, URL: {download_url}')
-            except aiohttp.ClientError as e:
-                raise Exception(f'下载图片网络错误: {str(e)}, URL: {download_url}')
-    except Exception as e:
-        raise Exception(f'获取图片失败: {str(e)}') from e
-
-
-async def get_wecom_image_base64(pic_url: str) -> tuple[str, str]:
-    """
-    下载企业微信图片并转换为 base64
-    :param pic_url: 企业微信图片URL
-    :return: (base64_str, image_format)
-    """
-    async with aiohttp.ClientSession() as session:
-        async with session.get(pic_url) as response:
-            if response.status != 200:
-                raise Exception(f'Failed to download image: {response.status}')
-
-            # 读取图片数据
-            image_data = await response.read()
-
-            # 获取图片格式
-            content_type = response.headers.get('Content-Type', '')
-            image_format = content_type.split('/')[-1]  # 例如 'image/jpeg' -> 'jpeg'
-
-            # 转换为 base64
-            import base64
-
-            image_base64 = base64.b64encode(image_data).decode('utf-8')
-
-            return image_base64, image_format
-
-
-async def get_qq_official_image_base64(pic_url: str, content_type: str) -> tuple[str, str]:
-    """
-    下载QQ官方图片，
-    并且转换为base64格式
-    """
-    async with httpx.AsyncClient() as client:
-        response = await client.get(pic_url)
-        response.raise_for_status()  # 确保请求成功
-        image_data = response.content
-        base64_data = base64.b64encode(image_data).decode('utf-8')
-
-        return f'data:{content_type};base64,{base64_data}'
-
-
-def get_qq_image_downloadable_url(image_url: str) -> tuple[str, dict]:
-    """获取QQ图片的下载链接"""
-    parsed = urlparse(image_url)
-    query = parse_qs(parsed.query)
-    return f'http://{parsed.netloc}{parsed.path}', query
-
-
-async def get_qq_image_bytes(image_url: str, query: dict = {}) -> tuple[bytes, str]:
-    """[弃用]获取QQ图片的bytes"""
-    image_url, query_in_url = get_qq_image_downloadable_url(image_url)
-    query = {**query, **query_in_url}
-    ssl_context = ssl.create_default_context()
-    ssl_context.check_hostname = False
-    ssl_context.verify_mode = ssl.CERT_NONE
-    async with aiohttp.ClientSession(trust_env=False) as session:
-        async with session.get(image_url, params=query, ssl=ssl_context) as resp:
-            resp.raise_for_status()
-            file_bytes = await resp.read()
-            content_type = resp.headers.get('Content-Type')
-            if not content_type:
-                image_format = 'jpeg'
-            elif not content_type.startswith('image/'):
-                pil_img = PIL.Image.open(io.BytesIO(file_bytes))
-                image_format = pil_img.format.lower()
-            else:
-                image_format = content_type.split('/')[-1]
-            return file_bytes, image_format
-
-
-async def qq_image_url_to_base64(image_url: str) -> typing.Tuple[str, str]:
-    """[弃用]将QQ图片URL转为base64，并返回图片格式
-
-    Args:
-        image_url (str): QQ图片URL
-
-    Returns:
-        typing.Tuple[str, str]: base64编码和图片格式
-    """
-    image_url, query = get_qq_image_downloadable_url(image_url)
-
-    # Flatten the query dictionary
-    query = {k: v[0] for k, v in query.items()}
-
-    file_bytes, image_format = await get_qq_image_bytes(image_url, query)
-
-    base64_str = base64.b64encode(file_bytes).decode()
-
-    return base64_str, image_format
-
-
-async def extract_b64_and_format(image_base64_data: str) -> typing.Tuple[str, str]:
-    """提取base64编码和图片格式
-
-    data:image/jpeg;base64,xxx
-    提取出base64编码和图片格式
-    """
-    base64_str = image_base64_data.split(',')[-1]
-    image_format = image_base64_data.split(':')[-1].split(';')[0].split('/')[-1]
-    return base64_str, image_format
-
-
-async def get_slack_image_to_base64(pic_url: str, bot_token: str):
-    headers = {'Authorization': f'Bearer {bot_token}'}
-    try:
-        async with aiohttp.ClientSession() as session:
-            async with session.get(pic_url, headers=headers) as resp:
-                mime_type = resp.headers.get('Content-Type', 'application/octet-stream')
-                file_bytes = await resp.read()
-                base64_str = base64.b64encode(file_bytes).decode('utf-8')
-            return f'data:{mime_type};base64,{base64_str}'
-    except Exception as e:
-        raise (e)