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Code Issues Packages Projects Releases Wiki Activity

[verified] feat: synchronize Blog articles into Wiki (#113)

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Co-authored-by: dadachann <[email protected]>
This commit is contained in:
Hyu
2026-08-26 02:05:29 +08:00
committed by GitHub
co-authored by dadachann
parent fa48e37c20
commit 1fd6c29cc5
113 changed files with 21493 additions and 0 deletions
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.github/workflows/sync-blog-articles.yml
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name: Sync LangBot Blog articles
on:
workflow_dispatch:
schedule:
- cron: "17 2 * * *"
permissions:
contents: write
concurrency:
group: sync-langbot-blog-articles
cancel-in-progress: false
jobs:
sync:
runs-on: ubuntu-latest
steps:
- name: Check out Wiki
uses: actions/checkout@v5
with:
fetch-depth: 0
- name: Check out LangBot landing page
uses: actions/checkout@v5
with:
repository: langbot-app/langbot-landing-page
path: .article-source
persist-credentials: false
- name: Synchronize articles and images
run: node scripts/sync-blog-articles.mjs --source .article-source
- name: Validate generated articles
run: |
node scripts/sync-blog-articles.mjs --check --source .article-source
node scripts/sync-blog-articles.mjs --check
npm test
npx --yes [email protected] validate
- name: Commit synchronized content
run: |
rm -rf .article-source
if git diff --quiet && test -z "$(git status --short)"; then
echo "LangBot Blog articles are already synchronized."
exit 0
fi
git config user.name "langbot-wiki-bot"
git config user.email "[email protected]"
git add docs.json en/articles zh/articles ja/articles images/articles scripts/blog-articles-manifest.json
git commit -m "docs(articles): sync LangBot Blog"
git push
docs.json
+129
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@@ -710,6 +710,49 @@
}
]
},
{
"tab": "Articles",
"groups": [
{
"group": "Browse",
"pages": [
"en/articles/index"
]
},
{
"group": "Product Updates",
"pages": [
"en/articles/langbot-cloud-multi-user-workspaces"
]
},
{
"group": "Engineering",
"pages": [
"en/articles/langbot-v4100-sandbox-skills",
"en/articles/langbot-v490-rag-plugin-architecture",
"en/articles/langbot-plugin-system-deep-dive"
]
},
{
"group": "Tutorials & Integrations",
"pages": [
"en/articles/langtars-remote-pc-control-dify-n8n",
"en/articles/connect-deepseek-to-wechat",
"en/articles/deploy-ai-bot-in-5-minutes",
"en/articles/dify-agent-discord-telegram-slack",
"en/articles/langflow-drag-and-drop-ai-bot",
"en/articles/n8n-multi-platform-ai-chatbot",
"en/articles/dify-langbot-rag-knowledge-base"
]
},
{
"group": "Announcements",
"pages": [
"en/articles/welcome"
]
}
]
},
{
"tab": "API Reference",
"groups": [
@@ -949,6 +992,49 @@
}
]
},
{
"tab": "文章",
"groups": [
{
"group": "浏览",
"pages": [
"zh/articles/index"
]
},
{
"group": "产品动态",
"pages": [
"zh/articles/langbot-cloud-multi-user-workspaces"
]
},
{
"group": "技术解析",
"pages": [
"zh/articles/langbot-v4100-sandbox-skills",
"zh/articles/langbot-v490-rag-plugin-architecture",
"zh/articles/langbot-plugin-system-deep-dive"
]
},
{
"group": "教程与集成",
"pages": [
"zh/articles/langtars-remote-pc-control-dify-n8n",
"zh/articles/connect-deepseek-to-wechat",
"zh/articles/deploy-ai-bot-in-5-minutes",
"zh/articles/dify-agent-discord-telegram-slack",
"zh/articles/langflow-drag-and-drop-ai-bot",
"zh/articles/n8n-multi-platform-ai-chatbot",
"zh/articles/dify-langbot-rag-knowledge-base"
]
},
{
"group": "公告",
"pages": [
"zh/articles/welcome"
]
}
]
},
{
"tab": "API 参考",
"groups": [
@@ -1163,6 +1249,49 @@
}
]
},
{
"tab": "記事",
"groups": [
{
"group": "一覧",
"pages": [
"ja/articles/index"
]
},
{
"group": "製品アップデート",
"pages": [
"ja/articles/langbot-cloud-multi-user-workspaces"
]
},
{
"group": "エンジニアリング",
"pages": [
"ja/articles/langbot-v4100-sandbox-skills",
"ja/articles/langbot-v490-rag-plugin-architecture",
"ja/articles/langbot-plugin-system-deep-dive"
]
},
{
"group": "チュートリアルと連携",
"pages": [
"ja/articles/langtars-remote-pc-control-dify-n8n",
"ja/articles/connect-deepseek-to-wechat",
"ja/articles/deploy-ai-bot-in-5-minutes",
"ja/articles/dify-agent-discord-telegram-slack",
"ja/articles/langflow-drag-and-drop-ai-bot",
"ja/articles/n8n-multi-platform-ai-chatbot",
"ja/articles/dify-langbot-rag-knowledge-base"
]
},
{
"group": "お知らせ",
"pages": [
"ja/articles/welcome"
]
}
]
},
{
"tab": "API リファレンス",
"groups": [
en/articles/connect-deepseek-to-wechat.mdx
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---
title: "How to Connect DeepSeek R1 to WeChat, Discord & Telegram in 5 Minutes (FREE)"
description: "A step-by-step guide to connecting DeepSeek R1/V3 API to WeChat (Personal/Enterprise), Discord, Telegram, and more using LangBot. No coding required."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/connect-deepseek-to-wechat).
Published 2026-02-21 · Author: LangBot Team
![DeepSeek x WeChat Integration](/images/articles/source/images/covers/deepseek-wechat.png)
DeepSeek has taken the AI world by storm. Its R1 reasoning model rivals OpenAI's o1 but is open-source and significantly cheaper.
However, the official DeepSeek app currently only supports "AI Search" in WeChat, and direct integration into group chats or private workflows is limited.
**What if you could have a fully functional DeepSeek R1 bot in your WeChat groups, Discord servers, and Telegram chats right now?**
With **LangBot**, you can. And it takes less than 5 minutes.
## Why LangBot?
LangBot is an open-source, production-grade IM bot platform. Unlike simple scripts or single-platform bots, LangBot gives you:
* **Unified Platform:** Connect once, deploy to WeChat, Discord, Telegram, Slack, Lark, and more.
* **Model Agnostic:** Use DeepSeek R1, V3, Claude 3.5, GPT-4, or local Ollama models.
* **No Coding:** robust WebUI for configuration.
* **Enterprise Features:** Knowledge base (RAG), Plugin system, and multi-user management.
## Prerequisites
1. **A Server or PC:** Docker installed (VPS, local computer, or Synology/NAS).
2. **DeepSeek API Key:**
* **Official:** [platform.deepseek.com](https://platform.deepseek.com)
* **SiliconFlow (Recommended for speed):** [cloud.siliconflow.cn](https://cloud.siliconflow.cn) (Faster R1 inference).
## Step 1: Deploy LangBot
If you haven't deployed LangBot yet, run this one command:
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
Access your dashboard at `http://localhost:5300` (or your server IP).
## Step 2: Configure DeepSeek Model
1. Go to **Models** -> **Provider List**.
2. Find **DeepSeek** (or **SiliconFlow** / **OpenAI Compatible**).
3. Enter your API Key.
4. Click **Save**.
Now create a **Model Instance**:
* **Name:** `DeepSeek-R1-Bot`
* **Model:** `deepseek-reasoner` (for R1) or `deepseek-chat` (for V3).
## Step 3: Create a Chat Pipeline
LangBot uses "Pipelines" to manage bot logic.
1. Go to **Pipelines** -> **New Pipeline**.
2. Choose **Chat Pipeline**.
3. Select your `DeepSeek-R1-Bot` model.
4. (Optional) Add a System Prompt:
> "You are a helpful assistant powered by DeepSeek R1. You think deeply before answering."
## Step 4: Connect to WeChat (or any platform)
### For WeChat (Personal / Wechaty)
*Note: Personal WeChat access relies on third-party libraries and carries some risk. For business use, we recommend Enterprise WeChat (WeCom).*
1. Go to **Bots** -> **New Bot**.
2. Select **WeChat**.
3. Choose the **GeweChat** or **Wechaty** adapter (depending on your preference).
4. Click **Save**.
5. Scan the QR code that appears in the logs or UI.
### For Discord / Telegram
1. Select **Discord** or **Telegram**.
2. Paste your **Bot Token** (from Discord Developer Portal or BotFather).
3. Click **Save**.
## Step 5: Test It Out!
Open your chat app and send a message. You should see DeepSeek R1 "thinking" (if supported) and replying with high-quality reasoning.
## Advanced: Using Search & Tools
DeepSeek is great, but DeepSeek with **Internet Access** is better.
In LangBot:
1. Go to **Plugins**.
2. Install **Tavily Search** or **Google Search**.
3. Add the plugin to your DeepSeek Pipeline.
Now your bot can search the web for real-time info *before* reasoning with DeepSeek R1!
---
**Ready to build?**
[Star us on GitHub](https://github.com/langbot-app/LangBot) or [Read the Docs](https://docs.langbot.app).
en/articles/deploy-ai-bot-in-5-minutes.mdx
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---
title: "Deploy Your Own AI Bot to Discord, Telegram & WeChat in 5 Minutes"
description: "A step-by-step guide to deploying LangBot — the open-source platform that connects 20+ LLMs to 13+ messaging platforms with a single Docker command."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/deploy-ai-bot-in-5-minutes).
Published 2026-02-17 · Author: LangBot Team
![LangBot Dashboard](/images/articles/source/images/covers/deploy-ai-bot.png)
What if you could have GPT-5, Claude, DeepSeek, and Gemini all answering questions in your Discord server, Telegram group, and WeChat — at the same time?
No API wrangling. No weeks of development. Just one Docker command.
That's **LangBot** — and it just crossed **15,000 stars** on GitHub.
## The Problem Everyone Faces
You want an AI assistant in your team's chat. Maybe for customer support on Telegram. Maybe for a coding helper in Discord. Maybe for a knowledge base bot in your company's WeChat or Lark group.
But then reality hits:
- Each platform has its own bot API, webhook format, and auth flow
- You need to handle message queuing, session management, and error recovery
- Switching LLM providers means rewriting your integration layer
- Adding RAG or tool calling is yet another project
LangBot solves all of this with a single, unified platform.
## What Makes LangBot Different
### 13+ Messaging Platforms, One Codebase
Deploy a single LangBot instance and connect it to:
**Global:** Discord, Telegram, Slack, LINE, WhatsApp
**Asia:** WeChat (Official Account), WeCom, QQ, Lark, DingTalk, Feishu, KOOK
Each platform gets its own adapter — you just fill in your bot token in the WebUI and you're live.
![LangBot Bot Management Page](/images/articles/source/images/article-mcp/13-bot-page.png)
### 20+ LLM Models, Zero Lock-in
Through **LangBot Space**, you get instant access to 20 cloud models out of the box — no API keys to manage:
- **Claude** (Opus 4.6, Sonnet 4.5, Haiku 4.5)
- **GPT** (GPT-5.2, GPT-5-mini, GPT-4.1-mini)
- **Gemini** (3 Pro, 2.5 Pro, 2.5 Flash)
- **DeepSeek** (R1, V3)
- **Grok** (4, 4.1)
- **Qwen** (3 Max)
Or add your own providers — OpenAI-compatible endpoints, Ollama for local models, any provider you want.
![Model Selection](/images/articles/source/images/article-mcp/08-model-select.png)
### Built-in Agent with Tool Calling
LangBot's Local Agent isn't just a chat wrapper — it's a full agent runtime:
- **Multi-round conversations** with configurable memory
- **Function calling / tool use** for LLM-driven actions
- **MCP (Model Context Protocol)** support for connecting to 100+ pre-built tools
- **Knowledge base (RAG)** with built-in vector search
![Pipeline AI Configuration](/images/articles/source/images/article-mcp/06-pipeline-ai.png)
### Plugin Marketplace
37+ community plugins and growing — install with one click:
- **WebSearch** — Let your bot search the web
- **AI Image Generator** — Generate images from text
- **LinkAnaly** — Auto-preview links in chat
- **ScheNotify** — Schedule reminders with natural language
- **Google Search**, **Tavily Search**, **RAGFlow Retriever**, and more
![LangBot Space Plugin Market](/images/articles/source/images/article-mcp/14-space-market.png)
## Deploy in 5 Minutes — For Real
### Step 1: Run Docker Compose
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
That's it. LangBot is now running at `http://localhost:5300`.
### Step 2: Initialize with LangBot Space
Open the WebUI and click **"Initialize with Space"**. This connects your instance to LangBot Space, giving you:
- 20 cloud models ready to use (with free credits)
- One-click plugin installation
- Managed API keys
![Initialize with Space](/images/articles/source/images/article-mcp/01-init-page.png)
### Step 3: Configure Your Pipeline
Go to **Pipelines** and edit the default `ChatPipeline`:
1. Select your model (e.g., `deepseek-v3`, `gpt-5-mini`, `claude-sonnet-4-5`)
2. Customize the system prompt
3. Optionally attach a knowledge base or enable tools
![Pipelines Page](/images/articles/source/images/article-mcp/05-pipelines.png)
### Step 4: Connect a Platform
Go to **Bots** → click **+** → choose your platform (Discord, Telegram, etc.) → enter your bot token.
Done. Your bot is live.
### Step 5: Test It
Use the built-in **Debug Chat** to test your pipeline before going live:
![Debug Chat](/images/articles/source/images/article-mcp/07-debug-chat.png)
## Real Conversations, Real Value
Here's what it looks like when LangBot is running in a QQ group — users asking technical questions and getting instant, accurate answers:
![Group Chat Demo](/images/articles/source/images/article-mcp/10-group-chat.png)
And in private chat:
![Private Chat Demo](/images/articles/source/images/article-mcp/11-private-chat.png)
## Architecture That Scales
LangBot is built for production:
- **Pipeline architecture** — each bot binds to a pipeline; pipelines handle AI logic, triggers, safety controls, and output formatting
- **Cross-process plugin isolation** — a bad plugin can't crash your bot
- **Multiple runner backends** — use LangBot's Local Agent, or connect to Dify, n8n, Langflow, Coze for complex workflows
- **Database flexibility** — SQLite for dev, PostgreSQL for production
- **Vector DB options** — Chroma, Qdrant, Milvus, pgvector, SeekDB
## Why 15,000+ Developers Choose LangBot
| Feature | LangBot | Building from Scratch |
|---------|---------|----------------------|
| Platforms | 13+ ready | Weeks per platform |
| LLM Providers | 20+ models | Manual integration |
| Agent Runtime | Built-in | Build your own |
| RAG | Native + external | Separate project |
| Plugin System | Marketplace | DIY |
| Deployment | `docker compose up` | Days of setup |
| WebUI | Included | Build your own |
## Get Started
- **GitHub:** [github.com/langbot-app/LangBot](https://github.com/langbot-app/LangBot) — give us a star!
- **Documentation:** [docs.langbot.app](https://docs.langbot.app)
- **Plugin Market:** [space.langbot.app](https://space.langbot.app)
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
Your AI bot empire starts with one command.
en/articles/dify-agent-discord-telegram-slack.mdx
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---
title: "Finally Got My Dify Agent Working in Discord, Telegram and Slack"
description: "Finally Got My Dify Agent Working in Discord, Telegram and Slack"
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/dify-agent-discord-telegram-slack).
Published 2025-12-11 · Author: LangBot Team
![Finally Got My Dify Agent Working in Discord, Telegram and Slack](/images/articles/source/images/covers/dify-agent.png)
Want your Dify Agent to break free from the browser and chat directly in WeChat, QQ, or Telegram? Combine LangBot with Dify and you can set it up in under 10 minutes.
## Why This Combo?
[**LangBot**](https://github.com/langbot-app/LangBot) is the most powerful open-source multi-platform chatbot framework available. It supports major Chinese IMs (QQ, WeChat, Feishu, DingTalk) and international platforms (Telegram, Discord, Slack, LINE). Its core strength is connecting AI backends to messaging apps.
**Dify** is one of the hottest AI app development platforms, offering visual Agent orchestration, 50+ built-in tools, and RAG knowledge bases. Together, they let you build a multi-platform AI assistant fast.
## Deploy LangBot
Three commands with uvx:
```bash
mkdir langbot-instance
cd langbot-instance
uvx langbot@latest
```
Visit `http://127.0.0.1:5300` and register an admin account:
![LangBot Dashboard](/images/articles/external/c6d52212ba91-02-langbot-dashboard.png)
## Create a Dify Agent
Log in to [cloud.dify.ai](https://cloud.dify.ai) and create a new app. Choose **Agent** type - unlike basic chat apps, Agents can reason autonomously and call tools for complex tasks.
![Select Agent Type](/images/articles/external/3e5b1d8ec6ec-07-dify-create-app-types.png)
### Configure the Agent
In the orchestration interface, set up:
**System Instructions** - Define the Agent's role:
```
You are an intelligent assistant that helps users complete various tasks. You have the following capabilities:
1. Answer various user questions
2. Query weather information
3. Retrieve webpage content
Please always maintain a friendly and professional attitude. If you need to use tools to get information, proactively call the relevant tools.
```
**Add Tools** - Pick from Dify's 50+ built-in tools, like Weather and Web Scraper:
![Add Tools](/images/articles/external/9066d99b172c-11-dify-tools-panel.png)
**Select Model** - Supports GPT, Claude, Gemini, DeepSeek, and other major LLMs.
Final configuration:
![Configuration Complete](/images/articles/external/be278ca435e8-12-dify-agent-configured.png)
### Get the API Key
Click "Publish", then go to "Access API" to create a key:
![Create API Key](/images/articles/external/471b316018e5-13-dify-api-key-created.png)
Save the API key and server address `https://api.dify.ai/v1`.
## Connect Dify to LangBot
Back in LangBot, go to Pipelines > ChatPipeline > AI Capability:
![AI Capability Config](/images/articles/external/8f4b3820ed5b-14-langbot-pipeline-ai-capability.png)
Select **Dify Service API** as the Runner:
![Select Dify Runner](/images/articles/external/efa6fcae9c58-15-langbot-runner-dropdown.png)
Enter the configuration:
- **Base URL**: `https://api.dify.ai/v1`
- **API Key**: The key you just created
- **App Type**: Chat (including Chatflow)
![Enter Configuration](/images/articles/external/74f71a8bf1dc-17-langbot-dify-api-key-entered.png)
Save and click "Debug Conversation" to test:
![Test Conversation](/images/articles/external/9999c12907eb-20-langbot-chat-test.png)
If the Agent responds correctly and calls tools, the integration is working.
## Why This Architecture Rocks
- **Decoupled Design**: Agent capabilities live in Dify, LangBot handles message routing - each does what it's best at
- **One Config, Multi-Platform**: Same Agent serves QQ, WeChat, Telegram, and more simultaneously
- **Easy to Extend**: Add RAG knowledge bases, Workflow orchestration, or more tools in Dify later
## Next Steps
- Configure messaging platforms in LangBot (QQ bot, WeChat personal account, etc.)
- Add more tools in Dify (database queries, API calls)
- Try Dify Chatflow for complex conversation flows
If you're using n8n, FastGPT, Coze, or Langflow, LangBot supports those too.
---
**Resources**
- [LangBot Docs](https://docs.langbot.app) | [GitHub](https://github.com/langbot-app/LangBot)
- [Dify Docs](https://docs.dify.ai) | [Cloud Platform](https://cloud.dify.ai)
en/articles/dify-langbot-rag-knowledge-base.mdx
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---
title: "LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations"
description: "LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations"
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/dify-langbot-rag-knowledge-base).
Published 2025-12-02 · Author: LangBot Team
![LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations](/images/articles/source/images/covers/dify-rag.png)
LangBot 4.6.0 introduces external knowledge base functionality, allowing users to integrate external knowledge retrieval services such as Dify and RAGFlow into conversation pipelines. This tutorial demonstrates how to combine Dify knowledge base with LangBot to enable intelligent conversations based on domain-specific knowledge.
## Feature Overview
The external knowledge base feature enables LangBot to:
- Connect to various external knowledge retrieval services (Dify, RAGFlow, etc.)
- Provide professional answers based on domain-specific knowledge
- Flexibly extend knowledge retrieval capabilities through the plugin system
- Configure conveniently through WebUI without manual configuration file editing
## Prerequisites
Before starting, you need:
1. Deploy LangBot 4.6.0 or higher
2. Have a Dify account (register at https://cloud.dify.ai)
3. Configure a conversation model (this tutorial uses claude-opus-4-1-20250805)
## Step 1: Deploy LangBot
### 1.1 View LangBot Repository
First, visit the [LangBot GitHub repository](https://github.com/langbot-app/LangBot) to understand the project. LangBot is a production-grade instant messaging bot development platform supporting multiple messaging platforms and LLM services.
### 1.2 Learn About External Knowledge Base Feature
Visit the [official LangBot documentation](https://docs.langbot.app) to view the external knowledge base usage instructions. The documentation provides detailed explanations on how to build built-in knowledge bases and connect to external knowledge bases.
### 1.3 Start LangBot
Use the `uvx langbot` command to quickly start LangBot, then visit http://127.0.0.1:5300 for initialization. Fill in your email and password to complete registration, then log in.
### 1.4 Dashboard After Login
After successfully logging in, you will see the LangBot WebUI dashboard.
## Step 2: Configure Conversation Model
Before using the knowledge base, you need to configure a conversation model.
### 2.1 Navigate to Model Configuration Page
Click "Models" in the left navigation bar to enter the model configuration page.
### 2.2 Add New Model
Click the "+" button to open the model configuration dialog.
### 2.3 Fill in Model Information
Fill in the following information:
- **Model Name**: claude-opus-4-1-20250805
- **Model Provider**: New API
- **Request URL**: Your API endpoint
- **API Key**: Your API Key
- **Abilities**: Check Vision Ability and Function Call
Click "Submit" to save the configuration.
## Step 3: Create Knowledge Base in Dify
### 3.1 Log in to Dify Platform
Visit https://cloud.dify.ai and log in to your account.
### 3.2 Navigate to Knowledge Page
Click "Knowledge" in the top navigation bar to enter the knowledge base management page.
### 3.3 Create New Knowledge Base
Click "Create Knowledge" to start creating a knowledge base.
### 3.4 Select Data Source
You can choose from the following methods to import data:
- **Import from file**: Upload document files
- **Sync from Notion**: Sync from Notion
- **Sync from website**: Crawl website content
This tutorial selects "Import from file" and uploads prepared LangBot documentation.
### 3.5 Upload Document
After uploading the document, the system will display file information. Click "Next" to proceed.
### 3.6 Configure Document Processing Parameters
On the document processing page, you can configure chunk settings, index method, and retrieval settings:
- **Chunk Settings**: General mode, maximum chunk length 1024 characters
- **Index Method**: High Quality (uses embedding model)
- **Retrieval Setting**: Vector Search
Click "Save & Process" to start processing the document.
### 3.7 Knowledge Base Processing
The system starts processing the document and generating vector embeddings.
### 3.8 Embedding Completed
After processing is complete, the knowledge base is ready to use.
## Step 4: Get Dify API Information
### 4.1 Open Service API Panel
Click the "Service API" button in the bottom right. Record the following information:
- **Service API Endpoint**: https://api.dify.ai/v1
- **Dataset ID**: Get from the URL
### 4.2 Get API Key
Click the "API Key" button to view the API key. Record your API Key for use in subsequent configuration.
## Step 5: Configure External Knowledge Base in LangBot
### 5.1 Navigate to Knowledge Page
In the LangBot WebUI, click "Knowledge" in the left navigation bar.
### 5.2 Switch to External Knowledge Base Tab
Click the "External" tab.
### 5.3 Add External Knowledge Base
Click the "+" button to open the add external knowledge base dialog. In this dialog, you need to:
1. **Knowledge Base Name**: Give the knowledge base a name
2. **Knowledge Base Description**: Add a description (optional)
3. **Retriever**: Select a knowledge retriever plugin
**Note**: Using an external knowledge base requires installing the corresponding knowledge retriever plugin first. You can search for and install the Dify knowledge retriever plugin in the [plugin marketplace](https://space.langbot.app/market?category=KnowledgeRetriever).
After installing the plugin, select the corresponding plugin from the Retriever dropdown, then fill in the configuration information obtained from Dify (API Endpoint, API Key, Dataset ID).
## Step 6: Configure Pipeline
After configuring the external knowledge base, you need to enable it in the pipeline:
1. Navigate to the "Pipelines" page
2. Edit or create a new pipeline
3. On the "AI Capabilities" page, select "Built-in Agent" as the runner
4. In the knowledge base selection, check the external knowledge base you just configured
5. Save the pipeline configuration
## Step 7: Test the Effect
Test in the pipeline's "Conversation Testing" page, or bind the pipeline to a bot for actual conversation testing. When users ask questions, LangBot will automatically retrieve relevant content from the Dify knowledge base and generate answers combined with the retrieved knowledge.
## Frequently Asked Questions
### 1. How to Install Knowledge Retriever Plugins?
Visit the [LangBot plugin marketplace](https://space.langbot.app/market), search for "Knowledge Retriever" or "Dify", find the corresponding plugin and click install.
### 2. Which External Knowledge Retrieval Services Are Supported?
LangBot currently supports multiple external knowledge retrieval services through the plugin system, including:
- Dify
- RAGFlow
- Other custom knowledge retrieval services
Specific supported services can be viewed in the plugin marketplace.
### 3. What's the Difference Between External and Built-in Knowledge Bases?
- **Built-in Knowledge Base**: Data is stored locally in LangBot, with LangBot handling vectorization and retrieval
- **External Knowledge Base**: Data is stored in external services (like Dify), with retrieval performed through API calls
The advantage of external knowledge bases is the ability to leverage the capabilities of professional LLMOps platforms, such as Dify's advanced document processing and multiple retrieval strategies.
### 4. Can Multiple Knowledge Bases Be Used Simultaneously?
Yes. In the pipeline configuration, you can check multiple knowledge bases (built-in or external) simultaneously, and LangBot will comprehensively utilize the content from these knowledge bases to generate answers.
## Summary
Through this tutorial, you learned how to:
1. Create a knowledge base and upload documents on the Dify platform
2. Obtain Dify API key and related configuration information
3. Configure a conversation model in LangBot
4. Add an external knowledge base in LangBot
5. Configure pipelines to use external knowledge bases
The external knowledge base feature provides LangBot with more powerful and flexible knowledge management capabilities. Combined with professional LLMOps platforms like Dify, you can build more intelligent and professional conversational bots.
## Related Resources
- [LangBot Official Website](https://langbot.app)
- [LangBot Documentation](https://docs.langbot.app)
- [LangBot GitHub](https://github.com/langbot-app/LangBot)
- [Dify Official Website](https://dify.ai)
- [LangBot Plugin Marketplace](https://space.langbot.app/market)
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---
title: "LangBot Articles"
description: "Product updates, engineering deep dives, tutorials, integrations, and announcements from the LangBot Blog."
---
Product updates, engineering deep dives, tutorials, integrations, and announcements from the LangBot Blog.
## Product Updates
- [The new LangBot Cloud is here — and multi-user Workspaces are now in open source](/en/articles/langbot-cloud-multi-user-workspaces) — 2026-08-25
## Engineering
- [LangBot v4.10.0: Agentic Sandbox & Skills — Letting Your Agent Work Like a Developer](/en/articles/langbot-v4100-sandbox-skills) — 2026-06-04
- [LangBot v4.9.0: Full RAG Plugin Architecture — Knowledge Without Borders](/en/articles/langbot-v490-rag-plugin-architecture) — 2026-03-10
- [Deep Dive into the LangBot Plugin System: Process Isolation, Event-Driven Hooks, and Component Architecture](/en/articles/langbot-plugin-system-deep-dive) — 2026-02-23
## Tutorials & Integrations
- [LangTARS: Open-Source AI Agent for Remote PC Control — Works with Dify, n8n & 10+ Messaging Platforms](/en/articles/langtars-remote-pc-control-dify-n8n) — 2026-03-23
- [How to Connect DeepSeek R1 to WeChat, Discord & Telegram in 5 Minutes (FREE)](/en/articles/connect-deepseek-to-wechat) — 2026-02-21
- [Deploy Your Own AI Bot to Discord, Telegram & WeChat in 5 Minutes](/en/articles/deploy-ai-bot-in-5-minutes) — 2026-02-17
- [Finally Got My Dify Agent Working in Discord, Telegram and Slack](/en/articles/dify-agent-discord-telegram-slack) — 2025-12-11
- [How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows](/en/articles/langflow-drag-and-drop-ai-bot) — 2025-12-05
- [How I Built a Multi-Platform AI Chatbot with n8n and LangBot](/en/articles/n8n-multi-platform-ai-chatbot) — 2025-12-03
- [LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations](/en/articles/dify-langbot-rag-knowledge-base) — 2025-12-02
## Announcements
- [Introducing the LangBot Blog](/en/articles/welcome) — 2026-02-17
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---
title: "The new LangBot Cloud is here — and multi-user Workspaces are now in open source"
description: "LangBot is an open-source AI bot platform connecting LLMs with messaging apps. The new Cloud and open-source editions now support multi-user Workspaces, invitations, and role-based access."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langbot-cloud-multi-user-workspaces).
Published 2026-08-25 · Author: LangBot Team
![LangBot multi-user Workspace dashboard](/images/articles/source/images/cloud-multi-user/oss-dashboard.png)
The new [LangBot Cloud](https://cloud.langbot.app) is live. This release tackles a practical question: when a team uses LangBot together, who can access which resources?
The same multi-user foundation is now available in the open-source edition. Cloud and self-hosted LangBot still have different operating models, but they share the same basic approach to members, invitations, and permissions.
## What is LangBot?
LangBot is an open-source platform for building and operating AI bots across messaging apps. A single dashboard manages bots, message pipelines, knowledge bases, models, and extensions, then connects them to Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, and KOOK. LangBot also supports Satori and Matrix; Matrix bridges can extend a deployment to networks such as WhatsApp, Signal, Messenger, Mattermost, Google Chat, IRC, XMPP, and Zulip.
The model layer is provider-neutral. LangBot connects to OpenAI, Anthropic Claude, DeepSeek, Google Gemini, xAI Grok, Moonshot Kimi, and Zhipu GLM, while Ollama and LM Studio cover local models. Gateway options include SiliconFlow, Aliyun Bailian, Volcengine Ark, ModelScope, and GiteeAI. Existing agent workflows can also be connected through Dify and MCP.
A team can therefore run AI assistants on several messaging platforms from one place and choose a hosted model, a local model, or an existing agent workflow for each use case.
![The new LangBot Cloud sign-in page](/images/articles/source/images/cloud-multi-user/cloud-login.png)
## Cloud: start in your own Workspace
LangBot Cloud now uses LangBot Account for sign-in. New users start with a personal Workspace and can accept invitations to join team Workspaces. If an account belongs to several Workspaces, the current one can be switched from the interface.
Bots, pipelines, models, knowledge bases, and extensions belong to the active Workspace. Switching Workspaces does not mix one team's configuration or data with another's.
Cloud continues to provide the hosting, updates, and runtime environment. It is the simpler option for teams that want to configure a bot without maintaining a server first.
## Invite people instead of sharing an admin account
An Owner, or another role with member-management permission, can send a one-time invitation. The recipient accepts it with their own account and enters the Workspace with the assigned role.
![LangBot Cloud invitation page with the Workspace name anonymized](/images/articles/source/images/cloud-multi-user/cloud-invitation.png)
That is a better long-term setup than sharing one administrator password: each person has an identity, and access can match the work they actually do.
Five fixed roles are available today:
- **Owner** — owns the Workspace and its highest-level membership controls.
- **Admin** — manages members and most Workspace settings.
- **Developer** — configures bots, pipelines, models, and extensions.
- **Operator** — handles day-to-day operations.
- **Viewer** — read-only access.
## Open source: one instance, one Workspace, multiple users
The self-hosted edition is not turning into a multi-Workspace SaaS. One LangBot instance still has one Workspace, but that Workspace can now include several users.
The account created during initialization becomes the Owner. It can invite other people later. When SMTP is configured, LangBot can deliver the invitation by email; otherwise it shows a one-time link that can be shared through a trusted channel.
Members still use the familiar LangBot dashboard. The difference is that resource access is checked against both the Workspace and the member's role, rather than treating every signed-in user as a full administrator.
## Which edition should you use?
| | LangBot Cloud | Open-source self-hosted |
|---|---|---|
| Deployment | Hosted and maintained by LangBot | You operate the server and data |
| Workspaces | Own or join multiple | One per instance |
| Members and invitations | Included | Included |
| Fixed role-based access | Included | Included |
| Best for | Getting a team running quickly | Private deployment and infrastructure control |
To start with the hosted edition, visit [cloud.langbot.app](https://cloud.langbot.app). To keep the runtime and data on your own infrastructure, deploy LangBot from [GitHub](https://github.com/langbot-app/LangBot).
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---
title: "Deep Dive into the LangBot Plugin System: Process Isolation, Event-Driven Hooks, and Component Architecture"
description: "A source-level deep dive into LangBot 4.0's plugin architecture — process-isolated communication, event-driven hooks, a component-based extension model, and comparisons with Dify and MCP."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langbot-plugin-system-deep-dive).
Published 2026-02-23 · Author: LangBot Team
![LangBot Plugin System](/images/articles/source/images/covers/plugin-system.png)
Most chatbot frameworks call their "plugin system" a glorified dynamic import of Python modules. LangBot 4.0 takes a harder but more principled approach — **every plugin runs in its own process**, communicating with the host through a structured JSON-RPC-style protocol.
This article dissects the system from source code, end to end.
## Overall Architecture: A Three-Layer Process Model
LangBot's plugin system consists of three cooperating process layers:
![LangBot Plugin System Architecture](/images/articles/source/images/plugin-system/architecture.png)
**Each layer has a distinct responsibility:**
1. **LangBot Main Process**: Runs business logic (message pipelines, platform adapters, model invocations), connects to Runtime via `PluginRuntimeConnector`.
2. **Plugin Runtime**: The orchestration layer — discovers, launches, and manages all plugin subprocesses, routes requests from the main process to the appropriate plugin.
3. **Plugin Subprocesses**: Each plugin runs in its own Python process, communicating with Runtime via stdio pipes.
### Why Three Layers Instead of Two?
The intuitive design would have the main process manage plugin processes directly. LangBot adds the Runtime layer for **deployment flexibility**:
- **Local development**: Main process spawns Runtime as a child via stdio (zero config)
- **Docker production**: Runtime runs as a separate container, connected via WebSocket
- **Windows compatibility**: Since Windows asyncio has incomplete stdio subprocess support, it automatically falls back to WebSocket
The same codebase — no config changes — adapts from development to production.
## Communication Protocol: JSON-RPC-Style Request/Response
All cross-process communication runs on a unified protocol layer. The core data structures are minimal:
```python
# Request
class ActionRequest(pydantic.BaseModel):
seq_id: int # Sequence number for matching request/response
action: str # Action name
data: dict # Payload
# Response
class ActionResponse(pydantic.BaseModel):
seq_id: int
code: int # 0 = success
message: str
data: dict
chunk_status: str # "continue" | "end" (streaming support)
```
The `Handler` class is the system's core abstraction, acting as both RPC client and server:
```python
class Handler:
async def call_action(self, action, data, timeout=15.0) -> dict:
"""Actively call an action provided by the peer, wait for response"""
self.seq_id_index += 1
request = ActionRequest.make_request(self.seq_id_index, action.value, data)
future = asyncio.Future()
self.resp_waiters[self.seq_id_index] = future
await self.conn.send(json.dumps(request.model_dump()))
response = await asyncio.wait_for(future, timeout)
return response.data
@action(SomeAction.DO_SOMETHING)
async def handle_something(data: dict) -> ActionResponse:
"""Register an action for the peer to call"""
return ActionResponse.success({"result": "ok"})
```
**Key design points:**
- `seq_id`-based request/response matching enables full-duplex concurrent calls
- Streaming responses via `chunk_status` for long-running operations like command execution
- Large messages auto-chunk (stdio: 16KB / WebSocket: 64KB per chunk)
- File transfer uses a separate base64 chunking mechanism
### Action Enums: Clear API Contracts
The system defines all cross-process calls through four enum groups:
```python
# Plugin → Runtime (plugin-initiated requests)
class PluginToRuntimeAction:
REGISTER_PLUGIN = "register_plugin"
SEND_MESSAGE = "send_message" # Send message to a platform
INVOKE_LLM = "invoke_llm" # Call an LLM
SET_PLUGIN_STORAGE = "set_plugin_storage" # Persistent storage
# ...
# Runtime → Plugin (runtime-dispatched commands)
class RuntimeToPluginAction:
INITIALIZE_PLUGIN = "initialize_plugin"
EMIT_EVENT = "emit_event"
CALL_TOOL = "call_tool"
EXECUTE_COMMAND = "execute_command"
SHUTDOWN = "shutdown"
# ...
# LangBot Main → Runtime
class LangBotToRuntimeAction:
INSTALL_PLUGIN = "install_plugin"
EMIT_EVENT = "emit_event"
LIST_TOOLS = "list_tools"
# ...
# Runtime → LangBot Main
class RuntimeToLangBotAction:
GET_PLUGIN_SETTINGS = "get_plugin_settings"
SET_BINARY_STORAGE = "set_binary_storage"
# ...
```
This makes API boundaries crystal clear — what a plugin can and cannot do is defined entirely by these enums.
## Plugin Lifecycle
A plugin goes through these stages from installation to execution:
### 1. Discovery
On startup, Runtime scans the `data/plugins/` directory:
```python
async def launch_all_plugins(self):
for plugin_path in glob.glob("data/plugins/*"):
if not os.path.isdir(plugin_path):
continue
task = self.launch_plugin(plugin_path)
self.plugin_run_tasks.append(task)
```
Directory names follow the `{author}__{name}` convention, each containing a `manifest.yaml` and plugin code.
### 2. Launch
Runtime spawns an independent subprocess for each plugin:
```python
async def launch_plugin(self, plugin_path: str):
python_path = sys.executable
args = ["-m", "langbot_plugin.cli.__init__", "run", "-s", "--prod"]
ctrl = StdioClientController(
command=python_path,
args=args,
working_dir=plugin_path, # Each plugin runs in its own directory
)
await ctrl.run(new_plugin_connection_callback)
```
**Key detail**: The subprocess working directory is set to the plugin's own directory — natural filesystem isolation.
### 3. Registration
After starting, the plugin process actively registers itself with Runtime:
```python
# Runtime-side registration handler
async def register_plugin(self, handler, container_data, debug_plugin=False):
plugin_container = PluginContainer.from_dict(container_data)
# Fetch plugin settings from the main process
plugin_settings = await self.context.control_handler.call_action(
RuntimeToLangBotAction.GET_PLUGIN_SETTINGS, {...}
)
# Initialize the plugin (send config)
await handler.initialize_plugin(plugin_settings)
# Store the plugin container
self.plugins.append(plugin_container)
```
### 4. Running
Once in `INITIALIZED` state, the plugin can receive events, tool calls, and command executions.
### 5. Shutdown
```python
async def shutdown_plugin(self, plugin_container):
# 1. Notify the plugin to shut down gracefully
await plugin_container._runtime_plugin_handler.shutdown_plugin()
# 2. Close the communication connection
await plugin_container._runtime_plugin_handler.conn.close()
# 3. Kill the subprocess
if handler.stdio_process is not None:
handler.stdio_process.kill()
await asyncio.wait_for(handler.stdio_process.wait(), timeout=2)
```
## Component System: Four Extension Types
A LangBot plugin isn't a single hook function — it's a **component container**. A single plugin can provide multiple component types simultaneously:
### EventListener
The most fundamental extension — listen for events in the message pipeline:
```python
from langbot_plugin.api.definition.components.common.event_listener import EventListener
from langbot_plugin.api.entities.events import PersonNormalMessageReceived
from langbot_plugin.api.entities.context import EventContext
class MyListener(EventListener):
@EventListener.handler(PersonNormalMessageReceived)
async def on_person_message(self, ctx: EventContext):
event = ctx.event
# Modify the user message before it reaches the LLM
event.user_message_alter = "Answer in poetry: " + event.text_message
# Or block further processing
# ctx.prevent_default()
# ctx.prevent_postorder()
```
Supported events cover the full message lifecycle:
| Event | Trigger |
|-------|---------|
| `PersonMessageReceived` | Any private message received |
| `GroupMessageReceived` | Any group message received |
| `PersonNormalMessageReceived` | Private message deemed processable |
| `GroupNormalMessageReceived` | Group message deemed processable |
| `NormalMessageResponded` | LLM response completed |
| `PromptPreProcessing` | Prompt preprocessing stage |
Event propagation supports two interruption modes:
- `prevent_default()`: Skip default behavior (e.g., skip the LLM call)
- `prevent_postorder()`: Stop subsequent plugins from running
### Tool
Tools for LLM Function Calling:
```python
from langbot_plugin.api.definition.components.tool.tool import Tool
class WeatherTool(Tool):
async def call(self, params: dict, session, query_id: int) -> str:
city = params.get("city", "Beijing")
# Call weather API...
return f"{city}: Sunny, 25°C"
```
Tool metadata (name, description, parameter schema) is defined in a companion YAML manifest file. LangBot automatically converts this into the Function definition that LLMs understand.
### Command
User-triggered commands via `!command`, with subcommand support:
```python
from langbot_plugin.api.definition.components.command.command import Command
class MyCommand(Command):
def __init__(self):
super().__init__()
@self.subcommand("hello", help="Say hello")
async def hello(self, ctx):
yield CommandReturn(text="Hello from plugin!")
@self.subcommand("status", help="Show status")
async def status(self, ctx):
yield CommandReturn(text="All systems operational.")
```
Command results are returned via `AsyncGenerator`, providing natural streaming output.
### KnowledgeRetriever
A multi-instance component for connecting external knowledge bases:
```python
from langbot_plugin.api.definition.components.knowledge_retriever.retriever import KnowledgeRetriever
class MyRetriever(KnowledgeRetriever):
async def retrieve(self, context) -> list:
results = await self.search_external_db(context.query)
return [RetrievalResultEntry(content=r) for r in results]
```
KnowledgeRetriever is a **polymorphic component** — a single retriever class can spawn multiple instances, each with independent configuration. This allows users to connect multiple different external knowledge bases.
## SDK API: What Plugins Can Do
Plugins gain rich capabilities through the `LangBotAPIProxy` inherited by `BasePlugin`:
```python
class LangBotAPIProxy:
# Message operations
async def send_message(self, bot_uuid, target_type, target_id, message_chain)
# Model invocation
async def get_llm_models(self) -> list[str]
async def invoke_llm(self, model_uuid, messages, funcs=[], extra_args={})
# Persistent storage (plugin-level isolation)
async def set_plugin_storage(self, key, value: bytes)
async def get_plugin_storage(self, key) -> bytes
# Workspace storage (cross-plugin shared)
async def set_workspace_storage(self, key, value: bytes)
async def get_workspace_storage(self, key) -> bytes
# System info
async def get_langbot_version(self) -> str
async def get_bots(self) -> list[str]
async def list_plugins_manifest(self) -> list
```
**The storage API design is worth noting**: Two levels of KV storage — `plugin_storage` (plugin-private) and `workspace_storage` (globally shared), storing data as bytes (base64-serialized in transit). Simple but flexible enough.
## Event Dispatch Mechanism
The complete path from main process to plugin:
![Event Dispatch Flow](/images/articles/source/images/plugin-system/event-dispatch.png)
Key source code:
```python
async def emit_event(self, event_context, include_plugins=None):
for plugin in self.plugins:
if plugin.status != RuntimeContainerStatus.INITIALIZED:
continue
if not plugin.enabled:
continue
# Pipeline-level plugin filtering
if include_plugins is not None:
plugin_id = f"{plugin.manifest.metadata.author}/{plugin.manifest.metadata.name}"
if plugin_id not in include_plugins:
continue
resp = await plugin._runtime_plugin_handler.emit_event(
event_context.model_dump()
)
event_context = EventContext.model_validate(resp["event_context"])
# Plugin requested propagation stop
if event_context.is_prevented_postorder():
break
return emitted_plugins, event_context
```
The `include_plugins` parameter enables **pipeline-level plugin binding** — different message processing pipelines can use different subsets of plugins.
## Installation & Distribution
Plugins support three installation sources:
1. **Local upload**: `.lbpkg` files (actually zip archives containing manifest.yaml and code)
2. **Marketplace**: Install from LangBot Space online
3. **GitHub Release**: Download from a GitHub repository's Release assets
The installation flow:
```python
async def install_plugin(self, source, install_info):
yield {"current_action": "downloading plugin package"}
# 1. Fetch and extract the plugin package (unzip)
plugin_path, author, name, version = await self.install_plugin_from_file(plugin_file)
yield {"current_action": "installing dependencies"}
# 2. Install dependencies (pip install -r requirements.txt)
pkgmgr_helper.install_requirements(requirements_file)
yield {"current_action": "initializing plugin settings"}
# 3. Initialize configuration
await self.context.control_handler.call_action(
RuntimeToLangBotAction.INITIALIZE_PLUGIN_SETTINGS, {...}
)
yield {"current_action": "launching plugin"}
# 4. Launch the plugin process
task = self.launch_plugin(plugin_path)
```
The entire process reports progress via `AsyncGenerator`, enabling real-time installation status in the frontend.
## Developer Experience
The SDK provides a complete developer toolchain:
```bash
# Initialize a new plugin
lbp init
# Add a component
lbp component add
# Run locally for debugging
lbp run
# Package for publication
lbp publish
```
**Debug mode** has a particularly clever design: the developer's plugin connects to the running Runtime via WebSocket (instead of stdio), meaning you can hot-reload plugin code without restarting LangBot. Debug plugins are specially marked in the UI and protected from accidental deletion.
## Comparisons with Other Systems
### vs Dify Plugins
Dify's plugin system (`dify-plugin-daemon`) shares the process isolation philosophy with LangBot, but the focus differs:
- **Dify**: Plugins extend workflow node types (Tool, Model, Extension) — designed for AI application orchestration
- **LangBot**: Plugins extend the message processing pipeline (Event, Tool, Command, KnowledgeRetriever) — designed for instant messaging scenarios
LangBot's `EventListener` component provides a capability Dify lacks — injecting logic at any stage of message processing.
### vs MCP (Model Context Protocol)
MCP is a standardized protocol for AI tool invocation. LangBot's Tool component and MCP services overlap functionally, but serve different purposes:
- **MCP**: A universal "AI calls external capabilities" protocol, usable by any LLM application
- **LangBot Tool**: Deeply integrated with message processing context, with access to session info, user identity, etc.
In practice, LangBot natively supports MCP — users can configure MCP servers directly in LangBot without writing plugins. LangBot's Tool component is for scenarios requiring access to LangBot's internal context.
## Design Decisions Explained
**Why process isolation instead of threads/coroutines?**
- Plugin code quality is unpredictable; a segfault shouldn't crash the entire service
- Dependency isolation: different plugins may depend on different versions of the same library
- Resource control: you can set per-plugin process resource limits
**Why JSON instead of Protobuf/MessagePack?**
- Debug-friendly: developers can directly read communication logs
- Natively supported in Python, no extra dependencies
- The performance bottleneck isn't serialization (plugin call frequency is far below database queries)
**Why stdio over WebSocket by default?**
- stdio requires no network stack — lower latency
- Simpler process lifecycle management (child processes auto-cleanup when parent exits)
- WebSocket is only used where stdio isn't supported (Docker, Windows)
## Conclusion
LangBot's plugin system is a **production-grade, process-isolated, event-driven component framework for extensibility**.
Its core design principles:
1. **Safety first**: Process isolation ensures plugins can't destabilize the main service
2. **Deployment flexibility**: Dual stdio/WebSocket modes adapt to all environments
3. **Developer-friendly**: Complete SDK, CLI, and debug support
4. **Component-based**: Four component types cover the major extension needs
If you're interested in developing LangBot plugins, start with the [plugin development docs](https://docs.langbot.app/en/plugin/dev/tutor), or browse existing plugins on the [marketplace](https://space.langbot.app/market) for inspiration.
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---
title: "LangBot v4.10.0: Agentic Sandbox & Skills — Letting Your Agent Work Like a Developer"
description: "LangBot v4.10.0 brings a real sandbox (Box Runtime) and an on-demand Skills system to the built-in Agent. It can now run commands and edit files in an isolated environment, and pull in specialized know-how exactly when a task needs it."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langbot-v4100-sandbox-skills).
Published 2026-06-04 · Author: LangBot Team
![LangBot v4.10.0 Agentic Sandbox & Skills](/images/articles/source/images/covers/sandbox-skills.png)
LangBot v4.10.0 is our biggest Agent update yet. It ships two features that point in the same direction — **letting the built-in Agent work like a real developer**:
1. **Sandbox**: the Agent now runs in a genuinely isolated environment where it can execute commands and work with files.
2. **Skills**: the Agent can load specialized know-how on demand, instead of cramming everything into its context.
Both capabilities were led by community contributor [@huanghuoguoguo](https://github.com/huanghuoguoguo). Let's dig in.
> **New to LangBot?** [LangBot](https://github.com/langbot-app/LangBot) is an open-source LLM bot platform (15k+ GitHub stars) that lets you deploy AI chatbots across **Discord, Telegram, WeChat (个人微信 / 企业微信), Slack, Lark (Feishu), DingTalk, QQ** and more — all from a single instance. It connects 20+ large language models (OpenAI GPT, Anthropic Claude, Google Gemini, DeepSeek, and others) to 13+ messaging platforms, with built-in Agent, RAG knowledge bases, MCP support, and a plugin ecosystem. You can self-host it with one Docker command or run it managed on [LangBot Cloud](https://space.langbot.app/cloud).
## 📦 Sandbox: a real workspace for your Agent
Until now, LangBot's built-in Agent could only call predefined tools. It couldn't "open a terminal and run a command," nor "write an intermediate result to a file and read it back later" — which is exactly what human developers do all day.
v4.10.0 introduces the new **Box Runtime**. It gives the Agent an isolated sandbox environment with six native tools built in:
| Tool | Purpose |
|------|---------|
| `exec` | Run shell commands |
| `read` | Read file contents |
| `write` | Write files |
| `edit` | Make targeted edits to files |
| `glob` | Find files by pattern |
| `grep` | Search inside file contents |
With this toolkit, the Agent can work like a developer: run a script, save the output to a file, read it back to analyze, and post-process the result. All of it happens inside an isolated environment that never touches the host.
![LangBot sandbox in action](/images/articles/source/images/sandbox-skills/sandbox.png)
### Pluggable backends
The sandbox's execution backend is **pluggable**, and LangBot selects one automatically based on your deployment:
- **Docker**: spins up a dedicated `langbot_box` container that talks to the main process over WebSocket. Great for containerized deployments.
- **nsjail**: lightweight isolation based on Linux namespaces, no extra container needed. Great for bare-metal or single-container setups.
- **E2B**: cloud sandbox that runs execution in a remote isolated environment.
You don't have to pick manually — `box.enabled` is the master switch. Once on, LangBot probes for the most suitable backend and degrades gracefully if probing fails.
![Sandbox native tools](/images/articles/source/images/sandbox-skills/sandbox-tools.png)
### It also hosts MCP Servers
Beyond native tools, the Box Runtime takes on another important job: **hosting stdio-mode MCP Servers**.
Previously, stdio-mode MCP Servers had to be spawned as subprocesses directly inside the main process — neither safe nor easy to manage. Now they all run inside the sandbox, sharing the same isolation boundary as the Agent's execution environment. `http` / `sse` mode MCP is unaffected and works as before.
## 🎯 Skills: know-how, loaded on demand
The second big update is **Skills**.
As the Agent's capabilities grow, an old problem surfaces: **the context window is finite**. If you stuff every possible instruction, procedure, and reference doc into the system prompt, the context fills up fast — and most of it is irrelevant to the task at hand.
The design idea behind Skills is simple: **keep the table of contents in view, and open the book only when you need it.**
A Skill is an **on-demand instruction pack** containing:
- **A prompt**: tells the Agent what the skill does and when to use it
- **A procedure**: the steps to accomplish a class of tasks
- **Scripts**: ready-to-run helper scripts
- **Reference files**: API docs, templates, examples, and so on
By default the Agent only sees a **list of available skills** (name + summary), which costs almost nothing in context. When it decides a task needs a particular skill, it pulls the full content just in time via the `activate` action.
![LangBot Skills system](/images/articles/source/images/sandbox-skills/skills.png)
The benefits are clear:
1. **Lean context**: irrelevant skill content never takes up the window.
2. **Extensible capabilities**: want to teach the Agent a new trick? Write a skill pack — no core code changes.
3. **Instant effect**: skills load dynamically during execution, no restart required.
If you've used Claude's Skills or a similar mechanism, this pattern will feel familiar — it decouples "what the Agent knows" from "what the Agent needs right now."
## 🧩 Shipped early across 4.9.x
These were planned for v4.10.0 but shipped early in the 4.9.x line:
- **Plugin Extension Pages**: plugins can render custom UI via iframe + a Page SDK ([@TyperBody](https://github.com/TyperBody), #2107)
- **Monitoring**: feedback linked to message IDs, plus feedback export ([@6mvp6](https://github.com/6mvp6), #2120)
- **Security hardening**: path-traversal fixes and removal of an authenticated RCE endpoint ([@sebastiondev](https://github.com/sebastiondev), #2087 / #2178)
## 🚀 Getting started
The sandbox needs an execution backend, depending on how you deploy:
- **Docker**: run `docker compose --profile all up`, which also launches the `langbot_box` container.
- **Manual / `uvx`**: runs as a stdio subprocess automatically, nothing to do.
- **LangBot Cloud**: ready out of the box — the sandbox is already set up for you, nothing to install.
To turn the sandbox off, set `box.enabled: false` — built-in tools, Skill create/edit, and stdio-mode MCP are all disabled; `http` / `sse` mode MCP is unaffected. See the [Sandbox docs](https://docs.langbot.app/en/usage/sandbox/readme) for details.
This release also upgrades the plugin SDK to **`langbot-plugin 0.4.1`**.
## Closing thoughts
Sandbox and Skills are, at their core, answering the same question: **how do we let an Agent handle genuinely complex, open-ended tasks?**
The sandbox solves the "**hands**" problem — giving the Agent the ability to execute and room to experiment. Skills solve the "**brain**" problem — letting the Agent reach for the right knowledge at the right moment, without drowning in irrelevant information.
The fastest way to try it is to spin up a managed instance on [LangBot Cloud](https://space.langbot.app/cloud), where the sandbox is ready to go. You can also read the [full release notes](https://github.com/langbot-app/LangBot/releases/tag/v4.10.0) for every change.
Thank you to everyone building with LangBot. We'll keep making the cloud Agent experience more stable and more capable.
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---
title: "LangBot v4.9.0: Full RAG Plugin Architecture — Knowledge Without Borders"
description: "LangBot v4.9.0 refactors the entire knowledge base system from a built-in implementation to a plugin-driven architecture, introducing KnowledgeEngine and Parser component types for boundless extensibility."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langbot-v490-rag-plugin-architecture).
Published 2026-03-10 · Author: LangBot Team
![LangBot v4.9.0 RAG Plugin Architecture](/images/articles/source/images/covers/rag-plugin.png)
LangBot v4.9.0, codenamed **"Knowledge Without Borders,"** does exactly what the name says: **the entire knowledge base capability has been refactored from a built-in implementation to a plugin-driven architecture.**
This isn't a minor tweak — it redefines what "knowledge" means in LangBot.
## The Problem with the Old Approach
Before v4.9.0, LangBot's knowledge base was split into two separate systems:
- **Built-in Knowledge Base**: Used Chroma as the vector database, with embedding models managed by LangBot directly. Document parsing, chunking, and indexing were all hardcoded.
- **External Knowledge Base**: Bridged services like Dify, RAGFlow, and FastGPT through the `KnowledgeRetriever` plugin component — retrieval only, no document ingestion.
These two systems lived behind separate UI tabs, with completely different data models and management flows.
**Why this was painful:**
1. **Poor extensibility**: Want a different vector database? A custom chunking strategy? Sorry, that's hardcoded.
2. **High maintenance cost**: Every RAG improvement required changes to LangBot's core code and a new release.
3. **Fragmented UX**: Two completely different knowledge base management flows meant a steep learning curve.
v4.9.0 solves this decisively: **extract RAG capabilities from LangBot's core and hand them to plugins.**
## What Changed
### 1. Unified Knowledge Base Model
The `internal` / `external` distinction is gone. All knowledge bases are managed through a single interface, differentiated only by their `rag_engine_plugin_id`. One list, one creation flow — just pick your engine.
### 2. KnowledgeEngine Component
This is the headline addition. `KnowledgeEngine` replaces the old `KnowledgeRetriever` and takes ownership of the full knowledge base lifecycle:
- **Document Ingestion**: The complete pipeline from file parsing to vector indexing
- **Knowledge Retrieval**: Returning relevant chunks at query time
- **Document Deletion**: Cleaning up documents and their associated vector data
- **Lifecycle Hooks**: Callbacks when knowledge bases are created or deleted
A KnowledgeEngine plugin has full control over indexing and retrieval strategies — not just retrieval.
![Knowledge Engine plugins in the Marketplace](/images/articles/external/b9a33a8551a7-8dd37939-4f10-4046-bd84-cce798005cd4.png)
### 3. Parser Component
Document parsing has been extracted into its own plugin component type. A Parser converts binary files (PDF, Word, Markdown, etc.) into structured text, which is then handed to the RAG engine for chunking and indexing.
**The data flow:**
![Data flow: Parser → KnowledgeEngine](/images/articles/source/images/rag-plugin/data-flow.png)
If a RAG engine declares `DOC_PARSING` capability, it can handle parsing internally and skip the external Parser.
### 4. Host RAG API
LangBot's core no longer executes RAG operations directly, but it still provides essential infrastructure through `RAGRuntimeService`, accessible to plugins via RPC:
- **Embedding invocation**: `invoke_embedding()` — plugins don't need to manage model connections
- **Vector database operations**: `vector_upsert()` / `vector_search()` / `vector_delete()`
- **File access**: `get_knowledge_file_stream()` — read raw files from storage
This means plugins can focus on RAG strategy (chunking algorithms, retrieval logic, re-ranking) while the host handles the "heavy" operations like vector storage and embedding models.
### 5. KnowledgeRetriever Deprecated
The old `KnowledgeRetriever` component has been removed. If you had external knowledge base plugins, they'll need to migrate to `KnowledgeEngine`. The good news: the new API is cleaner and migration is straightforward.
## Building a RAG Engine Plugin
### Scaffold the Component
```bash
lbp comp KnowledgeEngine
```
This generates the directory structure:
![Plugin directory structure](/images/articles/source/images/rag-plugin/dir-structure.png)
### Define Configuration Schemas
The YAML manifest defines two configuration schemas:
- **`creation_schema`**: Parameters filled when creating a knowledge base (e.g., chunk size, embedding model)
- **`retrieval_schema`**: Parameters adjustable at retrieval time (e.g., score threshold, top-K)
```yaml
spec:
creation_schema:
- name: chunk_size
label:
en_US: Chunk Size
type: integer
default: 500
- name: chunk_overlap
label:
en_US: Chunk Overlap
type: integer
default: 50
retrieval_schema:
- name: score_threshold
label:
en_US: Score Threshold
type: float
default: 0.5
```
LangBot **dynamically renders** creation and retrieval forms based on these schemas — different engines show different configuration fields.
### Declare Capabilities
```python
class SimpleRag(KnowledgeEngine):
@classmethod
def get_capabilities(cls) -> list[str]:
return [
KnowledgeEngineCapability.DOC_INGESTION, # Supports document upload
# KnowledgeEngineCapability.DOC_PARSING, # Optional: built-in parsing
]
```
| Capability | Description |
|------------|-------------|
| `DOC_INGESTION` | Supports document upload and processing. UI shows "Documents" tab |
| `DOC_PARSING` | Supports built-in document parsing. Without this, an external Parser plugin is required |
### Implement Core Methods
The three essential methods:
**Document Ingestion:**
```python
async def ingest(self, context: IngestionContext) -> IngestionResult:
# 1. Get file content (or use Parser's pre-parsed result)
if context.parsed_content:
text = context.parsed_content.text
else:
file_bytes = await self.plugin.get_knowledge_file_stream(
context.file_object.storage_path
)
text = file_bytes.decode('utf-8')
# 2. Chunk the text
chunks = self._split_text(text, chunk_size=500)
# 3. Call host embedding model
vectors = await self.plugin.invoke_embedding(
embedding_model_uuid, chunks
)
# 4. Write to host vector database
await self.plugin.vector_upsert(
collection_id, vectors, ids, metadata
)
return IngestionResult(
document_id=context.file_object.metadata.document_id,
status=DocumentStatus.COMPLETED,
chunks_created=len(chunks),
)
```
**Knowledge Retrieval:**
```python
async def retrieve(self, context: RetrievalContext) -> RetrievalResponse:
# 1. Generate query vector
query_vectors = await self.plugin.invoke_embedding(
embedding_model_uuid, [context.query]
)
# 2. Vector search
results = await self.plugin.vector_search(
collection_id, query_vectors[0], top_k=5
)
# 3. Convert and return
return RetrievalResponse(results=entries, total_found=len(entries))
```
**Document Deletion:**
```python
async def delete_document(self, kb_id: str, document_id: str) -> bool:
deleted = await self.plugin.vector_delete(
collection_id=kb_id, file_ids=[document_id]
)
return deleted > 0
```
### Bridging External Services
If your goal is to bridge Dify, RAGFlow, FastGPT, or other external services rather than building a custom RAG pipeline, the implementation is even simpler — **don't declare `DOC_INGESTION` capability** and only implement `retrieve`:
```python
class DifyRAGEngine(KnowledgeEngine):
@classmethod
def get_capabilities(cls) -> list[str]:
return [] # No document upload — managed externally
async def retrieve(self, context: RetrievalContext) -> RetrievalResponse:
# Call Dify/RAGFlow/FastGPT retrieval API
...
```
The knowledge base won't show a "Documents" tab — all content management happens in the external service.
## Building a Parser Plugin
Parser development is even more concise:
```bash
lbp comp Parser
```
Declare supported MIME types in the manifest:
```yaml
spec:
supported_mime_types:
- application/pdf
- application/vnd.openxmlformats-officedocument.wordprocessingml.document
```
Implement the `parse` method:
```python
class PdfParser(Parser):
async def parse(self, context: ParseContext) -> ParseResult:
# context.file_content: raw file bytes
# context.mime_type: detected MIME type
# context.filename: original filename
text = extract_text_from_pdf(context.file_content)
return ParseResult(
text=text,
sections=[
TextSection(content=page_text, heading=f"Page {i}", page=i)
for i, page_text in enumerate(pages)
],
)
```
Parsers also support **cross-plugin invocation** — a RAG engine plugin can call another plugin's Parser:
```python
result = await self.plugin.invoke_parser(
plugin_author="author_name",
plugin_name="plugin_name",
storage_path=context.file_object.storage_path,
mime_type=context.file_object.metadata.mime_type,
filename=context.file_object.metadata.filename,
)
```
## Upgrade Notes
- Knowledge bases created in previous versions are **automatically migrated**. After updating, visit the Knowledge Base page to verify.
- The `KnowledgeRetriever` component is deprecated. Existing plugins need to migrate to `KnowledgeEngine`.
- Browse the [Plugin Marketplace](https://space.langbot.app/market?category=KnowledgeEngine) for available RAG engine plugins.
## The Bigger Picture
v4.9.0's knowledge base refactoring is the latest step in LangBot's plugin-first evolution. From event handlers and tools in v4.0, to knowledge retrievers, to now full RAG engines and parsers — LangBot's core capabilities are progressively moving from "built-in" to "pluggable."
The endgame: **LangBot's core provides pipeline orchestration and infrastructure; all business capabilities are plugin-driven.**
Custom chunking strategy? Write a KnowledgeEngine plugin. PDF parsing? Write a Parser plugin. Bridge your company's internal knowledge service? Also a plugin.
Knowledge, without borders.
---
**Links:**
- [v4.9.0 Release Notes](https://github.com/langbot-app/LangBot/releases/tag/v4.9.0)
- [KnowledgeEngine Development Guide](https://docs.langbot.app/en/plugin/dev/components/knowledge-engine)
- [Parser Development Guide](https://docs.langbot.app/en/plugin/dev/components/parser)
- [Plugin Marketplace](https://space.langbot.app/market)
- [LangBot Cloud](https://space.langbot.app/cloud)
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---
title: "How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows"
description: "How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows"
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langflow-drag-and-drop-ai-bot).
Published 2025-12-05 · Author: LangBot Team
![How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows](/images/articles/source/images/covers/langflow.png)
Drive chatbots across QQ, WeChat, Telegram, Discord, and more using visual workflows - no coding required.
---
[LangBot](https://github.com/langbot-app/LangBot) is an open-source instant messaging bot platform that connects AI workflow engines like Langflow, n8n, Dify, FastGPT, and Coze to platforms including WeChat, QQ, Feishu, DingTalk, Telegram, Discord, Slack, and LINE. This tutorial demonstrates how to use Langflow's visual workflows as LangBot's conversation engine.
## Why This Approach Works
- **True Multi-Platform**: One workflow powering 8+ messaging platforms simultaneously
- **Visual Orchestration**: Drag-and-drop conversation design with conditional branches, multi-turn dialogs, and external API calls
- **Flexible AI Models**: Support for OpenAI, Claude, Gemini, DeepSeek, and local models
- **Fully Open Source**: Both LangBot and Langflow are open-source projects for free deployment and customization
## Prerequisites
- Python 3.10+
- Docker (recommended for quick deployment)
- OpenAI API Key or API keys for other LLM services
## Step 1: Deploy LangBot
Launch with uvx in one command:
```bash
uvx langbot
```
First run auto-initializes and opens your browser to http://127.0.0.1:5300.
![LangBot Initial Page](/images/articles/external/0c8aa2c940ab-01-langbot-initial-page.png)
After registration, log in to access the dashboard:
![LangBot Dashboard](/images/articles/external/b93d97b5b830-04-langbot-dashboard.png)
## Step 2: Deploy Langflow
Deploy quickly with Docker:
```bash
docker run -d --name langflow -p 7860:7860 langflowai/langflow:latest
```
Visit http://localhost:7860 to access Langflow:
![Langflow Welcome Page](/images/articles/external/7e7ebfc3ba58-01-langflow-welcome.png)
## Step 3: Create a Langflow Workflow
In Langflow, select the "Basic Prompting" template to get started quickly:
![Langflow Template Selection](/images/articles/external/b304cebf8ce9-02-langflow-template-selection.png)
This template includes four basic components:
- **Chat Input**: Receives user messages
- **Prompt**: Sets system instructions
- **Language Model**: Calls LLM to generate responses
- **Chat Output**: Returns results
![Langflow Workflow Editor](/images/articles/external/6e24402fd4a8-03-langflow-workflow-editor.png)
### Configure Language Model
Click the Language Model component and configure:
1. **Model Provider**: Select OpenAI (or other compatible providers like SiliconFlow, New API)
2. **Model Name**: Select gpt-4o-mini or deepseek-chat
3. **OpenAI API Key**: Enter your API Key
![Langflow OpenAI API Key Configured](/images/articles/external/bcd91ef5e4b3-14-langflow-openai-api-key-configured.png)
> Tip: You can use OpenAI-compatible API services like SiliconFlow or New API by simply modifying the Base URL.
Save the workflow after configuration.
## Step 4: Get Langflow API Information
### Generate API Key
In Langflow's upper right: Settings → API Keys, navigate to the API Keys page:
![Langflow API Keys Page](/images/articles/external/21fc47358066-05-langflow-api-keys-page.png)
Click Create New Key:
![Langflow Create API Key Dialog](/images/articles/external/8f04c7b00759-06-langflow-create-api-key-dialog.png)
Generate and save the API Key:
![Langflow API Key Generated](/images/articles/external/e5a019491ef8-07-langflow-api-key-generated.png)
Format: `sk-xxxxxxxxxxxxxxxxxxxxxxxx`
### Get Flow ID
Extract from the flow editor's URL:
```
http://localhost:7860/flow/{flow-id}
```
Record this `flow-id`.
## Step 5: Configure Langflow in LangBot
Return to LangBot dashboard and go to **Pipelines** page.
Click ChatPipeline to edit, in the AI tab:
![LangBot Pipeline AI Tab](/images/articles/external/3c7d6bc96738-08-langbot-pipeline-ai-tab.png)
Configure **Runner**, select **Langflow API**:
![LangBot Runner Dropdown](/images/articles/external/ef9bbef78165-09-langbot-runner-dropdown.png)
Fill in the Langflow configuration:
![LangBot Langflow Config Form](/images/articles/external/82d5e2ffb9a0-10-langbot-langflow-config-form.png)
Configuration items:
- **Base URL**: `http://localhost:7860` (local) or `http://langflow:7860` (Docker network)
- **API Key**: The API Key generated in Langflow
- **Flow ID**: The Flow ID recorded earlier
![LangBot Langflow Config Filled](/images/articles/external/2fb54ce0f7a2-11-langbot-langflow-config-filled.png)
> Docker Tip: If both services run in containers, ensure they're on the same network and use the container name for Base URL.
Click **Save** to save the configuration.
## Step 6: Test the Conversation
Click **Debug Chat** on the Pipelines page to open the debug chat interface:
![LangBot Debug Chat Interface](/images/articles/external/b36d6dea3702-12-langbot-debug-chat-interface.png)
Enter a test message like "Hello" and view the AI response:
![LangBot Chat Test Success](/images/articles/external/3d7fa393f210-13-langbot-chat-with-response.png)
## How It Works
1. User sends a message on a messaging platform
2. LangBot receives and passes it to the Pipeline
3. Pipeline calls Langflow API
4. Langflow executes the workflow: receives input → adds prompt → calls LLM → returns result
5. LangBot sends the response back to the user
## Common Issues
**Cannot connect to Langflow?**
Check Base URL. For Docker deployment, ensure containers are on the same network:
```bash
docker network create langbot_network
docker network connect langbot_network langflow
docker network connect langbot_network langbot
```
Use container name: `http://langflow:7860`
**API call fails?**
- Confirm API Key and Flow ID are correct
- Verify the Language Model in Langflow has a valid LLM API Key configured
## Advanced Use Cases
Langflow's power lies in visually orchestrating complex AI workflows:
- **Multi-Turn Memory**: Add Memory components for contextual understanding
- **Conditional Branches**: Execute different logic based on user input
- **External API Integration**: Connect databases, search engines, third-party services
- **Multi-Agent Collaboration**: Multiple LLM models working together
- **RAG Applications**: Integrate vector databases for knowledge base Q&A
All achievable through drag-and-drop without writing code.
## Summary
With LangBot + Langflow, you can rapidly build powerful multi-platform AI chatbots. Langflow provides visual workflow orchestration, LangBot handles messaging platform integration - together they create a complete loop from workflow design to multi-platform deployment.
This approach is ideal for:
- Scenarios requiring the same AI capabilities across multiple platforms
- Teams wanting rapid iteration and testing of different conversation flows
- Developers wanting to build complex AI applications without deep coding
## Related Resources
- [LangBot Official Site](https://langbot.app) | [Documentation](https://docs.langbot.app) | [GitHub](https://github.com/langbot-app/LangBot)
- [Langflow Official Site](https://www.langflow.org) | [Documentation](https://docs.langflow.org) | [GitHub](https://github.com/langflow-ai/langflow)
---
*This article is based on the latest version of LangBot. LangBot supports integration with Dify, n8n, FastGPT, Coze, and other AI platforms - choose the workflow engine that best fits your needs.*
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---
title: "LangTARS: Open-Source AI Agent for Remote PC Control — Works with Dify, n8n & 10+ Messaging Platforms"
description: "LangTARS is a LangBot native plugin that lets you remotely control Mac/Windows/Linux from Telegram, Discord & WeChat — with Dify and n8n workflow support."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/langtars-remote-pc-control-dify-n8n).
Published 2026-03-23 · Author: LangBot Team
![LangTARS Logo](/images/articles/external/3fe5bdc18716-icon.svg)
**LangBot Native Plugin | OpenClaw-like Experience | Ready to Use with One-Click Deployment**
---
## ✨ What is LangTARS?
**LangTARS** is a powerful **OpenClaw alternative** and native plugin for LangBot, inspired by the loyal and reliable robot TARS from the movie *Interstellar*.
As a versatile **AI agent** for **remote computer control**, LangTARS allows you to remotely command your **Mac, Windows, or Linux** machine directly through your favorite messaging apps like **Telegram, Discord, DingTalk, and WeChat**! If you've been looking for an **OpenClaw-like** computer use agent that integrates seamlessly into your existing **chatbot framework**, LangTARS is the perfect solution.
> 💡 **Deploy LangBot, and you can experience LangTARS instantly!** No extra standalone agent installation needed—just get it with one click from the plugin marketplace.
![LangTARS Demo](/images/articles/external/70b26e660a60-tars.gif)
---
## 🤖 The LangBot Ecosystem
LangTARS is just ONE plugin in the massive LangBot ecosystem. **LangBot** is a leading **open source chatbot** framework designed for connecting LLMs to messaging platforms.
- **Multi-platform bot integration**: Native support for Telegram, Discord, WeChat, DingTalk, Lark, LINE, Slack, and more.
- **Multi-model LLM chatbot**: Works flawlessly with OpenAI, Claude, Gemini, DeepSeek, and local models.
- **Rich Plugin Marketplace**: Access over 30+ plugins for endless functionality.
- **Advanced Features**: Pipeline-based message processing, RAG support, and multi-agent workflows.
- **Self-hosted AI**: Total control over your data and deployments.
- **AI Workflow Integrations**: Connect LangBot to platforms like **Dify**, **n8n**, **Langflow**, and **Coze**. LangBot can act as the messaging frontend for your complex AI workflows, meaning LangTARS + Dify/n8n = powerful automation!
Get started with LangBot:
- **Easiest**: [LangBot Cloud](https://cloud.langbot.app)
- Self-host: `uvx langbot@latest`
- Documentation: [docs.langbot.app](https://docs.langbot.app)
- GitHub: [github.com/langbot-app/LangBot](https://github.com/langbot-app/LangBot) (15k+ stars)
---
## 🎯 What Can It Do?
As a comprehensive **automation tool**, just send a message, and the AI will handle your **task automation** and **browser automation**:
| You Say | AI Does |
|------|-------|
| `!tars open Reddit in Chrome, post a thread with the title "Hello from LangTARS" and say hi` | 🌐 Automatically controls Chrome to open Reddit and publish a post |
| `!tars organize my desktop files` | 📁 Automatically categorizes and moves files |
| `!tars open the browser and search for today's AI news` | 🌐 Opens a browser, searches the web, and summarizes the news |
| `!tars create a meeting note for our marketing sync` | 📝 Automatically creates a markdown note file |
| `!tars check system memory and CPU` | 💻 Uses MCP tools to get computer status |
![Usage Example](/images/articles/external/6613b3025b8b-usage-example.png)
![Plan Mode](/images/articles/external/ff42857251ab-planner-mode.png)
---
## 🔗 Supercharge with Dify & n8n
LangTARS becomes even more powerful when combined with **AI workflow platforms**:
- **Dify Integration**: Connect LangBot to [Dify](https://dify.ai) as a messaging frontend. Build complex AI workflows in Dify's visual editor, then trigger them from Telegram/Discord via LangTARS.
- **n8n Integration**: Use [n8n](https://n8n.io) workflows with LangBot for advanced automation chains. Combine LangTARS's computer control with n8n's 400+ integrations for unlimited possibilities.
- **Also supports**: Langflow, Coze, and any OpenAI-compatible API.
> 💡 **Example**: "Hey TARS, run my Dify workflow to analyze today's sales data and save the report to my desktop" — LangTARS handles the entire chain.
---
## 🚀 Why Choose LangTARS?
### 1️⃣ LangBot Native Plugin, Out of the Box
Instead of deploying a complex standalone agent, you can easily **deploy AI bot** capabilities right where you already chat.
```bash
# Deploy LangBot with one command
uvx langbot@latest
# Install LangTARS from the plugin market, configure your LLM, and start using!
```
**No complex configuration, no extra deployment. LangBot users can experience OpenClaw-style computer control directly!**
![Installation Flow](/images/articles/external/2a3a0d5f4ae9-install-flow.png)
---
### 2️⃣ Intelligent Task Planning, Autonomous Execution
Powered by an AI planning engine based on the **ReAct agent** loop:
```
Your Command → Understand Needs → Make a Plan → Execute Step-by-Step → Feedback Results
```
The AI assistant thinks like a human and automatically breaks down complex tasks using integrated **MCP tools**!
---
### 3️⃣ Multi-Browser Support for Web Automation
| Browser | macOS | Windows | Linux |
|--------|-------|---------|-------|
| Playwright (Headless) | ✅ | ✅ | ✅ |
| Safari | ✅ | - | - |
| Chrome | ✅ | ✅ | ✅ |
| Edge | - | ✅ | - |
| Firefox | - | - | ✅ |
---
### 4️⃣ Security-First Design
- 🚫 **Dangerous Command Interception** — Automatically blocks commands like `rm -rf /`
- 📂 **Workspace Isolation** — Restricts file operations to safe directories
- 📋 **Command Whitelist** — Configurable allowed commands
- 👤 **User Access Control** — Restricts usage to specific authorized users
![Security Features](/images/articles/external/a618d3d41416-security.png)
---
## 📱 Quick Start
### Step 1: Deploy LangBot
> 🚀 **Fastest way to start:** Use [LangBot Cloud](https://cloud.langbot.app) — deploy your LangBot instance in one click, no server needed! Install LangTARS from the plugin marketplace and start controlling your computer immediately.
*Alternative for power users (Self-hosted):*
```bash
uvx langbot@latest
```
### Step 2: Configure Chat Platform
Follow the documentation to configure your **Telegram bot**, **WeChat bot**, or **Discord bot**.
### Step 3: Install LangTARS
Search for **LangTARS** in the **plugin marketplace** and install it.
### Step 4: Start Chatting
```
!tars Hello, tell me what you can do
```
> 📱 **Recommendation**: Use it on Telegram or Discord for the best interactive experience!
---
## 🎮 Common Commands
| Command | Function |
|------|------|
| `!tars <Task Description>` | Execute a task |
| `!tars stop` | Stop the current task |
| `!tars what` | Check execution status |
| `!tars reset` | Reset the conversation |
| `!tars help` | Show help |
---
## 🌟 Project Information
| Item | Information |
|------|------|
| **GitHub** | [github.com/langbot-app/LangTARS](https://github.com/langbot-app/LangTARS) |
| **Language** | Python 100% |
| **License** | CC BY-NC-ND 4.0 |
| **Platforms** | macOS / Windows / Linux |
---
## 🔗 Related Links
- 📖 **LangBot Documentation**: [docs.langbot.app](https://docs.langbot.app)
- 💬 **Issue Feedback**: [GitHub Issues](https://github.com/langbot-app/LangTARS/issues)
- ⭐ **Support with a Star**: [GitHub](https://github.com/langbot-app/LangTARS)
---
## 🎉 Deploy LangBot and Experience LangTARS!
**Let AI be your loyal digital assistant, just like TARS.**
> 🚀 **Fastest way to start:** Use [LangBot Cloud](https://cloud.langbot.app) — deploy your LangBot instance in one click, no server needed! Install LangTARS from the plugin marketplace and start controlling your computer immediately.
*Alternative for power users (Self-hosted):*
```bash
uvx langbot@latest
```
en/articles/n8n-multi-platform-ai-chatbot.mdx
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---
title: "How I Built a Multi-Platform AI Chatbot with n8n and LangBot"
description: "How I Built a Multi-Platform AI Chatbot with n8n and LangBot"
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/n8n-multi-platform-ai-chatbot).
Published 2025-12-03 · Author: LangBot Team
![How I Built a Multi-Platform AI Chatbot with n8n and LangBot](/images/articles/source/images/covers/n8n.png)
Connecting n8n's visual workflow automation with LangBot's multi-platform bot framework creates a powerful, code-free way to deploy AI chatbots across QQ, WeChat, Discord, Telegram, Slack, and more. This tutorial shows you how to integrate these tools in minutes.
## What You'll Need
- Python 3.8+ installed
- Node.js 18+ installed
- npm or npx available
- 15 minutes of your time
## Deploy LangBot in 3 Commands
LangBot is a production-ready bot framework that connects to multiple messaging platforms and AI services including Dify, FastGPT, Coze, OpenAI, Claude, and Gemini. Deploy it instantly:
```bash
cd your-workspace
mkdir -p langbot-instance && cd langbot-instance
uvx langbot@latest
```
On first launch, LangBot initializes automatically. Open `http://127.0.0.1:5300` in your browser.
![LangBot initialization screen](/images/articles/external/70ee40a6d232-01-langbot-initialization.png)
Register your admin account when prompted. You'll land on the dashboard where you can manage bots, models, pipelines, and integrations.
![LangBot dashboard](/images/articles/external/4f1f5c28fbfc-03-langbot-dashboard.png)
## Set Up n8n Workflow Automation
n8n is an open-source automation platform with 400+ integrations and powerful AI capabilities. Launch it locally:
```bash
cd your-workspace
mkdir -p n8n-data
export N8N_USER_FOLDER=$(pwd)/n8n-data
npx n8n
```
Visit `http://127.0.0.1:5678` and create your owner account.
![n8n initial setup](/images/articles/external/12d0e04600c5-05-n8n-setup.png)
## Build Your AI Workflow
Create a new workflow in n8n. You'll need two essential nodes:
![n8n workflow editor](/images/articles/external/e36d04ca4a5b-07-n8n-workflow-editor.png)
### Add the Webhook Trigger
Click "+" on the canvas and add a **Webhook** node. Configure it:
- **HTTP Method**: POST
- **Response Mode**: Streaming (enables real-time chat responses)
- **Authentication**: None (adjust for production)
![Webhook node configuration](/images/articles/external/03443176ae5c-08-n8n-webhook-node.png)
### Add the AI Agent
Press Tab, navigate to the "AI" category, and select **AI Agent**.
**Configure the Chat Model**: Click "Chat Model" and choose "OpenAI Chat Model". Add your credentials:
- **API Key**: Your OpenAI API key (or compatible service key)
- **Base URL**: For OpenAI alternatives like Claude, Gemini, or local models, update to your provider's endpoint
**Critical Step - Fix the Prompt Source**: By default, the AI Agent expects a Chat Trigger node, which won't work with webhooks. Here's how to fix it:
1. Find "Source for Prompt (User Message)" in the AI Agent settings
2. Change from "Connected Chat Trigger Node" to "Define below"
3. Switch to "Expression" mode
4. Enter: `{{ $json.body }}`
This expression pulls the user's message from the webhook request body.
![Configured webhook with AI Agent](/images/articles/external/621f3d526678-09-n8n-webhook-configured.png)
### Activate and Get Your Webhook URL
Save the workflow and toggle the activation switch (top-right). Switch to the "Production URL" tab and copy the webhook URL:
```
http://localhost:5678/webhook/{your-webhook-id}
```
## Connect LangBot to n8n
Back in the LangBot dashboard, navigate to **Pipelines** and click the default "ChatPipeline".
![LangBot pipelines page](/images/articles/external/ef2b2f83bd16-04-langbot-pipelines.png)
Switch to the **AI** tab and select "n8n Workflow API" from the Runner dropdown. Configure:
- **Webhook URL**: Paste your n8n production webhook URL
- **Authentication Type**: None (match your n8n webhook settings)
- **Timeout**: 120 seconds
- **Output Key**: response
Click **Save**.
## Test It Out
In the Pipeline editor, click "Debug Chat" on the left sidebar. Send a test message like "What is LangBot?"
If everything works, you'll see LangBot send the message to n8n, where the AI Agent processes it and streams back a response.
## Troubleshooting
**Error: "Expected to find the prompt in an input field called 'chatInput'"**
This means the AI Agent is still configured for a Chat Trigger node. Fix it:
1. Open the AI Agent configuration
2. Set "Source for Prompt (User Message)" to "Define below"
3. Switch to Expression mode
4. Enter: `{{ $json.body }}`
**Test Your Webhook Directly**
Verify the webhook works with curl:
```bash
curl -X POST http://localhost:5678/webhook/your-webhook-id \
-H "Content-Type: application/json" \
-d '{"body": "Hello, can you introduce yourself?"}'
```
You should receive streaming JSON with the AI's response.
## How the Integration Works
Here's the complete flow:
1. User sends a message via QQ, WeChat, Discord, Telegram, Slack, LINE, or any LangBot-supported platform
2. LangBot's Pipeline receives the message and calls the n8n Workflow API
3. n8n's Webhook node captures the request and passes it to the AI Agent
4. The AI Agent uses OpenAI, Claude, Gemini, or your configured LLM to generate a response
5. n8n streams the response back to LangBot
6. LangBot delivers the response to the user on their original platform
## Why This Combination Works
**LangBot + n8n** unlocks powerful capabilities:
1. **No-Code AI Logic**: Design conversation flows visually in n8n without touching code
2. **Multi-Platform Reach**: Deploy the same AI across QQ, WeChat, Discord, Telegram, Slack, LINE, DingTalk, and Lark simultaneously
3. **Flexible AI Models**: Swap between OpenAI GPT, Anthropic Claude, Google Gemini, Coze, Dify, local models, and more
4. **Rich Integrations**: Connect n8n's 400+ integrations - databases, APIs, Notion, Airtable, Google Sheets, Slack, and beyond
5. **Tool-Calling Agents**: AI Agent can trigger n8n tools like database queries, API calls, or custom functions
6. **Workflow Extensions**: Add preprocessing, content moderation, logging, or custom business logic
**Perfect For**:
- Enterprise customer support bots
- Knowledge base Q&A systems
- Multi-platform community management
- Task automation assistants
- Unified chat interfaces for teams
## Next Steps
Extend your bot further:
- Integrate Dify or FastGPT for advanced RAG (retrieval-augmented generation)
- Add vector database nodes (Pinecone, Qdrant, Weaviate) for knowledge retrieval
- Connect business APIs for real-time data
- Implement conversation memory and context tracking
- Add content filtering and moderation workflows
- Use Langflow or Coze for additional AI orchestration
This integration gives you the flexibility of code-based AI frameworks like LangChain with the simplicity of visual workflow builders - all while reaching users across every major messaging platform.
Ready to deploy your multi-platform AI assistant? Start with LangBot and n8n today.
en/articles/welcome.mdx
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---
title: "Introducing the LangBot Blog"
description: "Welcome to the official LangBot blog. Stay up to date with product updates, new features, and milestones."
---
This article is synchronized from the LangBot Blog. [Read the canonical version](https://langbot.app/en/blog/welcome).
Published 2026-02-17 · Author: LangBot Team
![LangBot Blog](/images/articles/source/images/covers/welcome.png)
We're excited to launch the official LangBot blog! This is where we'll share product updates, new features, milestones, and insights from the LangBot team.
## What is LangBot?
LangBot is an open-source LLM-native instant messaging bot platform. It supports multiple messaging platforms and model providers, with integrations for Dify, Alibaba Cloud Bailian, and other LLMOps platforms. LangBot also provides a WebUI for quick deployment and management.
## What to Expect
On this blog, you'll find:
- **Release Notes** - Detailed breakdowns of each new version
- **Feature Spotlights** - Deep dives into new capabilities
- **Milestones** - Project growth and community achievements
- **Guides & Tips** - Best practices for getting the most out of LangBot
## Stay Connected
- [GitHub](https://github.com/langbot-app/LangBot) - Star us and follow development
- [Documentation](https://docs.langbot.app) - Full guides and API reference
- [LangBot Home](https://langbot.app) - Main website
Stay tuned for more updates!
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---
title: "DeepSeek R1 を WeChat・Discord・Telegram に5分で接続する方法"
description: "A step-by-step guide to connecting DeepSeek R1/V3 API to WeChat (Personal/Enterprise), Discord, Telegram, and more using LangBot. No coding required."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/connect-deepseek-to-wechat).
公開日：2026-02-21 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![DeepSeek x WeChat Integration](/images/articles/source/images/covers/deepseek-wechat.png)
DeepSeek has taken the AI world by storm. Its R1 reasoning model rivals OpenAI's o1 but is open-source and significantly cheaper.
However, the official DeepSeek app currently only supports "AI Search" in WeChat, and direct integration into group chats or private workflows is limited.
**What if you could have a fully functional DeepSeek R1 bot in your WeChat groups, Discord servers, and Telegram chats right now?**
With **LangBot**, you can. And it takes less than 5 minutes.
## Why LangBot?
LangBot is an open-source, production-grade IM bot platform. Unlike simple scripts or single-platform bots, LangBot gives you:
* **Unified Platform:** Connect once, deploy to WeChat, Discord, Telegram, Slack, Lark, and more.
* **Model Agnostic:** Use DeepSeek R1, V3, Claude 3.5, GPT-4, or local Ollama models.
* **No Coding:** robust WebUI for configuration.
* **Enterprise Features:** Knowledge base (RAG), Plugin system, and multi-user management.
## Prerequisites
1. **A Server or PC:** Docker installed (VPS, local computer, or Synology/NAS).
2. **DeepSeek API Key:**
* **Official:** [platform.deepseek.com](https://platform.deepseek.com)
* **SiliconFlow (Recommended for speed):** [cloud.siliconflow.cn](https://cloud.siliconflow.cn) (Faster R1 inference).
## Step 1: Deploy LangBot
If you haven't deployed LangBot yet, run this one command:
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
Access your dashboard at `http://localhost:5300` (or your server IP).
## Step 2: Configure DeepSeek Model
1. Go to **Models** -> **Provider List**.
2. Find **DeepSeek** (or **SiliconFlow** / **OpenAI Compatible**).
3. Enter your API Key.
4. Click **Save**.
Now create a **Model Instance**:
* **Name:** `DeepSeek-R1-Bot`
* **Model:** `deepseek-reasoner` (for R1) or `deepseek-chat` (for V3).
## Step 3: Create a Chat Pipeline
LangBot uses "Pipelines" to manage bot logic.
1. Go to **Pipelines** -> **New Pipeline**.
2. Choose **Chat Pipeline**.
3. Select your `DeepSeek-R1-Bot` model.
4. (Optional) Add a System Prompt:
> "You are a helpful assistant powered by DeepSeek R1. You think deeply before answering."
## Step 4: Connect to WeChat (or any platform)
### For WeChat (Personal / Wechaty)
*Note: Personal WeChat access relies on third-party libraries and carries some risk. For business use, we recommend Enterprise WeChat (WeCom).*
1. Go to **Bots** -> **New Bot**.
2. Select **WeChat**.
3. Choose the **GeweChat** or **Wechaty** adapter (depending on your preference).
4. Click **Save**.
5. Scan the QR code that appears in the logs or UI.
### For Discord / Telegram
1. Select **Discord** or **Telegram**.
2. Paste your **Bot Token** (from Discord Developer Portal or BotFather).
3. Click **Save**.
## Step 5: Test It Out!
Open your chat app and send a message. You should see DeepSeek R1 "thinking" (if supported) and replying with high-quality reasoning.
## Advanced: Using Search & Tools
DeepSeek is great, but DeepSeek with **Internet Access** is better.
In LangBot:
1. Go to **Plugins**.
2. Install **Tavily Search** or **Google Search**.
3. Add the plugin to your DeepSeek Pipeline.
Now your bot can search the web for real-time info *before* reasoning with DeepSeek R1!
---
**Ready to build?**
[Star us on GitHub](https://github.com/langbot-app/LangBot) or [Read the Docs](https://docs.langbot.app).
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---
title: "AI Bot を Discord・Telegram・WeChat に5分でデプロイ"
description: "A step-by-step guide to deploying LangBot — the open-source platform that connects 20+ LLMs to 13+ messaging platforms with a single Docker command."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/deploy-ai-bot-in-5-minutes).
公開日：2026-02-17 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot Dashboard](/images/articles/source/images/covers/deploy-ai-bot.png)
What if you could have GPT-5, Claude, DeepSeek, and Gemini all answering questions in your Discord server, Telegram group, and WeChat — at the same time?
No API wrangling. No weeks of development. Just one Docker command.
That's **LangBot** — and it just crossed **15,000 stars** on GitHub.
## The Problem Everyone Faces
You want an AI assistant in your team's chat. Maybe for customer support on Telegram. Maybe for a coding helper in Discord. Maybe for a knowledge base bot in your company's WeChat or Lark group.
But then reality hits:
- Each platform has its own bot API, webhook format, and auth flow
- You need to handle message queuing, session management, and error recovery
- Switching LLM providers means rewriting your integration layer
- Adding RAG or tool calling is yet another project
LangBot solves all of this with a single, unified platform.
## What Makes LangBot Different
### 13+ Messaging Platforms, One Codebase
Deploy a single LangBot instance and connect it to:
**Global:** Discord, Telegram, Slack, LINE, WhatsApp
**Asia:** WeChat (Official Account), WeCom, QQ, Lark, DingTalk, Feishu, KOOK
Each platform gets its own adapter — you just fill in your bot token in the WebUI and you're live.
![LangBot Bot Management Page](/images/articles/source/images/article-mcp/13-bot-page.png)
### 20+ LLM Models, Zero Lock-in
Through **LangBot Space**, you get instant access to 20 cloud models out of the box — no API keys to manage:
- **Claude** (Opus 4.6, Sonnet 4.5, Haiku 4.5)
- **GPT** (GPT-5.2, GPT-5-mini, GPT-4.1-mini)
- **Gemini** (3 Pro, 2.5 Pro, 2.5 Flash)
- **DeepSeek** (R1, V3)
- **Grok** (4, 4.1)
- **Qwen** (3 Max)
Or add your own providers — OpenAI-compatible endpoints, Ollama for local models, any provider you want.
![Model Selection](/images/articles/source/images/article-mcp/08-model-select.png)
### Built-in Agent with Tool Calling
LangBot's Local Agent isn't just a chat wrapper — it's a full agent runtime:
- **Multi-round conversations** with configurable memory
- **Function calling / tool use** for LLM-driven actions
- **MCP (Model Context Protocol)** support for connecting to 100+ pre-built tools
- **Knowledge base (RAG)** with built-in vector search
![Pipeline AI Configuration](/images/articles/source/images/article-mcp/06-pipeline-ai.png)
### Plugin Marketplace
37+ community plugins and growing — install with one click:
- **WebSearch** — Let your bot search the web
- **AI Image Generator** — Generate images from text
- **LinkAnaly** — Auto-preview links in chat
- **ScheNotify** — Schedule reminders with natural language
- **Google Search**, **Tavily Search**, **RAGFlow Retriever**, and more
![LangBot Space Plugin Market](/images/articles/source/images/article-mcp/14-space-market.png)
## Deploy in 5 Minutes — For Real
### Step 1: Run Docker Compose
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
That's it. LangBot is now running at `http://localhost:5300`.
### Step 2: Initialize with LangBot Space
Open the WebUI and click **"Initialize with Space"**. This connects your instance to LangBot Space, giving you:
- 20 cloud models ready to use (with free credits)
- One-click plugin installation
- Managed API keys
![Initialize with Space](/images/articles/source/images/article-mcp/01-init-page.png)
### Step 3: Configure Your Pipeline
Go to **Pipelines** and edit the default `ChatPipeline`:
1. Select your model (e.g., `deepseek-v3`, `gpt-5-mini`, `claude-sonnet-4-5`)
2. Customize the system prompt
3. Optionally attach a knowledge base or enable tools
![Pipelines Page](/images/articles/source/images/article-mcp/05-pipelines.png)
### Step 4: Connect a Platform
Go to **Bots** → click **+** → choose your platform (Discord, Telegram, etc.) → enter your bot token.
Done. Your bot is live.
### Step 5: Test It
Use the built-in **Debug Chat** to test your pipeline before going live:
![Debug Chat](/images/articles/source/images/article-mcp/07-debug-chat.png)
## Real Conversations, Real Value
Here's what it looks like when LangBot is running in a QQ group — users asking technical questions and getting instant, accurate answers:
![Group Chat Demo](/images/articles/source/images/article-mcp/10-group-chat.png)
And in private chat:
![Private Chat Demo](/images/articles/source/images/article-mcp/11-private-chat.png)
## Architecture That Scales
LangBot is built for production:
- **Pipeline architecture** — each bot binds to a pipeline; pipelines handle AI logic, triggers, safety controls, and output formatting
- **Cross-process plugin isolation** — a bad plugin can't crash your bot
- **Multiple runner backends** — use LangBot's Local Agent, or connect to Dify, n8n, Langflow, Coze for complex workflows
- **Database flexibility** — SQLite for dev, PostgreSQL for production
- **Vector DB options** — Chroma, Qdrant, Milvus, pgvector, SeekDB
## Why 15,000+ Developers Choose LangBot
| Feature | LangBot | Building from Scratch |
|---------|---------|----------------------|
| Platforms | 13+ ready | Weeks per platform |
| LLM Providers | 20+ models | Manual integration |
| Agent Runtime | Built-in | Build your own |
| RAG | Native + external | Separate project |
| Plugin System | Marketplace | DIY |
| Deployment | `docker compose up` | Days of setup |
| WebUI | Included | Build your own |
## Get Started
- **GitHub:** [github.com/langbot-app/LangBot](https://github.com/langbot-app/LangBot) — give us a star!
- **Documentation:** [docs.langbot.app](https://docs.langbot.app)
- **Plugin Market:** [space.langbot.app](https://space.langbot.app)
```bash
git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
```
Your AI bot empire starts with one command.
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---
title: "Dify Agent を Discord・Telegram・Slack で動かす"
description: "Finally Got My Dify Agent Working in Discord, Telegram and Slack"
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/dify-agent-discord-telegram-slack).
公開日：2025-12-11 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![Finally Got My Dify Agent Working in Discord, Telegram and Slack](/images/articles/source/images/covers/dify-agent.png)
Want your Dify Agent to break free from the browser and chat directly in WeChat, QQ, or Telegram? Combine LangBot with Dify and you can set it up in under 10 minutes.
## Why This Combo?
[**LangBot**](https://github.com/langbot-app/LangBot) is the most powerful open-source multi-platform chatbot framework available. It supports major Chinese IMs (QQ, WeChat, Feishu, DingTalk) and international platforms (Telegram, Discord, Slack, LINE). Its core strength is connecting AI backends to messaging apps.
**Dify** is one of the hottest AI app development platforms, offering visual Agent orchestration, 50+ built-in tools, and RAG knowledge bases. Together, they let you build a multi-platform AI assistant fast.
## Deploy LangBot
Three commands with uvx:
```bash
mkdir langbot-instance
cd langbot-instance
uvx langbot@latest
```
Visit `http://127.0.0.1:5300` and register an admin account:
![LangBot Dashboard](/images/articles/external/c6d52212ba91-02-langbot-dashboard.png)
## Create a Dify Agent
Log in to [cloud.dify.ai](https://cloud.dify.ai) and create a new app. Choose **Agent** type - unlike basic chat apps, Agents can reason autonomously and call tools for complex tasks.
![Select Agent Type](/images/articles/external/3e5b1d8ec6ec-07-dify-create-app-types.png)
### Configure the Agent
In the orchestration interface, set up:
**System Instructions** - Define the Agent's role:
```
You are an intelligent assistant that helps users complete various tasks. You have the following capabilities:
1. Answer various user questions
2. Query weather information
3. Retrieve webpage content
Please always maintain a friendly and professional attitude. If you need to use tools to get information, proactively call the relevant tools.
```
**Add Tools** - Pick from Dify's 50+ built-in tools, like Weather and Web Scraper:
![Add Tools](/images/articles/external/9066d99b172c-11-dify-tools-panel.png)
**Select Model** - Supports GPT, Claude, Gemini, DeepSeek, and other major LLMs.
Final configuration:
![Configuration Complete](/images/articles/external/be278ca435e8-12-dify-agent-configured.png)
### Get the API Key
Click "Publish", then go to "Access API" to create a key:
![Create API Key](/images/articles/external/471b316018e5-13-dify-api-key-created.png)
Save the API key and server address `https://api.dify.ai/v1`.
## Connect Dify to LangBot
Back in LangBot, go to Pipelines > ChatPipeline > AI Capability:
![AI Capability Config](/images/articles/external/8f4b3820ed5b-14-langbot-pipeline-ai-capability.png)
Select **Dify Service API** as the Runner:
![Select Dify Runner](/images/articles/external/efa6fcae9c58-15-langbot-runner-dropdown.png)
Enter the configuration:
- **Base URL**: `https://api.dify.ai/v1`
- **API Key**: The key you just created
- **App Type**: Chat (including Chatflow)
![Enter Configuration](/images/articles/external/74f71a8bf1dc-17-langbot-dify-api-key-entered.png)
Save and click "Debug Conversation" to test:
![Test Conversation](/images/articles/external/9999c12907eb-20-langbot-chat-test.png)
If the Agent responds correctly and calls tools, the integration is working.
## Why This Architecture Rocks
- **Decoupled Design**: Agent capabilities live in Dify, LangBot handles message routing - each does what it's best at
- **One Config, Multi-Platform**: Same Agent serves QQ, WeChat, Telegram, and more simultaneously
- **Easy to Extend**: Add RAG knowledge bases, Workflow orchestration, or more tools in Dify later
## Next Steps
- Configure messaging platforms in LangBot (QQ bot, WeChat personal account, etc.)
- Add more tools in Dify (database queries, API calls)
- Try Dify Chatflow for complex conversation flows
If you're using n8n, FastGPT, Coze, or Langflow, LangBot supports those too.
---
**Resources**
- [LangBot Docs](https://docs.langbot.app) | [GitHub](https://github.com/langbot-app/LangBot)
- [Dify Docs](https://docs.dify.ai) | [Cloud Platform](https://cloud.dify.ai)
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---
title: "LangBot 4.6.0 外部ナレッジベース入門：Dify と連携した RAG 会話"
description: "LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations"
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/dify-langbot-rag-knowledge-base).
公開日：2025-12-02 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot 4.6.0 External Knowledge Base Tutorial: Integrating Dify with LangBot for RAG-powered Conversations](/images/articles/source/images/covers/dify-rag.png)
LangBot 4.6.0 introduces external knowledge base functionality, allowing users to integrate external knowledge retrieval services such as Dify and RAGFlow into conversation pipelines. This tutorial demonstrates how to combine Dify knowledge base with LangBot to enable intelligent conversations based on domain-specific knowledge.
## Feature Overview
The external knowledge base feature enables LangBot to:
- Connect to various external knowledge retrieval services (Dify, RAGFlow, etc.)
- Provide professional answers based on domain-specific knowledge
- Flexibly extend knowledge retrieval capabilities through the plugin system
- Configure conveniently through WebUI without manual configuration file editing
## Prerequisites
Before starting, you need:
1. Deploy LangBot 4.6.0 or higher
2. Have a Dify account (register at https://cloud.dify.ai)
3. Configure a conversation model (this tutorial uses claude-opus-4-1-20250805)
## Step 1: Deploy LangBot
### 1.1 View LangBot Repository
First, visit the [LangBot GitHub repository](https://github.com/langbot-app/LangBot) to understand the project. LangBot is a production-grade instant messaging bot development platform supporting multiple messaging platforms and LLM services.
### 1.2 Learn About External Knowledge Base Feature
Visit the [official LangBot documentation](https://docs.langbot.app) to view the external knowledge base usage instructions. The documentation provides detailed explanations on how to build built-in knowledge bases and connect to external knowledge bases.
### 1.3 Start LangBot
Use the `uvx langbot` command to quickly start LangBot, then visit http://127.0.0.1:5300 for initialization. Fill in your email and password to complete registration, then log in.
### 1.4 Dashboard After Login
After successfully logging in, you will see the LangBot WebUI dashboard.
## Step 2: Configure Conversation Model
Before using the knowledge base, you need to configure a conversation model.
### 2.1 Navigate to Model Configuration Page
Click "Models" in the left navigation bar to enter the model configuration page.
### 2.2 Add New Model
Click the "+" button to open the model configuration dialog.
### 2.3 Fill in Model Information
Fill in the following information:
- **Model Name**: claude-opus-4-1-20250805
- **Model Provider**: New API
- **Request URL**: Your API endpoint
- **API Key**: Your API Key
- **Abilities**: Check Vision Ability and Function Call
Click "Submit" to save the configuration.
## Step 3: Create Knowledge Base in Dify
### 3.1 Log in to Dify Platform
Visit https://cloud.dify.ai and log in to your account.
### 3.2 Navigate to Knowledge Page
Click "Knowledge" in the top navigation bar to enter the knowledge base management page.
### 3.3 Create New Knowledge Base
Click "Create Knowledge" to start creating a knowledge base.
### 3.4 Select Data Source
You can choose from the following methods to import data:
- **Import from file**: Upload document files
- **Sync from Notion**: Sync from Notion
- **Sync from website**: Crawl website content
This tutorial selects "Import from file" and uploads prepared LangBot documentation.
### 3.5 Upload Document
After uploading the document, the system will display file information. Click "Next" to proceed.
### 3.6 Configure Document Processing Parameters
On the document processing page, you can configure chunk settings, index method, and retrieval settings:
- **Chunk Settings**: General mode, maximum chunk length 1024 characters
- **Index Method**: High Quality (uses embedding model)
- **Retrieval Setting**: Vector Search
Click "Save & Process" to start processing the document.
### 3.7 Knowledge Base Processing
The system starts processing the document and generating vector embeddings.
### 3.8 Embedding Completed
After processing is complete, the knowledge base is ready to use.
## Step 4: Get Dify API Information
### 4.1 Open Service API Panel
Click the "Service API" button in the bottom right. Record the following information:
- **Service API Endpoint**: https://api.dify.ai/v1
- **Dataset ID**: Get from the URL
### 4.2 Get API Key
Click the "API Key" button to view the API key. Record your API Key for use in subsequent configuration.
## Step 5: Configure External Knowledge Base in LangBot
### 5.1 Navigate to Knowledge Page
In the LangBot WebUI, click "Knowledge" in the left navigation bar.
### 5.2 Switch to External Knowledge Base Tab
Click the "External" tab.
### 5.3 Add External Knowledge Base
Click the "+" button to open the add external knowledge base dialog. In this dialog, you need to:
1. **Knowledge Base Name**: Give the knowledge base a name
2. **Knowledge Base Description**: Add a description (optional)
3. **Retriever**: Select a knowledge retriever plugin
**Note**: Using an external knowledge base requires installing the corresponding knowledge retriever plugin first. You can search for and install the Dify knowledge retriever plugin in the [plugin marketplace](https://space.langbot.app/market?category=KnowledgeRetriever).
After installing the plugin, select the corresponding plugin from the Retriever dropdown, then fill in the configuration information obtained from Dify (API Endpoint, API Key, Dataset ID).
## Step 6: Configure Pipeline
After configuring the external knowledge base, you need to enable it in the pipeline:
1. Navigate to the "Pipelines" page
2. Edit or create a new pipeline
3. On the "AI Capabilities" page, select "Built-in Agent" as the runner
4. In the knowledge base selection, check the external knowledge base you just configured
5. Save the pipeline configuration
## Step 7: Test the Effect
Test in the pipeline's "Conversation Testing" page, or bind the pipeline to a bot for actual conversation testing. When users ask questions, LangBot will automatically retrieve relevant content from the Dify knowledge base and generate answers combined with the retrieved knowledge.
## Frequently Asked Questions
### 1. How to Install Knowledge Retriever Plugins?
Visit the [LangBot plugin marketplace](https://space.langbot.app/market), search for "Knowledge Retriever" or "Dify", find the corresponding plugin and click install.
### 2. Which External Knowledge Retrieval Services Are Supported?
LangBot currently supports multiple external knowledge retrieval services through the plugin system, including:
- Dify
- RAGFlow
- Other custom knowledge retrieval services
Specific supported services can be viewed in the plugin marketplace.
### 3. What's the Difference Between External and Built-in Knowledge Bases?
- **Built-in Knowledge Base**: Data is stored locally in LangBot, with LangBot handling vectorization and retrieval
- **External Knowledge Base**: Data is stored in external services (like Dify), with retrieval performed through API calls
The advantage of external knowledge bases is the ability to leverage the capabilities of professional LLMOps platforms, such as Dify's advanced document processing and multiple retrieval strategies.
### 4. Can Multiple Knowledge Bases Be Used Simultaneously?
Yes. In the pipeline configuration, you can check multiple knowledge bases (built-in or external) simultaneously, and LangBot will comprehensively utilize the content from these knowledge bases to generate answers.
## Summary
Through this tutorial, you learned how to:
1. Create a knowledge base and upload documents on the Dify platform
2. Obtain Dify API key and related configuration information
3. Configure a conversation model in LangBot
4. Add an external knowledge base in LangBot
5. Configure pipelines to use external knowledge bases
The external knowledge base feature provides LangBot with more powerful and flexible knowledge management capabilities. Combined with professional LLMOps platforms like Dify, you can build more intelligent and professional conversational bots.
## Related Resources
- [LangBot Official Website](https://langbot.app)
- [LangBot Documentation](https://docs.langbot.app)
- [LangBot GitHub](https://github.com/langbot-app/LangBot)
- [Dify Official Website](https://dify.ai)
- [LangBot Plugin Marketplace](https://space.langbot.app/market)
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---
title: "LangBot 記事"
description: "LangBot Blog の製品アップデート、技術解説、チュートリアル、連携事例、お知らせをまとめています。"
---
LangBot Blog の製品アップデート、技術解説、チュートリアル、連携事例、お知らせをまとめています。
## 製品アップデート
- [新しい LangBot Cloud が公開—オープンソース版もマルチユーザー Workspace に対応](/ja/articles/langbot-cloud-multi-user-workspaces) — 2026-08-25
## エンジニアリング
- [LangBot v4.10.0：Agent Sandbox と Skills](/ja/articles/langbot-v4100-sandbox-skills) — 2026-06-04
- [LangBot v4.9.0：完全プラグイン化された RAG アーキテクチャ](/ja/articles/langbot-v490-rag-plugin-architecture) — 2026-03-10
- [LangBot プラグインシステム詳解：プロセス分離・イベント駆動・コンポーネント設計](/ja/articles/langbot-plugin-system-deep-dive) — 2026-02-23
## チュートリアルと連携
- [LangTARS：Dify・n8n と連携するオープンソース PC 操作 Agent](/ja/articles/langtars-remote-pc-control-dify-n8n) — 2026-03-23
- [DeepSeek R1 を WeChat・Discord・Telegram に5分で接続する方法](/ja/articles/connect-deepseek-to-wechat) — 2026-02-21
- [AI Bot を Discord・Telegram・WeChat に5分でデプロイ](/ja/articles/deploy-ai-bot-in-5-minutes) — 2026-02-17
- [Dify Agent を Discord・Telegram・Slack で動かす](/ja/articles/dify-agent-discord-telegram-slack) — 2025-12-11
- [Langflow のドラッグ＆ドロップでマルチプラットフォーム AI Bot を構築](/ja/articles/langflow-drag-and-drop-ai-bot) — 2025-12-05
- [n8n と LangBot でマルチプラットフォーム AI Chatbot を構築](/ja/articles/n8n-multi-platform-ai-chatbot) — 2025-12-03
- [LangBot 4.6.0 外部ナレッジベース入門：Dify と連携した RAG 会話](/ja/articles/dify-langbot-rag-knowledge-base) — 2025-12-02
## お知らせ
- [LangBot Blog のご紹介](/ja/articles/welcome) — 2026-02-17
ja/articles/langbot-cloud-multi-user-workspaces.mdx
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---
title: "新しい LangBot Cloud が公開—オープンソース版もマルチユーザー Workspace に対応"
description: "LangBot は LLM とメッセージングアプリを接続するオープンソース AI Bot プラットフォームです。新しい Cloud とオープンソース版はマルチユーザー Workspace、招待、ロール権限に対応しました。"
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/ja/blog/langbot-cloud-multi-user-workspaces).
公開日：2026-08-25 · 著者：LangBot Team
![LangBot マルチユーザー Workspace ダッシュボード](/images/articles/source/images/cloud-multi-user/oss-dashboard.png)
新しい [LangBot Cloud](https://cloud.langbot.app) を公開しました。今回まず整理したのは、複数人で LangBot を使うときに、誰がどのリソースへアクセスできるかという実務的な問題です。
同じマルチユーザーの基盤は、オープンソース版でも利用できます。Cloud とセルフホスト版では運用方法が異なりますが、メンバー、招待、権限の基本的な考え方は共通になりました。
## LangBot とは
LangBot は、複数のメッセージングアプリで AI Bot を構築・運用するためのオープンソースプラットフォームです。1つのダッシュボードで Bot、メッセージ Pipeline、ナレッジベース、モデル、拡張機能を管理し、LINE、Slack、Discord、Telegram、QQ、WeChat、WeCom、Lark、DingTalk、KOOK に接続できます。Satori と Matrix にも対応し、Matrix ブリッジを使えば WhatsApp、Signal、Messenger、Mattermost、Google Chat、IRC、XMPP、Zulip などにも展開できます。
モデルは特定の事業者に限定されません。OpenAI、Anthropic Claude、DeepSeek、Google Gemini、xAI Grok、Moonshot Kimi、Zhipu GLM に接続でき、Ollama と LM Studio ではローカルモデルも利用できます。SiliconFlow、Alibaba Cloud Model Studio（百錬）、Volcengine Ark、ModelScope、GiteeAI などのゲートウェイに加え、Dify と MCP を使った既存の Agent ワークフローにも対応します。
チームは複数のチャットサービス上の AI アシスタントを1か所で管理し、用途に応じてクラウドモデル、ローカルモデル、既存の Agent ワークフローを使い分けられます。
![新しい LangBot Cloud のログイン画面](/images/articles/source/images/cloud-multi-user/cloud-login.png)
## Cloud：自分の Workspace から始める
LangBot Cloud は LangBot Account でログインします。初回利用時には個人用 Workspace が用意され、招待を受けてチームの Workspace に参加できます。複数の Workspace に所属している場合は、画面上で作業先を切り替えられます。
Bot、Pipeline、モデル、ナレッジベース、拡張機能は、現在選択している Workspace に属します。Workspace を切り替えても、別のチームの設定やデータが混ざることはありません。
ホスティング、更新、実行環境は引き続き Cloud 側が管理します。サーバーを保守せず、チームですぐに Bot の設定を始めたい場合に向いています。
## 管理者アカウントを共有せず、メンバーを招待
Owner、またはメンバー管理権限を持つロールは、一度だけ使える招待を発行できます。招待された人は自分のアカウントで参加し、割り当てられたロールで Workspace を利用します。
![Workspace 名を匿名化した LangBot Cloud の招待画面](/images/articles/source/images/cloud-multi-user/cloud-invitation.png)
1つの管理者パスワードを共有するより、長期運用に適した形です。利用者ごとに独立した本人性があり、担当に合わせて権限を分けられます。
現在は5つの固定ロールを用意しています。
- **Owner**：Workspace の所有者。最上位の権限とメンバー管理を担当します。
- **Admin**：メンバーと大部分の Workspace 設定を管理します。
- **Developer**：Bot、Pipeline、モデル、拡張機能を設定します。
- **Operator**：日常的な運用操作を担当します。
- **Viewer**：読み取り専用で確認します。
## オープンソース版：1インスタンス、1 Workspace、複数ユーザー
セルフホスト版がマルチ Workspace の SaaS になるわけではありません。1つの LangBot インスタンスは引き続き1つの Workspace に対応しますが、その Workspace に複数のユーザーを追加できます。
初期設定で作成したアカウントが Owner になります。その後、ほかのメンバーを招待できます。SMTP を設定している場合は招待メールを送信でき、メール設定がない場合は一度だけ表示される招待リンクを信頼できる手段で共有できます。
参加後に使うのは、これまでと同じ LangBot のダッシュボードです。異なる点は、ログインした全員を管理者として扱うのではなく、Workspace とロールの両方に基づいてアクセスを確認することです。
## どちらを選ぶべきか
| | LangBot Cloud | オープンソース・セルフホスト版 |
|---|---|---|
| 導入と保守 | LangBot がホスト・保守 | サーバーとデータを自分で管理 |
| Workspace | 複数を所有・参加可能 | 1インスタンスにつき1つ |
| メンバーと招待 | 対応 | 対応 |
| 固定ロール権限 | 対応 | 対応 |
| 向いている用途 | チームですぐに使い始める | プライベート環境とインフラを自分で管理する |
ホスト版を使う場合は [cloud.langbot.app](https://cloud.langbot.app) へ。実行環境とデータを自分のサーバーに置く場合は、[GitHub](https://github.com/langbot-app/LangBot) からオープンソース版を導入できます。
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---
title: "LangBot プラグインシステム詳解：プロセス分離・イベント駆動・コンポーネント設計"
description: "A source-level deep dive into LangBot 4.0's plugin architecture — process-isolated communication, event-driven hooks, a component-based extension model, and comparisons with Dify and MCP."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/langbot-plugin-system-deep-dive).
公開日：2026-02-23 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot Plugin System](/images/articles/source/images/covers/plugin-system.png)
Most chatbot frameworks call their "plugin system" a glorified dynamic import of Python modules. LangBot 4.0 takes a harder but more principled approach — **every plugin runs in its own process**, communicating with the host through a structured JSON-RPC-style protocol.
This article dissects the system from source code, end to end.
## Overall Architecture: A Three-Layer Process Model
LangBot's plugin system consists of three cooperating process layers:
![LangBot Plugin System Architecture](/images/articles/source/images/plugin-system/architecture.png)
**Each layer has a distinct responsibility:**
1. **LangBot Main Process**: Runs business logic (message pipelines, platform adapters, model invocations), connects to Runtime via `PluginRuntimeConnector`.
2. **Plugin Runtime**: The orchestration layer — discovers, launches, and manages all plugin subprocesses, routes requests from the main process to the appropriate plugin.
3. **Plugin Subprocesses**: Each plugin runs in its own Python process, communicating with Runtime via stdio pipes.
### Why Three Layers Instead of Two?
The intuitive design would have the main process manage plugin processes directly. LangBot adds the Runtime layer for **deployment flexibility**:
- **Local development**: Main process spawns Runtime as a child via stdio (zero config)
- **Docker production**: Runtime runs as a separate container, connected via WebSocket
- **Windows compatibility**: Since Windows asyncio has incomplete stdio subprocess support, it automatically falls back to WebSocket
The same codebase — no config changes — adapts from development to production.
## Communication Protocol: JSON-RPC-Style Request/Response
All cross-process communication runs on a unified protocol layer. The core data structures are minimal:
```python
# Request
class ActionRequest(pydantic.BaseModel):
seq_id: int # Sequence number for matching request/response
action: str # Action name
data: dict # Payload
# Response
class ActionResponse(pydantic.BaseModel):
seq_id: int
code: int # 0 = success
message: str
data: dict
chunk_status: str # "continue" | "end" (streaming support)
```
The `Handler` class is the system's core abstraction, acting as both RPC client and server:
```python
class Handler:
async def call_action(self, action, data, timeout=15.0) -> dict:
"""Actively call an action provided by the peer, wait for response"""
self.seq_id_index += 1
request = ActionRequest.make_request(self.seq_id_index, action.value, data)
future = asyncio.Future()
self.resp_waiters[self.seq_id_index] = future
await self.conn.send(json.dumps(request.model_dump()))
response = await asyncio.wait_for(future, timeout)
return response.data
@action(SomeAction.DO_SOMETHING)
async def handle_something(data: dict) -> ActionResponse:
"""Register an action for the peer to call"""
return ActionResponse.success({"result": "ok"})
```
**Key design points:**
- `seq_id`-based request/response matching enables full-duplex concurrent calls
- Streaming responses via `chunk_status` for long-running operations like command execution
- Large messages auto-chunk (stdio: 16KB / WebSocket: 64KB per chunk)
- File transfer uses a separate base64 chunking mechanism
### Action Enums: Clear API Contracts
The system defines all cross-process calls through four enum groups:
```python
# Plugin → Runtime (plugin-initiated requests)
class PluginToRuntimeAction:
REGISTER_PLUGIN = "register_plugin"
SEND_MESSAGE = "send_message" # Send message to a platform
INVOKE_LLM = "invoke_llm" # Call an LLM
SET_PLUGIN_STORAGE = "set_plugin_storage" # Persistent storage
# ...
# Runtime → Plugin (runtime-dispatched commands)
class RuntimeToPluginAction:
INITIALIZE_PLUGIN = "initialize_plugin"
EMIT_EVENT = "emit_event"
CALL_TOOL = "call_tool"
EXECUTE_COMMAND = "execute_command"
SHUTDOWN = "shutdown"
# ...
# LangBot Main → Runtime
class LangBotToRuntimeAction:
INSTALL_PLUGIN = "install_plugin"
EMIT_EVENT = "emit_event"
LIST_TOOLS = "list_tools"
# ...
# Runtime → LangBot Main
class RuntimeToLangBotAction:
GET_PLUGIN_SETTINGS = "get_plugin_settings"
SET_BINARY_STORAGE = "set_binary_storage"
# ...
```
This makes API boundaries crystal clear — what a plugin can and cannot do is defined entirely by these enums.
## Plugin Lifecycle
A plugin goes through these stages from installation to execution:
### 1. Discovery
On startup, Runtime scans the `data/plugins/` directory:
```python
async def launch_all_plugins(self):
for plugin_path in glob.glob("data/plugins/*"):
if not os.path.isdir(plugin_path):
continue
task = self.launch_plugin(plugin_path)
self.plugin_run_tasks.append(task)
```
Directory names follow the `{author}__{name}` convention, each containing a `manifest.yaml` and plugin code.
### 2. Launch
Runtime spawns an independent subprocess for each plugin:
```python
async def launch_plugin(self, plugin_path: str):
python_path = sys.executable
args = ["-m", "langbot_plugin.cli.__init__", "run", "-s", "--prod"]
ctrl = StdioClientController(
command=python_path,
args=args,
working_dir=plugin_path, # Each plugin runs in its own directory
)
await ctrl.run(new_plugin_connection_callback)
```
**Key detail**: The subprocess working directory is set to the plugin's own directory — natural filesystem isolation.
### 3. Registration
After starting, the plugin process actively registers itself with Runtime:
```python
# Runtime-side registration handler
async def register_plugin(self, handler, container_data, debug_plugin=False):
plugin_container = PluginContainer.from_dict(container_data)
# Fetch plugin settings from the main process
plugin_settings = await self.context.control_handler.call_action(
RuntimeToLangBotAction.GET_PLUGIN_SETTINGS, {...}
)
# Initialize the plugin (send config)
await handler.initialize_plugin(plugin_settings)
# Store the plugin container
self.plugins.append(plugin_container)
```
### 4. Running
Once in `INITIALIZED` state, the plugin can receive events, tool calls, and command executions.
### 5. Shutdown
```python
async def shutdown_plugin(self, plugin_container):
# 1. Notify the plugin to shut down gracefully
await plugin_container._runtime_plugin_handler.shutdown_plugin()
# 2. Close the communication connection
await plugin_container._runtime_plugin_handler.conn.close()
# 3. Kill the subprocess
if handler.stdio_process is not None:
handler.stdio_process.kill()
await asyncio.wait_for(handler.stdio_process.wait(), timeout=2)
```
## Component System: Four Extension Types
A LangBot plugin isn't a single hook function — it's a **component container**. A single plugin can provide multiple component types simultaneously:
### EventListener
The most fundamental extension — listen for events in the message pipeline:
```python
from langbot_plugin.api.definition.components.common.event_listener import EventListener
from langbot_plugin.api.entities.events import PersonNormalMessageReceived
from langbot_plugin.api.entities.context import EventContext
class MyListener(EventListener):
@EventListener.handler(PersonNormalMessageReceived)
async def on_person_message(self, ctx: EventContext):
event = ctx.event
# Modify the user message before it reaches the LLM
event.user_message_alter = "Answer in poetry: " + event.text_message
# Or block further processing
# ctx.prevent_default()
# ctx.prevent_postorder()
```
Supported events cover the full message lifecycle:
| Event | Trigger |
|-------|---------|
| `PersonMessageReceived` | Any private message received |
| `GroupMessageReceived` | Any group message received |
| `PersonNormalMessageReceived` | Private message deemed processable |
| `GroupNormalMessageReceived` | Group message deemed processable |
| `NormalMessageResponded` | LLM response completed |
| `PromptPreProcessing` | Prompt preprocessing stage |
Event propagation supports two interruption modes:
- `prevent_default()`: Skip default behavior (e.g., skip the LLM call)
- `prevent_postorder()`: Stop subsequent plugins from running
### Tool
Tools for LLM Function Calling:
```python
from langbot_plugin.api.definition.components.tool.tool import Tool
class WeatherTool(Tool):
async def call(self, params: dict, session, query_id: int) -> str:
city = params.get("city", "Beijing")
# Call weather API...
return f"{city}: Sunny, 25°C"
```
Tool metadata (name, description, parameter schema) is defined in a companion YAML manifest file. LangBot automatically converts this into the Function definition that LLMs understand.
### Command
User-triggered commands via `!command`, with subcommand support:
```python
from langbot_plugin.api.definition.components.command.command import Command
class MyCommand(Command):
def __init__(self):
super().__init__()
@self.subcommand("hello", help="Say hello")
async def hello(self, ctx):
yield CommandReturn(text="Hello from plugin!")
@self.subcommand("status", help="Show status")
async def status(self, ctx):
yield CommandReturn(text="All systems operational.")
```
Command results are returned via `AsyncGenerator`, providing natural streaming output.
### KnowledgeRetriever
A multi-instance component for connecting external knowledge bases:
```python
from langbot_plugin.api.definition.components.knowledge_retriever.retriever import KnowledgeRetriever
class MyRetriever(KnowledgeRetriever):
async def retrieve(self, context) -> list:
results = await self.search_external_db(context.query)
return [RetrievalResultEntry(content=r) for r in results]
```
KnowledgeRetriever is a **polymorphic component** — a single retriever class can spawn multiple instances, each with independent configuration. This allows users to connect multiple different external knowledge bases.
## SDK API: What Plugins Can Do
Plugins gain rich capabilities through the `LangBotAPIProxy` inherited by `BasePlugin`:
```python
class LangBotAPIProxy:
# Message operations
async def send_message(self, bot_uuid, target_type, target_id, message_chain)
# Model invocation
async def get_llm_models(self) -> list[str]
async def invoke_llm(self, model_uuid, messages, funcs=[], extra_args={})
# Persistent storage (plugin-level isolation)
async def set_plugin_storage(self, key, value: bytes)
async def get_plugin_storage(self, key) -> bytes
# Workspace storage (cross-plugin shared)
async def set_workspace_storage(self, key, value: bytes)
async def get_workspace_storage(self, key) -> bytes
# System info
async def get_langbot_version(self) -> str
async def get_bots(self) -> list[str]
async def list_plugins_manifest(self) -> list
```
**The storage API design is worth noting**: Two levels of KV storage — `plugin_storage` (plugin-private) and `workspace_storage` (globally shared), storing data as bytes (base64-serialized in transit). Simple but flexible enough.
## Event Dispatch Mechanism
The complete path from main process to plugin:
![Event Dispatch Flow](/images/articles/source/images/plugin-system/event-dispatch.png)
Key source code:
```python
async def emit_event(self, event_context, include_plugins=None):
for plugin in self.plugins:
if plugin.status != RuntimeContainerStatus.INITIALIZED:
continue
if not plugin.enabled:
continue
# Pipeline-level plugin filtering
if include_plugins is not None:
plugin_id = f"{plugin.manifest.metadata.author}/{plugin.manifest.metadata.name}"
if plugin_id not in include_plugins:
continue
resp = await plugin._runtime_plugin_handler.emit_event(
event_context.model_dump()
)
event_context = EventContext.model_validate(resp["event_context"])
# Plugin requested propagation stop
if event_context.is_prevented_postorder():
break
return emitted_plugins, event_context
```
The `include_plugins` parameter enables **pipeline-level plugin binding** — different message processing pipelines can use different subsets of plugins.
## Installation & Distribution
Plugins support three installation sources:
1. **Local upload**: `.lbpkg` files (actually zip archives containing manifest.yaml and code)
2. **Marketplace**: Install from LangBot Space online
3. **GitHub Release**: Download from a GitHub repository's Release assets
The installation flow:
```python
async def install_plugin(self, source, install_info):
yield {"current_action": "downloading plugin package"}
# 1. Fetch and extract the plugin package (unzip)
plugin_path, author, name, version = await self.install_plugin_from_file(plugin_file)
yield {"current_action": "installing dependencies"}
# 2. Install dependencies (pip install -r requirements.txt)
pkgmgr_helper.install_requirements(requirements_file)
yield {"current_action": "initializing plugin settings"}
# 3. Initialize configuration
await self.context.control_handler.call_action(
RuntimeToLangBotAction.INITIALIZE_PLUGIN_SETTINGS, {...}
)
yield {"current_action": "launching plugin"}
# 4. Launch the plugin process
task = self.launch_plugin(plugin_path)
```
The entire process reports progress via `AsyncGenerator`, enabling real-time installation status in the frontend.
## Developer Experience
The SDK provides a complete developer toolchain:
```bash
# Initialize a new plugin
lbp init
# Add a component
lbp component add
# Run locally for debugging
lbp run
# Package for publication
lbp publish
```
**Debug mode** has a particularly clever design: the developer's plugin connects to the running Runtime via WebSocket (instead of stdio), meaning you can hot-reload plugin code without restarting LangBot. Debug plugins are specially marked in the UI and protected from accidental deletion.
## Comparisons with Other Systems
### vs Dify Plugins
Dify's plugin system (`dify-plugin-daemon`) shares the process isolation philosophy with LangBot, but the focus differs:
- **Dify**: Plugins extend workflow node types (Tool, Model, Extension) — designed for AI application orchestration
- **LangBot**: Plugins extend the message processing pipeline (Event, Tool, Command, KnowledgeRetriever) — designed for instant messaging scenarios
LangBot's `EventListener` component provides a capability Dify lacks — injecting logic at any stage of message processing.
### vs MCP (Model Context Protocol)
MCP is a standardized protocol for AI tool invocation. LangBot's Tool component and MCP services overlap functionally, but serve different purposes:
- **MCP**: A universal "AI calls external capabilities" protocol, usable by any LLM application
- **LangBot Tool**: Deeply integrated with message processing context, with access to session info, user identity, etc.
In practice, LangBot natively supports MCP — users can configure MCP servers directly in LangBot without writing plugins. LangBot's Tool component is for scenarios requiring access to LangBot's internal context.
## Design Decisions Explained
**Why process isolation instead of threads/coroutines?**
- Plugin code quality is unpredictable; a segfault shouldn't crash the entire service
- Dependency isolation: different plugins may depend on different versions of the same library
- Resource control: you can set per-plugin process resource limits
**Why JSON instead of Protobuf/MessagePack?**
- Debug-friendly: developers can directly read communication logs
- Natively supported in Python, no extra dependencies
- The performance bottleneck isn't serialization (plugin call frequency is far below database queries)
**Why stdio over WebSocket by default?**
- stdio requires no network stack — lower latency
- Simpler process lifecycle management (child processes auto-cleanup when parent exits)
- WebSocket is only used where stdio isn't supported (Docker, Windows)
## Conclusion
LangBot's plugin system is a **production-grade, process-isolated, event-driven component framework for extensibility**.
Its core design principles:
1. **Safety first**: Process isolation ensures plugins can't destabilize the main service
2. **Deployment flexibility**: Dual stdio/WebSocket modes adapt to all environments
3. **Developer-friendly**: Complete SDK, CLI, and debug support
4. **Component-based**: Four component types cover the major extension needs
If you're interested in developing LangBot plugins, start with the [plugin development docs](https://docs.langbot.app/en/plugin/dev/tutor), or browse existing plugins on the [marketplace](https://space.langbot.app/market) for inspiration.
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title: "LangBot v4.10.0：Agent Sandbox と Skills"
description: "LangBot v4.10.0 brings a real sandbox (Box Runtime) and an on-demand Skills system to the built-in Agent. It can now run commands and edit files in an isolated environment, and pull in specialized know-how exactly when a task needs it."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/langbot-v4100-sandbox-skills).
公開日：2026-06-04 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot v4.10.0 Agentic Sandbox & Skills](/images/articles/source/images/covers/sandbox-skills.png)
LangBot v4.10.0 is our biggest Agent update yet. It ships two features that point in the same direction — **letting the built-in Agent work like a real developer**:
1. **Sandbox**: the Agent now runs in a genuinely isolated environment where it can execute commands and work with files.
2. **Skills**: the Agent can load specialized know-how on demand, instead of cramming everything into its context.
Both capabilities were led by community contributor [@huanghuoguoguo](https://github.com/huanghuoguoguo). Let's dig in.
> **New to LangBot?** [LangBot](https://github.com/langbot-app/LangBot) is an open-source LLM bot platform (15k+ GitHub stars) that lets you deploy AI chatbots across **Discord, Telegram, WeChat (个人微信 / 企业微信), Slack, Lark (Feishu), DingTalk, QQ** and more — all from a single instance. It connects 20+ large language models (OpenAI GPT, Anthropic Claude, Google Gemini, DeepSeek, and others) to 13+ messaging platforms, with built-in Agent, RAG knowledge bases, MCP support, and a plugin ecosystem. You can self-host it with one Docker command or run it managed on [LangBot Cloud](https://space.langbot.app/cloud).
## 📦 Sandbox: a real workspace for your Agent
Until now, LangBot's built-in Agent could only call predefined tools. It couldn't "open a terminal and run a command," nor "write an intermediate result to a file and read it back later" — which is exactly what human developers do all day.
v4.10.0 introduces the new **Box Runtime**. It gives the Agent an isolated sandbox environment with six native tools built in:
| Tool | Purpose |
|------|---------|
| `exec` | Run shell commands |
| `read` | Read file contents |
| `write` | Write files |
| `edit` | Make targeted edits to files |
| `glob` | Find files by pattern |
| `grep` | Search inside file contents |
With this toolkit, the Agent can work like a developer: run a script, save the output to a file, read it back to analyze, and post-process the result. All of it happens inside an isolated environment that never touches the host.
![LangBot sandbox in action](/images/articles/source/images/sandbox-skills/sandbox.png)
### Pluggable backends
The sandbox's execution backend is **pluggable**, and LangBot selects one automatically based on your deployment:
- **Docker**: spins up a dedicated `langbot_box` container that talks to the main process over WebSocket. Great for containerized deployments.
- **nsjail**: lightweight isolation based on Linux namespaces, no extra container needed. Great for bare-metal or single-container setups.
- **E2B**: cloud sandbox that runs execution in a remote isolated environment.
You don't have to pick manually — `box.enabled` is the master switch. Once on, LangBot probes for the most suitable backend and degrades gracefully if probing fails.
![Sandbox native tools](/images/articles/source/images/sandbox-skills/sandbox-tools.png)
### It also hosts MCP Servers
Beyond native tools, the Box Runtime takes on another important job: **hosting stdio-mode MCP Servers**.
Previously, stdio-mode MCP Servers had to be spawned as subprocesses directly inside the main process — neither safe nor easy to manage. Now they all run inside the sandbox, sharing the same isolation boundary as the Agent's execution environment. `http` / `sse` mode MCP is unaffected and works as before.
## 🎯 Skills: know-how, loaded on demand
The second big update is **Skills**.
As the Agent's capabilities grow, an old problem surfaces: **the context window is finite**. If you stuff every possible instruction, procedure, and reference doc into the system prompt, the context fills up fast — and most of it is irrelevant to the task at hand.
The design idea behind Skills is simple: **keep the table of contents in view, and open the book only when you need it.**
A Skill is an **on-demand instruction pack** containing:
- **A prompt**: tells the Agent what the skill does and when to use it
- **A procedure**: the steps to accomplish a class of tasks
- **Scripts**: ready-to-run helper scripts
- **Reference files**: API docs, templates, examples, and so on
By default the Agent only sees a **list of available skills** (name + summary), which costs almost nothing in context. When it decides a task needs a particular skill, it pulls the full content just in time via the `activate` action.
![LangBot Skills system](/images/articles/source/images/sandbox-skills/skills.png)
The benefits are clear:
1. **Lean context**: irrelevant skill content never takes up the window.
2. **Extensible capabilities**: want to teach the Agent a new trick? Write a skill pack — no core code changes.
3. **Instant effect**: skills load dynamically during execution, no restart required.
If you've used Claude's Skills or a similar mechanism, this pattern will feel familiar — it decouples "what the Agent knows" from "what the Agent needs right now."
## 🧩 Shipped early across 4.9.x
These were planned for v4.10.0 but shipped early in the 4.9.x line:
- **Plugin Extension Pages**: plugins can render custom UI via iframe + a Page SDK ([@TyperBody](https://github.com/TyperBody), #2107)
- **Monitoring**: feedback linked to message IDs, plus feedback export ([@6mvp6](https://github.com/6mvp6), #2120)
- **Security hardening**: path-traversal fixes and removal of an authenticated RCE endpoint ([@sebastiondev](https://github.com/sebastiondev), #2087 / #2178)
## 🚀 Getting started
The sandbox needs an execution backend, depending on how you deploy:
- **Docker**: run `docker compose --profile all up`, which also launches the `langbot_box` container.
- **Manual / `uvx`**: runs as a stdio subprocess automatically, nothing to do.
- **LangBot Cloud**: ready out of the box — the sandbox is already set up for you, nothing to install.
To turn the sandbox off, set `box.enabled: false` — built-in tools, Skill create/edit, and stdio-mode MCP are all disabled; `http` / `sse` mode MCP is unaffected. See the [Sandbox docs](https://docs.langbot.app/en/usage/sandbox/readme) for details.
This release also upgrades the plugin SDK to **`langbot-plugin 0.4.1`**.
## Closing thoughts
Sandbox and Skills are, at their core, answering the same question: **how do we let an Agent handle genuinely complex, open-ended tasks?**
The sandbox solves the "**hands**" problem — giving the Agent the ability to execute and room to experiment. Skills solve the "**brain**" problem — letting the Agent reach for the right knowledge at the right moment, without drowning in irrelevant information.
The fastest way to try it is to spin up a managed instance on [LangBot Cloud](https://space.langbot.app/cloud), where the sandbox is ready to go. You can also read the [full release notes](https://github.com/langbot-app/LangBot/releases/tag/v4.10.0) for every change.
Thank you to everyone building with LangBot. We'll keep making the cloud Agent experience more stable and more capable.
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---
title: "LangBot v4.9.0：完全プラグイン化された RAG アーキテクチャ"
description: "LangBot v4.9.0 refactors the entire knowledge base system from a built-in implementation to a plugin-driven architecture, introducing KnowledgeEngine and Parser component types for boundless extensibility."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/langbot-v490-rag-plugin-architecture).
公開日：2026-03-10 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot v4.9.0 RAG Plugin Architecture](/images/articles/source/images/covers/rag-plugin.png)
LangBot v4.9.0, codenamed **"Knowledge Without Borders,"** does exactly what the name says: **the entire knowledge base capability has been refactored from a built-in implementation to a plugin-driven architecture.**
This isn't a minor tweak — it redefines what "knowledge" means in LangBot.
## The Problem with the Old Approach
Before v4.9.0, LangBot's knowledge base was split into two separate systems:
- **Built-in Knowledge Base**: Used Chroma as the vector database, with embedding models managed by LangBot directly. Document parsing, chunking, and indexing were all hardcoded.
- **External Knowledge Base**: Bridged services like Dify, RAGFlow, and FastGPT through the `KnowledgeRetriever` plugin component — retrieval only, no document ingestion.
These two systems lived behind separate UI tabs, with completely different data models and management flows.
**Why this was painful:**
1. **Poor extensibility**: Want a different vector database? A custom chunking strategy? Sorry, that's hardcoded.
2. **High maintenance cost**: Every RAG improvement required changes to LangBot's core code and a new release.
3. **Fragmented UX**: Two completely different knowledge base management flows meant a steep learning curve.
v4.9.0 solves this decisively: **extract RAG capabilities from LangBot's core and hand them to plugins.**
## What Changed
### 1. Unified Knowledge Base Model
The `internal` / `external` distinction is gone. All knowledge bases are managed through a single interface, differentiated only by their `rag_engine_plugin_id`. One list, one creation flow — just pick your engine.
### 2. KnowledgeEngine Component
This is the headline addition. `KnowledgeEngine` replaces the old `KnowledgeRetriever` and takes ownership of the full knowledge base lifecycle:
- **Document Ingestion**: The complete pipeline from file parsing to vector indexing
- **Knowledge Retrieval**: Returning relevant chunks at query time
- **Document Deletion**: Cleaning up documents and their associated vector data
- **Lifecycle Hooks**: Callbacks when knowledge bases are created or deleted
A KnowledgeEngine plugin has full control over indexing and retrieval strategies — not just retrieval.
![Knowledge Engine plugins in the Marketplace](/images/articles/external/b9a33a8551a7-8dd37939-4f10-4046-bd84-cce798005cd4.png)
### 3. Parser Component
Document parsing has been extracted into its own plugin component type. A Parser converts binary files (PDF, Word, Markdown, etc.) into structured text, which is then handed to the RAG engine for chunking and indexing.
**The data flow:**
![Data flow: Parser → KnowledgeEngine](/images/articles/source/images/rag-plugin/data-flow.png)
If a RAG engine declares `DOC_PARSING` capability, it can handle parsing internally and skip the external Parser.
### 4. Host RAG API
LangBot's core no longer executes RAG operations directly, but it still provides essential infrastructure through `RAGRuntimeService`, accessible to plugins via RPC:
- **Embedding invocation**: `invoke_embedding()` — plugins don't need to manage model connections
- **Vector database operations**: `vector_upsert()` / `vector_search()` / `vector_delete()`
- **File access**: `get_knowledge_file_stream()` — read raw files from storage
This means plugins can focus on RAG strategy (chunking algorithms, retrieval logic, re-ranking) while the host handles the "heavy" operations like vector storage and embedding models.
### 5. KnowledgeRetriever Deprecated
The old `KnowledgeRetriever` component has been removed. If you had external knowledge base plugins, they'll need to migrate to `KnowledgeEngine`. The good news: the new API is cleaner and migration is straightforward.
## Building a RAG Engine Plugin
### Scaffold the Component
```bash
lbp comp KnowledgeEngine
```
This generates the directory structure:
![Plugin directory structure](/images/articles/source/images/rag-plugin/dir-structure.png)
### Define Configuration Schemas
The YAML manifest defines two configuration schemas:
- **`creation_schema`**: Parameters filled when creating a knowledge base (e.g., chunk size, embedding model)
- **`retrieval_schema`**: Parameters adjustable at retrieval time (e.g., score threshold, top-K)
```yaml
spec:
creation_schema:
- name: chunk_size
label:
en_US: Chunk Size
type: integer
default: 500
- name: chunk_overlap
label:
en_US: Chunk Overlap
type: integer
default: 50
retrieval_schema:
- name: score_threshold
label:
en_US: Score Threshold
type: float
default: 0.5
```
LangBot **dynamically renders** creation and retrieval forms based on these schemas — different engines show different configuration fields.
### Declare Capabilities
```python
class SimpleRag(KnowledgeEngine):
@classmethod
def get_capabilities(cls) -> list[str]:
return [
KnowledgeEngineCapability.DOC_INGESTION, # Supports document upload
# KnowledgeEngineCapability.DOC_PARSING, # Optional: built-in parsing
]
```
| Capability | Description |
|------------|-------------|
| `DOC_INGESTION` | Supports document upload and processing. UI shows "Documents" tab |
| `DOC_PARSING` | Supports built-in document parsing. Without this, an external Parser plugin is required |
### Implement Core Methods
The three essential methods:
**Document Ingestion:**
```python
async def ingest(self, context: IngestionContext) -> IngestionResult:
# 1. Get file content (or use Parser's pre-parsed result)
if context.parsed_content:
text = context.parsed_content.text
else:
file_bytes = await self.plugin.get_knowledge_file_stream(
context.file_object.storage_path
)
text = file_bytes.decode('utf-8')
# 2. Chunk the text
chunks = self._split_text(text, chunk_size=500)
# 3. Call host embedding model
vectors = await self.plugin.invoke_embedding(
embedding_model_uuid, chunks
)
# 4. Write to host vector database
await self.plugin.vector_upsert(
collection_id, vectors, ids, metadata
)
return IngestionResult(
document_id=context.file_object.metadata.document_id,
status=DocumentStatus.COMPLETED,
chunks_created=len(chunks),
)
```
**Knowledge Retrieval:**
```python
async def retrieve(self, context: RetrievalContext) -> RetrievalResponse:
# 1. Generate query vector
query_vectors = await self.plugin.invoke_embedding(
embedding_model_uuid, [context.query]
)
# 2. Vector search
results = await self.plugin.vector_search(
collection_id, query_vectors[0], top_k=5
)
# 3. Convert and return
return RetrievalResponse(results=entries, total_found=len(entries))
```
**Document Deletion:**
```python
async def delete_document(self, kb_id: str, document_id: str) -> bool:
deleted = await self.plugin.vector_delete(
collection_id=kb_id, file_ids=[document_id]
)
return deleted > 0
```
### Bridging External Services
If your goal is to bridge Dify, RAGFlow, FastGPT, or other external services rather than building a custom RAG pipeline, the implementation is even simpler — **don't declare `DOC_INGESTION` capability** and only implement `retrieve`:
```python
class DifyRAGEngine(KnowledgeEngine):
@classmethod
def get_capabilities(cls) -> list[str]:
return [] # No document upload — managed externally
async def retrieve(self, context: RetrievalContext) -> RetrievalResponse:
# Call Dify/RAGFlow/FastGPT retrieval API
...
```
The knowledge base won't show a "Documents" tab — all content management happens in the external service.
## Building a Parser Plugin
Parser development is even more concise:
```bash
lbp comp Parser
```
Declare supported MIME types in the manifest:
```yaml
spec:
supported_mime_types:
- application/pdf
- application/vnd.openxmlformats-officedocument.wordprocessingml.document
```
Implement the `parse` method:
```python
class PdfParser(Parser):
async def parse(self, context: ParseContext) -> ParseResult:
# context.file_content: raw file bytes
# context.mime_type: detected MIME type
# context.filename: original filename
text = extract_text_from_pdf(context.file_content)
return ParseResult(
text=text,
sections=[
TextSection(content=page_text, heading=f"Page {i}", page=i)
for i, page_text in enumerate(pages)
],
)
```
Parsers also support **cross-plugin invocation** — a RAG engine plugin can call another plugin's Parser:
```python
result = await self.plugin.invoke_parser(
plugin_author="author_name",
plugin_name="plugin_name",
storage_path=context.file_object.storage_path,
mime_type=context.file_object.metadata.mime_type,
filename=context.file_object.metadata.filename,
)
```
## Upgrade Notes
- Knowledge bases created in previous versions are **automatically migrated**. After updating, visit the Knowledge Base page to verify.
- The `KnowledgeRetriever` component is deprecated. Existing plugins need to migrate to `KnowledgeEngine`.
- Browse the [Plugin Marketplace](https://space.langbot.app/market?category=KnowledgeEngine) for available RAG engine plugins.
## The Bigger Picture
v4.9.0's knowledge base refactoring is the latest step in LangBot's plugin-first evolution. From event handlers and tools in v4.0, to knowledge retrievers, to now full RAG engines and parsers — LangBot's core capabilities are progressively moving from "built-in" to "pluggable."
The endgame: **LangBot's core provides pipeline orchestration and infrastructure; all business capabilities are plugin-driven.**
Custom chunking strategy? Write a KnowledgeEngine plugin. PDF parsing? Write a Parser plugin. Bridge your company's internal knowledge service? Also a plugin.
Knowledge, without borders.
---
**Links:**
- [v4.9.0 Release Notes](https://github.com/langbot-app/LangBot/releases/tag/v4.9.0)
- [KnowledgeEngine Development Guide](https://docs.langbot.app/en/plugin/dev/components/knowledge-engine)
- [Parser Development Guide](https://docs.langbot.app/en/plugin/dev/components/parser)
- [Plugin Marketplace](https://space.langbot.app/market)
- [LangBot Cloud](https://space.langbot.app/cloud)
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---
title: "Langflow のドラッグ＆ドロップでマルチプラットフォーム AI Bot を構築"
description: "How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows"
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/langflow-drag-and-drop-ai-bot).
公開日：2025-12-05 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![How I Built a Multi-Platform AI Bot with Langflow's Drag-and-Drop Workflows](/images/articles/source/images/covers/langflow.png)
Drive chatbots across QQ, WeChat, Telegram, Discord, and more using visual workflows - no coding required.
---
[LangBot](https://github.com/langbot-app/LangBot) is an open-source instant messaging bot platform that connects AI workflow engines like Langflow, n8n, Dify, FastGPT, and Coze to platforms including WeChat, QQ, Feishu, DingTalk, Telegram, Discord, Slack, and LINE. This tutorial demonstrates how to use Langflow's visual workflows as LangBot's conversation engine.
## Why This Approach Works
- **True Multi-Platform**: One workflow powering 8+ messaging platforms simultaneously
- **Visual Orchestration**: Drag-and-drop conversation design with conditional branches, multi-turn dialogs, and external API calls
- **Flexible AI Models**: Support for OpenAI, Claude, Gemini, DeepSeek, and local models
- **Fully Open Source**: Both LangBot and Langflow are open-source projects for free deployment and customization
## Prerequisites
- Python 3.10+
- Docker (recommended for quick deployment)
- OpenAI API Key or API keys for other LLM services
## Step 1: Deploy LangBot
Launch with uvx in one command:
```bash
uvx langbot
```
First run auto-initializes and opens your browser to http://127.0.0.1:5300.
![LangBot Initial Page](/images/articles/external/0c8aa2c940ab-01-langbot-initial-page.png)
After registration, log in to access the dashboard:
![LangBot Dashboard](/images/articles/external/b93d97b5b830-04-langbot-dashboard.png)
## Step 2: Deploy Langflow
Deploy quickly with Docker:
```bash
docker run -d --name langflow -p 7860:7860 langflowai/langflow:latest
```
Visit http://localhost:7860 to access Langflow:
![Langflow Welcome Page](/images/articles/external/7e7ebfc3ba58-01-langflow-welcome.png)
## Step 3: Create a Langflow Workflow
In Langflow, select the "Basic Prompting" template to get started quickly:
![Langflow Template Selection](/images/articles/external/b304cebf8ce9-02-langflow-template-selection.png)
This template includes four basic components:
- **Chat Input**: Receives user messages
- **Prompt**: Sets system instructions
- **Language Model**: Calls LLM to generate responses
- **Chat Output**: Returns results
![Langflow Workflow Editor](/images/articles/external/6e24402fd4a8-03-langflow-workflow-editor.png)
### Configure Language Model
Click the Language Model component and configure:
1. **Model Provider**: Select OpenAI (or other compatible providers like SiliconFlow, New API)
2. **Model Name**: Select gpt-4o-mini or deepseek-chat
3. **OpenAI API Key**: Enter your API Key
![Langflow OpenAI API Key Configured](/images/articles/external/bcd91ef5e4b3-14-langflow-openai-api-key-configured.png)
> Tip: You can use OpenAI-compatible API services like SiliconFlow or New API by simply modifying the Base URL.
Save the workflow after configuration.
## Step 4: Get Langflow API Information
### Generate API Key
In Langflow's upper right: Settings → API Keys, navigate to the API Keys page:
![Langflow API Keys Page](/images/articles/external/21fc47358066-05-langflow-api-keys-page.png)
Click Create New Key:
![Langflow Create API Key Dialog](/images/articles/external/8f04c7b00759-06-langflow-create-api-key-dialog.png)
Generate and save the API Key:
![Langflow API Key Generated](/images/articles/external/e5a019491ef8-07-langflow-api-key-generated.png)
Format: `sk-xxxxxxxxxxxxxxxxxxxxxxxx`
### Get Flow ID
Extract from the flow editor's URL:
```
http://localhost:7860/flow/{flow-id}
```
Record this `flow-id`.
## Step 5: Configure Langflow in LangBot
Return to LangBot dashboard and go to **Pipelines** page.
Click ChatPipeline to edit, in the AI tab:
![LangBot Pipeline AI Tab](/images/articles/external/3c7d6bc96738-08-langbot-pipeline-ai-tab.png)
Configure **Runner**, select **Langflow API**:
![LangBot Runner Dropdown](/images/articles/external/ef9bbef78165-09-langbot-runner-dropdown.png)
Fill in the Langflow configuration:
![LangBot Langflow Config Form](/images/articles/external/82d5e2ffb9a0-10-langbot-langflow-config-form.png)
Configuration items:
- **Base URL**: `http://localhost:7860` (local) or `http://langflow:7860` (Docker network)
- **API Key**: The API Key generated in Langflow
- **Flow ID**: The Flow ID recorded earlier
![LangBot Langflow Config Filled](/images/articles/external/2fb54ce0f7a2-11-langbot-langflow-config-filled.png)
> Docker Tip: If both services run in containers, ensure they're on the same network and use the container name for Base URL.
Click **Save** to save the configuration.
## Step 6: Test the Conversation
Click **Debug Chat** on the Pipelines page to open the debug chat interface:
![LangBot Debug Chat Interface](/images/articles/external/b36d6dea3702-12-langbot-debug-chat-interface.png)
Enter a test message like "Hello" and view the AI response:
![LangBot Chat Test Success](/images/articles/external/3d7fa393f210-13-langbot-chat-with-response.png)
## How It Works
1. User sends a message on a messaging platform
2. LangBot receives and passes it to the Pipeline
3. Pipeline calls Langflow API
4. Langflow executes the workflow: receives input → adds prompt → calls LLM → returns result
5. LangBot sends the response back to the user
## Common Issues
**Cannot connect to Langflow?**
Check Base URL. For Docker deployment, ensure containers are on the same network:
```bash
docker network create langbot_network
docker network connect langbot_network langflow
docker network connect langbot_network langbot
```
Use container name: `http://langflow:7860`
**API call fails?**
- Confirm API Key and Flow ID are correct
- Verify the Language Model in Langflow has a valid LLM API Key configured
## Advanced Use Cases
Langflow's power lies in visually orchestrating complex AI workflows:
- **Multi-Turn Memory**: Add Memory components for contextual understanding
- **Conditional Branches**: Execute different logic based on user input
- **External API Integration**: Connect databases, search engines, third-party services
- **Multi-Agent Collaboration**: Multiple LLM models working together
- **RAG Applications**: Integrate vector databases for knowledge base Q&A
All achievable through drag-and-drop without writing code.
## Summary
With LangBot + Langflow, you can rapidly build powerful multi-platform AI chatbots. Langflow provides visual workflow orchestration, LangBot handles messaging platform integration - together they create a complete loop from workflow design to multi-platform deployment.
This approach is ideal for:
- Scenarios requiring the same AI capabilities across multiple platforms
- Teams wanting rapid iteration and testing of different conversation flows
- Developers wanting to build complex AI applications without deep coding
## Related Resources
- [LangBot Official Site](https://langbot.app) | [Documentation](https://docs.langbot.app) | [GitHub](https://github.com/langbot-app/LangBot)
- [Langflow Official Site](https://www.langflow.org) | [Documentation](https://docs.langflow.org) | [GitHub](https://github.com/langflow-ai/langflow)
---
*This article is based on the latest version of LangBot. LangBot supports integration with Dify, n8n, FastGPT, Coze, and other AI platforms - choose the workflow engine that best fits your needs.*
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---
title: "LangTARS：Dify・n8n と連携するオープンソース PC 操作 Agent"
description: "LangTARS is a LangBot native plugin that lets you remotely control Mac/Windows/Linux from Telegram, Discord & WeChat — with Dify and n8n workflow support."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/langtars-remote-pc-control-dify-n8n).
公開日：2026-03-23 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangTARS Logo](/images/articles/external/3fe5bdc18716-icon.svg)
**LangBot Native Plugin | OpenClaw-like Experience | Ready to Use with One-Click Deployment**
---
## ✨ What is LangTARS?
**LangTARS** is a powerful **OpenClaw alternative** and native plugin for LangBot, inspired by the loyal and reliable robot TARS from the movie *Interstellar*.
As a versatile **AI agent** for **remote computer control**, LangTARS allows you to remotely command your **Mac, Windows, or Linux** machine directly through your favorite messaging apps like **Telegram, Discord, DingTalk, and WeChat**! If you've been looking for an **OpenClaw-like** computer use agent that integrates seamlessly into your existing **chatbot framework**, LangTARS is the perfect solution.
> 💡 **Deploy LangBot, and you can experience LangTARS instantly!** No extra standalone agent installation needed—just get it with one click from the plugin marketplace.
![LangTARS Demo](/images/articles/external/70b26e660a60-tars.gif)
---
## 🤖 The LangBot Ecosystem
LangTARS is just ONE plugin in the massive LangBot ecosystem. **LangBot** is a leading **open source chatbot** framework designed for connecting LLMs to messaging platforms.
- **Multi-platform bot integration**: Native support for Telegram, Discord, WeChat, DingTalk, Lark, LINE, Slack, and more.
- **Multi-model LLM chatbot**: Works flawlessly with OpenAI, Claude, Gemini, DeepSeek, and local models.
- **Rich Plugin Marketplace**: Access over 30+ plugins for endless functionality.
- **Advanced Features**: Pipeline-based message processing, RAG support, and multi-agent workflows.
- **Self-hosted AI**: Total control over your data and deployments.
- **AI Workflow Integrations**: Connect LangBot to platforms like **Dify**, **n8n**, **Langflow**, and **Coze**. LangBot can act as the messaging frontend for your complex AI workflows, meaning LangTARS + Dify/n8n = powerful automation!
Get started with LangBot:
- **Easiest**: [LangBot Cloud](https://cloud.langbot.app)
- Self-host: `uvx langbot@latest`
- Documentation: [docs.langbot.app](https://docs.langbot.app)
- GitHub: [github.com/langbot-app/LangBot](https://github.com/langbot-app/LangBot) (15k+ stars)
---
## 🎯 What Can It Do?
As a comprehensive **automation tool**, just send a message, and the AI will handle your **task automation** and **browser automation**:
| You Say | AI Does |
|------|-------|
| `!tars open Reddit in Chrome, post a thread with the title "Hello from LangTARS" and say hi` | 🌐 Automatically controls Chrome to open Reddit and publish a post |
| `!tars organize my desktop files` | 📁 Automatically categorizes and moves files |
| `!tars open the browser and search for today's AI news` | 🌐 Opens a browser, searches the web, and summarizes the news |
| `!tars create a meeting note for our marketing sync` | 📝 Automatically creates a markdown note file |
| `!tars check system memory and CPU` | 💻 Uses MCP tools to get computer status |
![Usage Example](/images/articles/external/6613b3025b8b-usage-example.png)
![Plan Mode](/images/articles/external/ff42857251ab-planner-mode.png)
---
## 🔗 Supercharge with Dify & n8n
LangTARS becomes even more powerful when combined with **AI workflow platforms**:
- **Dify Integration**: Connect LangBot to [Dify](https://dify.ai) as a messaging frontend. Build complex AI workflows in Dify's visual editor, then trigger them from Telegram/Discord via LangTARS.
- **n8n Integration**: Use [n8n](https://n8n.io) workflows with LangBot for advanced automation chains. Combine LangTARS's computer control with n8n's 400+ integrations for unlimited possibilities.
- **Also supports**: Langflow, Coze, and any OpenAI-compatible API.
> 💡 **Example**: "Hey TARS, run my Dify workflow to analyze today's sales data and save the report to my desktop" — LangTARS handles the entire chain.
---
## 🚀 Why Choose LangTARS?
### 1️⃣ LangBot Native Plugin, Out of the Box
Instead of deploying a complex standalone agent, you can easily **deploy AI bot** capabilities right where you already chat.
```bash
# Deploy LangBot with one command
uvx langbot@latest
# Install LangTARS from the plugin market, configure your LLM, and start using!
```
**No complex configuration, no extra deployment. LangBot users can experience OpenClaw-style computer control directly!**
![Installation Flow](/images/articles/external/2a3a0d5f4ae9-install-flow.png)
---
### 2️⃣ Intelligent Task Planning, Autonomous Execution
Powered by an AI planning engine based on the **ReAct agent** loop:
```
Your Command → Understand Needs → Make a Plan → Execute Step-by-Step → Feedback Results
```
The AI assistant thinks like a human and automatically breaks down complex tasks using integrated **MCP tools**!
---
### 3️⃣ Multi-Browser Support for Web Automation
| Browser | macOS | Windows | Linux |
|--------|-------|---------|-------|
| Playwright (Headless) | ✅ | ✅ | ✅ |
| Safari | ✅ | - | - |
| Chrome | ✅ | ✅ | ✅ |
| Edge | - | ✅ | - |
| Firefox | - | - | ✅ |
---
### 4️⃣ Security-First Design
- 🚫 **Dangerous Command Interception** — Automatically blocks commands like `rm -rf /`
- 📂 **Workspace Isolation** — Restricts file operations to safe directories
- 📋 **Command Whitelist** — Configurable allowed commands
- 👤 **User Access Control** — Restricts usage to specific authorized users
![Security Features](/images/articles/external/a618d3d41416-security.png)
---
## 📱 Quick Start
### Step 1: Deploy LangBot
> 🚀 **Fastest way to start:** Use [LangBot Cloud](https://cloud.langbot.app) — deploy your LangBot instance in one click, no server needed! Install LangTARS from the plugin marketplace and start controlling your computer immediately.
*Alternative for power users (Self-hosted):*
```bash
uvx langbot@latest
```
### Step 2: Configure Chat Platform
Follow the documentation to configure your **Telegram bot**, **WeChat bot**, or **Discord bot**.
### Step 3: Install LangTARS
Search for **LangTARS** in the **plugin marketplace** and install it.
### Step 4: Start Chatting
```
!tars Hello, tell me what you can do
```
> 📱 **Recommendation**: Use it on Telegram or Discord for the best interactive experience!
---
## 🎮 Common Commands
| Command | Function |
|------|------|
| `!tars <Task Description>` | Execute a task |
| `!tars stop` | Stop the current task |
| `!tars what` | Check execution status |
| `!tars reset` | Reset the conversation |
| `!tars help` | Show help |
---
## 🌟 Project Information
| Item | Information |
|------|------|
| **GitHub** | [github.com/langbot-app/LangTARS](https://github.com/langbot-app/LangTARS) |
| **Language** | Python 100% |
| **License** | CC BY-NC-ND 4.0 |
| **Platforms** | macOS / Windows / Linux |
---
## 🔗 Related Links
- 📖 **LangBot Documentation**: [docs.langbot.app](https://docs.langbot.app)
- 💬 **Issue Feedback**: [GitHub Issues](https://github.com/langbot-app/LangTARS/issues)
- ⭐ **Support with a Star**: [GitHub](https://github.com/langbot-app/LangTARS)
---
## 🎉 Deploy LangBot and Experience LangTARS!
**Let AI be your loyal digital assistant, just like TARS.**
> 🚀 **Fastest way to start:** Use [LangBot Cloud](https://cloud.langbot.app) — deploy your LangBot instance in one click, no server needed! Install LangTARS from the plugin marketplace and start controlling your computer immediately.
*Alternative for power users (Self-hosted):*
```bash
uvx langbot@latest
```
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---
title: "n8n と LangBot でマルチプラットフォーム AI Chatbot を構築"
description: "How I Built a Multi-Platform AI Chatbot with n8n and LangBot"
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/n8n-multi-platform-ai-chatbot).
公開日：2025-12-03 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![How I Built a Multi-Platform AI Chatbot with n8n and LangBot](/images/articles/source/images/covers/n8n.png)
Connecting n8n's visual workflow automation with LangBot's multi-platform bot framework creates a powerful, code-free way to deploy AI chatbots across QQ, WeChat, Discord, Telegram, Slack, and more. This tutorial shows you how to integrate these tools in minutes.
## What You'll Need
- Python 3.8+ installed
- Node.js 18+ installed
- npm or npx available
- 15 minutes of your time
## Deploy LangBot in 3 Commands
LangBot is a production-ready bot framework that connects to multiple messaging platforms and AI services including Dify, FastGPT, Coze, OpenAI, Claude, and Gemini. Deploy it instantly:
```bash
cd your-workspace
mkdir -p langbot-instance && cd langbot-instance
uvx langbot@latest
```
On first launch, LangBot initializes automatically. Open `http://127.0.0.1:5300` in your browser.
![LangBot initialization screen](/images/articles/external/70ee40a6d232-01-langbot-initialization.png)
Register your admin account when prompted. You'll land on the dashboard where you can manage bots, models, pipelines, and integrations.
![LangBot dashboard](/images/articles/external/4f1f5c28fbfc-03-langbot-dashboard.png)
## Set Up n8n Workflow Automation
n8n is an open-source automation platform with 400+ integrations and powerful AI capabilities. Launch it locally:
```bash
cd your-workspace
mkdir -p n8n-data
export N8N_USER_FOLDER=$(pwd)/n8n-data
npx n8n
```
Visit `http://127.0.0.1:5678` and create your owner account.
![n8n initial setup](/images/articles/external/12d0e04600c5-05-n8n-setup.png)
## Build Your AI Workflow
Create a new workflow in n8n. You'll need two essential nodes:
![n8n workflow editor](/images/articles/external/e36d04ca4a5b-07-n8n-workflow-editor.png)
### Add the Webhook Trigger
Click "+" on the canvas and add a **Webhook** node. Configure it:
- **HTTP Method**: POST
- **Response Mode**: Streaming (enables real-time chat responses)
- **Authentication**: None (adjust for production)
![Webhook node configuration](/images/articles/external/03443176ae5c-08-n8n-webhook-node.png)
### Add the AI Agent
Press Tab, navigate to the "AI" category, and select **AI Agent**.
**Configure the Chat Model**: Click "Chat Model" and choose "OpenAI Chat Model". Add your credentials:
- **API Key**: Your OpenAI API key (or compatible service key)
- **Base URL**: For OpenAI alternatives like Claude, Gemini, or local models, update to your provider's endpoint
**Critical Step - Fix the Prompt Source**: By default, the AI Agent expects a Chat Trigger node, which won't work with webhooks. Here's how to fix it:
1. Find "Source for Prompt (User Message)" in the AI Agent settings
2. Change from "Connected Chat Trigger Node" to "Define below"
3. Switch to "Expression" mode
4. Enter: `{{ $json.body }}`
This expression pulls the user's message from the webhook request body.
![Configured webhook with AI Agent](/images/articles/external/621f3d526678-09-n8n-webhook-configured.png)
### Activate and Get Your Webhook URL
Save the workflow and toggle the activation switch (top-right). Switch to the "Production URL" tab and copy the webhook URL:
```
http://localhost:5678/webhook/{your-webhook-id}
```
## Connect LangBot to n8n
Back in the LangBot dashboard, navigate to **Pipelines** and click the default "ChatPipeline".
![LangBot pipelines page](/images/articles/external/ef2b2f83bd16-04-langbot-pipelines.png)
Switch to the **AI** tab and select "n8n Workflow API" from the Runner dropdown. Configure:
- **Webhook URL**: Paste your n8n production webhook URL
- **Authentication Type**: None (match your n8n webhook settings)
- **Timeout**: 120 seconds
- **Output Key**: response
Click **Save**.
## Test It Out
In the Pipeline editor, click "Debug Chat" on the left sidebar. Send a test message like "What is LangBot?"
If everything works, you'll see LangBot send the message to n8n, where the AI Agent processes it and streams back a response.
## Troubleshooting
**Error: "Expected to find the prompt in an input field called 'chatInput'"**
This means the AI Agent is still configured for a Chat Trigger node. Fix it:
1. Open the AI Agent configuration
2. Set "Source for Prompt (User Message)" to "Define below"
3. Switch to Expression mode
4. Enter: `{{ $json.body }}`
**Test Your Webhook Directly**
Verify the webhook works with curl:
```bash
curl -X POST http://localhost:5678/webhook/your-webhook-id \
-H "Content-Type: application/json" \
-d '{"body": "Hello, can you introduce yourself?"}'
```
You should receive streaming JSON with the AI's response.
## How the Integration Works
Here's the complete flow:
1. User sends a message via QQ, WeChat, Discord, Telegram, Slack, LINE, or any LangBot-supported platform
2. LangBot's Pipeline receives the message and calls the n8n Workflow API
3. n8n's Webhook node captures the request and passes it to the AI Agent
4. The AI Agent uses OpenAI, Claude, Gemini, or your configured LLM to generate a response
5. n8n streams the response back to LangBot
6. LangBot delivers the response to the user on their original platform
## Why This Combination Works
**LangBot + n8n** unlocks powerful capabilities:
1. **No-Code AI Logic**: Design conversation flows visually in n8n without touching code
2. **Multi-Platform Reach**: Deploy the same AI across QQ, WeChat, Discord, Telegram, Slack, LINE, DingTalk, and Lark simultaneously
3. **Flexible AI Models**: Swap between OpenAI GPT, Anthropic Claude, Google Gemini, Coze, Dify, local models, and more
4. **Rich Integrations**: Connect n8n's 400+ integrations - databases, APIs, Notion, Airtable, Google Sheets, Slack, and beyond
5. **Tool-Calling Agents**: AI Agent can trigger n8n tools like database queries, API calls, or custom functions
6. **Workflow Extensions**: Add preprocessing, content moderation, logging, or custom business logic
**Perfect For**:
- Enterprise customer support bots
- Knowledge base Q&A systems
- Multi-platform community management
- Task automation assistants
- Unified chat interfaces for teams
## Next Steps
Extend your bot further:
- Integrate Dify or FastGPT for advanced RAG (retrieval-augmented generation)
- Add vector database nodes (Pinecone, Qdrant, Weaviate) for knowledge retrieval
- Connect business APIs for real-time data
- Implement conversation memory and context tracking
- Add content filtering and moderation workflows
- Use Langflow or Coze for additional AI orchestration
This integration gives you the flexibility of code-based AI frameworks like LangChain with the simplicity of visual workflow builders - all while reaching users across every major messaging platform.
Ready to deploy your multi-platform AI assistant? Start with LangBot and n8n today.
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---
title: "LangBot Blog のご紹介"
description: "Welcome to the official LangBot blog. Stay up to date with product updates, new features, and milestones."
---
この記事は LangBot Blog から同期されています。 [Blog の原文を読む](https://langbot.app/en/blog/welcome).
公開日：2026-02-17 · 著者：LangBot Team
> この記事の日本語版はまだ公開されていないため、英語版を掲載しています。
![LangBot Blog](/images/articles/source/images/covers/welcome.png)
We're excited to launch the official LangBot blog! This is where we'll share product updates, new features, milestones, and insights from the LangBot team.
## What is LangBot?
LangBot is an open-source LLM-native instant messaging bot platform. It supports multiple messaging platforms and model providers, with integrations for Dify, Alibaba Cloud Bailian, and other LLMOps platforms. LangBot also provides a WebUI for quick deployment and management.
## What to Expect
On this blog, you'll find:
- **Release Notes** - Detailed breakdowns of each new version
- **Feature Spotlights** - Deep dives into new capabilities
- **Milestones** - Project growth and community achievements
- **Guides & Tips** - Best practices for getting the most out of LangBot
## Stay Connected
- [GitHub](https://github.com/langbot-app/LangBot) - Star us and follow development
- [Documentation](https://docs.langbot.app) - Full guides and API reference
- [LangBot Home](https://langbot.app) - Main website
Stay tuned for more updates!
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"scripts": {
"dev": "npx mintlify dev",
"generate:sitemap-alternates": "node scripts/generate-alternate-sitemap.mjs",
"sync:articles": "node scripts/sync-blog-articles.mjs",
"check:articles": "node scripts/sync-blog-articles.mjs --check",
"test": "node --test tests/*.test.mjs"
},
"dependencies": {
scripts/README-blog-articles.md
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# LangBot Blog → Wiki article synchronization
`langbot-app/langbot-landing-page/src/content/blog` is the canonical article source. The Wiki publishes generated copies under `{en,zh,ja}/articles/` and stores every rendered image locally under `images/articles/` so Wiki pages do not depend on third-party hotlinks.
## Run locally
From the Wiki checkout, with the landing-page repository available as a sibling checkout:
```bash
node scripts/sync-blog-articles.mjs --source ../langbot-landing-page
node scripts/sync-blog-articles.mjs --check --source ../langbot-landing-page
node scripts/sync-blog-articles.mjs --check
npm test
```
The synchronizer:
- requires matching English and Chinese slug sets;
- uses the canonical Japanese article when one exists and a clearly marked English fallback otherwise;
- groups navigation into product updates, engineering, tutorials/integrations, and announcements with localized labels;
- copies source images and downloads external images into the Wiki repository;
- rewrites Markdown image URLs to Wiki-local paths;
- escapes prose that would otherwise be parsed as MDX expressions;
- replaces the generated article directories and updates the `docs.json` article tabs deterministically.
Do not edit generated article pages or mirrored images manually. Fix the canonical Blog post or the synchronizer, then regenerate.
A daily GitHub Actions workflow provides drift recovery. Article publishing work should still synchronize and verify the Wiki in the same task so a new Blog post does not wait for the scheduled run.
scripts/blog-articles-manifest.json
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{
"source": "https://github.com/langbot-app/langbot-landing-page/tree/main/src/content/blog",
"articles": [
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scripts/sync-blog-articles.mjs
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#!/usr/bin/env node
import { createHash } from "node:crypto";
import { existsSync } from "node:fs";
import { cp, lstat, mkdir, mkdtemp, readFile, readdir, realpath, rm, stat, writeFile } from "node:fs/promises";
import { tmpdir } from "node:os";
import path from "node:path";
import process from "node:process";
const ROOT = path.resolve(import.meta.dirname, "..");
const ARTICLE_LOCALES = ["en", "zh", "ja"];
const MAX_IMAGE_BYTES = 25 * 1024 * 1024;
const LANGUAGE_CONFIG = {
en: { mintlify: "en", tab: "Articles", introTitle: "LangBot Articles", introDescription: "Product updates, engineering deep dives, tutorials, integrations, and announcements from the LangBot Blog.", sourceLabel: "This article is synchronized from the LangBot Blog.", sourceAction: "Read the canonical version", fallbackLabel: "", overviewGroup: "Browse", groups: { updates: "Product Updates", engineering: "Engineering", tutorials: "Tutorials & Integrations", announcements: "Announcements" } },
zh: { mintlify: "cn", tab: "文章", introTitle: "LangBot 文章", introDescription: "同步自 LangBot Blog 的产品动态、技术解析、教程、集成实践与公告。", sourceLabel: "本文同步自 LangBot Blog。", sourceAction: "阅读 Blog 原文", fallbackLabel: "", overviewGroup: "浏览", groups: { updates: "产品动态", engineering: "技术解析", tutorials: "教程与集成", announcements: "公告" } },
ja: { mintlify: "jp", tab: "記事", introTitle: "LangBot 記事", introDescription: "LangBot Blog の製品アップデート、技術解説、チュートリアル、連携事例、お知らせをまとめています。", sourceLabel: "この記事は LangBot Blog から同期されています。", sourceAction: "Blog の原文を読む", fallbackLabel: "この記事の日本語版はまだ公開されていないため、英語版を掲載しています。", overviewGroup: "一覧", groups: { updates: "製品アップデート", engineering: "エンジニアリング", tutorials: "チュートリアルと連携", announcements: "お知らせ" } },
};
const JAPANESE_TITLES = {
"connect-deepseek-to-wechat": "DeepSeek R1 を WeChat・Discord・Telegram に5分で接続する方法",
"deploy-ai-bot-in-5-minutes": "AI Bot を Discord・Telegram・WeChat に5分でデプロイ",
"dify-agent-discord-telegram-slack": "Dify Agent を Discord・Telegram・Slack で動かす",
"dify-langbot-rag-knowledge-base": "LangBot 4.6.0 外部ナレッジベース入門：Dify と連携した RAG 会話",
"langbot-plugin-system-deep-dive": "LangBot プラグインシステム詳解：プロセス分離・イベント駆動・コンポーネント設計",
"langbot-v4100-sandbox-skills": "LangBot v4.10.0：Agent Sandbox と Skills",
"langbot-v490-rag-plugin-architecture": "LangBot v4.9.0：完全プラグイン化された RAG アーキテクチャ",
"langflow-drag-and-drop-ai-bot": "Langflow のドラッグ＆ドロップでマルチプラットフォーム AI Bot を構築",
"langtars-remote-pc-control-dify-n8n": "LangTARS：Dify・n8n と連携するオープンソース PC 操作 Agent",
"n8n-multi-platform-ai-chatbot": "n8n と LangBot でマルチプラットフォーム AI Chatbot を構築",
welcome: "LangBot Blog のご紹介",
};
function parseArgs(argv) {
const result = { check: false, source: process.env.LANGBOT_LANDING_REPO || "" };
for (let i = 0; i < argv.length; i += 1) {
if (argv[i] === "--check") result.check = true;
else if (argv[i] === "--source") result.source = argv[++i];
else throw new Error(`Unknown argument: ${argv[i]}`);
}
if (result.source) result.source = path.resolve(result.source);
return result;
}
function parseScalar(value) {
const trimmed = value.trim();
if ((trimmed.startsWith('"') && trimmed.endsWith('"')) || (trimmed.startsWith("'") && trimmed.endsWith("'"))) return trimmed.slice(1, -1).replaceAll('\\"', '"');
return trimmed;
}
function parseFrontmatter(markdown, file) {
if (!markdown.startsWith("---\n")) throw new Error(`${file}: missing frontmatter`);
const end = markdown.indexOf("\n---\n", 4);
if (end < 0) throw new Error(`${file}: unterminated frontmatter`);
const raw = markdown.slice(4, end);
const body = markdown.slice(end + 5).trimStart();
const lines = raw.split("\n");
const data = {};
for (let i = 0; i < lines.length; i += 1) {
const match = /^([A-Za-z][A-Za-z0-9_-]*):\s*(.*)$/.exec(lines[i]);
if (!match) continue;
const [, key, value] = match;
if (key === "cover") {
const cover = {};
while (i + 1 < lines.length && /^\s+/.test(lines[i + 1])) {
const nested = /^\s+([A-Za-z][A-Za-z0-9_-]*):\s*(.*)$/.exec(lines[++i]);
if (nested) cover[nested[1]] = parseScalar(nested[2]);
}
data.cover = cover;
} else if (value.startsWith("[") && value.endsWith("]")) {
data[key] = [...value.matchAll(/"((?:\\.|[^"\\])*)"|'((?:\\.|[^'\\])*)'/g)].map((item) => item[1] ?? item[2]);
} else data[key] = parseScalar(value);
}
for (const required of ["title", "date", "description", "author"]) if (!data[required]) throw new Error(`${file}: missing ${required}`);
return { data, body };
}
function yamlQuote(value) { return JSON.stringify(String(value)); }
function classify(post) {
const category = String(post.data.categories?.[0] || "").toLowerCase();
if (category.includes("product") || category.includes("产品") || category.includes("製品")) return "updates";
if (category.includes("engineering") || category.includes("technical") || category.includes("技术") || category.includes("技術")) return "engineering";
if (category.includes("announcement") || category.includes("公告") || category.includes("お知らせ")) return "announcements";
return "tutorials";
}
function canonicalUrl(locale, slug) { return `https://langbot.app/${locale}/blog/${slug}`; }
function attribution(locale, post) {
if (locale === "zh") return `发布于 ${post.data.date} · 作者：${post.data.author}`;
if (locale === "ja") return `公開日：${post.data.date} · 著者：${post.data.author}`;
return `Published ${post.data.date} · Author: ${post.data.author}`;
}
function escapeMdxProse(markdown) {
const lines = markdown.split("\n");
let fenced = false;
return lines.map((line) => {
if (/^\s*```/.test(line) || /^\s*~~~/.test(line)) { fenced = !fenced; return line; }
if (fenced) return line.trimEnd();
return line.split(/(`[^`]*`)/g).map((piece, index) => index % 2 === 1 ? piece : piece.replaceAll("{", "&#123;").replaceAll("}", "&#125;")).join("").trimEnd();
}).join("\n");
}
function imageReferences(markdown) { return [...markdown.matchAll(/!\[([^\]]*)\]\(([^)\s]+)(?:\s+["'][^"']*["'])?\)/g)].map((match) => ({ alt: match[1], url: match[2], full: match[0] })); }
function contentTypeExtension(contentType) {
if (contentType.includes("image/png")) return ".png";
if (contentType.includes("image/jpeg")) return ".jpg";
if (contentType.includes("image/gif")) return ".gif";
if (contentType.includes("image/svg")) return ".svg";
if (contentType.includes("image/webp")) return ".webp";
return "";
}
function localImageTarget(sourceUrl) {
if (sourceUrl.startsWith("/")) return `/images/articles/source${sourceUrl}`;
const parsed = new URL(sourceUrl);
let base = path.posix.basename(parsed.pathname) || "image";
const digest = createHash("sha256").update(sourceUrl).digest("hex").slice(0, 12);
base = base.replace(/[^A-Za-z0-9._-]/g, "-");
return `/images/articles/external/${digest}-${base}`;
}
function assertWithin(base, candidate, label) {
const normalizedBase = path.resolve(base);
const normalizedCandidate = path.resolve(candidate);
if (!normalizedCandidate.startsWith(`${normalizedBase}${path.sep}`)) throw new Error(`${label} escapes ${normalizedBase}: ${normalizedCandidate}`);
}
async function assertDirectoryWithoutSymlinks(directory, label) {
if (!existsSync(directory)) throw new Error(`${label} does not exist: ${directory}`);
const info = await lstat(directory);
if (!info.isDirectory() || info.isSymbolicLink()) throw new Error(`${label} must be a real non-symlink directory: ${directory}`);
const resolved = await realpath(directory);
if (resolved !== path.resolve(directory)) throw new Error(`${label} path must not traverse symlinks: ${directory}`);
return resolved;
}
async function readRegularFileWithin(base, file, label, encoding = null) {
const baseReal = await assertDirectoryWithoutSymlinks(base, `${label} directory`);
if (!existsSync(file)) throw new Error(`${label} does not exist: ${file}`);
const info = await lstat(file);
if (!info.isFile() || info.isSymbolicLink()) throw new Error(`${label} must be a regular non-symlink file: ${file}`);
const fileReal = await realpath(file);
assertWithin(baseReal, fileReal, label);
return encoding ? readFile(fileReal, encoding) : readFile(fileReal);
}
function validateImageBytes(bytes, label) {
const ascii = bytes.subarray(0, 16).toString("ascii");
const trimmed = bytes.toString("utf8", 0, Math.min(bytes.length, 1024)).trimStart();
const valid =
bytes.subarray(0, 8).equals(Buffer.from([0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a])) ||
(bytes[0] === 0xff && bytes[1] === 0xd8 && bytes[2] === 0xff) ||
ascii.startsWith("GIF87a") || ascii.startsWith("GIF89a") ||
(ascii.startsWith("RIFF") && ascii.slice(8, 12) === "WEBP") ||
((trimmed.startsWith("<svg") || trimmed.startsWith("<?xml")) && trimmed.includes("<svg"));
if (!valid) throw new Error(`Invalid image signature: ${label}`);
}
async function prepareImageTarget(assetRoot, target) {
const imageRoot = path.join(assetRoot, "images/articles");
assertWithin(imageRoot, target, "Article image target");
await mkdir(path.dirname(target), { recursive: true });
const imageRootReal = await realpath(imageRoot);
const targetParentReal = await realpath(path.dirname(target));
if (targetParentReal !== path.resolve(path.dirname(target))) throw new Error(`Article image target directory must not be a symlink: ${path.dirname(target)}`);
assertWithin(imageRootReal, path.join(targetParentReal, path.basename(target)), "Resolved article image target");
if (existsSync(target) && (await lstat(target)).isSymbolicLink()) throw new Error(`Article image target must not be a symlink: ${target}`);
}
async function materializeImage(sourceRoot, sourceUrl, assetRoot) {
let targetUrl = localImageTarget(sourceUrl);
let target = path.join(assetRoot, targetUrl.slice(1));
await prepareImageTarget(assetRoot, target);
if (sourceUrl.startsWith("/")) {
const publicRoot = path.join(sourceRoot, "public");
const source = path.join(publicRoot, sourceUrl.slice(1));
assertWithin(publicRoot, source, "Blog image source");
if (!existsSync(source)) throw new Error(`Missing source image: ${sourceUrl} (${source})`);
const sourceStat = await lstat(source);
if (!sourceStat.isFile() || sourceStat.isSymbolicLink()) throw new Error(`Blog image source must be a regular non-symlink file: ${source}`);
const publicRootReal = await realpath(publicRoot);
const sourceReal = await realpath(source);
assertWithin(publicRootReal, sourceReal, "Resolved Blog image source");
const bytes = await readFile(sourceReal);
validateImageBytes(bytes, sourceUrl);
await writeFile(target, bytes);
return targetUrl;
}
const parsedSource = new URL(sourceUrl);
if (parsedSource.protocol !== "https:") throw new Error(`External images must use HTTPS: ${sourceUrl}`);
const response = await fetch(sourceUrl, { headers: { "User-Agent": "LangBot-Wiki-Article-Sync/1.0" }, redirect: "follow" });
if (!response.ok) throw new Error(`Failed to fetch image ${sourceUrl}: HTTP ${response.status}`);
const declaredSize = Number(response.headers.get("content-length") || 0);
if (declaredSize > MAX_IMAGE_BYTES) throw new Error(`Image exceeds ${MAX_IMAGE_BYTES} bytes: ${sourceUrl}`);
const bytes = Buffer.from(await response.arrayBuffer());
const contentType = response.headers.get("content-type") || "";
if (!bytes.length || bytes.length > MAX_IMAGE_BYTES || !contentType.toLowerCase().startsWith("image/")) throw new Error(`Invalid image ${sourceUrl}: ${contentType}, ${bytes.length} bytes`);
validateImageBytes(bytes, sourceUrl);
if (!path.extname(target)) {
const extension = contentTypeExtension(contentType);
if (!extension) throw new Error(`Unsupported image content type for extensionless URL: ${contentType}`);
target += extension;
targetUrl += extension;
await prepareImageTarget(assetRoot, target);
}
if (contentType.toLowerCase().includes("image/svg")) {
await writeFile(target, bytes.toString("utf8").replace(/[ \t]+$/gm, ""));
} else {
await writeFile(target, bytes);
}
return targetUrl;
}
async function loadPosts(sourceRoot) {
const byLocale = new Map();
const blogRoot = path.join(sourceRoot, "src/content/blog");
const blogRootReal = await assertDirectoryWithoutSymlinks(blogRoot, "Blog content root");
for (const locale of ARTICLE_LOCALES) {
const dir = path.join(blogRoot, locale);
const files = existsSync(dir) ? await readdir(dir) : [];
if (existsSync(dir)) {
const localeRootReal = await assertDirectoryWithoutSymlinks(dir, `Blog ${locale} content root`);
assertWithin(blogRootReal, localeRootReal, `Blog ${locale} content root`);
}
const posts = new Map();
for (const name of files.filter((item) => item.endsWith(".md")).sort()) {
const file = path.join(dir, name);
const parsed = parseFrontmatter(await readRegularFileWithin(dir, file, `Blog ${locale} article`, "utf8"), file);
const slug = name.slice(0, -3);
posts.set(slug, { ...parsed, slug, locale, file });
}
byLocale.set(locale, posts);
}
const enSlugs = [...byLocale.get("en").keys()].sort();
const zhSlugs = [...byLocale.get("zh").keys()].sort();
if (JSON.stringify(enSlugs) !== JSON.stringify(zhSlugs)) throw new Error("English and Chinese blog slug sets differ");
return { byLocale, slugs: enSlugs };
}
async function renderArticle(sourceRoot, post, locale, fallback, assetRoot, imageCache) {
const config = LANGUAGE_CONFIG[locale];
const title = locale === "ja" && fallback ? (JAPANESE_TITLES[post.slug] || post.data.title) : post.data.title;
let body = post.body;
const refs = imageReferences(body);
for (const ref of refs) if (!imageCache.has(ref.url)) imageCache.set(ref.url, await materializeImage(sourceRoot, ref.url, assetRoot));
if (post.data.cover?.image && !imageCache.has(post.data.cover.image)) imageCache.set(post.data.cover.image, await materializeImage(sourceRoot, post.data.cover.image, assetRoot));
for (const ref of refs) body = body.replaceAll(ref.full, `![${ref.alt}](${imageCache.get(ref.url)})`);
body = escapeMdxProse(body);
const cover = post.data.cover?.image ? imageCache.get(post.data.cover.image) : "";
const bodyHasCover = cover && body.includes(`](${cover})`);
const notice = fallback ? `\n> ${config.fallbackLabel}\n` : "";
const coverBlock = cover && !bodyHasCover ? `\n![${post.data.cover.alt || title}](${cover})\n` : "";
return `---\ntitle: ${yamlQuote(title)}\ndescription: ${yamlQuote(post.data.description)}\n---\n\n${config.sourceLabel} [${config.sourceAction}](${canonicalUrl(post.locale, post.slug)}).\n\n${attribution(locale, post)}\n${notice}${coverBlock}\n${body.trim()}\n`;
}
function articlePage(locale, slug) { return `${locale}/articles/${slug}`; }
function renderIndex(locale, posts) {
const config = LANGUAGE_CONFIG[locale];
const lines = ["---", `title: ${yamlQuote(config.introTitle)}`, `description: ${yamlQuote(config.introDescription)}`, "---", "", config.introDescription, ""];
for (const key of ["updates", "engineering", "tutorials", "announcements"]) {
const inGroup = posts.filter((post) => post.group === key);
if (!inGroup.length) continue;
lines.push(`## ${config.groups[key]}`, "");
for (const post of inGroup) lines.push(`- [${post.title}](/${articlePage(locale, post.slug)}) — ${post.date}`);
lines.push("");
}
return `${lines.join("\n").trim()}\n`;
}
function navigationTab(locale, posts) {
const config = LANGUAGE_CONFIG[locale];
const groups = [{ group: config.overviewGroup, pages: [`${locale}/articles/index`] }];
for (const key of ["updates", "engineering", "tutorials", "announcements"]) {
const pages = posts.filter((post) => post.group === key).map((post) => articlePage(locale, post.slug));
if (pages.length) groups.push({ group: config.groups[key], pages });
}
return { tab: config.tab, groups };
}
async function validateGenerated(manifest) {
const docs = JSON.parse(await readFile(path.join(ROOT, "docs.json"), "utf8"));
const failures = [];
const referencedImages = new Set();
for (const locale of ARTICLE_LOCALES) {
const config = LANGUAGE_CONFIG[locale];
const articleRoot = path.join(ROOT, locale, "articles");
await assertDirectoryWithoutSymlinks(articleRoot, `${locale} generated article root`);
const language = docs.navigation.languages.find((item) => item.language === config.mintlify);
const tabs = language?.tabs.filter((item) => item.tab === config.tab) || [];
if (tabs.length !== 1) failures.push(`${locale}: expected exactly one ${config.tab} navigation tab, found ${tabs.length}`);
for (const article of manifest.articles.filter((item) => item.locale === locale)) {
const file = path.join(ROOT, `${article.page}.mdx`);
if (!existsSync(file)) failures.push(`missing article: ${article.page}.mdx`);
else {
const content = await readRegularFileWithin(articleRoot, file, `${locale} generated article`, "utf8");
for (const match of content.matchAll(/!\[[^\]]*\]\((\/images\/[^)]+)\)/g)) {
referencedImages.add(match[1]);
const image = path.join(ROOT, match[1].slice(1));
if (!existsSync(image) || (await stat(image)).size === 0) failures.push(`${article.page}: missing image ${match[1]}`);
}
}
}
}
const mirroredMetadata = await imageMetadata(ROOT);
const mirroredImages = new Set(mirroredMetadata.map((image) => image.path));
for (const image of mirroredImages) if (!referencedImages.has(image)) failures.push(`unreferenced mirrored image: ${image}`);
for (const image of referencedImages) if (!mirroredImages.has(image)) failures.push(`referenced image is not mirrored: ${image}`);
const expectedImages = new Map((manifest.images || []).map((image) => [image.path, image]));
for (const image of mirroredMetadata) {
const expected = expectedImages.get(image.path);
if (!expected) failures.push(`mirrored image missing from manifest: ${image.path}`);
else if (expected.sha256 !== image.sha256 || expected.bytes !== image.bytes) failures.push(`mirrored image digest mismatch: ${image.path}`);
}
for (const image of expectedImages.keys()) if (!mirroredImages.has(image)) failures.push(`manifest image is missing: ${image}`);
if (failures.length) throw new Error(`Generated article validation failed:\n${failures.join("\n")}`);
}
async function listFiles(directory) {
if (!existsSync(directory)) return [];
const files = [];
for (const entry of await readdir(directory, { withFileTypes: true })) {
const full = path.join(directory, entry.name);
if (entry.isSymbolicLink()) throw new Error(`Generated content tree must not contain symlinks: ${full}`);
if (entry.isDirectory()) files.push(...await listFiles(full));
else if (entry.isFile()) files.push(full);
else throw new Error(`Generated content tree must contain only directories and regular files: ${full}`);
}
return files;
}
async function imageMetadata(assetRoot) {
const metadata = [];
for (const file of await listFiles(path.join(assetRoot, "images/articles"))) {
const bytes = await readFile(file);
validateImageBytes(bytes, file);
metadata.push({
path: `/${path.relative(assetRoot, file).split(path.sep).join("/")}`,
sha256: createHash("sha256").update(bytes).digest("hex"),
bytes: bytes.length,
});
}
return metadata.sort((a, b) => a.path.localeCompare(b.path));
}
async function referencedImagesIn(directory) {
const images = new Set();
for (const file of await listFiles(directory)) {
if (!file.endsWith(".mdx")) continue;
const content = await readFile(file, "utf8");
for (const match of content.matchAll(/!\[[^\]]*\]\((\/images\/articles\/[^)]+)\)/g)) images.add(match[1]);
}
return images;
}
async function main() {
const args = parseArgs(process.argv.slice(2));
const manifestPath = path.join(ROOT, "scripts/blog-articles-manifest.json");
if (args.check && !args.source) {
await validateGenerated(JSON.parse(await readFile(manifestPath, "utf8")));
console.log("Generated article pages, navigation, and local images are valid.");
return;
}
if (!args.source) throw new Error("Pass --source /path/to/langbot-landing-page or set LANGBOT_LANDING_REPO");
if (!existsSync(path.join(args.source, "src/content/blog"))) throw new Error(`Not a LangBot landing repository: ${args.source}`);
const { byLocale, slugs } = await loadPosts(args.source);
const outputRoot = await mkdtemp(path.join(tmpdir(), "langbot-wiki-article-sync-"));
const assetRoot = args.check ? outputRoot : ROOT;
const imageCache = new Map();
const localePosts = new Map();
const manifest = { source: "https://github.com/langbot-app/langbot-landing-page/tree/main/src/content/blog", articles: [] };
for (const locale of ARTICLE_LOCALES) {
const renderedPosts = [];
for (const slug of slugs) {
const localized = byLocale.get(locale).get(slug);
const post = localized || byLocale.get("en").get(slug);
const fallback = !localized;
const title = locale === "ja" && fallback ? (JAPANESE_TITLES[slug] || post.data.title) : post.data.title;
renderedPosts.push({ slug, title, date: post.data.date, group: classify(byLocale.get("en").get(slug)), fallback });
const target = path.join(outputRoot, locale, "articles", `${slug}.mdx`);
await mkdir(path.dirname(target), { recursive: true });
await writeFile(target, await renderArticle(args.source, post, locale, fallback, assetRoot, imageCache));
manifest.articles.push({ locale, slug, page: articlePage(locale, slug), sourceLocale: post.locale, fallback });
}
renderedPosts.sort((a, b) => b.date.localeCompare(a.date) || a.slug.localeCompare(b.slug));
localePosts.set(locale, renderedPosts);
await writeFile(path.join(outputRoot, locale, "articles", "index.mdx"), renderIndex(locale, renderedPosts));
}
if (!args.check) {
const referencedImages = await referencedImagesIn(outputRoot);
for (const file of await listFiles(path.join(ROOT, "images/articles"))) {
const publicPath = `/${path.relative(ROOT, file).split(path.sep).join("/")}`;
if (!referencedImages.has(publicPath)) await rm(file, { force: true });
}
}
manifest.images = await imageMetadata(assetRoot);
if (args.check) {
const mismatches = [];
for (const locale of ARTICLE_LOCALES) {
const generatedDir = path.join(outputRoot, locale, "articles");
const currentDir = path.join(ROOT, locale, "articles");
const generatedNames = (await readdir(generatedDir)).sort();
const currentEntries = existsSync(currentDir) ? await readdir(currentDir, { withFileTypes: true }) : [];
if (currentEntries.some((entry) => !entry.isFile() || entry.isSymbolicLink())) mismatches.push(`${locale}/articles contains non-regular files`);
const currentNames = currentEntries.map((entry) => entry.name).sort();
if (JSON.stringify(generatedNames) !== JSON.stringify(currentNames)) mismatches.push(`${locale}/articles file set`);
for (const name of generatedNames) {
const generated = await readFile(path.join(generatedDir, name), "utf8");
const currentFile = path.join(currentDir, name);
const current = existsSync(currentFile) ? await readRegularFileWithin(currentDir, currentFile, `${locale} generated article`, "utf8") : "";
if (generated !== current) mismatches.push(`${locale}/articles/${name}`);
}
}
const currentManifest = existsSync(manifestPath) ? JSON.parse(await readFile(manifestPath, "utf8")) : null;
if (JSON.stringify(currentManifest) !== JSON.stringify(manifest)) mismatches.push("scripts/blog-articles-manifest.json");
const currentDocs = JSON.parse(await readFile(path.join(ROOT, "docs.json"), "utf8"));
for (const locale of ARTICLE_LOCALES) {
const config = LANGUAGE_CONFIG[locale];
const language = currentDocs.navigation.languages.find((item) => item.language === config.mintlify);
const matchingTabs = language?.tabs.filter((item) => item.tab === config.tab) || [];
const currentTab = matchingTabs.length === 1 ? matchingTabs[0] : null;
const expectedTab = navigationTab(locale, localePosts.get(locale));
if (matchingTabs.length !== 1 || JSON.stringify(currentTab) !== JSON.stringify(expectedTab)) mismatches.push(`docs.json ${config.tab} tab`);
}
await rm(outputRoot, { recursive: true, force: true });
if (mismatches.length) throw new Error(`Blog articles are out of sync:\n${mismatches.join("\n")}`);
await validateGenerated(JSON.parse(await readFile(manifestPath, "utf8")));
console.log(`All ${manifest.articles.length} localized article pages are synchronized.`);
return;
}
for (const locale of ARTICLE_LOCALES) {
const targetDir = path.join(ROOT, locale, "articles");
await rm(targetDir, { recursive: true, force: true });
await mkdir(path.dirname(targetDir), { recursive: true });
await cp(path.join(outputRoot, locale, "articles"), targetDir, { recursive: true });
}
await rm(outputRoot, { recursive: true, force: true });
const docsPath = path.join(ROOT, "docs.json");
const docs = JSON.parse(await readFile(docsPath, "utf8"));
for (const locale of ARTICLE_LOCALES) {
const config = LANGUAGE_CONFIG[locale];
const language = docs.navigation.languages.find((item) => item.language === config.mintlify);
if (!language) throw new Error(`docs.json missing language ${config.mintlify}`);
language.tabs = language.tabs.filter((item) => item.tab !== config.tab);
const apiIndex = language.tabs.findIndex((item) => /API/.test(item.tab));
language.tabs.splice(apiIndex >= 0 ? apiIndex : language.tabs.length, 0, navigationTab(locale, localePosts.get(locale)));
}
await writeFile(docsPath, `${JSON.stringify(docs, null, 2)}\n`);
await writeFile(manifestPath, `${JSON.stringify(manifest, null, 2)}\n`);
await validateGenerated(manifest);
console.log(`Synchronized ${slugs.length} posts into ${manifest.articles.length} localized Wiki pages.`);
}
main().catch((error) => { console.error(error.stack || error.message); process.exitCode = 1; });
tests/blog-articles.test.mjs
+59
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@@ -0,0 +1,59 @@
import assert from "node:assert/strict";
import { createHash } from "node:crypto";
import { existsSync, readFileSync, statSync } from "node:fs";
import path from "node:path";
import test from "node:test";
const root = path.resolve(import.meta.dirname, "..");
const docs = JSON.parse(readFileSync(path.join(root, "docs.json"), "utf8"));
const manifest = JSON.parse(readFileSync(path.join(root, "scripts/blog-articles-manifest.json"), "utf8"));
const localeConfig = {
en: { language: "en", tab: "Articles" },
zh: { language: "cn", tab: "文章" },
ja: { language: "jp", tab: "記事" },
};
test("all canonical Blog slugs are published in every Wiki locale", () => {
const slugsByLocale = new Map();
for (const locale of Object.keys(localeConfig)) {
const items = manifest.articles.filter((article) => article.locale === locale);
assert.ok(items.length > 0, `${locale} has no articles`);
slugsByLocale.set(locale, items.map((article) => article.slug).sort());
}
assert.deepEqual(slugsByLocale.get("zh"), slugsByLocale.get("en"));
assert.deepEqual(slugsByLocale.get("ja"), slugsByLocale.get("en"));
});
test("article tabs group every generated page exactly once", () => {
for (const [locale, config] of Object.entries(localeConfig)) {
const language = docs.navigation.languages.find((item) => item.language === config.language);
const matchingTabs = language.tabs.filter((item) => item.tab === config.tab);
assert.equal(matchingTabs.length, 1, `${locale} must have exactly one ${config.tab} tab`);
const tab = matchingTabs[0];
const pages = tab.groups.flatMap((group) => group.pages).filter((page) => page !== `${locale}/articles/index`);
const expected = manifest.articles.filter((article) => article.locale === locale).map((article) => article.page);
assert.deepEqual([...pages].sort(), [...expected].sort());
assert.equal(new Set(pages).size, pages.length, `${locale} has duplicate article navigation entries`);
}
});
test("generated articles use only non-empty Wiki-local images", () => {
const expectedImages = new Map(manifest.images.map((image) => [image.path, image]));
for (const article of manifest.articles) {
const file = path.join(root, `${article.page}.mdx`);
assert.ok(existsSync(file), `missing ${article.page}.mdx`);
const content = readFileSync(file, "utf8");
const imageUrls = [...content.matchAll(/!\[[^\]]*\]\(([^)]+)\)/g)].map((match) => match[1]);
for (const imageUrl of imageUrls) {
assert.match(imageUrl, /^\/images\/articles\//, `${article.page} hotlinks ${imageUrl}`);
const image = path.join(root, imageUrl.slice(1));
assert.ok(existsSync(image), `${article.page} references missing ${imageUrl}`);
assert.ok(statSync(image).size > 0, `${imageUrl} is empty`);
const bytes = readFileSync(image);
const expected = expectedImages.get(imageUrl);
assert.ok(expected, `${imageUrl} is missing from the synchronization manifest`);
assert.equal(bytes.length, expected.bytes, `${imageUrl} size differs from the manifest`);
assert.equal(createHash("sha256").update(bytes).digest("hex"), expected.sha256, `${imageUrl} digest differs from the manifest`);
}
}
});

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