Deploy Your Own AI Bot to Discord, Telegram & WeChat in 5 Minutes

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.

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.

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

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

Deploy in 5 Minutes — For Real

Step 1: Run Docker Compose

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

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

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:

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:

And in private chat:

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

Get Started

• GitHub: github.com/langbot-app/LangBot — give us a star!
• Documentation: docs.langbot.app
• Plugin Market: space.langbot.app

git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d

Your AI bot empire starts with one command.