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

Source: https://langbot.app/docs/en/articles/deploy-ai-bot-in-5-minutes

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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/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](https://langbot.app/docs/images/articles/source/images/article-mcp/10-group-chat.png)

And in private chat:

![Private Chat Demo](https://langbot.app/docs/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:** [langbot.app/docs](https://langbot.app/docs/en/insight/guide.md)
- **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.