Production-grade platform for building agentic IM bots.

Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.

English / 简体中文 / 繁體中文 / 日本語 / Español / Français / 한국어 / Русский / Tiếng Việt

Website ｜ Features ｜ Docs ｜ API ｜ Cloud ｜ Plugin Market ｜ Roadmap

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What is LangBot?

LangBot is an open-source, production-grade platform for building AI-powered instant messaging bots. It connects Large Language Models (LLMs) to any chat platform, enabling you to create intelligent agents that can converse, execute tasks, and integrate with your existing workflows.

Key Capabilities

• AI Conversations & Agents — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to Dify, Coze, n8n, Langflow.
• Universal IM Platform Support — One codebase for Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
• Production-Ready — Access control, rate limiting, sensitive word filtering, comprehensive monitoring, and exception handling. Trusted by enterprises.
• Plugin Ecosystem — Hundreds of plugins, event-driven architecture, component extensions, and MCP protocol support.
• Web Management Panel — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
• Multi-Pipeline Architecture — Different bots for different scenarios, with comprehensive monitoring and exception handling.

→ Learn more about all features

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Quick Start

☁️ LangBot Cloud (Recommended)

LangBot Cloud — Zero deployment, ready to use.

One-Line Launch

uvx langbot

Requires uv. Visit http://localhost:5300 — done.

Docker Compose

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

One-Click Cloud Deploy

More options: Docker · Manual · BTPanel · Kubernetes

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Local development

Inspecting the SQLite database

When you run LangBot from a git checkout, the default database file is data/langbot.db at the repository root.

Command line:

sqlite3 data/langbot.db "select uuid, name, adapter from bots;"

GUI (e.g. TablePlus): Add a SQLite connection whose database path is data/langbot.db, connect, then open the bots table in the sidebar (other tables such as api_keys are listed the same way). This is equivalent to running the SQL above in a shell.

Treat adapter_config and similar columns as sensitive—they can contain tokens. Redact them in screenshots or exports if you share anything publicly.

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Supported Platforms

Platform	Status	Notes
Discord	✅
Telegram	✅
Slack	✅
LINE	✅
QQ	✅	Personal & Official API
WeCom	✅	Enterprise WeChat, External CS, AI Bot
WeChat	✅	Personal & Official Account
Lark	✅
DingTalk	✅
KOOK	✅
Satori	✅

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Supported LLMs & Integrations

Provider	Type	Status
OpenAI	LLM	✅
Anthropic	LLM	✅
DeepSeek	LLM	✅
Google Gemini	LLM	✅
xAI	LLM	✅
Moonshot	LLM	✅
Zhipu AI	LLM	✅
Ollama	Local LLM	✅
LM Studio	Local LLM	✅
Dify	LLMOps	✅
MCP	Protocol	✅
SiliconFlow	Gateway	✅
Aliyun Bailian	Gateway	✅
Volc Engine Ark	Gateway	✅
ModelScope	Gateway	✅
GiteeAI	Gateway	✅
CompShare	GPU Platform	✅
PPIO	GPU Platform	✅
ShengSuanYun	GPU Platform	✅
接口 AI	Gateway	✅
302.AI	Gateway	✅

→ View all integrations

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Why LangBot?

Use Case	How LangBot Helps
Customer Support	Deploy AI agents to Slack/Discord/Telegram that answer questions using your knowledge base
Internal Tools	Connect n8n/Dify workflows to WeCom/DingTalk for automated business processes
Community Management	Moderate QQ/Discord groups with AI-powered content filtering and interaction
Multi-Platform Presence	One bot, all platforms. Manage from a single dashboard

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Live Demo

Try it now: https://demo.langbot.dev/

• Email: demo@langbot.app
• Password: langbot123456

Note: Public demo environment. Do not enter sensitive information.

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Community

• Discord Community

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Star History

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Contributors

Thanks to all contributors who have helped make LangBot better: