#LangBot: One AI bot across every chat app

Want one AI bot running on Telegram, Discord, Slack, and WeChat without rewriting it four times? LangBot is an open source project that tries to solve exactly that problem.

#What it is

LangBot is a production-grade, open source platform for building agentic instant messaging bots. The core value is one codebase for many channels: Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK, and Matrix. Instead of duplicating bot logic for each platform's webhook and API, LangBot acts as a coordination layer — chat apps in front, models and workflows behind.

It is more than a chatbot relay. LangBot supports multi-turn conversation, tool calling, multi-modal input, streaming, RAG, plugins, and MCP. You can connect OpenAI, Claude, DeepSeek, Gemini, Ollama, Dify, or n8n depending on your use case, and a web panel lets you configure and observe the bot without editing YAML for every change.

#Why it matters

• One bot can serve many chat channels, which matters when your community or team lives across multiple platforms.
• It connects easily to LLMs, RAG pipelines, Dify, n8n, and plugins, so you can compose a real agent workflow rather than just forwarding messages to a model.
• A web dashboard replaces hand-editing config files for routine changes, giving you a place to manage bots, pipelines, plugins, and observe traffic, model calls, and sessions.

#How it works

LangBot sits in the middle. Chat platforms connect on the front side; models, knowledge bases, plugins, and workflows connect on the back. A single bot is coordinated from a central point, and the platform handles the per-channel plumbing. The repo includes a quick-start command, uvx langbot, which opens a local dashboard on port 5300.

The agent layer is where the real capability lives. Multi-turn chat, tool calling, streaming responses, RAG, plugins, and MCP are all supported, which means the bot can do more than answer questions — it can take actions and pull context.

#Caveats

Each platform has its own rules, webhooks, and rate limits, so a broad feature list does not mean production deployment is one click. Bringing a bot to production still requires managing access control, rate limits, API keys, sensitive data, and model costs. The sensible path is to start with one channel and one use case before turning on every integration.

#Who it's for

LangBot is for teams and communities that need to deploy an AI bot across multiple chat platforms and want a single coordination layer rather than a tangle of per-channel scripts. If you only need one channel, a simpler tool may suffice.

If you need a multi-channel AI bot coordination layer, LangBot is worth a trial — just bring production discipline with it.

Source: https://github.com/langbot-app/LangBot