🚀 Connect your communication app to AI in just one minute

MuseBot

This repository provides a Chat bot (Telegram, Discord, Slack, Lark（飞书），钉钉, 企业微信, QQ, 微信) that integrates with LLM API to provide AI-powered responses. The bot supports openai deepseek gemini openrouter orcarouter LLMs, making interactions feel more natural and dynamic.
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Китайская документация

Thanks

感谢阮一峰老师的weekly，很荣幸能登榜：https://github.com/ruanyf/weekly
感谢linux.do 社区，佬友们很给力，给一个我的介绍贴链接：https://linux.do/t/topic/1128110
Thanks to the Reddit community as well, even though a few of my subreddits got banned 😅: https://www.reddit.com/

🚀 Features

• 🤖 AI Responses: Uses LLM API for chatbot replies.
• ⏳ Streaming Output: Sends responses in real-time to improve user experience.
• 🏗 Easy Deployment: Run locally or deploy to a cloud server.
• 👀 Identify Image: use image to communicate with LLM, see doc.
• 🎺 Support Voice: use voice to communicate with LLM, see doc.
• 🐂 Function Call: transform mcp protocol to function call, see doc.
• 🧩 Skills: load local SKILL.md instructions into the unified /task workflow, see doc.
• 🛠️ Conversational System Operations: use natural language directly to manage runtime settings, MCP, skills, and cron jobs, and to run commands and file operations inside safety boundaries, see doc.
• 🌊 RAG: Support Rag to fill context, see doc.
• 🌞 AdminPlatform: Use platform to manage MuseBot, see doc.
• 🌛 Register: With the service registration module, robot instances can be automatically registered to the registration center doc
• 🌈 Metrics: Support Metrics for monitoring, see doc.
• 🐶 Cron: Support Cron to trigger LLM, see doc.

Usage Video

easiest way to use: https://www.youtube.com/watch?v=4UHoKRMfNZg
deepseek: https://www.youtube.com/watch?v=kPtNdLjKVn0
gemini: https://www.youtube.com/watch?v=7mV9RYvdE6I
chatgpt: https://www.youtube.com/watch?v=G_DZYMvd5Ug

📸 Support Platform

Supported Large Language Models

🤖 Text Example

🎺 Multimodal Example

📥 Installation

1. Clone the repository

   git clone https://github.com/yincongcyincong/MuseBot.git
   cd MuseBot
   

2. Install dependencies

    go mod tidy
   

3. Set up environment variables

    export TELEGRAM_BOT_TOKEN="your_telegram_bot_token"
    export DEEPSEEK_TOKEN="your_deepseek_api_key"
   

🚀 Usage

Run the bot locally:

 go run main.go -telegram_bot_token=telegram-bot-token -deepseek_token=deepseek-auth-token

Use docker

  docker pull jackyin0822/musebot:latest
  chmod 777 /home/user/data
  docker run -d -v /home/user/data:/app/data -e TELEGRAM_BOT_TOKEN="telegram-bot-token" -e DEEPSEEK_TOKEN="deepseek-auth-token" -p 36060:36060 --name my-bot  jackyin0822/MuseBot:latest

 ALIYUN:
 docker pull crpi-i1dsvpjijxpgjgbv.cn-hangzhou.personal.cr.aliyuncs.com/jackyin0822/musebot:latest
 chmod 777 /home/user/data
  docker run -d -v /home/user/data:/app/data -e TELEGRAM_BOT_TOKEN="telegram-bot-token" -e DEEPSEEK_TOKEN="deepseek-auth-token" -p 36060:36060 --name my-bot  crpi-i1dsvpjijxpgjgbv.cn-hangzhou.personal.cr.aliyuncs.com/jackyin0822/musebot:latest

command: (doc)[https://github.com/yincongcyincong/MuseBot/blob/main/static/doc/param_conf.md]

⚙️ Configuration

You can configure the bot via environment variables:

Here’s the English version of your environment variable table: If you use parameter. Please use lower letter and underscore. for example: ./MuseBot -telegram_bot_token=xxx

CUSTOM_URL

If you are using a self-deployed llm, you can set CUSTOM_URL to route requests to your self-deployed llm.

DB_TYPE

support sqlite3 or mysql

DB_CONF

if DB_TYPE is sqlite3, give a file path, such as ./data/telegram_bot.db if DB_TYPE is mysql, give a mysql link, such as root:admin@tcp(127.0.0.1:3306)/dbname?charset=utf8mb4&parseTime=True&loc=Local, database must be created.

LANG

choose a language for bot, English (en), Chinese (zh), Russian (ru).

other config

deepseek_conf
photo_conf
video_conf
audio_conf

Command

/clear $clear

clear all of your communication record with deepseek. this record use for helping deepseek to understand the context.

/retry $retry

retry last question.

/txt_type /photo_type /video_type /rec_type $txt_type $photo_type $video_type $rec_type

choose txt/photo/video/recognize model type.

/txt_model /img_model /video_model /rec_model $txt_model $img_model $video_model $rec_model

choose txt/photo/video/recognize model.

/mode $mode

show current model type and model.

/state $state

calculate one user token usage.

/photo /edit_photo $photo $edit_photo

/edit_photo will update you photo base on your description.

/video $video

/chat $chat

allows the bot to chat through /chat command in groups, without the bot being set as admin of the group.

/help $help

/task $task

multi agent communicate with each other!

Deployment

Deploy with Docker

1. Build the Docker image

    docker build -t MuseBot .
   

2. Run the container

     docker run -d -v /home/user/xxx/data:/app/data -e TELEGRAM_BOT_TOKEN="telegram-bot-token" -e DEEPSEEK_TOKEN="deepseek-auth-token" --name my-bot MuseBot
   

Contributing

Feel free to submit issues and pull requests to improve this bot. 🚀

group

telegram-group: https://t.me/+WtaMcDpaMOlhZTE1 , or you can have a try robot Guanwushan_bot. every body have 10000 token to try this bot, please give me a star!

QQ群：1031411708

License

MIT License © 2025 jack yin