AstrBot

Overview

AstrBot is an open-source, all-in-one agent chatbot platform that integrates with mainstream instant messaging apps. It provides reliable and scalable conversational AI infrastructure for individuals, developers, and teams, supporting personal AI companions, customer service, automation assistants, and enterprise knowledge bases.

Key Features

• AI LLM conversations with multimodal input, agent capabilities, MCP, skills, knowledge base, persona settings, and auto context compression.
• Multi-platform access to QQ, OneBot v11, Telegram, WeCom, WeChat Official Accounts, Feishu, DingTalk, Slack, Discord, LINE, Satori, KOOK, Misskey, and Mattermost.
• Plugin extensions with 1000+ plugins available for one-click installation and a plugin-driven architecture.
• Agent sandbox for isolated, safe execution of code, shell calls, and session-level resource reuse.
• WebUI and Web ChatUI support for configuration, debugging, logs, plugin management, and chat.
• Multi-provider AI support including OpenAI, Anthropic, Google Gemini, DeepSeek, Ollama, LM Studio, Dify, Coze, and many speech services.

Use Cases

• Personal AI companion with emotional companionship, persona editing, long-term memory, and proactive dialog.
• Intelligent customer service and enterprise knowledge base assistants inside IM platforms.
• Automation assistant for group operations, content workflows, and scheduled tasks with cron jobs.
• Developer platform for building production-ready AI applications on instant messaging workflows.

Getting Started

• Install uv, then run uv tool install astrbot --python 3.12, astrbot init for first-time initialization, and astrbot run.
• Deploy with Docker or Docker Compose by following the official documentation for a stable production setup.
• Use the AstrBot desktop application or AstrBot Launcher for desktop usage and isolated multi-instance deployment.
• Use one-click cloud deployment via RainYun for 24-hour-online deployment without managing servers.

Deployment & Requirements

• One-click uv deployment requires uv to be installed and uses Python 3.12.
• Docker or Docker Compose is recommended for a more stable, production-ready deployment.
• Desktop application deployment is designed for desktop usage and is not recommended for server scenarios.
• Launcher deployment is intended for desktop users who want fast deployment and isolated multi-instance usage.

Before You Adopt

• License: AGPL-3.0. Review its terms before using, modifying, or distributing the project.
• Desktop application deployment is intended for desktop use and is not recommended for server scenarios.