tencentcloud/octop

A smarter, self-hosted AI assistant — multi-user, multi-agent.

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Summary Information

Updated 25 minutes ago
Added to GitGenius on September 18th, 2026
Created on July 8th, 2026
Open Issues & Pull Requests: 299 (+3)
GitHub issues: Enabled
Number of forks: 485
Total Stargazers: 4,367 (+12)
Total Subscribers: 45 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 11.6 hours
Mean response time: 3.9 days
90th percentile: 13.1 days
Tracked items: 192

Most active contributors

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How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Almost all tracked open issues have seen activity in the last three months. Only 38% of issues opened in the past year have been closed. Three people close 81% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 200
New in 7 days: 89
Closed in 7 days: 19
Avg open age: 18 days
Stale 30+ days: 57
Stale 90+ days: 0

Recent activity

Opened in 7 days: 86
Closed in 7 days: 18
Comments in 7 days: 11
Events in 7 days: 52

Top labels

  • bug (148)
  • enhancement (120)

Detailed Description

Octop is a self-hosted AI assistant platform that supports multi-user and multi-agent architectures.

The tool addresses the need for privacy-preserving AI assistance by running entirely on your own infrastructure rather than relying on cloud services. It uses a multi-agent architecture where agents can operate in parallel and collaborate within an intelligent environment. The system is designed to be accessible through multiple interfaces: a web dashboard, command-line interface, and integrations with messaging platforms including Feishu, DingTalk, QQ, Discord, WeCom, and programmatic access via HTTP, SSE, and WebSocket. Extensibility is built in through an expert library, Connectors that support OAuth and MCP protocols, and ACP integration for IDE workflows.

Teams, families, and individuals seeking a self-hosted alternative to cloud-based AI assistants should consider this tool, particularly those prioritizing data privacy and control. The single-process startup design makes deployment straightforward, and the multi-interface support means users can interact through their preferred communication channels. The project is written in Python and distributed via PyPI, making installation accessible to developers comfortable with Python tooling.

Development activity shows consistent engagement with code quality standards enforced through Ruff for linting and formatting. The project maintains an active community presence with a Discord channel for user support and discussion. The codebase demonstrates attention to modern Python practices and appears to be actively maintained with regular updates to the platform.