No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by microsoft · Agent Tool · ★ 61.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is autogen safe to install? View the security audit →
AutoGen AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans. Important: if you are new to AutoGen, please checkout Microsoft Agent Framework. AutoGen will still be maintained and continue to receive bug fixes and critical security patches. Read our announcement. Installation AutoGen requires Python 3.10 or later. The current stable version can be found in the [releases](htt
| Stars | 61,075 |
| Forks | 9,238 |
| Language | Python |
| Category | Agent Tool |
| License | CC-BY-4.0 |
| Quality Score | 62.8832906712694/100 |
| Open Issues | 1086 |
| Last Updated | 2026-04-15 |
| Created | 2023-08-18 |
| Platforms | python |
| Est. Tokens | ~9640k |
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autogen is A programming framework for agentic AI. It is categorized as a Agent Tool with 61.1k GitHub stars.
autogen is primarily written in Python. It covers topics such as agentic, agentic-agi, agents.
You can find installation instructions and usage details in the autogen GitHub repository at github.com/microsoft/autogen. The project has 61.1k stars and 9238 forks, indicating an active community.
autogen is released under the CC-BY-4.0 license, making it free to use and modify according to the license terms.
The top alternatives to autogen on Agent Skills Hub include langchain, openai-agents-python, ragflow. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: