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 XMUDeepLIT · Agent Tool · ★ 426
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-Self-Evolving-Agents safe to install? View the security audit →
Awesome-Self-Evolving-Agents This repository provides a comprehensive collection of research papers, benchmarks, and open-source projects on Self-Evolving Agents. It includes contents from our survey paper 📖"A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution" and will be continuously updated. 🤗 You're very welcome to contribute to this repository. If you find any missing resources or come across interesting new research works, please don’t hesitate to launch an issue or submit a pull request! 📫 Contact us via emails: , 📃 Please cite our paper if you find our survey or repository helpful! @article{xiang2026systematic, title={A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environm
| Stars | 426 |
| Forks | 31 |
| Category | Agent Tool |
| Quality Score | 55.6598359308749/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-12 |
| Created | 2026-02-09 |
| Platforms | aws |
| Est. Tokens | ~28k |
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Awesome-Self-Evolving-Agents is A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents.. It is categorized as a Agent Tool with 426 GitHub stars.
You can find installation instructions and usage details in the Awesome-Self-Evolving-Agents GitHub repository at github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents. The project has 426 stars and 31 forks, indicating an active community.
The top alternatives to Awesome-Self-Evolving-Agents on Agent Skills Hub include awesome-agent-evolution, COG-second-brain, CORAL. 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: