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 NipunaRanasinghe · Agent Tool · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-agents safe to install? View the security audit →
Awesome AI Agents A curated list of frameworks, tools, and resources for building and deploying AI agents. From multi-agent systems to autonomous coding assistants, this repository covers the latest advancements in AI agent technology. Contents 🌟 Core Frameworks 🚀 Specialized Agents 💻 Coding Agents 🔬 Research Agents
| Stars | 57 |
| Forks | 67 |
| Language | HTML |
| Category | Agent Tool |
| License | CC0-1.0 |
| Quality Score | 56.7713250042824/100 |
| Open Issues | 26 |
| Last Updated | 2026-09-30 |
| Created | 2025-01-25 |
| Est. Tokens | ~23k |
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awesome-ai-agents is A curated list of frameworks, tools, and resources for building and deploying AI agents.. It is categorized as a Agent Tool with 57 GitHub stars.
awesome-ai-agents is primarily written in HTML. It covers topics such as ai, ai-agent, ai-agents.
You can find installation instructions and usage details in the awesome-ai-agents GitHub repository at github.com/NipunaRanasinghe/awesome-ai-agents. The project has 57 stars and 67 forks, indicating an active community.
awesome-ai-agents is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-ai-agents on Agent Skills Hub include toolsdk-mcp-registry, awesome-ai-agents-directories, awesome-prompt-engineering. 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: