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 mahmoudrabie · MCP Server · ★ 159
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentic-ai safe to install? View the security audit →
Agentic AI Research Radar 300+ curated agentic AI papers, benchmarks, frameworks, and real-world systems across 24 domains. Start with Frameworks Contribute a Resource Why This Repo Agentic AI moves fast, and useful work is scattered across papers, code repositories, benchmark releases, technical posts, and domain-specific case studies. This repo tracks high-signal work in one place so builders and researchers can quickly find: frameworks and orchestration patterns for building agents evaluation, testing, and benchmark resources security, safety, and governance research domain applications in healthcare, Physical AI/robotics, software engineering, IoT, education, and more If this saves you research time, starring the repo helps more builders di
| Stars | 159 |
| Forks | 6 |
| Category | MCP Server |
| License | MIT |
| Quality Score | 56.56481216746/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-25 |
| Created | 2025-03-21 |
| Platforms | mcp |
| Est. Tokens | ~17k |
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agentic-ai is Agentic AI research papers, benchmarks, frameworks, and tools curated across 24 domains.. It is categorized as a MCP Server with 159 GitHub stars.
You can find installation instructions and usage details in the agentic-ai GitHub repository at github.com/mahmoudrabie/agentic-ai. The project has 159 stars and 6 forks, indicating an active community.
agentic-ai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agentic-ai on Agent Skills Hub include awesome-ai-agents, agent-builder, oreilly-ai-agents. 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: