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 YuanchenBei · Agent Tool · ★ 204
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-Graphs-Meet-Agents safe to install? View the security audit →
🥂 Awesome-Graphs-Meet-Agents 📚 This repository contains a curated list of related papers on graphs & agents, based on the survey paper "Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities". 🤗 Welcome to update this repository by launching an issue or a pull request if you find any missing resources or want to include some new resources. 🥂 Awesome-Graphs-Meet-Agents 🪴 Overall Description 🚀 Taxonomy Graph for Agent Planning Task Reasoning Task Decomposition Task Decision Searching Graph for Agent Execution Tool Usage Environment Interaction Graph for Agent Memory Memory Organization Memory Retrieval Memory Maintenance [Graphs for Multi-Agent Coordination](#graphs-for
| Stars | 204 |
| Forks | 15 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 51.9386146874532/100 |
| Last Updated | 2026-07-28 |
| Created | 2025-06-13 |
| Est. Tokens | ~23k |
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Awesome-Graphs-Meet-Agents is [Up-to-date] A curated list of resources on graph-empowered agents and agent-facilitated graph learning (Graphs Meet Agents).. It is categorized as a Agent Tool with 204 GitHub stars.
You can find installation instructions and usage details in the Awesome-Graphs-Meet-Agents GitHub repository at github.com/YuanchenBei/Awesome-Graphs-Meet-Agents. The project has 204 stars and 15 forks, indicating an active community.
Awesome-Graphs-Meet-Agents is released under the MIT license, making it free to use and modify according to the license terms.
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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: