Awesome-Graphs-Meet-Agents: [Up-to-date] A curated list of resources on graph-empowered agents and agent-facilitated graph learning

by YuanchenBei · Agent Tool · ★ 211

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 52/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentagentic-aigraphgraph-engineeringgraph-learninglarge-language-modelsllm-agentreinforcement-learningrl-agent

Quick Facts

Stars211
Forks17
CategoryAgent Tool
LicenseMIT
Quality Score52/100
Open Issues1
Last Updated2026-07-28
Created2025-06-13
Est. Tokens~23k

About Awesome-Graphs-Meet-Agents

🥂 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.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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