laya-jev-GraphRAG: A database-agnostic Agentic GraphRAG framework using swappable System One models (local Laya / cloud Jev)

by bodepudimuneendra-netizen · Agent Tool · ★ 51

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How to install laya-jev-GraphRAG

git clone https://github.com/bodepudimuneendra-netizen/laya-jev-GraphRAG.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 69/100

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. Full audit →

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.

astar-algorithmeval-harnessfine-tuninggraphdatabasegrapragjevlayaneo4jragtypesafe-ai

Quick Facts

Stars51
Forks3
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score69/100
Last Updated2026-09-29
Created2026-09-23
Platformspython
Est. Tokens~11k

About laya-jev-GraphRAG

laya-jev-GraphRAG A production-grade Agentic GraphRAG engine with swappable System One decision models (local Laya / cloud Jev) that evaluate every edge and relationship across the entire graph lifecycle.

Compatible Skills

These tools work well together with laya-jev-GraphRAG for enhanced workflows:

  • laya-ultrafast — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)

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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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