KAG: Logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs

by OpenSPG · Agent Tool · ★ 9.1k

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

Security audit verdict: SAFE · quality 66/100

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.

knowledge-graphlarge-language-modellogical-reasoningmulti-hop-question-answeringtrustfulness

Quick Facts

Stars9,085
Forks723
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score66/100
Open Issues173
Last Updated2026-01-28
Created2024-09-21
Platformspython
Est. Tokens~16k

About KAG

KAG: Knowledge Augmented Generation English | 简体中文 | 日本語版ドキュメント What is KAG? KAG is a logical reasoning and Q&A framework based on the [OpenSPG](https://github.com/OpenSPG/openspg

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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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