RAGLight: Modular framework for Retrieval-Augmented Generation (RAG)

by Bessouat40 · MCP Server · ★ 674

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

Featured in: Knowledge Base & RAG

How to install RAGLight

pip install raglight
pip install "raglight[qdrant]"           # Qdrant only (Windows-friendly)

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

Security audit verdict: SAFE · quality 67/100

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.

agentic-aiagentic-ragagentic-workflowartificial-intelligencedata-scienceframeworkhuggingfacelmstudiomcpmcp-tools

Quick Facts

Stars674
Forks103
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score67/100
Open Issues16
Last Updated2026-10-16
Created2024-12-12
Platformsdocker, mcp, python
Est. Tokens~23k

About RAGLight

RAGLight RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG). It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference.

Compatible Skills

These tools work well together with RAGLight for enhanced workflows:

  • CodeRAG — semantic(0.35)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • autollm — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (64%)
  • AI-Lawyer-RAG-with-Deepseek — semantic(0.37)+complementary+rare_topics+same_lang+shared_platform (61%)
  • BambooAI — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • langroid — semantic(0.30)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)

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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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