erag: An AI interaction tool with RAG hybrid search, conversation context, web content processing and structured

by EduardTalianu · Agent Tool · ★ 214

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

Featured in: Knowledge Base & RAG

How to install erag

git clone https://github.com/EdwardDali/erag.git && cd erag

Taken from the project's README on GitHub.

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.

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.

Quick Facts

Stars214
Forks25
LanguagePython
CategoryAgent Tool
Quality Score66/100
Last Updated2025-06-17
Created2024-04-25
Platformsbrowser, python
Est. Tokens~473k

About erag

ERAG Overview You can use this application to: Talk privately with your documents using Ollama or talk fast using Groq and others. Perform Retrieval-Augmented Generation activities (RAG) using various APIs (Ollama, LLaMA, Groq, Gemini, Cohere). Perform AI powered web search. Talk with a specific url. Analyze and summarize GitHub repositories.

Compatible Skills

These tools work well together with erag for enhanced workflows:

  • datagouv-mcp — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)
  • flexible-graphrag — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
  • dbt-llm-agent — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
  • data-analysis-llm-agent — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • lex — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (54%)

erag alternative? Top 5 similar tools

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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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