by EduardTalianu · Agent Tool · ★ 213
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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. Do AI powered Exploratory Data Analysis (EDA) with AI generated Business Intelligence and insights on excels and csv (see some examples in images below). Utilize multiple AI models in collaboration (worker, supervisor, manager) for pre-defined complex tasks. Generate specific knowledge entries (knol), or generate full size textbooks or use AI generated questions and answers to create datasets. Thus, ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently.
| Stars | 213 |
| Forks | 25 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 65.9003159301238/100 |
| Last Updated | 2025-06-17 |
| Created | 2024-04-25 |
| Platforms | browser, python |
| Est. Tokens | ~473k |
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erag is an AI interaction tool with RAG hybrid search, conversation context, web content processing and structured data analysis with LLM / GPT. It is categorized as a Agent Tool with 213 GitHub stars.
erag is primarily written in Python.
You can find installation instructions and usage details in the erag GitHub repository at github.com/EduardTalianu/erag. The project has 213 stars and 25 forks, indicating an active community.
The top alternatives to erag on Agent Skills Hub include awesome-android-agent-skills, claude-code-skill-factory, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.