RAGLight

by Bessouat40 · Python · ★ 659

RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

#agentic-ai#agentic-rag#agentic-workflow#artificial-intelligence#data-science#framework#huggingface#lmstudio#mcp#mcp-tools#mistral-api#mistralai#ollama#openai#openai-api#rag#retrieval-augmented#retrieval-augmented-generation#vector-database

Install

pip install git+https://github.com/Bessouat40/RAGLight.git

Claude Desktop config

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "raglight": {
      "command": "uvx",
      "args": [
        "git+https://github.com/Bessouat40/RAGLight.git"
      ]
    }
  }
}

From the README

[](https://pepy.tech/projects/raglight) [](https://github.com/Bessouat40/RAGLight/actions/workflows/test.yml) **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. Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context-aware AI solutions. > ## ⚠️ Requirements > > Actual…

Read full README on GitHub →