by Bessouat40 · MCP Server · ★ 656
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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. 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. 📚 Table of Contents Requirements Features Import library Chat with Your Documents Instantly With CLI Ignore Folders Feature Ignore Folders in Configuration Classes Deploy as a REST API (raglight serve) Start the server [Launc
| Stars | 656 |
| Forks | 99 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 37.3/100 |
| Open Issues | 9 |
| Last Updated | 2026-03-24 |
| Created | 2024-12-12 |
| Platforms | mcp, python |
| Est. Tokens | ~991k |
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RAGLight is 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 connec. It is categorized as a MCP Server with 656 GitHub stars.
RAGLight is primarily written in Python. It covers topics such as agentic-ai, agentic-rag, agentic-workflow.
You can find installation instructions and usage details in the RAGLight GitHub repository at github.com/Bessouat40/RAGLight. The project has 656 stars and 99 forks, indicating an active community.
RAGLight is released under the MIT license, making it free to use and modify according to the license terms.
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