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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.

664 starsPython

RAGLight, serve a RAG pipeline as a REST API and chat UI in one command

by bessouat40·Mar 5, 2026·1 point·0 comments

AI Analysis

MidShip It

RAG library with serve command, but Langchain, LlamaIndex, and Verba already dominate.

Strengths
  • Modular design lets you swap LLM and embedding providers without rewriting
  • Docker Compose deployment ready, no complex orchestration needed
  • MCP integration enables connecting external tools and data sources cleanly
Weaknesses
  • RAG-as-a-service is saturated; no clear differentiation from established frameworks
  • Limited provider support (Ollama, LMStudio, OpenAI) vs competitors' ecosystems
Target Audience

Backend developers, ML engineers building RAG applications

Similar To

LangChain · LlamaIndex · Verba

Post Description

I added a `serve` command to RAGLight, a modular RAG library I've been building.

raglight serve # FastAPI server (ingest, query, list collections) raglight serve --ui # same + Streamlit chat UI

Config is env vars (LLM provider, embeddings provider, collection name, port).

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