RAGLight and rag_blueprint
These are competitors offering overlapping modular RAG frameworks, though RAGLight emphasizes LLM/embedding/vector store flexibility with MCP integration while rag_blueprint focuses more heavily on built-in evaluation and monitoring capabilities.
About RAGLight
Bessouat40/RAGLight
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.
Supports hybrid retrieval combining BM25 keyword search with semantic vector similarity using Reciprocal Rank Fusion, and offers agentic RAG capabilities with query reformulation for multi-turn conversations. Built on pluggable document processors and vector store backends (Chroma, Qdrant) with optional observability via Langfuse tracing. Provides both programmatic Python APIs and CLI/REST interfaces for rapid deployment, including a Docker Compose setup for production environments.
About rag_blueprint
feld-m/rag_blueprint
A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.
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