RAGLight and RAG-Boilerplate
These are competitors—both provide end-to-end RAG frameworks with configurable components (LLMs, embeddings, vector stores), but RAGLight emphasizes modularity and MCP integration while RAG-Boilerplate emphasizes advanced retrieval techniques (hybrid search, reranking) and orchestration (CrewAI), so teams would typically choose one based on their architectural priorities rather than use both together.
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-Boilerplate
mburaksayici/RAG-Boilerplate
RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).
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