LightRAG and OpenRag
These two tools are competitors, with LightRAG focusing on simplicity and speed for RAG, while OpenRag offers a multi-strategy RAG system with advanced techniques like RAPTOR, knowledge graphs, and neural reranking to achieve higher recall.
About LightRAG
HKUDS/LightRAG
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
Constructs a dual-level retrieval system combining vector similarity search with knowledge graph extraction to handle both entity-centric and content-based queries. Supports multiple storage backends including Neo4j, MongoDB, and PostgreSQL, with integrated reranking, citation tracking, and multimodal document processing via RAG-Anything. Designed for Python 3.10+ with built-in evaluation (RAGAS) and tracing (Langfuse) capabilities.
About OpenRag
incidentfox/OpenRag
Multi-strategy RAG system achieving 74% Recall@10 on MultiHop-RAG. Combines RAPTOR hierarchical retrieval, knowledge graphs, HyDE, BM25, and Cohere neural reranking.
Implements a FastAPI server with pluggable retrieval strategies (semantic search, HyDE query expansion, BM25 hybrid matching, multi-hop decomposition) that run in parallel before Cohere neural reranking, with built-in persistence for RAPTOR hierarchies and a comprehensive benchmark suite supporting MultiHop-RAG and CRAG datasets. Ablation studies show the neural reranker alone contributes +9.3% recall improvement, while local cross-encoder alternatives are available for privacy-sensitive deployments.
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