OneRAG and Advanced-RAG-monorepo
Both frameworks offer production-ready RAG solutions with modular components like vector databases and LLM integrations, making them direct competitors in the "rag-quality-assurance" category.
About OneRAG
notadev-iamaura/OneRAG
Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.
Supports hybrid search (dense + BM25), GraphRAG for knowledge graph reasoning, and pluggable rerankers (6 options including Jina and Cohere) through a modular pipeline architecture. Includes built-in PII detection/masking, semantic/Redis caching layers, and query routing that classifies requests before retrieval. Designed for gradual complexity—start with basic vector search and layer in advanced features like agents and tool execution without refactoring the codebase.
About Advanced-RAG-monorepo
MERakram/Advanced-RAG-monorepo
🚀 Production-ready modular RAG monorepo: Local LLM inference (vLLM) • Hybrid retrieval with Qdrant • Semantic caching • Docling document parsing • Cross-encoder reranking • DeepEval evaluation • Full observability with Langfuse • Open WebUI chat interface • OpenAI-compatible API • Fully Dockerized
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