Best RAG framework

Combined ranking

1. 1
   LlamaIndex—15 pts

   GPT #1Claude #1Gemini #1

   Best RAG-specialized developer framework: strong ingestion, indexing, chunking, retrievers, query engines, reranking, graph/hybrid retrieval patterns, evaluation hooks, and broad vector-store/model integrations

   To stay #1 Make production deployment, tracing, and hosted ops as polished and standard as its indexing APIs

2. 2
   LangChainincumbent—12 pts

   GPT #2Claude #2Gemini #2

   Largest ecosystem and strongest end-to-end app stack, with huge integrations, LangGraph for reliable orchestration, LangSmith for tracing/evals, and mature production patterns around retrieval workflows

   To rank higher Reduce abstraction churn and make RAG-specific paths simpler, more opinionated, and easier to maintain

3. 3
   Haystack—9 pts

   GPT #3Claude #3Gemini #3

   Clean production pipeline model, excellent hybrid search/reranking support, strong document QA heritage, transparent components, and enterprise-friendly deployment discipline

   To rank higher Expand ecosystem momentum and community integrations to match LangChain/LlamaIndex breadth

4. 4
   DSPy—4 pts

   GPT #4Claude —Gemini #4

   Best for optimizing RAG behavior instead of hand-tuning prompts, with programmatic retrieval-generation pipelines, metric-driven compilation, and strong experimentation discipline

   To rank higher Add more turnkey ingestion, connector, UI, and deployment ergonomics for normal RAG app teams

5. 5
   RAGFlow—3 pts

   GPT #5Claude #5Gemini #5

   Strong full-stack document RAG product with practical parsing, OCR/table handling, citation-focused workflows, and an out-of-the-box app experience for knowledge-base QA

   To rank higher Mature the developer-framework ecosystem, extensibility, and production customization depth

6. 6
   Difynew2 pts

   GPT —Claude #4Gemini —

   Fastest path from zero to a working RAG app — visual pipeline builder, built-in knowledge-base management with chunking/reranking controls, self-hostable, lets non-ML teams ship internal RAG assistants without writing orchestration code

   To rank higher Expose more low-level retrieval control — power users hit the ceiling of the visual builder when they need custom retrievers, advanced chunking, or eval-driven tuning

Rank history

By model

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled continuously