Pyyan / Compare / RAGFlow vs LlamaIndex vs LangChain & LangGraph vs GraphRAG

RAGFlow vs LlamaIndex vs LangChain & LangGraph vs GraphRAG

4 of 5

RAG & Knowledge Graphs · verified 13 Aug 2026

×RAGFlowInfiniFlowcurrent
×LlamaIndexLlamaIndexcurrent
×LangChain & LangGraphLangChaincurrent
×GraphRAGMicrosoftcurrent
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SpecificationRAGFlowLlamaIndexLangChain & LangGraphGraphRAG
SummaryVisual pipeline builder, deep document understanding.The strongest option for document-centric retrieval.Orchestration, with retrieval as one piece.Build a knowledge graph first, then query it.
KindPlatformFrameworkFrameworkLibrary
GraphYesYes, property graph indexVia integrationsYes, core
LanguagePythonPython, TypeScriptPython, TypeScriptPython
LicenceApache 2.0MITMITMIT
GitHub stars~35k~45k~120k~25k
CategoryRAG & Knowledge GraphsRAG & Knowledge GraphsRAG & Knowledge GraphsRAG & Knowledge Graphs
OfficialInfiniFlowLlamaIndexLangChainMicrosoft

Highlighted rows are where these differ.

RAGFlow

  • Strong document parsing built in
  • The usual entry point for non-developers

Best for teams without a Python engineer.

Full spec sheet →

LlamaIndex

  • Deep document handling and many indexing strategies
  • The common advice is LlamaIndex for retrieval, LangGraph for orchestration

Best for PDFs, knowledge bases, structured data.

Full spec sheet →

LangChain & LangGraph

  • LangGraph adds durable state and checkpointing
  • Widely used, and widely argued about

Best for agents that retrieve as one step among many.

Full spec sheet →

GraphRAG

  • Extracts entities and relationships, then summarises communities
  • Answers “what are the themes here” which vector search cannot
  • Expensive to index

Best for questions no single chunk can answer.

Full spec sheet →