Pyyan / Compare / R2R vs LlamaIndex vs LangChain & LangGraph vs RAGFlow
R2R vs LlamaIndex vs LangChain & LangGraph vs RAGFlow
4 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
×LangChain & LangGraphLangChaincurrent
| Specification | R2R | LlamaIndex | LangChain & LangGraph | RAGFlow |
|---|---|---|---|---|
| Summary | RAG as a service you deploy yourself. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. | Visual pipeline builder, deep document understanding. |
| Kind | Platform | Framework | Framework | Platform |
| Graph | Yes | Yes, property graph index | Via integrations | Yes |
| Language | Python | Python, TypeScript | Python, TypeScript | Python |
| Licence | MIT | MIT | MIT | Apache 2.0 |
| GitHub stars | ~7k | ~45k | ~120k | ~35k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | SciPhi ↗ | LlamaIndex ↗ | LangChain ↗ | InfiniFlow ↗ |
Highlighted rows are where these differ.
R2R
- Ingestion, retrieval, auth and observability included
- Graph extraction built in
Best for a working RAG API without building one.
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.
LangChain & LangGraph
- LangGraph adds durable state and checkpointing
- Widely used, and widely argued about
Best for agents that retrieve as one step among many.
RAGFlow
- Strong document parsing built in
- The usual entry point for non-developers
Best for teams without a Python engineer.