Pyyan / Compare / R2R vs LlamaIndex vs LangChain & LangGraph
R2R vs LlamaIndex vs LangChain & LangGraph
3 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | R2R | LlamaIndex | LangChain & LangGraph |
|---|---|---|---|
| Summary | RAG as a service you deploy yourself. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. |
| Kind | Platform | Framework | Framework |
| Graph | Yes | Yes, property graph index | Via integrations |
| Language | Python | Python, TypeScript | Python, TypeScript |
| Licence | MIT | MIT | MIT |
| GitHub stars | ~7k | ~45k | ~120k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | SciPhi ↗ | LlamaIndex ↗ | LangChain ↗ |
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.