Pyyan / Compare / Neo4j vs LlamaIndex vs LangChain & LangGraph vs RAGFlow
Neo4j vs LlamaIndex vs LangChain & LangGraph vs RAGFlow
4 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | Neo4j | LlamaIndex | LangChain & LangGraph | RAGFlow |
|---|---|---|---|---|
| Summary | The graph database most knowledge graphs are stored in. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. | Visual pipeline builder, deep document understanding. |
| Kind | Database | Framework | Framework | Platform |
| Graph | Yes, native | Yes, property graph index | Via integrations | Yes |
| Language | Cypher | Python, TypeScript | Python, TypeScript | Python |
| Licence | GPL-3.0 / commercial | MIT | MIT | Apache 2.0 |
| GitHub stars | ~14k | ~45k | ~120k | ~35k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | Neo4j ↗ | LlamaIndex ↗ | LangChain ↗ | InfiniFlow ↗ |
Highlighted rows are where these differ.
Neo4j
- Native vector index alongside graph traversal
- GraphRAG and Graphiti both commonly sit on it
Best for the storage layer under a knowledge graph.
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.