Pyyan / Compare / Neo4j vs LlamaIndex vs RAGFlow
Neo4j vs LlamaIndex vs RAGFlow
3 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | Neo4j | LlamaIndex | RAGFlow |
|---|---|---|---|
| Summary | The graph database most knowledge graphs are stored in. | The strongest option for document-centric retrieval. | Visual pipeline builder, deep document understanding. |
| Kind | Database | Framework | Platform |
| Graph | Yes, native | Yes, property graph index | Yes |
| Language | Cypher | Python, TypeScript | Python |
| Licence | GPL-3.0 / commercial | MIT | Apache 2.0 |
| GitHub stars | ~14k | ~45k | ~35k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | Neo4j ↗ | LlamaIndex ↗ | 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.
RAGFlow
- Strong document parsing built in
- The usual entry point for non-developers
Best for teams without a Python engineer.