Pyyan / Compare / Neo4j vs LlamaIndex vs LangChain & LangGraph vs Haystack
Neo4j vs LlamaIndex vs LangChain & LangGraph vs Haystack
4 of 5RAG & Knowledge Graphs · verified 13 Aug 2026
| Specification | Neo4j | LlamaIndex | LangChain & LangGraph | Haystack |
|---|---|---|---|---|
| Summary | The graph database most knowledge graphs are stored in. | The strongest option for document-centric retrieval. | Orchestration, with retrieval as one piece. | A strict pipeline abstraction, built for regulated work. |
| Kind | Database | Framework | Framework | Framework |
| Graph | Yes, native | Yes, property graph index | Via integrations | Via integrations |
| Language | Cypher | Python, TypeScript | Python, TypeScript | Python |
| Licence | GPL-3.0 / commercial | MIT | MIT | Apache 2.0 |
| GitHub stars | ~14k | ~45k | ~120k | ~20k |
| Category | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs | RAG & Knowledge Graphs |
| Official | Neo4j ↗ | LlamaIndex ↗ | LangChain ↗ | deepset ↗ |
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.
Haystack
- Every step is declared, which is what audits need
- Cleanest option where a wrong answer has consequences
Best for finance, health, legal and government.