# Neo4j GraphRAG Python

> Official Neo4j package for building graph retrieval augmented generation (GraphRAG) applications in Python. Enables developers to create knowledge graphs and implement advanced retrieval methods including graph traversals, text-to-Cypher, and vector searches.

- **URL:** https://github.com/neo4j/neo4j-graphrag-python
- **Category:** LLM Frameworks
- **Tags:** graphrag, Knowledge Graph, Rag
- **Updated:** 2026-03-15 12:39
- **Canonical page:** https://vectordb.ever.works/ar/items/neo4j-graphrag-python

## Details

## Overview

The official Neo4j GraphRAG package for Python enables developers to build graph retrieval augmented generation (GraphRAG) applications using the power of Neo4j and Python. It provides a robust, feature-rich solution with long-term support directly from Neo4j.

## Features

- **Knowledge Graph Creation**: Build and manage knowledge graphs from unstructured data
- **Advanced Retrieval Methods**: Graph traversals, text-to-Cypher query generation, vector searches, and full-text searches
- **Hybrid Search**: Combine vector similarity with graph relationships
- **LLM Integration**: Works with popular LLM providers (OpenAI, Anthropic, etc.)
- **Production Ready**: Official support and maintenance from Neo4j
- **Explainable Results**: Graph-based retrieval provides transparent reasoning chains

## Key Capabilities

- Entity extraction and relationship mapping
- Multi-hop question answering through graph traversals
- Cypher query generation from natural language
- Integration with vector embeddings for semantic search
- Structured and unstructured data combination

## Use Cases

- Question answering over complex, interconnected data
- Multi-hop reasoning tasks
- Knowledge base construction
- Explainable AI applications
- Enterprise knowledge management

## Pricing

Free and open-source. Neo4j database licensing applies separately.

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