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RAG & Knowledge Systems

Neo4J Graphrag Python

Neo4j GraphRAG for Python is a Python library for building graph retrieval-augmented generation applications that use Neo4j. It supports retrieval from populated graphs and knowledge-graph construction from text and PDFs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Neo4J Graphrag Python — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when developing a Python RAG application that should retrieve context from Neo4j, or when building a Neo4j knowledge graph from text or PDF sources.

Who it is for

  • Python developers building generative AI applications grounded in Neo4j graph data.
  • Teams that need to construct knowledge graphs from text or PDF documents.

Documented uses

  • Retrieve relevant graph context for a generative AI application.
  • Build question-answering applications grounded in a Neo4j graph.
  • Construct a knowledge graph from text or PDFs using a customizable or streamlined pipeline.
Before you choose

Requirements & limits

  • A running Neo4j instance and connection details are needed for the documented examples.
  • RAG and the knowledge-graph builder pipeline require at least one LLM provider integration.
  • Graph construction requires APOC core installed in Neo4j.
  • The Pipeline offers more customization; SimpleKGPipeline provides a more streamlined abstraction.
  • The optional spaCy-based NLP features are not supported on Python 3.14.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Neo4j GraphRAG Python package

Set up the library in an isolated Python environment using the documented pip installation.

Before you start

  • Python 3.10–3.14
  • An activated virtual environment

Steps

  1. Activate a Python virtual environment.

  2. Run the command below.

    Documented command
    pip install neo4j-graphrag

Expected outcome

The Neo4j GraphRAG package is installed in the active environment.

Guide limitations

  • Installation alone does not configure a Neo4j connection or an LLM provider.
  • At least one LLM provider dependency is required for RAG and the knowledge-graph builder pipeline.
Documentation 1 ↗
Source-based practical guide

Prepare a knowledge-graph construction example

Configure the documented example’s Neo4j and OpenAI prerequisites before running it.

Before you start

  • A running Neo4j instance
  • Neo4j URI, username, and password
  • An OpenAI API key
  • The APOC core library installed in the Neo4j instance
  • The package and the example's required dependencies

Steps

  1. Set the example's NEO4J_URI, NEO4J_USERNAME, and NEO4J_PASSWORD values to match the Neo4j instance.

  2. Export the OpenAI key as OPENAI_API_KEY.

  3. Choose the documented Pipeline for customization or SimpleKGPipeline for a more streamlined construction workflow.

Expected outcome

The documented prerequisites and pipeline choice are prepared for constructing a graph from text or PDFs.

Guide limitations

  • The knowledge-graph construction feature requires APOC core.
  • The experimental namespace is not recommended for production use.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Neo4J Graphrag PythonRetrieve relevant graph context for a generative AI application.Server
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
neo4j/neo4j-graphrag-python · Official homepage title · retained in its source language
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