Use case

Smarter AI starts with GraphRAG

When your AI follows the real connections in your data, every answer traces back to its source. That’s context you can act on with confidence.

Get started

View resources

Overview
GraphRAG
Capabilities
Use cases
Customers
Partners
Resources

Move from pilot to
production faster

Deliver an AI system with 50% fewer hallucinations by grounding in connected data.

Make decisions with information you can trust

Reason across facts several steps apart in your data for answers that are 80% more truthful.

Stay compliant and
audit-ready

Trace the sources and logic behind every response for full transparency.

Anyone can ask, not just experts

No need to learn a programming language. Just ask like you would a colleague.

GraphRAG explained

GraphRAG combines knowledge graphs and vector search, giving your AI the context to get answers right.

Capabilities

How Neo4j powers GraphRAG

Knowledge graph

Model your data as a graph of entities, attributes, and relationships instead of disconnected records. Continuously enrich that graph with new data to surface patterns a single lookup would miss.

See how knowledge graphs work

Framework and tools

Accelerate GenAI development with integrations for popular AI frameworks and tools. Accelerate development with integrations for popular AI frameworks and tools, like LangChain, HuggingFace, and MCP.

See the GraphRAG developer guide

Native vector search

Give your AI fast semantic search that finds what’s related, not just what matches word-for-word.

Explore vector search

GraphRAG Python Package

Build a complete GraphRAG pipeline in code, from knowledge graph construction to retrieval and natural language querying with Text2Cypher.

See the Python library

Use Cases

GraphRAG for real-world problems

Detect fraud and compliance risk in connected data

Know-Your-Customer investigations are a connected-data problem. Customers, accounts, transactions, and devices form a web that traditional document search cannot untangle. A GraphRAG-powered agent traces these connections to reveal circular transactions, shared addresses, and other signals of fraud.

See how you can build it

Build agents that reason across your data

A basic support agent can answer simple FAQs. But if a question needs an order, its shipment status, and a return policy all at once, FAQ-matching alone falls apart. An agent built on Neo4j reasons across those connections, answering in one response instead of checking three separate systems.

See an example application

Recognize the intent  behind every search

Keyword search matches words on a page. It can’t tell a browsing question from an urgent one. And it can’t connect a search term to the account, order, or policy behind it. Graph-backed search understands what you’re actually asking, so results match what you meant, not just what you typed.

See an example with semantic layers

Learn how to feed LLMs context for complete, relevant GenAI results

Read now

Loved by devs. Deployed worldwide.

1,700+ organizations build on Neo4j for data breakthroughs.

Customers

Real-world AI innovations. Powered by graph.

“Agentic AI without a knowledge graph is like a self-driving car with no GPS map. Kind of brilliant but dangerously unaware.”

Natalie Romanov

Associate Director, Knowledge Management & Data Strategy, Merck

Link to full story

“Neo4j is our go-to graph database. Its capabilities like vector similarity search for node and the vector search index make implementing and maintaining AI solutions easier when working with graphs.”

Martin Elwin

Senior Director, Klarna

Link to full story

“A strong data foundation is the only path to strong AI. You can have the best models in the world, but if your data lacks integrity and transparency, you are building on sand… Clients come to us thinking they have a use case problem. They leave realizing they had a data connectivity problem. Once you solve the connectivity in the graph, the AI becomes simple. You move from guessing to knowing.”

Chiru Bhavansikar

Chief AI Officer, Arhasi

Link to full story

“By combining graph technology with large language models, we turn live enterprise activity into knowledge so that the organization can understand what it’s actually doing right now, make faster decisions, and solve problems in minutes instead of weeks.”

Conor O’Shea

AI Strategy and Architect, Daimler Truck North America