Neo4j blog

The knowledge layer for enterprise AI

A manifesto for context-rich enterprise AI

19 min read
Chart of the knowledge layer comprising ontologies, data, and memory

Independent study: GraphRAG makes AI agents 80% more truthful

4 min read
Illustration representing a context graph for AI agent memory

What is a context graph?

13 min read

Welcome, GraphAware

4 min read

Emil Eifrem

CEO & Founder, Neo4j

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Sudhir Hasbe

Chief Product Officer, Neo4j

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Philip Rathle

Chief Technology Officer, Neo4j

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Most recent

Why Neo4j’s IMDA accreditation matters for graph tech adoption in Singapore

4 min read

Under the covers with LightRAG: Retrieval

19 min read

Maximize generative AI outcomes, minimize generative AI risks

7 min read

Graph analytics applications and use cases

17 min read

Under the covers with LightRAG: Extraction

24 min read

Mahabharata 2.0: From scrolls to soundwaves (Part 4)

6 min read

Announcing the Neo4j graph workload for Microsoft Fabric

7 min read

Run Cypher from Java

3 min read

Integrating Neo4j with LangChain4j for GraphRAG vector stores and retrievers

19 min read

Aura graph analytics: A technical deep dive

14 min read

Introducing Neo4j Aura Graph Analytics: Scalable, easy-to-deploy graph analytics for any data source

6 min read

Agentic GraphRAG for commercial contracts

29 min read