Neo4j blog

Agentic AI

The knowledge layer for enterprise AI

A manifesto for context-rich enterprise AI

Chart of the knowledge layer comprising ontologies, data, and memory

Scaling Karpathy’s LLM wiki: Why your knowledge base needs a graph

Independent study: GraphRAG makes AI agents 80% more truthful

Illustration representing a context graph for AI agent memory

Agentic AI

What is a context graph?

Featured voices

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

Catch this week's GraphCast on how graph databases help with drug discovery for COVID-19 research

#GraphCast: Graphs Connecting COVID-19 Research

Graph visualization

Your World Seen Through Your Eyes with Neo4j Bloom

Graph-Powered Recommendations: Developing and Deploying Recommendations

Mind-Blowing Knowledge Graphs: 5-Minute Interview with Mayank Gupta, SVP of Data, LPL Financial

Fraud detection

7 reasons why effective compliance management is only possible with a graph-based solution

Graph-powered recommendations: Hybrid scoring and Graph Data Science

Learn about the three new Neo4j online training courses.

Announcing New Neo4j Online Training Courses!

Creative use of network science with Graph Commons

Learn about analytical subgraph overlays in Neo4j.

Analytical subgraph overlays in Neo4j

Graph-powered recommendations: A framework for faster development

Graph visualization

Unlimited possibilities: 5-minute interview with David Mohr

Learn how automotive and manufacturing industries use Neo4j for supply chain management and customer 360.

Graphs in automotive and manufacturing: Unlock new value from your data