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

Context graph diagram illustrating decision traces for AI agents

Agentic AI

What are decision traces in a context graph? How they reveal agentic reasoning

Featured voices

Emil Eifrem

CEO & Founder, Neo4j

See all articles

Sudhir Hasbe

Chief Product Officer, Neo4j

See all articles

Philip Rathle

Chief Technology Officer, Neo4j

See all articles

Most recent

Learn how EY used Neo4j to design enterprise HR applications at scale.

Designing the Enterprise: HR Applications at Scale

Learn how graph technology improve the KYC process.

From Know Your Customer to Know Your Network: A Paradigm Shift Empowered by Neo4j

Rethink Your Master Data: From MDM to Innovation

Check out this tv show in which they use graph theory to solve crimes.

Graph Data Science

#GraphCast: Numb3rs and Graph Theory

Learn more about Cloud Foundry and Neo4j.

Neo4j and Cloud Foundry

Upgrade your Graph Data Science library now (or miss out on cool new features)

Master data management

Rethink Your Master Data: Addressing Governance, Risk and Compliance

Graph Data Science

The Power of Graph Data Science: 5-Minute Interview with Alicia Frame

Building the enterprise knowledge graph

Learn about Drivine, a database client for Node.js and Typescript

Introducing Drivine: Graph Database Client for Node.js and Typescript

Master data management

Rethink Your Master Data: How Graphs and MDM Intersect

Catch this week's GraphCast: A high-level explainer of what a native graph database is in 10 minutes

#GraphCast: What Is a Graph Database in 10 Minutes