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

Graph Data Science

Capgemini’s UK Graph Guild: 5-minute interview with Calum Chalmers

Graph algorithms for community detection & recommendations

Achieving a positive effect on payments, Transaction Monitoring and OPEX simultaneously is impossible without graph technology.

Financial services: The next evolutionary step is graph technology

Peformance tuning and troubleshooting the Neo4j BI Connector

Graph Data Science

The Neo4j BI Connector: Performance

Catch this week's GraphCast: a chat with Emil Eifrem about the future of graph database technology.

GraphCast: The Emil update

Learn more about graph data science.

Evolving your application of GDS technology

Learn more about how the GRANDstack makes handling complex data easy.

How the GRANDstack makes handling complex data easy

Check out the winners of this year's Neo4j Graphies.

Announcing the 2020 Neo4j Graphie Award winners

Security

The Neo4j BI Connector: Security

Graph visualization

This week in Neo4j – Creating charts from your graphs, Neo4j 4.0 certification exam, working with a multilingual thesaurus

Simplifying data visualization: 5-minute interview with Varun Bhandary

Learn about Neo4j as a graph data platform leader.

Graph data platforms: From a napkin sketch to a category leader