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

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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

ServiceNow+Neo4j

Turning ServiceNow data into connected enterprise intelligence

Graph Data Science for supply chains – Part 3: Pathfinding, optimization, and what-if scenarios

Graph embeddings: AI that learns from your data to solve problems

Neo4j as an embedded database: Understanding embedding and graph databases

Discover the data trends and challenges today that requires the power of knowledge graphs.

Knowledge graph

From graph to Knowledge Graph: Data trends and challenges

Developer

Java 17: Explore the newly-released Java version in a graph database!

Knowledge Graph book

Knowledge graphs: Data in context for responsive businesses [New Book]

Achieving Liftoff: Reflections of Six Amazing Months in Graph Databases

News

Achieving liftoff: Reflections on six amazing months in graph databases

Top 10 use cases: Data lineage

NODES

Unearthing historical networks using Neo4j and archival documents

Graph data platforms: A brief introduction

Learn about the new, free book, Graph Data Science for Dummies

Understanding graphs and Graph Data Science