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

Agentic AI

How AI decision-making works and how to improve it

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections

Developer

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections

Government

Public sector workforce intelligence and compound questions

Connected Intelligence: Operationalizing Production-Grade Graph Solutions Across Enterprise Networks

Graph used right & built fast: Neo4j at HackwithBay 3.0

Hybrid Search in Neo4j: Full-Text, Vectors, and Graph Topology with Cypher

Developer

What 40 AI Agents Revealed About the Future of Graph Intelligence

Deep Dive: Neo4j Virtual Graph on BigQuery

What is AI-ready data? How a knowledge layer gets you there

I built a digital twin of my amateur-built airplane

A workbench for teams to query, explore, and visualize graph data

A workbench for teams to query, explore, and visualize graph data

Neo4j Named “One to Watch” in Snowflake’s 2026 Modern Marketing Data Stack Report