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

See all articles

Sudhir Hasbe

Chief Product Officer, Neo4j

See all articles

Philip Rathle

Chief Technology Officer, Neo4j

See all articles

Most recent

AgentMemory for .NET: A native sibling to Neo4j Agent Memory

Neo4j Virtual Graph is now in public preview

Why public sector AI needs a workforce knowledge layer

Introducing MCP for Aura: Hosted MCP, built into every Aura instance

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