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

The startup’s guide to getting started building on Neo4j Aura

Get start With Neo4j Aura Cloud Graph Database Network

Getting started with Neo4j Aura: A guide to the leading cloud graph database

Graph-driven AI for all: Neo4j Aura Agent enters general availability

Aura activity feed: Enhanced security and compliance for Neo4j Aura

Streamline data ingestion with the Neo4j Aura Import API

Find similar patient journeys with Neo4j Aura graph analytics

Managing risk in a manufacturing plant with Neo4j Aura graph analytics

Illustration showing how vector similarity, Cypher templates, and text-to-Cypher meet to create the Aura Agent

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Neo4j Aura Agent: Create your own GraphRAG agent in minutes

IP filtering for Neo4j Aura—Simpler and stronger network security

Introducing Neo4j Aura Graph Analytics: Scalable, easy-to-deploy graph analytics for any data source

Simplify modern application development with GraphQL on Neo4j AuraDB

From click to chaos: Chaos testing within Neo4j Aura