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

Unleash graph data science with Neo4j AuraDS Enterprise on Microsoft Azure

Neo4j AuraDS Enterprise on Microsoft Azure (EAP): The future of graph data science

Graph Data Science

Unlock the power of graph data science with AuraDS Enterprise on AWS

Announcing Neo4j AuraDS Enterprise and Neo4j Graph Data Science 2.2

Graph data science on Google Cloud: Neo4j AuraDS and Vertex AI

Graph Data Science 2.0

Graph Data Science

New in 2022: Introducing Graph Data Science 2.0 and general availability of AuraDS

The efficiency of on-demand graph analytics.

The persistence tax: Why it’s time to let your graph analytics sleep

Agentic AI

Hands on with context graphs and Neo4j

Build a private network path to Aura on AWS, Azure, and Google Cloud using Terraform

Optimize weakly connected component projections

Use weakly connected components to avoid Cypher query crashing

Automate start and stop of Neo4j AuraDB instances using GitHub Actions

LLM knowledge graph builder—First release of 2025