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

Implementing DRIFT search with Neo4j and LlamaIndex

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

News

Reflections on the 2025 Gartner® Cloud DBMS Magic Quadrant™ and the future of AI-ready data

Introducing Neo4j’s native vector data type

Stranger Graphs illustration

News

“Stranger Things” — The gate to true sight with graph intelligence

Aura activity feed: Enhanced security and compliance for Neo4j Aura

Modeling disruptions in the MTA subway

Cypher and the cipher manuscript

Build better recommendations with Aura graph analytics

Conquering Neo4j errors: GQLSTATUS code to the rescue

Why startups in the Google ecosystem choose Neo4j

Declarative route planning with Cypher 25—Graph traversal grows up