Neo4j Blog

From data to intelligence: Why every enterprise needs an AI knowledge layer

The knowledge layer maps and resolves data so AI can accurately answer questions, make better decisions, and be explainable.

Agentic AI

Agent tools: What they are, how they work, and how AI agents use them

Agent Memory

A knowledge layer for your agentic systems on Google Cloud

The data strategy that makes truly personalized customer experiences possible

Featured Voices

Emil Eifrem

CEO & Co-Founder, Neo4j, Inc.

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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

Introducing Create Context Graph

How a Neo4j semantic layer makes your Text-to-SQL agent smarter and cheaper

NODES 2026 Call for Papers open until June 15, 2026

Graph Database

NODES 2026 Is Coming Up: Submit Your Talk

ServiceNow+Neo4j

Turning ServiceNow Data into Connected Enterprise Intelligence

Building Graph-Based Agentic Systems: Failures, Fixes, and How the Answer Gets There — Part 2

SumoDB in Neo4j: Graph Analytics in Snowflake — Part 2

Get start With Neo4j Aura Cloud Graph Database Network

Getting Started With Neo4j Aura: A Guide to the Leading Cloud Graph Database

Learn what agentic RAG is, how it works, which design patterns to use, and when it beats traditional RAG.

Agentic AI

Agentic RAG: What it is, how it works, and when to use it

Introducing Neo4j Agent Skills

Introducing Asynchronous I/O in Neo4j using io_uring

SumoDB in Neo4j: Graph Analytics of Grand Sumo

Building Stateful AI: Integrating Aura Agent Lifecycle with MCP and Persistent Memory