Neo4j blog

Welcome, GraphAware

Neo4j is acquiring GraphAware, our longtime partner and a leading provider of intelligence analysis software for government agencies.

New research finds enterprises earn 230% ROI with Neo4j Graph Intelligence Platform

Context graphs: Why AI agents need three types of memory

What is AI-ready data? How a knowledge layer gets you there

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

Introducing Neo4j Agent Skills

Combining knowledge graph and graph algorithms to find hidden skills at NASA

How the Professional Services Team at Neo4j built an enterprise knowledge graph.

Discovering hidden skills with an enterprise knowledge graph

Hybrid Search in Neo4j: Full-Text, Vectors, and Graph Topology with Cypher

Developer

What 40 AI Agents Revealed About the Future of Graph Intelligence

Illustration of how a supplier graph connectes materials, routes, and contracts

What’s invisible in your supply chain could cost you

Now available: Dedicated Neo4j field engineers empowering AI startups

Agentic AI

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

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

GraphAcademy content is moved into Neo4j Aura, and an Aura Agent used to provide MCP server access to VS Code, Cursor, and Claude agents.

Can I use Aura Agents to create an MCP enabled teacher?

Developer

Building a Neo4j graph agent for Gemini Enterprise

iscover the best tool for context engineering and build agentic AI with MCP, knowledge graphs, GraphRAG, and practical evaluation and tracing.

Agentic AI

Best tools for context engineering in agentic AI systems