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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

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?

Welcome, GraphAware

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

From first graph algorithm experiment to production with Aura Graph Analytics

Introducing Document Intelligence: From documents to a knowledge graph, right inside Aura

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

Get start With Neo4j Aura Cloud Graph Database Network

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

Developer

Building a Neo4j graph agent for Gemini Enterprise

Architecting graph-based agentic system: When a regulator asks “Why was this loan approved?”

Architecting graph-based agentic system: When a regulator asks “Why was this loan approved?”

Using agents to secure satellites’ supply chain systems

Using agents to secure satellites’ supply chain systems

The efficiency of on-demand graph analytics.

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

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

Why healthcare CIOs can’t afford to scale AI without a knowledge graph foundation

Neo4j Community Edition advances for graph-powered AI and intelligent applications

Graph database

Neo4j Community Edition advances for graph-powered AI and intelligent applications

What is GraphRAG?