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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

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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

The enterprise knowledge layer: Operating Structure

The startup’s guide to getting started building on Neo4j Aura

Train once, embed forever: GraphSAGE and the model repository in Aura Graph Analytics

Understanding GraphSAGE

Secure GraphRAG: Wiring Microsoft Copilot Studio to Neo4j with per-user Okta SSO

Secure GraphRAG: Wiring Microsoft Copilot Studio to Neo4j with per-user Okta SSO

Graph database

What is graph technology? And why it’s the future of enterprise AI

From first graph algorithm experiment to production with Aura Graph Analytics

SumoDB in Neo4j: Microsoft Fabric bridging Decades of Rikishi — Part 4

The payload: the semantic layer, memory, and the loop that fills it

From Agent Memory to Portable Skills

From Agent Memory to Portable Skills

Constant-cost semantic memory for multi-agent systems

a recipe knowledge graph and agent

One starter repo, three winners: Neo4j at Hack the Video Agent Context Graph