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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

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

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

AgentMemory for .NET: A native sibling to Neo4j Agent Memory

Neo4j Virtual Graph is now in public preview

Why public sector AI needs a workforce knowledge layer

Introducing MCP for Aura: Hosted MCP, built into every Aura instance

Agentic AI

How AI decision-making works and how to improve it

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections

Developer

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections