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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

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

Government

Public sector workforce intelligence and compound questions

Connected Intelligence: Operationalizing Production-Grade Graph Solutions Across Enterprise Networks