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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

Designing agentic AI for pharma & life sciences with graph technology: From complex evidence to explainable insights

Graph database

Designing agentic AI for pharma & life sciences with graph technology: From complex evidence to explainable insights

GraphTalk Pharma & Life Sciences 2025—a recap

Constant-cost semantic memory for multi-agent systems

Zero-Copy Graph Reasoning on Snowflake: Getting Started With Neo4j Virtual Graph

Zero-Copy Graph Reasoning on Snowflake: Getting Started With Neo4j Virtual Graph

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

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

Visualization of how Neo4j Graph Intelligence reveals hidden patterns in massive datasets

How graph intelligence drives breakthroughs in science and society

Graph-driven AI for all: Neo4j Aura Agent enters general availability

News

Neo4j earns new AWS competencies and expands innovation ahead of AWS re:Invent

Illustration showing how vector similarity, Cypher templates, and text-to-Cypher meet to create the Aura Agent

GenAI
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Neo4j Aura Agent: Create your own GraphRAG agent in minutes

Government

The most powerful fraud prevention tool for federal agencies: Graph technology

GenAI

Maximize generative AI outcomes, minimize generative AI risks