Neo4j blog

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

Scaling Karpathy’s LLM wiki: Why your knowledge base needs a graph

Independent study: GraphRAG makes AI agents 80% more truthful

Context graph diagram illustrating decision traces for AI agents

Agentic AI

What are decision traces in a context graph? How they reveal agentic reasoning

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

A Tour of the Neo4j Agent Memory Service (NAMS)

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

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

Developer

Graph and Lakehouse, Friends at Last

Knowledge graph

Taxonomy vs. ontology vs. knowledge graph: What’s the difference?

Developer

Neo4j GRAPH TYPE: A Pragmatic Comparison with the RDF/SHACL World

POLE+O: The 5-Type Ontology That Solves the Hardest Part of Building a Knowledge Graph

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

Developer

How Do You Defend Neo4j Against Quantum Computers?

Build a Semantic Layer from GCP with Neocarta

1 of 3: The difference between a graph, a knowledge graph, and a context graph

2 of 3: Why graphs, knowledge graphs, and context graphs matter to customers

3 of 3: The graph ecosystem: Bringing connected context to enterprise AI