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

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

Understanding GraphSAGE

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

Knowledge Graphs Are the Accountability Layer for High-Stakes AI

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

Context graphs: Why AI agents need three types of memory

Grounding Salesforce Agentforce with Neo4j knowledge graphs

From recall to reasoning: How context graphs upgrade an agent’s brain

Context graphs and AI memory across the globe

Meet Lenny’s Memory: Building context graphs for AI agents

Developer

Boundary graph: Exploring the IPL through the lens of graphs

Empowering Microsoft Agent Framework with Neo4j knowledge graphs

Agentic AI

Hands on with context graphs and Neo4j