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

Knowledge graph generation

Introducing ToolsRetriever in the Neo4j GraphRAG Python package

The Neo4j Startup Program: Tomorrow’s AI unicorns build on Neo4j

Optimize weakly connected component projections

Creating a Neo4j agentic memory multi-user dungeon

Agentic AI

What is an AI agent?

Use weakly connected components to avoid Cypher query crashing

Implementing Neo4j GraphRAG retrievers as MCP server

Graph database

Property sharding in Infinigraph: Smarter scaling for rich graph databases

Graph database

Neo4j launches breakthrough architecture to unify transactional and operational workloads

Evaluating graph retrieval in MCP agentic systems

Rewire your integration strategy: a graph-first approach with Neo4j and AI