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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

Learn How the Future of Real-Time Recommendation Engines Merges with Graph-Aided Search

Real-time recommendations

The Future of Recommendation Engines: Graph-Aided Search

Hybrid Search in Neo4j: Full-Text, Vectors, and Graph Topology with Cypher

Now available: Dedicated Neo4j field engineers empowering AI startups

iscover the best tool for context engineering and build agentic AI with MCP, knowledge graphs, GraphRAG, and practical evaluation and tracing.

Agentic AI

Best tools for context engineering in agentic AI systems

Why AI teams are moving from prompt engineering to context engineering

What is context engineering in AI agents? A practical guide

Implementing DRIFT search with Neo4j and LlamaIndex

An extremely simple but effective way to improve search over text embeddings

Developer

Conversational Artificial Intelligence with Neo4j and the Unreal Engine—Part 3

Building a recommendation engine using Neo4j hands-on—Part 2

Conversational AI with Neo4j and Unreal Engine – part 2

LangChain Cypher search: Tips & tricks