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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

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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

AWS and Neo4j join forces to solve LLM hallucinations and evolve GenAI

Connected Intelligence: Operationalizing Production-Grade Graph Solutions Across Enterprise Networks

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

New research finds enterprises earn 230% ROI with Neo4j Graph Intelligence Platform

How a Neo4j semantic layer makes your Text-to-SQL agent smarter and cheaper

Graph database

Connected Intelligence: Scaling Graph-Powered Reasoning Across the AI Ecosystem

Developer

“Boost” the power of your AI agents: Integrating Neo4j MCP with Laravel

Amnesia is all you need

Expensive AI models may sound smart, but without context, they don’t actually understand your business. It’s Ferris Bueller’s Ferrari with nowhere to drive. All engine. No roads.

The context gap: Why your smart-sounding AI struggles to reason

Graph database

Capgemini turns data and AI into connected intelligence with Neo4j and Databricks