Neo4j Blog

From data to intelligence: Why every enterprise needs an AI knowledge layer

The knowledge layer maps and resolves data so AI can accurately answer questions, make better decisions, and be explainable.

Agentic AI

Agent tools: What they are, how they work, and how AI agents use them

Agent Memory

A knowledge layer for your agentic systems on Google Cloud

The data strategy that makes truly personalized customer experiences possible

Featured Voices

Emil Eifrem

CEO & Co-Founder, Neo4j, Inc.

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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

Why Healthcare CIOs Can’t Afford to Scale AI Without a Knowledge Graph Foundation

ServiceNow+Neo4j

Turning ServiceNow Data into Connected Enterprise Intelligence

Learn what agentic RAG is, how it works, which design patterns to use, and when it beats traditional RAG.

Agentic AI

Agentic RAG: What it is, how it works, and when to use it

Precise Economic Policy in a World of Chaos

From Identity Vacuum to Identity Driven Access Control: Securing LLM Agents in the Enterprise

Amnesia Is All You Need

Developer

Building a Neo4j Graph Agent for Gemini Enterprise

Developer

Neo4j GraphML Detects Network Intrusion in Snowflake

Architecting Graph-Based Agentic System: When a Regulator Asks “Why Was This Loan Approved?”

Architecting Graph-Based Agentic System: When a Regulator Asks “Why Was This Loan Approved?”

When Your Agents Share a Brain: Building Multi-Agent Memory with Neo4j

When Your Agents Share a Brain: Building Multi-Agent Memory with Neo4j

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

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