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

Fraud is detected when relationships are visible, not when transactions are viewed in isolation.

Detect fraud faster with a Transaction Graph

Text2Cypher guide

GenAI
News

2025: Year of AI and scalability

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Why AI needs graph

Graph-driven AI for all: Neo4j Aura Agent enters general availability

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

Developer

Boundary graph: Exploring the IPL through the lens of graphs

Agentic AI vs. generative AI: Why agents need memory, context, and guardrails

Why LeBron James shouldn’t drive your recommendations: The intuition behind the Jaccard coefficient

Graph database

Building a seamless data pipeline with Neo4j CDC and GCP Pub/Sub

Getting started with MCP servers: A technical deep dive

Scaling without limits: Infinigraph is now generally available