Neo4j blog

Introducing Neo4j GraphAware Financial Crime Intelligence

Move from fragmented data to connected intelligence, for better detection, deeper investigations, and defensible decisions.

Vector RAG vs. GraphRAG: Which retrieval do you need?

Scaling Karpathy’s LLM wiki: Why your knowledge base needs a graph

Independent study: GraphRAG makes AI agents 80% more truthful

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

Agentic AI

Connected context and persistent memory: Neo4j providers for the Microsoft Agent Framework

Agent Memory

A knowledge layer for your agentic systems on Google Cloud

Developer

Building a Neo4j graph agent for Gemini Enterprise

How to create and integrate an Okta OIDC service account with Neo4j

Developer

How to create and integrate an Okta OIDC service account with Neo4j

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?”

Batching like a pro

Batching like a pro

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

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

Using agents to secure satellites’ supply chain systems

Using agents to secure satellites’ supply chain systems