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

Illustration representing a context graph for AI agent memory

Agentic AI

What is a context graph?

Constant-cost semantic memory for multi-agent systems

a recipe knowledge graph and agent

One starter repo, three winners: Neo4j at Hack the Video Agent Context Graph

AgentMemory for .NET: A native sibling to Neo4j Agent Memory

Neo4j Virtual Graph is now in public preview

Why public sector AI needs a workforce knowledge layer

Introducing MCP for Aura: Hosted MCP, built into every Aura instance

Agentic AI

How AI decision-making works and how to improve it

Chart of the knowledge layer comprising ontologies, data, and memory

Agentic AI

The knowledge layer for enterprise AI

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections

Developer

Seeds of Science, Visuals in Bloom: Introducing Whole-Graph Projections

Government

Public sector workforce intelligence and compound questions

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