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

See all articles

Sudhir Hasbe

Chief Product Officer, Neo4j

See all articles

Philip Rathle

Chief Technology Officer, Neo4j

See all articles

Most recent

Neo4j graph analytics for Snowflake: Bringing graph-powered insights to the AI Data Cloud

Beyond vector search: Unleashing the power of GraphRAG for smarter recommendations

What are the different types of graph algorithms & when to use them?

News

Why Neo4j’s IMDA accreditation matters for graph tech adoption in Singapore

Under the covers with LightRAG: Retrieval

GenAI

Maximize generative AI outcomes, minimize generative AI risks

Graph analytics applications and use cases

Under the covers with LightRAG: Extraction

Mahabharata 2.0: From scrolls to soundwaves (Part 4)

Announcing the Neo4j graph workload for Microsoft Fabric

Run Cypher from Java

Integrating Neo4j with LangChain4j for GraphRAG vector stores and retrievers