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

Illustration representing a context graph for AI agent memory

Agentic AI

What is a context graph?

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

Safeguarding elections: How graph technology powers groundbreaking research on political ads

Using LlamaParse to create knowledge graphs from documents

Topic extraction with Neo4j GDS for better semantic search in RAG applications

Creating the GQL Database Language Standard.

Cypher & GQL

Creating the GQL database language standard

Graph-based metadata filtering to improve vector search in RAG applications

Adding RAG to your GraphQL API

GQL is here: Your Cypher queries in a GQL world.

Cypher & GQL

GQL is here: Your Cypher queries in a GQL world

GQL: The ISO Standard for Graphs Has Arrived!

GQL: The ISO standard for graphs has arrived

Building RAG Applications With the Neo4j GenAI Stack: A Comprehensive Guide

Building RAG applications with the Neo4j GenAI stack: a comprehensive guide

Node embeddings in a knowledge graph.

The future of knowledge graph: Will structured and semantic search become one?

What is retrieval-augmented generation RAG)?

GenAI

What is retrieval-augmented generation (RAG)?

Entity resolved knowledge graphs: A tutorial

Entity resolved knowledge graphs: A tutorial