Neo4j Blog

From Data to Intelligence: Why Every Enterprise Needs an AI Knowledge Layer

The knowledge layer maps and resolves data so AI can accurately answer questions, make better decisions, and be explainable.

Agent Memory

A Knowledge Layer For Your Agentic Systems on Google Cloud

The Data Strategy That Makes Truly Personalized Customer Experiences Possible

iscover the best tool for context engineering and build agentic AI with MCP, knowledge graphs, GraphRAG, and practical evaluation and tracing.

Agentic AI

Best Tools for Context Engineering in Agentic AI Systems

Featured Voices

Emil Eifrem

CEO & Co-Founder, Neo4j, Inc.

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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

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

GQL, the future of graph databases is here.

ISO GQL: A Defining Moment in the History of Database Innovation

LangChain Library now supports Neo4j vector index.

LangChain Library Adds Full Support for Neo4j Vector Index

Developer

Maximizing Your Neo4j Project’s Potential: An In-depth Guide to Solution Assessment

Graph Data Models for RAG Applications

Neo4j Brings GraphRAG Capabilities for GenAI to Google Cloud

Neo4j Is Now SOC2 Type 2 Compliant

Enhancing RAG with Neo4j Cypher and Vector Templates Using LangChain Agents

Enhancing the Accuracy of RAG Applications With Knowledge Graphs