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.

Visualization of how Neo4j Graph Intelligence reveals hidden patterns in massive datasets

How Graph Intelligence Drives Breakthroughs in Science and Society

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

The Data Strategy That Makes Truly Personalized Customer Experiences Possible

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

Exploring the Intersection of Neo4j and Large Language Models

Harnessing Large Language Models With Neo4j

Harnessing Large Language Models With Neo4j

Model Context Protocol (MCP)

GenAI

What Is Model Context Protocol (MCP)?

Diagram of the enterprise graphs: Employee, Network & Security, Supplier, Product, Process, Transaction, Customer

Model Your Data Like Your Business for Better Insights

Text2Cypher Across Languages: Evaluating Foundational Models Beyond English

Google Cloud & Neo4j: Teaming Up at the Intersection of Knowledge Graphs, Agents, MCP, and Natural Language Interfaces

Introducing the Fine-Tuned Neo4j Text2Cypher (2024) Model

Generative Transformation from ER Diagram to Graph Model Using Google’s Gemini Pro

Graph Database

Data Consistency Models: ACID vs. BASE Explained

NeoDash 2.3: Create Graph Dashboards With LLM-Powered Natural Language Queries

Predictive Modeling Techniques: Types, Benefits & Algorithms

Discover what makes Neo4j the #1 graph database.

The Secret Sauce of Neo4j: Modeling and Querying Graphs