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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

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?

Welcome, GraphAware

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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

A brief history of SQL and the rise of graph queries

LLM Knowledge Graph Builder: From zero to GraphRAG in five minutes

Get Started With GraphRAG: Neo4j Ecosystem Tools

Get started with GraphRAG: Neo4j’s ecosystem tools

The GenAI graph gathering.

A tale of LLMs and graphs: The GenAI Graph Gathering

Easy data ingestion with Neo4j Runway and arrows.app

Data exploration with the Neo4j Runway Python library

Rags to Reqs: Making ASVS Accessible Through the Power of Graphs and Chatbots

Rags to reqs: Making ASVS accessible through the power of graphs and chatbots

GenAI starter kit: Everything you need to build an application with Spring AI in Java

Developer

Unleashing the power of NLP with LlamaIndex and Neo4j

From ancient epic to modern marvel: demystifying the Mahabharata chatbot with GraphRAG (part 3)

Neo4j-Databricks Connector Delivers Deeper Insights, Faster GenAI Development.

Neo4j-Databricks connector delivers deeper insights, faster GenAI development

Knowledge graph vs. vector RAG: Benchmarking, optimization levers, and a financial analysis example