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

Neo4j enables up to 100x faster analytics and real-time decision-making

Train once, embed forever: GraphSAGE and the model repository in Aura Graph Analytics

Deep Dive: Neo4j Virtual Graph on BigQuery

Build a Semantic Layer from GCP with Neocarta

Building graph-based agentic systems: Failures, fixes, and how the answer gets there — Part 2

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Developer

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Structured AI with Neo4j

New Cypher AI procedures

Debugging with Aura Agent

Agentic AI

What is agentic AI? The shift toward autonomous, goal-driven AI systems

News

Reflections on the 2025 Gartner® Cloud DBMS Magic Quadrant™ and the future of AI-ready data

Cypher and the cipher manuscript