
Three Memory Types Every Production AI Agent Needs
Join us on April 7 to learn how neo4j-agent-memory enables you to build context graphs that give your production AI agents persistent, graph-connected memory.
Check out our on-demand library of content to learn more about graph technology, get a deeper understanding of use cases, and more - all at your convenience!

Join us on April 7 to learn how neo4j-agent-memory enables you to build context graphs that give your production AI agents persistent, graph-connected memory.

Join us for an introduction to graph-powered financial crime detection. Learn practical strategies for building a Transaction Graph to uncover coordinated fraud, trace hidden fund flows, and deliver more accurate, relationship-aware detection.

Join us on March 24 and learn how context graphs, or “living graphs,” enable agents to search for precedents by linking decisions, constraints, tool calls, and outcomes.

Join us for a 30-minute session on how domain modelling and ontology integration come together in graph schema design. Learn how to build scalable, semantically rich, and AI-ready data foundations that reflect the real world.

Join us on February 26 to learn how context engineering and knowledge graphs power GraphRAG to boost AI accuracy up to 3x. Discover how connected data can improve explainability, reliability, and production-readiness.

Join us for an introduction to GraphRAG and learn practical tips for building knowledge graphs that empower your GenAI and agentic apps to return more accurate, trustworthy, and business-relevant outcomes.

Join us for a 30 minute session that will explore effective context engineering techniques and show you how to provide the foundational context AI needs to be trustworthy and impactful.

Join us for a 45-min session that will show you how to turn enterprise data into reliable, actionable insights using knowledge-graph-powered GenAI.

Join us for a 30-minute rundown on how to use the open-source MCP for Neo4j to improve AI reasoning, so it can assist in graph data modeling.