Graph Database - Amsterdam

Register:

Join our technical meetup for the biggest AI nerds!
We share the latest AI devtools, APIs, RAG & Agent frameworks, techniques, models and much more!
​**:: FOR WHO ::**
✅ Anyone actively building with Generative AI
✅ Devs, Product peeps, Data lovers, ML engineers, Founders
⚠️ Basic LLM knowledge required to fully enjoy the talks!

Neo4j Session:
WhatsThat? Using graphs and AI to make sense of your friends WhatsApp group by Martin O'Hanlon

Communication, messaging, and memory are complicated. How are you supposed to keep up with it all? It's difficult for you to keep up with the constant stream of messages you get. How can we expect an LLM to do the same? Let's look at how we can use graphs and AI to summarise the complex streams of messages you get in that oddly titled WhatsApp group.

This session is designed to show you how to accelerate Neo4j GenAI projects by using the Model Context Protocol (MCP) to create a fully integrated developer workspace. Proof-of-Value projects are often where ideas slow down, as teams move from data exploration to modeling, ingestion, and validation. In this workshop, you will see how an MCP-based IDE template streamlines this entire workflow and significantly reduces time-to-value.
The workshop demonstrates how to combine Neo4j and third-party MCP servers to support every stage of development, from data discovery and graph modeling to ingestion and use case verification. You will explore how this approach enables rapid prototyping across both structured and unstructured data, making it easier to test and validate the broad range of use cases supported by Neo4j.
Through practical examples and walkthroughs, you will learn how to apply this end-to-end workspace pattern to your own GenAI initiatives. You will leave with a clear understanding of how to adopt MCP in Neo4j projects to move faster from idea to working solution.
You will learn:

• How to use MCP to build an end-to-end Neo4j GenAI developer workspace
• How to accelerate data discovery, modeling, and ingestion for Proof-of-Value projects
• How to prototype and validate GenAI use cases across structured and unstructured data
• How to reduce time-to-value using an MCP-based development approach

This session is designed to demonstrate how AI agents can move beyond stateless execution and begin learning from their own experiences. While techniques such as RAG and prompt engineering improve individual LLM interactions, they do not provide agents with a structured way to retain, analyze, and reuse knowledge from past executions. In this workshop, you will explore how Neo4j can be used as a long-term memory system that enables agentic workflows to improve continuously over time.
The workshop introduces a graph-based memory architecture that captures complete agent execution traces, including tool choices, reasoning steps, reflections, and retrieved context, as connected graph structures. You will see how these traces form a dynamic and evolving “playbook” that agents refine through reinforcement learning and human feedback. By querying and analyzing this graph memory, agents learn not just better answers, but better strategies: when to invoke specific tools, how to structure multi-step workflows, and how to apply successful patterns to new problems.
Through practical examples and demonstrations, you will learn how to model agent executions in Neo4j, extract actionable patterns using Cypher, and retrieve relevant strategies based on structural similarity. Real-world use cases, including applications in law enforcement such as criminal network analysis and suspect nomination, will illustrate how graph-based memory enables agents to develop domain-specific orchestration patterns that transfer across contexts.
You will learn:

• How to design graph-based long-term memory for agentic workflows using Neo4j
• How to capture and model agent execution traces as reusable graph structures
• How agents can improve tool selection, reflection, and retrieval strategies over time
• How to query graph memory to distil and reuse successful decision-making patterns
• How to apply graph-based memory to real-world, high-impact domains