Name: Context Graphs and AI Memory
Start: 2026-04-07T17:00:00.000Z
Location: Neo4j UK

This session is designed to demonstrate how GraphRAG architectures can be applied to healthcare and other regulated domains where data is heterogeneous, distributed, and subject to stringent governance requirements. You will explore how knowledge graphs, graph neural networks (GNNs), and LLM-based agents collaborate to overcome the limitations of traditional text-centric RAG approaches, particularly in terms of temporal reasoning, explainability, and compliance.
The workshop introduces an agent-driven GraphRAG architecture built on Neo4j that unifies medical ontologies, clinical documents, and research publications in a materialized knowledge graph, while keeping sensitive electronic health record (EHR) data virtually integrated rather than centralized. Through architectural walkthroughs and examples, you will see how agents plan and orchestrate graph queries, apply GNN-based relevance and similarity scoring, and enforce controlled access to regulated data sources.
By grounding retrieval and reasoning in graph structure, this approach enables multi-hop, temporally aware reasoning with traceable and explainable outputs. You will leave with a clear understanding of when and how to combine knowledge graphs, GNNs, and agents to design GraphRAG systems suitable for high-trust environments such as healthcare.
**You will learn:**

* How to design an agent-driven GraphRAG architecture for regulated medical domains
* How knowledge graphs unify ontologies, documents, and reports while preserving data privacy
* How GNNs support relevance, similarity scoring, and multi-hop reasoning
* How to separate structural knowledge from patient-level data for governance and compliance
* How graph-powered RAG enables explainable, temporally grounded answers

Graph Database - British Columbia & Alberta

Neo4j

Mykola Pereyma

Road to NODES AI - Design Agent-Driven GraphRAG Systems with GNNs for Healthcare

Online event

Register: [https://luma.com/cc38ppvg ](https://luma.com/cc38ppvg)

Supercharge your dev game at the **Agents and APIs Developer Meetup** brought to you by Postman.
​​​Do you:

* ​​​Need to put your AI agents into production?
* ​​​Create APIs that actually scale with your AI app?
* ​​​Want to meet other AI & API builders and share best practices?

​​​Then this event is for you!
​​​Join us, along with special guests from \*\*[Monoscope ](https://monoscope.tech/)\*\*and **[Neo4j](https://neo4j.com/)**, for food, networking, live demos, and best practices on how to scale API development and get your AI apps ready for production. Come for the learning, stay for the vibes.

Graph Database - DACH (Germany, Austria, Switzerland )

ismail Abdul Maroof

Sneha Panda

Zeus Intuivo

hemanthmm1325

Paul Edward

Divya

Ritesh Jadhav

moh.alnsour

Rohan Jain

Bansari Gohil

Ivan Zakharov

Anchal

Asha Niwale

Kevin Stubbs

dorodri3

Tina

Özge Şahin

Agents & APIs Berlin Developer Meetup

The Social Hub Berlin

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.

Graph Database - Amsterdam

Nitin

ayush khandelwal

Simona

AI Builders Amsterdam

StartDock Coworking Prins Hendrikkade

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

Road to NODES AI - End-to-End Neo4j Developer Workspace with MCP

We're excited to invite you to ***our next Meetup*** *during the week of **AI Engineers Europe!***

Join fellow graph enthusiasts, Neo4j developers, and members of the AI community as we explore how graphs, knowledge graphs, and context engineering can unlock smarter applications and better answers.

We look forward to connecting with the community to exchange ideas on graphs, tech, and AI - join the discussion!

Session 1:
**Context Graphs for Explainable, Decision-Aware AI Agents**
Zaid Zaim, Developer Advocate - Neo4j

AI agents can follow prompts and use tools, but often lack the institutional context needed to explain why a decision is made. That reasoning: policies, precedents, and past outcomes are usually scattered across systems and human memory.
Context graphs capture this missing layer by modeling decision traces over time, including causality and context. By giving agents access to just enough historical and organizational knowledge, context graphs enable more explainable, consistent, and auditable decisions.

Session 2: tbd

Graph Database - United Kingdom

Ayman Arif

Fox Reymann

Context Graphs and AI Memory

Neo4j UK

Neo4j MeetUp

スポンサー

グループ

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Connect with our diverse Neo4j community: software engineers, data scientists, mathematicians, and academics. We're for anyone interested in learning about and working with graph databases for social, spatial, hierarchical, or other highly connected data sets. We host hands-on lab sessions, technology reviews, topical lectures, and plenty of social beer nights. Curious about graphs or noSQL tech? Join us!

A comprehensive guide to building GenAI applications using Neo4j's KGs.

Building Neo4j-Powered Apps with Gen-AI

Learn about LLMs + Knowledge Graphs, RAG and more

Free Hands-on Online Training

Join the Neo4j experts in the forum for Graph Database knowledge & more!

Neo4j Community Forum

A comprehensive guide on how to build a GraphRAG system from scratch.

Neo4j MeetUp

私たちについて

スポンサー

Building Neo4j-Powered Apps with Gen-AI

Free Hands-on Online Training

Neo4j Community Forum

Essential GraphRAG Ebook

今後のイベント

私たちについて

スポンサー

Building Neo4j-Powered Apps with Gen-AI

Free Hands-on Online Training

Neo4j Community Forum

Essential GraphRAG Ebook

グループ