Über uns
Willkommen bei der Gruppe für Deutschland, Österreich und der Schweiz von Neo4j, der weltweit führenden Graphdatenbank. KI ist nur so gut wie der Kontext, den man ihr gibt, und Kontext sind vernetzte Daten. Deshalb stehen Graphen im Zentrum moderner KI: GraphRAG, Knowledge Graphs und Context Graphs geben LLMs und Agenten die Beziehungen, die sie brauchen, um die richtigen Fakten zu finden, darüber zu schlussfolgern und ihre Antworten zu erklären.
Wir veranstalten regelmäßig Events mit Vorträgen und praktischen Workshops zum Bau von KI-Anwendungen mit Graphen, von GraphRAG und Agent Memory bis zu Context Graphs und der Neo4j-Datenbank selbst.
Ob du neu bei Graphen bist oder schon lange damit arbeitest, du bist willkommen. Komm danach auf ein Getränk auf uns mit dazu. Eine tolle Gelegenheit zum Netzwerken (Graphen sind eben überall!) und um dem Neo4j-Team deine Fragen zu stellen.
Welcome to the Germany, Austria & Switzerland group for Neo4j, the world’s leading graph database. AI is only as good as the context you give it, and context is connected data. That’s why graphs sit at the center of modern AI: GraphRAG, knowledge graphs, and context graphs give LLMs and agents the relationships they need to retrieve the right facts, reason across them, and explain their answers.
We host regular events with talks and hands-on sessions on building AI applications with graphs, from GraphRAG and agent memory to context graphs and the core Neo4j database.
Whether you’re new to graphs or an experienced graphista, you’re welcome.
Join us afterwards for a drink on us. It’s a great chance to network (see, graphs are everywhere!) and to ask the Neo4j team your questions.
Vorgestelltes Event
From Demo to Production: Deploying AI Responsibly in a Complex World
Register: https://www.meetup.com/vienna-data-science-group-meetup/events/315639920/
Legit Check for AI - Grounding AI with Knowledge Graphs for Transparency and Trust at the Core
AI systems are only as trustworthy as the data they reason over. But when that data is highly interconnected — products, brands, trends, relationships — flat retrieval architectures like RAG fall short: they can't traverse context, can't explain their paths, and can't make their reasoning visible.
This talk shows how Knowledge Graphs change that equation.
Using a real-world dataset scraped from sources like Sneaker News, WWD, and StockX, we demonstrate how to build a Knowledge Graph from unstructured documents and use it to ground Large Language Models, making their outputs not just more accurate, but genuinely explainable.
We'll walk through:
- Building the graph — extracting structured knowledge from unstructured web sources and modeling it as a connected graph
- Grounding LLMs — how Knowledge Graph context replaces hallucination-prone retrieval and gives the model structured facts to reason over
- Visualizing trust — how graph-based visualizations let users see why the AI answered the way it did, not just what it answered
Kommende Veranstaltungen
6
From Demo to Production: Deploying AI Responsibly in a Complex World
TUtheSky, Getreidemarkt 9, 1060, Wien, ATRegister: https://www.meetup.com/vienna-data-science-group-meetup/events/315639920/
Legit Check for AI - Grounding AI with Knowledge Graphs for Transparency and Trust at the Core
AI systems are only as trustworthy as the data they reason over. But when that data is highly interconnected — products, brands, trends, relationships — flat retrieval architectures like RAG fall short: they can't traverse context, can't explain their paths, and can't make their reasoning visible.
This talk shows how Knowledge Graphs change that equation.
Using a real-world dataset scraped from sources like Sneaker News, WWD, and StockX, we demonstrate how to build a Knowledge Graph from unstructured documents and use it to ground Large Language Models, making their outputs not just more accurate, but genuinely explainable.
We'll walk through:- Building the graph — extracting structured knowledge from unstructured web sources and modeling it as a connected graph
- Grounding LLMs — how Knowledge Graph context replaces hallucination-prone retrieval and gives the model structured facts to reason over
- Visualizing trust — how graph-based visualizations let users see why the AI answered the way it did, not just what it answered
11 Teilnehmer
Knowledge-BASED #7: GraphRAG edition
Riesstraße 22, Riesstraße 22, 80992 München-Moosach, Germany, München, BY, DERegister: https://luma.com/5tf46f5t
Search is evolving fast; from new algorithms and tools to AI-driven solutions, the field is constantly shifting.
BASED Meetup is where professionals & enthusiasts discuss the latest trends, breakthroughs, and challenges in modern search.
We look into topics like:
- Agentic Retrieval-Augmented Generation (RAG), Memory & Search;
- Knowledge graphs & GraphRAG;
- Innovative approaches to retrieval;
- Modern metrics for search quality;
- Optimisation techniques for production-scale systems;
- Cross-lingual and multimodal search challenges;
… and much more.
No sales pitches, no gatekeeping; the goal is to have an open space to share ideas, learn from each other, and explore the technologies shaping the future of search!:)14 Teilnehmer- Netzwerkveranstaltung
NODES 2026
·OnlineOnline25 Teilnehmer aus 10 GruppenNODES, the biggest community gathering dedicated to engineering better intelligence returns on November 12, 2026, for its eighth year.
Join thousands of developers and data and AI practitioners for this free, 24-hour conference to learn about the latest advancements in intelligent systems and gain insights from speakers who showcase their implementations, tools, and models.The agenda is out!!! Here is a snapshot from the AI Engineering track
- From Vector Search to GraphRAG: Architecting AI Systems That Actually Work in Production, Emmanuela Opurum, SoftNet Technologies
- Building an Explainable GraphRAG System: From Clinical Notes to Knowledge Graphs with Neo4j, Joakim Nilsson, Capgemini & Johan Müllern-Aspegren, AI Futures Lab
- Sovereign Transaction Graphs – Adaptive Agents Without Context Leakage: Mandy Goram, pmOne AG
- Why Did the Agent Do That? A Neo4j Decision-Trace Layer for AI Agents: Alexis Ngoga
- Inside the AI Detective's Brain: Multi-Agent Deduction on Knowledge Graphs, Paweł Gołąb & Michał Domański, BCNN
Check out the full agenda of unique live sessions
Before NODES 2026: Get hands-on with GraphRAG, agent memory, context graphs, and agentic engineering in our 2-hour workshop series—and share NODES with your fellow devs for a chance to win $200 in cool prizes. Come to NODES with a few new patterns, ideas, and questions ready to dig into.
4 Teilnehmer aus dieser Gruppe 
AI Hands-On Workshop with Neo4j and Databricks
Design Offices München Campus Königsplatz, Brienner Str. 45/a-d, 80333 München, Germany, München, BY, DERegister: https://events.neo4j.com/workshopwithneo4janddatabricks
Join Neo4j and Databricks experts for an in-person, hands-on workshop. It is built for data scientists, data engineers, AI/ML engineers, and AI developers who want to build production-style AI agents grounded in governed lakehouse data and connected graph context.
In this half-day Neo4j Graph Workshop, you will combine the Databricks Lakehouse, Databricks Genie Agents, and Neo4j graph intelligence to build a multi-agent system with memory. The system answers natural-language questions across structured analytics data and connected graph context.
Working with an Aircraft digital twin dataset, you will see how Databricks handles high-volume sensor telemetry and SQL analytics while Neo4j captures the relationships between aircraft, systems, components, flights, delays, maintenance events, airports, and documentation. You will then connect both platforms through a Supervisor Agent that routes each question to the right specialist: Genie for sensor trends and aggregations, Neo4j for graph traversal and relationship questions, or both when the answer spans data sources.
9 Teilnehmer
Vergangene Veranstaltungen
305
