Graph Database - DACH (Germany, Austria, Switzerland )

Guest: Jose Latorre ( https://www.linkedin.com/in/joslat/ )

Blog: https://neo4j.com/blog/developer/agentmemory-for-net-a-native-sibling-to-neo4j-agent-memory/

#neo4j #graphdatabase #agenticai #knowledgelayer #knowledgegraph #graphrag #AIAgents #dotnet #agentframework

Register: https://globalai.community/e/ack9t53d

Bring your coding agent. Pick an idea. Build something.
Graphs Gone Wild is for people experimenting with AI agents, memory, documents, graphs, MCP, and new developer tooling.
Use whatever stack you like: Cursor, Claude Code, Codex, Copilot, your own agent setup, frameworks, databases, models, or something completely experimental.
For this NODES 2026 pop-up, Neo4j will provide some of the building blocks, including Document Intelligence, Aura MCP, knowledge graphs, and Agent Memory.
More tools, technologies, and partners will be revealed soon.
The goal: build something interesting in a few hours. It does not need to be polished. A working experiment and a good demo are enough.
What can you build?
Anything around agents, connected knowledge, memory, documents, tools, or graphs.

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

NODES, 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.

AI Engineering: Generative AI, RAG, and Agents
Build software with AI. From architecting multi-agent systems to exploring new coding workflows, AI tools and techniques are evolving rapidly—and graphs play a central role. Learn from the latest research and real-world applications. Topics include knowledge graph construction from structured and unstructured data, context graphs and memory for more reliable AI systems, GraphRAG retrieval patterns, full-stack agentic strategies, and the libraries, tools, and services used to bring these systems into production.

Modern Applications: Libraries, Frameworks, and Platforms
Discover how developers build modern applications and AI-powered systems using graph databases alongside popular libraries, frameworks, and platforms. Learn how teams solve real-world problems across software stacks, deployments, integrations, and programming languages. Explore tools and practices tailored to different use cases that streamline development, improve the developer workflow, and accelerate the delivery of intelligent applications.

Data Intelligence: Knowledge Graphs, LLMs, and Fully Fledged Context
Explore how developers, data scientists, and AI builders unlock deeper intelligence from data. Learn how knowledge graphs and modern data workflows combine data engineering and domain expertise to build scalable pipelines, models, and knowledge structures that continuously improve outcomes. Sessions highlight practical techniques and real-world applications including GraphRAG, centrality, entity recognition, graph visualization, and scalable data integration.

To see the talks from last year, check out the NODES 2025 Playlist.