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Mar 19, 2025

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

Andreas Kollegger argues that AI agents shouldn't be autonomous black boxes. Discover how combining GraphRAG with local LLMs turns agents into safe, composable software design patterns.

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#1 about 4 min

Introduction to Neo4j and remote developer relations work

An overview of Neo4j's growth and the global travel patterns of their developer relations team.

#2 about 3 min

Understanding graph databases and node relationship modeling

How graph databases use nodes and relationships to connect and organize data efficiently without massive join tables.

#3 about 3 min

The fundamentals of retrieval augmented generation operations

How preparing language model prompts with external retrieved context guarantees more accurate operational answers.

#4 about 3 min

Enhancing language models with graph retrieval augmented generation

Using knowledge graphs to distill information and provide highly focused context for answering language model queries.

#5 about 2 min

Merging structured and unstructured data for business insights

Techniques for connecting fragmented text documents with existing business data records to build comprehensive query systems.

#6 about 2 min

Resources and introductory tools for learning graph technologies

Available online courses and bundled functional packages that assist developers with implementing graph knowledge representations.

#7 about 5 min

Handling messy enterprise data with evolvable graph schemas

Mapping fragmented spreadsheets and legacy documentation into incrementally refined graphs simplifies massive enterprise data migrations.

#8 about 1 min

Returning to basic text formats for machine learning

Why straightforward text formats like Markdown and CSV are preferred for feeding clean unstructured data directly to automated AI scrapers.

#9 about 2 min

Addressing data privacy concerns with local language models

Running self-hosted language models ensures corporate data governance and prevents critical proprietary information from leaking.

#10 about 3 min

The impact of open source models on industry dynamics

How advanced open source releases disrupt traditional funding incentives and democratize advanced artificial intelligence capabilities.

#11 about 3 min

Overcoming language model limitations via streaming data ingestion

Processing real-time information updates with specialized small models maintains current contextual relevance without high resource consumption.

#12 about 2 min

Embedding local language models into user operating systems

How small models operating as personal desktop agents enable advanced contextual search and independent file interactions.

#13 about 6 min

Treating artificial intelligence agents as composable software units

Reframing machine learning agents from autonomous actors into controllable software architectures managed by traditional design patterns.

#14 about 4 min

Comparing graph augmented retrieval with vector similarity search

Why basic vector matching lacks the specific contextual relationships that knowledge graph cross-linking effectively secures.

#15 about 4 min

Overcoming developer intimidation when integrating foundational graph structures

Recognizing that functional graph querying relies heavily on accessible pattern matching approaches rather than entirely native data components.

#16 about 3 min

The future potential for native graph programming languages

Exploring how contemporary coding frameworks abstract internal graph operations and the viable opportunity for newly tailored native syntax.

#17 about 2 min

Elevating developer workflows by automating repetitive foundational tasks

How automated tools securely shoulder repetitive dependency tracking constraints so programmers can target impactful software challenges.

#18 about 2 min

Implementing sensible adoption strategies for generative programming systems

Applying measured incremental implementation strategies lets teams confidently adopt emerging capabilities without enduring overwhelming complexity.

Matching moments

1:44 min

Introduction to generative AI and knowledge graphs

Michael Hunger Michael Hunger · World Congress 2024

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 · World Congress 2026 Europe

4:11 min

Protecting enterprise data with local models and RAG

Ash Ryan Arnwine Ash Ryan Arnwine +3 · World Congress 2024

2:24 min

Transforming unstructured email data into customized user knowledge graphs

Dana Lawson Dana Lawson +1 · World Congress 2026 Europe

2:26 min

Automating structural graph generation using large language models

Prof Smoke Prof Smoke · World Congress 2025

3:50 min

Building local RAG architectures using the Anything LLM tool

Cedric Clyburn Cedric Clyburn +1 · World Congress 2025

Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 14:10–14:40

Stage 2

Store Your AI Agent's Memory and Context in a Knowledge Graph

Guy Korland

CEO of FalkorDB

Guy Korland
Open session

World Congress 2026 North America

September 23, 2026 · 10:00–17:00

Stage 11

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

September 25, 2026 · 16:10–16:40

Stage 2

Beyond SQL Generation: How to Teach Agents What Your Database Actually Means

Kris Jenkins

Host / Developer Voices

Kris Jenkins
Open session

World Congress 2026 North America

September 23, 2026 · 13:15–15:15

Stage 9

DeepAgents: Build Multi-Agent AI Systems That Actually Work

Anagha Rumade, Anjana Umapathy, Apoorva Jaiswal

Anagha Rumade
Anjana Umapathy
Apoorva Jaiswal
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 5

Edge AI: Running Agentic Intelligence Where Internet Can't Reach

Nitin Eusebius

AWS - Principal Solutions Architect

Nitin Eusebius
Open session

World Congress 2026 North America

September 25, 2026 · 09:40–10:10

Stage 7

Loop Engineering

Li Yin

CEO

Li Yin

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