NODES 2025 - Agentic GraphRAG: Multi-Agent Knowledge Graph Construction for Research Teams

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9 learners

What you'll learn

This course includes

51.3 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

neo4j graph databases graphs LLM retrieval augmented generation RAG vector database vector search GenAI AI Artificial intelligence nodes25

Full Transcript

This session will explore a multiagent approach to GraphRAG that democratizes knowledge graph construction for research teams. Watch as four specialized AI agents collaborate: analyzing research focus to suggest entities, extracting knowledge from documents in parallel, merging duplicate entities, and optimizing graph structure—all while keeping researchers in control. The session showcases a production-ready platform where teams upload PDFs, define their research domain, and receive AI-generated suggestions they can refine with domain expertise. Built with Neo4j, LangChain, and deployed on Cloudflare Workers, this agentic architecture dramatically improves extraction quality while maintaining accessibility for non-technical users. You will learn advanced techniques for agent coordination, streaming processing with real-time progress tracking, and hybrid human-AI decision making. The live demonstration processes research documents in real-time, showcasing how agent specialization produces higher quality knowledge graphs than traditional single LLM approaches. Speaker: Akhil Hemanth Resources: Get Started with Aura - https://bit.ly/3LOLrjh Deployment Center - https://bit.ly/4jOelM3 Ground AI Systems and Agents with Neo4j - https://bit.ly/4oVsnyb #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph