NODES 2025 - Building Modular AI Agents with LangGraph, MCP, and Neo4j

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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

In this session, Shubham Shardul will introduce the Model Context Protocol (MCP), LangGraph, and Neo4j and demonstrate their integration in a real-world agent pipeline. You will learn: 1. The challenge of managing disparate AI tool connectors and how MCP standardizes interactions 2. How LangGraph coordinates complex agent workflows with built-in memory and dynamic tool access 3. How Neo4j supports reasoning over connected data to power intelligent agents Shubham will guide you through the architecture—MCP server and client configuration, tool registration, and agent setup—using clear, step-by-step slides that walk through an exemplar pipeline, showcasing session memory, vector-store queries, and graph-based reasoning. By the end, you’ll be equipped to implement MCP servers and clients, define custom tools, and assemble robust, context-aware AI agents with LangGraph and Neo4j. Speaker: Shubham Shardul 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