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Building Modular AI Agents with LangGraph, MCP, and Neo4j

Session Track: App Dev

Session Time:

Session description

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

photo of Shubham Shardul

Shubham Shardul

Data & AI Senior Analyst (Capability), Accenture

Shubham Shardul is a data and AI senior analyst at Accenture, specializing in data engineering and GenAI applications. With hands-on expertise in Python development, knowledge graphs, and GenAI-powered tools, he builds scalable, intelligent data solutions. Shubham is also a community speaker who shares thought leadership on modern data practices, AI integration, and insights into emerging AI technologies.