Join Aishwarya Srinivasan and Arvind Narayanamurthy for a beginner–intermediate, 1.5-hour live workshop on LangChain & LangGraph: Orchestrating AI Applications and Agentic Workflows.

This session will give you the core mental models and practical skills needed to move from simple prompt-based scripts to structured pipelines and agentic systems. You’ll learn where LangChain fits, when to use LangGraph, and how to build real applications step-by-step.

Prerequisite: Basic Python knowledge is required.

Note: This workshop session will be recorded, and all participants will receive the recording post-event.

You’ll learn:

→ Foundations & Mental Models

A clear understanding of LLM orchestration challenges, what LangChain solves, and how LangGraph fits into workflow-based AI systems.

→ LangChain Essentials

The core building blocks: chat models, prompting patterns, structured outputs, and chaining using LCEL.

→ Tool Calling

How to extend LLMs with real-world capabilities, including using MCP adapters inside LangChain.

→ RAG Basics

The end-to-end flow of a simple RAG pipeline—document loading, vector stores, retrievers, and wiring them together.

→ Lab 1: Micro-App in Colab

Build a small functional app using LangChain.

→ LangGraph Workflows & Agents

How to build agentic architectures in LangGraph by working with states, nodes, edges, interrupts, and tool-augmented agents.

→ Lab 2: Agentic App with LangGraph

Create a simple agent with tools, memory, and workflows.

→ Observability with LangSmith

Quick look at Langsmith traces to debug chains

We’ll wrap up with a Q&A to help you apply these ideas to your own projects.

By the end, you’ll have the confidence and practical foundation to build real-world AI applications and agentic systems using LangChain and LangGraph.