Your first Implementation of LangGraph | Agentic AI

Description

🚀 In this session of the LangGraph Series, we move from theory to practical implementation using a simple Jupyter Notebook example. After understanding the core concepts of LangGraph in the previous lectures, this video focuses on building and running a complete LangGraph workflow step-by-step in Python. We implement: ✅ State using `TypedDict` ✅ Nodes using Python functions ✅ Edges & Conditional Edges ✅ START & END markers ✅ StateGraph initialization ✅ Graph Compilation & Invocation ✅ Workflow Visualization 📌 The workflow example used in this lecture is a Hiring Process Workflow, where we simulate stages like: * Resume Receiving * Resume Review * Interview Selection/Rejection * Final State Updates This lecture is intentionally kept beginner-friendly and implementation-focused so that anyone new to LangGraph can understand how the framework actually works before moving to advanced Agentic AI workflows. 🎯 By the end of this video, you will understand: * How LangGraph internally manages nodes, edges, and state * Why graph compilation is necessary * How state updates happen across nodes * How to validate whether your workflow implementation is correct * How to invoke and test workflows with different initial states 📚 Perfect for: * AI Engineers * GenAI Developers * LangChain/LangGraph Beginners * Agentic AI Enthusiasts * Workflow Automation Learners