LangGraph Components #2–Pydantic for State Schema -Type Validation in LangGraph| Agentic AI Bootcamp

Description

Welcome to LangGraph Components Series — Part 1! 🎬 In this video, we’ll explore how to use Pydantic to define a state schema in LangGraph. Pydantic helps you validate, structure, and manage data inside LangGraph — ensuring your Agentic workflows are robust and type-safe. This is a must-watch if you’re learning LangGraph + LangChain and want to build real-world AI agents with structured data management. 💡 What You’ll Learn ✅ What is Pydantic and why use it in LangGraph ✅ How to create Pydantic models for GraphState ✅ Validation & error handling inside LangGraph ✅ When to use Pydantic vs TypedDict or Dataclass ✅ Practical example with ChatGroq / OpenAI models 🧰 Tech Stack LangGraph LangChain Core Pydantic (v2) Groq / OpenAI APIs Python (Jupyter Notebook) 📌 GitHub Repository (Code + Notes): 👉 https://github.com/dearnidhi/Agentic-AI-HandsOn-Bootcamp 📩 Connect with Me: ✉️ Email: nidhiyachouhan12@gmail.com 📸 Instagram: @dear_nidhi | @codenidhi 💼 LinkedIn: https://www.linkedin.com/in/nidhi-chouhan-544650b4/ ✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more LangGraph + Agentic AI tutorials! LangGraph Pydantic, Pydantic in LangGraph, LangGraph State Schema, LangGraph Components, LangGraph TypedDict vs Pydantic, LangChain LangGraph Tutorial, Agentic AI Bootcamp, LangGraph Validation, Define GraphState in LangGraph, LangGraph BaseModel Example, LangGraph Pydantic Model, LangGraph for Beginners, CodeNidhi LangGraph, Nidhi Chouhan AI, AI Workflow LangGraph, LangGraph Python Example #LangGraph #LangChain #Pydantic #AgenticAI #AIWorkflow #LangGraphTutorial #Python #OpenAI #Groq #CodeNidhi #NidhiChouhan #MachineLearning #ArtificialIntelligence #GenAI #LangGraphComponents