Structured LLM Output with Pydantic and LangChain

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Have you ever struggled to get clean, structured data out of your LLM calls or agent networks?
If you’ve been piecing together JSON responses, wrestling with data validation, or just trying to keep your codebase tidy, you’re in the right place!

In today’s video, I’ll show you how to combine three powerful tools—LangGraph, Pydantic, and JSON—to streamline how your agents return structured data. Whether you're building an AI assistant, a data processing pipeline, or an API response generator, this workflow will save you time and headaches.

We’ll start by building a 4-agent network with LangGraph, the powerful framework for agentic flows. Then, we’ll use Pydantic and JSON to parse and validate the agent responses into structured data, ready for consumption by your applications.

🔗 Links & Resources:
Skool: https://www.skool.com/ai-software-dev...
Code the Revolution: Newsletter - https://aidev9.substack.com/
Discord server:   / discord  
LangGraph Python docs: https://www.langchain.com/langgraph
Langsmith: https://www.langchain.com/langsmith

#ai #openai #langgraph #chatbot #langsmith #agent

🕒 TIMESTAMPS:
00:00 - Intro
00:14 - Project setup
00:57 - Coding tutorial
11:18 - Testing
12:08 - LangSmith
13:57 - More complex task
14:36 - Conclusion