📌 Quick Reference Guide for This Tutorial: 00:06 Intro 00:53 Introduction 01:44 PydanticAI Philosophy 02:38 PydanticAI Core Components 05:19 Demo: Building Tech Support AI Agent using PydanticAI 24:32 My Final Thoughts about PydanticAI 💡 Pro Tips: - Install requirements first: pydantic-ai, openai - Keep your OpenAI API key ready Got questions? Drop them below! I respond to all comments about implementation challenges. Full code: tinyurl.com/ysz6cuf7
¡Me alegro que te haya gustado la música de fondo! Siempre intento seleccionar pistas de audio que complementen bien el contenido técnico sin distraer del aprendizaje. ¡Gracias por tu comentario! 😊
@arthuraquino8356 Thanks for your question. PydanticAI and LangGraph are both amazing tools for building AI applications, but they serve different primary purposes and excel in different areas. PydanticAI focuses on bringing type safety and validation to AI agent development making it particularly strong for building robust production ready applications. Its integration with Pydantic models makes it especially powerful for Python developers who need to ensure reliable data handling in their AI systems. LangGraph, specializes in orchestrating complex workflows between language models and tools. It's particularly good at managing multi-step processes and creating sophisticated interaction flows betwen different components of an AI system. You might think of it as being more focused on the coordination and flow of operations. I recommend using PydanticAI if you're prioritizing type safety and want to build production ready applications with strong data validation. Choose LangGraph if your primary need is orchestrating complex workflows. Please note that in practice, they can actually complement each other. You can use PydanticAI's type safety features alongside LangGraph's workflow management capabilitities for complex application. Let me know if you need more information about how to use both in the same application.
@@atefataya Thanks for your point of view. It would be interesting for you to do an example later using both in a complementary way, demonstrating the best side of each one.
@@arthuraquino8356 Thanks. Yes it is in the plan I am preparing the guide for building Robust AI Agents which I will share on my blog and a short video on my channel.
سلام و ممنون از نظرتون. کانال من روی آموزش هوش مصنوعی و آخرین اخبار تکنولوژی تمرکز داره. خوشحال میشم از طریق نظرات زیر ویدیوها در مورد محتوای آموزشی بحث و تبادل نظر کنیم. 🤖💻
📌 Quick Reference Guide for This Tutorial:
00:06 Intro
00:53 Introduction
01:44 PydanticAI Philosophy
02:38 PydanticAI Core Components
05:19 Demo: Building Tech Support AI Agent using PydanticAI
24:32 My Final Thoughts about PydanticAI
💡 Pro Tips:
- Install requirements first: pydantic-ai, openai
- Keep your OpenAI API key ready
Got questions? Drop them below! I respond to all comments about implementation challenges.
Full code: tinyurl.com/ysz6cuf7
Gracias por las pista de audio!!!
¡Me alegro que te haya gustado la música de fondo! Siempre intento seleccionar pistas de audio que complementen bien el contenido técnico sin distraer del aprendizaje. ¡Gracias por tu comentario! 😊
What is your opinion between it and langgraph?
@arthuraquino8356 Thanks for your question. PydanticAI and LangGraph are both amazing tools for building AI applications, but they serve different primary purposes and excel in different areas.
PydanticAI focuses on bringing type safety and validation to AI agent development making it particularly strong for building robust production ready applications. Its integration with Pydantic models makes it especially powerful for Python developers who need to ensure reliable data handling in their AI systems.
LangGraph, specializes in orchestrating complex workflows between language models and tools. It's particularly good at managing multi-step processes and creating sophisticated interaction flows betwen different components of an AI system. You might think of it as being more focused on the coordination and flow of operations.
I recommend using PydanticAI if you're prioritizing type safety and want to build production ready applications with strong data validation. Choose LangGraph if your primary need is orchestrating complex workflows.
Please note that in practice, they can actually complement each other. You can use PydanticAI's type safety features alongside LangGraph's workflow management capabilitities for complex application. Let me know if you need more information about how to use both in the same application.
@@atefataya Thanks for your point of view. It would be interesting for you to do an example later using both in a complementary way, demonstrating the best side of each one.
@@arthuraquino8356 Thanks. Yes it is in the plan I am preparing the guide for building Robust AI Agents which I will share on my blog and a short video on my channel.
سلام عاتف جان شما ایرانی که بتونیم با همدیگه همکاری داشته باشیم؟
سلام و ممنون از نظرتون. کانال من روی آموزش هوش مصنوعی و آخرین اخبار تکنولوژی تمرکز داره. خوشحال میشم از طریق نظرات زیر ویدیوها در مورد محتوای آموزشی بحث و تبادل نظر کنیم. 🤖💻