Create AI Agent CRUD Application with PydanticAI: Step by Step

#aiagents #aiagent #aiapplications

🤖 Create AI Agent CRUD Application with PydanticAI: Step by Step

Welcome to our Beginner's Guide to AI Agents! In this video, we'll take you through the entire process of building a CRUD (Create, Read, Update, Delete) application using PydanticAI, OpenAI, PostgreSQL and Streamlit. Whether you're new to AI Agents or looking to enhance your skills, this tutorial is perfect for you.

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🔗 Useful Links:

Github with notes and code

PydanticAI Documentation

Get Digital Ocean Account with Credit

Buy me a Beer

Medium Article on the Video

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⏰ Timestamps:

00:00:49 Demonstration of the application
00:04:53 Background on Generative AI, Conversational AI, AI and AI Agents
00:09:14 Introduction to PydanticAI
00:29:40 DigitalOcean PostgreSQL Database set-up
00:34:24 AI Agent Database Connection model code (Database Tool)
01:13:31 Creating the streamlit application front-end
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🌟 Why PydanticAI, AI Agents, and GenerativeAI?

PydanticAI is a groundbreaking Python framework developed by the creators of Pydantic, renowned for its exceptional ability to create and verify structured data models with ease. This capability is a true superpower when it comes to utilizing GenerativeAI for production-grade applications. One of the biggest challenges with GenerativeAI in applications has been the unpredictable nature of its outputs—especially JSON outputs, which were often unreliable and inconsistent. PydanticAI addresses this challenge by allowing developers to precisely specify both the input and output data models using Pydantic.

By defining strict schemas for data exchange, PydanticAI ensures that the data flowing through your AI Agents is well-structured, validated, and predictable. This structured approach eliminates the uncertainties associated with GenerativeAI outputs, making it feasible to deploy AI Agents in mission-critical applications where reliability is essential. With PydanticAI, you can seamlessly integrate GenerativeAI into your applications without worrying about data inconsistencies or unexpected behaviors.

In this video, we demonstrate the powerful synergy between PydanticAI, AI Agents, and GenerativeAI by showcasing how two AI Agents can effectively hand over tasks with structured outputs powered by PydanticAI. This integration not only enhances the robustness and scalability of your applications but also unlocks the full potential of GenerativeAI, enabling the creation of intelligent, reliable, and efficient AI-driven solutions. By leveraging PydanticAI, you ensure that your AI Agents deliver consistent and dependable results, making your applications more trustworthy and effective.
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Thanks for the video, I've tried the same code using the Gemini 2.0 Flash model, but there is a problem with Gemini model and markdown fields in pydantic.

[AjinkyaBhore-xq]

Your AI agent will update database data ? What's the scenario you will feed to train it on