How to Build a Price Monitoring Agent with Pydantic AI
Part 1 of the Pydantic AI series
Video Tutorial
Keeping track of fluctuating product prices across e-commerce platforms can be a daunting task.
Whether you're tracking a personal wishlist or monitoring competitors' pricing for your business, automating this process can save time and effort.
In this guide, we’ll explore how to build a price monitoring agent using the Pydantic AI framework—a robust agentic framework from the creators of Pantic, a popular data validation library.
This tutorial is part one of a series. Today, we’ll focus on building a scraper agent to extract key product details like title, description, price, and more.
In the next part, we’ll expand this agent to store data in a database and send notifications for price changes.
What is Pydantic AI?
Pydantic AI is revolutionizing the way developers build applications that leverage Generative AI. As a Python Agent Framework, it simplifies the creation of production-grade applications by integrating robust data validation with the power of LLMs. Here’s why Pydantic AI stands out:
Built on Proven Foundations: Developed by the creators of Pydantic, which is widely used in various AI frameworks like OpenAI and LangChain, Pydantic AI inherits a strong legacy of type safety and structured data management.
Model-Agnostic Flexibility: Currently supporting models like OpenAI, Gemini, and Groq, Pydantic AI allows developers to easily implement support for additional models through a simple interface. This flexibility ensures that your application can adapt to various AI technologies without significant overhead.
Enhanced Developer Experience: With features like vanilla Python control flow and a novel dependency injection system, Pydantic AI empowers developers to apply familiar coding practices. This leads to more maintainable code and a smoother development process.
Streamlined Response Validation: The framework not only validates incoming data but also ensures that responses from LLMs are structured and validated, enhancing reliability in application behavior.
Overview of the Price Monitoring Agent
Our agent will:
Scrape product details (title, description, price, currency, and image URL) from a given URL.
Parse the information into a structured format.
Prepare for database storage and notification handling (to be implemented in part two).
Here’s how the process works (diagram above)
Input: Product page URL
Scraper Tool: Extracts structured data using Beautiful Soup and Markdownify.
Agent: Processes the scraped data using Pydantic AI for type-safe responses.
Code
https://colab.research.google.com/drive/1wEFO0_W13J_DBtjd55vNK7vQa_UGo7Hm#scrollTo=3O2V5Jth2Q8F
To be continued in Part 2