Best of Pydantic — 2025

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   Article

   Community Picks·1y

   PydanticAI

   PydanticAI is a Python agent framework designed to facilitate the development of production-grade applications with Generative AI. Built by the Pydantic team, it supports multiple models such as OpenAI, Anthropic, and Cohere. The framework integrates seamlessly with Pydantic Logfire for real-time debugging and performance monitoring. It offers type-safe and structured responses, dependency injection, and streamed outputs to ensure efficient and accurate AI operations. PydanticAI also supports advanced features like graph definitions with typing hints for complex applications.

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2. 2
   Article

   Community Picks·1y

   zhanymkanov/fastapi-best-practices: FastAPI Best Practices and Conventions we used at our startup

   Opinionated list of best practices for FastAPI development, including project structure, async routes, Pydantic, and dependency management. It covers a comprehensive folder structure inspired by Netflix's Dispatch, explains the handling of async and sync routes, emphasizes the importance of Pydantic for validations, and provides insights on organizing dependencies for effective request validation. Additional notes on database naming conventions, migration practices, API documentation, and integration tests are also included.

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   Article

   Data Engineer Things·1y

   Airflow 3 and Airflow AI SDK in Action — Analyzing League of Legends

   This post demonstrates how to create an end-to-end data pipeline using Airflow 3 and the Airflow AI SDK to analyze League of Legends data. It covers setting up the environment, exploring the Riot Games API, building a Python client for API interaction, and using AI to generate a champion tier list. The pipeline showcases modern Airflow features like Dynamic Task Mapping and emphasizes newer AI integration capabilities with Large Language Models.

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   Article

   Medium·1y

   How I built an agent with Pydantic AI and Google Gemini

   Building an AI agent leveraging Pydantic AI and Google Gemini can streamline the synthesis of strategic insights. This tutorial demonstrates how to build such an agent using frameworks like FastAPI for the backend, HTMX for dynamic UI updates, Tailwind CSS for styling, and Cloud Run for deployment. The agent performs web content analysis and generates a SWOT analysis, incorporating community insights and competitive analysis through various integrated tools.

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