Day 24 of 100 Days Agentic Engineer Challenge: PydanticAI

During my recent journey, I realized that to effectively build AI agents, I needed a solid understanding of Python programming. I also considered using FastAPI for server-side development and API creation. FastAPI is appealing because it utilizes Pydantic, a library that ensures type safety and handles data validation.

Recently, the team behind Pydantic introduced a new framework called PydanticAI. This framework is designed to simplify the creation of AI agents by integrating seamlessly with various language models like OpenAI, Anthropic, Gemini, and others. PydanticAI emphasizes type safety, modularity, and provides a type-safe dependency injection system, which is crucial for building reliable AI applications.

Although PydanticAI is currently in its beta phase, I believe it’s the perfect time to start learning and experimenting with it. By diving into this framework now, I can stay ahead of the curve and develop efficient, type-safe AI applications. To get started with PydanticAI, I plan to explore its official documentation, which offers comprehensive guides and examples to assist developers in integrating it into their projects. Before I continue about PydanticAI, lets check my daily tasks routine.

Daily Tasks Routine

1.Physical Activity — I did 26 pushups again and am slowly moving towards 30.
2. Seven hours of sleep — Again, I went to bed too late, but earlier than the last few days, so I see progress.
3.AI Agents — Started studying the PydanticAI docs.
4.PAIC — In queue.
5.Data Science — In queue.

If you want to know what all these tasks are about, read the introduction to the 100 Days Agentic Engineer Challenge.

PydanticAI

I’ve been thinking about building AI agents and figured it’s best to stick with one programming language and a few frameworks. I’m leaning towards Python because it’s versatile and widely used. For the backend, FastAPI seems like a solid choice since it’s fast and user-friendly. To handle data validation and type safety, PydanticAI looks promising. As for the frontend, I’m still deciding between Next.js, Nuxt.js, or SvelteKit, but I’ll tackle that later. I did some research on PydanticAI’s features with the help of GPT and web searches, and it seems like a great tool for my project.

Key Features of PydanticAI

• Model-Agnostic Support: PydanticAI is designed to work seamlessly with various AI models, including OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral. This flexibility allows developers to choose the most suitable model for their specific application needs.
  InfoQ
• Type Safety: Building upon the foundation of Pydantic, PydanticAI ensures robust data validation and type safety. This integration guarantees that the data structures used within AI agents are consistent and error-free, enhancing the reliability of applications.
  Stephen Collins.tech
• Pythonic Design: PydanticAI emphasizes a design philosophy that aligns with standard Python development practices. By utilizing familiar control flows and agent composition patterns, it makes the development process intuitive for Python developers.
  InfoQ
• Dependency Injection System: The framework offers a type-safe dependency injection mechanism, which is particularly useful for testing and iterative development. This system allows for the clean provision of data and services to an agent’s system prompts, tools, and result validators.
  Medium
• Logfire Integration: For real-time debugging and monitoring, PydanticAI integrates with Pydantic Logfire. This feature enables developers to track application behavior and performance effectively, facilitating easier troubleshooting and optimization.
  InfoQ
• Streamed Responses: PydanticAI supports the streaming of Large Language Model (LLM) outputs with immediate validation. This capability ensures that responses are processed and validated in real-time, leading to faster and more accurate results.
  Medium

My next step is to go through all the examples provided in the PydanticAI docs and then think about how I can implement the agentic approach in one of my app ideas. I will also try to go through the examples with an AI coding assistant.