Building AI Applications the Pydantic Way (Sponsor Pydantic)
AI Summary
In this talk, Samuel discusses building AI applications using the Pyantic framework. He introduces Pyantic, a library that has gained immense popularity, notably due to its use in AI and web frameworks like FastAPI. Samuel shares insights into new developments, including Pyantic Logire—an observability platform for developers—and Pyantic AI, an agent framework. He emphasizes the importance of type safety in Python and how it benefits AI development, especially as AI-generated code becomes more prevalent. Samuel also explains the Model Context Protocol (MCP) and its role in enhancing agent behavior. He explores various examples and use cases, especially focused on memory management in AI applications and the process of running evaluations (evals) for AI models. Throughout the discussion, he highlights the challenges and nuances of working with AI frameworks and how Pyantic seeks to make this easier, providing practical demonstrations and encouraging collaboration and feedback from the community.