Software Engineering Radio - the podcast for professional software developers SE Radio 676: Samuel Colvin on the Pydantic Ecosystem
Jul 10, 2025
Samuel Colvin, CEO of Pydantic and creator of its ecosystem, discusses the innovative Pydantic frameworks designed for Python. He dives into Pydantic AI's role as a model-agnostic library for large language models and introduces Logfire for observability in AI applications. Colvin explores data validation, contrasting coercion with strict modes, and emphasizes the performance gains from Pydantic's rewrite in Rust. He shares success stories and outlines the integration of Pydantic with various databases while advocating for open standards and collaboration in engineering.
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Type Hints Enforced At Runtime
- Pydantic enforces Python type hints at runtime to validate, coerce, and serialize data reliably.
- It converts annotations into validated Python objects and generates JSON schemas for interoperability.
Validate At Boundaries
- Use Pydantic where data crosses application boundaries to enforce types and avoid runtime surprises.
- Prefer coercion when receiving strings (e.g., "123") and convert them to expected types to reduce errors.
Lax Versus Strict Modes
- Pydantic supports both lax (coercing) and strict modes with fine-grained control over coercion semantics.
- The library guarantees you get the Python object you declared, within practical open-source limits.