Pydantic is a Python library that uses standard type hints to define data models and enforce data schemas at run-time. Originally, type annotations were added to Python for static analysis, but their growing versatility has led to broader uses, including run-time validation. Built on a fast Rust core, it provides efficient data validation, parsing and serialization.
While it’s best known for web API development, Pydantic has also become essential in LLM applications. We typically use the structured output from LLMs technique to manage the unpredictable nature of LLMs. By defining a strict data schema, it acts as a safety net for the unpredictable nature of model output — converting free-form text responses into deterministic, type-safe Python objects (e.g., JSON). This approach, often implemented through Pydantic AI or LangChain, turns potentially brittle LLM interactions into reliable, machine-readable data contracts. Our teams have successfully used Pydantic in production to extract structured representations from unstructured documents, ensuring the output conforms to a valid structure. Given its maturity, performance and reliability, Pydantic is now our default choice for production-level Python AI applications.
Originally type annotations were added to Python to support static analysis. However, considering how widely type annotations, and annotations in general, are used in other programming languages, it was only a matter of time before developers would begin to use Python's type annotations for other purposes. pydantic falls into this category. It allows you to use type annotations for data validation and settings management at run time. When data arrives as, say, a JSON document and needs to be parsed into a complex Python structure, pydantic ensures that the incoming data matches the expected types or reports an error if it doesn't. Although you can use pydantic directly, many developers have used it as part of FastAPI, one of the most popular Python web frameworks. In fact, using pydantic in FastAPI is considered so indispensable that a recently proposed change to Python, aimed at reducing the cost of loading annotated code into memory, was reconsidered because it would have broken the use of type annotations at run time.
