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| Pydantic | |
|---|---|
| Developers | Samuel Colvin; Pydantic Services Inc. |
| Release | 2017 |
| Written in | Python, Rust |
| Type | Data validation |
| License | MIT License |
| Website | pydantic |
| Repository | github |
Pydantic is an open-source data validation library for the Python programming language. It uses Python type hints to validate and serialize data at runtime: developers declare the expected structure of their data as Python classes, and Pydantic checks incoming data against those declarations, converting compatible types where appropriate and returning structured errors when data does not conform.[1][2]
The library is developed by Pydantic Services Inc., a company founded by its original author, Samuel Colvin. The company also produces Pydantic AI, an open-source framework for building applications with large language models, and Pydantic Logfire, a commercial AI observability platform.[3][4]
History
[edit]Pydantic was created by the software developer Samuel Colvin, who started the project in 2017.[5] Its adoption grew substantially through its use in FastAPI, a Python web framework that relies on Pydantic to validate request and response data.[5][6]
Pydantic V2, released in 2023, was a rewrite in which the core validation logic was reimplemented in Rust as a separate package, pydantic-core, with the stated goal of improving performance.[7]
In 2022, Colvin founded a commercial entity, Pydantic Services Inc. The company emerged from stealth in February 2023 with US$4.7 million in seed funding led by Sequoia Capital, with participation from Partech and a group of angel investors.[5] In October 2024 it released Pydantic Logfire, an AI observability platform for applications, and announced a US$12.5 million Series A round, also led by Sequoia.[3] The company subsequently released Pydantic AI, an open-source framework for building applications and agents with large language models, which reached version 1.0 in September 2025 and version 2.0 in June 2026.[4][8]
Design
[edit]A Pydantic model is defined as a Python class that inherits from a base model class, with each field declared using a standard type annotation. When a model is instantiated, Pydantic validates the supplied data against those annotations.[1] The library can emit JSON Schema from a model definition and supports user-defined validators and serializers for cases that the built-in types do not cover.[1] Technical literature on Python type checking has discussed Pydantic's distinction between validation and parsing as part of its approach to runtime data checking.[2]
Reception and use
[edit]Pydantic has been described as the most widely used data validation library for Python.[1] Reporting by TechCrunch stated that it is used by developers at large technology companies, including Google, Meta, Microsoft, Amazon, Apple, Nvidia, Netflix, and OpenAI.[5][3] The library is the subject of dedicated chapters or sections in programming books, including Patrick Viafore's Robust Python and Bill Lubanovic's FastAPI, both published by O'Reilly Media, and Steven F. Lott and Dusty Phillips's Python Object-Oriented Programming, published by Packt Publishing.[2][6][9]
References
[edit]- 1 2 3 4 "Pydantic: Simplifying Data Validation in Python". Real Python. Retrieved 2026-06-09.
- 1 2 3 Viafore, Patrick (2021). "Runtime Checking With pydantic". Robust Python. O'Reilly Media. ISBN 978-1-098-10066-7.
- 1 2 3 Sawers, Paul (2024-10-01). "Sequoia backs Pydantic to expand beyond its open source data-validation framework". TechCrunch. Retrieved 2026-06-09.
- 1 2 "Pydantic AI". Pydantic Documentation. Retrieved 2026-06-09.
- 1 2 3 4 Sawers, Paul (2023-02-16). "Sequoia backs open source data-validation framework Pydantic to commercialize with cloud services". TechCrunch. Retrieved 2026-06-09.
- 1 2 Lubanovic, Bill (2023). "Pydantic, Type Hints, and Models Tour". FastAPI. O'Reilly Media. ISBN 978-1-098-13550-8.
- ↑ "pydantic/pydantic". GitHub. Retrieved 2026-06-09.
- ↑ "Version Policy". Pydantic AI Documentation. Retrieved 2026-06-09.
- ↑ Lott, Steven F.; Phillips, Dusty (2025). "Runtime value checking and the Pydantic package". Python Object-Oriented Programming (5th ed.). Packt Publishing. ISBN 978-1-83664-259-6.