Metadata-Version: 2.4
Name: pydantic-evals
Version: 0.0.48
Summary: Framework for evaluating stochastic code execution, especially code making use of LLMs
Author-email: David Montague <david@pydantic.dev>
License-Expression: MIT
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Requires-Dist: anyio>=0
Requires-Dist: eval-type-backport>=0; python_version < '3.11'
Requires-Dist: logfire-api>=1.2.0
Requires-Dist: pydantic>=2.10
Requires-Dist: rich>=13.9.4
Provides-Extra: logfire
Requires-Dist: logfire>=2.3; extra == 'logfire'
Description-Content-Type: text/markdown

# Pydantic Evals

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This is a library for evaluating non-deterministic (or "stochastic") functions in Python. It provides a simple,
Pythonic interface for defining and running stochastic functions, and analyzing the results of running those functions.

While this library is developed as part of [PydanticAI](https://ai.pydantic.dev), it only uses PydanticAI for a small
subset of generative functionality internally, and it is designed to be used with arbitrary "stochastic function"
implementations. In particular, it can be used with other (non-PydanticAI) AI libraries, agent frameworks, etc.

As with PydanticAI, this library prioritizes type safety and use of common Python syntax over esoteric, domain-specific
use of Python syntax.

Full documentation is available at [ai.pydantic.dev/evals](https://ai.pydantic.dev/evals).

[//]: # (TODO: Add a basic example here.)
[//]: # (TODO: Add a note about how you can view the results in the terminal or in any OTel sink, e.g. Logfire.)
