# EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

arXiv: https://arxiv.org/abs/2008.04175 · HTML: https://mlsanity.com/paper/2008.04175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Towards Standardizing AI Bias Exploration](https://mlsanity.com/paper/2405.19022/) (2024)
- [Towards Migrating Neural Network Implementations](https://mlsanity.com/paper/2511.02610/) (2025)
- [WAX-ML: A Python library for machine learning and feedback loops on streaming data](https://mlsanity.com/paper/2106.06524/) (2021)

## Abstract (arXiv)

EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose between supporting just one of these frameworks or reimplementing the library for each framework and dealing with code duplication. Users of such libraries can more easily switch frameworks without being locked in by a specific 3rd party library. Beyond multi-framework support, EagerPy also brings comprehensive type annotations and consistent support for method chaining to any framework. The latest documentation is available online at https://eagerpy.jonasrauber.de and the code can be found on GitHub at https://github.com/jonasrauber/eagerpy.