einops is a tensor manipulation library that provides readable and expressive operations for deep learning frameworks including PyTorch, JAX, TensorFlow, NumPy, and others.
The library addresses the problem of writing tensor reshaping and rearrangement code that is both clear and maintainable. Traditional tensor operations often require developers to manually track dimensions and write error-prone index manipulations. einops solves this by introducing a notation-based approach where operations are expressed using Einstein summation-like syntax, making the intent of tensor transformations explicit and self-documenting. The library handles the underlying implementation details across different frameworks, allowing the same code to work with multiple backends.
Developers should adopt einops if they work regularly with multidimensional arrays and want to reduce bugs related to dimension handling. It suits any project using NumPy, PyTorch, JAX, TensorFlow, MLX, or other supported frameworks where tensor reshaping, transposition, or rearrangement is common. The tool is particularly valuable in deep learning workflows where complex tensor operations are frequent. The library includes specialized operations like packing and unpacking for advanced use cases, and supports modern features such as torch.compile and the array API standard.
The project maintains active development with regular updates introducing new backends and features. Testing infrastructure is in place with continuous integration workflows. The tool has gained substantial adoption across the ecosystem, with documentation available both online and through interactive browser-based notebooks. Recent work has focused on improving type annotations and expanding framework support to include emerging platforms.
