array-api-compat
A wrapper around NumPy and other array libraries to make them compatible with the Array API standard
Decision gist · record as of 2026-08-14
Yes, if you are building a library or application that needs to support multiple array backends. The package is actively maintained, has no external dependencies, and MIT-licensed. Install it if you want to write array code once and let users choose their backend; skip it if you are locked to a single array library.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; the underlying array library (NumPy, CuPy, PyTorch, Dask, JAX, ndonnx, or sparse) must be installed separately.
- Low friction install with no runtime dependencies.
- Actively maintained with a recent release and steady commit activity.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
last release 2026-06-07 (68 days) · last repo commit 2026-08-10 · 129 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,341,327 downloads/mo, #3,125 on PyPI
Alternatives
Verify before relying
pip install array-api-compat
# Then import and use with your chosen array library
import array_api_compat- Whether the wrapper introduces measurable performance overhead compared to direct library calls
- Coverage completeness: which Array API operations are fully supported versus partially implemented across all supported libraries
- How to select or switch between supported array backends at runtime
What it is and what it does
array-api-compat is a lightweight wrapper that lets you write array code once and run it against multiple array libraries—NumPy, CuPy, PyTorch, Dask, JAX, ndonnx, and sparse—all through a single standardized interface defined by the Array API standard. It abstracts away library-specific quirks and naming differences so your code doesn't need conditional imports or branching logic for different backends.
The package has no runtime dependencies of its own and is designed to be a thin compatibility layer. You import from it instead of directly from your array library, and it delegates to whichever backend you're using. This is useful when building libraries or applications that need to support multiple array backends without duplicating logic, or when you want to let users choose their preferred array library without rewriting your code.
Use it for
- Build a scientific library that works with NumPy, CuPy, PyTorch, Dask, JAX, ndonnx, and sparse without maintaining separate code paths for each.
- Write code that can run on CPU or GPU by swapping the array backend without changing operations.
- Create a machine learning framework that accepts arrays from any supported library and processes them uniformly.
- Migrate code between array libraries by changing only the import statement, not the operations themselves.
- Develop educational materials or examples that work across multiple array libraries without duplication.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a library or application that needs to support multiple array backends.
The package is actively maintained, has no external dependencies, and MIT-licensed. Install it if you want to write array code once and let users choose their backend; skip it if you are locked to a single array library.
Install
array-api-compat on PyPI
Before you install
Low friction install with no runtime dependencies. Actively maintained with a recent release and steady commit activity.
Requires Python 3.10 or later; the underlying array library (NumPy, CuPy, PyTorch, Dask, JAX, ndonnx, or sparse) must be installed separately.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
Quickstart
pip install array-api-compat
# Then import and use with your chosen array library
import array_api_compatVerify before relying
- Whether the wrapper introduces measurable performance overhead compared to direct library calls
- Coverage completeness: which Array API operations are fully supported versus partially implemented across all supported libraries
- How to select or switch between supported array backends at runtime
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 68 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,341,327 / month, #3,125 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: array_api_compat-1.15.0-py3-none-any.whl
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See also array-api-strict · fast-array-utils · array-api-extra · autoray · dataframe-api-compat · cupy-cuda12x · cupy-cuda13x · sparse · jax-jumpy · coola