Infrastructure / Core ML frameworks & libraries

JAX

JAX is a Python library for accelerator-oriented array computation and program transformation, combining NumPy-style arrays with composable function transforms: automatic differentiation, just-in-time compilation through XLA, and automatic vectorization. It is developed by the jax-ml organization, having originated at Google, and targets high-performance numerical computing and large-scale ML research.

Verified 2026-08-13 via GitHub and the GitHub license API.

Openness

5 high confidence

5.0

license: Apache-2.0(OSI)
source: public
core-gated: ungated

Apache-2.0 licensed with full public source. The repository is public and unarchived, and the README describes the whole library with no paid, enterprise or hosted tier beside it, so source is public and the core ungated.

https://github.com/jax-ml/jax recorded 2026-08-13
Repo page still shows the Apache-2.0 license and the full public source tree
https://api.github.com/repos/jax-ml/jax recorded 2026-08-13
Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch main - for jax-ml/jax.
https://raw.githubusercontent.com/jax-ml/jax/main/README.md recorded 2026-08-13
README describes JAX as a Python library for accelerator-oriented array computation and program transformation, installable from PyPI. No paid tier or hosted edition appears.

Adoption

5 high confidence

5.0

20,195,276 PyPI downloads of `jax` in the trailing 30 days, which clears the top band of the software usage scale, over 10M a month, level 5. That is the same band other products on this map hold at comparable volume - pydantic-ai at around 31M, llama-index at around 10.18M, langgraph at around 58M. JAX is the foundation of a major ML ecosystem, including Flax and the Google research stack.

https://pypistats.org/api/packages/jax/recent recorded 2026-08-13
last_month downloads = 20,195,276 for jax

Capability

5 high confidence

5.0

Defines the functional/accelerated-compute layer for ML research.

https://github.com/jax-ml/jax recorded 2026-08-13
README still describes accelerator-oriented array computation and program transformation - autodiff, XLA JIT, vectorization

Verified 2026-08-13