MLX

license

MIT(OSI)

source

public(ml-explore/mlx)

core-gated

ungated(no enterprise or ee path in the repository root and no paid tier described in the README)

MIT, an OSI licence, with the full source public and the shipped code in this repository. The LICENSE body was read in full: the stock MIT text, with no appended condition. No enterprise or ee path in the repository root and no paid tier described in the readme.

• https://raw.githubusercontent.com/ml-explore/mlx/main/LICENSE recorded 2026-08-31

  LICENSE body, read in full: the stock MIT licence text at 1,066 bytes, carrying no appended term or additional condition.

• https://raw.githubusercontent.com/ml-explore/mlx/main/README.md recorded 2026-08-31

  README, read for the distribution and gating question: it documents installing from the mlx package or building from this repository, and names no enterprise, paid or licence-gated build.

Banded on the authoritative PyPI route: 1,464,066 downloads in the last 30 days for the mlx package, which places it at level 4. The repository carries 28,237 stars, recorded only as corroboration since stars rank below every real usage signal.

• https://pypistats.org/api/packages/mlx/recent recorded 2026-08-31

  last_month 1464066, last_week 250585, last_day 32853 for the mlx package.

• https://api.github.com/repos/ml-explore/mlx recorded 2026-08-31

  Repo metadata for ml-explore/mlx: stargazers_count 28237, archived false, pushed_at 2026-08-30.

Scored 4 rather than 5 on reach of hardware rather than depth of design. MLX has the shape of a foundational framework - its own array type, composable transforms and autodiff, and an ecosystem built on it - but it runs on Apple silicon alone, where pytorch, tensorflow and jax at 5 are hardware-portable. openvino sets the precedent in this category at 4: a technically strong framework scoped to one vendor's hardware.

• https://raw.githubusercontent.com/ml-explore/mlx/main/README.md recorded 2026-08-31

  The README's key-features list documents a NumPy-following Python API with C++, C and Swift APIs, higher-level mlx.nn and mlx.optimizers packages following PyTorch, composable function transformations for automatic differentiation, automatic vectorization and graph optimization, lazy computation, and dynamic graph construction.