Ivy is an open-source tool that converts machine learning code between frameworks, so a model written for one ecosystem can run in another without a rewrite. Its core feature, ivy.transpile, takes PyTorch, TensorFlow, JAX, or NumPy code and converts it across frameworks automatically. The project has earned more than 14,000 stars on GitHub and is still actively developed, with updates through late 2026.
Framework lock-in is a quiet tax on ML teams: a research model exists only in PyTorch, a deployment target wants TensorFlow, a TPU pipeline needs JAX. Ivy attacks that problem by transpiling at the source level, tracing your code and re-emitting it for the target framework while preserving its structure, so the output is readable code you own, not a black-box runtime. Installation is a single pip command, and the documentation includes examples and demos for common conversion paths, such as turning PyTorch models into TensorFlow, JAX, or NumPy equivalents. Conversion coverage is still growing: source and target support varies by framework pair, and the project’s matrix is explicit about what is available today versus planned. For teams evaluating a migration, Ivy offers a realistic way to test target frameworks on real models before committing.
Ivy is free and open source. There is no paid hosted converter or commercial tier attached to the project; you install it locally and convert as much code as you need at no cost.
Ivy is for ML engineers and researchers who need code to move: porting a model to a deployment framework, comparing framework performance on equal footing, or maintaining one codebase across ecosystems. It is also valuable for library authors who want their tools usable by the widest possible audience without maintaining parallel implementations.