---
id: tensorflowjs
version: "4.22.0"
license: Apache 2.0
license_treatment: permissive
maintenance: dormant
---

# tensorflowjs

License: permissive · Maintenance: dormant · Downloads: 4.0M/mo

## What it is and what it does

tensorflowjs is a Python package that bridges TensorFlow models and TensorFlow.js, the JavaScript machine-learning library. It provides command-line tools and a Python API to convert TensorFlow SavedModels and other formats into a format that TensorFlow.js can load and run in browsers or Node.js environments. The package includes a CLI converter (tensorflowjs_converter) and an interactive wizard (tensorflowjs_wizard) to guide users through the conversion process.

The package depends on a large stack of machine-learning libraries—tensorflow, tf-keras, jax, jaxlib, flax, and tensorflow-decision-forests—making it a heavy install. It is intended for developers and researchers who need to deploy trained TensorFlow models to web or JavaScript environments. The dormant maintenance status (no release in 662 days) suggests the package may not be actively developed, though the conversion logic itself may be stable enough for existing workflows.

Use it for:

- Convert a trained TensorFlow SavedModel to TensorFlow.js format for inference in a web browser.
- Deploy a Keras model to a Node.js server using the converted TensorFlow.js format.
- Automate model conversion workflows with the tensorflowjs_converter CLI in a build pipeline.
- Use the interactive wizard to guide non-technical team members through model conversion without writing code.
- Integrate TensorFlow model serving into a full-stack JavaScript application by converting and bundling models.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Converts TensorFlow models to TensorFlow.js format for browser and Node.js deployment, with CLI tools and a wizard for model conversion workflows.

Yes, if you need to deploy TensorFlow models to JavaScript environments and can tolerate dormant maintenance. The low install friction and permissive license are favorable, but the 662-day gap since the last release and lack of repository metadata raise questions about active support. Verify compatibility with your TensorFlow and TensorFlow.js versions before committing to production use.

## Install

pip install tensorflowjs
uv add tensorflowjs
poetry add tensorflowjs

## Installing tensorflowjs

Before you install: Low install friction with a pure-Python wheel. Maintenance is dormant—no release in 662 days—which may signal stalled development or stable-enough code; the large runtime dependency tree (tensorflow, jax, flax, and others) means setup requires resolving a complex ecosystem.

License in practice: Apache 2.0 permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute the package provided you include the license notice.

Quickstart:

pip install tensorflowjs
python -c "from tensorflowjs import converters; help(converters.convert_tf_saved_model)"

Requires TensorFlow and its full dependency stack (including jaxlib, flax) to be installed and functional; model conversion workflows typically need a working TensorFlow environment.

Verify before relying:

- Whether the 662-day gap since last release reflects active maintenance or abandonment; no repo or commit data available.
- Actual Python version support—classifiers list Python 2 and 3 but requires_python is unspecified.
- Whether the package works with current TensorFlow and TensorFlow.js versions, given the dormant maintenance status.

## Package facts

- License: Apache 2.0 (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 4.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tensorflow model to javascript conversion, tensorflowjs converter, deploy tensorflow models browser, tfjs model conversion cli, tensorflow.js python tools, convert saved models to tfjs, tensorflow browser deployment, model-conversion, tensorflow-deployment, javascript-ml

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