tensorflowjs
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow and its full dependency stack (including jaxlib, flax) to be installed and functional; model conversion workflows typically need a working TensorFlow environment.
- 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 · maintenance · safety
Apache 2.0 (permissive) — 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.
last release 2024-10-21 (662 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,979,871 downloads/mo, #2,431 on PyPI
Alternatives
Verify before relying
pip install tensorflowjs
python -c "from tensorflowjs import converters; help(converters.convert_tf_saved_model)"- 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.
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 on this page. Verify before relying on it.
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
tensorflowjs on PyPI
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.
Requires TensorFlow and its full dependency stack (including jaxlib, flax) to be installed and functional; model conversion workflows typically need a working TensorFlow environment.
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)"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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesflaximportlib_resourcesjaxjaxlibtensorflowtf-kerastensorflow-decision-forestssixtensorflow-hubpackaging |
| Maintenance | Dormant 662 days since the last release |
| First released | |
| Downloads | 3,979,871 / month, #2,431 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: JavaScriptProgramming Language :: Python :: 2Programming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflowjs-4.22.0-py3-none-any.whl
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See also tf2onnx · onnxmltools · tf-estimator-nightly · onnxconverter-common · tfp-nightly · tensorflow-probability · tensorflow-estimator · litert-converter · ai-edge-model-explorer · tensorflow-serving-api