ax-platform
Adaptive Experimentation
Decision gist · record as of 2026-08-14
Yes. Ax is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively (MIT). It is production-stable and suitable for anyone doing Bayesian or adaptive optimization. Install it if you need a general-purpose optimization platform; the main constraint is the Python 3.11+ requirement.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or newer.
- Low friction: pure Python wheel distribution.
- Active maintenance with recent releases; last commit 2026-08-13.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) means you can use, modify, and distribute Ax with minimal restrictions in commercial and open-source projects, provided you include the license notice.
last release 2026-06-09 (66 days) · last repo commit 2026-08-13 · 2,787 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 218,740 downloads/mo, #9,335 on PyPI
Alternatives
Verify before relying
pip install ax-platform
from ax import Client, RangeParameterConfig
from ax.utils.common import ParameterType
client = Client()
client.configure_experiment(
parameters=[
RangeParameterConfig(
name="x",
bounds=(-10.0, 10.0),
parameter_type=ParameterType.FLOAT,
)
]
)
client.configure_optimization(objective="my_metric")
for trial_index, params in client.get_next_trials(max_trials=1).items():
client.complete_trial(trial_index=trial_index, raw_data={"my_metric": 0.5})- Whether the notebook extra (ipywidgets integration) is required for Jupyter use or optional.
- Performance characteristics and scalability limits for large parameter spaces or many parallel trials.
- Whether fully_bayesian extra (JAX/NumPyro) is necessary for production use or only for specific model types.
What it is and what it does
Ax is a machine-learning-guided optimization platform designed to automate the process of exploring parameter spaces and finding optimal configurations with minimal resource expenditure. It abstracts away complex optimization details by providing sensible defaults while remaining highly configurable for researchers and practitioners. The platform supports Bayesian optimization (via BoTorch) and bandit optimization, handles complex search spaces with multiple objectives, parameter constraints, and noisy observations, and can suggest multiple designs for parallel evaluation.
The package is built for production deployment with automation, orchestration, and robust error handling. It integrates with visualization tools (plotly, graphviz) and optional storage backends (MySQL via extras), making it suitable for both interactive experimentation in notebooks and large-scale automated optimization workflows. Its main dependencies are BoTorch for Bayesian optimization algorithms, pandas and scipy for data handling, scikit-learn for machine learning utilities, and various visualization and symbolic computation libraries.
Use it for
- Hyperparameter tuning for machine learning models by iteratively exploring configurations and identifying optimal settings.
- Multi-objective optimization where you need to balance competing goals and constraints across a parameter space.
- Automated experiment design and execution in research or engineering where you want to minimize evaluation cost.
- A/B testing and online experimentation with adaptive allocation of resources to promising variants.
- Bandit-style exploration problems where you need to balance exploration and exploitation in real time.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Ax is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively (MIT). It is production-stable and suitable for anyone doing Bayesian or adaptive optimization. Install it if you need a general-purpose optimization platform; the main constraint is the Python 3.11+ requirement.
Install
ax-platform on PyPI
Before you install
Low friction: pure Python wheel distribution. Active maintenance with recent releases; last commit 2026-08-13. Requires Python 3.11 or newer. Eleven runtime dependencies including botorch, scipy, scikit-learn, and visualization libraries (plotly, graphviz) are all standard packages.
Requires Python 3.11 or newer.
License in practice
MIT license (permissive) means you can use, modify, and distribute Ax with minimal restrictions in commercial and open-source projects, provided you include the license notice.
Quickstart
pip install ax-platform
from ax import Client, RangeParameterConfig
from ax.utils.common import ParameterType
client = Client()
client.configure_experiment(
parameters=[
RangeParameterConfig(
name="x",
bounds=(-10.0, 10.0),
parameter_type=ParameterType.FLOAT,
)
]
)
client.configure_optimization(objective="my_metric")
for trial_index, params in client.get_next_trials(max_trials=1).items():
client.complete_trial(trial_index=trial_index, raw_data={"my_metric": 0.5})Verify before relying
- Whether the notebook extra (ipywidgets integration) is required for Jupyter use or optional.
- Performance characteristics and scalability limits for large parameter spaces or many parallel trials.
- Whether fully_bayesian extra (JAX/NumPyro) is necessary for production use or only for specific model types.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesbotorchjinja2pandasscipyscikit-learnipywidgetsplotlypyre-extensionssympymarkdowngraphviz |
| Maintenance | Actively maintained 66 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 218,740 / month, #9,335 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 |
Evidence: ax_platform-1.3.1-py3-none-any.whl
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