FLAML
A fast library for automated machine learning and tuning
Decision gist · record as of 2026-08-14
Yes. FLAML is actively maintained, has low install friction, carries a permissive MIT license, and offers practical automation for both standard ML tasks and custom tuning scenarios. The single NumPy dependency and support for current Python versions make it straightforward to integrate. No known vulnerabilities and strong community adoption (4385 stars) support its reliability.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10 and < 3.14; full model support not guaranteed on other versions.
- Low friction install with only NumPy as a runtime dependency.
- Active maintenance with recent release (108 days ago) and 4385 repository stars indicate ongoing development and community adoption.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
last release 2026-04-28 (108 days) · last repo commit 2026-08-14 · 4,385 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 649,104 downloads/mo, #5,579 on PyPI
Alternatives
Verify before relying
pip install flaml
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")- Whether the 'extra options' installation variants (e.g., flaml[automl]) introduce additional dependencies beyond NumPy.
- Performance characteristics and typical runtime for hyperparameter tuning on datasets of varying sizes.
- Specific ML frameworks and estimators supported by default versus those requiring optional dependencies.
What it is and what it does
FLAML is a Python library that automates machine learning workflows by handling model selection and hyperparameter optimization. It works as a scikit-learn-style estimator for standard classification and regression tasks, but also supports generic hyperparameter tuning for custom functions and existing ML libraries like XGBoost and LightGBM. The library is designed to work efficiently under resource constraints, making it practical for scenarios where computational budget is limited.
The package depends only on NumPy at runtime and supports Python 3.10 through 3.13. It offers multiple usage patterns: direct AutoML for quick model discovery, restricted learner lists for tuning specific algorithms, custom function tuning, and zero-shot AutoML that applies pre-optimized hyperparameter configurations. The library is actively maintained by Microsoft Research with contributions from academic collaborators.
Use it for
- Quickly find a well-tuned classification or regression model for a new dataset without manual hyperparameter experimentation.
- Tune hyperparameters for a specific algorithm like LightGBM or XGBoost within a fixed time or resource budget.
- Optimize custom evaluation functions or ML pipelines where standard AutoML tools don't apply.
- Apply pre-optimized hyperparameter configurations to new datasets using zero-shot AutoML patterns.
- Automate hyperparameter tuning for foundation models and LLM inference configurations in production workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
FLAML is actively maintained, has low install friction, carries a permissive MIT license, and offers practical automation for both standard ML tasks and custom tuning scenarios. The single NumPy dependency and support for current Python versions make it straightforward to integrate. No known vulnerabilities and strong community adoption (4385 stars) support its reliability.
Install
flaml on PyPI
Before you install
Low friction install with only NumPy as a runtime dependency. Active maintenance with recent release (108 days ago) and 4385 repository stars indicate ongoing development and community adoption.
Requires Python >= 3.10 and < 3.14; full model support not guaranteed on other versions.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
Quickstart
pip install flaml
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")Verify before relying
- Whether the 'extra options' installation variants (e.g., flaml[automl]) introduce additional dependencies beyond NumPy.
- Performance characteristics and typical runtime for hyperparameter tuning on datasets of varying sizes.
- Specific ML frameworks and estimators supported by default versus those requiring optional dependencies.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageNumPy |
| Maintenance | Actively maintained 108 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 649,104 / month, #5,579 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: flaml-2.6.0-py3-none-any.whl
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