FlexML offers an easy to use and flexible automated machine learning library for Python.
FLAML
FLAML provides a fast Python library for automated machine learning and tuning.

About FLAML
FLAML is an open-source Python library for automated machine learning and tuning. It automates model selection and hyperparameter optimization for ML and AI workflows, including classification and regression on user-provided data.
It can tune specific learners such as XGBoost, LightGBM and Random Forest, run generic tuning for custom functions, and tune configurations for foundation-model inference and MLOps workflows. The latest version requires Python >=3.10 and
Key features
- Automates model selection and hyperparameter optimization under resource constraints
- Supports classification and regression on user-provided data
- Tunes XGBoost, LightGBM, Random Forest, or custom learners
- Runs generic tuning for custom functions with time budgets
- Provides zero-shot hyperparameter defaults for LightGBM and XGBoost APIs
- Integrates with MLflow and Microsoft Fabric Data Science
- Uses scikit-learn style API that starts in three lines of code
- Customizable and extendable for different learners and search spaces
- Installs minimal dependencies by default with optional extras per feature
- Latest version requires Python >=3.10 and
- Features such as automl require separate extra installs
- Does not include autogen module; AutoGen is in a separate repository
FLAML at a glance
- Website
- github.com/microsoft/FLAML
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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FLAML FAQ
What Python versions does FLAML support?
How do I install FLAML with AutoML dependencies?
Does FLAML include AutoGen?
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