#135135th of 206 in ML frameworks and libraries
flaml
Python AutoML and hyperparameter tuning library with a scikit-learn style API
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- Stars
- 4.4k
- License
- MIT
- Last commit
- Nov 2026
- Last release
- Sep 2026
- Language
- Jupyter Notebook
Missed the latest GitHub check; these facts may be older.
Overview
FLAML is a Python library that searches for models and hyperparameters under a compute or time budget. AutoML().fit() handles classification and regression with learners such as LightGBM, XGBoost and Random Forest, and flaml.tune runs generic tuning of any user-defined function. It also offers zero-shot wrappers that set LightGBM and XGBoost hyperparameters from the training data.
Who it is for: ML engineers who want budgeted AutoML or hyperparameter tuning in Python
Strengths
- Three-line scikit-learn style AutoML fit with time budget control
- Generic tune.run for arbitrary functions, with cost-aware search and early stopping
- Zero-shot LGBMRegressor drop-in picks hyperparameters from the data
- Installs via pip with minimal core dependencies; extras are optional
Weaknesses
- The autogen module was removed; agent use now requires the separate AutoGen repo
- Requires Python >= 3.10 and < 3.14; other versions not guaranteed
- Focused on tabular AutoML and tuning; not for LLM chat or serving
- Some notebook examples need specific extras installed
What it needs
- GPU optional
- Docker
- Needs Python >= 3.10, < 3.14, MLflow (optional)
- Models: LightGBM, XGBoost, Random Forest, custom learners
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