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AutoGL provides automated graph learning through a machine learning framework and toolkit.
About AutoGL
AutoGL is an open-source AutoML framework and toolkit for machine learning on graphs. It provides solvers for graph tasks that use auto feature engineering, neural architecture search, auto model, hyperparameter optimization, and auto ensemble, and it allows users to implement and test their own AutoML or graph learning models.
It requires Python >= 3.6.0, PyTorch >= 1.6.0, and a PyTorch Geometric or Deep Graph Library backend. Documented work includes homogeneous and heterogeneous node classification, link prediction, and graph classification, along with graph self-supervised learning and robustness methods.
Key features
- Solvers combining feature engineering, NAS, HPO and ensemble
- Node classification, link prediction and graph classification
- Works with PyTorch Geometric or DGL backends
- Neural architecture search with multiple algorithms and spaces
- Hyperparameter optimization including grid, random and Bayesian methods
- Graph self-supervised learning and robustness algorithms
Best for: Researchers and developers conducting AutoML on graph datasets and tasks.
Good for
- Covers feature engineering, NAS, modeling, HPO and ensemble
- Compatible with both PyTorch Geometric and DGL
- Supports custom datasets and user-implemented models
Watch out for
- Requires Python, PyTorch and PyG or DGL backend setup
AutoGL at a glance
- Website
- github.com/THUMNLab/AutoGL
- 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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AutoGL FAQ
What is required to install AutoGL?
Python >= 3.6.0, PyTorch >= 1.6.0, and either PyTorch Geometric or Deep Graph Library as the backend.
Which graph tasks does AutoGL support?
Homogeneous and heterogeneous node classification, link prediction, and graph classification, plus self-supervised learning and robustness methods.
What are the five main modules used by AutoGL solvers?
Auto feature engineer, neural architecture search, auto model, hyperparameter optimization, and auto ensemble.
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