AutoGL

Overview

AutoGL is an open-source AutoML framework for graph machine learning. It automates tasks like model selection, hyperparameter tuning, and graph feature engineering. Users define a graph dataset and a task, and AutoGL searches for the best pipeline.

Best for

Best for
Researchers and developers who want to automate graph neural network experimentation

Use cases

• Automate graph neural network model selection and tuning
• Benchmark different graph learning pipelines on custom datasets
• Rapidly prototype graph-based ML solutions without manual tuning

Notes

AutoGL is an open-source AutoML framework for graph machine learning. It automates tasks like model selection, hyperparameter tuning, and graph feature engineering. Users define a graph dataset and a task, and AutoGL searches for the best pipeline.

1,134 stars on GitHub. Last updated 2025-11-20. Licensed Apache-2.0.

Use cases

• Automate graph neural network model selection and tuning
• Benchmark different graph learning pipelines on custom datasets
• Rapidly prototype graph-based ML solutions without manual tuning

Pros

• Reduces manual effort in graph ML pipeline design
• Open-source with active community support
• Supports a variety of graph tasks and datasets

Cons

• Limited to graph-structured data, not general AutoML
• May require understanding of graph ML concepts to interpret results
• Performance depends on search space and computational resources

Indexed from awesome-llmops and enriched against its public facts.