Comparison
auto-sklearn vs AutoGL
Verdict
Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick AutoGL if autoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks.
Markdown twin · auto-sklearn alternatives · AutoGL alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | AutoGL |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Slowing (256d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- auto-sklearn
- Automated Machine Learning with scikit-learn
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- auto-sklearn
- 8.1k
- AutoGL
- 1.1k
Forks
- auto-sklearn
- 1.3k
- AutoGL
- 123
Open issues
- auto-sklearn
- 209
- AutoGL
- 20
Language
- auto-sklearn
- Python
- AutoGL
- Python
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- AutoGL
- AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks.
Persona
- auto-sklearn
- -
- AutoGL
- -
Runtime
- auto-sklearn
- -
- AutoGL
- -
License
- auto-sklearn
- BSD-3-Clause
- AutoGL
- Apache-2.0
Last pushed
- auto-sklearn
- Jun 29, 2026
- AutoGL
- Nov 20, 2025
Categories
- auto-sklearn
- Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- AutoGL
- Slowing (36%)
Days since push
- auto-sklearn
- 35d
- AutoGL
- 256d
Open issues (now)
- auto-sklearn
- 209
- AutoGL
- 20
OSV dependency advisories
- auto-sklearn
- Published findings
- AutoGL
- No lockfile (source not queried)
Full report
- auto-sklearn
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · AutoGL: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, AutoGL is Apache-2.0.
- Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search.
- auto-sklearn ships Docker support for self-hosted deployment.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Choose AutoGL if…
- License: AutoGL is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0..
- Tags unique to AutoGL: deep-learning, graph-neural-networks, hyper-parameter-optimization, machine-learning.
- When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets.
When NOT to use AutoGL
- For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets.
- If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/auto-sklearn) · observed Aug 4, 2026
- GitHub forks (automl/auto-sklearn) · observed Aug 4, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUMNLab/AutoGL) · observed Aug 4, 2026
- GitHub forks (THUMNLab/AutoGL) · observed Aug 4, 2026
- Last push (THUMNLab/AutoGL) · observed Nov 20, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and AutoGL?
- auto-sklearn: Automated Machine Learning with scikit-learn. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over AutoGL?
- Choose auto-sklearn over AutoGL when License: auto-sklearn is BSD-3-Clause, AutoGL is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- When should I choose AutoGL over auto-sklearn?
- Choose AutoGL over auto-sklearn when License: AutoGL is Apache-2.0, auto-sklearn is BSD-3-Clause; Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.; Tags unique to AutoGL: deep-learning, graph-neural-networks, hyper-parameter-optimization, machine-learning; When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- When should I avoid AutoGL?
- For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies.
- Is auto-sklearn or AutoGL more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 1,138). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and AutoGL open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, AutoGL: Apache-2.0).
- Where can I find alternatives to auto-sklearn or AutoGL?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and AutoGL alternatives (auto-sklearn markdown twin, AutoGL markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, auto-sklearn or AutoGL?
- auto-sklearn: Steady. AutoGL: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for auto-sklearn and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; AutoGL trust report.