Comparison
AutoGL vs awesome-AutoML
Verdict
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; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · AutoGL alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | AutoGL | awesome-AutoML |
|---|---|---|
| Maintenance | Slowing (256d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
- awesome-AutoML
- Curating AutoML research and resources
Stars
- AutoGL
- 1.1k
- awesome-AutoML
- 941
Forks
- AutoGL
- 123
- awesome-AutoML
- 156
Open issues
- AutoGL
- 20
- awesome-AutoML
- 1
Language
- AutoGL
- Python
- awesome-AutoML
- -
Adopt for
- 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.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- AutoGL
- -
- awesome-AutoML
- -
Runtime
- AutoGL
- -
- awesome-AutoML
- -
License
- AutoGL
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- AutoGL
- Nov 20, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- AutoGL
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Days since push
- AutoGL
- 256d
- awesome-AutoML
- 133d
Open issues (now)
- AutoGL
- 20
- awesome-AutoML
- 1
Owner type
- AutoGL
- Organization
- awesome-AutoML
- User
Full report
- AutoGL
- Trust report
- awesome-AutoML
- Trust report
Choose AutoGL if…
- License: AutoGL is Apache-2.0, awesome-AutoML is GPL-3.0.
- 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.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, AutoGL is Apache-2.0.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AutoGL 1.1k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between AutoGL and awesome-AutoML?
- AutoGL: AutoML framework & toolkit for machine learning on graphs. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose AutoGL over awesome-AutoML?
- Choose AutoGL over awesome-AutoML when License: AutoGL is Apache-2.0, awesome-AutoML is GPL-3.0; 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 choose awesome-AutoML over AutoGL?
- Choose awesome-AutoML over AutoGL when License: awesome-AutoML is GPL-3.0, AutoGL is Apache-2.0; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- 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.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is AutoGL or awesome-AutoML more popular on GitHub?
- AutoGL has more GitHub stars (1,138 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are AutoGL and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (AutoGL: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to AutoGL or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at AutoGL alternatives and awesome-AutoML alternatives (AutoGL markdown twin, awesome-AutoML 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, AutoGL or awesome-AutoML?
- AutoGL: Slowing. awesome-AutoML: 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 AutoGL and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AutoGL trust report; awesome-AutoML trust report.