Comparison
autokeras vs AutoGL
Verdict
Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; 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 · autokeras alternatives · AutoGL alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | autokeras | AutoGL |
|---|---|---|
| Maintenance | Slowing (251d 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 | 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
- autokeras
- AutoML library for deep learning
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- autokeras
- 9.3k
- AutoGL
- 1.1k
Forks
- autokeras
- 1.4k
- AutoGL
- 123
Open issues
- autokeras
- 161
- AutoGL
- 20
Language
- autokeras
- Python
- AutoGL
- Python
Adopt for
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
- 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
- autokeras
- -
- AutoGL
- -
Runtime
- autokeras
- -
- AutoGL
- -
License
- autokeras
- Apache-2.0
- AutoGL
- Apache-2.0
Last pushed
- autokeras
- Nov 25, 2025
- AutoGL
- Nov 20, 2025
Categories
- autokeras
- Developer Tools, Model Training
- AutoGL
- Model Training
Trust and health
Days since push
- autokeras
- 251d
- AutoGL
- 256d
Open issues (now)
- autokeras
- 161
- AutoGL
- 20
Full report
- autokeras
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · autokeras: Python runtime · AutoGL: Python runtime
Choose autokeras if…
- Tags unique to autokeras: autodl, keras, tensorflow.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When NOT to use autokeras
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
Choose AutoGL if…
- 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: graph-neural-networks, hyper-parameter-optimization, pytorch, pytorch-geometric.
- 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 (keras-team/autokeras) · observed Aug 4, 2026
- GitHub forks (keras-team/autokeras) · observed Aug 4, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 2025
- License file (Apache-2.0) · 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: autokeras 9.3k · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between autokeras and AutoGL?
- autokeras: AutoML library for deep learning. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose autokeras over AutoGL?
- Choose autokeras over AutoGL when Tags unique to autokeras: autodl, keras, tensorflow; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- When should I choose AutoGL over autokeras?
- Choose AutoGL over autokeras when 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: graph-neural-networks, hyper-parameter-optimization, pytorch, pytorch-geometric; 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 autokeras?
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
- 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 autokeras or AutoGL more popular on GitHub?
- autokeras has more GitHub stars (9,328 vs 1,138). Stars measure visibility, not whether either tool fits your constraints.
- Are autokeras and AutoGL open source?
- Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, AutoGL: Apache-2.0).
- Where can I find alternatives to autokeras or AutoGL?
- GraphCanon lists graph-backed alternatives at autokeras alternatives and AutoGL alternatives (autokeras 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, autokeras or AutoGL?
- autokeras: Slowing. 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 autokeras and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autokeras trust report; AutoGL trust report.