Comparison
autogluon vs AutoGL
Verdict
Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; 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 · autogluon alternatives · AutoGL alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | autogluon | AutoGL |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Slowing (256d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization 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
- autogluon
- Fast and Accurate ML in 3 Lines of Code
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- autogluon
- 11k
- AutoGL
- 1.1k
Forks
- autogluon
- 1.2k
- AutoGL
- 123
Open issues
- autogluon
- 388
- AutoGL
- 20
Language
- autogluon
- Python
- AutoGL
- Python
Adopt for
- autogluon
- AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
- 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
- autogluon
- -
- AutoGL
- -
Runtime
- autogluon
- -
- AutoGL
- -
License
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
- AutoGL
- Apache-2.0
Last pushed
- autogluon
- Aug 3, 2026
- AutoGL
- Nov 20, 2025
Categories
- autogluon
- Developer Tools, Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- autogluon
- Very active (96%)
- AutoGL
- Slowing (36%)
Days since push
- autogluon
- 0d
- AutoGL
- 256d
Open issues (now)
- autogluon
- 388
- AutoGL
- 20
Full report
- autogluon
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · autogluon: Python runtime · AutoGL: Python runtime
Choose autogluon if…
- Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning.
- Also covers Developer Tools.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
When NOT to use autogluon
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation.
- For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
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, machine-learning, neural-architecture-search.
- 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 (autogluon/autogluon) · observed Aug 4, 2026
- GitHub forks (autogluon/autogluon) · observed Aug 4, 2026
- Last push (autogluon/autogluon) · observed Aug 3, 2026
- 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: autogluon 11k · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between autogluon and AutoGL?
- autogluon: Fast and Accurate ML in 3 Lines of Code. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogluon over AutoGL?
- Choose autogluon over AutoGL when Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, ensemble-learning; Also covers Developer Tools; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- When should I choose AutoGL over autogluon?
- Choose AutoGL over autogluon 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, machine-learning, neural-architecture-search; 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 autogluon?
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
- 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 autogluon or AutoGL more popular on GitHub?
- autogluon has more GitHub stars (10,576 vs 1,138). Stars measure visibility, not whether either tool fits your constraints.
- Are autogluon and AutoGL open source?
- Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, AutoGL: Apache-2.0).
- Where can I find alternatives to autogluon or AutoGL?
- GraphCanon lists graph-backed alternatives at autogluon alternatives and AutoGL alternatives (autogluon 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, autogluon or AutoGL?
- autogluon: Very active. 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 autogluon and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogluon trust report; AutoGL trust report.