Comparison
autogluon vs Hypernets
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 Hypernets if hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Markdown twin · autogluon alternatives · Hypernets alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | autogluon | Hypernets |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Slowing (106d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · 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 | Published findings 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
- Hypernets
- A General Automated Machine Learning framework for building domain-specific AutoML toolkits.
Stars
- autogluon
- 11k
- Hypernets
- 265
Forks
- autogluon
- 1.2k
- Hypernets
- 39
Open issues
- autogluon
- 388
- Hypernets
- 0
Language
- autogluon
- Python
- Hypernets
- 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.
- Hypernets
- Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Persona
- autogluon
- -
- Hypernets
- -
Runtime
- autogluon
- -
- Hypernets
- -
License
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
- Hypernets
- Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved.
Last pushed
- autogluon
- Aug 3, 2026
- Hypernets
- Apr 20, 2026
Categories
- autogluon
- Developer Tools, Model Training
- Hypernets
- Developer Tools, Model Training
Trust and health
Maintenance
- autogluon
- Very active (96%)
- Hypernets
- Slowing (36%)
Days since push
- autogluon
- 0d
- Hypernets
- 106d
Open issues (now)
- autogluon
- 388
- Hypernets
- 0
OSV dependency advisories
- autogluon
- No lockfile (source not queried)
- Hypernets
- Published findings
Full report
- autogluon
- Trust report
- Hypernets
- Trust report
Shared compatibility
- Python · autogluon: Python runtime · Hypernets: Python runtime
Choose autogluon if…
- Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, deep-learning.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- More GitHub stars (11k vs 265) - visibility, not fit.
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 Hypernets if…
- Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, neural-architecture-search.
- If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline
- Leaner open-issue backlog (0).
When NOT to use Hypernets
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus
- Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
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 (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- GitHub forks (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- Last push (DataCanvasIO/Hypernets) · observed Apr 20, 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 on cards: autogluon 11k · Hypernets 265 (synced Aug 4, 2026).
Common questions
- What is the difference between autogluon and Hypernets?
- autogluon: Fast and Accurate ML in 3 Lines of Code. Hypernets: A General Automated Machine Learning framework for building domain-specific AutoML toolkits.. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogluon over Hypernets?
- Choose autogluon over Hypernets when Tags unique to autogluon: automated-machine-learning, computer-vision, data-science, deep-learning; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis; More GitHub stars (11k vs 265) - visibility, not fit.
- When should I choose Hypernets over autogluon?
- Choose Hypernets over autogluon when Tags unique to Hypernets: hyperparameter-optimization, keras, lightgbm, neural-architecture-search; If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline; Leaner open-issue backlog (0).
- 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 Hypernets?
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
- Is autogluon or Hypernets more popular on GitHub?
- autogluon has more GitHub stars (10,576 vs 265). Stars measure visibility, not whether either tool fits your constraints.
- Are autogluon and Hypernets open source?
- Yes - both are open-source projects on GitHub (autogluon: Apache-2.0, Hypernets: Apache-2.0).
- Where can I find alternatives to autogluon or Hypernets?
- GraphCanon lists graph-backed alternatives at autogluon alternatives and Hypernets alternatives (autogluon markdown twin, Hypernets 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 Hypernets?
- autogluon: Very active. Hypernets: 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 Hypernets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogluon trust report; Hypernets trust report.