Comparison
penzai vs awesome-AutoML
Verdict
Pick penzai if penzai supports fine-tuning and interpretability features in neural network research through JAX; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · penzai alternatives · awesome-AutoML alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | penzai | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (427d since push) As of 1d · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · 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
- penzai
- A JAX research toolkit for building, editing, and visualizing neural networks.
- awesome-AutoML
- Curating AutoML research and resources
Stars
- penzai
- 1.9k
- awesome-AutoML
- 941
Forks
- penzai
- 70
- awesome-AutoML
- 156
Open issues
- penzai
- 21
- awesome-AutoML
- 1
Language
- penzai
- Python
- awesome-AutoML
- -
Adopt for
- penzai
- Penzai supports fine-tuning and interpretability features in neural network research through JAX.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- penzai
- -
- awesome-AutoML
- -
Runtime
- penzai
- -
- awesome-AutoML
- -
License
- penzai
- Penzai operates under an Apache-2.0 license, offering permissive rights for software use, modification, and distribution.
- awesome-AutoML
- GPL-3.0
Last pushed
- penzai
- Jun 22, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- penzai
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- penzai
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- penzai
- 427d
- awesome-AutoML
- 133d
Open issues (now)
- penzai
- 21
- awesome-AutoML
- 1
Stars delta
- penzai
- +9 (30d)
- awesome-AutoML
- Unknown
Open issues delta
- penzai
- +4 (30d)
- awesome-AutoML
- Unknown
Owner type
- penzai
- Organization
- awesome-AutoML
- User
Full report
- penzai
- Trust report
- awesome-AutoML
- Trust report
Choose penzai if…
- License: penzai is Apache-2.0, awesome-AutoML is GPL-3.0.
- Requirements: Ensure compatibility with Python and JAX libraries as they are crucial for leveraging Penzai's toolkit..
- Tags unique to penzai: fine-tuning, interpretability, jax, neural-networks.
- When your AI development tasks require detailed visualization capabilities for neural networks, as Penzai integrates advanced visual components tailored to these needs.
When NOT to use penzai
- Avoid if you are strictly working with frameworks that do not support or are incompatible with JAX, as Penzai is specifically designed for use within the JAX ecosystem.
- Do not choose Penzai if your project requires a focus on backend model deployment rather than research-oriented functionalities like visualization and interpretability.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, penzai is Apache-2.0.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- 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 (google-deepmind/penzai) · observed Aug 24, 2026
- GitHub forks (google-deepmind/penzai) · observed Aug 24, 2026
- Last push (google-deepmind/penzai) · observed Jun 22, 2025
- License file (Apache-2.0) · observed Aug 24, 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: penzai 1.9k · awesome-AutoML 941 (synced Aug 24, 2026).
Common questions
- What is the difference between penzai and awesome-AutoML?
- penzai: A JAX research toolkit for building, editing, and visualizing neural networks.. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose penzai over awesome-AutoML?
- Choose penzai over awesome-AutoML when License: penzai is Apache-2.0, awesome-AutoML is GPL-3.0; Requirements: Ensure compatibility with Python and JAX libraries as they are crucial for leveraging Penzai's toolkit.; Tags unique to penzai: fine-tuning, interpretability, jax, neural-networks; When your AI development tasks require detailed visualization capabilities for neural networks, as Penzai integrates advanced visual components tailored to these needs.
- When should I choose awesome-AutoML over penzai?
- Choose awesome-AutoML over penzai when License: awesome-AutoML is GPL-3.0, penzai is Apache-2.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid penzai?
- Avoid if you are strictly working with frameworks that do not support or are incompatible with JAX, as Penzai is specifically designed for use within the JAX ecosystem. Do not choose Penzai if your project requires a focus on backend model deployment rather than research-oriented functionalities like visualization and interpretability.
- 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 penzai or awesome-AutoML more popular on GitHub?
- penzai has more GitHub stars (1,901 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are penzai and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (penzai: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to penzai or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at penzai alternatives and awesome-AutoML alternatives (penzai 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, penzai or awesome-AutoML?
- penzai: Dormant. 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 penzai and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: penzai trust report; awesome-AutoML trust report.