--- title: "penzai vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-deepmind-penzai-vs-microsoft-archai" tools: ["google-deepmind-penzai", "microsoft-archai"] --- # penzai vs archai *GraphCanon updated Aug 24, 2026* ## Verdict Pick penzai if penzai supports fine-tuning and interpretability features in neural network research through JAX; pick archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. [penzai](https://penzai.readthedocs.io/) reports 1.9k GitHub stars, 70 forks, and 21 open issues, last pushed Jun 22, 2025. [archai](https://microsoft.github.io/archai) has 485 stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [penzai's repository](https://github.com/google-deepmind/penzai) and [archai's repository](https://github.com/microsoft/archai). | | [penzai](/tools/google-deepmind-penzai.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | A JAX research toolkit for building, editing, and visualizing neural networks. | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 1,901 | 485 | | Forks | 70 | 93 | | Open issues | 21 | 4 | | Language | Python | Python | | Adopt for | Penzai supports fine-tuning and interpretability features in neural network research through JAX. | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | | Persona | - | - | | Runtime | - | - | | License | Penzai operates under an Apache-2.0 license, offering permissive rights for software use, modification, and distribution. | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [penzai](/tools/google-deepmind-penzai.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 427d | 252d | | Open issues (now) | 21 | 4 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | +4 (30d) | Unknown | | Full report | [trust report](/tools/google-deepmind-penzai/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [penzai](/tools/google-deepmind-penzai.md) - Python runtime; [archai](/tools/microsoft-archai.md) - Python runtime ## Decision facts: penzai - **Requirements:** Ensure compatibility with Python and JAX libraries as they are crucial for leveraging Penzai's toolkit. - **Adopt for:** Penzai supports fine-tuning and interpretability features in neural network research through JAX. - **License detail:** Penzai operates under an Apache-2.0 license, offering permissive rights for software use, modification, and distribution. ## Decision facts: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## Choose when ### Choose penzai if… - License: penzai is Apache-2.0, archai is MIT. - 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. ### Choose archai if… - License: archai is MIT, penzai is Apache-2.0. - Tags unique to archai: automated-machine-learning, automl, darts, deep-learning. - Need rapid iteration in NAS projects while ensuring reproducibility ## 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. ## When NOT to use archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## Common questions ### What is the difference between penzai and archai? penzai: A JAX research toolkit for building, editing, and visualizing neural networks.. archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose penzai over archai? Choose penzai over archai when License: penzai is Apache-2.0, archai is MIT; 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 archai over penzai? Choose archai over penzai when License: archai is MIT, penzai is Apache-2.0; Tags unique to archai: automated-machine-learning, automl, darts, deep-learning; Need rapid iteration in NAS projects while ensuring reproducibility. ### 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 archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### Is penzai or archai more popular on GitHub? penzai has more GitHub stars (1,901 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are penzai and archai open source? Yes - both are open-source projects on GitHub (penzai: Apache-2.0, archai: MIT). ### Where can I find alternatives to penzai or archai? GraphCanon lists graph-backed alternatives at [penzai alternatives](/tools/google-deepmind-penzai/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([penzai markdown twin](/tools/google-deepmind-penzai/alternatives.md), [archai markdown twin](/tools/microsoft-archai/alternatives.md)), 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](/compare/google-deepmind-penzai-vs-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, penzai or archai? penzai: Dormant. archai: 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 archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [penzai trust report](/tools/google-deepmind-penzai/trust); [archai trust report](/tools/microsoft-archai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-deepmind-penzai`](/api/graphcanon/graph?tool=google-deepmind-penzai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_