--- title: "hyperopt vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hyperopt-hyperopt-vs-microsoft-archai" tools: ["hyperopt-hyperopt", "microsoft-archai"] --- # hyperopt vs archai *GraphCanon updated Aug 4, 2026* ## Verdict Pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing; 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. [hyperopt](http://hyperopt.github.io/hyperopt) reports 7.6k GitHub stars, 1.1k forks, and 9 open issues, last pushed Aug 3, 2026. [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 [hyperopt's repository](https://github.com/hyperopt/hyperopt) and [archai's repository](https://github.com/microsoft/archai). | | [hyperopt](/tools/hyperopt-hyperopt.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | Distributed Asynchronous Hyperparameter Optimization in Python | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 7,598 | 485 | | Forks | 1,075 | 93 | | Open issues | 9 | 4 | | Language | Python | Python | | Adopt for | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | 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 | Other | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hyperopt](/tools/hyperopt-hyperopt.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 252d | | Open issues (now) | 9 | 4 | | Full report | [trust report](/tools/hyperopt-hyperopt/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [hyperopt](/tools/hyperopt-hyperopt.md) - Python runtime; [archai](/tools/microsoft-archai.md) - Python runtime ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## 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 hyperopt if… - License: hyperopt is Other, archai is MIT. - Tags unique to hyperopt: annealing, asynchronous, distributed-computing, machine-learning. - When you need to optimize machine learning model parameters on a distributed system asynchronously. ### Choose archai if… - License: archai is MIT, hyperopt is Other. - Tags unique to archai: automated-machine-learning, automl, darts, deep-learning. - Need rapid iteration in NAS projects while ensuring reproducibility ## When NOT to use hyperopt - If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. - Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ## 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 hyperopt and archai? hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. 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 hyperopt over archai? Choose hyperopt over archai when License: hyperopt is Other, archai is MIT; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, machine-learning; When you need to optimize machine learning model parameters on a distributed system asynchronously. ### When should I choose archai over hyperopt? Choose archai over hyperopt when License: archai is MIT, hyperopt is Other; Tags unique to archai: automated-machine-learning, automl, darts, deep-learning; Need rapid iteration in NAS projects while ensuring reproducibility. ### When should I avoid hyperopt? If your project does not support asynchronous execution, opting for synchronous tools might be more suitable. Avoid if you prefer a simpler setup without the complexity of distributed systems and instead need straightforward hyperparameter tuning options. ### 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 hyperopt or archai more popular on GitHub? hyperopt has more GitHub stars (7,598 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are hyperopt and archai open source? Yes - both are open-source projects on GitHub (hyperopt: Other, archai: MIT). ### Where can I find alternatives to hyperopt or archai? GraphCanon lists graph-backed alternatives at [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([hyperopt markdown twin](/tools/hyperopt-hyperopt/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/hyperopt-hyperopt-vs-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hyperopt or archai? hyperopt: Very active. 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 hyperopt and archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hyperopt trust report](/tools/hyperopt-hyperopt/trust); [archai trust report](/tools/microsoft-archai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hyperopt-hyperopt`](/api/graphcanon/graph?tool=hyperopt-hyperopt) - 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/_