--- title: "RoBO vs hyperopt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-robo-vs-hyperopt-hyperopt" tools: ["automl-robo", "hyperopt-hyperopt"] --- # RoBO vs hyperopt *GraphCanon updated Aug 4, 2026* ## Verdict Pick RoBO if roBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. [RoBO](https://github.com/automl/RoBO) reports 492 GitHub stars, 129 forks, and 25 open issues, last pushed Apr 30, 2019. [hyperopt](http://hyperopt.github.io/hyperopt) has 7.6k stars, 1.1k forks, and 9 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [RoBO's repository](https://github.com/automl/RoBO) and [hyperopt's repository](https://github.com/hyperopt/hyperopt). | | [RoBO](/tools/automl-robo.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Tagline | A Robust Bayesian Optimization framework | Distributed Asynchronous Hyperparameter Optimization in Python | | Stars | 492 | 7,598 | | Forks | 129 | 1,075 | | Open issues | 25 | 9 | | Language | Python | Python | | Adopt for | RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RoBO](/tools/automl-robo.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 2653d | 0d | | Open issues (now) | 25 | 9 | | Full report | [trust report](/tools/automl-robo/trust.md) | [trust report](/tools/hyperopt-hyperopt/trust.md) | ## Shared compatibility - **Python**: [RoBO](/tools/automl-robo.md) - Python runtime; [hyperopt](/tools/hyperopt-hyperopt.md) - Python runtime ## Decision facts: RoBO - **Adopt for:** RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Choose when ### Choose RoBO if… - License: RoBO is BSD-3-Clause, hyperopt is Other. - Tags unique to RoBO: bayesian-optimization, gaussian processes, python, random forests. - For tasks requiring robust handling of noisy data in Bayesian Optimization ### Choose hyperopt if… - License: hyperopt is Other, RoBO is BSD-3-Clause. - Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization. - When you need to optimize machine learning model parameters on a distributed system asynchronously. ## When NOT to use RoBO - Avoid if your project strictly requires open-source licenses other than BSD-3-Clause - Not suitable for users not comfortable installing external dependencies manually ## 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. ## Common questions ### What is the difference between RoBO and hyperopt? RoBO: A Robust Bayesian Optimization framework. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose RoBO over hyperopt? Choose RoBO over hyperopt when License: RoBO is BSD-3-Clause, hyperopt is Other; Tags unique to RoBO: bayesian-optimization, gaussian processes, python, random forests; For tasks requiring robust handling of noisy data in Bayesian Optimization. ### When should I choose hyperopt over RoBO? Choose hyperopt over RoBO when License: hyperopt is Other, RoBO is BSD-3-Clause; Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization; When you need to optimize machine learning model parameters on a distributed system asynchronously. ### When should I avoid RoBO? Avoid if your project strictly requires open-source licenses other than BSD-3-Clause Not suitable for users not comfortable installing external dependencies manually ### 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. ### Is RoBO or hyperopt more popular on GitHub? hyperopt has more GitHub stars (7,598 vs 492). Stars measure visibility, not whether either tool fits your constraints. ### Are RoBO and hyperopt open source? Yes - both are open-source projects on GitHub (RoBO: BSD-3-Clause, hyperopt: Other). ### Where can I find alternatives to RoBO or hyperopt? GraphCanon lists graph-backed alternatives at [RoBO alternatives](/tools/automl-robo/alternatives) and [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) ([RoBO markdown twin](/tools/automl-robo/alternatives.md), [hyperopt markdown twin](/tools/hyperopt-hyperopt/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/automl-robo-vs-hyperopt-hyperopt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RoBO or hyperopt? RoBO: Dormant. hyperopt: Very active. 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 RoBO and hyperopt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RoBO trust report](/tools/automl-robo/trust); [hyperopt trust report](/tools/hyperopt-hyperopt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-robo`](/api/graphcanon/graph?tool=automl-robo) - 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/_