--- title: "RoBO vs scikit-optimize" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-robo-vs-scikit-optimize-scikit-optimize" tools: ["automl-robo", "scikit-optimize-scikit-optimize"] --- # RoBO vs scikit-optimize *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 scikit-optimize if scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize. [RoBO](https://github.com/automl/RoBO) reports 492 GitHub stars, 129 forks, and 25 open issues, last pushed Apr 30, 2019. [scikit-optimize](https://scikit-optimize.github.io) has 2.8k stars, 559 forks, and 318 open issues, last pushed Feb 23, 2024. Figures are from public GitHub metadata via [RoBO's repository](https://github.com/automl/RoBO) and [scikit-optimize's repository](https://github.com/scikit-optimize/scikit-optimize). | | [RoBO](/tools/automl-robo.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) | | --- | --- | --- | | Tagline | A Robust Bayesian Optimization framework | Sequential model-based optimization library with scipy.optimize interface | | Stars | 492 | 2,829 | | Forks | 129 | 559 | | Open issues | 25 | 318 | | Language | Python | Python | | Adopt for | RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. | Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | BSD-3-Clause | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RoBO](/tools/automl-robo.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Archived (8%) | | Days since push | 2653d | 893d | | Archived on GitHub | No | Yes | | Open issues (now) | 25 | 318 | | Full report | [trust report](/tools/automl-robo/trust.md) | [trust report](/tools/scikit-optimize-scikit-optimize/trust.md) | ## Shared compatibility - **Python**: [RoBO](/tools/automl-robo.md) - Python runtime; [scikit-optimize](/tools/scikit-optimize-scikit-optimize.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: scikit-optimize - **Adopt for:** Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize. ## Choose when ### Choose RoBO if… - Tags unique to RoBO: gaussian processes, python, random forests. - For tasks requiring robust handling of noisy data in Bayesian Optimization - Leaner open-issue backlog (25). ### Choose scikit-optimize if… - Tags unique to scikit-optimize: hyperparameter-tuning, machine-learning, scikit-learn. - Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective. - More GitHub stars (2.8k vs 492) - visibility, not fit. ## 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 scikit-optimize - Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient. - Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments. - Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified. ## Common questions ### What is the difference between RoBO and scikit-optimize? RoBO: A Robust Bayesian Optimization framework. scikit-optimize: Sequential model-based optimization library with scipy.optimize interface. See the comparison table for live GitHub stats and shared categories. ### When should I choose RoBO over scikit-optimize? Choose RoBO over scikit-optimize when Tags unique to RoBO: gaussian processes, python, random forests; For tasks requiring robust handling of noisy data in Bayesian Optimization; Leaner open-issue backlog (25). ### When should I choose scikit-optimize over RoBO? Choose scikit-optimize over RoBO when Tags unique to scikit-optimize: hyperparameter-tuning, machine-learning, scikit-learn; Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective; More GitHub stars (2.8k vs 492) - visibility, not fit. ### 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 scikit-optimize? Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient. Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments. Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified. ### Is RoBO or scikit-optimize more popular on GitHub? scikit-optimize has more GitHub stars (2,829 vs 492). Stars measure visibility, not whether either tool fits your constraints. ### Are RoBO and scikit-optimize open source? Yes - both are open-source projects on GitHub (RoBO: BSD-3-Clause, scikit-optimize: BSD-3-Clause). ### Where can I find alternatives to RoBO or scikit-optimize? GraphCanon lists graph-backed alternatives at [RoBO alternatives](/tools/automl-robo/alternatives) and [scikit-optimize alternatives](/tools/scikit-optimize-scikit-optimize/alternatives) ([RoBO markdown twin](/tools/automl-robo/alternatives.md), [scikit-optimize markdown twin](/tools/scikit-optimize-scikit-optimize/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-scikit-optimize-scikit-optimize.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RoBO or scikit-optimize? RoBO: Dormant. scikit-optimize: Archived. 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 scikit-optimize? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RoBO trust report](/tools/automl-robo/trust); [scikit-optimize trust report](/tools/scikit-optimize-scikit-optimize/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/_