--- title: "RoBO vs optuna" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-robo-vs-optuna-optuna" tools: ["automl-robo", "optuna-optuna"] --- # RoBO vs optuna *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 optuna if optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. [RoBO](https://github.com/automl/RoBO) reports 492 GitHub stars, 129 forks, and 25 open issues, last pushed Apr 30, 2019. [optuna](https://optuna.org) has 15k stars, 1.4k forks, and 16 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [RoBO's repository](https://github.com/automl/RoBO) and [optuna's repository](https://github.com/optuna/optuna). | | [RoBO](/tools/automl-robo.md) | [optuna](/tools/optuna-optuna.md) | | --- | --- | --- | | Tagline | A Robust Bayesian Optimization framework | A hyperparameter optimization framework | | Stars | 492 | 14,603 | | Forks | 129 | 1,361 | | Open issues | 25 | 16 | | Language | Python | Python | | Adopt for | RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests. | Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RoBO](/tools/automl-robo.md) | [optuna](/tools/optuna-optuna.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 2653d | 1d | | Open issues (now) | 25 | 16 | | Full report | [trust report](/tools/automl-robo/trust.md) | [trust report](/tools/optuna-optuna/trust.md) | ## Shared compatibility - **Python**: [RoBO](/tools/automl-robo.md) - Python runtime; [optuna](/tools/optuna-optuna.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: optuna - **Adopt for:** Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks. ## Choose when ### Choose RoBO if… - License: RoBO is BSD-3-Clause, optuna is MIT. - Tags unique to RoBO: bayesian-optimization, gaussian processes, random forests. - For tasks requiring robust handling of noisy data in Bayesian Optimization ### Choose optuna if… - License: optuna is MIT, RoBO is BSD-3-Clause. - Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel. - When you need to streamline the hyperparameter tuning process for machine learning models built in Python. ## 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 optuna - If your project is not compatible with Python, as Optuna does not support other languages directly out of box. - Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ## Common questions ### What is the difference between RoBO and optuna? RoBO: A Robust Bayesian Optimization framework. optuna: A hyperparameter optimization framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose RoBO over optuna? Choose RoBO over optuna when License: RoBO is BSD-3-Clause, optuna is MIT; Tags unique to RoBO: bayesian-optimization, gaussian processes, random forests; For tasks requiring robust handling of noisy data in Bayesian Optimization. ### When should I choose optuna over RoBO? Choose optuna over RoBO when License: optuna is MIT, RoBO is BSD-3-Clause; Tags unique to optuna: distributed, hyperparameter-optimization, machine-learning, parallel; When you need to streamline the hyperparameter tuning process for machine learning models built in Python. ### 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 optuna? If your project is not compatible with Python, as Optuna does not support other languages directly out of box. Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs. ### Is RoBO or optuna more popular on GitHub? optuna has more GitHub stars (14,603 vs 492). Stars measure visibility, not whether either tool fits your constraints. ### Are RoBO and optuna open source? Yes - both are open-source projects on GitHub (RoBO: BSD-3-Clause, optuna: MIT). ### Where can I find alternatives to RoBO or optuna? GraphCanon lists graph-backed alternatives at [RoBO alternatives](/tools/automl-robo/alternatives) and [optuna alternatives](/tools/optuna-optuna/alternatives) ([RoBO markdown twin](/tools/automl-robo/alternatives.md), [optuna markdown twin](/tools/optuna-optuna/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-optuna-optuna.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RoBO or optuna? RoBO: Dormant. optuna: 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 optuna? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RoBO trust report](/tools/automl-robo/trust); [optuna trust report](/tools/optuna-optuna/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/_