--- title: "hyperopt vs rembo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hyperopt-hyperopt-vs-ziyuw-rembo" tools: ["hyperopt-hyperopt", "ziyuw-rembo"] --- # hyperopt vs rembo *GraphCanon updated Aug 4, 2026* ## Verdict Pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing; pick rembo if rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. [hyperopt](http://hyperopt.github.io/hyperopt) reports 7.6k GitHub stars, 1.1k forks, and 9 open issues, last pushed Aug 3, 2026. [rembo](https://github.com/ziyuw/rembo) has 117 stars, 25 forks, and 3 open issues, last pushed Aug 4, 2013. Figures are from public GitHub metadata via [hyperopt's repository](https://github.com/hyperopt/hyperopt) and [rembo's repository](https://github.com/ziyuw/rembo). | | [hyperopt](/tools/hyperopt-hyperopt.md) | [rembo](/tools/ziyuw-rembo.md) | | --- | --- | --- | | Tagline | Distributed Asynchronous Hyperparameter Optimization in Python | Bayesian optimization in high-dimensions via random embedding. | | Stars | 7,598 | 117 | | Forks | 1,075 | 25 | | Open issues | 9 | 3 | | Language | Python | Matlab | | Adopt for | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. | | Persona | - | - | | Runtime | - | - | | License | Other | - | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hyperopt](/tools/hyperopt-hyperopt.md) | [rembo](/tools/ziyuw-rembo.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 4747d | | Open issues (now) | 9 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/hyperopt-hyperopt/trust.md) | [trust report](/tools/ziyuw-rembo/trust.md) | ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Decision facts: rembo - **Adopt for:** Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. ## Choose when ### Choose hyperopt if… - hyperopt is primarily Python; rembo is Matlab. - Tags unique to hyperopt: annealing, asynchronous, distributed-computing, hyperparameter-optimization. - When you need to optimize machine learning model parameters on a distributed system asynchronously. ### Choose rembo if… - rembo is primarily Matlab; hyperopt is Python. - Tags unique to rembo: bayesian-optimization, high-dimensional space, random embedding. - When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions ## 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 rembo - For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding - In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects ## Common questions ### What is the difference between hyperopt and rembo? hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. rembo: Bayesian optimization in high-dimensions via random embedding.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hyperopt over rembo? Choose hyperopt over rembo when hyperopt is primarily Python; rembo is Matlab; 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 choose rembo over hyperopt? Choose rembo over hyperopt when rembo is primarily Matlab; hyperopt is Python; Tags unique to rembo: bayesian-optimization, high-dimensional space, random embedding; When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions. ### 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 rembo? For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects ### Is hyperopt or rembo more popular on GitHub? hyperopt has more GitHub stars (7,598 vs 117). Stars measure visibility, not whether either tool fits your constraints. ### Are hyperopt and rembo open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to hyperopt or rembo? GraphCanon lists graph-backed alternatives at [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) and [rembo alternatives](/tools/ziyuw-rembo/alternatives) ([hyperopt markdown twin](/tools/hyperopt-hyperopt/alternatives.md), [rembo markdown twin](/tools/ziyuw-rembo/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-ziyuw-rembo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hyperopt or rembo? hyperopt: Very active. rembo: Dormant. 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 rembo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hyperopt trust report](/tools/hyperopt-hyperopt/trust); [rembo trust report](/tools/ziyuw-rembo/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/_