--- title: "autoai vs hyperopt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-hyperopt-hyperopt" tools: ["blobcity-autoai", "hyperopt-hyperopt"] --- # autoai vs hyperopt *GraphCanon updated Aug 4, 2026* ## Verdict Pick autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation; pick hyperopt if hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [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 [autoai's repository](https://github.com/blobcity/autoai) and [hyperopt's repository](https://github.com/hyperopt/hyperopt). | | [autoai](/tools/blobcity-autoai.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | Distributed Asynchronous Hyperparameter Optimization in Python | | Stars | 186 | 7,598 | | Forks | 46 | 1,075 | | Open issues | 9 | 9 | | Language | Python | Python | | Adopt for | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [hyperopt](/tools/hyperopt-hyperopt.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 496d | 0d | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/hyperopt-hyperopt/trust.md) | ## Shared compatibility - **Python**: [autoai](/tools/blobcity-autoai.md) - Python runtime; [hyperopt](/tools/hyperopt-hyperopt.md) - Python runtime ## Decision facts: autoai - **Adopt for:** Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. ## Decision facts: hyperopt - **Adopt for:** Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing. ## Choose when ### Choose autoai if… - License: autoai is Apache-2.0, hyperopt is Other. - Tags unique to autoai: ai, autoai, automl, codegen. - Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### Choose hyperopt if… - License: hyperopt is Other, autoai is Apache-2.0. - 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 autoai - Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. - Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ## 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 autoai and hyperopt? autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose autoai over hyperopt? Choose autoai over hyperopt when License: autoai is Apache-2.0, hyperopt is Other; Tags unique to autoai: ai, autoai, automl, codegen; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### When should I choose hyperopt over autoai? Choose hyperopt over autoai when License: hyperopt is Other, autoai is Apache-2.0; 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 autoai? Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ### 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 autoai or hyperopt more popular on GitHub? hyperopt has more GitHub stars (7,598 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are autoai and hyperopt open source? Yes - both are open-source projects on GitHub (autoai: Apache-2.0, hyperopt: Other). ### Where can I find alternatives to autoai or hyperopt? GraphCanon lists graph-backed alternatives at [autoai alternatives](/tools/blobcity-autoai/alternatives) and [hyperopt alternatives](/tools/hyperopt-hyperopt/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/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/blobcity-autoai-vs-hyperopt-hyperopt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoai or hyperopt? autoai: 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 autoai and hyperopt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoai trust report](/tools/blobcity-autoai/trust); [hyperopt trust report](/tools/hyperopt-hyperopt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blobcity-autoai`](/api/graphcanon/graph?tool=blobcity-autoai) - 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/_