--- title: "autoai vs dragonfly" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-dragonfly-dragonfly" tools: ["blobcity-autoai", "dragonfly-dragonfly"] --- # autoai vs dragonfly *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 dragonfly if dragonfly is an open-source Python library that specializes in scalable Bayesian optimization. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [dragonfly](https://github.com/dragonfly/dragonfly) has 894 stars, 238 forks, and 43 open issues, last pushed Jun 19, 2023. Figures are from public GitHub metadata via [autoai's repository](https://github.com/blobcity/autoai) and [dragonfly's repository](https://github.com/dragonfly/dragonfly). | | [autoai](/tools/blobcity-autoai.md) | [dragonfly](/tools/dragonfly-dragonfly.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | An open source Python library for scalable Bayesian optimisation. | | Stars | 186 | 894 | | Forks | 46 | 238 | | Open issues | 9 | 43 | | 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. | Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [dragonfly](/tools/dragonfly-dragonfly.md) | | --- | --- | --- | | Days since push | 496d | 1141d | | Open issues (now) | 9 | 43 | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/dragonfly-dragonfly/trust.md) | ## Shared compatibility - **Python**: [autoai](/tools/blobcity-autoai.md) - Python runtime; [dragonfly](/tools/dragonfly-dragonfly.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: dragonfly - **Pricing:** freemium - Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works. - **Requirements:** Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager. - **Adopt for:** Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization ## Choose when ### Choose autoai if… - License: autoai is Apache-2.0, dragonfly is MIT. - 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 dragonfly if… - License: dragonfly is MIT, autoai is Apache-2.0. - Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.. - Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.. - Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation. - When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ## 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 dragonfly - If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. - In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ## Common questions ### What is the difference between autoai and dragonfly? autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. dragonfly: An open source Python library for scalable Bayesian optimisation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose autoai over dragonfly? Choose autoai over dragonfly when License: autoai is Apache-2.0, dragonfly is MIT; 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 dragonfly over autoai? Choose dragonfly over autoai when License: dragonfly is MIT, autoai is Apache-2.0; Pricing: Available under the MIT License, free to use but does require attention to licensing when redistributing derivative works.; Requirements: Installation requires Python and gfortran.; Additional dependencies can be installed via the `pip` package manager.; Tags unique to dragonfly: bayesian optimisation, python library, scalable optimisation; When dealing with large-scale problems where traditional optimization methods may not be efficient enough. ### 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 dragonfly? If the problem at hand can be effectively managed by simpler or more lightweight optimization tools; Dragonfly’s strength lies in scalability and complex scenario management. In environments where Python or extensive dependencies are not desirable, as installing and running Dragonfly requires specific setup including gfortran for certain operations. ### Is autoai or dragonfly more popular on GitHub? dragonfly has more GitHub stars (894 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are autoai and dragonfly open source? Yes - both are open-source projects on GitHub (autoai: Apache-2.0, dragonfly: MIT). ### Where can I find alternatives to autoai or dragonfly? GraphCanon lists graph-backed alternatives at [autoai alternatives](/tools/blobcity-autoai/alternatives) and [dragonfly alternatives](/tools/dragonfly-dragonfly/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/alternatives.md), [dragonfly markdown twin](/tools/dragonfly-dragonfly/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-dragonfly-dragonfly.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoai or dragonfly? autoai: Dormant. dragonfly: 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 autoai and dragonfly? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoai trust report](/tools/blobcity-autoai/trust); [dragonfly trust report](/tools/dragonfly-dragonfly/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/_