Comparison
autoai vs dragonfly
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.
Markdown twin · autoai alternatives · dragonfly alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | autoai | dragonfly |
|---|---|---|
| Maintenance | Dormant (496d since push) As of 2w · github_public_v1 | Dormant (1141d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- autoai
- 186
- dragonfly
- 894
Forks
- autoai
- 46
- dragonfly
- 238
Open issues
- autoai
- 9
- dragonfly
- 43
Language
- autoai
- Python
- dragonfly
- Python
Adopt for
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
- dragonfly
- Dragonfly is an open-source Python library that specializes in scalable Bayesian optimization
Persona
- autoai
- -
- dragonfly
- -
Runtime
- autoai
- -
- dragonfly
- -
License
- autoai
- Apache-2.0
- dragonfly
- MIT
Last pushed
- autoai
- Mar 25, 2025
- dragonfly
- Jun 19, 2023
Categories
- autoai
- Model Training
- dragonfly
- Model Training
Trust and health
Days since push
- autoai
- 496d
- dragonfly
- 1141d
Open issues (now)
- autoai
- 9
- dragonfly
- 43
OSV dependency advisories
- autoai
- Published findings
- dragonfly
- No published findings from this source as of 2026-07-11
Full report
- autoai
- Trust report
- dragonfly
- Trust report
Shared compatibility
- Python · autoai: Python runtime · dragonfly: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (blobcity/autoai) · observed Aug 4, 2026
- GitHub forks (blobcity/autoai) · observed Aug 4, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (dragonfly/dragonfly) · observed Aug 4, 2026
- GitHub forks (dragonfly/dragonfly) · observed Aug 4, 2026
- Last push (dragonfly/dragonfly) · observed Jun 19, 2023
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autoai 186 · dragonfly 894 (synced Aug 4, 2026).
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
pippackage 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 and dragonfly alternatives (autoai markdown twin, dragonfly markdown twin), 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 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; dragonfly trust report.