Comparison
autoai vs hyperopt
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.
Markdown twin · autoai alternatives · hyperopt alternatives
GraphCanon updated Oct 2, 2026
13views this month
Trust & integrity
| Signal | autoai | hyperopt |
|---|---|---|
| Maintenance | Dormant (554d since push) As of Sep 30, 2026 · github_public_v1 | Very active (3d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- hyperopt
- Distributed Asynchronous Hyperparameter Optimization in Python
Stars
- autoai
- 186
- hyperopt
- 7.6k
Forks
- autoai
- 46
- hyperopt
- 1.1k
Open issues
- autoai
- 9
- hyperopt
- 10
Language
- autoai
- Python
- hyperopt
- 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.
- hyperopt
- Hyperopt offers distributed asynchronous hyperparameter optimization with multiple optimizers like TPE and Annealing.
Persona
- autoai
- -
- hyperopt
- -
Runtime
- autoai
- -
- hyperopt
- -
License
- autoai
- Apache-2.0
- hyperopt
- Other
Last pushed
- autoai
- Mar 25, 2025
- hyperopt
- Sep 28, 2026
Categories
- autoai
- Model Training
- hyperopt
- Model Training
Trust and health
Maintenance
- autoai
- Dormant (18%)
- hyperopt
- Very active (96%)
Days since push
- autoai
- 554d
- hyperopt
- 3d
Open issues (now)
- autoai
- 9
- hyperopt
- 10
Stars delta
- autoai
- 0 (30d)
- hyperopt
- -3 (30d)
Open issues delta
- autoai
- 0 (30d)
- hyperopt
- +1 (30d)
OSV dependency advisories
- autoai
- Published findings
- hyperopt
- No lockfile (source not queried)
Full report
- autoai
- Trust report
- hyperopt
- Trust report
Shared compatibility
- Python · autoai: Python runtime · hyperopt: Python runtime
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.
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 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 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.
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 Sep 30, 2026
- GitHub forks (blobcity/autoai) · observed Sep 30, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hyperopt/hyperopt) · observed Oct 2, 2026
- GitHub forks (hyperopt/hyperopt) · observed Oct 2, 2026
- Last push (hyperopt/hyperopt) · observed Sep 28, 2026
- License file (Other) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autoai 186 · hyperopt 7.6k (synced Sep 30, 2026).
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 (7,595 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 and hyperopt alternatives (autoai markdown twin, hyperopt 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 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; hyperopt trust report.