Comparison
evalml vs autoai
Verdict
Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; 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.
Markdown twin · evalml alternatives · autoai alternatives
GraphCanon updated Sep 30, 2026
9views this month
Trust & integrity
| Signal | evalml | autoai |
|---|---|---|
| Maintenance | Slowing (258d since push) As of Sep 30, 2026 · github_public_v1 | Dormant (554d since push) As of Sep 30, 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 Sep 30, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings 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
- evalml
- An AutoML library written in Python
- autoai
- Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
Stars
- evalml
- 850
- autoai
- 186
Forks
- evalml
- 96
- autoai
- 46
Open issues
- evalml
- 325
- autoai
- 9
Language
- evalml
- Python
- autoai
- Python
Adopt for
- evalml
- EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license.
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
Persona
- evalml
- -
- autoai
- -
Runtime
- evalml
- -
- autoai
- -
License
- evalml
- EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
- autoai
- Apache-2.0
Last pushed
- evalml
- Jan 14, 2026
- autoai
- Mar 25, 2025
Categories
- evalml
- Evaluation & Observability, Model Training
- autoai
- Model Training
Trust and health
Maintenance
- evalml
- Slowing (36%)
- autoai
- Dormant (18%)
Days since push
- evalml
- 258d
- autoai
- 554d
Open issues (now)
- evalml
- 325
- autoai
- 9
Stars delta
- evalml
- -2 (30d)
- autoai
- 0 (30d)
Open issues delta
- evalml
- +1 (30d)
- autoai
- 0 (30d)
OSV dependency advisories
- evalml
- No lockfile (source not queried)
- autoai
- Published findings
Full report
- evalml
- Trust report
- autoai
- Trust report
Shared compatibility
- Python · evalml: Python runtime · autoai: Python runtime
Choose evalml if…
- License: evalml is BSD-3-Clause, autoai is Apache-2.0.
- Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased..
- Requirements: Min 2 GB RAM.
- Tags unique to evalml: data-science, feature-engineering, feature-selection, hyperparameter-tuning.
- Also covers Evaluation & Observability.
- You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
When NOT to use evalml
- You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box.
- Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
Choose autoai if…
- License: autoai is Apache-2.0, evalml is BSD-3-Clause.
- Tags unique to autoai: ai, autoai, codegen, deep-learning.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alteryx/evalml) · observed Sep 30, 2026
- GitHub forks (alteryx/evalml) · observed Sep 30, 2026
- Last push (alteryx/evalml) · observed Jan 14, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: evalml 850 · autoai 186 (synced Sep 30, 2026).
Common questions
- What is the difference between evalml and autoai?
- evalml: An AutoML library written in Python. autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. See the comparison table for live GitHub stats and shared categories.
- When should I choose evalml over autoai?
- Choose evalml over autoai when License: evalml is BSD-3-Clause, autoai is Apache-2.0; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: data-science, feature-engineering, feature-selection, hyperparameter-tuning; Also covers Evaluation & Observability; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
- When should I choose autoai over evalml?
- Choose autoai over evalml when License: autoai is Apache-2.0, evalml is BSD-3-Clause; Tags unique to autoai: ai, autoai, codegen, deep-learning; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
- When should I avoid evalml?
- You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
- 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.
- Is evalml or autoai more popular on GitHub?
- evalml has more GitHub stars (850 vs 186). Stars measure visibility, not whether either tool fits your constraints.
- Are evalml and autoai open source?
- Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, autoai: Apache-2.0).
- Where can I find alternatives to evalml or autoai?
- GraphCanon lists graph-backed alternatives at evalml alternatives and autoai alternatives (evalml markdown twin, autoai 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, evalml or autoai?
- evalml: Slowing. autoai: 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 evalml and autoai?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evalml trust report; autoai trust report.