--- title: "evalml vs autoai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-blobcity-autoai" tools: ["alteryx-evalml", "blobcity-autoai"] --- # evalml vs autoai *GraphCanon updated Sep 30, 2026* ## 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. [evalml](https://evalml.alteryx.com) reports 850 GitHub stars, 96 forks, and 325 open issues, last pushed Jan 14, 2026. [autoai](https://github.com/blobcity/autoai) has 186 stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [autoai's repository](https://github.com/blobcity/autoai). | | [evalml](/tools/alteryx-evalml.md) | [autoai](/tools/blobcity-autoai.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | | Stars | 850 | 186 | | Forks | 96 | 46 | | Open issues | 325 | 9 | | Language | Python | Python | | Adopt for | EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [autoai](/tools/blobcity-autoai.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 258d | 554d | | Open issues (now) | 325 | 9 | | Stars delta | -2 (30d) | 0 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/blobcity-autoai/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [autoai](/tools/blobcity-autoai.md) - Python runtime ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/alteryx-evalml/alternatives) and [autoai alternatives](/tools/blobcity-autoai/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/alternatives.md), [autoai markdown twin](/tools/blobcity-autoai/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/alteryx-evalml-vs-blobcity-autoai.md) 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](/tools/alteryx-evalml/trust); [autoai trust report](/tools/blobcity-autoai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - 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/_