--- title: "evalml vs athina-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-athina-ai-athina-evals" tools: ["alteryx-evalml", "athina-ai-athina-evals"] --- # evalml vs athina-evals *GraphCanon updated Aug 4, 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 athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. [evalml](https://evalml.alteryx.com) reports 852 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 2026. [athina-evals](https://docs.athina.ai) has 301 stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [athina-evals's repository](https://github.com/athina-ai/athina-evals). | | [evalml](/tools/alteryx-evalml.md) | [athina-evals](/tools/athina-ai-athina-evals.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | Python SDK for evaluating LLM generated responses | | Stars | 852 | 301 | | Forks | 93 | 22 | | Open issues | 324 | 3 | | 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. | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | - | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [athina-evals](/tools/athina-ai-athina-evals.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 201d | 417d | | Open issues (now) | 324 | 3 | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/athina-ai-athina-evals/trust.md) | ## 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: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Choose when ### Choose evalml if… - 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: automl, data-science, feature-engineering, feature-selection. - Also covers Model Training. - You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### Choose athina-evals if… - Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - Leaner open-issue backlog (3). ## 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 athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## Common questions ### What is the difference between evalml and athina-evals? evalml: An AutoML library written in Python. athina-evals: Python SDK for evaluating LLM generated responses. See the comparison table for live GitHub stats and shared categories. ### When should I choose evalml over athina-evals? Choose evalml over athina-evals when 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: automl, data-science, feature-engineering, feature-selection; Also covers Model Training; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### When should I choose athina-evals over evalml? Choose athina-evals over evalml when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; Leaner open-issue backlog (3). ### 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 athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### Is evalml or athina-evals more popular on GitHub? evalml has more GitHub stars (852 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and athina-evals open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to evalml or athina-evals? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/alternatives.md), [athina-evals markdown twin](/tools/athina-ai-athina-evals/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-athina-ai-athina-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evalml or athina-evals? evalml: Slowing. athina-evals: 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 athina-evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [athina-evals trust report](/tools/athina-ai-athina-evals/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/_