Comparison
evalml vs athina-evals
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.
Markdown twin · evalml alternatives · athina-evals alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | evalml | athina-evals |
|---|---|---|
| Maintenance | Slowing (201d since push) As of 2w · github_public_v1 | Dormant (417d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- evalml
- An AutoML library written in Python
- athina-evals
- Python SDK for evaluating LLM generated responses
Stars
- evalml
- 852
- athina-evals
- 301
Forks
- evalml
- 93
- athina-evals
- 22
Open issues
- evalml
- 324
- athina-evals
- 3
Language
- evalml
- Python
- athina-evals
- 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.
- athina-evals
- athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
Persona
- evalml
- -
- athina-evals
- -
Runtime
- evalml
- -
- athina-evals
- -
License
- evalml
- EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
- athina-evals
- -
Last pushed
- evalml
- Jan 14, 2026
- athina-evals
- Jun 6, 2025
Categories
- evalml
- Evaluation & Observability, Model Training
- athina-evals
- Evaluation & Observability
Trust and health
Maintenance
- evalml
- Slowing (36%)
- athina-evals
- Dormant (18%)
Days since push
- evalml
- 201d
- athina-evals
- 417d
Open issues (now)
- evalml
- 324
- athina-evals
- 3
Full report
- evalml
- Trust report
- athina-evals
- Trust report
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.
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 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 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
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 Aug 4, 2026
- GitHub forks (alteryx/evalml) · observed Aug 4, 2026
- Last push (alteryx/evalml) · observed Jan 14, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (athina-ai/athina-evals) · observed Jul 28, 2026
- GitHub forks (athina-ai/athina-evals) · observed Jul 28, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: evalml 852 · athina-evals 301 (synced Aug 4, 2026).
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 and athina-evals alternatives (evalml markdown twin, athina-evals 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 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; athina-evals trust report.