Home/Compare/athina-evals vs every_eval_ever

Comparison

athina-evals vs every_eval_ever

Verdict

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; pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results.

Markdown twin · athina-evals alternatives · every_eval_ever alternatives

GraphCanon updated Sep 20, 2026

16views this month

athina-evals logo

athina-evals

athina-ai/athina-evals

301pushed Jun 6, 2025
vs
every_eval_ever logo

every_eval_ever

evaleval/every_eval_ever

111pushed Sep 7, 2026

Trust & integrity

Signalathina-evalsevery_eval_ever
Maintenance
Dormant (470d since push)
As of Sep 20, 2026 · github_public_v1
Very active (1d since push)
As of Sep 9, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 9, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 15, 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

athina-evals
Python SDK for evaluating LLM generated responses
every_eval_ever
Shared schema and crowdsourced eval database

Stars

athina-evals
301
every_eval_ever
111

Forks

athina-evals
22
every_eval_ever
49

Open issues

athina-evals
4
every_eval_ever
27

Language

athina-evals
Python
every_eval_ever
Python

Adopt for

athina-evals
athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
every_eval_ever
Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results.

Persona

athina-evals
-
every_eval_ever
-

Runtime

athina-evals
-
every_eval_ever
-

License

athina-evals
-
every_eval_ever
MIT

Last pushed

athina-evals
Jun 6, 2025
every_eval_ever
Sep 7, 2026

Categories

athina-evals
Evaluation & Observability
every_eval_ever
Evaluation & Observability

Trust and health

Maintenance

athina-evals
Dormant (18%)
every_eval_ever
Very active (96%)

Days since push

athina-evals
470d
every_eval_ever
1d

Open issues (now)

athina-evals
4
every_eval_ever
27

Stars delta

athina-evals
0 (30d)
every_eval_ever
+9 (30d)

Open issues delta

athina-evals
+1 (30d)
every_eval_ever
+3 (30d)

Full report

athina-evals
Trust report
every_eval_ever
Trust report

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
  • More GitHub stars (301 vs 111) - visibility, not fit.

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

Choose every_eval_ever if…

  • Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database..
  • Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip..
  • Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra.
  • Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema.

When NOT to use every_eval_ever

  • Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset.
  • If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: athina-evals 301 · every_eval_ever 111 (synced Sep 20, 2026).

Common questions

What is the difference between athina-evals and every_eval_ever?
athina-evals: Python SDK for evaluating LLM generated responses. every_eval_ever: Shared schema and crowdsourced eval database. See the comparison table for live GitHub stats and shared categories.
When should I choose athina-evals over every_eval_ever?
Choose athina-evals over every_eval_ever 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; More GitHub stars (301 vs 111) - visibility, not fit.
When should I choose every_eval_ever over athina-evals?
Choose every_eval_ever over athina-evals when Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.; Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema.
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
When should I avoid every_eval_ever?
Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable.
Is athina-evals or every_eval_ever more popular on GitHub?
athina-evals has more GitHub stars (301 vs 111). Stars measure visibility, not whether either tool fits your constraints.
Are athina-evals and every_eval_ever open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to athina-evals or every_eval_ever?
GraphCanon lists graph-backed alternatives at athina-evals alternatives and every_eval_ever alternatives (athina-evals markdown twin, every_eval_ever 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, athina-evals or every_eval_ever?
athina-evals: Dormant. every_eval_ever: 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 athina-evals and every_eval_ever?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; every_eval_ever trust report.

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