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Comparison

every_eval_ever vs auto-evaluator

Verdict

Pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results; pick auto-evaluator if auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI.

Markdown twin · every_eval_ever alternatives · auto-evaluator alternatives

GraphCanon updated Sep 20, 2026

15views this month

every_eval_ever logo

every_eval_ever

evaleval/every_eval_ever

★ 111pushed Sep 7, 2026
vs
auto-evaluator logo

auto-evaluator

rlancemartin/auto-evaluator

★ 1.1kpushed May 10, 2023

Trust & integrity

Signalevery_eval_everauto-evaluator
Maintenance
Very active (1d since push)
As of Sep 9, 2026 · github_public_v1
Dormant (1216d since push)
As of Sep 8, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 9, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 8, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 15, 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
every_eval_ever trust reportauto-evaluator trust reportTrust methodology

Tagline

every_eval_ever
Shared schema and crowdsourced eval database
auto-evaluator
A lightweight evaluation tool for question-answering using Langchain

Stars

every_eval_ever
111
auto-evaluator
1.1k

Forks

every_eval_ever
49
auto-evaluator
92

Open issues

every_eval_ever
27
auto-evaluator
3

Language

every_eval_ever
Python
auto-evaluator
Python

Adopt for

every_eval_ever
Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results.
auto-evaluator
Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI.

Persona

every_eval_ever
-
auto-evaluator
-

Runtime

every_eval_ever
-
auto-evaluator
-

License

every_eval_ever
MIT
auto-evaluator
-

Last pushed

every_eval_ever
Sep 7, 2026
auto-evaluator
May 10, 2023

Categories

every_eval_ever
Evaluation & Observability
auto-evaluator
Evaluation & Observability

Trust and health

Maintenance

every_eval_ever
Very active (96%)
auto-evaluator
Dormant (18%)

Days since push

every_eval_ever
1d
auto-evaluator
1216d

Open issues (now)

every_eval_ever
27
auto-evaluator
3

Stars delta

every_eval_ever
+9 (30d)
auto-evaluator
-3 (30d)

Open issues delta

every_eval_ever
+3 (30d)
auto-evaluator
0 (30d)

Owner type

every_eval_ever
Organization
auto-evaluator
User

OSV dependency advisories

every_eval_ever
No lockfile (source not queried)
auto-evaluator
Published findings

Full report

every_eval_ever
Trust report
auto-evaluator
Trust report

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.
GitHubDetails

Choose auto-evaluator if…

  • Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm.
  • Use when you need a lightweight solution for testing question-answering capabilities of Langchain models.
  • More GitHub stars (1.1k vs 111) - visibility, not fit.

When NOT to use auto-evaluator

  • Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings.
  • If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature.
GitHubDetails

Explore

every_eval_ever trust report →auto-evaluator trust report →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: every_eval_ever 111 · auto-evaluator 1.1k (synced Sep 20, 2026).

Common questions

What is the difference between every_eval_ever and auto-evaluator?
every_eval_ever: Shared schema and crowdsourced eval database. auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. See the comparison table for live GitHub stats and shared categories.
When should I choose every_eval_ever over auto-evaluator?
Choose every_eval_ever over auto-evaluator 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 choose auto-evaluator over every_eval_ever?
Choose auto-evaluator over every_eval_ever when Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; More GitHub stars (1.1k vs 111) - visibility, not fit.
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.
When should I avoid auto-evaluator?
Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature.
Is every_eval_ever or auto-evaluator more popular on GitHub?
auto-evaluator has more GitHub stars (1,102 vs 111). Stars measure visibility, not whether either tool fits your constraints.
Are every_eval_ever and auto-evaluator open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to every_eval_ever or auto-evaluator?
GraphCanon lists graph-backed alternatives at every_eval_ever alternatives and auto-evaluator alternatives (every_eval_ever markdown twin, auto-evaluator 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, every_eval_ever or auto-evaluator?
every_eval_ever: Very active. auto-evaluator: 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 every_eval_ever and auto-evaluator?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: every_eval_ever trust report; auto-evaluator trust report.

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