Comparison
auto-evaluator vs qa_metrics
Verdict
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; pick qa_metrics if qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from.
Markdown twin · auto-evaluator alternatives · qa_metrics alternatives
GraphCanon updated Sep 20, 2026
6views this month
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Trust & integrity
| Signal | auto-evaluator | qa_metrics |
|---|---|---|
| Maintenance | Dormant (1216d since push) As of Sep 8, 2026 · github_public_v1 | Dormant (417d since push) As of Sep 9, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 8, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 9, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings 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
- auto-evaluator
- A lightweight evaluation tool for question-answering using Langchain
- qa_metrics
- A Python package for basic QA evaluations of large language models.
Stars
- auto-evaluator
- 1.1k
- qa_metrics
- 64
Forks
- auto-evaluator
- 92
- qa_metrics
- 6
Open issues
- auto-evaluator
- 3
- qa_metrics
- 0
Language
- auto-evaluator
- Python
- qa_metrics
- Python
Adopt for
- 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.
- qa_metrics
- qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic.
Persona
- auto-evaluator
- -
- qa_metrics
- -
Runtime
- auto-evaluator
- -
- qa_metrics
- -
License
- auto-evaluator
- -
- qa_metrics
- MIT License allows for free use and distribution with attribution required by retaining the copyright notice and license text in any redistribution.
Last pushed
- auto-evaluator
- May 10, 2023
- qa_metrics
- Jul 18, 2025
Categories
- auto-evaluator
- Evaluation & Observability
- qa_metrics
- Evaluation & Observability
Trust and health
Days since push
- auto-evaluator
- 1216d
- qa_metrics
- 417d
Open issues (now)
- auto-evaluator
- 3
- qa_metrics
- 0
Stars delta
- auto-evaluator
- -3 (30d)
- qa_metrics
- +2 (30d)
OSV dependency advisories
- auto-evaluator
- Published findings
- qa_metrics
- No lockfile (source not queried)
Full report
- auto-evaluator
- Trust report
- qa_metrics
- Trust report
Shared compatibility
- Python · auto-evaluator: Python runtime · qa_metrics: Python runtime
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 64) - 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.
Choose qa_metrics if…
- Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test.
- When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score.
- More recently updated (last pushed Jul 18, 2025).
When NOT to use qa_metrics
- Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set.
- Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (rlancemartin/auto-evaluator) · observed Sep 20, 2026
- GitHub forks (rlancemartin/auto-evaluator) · observed Sep 20, 2026
- Last push (rlancemartin/auto-evaluator) · observed May 10, 2023
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zli12321/qa_metrics) · observed Sep 20, 2026
- GitHub forks (zli12321/qa_metrics) · observed Sep 20, 2026
- Last push (zli12321/qa_metrics) · observed Jul 18, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: auto-evaluator 1.1k · qa_metrics 64 (synced Sep 20, 2026).
Common questions
- What is the difference between auto-evaluator and qa_metrics?
- auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. qa_metrics: A Python package for basic QA evaluations of large language models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-evaluator over qa_metrics?
- Choose auto-evaluator over qa_metrics 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 64) - visibility, not fit.
- When should I choose qa_metrics over auto-evaluator?
- Choose qa_metrics over auto-evaluator when Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test; When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score; More recently updated (last pushed Jul 18, 2025).
- 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.
- When should I avoid qa_metrics?
- Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set. Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily.
- Is auto-evaluator or qa_metrics more popular on GitHub?
- auto-evaluator has more GitHub stars (1,102 vs 64). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-evaluator and qa_metrics open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to auto-evaluator or qa_metrics?
- GraphCanon lists graph-backed alternatives at auto-evaluator alternatives and qa_metrics alternatives (auto-evaluator markdown twin, qa_metrics 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, auto-evaluator or qa_metrics?
- auto-evaluator: Dormant. qa_metrics: 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 auto-evaluator and qa_metrics?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-evaluator trust report; qa_metrics trust report.