Comparison
awesome-evals vs qa_metrics
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; 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 OpenAI and Anthropic.
Markdown twin · awesome-evals alternatives · qa_metrics alternatives
GraphCanon updated Sep 20, 2026
6views this month
vs
Trust & integrity
| Signal | awesome-evals | qa_metrics |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (417d 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 · Personal 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
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- qa_metrics
- A Python package for basic QA evaluations of large language models.
Stars
- awesome-evals
- 900
- qa_metrics
- 64
Forks
- awesome-evals
- 104
- qa_metrics
- 6
Open issues
- awesome-evals
- 34
- qa_metrics
- 0
Language
- awesome-evals
- -
- qa_metrics
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- 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
- awesome-evals
- -
- qa_metrics
- -
Runtime
- awesome-evals
- -
- qa_metrics
- -
License
- awesome-evals
- Other
- 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
- awesome-evals
- Sep 15, 2026
- qa_metrics
- Jul 18, 2025
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- qa_metrics
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- qa_metrics
- Dormant (18%)
Days since push
- awesome-evals
- 4d
- qa_metrics
- 417d
Open issues (now)
- awesome-evals
- 34
- qa_metrics
- 0
Stars delta
- awesome-evals
- +139 (30d)
- qa_metrics
- +2 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- qa_metrics
- 0 (30d)
Owner type
- awesome-evals
- Organization
- qa_metrics
- User
Full report
- awesome-evals
- Trust report
- qa_metrics
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, qa_metrics is MIT.
- Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks.
- Also covers AI Agents.
- Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation
When NOT to use awesome-evals
- Require real-time interactive support or direct tool integrations not covered by a static resource list
- Seeking proprietary tools from specific vendors rather than open resources and community content
Choose qa_metrics if…
- License: qa_metrics is MIT, awesome-evals is Other.
- Tags unique to qa_metrics: exact-matching, 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.
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 (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- Last push (benchflow-ai/awesome-evals) · observed Sep 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 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: awesome-evals 900 · qa_metrics 64 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and qa_metrics?
- awesome-evals: A curated library of resources for building and evaluating AI agents. 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 awesome-evals over qa_metrics?
- Choose awesome-evals over qa_metrics when License: awesome-evals is Other, qa_metrics is MIT; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose qa_metrics over awesome-evals?
- Choose qa_metrics over awesome-evals when License: qa_metrics is MIT, awesome-evals is Other; Tags unique to qa_metrics: exact-matching, 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.
- When should I avoid awesome-evals?
- Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content
- 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 awesome-evals or qa_metrics more popular on GitHub?
- awesome-evals has more GitHub stars (900 vs 64). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and qa_metrics open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, qa_metrics: MIT).
- Where can I find alternatives to awesome-evals or qa_metrics?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and qa_metrics alternatives (awesome-evals 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, awesome-evals or qa_metrics?
- awesome-evals: Very active. 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 awesome-evals and qa_metrics?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; qa_metrics trust report.