Comparison
awesome-evals vs evals
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick evals if evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations.
Markdown twin · awesome-evals alternatives · evals alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | awesome-evals | evals |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (145d since push) As of Sep 7, 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 7, 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 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 |
Tagline
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- evals
- Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.
Stars
- awesome-evals
- 901
- evals
- 19k
Forks
- awesome-evals
- 104
- evals
- 3.1k
Open issues
- awesome-evals
- 34
- evals
- 333
Language
- awesome-evals
- -
- evals
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- evals
- Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations.
Persona
- awesome-evals
- -
- evals
- -
Runtime
- awesome-evals
- -
- evals
- -
License
- awesome-evals
- Other
- evals
- Other
Last pushed
- awesome-evals
- Sep 15, 2026
- evals
- Apr 14, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- evals
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- evals
- Slowing (36%)
Days since push
- awesome-evals
- 4d
- evals
- 145d
Open issues (now)
- awesome-evals
- 34
- evals
- 333
Stars delta
- awesome-evals
- +140 (30d)
- evals
- +271 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- evals
- +120 (30d)
Full report
- awesome-evals
- Trust report
- evals
- Trust report
Choose awesome-evals if…
- 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 evals if…
- Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large-language-models.
- * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem.
- More GitHub stars (19k vs 901) - visibility, not fit.
When NOT to use evals
- * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key.
- * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a
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 (openai/evals) · observed Sep 20, 2026
- GitHub forks (openai/evals) · observed Sep 20, 2026
- Last push (openai/evals) · observed Apr 14, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · evals 19k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and evals?
- awesome-evals: A curated library of resources for building and evaluating AI agents. evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over evals?
- Choose awesome-evals over evals when 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 evals over awesome-evals?
- Choose evals over awesome-evals when Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, large-language-models; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem; More GitHub stars (19k vs 901) - visibility, not fit.
- 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 evals?
- * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a
- Is awesome-evals or evals more popular on GitHub?
- evals has more GitHub stars (19,398 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and evals open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, evals: Other).
- Where can I find alternatives to awesome-evals or evals?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and evals alternatives (awesome-evals markdown twin, 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, awesome-evals or evals?
- awesome-evals: Very active. evals: Slowing. 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 evals?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; evals trust report.