Comparison
awesome-evals vs lmms-eval
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick lmms-eval if lmms-eval is a one-stop solution for benchmarking multimodal large language models across various tasks including text, image, video, and audio.
Markdown twin · awesome-evals alternatives · lmms-eval alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | awesome-evals | lmms-eval |
|---|---|---|
| Maintenance | Active (26d since push) As of 3w · github_public_v1 | Active (11d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- lmms-eval
- One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks
Stars
- awesome-evals
- 761
- lmms-eval
- 4.4k
Forks
- awesome-evals
- 71
- lmms-eval
- 639
Open issues
- awesome-evals
- 21
- lmms-eval
- 49
Language
- awesome-evals
- -
- lmms-eval
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- lmms-eval
- lmms-eval is a one-stop solution for benchmarking multimodal large language models across various tasks including text, image, video, and audio.
Persona
- awesome-evals
- -
- lmms-eval
- -
Runtime
- awesome-evals
- -
- lmms-eval
- -
License
- awesome-evals
- Other
- lmms-eval
- Other
Last pushed
- awesome-evals
- Jul 1, 2026
- lmms-eval
- Aug 6, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- lmms-eval
- Evaluation & Observability
Trust and health
Days since push
- awesome-evals
- 26d
- lmms-eval
- 11d
Open issues (now)
- awesome-evals
- 21
- lmms-eval
- 49
Stars delta
- awesome-evals
- Unknown
- lmms-eval
- +52 (30d)
Open issues delta
- awesome-evals
- Unknown
- lmms-eval
- +9 (30d)
Full report
- awesome-evals
- Trust report
- lmms-eval
- 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 lmms-eval if…
- Tags unique to lmms-eval: agi, audio-evaluation, benchmark, evaluation.
- You need to evaluate LLaVA series models on different datasets with precise control over reproducibility details like torch/cuda versions.
- More GitHub stars (4.4k vs 761) - visibility, not fit.
When NOT to use lmms-eval
- Looking for a tool that supports less than Python 3.12, as uv setup mandates this version.
- Requiring support beyond text, image, video, and audio modalities which lmms-eval specifically covers.
- Your project doesn't benefit from extensive results tracking in Google Sheets or relies solely on alternative reproducibility mechanisms without external dependencies.
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 Jul 28, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Jul 28, 2026
- Last push (benchflow-ai/awesome-evals) · observed Jul 1, 2026
- License file (Other) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (EvolvingLMMs-Lab/lmms-eval) · observed Aug 17, 2026
- GitHub forks (EvolvingLMMs-Lab/lmms-eval) · observed Aug 17, 2026
- Last push (EvolvingLMMs-Lab/lmms-eval) · observed Aug 6, 2026
- License file (Other) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 761 · lmms-eval 4.4k (synced Jul 28, 2026).
Common questions
- What is the difference between awesome-evals and lmms-eval?
- awesome-evals: A curated library of resources for building and evaluating AI agents. lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over lmms-eval?
- Choose awesome-evals over lmms-eval 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 lmms-eval over awesome-evals?
- Choose lmms-eval over awesome-evals when Tags unique to lmms-eval: agi, audio-evaluation, benchmark, evaluation; You need to evaluate LLaVA series models on different datasets with precise control over reproducibility details like torch/cuda versions; More GitHub stars (4.4k vs 761) - 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 lmms-eval?
- Looking for a tool that supports less than Python 3.12, as uv setup mandates this version. Requiring support beyond text, image, video, and audio modalities which lmms-eval specifically covers. Your project doesn't benefit from extensive results tracking in Google Sheets or relies solely on alternative reproducibility mechanisms without external dependencies.
- Is awesome-evals or lmms-eval more popular on GitHub?
- lmms-eval has more GitHub stars (4,368 vs 761). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and lmms-eval open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, lmms-eval: Other).
- Where can I find alternatives to awesome-evals or lmms-eval?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and lmms-eval alternatives (awesome-evals markdown twin, lmms-eval 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 lmms-eval?
- awesome-evals: Active. lmms-eval: 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 awesome-evals and lmms-eval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; lmms-eval trust report.