Comparison
IndustryBench vs awesome-evals
Verdict
Pick IndustryBench if industryBench is a multi-lingual benchmark for assessing the industrial domain knowledge of LLMs, grounded in Chinese national standards and structured industrial product records; pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance.
Markdown twin · IndustryBench alternatives · awesome-evals alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | IndustryBench | awesome-evals |
|---|---|---|
| Maintenance | Steady (43d since push) As of 3w · github_public_v1 | Active (26d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 4w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- IndustryBench
- A multi-lingual benchmark for evaluating industrial domain knowledge of LLMs
- awesome-evals
- A curated library of resources for building and evaluating AI agents
Stars
- IndustryBench
- 155
- awesome-evals
- 761
Forks
- IndustryBench
- 10
- awesome-evals
- 71
Open issues
- IndustryBench
- 1
- awesome-evals
- 21
Language
- IndustryBench
- Python
- awesome-evals
- -
Adopt for
- IndustryBench
- IndustryBench is a multi-lingual benchmark for assessing the industrial domain knowledge of LLMs, grounded in Chinese national standards and structured industrial product records.
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
Persona
- IndustryBench
- -
- awesome-evals
- -
Runtime
- IndustryBench
- -
- awesome-evals
- -
License
- IndustryBench
- MIT
- awesome-evals
- Other
Last pushed
- IndustryBench
- Jun 15, 2026
- awesome-evals
- Jul 1, 2026
Categories
- IndustryBench
- Evaluation & Observability
- awesome-evals
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- IndustryBench
- Steady (60%)
- awesome-evals
- Active (82%)
Days since push
- IndustryBench
- 43d
- awesome-evals
- 26d
Open issues (now)
- IndustryBench
- 1
- awesome-evals
- 21
OSV dependency advisories
- IndustryBench
- Published findings
- awesome-evals
- No lockfile (source not queried)
Full report
- IndustryBench
- Trust report
- awesome-evals
- Trust report
Choose IndustryBench if…
- License: IndustryBench is MIT, awesome-evals is Other.
- Tags unique to IndustryBench: industry-benchmark.
- When evaluating LLM performance on industry-specific inquiries across English, Russian, Vietnamese, and source Chinese content
When NOT to use IndustryBench
- If the focus is solely on natural language understanding without a specific industrial knowledge requirement
- For benchmarking models where non-Chinese national standard data sources are preferred over GB/T excerpts and structured records
Choose awesome-evals if…
- License: awesome-evals is Other, IndustryBench 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alibaba-multimodal-industrial-ai/IndustryBench) · observed Jul 29, 2026
- GitHub forks (alibaba-multimodal-industrial-ai/IndustryBench) · observed Jul 29, 2026
- Last push (alibaba-multimodal-industrial-ai/IndustryBench) · observed Jun 15, 2026
- License file (MIT) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: IndustryBench 155 · awesome-evals 761 (synced Jul 29, 2026).
Common questions
- What is the difference between IndustryBench and awesome-evals?
- IndustryBench: A multi-lingual benchmark for evaluating industrial domain knowledge of LLMs. awesome-evals: A curated library of resources for building and evaluating AI agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose IndustryBench over awesome-evals?
- Choose IndustryBench over awesome-evals when License: IndustryBench is MIT, awesome-evals is Other; Tags unique to IndustryBench: industry-benchmark; When evaluating LLM performance on industry-specific inquiries across English, Russian, Vietnamese, and source Chinese content.
- When should I choose awesome-evals over IndustryBench?
- Choose awesome-evals over IndustryBench when License: awesome-evals is Other, IndustryBench 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 avoid IndustryBench?
- If the focus is solely on natural language understanding without a specific industrial knowledge requirement For benchmarking models where non-Chinese national standard data sources are preferred over GB/T excerpts and structured records
- 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
- Is IndustryBench or awesome-evals more popular on GitHub?
- awesome-evals has more GitHub stars (761 vs 155). Stars measure visibility, not whether either tool fits your constraints.
- Are IndustryBench and awesome-evals open source?
- Yes - both are open-source projects on GitHub (IndustryBench: MIT, awesome-evals: Other).
- Where can I find alternatives to IndustryBench or awesome-evals?
- GraphCanon lists graph-backed alternatives at IndustryBench alternatives and awesome-evals alternatives (IndustryBench markdown twin, awesome-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, IndustryBench or awesome-evals?
- IndustryBench: Steady. awesome-evals: 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 IndustryBench and awesome-evals?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: IndustryBench trust report; awesome-evals trust report.