Comparison
awesome-evals vs Open-LLM-Leaderboard
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick Open-LLM-Leaderboard if open-LLM-Leaderboard evaluates large language models on open-style questions using a GPT-4-based evaluator and aggregates results in an accessible leaderboard format.
Markdown twin · awesome-evals alternatives · Open-LLM-Leaderboard alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | awesome-evals | Open-LLM-Leaderboard |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (804d since push) As of Sep 10, 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 10, 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
- Open-LLM-Leaderboard
- Tracks LLM performance on open-style questions
Stars
- awesome-evals
- 900
- Open-LLM-Leaderboard
- 53
Forks
- awesome-evals
- 104
- Open-LLM-Leaderboard
- 7
Open issues
- awesome-evals
- 34
- Open-LLM-Leaderboard
- 1
Language
- awesome-evals
- -
- Open-LLM-Leaderboard
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- Open-LLM-Leaderboard
- Open-LLM-Leaderboard evaluates large language models on open-style questions using a GPT-4-based evaluator and aggregates results in an accessible leaderboard format.
Persona
- awesome-evals
- -
- Open-LLM-Leaderboard
- -
Runtime
- awesome-evals
- -
- Open-LLM-Leaderboard
- -
License
- awesome-evals
- Other
- Open-LLM-Leaderboard
- CC-BY-4.0
Last pushed
- awesome-evals
- Sep 15, 2026
- Open-LLM-Leaderboard
- Jun 27, 2024
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- Open-LLM-Leaderboard
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- Open-LLM-Leaderboard
- Dormant (18%)
Days since push
- awesome-evals
- 4d
- Open-LLM-Leaderboard
- 804d
Open issues (now)
- awesome-evals
- 34
- Open-LLM-Leaderboard
- 1
Stars delta
- awesome-evals
- +139 (30d)
- Open-LLM-Leaderboard
- 0 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- Open-LLM-Leaderboard
- 0 (30d)
Full report
- awesome-evals
- Trust report
- Open-LLM-Leaderboard
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, Open-LLM-Leaderboard is CC-BY-4.0.
- 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 Open-LLM-Leaderboard if…
- License: Open-LLM-Leaderboard is CC-BY-4.0, awesome-evals is Other.
- Tags unique to Open-LLM-Leaderboard: leaderboard, model-performance-tracking, open-style-questions.
- You need to evaluate your LLM's performance on open-ended, human-like question formats across multiple datasets without setting up the evaluation process yourself.
When NOT to use Open-LLM-Leaderboard
- You are seeking evaluations solely based on closed-response or multiple-choice questions where specific answers can be easily verified by non-LLM means.
- Your project has constraints against using commercial LLMs like GPT-4 for evaluation due to cost, licensing issues, or the need for open-source alternatives.
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 (VILA-Lab/Open-LLM-Leaderboard) · observed Sep 20, 2026
- GitHub forks (VILA-Lab/Open-LLM-Leaderboard) · observed Sep 20, 2026
- Last push (VILA-Lab/Open-LLM-Leaderboard) · observed Jun 27, 2024
- License file (CC-BY-4.0) · 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 · Open-LLM-Leaderboard 53 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and Open-LLM-Leaderboard?
- awesome-evals: A curated library of resources for building and evaluating AI agents. Open-LLM-Leaderboard: Tracks LLM performance on open-style questions. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over Open-LLM-Leaderboard?
- Choose awesome-evals over Open-LLM-Leaderboard when License: awesome-evals is Other, Open-LLM-Leaderboard is CC-BY-4.0; 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 Open-LLM-Leaderboard over awesome-evals?
- Choose Open-LLM-Leaderboard over awesome-evals when License: Open-LLM-Leaderboard is CC-BY-4.0, awesome-evals is Other; Tags unique to Open-LLM-Leaderboard: leaderboard, model-performance-tracking, open-style-questions; You need to evaluate your LLM's performance on open-ended, human-like question formats across multiple datasets without setting up the evaluation process yourself.
- 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 Open-LLM-Leaderboard?
- You are seeking evaluations solely based on closed-response or multiple-choice questions where specific answers can be easily verified by non-LLM means. Your project has constraints against using commercial LLMs like GPT-4 for evaluation due to cost, licensing issues, or the need for open-source alternatives.
- Is awesome-evals or Open-LLM-Leaderboard more popular on GitHub?
- awesome-evals has more GitHub stars (900 vs 53). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and Open-LLM-Leaderboard open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, Open-LLM-Leaderboard: CC-BY-4.0).
- Where can I find alternatives to awesome-evals or Open-LLM-Leaderboard?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and Open-LLM-Leaderboard alternatives (awesome-evals markdown twin, Open-LLM-Leaderboard 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 Open-LLM-Leaderboard?
- awesome-evals: Very active. Open-LLM-Leaderboard: 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 Open-LLM-Leaderboard?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; Open-LLM-Leaderboard trust report.