Comparison
awesome-evals vs lm-evaluation-harness
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick lm-evaluation-harness if lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend.
Markdown twin · awesome-evals alternatives · lm-evaluation-harness alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | awesome-evals | lm-evaluation-harness |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Very active (5d since push) As of Sep 6, 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 6, 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
- lm-evaluation-harness
- A framework for few-shot evaluation of language models.
Stars
- awesome-evals
- 901
- lm-evaluation-harness
- 14k
Forks
- awesome-evals
- 104
- lm-evaluation-harness
- 3.5k
Open issues
- awesome-evals
- 34
- lm-evaluation-harness
- 945
Language
- awesome-evals
- -
- lm-evaluation-harness
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- lm-evaluation-harness
- lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend.
Persona
- awesome-evals
- -
- lm-evaluation-harness
- -
Runtime
- awesome-evals
- -
- lm-evaluation-harness
- -
License
- awesome-evals
- Other
- lm-evaluation-harness
- MIT
Last pushed
- awesome-evals
- Sep 15, 2026
- lm-evaluation-harness
- Sep 1, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- lm-evaluation-harness
- Evaluation & Observability
Trust and health
Days since push
- awesome-evals
- 4d
- lm-evaluation-harness
- 5d
Open issues (now)
- awesome-evals
- 34
- lm-evaluation-harness
- 945
Stars delta
- awesome-evals
- +140 (30d)
- lm-evaluation-harness
- +346 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- lm-evaluation-harness
- +7 (30d)
Full report
- awesome-evals
- Trust report
- lm-evaluation-harness
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, lm-evaluation-harness 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 lm-evaluation-harness if…
- License: lm-evaluation-harness is MIT, awesome-evals is Other.
- Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model.
- - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations.
When NOT to use lm-evaluation-harness
- - If your evaluation setup requires pipeline parallelism not currently supported by this framework.
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 (EleutherAI/lm-evaluation-harness) · observed Sep 20, 2026
- GitHub forks (EleutherAI/lm-evaluation-harness) · observed Sep 20, 2026
- Last push (EleutherAI/lm-evaluation-harness) · observed Sep 1, 2026
- License file (MIT) · 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 · lm-evaluation-harness 14k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and lm-evaluation-harness?
- awesome-evals: A curated library of resources for building and evaluating AI agents. lm-evaluation-harness: A framework for few-shot evaluation of language models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over lm-evaluation-harness?
- Choose awesome-evals over lm-evaluation-harness when License: awesome-evals is Other, lm-evaluation-harness 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 lm-evaluation-harness over awesome-evals?
- Choose lm-evaluation-harness over awesome-evals when License: lm-evaluation-harness is MIT, awesome-evals is Other; Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model; - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations.
- 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 lm-evaluation-harness?
- - If your evaluation setup requires pipeline parallelism not currently supported by this framework.
- Is awesome-evals or lm-evaluation-harness more popular on GitHub?
- lm-evaluation-harness has more GitHub stars (13,906 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and lm-evaluation-harness open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, lm-evaluation-harness: MIT).
- Where can I find alternatives to awesome-evals or lm-evaluation-harness?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and lm-evaluation-harness alternatives (awesome-evals markdown twin, lm-evaluation-harness 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 lm-evaluation-harness?
- awesome-evals: Very active. lm-evaluation-harness: Very 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 lm-evaluation-harness?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; lm-evaluation-harness trust report.