Comparison
awesome-evals vs helm
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick helm if helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes.
Markdown twin · awesome-evals alternatives · helm alternatives
GraphCanon updated Oct 12, 2026
5views this month
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Trust & integrity
| Signal | awesome-evals | helm |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Steady (41d since push) As of Oct 12, 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 Oct 12, 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 | No public record from this source As of Sep 20, 2026 · openssf-scorecard@v1 |
Tagline
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- helm
- Holistic, reproducible and transparent evaluation of foundation models
Stars
- awesome-evals
- 901
- helm
- 2.9k
Forks
- awesome-evals
- 104
- helm
- 421
Open issues
- awesome-evals
- 34
- helm
- 112
Language
- awesome-evals
- -
- helm
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- helm
- Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes.
Persona
- awesome-evals
- -
- helm
- -
Runtime
- awesome-evals
- -
- helm
- -
License
- awesome-evals
- Other
- helm
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- helm
- Sep 1, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- helm
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- helm
- Steady (60%)
Days since push
- awesome-evals
- 4d
- helm
- 41d
Open issues (now)
- awesome-evals
- 34
- helm
- 112
Stars delta
- awesome-evals
- +140 (30d)
- helm
- +37 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- helm
- +8 (30d)
OpenSSF Scorecard
- awesome-evals
- Not queried
- helm
- No public record from this source
Full report
- awesome-evals
- Trust report
- helm
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, helm is Apache-2.0.
- 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 helm if…
- License: helm is Apache-2.0, awesome-evals is Other.
- Tags unique to helm: evaluation, foundation models, framework, language models.
- When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way.
When NOT to use helm
- Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models.
- If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity.
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 (stanford-crfm/helm) · observed Oct 12, 2026
- GitHub forks (stanford-crfm/helm) · observed Oct 12, 2026
- Last push (stanford-crfm/helm) · observed Sep 1, 2026
- License file (Apache-2.0) · observed Oct 12, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · helm 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and helm?
- awesome-evals: A curated library of resources for building and evaluating AI agents. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over helm?
- Choose awesome-evals over helm when License: awesome-evals is Other, helm is Apache-2.0; 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 helm over awesome-evals?
- Choose helm over awesome-evals when License: helm is Apache-2.0, awesome-evals is Other; Tags unique to helm: evaluation, foundation models, framework, language models; When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way.
- 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 helm?
- Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity.
- Is awesome-evals or helm more popular on GitHub?
- helm has more GitHub stars (2,939 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and helm open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, helm: Apache-2.0).
- Where can I find alternatives to awesome-evals or helm?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and helm alternatives (awesome-evals markdown twin, helm 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 helm?
- awesome-evals: Very active. helm: Steady. 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 helm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; helm trust report.