--- title: "awesome-evals vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-stanford-crfm-helm" tools: ["benchflow-ai-awesome-evals", "stanford-crfm-helm"] --- # awesome-evals vs helm *GraphCanon updated Oct 12, 2026* ## 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. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 901 GitHub stars, 104 forks, and 34 open issues, last pushed Sep 15, 2026. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 421 forks, and 112 open issues, last pushed Sep 1, 2026. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [helm's repository](https://github.com/stanford-crfm/helm). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 901 | 2,939 | | Forks | 104 | 421 | | Open issues | 34 | 112 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | 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 | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 4d | 41d | | Open issues (now) | 34 | 112 | | Stars delta | +140 (30d) | +37 (30d) | | Open issues delta | +13 (30d) | +8 (30d) | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: helm - **Adopt for:** Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/benchflow-ai-awesome-evals/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [helm markdown twin](/tools/stanford-crfm-helm/alternatives.md)), 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](/compare/benchflow-ai-awesome-evals-vs-stanford-crfm-helm.md) 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](/tools/benchflow-ai-awesome-evals/trust); [helm trust report](/tools/stanford-crfm-helm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_