--- title: "deepeval vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-stanford-crfm-helm" tools: ["confident-ai-deepeval", "stanford-crfm-helm"] --- # deepeval vs helm *GraphCanon updated Jul 12, 2026* ## Verdict Pick deepeval when tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; pick helm when tags unique to helm: evaluation, foundation models, framework, language models. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.6k forks, and 334 open issues, last pushed Jul 10, 2026. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 400 forks, and 84 open issues, last pushed Jul 1, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [helm's repository](https://github.com/stanford-crfm/helm). | | [deepeval](/tools/confident-ai-deepeval.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | The LLM Evaluation Framework | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 16,767 | 2,850 | | Forks | 1,641 | 400 | | Open issues | 334 | 84 | | Language | Python | Python | | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 10d | | Open issues (now) | 334 | 84 | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## 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 deepeval if… - Tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework. - Also covers LLM Frameworks. - More GitHub stars (17k vs 2.9k) - visibility, not fit. ### Choose helm if… - 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. - Leaner open-issue backlog (84). ## When NOT to use deepeval - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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 deepeval and helm? deepeval: The LLM Evaluation Framework. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over helm? Choose deepeval over helm when Tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; Also covers LLM Frameworks; More GitHub stars (17k vs 2.9k) - visibility, not fit. ### When should I choose helm over deepeval? Choose helm over deepeval when 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; Leaner open-issue backlog (84). ### When should I avoid deepeval? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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 deepeval or helm more popular on GitHub? deepeval has more GitHub stars (16,767 vs 2,850). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and helm open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, helm: Apache-2.0). ### Where can I find alternatives to deepeval or helm? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/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/confident-ai-deepeval-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, deepeval or helm? deepeval: Very active. helm: 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 deepeval and helm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [helm trust report](/tools/stanford-crfm-helm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_