--- title: "deepeval vs lighteval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-huggingface-lighteval" tools: ["confident-ai-deepeval", "huggingface-lighteval"] --- # deepeval vs lighteval *GraphCanon updated Jul 12, 2026* ## Verdict Pick deepeval when license: deepeval is Apache-2.0, lighteval is MIT; pick lighteval when license: lighteval is MIT, deepeval is Apache-2.0. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.6k forks, and 334 open issues, last pushed Jul 10, 2026. [lighteval](https://huggingface.co/docs/lighteval/en/index) has 2.5k stars, 506 forks, and 347 open issues, last pushed Jun 29, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [lighteval's repository](https://github.com/huggingface/lighteval). | | [deepeval](/tools/confident-ai-deepeval.md) | [lighteval](/tools/huggingface-lighteval.md) | | --- | --- | --- | | Tagline | The LLM Evaluation Framework | All-in-one toolkit for evaluating LLMs across multiple backends | | Stars | 16,767 | 2,472 | | Forks | 1,641 | 506 | | Open issues | 334 | 347 | | Language | Python | Python | | Adopt for | - | Lighteval is designed for evaluating language models across multiple backends. It integrates well with Hugging Face and provides a wide range of extras, making it particularly handy in non-Windows environments. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [lighteval](/tools/huggingface-lighteval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 334 | 347 | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/huggingface-lighteval/trust.md) | ## Decision facts: lighteval - **Adopt for:** Lighteval is designed for evaluating language models across multiple backends. It integrates well with Hugging Face and provides a wide range of extras, making it particularly handy in non-Windows environments. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, lighteval is MIT. - Tags unique to deepeval: llm-evaluation-framework, llm-evaluation-metrics, llm-evaluation. - Also covers LLM Frameworks. ### Choose lighteval if… - License: lighteval is MIT, deepeval is Apache-2.0. - Tags unique to lighteval: evaluation, huggingface. - When you need to evaluate the performance of various LLMs on different backend infrastructures, especially if you are working within Mac/Linux environments. ## When NOT to use deepeval - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use lighteval - Avoid Lighteval for evaluations on Windows systems as it is currently untested and not supported there. - Should you require a solution that does not integrate with or depend on the Hugging Face ecosystem, Lighteval might not fulfill your needs. ## Common questions ### What is the difference between deepeval and lighteval? deepeval: The LLM Evaluation Framework. lighteval: All-in-one toolkit for evaluating LLMs across multiple backends. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over lighteval? Choose deepeval over lighteval when License: deepeval is Apache-2.0, lighteval is MIT; Tags unique to deepeval: llm-evaluation-framework, llm-evaluation-metrics, llm-evaluation; Also covers LLM Frameworks. ### When should I choose lighteval over deepeval? Choose lighteval over deepeval when License: lighteval is MIT, deepeval is Apache-2.0; Tags unique to lighteval: evaluation, huggingface; When you need to evaluate the performance of various LLMs on different backend infrastructures, especially if you are working within Mac/Linux environments. ### When should I avoid deepeval? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid lighteval? Avoid Lighteval for evaluations on Windows systems as it is currently untested and not supported there. Should you require a solution that does not integrate with or depend on the Hugging Face ecosystem, Lighteval might not fulfill your needs. ### Is deepeval or lighteval more popular on GitHub? deepeval has more GitHub stars (16,767 vs 2,472). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and lighteval open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, lighteval: MIT). ### Where can I find alternatives to deepeval or lighteval? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [lighteval alternatives](/tools/huggingface-lighteval/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [lighteval markdown twin](/tools/huggingface-lighteval/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-huggingface-lighteval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or lighteval? deepeval: Very active. lighteval: 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 lighteval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [lighteval trust report](/tools/huggingface-lighteval/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/_