--- title: "deepeval vs lm-evaluation-harness" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-eleutherai-lm-evaluation-harness" tools: ["confident-ai-deepeval", "eleutherai-lm-evaluation-harness"] --- # deepeval vs lm-evaluation-harness *GraphCanon updated Aug 7, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; 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. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. [lm-evaluation-harness](https://www.eleuther.ai) has 14k stars, 3.5k forks, and 938 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [lm-evaluation-harness's repository](https://github.com/EleutherAI/lm-evaluation-harness). | | [deepeval](/tools/confident-ai-deepeval.md) | [lm-evaluation-harness](/tools/eleutherai-lm-evaluation-harness.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | A framework for few-shot evaluation of language models. | | Stars | 17,226 | 13,560 | | Forks | 1,736 | 3,467 | | Open issues | 404 | 938 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [lm-evaluation-harness](/tools/eleutherai-lm-evaluation-harness.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 24d | | Open issues (now) | 404 | 938 | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/eleutherai-lm-evaluation-harness/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [lm-evaluation-harness](/tools/eleutherai-lm-evaluation-harness.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: lm-evaluation-harness - **Adopt for:** 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. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, lm-evaluation-harness is MIT. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose lm-evaluation-harness if… - License: lm-evaluation-harness is MIT, deepeval is Apache-2.0. - 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 deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use lm-evaluation-harness - - If your evaluation setup requires pipeline parallelism not currently supported by this framework. ## Common questions ### What is the difference between deepeval and lm-evaluation-harness? deepeval: LLM Evaluation Framework.. 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 deepeval over lm-evaluation-harness? Choose deepeval over lm-evaluation-harness when License: deepeval is Apache-2.0, lm-evaluation-harness is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose lm-evaluation-harness over deepeval? Choose lm-evaluation-harness over deepeval when License: lm-evaluation-harness is MIT, deepeval is Apache-2.0; 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 deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid lm-evaluation-harness? - If your evaluation setup requires pipeline parallelism not currently supported by this framework. ### Is deepeval or lm-evaluation-harness more popular on GitHub? deepeval has more GitHub stars (17,226 vs 13,560). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and lm-evaluation-harness open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, lm-evaluation-harness: MIT). ### Where can I find alternatives to deepeval or lm-evaluation-harness? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [lm-evaluation-harness alternatives](/tools/eleutherai-lm-evaluation-harness/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [lm-evaluation-harness markdown twin](/tools/eleutherai-lm-evaluation-harness/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-eleutherai-lm-evaluation-harness.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or lm-evaluation-harness? deepeval: Very active. lm-evaluation-harness: 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 lm-evaluation-harness? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [lm-evaluation-harness trust report](/tools/eleutherai-lm-evaluation-harness/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/_