Comparison
deepeval vs lm-evaluation-harness
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.
Markdown twin · deepeval alternatives · lm-evaluation-harness alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | deepeval | lm-evaluation-harness |
|---|---|---|
| Maintenance | Very active (1d since push) As of 3w · github_public_v1 | Active (24d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deepeval
- LLM Evaluation Framework.
- lm-evaluation-harness
- A framework for few-shot evaluation of language models.
Stars
- deepeval
- 17k
- lm-evaluation-harness
- 14k
Forks
- deepeval
- 1.7k
- lm-evaluation-harness
- 3.5k
Open issues
- deepeval
- 404
- lm-evaluation-harness
- 938
Language
- deepeval
- Python
- lm-evaluation-harness
- Python
Adopt for
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- lm-evaluation-harness
- 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
- deepeval
- -
- lm-evaluation-harness
- -
Runtime
- deepeval
- -
- lm-evaluation-harness
- -
License
- deepeval
- Apache-2.0 License
- lm-evaluation-harness
- MIT
Last pushed
- deepeval
- Jul 27, 2026
- lm-evaluation-harness
- Jul 13, 2026
Categories
- deepeval
- Evaluation & Observability
- lm-evaluation-harness
- Evaluation & Observability
Trust and health
Maintenance
- deepeval
- Very active (96%)
- lm-evaluation-harness
- Active (82%)
Days since push
- deepeval
- 1d
- lm-evaluation-harness
- 24d
Open issues (now)
- deepeval
- 404
- lm-evaluation-harness
- 938
Full report
- deepeval
- Trust report
- lm-evaluation-harness
- Trust report
Shared compatibility
- Python · deepeval: Python runtime · lm-evaluation-harness: Python runtime
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.
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.
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 lm-evaluation-harness
- - If your evaluation setup requires pipeline parallelism not currently supported by this framework.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (confident-ai/deepeval) · observed Jul 28, 2026
- GitHub forks (confident-ai/deepeval) · observed Jul 28, 2026
- Last push (confident-ai/deepeval) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (EleutherAI/lm-evaluation-harness) · observed Aug 7, 2026
- GitHub forks (EleutherAI/lm-evaluation-harness) · observed Aug 7, 2026
- Last push (EleutherAI/lm-evaluation-harness) · observed Jul 13, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deepeval 17k · lm-evaluation-harness 14k (synced Jul 28, 2026).
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 and lm-evaluation-harness alternatives (deepeval markdown twin, lm-evaluation-harness 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, 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; lm-evaluation-harness trust report.