--- title: "deepeval vs hypertunity" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-gdikov-hypertunity" tools: ["confident-ai-deepeval", "gdikov-hypertunity"] --- # deepeval vs hypertunity *GraphCanon updated Jul 11, 2026* ## Verdict Pick deepeval when tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; pick hypertunity when tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm. [deepeval](https://deepeval.com) reports 17k GitHub stars, 1.6k forks, and 334 open issues, last pushed Jul 10, 2026. [hypertunity](https://hypertunity.readthedocs.io) has 137 stars, 10 forks, and 0 open issues, last pushed Jan 26, 2020. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [hypertunity's repository](https://github.com/gdikov/hypertunity). | | [deepeval](/tools/confident-ai-deepeval.md) | [hypertunity](/tools/gdikov-hypertunity.md) | | --- | --- | --- | | Tagline | The LLM Evaluation Framework | A toolset for black-box hyperparameter optimisation. | | Stars | 16,767 | 137 | | Forks | 1,641 | 10 | | Open issues | 334 | 0 | | Language | Python | Python | | Adopt for | - | - | | 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) | [hypertunity](/tools/gdikov-hypertunity.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 2358d | | Open issues (now) | 334 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/gdikov-hypertunity/trust.md) | ## 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 137) - visibility, not fit. ### Choose hypertunity if… - Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm. - Leaner open-issue backlog (0). ## 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 hypertunity - Last GitHub push was 2358 days ago (dormant maintenance, Jan 26, 2020). Validate activity before betting a new project on hypertunity. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between deepeval and hypertunity? deepeval: The LLM Evaluation Framework. hypertunity: A toolset for black-box hyperparameter optimisation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over hypertunity? Choose deepeval over hypertunity when Tags unique to deepeval: evaluation framework, evaluation-metrics, llm-evaluation, llm-evaluation-framework; Also covers LLM Frameworks; More GitHub stars (17k vs 137) - visibility, not fit. ### When should I choose hypertunity over deepeval? Choose hypertunity over deepeval when Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm; Leaner open-issue backlog (0). ### 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 hypertunity? Last GitHub push was 2358 days ago (dormant maintenance, Jan 26, 2020). Validate activity before betting a new project on hypertunity. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is deepeval or hypertunity more popular on GitHub? deepeval has more GitHub stars (16,767 vs 137). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and hypertunity open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, hypertunity: Apache-2.0). ### Where can I find alternatives to deepeval or hypertunity? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [hypertunity alternatives](/tools/gdikov-hypertunity/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [hypertunity markdown twin](/tools/gdikov-hypertunity/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-gdikov-hypertunity.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or hypertunity? deepeval: Very active. hypertunity: Dormant. 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 hypertunity? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [hypertunity trust report](/tools/gdikov-hypertunity/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/_