--- title: "athina-evals vs jailbreak-evaluation" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-controllability-jailbreak-evaluation" tools: ["athina-ai-athina-evals", "controllability-jailbreak-evaluation"] --- # athina-evals vs jailbreak-evaluation *GraphCanon updated Aug 5, 2026* ## Verdict Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick jailbreak-evaluation if jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [jailbreak-evaluation](https://arxiv.org/abs/2404.06407) has 27 stars, 8 forks, and 0 open issues, last pushed Nov 4, 2024. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [jailbreak-evaluation's repository](https://github.com/controllability/jailbreak-evaluation). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [jailbreak-evaluation](/tools/controllability-jailbreak-evaluation.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Python package for language model jailbreak evaluation | | Stars | 301 | 27 | | Forks | 22 | 8 | | Open issues | 3 | 0 | | Language | Python | Python | | Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [jailbreak-evaluation](/tools/controllability-jailbreak-evaluation.md) | | --- | --- | --- | | Days since push | 417d | 638d | | Open issues (now) | 3 | 0 | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/controllability-jailbreak-evaluation/trust.md) | ## Decision facts: athina-evals - **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. ## Decision facts: jailbreak-evaluation - **Requirements:** The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality - **Adopt for:** jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming. ## Choose when ### Choose athina-evals if… - Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - More GitHub stars (301 vs 27) - visibility, not fit. ### Choose jailbreak-evaluation if… - Requirements: The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality. - Tags unique to jailbreak-evaluation: ai safety, evaluation tools, jailbreaks, language-models. - When you need to assess whether an AI model can be manipulated to produce unpredictable or unintended outcomes through specific inputs, such as jailbreaking. ## When NOT to use athina-evals - If open-source alternatives with transparent customization options are preferred over athina-evals' approach - In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ## When NOT to use jailbreak-evaluation - If your project does not involve assessing the security or integrity of how an AI model responds to manipulative input techniques designed to exploit design weaknesses. - When you do not need dependencies on specific frameworks like PyTorch and FastChat, as jailbreak-evaluation requires these without automating their installation. ## Common questions ### What is the difference between athina-evals and jailbreak-evaluation? athina-evals: Python SDK for evaluating LLM generated responses. jailbreak-evaluation: Python package for language model jailbreak evaluation. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over jailbreak-evaluation? Choose athina-evals over jailbreak-evaluation when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More GitHub stars (301 vs 27) - visibility, not fit. ### When should I choose jailbreak-evaluation over athina-evals? Choose jailbreak-evaluation over athina-evals when Requirements: The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality; Tags unique to jailbreak-evaluation: ai safety, evaluation tools, jailbreaks, language-models; When you need to assess whether an AI model can be manipulated to produce unpredictable or unintended outcomes through specific inputs, such as jailbreaking. ### When should I avoid athina-evals? If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments ### When should I avoid jailbreak-evaluation? If your project does not involve assessing the security or integrity of how an AI model responds to manipulative input techniques designed to exploit design weaknesses. When you do not need dependencies on specific frameworks like PyTorch and FastChat, as jailbreak-evaluation requires these without automating their installation. ### Is athina-evals or jailbreak-evaluation more popular on GitHub? athina-evals has more GitHub stars (301 vs 27). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and jailbreak-evaluation open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or jailbreak-evaluation? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [jailbreak-evaluation alternatives](/tools/controllability-jailbreak-evaluation/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [jailbreak-evaluation markdown twin](/tools/controllability-jailbreak-evaluation/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/athina-ai-athina-evals-vs-controllability-jailbreak-evaluation.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or jailbreak-evaluation? athina-evals: Dormant. jailbreak-evaluation: 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 athina-evals and jailbreak-evaluation? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [jailbreak-evaluation trust report](/tools/controllability-jailbreak-evaluation/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=athina-ai-athina-evals`](/api/graphcanon/graph?tool=athina-ai-athina-evals) - 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/_