--- title: "athina-evals vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-declare-lab-instruct-eval" tools: ["athina-ai-athina-evals", "declare-lab-instruct-eval"] --- # athina-evals vs instruct-eval *GraphCanon updated Aug 7, 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 instruct-eval if key facts about instruct-eval. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Quantitative evaluation for instruction-tuned language models | | Stars | 301 | 552 | | Forks | 22 | 45 | | Open issues | 3 | 24 | | 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. | Key facts about instruct-eval | | Persona | - | - | | Runtime | - | - | | License | - | The tool is distributed under Apache-2.0 license | | 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) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Days since push | 417d | 879d | | Open issues (now) | 3 | 24 | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/declare-lab-instruct-eval/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: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Choose when ### Choose athina-evals if… - Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation. - When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics - More recently updated (last pushed Jun 6, 2025). ### Choose instruct-eval if… - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ## 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 instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## Common questions ### What is the difference between athina-evals and instruct-eval? athina-evals: Python SDK for evaluating LLM generated responses. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over instruct-eval? Choose athina-evals over instruct-eval when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More recently updated (last pushed Jun 6, 2025). ### When should I choose instruct-eval over athina-evals? Choose instruct-eval over athina-evals when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, instruct-tuning, llm, safety; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### 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 instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### Is athina-evals or instruct-eval more popular on GitHub? instruct-eval has more GitHub stars (552 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and instruct-eval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or instruct-eval? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/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-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or instruct-eval? athina-evals: Dormant. instruct-eval: 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 instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/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/_