--- title: "athina-evals vs Promptify" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-promptslab-promptify" tools: ["athina-ai-athina-evals", "promptslab-promptify"] --- # athina-evals vs Promptify *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 Promptify if promptify is a Python library designed for task-based Natural Language Processing with Pydantic structured outputs and built-in evaluation features, leveraging LiteLLM as its universal LLM backend. It supports prompt版本控制. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [Promptify](https://discord.gg/m88xfYMbK6) has 4.6k stars, 363 forks, and 60 open issues, last pushed Mar 27, 2026. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [Promptify's repository](https://github.com/promptslab/Promptify). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [Promptify](/tools/promptslab-promptify.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Task-based NLP engine with Pydantic structured outputs | | Stars | 301 | 4,630 | | Forks | 22 | 363 | | Open issues | 3 | 60 | | 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. | Promptify is a Python library designed for task-based Natural Language Processing with Pydantic structured outputs and built-in evaluation features, leveraging LiteLLM as its universal LLM backend. It supports prompt版本控制 | | Persona | - | - | | Runtime | - | - | | License | - | Promptify is available under the Apache-2.0 license, granting users permissions to use, modify, distribute, and sell this software. | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [athina-evals](/tools/athina-ai-athina-evals.md) | [Promptify](/tools/promptslab-promptify.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 417d | 133d | | Open issues (now) | 3 | 60 | | Stars delta | Unknown | +11 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/promptslab-promptify/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: Promptify - **Requirements:** Requires Python 3.9 or higher; Can be installed via pip or directly from GitHub - **Adopt for:** Promptify is a Python library designed for task-based Natural Language Processing with Pydantic structured outputs and built-in evaluation features, leveraging LiteLLM as its universal LLM backend. It supports prompt版本控制 - **License detail:** Promptify is available under the Apache-2.0 license, granting users permissions to use, modify, distribute, and sell this software. ## 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 - Leaner open-issue backlog (3). ### Choose Promptify if… - Requirements: Requires Python 3.9 or higher; Can be installed via pip or directly from GitHub. - Tags unique to Promptify: chatgpt, chatgpt-api, gpt-3, gpt-4. - Also covers LLM Frameworks. - When your application requires structured NLP outputs with clear schemas defined using Pydantic ## 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 Promptify - When your project does not require structured outputs or if Pydantic schemas are not suitable for your use case - If you do not need built-in evaluation metrics for prompt performance and prefer more customization in the evaluation process - In situations where integration with only a few specific LLMs is required, as Promptify's advantage lies in its flexibility across various providers ## Common questions ### What is the difference between athina-evals and Promptify? athina-evals: Python SDK for evaluating LLM generated responses. Promptify: Task-based NLP engine with Pydantic structured outputs. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over Promptify? Choose athina-evals over Promptify 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; Leaner open-issue backlog (3). ### When should I choose Promptify over athina-evals? Choose Promptify over athina-evals when Requirements: Requires Python 3.9 or higher; Can be installed via pip or directly from GitHub; Tags unique to Promptify: chatgpt, chatgpt-api, gpt-3, gpt-4; Also covers LLM Frameworks; When your application requires structured NLP outputs with clear schemas defined using Pydantic. ### 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 Promptify? When your project does not require structured outputs or if Pydantic schemas are not suitable for your use case If you do not need built-in evaluation metrics for prompt performance and prefer more customization in the evaluation process In situations where integration with only a few specific LLMs is required, as Promptify's advantage lies in its flexibility across various providers ### Is athina-evals or Promptify more popular on GitHub? Promptify has more GitHub stars (4,630 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and Promptify open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or Promptify? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [Promptify alternatives](/tools/promptslab-promptify/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [Promptify markdown twin](/tools/promptslab-promptify/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-promptslab-promptify.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or Promptify? athina-evals: Dormant. Promptify: Slowing. 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 Promptify? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [Promptify trust report](/tools/promptslab-promptify/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/_