Comparison
athina-evals vs Promptify
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.
Markdown twin · athina-evals alternatives · Promptify alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | athina-evals | Promptify |
|---|---|---|
| Maintenance | Dormant (417d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · 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
- athina-evals
- Python SDK for evaluating LLM generated responses
- Promptify
- Task-based NLP engine with Pydantic structured outputs
Stars
- athina-evals
- 301
- Promptify
- 4.6k
Forks
- athina-evals
- 22
- Promptify
- 363
Open issues
- athina-evals
- 3
- Promptify
- 60
Language
- athina-evals
- Python
- Promptify
- Python
Adopt for
- athina-evals
- athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
- Promptify
- 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
- athina-evals
- -
- Promptify
- -
Runtime
- athina-evals
- -
- Promptify
- -
License
- athina-evals
- -
- Promptify
- Promptify is available under the Apache-2.0 license, granting users permissions to use, modify, distribute, and sell this software.
Last pushed
- athina-evals
- Jun 6, 2025
- Promptify
- Mar 27, 2026
Categories
- athina-evals
- Evaluation & Observability
- Promptify
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- athina-evals
- Dormant (18%)
- Promptify
- Slowing (36%)
Days since push
- athina-evals
- 417d
- Promptify
- 133d
Open issues (now)
- athina-evals
- 3
- Promptify
- 60
Stars delta
- athina-evals
- Unknown
- Promptify
- +11 (30d)
Open issues delta
- athina-evals
- Unknown
- Promptify
- 0 (30d)
Full report
- athina-evals
- Trust report
- Promptify
- Trust report
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).
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
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (athina-ai/athina-evals) · observed Jul 28, 2026
- GitHub forks (athina-ai/athina-evals) · observed Jul 28, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (promptslab/Promptify) · observed Aug 7, 2026
- GitHub forks (promptslab/Promptify) · observed Aug 7, 2026
- Last push (promptslab/Promptify) · observed Mar 27, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · Promptify 4.6k (synced Jul 28, 2026).
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 and Promptify alternatives (athina-evals markdown twin, Promptify 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, 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; Promptify trust report.