Comparison
athina-evals vs uptrain
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 uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Markdown twin · athina-evals alternatives · uptrain alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | athina-evals | uptrain |
|---|---|---|
| Maintenance | Dormant (470d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (762d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- uptrain
- Unified platform for evaluating and improving Generative AI applications
Stars
- athina-evals
- 301
- uptrain
- 2.4k
Forks
- athina-evals
- 22
- uptrain
- 205
Open issues
- athina-evals
- 4
- uptrain
- 65
Language
- athina-evals
- Python
- uptrain
- 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.
- uptrain
- UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights.
Persona
- athina-evals
- -
- uptrain
- -
Runtime
- athina-evals
- -
- uptrain
- -
License
- athina-evals
- -
- uptrain
- The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution.
Last pushed
- athina-evals
- Jun 6, 2025
- uptrain
- Aug 18, 2024
Categories
- athina-evals
- Evaluation & Observability
- uptrain
- Evaluation & Observability
Trust and health
Days since push
- athina-evals
- 470d
- uptrain
- 762d
Open issues (now)
- athina-evals
- 4
- uptrain
- 65
Stars delta
- athina-evals
- 0 (30d)
- uptrain
- +6 (30d)
Open issues delta
- athina-evals
- +1 (30d)
- uptrain
- +7 (30d)
Full report
- athina-evals
- Trust report
- uptrain
- Trust report
Choose athina-evals if…
- Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-evaluation, llm-evaluation-toolkit.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- More recently updated (last pushed Jun 6, 2025).
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 uptrain if…
- UpTrain can be installed on-premises using pip or accessed through a managed version.
- Tags unique to uptrain: autoevaluation, experimentation, hallucination-detection, jailbreak-detection.
- uptrain ships Docker support for self-hosted deployment.
- - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
When NOT to use uptrain
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis.
- - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
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 Sep 20, 2026
- GitHub forks (athina-ai/athina-evals) · observed Sep 20, 2026
- Last push (athina-ai/athina-evals) · observed Jun 6, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (uptrain-ai/uptrain) · observed Sep 20, 2026
- GitHub forks (uptrain-ai/uptrain) · observed Sep 20, 2026
- Last push (uptrain-ai/uptrain) · observed Aug 18, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: athina-evals 301 · uptrain 2.4k (synced Sep 20, 2026).
Common questions
- What is the difference between athina-evals and uptrain?
- athina-evals: Python SDK for evaluating LLM generated responses. uptrain: Unified platform for evaluating and improving Generative AI applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose athina-evals over uptrain?
- Choose athina-evals over uptrain when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-evaluation, llm-evaluation-toolkit; 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 uptrain over athina-evals?
- Choose uptrain over athina-evals when UpTrain can be installed on-premises using pip or accessed through a managed version; Tags unique to uptrain: autoevaluation, experimentation, hallucination-detection, jailbreak-detection; uptrain ships Docker support for self-hosted deployment; - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings.
- 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 uptrain?
- - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it.
- Is athina-evals or uptrain more popular on GitHub?
- uptrain has more GitHub stars (2,365 vs 301). Stars measure visibility, not whether either tool fits your constraints.
- Are athina-evals and uptrain open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to athina-evals or uptrain?
- GraphCanon lists graph-backed alternatives at athina-evals alternatives and uptrain alternatives (athina-evals markdown twin, uptrain 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 uptrain?
- athina-evals: Dormant. uptrain: 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 uptrain?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; uptrain trust report.