--- title: "athina-evals vs uptrain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-uptrain-ai-uptrain" tools: ["athina-ai-athina-evals", "uptrain-ai-uptrain"] --- # athina-evals vs uptrain *GraphCanon updated Sep 20, 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 uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 4 open issues, last pushed Jun 6, 2025. [uptrain](https://uptrain.ai/) has 2.4k stars, 205 forks, and 65 open issues, last pushed Aug 18, 2024. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [uptrain's repository](https://github.com/uptrain-ai/uptrain). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Unified platform for evaluating and improving Generative AI applications | | Stars | 301 | 2,365 | | Forks | 22 | 205 | | Open issues | 4 | 65 | | 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. | UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. | | Persona | - | - | | Runtime | - | - | | License | - | The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. | | 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) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Days since push | 470d | 762d | | Open issues (now) | 4 | 65 | | Stars delta | 0 (30d) | +6 (30d) | | Open issues delta | +1 (30d) | +7 (30d) | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/uptrain-ai-uptrain/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: uptrain - **Hosting:** self hosted - UpTrain can be installed on-premises using pip or accessed through a managed version. - **Adopt for:** UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. - **License detail:** The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. ## Choose when ### 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). ### 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 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 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. ## 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](/tools/athina-ai-athina-evals/alternatives) and [uptrain alternatives](/tools/uptrain-ai-uptrain/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [uptrain markdown twin](/tools/uptrain-ai-uptrain/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-uptrain-ai-uptrain.md) 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](/tools/athina-ai-athina-evals/trust); [uptrain trust report](/tools/uptrain-ai-uptrain/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/_