--- title: "athina-evals vs ARES" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-stanford-futuredata-ares" tools: ["athina-ai-athina-evals", "stanford-futuredata-ares"] --- # athina-evals vs ARES *GraphCanon updated Aug 1, 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 ARES if automated evaluation for RAG systems with API integrations like OpenAI. [athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [ARES](https://ares-ai.vercel.app/) has 731 stars, 67 forks, and 21 open issues, last pushed Mar 28, 2025. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [ARES's repository](https://github.com/stanford-futuredata/ARES). | | [athina-evals](/tools/athina-ai-athina-evals.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Tagline | Python SDK for evaluating LLM generated responses | Automated Evaluation of RAG Systems | | Stars | 301 | 731 | | Forks | 22 | 67 | | Open issues | 3 | 21 | | 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. | Automated evaluation for RAG systems with API integrations like OpenAI. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | 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) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Days since push | 417d | 491d | | Open issues (now) | 3 | 21 | | Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/stanford-futuredata-ares/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: ARES - **Adopt for:** Automated evaluation for RAG systems with API integrations like OpenAI. ## 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 - More recently updated (last pushed Jun 6, 2025). ### Choose ARES if… - Tags unique to ARES: automated scoring, human validation sets, python, rag evaluation. - Evaluating Retrieval-Augmented Generation (RAG) systems that require automatic scoring using human-annotated data and few-shot examples. - More GitHub stars (731 vs 301) - visibility, not fit. ## 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 ARES - Avoid if limited to non-GPU machines with less than ~100GB available disk space, as it encounters CUDA out-of-memory errors without compatible GPU setups. ## Common questions ### What is the difference between athina-evals and ARES? athina-evals: Python SDK for evaluating LLM generated responses. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose athina-evals over ARES? Choose athina-evals over ARES 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; More recently updated (last pushed Jun 6, 2025). ### When should I choose ARES over athina-evals? Choose ARES over athina-evals when Tags unique to ARES: automated scoring, human validation sets, python, rag evaluation; Evaluating Retrieval-Augmented Generation (RAG) systems that require automatic scoring using human-annotated data and few-shot examples; More GitHub stars (731 vs 301) - visibility, not fit. ### 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 ARES? Avoid if limited to non-GPU machines with less than ~100GB available disk space, as it encounters CUDA out-of-memory errors without compatible GPU setups. ### Is athina-evals or ARES more popular on GitHub? ARES has more GitHub stars (731 vs 301). Stars measure visibility, not whether either tool fits your constraints. ### Are athina-evals and ARES open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to athina-evals or ARES? GraphCanon lists graph-backed alternatives at [athina-evals alternatives](/tools/athina-ai-athina-evals/alternatives) and [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) ([athina-evals markdown twin](/tools/athina-ai-athina-evals/alternatives.md), [ARES markdown twin](/tools/stanford-futuredata-ares/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-stanford-futuredata-ares.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, athina-evals or ARES? athina-evals: Dormant. ARES: 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 ARES? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [ARES trust report](/tools/stanford-futuredata-ares/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/_