--- title: "ARES vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/stanford-futuredata-ares-vs-vibrantlabsai-ragas" tools: ["stanford-futuredata-ares", "vibrantlabsai-ragas"] --- # ARES vs ragas *GraphCanon updated Aug 20, 2026* ## Verdict Pick ARES if automated evaluation for RAG systems with API integrations like OpenAI; pick ragas if ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. [ARES](https://ares-ai.vercel.app/) reports 731 GitHub stars, 67 forks, and 21 open issues, last pushed Mar 28, 2025. [ragas](https://docs.ragas.io) has 15k stars, 1.6k forks, and 562 open issues, last pushed Feb 24, 2026. Figures are from public GitHub metadata via [ARES's repository](https://github.com/stanford-futuredata/ARES) and [ragas's repository](https://github.com/vibrantlabsai/ragas). | | [ARES](/tools/stanford-futuredata-ares.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Automated Evaluation of RAG Systems | Supercharge Your LLM Application Evaluations 🚀 | | Stars | 731 | 15,388 | | Forks | 67 | 1,637 | | Open issues | 21 | 562 | | Language | Python | Python | | Adopt for | Automated evaluation for RAG systems with API integrations like OpenAI. | Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. | | Persona | - | developer harness | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ARES](/tools/stanford-futuredata-ares.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 491d | 176d | | Open issues (now) | 21 | 562 | | Stars delta | Unknown | +470 (30d) | | Open issues delta | Unknown | +45 (30d) | | Full report | [trust report](/tools/stanford-futuredata-ares/trust.md) | [trust report](/tools/vibrantlabsai-ragas/trust.md) | ## Shared compatibility - **Python**: [ARES](/tools/stanford-futuredata-ares.md) - Python runtime; [ragas](/tools/vibrantlabsai-ragas.md) - Python runtime ## Decision facts: ARES - **Adopt for:** Automated evaluation for RAG systems with API integrations like OpenAI. ## Decision facts: ragas - **Requirements:** Min 4 GB RAM - **Adopt for:** Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights. - **Persona:** developer harness ## Choose when ### 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. - Leaner open-issue backlog (21). ### Choose ragas if… - Requirements: Min 4 GB RAM. - Tags unique to ragas: evaluation, llm, llmops. - When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks. ## 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. ## When NOT to use ragas - If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. - For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support. ## Common questions ### What is the difference between ARES and ragas? ARES: Automated Evaluation of RAG Systems. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories. ### When should I choose ARES over ragas? Choose ARES over ragas 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; Leaner open-issue backlog (21). ### When should I choose ragas over ARES? Choose ragas over ARES when Requirements: Min 4 GB RAM; Tags unique to ragas: evaluation, llm, llmops; When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks. ### 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. ### When should I avoid ragas? If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support. ### Is ARES or ragas more popular on GitHub? ragas has more GitHub stars (15,388 vs 731). Stars measure visibility, not whether either tool fits your constraints. ### Are ARES and ragas open source? Yes - both are open-source projects on GitHub (ARES: Apache-2.0, ragas: Apache-2.0). ### Where can I find alternatives to ARES or ragas? GraphCanon lists graph-backed alternatives at [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) and [ragas alternatives](/tools/vibrantlabsai-ragas/alternatives) ([ARES markdown twin](/tools/stanford-futuredata-ares/alternatives.md), [ragas markdown twin](/tools/vibrantlabsai-ragas/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/stanford-futuredata-ares-vs-vibrantlabsai-ragas.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ARES or ragas? ARES: Dormant. ragas: 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 ARES and ragas? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ARES trust report](/tools/stanford-futuredata-ares/trust); [ragas trust report](/tools/vibrantlabsai-ragas/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=stanford-futuredata-ares`](/api/graphcanon/graph?tool=stanford-futuredata-ares) - 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/_