--- title: "deepeval vs ARES" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-stanford-futuredata-ares" tools: ["confident-ai-deepeval", "stanford-futuredata-ares"] --- # deepeval vs ARES *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick ARES if aRES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [ARES](https://ares-ai.vercel.app/) has 733 stars, 67 forks, and 22 open issues, last pushed Mar 28, 2025. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [ARES's repository](https://github.com/stanford-futuredata/ARES). | | [deepeval](/tools/confident-ai-deepeval.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | Automated Evaluation of RAG Systems | | Stars | 18,342 | 733 | | Forks | 1,953 | 67 | | Open issues | 624 | 22 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 538d | | Open issues (now) | 624 | 22 | | Stars delta | +1.1k (30d) | +2 (30d) | | Open issues delta | +220 (30d) | +1 (30d) | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/stanford-futuredata-ares/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [ARES](/tools/stanford-futuredata-ares.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: ARES - **Requirements:** Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup. - **Adopt for:** ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities. ## Choose when ### Choose deepeval if… - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose ARES if… - Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup.. - Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria. - Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use ARES - Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring. - Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100. - Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine. ## Common questions ### What is the difference between deepeval and ARES? deepeval: LLM Evaluation Framework.. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over ARES? Choose deepeval over ARES when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose ARES over deepeval? Choose ARES over deepeval when Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup.; Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria; Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid ARES? Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring. Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100. Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine. ### Is deepeval or ARES more popular on GitHub? deepeval has more GitHub stars (18,342 vs 733). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and ARES open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, ARES: Apache-2.0). ### Where can I find alternatives to deepeval or ARES? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/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/confident-ai-deepeval-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, deepeval or ARES? deepeval: Very active. 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 deepeval and ARES? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [ARES trust report](/tools/stanford-futuredata-ares/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_