--- title: "auto-evaluator vs ARES" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rlancemartin-auto-evaluator-vs-stanford-futuredata-ares" tools: ["rlancemartin-auto-evaluator", "stanford-futuredata-ares"] --- # auto-evaluator vs ARES *GraphCanon updated Aug 8, 2026* ## Verdict Pick auto-evaluator if auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI; pick ARES if automated evaluation for RAG systems with API integrations like OpenAI. [auto-evaluator](https://autoevaluator.langchain.com/) reports 1.1k GitHub stars, 92 forks, and 3 open issues, last pushed May 10, 2023. [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 [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator) and [ARES's repository](https://github.com/stanford-futuredata/ARES). | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Tagline | A lightweight evaluation tool for question-answering using Langchain | Automated Evaluation of RAG Systems | | Stars | 1,105 | 731 | | Forks | 92 | 67 | | Open issues | 3 | 21 | | Language | Python | Python | | Adopt for | Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. | 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._ | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [ARES](/tools/stanford-futuredata-ares.md) | | --- | --- | --- | | Days since push | 1186d | 491d | | Open issues (now) | 3 | 21 | | Owner type | User | Organization | | Full report | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | [trust report](/tools/stanford-futuredata-ares/trust.md) | ## Shared compatibility - **OpenAI API**: [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - OpenAI API; [ARES](/tools/stanford-futuredata-ares.md) - OpenAI API - **Python**: [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - Python runtime; [ARES](/tools/stanford-futuredata-ares.md) - Python runtime ## Decision facts: auto-evaluator - **Adopt for:** Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. ## Decision facts: ARES - **Adopt for:** Automated evaluation for RAG systems with API integrations like OpenAI. ## Choose when ### Choose auto-evaluator if… - Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm. - Use when you need a lightweight solution for testing question-answering capabilities of Langchain models. - More GitHub stars (1.1k vs 731) - visibility, not fit. ### 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 recently updated (last pushed Mar 28, 2025). ## When NOT to use auto-evaluator - Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. - If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ## 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 auto-evaluator and ARES? auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-evaluator over ARES? Choose auto-evaluator over ARES when Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; More GitHub stars (1.1k vs 731) - visibility, not fit. ### When should I choose ARES over auto-evaluator? Choose ARES over auto-evaluator 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 recently updated (last pushed Mar 28, 2025). ### When should I avoid auto-evaluator? Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ### 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 auto-evaluator or ARES more popular on GitHub? auto-evaluator has more GitHub stars (1,105 vs 731). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-evaluator and ARES open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to auto-evaluator or ARES? GraphCanon lists graph-backed alternatives at [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) and [ARES alternatives](/tools/stanford-futuredata-ares/alternatives) ([auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/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/rlancemartin-auto-evaluator-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, auto-evaluator or ARES? auto-evaluator: 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 auto-evaluator and ARES? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust); [ARES trust report](/tools/stanford-futuredata-ares/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rlancemartin-auto-evaluator`](/api/graphcanon/graph?tool=rlancemartin-auto-evaluator) - 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/_