--- title: "autoarena vs simple-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kolenaio-autoarena-vs-openai-simple-evals" tools: ["kolenaio-autoarena", "openai-simple-evals"] --- # autoarena vs simple-evals *GraphCanon updated Aug 7, 2026* ## Verdict Pick autoarena if autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users; pick simple-evals if simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. [autoarena](https://www.kolena.com/autoarena/) reports 108 GitHub stars, 9 forks, and 4 open issues, last pushed Dec 16, 2024. [simple-evals](https://github.com/openai/simple-evals) has 4.6k stars, 501 forks, and 56 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [autoarena's repository](https://github.com/kolenaIO/autoarena) and [simple-evals's repository](https://github.com/openai/simple-evals). | | [autoarena](/tools/kolenaio-autoarena.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Tagline | Automated evaluation of LLMs and RAG systems | A lightweight library for evaluating language models. | | Stars | 108 | 4,595 | | Forks | 9 | 501 | | Open issues | 4 | 56 | | Language | TypeScript | Python | | Adopt for | autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. | simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 license | MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoarena](/tools/kolenaio-autoarena.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 589d | 106d | | Open issues (now) | 4 | 56 | | Full report | [trust report](/tools/kolenaio-autoarena/trust.md) | [trust report](/tools/openai-simple-evals/trust.md) | ## Decision facts: autoarena - **Hosting:** self hosted - **Requirements:** Python environment and internet access are needed for PyPI installation via pip. - **Adopt for:** autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. - **License detail:** Apache-2.0 license ## Decision facts: simple-evals - **Adopt for:** simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. - **License detail:** MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. ## Choose when ### Choose autoarena if… - autoarena is primarily TypeScript; simple-evals is Python. - License: autoarena is Apache-2.0, simple-evals is MIT. - Requirements: Python environment and internet access are needed for PyPI installation via pip.. - Tags unique to autoarena: ai, llm-evaluation, rag, testing. - When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ### Choose simple-evals if… - simple-evals is primarily Python; autoarena is TypeScript. - License: simple-evals is MIT, autoarena is Apache-2.0. - Tags unique to simple-evals: benchmark, depreciation notice, language-models. - When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025 ## When NOT to use autoarena - If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. - When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ## When NOT to use simple-evals - For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks - When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025 ## Common questions ### What is the difference between autoarena and simple-evals? autoarena: Automated evaluation of LLMs and RAG systems. simple-evals: A lightweight library for evaluating language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose autoarena over simple-evals? Choose autoarena over simple-evals when autoarena is primarily TypeScript; simple-evals is Python; License: autoarena is Apache-2.0, simple-evals is MIT; Requirements: Python environment and internet access are needed for PyPI installation via pip.; Tags unique to autoarena: ai, llm-evaluation, rag, testing; When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ### When should I choose simple-evals over autoarena? Choose simple-evals over autoarena when simple-evals is primarily Python; autoarena is TypeScript; License: simple-evals is MIT, autoarena is Apache-2.0; Tags unique to simple-evals: benchmark, depreciation notice, language-models; When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025. ### When should I avoid autoarena? If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ### When should I avoid simple-evals? For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025 ### Is autoarena or simple-evals more popular on GitHub? simple-evals has more GitHub stars (4,595 vs 108). Stars measure visibility, not whether either tool fits your constraints. ### Are autoarena and simple-evals open source? Yes - both are open-source projects on GitHub (autoarena: Apache-2.0, simple-evals: MIT). ### Where can I find alternatives to autoarena or simple-evals? GraphCanon lists graph-backed alternatives at [autoarena alternatives](/tools/kolenaio-autoarena/alternatives) and [simple-evals alternatives](/tools/openai-simple-evals/alternatives) ([autoarena markdown twin](/tools/kolenaio-autoarena/alternatives.md), [simple-evals markdown twin](/tools/openai-simple-evals/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/kolenaio-autoarena-vs-openai-simple-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoarena or simple-evals? autoarena: Dormant. simple-evals: 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 autoarena and simple-evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoarena trust report](/tools/kolenaio-autoarena/trust); [simple-evals trust report](/tools/openai-simple-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kolenaio-autoarena`](/api/graphcanon/graph?tool=kolenaio-autoarena) - 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/_