--- title: "evidently vs simple-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evidentlyai-evidently-vs-openai-simple-evals" tools: ["evidentlyai-evidently", "openai-simple-evals"] --- # evidently vs simple-evals *GraphCanon updated Jul 11, 2026* ## Verdict Pick evidently when evidently is primarily Jupyter Notebook; simple-evals is Python; pick simple-evals when simple-evals is primarily Python; evidently is Jupyter Notebook. [evidently](https://discord.gg/xZjKRaNp8b) reports 7.7k GitHub stars, 875 forks, and 285 open issues, last pushed May 2, 2026. [simple-evals](https://github.com/openai/simple-evals) has 4.6k stars, 493 forks, and 56 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [evidently's repository](https://github.com/evidentlyai/evidently) and [simple-evals's repository](https://github.com/openai/simple-evals). | | [evidently](/tools/evidentlyai-evidently.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Tagline | Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics. | simple-evals | | Stars | 7,682 | 4,565 | | Forks | 875 | 493 | | Open issues | 285 | 56 | | Language | Jupyter Notebook | Python | | Adopt for | Evidently is an open-source observability framework for assessing and monitoring AI systems, with support for over 100 different metrics. It can easily integrate into existing ML pipelines via Jupyter Notebooks. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, LLM Frameworks, Evaluation & Observability | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evidently](/tools/evidentlyai-evidently.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Days since push | 69d | 79d | | Open issues (now) | 285 | 56 | | Full report | [trust report](/tools/evidentlyai-evidently/trust.md) | [trust report](/tools/openai-simple-evals/trust.md) | ## Decision facts: evidently - **Pricing:** freemium - Evidently is available under the Apache-2.0 license and open-source on GitHub, making the core framework free to use. However, advanced or specific-use-case features might necessitate community or own - **Requirements:** Installation straightforward through PyPI or Conda Forge. - **Adopt for:** Evidently is an open-source observability framework for assessing and monitoring AI systems, with support for over 100 different metrics. It can easily integrate into existing ML pipelines via Jupyter Notebooks. ## Choose when ### Choose evidently if… - evidently is primarily Jupyter Notebook; simple-evals is Python. - License: evidently is Apache-2.0, simple-evals is MIT. - Pricing: Evidently is available under the Apache-2.0 license and open-source on GitHub, making the core framework free to use. However, advanced or specific-use-case features might necessitate community or own. - Requirements: Installation straightforward through PyPI or Conda Forge.. - Tags unique to evidently: data-validation, data-science, data-drift, html-report. - Also covers Data & Retrieval. - Use Evidently when you need a robust solution to evaluate model performance across various stages of the machine learning lifecycle, including generative AI applications. ### Choose simple-evals if… - simple-evals is primarily Python; evidently is Jupyter Notebook. - License: simple-evals is MIT, evidently is Apache-2.0. - Tags unique to simple-evals: python. ## When NOT to use evidently - Avoid using Evidently for projects where custom metric definitions are critical, as it may require significant effort to expand beyond its pre-implemented 100+ metrics. - Do not opt for Evidently if your organization strictly prefers lightweight, minimalistic tools; it can be more feature-rich than necessary for simple monitoring tasks. ## When NOT to use simple-evals - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between evidently and simple-evals? evidently: Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.. simple-evals: simple-evals. See the comparison table for live GitHub stats and shared categories. ### When should I choose evidently over simple-evals? Choose evidently over simple-evals when evidently is primarily Jupyter Notebook; simple-evals is Python; License: evidently is Apache-2.0, simple-evals is MIT; Pricing: Evidently is available under the Apache-2.0 license and open-source on GitHub, making the core framework free to use. However, advanced or specific-use-case features might necessitate community or own; Requirements: Installation straightforward through PyPI or Conda Forge.; Tags unique to evidently: data-validation, data-science, data-drift, html-report; Also covers Data & Retrieval; Use Evidently when you need a robust solution to evaluate model performance across various stages of the machine learning lifecycle, including generative AI applications. ### When should I choose simple-evals over evidently? Choose simple-evals over evidently when simple-evals is primarily Python; evidently is Jupyter Notebook; License: simple-evals is MIT, evidently is Apache-2.0; Tags unique to simple-evals: python. ### When should I avoid evidently? Avoid using Evidently for projects where custom metric definitions are critical, as it may require significant effort to expand beyond its pre-implemented 100+ metrics. Do not opt for Evidently if your organization strictly prefers lightweight, minimalistic tools; it can be more feature-rich than necessary for simple monitoring tasks. ### When should I avoid simple-evals? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is evidently or simple-evals more popular on GitHub? evidently has more GitHub stars (7,682 vs 4,565). Stars measure visibility, not whether either tool fits your constraints. ### Are evidently and simple-evals open source? Yes - both are open-source projects on GitHub (evidently: Apache-2.0, simple-evals: MIT). ### Where can I find alternatives to evidently or simple-evals? GraphCanon lists graph-backed alternatives at [evidently alternatives](/tools/evidentlyai-evidently/alternatives) and [simple-evals alternatives](/tools/openai-simple-evals/alternatives) ([evidently markdown twin](/tools/evidentlyai-evidently/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/evidentlyai-evidently-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, evidently or simple-evals? evidently: Steady. simple-evals: Steady. 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 evidently and simple-evals? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evidently trust report](/tools/evidentlyai-evidently/trust); [simple-evals trust report](/tools/openai-simple-evals/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evidentlyai-evidently`](/api/graphcanon/graph?tool=evidentlyai-evidently) - 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/_