---
title: "Awesome-LLM-Eval vs evals"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/onejune2018-awesome-llm-eval-vs-openai-evals"
tools: ["onejune2018-awesome-llm-eval", "openai-evals"]
---

# Awesome-LLM-Eval vs evals

*GraphCanon updated Aug 7, 2026*

## Verdict

Pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks; pick evals if evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations.

[Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) reports 654 GitHub stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. [evals](https://github.com/openai/evals) has 19k stars, 3.0k forks, and 213 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval) and [evals's repository](https://github.com/openai/evals).

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [evals](/tools/openai-evals.md) |
| --- | --- | --- |
| Tagline | Curated list for evaluation of large language models | Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks. |
| Stars | 654 | 19,127 |
| Forks | 82 | 3,050 |
| Open issues | 44 | 213 |
| Language | - | Python |
| Adopt for | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Other |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [evals](/tools/openai-evals.md) |
| --- | --- | --- |
| Days since push | 246d | 115d |
| Open issues (now) | 44 | 213 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | [trust report](/tools/openai-evals/trust.md) |

## Decision facts: Awesome-LLM-Eval

- **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.
- **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications.
- **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks.

## Decision facts: evals

- **Adopt for:** Evals is an evaluation framework from OpenAI for assessing large language models and systems built with them. It includes an open-source registry of benchmarks and tools to create custom evaluations.

## Choose when

### Choose Awesome-LLM-Eval if…

- License: Awesome-LLM-Eval is MIT, evals is Other.
- Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms..
- Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications..
- Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation.
- When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### Choose evals if…

- License: evals is Other, Awesome-LLM-Eval is MIT.
- Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, llm systems.
- * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem.

## When NOT to use Awesome-LLM-Eval

- You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform.
- If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

## When NOT to use evals

- * When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key.
- * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a

## Common questions

### What is the difference between Awesome-LLM-Eval and evals?

Awesome-LLM-Eval: Curated list for evaluation of large language models. evals: Framework for evaluating LLMs and LLM systems with an open-source registry of benchmarks.. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-LLM-Eval over evals?

Choose Awesome-LLM-Eval over evals when License: Awesome-LLM-Eval is MIT, evals is Other; Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### When should I choose evals over Awesome-LLM-Eval?

Choose evals over Awesome-LLM-Eval when License: evals is Other, Awesome-LLM-Eval is MIT; Tags unique to evals: benchmarking, custom eval creation, evaluation-framework, llm systems; * When you need a comprehensive set of pre-existing evals and the ability to create your own tailored tests using specific use cases, especially within the OpenAI model ecosystem.

### When should I avoid Awesome-LLM-Eval?

You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

### When should I avoid evals?

* When evaluating models or systems that do not benefit from being integrated with the OpenAI API, as some features like direct evals configuration in the OpenAI Dashboard require an OpenAI key. * If you are looking for an evaluation framework that doesn’t involve external dependencies such as Git Large File Storage (LFS) and specific Python version requirements (Python 3.9 minimum), or if a

### Is Awesome-LLM-Eval or evals more popular on GitHub?

evals has more GitHub stars (19,127 vs 654). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-LLM-Eval and evals open source?

Yes - both are open-source projects on GitHub (Awesome-LLM-Eval: MIT, evals: Other).

### Where can I find alternatives to Awesome-LLM-Eval or evals?

GraphCanon lists graph-backed alternatives at [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) and [evals alternatives](/tools/openai-evals/alternatives) ([Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/alternatives.md), [evals markdown twin](/tools/openai-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/onejune2018-awesome-llm-eval-vs-openai-evals.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-LLM-Eval or evals?

Awesome-LLM-Eval: Slowing. 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 Awesome-LLM-Eval and evals?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust); [evals trust report](/tools/openai-evals/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval`](/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval)
- 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/_