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

# Awesome-LLM-Eval vs prometheus-eval

*GraphCanon updated Sep 20, 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 prometheus-eval if prometheus-Eval integrates Prometheus metrics with GPT-4 for evaluating LLM responses in Python, under the Apache-2.0 license.

[Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) reports 656 GitHub stars, 90 forks, and 55 open issues, last pushed Nov 24, 2025. [prometheus-eval](https://github.com/prometheus-eval/prometheus-eval) has 1.1k stars, 69 forks, and 13 open issues, last pushed Apr 25, 2025. Figures are from public GitHub metadata via [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval) and [prometheus-eval's repository](https://github.com/prometheus-eval/prometheus-eval).

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [prometheus-eval](/tools/prometheus-eval-prometheus-eval.md) |
| --- | --- | --- |
| Tagline | Curated list for evaluation of large language models | Evaluate your LLM's response with Prometheus and GPT4 |
| Stars | 656 | 1,116 |
| Forks | 90 | 69 |
| Open issues | 55 | 13 |
| Language | - | Python |
| Adopt for | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | Prometheus-Eval integrates Prometheus metrics with GPT-4 for evaluating LLM responses in Python, under the Apache-2.0 license. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| 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) | [prometheus-eval](/tools/prometheus-eval-prometheus-eval.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 300d | 513d |
| Open issues (now) | 55 | 13 |
| Stars delta | +2 (30d) | +9 (30d) |
| Open issues delta | +11 (30d) | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | [trust report](/tools/prometheus-eval-prometheus-eval/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: prometheus-eval

- **Adopt for:** Prometheus-Eval integrates Prometheus metrics with GPT-4 for evaluating LLM responses in Python, under the Apache-2.0 license.

## Choose when

### Choose Awesome-LLM-Eval if…

- License: Awesome-LLM-Eval is MIT, prometheus-eval is Apache-2.0.
- 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, large-language-models.
- When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### Choose prometheus-eval if…

- License: prometheus-eval is Apache-2.0, Awesome-LLM-Eval is MIT.
- Tags unique to prometheus-eval: gpt4, litellm, llm, llmops.
- - When you need detailed and automated evaluations of instruction-response pairs from large language models using both Prometheus metrics and insights from GPT-4.

## 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 prometheus-eval

- - If your project does not require Prometheus metrics or if you prefer not to integrate an additional service for evaluation.
- - When your organization has strict data policies that prohibit using GPT-4 for assessment purposes, such as in scenarios with sensitive data processing outside AWS.

## Common questions

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

Awesome-LLM-Eval: Curated list for evaluation of large language models. prometheus-eval: Evaluate your LLM's response with Prometheus and GPT4. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-LLM-Eval over prometheus-eval?

Choose Awesome-LLM-Eval over prometheus-eval when License: Awesome-LLM-Eval is MIT, prometheus-eval is Apache-2.0; 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, large-language-models; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### When should I choose prometheus-eval over Awesome-LLM-Eval?

Choose prometheus-eval over Awesome-LLM-Eval when License: prometheus-eval is Apache-2.0, Awesome-LLM-Eval is MIT; Tags unique to prometheus-eval: gpt4, litellm, llm, llmops; - When you need detailed and automated evaluations of instruction-response pairs from large language models using both Prometheus metrics and insights from GPT-4.

### 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 prometheus-eval?

- If your project does not require Prometheus metrics or if you prefer not to integrate an additional service for evaluation. - When your organization has strict data policies that prohibit using GPT-4 for assessment purposes, such as in scenarios with sensitive data processing outside AWS.

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

prometheus-eval has more GitHub stars (1,116 vs 656). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-LLM-Eval and prometheus-eval open source?

Yes - both are open-source projects on GitHub (Awesome-LLM-Eval: MIT, prometheus-eval: Apache-2.0).

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

GraphCanon lists graph-backed alternatives at [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) and [prometheus-eval alternatives](/tools/prometheus-eval-prometheus-eval/alternatives) ([Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/alternatives.md), [prometheus-eval markdown twin](/tools/prometheus-eval-prometheus-eval/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-prometheus-eval-prometheus-eval.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 prometheus-eval?

Awesome-LLM-Eval: Slowing. prometheus-eval: 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 Awesome-LLM-Eval and prometheus-eval?

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