---
title: "every_eval_ever vs awesome-llm-human-preference-datasets"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/evaleval-every-eval-ever-vs-glgh-awesome-llm-human-preference-datasets"
tools: ["evaleval-every-eval-ever", "glgh-awesome-llm-human-preference-datasets"]
---

# every_eval_ever vs awesome-llm-human-preference-datasets

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results; pick awesome-llm-human-preference-datasets if awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被.

[every_eval_ever](https://evalevalai.com/projects/every-eval-ever/) reports 111 GitHub stars, 49 forks, and 27 open issues, last pushed Sep 7, 2026. [awesome-llm-human-preference-datasets](https://github.com/glgh/awesome-llm-human-preference-datasets) has 391 stars, 19 forks, and 0 open issues, last pushed Oct 4, 2023. Figures are from public GitHub metadata via [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever) and [awesome-llm-human-preference-datasets's repository](https://github.com/glgh/awesome-llm-human-preference-datasets).

| | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Tagline | Shared schema and crowdsourced eval database | Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval |
| Stars | 111 | 391 |
| Forks | 49 | 19 |
| Open issues | 27 | 0 |
| Language | Python | - |
| Adopt for | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被 |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability, Model Training |

## Trust and health

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

| | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 1d | 1067d |
| Open issues (now) | 27 | 0 |
| Stars delta | +9 (30d) | +1 (30d) |
| Open issues delta | +3 (30d) | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/evaleval-every-eval-ever/trust.md) | [trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust.md) |

## Decision facts: every_eval_ever

- **Pricing:** freemium - Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.
- **Requirements:** Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.
- **Adopt for:** Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results.

## Decision facts: awesome-llm-human-preference-datasets

- **Adopt for:** awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被

## Choose when

### Choose every_eval_ever if…

- Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database..
- Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip..
- Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra.
- Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema.

### Choose awesome-llm-human-preference-datasets if…

- Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences.
- Also covers Model Training.
- 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。

## When NOT to use every_eval_ever

- Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset.
- If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable.

## When NOT to use awesome-llm-human-preference-datasets

- NLP，LLM、，。

## Common questions

### What is the difference between every_eval_ever and awesome-llm-human-preference-datasets?

every_eval_ever: Shared schema and crowdsourced eval database. awesome-llm-human-preference-datasets: Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval. See the comparison table for live GitHub stats and shared categories.

### When should I choose every_eval_ever over awesome-llm-human-preference-datasets?

Choose every_eval_ever over awesome-llm-human-preference-datasets when Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.; Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema.

### When should I choose awesome-llm-human-preference-datasets over every_eval_ever?

Choose awesome-llm-human-preference-datasets over every_eval_ever when Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences; Also covers Model Training; 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。.

### When should I avoid every_eval_ever?

Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable.

### When should I avoid awesome-llm-human-preference-datasets?

NLP，LLM、，。

### Is every_eval_ever or awesome-llm-human-preference-datasets more popular on GitHub?

awesome-llm-human-preference-datasets has more GitHub stars (391 vs 111). Stars measure visibility, not whether either tool fits your constraints.

### Are every_eval_ever and awesome-llm-human-preference-datasets open source?

Yes - both are open-source projects on GitHub (every_eval_ever: MIT, awesome-llm-human-preference-datasets: MIT).

### Where can I find alternatives to every_eval_ever or awesome-llm-human-preference-datasets?

GraphCanon lists graph-backed alternatives at [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) and [awesome-llm-human-preference-datasets alternatives](/tools/glgh-awesome-llm-human-preference-datasets/alternatives) ([every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/alternatives.md), [awesome-llm-human-preference-datasets markdown twin](/tools/glgh-awesome-llm-human-preference-datasets/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/evaleval-every-eval-ever-vs-glgh-awesome-llm-human-preference-datasets.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, every_eval_ever or awesome-llm-human-preference-datasets?

every_eval_ever: Very active. awesome-llm-human-preference-datasets: 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 every_eval_ever and awesome-llm-human-preference-datasets?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust); [awesome-llm-human-preference-datasets trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=evaleval-every-eval-ever`](/api/graphcanon/graph?tool=evaleval-every-eval-ever)
- 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/_