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

# deepeval vs Awesome-LLM-Eval

*GraphCanon updated Jul 28, 2026*

## Verdict

Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; 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.

[deepeval](https://deepeval.com) reports 17k GitHub stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 654 stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval).

| | [deepeval](/tools/confident-ai-deepeval.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) |
| --- | --- | --- |
| Tagline | LLM Evaluation Framework. | Curated list for evaluation of large language models |
| Stars | 17,226 | 654 |
| Forks | 1,736 | 82 |
| Open issues | 404 | 44 |
| Language | Python | - |
| Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 License | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [deepeval](/tools/confident-ai-deepeval.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 1d | 246d |
| Open issues (now) | 404 | 44 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) |

## Decision facts: deepeval

- **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.
- **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- **License detail:** Apache-2.0 License

## 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.

## Choose when

### Choose deepeval if…

- License: deepeval is Apache-2.0, Awesome-LLM-Eval is MIT.
- Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities..
- Tags unique to deepeval: metrics.
- When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.

### Choose Awesome-LLM-Eval if…

- License: Awesome-LLM-Eval is MIT, deepeval 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 NOT to use deepeval

- For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill.
- In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.

## 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.

## Common questions

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

deepeval: LLM Evaluation Framework.. Awesome-LLM-Eval: Curated list for evaluation of large language models. See the comparison table for live GitHub stats and shared categories.

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

Choose deepeval over Awesome-LLM-Eval when License: deepeval is Apache-2.0, Awesome-LLM-Eval is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.

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

Choose Awesome-LLM-Eval over deepeval when License: Awesome-LLM-Eval is MIT, deepeval 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 avoid deepeval?

For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.

### 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.

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

deepeval has more GitHub stars (17,226 vs 654). Stars measure visibility, not whether either tool fits your constraints.

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

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

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

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

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

deepeval: Very active. Awesome-LLM-Eval: 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 deepeval and Awesome-LLM-Eval?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval)
- 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/_