---
title: "athina-evals vs deepeval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/athina-ai-athina-evals-vs-confident-ai-deepeval"
tools: ["athina-ai-athina-evals", "confident-ai-deepeval"]
---

# athina-evals vs deepeval

*GraphCanon updated Jul 28, 2026*

## Verdict

Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.

[athina-evals](https://docs.athina.ai) reports 301 GitHub stars, 22 forks, and 3 open issues, last pushed Jun 6, 2025. [deepeval](https://deepeval.com) has 17k stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [athina-evals's repository](https://github.com/athina-ai/athina-evals) and [deepeval's repository](https://github.com/confident-ai/deepeval).

| | [athina-evals](/tools/athina-ai-athina-evals.md) | [deepeval](/tools/confident-ai-deepeval.md) |
| --- | --- | --- |
| Tagline | Python SDK for evaluating LLM generated responses | LLM Evaluation Framework. |
| Stars | 301 | 17,226 |
| Forks | 22 | 1,736 |
| Open issues | 3 | 404 |
| Language | Python | Python |
| Adopt for | athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks. | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 License |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [athina-evals](/tools/athina-ai-athina-evals.md) | [deepeval](/tools/confident-ai-deepeval.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 417d | 1d |
| Open issues (now) | 3 | 404 |
| Full report | [trust report](/tools/athina-ai-athina-evals/trust.md) | [trust report](/tools/confident-ai-deepeval/trust.md) |

## Decision facts: athina-evals

- **Adopt for:** athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.

## 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

## Choose when

### Choose athina-evals if…

- Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation-toolkit.
- When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
- Leaner open-issue backlog (3).

### Choose deepeval if…

- 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 NOT to use athina-evals

- If open-source alternatives with transparent customization options are preferred over athina-evals' approach
- In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments

## 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.

## Common questions

### What is the difference between athina-evals and deepeval?

athina-evals: Python SDK for evaluating LLM generated responses. deepeval: LLM Evaluation Framework.. See the comparison table for live GitHub stats and shared categories.

### When should I choose athina-evals over deepeval?

Choose athina-evals over deepeval when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation-toolkit; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; Leaner open-issue backlog (3).

### When should I choose deepeval over athina-evals?

Choose deepeval over athina-evals when 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 avoid athina-evals?

If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments

### 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.

### Is athina-evals or deepeval more popular on GitHub?

deepeval has more GitHub stars (17,226 vs 301). Stars measure visibility, not whether either tool fits your constraints.

### Are athina-evals and deepeval open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to athina-evals or deepeval?

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

### Which is better maintained, athina-evals or deepeval?

athina-evals: Dormant. deepeval: Very active. 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 athina-evals and deepeval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [athina-evals trust report](/tools/athina-ai-athina-evals/trust); [deepeval trust report](/tools/confident-ai-deepeval/trust).

---

**Machine-readable endpoints**

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