Comparison
deepeval vs ARES
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 ARES if aRES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities.
Markdown twin · deepeval alternatives · ARES alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | deepeval | ARES |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (538d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deepeval
- LLM Evaluation Framework.
- ARES
- Automated Evaluation of RAG Systems
Stars
- deepeval
- 18k
- ARES
- 733
Forks
- deepeval
- 2.0k
- ARES
- 67
Open issues
- deepeval
- 624
- ARES
- 22
Language
- deepeval
- Python
- ARES
- Python
Adopt for
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
- ARES
- ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities.
Persona
- deepeval
- -
- ARES
- -
Runtime
- deepeval
- -
- ARES
- -
License
- deepeval
- Apache-2.0 License
- ARES
- Apache-2.0
Last pushed
- deepeval
- Sep 18, 2026
- ARES
- Mar 28, 2025
Categories
- deepeval
- Evaluation & Observability
- ARES
- Evaluation & Observability
Trust and health
Maintenance
- deepeval
- Very active (96%)
- ARES
- Dormant (18%)
Days since push
- deepeval
- 1d
- ARES
- 538d
Open issues (now)
- deepeval
- 624
- ARES
- 22
Stars delta
- deepeval
- +1.1k (30d)
- ARES
- +2 (30d)
Open issues delta
- deepeval
- +220 (30d)
- ARES
- +1 (30d)
OSV dependency advisories
- deepeval
- No lockfile (source not queried)
- ARES
- Published findings
Full report
- deepeval
- Trust report
- ARES
- Trust report
Shared compatibility
- Python · deepeval: Python runtime · ARES: Python runtime
Choose deepeval if…
- Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities..
- Tags unique to deepeval: evaluation, llm-evaluation, metrics.
- When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.
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.
Choose ARES if…
- Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup..
- Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria.
- Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance.
When NOT to use ARES
- Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring.
- Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100.
- Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (confident-ai/deepeval) · observed Sep 20, 2026
- GitHub forks (confident-ai/deepeval) · observed Sep 20, 2026
- Last push (confident-ai/deepeval) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (stanford-futuredata/ARES) · observed Sep 20, 2026
- GitHub forks (stanford-futuredata/ARES) · observed Sep 20, 2026
- Last push (stanford-futuredata/ARES) · observed Mar 28, 2025
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: deepeval 18k · ARES 733 (synced Sep 20, 2026).
Common questions
- What is the difference between deepeval and ARES?
- deepeval: LLM Evaluation Framework.. ARES: Automated Evaluation of RAG Systems. See the comparison table for live GitHub stats and shared categories.
- When should I choose deepeval over ARES?
- Choose deepeval over ARES when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.
- When should I choose ARES over deepeval?
- Choose ARES over deepeval when Requirements: Requires over 100 GB of available disk space and a compatible GPU like the A100.; Installation of conda and gcc is necessary for setup.; Tags unique to ARES: api integration, automated evaluation, comparison, evaluation criteria; Use ARES when you need to evaluate RAG systems with specific criteria such as context relevance, answer faithfulness, and answer relevance.
- 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 ARES?
- Do not use ARES if you lack the necessary human-annotated datasets for evaluation criteria or if you do not have a large set of unlabeled triples for scoring. Avoid ARES if your machine does not meet the requirements, such as having over 100 GB of available disk space and a compatible GPU like the A100. Do not use ARES if you cannot set up the required environment, including conda and gcc, on your machine.
- Is deepeval or ARES more popular on GitHub?
- deepeval has more GitHub stars (18,342 vs 733). Stars measure visibility, not whether either tool fits your constraints.
- Are deepeval and ARES open source?
- Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, ARES: Apache-2.0).
- Where can I find alternatives to deepeval or ARES?
- GraphCanon lists graph-backed alternatives at deepeval alternatives and ARES alternatives (deepeval markdown twin, ARES markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, deepeval or ARES?
- deepeval: Very active. ARES: 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 deepeval and ARES?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deepeval trust report; ARES trust report.