Comparison
ARES vs ragas
Verdict
Pick ARES if aRES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities; pick ragas if ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Markdown twin · ARES alternatives · ragas alternatives
GraphCanon updated Sep 20, 2026
11views this month
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Trust & integrity
| Signal | ARES | ragas |
|---|---|---|
| Maintenance | Dormant (538d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (207d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- ARES
- Automated Evaluation of RAG Systems
- ragas
- Supercharge Your LLM Application Evaluations 🚀
Stars
- ARES
- 733
- ragas
- 16k
Forks
- ARES
- 67
- ragas
- 1.7k
Open issues
- ARES
- 22
- ragas
- 601
Language
- ARES
- Python
- ragas
- Python
Adopt for
- ARES
- ARES is a Python-based tool for automated evaluation of RAG systems, offering scoring and comparison functionalities.
- ragas
- Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Persona
- ARES
- -
- ragas
- developer harness
Runtime
- ARES
- -
- ragas
- -
License
- ARES
- Apache-2.0
- ragas
- Apache-2.0
Last pushed
- ARES
- Mar 28, 2025
- ragas
- Feb 24, 2026
Categories
- ARES
- Evaluation & Observability
- ragas
- Evaluation & Observability
Trust and health
Maintenance
- ARES
- Dormant (18%)
- ragas
- Slowing (36%)
Days since push
- ARES
- 538d
- ragas
- 207d
Open issues (now)
- ARES
- 22
- ragas
- 601
Stars delta
- ARES
- +2 (30d)
- ragas
- +395 (30d)
Open issues delta
- ARES
- +1 (30d)
- ragas
- +39 (30d)
OSV dependency advisories
- ARES
- Published findings
- ragas
- No lockfile (source not queried)
Full report
- ARES
- Trust report
- ragas
- Trust report
Shared compatibility
- Python · ARES: Python runtime · ragas: Python runtime
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.
Choose ragas if…
- Requirements: Min 4 GB RAM.
- Tags unique to ragas: evaluation, llm, llmops.
- When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
When NOT to use ragas
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems.
- For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (vibrantlabsai/ragas) · observed Sep 20, 2026
- GitHub forks (vibrantlabsai/ragas) · observed Sep 20, 2026
- Last push (vibrantlabsai/ragas) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ARES 733 · ragas 16k (synced Sep 20, 2026).
Common questions
- What is the difference between ARES and ragas?
- ARES: Automated Evaluation of RAG Systems. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories.
- When should I choose ARES over ragas?
- Choose ARES over ragas 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 choose ragas over ARES?
- Choose ragas over ARES when Requirements: Min 4 GB RAM; Tags unique to ragas: evaluation, llm, llmops; When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
- 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.
- When should I avoid ragas?
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
- Is ARES or ragas more popular on GitHub?
- ragas has more GitHub stars (15,783 vs 733). Stars measure visibility, not whether either tool fits your constraints.
- Are ARES and ragas open source?
- Yes - both are open-source projects on GitHub (ARES: Apache-2.0, ragas: Apache-2.0).
- Where can I find alternatives to ARES or ragas?
- GraphCanon lists graph-backed alternatives at ARES alternatives and ragas alternatives (ARES markdown twin, ragas 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, ARES or ragas?
- ARES: Dormant. ragas: 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 ARES and ragas?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ARES trust report; ragas trust report.