Comparison
dingo vs ragas
Verdict
Pick dingo if dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks; 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 · dingo alternatives · ragas alternatives
GraphCanon updated Sep 20, 2026
8views this month
Trust & integrity
| Signal | dingo | ragas |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 6, 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 6, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of Jul 11, 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
- dingo
- Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool
- ragas
- Supercharge Your LLM Application Evaluations 🚀
Stars
- dingo
- 753
- ragas
- 16k
Forks
- dingo
- 77
- ragas
- 1.7k
Open issues
- dingo
- 5
- ragas
- 601
Language
- dingo
- Python
- ragas
- Python
Adopt for
- dingo
- Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks.
- 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
- dingo
- -
- ragas
- developer harness
Runtime
- dingo
- -
- ragas
- -
License
- dingo
- Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License.
- ragas
- Apache-2.0
Last pushed
- dingo
- Sep 3, 2026
- ragas
- Feb 24, 2026
Categories
- dingo
- Data & Retrieval, Evaluation & Observability
- ragas
- Evaluation & Observability
Trust and health
Maintenance
- dingo
- Very active (96%)
- ragas
- Slowing (36%)
Days since push
- dingo
- 3d
- ragas
- 207d
Open issues (now)
- dingo
- 5
- ragas
- 601
Stars delta
- dingo
- +20 (30d)
- ragas
- +395 (30d)
Open issues delta
- dingo
- +1 (30d)
- ragas
- +39 (30d)
OSV dependency advisories
- dingo
- No published findings from this source as of 2026-07-11
- ragas
- No lockfile (source not queried)
Full report
- dingo
- Trust report
- ragas
- Trust report
Choose dingo if…
- Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost..
- Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection.
- Also covers Data & Retrieval.
- When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination.
When NOT to use dingo
- If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice.
- In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development.
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 (MigoXLab/dingo) · observed Sep 20, 2026
- GitHub forks (MigoXLab/dingo) · observed Sep 20, 2026
- Last push (MigoXLab/dingo) · observed Sep 3, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 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: dingo 753 · ragas 16k (synced Sep 20, 2026).
Common questions
- What is the difference between dingo and ragas?
- dingo: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories.
- When should I choose dingo over ragas?
- Choose dingo over ragas when Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.; Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection; Also covers Data & Retrieval; When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination.
- When should I choose ragas over dingo?
- Choose ragas over dingo 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 dingo?
- If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development.
- 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 dingo or ragas more popular on GitHub?
- ragas has more GitHub stars (15,783 vs 753). Stars measure visibility, not whether either tool fits your constraints.
- Are dingo and ragas open source?
- Yes - both are open-source projects on GitHub (dingo: Apache-2.0, ragas: Apache-2.0).
- Where can I find alternatives to dingo or ragas?
- GraphCanon lists graph-backed alternatives at dingo alternatives and ragas alternatives (dingo 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, dingo or ragas?
- dingo: Very active. 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 dingo and ragas?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dingo trust report; ragas trust report.