--- title: "dingo vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/migoxlab-dingo-vs-vibrantlabsai-ragas" tools: ["migoxlab-dingo", "vibrantlabsai-ragas"] --- # dingo vs ragas *GraphCanon updated Aug 20, 2026* ## 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. [dingo](https://dingo.openxlab.org.cn/) reports 733 GitHub stars, 74 forks, and 4 open issues, last pushed Aug 6, 2026. [ragas](https://docs.ragas.io) has 15k stars, 1.6k forks, and 562 open issues, last pushed Feb 24, 2026. Figures are from public GitHub metadata via [dingo's repository](https://github.com/MigoXLab/dingo) and [ragas's repository](https://github.com/vibrantlabsai/ragas). | | [dingo](/tools/migoxlab-dingo.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool | Supercharge Your LLM Application Evaluations 🚀 | | Stars | 733 | 15,388 | | Forks | 74 | 1,637 | | Open issues | 4 | 562 | | Language | Python | Python | | Adopt for | Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. | 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 | - | developer harness | | Runtime | - | - | | License | Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. | Apache-2.0 | | Categories | Data & Retrieval, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dingo](/tools/migoxlab-dingo.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 176d | | Open issues (now) | 4 | 562 | | Stars delta | Unknown | +470 (30d) | | Open issues delta | Unknown | +45 (30d) | | Full report | [trust report](/tools/migoxlab-dingo/trust.md) | [trust report](/tools/vibrantlabsai-ragas/trust.md) | ## Decision facts: dingo - **Pricing:** freemium - 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. - **Adopt for:** Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. - **License detail:** Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. ## Decision facts: ragas - **Requirements:** Min 4 GB RAM - **Adopt for:** 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:** developer harness ## Choose when ### 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. ### 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 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 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. ## 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 (15,388 vs 733). 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](/tools/migoxlab-dingo/alternatives) and [ragas alternatives](/tools/vibrantlabsai-ragas/alternatives) ([dingo markdown twin](/tools/migoxlab-dingo/alternatives.md), [ragas markdown twin](/tools/vibrantlabsai-ragas/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/migoxlab-dingo-vs-vibrantlabsai-ragas.md) 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](/tools/migoxlab-dingo/trust); [ragas trust report](/tools/vibrantlabsai-ragas/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=migoxlab-dingo`](/api/graphcanon/graph?tool=migoxlab-dingo) - 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/_