--- title: "continuous-eval vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/relari-ai-continuous-eval-vs-vibrantlabsai-ragas" tools: ["relari-ai-continuous-eval", "vibrantlabsai-ragas"] --- # continuous-eval vs ragas Neutral, constraint-first comparison with live GitHub stats. | | [continuous-eval](/tools/relari-ai-continuous-eval.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Data-Driven Evaluation for LLM-Powered Applications | Supercharge Your LLM Application Evaluations | | Stars | 516 | 14,717 | | Forks | 38 | 1,539 | | Open issues | 12 | 478 | | Language | Python | Python | | Adopt for | `continuous-eval` is an open-source Python library for data-driven evaluation of applications using Large Language Models (LLMs). It supports modularized and probabilistic evaluations with a comprehensive metric library. | Ragas is an essential toolkit for evaluating and improving Large Language Model applications through objective metrics, intelligent test generation, and seamless integration with popular frameworks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [continuous-eval](/tools/relari-ai-continuous-eval.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 531d | 134d | | Open issues (now) | 12 | 478 | | Full report | [trust report](/tools/relari-ai-continuous-eval/trust.md) | [trust report](/tools/vibrantlabsai-ragas/trust.md) | **Typed relationship:** continuous-eval _(alternative)_ ragas Both `continuous-eval` and `ragas` aim to provide comprehensive evaluation capabilities for LLM applications, making them alternatives. ## Shared compatibility - **Python**: [continuous-eval](/tools/relari-ai-continuous-eval.md) - Python runtime; [ragas](/tools/vibrantlabsai-ragas.md) - Python runtime ## Decision facts: continuous-eval - **Adopt for:** `continuous-eval` is an open-source Python library for data-driven evaluation of applications using Large Language Models (LLMs). It supports modularized and probabilistic evaluations with a comprehensive metric library. - **License detail:** Apache-2.0 ## Decision facts: ragas - **Adopt for:** Ragas is an essential toolkit for evaluating and improving Large Language Model applications through objective metrics, intelligent test generation, and seamless integration with popular frameworks. ## Choose when ### Choose continuous-eval if… - Both `continuous-eval` and `ragas` aim to provide comprehensive evaluation capabilities for LLM applications, making them alternatives. - Tags unique to continuous-eval: rag, information-retrieval, retrieval-augmented-generation, evaluation-metrics. - - When you need to evaluate the performance of LLMs across multiple modules within your application, such as retrieval systems or code generation. ### Choose ragas if… - Both `continuous-eval` and `ragas` aim to provide comprehensive evaluation capabilities for LLM applications, making them alternatives. - Tags unique to ragas: evaluation, llm. - - When you need to assess the performance of your LLM applications with quantitative metrics beyond subjective evaluations. ## When NOT to use continuous-eval - - When the specific use case does not benefit from modularized evaluation across various modules (e.g., simple chatbots without complex module interactions). - - If your application does not require a variety of metric types including LLM-based, semantic, and deterministic metrics. - - In scenarios where you prefer a less flexible framework with fewer customization options for evaluation methods. ## When NOT to use ragas - - Avoid using Ragas if your LLM evaluation solely relies on qualitative assessments without the need for quantitative metrics. - - If you prefer a toolkit that does not offer out-of-the-box integrations with commonly used LLM frameworks like LangChain. - - When specific custom evaluations are needed outside of predefined templates such as Aspect Critique or prompt analysis. ## Common questions ### What is the difference between continuous-eval and ragas? continuous-eval: Data-Driven Evaluation for LLM-Powered Applications. ragas: Supercharge Your LLM Application Evaluations. See the comparison table for live GitHub stats and shared categories. ### When should I choose continuous-eval over ragas? Choose continuous-eval over ragas when Both `continuous-eval` and `ragas` aim to provide comprehensive evaluation capabilities for LLM applications, making them alternatives; Tags unique to continuous-eval: rag, information-retrieval, retrieval-augmented-generation, evaluation-metrics; - When you need to evaluate the performance of LLMs across multiple modules within your application, such as retrieval systems or code generation. ### When should I choose ragas over continuous-eval? Choose ragas over continuous-eval when Both `continuous-eval` and `ragas` aim to provide comprehensive evaluation capabilities for LLM applications, making them alternatives; Tags unique to ragas: evaluation, llm; - When you need to assess the performance of your LLM applications with quantitative metrics beyond subjective evaluations. ### When should I avoid continuous-eval? - When the specific use case does not benefit from modularized evaluation across various modules (e.g., simple chatbots without complex module interactions). - If your application does not require a variety of metric types including LLM-based, semantic, and deterministic metrics. - In scenarios where you prefer a less flexible framework with fewer customization options for evaluation methods. ### When should I avoid ragas? - Avoid using Ragas if your LLM evaluation solely relies on qualitative assessments without the need for quantitative metrics. - If you prefer a toolkit that does not offer out-of-the-box integrations with commonly used LLM frameworks like LangChain. - When specific custom evaluations are needed outside of predefined templates such as Aspect Critique or prompt analysis. ### Is continuous-eval or ragas more popular on GitHub? ragas has more GitHub stars (14,717 vs 516). Stars measure visibility, not whether either tool fits your constraints. ### Are continuous-eval and ragas open source? Yes - both are open-source projects on GitHub (continuous-eval: Apache-2.0, ragas: Apache-2.0). ### Where can I find alternatives to continuous-eval or ragas? GraphCanon lists graph-backed alternatives at /tools/relari-ai-continuous-eval/alternatives and /tools/vibrantlabsai-ragas/alternatives (/tools/relari-ai-continuous-eval/alternatives.md, /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 /compare/relari-ai-continuous-eval-vs-vibrantlabsai-ragas.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, continuous-eval or ragas? continuous-eval: 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 continuous-eval and ragas? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: continuous-eval: /tools/relari-ai-continuous-eval/trust; ragas: /tools/vibrantlabsai-ragas/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=relari-ai-continuous-eval`](/api/graphcanon/graph?tool=relari-ai-continuous-eval) - 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/_