--- title: Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow description: MLflow 3.9.0 integrates over 50 evaluation metrics from DeepEval, RAGAS, and Arize Phoenix frameworks into a unified API. This integration enables developers... canonical: https://daily.dev/posts/introducing-deepeval-ragas-and-phoenix-judges-in-mlflow-zvcgzncs0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow | daily.dev og:description: MLflow 3.9.0 integrates over 50 evaluation metrics from DeepEval, RAGAS, and Arize Phoenix frameworks into a unified API. This integration enables developers... og:url: https://daily.dev/posts/introducing-deepeval-ragas-and-phoenix-judges-in-mlflow-zvcgzncs0 og:image: https://api.daily.dev/og/posts/zVcgznCs0.png og:image:alt: Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow **[mlflow](https://daily.dev/sources/MLflow)** · 6 min read · 19 upvotes · 0 comments ## Summary MLflow 3.9.0 integrates over 50 evaluation metrics from DeepEval, RAGAS, and Arize Phoenix frameworks into a unified API. This integration enables developers to evaluate LLM agents and RAG systems using multiple judge frameworks simultaneously, compare results side-by-side in MLflow UI, and access specialized metrics for conversational agents, retrieval quality, hallucination detection, and safety. The unified interface eliminates the need for custom wrappers and provides visualization, filtering, and iteration tools for improving agent quality before production deployment. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Structuring AI Evaluation and Observability with MLflow: From Development to Production](https://daily.dev/posts/structuring-ai-evaluation-and-observability-with-mlflow-from-development-to-production-wdjrvh5aa) · mlflow · 0 upvotes · 0 comments - [A Hands-On Guide to Testing Agents with RAGAs and G-Eval](https://daily.dev/posts/a-hands-on-guide-to-testing-agents-with-ragas-and-g-eval-edgdasjs0) · Machine Learning Mastery · 1 upvotes · 0 comments - [Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned](https://daily.dev/posts/evaluating-ai-agents-in-practice-benchmarks-frameworks-and-lessons-learned-tohpewvfd) · InfoQ · 2 upvotes · 0 comments - [Building Responsible and Calibrated AI Agents with Databricks and MLflow: A Real-World Use Case Deep Dive](https://daily.dev/posts/building-responsible-and-calibrated-ai-agents-with-databricks-and-mlflow-a-real-world-use-case-deep-yfco81kv2) · databricks · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#llm](https://daily.dev/tags/llm), [#machine-learning](https://daily.dev/tags/machine-learning), [#rag](https://daily.dev/tags/rag) [View this post on daily.dev](https://daily.dev/posts/introducing-deepeval-ragas-and-phoenix-judges-in-mlflow-zvcgzncs0) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introducing DeepEval, RAGAS, and Phoenix Judges in MLflow","url":"https://daily.dev/posts/introducing-deepeval-ragas-and-phoenix-judges-in-mlflow-zvcgzncs0","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introducing-deepeval-ragas-and-phoenix-judges-in-mlflow-zvcgzncs0"},"datePublished":"2026-02-02T04:18:35.715Z","dateModified":"2026-03-15T03:53:01.537Z","description":"MLflow 3.9.0 integrates over 50 evaluation metrics from DeepEval, RAGAS, and Arize Phoenix frameworks into a unified API. 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