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MLflow GenAI Evaluation

mlflow.org

Free plan WebAPISelf-hosted
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Spec score 7.5 #17 of 39 in ML Experiment Tracking Software

What it is

MLflow GenAI Evaluation is a free, open-source API and UI for measuring, improving, and monitoring LLM applications and AI agents from development through production. Evaluation Datasets centralize test cases, expected answers, and evaluation data. Teams can collect feedback from end users and domain experts, attach it to traces, and retain metadata. Scoring includes LLM-as-a-Judge, custom LLM judges, and code-based scorers; automatic evaluation supports LLM judges but not code-based scorers. Results appear in the MLflow UI with per-record details and comparisons between agent versions. MLflow Tracing records production metrics such as latency and token usage. Integrations are listed for more than 40 LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic. Judge adapters include Anthropic, Bedrock, Google, and xAI. API, self-hosted, and web options are available, with Python SDK support, prompt history and testing, regression runs, safety evaluations, tool-call checks, deployment controls, and optional basic HTTP authentication.

Spec sheet

Purpose
MLflow helps teams measure, improve, and monitor the quality of LLM applications and AI agents from development through production.Read 29 Sep 2026 · mlflow.org
Integrations
MLflow Tracing lists integrations with 40+ LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic.Read 29 Sep 2026 · mlflow.org
Access control detail
Basic HTTP authentication requires a configured secret key for CSRF protection and an admin password when first enabled.Read 29 Sep 2026 · mlflow.org
Automatic evaluation limit
Automatic evaluation supports LLM judges; code-based scorers are not supported in that mode.Read 29 Sep 2026 · mlflow.org
Evaluation data
Evaluation Datasets provide a centralized place to manage test cases, ground-truth expectations, and evaluation data.Read 29 Sep 2026 · mlflow.org
Human feedback
Users can collect and manage feedback from end users and domain experts, with feedback attached to traces and recorded with metadata.Read 29 Sep 2026 · mlflow.org
Judge providers
MLflow's evaluation quickstart says judge models can use providers including Anthropic, Bedrock, Google, and xAI through built-in adapters.Read 29 Sep 2026 · mlflow.org
Judges and scorers
MLflow includes LLM-as-a-Judge scorers and supports custom LLM judges and code-based scorers.Read 29 Sep 2026 · mlflow.org
Production monitoring
MLflow Tracing captures metrics such as latency and token usage to help monitor application performance in production.Read 29 Sep 2026 · mlflow.org
Review and compare
Evaluation results can be reviewed in the MLflow UI, including per-record results and comparisons across agent versions.Read 29 Sep 2026 · mlflow.org
Security controls
MLflow supports optional basic HTTP authentication for access control over experiments, registered models, and scorers.Read 29 Sep 2026 · mlflow.org
Who it is for
The product is designed for teams building and operating LLM applications and AI agents who want evaluation and monitoring across the application lifecycle.Read 29 Sep 2026 · mlflow.org

Plans and prices

MLflow GenAI Evaluation Free Open source; evaluation API and UI Read 29 Sep 2026 · mlflow.org

Every MLflow GenAI Evaluation plan and price →

Strong points and limits

Strong points

  • Free open-source evaluation API and UI
  • Centralized datasets for tests and expected answers
  • Supports human feedback attached to traces
  • Compares results across agent versions
  • Offers tracing for latency and token usage

Limits

  • Automatic evaluation excludes code-based scorers
  • Authentication setup requires a CSRF secret and admin password
  • SDK language support listed as Python

Who it suits

It is designed for teams building and operating LLM applications and AI agents that need evaluation and monitoring across the application lifecycle.

Verdict

MLflow GenAI Evaluation covers dataset management, scoring, feedback, tracing, and regression review in one free package. Teams using code-based scorers must run them outside automatic evaluation.

Where Specifiction places it

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