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MLflow GenAI Evaluation

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What MLflow GenAI Evaluation is

MLflow GenAI Evaluation helps teams assess LLM applications and AI agents during development and monitor them in production. Evaluation Datasets keep test cases, expected outcomes, and other evaluation material in one place. Teams can collect feedback from users and domain experts, link it to traces, and retain metadata. Scoring options include LLM-as-a-Judge, custom LLM judges, and code-based scorers; automatic evaluation supports LLM judges, but not code-based scorers. Results are available in the MLflow UI, with per-record views and comparisons between agent versions. MLflow Tracing captures measures such as latency and token use, and lists integrations with more than 40 LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic. Built-in adapters support judge models from providers including Anthropic, Bedrock, Google, and xAI. The open-source evaluation API and UI are free, with API, self-hosted, and web options. Optional basic HTTP authentication can control access to experiments, registered models, and scorers; enabling it requires a CSRF secret key and an admin password.

Who it suits

It suits teams building or operating LLM applications and AI agents that need evaluation and monitoring across the product lifecycle. Python SDK support and API access are listed.

What it does well

  • Free open-source evaluation API and UI.
  • Compares results across agent versions.
  • Captures latency and token usage in traces.
  • Supports human feedback attached to traces.
  • Lists integrations with 40+ libraries and frameworks.

What to know first

  • Automatic evaluation does not support code-based scorers.
  • Authentication setup requires a CSRF secret key and admin password.

Plans

PlanBilled and limitsPrice
MLflow GenAI EvaluationOpen source; evaluation API and UIFree

As mlflow.org lists them, read 29 Sept 2026. The maker's own page has the final word.

Getting started

  1. Open the MLflow GenAI Evaluation website.
  2. Choose API, self-hosted, or web access.
  3. Create or manage Evaluation Datasets for test cases and expectations.
  4. Select LLM-as-a-Judge, a custom LLM judge, or a code-based scorer.
  5. Review evaluation results in the MLflow UI.
  6. Configure a CSRF secret key and admin password if enabling basic HTTP authentication.

Verdict

MLflow GenAI Evaluation combines dataset management, scoring, feedback, and production tracing in one offering. Teams should account for the limit on code-based scorers in automatic evaluation and the setup requirements for optional authentication.

Questions

How much does MLflow GenAI Evaluation cost?

The open-source evaluation API and UI are free.

Where can I use it?

The listed options are API, self-hosted, and web.

What evaluation methods does it support?

It supports LLM-as-a-Judge scorers, custom LLM judges, and code-based scorers. Automatic evaluation supports LLM judges but not code-based scorers.

Can teams compare versions of an agent?

Yes. The MLflow UI can show per-record results and comparisons across agent versions.

What does production tracing capture?

MLflow Tracing captures metrics such as latency and token usage. It lists integrations with 40+ LLM and agent libraries and frameworks.

What is needed to enable basic HTTP authentication?

A configured secret key for CSRF protection and an admin password when authentication is first enabled.

Facts from MLflow GenAI Evaluation's own pages

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

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