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AI Evaluation & Observability

MLflow

MLflow is an open-source platform for developing and operating machine-learning models, LLM applications, and agents. It provides experiment tracking, model evaluation and lifecycle tools, plus tracing, prompt management, and evaluation workflows for LLM and agent applications.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

MLflow — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use MLflow when a development workflow needs experiment or model lifecycle tracking, or when an LLM or agent team needs traces, systematic evaluation, or managed prompt versions. The documentation offers separate getting-started paths for traditional ML and for agents and LLM applications.

Who it is for

  • Developers building LLM applications or agents who need to inspect runs and evaluate application quality.
  • ML practitioners tracking experiments, comparing models, managing versions, and preparing deployment workflows.
  • Teams integrating application tracing with an existing OpenTelemetry-compatible observability stack.

Documented uses

  • Inspect prompts, retrievals, tool calls, and responses in an application trace.
  • Evaluate agent or LLM output using pre-built or custom judges and metrics.
  • Create, compare, edit, and version prompt templates.
  • Log ML experiment parameters, metrics, and artifacts and compare model runs.
  • Version models and use registry and deployment workflows.
Before you choose

Requirements & limits

The project describes itself as open source. Documentation describes use in local environments, on-premises clusters, cloud platforms, and managed services.

  • MLflow combines LLM and agent observability and evaluation with traditional ML tracking and model lifecycle workflows.
  • Its tracing is described as OpenTelemetry-compatible, including native support for GenAI Semantic Conventions.
  • The documentation describes framework and model-provider integrations, while the technical overview names ecosystems including OpenAI, Claude, Gemini, LangChain, LangGraph, Google ADK, CrewAI, and Vercel AI SDK.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Explore MLflow’s hosted LLM and agent demo

Open a publicly hosted MLflow instance populated with sample data to explore its interface.

Steps

  1. Visit demo.mlflow.org.

  2. Explore the hosted MLflow instance and its sample data.

Expected outcome

You can inspect a public MLflow instance with sample data.

Guide limitations

  • The documented demo is pre-loaded with sample data; these steps do not set up or run your own application.
Documentation 1 ↗
Evidence and transparency

Sources & review

mlflow.org ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
MLflow - Open Source AI Platform for Agents, LLMs & Models · Official homepage title · retained in its source language
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