mlflow/MLflow
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
MLflow is the long-running open-source platform for the machine-learning lifecycle, now extended to cover LLM and AI-agent engineering. Teams use it to track experiments, register and version models, and debug, evaluate, and monitor production AI apps, all in one open tool.
Our Review
MLflow has 26,000 GitHub stars and roots going back to 2018, when Databricks open-sourced it as the standard way to track machine-learning experiments. In 2026 it is much broader: the same platform now handles LLM tracing, evaluation, prompt management, and agent observability, so classic ML and modern AI engineering share one tool.
What MLflow does:
- •Experiment tracking log parameters, metrics, code, and artifacts for every ML and LLM run, then compare them in a UI.
- •Model registry version, stage, and manage models and prompts with lineage and approvals.
- •LLM and agent observability trace model and tool calls, so you can debug agent behavior the way you debug ML runs.
- •Evaluation score model and LLM outputs with built-in and custom metrics on datasets.
- •Deployment and serving package models and serve them, or deploy to common platforms.
- •Open and integration-friendly works with PyTorch, scikit-learn, LangChain, OpenAI, and most ML and LLM tooling.
Getting started:
Install with pip install mlflow, run mlflow ui to launch the tracking server, and wrap your training or LLM code with MLflow logging. Docs at mlflow.org.
Limitations:
MLflow is broad and mature, which means a learning curve: it covers a lot (tracking, registry, eval, serving), so a small project may only need a slice of it. For pure LLM-app observability, focused tools can feel lighter than MLflow's full platform. Self-hosting the tracking server and artifact store is your responsibility, and the high issue count reflects a large, busy project. Deep enterprise features lean toward managed Databricks.
Our Verdict
MLflow is the safe, proven open-source backbone in 2026 for teams that need to track, version, and monitor AI work across both classic ML and LLMs. If you want one tool that records every experiment, manages models and prompts, and traces agent runs, MLflow has the longest track record, with 26,000 stars and an Apache-2.0 license.
For developers and ML engineers, MLflow fits naturally into training and AI-app code: a few logging calls capture parameters, metrics, and traces, and the registry gives you versioned models and prompts with lineage. Its breadth means one tool spans data science and LLM engineering instead of stitching several together.
Skip MLflow if you only need lightweight LLM tracing and evals; a focused tool like Langfuse is quicker to set up for that narrow job. If you are not doing classic ML at all, much of MLflow's surface will go unused.
Frequently Asked Questions
What is MLflow?
MLflow is an open-source platform for managing the machine-learning and AI lifecycle, originally created at Databricks. It provides experiment tracking, a model registry, evaluation, and deployment, and as of 2026 it also covers LLM and agent engineering with tracing, prompt management, and observability. Teams use it to debug, evaluate, and monitor production AI applications.
Is MLflow free and open source?
Yes. MLflow is released under the Apache-2.0 license and is free and open source as of 2026. You can self-host the tracking server and model registry at no licensing cost. Managed MLflow is available through Databricks and some cloud providers if you prefer not to operate the infrastructure yourself.
Can MLflow be used for LLM and agent apps?
Yes. As of 2026, MLflow has expanded well beyond classic machine learning to support LLM and agent engineering. It can trace model and tool calls, evaluate LLM outputs, manage prompts, and monitor production AI applications, alongside its long-standing experiment tracking and model registry. That lets one platform cover both traditional ML and modern AI.
How is MLflow different from Langfuse?
Langfuse is focused specifically on LLM observability, evals, and prompt management. MLflow is a broader platform that also covers classic ML experiment tracking, a model registry, and deployment, plus LLM features. Choose Langfuse for a lightweight, LLM-only setup; choose MLflow when you want one tool across machine learning and AI engineering.
Who maintains MLflow?
MLflow was created at Databricks and open-sourced in 2018, and it is now a widely used community project with thousands of contributors, governed as an open-source platform. Databricks offers a managed version, but the core is free, Apache-2.0 licensed, and self-hostable as of 2026, with broad adoption across data science and AI teams.
How do I install MLflow?
Visit the GitHub repository at https://github.com/mlflow/mlflow for installation instructions.
What license does MLflow use?
MLflow uses the Apache-2.0 license.
What are alternatives to MLflow?
Explore related tools and alternatives on My AI Guide.
Open source & community-verified
Apache-2.0 licensed: free to use in any project, no strings attached. 26,286 developers have starred this, meaning the community has reviewed and trusted it.
Reviewed by My AI Guide for relevance, quality, and active maintenance before listing.
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