---
title: "MLflow"
id: mlflow
slug: "/tracing-mlflow"
description: "Learn how to trace your Haystack pipelines with MLflow."
---

# MLflow

Learn how to trace your Haystack pipelines with MLflow.

<div className="key-value-table">

|  |  |
| --- | --- |
| **How to enable** | `mlflow.haystack.autolog()` |
| **Content tracing** | Captured automatically, including latencies, token usage, cost, and exceptions |
| **Package** | `mlflow` |
| **Integration guide** | https://haystack.deepset.ai/integrations/mlflow |

</div>

## Overview

[MLflow](https://mlflow.org/) is an open-source platform for managing the end-to-end machine learning and AI lifecycle. MLflow provides native tracing support for Haystack, so you can capture traces from all your pipelines and components with a single line of code.

## Installation

Install MLflow:

```shell
pip install mlflow
```

## Usage

Enable automatic tracing for all Haystack pipelines and components:

```python
import mlflow

mlflow.haystack.autolog()
# Optionally set an experiment name
mlflow.set_experiment("Haystack")
```

This automatically captures traces from all Haystack pipelines and components, including latencies, token usage, cost, and any exceptions.

:::info
Check out the [MLflow Haystack integration guide](https://haystack.deepset.ai/integrations/mlflow) for a full walkthrough with examples.
:::