> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unpage.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# unpage mlflow

> Debug and monitor agents using MLflow tracking

The `mlflow` command provides functionality to set up MLflow for debugging and monitoring agent performance and behavior.

## Usage

```shell theme={null}
unpage mlflow [OPTIONS] COMMAND [ARGS]...
```

## Commands

| Command | Description                                         |
| ------- | --------------------------------------------------- |
| `serve` | Start an MLflow tracking server for agent debugging |

## Subcommand: serve

Starts a local MLflow tracking server that can be used to capture and visualize agent runs, prompts, and responses.

### Usage

```shell theme={null}
unpage mlflow serve [OPTIONS]
```

### Options

| Option           | Description                                                                            |
| ---------------- | -------------------------------------------------------------------------------------- |
| `--profile TEXT` | Use profiles to manage multiple graphs \[env var: UNPAGE\_PROFILE] \[default: default] |
| `--port INTEGER` | Port for MLflow server to listen on \[default: 5566]                                   |
| `-h, --help`     | Show help message and exit                                                             |

## Examples

### Start MLflow Server

```shell theme={null}
# Start MLflow server with default settings
unpage mlflow serve

# Start MLflow on a custom port
unpage mlflow serve --port 8080
```

### Use MLflow with Agents

To use MLflow tracking with your agents, set the environment variable before running:

```shell theme={null}
# Set the MLflow tracking URI
export MLFLOW_TRACKING_URI=http://127.0.0.1:5566

# Run an agent with tracing enabled
unpage agent run my-agent '{"alert": "Disk usage exceeds 80%"}'
```