Deploy Crew

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Option 1: CLI Deployment

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Prerequisites

Before starting the deployment process, make sure you have:

• A CrewAI project built locally (follow our quickstart guide if you haven’t created one yet)
• Your code pushed to a GitHub repository
• The latest version of the CrewAI CLI installed (uv tool install crewai)

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Step 1: Authenticate with the Enterprise Platform

First, you need to authenticate your CLI with the CrewAI Enterprise platform:

# If you already have a CrewAI Enterprise account
crewai login

# If you're creating a new account
crewai signup

When you run either command, the CLI will:

1. Display a URL and a unique device code
2. Open your browser to the authentication page
3. Prompt you to confirm the device
4. Complete the authentication process

Upon successful authentication, you’ll see a confirmation message in your terminal!

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Step 2: Create a Deployment

From your project directory, run:

crewai deploy create

This command will:

1. Detect your GitHub repository information
2. Identify environment variables in your local .env file
3. Securely transfer these variables to the Enterprise platform
4. Create a new deployment with a unique identifier

On successful creation, you’ll see a message like:

Deployment created successfully!
Name: your_project_name
Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
Current Status: Deploy Enqueued

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Step 3: Monitor Deployment Progress

Track the deployment status with:

crewai deploy status

For detailed logs of the build process:

crewai deploy logs

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Additional CLI Commands

The CrewAI CLI offers several commands to manage your deployments:

# List all your deployments
crewai deploy list

# Get the status of your deployment
crewai deploy status

# View the logs of your deployment
crewai deploy logs

# Push updates after code changes
crewai deploy push

# Remove a deployment
crewai deploy remove <deployment_id>

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Option 2: Deploy Directly via Web Interface

You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn’t require using the CLI on your local machine.

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Step 1: Pushing to GitHub

First, you need to push your crew to a GitHub repository. If you haven’t created a crew yet, you can follow this tutorial.

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Step 2: Connecting GitHub to CrewAI Enterprise

1. Log in to CrewAI Enterprise
2. Click on the button “Connect GitHub”

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Step 3: Select the Repository

After connecting your GitHub account, you’ll be able to select which repository to deploy:

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Step 4: Set Environment Variables

Before deploying, you’ll need to set up your environment variables to connect to your LLM provider or other services:

1. You can add variables individually or in bulk
2. Enter your environment variables in KEY=VALUE format (one per line)

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Step 5: Deploy Your Crew

1. Click the “Deploy” button to start the deployment process
2. You can monitor the progress through the progress bar
3. The first deployment typically takes around 10-15 minutes; subsequent deployments will be faster

Once deployment is complete, you’ll see:

• Your crew’s unique URL
• A Bearer token to protect your crew API
• A “Delete” button if you need to remove the deployment

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Interact with Your Deployed Crew

Once deployment is complete, you can access your crew through:

1. REST API: The platform generates a unique HTTPS endpoint with these key routes:

   • /inputs: Lists the required input parameters
   • /kickoff: Initiates an execution with provided inputs
   • /status/{kickoff_id}: Checks the execution status

2. Web Interface: Visit app.crewai.com to access:

   • Status tab: View deployment information, API endpoint details, and authentication token
   • Run tab: Visual representation of your crew’s structure
   • Executions tab: History of all executions
   • Metrics tab: Performance analytics
   • Traces tab: Detailed execution insights

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Trigger an Execution

From the Enterprise dashboard, you can:

1. Click on your crew’s name to open its details
2. Select “Trigger Crew” from the management interface
3. Enter the required inputs in the modal that appears
4. Monitor progress as the execution moves through the pipeline

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Monitoring and Analytics

The Enterprise platform provides comprehensive observability features:

• Execution Management: Track active and completed runs
• Traces: Detailed breakdowns of each execution
• Metrics: Token usage, execution times, and costs
• Timeline View: Visual representation of task sequences

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Advanced Features

The Enterprise platform also offers:

• Environment Variables Management: Securely store and manage API keys
• LLM Connections: Configure integrations with various LLM providers
• Custom Tools Repository: Create, share, and install tools
• Crew Studio: Build crews through a chat interface without writing code