After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.

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Prerequisites

Crew Ready for Deployment

You should have a working crew either built locally or created through Crew Studio

GitHub Repository

Your crew code should be in a GitHub repository (for GitHub integration method)

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Option 1: Deploy Using CrewAI CLI

The CLI provides the fastest way to deploy locally developed crews to the Enterprise platform.
1

Install CrewAI CLI

If you haven’t already, install the CrewAI CLI:
pip install crewai[tools]
The CLI comes with the main CrewAI package, but the [tools] extra ensures you have all deployment dependencies.
2

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, or want to create one:
crewai login
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!
3

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
4

Monitor Deployment Progress

Track the deployment status with:
crewai deploy status
For detailed logs of the build process:
crewai deploy logs
The first deployment typically takes 10-15 minutes as it builds the container images. Subsequent deployments are much faster.

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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.
1

Pushing to GitHub

You need to push your crew to a GitHub repository. If you haven’t created a crew yet, you can follow this tutorial.
2

Connecting GitHub to CrewAI Enterprise

  1. Log in to CrewAI Enterprise
  2. Click on the button “Connect GitHub”
Connect GitHub Button
3

Select the Repository

After connecting your GitHub account, you’ll be able to select which repository to deploy:
Select Repository
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)
Set Environment Variables
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
Deploy Progress
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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⚠️ Environment Variable Security Requirements

Important: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.

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Blocked Environment Variable Patterns

For security reasons, the following environment variable naming patterns are automatically filtered and will cause deployment issues: Blocked Patterns:
  • Variables ending with _TOKEN (e.g., MY_API_TOKEN)
  • Variables ending with _PASSWORD (e.g., DB_PASSWORD)
  • Variables ending with _SECRET (e.g., API_SECRET)
  • Variables ending with _KEY in certain contexts
Specific Blocked Variables:
  • GITHUB_USER, GITHUB_TOKEN
  • AWS_REGION, AWS_DEFAULT_REGION
  • Various internal CrewAI system variables

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Allowed Exceptions

Some variables are explicitly allowed despite matching blocked patterns:
  • AZURE_AD_TOKEN
  • AZURE_OPENAI_AD_TOKEN
  • ENTERPRISE_ACTION_TOKEN
  • CREWAI_ENTEPRISE_TOOLS_TOKEN

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How to Fix Naming Issues

If your deployment fails due to environment variable restrictions: 
# ❌ These will cause deployment failures
OPENAI_TOKEN=sk-...
DATABASE_PASSWORD=mypassword
API_SECRET=secret123

# ✅ Use these naming patterns instead
OPENAI_API_KEY=sk-...
DATABASE_CREDENTIALS=mypassword
API_CONFIG=secret123

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Best Practices

  1. Use standard naming conventions: PROVIDER_API_KEY instead of PROVIDER_TOKEN
  2. Test locally first: Ensure your crew works with the renamed variables
  3. Update your code: Change any references to the old variable names
  4. Document changes: Keep track of renamed variables for your team
If you encounter deployment failures with cryptic environment variable errors, check your variable names against these patterns first.

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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

Need Help?

Contact our support team for assistance with deployment issues or questions about the Enterprise platform.