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

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Prerequisites

Project Ready for Deployment

You should have a working Crew or Flow that runs successfully locally. Follow our preparation guide to verify your project structure.

GitHub Repository

Your code should be in a GitHub repository (for GitHub integration method)
Crews vs Flows: Both project types can be deployed as “automations” on CrewAI AMP. The deployment process is the same, but they have different project structures. See Prepare for Deployment for details.

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Option 1: Deploy Using CrewAI CLI

The CLI provides the fastest way to deploy locally developed Crews or Flows to the AMP platform. The CLI automatically detects your project type from pyproject.toml and builds accordingly.
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 AMP platform:
# If you already have a CrewAI AMP 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 or Flows directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn’t require using the CLI on your local machine. The platform automatically detects your project type and handles the build appropriately.
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 AMP

  1. Log in to CrewAI AMP
  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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Option 3: Redeploy Using API (CI/CD Integration)

For automated deployments in CI/CD pipelines, you can use the CrewAI API to trigger redeployments of existing crews. This is particularly useful for GitHub Actions, Jenkins, or other automation workflows.
1

Get Your Personal Access Token

Navigate to your CrewAI AMP account settings to generate an API token:
  1. Go to app.crewai.com
  2. Click on Settings → Account → Personal Access Token
  3. Generate a new token and copy it securely
  4. Store this token as a secret in your CI/CD system
2

Find Your Automation UUID

Locate the unique identifier for your deployed crew:
  1. Go to Automations in your CrewAI AMP dashboard
  2. Select your existing automation/crew
  3. Click on Additional Details
  4. Copy the UUID - this identifies your specific crew deployment
3

Trigger Redeployment via API

Use the Deploy API endpoint to trigger a redeployment:
curl -i -X POST \
     -H "Authorization: Bearer YOUR_PERSONAL_ACCESS_TOKEN" \
     https://app.crewai.com/crewai_plus/api/v1/crews/YOUR-AUTOMATION-UUID/deploy

# HTTP/2 200
# content-type: application/json
#
# {
#   "uuid": "your-automation-uuid",
#   "status": "Deploy Enqueued",
#   "public_url": "https://your-crew-deployment.crewai.com",
#   "token": "your-bearer-token"
# }
If your automation was first created connected to Git, the API will automatically pull the latest changes from your repository before redeploying.
4

GitHub Actions Integration Example

Here’s a GitHub Actions workflow with more complex deployment triggers:
name: Deploy CrewAI Automation

on:
  push:
    branches: [ main ]
  pull_request:
    types: [ labeled ]
  release:
    types: [ published ]

jobs:
  deploy:
    runs-on: ubuntu-latest
    if: |
      (github.event_name == 'push' && github.ref == 'refs/heads/main') ||
      (github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'deploy')) ||
      (github.event_name == 'release')
    steps:
      - name: Trigger CrewAI Redeployment
        run: |
          curl -X POST \
               -H "Authorization: Bearer ${{ secrets.CREWAI_PAT }}" \
               https://app.crewai.com/crewai_plus/api/v1/crews/${{ secrets.CREWAI_AUTOMATION_UUID }}/deploy
Add CREWAI_PAT and CREWAI_AUTOMATION_UUID as repository secrets. For PR deployments, add a “deploy” label to trigger the workflow.

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Interact with Your Deployed Automation

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

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Troubleshooting Deployment Failures

If your deployment fails, check these common issues:

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Build Failures

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Missing uv.lock File

Symptom: Build fails early with dependency resolution errors Solution: Generate and commit the lock file: 
uv lock
git add uv.lock
git commit -m "Add uv.lock for deployment"
git push
The uv.lock file is required for all deployments. Without it, the platform cannot reliably install your dependencies.

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Wrong Project Structure

Symptom: “Could not find entry point” or “Module not found” errors Solution: Verify your project matches the expected structure:
  • Both Crews and Flows: Must have entry point at src/project_name/main.py
  • Crews: Use a run() function as entry point
  • Flows: Use a kickoff() function as entry point
See Prepare for Deployment for detailed structure diagrams.

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Missing CrewBase Decorator

Symptom: “Crew not found”, “Config not found”, or agent/task configuration errors Solution: Ensure all crew classes use the @CrewBase decorator: 
from crewai.project import CrewBase, agent, crew, task

@CrewBase  # This decorator is REQUIRED
class YourCrew():
    """Your crew description"""

    @agent
    def my_agent(self) -> Agent:
        return Agent(
            config=self.agents_config['my_agent'],  # type: ignore[index]
            verbose=True
        )

    # ... rest of crew definition
This applies to standalone Crews AND crews embedded inside Flow projects. Every crew class needs the decorator.

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Incorrect pyproject.toml Type

Symptom: Build succeeds but runtime fails, or unexpected behavior Solution: Verify the [tool.crewai] section matches your project type: 
# For Crew projects:
[tool.crewai]
type = "crew"

# For Flow projects:
[tool.crewai]
type = "flow"

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Runtime Failures

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LLM Connection Failures

Symptom: API key errors, “model not found”, or authentication failures Solution:
  1. Verify your LLM provider’s API key is correctly set in environment variables
  2. Ensure the environment variable names match what your code expects
  3. Test locally with the exact same environment variables before deploying

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Crew Execution Errors

Symptom: Crew starts but fails during execution Solution:
  1. Check the execution logs in the AMP dashboard (Traces tab)
  2. Verify all tools have required API keys configured
  3. Ensure agent configurations in agents.yaml are valid
  4. Check task configurations in tasks.yaml for syntax errors

Need Help?

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