Prepare for Deployment

Ensure your Crew or Flow is ready for deployment to CrewAI AMP

Before deploying to CrewAI AMP, it’s crucial to verify your project is correctly structured. Both Crews and Flows can be deployed as “automations,” but they have different project structures and requirements that must be met for successful deployment.

Understanding Automations

In CrewAI AMP, automations is the umbrella term for deployable Agentic AI projects. An automation can be either:

• A Crew: A standalone team of AI agents working together on tasks
• A Flow: An orchestrated workflow that can combine multiple crews, direct LLM calls, and procedural logic

Understanding which type you’re deploying is essential because they have different project structures and entry points.

Crews vs Flows: Key Differences

Crew Projects

Standalone AI agent teams with crew.py defining agents and tasks. Best for focused, collaborative tasks.

Flow Projects

Orchestrated workflows with embedded crews in a crews/ folder. Best for complex, multi-stage processes.

Aspect	Crew	Flow
Project structure	src/project_name/ with crew.py	src/project_name/ with crews/ folder
Main logic location	src/project_name/crew.py	src/project_name/main.py (Flow class)
Entry point function	run() in main.py	kickoff() in main.py
pyproject.toml type	type = "crew"	type = "flow"
CLI create command	crewai create crew name	crewai create flow name
Config location	src/project_name/config/	src/project_name/crews/crew_name/config/
Can contain other crews	No	Yes (in crews/ folder)

Project Structure Reference

Crew Project Structure

When you run crewai create crew my_crew, you get this structure:

my_crew/
├── .gitignore
├── pyproject.toml          # Must have type = "crew"
├── README.md
├── .env
├── uv.lock                  # REQUIRED for deployment
└── src/
    └── my_crew/
        ├── __init__.py
        ├── main.py          # Entry point with run() function
        ├── crew.py          # Crew class with @CrewBase decorator
        ├── tools/
        │   ├── custom_tool.py
        │   └── __init__.py
        └── config/
            ├── agents.yaml  # Agent definitions
            └── tasks.yaml   # Task definitions

The nested src/project_name/ structure is critical for Crews. Placing files at the wrong level will cause deployment failures.

Flow Project Structure

When you run crewai create flow my_flow, you get this structure:

my_flow/
├── .gitignore
├── pyproject.toml          # Must have type = "flow"
├── README.md
├── .env
├── uv.lock                  # REQUIRED for deployment
└── src/
    └── my_flow/
        ├── __init__.py
        ├── main.py          # Entry point with kickoff() function + Flow class
        ├── crews/           # Embedded crews folder
        │   └── poem_crew/
        │       ├── __init__.py
        │       ├── poem_crew.py  # Crew with @CrewBase decorator
        │       └── config/
        │           ├── agents.yaml
        │           └── tasks.yaml
        └── tools/
            ├── __init__.py
            └── custom_tool.py

Both Crews and Flows use the src/project_name/ structure. The key difference is that Flows have a crews/ folder for embedded crews, while Crews have crew.py directly in the project folder.

Pre-Deployment Checklist

Use this checklist to verify your project is ready for deployment.

1. Verify pyproject.toml Configuration

Your pyproject.toml must include the correct [tool.crewai] section:

For Crews

[tool.crewai]
type = "crew"

For Flows

[tool.crewai]
type = "flow"

If the type doesn’t match your project structure, the build will fail or the automation won’t run correctly.

2. Ensure uv.lock File Exists

CrewAI uses uv for dependency management. The uv.lock file ensures reproducible builds and is required for deployment.

# Generate or update the lock file
uv lock

# Verify it exists
ls -la uv.lock

If the file doesn’t exist, run uv lock and commit it to your repository:

uv lock
git add uv.lock
git commit -m "Add uv.lock for deployment"
git push

3. Validate CrewBase Decorator Usage

Every crew class must use the @CrewBase decorator. This applies to:

• Standalone crew projects
• Crews embedded inside Flow projects

from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List

@CrewBase  # This decorator is REQUIRED
class MyCrew():
    """My crew description"""

    agents: List[BaseAgent]
    tasks: List[Task]

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

    @task
    def my_task(self) -> Task:
        return Task(
            config=self.tasks_config['my_task']  # type: ignore[index]
        )

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=True,
        )

If you forget the @CrewBase decorator, your deployment will fail with errors about missing agents or tasks configurations.

4. Check Project Entry Points

Both Crews and Flows have their entry point in src/project_name/main.py:

For Crews

The entry point uses a run() function:

# src/my_crew/main.py
from my_crew.crew import MyCrew

def run():
    """Run the crew."""
    inputs = {'topic': 'AI in Healthcare'}
    result = MyCrew().crew().kickoff(inputs=inputs)
    return result

if __name__ == "__main__":
    run()

For Flows

The entry point uses a kickoff() function with a Flow class:

# src/my_flow/main.py
from crewai.flow import Flow, listen, start
from my_flow.crews.poem_crew.poem_crew import PoemCrew

class MyFlow(Flow):
    @start()
    def begin(self):
        # Flow logic here
        result = PoemCrew().crew().kickoff(inputs={...})
        return result

def kickoff():
    """Run the flow."""
    MyFlow().kickoff()

if __name__ == "__main__":
    kickoff()

5. Prepare Environment Variables

Before deployment, ensure you have:

1. LLM API keys ready (OpenAI, Anthropic, Google, etc.)
2. Tool API keys if using external tools (Serper, etc.)

If your project depends on packages from a private PyPI registry, you’ll also need to configure registry authentication credentials as environment variables. See the Private Package Registries guide for details.

Test your project locally with the same environment variables before deploying to catch configuration issues early.

Quick Validation Commands

Run these commands from your project root to quickly verify your setup:

# 1. Check project type in pyproject.toml
grep -A2 "\[tool.crewai\]" pyproject.toml

# 2. Verify uv.lock exists
ls -la uv.lock || echo "ERROR: uv.lock missing! Run 'uv lock'"

# 3. Verify src/ structure exists
ls -la src/*/main.py 2>/dev/null || echo "No main.py found in src/"

# 4. For Crews - verify crew.py exists
ls -la src/*/crew.py 2>/dev/null || echo "No crew.py (expected for Crews)"

# 5. For Flows - verify crews/ folder exists
ls -la src/*/crews/ 2>/dev/null || echo "No crews/ folder (expected for Flows)"

# 6. Check for CrewBase usage
grep -r "@CrewBase" . --include="*.py"

Common Setup Mistakes

Mistake	Symptom	Fix
Missing uv.lock	Build fails during dependency resolution	Run uv lock and commit
Wrong type in pyproject.toml	Build succeeds but runtime fails	Change to correct type
Missing @CrewBase decorator	”Config not found” errors	Add decorator to all crew classes
Files at root instead of src/	Entry point not found	Move to src/project_name/
Missing run() or kickoff()	Cannot start automation	Add correct entry function

Next Steps

Once your project passes all checklist items, you’re ready to deploy:

Deploy to AMP

Follow the deployment guide to deploy your Crew or Flow to CrewAI AMP using the CLI, web interface, or CI/CD integration.