Prepare for 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. New crews are JSON-first with crew.jsonc and agents/; classic crews can still use crew.py.

Flow Projects

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

Aspect	Crew	Flow
Project structure	Project root with `crew.jsonc` and `agents/`	`src/project_name/` with `crews/` folder
Main logic location	`crew.jsonc` (classic: `src/project_name/crew.py`)	`src/project_name/main.py` (Flow class)
Entry point function	Loaded from `crew.jsonc` (classic: `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	`crew.jsonc`, `agents/`, optional `tools/`	`src/project_name/crews/crew_name/config/` or embedded JSON crew folders
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 the JSON-first structure:

my_crew/
├── .gitignore
├── pyproject.toml          # Must have type = "crew"
├── README.md
├── .env
├── uv.lock                  # REQUIRED for deployment
├── crew.jsonc               # Crew settings, tasks, process, inputs
├── agents/
│   └── researcher.jsonc     # Agent definitions
├── tools/                   # Optional custom:<name> tools
├── knowledge/
└── skills/

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

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"

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 the Crew Definition

JSON-first Crews

JSON-first crews must have a crew.jsonc or crew.json file at the project root. The agents array must reference files in agents/, and each task should reference a valid agent name.

{
  "name": "Research Crew",
  "agents": ["researcher"],
  "tasks": [
    {
      "name": "research_task",
      "description": "Research {topic}.",
      "expected_output": "A concise report.",
      "agent": "researcher"
    }
  ],
  "inputs": {
    "topic": "AI Agents"
  }
}

Custom tools are referenced as "custom:<name>" and must be implemented in tools/<name>.py with a BaseTool subclass.

Classic Python/YAML Crews

Classic crews and Python crews embedded in Flows must use the @CrewBase decorator.

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
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,
        )

4. Check Project Entry Points

JSON-first standalone crews do not need a hand-written src/project_name/main.py; crewai run and deployment packaging load crew.jsonc directly. Classic crews and Flows use Python entry points:

JSON-first Crews

Run locally from the project root:

crewai run

Classic Crews

The entry point uses a run() function:

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:

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:

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

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. For JSON-first crews, verify crew.jsonc and agents/ exist
([ -f crew.jsonc ] || [ -f crew.json ]) || echo "No crew.jsonc or crew.json found"
test -d agents || echo "No agents/ directory found"

# 4. For classic 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. For classic Python crews - 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 `crew.jsonc` or `agents/` in a JSON-first crew	Crew definition not found	Keep `crew.jsonc` and `agents/` at the project root
Missing `@CrewBase` decorator in a classic crew	”Config not found” errors	Add decorator to all classic crew classes
Classic files at root instead of `src/`	Entry point not found	Move classic Python files 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.