CLI

Overview

The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.

Installation

To use the CrewAI CLI, make sure you have CrewAI installed:

pip install crewai

Basic Usage

The basic structure of a CrewAI CLI command is:

crewai [COMMAND] [OPTIONS] [ARGUMENTS]

Available Commands

1. Create

Create a new crew or flow.

crewai create [OPTIONS] TYPE NAME

• TYPE: Choose between "crew" or "flow"
• NAME: Name of the crew or flow

Example:

crewai create crew my_new_crew
crewai create flow my_new_flow

2. Version

Show the installed version of CrewAI.

crewai version [OPTIONS]

• --tools: (Optional) Show the installed version of CrewAI tools

Example:

crewai version
crewai version --tools

3. Train

Train the crew for a specified number of iterations.

crewai train [OPTIONS]

• -n, --n_iterations INTEGER: Number of iterations to train the crew (default: 5)
• -f, --filename TEXT: Path to a custom file for training (default: "trained_agents_data.pkl")

Example:

crewai train -n 10 -f my_training_data.pkl

4. Replay

Replay the crew execution from a specific task.

crewai replay [OPTIONS]

• -t, --task_id TEXT: Replay the crew from this task ID, including all subsequent tasks

Example:

crewai replay -t task_123456

5. Log-tasks-outputs

Retrieve your latest crew.kickoff() task outputs.

crewai log-tasks-outputs

6. Reset-memories

Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).

crewai reset-memories [OPTIONS]

• -l, --long: Reset LONG TERM memory
• -s, --short: Reset SHORT TERM memory
• -e, --entities: Reset ENTITIES memory
• -k, --kickoff-outputs: Reset LATEST KICKOFF TASK OUTPUTS
• -kn, --knowledge: Reset KNOWLEDGE storage
• -akn, --agent-knowledge: Reset AGENT KNOWLEDGE storage
• -a, --all: Reset ALL memories

Example:

crewai reset-memories --long --short
crewai reset-memories --all

7. Test

Test the crew and evaluate the results.

crewai test [OPTIONS]

• -n, --n_iterations INTEGER: Number of iterations to test the crew (default: 3)
• -m, --model TEXT: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")

Example:

crewai test -n 5 -m gpt-3.5-turbo

8. Run

Run the crew or flow.

crewai run

9. Chat

Starting in version 0.98.0, when you run the crewai chat command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.

After receiving the results, you can continue interacting with the assistant for further instructions or questions.

crewai chat

@crew
def crew(self) -> Crew:
    return Crew(
        agents=self.agents,
        tasks=self.tasks,
        process=Process.sequential,
        verbose=True,
        chat_llm="gpt-4o",  # LLM for chat orchestration
    )

10. Deploy

Deploy the crew or flow to CrewAI AMP.

• Authentication: You need to be authenticated to deploy to CrewAI AMP. You can login or create an account with:

  shellCopy

  crewai login
  

• Create a deployment: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.

  shellCopy

  crewai deploy create
  

  • Reads your local project configuration.
  • Prompts you to confirm the environment variables (like OPENAI_API_KEY, SERPER_API_KEY) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a .env file) before running this.

11. Organization Management

Manage your CrewAI AMP organizations.

crewai org [COMMAND] [OPTIONS]

Commands:

• list: List all organizations you belong to

crewai org list

• current: Display your currently active organization

crewai org current

• switch: Switch to a specific organization

crewai org switch <organization_id>

• Create a deployment (continued):

  • Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).

• Deploy the Crew: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.

  shellCopy

  crewai deploy push
  

  • Initiates the deployment process on the CrewAI AMP platform.
  • Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).

• Deployment Status: You can check the status of your deployment with:

  shellCopy

  crewai deploy status
  

  This fetches the latest deployment status of your most recent deployment attempt (e.g., Building Images for Crew, Deploy Enqueued, Online).

• Deployment Logs: You can check the logs of your deployment with:

  shellCopy

  crewai deploy logs
  

  This streams the deployment logs to your terminal.

• List deployments: You can list all your deployments with:

  shellCopy

  crewai deploy list
  

  This lists all your deployments.

• Delete a deployment: You can delete a deployment with:

  shellCopy

  crewai deploy remove
  

  This deletes the deployment from the CrewAI AMP platform.

• Help Command: You can get help with the CLI with:

  shellCopy

  crewai deploy --help
  

  This shows the help message for the CrewAI Deploy CLI.

Watch this video tutorial for a step-by-step demonstration of deploying your crew to CrewAI AMP using the CLI.

11. Login

Authenticate with CrewAI AMP using a secure device code flow (no email entry required).

crewai login

What happens:

• A verification URL and short code are displayed in your terminal
• Your browser opens to the verification URL
• Enter/confirm the code to complete authentication

Notes:

• The OAuth2 provider and domain are configured via crewai config (defaults use login.crewai.com)
• After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
• If you reset your configuration, run crewai login again to re-authenticate

12. API Keys

When running crewai create crew command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.

Once you've selected an LLM provider and model, you will be prompted for API keys.

Available LLM Providers

Here's a list of the most popular LLM providers suggested by the CLI:

• OpenAI
• Groq
• Anthropic
• Google Gemini
• SambaNova

When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.

Other Options

If you select "other", you will be able to select from a list of LiteLLM supported providers.

When you select a provider, the CLI will prompt you to enter the Key name and the API key.

See the following link for each provider's key name:

• LiteLLM Providers

13. Configuration Management

Manage CLI configuration settings for CrewAI.

crewai config [COMMAND] [OPTIONS]

Commands:

• list: Display all CLI configuration parameters

crewai config list

• set: Set a CLI configuration parameter

crewai config set <key> <value>

• reset: Reset all CLI configuration parameters to default values

crewai config reset

Available Configuration Parameters

• enterprise_base_url: Base URL of the CrewAI AMP instance
• oauth2_provider: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
• oauth2_audience: OAuth2 audience value, typically used to identify the target API or resource
• oauth2_client_id: OAuth2 client ID issued by the provider, used during authentication requests
• oauth2_domain: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens

Examples

Display current configuration:

crewai config list

Example output:

Set the enterprise base URL:

crewai config set enterprise_base_url https://my-enterprise.crewai.com

Set OAuth2 provider:

crewai config set oauth2_provider auth0

Set OAuth2 domain:

crewai config set oauth2_domain my-company.auth0.com

Reset all configuration to defaults:

crewai config reset

14. Trace Management

Manage trace collection preferences for your Crew and Flow executions.

crewai traces [COMMAND]

Commands:

• enable: Enable trace collection for crew/flow executions

crewai traces enable

• disable: Disable trace collection for crew/flow executions

crewai traces disable

• status: Show current trace collection status

crewai traces status

How Tracing Works

Trace collection is controlled by checking three settings in priority order:

1. Explicit flag in code (highest priority - can enable OR disable):

   pythonCopy

   crew = Crew(agents=[...], tasks=[...], tracing=True)   # Always enable
   crew = Crew(agents=[...], tasks=[...], tracing=False)  # Always disable
   crew = Crew(agents=[...], tasks=[...])                 # Check lower priorities (default)
   

   • tracing=True will always enable tracing (overrides everything)
   • tracing=False will always disable tracing (overrides everything)
   • tracing=None or omitted will check lower priority settings

2. Environment variable (second priority):

   envCopy

   CREWAI_TRACING_ENABLED=true
   

   • Checked only if tracing is not explicitly set to True or False in code
   • Set to true or 1 to enable tracing

3. User preference (lowest priority):

   shellCopy

   crewai traces enable
   

   • Checked only if tracing is not set in code and CREWAI_TRACING_ENABLED is not set to true
   • Running crewai traces enable is sufficient to enable tracing by itself

To disable tracing, use any ONE of these methods:

• Set tracing=False in your Crew/Flow code (overrides everything), OR
• Remove or set to false the CREWAI_TRACING_ENABLED env var, OR
• Run crewai traces disable

Higher priority settings override lower ones.