Training Crews
Training lets you improve crew performance by running iterative training sessions directly from the Training tab in CrewAI AMP. The platform uses auto-train mode — it handles the iterative process automatically, unlike CLI training which requires interactive human feedback per iteration.
After training completes, CrewAI evaluates agent outputs and consolidates feedback into actionable suggestions for each agent. These suggestions are then applied to future crew runs to improve output quality.
Prerequisites
Section titled “Prerequisites”Active deployment
You need a CrewAI AMP account with an active deployment in Ready status (Crew type).
Run permission
Your account must have run permission for the deployment you want to train.
How to train a crew
Section titled “How to train a crew”- Open the Training tab
Navigate to Deployments, click your deployment, then select the Training tab.
- Enter a training name
Provide a Training Name — this becomes the
.pklfilename used to store training results. For example, “Expert Mode Training” producesexpert_mode_training.pkl. - Fill in the crew inputs
Enter the crew’s input fields. These are the same inputs you’d provide for a normal kickoff — they’re dynamically loaded based on your crew’s configuration.
- Start training
Click Train Crew. The button changes to “Training…” with a spinner while the process runs.
Behind the scenes:
- A training record is created for your deployment
- The platform calls the deployment’s auto-train endpoint
- The crew runs its iterations automatically — no manual feedback required
- Monitor progress
The Current Training Status panel displays:
- Status — Current state of the training run
- Nº Iterations — Number of training iterations configured
- Filename — The
.pklfile being generated - Started At — When training began
- Training Inputs — The inputs you provided
Understanding training results
Section titled “Understanding training results”Once training completes, you’ll see per-agent result cards with the following information:
- Agent Role — The name/role of the agent in your crew
- Final Quality — A score from 0 to 10 evaluating the agent’s output quality
- Final Summary — A summary of the agent’s performance during training
- Suggestions — Actionable recommendations for improving the agent’s behavior
Editing suggestions
Section titled “Editing suggestions”You can refine the suggestions for any agent:
- Click Edit
On any agent’s result card, click the Edit button next to the suggestions.
- Modify suggestions
Update the suggestions text to better reflect the improvements you want.
- Save changes
Click Save. The edited suggestions sync back to the deployment and are used in all future runs.
Using trained data
Section titled “Using trained data”To apply training results to your crew:
- Note the Training Filename (the
.pklfile) from your completed training session. - Specify this filename in your deployment’s kickoff or run configuration.
- The crew automatically loads the training file and applies the stored suggestions to each agent.
This means agents benefit from the feedback generated during training on every subsequent run.
Previous trainings
Section titled “Previous trainings”The bottom of the Training tab displays a history of all past training sessions for the deployment. Use this to review previous training runs, compare results, or select a different training file to use.
Error handling
Section titled “Error handling”If a training run fails, the status panel shows an error state along with a message describing what went wrong.
Common causes of training failures:
- Deployment runtime not updated — Ensure your deployment is running the latest version
- Crew execution errors — Issues within the crew’s task logic or agent configuration
- Network issues — Connectivity problems between the platform and the deployment