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.

<Tip> For details on how CrewAI training works under the hood, see the [Training Concepts](/en/concepts/training) page. </Tip>

Prerequisites

<CardGroup cols={2}> <Card title="Active deployment" icon="rocket"> You need a CrewAI AMP account with an active deployment in **Ready** status (Crew type). </Card> <Card title="Run permission" icon="key"> Your account must have run permission for the deployment you want to train. </Card> </CardGroup>

How to train a crew

<Steps> <Step title="Open the Training tab"> Navigate to **Deployments**, click your deployment, then select the **Training** tab. </Step> <Step title="Enter a training name"> Provide a **Training Name** — this becomes the `.pkl` filename used to store training results. For example, "Expert Mode Training" produces `expert_mode_training.pkl`. </Step> <Step title="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. </Step> <Step title="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

</Step> <Step title="Monitor progress"> The **Current Training Status** panel displays: - **Status** — Current state of the training run - **Nº Iterations** — Number of training iterations configured - **Filename** — The `.pkl` file being generated - **Started At** — When training began - **Training Inputs** — The inputs you provided </Step> </Steps>

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

You can refine the suggestions for any agent:

<Steps> <Step title="Click Edit"> On any agent's result card, click the **Edit** button next to the suggestions. </Step> <Step title="Modify suggestions"> Update the suggestions text to better reflect the improvements you want. </Step> <Step title="Save changes"> Click **Save**. The edited suggestions sync back to the deployment and are used in all future runs. </Step> </Steps>

Using trained data

To apply training results to your crew:

Note the Training Filename (the .pkl file) 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

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

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

Limitations

<Info> Keep these constraints in mind when planning your training workflow: - **One active training at a time** per deployment — wait for the current run to finish before starting another - **Auto-train mode only** — the platform does not support interactive per-iteration feedback like the CLI does - **Training data is deployment-specific** — training results are tied to the specific deployment instance and version </Info> <CardGroup cols={3}> <Card title="Training Concepts" icon="book" href="/en/concepts/training"> Learn how CrewAI training works under the hood. </Card> <Card title="Kickoff Crew" icon="play" href="/en/enterprise/guides/kickoff-crew"> Run your deployed crew from the AMP platform. </Card> <Card title="Deploy to AMP" icon="cloud-arrow-up" href="/en/enterprise/guides/deploy-to-amp"> Get your crew deployed and ready for training. </Card> </CardGroup>