CrewAI integration

Action security for CrewAI.

Govern crew tasks and tool execution from the application bootstrap.

Get the Python SDK on PyPITry the local demoRead the documentation

Get the package and source.

Connect the execution boundary.

The CrewAI integration attaches through the SDK bootstrap import. Verify the crew and every consequential tool path with the versions used by your application.

An example to test

A support crew receives a refund request with a substituted recipient. Evaluate the final refund arguments before the billing tool runs.

Illustrative workflow, not a customer deployment or a measured result.

Before connecting live policy

  1. Identify the handler that actually performs the side effect.
  2. Provide trusted identity, purpose and final action arguments.
  3. Configure the tenant’s assigned runtime and keep API credentials server-side.
  4. Test allowed, blocked and unavailable paths before production.

Integration example

Adapt this existing SDK pattern to your application. Names such as the agent, handler and session refer to your own objects. Follow the package documentation for compatible versions and complete setup.

# Install the stable Python SDK support for your framework from PyPI.
python -m pip install "agenticdome-python-sdk[crewai]"

# Production uses the tenant assigned runtime sidecar, never the admin/control-plane URL.
export AGENTICDOME_API_BASE="https://your-sidecar.example.com"
export AGENTICDOME_API_KEY="your_api_key"
export AGENTICDOME_TENANT_ID="your_tenant_id"

# Register runtime protection in your app bootstrap.
import agenticdome_sdk.crewai

# Run the local vulnerable-vs-protected demo.
agenticdome-demo --framework crewai --scenario refund_hijack

Python demo commands evaluate fixed inputs against a bundled local policy; they do not instantiate the selected framework or prove a live integration. The TypeScript client and OpenClaw runtime plugin need their documented tenant configuration.

Read the integration researchUnderstand runtime verification

See the decision before the action.

Try a local scenario, then bring your workflow to a deployment review. Start with one agent and one tool, and see exactly where the action can be stopped.

Try the local demo Book a deployment review