Integration Guide

CrewAI

Connect CrewAI to DashClaw and get your first governed action into /decisions in under 20 minutes.

Instance URL detected: https://your-dashclaw-instance.example.com

Deploy DashClaw

Get a running instance. Click the Vercel deploy button or run locally.

Already have an instance? Skip to Step 2.

Install the DashClaw Python SDK and CrewAI

Create a virtual environment and install the required packages. Requires Python 3.10+ (Python 3.14+ is not supported by CrewAI).

Terminal

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install dashclaw crewai==1.11.0 python-dotenv

Set environment variables

Create a .env file with your DashClaw connection details. No LLM API key required for the example.

.env

DASHCLAW_BASE_URL=https://your-dashclaw-instance.example.com
DASHCLAW_API_KEY=oc_live_...

Create a governed CrewAI tool with the @tool decorator

The @tool decorator creates a CrewAI tool. run_governed keeps policy, approval, execution claiming, callback, and outcome on one persisted action.

main.py

from crewai.tools import tool
from dashclaw import DashClaw
import os

claw = DashClaw(
    base_url=os.environ["DASHCLAW_BASE_URL"],
    api_key=os.environ["DASHCLAW_API_KEY"],
    agent_id="crewai-analyst-agent",
)

@tool("Analyze Customer Data")
def analyze_customer_data(query: str) -> str:
    """Analyze customer data. Governed by DashClaw policies."""
    return claw.run_governed(
        {"kind": "sql", "statement": f"/* customer analysis */ {query}"},
        {
            "action_type": "data_analysis",
            "declared_goal": f"Analyze customer data: {query}",
            "risk_score": 40,
            "systems_touched": ["customer_database"],
        },
        lambda: f"Analysis of '{query}': 42 segments, avg satisfaction 4.2/5.",
    )

The callback runs only after DashClaw confirms protocol-1 execution authority for the exact action and act.

Run the governed CrewAI tool

Execute the example and watch the governance flow.

Terminal

python main.py

No LLM API key needed: the example calls the tool directly. Only the DashClaw SDK calls are real.

See the result in DashClaw

Open your DashClaw dashboard to confirm the action was recorded.

Go to /decisions: you should see your action in the ledger with action_type 'data_analysis', agent_id 'crewai-analyst-agent', and status 'completed'.

Clone the full example

The complete runnable example is in the DashClaw repo.

Terminal

git clone https://github.com/ucsandman/DashClaw.git
cd DashClaw/examples/crewai-governed
pip install -r requirements.txt
python main.py

For production CrewAI integrations, the Python SDK also includes a DashClawCrewIntegration class (sdk-python/dashclaw/integrations/crewai.py) that provides automatic task callbacks for governing entire crews.

What success looks like

Go to /decisions: you should see your action in the ledger with action_type 'data_analysis', agent_id 'crewai-analyst-agent', and status 'completed'.

Navigate to /decisions in your DashClaw instance. Your action should appear in the ledger within seconds of the agent run.

Governance as Code

guardrails.yml is a policy-as-code template. Import it into your instance — POST the YAML to /api/policies/import or call the Python SDK's import_policies — and DashClaw evaluates these rules at the guard step before any action executes.

guardrails.yml

version: 1
project: my-crewai-agent
description: >
  Governance policy for a CrewAI data analysis crew.
  Customer data analysis requires audit trail.
  External API calls require approval.

policies:
  - id: audit_data_analysis
    description: All data analysis tools must record an audit trail
    applies_to:
      tools:
        - Analyze Customer Data
        - Generate Report
    rule:
      allow: true

  - id: approve_external_calls
    description: External API calls require human approval
    applies_to:
      tools:
        - Send Email
        - Post to Slack
    rule:
      require: approval