CrewAI Integration Guide

Why Multi-Agent Compliance Matters

Multi-agent systems introduce unique compliance challenges. The EU AI Act's Article 12 requires logging of "events relevant to identifying risks." For multi-agent systems, that means tracking every agent's contribution.

Basic Setup

1. Initialize the Callback

from protectron.crewai import ProtectronCallback

# Basic initialization
callback = ProtectronCallback(
    system_id="my-crew"
)

# Full configuration
callback = ProtectronCallback(
    system_id="my-crew",
    environment="production",
    
    # CrewAI-specific options
    log_agent_thoughts=True,      # Log agent reasoning
    log_delegation=True,          # Log task delegation
    log_collaboration=True,       # Log inter-agent communication
    
    # Content options
    log_llm_content=True,         # Log prompts/completions
    log_tool_outputs=True,        # Log tool results
    
    # Privacy
    pii_redaction=True,           # Auto-redact PII
)

2. Add to Your Crew

from crewai import Agent, Task, Crew, Process

# Define agents
researcher = Agent(
    role="Research Analyst",
    goal="Find accurate and relevant information",
    backstory="You are an expert researcher with attention to detail.",
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Create engaging and informative content",
    backstory="You are a skilled writer who makes complex topics accessible.",
    verbose=True
)

# Define tasks
research_task = Task(
    description="Research the EU AI Act requirements for high-risk AI systems",
    agent=researcher,
    expected_output="A detailed summary of key requirements"
)

writing_task = Task(
    description="Write a compliance guide based on the research",
    agent=writer,
    expected_output="A clear, actionable compliance guide"
)

# Create crew with Protectron callback
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential,
    callbacks=[callback]  # Add Protectron here
)

# Run crew - all agent actions logged
result = crew.kickoff()
print(result)

Human Oversight Integration

For Article 14 compliance, add human oversight to critical crew actions:

from crewai import Agent, Task, Crew
from protectron.crewai import ProtectronCallback
from protectron import Protectron

callback = ProtectronCallback(system_id="supervised-crew")
protectron = Protectron(system_id="supervised-crew")

class SupervisedCrew:
    """Crew wrapper with human oversight capabilities."""
    
    def __init__(self, crew: Crew, require_approval_for: list[str] = None):
        self.crew = crew
        self.require_approval_for = require_approval_for or []
    
    def kickoff(self, approver_email: str):
        """Run crew with human oversight checkpoints."""
        
        # Pre-execution approval for sensitive crews
        if "crew_start" in self.require_approval_for:
            if not self._get_approval("crew_start", approver_email):
                return {"status": "rejected", "reason": "Crew execution not approved"}
        
        # Run the crew
        result = self.crew.kickoff()
        
        # Post-execution approval for critical outputs
        if "crew_output" in self.require_approval_for:
            approved = self._get_approval("crew_output", approver_email)
            if approved:
                protectron.log_human_approval(
                    action_type="crew_output",
                    action_details={"output": str(result)[:500]},
                    approved_by=approver_email
                )
            else:
                protectron.log_human_rejection(
                    action_type="crew_output",
                    action_details={"output": str(result)[:500]},
                    rejected_by=approver_email,
                    rejection_reason="Output not approved for use"
                )
        
        return {"status": "approved", "result": result}

# Usage
supervised = SupervisedCrew(
    crew=crew,
    require_approval_for=["crew_output"]
)
result = supervised.kickoff(approver_email="supervisor@company.com")

Monitoring Crew Performance

Your Protectron dashboard shows real-time crew execution status:

┌─────────────────────────────────────────────────────────────────┐
│                    Crew: content-creation-crew                   │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Status: Running                    Elapsed: 00:03:45           │
│                                                                  │
│  Agents:                                                         │
│  ┌──────────────────┬────────────┬───────────┬─────────────┐   │
│  │ Agent            │ Status     │ Actions   │ Tools Used  │   │
│  ├──────────────────┼────────────┼───────────┼─────────────┤   │
│  │ Research Analyst │ ✓ Complete │ 12        │ 5           │   │
│  │ Content Writer   │ ● Active   │ 3         │ 0           │   │
│  │ Editor           │ ○ Pending  │ 0         │ 0           │   │
│  └──────────────────┴────────────┴───────────┴─────────────┘   │
│                                                                  │
│  Tasks:                                                          │
│  [████████████████████░░░░░░░░░░] 2/3 Complete                  │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Crew Analytics

from protectron import Protectron

protectron = Protectron(system_id="my-crew")

# Get crew execution summary
summary = protectron.get_crew_summary(
    crew_run_id="run_abc123",
    include_agent_breakdown=True
)

# Returns:
# {
#   "total_duration": 245.3,
#   "tasks_completed": 3,
#   "total_agent_actions": 28,
#   "total_tool_calls": 12,
#   "agents": {
#     "Research Analyst": {"actions": 15, "tool_calls": 10},
#     "Content Writer": {"actions": 13, "tool_calls": 2}
#   },
#   "errors": 0,
#   "human_interventions": 1
# }