Agent Communication

# Standard inter-agent message format
from datetime import datetime
from typing import Any

def create_message(
    from_agent: str,
    to_agent: str,
    message_type: str,
    payload: Any,
    correlation_id: str = None
) -> dict:
    """
    Create a structured inter-agent message.

    message_type options:
    - 'task_assignment': coordinator → worker
    - 'task_result': worker → coordinator
    - 'review_request': coordinator → reviewer
    - 'review_result': reviewer → coordinator
    - 'escalation': any → human
    """
    return {
        "message_id": f"MSG-{datetime.now().strftime('%Y%m%d%H%M%S')}",
        "correlation_id": correlation_id,  # Links request and response
        "from_agent": from_agent,
        "to_agent": to_agent,
        "message_type": message_type,
        "timestamp": datetime.now().isoformat(),
        "payload": payload
    }

# Coordinator assigns a task to a research agent
task_msg = create_message(
    from_agent="coordinator",
    to_agent="researcher_1",
    message_type="task_assignment",
    payload={
        "task_id": "T1",
        "instruction": "Find the top 5 Python AI frameworks released in 2024",
        "context": {"max_results": 5},
        "deadline": "2024-01-15T15:00:00Z",
    },
    correlation_id="WF-001-T1"
)

# Research agent returns a result
result_msg = create_message(
    from_agent="researcher_1",
    to_agent="coordinator",
    message_type="task_result",
    payload={
        "task_id": "T1",
        "status": "success",
        "output": {
            "frameworks": ["TensorFlow", "PyTorch", "JAX", "Keras", "MXNet"],
            "sources": ["https://example.com"],
        }
    },
    correlation_id="WF-001-T1"  # Same ID links request and response
)

print("Task message type:", task_msg["message_type"])
print("Result correlation:", result_msg["correlation_id"])
print("Same workflow?", task_msg["correlation_id"] == result_msg["correlation_id"])