Custom instrumentation with @traced

We’ve introduced the @traced decorator, a powerful new way to capture function-level traces directly in your Python code.

• Automatically logs function inputs, outputs, and metadata
• Supports nested spans and custom span types (LLM, agent, tool, etc.)
• Works seamlessly with the Orq SDK initialization (no separate init required)
• Integrates with OpenTelemetry for end-to-end distributed tracing

This makes it easier than ever to debug, monitor, and observe your applications in real time.

import time

@traced
def process_user(user_id: str, action: str) -> dict:
    print('process user')
    """Process user data with custom attributes."""
    # Simulate some processing
    time.sleep(0.1)
    
    result = {
        "user_id": user_id,
        "action": action,
        "status": "completed",
        "timestamp": time.time()
    }
    
    return result

To get started, install the Orq.ai SDK for Python

pip install orq-ai-sdk