Trace Decorator

Basic Usage

Simple Trace Creation

The @trace decorator automatically creates a trace span that encompasses the entire function execution. You can optionally specify custom names and tags to better organize and categorize your traces:

from agentops.sdk.decorators import trace
import agentops

# Initialize AgentOps
agentops.init("your-api-key", auto_start_session=False)

@trace(name="customer-workflow", tags=["production", "customer-service"])
def my_workflow():
    """A simple workflow wrapped in a trace"""
    print("🚀 Starting customer workflow...")
    print("📋 Processing customer request...")
    # Your application logic here
    print("✅ Customer workflow completed successfully!")
    return "Workflow completed"

# Run the function - this creates and manages the trace automatically
print("🎬 Running traced workflow...")
result = my_workflow()
print(f"📊 Result: {result}")

Both name and tags parameters are optional. If no name is provided, the function name will be used as the trace name.

Custom Trace Names

You can specify custom names for your traces:

@trace(name="customer-onboarding-flow")
def onboard_customer(customer_data):
    """Customer onboarding process"""
    print(f"👋 Onboarding customer: {customer_data['name']}")
    print("📝 Creating customer profile...")
    print("📧 Sending welcome email...")
    print("✅ Customer onboarding complete!")
    return f"Onboarded customer: {customer_data['name']}"

@trace(name="data-processing-pipeline")
def process_data(input_data):
    """Data processing workflow"""
    print(f"📊 Processing {len(input_data)} data items...")
    print("🔄 Applying transformations...")
    print("✅ Data processing complete!")
    return f"Processed {len(input_data)} items"

# Usage examples
customer = {"name": "Alice Johnson", "email": "alice@example.com"}
result1 = onboard_customer(customer)
print(f"📋 Onboarding result: {result1}")

data_items = ["item1", "item2", "item3", "item4", "item5"]
result2 = process_data(data_items)
print(f"📋 Processing result: {result2}")

Adding Tags to Traces

Tags help categorize and filter traces in your dashboard:

@trace(tags=["production", "high-priority"])
def critical_workflow():
    """Critical production workflow"""
    print("🚨 Executing critical production workflow...")
    print("⚡ High priority processing...")
    print("✅ Critical task completed successfully!")
    return "Critical task completed"

@trace(name="user-analysis", tags=["analytics", "user-behavior"])
def analyze_user_behavior(user_id):
    """Analyze user behavior patterns"""
    print(f"🔍 Analyzing behavior for user: {user_id}")
    print("📈 Gathering user interaction data...")
    print("🧠 Running behavior analysis algorithms...")
    print("✅ User behavior analysis complete!")
    return f"Analysis complete for user {user_id}"

# Usage examples
print("🎬 Running critical workflow...")
result1 = critical_workflow()
print(f"📊 Critical workflow result: {result1}")

print("\n🎬 Running user analysis...")
result2 = analyze_user_behavior("user_12345")
print(f"📊 Analysis result: {result2}")

Integration with Other Decorators

Combining with Agent and Operation Decorators

The @trace decorator works seamlessly with other AgentOps decorators:

import agentops
from agentops.sdk.decorators import trace, agent, operation, tool

# Initialize AgentOps without auto-starting session since we use @trace
agentops.init("your-api-key", auto_start_session=False)

@agent
class DataAnalysisAgent:
    def __init__(self):
        print("🤖 DataAnalysisAgent initialized")
    
    @operation
    def collect_data(self, source):
        print(f"📊 Collecting data from {source}...")
        data = f"Data collected from {source}"
        print(f"✅ Data collection complete: {data}")
        return data
    
    @tool(cost=0.05)
    def analyze_data(self, data):
        print(f"🧠 Analyzing data: {data}")
        analysis = f"Analysis of {data}"
        print(f"✅ Analysis complete: {analysis}")
        return analysis
    
    @operation
    def generate_report(self, analysis):
        print(f"📝 Generating report from: {analysis}")
        report = f"Report: {analysis}"
        print(f"✅ Report generated: {report}")
        return report

@trace(name="complete-analysis-workflow")
def run_analysis_workflow(data_source):
    """Complete data analysis workflow"""
    print(f"🚀 Starting analysis workflow for: {data_source}")
    print("=" * 50)
    
    agent = DataAnalysisAgent()
    
    # Collect data
    print("\n📋 Step 1: Data Collection")
    data = agent.collect_data(data_source)
    
    # Analyze data
    print("\n📋 Step 2: Data Analysis")
    analysis = agent.analyze_data(data)
    
    # Generate report
    print("\n📋 Step 3: Report Generation")
    report = agent.generate_report(analysis)
    
    print("\n🎉 Workflow completed successfully!")
    print("=" * 50)
    
    return {
        "source": data_source,
        "report": report
    }

# Usage
print("🎬 Running complete analysis workflow...")
result = run_analysis_workflow("customer_database")
print(f"\n📊 Final Result:")
print(f"   Source: {result['source']}")
print(f"   Report: {result['report']}")

Async Function Support

The @trace decorator fully supports async functions:

import asyncio
import agentops
from agentops.sdk.decorators import trace, operation

# Initialize AgentOps without auto-starting session since we use @trace
agentops.init("your-api-key", auto_start_session=False)

@operation
async def fetch_user_data(user_id):
    """Simulate async data fetching"""
    print(f"🌐 Fetching data for user: {user_id}")
    await asyncio.sleep(1)  # Simulate API call
    data = f"User data for {user_id}"
    print(f"✅ Data fetched: {data}")
    return data

@operation
async def process_user_data(user_data):
    """Simulate async data processing"""
    print(f"⚙️ Processing user data: {user_data}")
    await asyncio.sleep(0.5)  # Simulate processing
    processed = f"Processed: {user_data}"
    print(f"✅ Processing complete: {processed}")
    return processed

@trace(name="async-user-workflow")
async def async_user_workflow(user_id):
    """Async workflow for user processing"""
    print(f"🚀 Starting async workflow for user: {user_id}")
    print("=" * 45)
    
    print("\n📋 Step 1: Fetching user data")
    user_data = await fetch_user_data(user_id)
    
    print("\n📋 Step 2: Processing user data")
    processed_data = await process_user_data(user_data)
    
    print("\n🎉 Async workflow completed!")
    print("=" * 45)
    
    return processed_data

# Usage
async def main():
    print("🎬 Running async user workflow...")
    result = await async_user_workflow("user_123")
    print(f"\n📊 Final Result: {result}")
    print("✨ Check your AgentOps dashboard to see the traced async workflow!")

# Run the async workflow
print("🔄 Starting async demo...")
asyncio.run(main())

Error Handling and Trace States

Automatic Error Handling

The @trace decorator automatically handles exceptions and sets appropriate trace states:

import agentops
from agentops.sdk.decorators import trace

# Initialize AgentOps without auto-starting session since we use @trace
agentops.init("your-api-key", auto_start_session=False)

@trace(name="error-prone-workflow")
def risky_operation():
    """Operation that might fail"""
    import random
    
    print("🎲 Running risky operation...")
    print("⚠️ This operation has a 50% chance of failure")
    
    if random.random() < 0.5:
        print("❌ Operation failed!")
        raise ValueError("Random failure occurred")
    
    print("✅ Operation succeeded!")
    return "Operation succeeded"

# The trace will automatically be marked with failure state if an exception occurs
print("🎬 Testing automatic error handling...")
for i in range(3):
    print(f"\n🔄 Attempt {i+1}:")
    try:
        result = risky_operation()
        print(f"📊 Success: {result}")
        break
    except ValueError as e:
        print(f"📊 Operation failed: {e}")
        print("🔍 Trace automatically ended with error state")

Custom Error Handling

You can implement custom error handling within traced functions:

@trace(name="robust-workflow")
def robust_operation(data):
    """Operation with custom error handling"""
    print(f"🚀 Starting robust operation with data: {data}")
    
    try:
        # Risky operation
        if not data:
            print("⚠️ No data provided!")
            raise ValueError("No data provided")
        
        # Process data
        print("⚙️ Processing data...")
        result = f"Processed: {data}"
        print(f"✅ Processing successful: {result}")
        return {"success": True, "result": result}
        
    except ValueError as e:
        # Handle specific errors
        print(f"❌ Validation error: {e}")
        return {"success": False, "error": str(e)}
    except Exception as e:
        # Handle unexpected errors
        print(f"💥 Unexpected error: {e}")
        return {"success": False, "error": f"Unexpected error: {str(e)}"}

# Usage examples
print("\n🎬 Testing custom error handling...")

print("\n📋 Test 1: Valid data")
result1 = robust_operation("valid_data")
print(f"📊 Result: {result1}")

print("\n📋 Test 2: Empty data")
result2 = robust_operation("")
print(f"📊 Result: {result2}")

print("\n📋 Test 3: None data")
result3 = robust_operation(None)
print(f"📊 Result: {result3}")

Real-World Examples

E-commerce Order Processing

from agentops.sdk.decorators import trace, agent, operation, tool
from openai import OpenAI
import agentops

agentops.init("your-api-key", auto_start_session=False)

@agent
class OrderProcessor:
    def __init__(self):
        print("🛒 OrderProcessor initialized")
    
    @tool(cost=0.01)
    def validate_payment(self, payment_info):
        """Payment validation service"""
        print(f"💳 Validating payment: {payment_info['card']}")
        result = {"valid": True, "transaction_id": "txn_123"}
        print(f"✅ Payment validation successful: {result['transaction_id']}")
        return result
    
    @tool(cost=0.02)
    def check_inventory(self, product_id, quantity):
        """Inventory check service"""
        print(f"📦 Checking inventory for {product_id} (qty: {quantity})")
        result = {"available": True, "reserved": quantity}
        print(f"✅ Inventory check complete: {quantity} units available")
        return result
    
    @operation
    def calculate_shipping(self, address, items):
        """Calculate shipping costs"""
        print(f"🚚 Calculating shipping to {address['city']}, {address['state']}")
        result = {"cost": 9.99, "method": "standard"}
        print(f"✅ Shipping calculated: ${result['cost']} ({result['method']})")
        return result
    
    @tool(cost=0.005)
    def send_confirmation_email(self, email, order_details):
        """Email service"""
        print(f"📧 Sending confirmation email to {email}")
        result = f"Confirmation sent to {email}"
        print(f"✅ Email sent successfully")
        return result

@trace(name="order-processing", tags=["ecommerce", "orders"])
def process_order(order_data):
    """Complete order processing workflow"""
    print(f"🚀 Starting order processing for {order_data['customer_email']}")
    print("=" * 60)
    
    processor = OrderProcessor()
    
    try:
        # Validate payment
        print("\n📋 Step 1: Payment Validation")
        payment_result = processor.validate_payment(order_data["payment"])
        if not payment_result["valid"]:
            print("❌ Payment validation failed!")
            return {"success": False, "error": "Payment validation failed"}
        
        # Check inventory for all items
        print("\n📋 Step 2: Inventory Check")
        for item in order_data["items"]:
            inventory_result = processor.check_inventory(
                item["product_id"], 
                item["quantity"]
            )
            if not inventory_result["available"]:
                print(f"❌ Item {item['product_id']} not available!")
                return {"success": False, "error": f"Item {item['product_id']} not available"}
        
        # Calculate shipping
        print("\n📋 Step 3: Shipping Calculation")
        shipping = processor.calculate_shipping(
            order_data["shipping_address"], 
            order_data["items"]
        )
        
        # Send confirmation
        print("\n📋 Step 4: Confirmation Email")
        confirmation = processor.send_confirmation_email(
            order_data["customer_email"],
            {
                "items": order_data["items"],
                "shipping": shipping,
                "payment": payment_result
            }
        )
        
        print("\n🎉 Order processing completed successfully!")
        print("=" * 60)
        
        return {
            "success": True,
            "order_id": "ORD_12345",
            "payment": payment_result,
            "shipping": shipping,
            "confirmation": confirmation
        }
        
    except Exception as e:
        print(f"💥 Order processing failed: {e}")
        return {"success": False, "error": str(e)}

# Usage
print("🎬 Running e-commerce order processing demo...")

order = {
    "customer_email": "customer@example.com",
    "payment": {"card": "****1234", "amount": 99.99},
    "items": [{"product_id": "PROD_001", "quantity": 2}],
    "shipping_address": {"city": "New York", "state": "NY"}
}

result = process_order(order)

print(f"\n📊 ORDER PROCESSING RESULT:")
print(f"   Success: {result['success']}")
if result['success']:
    print(f"   Order ID: {result['order_id']}")
    print(f"   Transaction: {result['payment']['transaction_id']}")
    print(f"   Shipping: ${result['shipping']['cost']}")
else:
    print(f"   Error: {result['error']}")

Data Analysis Workflow

from agentops.sdk.decorators import trace, agent, operation, tool
from openai import OpenAI
import agentops

agentops.init("your-api-key", auto_start_session=False)

@agent
class DataAnalysisAgent:
    def __init__(self):
        self.client = OpenAI()
        print("🤖 DataAnalysisAgent initialized")
    
    @operation
    def collect_data(self, source):
        """Simulate data collection"""
        print(f"📊 Collecting data from {source}...")
        data = f"Raw data collected from {source}: [sample_data_1, sample_data_2, sample_data_3]"
        print(f"✅ Data collection complete: {len(data)} characters collected")
        return data
    
    @operation
    def analyze_data_with_llm(self, data):
        """Use LLM to analyze the collected data"""
        print("🧠 Analyzing data with LLM...")
        response = self.client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a data analyst. Analyze the provided data and give insights."},
                {"role": "user", "content": f"Please analyze this data: {data}"}
            ]
        )
        analysis = response.choices[0].message.content
        print(f"✅ LLM analysis complete: {len(analysis)} characters generated")
        return analysis
    
    @tool(cost=0.05)
    def generate_visualization(self, analysis):
        """Generate data visualization"""
        print("📈 Generating visualization...")
        visualization = f"Chart generated for: {analysis[:50]}..."
        print(f"✅ Visualization generated: {visualization}")
        return visualization
    
    @operation
    def generate_report(self, analysis, visualization):
        """Generate final report using LLM"""
        print("📝 Generating final report with LLM...")
        response = self.client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a report writer. Create a professional data analysis report."},
                {"role": "user", "content": f"Create a report based on this analysis: {analysis} and visualization: {visualization}"}
            ]
        )
        report = response.choices[0].message.content
        print(f"✅ Final report generated: {len(report)} characters")
        return report

@trace(name="data-analysis-workflow", tags=["analytics", "reporting"])
def run_data_analysis(data_source):
    """Complete data analysis workflow with LLM integration"""
    print(f"🚀 Starting data analysis workflow for: {data_source}")
    print("=" * 60)
    
    agent = DataAnalysisAgent()
    
    # Collect data
    print("\n📋 Step 1: Data Collection")
    raw_data = agent.collect_data(data_source)
    
    # Analyze data using LLM
    print("\n📋 Step 2: LLM Analysis")
    analysis = agent.analyze_data_with_llm(raw_data)
    
    # Generate visualization
    print("\n📋 Step 3: Visualization Generation")
    visualization = agent.generate_visualization(analysis)
    
    # Generate final report using LLM
    print("\n📋 Step 4: Report Generation")
    report = agent.generate_report(analysis, visualization)
    
    print("\n🎉 Workflow completed successfully!")
    print("=" * 60)
    
    return {
        "source": data_source,
        "raw_data": raw_data,
        "analysis": analysis,
        "visualization": visualization,
        "final_report": report
    }

# Usage
print("🎬 Running data analysis workflow demo...")

result = run_data_analysis("customer_database")

print(f"\n📊 ANALYSIS RESULTS:")
print(f"   Data Source: {result['source']}")
print(f"   Raw Data: {result['raw_data'][:80]}...")
print(f"   Analysis Preview: {result['analysis'][:100]}...")
print(f"   Visualization: {result['visualization']}")
print(f"   Final Report Preview: {result['final_report'][:150]}...")

print(f"\n✨ Analysis complete! Check your AgentOps dashboard to see the traced workflow.")

Best Practices

1. Use Meaningful Names

Choose descriptive names that clearly indicate what the trace represents:

# Good
@trace(name="user-authentication-flow")
def authenticate_user(credentials):
    pass

@trace(name="payment-processing-pipeline")
def process_payment(payment_data):
    pass

# Less descriptive
@trace(name="trace1")
def some_function():
    pass

2. Add Relevant Tags

Use tags to categorize traces for easier filtering and analysis:

@trace(name="order-fulfillment", tags=["ecommerce", "fulfillment", "high-priority"])
def fulfill_order(order_id):
    pass

@trace(name="data-sync", tags=["background-job", "data-processing"])
def sync_data():
    pass

3. Keep Traces Focused

Each trace should represent a logical unit of work:

# Good - focused on a single workflow
@trace(name="customer-onboarding")
def onboard_customer(customer_data):
    validate_customer(customer_data)
    create_account(customer_data)
    send_welcome_email(customer_data)

# Less focused - mixing different concerns
@trace(name="mixed-operations")
def do_everything():
    onboard_customer(data1)
    process_orders(data2)
    generate_reports(data3)

4. Handle Errors Appropriately

Implement proper error handling within traced functions:

@trace(name="data-processing")
def process_data(data):
    try:
        # Main processing logic
        result = complex_processing(data)
        return {"success": True, "result": result}
    except ValidationError as e:
        # Expected errors
        return {"success": False, "error": "validation_failed", "details": str(e)}
    except Exception as e:
        # Unexpected errors
        logger.error(f"Unexpected error in data processing: {e}")
        return {"success": False, "error": "processing_failed"}

The @trace decorator provides a powerful and flexible way to organize your application’s telemetry data. By creating logical groupings of operations, you can better understand your application’s behavior and performance characteristics in the AgentOps dashboard.

Getting Started

Concepts

Integrations

Usage

Other Info

Basic Usage

Simple Trace Creation

Custom Trace Names

Adding Tags to Traces

Integration with Other Decorators

Combining with Agent and Operation Decorators

Async Function Support

Error Handling and Trace States

Automatic Error Handling

Custom Error Handling

Real-World Examples

E-commerce Order Processing

Data Analysis Workflow

Best Practices

1. Use Meaningful Names

2. Add Relevant Tags

3. Keep Traces Focused

4. Handle Errors Appropriately

Getting Started

Concepts

Integrations

Usage

Other Info

​Basic Usage

​Simple Trace Creation

​Custom Trace Names

​Adding Tags to Traces

​Integration with Other Decorators

​Combining with Agent and Operation Decorators

​Async Function Support

​Error Handling and Trace States

​Automatic Error Handling

​Custom Error Handling

​Real-World Examples

​E-commerce Order Processing

​Data Analysis Workflow

​Best Practices

​1. Use Meaningful Names

​2. Add Relevant Tags

​3. Keep Traces Focused

​4. Handle Errors Appropriately

Basic Usage

Simple Trace Creation

Custom Trace Names

Adding Tags to Traces

Integration with Other Decorators

Combining with Agent and Operation Decorators

Async Function Support

Error Handling and Trace States

Automatic Error Handling

Custom Error Handling

Real-World Examples

E-commerce Order Processing

Data Analysis Workflow

Best Practices

1. Use Meaningful Names

2. Add Relevant Tags

3. Keep Traces Focused

4. Handle Errors Appropriately