The OpenTelemetry SDK provides direct, fine-grained control over instrumentation in your LLM applications. Unlike automatic instrumentation libraries, the SDK allows you to manually create spans and set attributes exactly where and how you need them.

Using the OpenTelemetry SDK directly with Portkey gives you complete control over what gets traced while benefiting from Portkey’s intelligent gateway features like caching, fallbacks, and load balancing.

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Why OpenTelemetry SDK + Portkey?

Full Control

Manually instrument exactly what you need with custom spans and attributes

Production Ready

Battle-tested OpenTelemetry standard used by enterprises worldwide

Custom Attributes

Add any metadata you need to traces for debugging and analysis

Gateway Intelligence

Portkey adds routing optimization and resilience to your LLM calls

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Quick Start

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Prerequisites

  • Python
  • Portkey account with API key
  • OpenAI API key (or use Portkey’s virtual keys)

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Step 1: Install Dependencies

Install the required packages:

pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http openai portkey-ai

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Step 2: Configure OpenTelemetry

Set up the tracer provider and OTLP exporter:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

# Setup tracer provider
provider = TracerProvider()
trace.set_tracer_provider(provider)

# Configure OTLP exporter to send to Portkey
otlp_exporter = OTLPSpanExporter(
    endpoint="https://api.portkey.ai/v1/logs/otel/v1/traces",
    headers={
        "x-portkey-api-key": "YOUR_PORTKEY_API_KEY",
    }
)

# Add batch span processor (recommended for production)
span_processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(span_processor)

# Get tracer
tracer = trace.get_tracer(__name__)

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Step 3: Configure Portkey Gateway

Set up the OpenAI client with Portkey’s gateway:

from openai import OpenAI
from portkey_ai import createHeaders

# Use Portkey's gateway for intelligent routing
client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_VIRTUAL_KEY"  # Optional: Use Portkey's secure key management
    )
)

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Step 4: Create Instrumented Functions

Manually instrument your LLM calls with custom spans:

def generate_ai_response(input_text):
    with tracer.start_as_current_span("OpenAI-Chat-Completion") as span:
        # Add input attributes
        span.set_attribute("input.value", input_text)
        span.set_attribute("model.name", "gpt-4")
        span.set_attribute("temperature", 0.7)
        span.set_attribute("gen_ai.prompt.0.role", "user")
        span.set_attribute("gen_ai.prompt.0.content", input_text)

        # Make the API call
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": input_text}],
            model="gpt-4",
            temperature=0.7,
        )

        # Add response attributes
        output_content = response.choices[0].message.content
        span.set_attribute("output.value", output_content)
        span.set_attribute("gen_ai.completion.0.role", "assistant")
        span.set_attribute("gen_ai.completion.0.content", output_content)

        return output_content

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Complete Example

Here’s a full working example:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from openai import OpenAI
from portkey_ai import createHeaders

# Step 1: Setup OpenTelemetry
provider = TracerProvider()
trace.set_tracer_provider(provider)

otlp_exporter = OTLPSpanExporter(
    endpoint="https://api.portkey.ai/v1/logs/otel/v1/traces",
    headers={"x-portkey-api-key": "YOUR_PORTKEY_API_KEY"}
)

span_processor = BatchSpanProcessor(otlp_exporter)
provider.add_span_processor(span_processor)

tracer = trace.get_tracer(__name__)

# Step 2: Configure Portkey Gateway
client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="YOUR_PORTKEY_API_KEY",
        virtual_key="YOUR_VIRTUAL_KEY"
    )
)

# Step 3: Create instrumented function
def generate_ai_response(input_text):
    with tracer.start_as_current_span("OpenAI-Chat-Completion") as span:
        # Set input attributes
        span.set_attribute("input.value", input_text)
        span.set_attribute("model.name", "gpt-4")
        span.set_attribute("temperature", 0.7)

        # Make API call
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": input_text}],
            model="gpt-4",
            temperature=0.7,
        )

        # Set output attributes
        output_content = response.choices[0].message.content
        span.set_attribute("output.value", output_content)

        return output_content

# Step 4: Use the instrumented function
if __name__ == "__main__":
    input_text = "Explain the concept of AI in 50 words"
    response = generate_ai_response(input_text)
    print(response)

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Next Steps

Configure Gateway

Set up intelligent routing, fallbacks, and caching

Explore Virtual Keys

Secure your API keys with Portkey’s vault

View Analytics

Analyze costs, performance, and usage patterns

Set Up Alerts

Configure alerts for anomalies and performance issues

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See Your Traces in Action

Once configured, navigate to the Portkey dashboard to see your custom OpenTelemetry traces enhanced with gateway intelligence: