Traceloop (OpenLLMetry)

pip install openai traceloop-sdk

from traceloop.sdk import Traceloop

# Initialize Traceloop with the AI Gateway's endpoint
Traceloop.init(
    disable_batch=True,  # Process traces immediately
    api_endpoint="https://aigw.portkey.ai/v1/logs/otel",
    headers="Authorization=Bearer YOUR_PORTKEY_API_KEY",
    telemetry_enabled=False  # Disable Traceloop's own telemetry
)

from openai import OpenAI

# Use the AI Gateway for intelligent routing
client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1",
    default_headers={
        "x-portkey-provider": "@openai-prod",  # Your AI Provider slug from Model Catalog
    }
)

# Make calls through the AI Gateway
# Traceloop automatically instruments the call
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain the benefits of OpenTelemetry for LLM applications"}],
    temperature=0.7
)

print(response.choices[0].message.content)

# You now get:
# 1. Automatic, non-intrusive tracing from Traceloop
# 2. Gateway features (caching, fallbacks, routing)
# 3. Combined insights in Strata Cloud Manager

from traceloop.sdk import Traceloop
from openai import OpenAI

# Step 1: Initialize Traceloop with the AI Gateway endpoint
Traceloop.init(
    disable_batch=True,
    api_endpoint="https://aigw.portkey.ai/v1/logs/otel",
    headers="Authorization=Bearer YOUR_PORTKEY_API_KEY",
    telemetry_enabled=False
)

# Step 2: Configure the AI Gateway
client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1"
)

# Step 3: Make instrumented calls
response = client.chat.completions.create(
    model="@openai-prod/gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What makes observability important for production AI?"}
    ]
)

print(response.choices[0].message.content)