Pydantic Logfire

pip install logfire openai

import os
import logfire

# Configure OpenTelemetry export to the AI Gateway
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://aigw.portkey.ai/v1/logs/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "Authorization=Bearer YOUR_PORTKEY_API_KEY"

# Initialize Logfire
logfire.configure(
    service_name='my-llm-app',
    send_to_logfire=False,  # Disable sending to Logfire cloud
)

# Instrument OpenAI globally
logfire.instrument_openai()

import logfire
import os
from openai import OpenAI

# Configure OpenTelemetry export
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://aigw.portkey.ai/v1/logs/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "Authorization=Bearer YOUR_PORTKEY_API_KEY"

# Initialize Logfire
logfire.configure(
    service_name='my-llm-app',
    send_to_logfire=False,
)

# Create OpenAI client with the AI Gateway
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
    }
)

# Instrument the Portkey-configured client
logfire.instrument_openai(client)

# Simple chat completion - automatically traced
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain the benefits of observability in LLM applications"}],
    temperature=0.7
)

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