OpenAI OpenTelemetry Integration

Overview

This integration provides support for using OpenTelemetry with the OpenAI framework. It enables tracing and monitoring of applications built with OpenAI.

Installation

1. Install traceAI OpenAI

pip install traceAI-openai

Set environment variables

Set the Future AGI keys for tracing and the OpenAI key for the Quickstart request:

export FI_API_KEY="your-future-agi-api-key"
export FI_SECRET_KEY="your-future-agi-secret-key"
export OPENAI_API_KEY="your-openai-api-key"

Quickstart

Register Tracer Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="openai_app"
)

Configure OpenAI Instrumentation

Set up your OpenAI client with built-in observability. This includes support for text, image, and audio models.

from traceai_openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=trace_provider)

Make a traced request

Create the client after calling OpenAIInstrumentor().instrument(...).

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Tell me a short joke."}],
)
print(response.choices[0].message.content)
trace_provider.force_flush()  # Flush before a short-lived script exits.

Vercel AI Gateway

Use the same OpenAIInstrumentor for Chat Completions sent to Vercel AI Gateway; no gateway-specific package or OpenAI key is needed. Set AI_GATEWAY_API_KEY to your Vercel AI Gateway key and AI_GATEWAY_MODEL to a current model ID:

export AI_GATEWAY_API_KEY="your-vercel-ai-gateway-key"
export AI_GATEWAY_MODEL="<provider>/<model>"

Keep the tracer registration and instrumentation from the Quickstart above, then replace its OpenAI client and request with:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://ai-gateway.vercel.sh/v1",
    api_key=os.environ["AI_GATEWAY_API_KEY"],
)
response = client.chat.completions.create(
    model=os.environ["AI_GATEWAY_MODEL"],
    messages=[{"role": "user", "content": "Say hello in five words."}],
)
print(response.choices[0].message.content)
trace_provider.force_flush()  # Flush before a short-lived script exits.

The documented gateway host is labelled gen_ai.provider.name="vercel". This identifies the service the client calls, not the upstream model provider or a fallback provider. Unknown proxies and look-alike hosts retain their existing provider label. Sync, async, streamed and failed calls use the same attribution.

Client spans capture prompts, responses and returned token usage. They do not reconstruct gateway routing, retries, cost or provider attempts. Vercel's separate Trace Drains provide those gateway details but omit prompts and completions. A drain is not configured by OpenAIInstrumentor; do not sum client and drain token counts for the same request as separate model usage.