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"

Ollama and self-hosted OpenAI-compatible clients

Keep using this package when your application calls Ollama through the OpenAI SDK. The official https://ollama.com/v1 endpoint is attributed to ollama automatically. A local address can host many providers, so set the provider explicitly for each local or self-hosted client:

from openai import OpenAI
from traceai_openai import OpenAIInstrumentor, set_client_provider

# Use the trace provider configured in the quickstart below.
OpenAIInstrumentor().instrument(tracer_provider=trace_provider)

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
set_client_provider(client, provider="ollama")

The override controls gen_ai.provider.name on sync and async requests, including streams, embeddings, Responses and failures. It changes tracing metadata only; it adds no spans and sends no provider setting to the server. Use a lowercase provider identifier (1–64 characters, starting with a letter; letters, digits, ., _ and - are accepted), not a URL or secret.

Set the override before making requests. It takes precedence over host detection; set_client_provider(client, provider=None) restores host detection. Other clients are unaffected. A client returned by copy() or with_options() is a separate instance: set its override explicitly too, especially if its endpoint changes. Unconfigured loopback, proxy and unknown hosts retain the historical openai label; the instrumentor never assumes a local server is Ollama.

For applications using the native ollama Python client instead, use traceai-ollama. Do not install both instrumentors for the same API call. See Ollama's OpenAI compatibility guide for supported endpoints and models.

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

# OpenAI reads OPENAI_API_KEY from your environment.
with OpenAI() as client:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Can you tell me a joke?"},
        ],
    )
    print((response.choices[0].message.content or "").strip())
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.