OpenAI

Documentation Index

Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt

Use this file to discover all available pages before exploring further.

OpenAI Python Tracing Tutorial (Google Colab)

OpenAI Python Tracing Tutorials on GitHub

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Prerequisites

• Python 3.9+ or Node.js 18+
• An Arize AX account (sign up)
• An OPENAI_API_KEY from the OpenAI Platform, or Azure OpenAI credentials

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Launch Arize AX

1. Sign in to your Arize AX account.
2. From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

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Install

pip install arize-otel openinference-instrumentation-openai openai

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Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="openai-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="openai-tracing-example"
export AZURE_OPENAI_API_KEY="<your-azure-key>"
export AZURE_OPENAI_ENDPOINT="<your-azure-endpoint>"
export AZURE_OPENAI_API_VERSION="<api-version>"  # optional

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Setup tracing

# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for OpenAI.")

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Run OpenAI

# example.py

# Importing instrumentation first ensures tracing is set up
# before `openai` is imported.
from instrumentation import tracer_provider

import openai

# The client reads OPENAI_API_KEY from the environment.
client = openai.OpenAI()

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "user",
            "content": "Write a haiku about observability.",
        },
    ],
)

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

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Expected output

Arize AX tracing initialized for OpenAI.
Logs whisper softly,
metrics rise like morning mist —
truth in every span.

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Verify in Arize AX

1. Open your Arize AX space and select project openai-tracing-example.
2. You should see a new trace within ~30 seconds containing an LLM span — ChatCompletion for the Python SDK or OpenAI Chat Completions for the Node.js SDK — with the prompt, response, and token usage attached.
3. If no traces appear, see Troubleshooting.

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Troubleshooting

• No traces in Arize AX. Confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs the example. Enable OpenTelemetry debug logs with export OTEL_LOG_LEVEL=debug and re-run.
• OpenAI spans missing but other spans present (Python). OpenAIInstrumentor().instrument(...) must run before any import openai in the application. Make sure instrumentation.py is the first import in your entry point.
• OpenAI spans missing but other spans present (TypeScript). instrumentation.manuallyInstrument(OpenAI) must run before any code creates an OpenAI client. Make sure import { provider } from "./instrumentation" (or a side-effect-only import "./instrumentation") is the first import in your entry point.
• 401 from OpenAI. Verify OPENAI_API_KEY is set and has access to the model in the example. Swap gpt-5 for a model your key can call.
• Azure OpenAI returns Resource not found. Confirm AZURE_OPENAI_ENDPOINT points to your deployment, AZURE_OPENAI_API_VERSION matches a version your deployment supports, and the example uses the Azure client constructor (openai.AzureOpenAI() / new AzureOpenAI()) rather than the standard OpenAI client.
• TypeScript process exits before spans flush. With SimpleSpanProcessor, spans are sent immediately, but make sure to await provider.forceFlush() (or call provider.shutdown()) before the process exits to avoid losing trailing spans.

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Resources