Azure OpenAI (tracing)

Trace calls made through the openai package’s AzureOpenAI client. The instrumentation differs by language:

TypeScript uses the dedicated @respan/instrumentation-azure-openai instrumentor. It also supports the legacy @azure/openai OpenAIClient when that package is present.
Python has no separate Azure package. The AzureOpenAI client ships inside the openai package, so the OpenAI instrumentation (respan-instrumentation-openai) captures Azure OpenAI calls.

This tracing setup uses your Azure OpenAI credentials directly. To route requests through Respan instead, use the Azure OpenAI gateway setup.

Set up Respan

Create an account at platform.respan.ai and grab an API key .

Paste this into your coding agent:


Help me set up Respan tracing with the Azure OpenAI integration.
Follow https://www.respan.ai/docs/integrations/azure-openai-tracing.md

Setup

Install packages


pip install respan-ai respan-instrumentation-openai openai

Python has no dedicated Azure instrumentation package. The respan-instrumentation-openai OpenAIInstrumentor traces AzureOpenAI client calls because that client is part of the openai package.

Set environment variables


export AZURE_OPENAI_API_KEY="YOUR_AZURE_OPENAI_API_KEY"
export AZURE_OPENAI_ENDPOINT="https://YOUR_RESOURCE.openai.azure.com"
export OPENAI_API_VERSION="2024-10-21"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and OPENAI_API_VERSION are used for Azure OpenAI requests. RESPAN_API_KEY exports traces to Respan.

Initialize and run


import os
from openai import AzureOpenAI
from respan import Respan
from respan_instrumentation_openai import OpenAIInstrumentor

respan = Respan(instrumentations=[OpenAIInstrumentor()])

client = AzureOpenAI(
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    api_version=os.environ["OPENAI_API_VERSION"],
    azure_deployment="gpt-4o-mini",
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Say hello in one sentence."}],
)
print(response.choices[0].message.content)

View your trace

Open the Traces page to see Azure OpenAI chat, completion, embedding, and streaming spans.

Configuration

The configuration, attributes, and captured-operations below describe the TypeScript AzureOpenAIInstrumentor. In Python, configuration lives on the OpenAI instrumentation — see the OpenAI SDK tracing page.

Parameter	Type	Default	Description
`apiKey`	`string \| undefined`	`RESPAN_API_KEY`	Respan API key used to export traces.
`baseURL`	`string \| undefined`	`RESPAN_BASE_URL`	Optional Respan trace export API URL.
`instrumentations`	`RespanInstrumentation[]`	`[]`	Include `new AzureOpenAIInstrumentor()` to activate Azure OpenAI tracing.
`openAIModule`	`object \| undefined`	dynamic import	Pass the imported `openai` module when your app imports `AzureOpenAI` before initializing Respan.
`azureOpenAIModule`	`object \| undefined`	dynamic import	Optional legacy `@azure/openai` module for `OpenAIClient` applications.
`traceContent`	`boolean`	`true`	Capture prompt and completion content when spans are exported.

Attributes

With propagateAttributes

Override per-request attributes using a context scope.


import { AzureOpenAI } from "openai";
import * as OpenAI from "openai";
import { Respan } from "@respan/respan";
import { AzureOpenAIInstrumentor } from "@respan/instrumentation-azure-openai";

const respan = new Respan({
  instrumentations: [
    new AzureOpenAIInstrumentor({ openAIModule: OpenAI }),
  ],
});
await respan.initialize();

const client = new AzureOpenAI({
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  endpoint: process.env.AZURE_OPENAI_ENDPOINT,
  apiVersion: process.env.OPENAI_API_VERSION,
  deployment: "gpt-4o-mini",
});

async function handleRequest(userId: string, question: string) {
  await respan.propagateAttributes(
    {
      customer_identifier: userId,
      thread_identifier: "conv_abc_123",
      metadata: { plan: "pro" },
    },
    async () => {
      const response = await client.chat.completions.create({
        model: "gpt-4o-mini",
        messages: [{ role: "user", content: question }],
      });
      console.log(response.choices[0]?.message?.content);
    }
  );
}

Attribute	Type	Description
`customer_identifier`	`string`	Identifies the end user in Respan analytics.
`thread_identifier`	`string`	Groups related messages into a conversation.
`metadata`	`Record<string, unknown>`	Custom key-value pairs.

Captured operations

AzureOpenAI.chat.completions.create()
AzureOpenAI.completions.create()
AzureOpenAI.embeddings.create()
Streaming chat and text completion calls
Legacy @azure/openai OpenAIClient chat, completion, embedding, and streaming methods

Gateway

See Azure OpenAI gateway setup to route calls through the Respan gateway.