OpenAI

Trace every OpenAI client call with Rius without touching your call sites.

Setup

pip install "glassflow-rius[openai]" openai

import rius
from openai import OpenAI
 
rius.init(api_key="ri_...", service_name="my-agent")
client = OpenAI()  # use the client exactly as before

npm install @arizeai/openinference-instrumentation-openai

import { init } from '@glassflow-ai/rius'
import OpenAI from 'openai'
 
const rius = init({ apiKey: 'ri_...', serviceName: 'my-agent' })
const client = new OpenAI()  // use the client exactly as before

What gets captured

Each chat completion becomes an LLM-kind span carrying the model, token usage, and input/output messages; cost is computed server-side from the model and tokens. The SDK maps the keys the integration’s package writes onto the canonical GenAI ones, including the request parameters, finish reasons, the completion id, and the tool definitions; see what the SDK normalizes. On streamed calls the Python integration also records time to first token, without any manual code.

A call OpenAI rejects, such as a 404 for an unknown model, a 429, or a timeout, is recorded as an ERROR span with error.type naming the error class (openai.NotFoundError, for example). In TypeScript this needs @arizeai/openinference-instrumentation-openai 4.4.1 or newer, which ends a rejected call’s span itself; @glassflow-ai/rius 1.1.0 and later require that version as their peer, so an older one gets a peer dependency warning.

Streaming and token usage

Streamed responses do not include usage by default; ask OpenAI to send it with the final chunk:

stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages,
    stream=True,
    stream_options={"include_usage": True},
)

const stream = await client.chat.completions.create({
  model: 'gpt-4o-mini',
  messages,
  stream: true,
  stream_options: { include_usage: true },
})

Without it, streamed spans have no token counts, and no tokens means no cost.

Verify

Run one call, then open the trace in the console: the span shows the resolved model (for example gpt-4o-mini-2024-07-18), input/output token counts, and a cost. Wrap the call in observe to see it nested inside your agent’s run instead of as a standalone trace.