OpenAI Integration

Trace every OpenAI call your agents make: model parameters, token usage, response content, and cost, captured by wrapping chat.completions.create in the OpenAI Python SDK.

Enforcement scope: this integration is observability-only. It wraps the LLM call (chat.completions.create), not tool execution, so it records what the model does but does not block, throttle, steer, or gate tool calls. Use it for tracing, log, and alert policies. To enforce policies on tool calls, instrument the agent framework that runs the tools, for example the OpenAI Agents SDK or another tool-executing integration.

Installation

bash

pip install "strathon[openai]"

Setup

python

from strathon import Client, instrument

client = Client(
    api_key="stra_...",
    endpoint="http://localhost:4318",
)
instrument(client, frameworks=["openai"])

# Use the OpenAI SDK as normal — Strathon traces automatically.
import openai

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

What Gets Captured

Model: name, provider
Token usage: prompt tokens, completion tokens, total
Latency: request duration
Messages: system, user, assistant messages
Function/tool calls: name, arguments (if using tools)

Example Policy

Alert when an expensive model is used in development, so you can track spend (use alert or log; this surface observes, it does not block):

cel

attrs["gen_ai.request.model"] == "gpt-4o"
  && attrs["strathon.project.environment"] == "development"

Alert on high token usage:

cel

attrs["gen_ai.usage.total_tokens"] > 10000

Notes

The wrapper intercepts chat.completions.create (sync and async).
Streaming responses are traced with token counts at completion.
Works with openai Python SDK 1.0+.