import argovaa
from openai import OpenAI

# Initialise with your Argovaa API key
argovaa.init(
  api_key="arg_live_xxxxxxxxxxxx",
  service_name="my-ai-service",
  environment="production"
)

# Wrap your existing LLM client
client = argovaa.wrap(OpenAI())

# Use exactly as before — tracking is automatic
response = client.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": prompt}],
  # Argovaa captures: tokens, latency, cost, model
)

# Optionally tag requests for cost attribution
with argovaa.trace(
  user_id=user_id,
  feature="document-summary",
  tenant=tenant_id       # for multi-tenant billing
):
  result = client.chat.completions.create(...)

import { Argovaa } from '@argovaa/sdk';
import OpenAI from 'openai';

// Initialise Argovaa
const argovaa = new Argovaa({
  apiKey: 'arg_live_xxxxxxxxxxxx',
  serviceName: 'my-ai-service',
  environment: 'production',
});

// Wrap your OpenAI client
const openai = argovaa.wrap(new OpenAI());

// Tracking is fully automatic from here
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: prompt }],
});

// Tag for multi-tenant cost attribution
await argovaa.trace({
  userId: userId,
  feature: 'document-summary',
  tenant: tenantId,
  customCost: 0.002  // optional fixed overhead
}, async () => {
  return openai.chat.completions.create({...});
});