Quickstart

1

Get an API key

Sign in at platform.opper.ai and create a key. Each key belongs to one project, so make a new project for each app or environment.Calls draw from your account balance, so add credits under Billing before your first call.Put the key in your shell:

export OPPER_API_KEY="your-api-key"

2

Pick an API and run your first call

There are two patterns. The Chat API is multi-turn chat with messages and tools, which suits chatbots and agents. The JSON API handles one-shot tasks: send an input, get a typed JSON object back. Pick the one that matches what you’re building.

Chat API
JSON API

Messages with roles, optional tools, multiple turns. It works with the OpenAI, Anthropic, and Google AI SDKs.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.opper.ai/v3/compat",
    api_key=os.environ["OPPER_API_KEY"],
)

response = client.chat.completions.create(
    model="openai/gpt-5-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

The same trick works with the Anthropic and Google AI SDKs. See Drop-in SDKs for all three.

One task per request. Send an input, describe the shape you want back, and get a typed JSON object. It works well for parsing, extraction, and multimodal jobs. Here we read a receipt image straight from a URL and pull out the structured data.

import os
from pydantic import BaseModel
from opperai import Opper

class Item(BaseModel):
    name: str
    quantity: float | None = None
    price: float

class Receipt(BaseModel):
    store_name: str
    date: str
    items: list[Item]
    subtotal: float
    tax: float
    total: float

opper = Opper(http_bearer=os.getenv("OPPER_API_KEY", ""))

result = opper.call(
    name="parse-receipt",
    model="anthropic/claude-sonnet-4-6",
    instructions="Extract all line items, totals, and metadata from this receipt image.",
    input={"image_url": "https://expensesreceipt.com/assets/img/standard-grocery-receipt-template.png"},
    input_schema={
        "type": "object",
        "properties": {"image_url": {"type": "string", "description": "URL of the receipt image"}},
        "required": ["image_url"],
    },
    output_schema=Receipt,
)
print(result.json_payload)

You get back a typed object:

{
  "store_name": "WALL-MART-SUPERSTORE",
  "date": "10/17/2020",
  "items": [
    {"name": "HAND TOWEL", "price": 2.97},
    {"name": "GATORADE",   "price": 2.00},
    {"name": "T-SHIRT",    "price": 16.88}
  ],
  "subtotal": 23.09,
  "tax": 4.18,
  "total": 27.27
}

3

See it in the platform

Open platform.opper.ai and click into the most recent trace. You’ll see your call, the model that ran it, the cost, the latency, and any Control Plane rules that fired.

Make your first call

What to do next

Pick the right API

Models

Drop-in SDKs

Control Plane

​Make your first call

​What to do next

Pick the right API

Models

Drop-in SDKs

Control Plane

Make your first call

What to do next