Python Agent Integration

​

Prerequisites

• Python 3.11 or later
• A Sellfern API key with the minimum scopes needed for the user’s goal
• An agent framework or model SDK of your choice

​

Store configuration

export SELLFERN_API_KEY="your_api_key_here"
export SELLFERN_API_URL="https://api.sellfern.com"

​

Make server-side API calls

import os
import requests

SELLFERN_API_URL = os.environ.get("SELLFERN_API_URL", "https://api.sellfern.com")
SELLFERN_API_KEY = os.environ["SELLFERN_API_KEY"]


def sellfern_get(path: str, params: dict | None = None) -> dict:
    response = requests.get(
        f"{SELLFERN_API_URL}{path}",
        headers={"x-api-key": SELLFERN_API_KEY},
        params=params,
        timeout=30,
    )
    response.raise_for_status()
    return response.json()

​

Design tool definitions safely

1. Give each tool one narrow job.
2. Match tool input schemas to the API Reference.
3. Request the minimum scope required for that tool.
4. For write tools, include an Idempotency-Key header and reuse it for retries of the same logical mutation.
5. Return Sellfern error codes to the agent without guessing about hidden resources or unsupported endpoints.

​

Agent workflow

1. Read https://docs.sellfern.com/llms.txt to discover the relevant documentation.
2. Read the product or developer page that explains the user’s goal.
3. Confirm the endpoint and schema in /api-reference.
4. Call the smallest required Sellfern tool.
5. If Sellfern returns 403 missing_scope, ask the user for a key with the minimum additional scope.
6. If Sellfern returns 404 not_found, do not infer that the resource exists in another organization.

​

Extending the agent