Extract OpenSea page data with AlterLab’s API. Use Python, Node.js, or cURL. Choose a tier that matches the page’s complexity. For most OpenSea pages, T3 — Stealth provides reliable results.

Why collect e-commerce data from OpenSea

OpenSea hosts millions of NFT listings and marketplace activity. Engineers scrape it for market research, price monitoring, and portfolio analysis. Public listings include titles, prices, rankings, and descriptions. Aggregating this data helps build dashboards, feed machine learning models, or power competitive intelligence tools.

Technical challenges

E-commerce sites like OpenSea apply anti-bot defenses. They check request headers, enforce rate limits, and sometimes require JavaScript execution to render content. Raw HTTP requests often fail because the server expects a full browser context. To bypass these barriers you need rotating proxies, realistic user agents, and sometimes headless browser support. AlterLab’s Smart Rendering API abstracts much of this complexity. Learn more about handling dynamic pages in the Smart Rendering API documentation.

Quick start with AlterLab API

Begin by installing the AlterLab SDK. The Getting started guide walks you through API key creation and authentication. Once you have a key you can call the scrape endpoint from Python, Node.js, or cURL.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://opensea.io/example-page")
print(response.text)

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://opensea.io/example-page");
console.log(response.text);

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://opensea.io/example-page"}'

These snippets hit a static page on OpenSea. For pages that load data via JSON APIs you can often retrieve the same information without full browser rendering. When JavaScript is required, set the tier to T3 or higher. The API will auto-escalate if a lower tier fails, so you only pay for the tier that succeeds.

Extracting structured data

OpenSea listings contain predictable HTML patterns. Identify the elements that hold the data you need. Common targets are the title container, price badge, ranking badge, and description block. Use CSS selectors that match the class names you observe in the page source.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://opensea.io/example-page",
    schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type": "number"},
            "rating": {"type": "number"},
            "description": {"type": "string"}
        }
    }
)
print(result.data)  # Typed JSON output

The extract method returns a Python dictionary that matches the schema you provide. This removes the need for manual string parsing and ensures type safety across your pipeline.

Structured JSON extraction with Cortex

Cortex adds AI powered extraction on top of the basic scrape endpoint. Define a JSON schema and let the service return typed data. This is useful when the page structure changes frequently.

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://opensea.io/example-page",
    schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type": "number"},
            "rating": {"type": "number"},
            "description": {"type": "string"}
        }
    }
)
print(result.data)  # Typed JSON output

The same schema can be reused across many URLs. Cortex handles the heavy lifting of locating the correct DOM nodes and mapping them to your schema.

Cost breakdown

Pricing depends on the tier you actually use. OpenSea pages typically need T3 — Stealth because of anti-bot checks. AlterLab auto-escalates, so you start at T1 and move up only when necessary.

For OpenSea, T3 is usually sufficient. You pay only for the tier that succeeds, keeping costs predictable