AI-Powered Structured Extraction from the Web

Who this is for

Engineers tired of writing brittle CSS selectors for every product page, job listing, or directory entry they need to ingest. The site redesigns twice a year, your selector breaks every time, and the alert that fires at 3am is always the same one.

fastCRW's structured extraction replaces the selector layer with a JSON Schema. You describe the fields, the LLM does the locating, and the response is already shaped for the database.

Why fastCRW for extraction

Three things matter for production extraction: the schema is the contract, the call is cheap to retry, and the inference is handled for you.

On the managed cloud, POST /v1/extract (docs.fastcrw.com/api-reference/extract/) is a convenience wrapper over /v1/scrape with formats: ["json"]. Self-hosters call POST /v1/scrape with jsonSchema in the body and get the same typed object back — there is no feature gap, just a different endpoint name.

Extraction runs on fastCRW's managed LLM, so there is nothing to wire up: no provider credentials, no model selection, no separate inference account. The managed LLM is available on paid plans (the Free plan has no LLM features), and the cost is folded into fastCRW's per-credit pricing — one predictable line item instead of a separate model invoice to reconcile.

For sites that need a real browser (most JS-heavy product pages do), the renderer field picks between http, lightpanda, and chrome with an automatic fallback chain, so the extraction call works without you having to know which engine the page needs.

The 5-step recipe

1. Describe the fields you want as a JSON Schema. Write a JSON Schema that mirrors the record you need — strings, numbers, enums, arrays. Be specific in field descriptions; the LLM uses them as inline prompts.
2. Pick managed /v1/extract or self-hosted /v1/scrape + jsonSchema. On the managed cloud, POST /v1/extract is a convenience wrapper. Self-hosters call POST /v1/scrape with formats ["json"] and jsonSchema in the body — same result, no convenience tax.
3. No keys to manage — extraction runs on the managed LLM. Structured extraction uses fastCRW's managed LLM, available on paid plans (the Free plan has no LLM features). You do not pass any model provider credentials; the inference is handled for you and billed in credits.
4. Validate the returned JSON against the schema. The response data.extract field carries the typed object. Re-validate client-side with ajv or pydantic so downstream code can fail loudly on the rare extraction miss.
5. Extract from many URLs in one call. /v1/extract accepts a urls array (up to 50 URLs per request) and returns a results array; for larger jobs, iterate /v1/scrape concurrently from your worker, or run a /v1/crawl and apply the schema inside the result loop.

# extract_product.py — run with: python3 extract_product.py
import os
import requests
from pydantic import BaseModel, HttpUrl, Field, ValidationError

CRW = "https://api.fastcrw.com/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['CRW_API_KEY']}"}

class Product(BaseModel):
    name: str = Field(description="Product display name")
    price_usd: float = Field(description="Current price in US dollars")
    in_stock: bool = Field(description="True if the buy button is enabled")
    image_url: HttpUrl | None = None

JSON_SCHEMA = {
    "type": "object",
    "required": ["name", "price_usd", "in_stock"],
    "properties": {
        "name": {"type": "string", "description": "Product display name"},
        "price_usd": {"type": "number", "description": "Current price in US dollars"},
        "in_stock": {"type": "boolean", "description": "True if the buy button is enabled"},
        "image_url": {"type": "string", "format": "uri"},
    },
}

def extract(url: str) -> Product:
    r = requests.post(
        f"{CRW}/scrape",
        json={
            "url": url,
            "formats": ["json"],
            "jsonSchema": JSON_SCHEMA,
        },
        headers=HEADERS,
        timeout=90,
    )
    r.raise_for_status()
    raw = r.json()["data"]["extract"]
    try:
        return Product(**raw)
    except ValidationError as e:
        raise RuntimeError(f"Schema drift on {url}: {e}") from e

if __name__ == "__main__":
    print(extract("https://example.com/products/widget-42").model_dump_json(indent=2))

Next steps

Full schema-mode docs and the managed extraction reference live at docs.fastcrw.com/api-reference/scrape/; managed-cloud credit pricing for /v1/extract is on fastcrw.com/pricing. Managed LLM extraction is available on paid plans; the Free plan has no LLM features.