Scrapfly Extraction

Scrapfly Extraction

Use the Scrapfly Extraction API to extract structured data from HTML, markdown, or text using LLM prompts, pre-trained AI models, or custom extraction templates.

When to use

• Extracting structured data from web page content
• Using natural language prompts to pull specific information from documents
• Extracting product, article, review, or real estate data with auto AI models
• Parsing HTML/markdown into structured formats with custom templates
• Asking questions about document content and getting AI-powered answers

Setup

pip install scrapfly-sdk

The API key must be provided via environment variable SCRAPFLY_API_KEY or passed directly to the client.

API Reference

Endpoint: POST https://api.scrapfly.io/extraction

ScrapflyClient

from scrapfly import ScrapflyClient, ExtractionConfig
import os

client = ScrapflyClient(key=os.environ["SCRAPFLY_API_KEY"])

ExtractionConfig Parameters

You must provide exactly one of: extraction_prompt, extraction_model, or extraction_template.

Three Extraction Methods

1. LLM Prompt Extraction

Use natural language to describe what data to extract. The AI interprets the content and returns structured results.

2. Auto AI Models

Pre-trained models for common data types. Returns standardized schemas with quality scores.

• "article" - News/blog articles (title, author, date, content, etc.)
• "event" - Events (name, date, location, description, etc.)
• "food_recipe" - Recipes (ingredients, steps, servings, etc.)
• "hotel" - Single hotel/property (name, amenities, rating, etc.)
• "hotel_listing" - Hotel search/list results
• "job_listing" - Job search/list results
• "job_posting" - Single job (title, company, salary, description, etc.)
• "organization" - Company/organization (name, contact, description, etc.)
• "product" - E-commerce product (name, price, description, images, etc.)
• "product_listing" - Product search/category listing
• "real_estate_property" - Single property (price, address, features, etc.)
• "real_estate_property_listing" - Property search/list results
• "review_list" - Lists of reviews (reviewer, rating, text, date, etc.)
• "search_engine_results" - SERP data (results, snippets, etc.)
• "social_media_post" - Social post (author, content, engagement, etc.)
• "software" - Software/app (name, description, pricing, etc.)
• "stock" - Stock/market data
• "vehicle_ad" - Single vehicle listing
• "vehicle_ad_listing" - Vehicle search/list results

3. Custom Templates

Structured extraction rules for consistent parsing across similar pages. Can be defined inline (a JSON object), or stored on Scrapfly and referenced by slug.

Saved Templates (reference by slug)

If you have saved a template in the Scrapfly dashboard (Extraction API → Templates), pass its slug as a plain string. The platform resolves it to the currently-published version and applies it:

result = client.extract(ExtractionConfig(
    body=html_content,
    content_type="text/html",
    url="https://example.com/product/1",
    extraction_template="product-card",
))

Saved templates support:

• A full version history with a separate publish pointer (publish a new version atomically; roll back at any time).
• Optional URL scope (match_domain / match_path) configured in the dashboard. Calls against non-matching URLs return ERR::EXTRACTION::TEMPLATE_URL_MISMATCH.
• Cache-on-read with a 60-second TTL: a newly-published version becomes live within one minute, no client redeployment needed.

The slug shape is lowercase letters / digits / dashes, 3 to 128 characters. See Saved Extraction Templates for the dashboard workflow.

Inline Template

Pass a Python dict to extract once without saving. Useful for one-off scripts and for templates that vary per call.

extraction_template = {
    "source": "html",
    "selectors": [    
        {
            "name": "title",
            "query": "h3.product-title::text",
            "type": "css",
            "formatters": [
                {
                    "name": "uppercase"
                }
            ],
        },
        {
            "name": "description",
            "query": "p.product-description::text",
            "type": "css"
        },
        {
            "extractor": {
                "name": "price"
            },
            "name": "price",
            "query": ".product-price::text",
            "type": "css"
        },
        {
            "name": "variants",
          	"query": "div.variants",
            "type": "css",
            "nested": [
                {
                    "name": "name",
                    "query": "//a[@data-variant-id]/@data-variant-id",
                    "type": "xpath",
                    "multiple": True,
                },
                {
                    "name": "link",
                    "query": "//a[@data-variant-id]/@href",
                    "type": "xpath",
                    "multiple": True,
                },
            ]
        },
        {
            "name": "reviews",
            "query": "div.review>p::text",
            "type": "css",
            "multiple": True,            
        }
    ]
}

Examples

LLM prompt extraction from HTML

from scrapfly import ScrapflyClient, ExtractionConfig
import os

client = ScrapflyClient(key=os.environ["SCRAPFLY_API_KEY"])

html_content = "<html><body><h1>iPhone 15</h1><span class='price'>$999</span><p>Latest Apple smartphone</p></body></html>"

result = client.extract(ExtractionConfig(
    body=html_content,
    content_type="text/html",
    extraction_prompt="Extract the product name, price, and description as JSON",
))

# result
result.extraction_result['data']
# or
print(result.data)

# result content_type
result.extraction_result['content_type']
# or
print(result.content_type)

LLM prompt extraction from markdown

markdown_content = """
# Best Restaurants in NYC

1. **Le Bernardin** - French, $$$, 4.8 stars
2. **Peter Luger** - Steakhouse, $$$, 4.5 stars
3. **Di Fara Pizza** - Italian, $, 4.7 stars
"""

result = client.extract(ExtractionConfig(
    body=markdown_content,
    content_type="text/markdown",
    extraction_prompt="Extract each restaurant as a JSON array with name, cuisine, price_range, and rating fields",
))
print(result.data)

Auto AI model: product extraction

from scrapfly import ScrapflyClient, ExtractionConfig, ScrapeConfig
# First scrape the page, then extract
scrape_result = client.scrape(ScrapeConfig(url="https://web-scraping.dev/product/1"))

result = client.extract(ExtractionConfig(
    body=scrape_result.content,
    content_type="text/html",
    url="https://web-scraping.dev/product/1",
    extraction_model="product",
))

print(result.data)
# Returns: {"name": "...", "price": "...", "currency": "...", "description": "...", ...}

Ask a question about content

result = client.extract(ExtractionConfig(
    body=html_content,
    content_type="text/html",
    extraction_prompt="What is the most expensive item on this page and how much does it cost?",
))
print(result.data)

Scrape + Extract in one flow

from scrapfly import ScrapflyClient, ScrapeConfig, ExtractionConfig
import os

client = ScrapflyClient(key=os.environ["SCRAPFLY_API_KEY"])

# Step 1: Scrape the page
scrape_result = client.scrape(ScrapeConfig(
    url="https://web-scraping.dev/product/1",
    format="markdown",
))

# Step 2: Extract structured data from the content
extraction_result = client.extract(ExtractionConfig(
    body=scrape_result.content,
    content_type="text/markdown",
    extraction_prompt="Extract all products as a JSON array with fields: name, price, availability",
))

print(extraction_result.data)

Inline extraction with the Scrape API

You can also extract data directly within a scrape request:

result = client.scrape(ScrapeConfig(
    url="https://web-scraping.dev/product/1",
    extraction_prompt="Extract the product name, price, and description as JSON",
))
# Extraction result is included in the scrape response
print(result.scrape_result["extracted_data"])

Custom extraction template (inline)

# First, scrape the web page to retrieve its HTML
api_response = client.scrape(scrape_config=ScrapeConfig(
    url='https://web-scraping.dev/product/1',
    render_js=True
))

html = api_response.content

# extraction template for HTML parsing instructions. It accepts the following:
# selectors: CSS, XPath, JMESPath, Regex, Nested (nesting multiple selector types)
# extractors: extracts commonly accessed data types: price, image, links, emails
# formatters: transforms the extracted data for common methods: lowercase, uppercase, datatime, etc.
# refer to the docs for more details: https://scrapfly.io/docs/extraction-api/rules-and-template#rules
extraction_template = {
    "source": "html",
    "selectors": [    
        {
            "name": "title",
            "query": "h3.product-title::text",
            "type": "css",
            "formatters": [
                {
                    "name": "uppercase"
                }
            ],
        },
        {
            "name": "description",
            "query": "p.product-description::text",
            "type": "css"
        },
        {
            "extractor": {
                "name": "price"
            },
            "name": "price",
            "query": ".product-price::text",
            "type": "css"
        },
        {
            "name": "variants",
          	"query": "div.variants",
            "type": "css",
            "nested": [
                {
                    "name": "name",
                    "query": "//a[@data-variant-id]/@data-variant-id",
                    "type": "xpath",
                    "multiple": True,
                },
                {
                    "name": "link",
                    "query": "//a[@data-variant-id]/@href",
                    "type": "xpath",
                    "multiple": True,
                },
            ]
        },
        {
            "name": "reviews",
            "query": "div.review>p::text",
            "type": "css",
            "multiple": True,            
        }
    ]
}

extraction_api_response = client.extract(
    extraction_config=ExtractionConfig(
        body=html, # pass the HTML content
        content_type='text/html', # content data type
        charset='utf-8', # passed content charset, use `auto` if you aren't sure
        extraction_ephemeral_template=extraction_template # declared template defintion or template name saved on the dashboard
    )
)

# extracted data
print(extraction_api_response.data)

# extracted data content_type
print(extraction_api_response.content_type)

Error Handling

from scrapfly.errors import ScrapflyError

try:
    result = client.extract(ExtractionConfig(
        body="<html><body><h1>iPhone 15</h1><span class='price'>$999</span><p>Latest Apple smartphone</p></body></html",
        content_type="text/html",
        extraction_prompt="Extract the product price",
    ))
    print(result.data)
except ScrapflyError as e:
    print(f"Extraction failed: {e.message}")

Important Notes

• Provide exactly one extraction method per request: extraction_prompt, extraction_model, or extraction_template
• For LLM prompts, be specific about the desired output format (e.g., "as JSON")
• The url parameter helps resolve relative links in HTML but is not required
• For large documents, consider using format="markdown" or format="text" in the scrape step first to reduce token usage
• Auto AI models return quality metrics alongside extracted data
• Extraction can also be done inline during a scrape by adding extraction_prompt or extraction_model to ScrapeConfig