Scrape

Overview

The Scrape service fetches a web page and returns content in one or more formats at the same time: markdown, HTML, links, images, summary, JSON extraction, branding, or screenshots. It replaces the previous Markdownify service and uses a flexible formats array so a single call can return any combination you need.

Try the Scrape service instantly in our interactive playground.

Pricing

Format	Credits
`markdown`	1
`html`	1
`links`	1
`images`	1
`summary`	1
`json`	5
`screenshot`	2
`branding`	25

When a request includes multiple formats, the per-format costs are summed. Enabling stealth in fetchConfig adds 5 credits; render mode (auto/fast/js) does not affect the cost. See the pricing page for the full breakdown.

Getting Started

Quick Start

from scrapegraph_py import ScrapeGraphAI, MarkdownFormatConfig

# reads SGAI_API_KEY from env, or pass explicitly: ScrapeGraphAI(api_key="...")
sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://example.com",
    formats=[MarkdownFormatConfig()],
)

if res.status == "success":
    md = res.data.results.get("markdown", {}).get("data", [])
    print(md[0] if md else None)
else:
    print("Failed:", res.error)

Parameters

Parameter	Type	Required	Description
`url`	string	Yes	The URL of the webpage to scrape.
`formats`	array	Yes	One or more output formats (see Formats).
`contentType`	string	No	Override auto-detected content type (e.g. `"text/html"`, `"application/pdf"`).
`fetchConfig` / `fetch_config`	object	No	Fetch options — `mode`, `stealth`, `headers`, `cookies`, `scrolls`, `wait`, `timeout`, `country`.

Get your API key from the dashboard.

Example Response

{
  "id": "03907b00-3c10-4b73-a6b5-e3b399a850b1",
  "results": {
    "markdown": {
      "data": [
        "# Example Domain\n\nThis domain is for use in documentation examples without needing permission. Avoid use in operations.\n\n[Learn more](https://iana.org/domains/example)\n"
      ]
    }
  },
  "metadata": {
    "contentType": "text/html"
  }
}

Output Formats

Pass an array of format objects. Each entry has a type and optional per-format options.

Format	Options	Description
`markdown`	`mode`: `"normal"` \| `"reader"` \| `"prune"`	Clean markdown conversion of the page.
`html`	`mode`: `"normal"` \| `"reader"` \| `"prune"`	Raw or processed HTML.
`links`	—	All outgoing links on the page.
`images`	—	All image URLs on the page.
`summary`	—	AI-generated short summary.
`json`	`prompt`, `schema`	Structured JSON extraction (AI).
`branding`	—	Brand colors, typography, and logos.
`screenshot`	`fullPage`, `width`, `height`, `quality`	Screenshot image URL.

Multi-format example

from scrapegraph_py import (
    ScrapeGraphAI,
    MarkdownFormatConfig,
    LinksFormatConfig,
    ScreenshotFormatConfig,
)

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://example.com",
    formats=[
        MarkdownFormatConfig(mode="reader"),
        LinksFormatConfig(),
        ScreenshotFormatConfig(width=1280, height=720),
    ],
)

if res.status == "success":
    results = res.data.results
    print("Markdown preview:", results.get("markdown", {}).get("data", [""])[0][:200])
    print("Links count:", len(results.get("links", {}).get("data", [])))
    print("Screenshot URL:", results.get("screenshot", {}).get("data", {}).get("url"))

Screenshot

Capture a screenshot of the page. Use fullPage to grab the entire scrollable area, or set width/height for a fixed viewport. quality (1–100) controls JPEG compression.

from scrapegraph_py import ScrapeGraphAI, ScreenshotFormatConfig

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://scrapegraphai.com",
    formats=[
        ScreenshotFormatConfig(
            full_page=True,
            width=1440,
            height=900,
            quality=90,
        ),
    ],
)

if res.status == "success":
    shot = res.data.results.get("screenshot", {}).get("data", {})
    print("URL:", shot.get("url"))
    print("Size:", f"{shot.get('width')}x{shot.get('height')}")

Option	Type	Default	Range	Description
`fullPage`	bool	`false`	—	Capture the whole scrollable page instead of just the viewport.
`width`	int	`1440`	`320`–`3840`	Viewport width in pixels.
`height`	int	`900`	`200`–`2160`	Viewport height in pixels.
`quality`	int	`80`	`1`–`100`	JPEG quality.

Branding

Extract a page’s brand identity — colors, typography, and logos — in a single call.

from scrapegraph_py import ScrapeGraphAI, BrandingFormatConfig

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://scrapegraphai.com",
    formats=[BrandingFormatConfig()],
)

if res.status == "success":
    branding = res.data.results.get("branding", {}).get("data")
    print(branding)

Branding costs 25 credits per call — significantly more than other formats because it runs additional vision and typography analysis on top of the page fetch.

Structured JSON extraction

Use the json format to extract structured data during the scrape.

from scrapegraph_py import ScrapeGraphAI, JsonFormatConfig

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://scrapegraphai.com",
    formats=[
        JsonFormatConfig(
            prompt="Extract the company name and tagline",
            schema={
                "type": "object",
                "properties": {
                    "companyName": {"type": "string"},
                    "tagline": {"type": "string"},
                },
                "required": ["companyName"],
            },
        ),
    ],
)

if res.status == "success":
    print(res.data.results.get("json", {}).get("data"))

Using a Pydantic schema (Python)

JsonFormatConfig.schema accepts any JSON Schema dict, so a Pydantic BaseModel works via model_json_schema():

from pydantic import BaseModel
from scrapegraph_py import ScrapeGraphAI, JsonFormatConfig

class Company(BaseModel):
    company_name: str
    tagline: str | None = None

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://scrapegraphai.com",
    formats=[
        JsonFormatConfig(
            prompt="Extract the company name and tagline",
            schema=Company.model_json_schema(),
        ),
    ],
)

if res.status == "success":
    parsed = Company.model_validate(res.data.results["json"]["data"])
    print(parsed.company_name, parsed.tagline)

FetchConfig

Control how pages are fetched — JS rendering, stealth, custom headers, etc.

from scrapegraph_py import ScrapeGraphAI, MarkdownFormatConfig, FetchConfig

sgai = ScrapeGraphAI()

res = sgai.scrape(
    "https://example.com",
    formats=[MarkdownFormatConfig()],
    fetch_config=FetchConfig(
        mode="js",
        stealth=True,
        wait=2000,
        scrolls=3,
        cookies={"session": "abc123"},
        country="us",
    ),
)

Parameter	Type	Description
`mode`	string	Fetch mode: `"auto"` (default), `"fast"`, or `"js"`.
`stealth`	bool	Enable stealth mode with residential proxy and anti-bot headers.
`headers`	object	Custom HTTP headers.
`cookies`	object	Cookies to include in the request.
`scrolls`	int	Number of page scrolls (0–100).
`wait`	int	Milliseconds to wait after page load (0–30000).
`timeout`	int	Request timeout in milliseconds (1000–60000).
`country`	string	Two-letter ISO country code for geo-targeted proxy routing.

Async Support (Python)

import asyncio
from scrapegraph_py import AsyncScrapeGraphAI, MarkdownFormatConfig

async def main():
    async with AsyncScrapeGraphAI() as sgai:
        res = await sgai.scrape(
            "https://example.com",
            formats=[MarkdownFormatConfig()],
        )
        if res.status == "success":
            md = res.data.results.get("markdown", {}).get("data", [])
            print(md[0] if md else None)

asyncio.run(main())

Key Features

Multiple Formats

Request any combination of markdown, HTML, links, images, summary, JSON, branding, or screenshots in a single call.

JavaScript Rendering

Handle JavaScript-heavy sites with mode: "js" on fetchConfig.

Structured Output

Use the json format with a JSON schema to get typed data back.

Reliable Output

Stealth mode and country-targeted proxies for difficult sources.

Integration Options

Official SDKs

Python SDK — perfect for automation and data processing
JavaScript SDK — ideal for web applications and Node.js (scrapegraph-js ≥ 2.1.0, Node ≥ 22)

AI Framework Integrations

LangChain Integration — use Scrape in your content pipelines
LlamaIndex Integration — create searchable knowledge bases

Support & Resources

Documentation

Comprehensive guides and tutorials

API Reference

Detailed API documentation

Community

Join our Discord community

GitHub

Check out our open-source projects

Get Started

Services

Official SDKs

LLM SDKs

Frameworks

No-code

Contribute

Overview

Pricing

Getting Started

Quick Start

Parameters

Output Formats

Multi-format example

Screenshot

Branding

Structured JSON extraction

Using a Pydantic schema (Python)

FetchConfig

Async Support (Python)

Key Features

Multiple Formats

JavaScript Rendering

Structured Output

Reliable Output

Integration Options

Official SDKs

AI Framework Integrations

Support & Resources

Documentation

API Reference

Community

GitHub

Ready to Start?

Get Started

Services

Official SDKs

LLM SDKs

Frameworks

No-code

Contribute

Documentation Index

​Overview

​Pricing

​Getting Started

​Quick Start

​Parameters

​Output Formats

​Multi-format example

​Screenshot

​Branding

​Structured JSON extraction

​Using a Pydantic schema (Python)

​FetchConfig

​Async Support (Python)

​Key Features

Multiple Formats

JavaScript Rendering

Structured Output

Reliable Output

​Integration Options

​Official SDKs

​AI Framework Integrations

​Support & Resources

Documentation

API Reference

Community

GitHub

Ready to Start?

Overview

Pricing

Getting Started

Quick Start

Parameters

Output Formats

Multi-format example

Screenshot

Branding

Structured JSON extraction

Using a Pydantic schema (Python)

FetchConfig

Async Support (Python)

Key Features

Integration Options

Official SDKs

AI Framework Integrations

Support & Resources