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v2 API Change: JSON schema extraction is fully supported in v2, but the API format has changed. In v2, the schema is embedded directly inside the format object as formats: [{type: "json", schema: {...}}]. The v1 jsonOptions parameter no longer exists in v2.

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Scrape and extract structured data with Firecrawl

Firecrawl uses AI to get structured data from web pages in 3 steps:
  1. Set the Schema (optional): Define a JSON schema (using OpenAI’s format) to specify the data you want, or just provide a prompt if you don’t need a strict schema, along with the webpage URL.
  2. Make the Request: Send your URL and schema to our scrape endpoint using JSON mode. See how here: Scrape Endpoint Documentation
  3. Get Your Data: Get back clean, structured data matching your schema that you can use right away.
This makes getting web data in the format you need quick and easy.

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Extract structured data

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JSON mode via /scrape

Used to extract structured data from scraped pages. 
from firecrawl import Firecrawl
from pydantic import BaseModel

app = Firecrawl(api_key="fc-YOUR-API-KEY")

class CompanyInfo(BaseModel):
    company_mission: str
    supports_sso: bool
    is_open_source: bool
    is_in_yc: bool

result = app.scrape(
    'https://firecrawl.dev',
    formats=[{
      "type": "json",
      "schema": CompanyInfo.model_json_schema()
    }],
    only_main_content=False,
    timeout=120000
)

print(result)
Output:
JSON
{
    "success": true,
    "data": {
      "json": {
        "company_mission": "AI-powered web scraping and data extraction",
        "supports_sso": true,
        "is_open_source": true,
        "is_in_yc": true
      },
      "metadata": {
        "title": "Firecrawl",
        "description": "AI-powered web scraping and data extraction",
        "robots": "follow, index",
        "ogTitle": "Firecrawl",
        "ogDescription": "AI-powered web scraping and data extraction",
        "ogUrl": "https://firecrawl.dev/",
        "ogImage": "https://firecrawl.dev/og.png",
        "ogLocaleAlternate": [],
        "ogSiteName": "Firecrawl",
        "sourceURL": "https://firecrawl.dev/"
      },
    }
}

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Structured data without schema

You can also extract without a schema by just passing a prompt to the endpoint. The llm chooses the structure of the data. 
from firecrawl import Firecrawl

app = Firecrawl(api_key="fc-YOUR-API-KEY")

result = app.scrape(
    'https://firecrawl.dev',
    formats=[{
      "type": "json",
      "prompt": "Extract the company mission from the page."
    }],
    only_main_content=False,
    timeout=120000
)

print(result)
Output:
JSON
{
    "success": true,
    "data": {
      "json": {
        "company_mission": "AI-powered web scraping and data extraction",
      },
      "metadata": {
        "title": "Firecrawl",
        "description": "AI-powered web scraping and data extraction",
        "robots": "follow, index",
        "ogTitle": "Firecrawl",
        "ogDescription": "AI-powered web scraping and data extraction",
        "ogUrl": "https://firecrawl.dev/",
        "ogImage": "https://firecrawl.dev/og.png",
        "ogLocaleAlternate": [],
        "ogSiteName": "Firecrawl",
        "sourceURL": "https://firecrawl.dev/"
      },
    }
}

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Real-world example: Extracting company information

Here’s a comprehensive example extracting structured company information from a website: 
from firecrawl import Firecrawl
from pydantic import BaseModel

app = Firecrawl(api_key="fc-YOUR-API-KEY")

class CompanyInfo(BaseModel):
    company_mission: str
    supports_sso: bool
    is_open_source: bool
    is_in_yc: bool

result = app.scrape(
    'https://firecrawl.dev/',
    formats=[{
        "type": "json",
        "schema": CompanyInfo.model_json_schema()
    }]
)

print(result)
Output:
Output
{
  "success": true,
  "data": {
    "json": {
      "company_mission": "Turn websites into LLM-ready data",
      "supports_sso": true,
      "is_open_source": true,
      "is_in_yc": true
    }
  }
}

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JSON format options

When using JSON mode in v2, include an object in formats with the schema embedded directly: formats: [{ type: 'json', schema: { ... }, prompt: '...' }] Parameters:
  • schema: JSON Schema describing the structured output you want (required for schema-based extraction).
  • prompt: Optional prompt to guide extraction (also used for no-schema extraction).
Important: Unlike v1, there is no separate jsonOptions parameter in v2. The schema must be included directly inside the format object in the formats array.
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