JSON mode - Structured result

Extract structured data from pages via LLMs

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.

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.

Extract structured data

JSON mode via /scrape

Used to extract structured data from scraped pages.

Python

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)

import Firecrawl from "@mendable/firecrawl-js";
import { z } from "zod";

const app = new Firecrawl({
  apiKey: "fc-YOUR_API_KEY"
});

// Define schema to extract contents into
const schema = z.object({
  company_mission: z.string(),
  supports_sso: z.boolean(),
  is_open_source: z.boolean(),
  is_in_yc: z.boolean()
});

const result = await app.scrape("https://firecrawl.dev", {
  formats: [{
    type: "json",
    schema: schema
  }],
});

console.log(result);

curl -X POST https://api.firecrawl.dev/v2/scrape \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer YOUR_API_KEY' \
    -d '{
      "url": "https://firecrawl.dev",
      "formats": [ {
        "type": "json",
        "schema": {
          "type": "object",
          "properties": {
            "company_mission": {
                      "type": "string"
            },
            "supports_sso": {
                      "type": "boolean"
            },
            "is_open_source": {
                      "type": "boolean"
            },
            "is_in_yc": {
                      "type": "boolean"
            }
          },
          "required": [
            "company_mission",
            "supports_sso",
            "is_open_source",
            "is_in_yc"
          ]
        }
      } ]
    }'

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/"
      },
    }
}

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.

Python

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)

import Firecrawl from "@mendable/firecrawl-js";

const app = new Firecrawl({
  apiKey: "fc-YOUR_API_KEY"
});

const result = await app.scrape("https://firecrawl.dev", {
  formats: [{
    type: "json",
    prompt: "Extract the company mission from the page."
  }]
});

console.log(result);

curl -X POST https://api.firecrawl.dev/v2/scrape \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer YOUR_API_KEY' \
    -d '{
      "url": "https://firecrawl.dev",
      "formats": [{
        "type": "json",
        "prompt": "Extract the company mission from the page."
      }]
    }'

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/"
      },
    }
}

Real-world example: Extracting company information

Here’s a comprehensive example extracting structured company information from a website:

Python

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)

import Firecrawl from "@mendable/firecrawl-js";
import { z } from "zod";

const app = new Firecrawl({
  apiKey: "fc-YOUR_API_KEY"
});

const companyInfoSchema = z.object({
  company_mission: z.string(),
  supports_sso: z.boolean(),
  is_open_source: z.boolean(),
  is_in_yc: z.boolean()
});

const result = await app.scrape("https://firecrawl.dev/", {
  formats: [{
    type: "json",
    schema: companyInfoSchema
  }]
});

console.log(result);

curl -X POST https://api.firecrawl.dev/v2/scrape \
    -H 'Content-Type: application/json' \
    -H 'Authorization: Bearer YOUR_API_KEY' \
    -d '{
      "url": "https://firecrawl.dev/",
      "formats": [{
        "type": "json",
        "schema": {
          "type": "object",
          "properties": {
            "company_mission": {
              "type": "string"
            },
            "supports_sso": {
              "type": "boolean"
            },
            "is_open_source": {
              "type": "boolean"
            },
            "is_in_yc": {
              "type": "boolean"
            }
          },
          "required": [
            "company_mission",
            "supports_sso",
            "is_open_source",
            "is_in_yc"
          ]
        }
      }]
    }'

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
    }
  }
}

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.

HTML attributes are not available in JSON extraction. JSON extraction works on the markdown conversion of the page, which only preserves visible text content. HTML attributes (e.g., data-id, custom attributes on elements) are stripped during conversion and the LLM cannot see them. If you need to extract HTML attribute values, use rawHtml format and parse attributes client-side, or use an executeJavascript action to inject attribute values into visible text before extraction.

Tips for consistent extraction

If you are seeing inconsistent or incomplete results from JSON extraction, these practices can help:

• Keep prompts short and focused. Long prompts with many rules increase variability. Move specific constraints (like allowed values) into the schema instead.
• Use concise property names. Avoid embedding instructions or enum lists in property names. Use a short key like "installation_type" and put allowed values in an enum array.
• Add enum arrays for constrained fields. When a field has a fixed set of values, list them in enum and make sure they match the exact text shown on the page.
• Include null-handling in field descriptions. Add "Return null if not found on the page." to each field’s description so the model does not guess missing values.
• Add location hints. Tell the model where to find data on the page, e.g. "Flow rate in GPM from the Specifications table.".
• Split large schemas into smaller requests. Schemas with many fields (e.g. 30+) produce less consistent results. Split them into 2–3 requests of 10–15 fields each.

Example of a well-structured schema:

{
  "type": "object",
  "properties": {
    "product_name": {
      "type": ["string", "null"],
      "description": "Full descriptive product name as shown on the page. Return null if not found."
    },
    "installation_type": {
      "type": ["string", "null"],
      "description": "Installation type from the Specifications section. Return null if not found.",
      "enum": ["Deck-mount", "Wall-mount", "Countertop", "Drop-in", "Undermount"]
    },
    "flow_rate_gpm": {
      "type": ["string", "null"],
      "description": "Flow rate in GPM from the Specifications section. Return null if not found."
    }
  }
}