Firecrawl’s search API allows you to perform web searches and optionally scrape the search results in one operation.
  • Choose specific output formats (markdown, HTML, links, screenshots)
  • Search the web with customizable parameters (location, etc.)
  • Optionally retrieve content from search results in various formats
  • Control the number of results and set timeouts
For details, see the Search Endpoint API Reference.

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Performing a Search with Firecrawl

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/search endpoint

Used to perform web searches and optionally retrieve content from the results.

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Installation

# pip install firecrawl-py

from firecrawl import Firecrawl

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

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Basic Usage

from firecrawl import Firecrawl

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

results = firecrawl.search(
    query="firecrawl",
    limit=3,
)
print(results)

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Response

SDKs will return the data object directly. cURL will return the complete payload.
JSON
{
  "success": true,
  "data": {
    "web": [
      {
        "url": "https://www.firecrawl.dev/",
        "title": "Firecrawl - The Web Data API for AI",
        "description": "The web crawling, scraping, and search API for AI. Built for scale. Firecrawl delivers the entire internet to AI agents and builders.",
        "position": 1
      },
      {
        "url": "https://github.com/mendableai/firecrawl",
        "title": "mendableai/firecrawl: Turn entire websites into LLM-ready ... - GitHub",
        "description": "Firecrawl is an API service that takes a URL, crawls it, and converts it into clean markdown or structured data.",
        "position": 2
      },
      ...
    ],
    "images": [
      {
        "title": "Quickstart | Firecrawl",
        "imageUrl": "https://mintlify.s3.us-west-1.amazonaws.com/firecrawl/logo/logo.png",
        "imageWidth": 5814,
        "imageHeight": 1200,
        "url": "https://docs.firecrawl.dev/",
        "position": 1
      },
      ...
    ],
    "news": [
      {
        "title": "Y Combinator startup Firecrawl is ready to pay $1M to hire three AI agents as employees",
        "url": "https://techcrunch.com/2025/05/17/y-combinator-startup-firecrawl-is-ready-to-pay-1m-to-hire-three-ai-agents-as-employees/",
        "snippet": "It's now placed three new ads on YC's job board for “AI agents only” and has set aside a $1 million budget total to make it happen.",
        "date": "3 months ago",
        "position": 1
      },
      ...
    ]
  }
}

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Search result types

In addition to regular web results, Search supports specialized result types via the sources parameter:
  • web: standard web results (default)
  • news: news-focused results
  • images: image search results

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Search Categories

Filter search results by specific categories using the categories parameter:
  • github: Search within GitHub repositories, code, issues, and documentation
  • research: Search academic and research websites (arXiv, Nature, IEEE, PubMed, etc.)
Search specifically within GitHub repositories:
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "web scraping python",
    "categories": ["github"],
    "limit": 10
  }'
Search academic and research websites:
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "machine learning transformers",
    "categories": ["research"],
    "limit": 10
  }'
Combine multiple categories in one search:
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "neural networks",
    "categories": ["github", "research"],
    "limit": 15
  }'

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Category Response Format

Each search result includes a category field indicating its source: 
{
  "success": true,
  "data": {
    "web": [
      {
        "url": "https://github.com/example/neural-network",
        "title": "Neural Network Implementation",
        "description": "A PyTorch implementation of neural networks",
        "category": "github"
      },
      {
        "url": "https://arxiv.org/abs/2024.12345",
        "title": "Advances in Neural Network Architecture",
        "description": "Research paper on neural network improvements",
        "category": "research"
      }
    ]
  }
}
Examples:
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "openai",
    "sources": ["news"],
    "limit": 5
  }'
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "jupiter",
    "sources": ["images"],
    "limit": 8
  }'

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HD Image Search with Size Filtering

Use Google Images operators to find high-resolution images:
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "sunset imagesize:1920x1080",
    "sources": ["images"],
    "limit": 5
  }'
cURL
curl -X POST https://api.firecrawl.dev/v2/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer fc-YOUR_API_KEY" \
  -d '{
    "query": "mountain wallpaper larger:2560x1440",
    "sources": ["images"],
    "limit": 8
  }'
Common HD resolutions:
  • imagesize:1920x1080 - Full HD (1080p)
  • imagesize:2560x1440 - QHD (1440p)
  • imagesize:3840x2160 - 4K UHD
  • larger:1920x1080 - HD and above
  • larger:2560x1440 - QHD and above

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Search with Content Scraping

Search and retrieve content from the search results in one operation. 
from firecrawl import Firecrawl

firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")

# Search and scrape content
results = firecrawl.search(
    "firecrawl web scraping",
    limit=3,
    scrape_options={
        "formats": ["markdown", "links"]
    }
)
Every option in scrape endpoint is supported by this search endpoint through the scrapeOptions parameter.

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Response with Scraped Content

{
  "success": true,
  "data": [
    {
      "title": "Firecrawl - The Ultimate Web Scraping API",
      "description": "Firecrawl is a powerful web scraping API that turns any website into clean, structured data for AI and analysis.",
      "url": "https://firecrawl.dev/",
      "markdown": "# Firecrawl\n\nThe Ultimate Web Scraping API\n\n## Turn any website into clean, structured data\n\nFirecrawl makes it easy to extract data from websites for AI applications, market research, content aggregation, and more...",
      "links": [
        "https://firecrawl.dev/pricing",
        "https://firecrawl.dev/docs",
        "https://firecrawl.dev/guides"
      ],
      "metadata": {
        "title": "Firecrawl - The Ultimate Web Scraping API",
        "description": "Firecrawl is a powerful web scraping API that turns any website into clean, structured data for AI and analysis.",
        "sourceURL": "https://firecrawl.dev/",
        "statusCode": 200
      }
    }
  ]
}

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Advanced Search Options

Firecrawl’s search API supports various parameters to customize your search:

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Location Customization

from firecrawl import Firecrawl

firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")

# Search with location settings (Germany)
search_result = firecrawl.search(
    "web scraping tools",
    limit=5,
    location="Germany"
)

# Process the results
for result in search_result.data:
    print(f"Title: {result['title']}")
    print(f"URL: {result['url']}")
Use the tbs parameter to filter results by time: 
from firecrawl import Firecrawl

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

results = firecrawl.search(
    query="firecrawl",
    limit=5,
    tbs="qdr:d",
)
print(len(results.get('web', [])))
Common tbs values:
  • qdr:h - Past hour
  • qdr:d - Past 24 hours
  • qdr:w - Past week
  • qdr:m - Past month
  • qdr:y - Past year
For more precise time filtering, you can specify exact date ranges using the custom date range format: 
from firecrawl import Firecrawl

# Initialize the client with your API key
firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")

# Search for results from December 2024
search_result = firecrawl.search(
    "firecrawl updates",
    limit=10,
    tbs="cdr:1,cd_min:12/1/2024,cd_max:12/31/2024"
)

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Custom Timeout

Set a custom timeout for search operations: 
from firecrawl import FirecrawlApp

# Initialize the client with your API key
app = FirecrawlApp(api_key="fc-YOUR_API_KEY")

# Set a 30-second timeout
search_result = app.search(
    "complex search query",
    limit=10,
    timeout=30000  # 30 seconds in milliseconds
)

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Cost Implications

The cost of using this endpoint is 1 credit per search result. There is no additional charge for basic scrapes of each search result. However, be aware of these cost factors:
  • PDF parsing: 1 credit per PDF page (can significantly increase costs for multi-page PDFs)
  • Stealth proxy mode: +4 additional credits per search result
  • JSON mode: +4 additional credits per search result
To control costs:
  • Set parsers: [] if you don’t need PDF content
  • Use proxy: "basic" instead of "stealth" when possible
  • Limit the number of search results with the limit parameter

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Advanced Scraping Options

For more details about the scraping options, refer to the Scrape Feature documentation. Everything except for the FIRE-1 Agent and Change-Tracking features are supported by this Search endpoint.