Overview The Web Search API allows you to perform AI-powered web searches using Linkup, returning up-to-date information from across the internet. This API supports multiple output formats, date filtering, domain filtering, and two search depth options.
Authentication Alternatively, you can use Bearer token authentication:
Request Body The search query to send to Linkup
Search depth. Options: “standard” or “deep”
standard : $0.006 per searchdeep : $0.06 per search outputType
string
default: "searchResults"
Output format. Options: “searchResults”, “sourcedAnswer”, or “structured”
Required when outputType is “structured”. JSON schema string defining the desired response format
Whether to include image results in the search
Filter results from this date (YYYY-MM-DD format)
Filter results until this date (YYYY-MM-DD format)
Array of domains to exclude from search results
Array of domains to search exclusively
Response Search results, answer, or structured data depending on outputType
The search query that was executed
The search depth used (“standard” or “deep”)
ISO 8601 timestamp of when the search was performed
The cost of the search in USD
Search Results (default) Returns an array of search results with text and image entries:
{ "data" : [ { "type" : "text" , "title" : "Article Title" , "url" : "https://example.com/article" , "content" : "Article content snippet..." }, { "type" : "image" , "title" : "Image Title" , "url" : "https://example.com/image.jpg" , "imageUrl" : "https://example.com/image.jpg" } ], "metadata" : { "query" : "your search query" , "depth" : "standard" , "outputType" : "searchResults" , "timestamp" : "2025-07-08T09:00:00.000Z" , "cost" : 0.006 } } Sourced Answer Returns a comprehensive answer with source citations:
{ "data" : { "answer" : "The comprehensive answer to your query..." , "sources" : [ { "name" : "Source Name" , "url" : "https://example.com" , "snippet" : "Relevant snippet from the source..." } ] }, "metadata" : { "query" : "your search query" , "depth" : "standard" , "outputType" : "sourcedAnswer" , "timestamp" : "2025-07-08T09:00:00.000Z" , "cost" : 0.006 } } Structured Output Returns data matching your provided JSON schema:
{ "data" : { // Your structured data according to the schema }, "metadata" : { "query" : "your search query" , "depth" : "standard" , "outputType" : "structured" , "timestamp" : "2025-07-08T09:00:00.000Z" , "cost" : 0.006 } } Examples import requests import json # Your API key api_key = "YOUR_API_KEY" # API endpoint url = "https://nano-gpt.com/api/web" # Headers headers = { "Content-Type" : "application/json" , "x-api-key" : api_key } # Basic search basic_search = { "query" : "artificial intelligence trends 2025" } response = requests.post(url, headers = headers, json = basic_search) results = response.json() # Print results if results[ "metadata" ][ "outputType" ] == "searchResults" : for result in results[ "data" ]: print ( f "Title: { result[ 'title' ] } " ) print ( f "URL: { result[ 'url' ] } " ) print ( f "Content: { result.get( 'content' , 'N/A' )[: 200 ] } ..." ) print ( "-" * 50 ) print ( f "Search cost: $ { results[ 'metadata' ][ 'cost' ] } " )
Advanced Examples Deep Search with Date Filtering def deep_search_with_dates ( query , from_date , to_date ): data = { "query" : query, "depth" : "deep" , "fromDate" : from_date, "toDate" : to_date } response = requests.post(url, headers = headers, json = data) return response.json() # Search for recent research results = deep_search_with_dates( "quantum computing breakthroughs" , "2025-01-01" , "2025-07-01" ) def extract_structured_data ( query , schema ): data = { "query" : query, "outputType" : "structured" , "structuredOutputSchema" : json.dumps(schema) } response = requests.post(url, headers = headers, json = data) return response.json() # Define schema for extracting programming language data schema = { "type" : "object" , "properties" : { "languages" : { "type" : "array" , "items" : { "type" : "object" , "properties" : { "rank" : { "type" : "number" }, "name" : { "type" : "string" }, "popularityScore" : { "type" : "string" }, "primaryUseCase" : { "type" : "string" } } } } } } # Extract structured data results = extract_structured_data( "top 5 programming languages 2025" , schema ) Domain-Specific Search def search_specific_domains ( query , domains , exclude = False ): data = { "query" : query, "outputType" : "sourcedAnswer" } if exclude: data[ "excludeDomains" ] = domains else : data[ "includeDomains" ] = domains response = requests.post(url, headers = headers, json = data) return response.json() # Search only trusted news sources news_results = search_specific_domains( "latest tech acquisitions" , [ "reuters.com" , "bloomberg.com" , "techcrunch.com" ], exclude = False ) # Exclude certain domains filtered_results = search_specific_domains( "python tutorials" , [ "w3schools.com" , "geeksforgeeks.org" ], exclude = True ) Error Handling 400 Bad Request
401 Unauthorized
402 Payment Required
429 Too Many Requests
500 Internal Server Error
{ "error" : "Query parameter is required and must be a string" }
Rate Limiting The API is rate-limited to 10 requests per minute per IP address. If you exceed this limit, you’ll receive a 429 status code.
Best Practices
Use Standard Search for General Queries : Standard search is 10x cheaper and sufficient for most use cases.
Use Deep Search for Research : Deep search provides more comprehensive results and is ideal for research tasks or when you need extensive information.
Leverage Domain Filtering : Use includeDomains to search specific trusted sources or excludeDomains to filter out unwanted sources.
Date Filtering for Current Events : Use fromDate and toDate to get the most recent information on rapidly evolving topics.
Structured Output for Data Extraction : Use structured output when you need to extract specific data points from search results in a predictable format.
Handle Errors Gracefully : Always implement error handling for rate limits, insufficient balance, and network errors.
Cache Results : Consider caching search results for identical queries to reduce costs and improve performance.
Output Type Selection Guide
searchResults : Best for general searches where you want to see multiple sources and snippets. Ideal for research and exploration.
sourcedAnswer : Best when you want a comprehensive answer synthesized from multiple sources. Great for factual questions and summaries.
structured : Best when you need to extract specific data points in a predictable format. Perfect for data collection and automation.
Pricing
Standard Search : $0.006 per searchDeep Search : $0.06 per search
A minimum balance of $1.00 is required to use the web search API.
