Installation Install the package using npm or yarn:
# Using npm npm i scrapegraph-js # Using yarn yarn add scrapegraph-js Features
AI-Powered Extraction : Smart web scraping with artificial intelligenceAsync by Design : Fully asynchronous architectureType Safety : Built-in TypeScript support with Zod schemasProduction Ready : Automatic retries and detailed loggingDeveloper Friendly : Comprehensive error handling
Quick Start Basic example Store your API keys securely in environment variables. Use .env files and libraries like dotenv to load them into your app.
import { smartScraper } from 'scrapegraph-js' ; import 'dotenv/config' ; // Initialize variables const apiKey = process . env . SGAI_APIKEY ; // Set your API key as an environment variable const websiteUrl = 'https://example.com' ; const prompt = 'What does the company do?' ; try { const response = await smartScraper ( apiKey , websiteUrl , prompt ); // call SmartScraper function console . log ( response . result ); } catch ( error ) { console . error ( 'Error:' , error ); } ; Services SmartScraper Extract specific information from any webpage using AI:
const response = await smartScraper ( apiKey , 'https://example.com' , 'Extract the main content' ); Parameters Parameter Type Required Description apiKey string Yes The ScrapeGraph API Key. websiteUrl string Yes The URL of the webpage that needs to be scraped. prompt string Yes A textual description of what you want to achieve. schema object No The Pydantic or Zod object that describes the structure and format of the response.
Define a simple schema using Zod:
import { z } from 'zod' ; const ArticleSchema = z . object ({ title: z . string (). describe ( 'The article title' ), author: z . string (). describe ( 'The author \' s name' ), publishDate: z . string (). describe ( 'Article publication date' ), content: z . string (). describe ( 'Main article content' ), category: z . string (). describe ( 'Article category' ) }); const ArticlesArraySchema = z . array ( ArticleSchema ). describe ( 'Array of articles' ); const response = await smartScraper ( apiKey , 'https://example.com/blog/article' , 'Extract the article information' , ArticlesArraySchema ); console . log ( `Title: ${ response . result . title } ` ); console . log ( `Author: ${ response . result . author } ` ); console . log ( `Published: ${ response . result . publishDate } ` );
Define a complex schema for nested data structures:
import { z } from 'zod' ; const EmployeeSchema = z . object ({ name: z . string (). describe ( 'Employee \' s full name' ), position: z . string (). describe ( 'Job title' ), department: z . string (). describe ( 'Department name' ), email: z . string (). describe ( 'Email address' ) }); const OfficeSchema = z . object ({ location: z . string (). describe ( 'Office location/city' ), address: z . string (). describe ( 'Full address' ), phone: z . string (). describe ( 'Contact number' ) }); const CompanySchema = z . object ({ name: z . string (). describe ( 'Company name' ), description: z . string (). describe ( 'Company description' ), industry: z . string (). describe ( 'Industry sector' ), foundedYear: z . number (). describe ( 'Year company was founded' ), employees: z . array ( EmployeeSchema ). describe ( 'List of key employees' ), offices: z . array ( OfficeSchema ). describe ( 'Company office locations' ), website: z . string (). url (). describe ( 'Company website URL' ) }); // Extract comprehensive company information const response = await smartScraper ( apiKey , 'https://example.com/about' , 'Extract detailed company information including employees and offices' , CompanySchema ); // Access nested data console . log ( `Company: ${ response . result . name } ` ); console . log ( ' \n Key Employees:' ); response . result . employees . forEach ( employee => { console . log ( `- ${ employee . name } ( ${ employee . position } )` ); }); console . log ( ' \n Office Locations:' ); response . result . offices . forEach ( office => { console . log ( `- ${ office . location } : ${ office . address } ` ); }); SearchScraper Search and extract information from multiple web sources using AI:
const response = await searchScraper ( apiKey , 'Find the best restaurants in San Francisco' , ); Parameters Parameter Type Required Description apiKey string Yes The ScrapeGraph API Key. prompt string Yes A textual description of what you want to achieve. schema object No The Pydantic or Zod object that describes the structure and format of the response
Define a simple schema using Zod:
import { z } from 'zod' ; const ArticleSchema = z . object ({ title: z . string (). describe ( 'The article title' ), author: z . string (). describe ( 'The author \' s name' ), publishDate: z . string (). describe ( 'Article publication date' ), content: z . string (). describe ( 'Main article content' ), category: z . string (). describe ( 'Article category' ) }); const response = await searchScraper ( apiKey , 'Find news about the latest trends in AI' , ArticleSchema ); console . log ( `Title: ${ response . result . title } ` ); console . log ( `Author: ${ response . result . author } ` ); console . log ( `Published: ${ response . result . publishDate } ` );
Define a complex schema for nested data structures:
import { z } from 'zod' ; const EmployeeSchema = z . object ({ name: z . string (). describe ( 'Employee \' s full name' ), position: z . string (). describe ( 'Job title' ), department: z . string (). describe ( 'Department name' ), email: z . string (). describe ( 'Email address' ) }); const OfficeSchema = z . object ({ location: z . string (). describe ( 'Office location/city' ), address: z . string (). describe ( 'Full address' ), phone: z . string (). describe ( 'Contact number' ) }); const RestaurantSchema = z . object ({ name: z . string (). describe ( 'Restaurant name' ), address: z . string (). describe ( 'Restaurant address' ), rating: z . number (). describe ( 'Restaurant rating' ), website: z . string (). url (). describe ( 'Restaurant website URL' ) }); // Extract comprehensive company information const response = await searchScraper ( apiKey , 'Find the best restaurants in San Francisco' , RestaurantSchema ); Markdownify Convert any webpage into clean, formatted markdown:
const response = await markdownify ( apiKey , 'https://example.com' ); Parameters Parameter Type Required Description apiKey string Yes The ScrapeGraph API Key. websiteUrl string Yes The URL of the webpage to convert to markdown.
API Credits Check your available API credits:
import { getCredits } from 'scrapegraph-js' ; try { const credits = await getCredits ( apiKey ); console . log ( 'Available credits:' , credits ); } catch ( error ) { console . error ( 'Error fetching credits:' , error ); } Feedback Help us improve by submitting feedback programmatically:
import { sendFeedback } from 'scrapegraph-js' ; try { await sendFeedback ( apiKey , 'request-id' , 5 , 'Great results!' ); } catch ( error ) { console . error ( 'Error sending feedback:' , error ); } Support
This project is licensed under the MIT License. See the LICENSE file for details.
