javascript.mdx

title	description	icon
JavaScript SDK	Official JavaScript/TypeScript SDK for ScrapeGraphAI	js

<img style={{ borderRadius: '0.5rem' }} src="https://raw.githubusercontent.com/VinciGit00/Scrapegraph-ai/main/docs/assets/api-banner.png" alt="ScrapeGraph API Banner" />

[](https://badge.fury.io/js/scrapegraph-js) [](https://opensource.org/licenses/MIT)

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 intelligence
• Async by Design: Fully asynchronous architecture
• Type Safety: Built-in TypeScript support with Zod schemas
• Production Ready: Automatic retries and detailed logging
• Developer 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('\nKey Employees:');
response.result.employees.forEach(employee => {
  console.log(`- ${employee.name} (${employee.position})`);
});

console.log('\nOffice 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

Report issues and contribute to the SDK Get help from our development team This project is licensed under the MIT License. See the [LICENSE](https://github.com/ScrapeGraphAI/scrapegraph-sdk/blob/main/LICENSE) file for details.