Google Scholar Scraper

Google Scholar Scraper - A tool to scrape Google Scholar search results with a simple API. Get academic articles, research papers, citations, authors, and publication information.

We provide the results in a structured JSON format, eliminating the need for parsing, coding, proxies, or any other web scraping headaches for developers.

How to scrape Google Scholar?

Using a simple GET request, you can retrieve Google Scholar search results:

https://serpapi.com/search.json?engine=google_scholar&q=machine+learning&api_key=YOUR_API_KEY

• Register for free at SerpApi to get your API Key
• q parameter: defines the search query. Supports author: and source: syntax.
• as_ylo/as_yhi parameters (optional): filter by year range.

Code examples

Here are some code examples based on your favorite programming languages.

cURL Integration

curl --get https://serpapi.com/search \
 -d engine="google_scholar" \
 -d q="machine learning" \
 -d api_key="secret_api_key"

Python Integration

Step 1: Create a new main.py file.

Step 2: Install requests package with:

pip install requests

Step 3: Add this code to your file:

import requests
SERPAPI_API_KEY = "YOUR_SERPAPI_API_KEY"

params = {
    "api_key": SERPAPI_API_KEY,
    "engine": "google_scholar",
    "q": "machine learning"
}

search = requests.get("https://serpapi.com/search", params=params)
response = search.json()
print(response)

If you're only interested in the organic_results, you can print them from the response directly:

print(response["organic_results"])

JavaScript Integration

Step 1: Install the SerpApi JavaScript package:

npm install serpapi

Step 2: Create a new index.js file.

Step 3: Add this to your file:

const { getJson } = require("serpapi");
getJson({
  api_key: API_KEY,
  engine: "google_scholar",
  q: "machine learning"
}, (json) => {
  console.log(json["organic_results"]);
});

Other Programming Languages

While you can use our APIs using a simple GET request with any programming language, you can also see our ready-to-use libraries here: SerpApi Integrations.

Google Scholar Scraper Parameters

Please find the parameters for the Google Scholar API below:

Name	Description	Requirement
q	Parameter defines the search query. Supports author: and source: syntax	Required*
Advanced Search
cites	Parameter defines unique article ID for "Cited By" searches	Optional
cluster	Parameter defines unique article ID for "All Versions" searches	Optional
Year Range
as_ylo	Parameter defines the minimum year for results	Optional
as_yhi	Parameter defines the maximum year for results	Optional
Localization
hl	Parameter defines the two-letter language code	Optional
lr	Parameter defines language restrictions using lang_{code} format	Optional
Pagination
start	Parameter defines result offset for pagination	Optional
num	Parameter defines results per page (1-20, default 10)	Optional
Filtering
scisbd	Parameter to sort by recent articles	Optional
as_vis	Parameter to include/exclude citations	Optional
as_rr	Parameter to show review articles only	Optional
as_sdt	Parameter defines search type (case law, patents) or filtering	Optional

*q is optional only when using the cites parameter.

Visit our documentation for more information on all available parameters.

Available data on Google Scholar (JSON Response)

Google Scholar can return different information from time to time. Here is what the organic_results array may contain:

 "organic_results": [
    {
      "position": "Integer - Position of the result",
      "title": "String - Article title",
      "result_id": "String - Unique identifier",
      "link": "String - URL to the article",
      "snippet": "String - Brief excerpt from the article",
      "publication_info": {
        "summary": "String - Authors, source, year",
        "authors": [
          {
            "name": "String - Author name",
            "link": "String - Link to author profile",
            "author_id": "String - Google Scholar author ID"
          }
        ]
      },
      "inline_links": {
        "cited_by": {
          "total": "Integer - Number of citations",
          "link": "String - URL to citing articles",
          "cites_id": "String - ID for cited by search"
        },
        "versions": {
          "total": "Integer - Number of versions",
          "link": "String - URL to all versions",
          "cluster_id": "String - ID for cluster search"
        },
        "related_pages_link": "String - URL to related articles"
      },
      "resources": [
        {
          "title": "String - Resource title (e.g., PDF)",
          "file_format": "String - File type",
          "link": "String - URL to resource"
        }
      ]
    }
  ],

The API response also includes:

• related_searches: Suggested related queries
• pagination: Navigation links for result pages

Use cases

Here are some use cases for the Google Scholar API:

• Build academic research tools for literature reviews.
• Track citations and measure research impact (h-index analysis).
• Monitor publications from specific authors or institutions.
• Create bibliography generators for academic writing.
• Analyze research trends in specific fields.
• Build tools to discover related papers and research gaps.

Blog tutorial

• How to Scrape Google Scholar Results

Video tutorial

• Google Scholar Scraper - scrape data to Excel (No-code solution)

Contacts

Feel free to reach out via contact@serpapi.com.

Check other Google Scrapers from SerpApi.