Google Scholar Scraper — Papers & Citations

Extract academic paper metadata from Google Scholar including titles, authors, abstracts, citation counts, publication venues, PDF links, and publication years. Search by keyword, research topic, or author name. Returns structured JSON ready for literature reviews, bibliometric analysis, and AI research pipelines.

Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools.

Features

• 50 papers in ~10 seconds — lightning-fast API-based extraction
• Rich academic metadata — title, authors, abstract, venue, year, citation count, PDF link
• Citation tracking — exact citation counts for research impact measurement
• Year filtering — narrow results to a specific publication year range
• Author search — find all papers by a specific researcher (e.g., "Geoffrey Hinton")
• Multi-query support — search multiple keywords or authors in a single run
• Up to 500 results per query — deep search coverage with automatic pagination
• PDF direct links — when available, extract direct PDF download URLs
• Journal and conference data — venue name extracted from publication info
• Source type detection — classify results as PDF, book, or HTML

Use Cases

Use Case	Description
Literature reviews	Systematically collect papers for academic research and systematic reviews
Bibliometric analysis	Measure research impact, track citation trends, map collaboration networks
Competitor research	Monitor competitor publications and R&D directions
Grant writing	Find related work, citation context, and research gaps for proposals
AI knowledge graphs	Feed structured academic data to LLMs for summarization and classification
Content creation	Generate research-backed articles, newsletters, and educational materials

Input

Field	Type	Default	Description
searchQueries	string[]	["machine learning"]	Keywords or topics to search
authorUrls	string[]	[]	Author names to search (e.g., "Geoffrey Hinton")
maxResults	integer	20	Max papers per query (1–500)
yearFrom	integer	—	Filter papers from this year onward
yearTo	integer	—	Filter papers up to this year

Output

Each record represents one academic paper:

{
  "title": "Attention Is All You Need",
  "authors": ["Ashish Vaswani", "Noam Shazeer", "Niki Parmar"],
  "publicationInfo": "Advances in neural information processing systems, 2017",
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
  "citationCount": 98432,
  "paperUrl": "https://proceedings.neurips.cc/paper/2017/hash/...",
  "pdfUrl": "https://arxiv.org/pdf/1706.03762.pdf",
  "sourceType": "PDF",
  "year": 2017,
  "citationsUrl": "https://scholar.google.com/scholar?cites=...",
  "relatedUrl": "https://scholar.google.com/scholar?q=related:...",
  "scrapedAt": "2025-08-01T12:00:00.000Z",
  "searchQuery": "transformer attention mechanism"
}

API Usage

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('USERNAME/google-scholar-scraper').call({
  searchQueries: ['large language models', 'RLHF reinforcement learning'],
  maxResults: 50,
  yearFrom: 2022,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Found ${items.length} papers`);

Pricing

This actor charges per paper returned.

Volume	Estimated Cost
100 papers	~$0.10
1,000 papers	~$1.00
5,000 papers	~$5.00

FAQ

Q: Does this require a Google account or API key? No — the scraper uses Google Scholar's public data. No credentials needed.

Q: What is the citation count accuracy? Citation counts are extracted directly from Google Scholar's displayed counts. They match what you see on the website.

Q: Can I search for papers by a specific author? Yes — use the authorUrls field with the author's name (e.g., "Yann LeCun") or their Scholar profile URL.

Q: Does it extract full paper text? No — it extracts the abstract/snippet shown on Google Scholar. For full text, use the pdfUrl field when available.

What is Google Scholar Scraper?

Google Scholar Scraper turns the target data into structured, reusable results on Apify. Use it when you need repeatable collection for researchers, analysts, education or recruiting teams, and structured-data workflows without maintaining a custom scraper or one-off integration. Run it manually, schedule recurring jobs, call it through the Apify API, or connect it to an AI agent through the Apify MCP server.

The Actor stores results in an Apify dataset, where they can be previewed and exported as JSON, CSV, Excel, XML, or RSS. Availability and completeness depend on the source, supplied inputs, public visibility, authentication requirements, and upstream rate limits.

Use cases for Google Scholar Scraper

• Build structured datasets for research, reporting, enrichment, or monitoring.
• Automate repetitive collection with schedules, webhooks, and API calls.
• Feed clean records into spreadsheets, databases, CRMs, BI tools, AI agents, or RAG pipelines.
• Track changes over time by running the same validated input on a schedule.
• Replace fragile manual copy-and-paste work with a reproducible Apify workflow.

How to use Google Scholar Scraper

1. Open the Actor input page and choose a focused, valid target.
2. Set a conservative result limit for the first run.
3. Start the Actor and inspect the dataset for coverage and field availability.
4. Export the results or connect the dataset to your downstream system.
5. Scale gradually and use scheduling, pagination, or proxies when supported.

Important input options

• searchQueries — Enter one or more keywords, topics, or research areas to search on Google Scholar (e.g., 'machine learning', 'cancer immunotherapy'). Each query returns up to maxResults papers. Use authorUr
• authorUrls — Enter author names to find their most cited and relevant papers on Google Scholar (e.g., 'Geoffrey Hinton', 'Yann LeCun'). Returns papers authored by or citing these researchers.
• maxResults — Maximum number of papers to extract per search query. Default value is 50. Set lower for faster test runs, higher for comprehensive searches (up to 500 per query).
• yearLow — Only include papers published in or after this year. Set to 2020 to focus on recent research, or leave as 2000 for a wider range.
• yearHigh — Only include papers published in or before this year. Set to 2025 to exclude upcoming papers.
• sortBy — Sort order for search results. Use 'relevance' for most relevant papers first. Use 'date' for newest papers first.
• articlesOnly — When enabled, only includes actual articles — excludes patents and citations from results.

API and automation example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('muhammadafzal/google-scholar-scraper').call({
  // Add the same input fields you use in the Apify Console.
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

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Frequently asked questions

How many results can I scrape with Google Scholar Scraper?

The practical total depends on the source, input limits, pagination, available records, run timeout, and upstream restrictions. Start with a small run, verify the output, and increase the limit gradually.

Can I integrate Google Scholar Scraper with other apps?

Yes. Use Apify integrations, webhooks, schedules, dataset exports, Make, Zapier, Google Sheets, cloud storage, or your own application.

Can I use Google Scholar Scraper with the Apify API?

Yes. Start runs with the Apify REST API or an official Apify client, then retrieve records from the run's default dataset. Keep your API token in a secret or environment variable.

Can I use Google Scholar Scraper through an MCP Server?

Yes. The Apify MCP server can expose the Actor to compatible AI clients and agents. Review the input and expected cost before allowing an autonomous workflow to run it at scale.

Do I need proxies?

It depends on the source and volume. Use the default configuration first. For larger or geographically sensitive jobs, select an appropriate proxy configuration only when the Actor supports it.

Is it legal to scrape this data?

Scraping rules vary by source, jurisdiction, data type, and intended use. Collect only data you are authorized to access, respect applicable terms and privacy laws, and avoid restricted or personal data misuse. This documentation is not legal advice.

Your feedback

If a field is missing, a source layout has changed, or you need a supported use case documented, open an issue on the Actor page with a reproducible input and run ID.