# Google Scholar Scraper — Papers, Authors, Cites (`scrape.badger/google-scholar-scraper`) Actor

Scrape Google Scholar at scale: paper search with year range + language filters, author profile lookup (h-index, i10-index, interests, co-authors, full article list), citation formats (MLA, APA, Chicago, Harvard, Vancouver) with BibTeX / RIS / EndNote / RefWorks exports.

- **URL**: https://apify.com/scrape.badger/google-scholar-scraper.md
- **Developed by:** [ScrapeBadger](https://apify.com/scrape.badger) (community)
- **Categories:** Automation, Developer tools, Other
- **Stats:** 44 total users, 11 monthly users, 97.8% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $7.00 / 1,000 scholar pages

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

### What does Google Scholar Scraper do?

Scrape [Google Scholar](https://scholar.google.com) at scale — papers, author profiles, author citation charts, and citation format exports (MLA / APA / Chicago / BibTeX / RIS).

### Why use Google Scholar Scraper?

- **Five modes.** Search Papers, Search Author Profiles, Get Author Profile (with articles), Get Author Citation Chart, Get Paper Citation Formats.
- **Year range filter.** `as_ylo` / `as_yhi` for date-bound literature reviews.
- **Citation export.** Direct MLA / APA / Chicago / Harvard / Vancouver + BibTeX / RIS.
- **Author citation chart.** Per-year citation counts for academic impact tracking.
- **Cheapest scholar actor on Apify.** $0.70 / 1k papers.

### What data can Google Scholar Scraper extract?

| Field | Type | Description |
|---|---|---|
| record\_type | string | `paper` / `author` / `article` / `citation_chart_point` / `citation_format` |
| title | string | Paper / author name |
| authors | string | Author list |
| publication | string | Journal / venue |
| cited\_by | number | Citation count |
| year | number | Publication year |
| link | string | Paper URL |
| snippet | string | Abstract preview |
| cluster\_id | string | Google Scholar's paper ID |
| author\_id | string | Google Scholar's author ID |

### How to scrape Google Scholar

1. Click **Try for free**.
2. Pick `mode`.
3. For Search Papers: enter `q`, optional `as_ylo` / `as_yhi` year range.
4. For Author Profile: enter `author_id`.
5. For Citation Formats: enter `q` (cluster\_id).
6. Click **Start** — papers / authors stream into the dataset.

### How much will it cost?

**$0.007 per page (list modes) · $0.003 per single-shot call.** ≈ $0.70 per 1,000 papers. Author chart / cite format = single-shot $0.003 each.

#### Competitor benchmark

| Actor | Author | Price | Notes |
|---|---|---|---|
| easyapi/google-scholar-scraper | easyapi | ~$10 / 1k papers | Search-only |
| scrapestorm/google-scholar-scraper | ScrapeStorm | ~$8.99 / 1k | Search-only |
| marco\_gullo/google-scholar | Marco Gullo | Variable | Per-run |
| **scrape-badger/google-scholar-scraper** | **ScrapeBadger** | **$0.70 / 1k papers** | **5 modes in one actor** |

### Input

Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.

| Field | Required | Description |
|---|---|---|
| mode | ✅ | One of 5 modes. |
| q | Search modes | Query (keyword / cluster\_id / author name). |
| author\_id | Author modes | Google Scholar author ID. |
| as\_ylo / as\_yhi | — | Year range filter. |
| as\_sdt | — | Article type filter. |
| num / page | — | Pagination. |
| mauthors | Search Author Profiles | Author name query. |
| cstart / pagesize | Get Author Profile articles | Pagination. |

### Output

Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.

Example record:

```json
{
  "record_type": "paper",
  "title": "Attention Is All You Need",
  "authors": "Vaswani, Ashish et al.",
  "publication": "NeurIPS 2017",
  "cited_by": 98234,
  "year": 2017,
  "link": "https://arxiv.org/abs/1706.03762",
  "snippet": "The dominant sequence transduction models\u2026",
  "cluster_id": "1234567890"
}
```

### Tips / Advanced options

- **Use `as_ylo` for literature reviews.** Bound by date — e.g. `as_ylo: 2020` for post-2020 ML papers.
- **Citation chart for academic impact.** Per-year citation counts — perfect for tenure-track impact reports.
- **BibTeX export for reference managers.** Get Paper Citation Formats → BibTeX → drop into Zotero / Mendeley.
- **Dedupe by `cluster_id`.** Same paper on arXiv + journal + preprint server — Google merges by cluster\_id.

### FAQ, Disclaimers, Support

#### What's `cluster_id`?

Google Scholar's internal paper identifier. All versions (arXiv, journal, preprint) of the same paper share a cluster\_id.

#### How do I find `author_id`?

Run Search Author Profiles first — each returned author has an `author_id`. Or grab it from the author's profile URL (`&user=…`).

#### Does this include PDF text?

No — only Scholar's metadata (title, authors, snippet, link). Download the PDF from `link` separately.

#### What's the citation count source?

Google Scholar's own `cited_by` count. Differs from Web of Science / Scopus / OpenCitations.

#### Disclaimer

This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.

#### Support

Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).

#### Powered by

[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.

# Actor input Schema

## `mode` (type: `string`):

Pick the Scholar call to run. Each mode uses different required inputs.

## `q` (type: `string`):

Search text (e.g. <b>transformer neural networks</b>). Required for <b>Search Papers</b>. Also used as the <b>cluster\_id</b> in <b>Get Paper Citation Formats</b> — paste the paper's cluster\_id from a Search Papers result.

## `mauthors` (type: `string`):

Author name to search (e.g. <b>Geoffrey Hinton</b>). Required for <b>Search Author Profiles</b>.

## `author_id` (type: `string`):

Google Scholar user ID (the <b>user</b> query parameter on a scholar.google.com profile URL, e.g. <b>JicYPdAAAAAJ</b>).

## `as_ylo` (type: `integer`):

Lower bound for publication year (e.g. <b>2020</b>). Optional.

## `as_yhi` (type: `integer`):

Upper bound for publication year (e.g. <b>2024</b>). Optional.

## `as_sdt` (type: `string`):

<b>0</b> excludes patents (default). <b>7</b> includes patents and case law.

## `num` (type: `integer`):

Scholar paper results per page. 1–20.

## `hl` (type: `string`):

Google language code (e.g. <b>en</b>, <b>es</b>, <b>de</b>, <b>fr</b>).

## `cstart` (type: `integer`):

Offset for the author's article list (0-based).

## `pagesize` (type: `integer`):

How many articles to fetch per author-profile page.

## `max_pages` (type: `integer`):

How many pages of results to fetch. Each page = 1 API call (credits). Applies to <b>Search Papers</b>, <b>Search Author Profiles</b>, and <b>Get Author Profile</b>.

## Actor input object example

```json
{
  "mode": "Search Papers",
  "q": "transformer neural networks",
  "as_sdt": "0",
  "num": 10,
  "hl": "en",
  "cstart": 0,
  "pagesize": 20,
  "max_pages": 3
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "q": "transformer neural networks"
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/google-scholar-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "q": "transformer neural networks" }

# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-scholar-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "q": "transformer neural networks"
}' |
apify call scrape.badger/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=scrape.badger/google-scholar-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/q2pFlKAZuvZpzHBLV/builds/aO773nYJO9wEkdARg/openapi.json