# Google Scholar Scraper (`masked_hacker/google-scholar-scraper`) Actor

Scrape Google Scholar search results into structured academic records (title, authors, year, venue, citation count, PDF link, cluster id).

- **URL**: https://apify.com/masked\_hacker/google-scholar-scraper.md
- **Developed by:** [Masked Hacker](https://apify.com/masked_hacker) (community)
- **Categories:** Other
- **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## Google Scholar Scraper: Papers, Citations & Authors

Turn any [Google Scholar](https://scholar.google.com) search into a clean, structured dataset of
academic papers. Search by keyword, author or exact phrase and get **title, authors, year, venue,
publisher, citation count and PDF link** for every result, fully paginated.

Perfect for **literature reviews, bibliometric analysis, citation tracking, and research
landscape mapping** across any field.

### What you get

- 🔎 **Search by keyword, author, or exact phrase**, with year-range filtering.
- 📚 **Full bibliographic data per paper:** title, authors, year, venue and publisher.
- 📈 **Citation count** and a direct link to the citing papers.
- 📄 **Free PDF link** when Scholar shows one.
- 🔗 **Related articles link** and Scholar's cluster ID for deduping paper versions.
- 📊 Clean JSON / CSV / Excel export, ready for a spreadsheet or a pipeline.

### Pricing

**Pay per event:** **$0.005 per result** returned, plus a small per-run start fee. You only pay
for results you actually receive; no monthly subscription.

### Input

| Field | Type | Description |
|---|---|---|
| `searchQueries` | string\[] | Keyword searches to run, e.g. `graph neural networks`. Required. |
| `exactPhrase` | string | Restrict every query to results containing this exact phrase. |
| `authors` | string | Restrict every query to these author(s), e.g. `Y LeCun`. |
| `yearFrom` / `yearTo` | int | Only include results published in this year range. |
| `maxResultsPerQuery` | int | Stop paginating each query after this many results (default 100). |
| `proxyConfiguration` | proxy | Residential proxy required. Scholar blocks datacenter IPs. |

#### Example input

```json
{
  "searchQueries": ["graph neural networks", "CRISPR gene editing"],
  "yearFrom": 2020,
  "maxResultsPerQuery": 100
}
```

### Output

One record per search result, in rank order for its query.

| Field | Description |
|---|---|
| `title`, `authors`, `year` | Paper identity. |
| `venue`, `publisher` | Journal, conference or publisher. |
| `snippet` | Abstract excerpt shown under the result. |
| `citationCount`, `citedByUrl` | Citation count and link to citing papers. |
| `pdfUrl` | Direct link to a free PDF, when available. |
| `resultUrl`, `relatedUrl`, `clusterId` | Paper link, related-articles link, and Scholar's cluster ID. |
| `query`, `rank`, `scrapedAt` | Source query, result rank, and scrape time. |

#### Example output

```json
{
  "title": "Deep residual learning for image recognition",
  "authors": ["K He", "X Zhang", "S Ren"],
  "year": 2016,
  "venue": "Proceedings of the IEEE conference on computer vision",
  "citationCount": 194832,
  "pdfUrl": "https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf",
  "resultUrl": "https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html",
  "query": "deep residual learning",
  "rank": 1
}
```

### FAQ

**Do I need a Google account or API key?** No. Just provide a search query.

**How far back does pagination go?** As far as Scholar's own result pages go, up to
`maxResultsPerQuery` per query.

**Is this legal?** The actor collects only publicly available search-result data. You are
responsible for complying with Google's terms and applicable law in your use of the data.

# Actor input Schema

## `searchQueries` (type: `array`):

Keyword searches to run on Google Scholar. Each entry is one search.

## `exactPhrase` (type: `string`):

Optional. Restrict every query to results containing this exact phrase.

## `authors` (type: `string`):

Optional. Restrict every query to these author(s), e.g. "Y LeCun".

## `yearFrom` (type: `integer`):

Optional. Only include results published in or after this year.

## `yearTo` (type: `integer`):

Optional. Only include results published in or before this year.

## `maxResultsPerQuery` (type: `integer`):

Stop paginating each query after this many results.

## `proxyConfiguration` (type: `object`):

Residential required — Scholar blocks datacenter IPs and rate-limits aggressively.

## Actor input object example

```json
{
  "searchQueries": [
    "graph neural networks",
    "CRISPR gene editing"
  ],
  "maxResultsPerQuery": 100,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `results` (type: `string`):

All scraped academic records in the default dataset.

# 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 = {
    "searchQueries": [
        "large language models"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("masked_hacker/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 = { "searchQueries": ["large language models"] }

# Run the Actor and wait for it to finish
run = client.actor("masked_hacker/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 '{
  "searchQueries": [
    "large language models"
  ]
}' |
apify call masked_hacker/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/2r13gVFnSi6I8mjuP/builds/Y2klU7oWH8riL7Sco/openapi.json