# Google Scholar Scraper (`moving_beacon-owner1/google-scholar-scraper`) Actor
Scrapes Google Scholar search results, including paper titles, authors, publication years, citation counts, article URLs, and PDF links. Supports multiple queries and year filters for research, literature reviews, and citation analysis.
- **URL**: https://apify.com/moving\_beacon-owner1/google-scholar-scraper.md
- **Developed by:** [Jamshaid Arif](https://apify.com/moving_beacon-owner1) (community)
- **Categories:** Developer tools, Other, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $10.00 / 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.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
Scrape **Google Scholar search results** — paper titles, authors, publication year, snippets, **citation counts**, article URLs, and **direct PDF links** when available. Filter by year range. Built for **literature reviews, citation analysis, and research monitoring**. Export to **JSON, CSV, or Excel**.
### What data does it extract?
| Field | Example |
|---|---|
| `title` | Machine learning: Trends, perspectives, and prospects |
| `authorsAndVenue` | MI Jordan, TM Mitchell - Science, 2015 - science.org |
| `year` | 2015 |
| `snippet` | Machine learning addresses the question of how... |
| `citedBy` | 12847 |
| `citedByUrl` | https://scholar.google.com/scholar?cites=... |
| `pdfUrl` | https://www.cs.cmu.edu/~tom/pubs/Science-ML.pdf |
| `url` | https://www.science.org/doi/abs/10.1126/science.aaa8415 |
### Features
- **Citation counts + Cited-by links** — rank papers by impact instantly.
- **Direct PDF detection** — grabs the free full-text link when Scholar shows one.
- **Year range filter** — restrict to a publication window.
- **Multiple queries per run** — batch your literature search.
- **CAPTCHA-aware** — detects blocks and rotates sessions automatically.
### Use cases
- **Literature reviews** — collect and rank papers for a topic in minutes.
- **Citation analysis** — track how a field or an author's work is being cited.
- **Research alerts** — scheduled runs for new papers matching your queries.
- **Meta-research** — build datasets of publications per topic per year.
### Input example
```json
{
"queries": ["large language models", "retrieval augmented generation"],
"maxResultsPerQuery": 50,
"yearFrom": 2022,
"proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}
````
# Actor input Schema
## `queries` (type: `array`):
One or more Scholar search queries, e.g. machine learning, crispr gene editing.
## `maxResultsPerQuery` (type: `integer`):
How many results to scrape per query (10 per page).
## `yearFrom` (type: `integer`):
Only papers published in or after this year.
## `yearTo` (type: `integer`):
Only papers published in or before this year.
## `language` (type: `string`):
Two-letter language code for result pages.
## `proxyConfiguration` (type: `object`):
Residential proxies are STRONGLY recommended — Google Scholar blocks datacenter IPs aggressively.
## Actor input object example
```json
{
"queries": [
"machine learning"
],
"maxResultsPerQuery": 20,
"language": "en",
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"queries": [
"machine learning"
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("moving_beacon-owner1/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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"queries": ["machine learning"],
"proxyConfiguration": {
"useApifyProxy": True,
"apifyProxyGroups": ["RESIDENTIAL"],
},
}
# Run the Actor and wait for it to finish
run = client.actor("moving_beacon-owner1/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 '{
"queries": [
"machine learning"
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}' |
apify call moving_beacon-owner1/google-scholar-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=moving_beacon-owner1/google-scholar-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google Scholar Scraper",
"description": "Scrapes Google Scholar search results, including paper titles, authors, publication years, citation counts, article URLs, and PDF links. Supports multiple queries and year filters for research, literature reviews, and citation analysis.",
"version": "0.0",
"x-build-id": "NxDoN5vX8btG0P3QR"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/moving_beacon-owner1~google-scholar-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-moving_beacon-owner1-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/moving_beacon-owner1~google-scholar-scraper/runs": {
"post": {
"operationId": "runs-sync-moving_beacon-owner1-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/moving_beacon-owner1~google-scholar-scraper/run-sync": {
"post": {
"operationId": "run-sync-moving_beacon-owner1-google-scholar-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"queries"
],
"properties": {
"queries": {
"title": "Search queries",
"type": "array",
"description": "One or more Scholar search queries, e.g. machine learning, crispr gene editing.",
"items": {
"type": "string"
}
},
"maxResultsPerQuery": {
"title": "Max results per query",
"minimum": 1,
"maximum": 500,
"type": "integer",
"description": "How many results to scrape per query (10 per page).",
"default": 20
},
"yearFrom": {
"title": "Year from",
"minimum": 1900,
"type": "integer",
"description": "Only papers published in or after this year."
},
"yearTo": {
"title": "Year to",
"minimum": 1900,
"type": "integer",
"description": "Only papers published in or before this year."
},
"language": {
"title": "Interface language",
"type": "string",
"description": "Two-letter language code for result pages.",
"default": "en"
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Residential proxies are STRONGLY recommended — Google Scholar blocks datacenter IPs aggressively.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```