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

Scrapes Google Scholar for academic papers. Extracts the full canonical scholar-vertical schema: title, authors (with profile links), publication venue, publisher, year, citation counts, DOI, PDF/HTML/bibtex/abstract URLs, versions count, cluster ID, subjects/keywords, free PDF flag, and more.

- **URL**: https://apify.com/searchapi/google-scholar-scraper.md
- **Developed by:** [Search API](https://apify.com/searchapi) (community)
- **Categories:** Developer tools, Automation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.99 / 1,000 google scholar scraper results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

Scrapes Google Scholar for academic papers. Extracts the full canonical scholar-vertical schema: title, authors (with profile links), publication venue, publisher, year, citation counts, DOI, PDF/HTML/bibtex/abstract URLs, versions count, cluster ID, subjects/keywords, free PDF flag, and more.

### What this Actor collects

The Actor converts Google Scholar results into one clean JSON record per scholarly work, including authors and profile links, venue and publisher, year, citation and version counts, DOI, document links, subjects, keywords, access status, and search provenance when available.

- Uses the input limits and filters below to control the crawl.
- Stores source-backed fields defined by the 48-field dataset schema.
- Omits optional fields when the source does not expose a value instead of writing nulls or fabricated placeholders.

### Use cases

- Literature and catalog research
- Entity and citation enrichment
- Specialized-source monitoring

### Input

Provide input in JSON. Fields marked required must be supplied. The Default / example column shows a schema default when one exists; otherwise it shows a documented prefill or fixture value.

| Field | Type | Required | Default / example | Description |
| --- | --- | :---: | --- | --- |
| `query` | string | Yes | `"machine learning"` | The academic search query to look up on Google Scholar |
| `maxItems` | integer | No | `50` | Maximum number of scholar results to retrieve |
| `yearFrom` | integer | No | — | Filter results published from this year onwards (optional) |
| `yearTo` | integer | No | — | Filter results published up to this year (optional) |
| `sortBy` | string | No | `"relevance"` | Sort results by relevance or date |
| `proxyConfiguration` | object | No | `{"useApifyProxy":true,"apifyProxyGroups":["RESIDENTIAL"]}` | Proxy settings for the scraper. Use Apify residential proxies to avoid blocks. |

#### Example input

```json
{
  "query": "transformer neural network",
  "maxItems": 50,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "GOOGLE_SERP"
    ]
  },
  "sortBy": "relevance"
}
```

### Output

The default dataset contains one item per scholarly publication result. The following are the most useful fields; DOI, citation links, versions, document URLs, subjects, and access fields depend on the Scholar result.

| Field | Type | Description |
| --- | --- | --- |
| `position` | integer | Position |
| `title` | string | Title |
| `authors` | array | Authors |
| `publication` | string | Publication |
| `year` | integer | Year |
| `citedBy` | integer | Cited By |
| `searchQuery` | string | Search Query |
| `scrapedAt` | string | Scraped At |
| `type` | string | Type |
| `description` | string | Description |
| `snippet` | string | Snippet |
| `url` | string | URL |
| `link` | string | Link |
| `query` | string | Query |
| `resultType` | string | Result Type |
| `page` | integer | Page |

<details>
<summary>All 48 declared dataset fields</summary>

`position`, `title`, `authors`, `publication`, `year`, `citedBy`, `searchQuery`, `scrapedAt`, `type`, `resultType`
`page`, `titleHtml`, `url`, `link`, `snippet`, `description`, `authorsLinks`, `publisher`, `venue`, `venueType`
`publishedAt`, `publishedAtRaw`, `date`, `citedByUrl`, `citing`, `citesUrl`, `clusterId`, `doi`, `pdfUrl`, `htmlUrl`
`versionsCount`, `versionsUrl`, `bibtexUrl`, `abstractUrl`, `domain`, `favicon`, `thumbnail`, `imageUrl`, `typeOfWork`, `subjects`
`keywords`, `tags`, `topics`, `language`, `isFreePdf`, `searchUrl`, `searchMetadata`, `query`

</details>

#### Example dataset item

This compact example is taken from local Actor storage. Long text and nested collections are shortened for documentation only.

```json
{
  "position": 1,
  "title": "Gradient-based learning applied to document recognition",
  "authors": [
    "Yann LeCun",
    "Léon Bottou"
  ],
  "publication": "Proceedings of the IEEE",
  "year": 1998,
  "citedBy": 58655,
  "searchQuery": "transformer neural network",
  "scrapedAt": "2026-07-23T11:51:14.711Z",
  "type": "scholar",
  "description": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network …",
  "snippet": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network …",
  "url": "https://doi.org/10.1109/5.726791"
}
```

### Related Actors

- [Google About This Result Scraper](https://apify.com/searchapi/google-about-this-result-scraper)
- [Google AI Overview Scraper](https://apify.com/searchapi/google-ai-overview-scraper)
- [Google Autocomplete Scraper](https://apify.com/searchapi/google-autocomplete-scraper)

# Actor input Schema

## `query` (type: `string`):

The academic search query to look up on Google Scholar

## `maxItems` (type: `integer`):

Maximum number of scholar results to retrieve

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

Filter results published from this year onwards (optional)

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

Filter results published up to this year (optional)

## `sortBy` (type: `string`):

Sort results by relevance or date

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

Proxy settings for the scraper. Use Apify residential proxies to avoid blocks.

## Actor input object example

```json
{
  "query": "machine learning",
  "maxItems": 50,
  "sortBy": "relevance",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

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

Canonical v2 schema for Google Scholar results — matches \_canonical/schemas/scholar.schema.json

## `paginationState` (type: `string`):

Continuation URLs retained while traversing Google Scholar result pages.

# 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 = {
    "query": "machine learning",
    "maxItems": 50,
    "sortBy": "relevance",
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("searchapi/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 = {
    "query": "machine learning",
    "maxItems": 50,
    "sortBy": "relevance",
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    },
}

# Run the Actor and wait for it to finish
run = client.actor("searchapi/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 '{
  "query": "machine learning",
  "maxItems": 50,
  "sortBy": "relevance",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call searchapi/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/8dbphbAw0vzL5MN85/builds/dASuzKVqvuq0ntATD/openapi.json