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

Google Scholar results by keyword: title, URL, authors, venue, year, source site, and citation count.

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

## Pricing

from $0.50 / 1,000 scholar 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Google Scholar Scraper

Google Scholar results by keyword: title, URL, authors, venue, year, source site, and citation count.

For researchers, students, and AI builders who collect literature. Every run tells you plainly what worked and what did not: no silent empty runs.

### What you can do with it

- **Literature lists:** collect papers for a topic with `authors`, `year`, and `venue`.
- **Citation checks:** sort by `citedBy` to find the most cited work.
- **RAG source lists:** feed paper URLs to your reading pipeline.

### What you get

| Field | What it is | Example |
| --- | --- | --- |
| `query` | The search term that produced this row. | large language models |
| `type` | article, pdf, book, or error. | article |
| `position` | 1-based rank in this Actor's list. | 1 |
| `page` | Scholar results page number. | 1 |
| `title` | Paper or book title. | Beyond efficiency: A systematic survey of resource-efficient large la… |
| `url` | Paper URL. | https://dl.acm.org/doi/abs/10.1145/3845797 |
| `description` | Abstract excerpt. | … Beyond Eficiency: A Systematic Survey of Resource-Eficient Large La… |
| `publication` | Full publication line as Scholar shows it. | G Bai, Z Chai, C Ling, S Wang, J Lu, N Zhang… - ACM Computing …, 2026… |
| `authors` | Author names as Scholar lists them (Scholar shortens long lists). | \[G Bai, …] |
| `venue` | Journal, book, or conference name as shown. | ACM Computing |
| `year` | Publication year. | 2026 |
| `sourceSite` | Site that hosts the item, for example JSTOR or books.google.com. | dl.acm.org |
| `citedBy` | Citation count when present. | 275 |
| `pdfUrl` | Direct PDF link when the parser returns one. | https://dl.acm.org/doi/pdf/10.1145/3845797 |
| `rankOrganic` | Position among organic results on the results page. | 1 |

Examples come from a real run. Every row has every field; a value the page did not show is `null`, never guessed.

### How to use it

1. Click **Try for free** (or **Start**) on this page.
2. Enter one search query per line in the first field. The form is prefilled with a working example.
3. Optional: set **Max results** and the other options.
4. Click **Start**. Open the **Output** tab, or export JSON, CSV, Excel, or HTML.

#### Example input

```json
{
  "queries": [
    "large language models"
  ],
  "pages": 1,
  "maxResults": 3
}
```

#### Example output

One real row from a run on 26 October 2026 (long values shortened here):

```json
{
  "query": "large language models",
  "type": "article",
  "position": 1,
  "page": 1,
  "title": "Beyond efficiency: A systematic survey of resource-efficient large language models",
  "url": "https://dl.acm.org/doi/abs/10.1145/3845797",
  "description": "… Beyond Eficiency: A Systematic Survey of Resource-Eficient Large Language Models • 19 \nIn this condensed examination of resource eiciency in Large Language…",
  "publication": "G Bai, Z Chai, C Ling, S Wang, J Lu, N Zhang… - ACM Computing …, 2026 - dl.acm.org",
  "authors": [
    "G Bai",
    "Z Chai",
    "C Ling",
    "…"
  ],
  "venue": "ACM Computing",
  "year": 2026,
  "sourceSite": "dl.acm.org",
  "citedBy": 275,
  "pdfUrl": "https://dl.acm.org/doi/pdf/10.1145/3845797",
  "rankOrganic": 1
}
```

### Pricing

Pay per result: **$0.50 per 1,000 rows**, plus **$0.001** per run start. No subscription.

| Event | When | Price |
| --- | --- | --- |
| Actor start | Once per run (per GB of memory; the default 256 MB counts as one) | $0.001 |
| Result row | Each row saved to the dataset | $0.0005 |

| Example | Rows | Cost |
| --- | --- | --- |
| 10 papers | 10 | $0.001 + $0.005 = **$0.006** |
| 100 papers | 100 | $0.001 + $0.05 = **$0.051** |
| 1,000 papers | 1,000 | $0.001 + $0.50 = **$0.501** |

- Retries after a bot check or a temporary error cost you nothing. You pay only for rows saved.
- An input that fails after all retries saves one error row (one row charge), so you can see what failed and why.
- On Apify's free plan a run stops at 20 rows. Any paid Apify plan removes this limit.
- Set **Max total charge per run** in the run options. The Actor stops cleanly at that limit and keeps every row saved so far.

### What you can count on

- **Same columns on every row.** Missing values are `null`, so spreadsheets and databases never break.
- **No silent failures.** An input that fails gets an error row with `errorCode` and a plain-language `errorMessage`. The other inputs keep running.
- **A clear run summary.** The run status says how many results were saved, how many inputs failed, and why. The same numbers are saved as the `RUN_SUMMARY` record in the run's key-value store, for automations.
- **Automatic retries.** Bot checks and temporary errors are retried with a fresh request before an input counts as failed.
- **Limits respected.** **Max results** and your spending limit stop the run cleanly.

### Use it from code, automations, and AI agents

**API** (returns the rows when the run finishes):

```bash
curl -X POST "https://api.apify.com/v2/acts/plumblinedata~google-scholar-scraper/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries": ["large language models"], "pages": 1, "maxResults": 3}'
```

**Python** (`pip install apify-client`):

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("plumblinedata/google-scholar-scraper").call(run_input={"queries": ["large language models"], "pages": 1, "maxResults": 3})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row)
```

**JavaScript** (`npm install apify-client`):

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

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor('plumblinedata/google-scholar-scraper').call({"queries": ["large language models"], "pages": 1, "maxResults": 3});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

**No-code:** connect the Actor in Make, Zapier, n8n, or Google Sheets through Apify's integrations, or add a webhook to get notified when a run finishes.

**AI agents (MCP):** add `https://mcp.apify.com?tools=plumblinedata/google-scholar-scraper` to Claude, Cursor, or any MCP client. The agent can call this Actor as a tool with the same input as above.

### Limitations

- Scholar shortens long author and venue lists; they are kept as shown.
- `pdfUrl` is set only when Scholar shows a PDF link.

### FAQ

**Do I get the abstract or full text?**
You get the snippet Scholar shows (`description`), not the full paper. `pdfUrl` links to the PDF when Scholar has one.

**Can I find the most cited papers on a topic?**
Yes. Run your topic, then sort the dataset by `citedBy`.

**What does an error row mean?**
It marks an input that failed after retries. `errorCode` is short (`blocked`, `empty_result`, `not_available`, `http_404`, or `invalid_...` for a bad ID or URL) and `errorMessage` says what to do. Remove error rows with a filter on `type`.

**Can I run it on a schedule?**
Yes. Create a task with your input in Apify Console, then add a schedule (for example every day at 8:00).

**Is it legal to collect this data?**
The Actor reads public pages that anyone can open without logging in. You are responsible for how you use the data, including copyright, personal data rules such as GDPR, and the site's terms. If you are not sure, ask a lawyer.

### Related Actors

- [Google Search Scraper](https://apify.com/plumblinedata/google-search-scraper): Google search results as rows.
- [Google News Scraper](https://apify.com/plumblinedata/google-news-scraper): Google News results by keyword.

### Support

Found a problem or need a field? Open an issue on the **Issues** tab. Include the run ID and one input that shows the problem. We read every issue.

# Changelog

This Actor's version history is a separate document: https://apify.com/plumblinedata/google-scholar-scraper/changelog.md

# Actor input Schema

## `queries` (type: `array`):

One search term per line.

## `pages` (type: `integer`):

Result pages per query (1–10). Page 2 is a second lookup.

## `geoLocation` (type: `string`):

Optional city, state, country, or coordinates for localized results.

## `locale` (type: `string`):

Optional Accept-Language value.

## `maxResults` (type: `integer`):

Stop after this many dataset rows across every query. Leave empty to keep every paper.

## Actor input object example

```json
{
  "queries": [
    "large language models"
  ],
  "pages": 1
}
```

# Actor output Schema

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

Dataset of flattened Google Scholar listings.

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

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

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

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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": [
    "large language models"
  ]
}' |
apify call plumblinedata/google-scholar-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,plumblinedata/google-scholar-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/W7Qn8l3h2Se1O5MYf/builds/4MEiJN9gAvKMUWwmd/openapi.json