# OpenAlex Scholarly Works Scraper (`hipersoft/openalex-scraper`) Actor

Search 250M+ academic papers via OpenAlex: title, abstract, authors, institutions, venue, citation count, DOI, open-access status and topics. Filter by year, type and open access. For research, R\&D and lit reviews. No key.

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

## Pricing

from $0.001 / work scraped

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

## OpenAlex Scholarly Works Scraper — 250M+ papers, citations & abstracts, structured JSON, no API key

Search the open catalog of scholarly works via the **OpenAlex API** and get back clean, structured JSON for every paper — title, reconstructed abstract, authors and their institutions, venue, publisher, citation count, DOI, open-access status and research topics. Filter by publication year, work type and open access, and collect thousands of works per search term. **No account, no API key.** Ideal for literature reviews, R\&D scouting, bibliometrics and building research datasets.

### Features

- 🔎 **Multi-term search** — pass any number of `searchTerms` (matched against title, abstract and fulltext); each term runs separately and results are deduped by OpenAlex work ID.
- 📅 **Year filtering** — bound results with `fromYear` and `toYear` to focus on a publication window (0 = no bound).
- 🔓 **Open-access filter** — flip `openAccessOnly` to keep only OA works, and get `oaStatus` plus a direct `oaUrl` for each.
- 🗂️ **Work-type filter** — narrow to `article`, `preprint`, `review`, `book-chapter`, `dataset` and more via `workType`.
- 📝 **Reconstructed abstracts** — toggle `includeAbstract` to rebuild readable abstract text from OpenAlex's inverted index.
- 👥 **Rich authorship** — every author with their affiliated institutions, plus a ready-to-read `authorsList` string and `authorCount`.
- 📈 **Bibliometrics built in** — citation counts, referenced-works counts, venue, publisher and research `topics` on every record; scale to `maxResultsPerSearch` up to 10,000.

### What you get

Each dataset row is one scholarly work:

```json
{
  "searchTerm": "machine learning",
  "title": "Deep Residual Learning for Image Recognition",
  "doi": "https://doi.org/10.1109/cvpr.2016.90",
  "openAlexId": "https://openalex.org/W2194775991",
  "publicationYear": 2016,
  "publicationDate": "2016-06-01",
  "type": "article",
  "language": "en",
  "citedByCount": 210453,
  "referencedWorksCount": 38,
  "authorsList": "Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun",
  "authors": [
    { "name": "Kaiming He", "institutions": ["Microsoft Research"] }
  ],
  "authorCount": 4,
  "venue": "IEEE Conference on Computer Vision and Pattern Recognition",
  "publisher": "IEEE",
  "isOpenAccess": true,
  "oaStatus": "green",
  "oaUrl": "https://arxiv.org/pdf/1512.03385",
  "topics": ["Deep Learning", "Image Classification"],
  "url": "https://doi.org/10.1109/cvpr.2016.90",
  "abstract": "Deeper neural networks are more difficult to train..."
}
```

### Input

```json
{
  "searchTerms": ["crispr gene editing", "large language models"],
  "fromYear": 2020,
  "toYear": 0,
  "openAccessOnly": false,
  "workType": "article",
  "maxResultsPerSearch": 50,
  "includeAbstract": true
}
```

| Field | Description |
| --- | --- |
| `searchTerms` | Terms to search across title, abstract and fulltext. One search per term. |
| `fromYear` / `toYear` | Publication-year bounds; 0 means no lower/upper bound. |
| `openAccessOnly` | Keep only open-access works. |
| `workType` | Optional type filter (e.g. `article`, `preprint`, `review`, `dataset`). |
| `maxResultsPerSearch` | Maximum works per search term (1–10,000). |
| `includeAbstract` | Reconstruct and include each paper's abstract text. |

### Use cases

- Run a systematic literature review and export a structured, citation-ranked corpus.
- Map the most-cited authors, institutions and venues for a research topic.
- Track emerging preprints and open-access work in a fast-moving field.
- Build training or RAG datasets of abstracts and metadata for research tooling.

### Pricing

Pay-per-event: you're billed a small amount per run and per item scraped — you only pay for what you get. See the **Pricing** tab for current rates.

### FAQ

**Do I need an API key?**
No. This Actor uses the public [OpenAlex](https://openalex.org) API with no account, login or API key required — just enter your search terms and run.

**How many papers can I scrape per run?**
Up to 10,000 works per search term via `maxResultsPerSearch`, and you can pass multiple `searchTerms` in one run, so a single run can collect tens of thousands of works (deduped by OpenAlex work ID).

**Is scraping OpenAlex legal?**
Yes. OpenAlex is a fully open catalog of scholarly metadata, and this Actor reads only those openly available records through the official API and returns them as-is.

**What format is the output?**
Structured JSON — one row per scholarly work — exportable as JSON, CSV or Excel. Each record includes title, reconstructed abstract, authors with institutions, venue, publisher, citation count, DOI and open-access status.

**Can I filter by year or open access?**
Yes. Bound results with `fromYear`/`toYear`, keep only open-access works with `openAccessOnly`, and narrow to a specific `workType` such as `article`, `preprint`, `review` or `dataset`.

### Related Actors

Building a bibliometrics or literature dataset? These other hipersoft scrapers work well alongside this one:

- [Crossref Scraper](https://apify.com/hipersoft/crossref-scraper) — DOIs, citation counts and metadata from 150M+ registered works
- [Semantic Scholar Scraper](https://apify.com/hipersoft/semantic-scholar-scraper) — papers with citation and influential-citation metrics
- [arXiv Papers Scraper](https://apify.com/hipersoft/arxiv-scraper) — preprints with full abstracts and PDF links
- [PubMed Scraper](https://apify.com/hipersoft/pubmed-scraper) — biomedical literature, abstracts and MeSH terms

### Notes

This Actor uses the public OpenAlex API and returns that catalog's own metadata as-is, over openly available records with no login required. It is an independent tool and is not affiliated with, endorsed by, or sponsored by OpenAlex or OurResearch.

# Actor input Schema

## `searchTerms` (type: `array`):

Search academic works matching these terms (title, abstract, fulltext).

## `fromYear` (type: `integer`):

Only include works published in this year or later. 0 = no lower bound.

## `toYear` (type: `integer`):

Only include works published in this year or earlier. 0 = no upper bound.

## `openAccessOnly` (type: `boolean`):

Only include open-access works.

## `workType` (type: `string`):

Optional filter, e.g. article, preprint, review, book-chapter, dataset. Empty = all.

## `maxResultsPerSearch` (type: `integer`):

Maximum works to collect per search term.

## `includeAbstract` (type: `boolean`):

Reconstruct and include the paper abstract.

## Actor input object example

```json
{
  "searchTerms": [
    "crispr gene editing",
    "large language models"
  ],
  "fromYear": 0,
  "toYear": 0,
  "openAccessOnly": false,
  "workType": "",
  "maxResultsPerSearch": 50,
  "includeAbstract": true
}
```

# 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 = {
    "searchTerms": [
        "machine learning"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("hipersoft/openalex-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 = { "searchTerms": ["machine learning"] }

# Run the Actor and wait for it to finish
run = client.actor("hipersoft/openalex-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 '{
  "searchTerms": [
    "machine learning"
  ]
}' |
apify call hipersoft/openalex-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/3qxTWgdAStCT624B2/builds/T5Ik27wEpk3wqK1jg/openapi.json