OpenAlex Academic Research Scraper - Scholarly Papers

Search and extract academic papers, authors, institutions, and research topics from OpenAlex. Free open API covering 250M+ scholarly works. Get citations, abstracts, open access URLs.

Use cases

• Map a research field's citation network and open-access share
• Evaluate an author's or institution's output
• Build a bibliometrics dashboard
• Find open-access full-text links for a reading list
• Feed a research RAG pipeline with abstracts

Input

Parameter	Type	Required	Default	Description
mode	string	Yes	"searchWorks"	What to search for Allowed: searchWorks, searchAuthors, searchInstitutions, searchTopics.
searchQuery	string	Yes	"machine learning"	Search term
filterYear	integer	No	—	Filter by publication year (optional)
filterOpenAccess	boolean	No	false	Only return open access papers
sortBy	string	No	"relevance"	Sort order for results Allowed: relevance, cited_by_count, publication_date.
maxResults	integer	No	50	Maximum number of results
email	string	No	—	Your email for polite pool (faster rate limits). Optional but recommended.

Example input

{
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": false,
  "sortBy": "relevance",
  "maxResults": 5
}

Output

The exact fields depend on the mode you run. This is real output from an actual run of this Actor:

{
  "id": "https://openalex.org/W2101234009",
  "doi": "https://doi.org/10.48550/arxiv.1201.0490",
  "title": "Scikit-learn: Machine Learning in Python",
  "authorNames": "Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Müller, Andreas, Nothman, Joe…",
  "authors": [
    {
      "name": "Fabián Pedregosa",
      "institution": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives"
    },
    {
      "name": "Gaël Varoquaux",
      "institution": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives"
    }
  ],
  "publicationYear": 2012,
  "publicationDate": "2012-01-02",
  "journal": "ORBi (University of Liège)",
  "citedByCount": 64029,
  "isOpenAccess": true,
  "openAccessUrl": "https://orbi.uliege.be/handle/2268/225787",
  "abstract": "Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised …",
  "concepts": [
    "Python (programming language)",
    "Documentation"
  ],
  "type": "article"
}

Field	Type
id	string
doi	string
title	string
authorNames	string
authors	array of object — each with name, institution
publicationYear	number
publicationDate	string
journal	string
citedByCount	number
isOpenAccess	boolean
openAccessUrl	string
abstract	string
concepts	array
type	string

The dataset also ships a preset table view (Overview), so the key columns are readable straight away in Apify Console, and exportable to JSON, CSV, Excel, or XML.

How to run it

In Apify Console — open the Actor, fill in the input form, click Start, then download the results from the Dataset tab.

With the JavaScript client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('cloud9_ai/openalex-scraper').call({
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": false,
  "sortBy": "relevance",
  "maxResults": 5
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

With the Python client

from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')

run = client.actor('cloud9_ai/openalex-scraper').call(run_input={
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": False,
  "sortBy": "relevance",
  "maxResults": 5
})

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

With the API — POST https://api.apify.com/v2/acts/cloud9_ai~openalex-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN with the input JSON as the body.

Notes and limits

• No API key, account, or login is needed — just the input above.
• maxResults caps how much a single run collects, which is also what caps the run's cost.
• Requests are paced and failed requests are retried automatically, so runs stay inside the source's rate limits.
• Only publicly available data is collected. How you use the output is your responsibility, including the source's terms of use and any applicable data-protection law.

Support

Found a bug, or need a field that isn't in the output? Open an issue on the Issues tab of this Actor in Apify Console. Issues there are read and answered.

License

Apache-2.0