OpenAlex Scraper: research papers, citations and authors API

Export research papers, citation counts, authors, institutions, journals and topics from OpenAlex, the free and open catalog of 250M+ scholarly works (the successor to Microsoft Academic Graph). You get clean, flat rows ready for Excel, Google Sheets, BI tools, pandas or your database, with the full abstract rebuilt as plain text.

Use it as a no-code OpenAlex API and Google Scholar alternative. It runs on the official OpenAlex REST API, so there are no proxies, no browser and no blocking, and it costs $3 per 1,000 results.

What does the OpenAlex Scraper do?

It turns an OpenAlex search into a dataset with one row per record. Pick what to export:

• Works (papers, articles, preprints, books, datasets, dissertations): OpenAlex ID, DOI, title, year, publication date, type, language, citation count, FWCI, authors, author IDs, institutions, countries, journal or repository, ISSN, publisher, open-access status, open-access URL, PDF URL, license, primary topic, field, keywords, PubMed ID, reference count, retraction flag and the abstract.
• Authors: name, ORCID, works count, citations, h-index, i10-index, current institution, country and top topics.
• Institutions: name, ROR, type, country, city, homepage, works, citations, h-index and top topics.
• Sources (journals, repositories, conferences): name, ISSN, publisher, open-access and DOAJ flags, APC price, works, citations and h-index.
• Topics and legacy concepts: name, description, subfield, field, domain, keywords, works and citations.

Every list (authors, institutions, keywords...) is joined with ; into one cell, so the CSV opens cleanly in Excel.

Who is it for?

• Researchers and librarians. Build literature-review datasets, bibliometric analyses and citation reports without writing code.
• AI and RAG teams. Collect titles and abstracts on a topic to build training sets, embeddings or a research-assistant knowledge base.
• R&D, pharma and tech intelligence. Track what a company, university or competitor publishes, and which topics are growing.
• Recruiters and BD teams. Find the most-cited authors in a field, with ORCID and current institution, for hiring or partnership outreach.
• Publishers, funders and universities. Benchmark journals and institutions by output, citations, h-index and open-access share.
• Monitoring. Schedule a daily run sorted by newest to catch new papers from an institution, author or topic.

Why use this OpenAlex scraper?

• Low price. $3 per 1,000 results, about 13x cheaper than other OpenAlex actors. You pay only for rows saved.
• Official API, not HTML scraping. It runs on the OpenAlex API, so it doesn't break when a website changes. It respects the 10 requests/second limit and retries automatically on rate limits and server errors.
• Abstracts included. OpenAlex stores abstracts as an inverted index. The Actor rebuilds the plain text for you.
• CSV-friendly. No nested JSON. Every field is a single value, ready for spreadsheets.
• Simple filters plus full power. Year range, open access, work type, institution, author, topic and country fields cover most needs, and the advanced field accepts any raw OpenAlex filter.
• Bring your own key (optional). Works out of the box with no key. Add your free OpenAlex API key for very large exports.

How to scrape OpenAlex

1. Click Try for free (or Start) on this page.
2. Choose What to scrape (works, authors, institutions, sources, topics or concepts).
3. Enter a search query and any filters.
4. Set Max results to cap the number of rows, and with it your cost.
5. Click Start, then download the results as CSV, Excel, JSON, XML or HTML, or read them via the API.

Input examples

The 50 most relevant papers on large language models since 2023 (the prefilled example, about $0.15):

{
  "entity": "works",
  "search": "large language models",
  "fromYear": 2023,
  "maxResults": 50
}

The 1,000 most-cited open-access articles from Harvard published 2020 to 2024:

{
  "entity": "works",
  "institutionId": "I136199984",
  "fromYear": 2020,
  "toYear": 2024,
  "openAccessOnly": true,
  "workType": "article",
  "sort": "cited_by_count:desc",
  "maxResults": 1000
}

The 200 most-cited authors working on CRISPR in Germany:

{
  "entity": "authors",
  "search": "CRISPR",
  "countryCode": "DE",
  "sort": "cited_by_count:desc",
  "maxResults": 200
}

Newest preprints with more than 10 citations, using a raw OpenAlex filter:

{
  "entity": "works",
  "workType": "preprint",
  "extraFilter": "cited_by_count:>10,language:en",
  "sort": "publication_date:desc",
  "maxResults": 500
}

Field	What it does	Example
entity	What to export	works, authors, institutions, sources, topics, concepts
search	Full-text search (title, abstract, full text for works; name for others)	"graph neural networks"
fromYear / toYear	Publication year range (works)	2020 / 2024
openAccessOnly	Only open-access works or fully OA journals	true
workType	Work type (works)	article, review, preprint, book, dataset
institutionId	OpenAlex institution ID or URL (works, authors)	I136199984
authorId	OpenAlex author ID or URL (works)	A5023888391
topicId	Topic ID, or legacy concept ID for works	T10346, C41008148
countryCode	Two-letter country code	US, KR
extraFilter	Any raw OpenAlex filter, appended with AND	cited_by_count:>100,has_doi:true
sort	Sort order	cited_by_count:desc, publication_date:desc
maxResults	Maximum rows to save (default 100)	1000
mailto	Optional email sent to OpenAlex as a courtesy	you@example.com
apiKey	Optional free OpenAlex API key

Don't know an institution, author or topic ID? Run the Actor once with entity set to institutions, authors or topics and a search query. The id column has the ID to use.

Output sample

A works row (abstract shortened):

{
  "entity": "works",
  "id": "W4362515116",
  "url": "https://openalex.org/W4362515116",
  "citedByCount": 2068,
  "doi": "10.1007/s11704-026-60308-3",
  "title": "A Survey of Large Language Models",
  "year": 2026,
  "publicationDate": "2026-05-09",
  "type": "article",
  "language": "en",
  "fwci": 1251.5361,
  "authors": "Wayne Xin Zhao; Kun Zhou; Junyi Li; Tianyi Tang; Zican Dong; Yupeng Hou; ...; Jian-Yun Nie; Ji-Rong Wen",
  "authorsCount": 23,
  "authorIds": "A5037145565; A5100363220; A5030566556; ...",
  "institutions": "Renmin University of China; Université de Montréal",
  "countries": "CN; CA",
  "sourceName": "Frontiers of Computer Science",
  "sourceType": "journal",
  "issn": "2095-2228",
  "publisher": "Higher Education Press",
  "isOa": true,
  "oaStatus": "hybrid",
  "oaUrl": "https://link.springer.com/content/pdf/10.1007/s11704-026-60308-3.pdf",
  "pdfUrl": "https://link.springer.com/content/pdf/10.1007/s11704-026-60308-3.pdf",
  "license": "cc-by",
  "primaryTopic": "Topic Modeling",
  "field": "Computer Science",
  "keywords": "large language models; pre-training; self-supervised learning; fine-tuning; alignment; in-context learning",
  "pmid": null,
  "referencedWorksCount": 0,
  "isRetracted": false,
  "abstract": "Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigm...",
  "updatedDate": "2026-10-22T09:42:04.148104",
  "scrapedAt": "2026-10-23T04:34:51.480Z"
}

Field	Type	Description
entity	string	Which entity the row is (works, authors...)
id, url	string	Short OpenAlex ID and full OpenAlex URL
citedByCount	number	Times cited
doi	string	DOI without the https://doi.org/ prefix
title, year, publicationDate, type, language	string, number	Bibliographic basics
fwci	number	Field-weighted citation impact
authors, authorIds, authorsCount	string, number	Authors in order, their OpenAlex IDs, and the count
institutions, countries	string	De-duplicated author affiliations and country codes
sourceName, sourceType, issn, publisher	string	Journal, repository or conference
isOa, oaStatus, oaUrl, pdfUrl, license	boolean, string	Open-access status and the best free copy
primaryTopic, field, keywords	string	Subject classification
pmid	string	PubMed ID, when available
referencedWorksCount, isRetracted	number, boolean	Reference count and retraction flag
abstract	string	Full abstract as plain text
name, orcid, worksCount, hIndex, i10Index, institution, countryCode, topTopics		Author, institution and source fields
scrapedAt	string	When the row was collected (ISO 8601)

How much does it cost to scrape OpenAlex?

This Actor uses pay-per-event pricing:

• $0.003 per result saved ($3 per 1,000 results)
• $0.00005 per run start per GB of memory (the Actor runs on 256 MB, so about $0.0000125 per run)

Worked example: exporting 10,000 papers on a topic costs 10,000 × $0.003 = $30.00, plus about $0.00001 for the run start. The prefilled 50-paper example costs about $0.15, so the $5 of monthly free credit on the Apify Free plan covers around 1,600 results.

Set Max results or a maximum charge per run, and the Actor stops as soon as it reaches the limit.

Integrations and API

Call the Actor from your own code with the official apify-client, or connect it to Make, Zapier, n8n, Google Sheets, Slack, webhooks and more from the Integrations tab.

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("deanko/openalex-scraper").call(run_input={
    "search": "retrieval augmented generation",
    "fromYear": 2024,
    "sort": "cited_by_count:desc",
    "maxResults": 200,
})
for paper in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(paper["year"], paper["citedByCount"], paper["title"])

JavaScript / Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('deanko/openalex-scraper').call({
    entity: 'authors',
    search: 'protein folding',
    sort: 'cited_by_count:desc',
    maxResults: 100,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.map((a) => `${a.name} (${a.institution}) h=${a.hIndex}`));

You can also run it with one HTTP request through the run-sync-get-dataset-items endpoint and get the rows back in the response.

FAQ

Where does the data come from? It comes from the official OpenAlex API, run by the nonprofit OurResearch. OpenAlex data is released under CC0, so you can reuse it freely, including commercially.

Do I need an OpenAlex API key? No. Without a key the Actor uses OpenAlex's free shared budget and can return up to 10,000 results per query. For bigger exports, or if you run many large jobs in a day, add your free key from openalex.org/settings/api in the OpenAlex API key field. The key is stored as a secret input.

What is the mailto field for? OpenAlex asks API users to identify themselves. If you enter your email, it is sent to OpenAlex with each request. It's optional and the Actor never stores it.

How fast is it? It fetches 100 records per request at up to 10 requests per second. The 50-row example finishes in a few seconds, and 10,000 rows take about a minute.

Is OpenAlex a good Google Scholar alternative? For bulk data, yes. Google Scholar has no API and blocks scrapers, while OpenAlex covers a similar body of literature with open, structured metadata, citation counts and abstracts.

How do I get only new papers every day? Schedule the Actor (Console → Schedules) with sort set to publication_date:desc and a small maxResults, or put from_publication_date:2026-10-01 in extraFilter.

Why are some abstracts empty? Some publishers don't allow abstracts to be redistributed, so OpenAlex doesn't have them. The field is null in that case.

Why does a search return a slightly odd top result when sorted by citations? Sorting by citations ranks every match, including loosely matching works. Use the default relevance sort, add a year range, or narrow with workType and extraFilter.

Limitations

• Without an API key, OpenAlex limits each query to the first 10,000 results. Add a free key to go further.
• Very large dumps (millions of rows) are better served by the free OpenAlex snapshot.
• Lists such as referenced works, related works and per-year citation counts are not included, to keep rows flat.
• Data is only as complete as OpenAlex itself, and author disambiguation is automated.

Legal and privacy

OpenAlex data is CC0. This Actor is not affiliated with or endorsed by OpenAlex or OurResearch. Author records contain names and ORCID IDs that researchers publish publicly. You are responsible for using the data in line with the laws that apply to you.

Support and feedback

Missing a field or filter you need, or found a bug? Open an issue in the Issues tab on this page. If the Actor saves you time, a short review helps other people find it.