🔬 OpenAlex Scraper: Research Papers, Citations & Authors

Overview

OpenAlex Scraper searches 250M+ scholarly works from OpenAlex, the open index of the world's research, and returns clean structured JSON. Filter by topic, publication year, minimum citation count, work type, or open-access status; get titles, DOIs, authors, institutions, venues, citation counts, top concepts, language, and reconstructed abstracts. Built for R&D landscaping, competitive intelligence, literature reviews, bibliometrics, and AI or RAG pipelines over scientific text.

OpenAlex is the free, open successor to Microsoft Academic Graph, with a complete index of papers, authors, institutions, venues, and citations. Its API is powerful but returns deeply structured records (nested authorships, inverted-index abstracts, scored concepts) and uses cursor pagination. This actor flattens all of that into one clean record per work.

Reliability posture: blocked, empty, or failed runs are never charged. You only pay for a work record that was actually delivered.

✅ No API key | ✅ 250M+ works indexed | ✅ Reconstructed abstracts on demand | ✅ First 10 works free | ✅ MCP-ready for AI agents

Features

Full-text search across titles, abstracts, and fulltext, with filters for year range, minimum citations, work type, and open-access. Flat, analysis-ready output: title, DOI, year, type, authors, institutions, venue, citation count, top concepts, language, direct link. Reconstructed abstracts: OpenAlex stores abstracts as an inverted index; this actor rebuilds readable text on request. Results returned most-cited first, so the highest-impact papers surface without extra sort logic. Fully open access via the OpenAlex polite pool, so there is no key, no login, and no anti-bot friction.

How it works

The actor calls the OpenAlex works endpoint (api.openalex.org/works) with your search term and filters, sorted by citation count in descending order. It walks cursor-paginated pages up to your maxResults cap. For each work it flattens the nested authorship, institution, and concept arrays into flat fields.

When includeAbstract is on, it reconstructs the abstract from OpenAlex's inverted index format back into readable text. Empty pulls (search terms that match nothing) are recognized before charging.

🧾 Input configuration

{
  "searchTerm": "perovskite solar cells",
  "fromYear": 2022,
  "minCitations": 25,
  "openAccessOnly": true,
  "includeAbstract": true,
  "maxResults": 200
}

📤 Output format

{
  "openalex_id": "https://openalex.org/W2064815984",
  "doi": "https://doi.org/10.1126/science.1231143",
  "title": "Multiplex Genome Engineering Using CRISPR/Cas Systems",
  "publication_year": 2013,
  "type": "article",
  "cited_by_count": 15686,
  "authors": ["Le Cong", "F. Ann Ran"],
  "author_institutions": ["Broad Institute", "MIT"],
  "venue": "Science",
  "is_open_access": false,
  "oa_status": "closed",
  "concepts": ["CRISPR", "Genome editing", "Cas9"],
  "language": "en",
  "openalex_url": "https://openalex.org/W2064815984",
  "scraped_at": "2026-06-10T15:00:00.000Z"
}

Every work record contains these fields:

Field	Description
🆔 openalex_id	Canonical OpenAlex work URI
🔗 doi	Digital Object Identifier
📝 title	Paper title
📅 publication_year	Year of publication
📖 type	article, review, preprint, book, dataset, and more
🔢 cited_by_count	Total citations recorded by OpenAlex
👥 authors[]	Author display names
🏛️ author_institutions[]	Institutions across all authors
📚 venue	Journal, conference, or repository name
🔓 is_open_access	Boolean OA flag
📗 oa_status	gold, green, hybrid, bronze, or closed
🧠 concepts[]	Top OpenAlex concepts scored for this work
🌐 language	Two-letter language code
✍️ abstract	Reconstructed abstract text (when includeAbstract is on)
🕒 scraped_at	ISO timestamp of capture

💼 Common use cases

R&D and competitive intelligence Map who is publishing on a technology, at which institution, and how often it is cited. Track a rival research group's output as a leading indicator of the pipeline.

Literature reviews Assemble a filtered, citation-ranked corpus on a topic in seconds. Enable includeAbstract for full-text triage without a second fetch.

Bibliometrics and trends Quantify research output by year, institution, or concept for a strategy or grant report. Filter to openAccessOnly when you need reproducible, share-friendly evidence.

AI and RAG pipelines Ingest titles and abstracts as grounded scientific context for a Claude or ChatGPT agent. Pair with OpenCitations to expand the graph via forward and backward chaining.

🚀 Getting started

1. Open the actor and enter a Search term (e.g. large language models), or leave blank to match all works within your other filters.
2. Optionally set From year, To year, and Minimum citations.
3. Optionally set Work type (article, review, preprint, book, dataset) and toggle Open access only.
4. Toggle Include abstract if you want reconstructed abstract text on each record.
5. Click Save & Start, then download as JSON, CSV, or Excel, or pull via API or MCP.

FAQ

Do I need an API key? No. OpenAlex is fully open. This actor uses the fast polite pool.

How many works are indexed? Over 250 million across every field of research.

Can I get abstracts? Yes. Turn on includeAbstract and the actor reconstructs readable abstract text from OpenAlex's inverted index format.

How do I get the most-cited papers on a topic? Just set searchTerm (optionally with minCitations); results come back most-cited first.

How am I charged? Pay per work at $0.002 per work ($2 per 1,000). The first 10 works are free for life on every Apify account.

Use in Claude, ChatGPT & any MCP agent

https://mcp.apify.com/?tools=themineworks/openalex-scholarly-works

Or call it programmatically with the Apify client:

import { ApifyClient } from 'apify-client';

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

const run = await client.actor('themineworks/openalex-scholarly-works').call({
  searchTerm: 'large language models',
  fromYear: 2023,
  minCitations: 25,
  includeAbstract: true,
  maxResults: 100,
});

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

🛠️ Complete your scholarly research pipeline

Papers are the nodes. Add the citation graph and the DOI backbone:

• OpenCitations Scraper: 1.6B citation links for backward and forward reference chaining.
• Crossref Scraper: DOI-level metadata for any published work.
• ClinicalTrials.gov Scraper: cross-reference biomedical papers with the underlying trial registry.

Typical flow: search OpenAlex for a topic, expand via OpenCitations, and cross-check biomedical hits against ClinicalTrials.gov.

Found a bug or have a feature request? Open an issue on the actor's Apify Console page or reach out through the Apify profile.