OpenAlex Academic Research Scraper - Papers and Citations
Pricing
Pay per event
OpenAlex Academic Research Scraper - Papers and Citations
Search 250M+ scholarly works in OpenAlex and export title, abstract, authors, institutions, venue, year, DOI, citation count and open access link as JSON or CSV. A free Google Scholar alternative for research and AI agents. Pay per work.
Pricing
Pay per event
Rating
0.0
(0)
Developer
Muhammad Ariq Fauzan
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
3 days ago
Last modified
Security
Limited permissionsShare
Use it as an OpenAlex academic research scraper: find papers and citation counts for literature reviews, bibliometrics and AI research agents.
OpenAlex Scraper searches more than 250 million scholarly works in OpenAlex, the free and open catalog of the world's research, and exports each paper with its title, abstract, authors, institutions, journal or venue, publication year, DOI, citation count, topics and open access link. Use it for literature reviews, citation analysis, research trend monitoring, academic lead lists and RAG over scientific literature.
Why this Actor
- Official source. It uses the free OpenAlex REST API, with no HTML scraping and no blocking.
- Covers every field. OpenAlex indexes journals, conferences, preprints, books and datasets across all disciplines, a free alternative to Scopus and Web of Science.
- Rich fields. Abstracts are rebuilt into plain text, with citation counts, institutions and open access PDF links.
- Polite. It pages with cursors and backs off on rate limits.
- Use cases: literature reviews, bibliometrics, finding highly cited papers, tracking a topic over time, building academic datasets and RAG pipelines.
Example run
Real input and the first output record from run WNPr12mcRD7CgCkbb on 2026-10-30. Long text fields are shortened here with "...".
Input:
{"query": "large language models","maxResults": 50,"sort": "relevance","openAccessOnly": false}
Output (first record):
{"openalexId": "W4362515116","doi": "10.1007/s11704-026-60308-3","title": "A Survey of Large Language Models","year": 2026,"publicationDate": "2026-05-09","type": "article","venue": "Frontiers of Computer Science","citedByCount": 2085,"isOpenAccess": true,"oaUrl": "https://link.springer.com/content/pdf/10.1007/s11704-026-60308-3.pdf","authors": ["Wayne Xin Zhao","Kun Zhou","Junyi Li","Tianyi Tang","Zican Dong","Yupeng Hou","..."],"institutions": ["Renmin University of China","Université de Montréal"],"topics": ["Topic Modeling","Natural Language Processing Techniques","Multimodal Machine Learning Applications"],"abstract": "Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecede...","url": "https://openalex.org/W4362515116"}
Input
| Field | Description |
|---|---|
query | Full-text search, e.g. large language models |
maxResults | 1 to 10,000 |
sort | relevance, cited (most cited first) or newest |
fromYear / toYear | Optional publication year range |
openAccessOnly | Only works with a free full-text version |
Output (one item per work)
{"openalexId":"W4385245566","doi":"10.48550/arxiv.2303.08774","title":"GPT-4 Technical Report","year":2023,"publicationDate":"2023-03-15","type":"preprint","venue":"arXiv","citedByCount":1234,"isOpenAccess":true,"oaUrl":"https://arxiv.org/pdf/2303.08774","authors":["..."],"institutions":["..."],"topics":["..."],"abstract":"...","url":"https://openalex.org/W4385245566"}
Notes
OpenAlex data is released under CC0 by OurResearch. Abstracts may be missing for some works because of publisher restrictions. This Actor is not affiliated with OpenAlex or OurResearch.
Pricing
Pay-per-event: a small start fee plus a fee per work returned.