🎓 Academic Paper Search — Google Scholar Alternative (OpenAlex)

Data source: OpenAlex (api.openalex.org, ~250M works including arXiv). This actor does not scrape Google Scholar; it is an open-data alternative with the same use cases (literature review, citation analysis, author lookup).

Papers & Citations — academic paper records with citation counts, authors, venues, DOIs and open-access links (source: OpenAlex, ~250M works including arXiv preprints).

📊 Sample Output

title	publicationDate	url	paperId	source	citationTier
Scikit-learn: Machine Learning in Python	2012-01-02	https://orbi.uliege.be/handle/2268/225787	W2101234009	openalex.org	landmark (500+)
Genetic algorithms in search, optimization, and machine learning	1989-10-01	https://doi.org/10.5860/choice.27-0936	W3023540311	openalex.org	landmark (500+)
C4.5: Programs for Machine Learning	1992-10-15	http://lib.myilibrary.com?id=677844	W2125055259	openalex.org	landmark (500+)
UCI Machine Learning Repository	2007-01-01	http://ci.nii.ac.jp/naid/20001247967	W3120740533	openalex.org	landmark (500+)
Data Mining: Practical Machine Learning Tools and Techniques	2011-01-01	https://doi.org/10.1016/c2009-0-19715-5	W1570448133	openalex.org	landmark (500+)
Pattern Recognition and Machine Learning	2007-01-01	https://doi.org/10.1117/1.2819119	W1663973292	openalex.org	landmark (500+)

Real rows from run bm0ZvgdwZ06vQkfHk on build 0.0.28 (2026-09-25), unedited apart from masked emails/phones and shortened long text; fields the source does not publish are empty.

🔧 Input reference

Field	Type	Default	What it does
outputMode	string (tracker, raw)	"tracker"	Choose output format. 'Research Tracker' adds citation analysis (tier ranking, citations/year velocity), author clustering, venue breakdown, publication trend timeline, and auto-g…
query	string	"machine learning"	Search query for academic papers (required). Examples: 'large language models', 'CRISPR gene editing', 'climate change adaptation'. Leave empty and the run succeeds with 0 rows an…
maxResults	integer	25	Number of papers to return. 25 for a quick overview, 100 for comprehensive landscape analysis.
year	string	""	Filter by year (e.g., '2025') or year range (e.g., '2023-2025'). Leave empty for all years.

🧾 JSON sample record

One real record from run bm0ZvgdwZ06vQkfHk (emails/phones masked, long text shortened):

{
  "title": "Scikit-learn: Machine Learning in Python",
  "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 problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.",
  "year": 2012,
  "citationCount": 64019,
  "influentialCitations": null,
  "referenceCount": 13,
  "authors": [
    "Fabián Pedregosa",
    "Gaël Varoquaux",
    "Alexandre Gramfort",
    "Vincent Michel",
    "Bertrand Thirion",
    "Olivier Grisel",
    "Mathieu Blondel",
    "Müller, Andreas",
    "…"
  ],
  "authorCount": 19,
  "venue": "ORBi (University of Liège)",
  "publicationDate": "2012-01-02",
  "url": "https://orbi.uliege.be/handle/2268/225787",
  "openAccessPdf": "https://orbi.uliege.be/handle/2268/225787",
  "doi": "10.48550/arxiv.1201.0490",
  "arxivId": "",
  "paperId": "W2101234009",
  "source": "openalex.org",
  "citationTier": "landmark (500+)",
  "influenceRatio": 0,
  "paperAge": 14,
  "recency": "older (5+ yr)",
  "citationsPerYear": 4572.8,
  "hasOpenAccess": true,
  "hasDOI": true,
  "abstractLength": 586,
  "abstractDetail": "detailed"
}

💰 Pricing

Event	Price (USD)	When it is charged
Actor Start (apify-actor-start)	$0.0005	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
Academic paper (apify-default-dataset-item)	$0.04	One academic paper record with metadata

Pay-per-event: you pay only for what the run delivers. A run that delivers nothing bills no result events (only the actor-start event, when the actor defines one). Example: a run that delivers 100 results costs 100 × $0.04 = $4.00 plus the start fee.

🔗 Related Actors

More from the NexGenData Patents & trademarks family:

• China (CN) Patent Search — via Google Patents Index
• Google Patents Scraper — Global Prior-Art Search API
• Patent & Trademark RAG — IP Docs to Markdown API
• Patent Inventor Network — Google Patents Index
• Trademark Conflict Watch — Brand Monitoring API

5 more in this family on the NexGenData Store page.

📊 What you get

Clean JSON, one dataset row per paper (16 fields):

• title — Paper title
• abstract — Abstract (reconstructed)
• year / publicationDate — Publication year and date
• citationCount — Citations to date
• referenceCount — Works referenced
• authors / authorCount — Author list (first 10) and total
• venue — Journal or conference
• url / openAccessPdf — Landing page and free PDF when available
• doi / arxivId / paperId — Identifiers
• source — openalex.org

In Research Tracker mode each row also carries citationTier, citationsPerYear, recency, paperAge, hasOpenAccess, hasDOI, abstractLength, abstractDetail, and the run-level landscape report (citation stats, year/venue/author distributions, top papers, auto-insights) is written to the run's key-value store under OUTPUT — it is a summary, not a paper, so it is never placed in the dataset and never billed as a result.

Pricing: $0.040 per paper (Pay-Per-Event) — about 25 papers per $1. Runs that return no papers (empty search, missing query) deliver 0 rows and are not charged per result.

🤖 Use with AI agents

Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it.

Sample agent prompt:

Run Academic Paper Search (OpenAlex) on my input and return the structured results.

Sample input:

{ "query": "large language models", "maxResults": 25, "outputMode": "tracker" }

Agentic payments (x402): Supports agentic payment via x402 — call this actor with USDC, no API key required.

🔗 Related tools — Academic research tools

PubMed Research

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A NexGenData utility actor.