SiteCheck Academic Paper Search is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-16).
Search academic papers via OpenAlex with filters for relevance, citations, date range, open access, and minimum citation count
Searches OpenAlex (CC0) for scholarly works: title, authors with institutions, year, venue, DOI, open-access PDF link, citation count, topic and abstract (max 2,000 characters). Filters: publication year range, open access only, minimum citations; sort by relevance, citations or date (citations and date search the words as one phrase). Limit max 25.
Returns a JSON object with the total count of matching works, the query, sort method, data source attribution (OpenAlex CC0), and an array of up to 25 paper records each containing OpenAlex ID, DOI, publication year, title, topic, venue, list of authors with their institutions, abstract text, citation count, and an open-access PDF URL where available.
GEThttps://api.sitecheck-api.workers.dev/api/research/papers?utm_source=zero.xyzUse this endpoint when you need to search a broad, multi-disciplinary academic literature index with citation metadata, open-access filtering, and year-range scoping. Prefer it over general web search when scholarly peer-reviewed sources are required, and over domain-specific databases (PubMed, arXiv) when you need cross-disciplinary coverage or citation-ranked results via OpenAlex's unified index.
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
{
"type": "json",
"example": {
"sort": "citations",
"count": 2,
"query": "retrieval augmented generation",
"total": 57526,
"source": "OpenAlex works (openalex.org, CC0 data; API terms at docs.openalex.org)",
"results": [
{
"id": "https://openalex.org/W3015883388",
"doi": "https://doi.org/10.18653/v1/2020.emnlp-main.550",
"year": 2020,
"title": "Dense Passage Retrieval for Open-Domain Question Answering",
"topic": "Topic Modeling",
"venue": "Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"authors": [
{
"name": "Vladimir Karpukhin",
"institutions": [
"Meta (Israel)"
]
},
{
"name": "Barlas Oğuz",
"institutions": [
"Meta (Israel)"
]
},
{
"name": "Sewon Min",
"institutions": [
"University of Washington"
]
}
],
"abstract": "Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.",
"citations": 3397,
"openAccessPdf": "https://www.aclweb.org/anthology/2020.emnlp-main.550.pdf"
},
{
"id": "https://openalex.org/W3027879771",
"doi": "https://doi.org/10.48550/arxiv.2005.11401",
"year": 2020,
"title": "Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems",
"topic": "Safety Systems Engineering in Autonomy",
"venue": "arXiv (Cornell University)",
"authors": [
{
"name": "Patrick Lewis",
"institutions": []
},
{
"name": "Ethan Perez",
"institutions": []
},
{
"name": "Aleksandra Piktus",
"institutions": []
}
],
"abstract": "Affordance-Compiled Intelligence develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for LLM-integrated systems. The paper studies how a fixed model-policy can exhibit different operational capability when the surrounding world is redesigned through observations, typed action handles, validators, repair paths, rollback modes, authority scopes, context summaries, and auditable receipts. CIMT treats system-level capability amplification as a world-side compilation problem rather than a model-weight improvement problem. It defines operatio…",
"citations": 3027,
"openAccessPdf": "https://arxiv.org/pdf/2005.11401"
}
],
"checkedAt": "2026-10-15T19:01:48.972Z"
}
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