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research.memoryapi.org – Paper Citations & Metadata via Crossref
x402
research.memoryapi.org – Paper Citations & Metadata via Crossref is a paid API for AI agents from research.memoryapi.org, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Retrieve academic paper citations and metadata using Crossref, supporting both topic-based search and direct DOI lookup
Price / call$0.005
Success—
StatusUnknown
How this service works
Scientific literature search API with x402 micropayments. Search PubMed, arXiv, and Crossref citations.
Output
Returns structured citation and bibliographic metadata for matching papers, including title, authors, journal, publication date, DOI, and citation count, sourced from the Crossref database.
Example use cases
Example prompt
“Can you look up the citation metadata for the paper with DOI 10.1038/nature12373, including its authors, journal, publication date, and how many times it's been cited?”
Technical details
Endpoint
GEThttps://research.memoryapi.org/x402/research/citationsWhen to prefer
Use this endpoint when you need authoritative bibliographic metadata or citation counts for academic papers, especially when you have a DOI or a specific research topic in mind and need Crossref-sourced data. Prefer this over OpenAlex search when Crossref coverage is important (e.g., journal articles, DOI-based lookup). Use the OpenAlex endpoint for broader 250M+ work discovery with more filtering options.
Side effects
- DOI not found in Crossref database returns empty or 404-style response
- Malformed DOI input may return no results
- Topic search with very broad or ambiguous terms may return irrelevant results
- Rate limiting or payment failure ($0.005 USDC per call) may block requests
- Crossref API downtime may cause upstream errors
Input
{
"doi": "10.1038/nature12373"
}Output
{
"count": 1,
"query": "10.1038/nature12373",
"papers": [
{
"doi": "10.1038/nature12373",
"url": "https://doi.org/10.1038/nature12373",
"title": "Nanometre-scale thermometry in a living cell",
"authors": [
"G. Kucsko",
"P. C. Maurer",
"N. Y. Yao",
"M. Kubo",
"H. J. Noh",
"P. K. Lo",
"H. Park",
"M. D. Lukin"
],
"subjects": [],
"publisher": "Springer Science and Business Media LLC",
"citation_count": 1773,
"published_year": 2013
}
],
"source": "Crossref",
"success": true
}Schema · Response
{
"example": {
"count": 10,
"query": "machine learning",
"papers": [
{
"doi": "10.1038/s41586-021-03819-2",
"url": "https://doi.org/10.1038/s41586-021-03819-2",
"title": "Highly accurate protein structure prediction with AlphaFold",
"authors": [
"Jumper J",
"Evans R"
],
"subjects": [
"Structural Biology"
],
"publisher": "Nature",
"citation_count": 22000,
"published_year": 2021
}
],
"source": "Crossref",
"success": true
}
}Tags
x402
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