Genexxis Research Paper Search

x402

Genexxis Research Paper Search is a paid API for AI agents from research.genexxis.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-30).

Search and retrieve merged, de-duplicated scholarly papers across OpenAlex, arXiv, and Crossref with metadata including title, authors, DOI, citation count, open-access PDF links, and abstracts.

Price / call$0.005

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StatusUnknown

How this service works

Search scholarly papers across OpenAlex, arXiv and Crossref (merged, de-duplicated): title, authors, year, venue, DOI, arXiv id, citation count, open-access PDF link, abstract, topics.

Output

A JSON object containing a list of matched papers (up to 50), each with title, authors list, total author count, publication year and date, venue, DOI, arXiv ID, OpenAlex ID, abstract, topic tags, citation count, open-access status and PDF URL, and links to DOI/arXiv/OpenAlex pages. Also includes the echoed query parameters, total match counts per source (OpenAlex, arXiv, Crossref), any source errors encountered, and a timestamp.

Example use cases

Literature review on a new topic

“Find me the 20 most-cited papers on CRISPR gene editing published between 2018 and 2024, sorted by citations, with open-access PDFs only — I need titles, authors, abstracts, and DOIs.”

Tracking recent AI safety research

“Search for papers about AI alignment and safety published in the last two years, sorted by date, across all sources — give me up to 15 results with their abstracts and any available PDF links.”

Verifying a specific paper's existence

“Can you search for 'Attention Is All You Need' by Vaswani et al. and pull back its DOI, arXiv ID, citation count, and abstract?”

Technical details

Endpoint

GEThttps://research.genexxis.com/papers?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to search across multiple academic databases simultaneously and want de-duplicated results in a single call. It is ideal for broad literature discovery, finding open-access papers, or when you don't know which specific database holds the papers you need. Prefer it over single-source APIs when coverage and deduplication matter. It is not suited for fetching a single known paper by ID (use the sibling detail endpoint) or for mapping citation networks (use the citation neighbourhood endpoint).

Side effects

Missing required 'q' query parameter returns an error
Invalid sort value (not relevance/citations/date) may be ignored or cause an error
Invalid source filter (not all/openalex/arxiv/crossref) may default or error
maxItems outside 1-50 range may be clamped or rejected
Partial source failures are surfaced in the 'errors' array while other sources still return results
No results found returns count:0 and an empty papers array
Rate limiting or payment failure returns HTTP 402

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "asOf": "2026-10-25T03:00:00.000Z",
    "count": 5,
    "query": {
      "q": "transformer attention",
      "sort": "relevance",
      "source": "all",
      "maxItems": 5
    },
    "errors": [],
    "papers": [
      {
        "doi": "10.48550/arxiv.1706.03762",
        "type": "preprint",
        "year": 2017,
        "links": {
          "doi": "https://doi.org/10.48550/arxiv.1706.03762",
          "arxiv": "https://arxiv.org/abs/1706.03762",
          "openalex": "https://openalex.org/W2626778328"
        },
        "title": "Attention Is All You Need",
        "venue": "arXiv",
        "topics": [
          "Natural Language Processing Techniques",
          "Topic Modeling",
          "Speech Recognition and Synthesis"
        ],
        "arxivId": "1706.03762",
        "authors": [
          "Ashish Vaswani",
          "Noam Shazeer",
          "Niki Parmar"
        ],
        "foundIn": [
          "openalex",
          "arxiv"
        ],
        "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
        "openAccess": {
          "isOa": true,
          "pdfUrl": "https://arxiv.org/pdf/1706.03762",
          "status": "green",
          "landingUrl": "https://arxiv.org/abs/1706.03762"
        },
        "openalexId": "W2626778328",
        "authorsTotal": 8,
        "citedByCount": 78297,
        "publicationDate": "2017-06-12"
      }
    ],
    "totalMatchesBySource": {
      "arxiv": 18000,
      "crossref": 285486,
      "openalex": 212345
    }
  }
}

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How this service works

Search scholarly papers across OpenAlex, arXiv and Crossref (merged, de-duplicated): title, authors, year, venue, DOI, arXiv id, citation count, open-access PDF link, abstract, topics.

Output

Example use cases

Literature review on a new topic

“Find me the 20 most-cited papers on CRISPR gene editing published between 2018 and 2024, sorted by citations, with open-access PDFs only — I need titles, authors, abstracts, and DOIs.”

Tracking recent AI safety research

Verifying a specific paper's existence

“Can you search for 'Attention Is All You Need' by Vaswani et al. and pull back its DOI, arXiv ID, citation count, and abstract?”

Technical details

Endpoint

GEThttps://research.genexxis.com/papers?utm_source=zero.xyz

When to prefer

Side effects

Missing required 'q' query parameter returns an error
Invalid sort value (not relevance/citations/date) may be ignored or cause an error
Invalid source filter (not all/openalex/arxiv/crossref) may default or error
maxItems outside 1-50 range may be clamped or rejected
Partial source failures are surfaced in the 'errors' array while other sources still return results
No results found returns count:0 and an empty papers array
Rate limiting or payment failure returns HTTP 402

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "asOf": "2026-10-25T03:00:00.000Z",
    "count": 5,
    "query": {
      "q": "transformer attention",
      "sort": "relevance",
      "source": "all",
      "maxItems": 5
    },
    "errors": [],
    "papers": [
      {
        "doi": "10.48550/arxiv.1706.03762",
        "type": "preprint",
        "year": 2017,
        "links": {
          "doi": "https://doi.org/10.48550/arxiv.1706.03762",
          "arxiv": "https://arxiv.org/abs/1706.03762",
          "openalex": "https://openalex.org/W2626778328"
        },
        "title": "Attention Is All You Need",
        "venue": "arXiv",
        "topics": [
          "Natural Language Processing Techniques",
          "Topic Modeling",
          "Speech Recognition and Synthesis"
        ],
        "arxivId": "1706.03762",
        "authors": [
          "Ashish Vaswani",
          "Noam Shazeer",
          "Niki Parmar"
        ],
        "foundIn": [
          "openalex",
          "arxiv"
        ],
        "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
        "openAccess": {
          "isOa": true,
          "pdfUrl": "https://arxiv.org/pdf/1706.03762",
          "status": "green",
          "landingUrl": "https://arxiv.org/abs/1706.03762"
        },
        "openalexId": "W2626778328",
        "authorsTotal": 8,
        "citedByCount": 78297,
        "publicationDate": "2017-06-12"
      }
    ],
    "totalMatchesBySource": {
      "arxiv": 18000,
      "crossref": 285486,
      "openalex": 212345
    }
  }
}