Genexxis Research Citation Neighbourhood

x402

Genexxis Research Citation Neighbourhood is a paid API for AI agents from research.genexxis.com, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-30).

Returns a citation neighbourhood around a seed paper (by DOI, arXiv ID, OpenAlex ID, or keyword query), including related works, top citing papers, and top references with full paper metadata.

Price / call$0.008

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How this service works

Citation neighbourhood of a seed paper (by DOI, arXiv id, OpenAlex id or query): the seed, OpenAlex related works, most-cited citing works and most-cited references, each with full paper fields.

Output

Returns a JSON object with: a 'seed' object containing full paper details (title, authors, year, abstract, venue, topics, DOI, arXiv ID, OpenAlex ID, citation count, open-access links); a 'related' array of OpenAlex-related papers with metadata; a 'citing' array of the most-cited papers that cite the seed; a 'references' array of the seed's most-cited references; counts and totals for each group; and any errors encountered during resolution.

Example use cases

Literature review for a new paper

“I'm writing a paper on transformer models. Can you get me the citation neighbourhood for arXiv:1706.03762 — the 'Attention Is All You Need' paper — so I can see what the most influential citing papers are and what references it builds on?”

Discovering related work by keyword

“I don't have a DOI handy, but can you find the citation neighbourhood for the paper about 'BERT pre-training of deep bidirectional transformers' — give me up to 10 related works, citing papers, and references?”

Tracing influence of a specific paper

“Using OpenAlex ID W2626778328, show me which papers most cite this work and what other papers OpenAlex considers related — I want to understand the research landscape around it.”

Technical details

Endpoint

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

When to prefer

Choose this endpoint when you need the full citation context around a specific paper — not just metadata for the paper itself but also what cites it, what it references, and what OpenAlex considers related. It is ideal for literature review automation, citation graph exploration, and finding influential neighbouring work. Prefer it over a simple paper lookup when you need relational, multi-directional bibliographic context in a single call. If you only need metadata for a single paper without neighbourhood context, use the sibling single-paper detail endpoint instead.

Side effects

Paper not found in OpenAlex, arXiv, or Crossref — returns empty or partial results with error flags
Ambiguous keyword query matches wrong paper as seed — use DOI or arXiv ID for precision
maxItems values above 15 are silently capped at 15
Rate limiting or upstream API unavailability may return incomplete citation sets
DOI format errors cause lookup failure — must include full DOI string

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "seed": {
      "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"
    },
    "citing": {
      "count": 5,
      "total": 78297,
      "papers": [
        {
          "year": 2020,
          "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"
        }
      ]
    },
    "errors": [],
    "related": {
      "count": 5,
      "total": 20,
      "papers": [
        {
          "year": 2018,
          "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
          "citedByCount": 52000
        }
      ]
    },
    "references": {
      "count": 5,
      "total": 28,
      "papers": [
        {
          "year": 1997,
          "title": "Long Short-Term Memory"
        }
      ]
    }
  }
}

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How this service works

Citation neighbourhood of a seed paper (by DOI, arXiv id, OpenAlex id or query): the seed, OpenAlex related works, most-cited citing works and most-cited references, each with full paper fields.

Output

Example use cases

Literature review for a new paper

Discovering related work by keyword

Tracing influence of a specific paper

“Using OpenAlex ID W2626778328, show me which papers most cite this work and what other papers OpenAlex considers related — I want to understand the research landscape around it.”

Technical details

Endpoint

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

When to prefer

Side effects

Paper not found in OpenAlex, arXiv, or Crossref — returns empty or partial results with error flags
Ambiguous keyword query matches wrong paper as seed — use DOI or arXiv ID for precision
maxItems values above 15 are silently capped at 15
Rate limiting or upstream API unavailability may return incomplete citation sets
DOI format errors cause lookup failure — must include full DOI string

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "seed": {
      "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"
    },
    "citing": {
      "count": 5,
      "total": 78297,
      "papers": [
        {
          "year": 2020,
          "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"
        }
      ]
    },
    "errors": [],
    "related": {
      "count": 5,
      "total": 20,
      "papers": [
        {
          "year": 2018,
          "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
          "citedByCount": 52000
        }
      ]
    },
    "references": {
      "count": 5,
      "total": 28,
      "papers": [
        {
          "year": 1997,
          "title": "Long Short-Term Memory"
        }
      ]
    }
  }
}