AgentBit Academic Paper Search

x402

AgentBit Academic Paper Search is a paid API for AI agents from agentbit.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).

Searches academic literature across OpenAlex, Crossref, and arXiv in one call, returning deduplicated results with metadata, abstracts, citation counts, and open-access PDF links.

Price / call$0.01

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StatusUnknown

How this service works

Search academic literature across OpenAlex, Crossref and arXiv in one call: title, authors, year, venue, DOI, citation count, abstract and a DIRECT open-access PDF link where one exists. Deduplicated across sources, filterable by year and open-access, optional BibTeX per paper. One request replaces three APIs.

Output

A JSON object containing a deduplicated list of up to 25 papers, each with title, authors, year, venue, DOI, citation count, abstract, list of source databases (OpenAlex, Crossref, arXiv), and a direct open-access PDF URL where available. Optionally includes a BibTeX entry per paper. Also returns per-source result counts and a note on deduplication methodology.

Example use cases

Literature review for a research paper

“Find me the top 10 most-cited papers on CRISPR gene editing published after 2018 — I need their abstracts, DOIs, and BibTeX entries so I can build my literature review.”

Discovering open-access ML papers

“Search for recent papers on diffusion models for image generation published since 2022, but only show me ones I can actually download for free — up to 20 results please.”

Verifying a specific paper's details

“Look up 'Attention Is All You Need' by Vaswani et al. in academic databases and give me the citation count, venue, DOI, and a direct PDF link if available.”

Technical details

Endpoint

POSThttps://agentbit.app/v1/research/papers

When to prefer

Choose this endpoint when you need to search academic literature across multiple databases simultaneously and want deduplicated, enriched results in a single API call. It replaces separate calls to OpenAlex, Crossref, and arXiv, saving latency and complexity. Prefer it over raw database APIs when you need open-access PDF links, BibTeX output, or citation counts alongside standard metadata. Ideal for research agents, literature review automation, or any workflow that needs to quickly surface peer-reviewed papers on a topic.

Side effects

Query too short or too long (must be 3-300 characters) returns a validation error
No results found for highly specific or niche queries
PDF link absent when no open-access version exists for a paper
open_access_only=true with narrow queries may return zero results
Source APIs temporarily unavailable may reduce result count or return partial data
year_from filter eliminating all results for very recent topics

Input

Schema · Request body

Field	Type	Description
limit	integer	Max papers to return (1-25, default 10)
query	string	Search query: topic, title fragment, or author + topic (3-300 chars)
format	string	'bibtex' adds a ready-to-cite BibTeX entry per paper
year_from	integer	Only papers published in or after this year
open_access_only	boolean	Only papers with a direct open-access PDF link

Output

Schema · Response

{
  "type": "json",
  "example": {
    "note": "Merged and deduplicated across OpenAlex, Crossref and arXiv; citations from the richest source; pdf_url is a direct open-access link where one exists.",
    "query": "attention is all you need transformer",
    "papers": [
      {
        "doi": "10.48550/arxiv.1706.03762",
        "url": "https://doi.org/10.48550/arxiv.1706.03762",
        "year": 2017,
        "title": "Attention Is All You Need",
        "venue": "Neural Information Processing Systems",
        "bibtex": "@article{vaswani2017,\n  title = {Attention Is All You Need},\n  author = {Ashish Vaswani and Noam Shazeer and Niki Parmar},\n  year = {2017}\n}",
        "authors": [
          "Ashish Vaswani",
          "Noam Shazeer",
          "Niki Parmar"
        ],
        "pdf_url": "https://arxiv.org/pdf/1706.03762",
        "sources": [
          "openalex",
          "arxiv"
        ],
        "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
        "citations": 100000
      }
    ],
    "sources": {
      "arxiv": {
        "ok": true,
        "results": 15
      },
      "crossref": {
        "ok": true,
        "results": 15
      },
      "openalex": {
        "ok": true,
        "results": 15
      }
    },
    "result_count": 5
  }
}

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How this service works

Output

Example use cases

Literature review for a research paper

“Find me the top 10 most-cited papers on CRISPR gene editing published after 2018 — I need their abstracts, DOIs, and BibTeX entries so I can build my literature review.”

Discovering open-access ML papers

“Search for recent papers on diffusion models for image generation published since 2022, but only show me ones I can actually download for free — up to 20 results please.”

Verifying a specific paper's details

“Look up 'Attention Is All You Need' by Vaswani et al. in academic databases and give me the citation count, venue, DOI, and a direct PDF link if available.”

Technical details

Endpoint

POSThttps://agentbit.app/v1/research/papers

When to prefer

Side effects

Query too short or too long (must be 3-300 characters) returns a validation error
No results found for highly specific or niche queries
PDF link absent when no open-access version exists for a paper
open_access_only=true with narrow queries may return zero results
Source APIs temporarily unavailable may reduce result count or return partial data
year_from filter eliminating all results for very recent topics

Input

Schema · Request body

Field	Type	Description
limit	integer	Max papers to return (1-25, default 10)
query	string	Search query: topic, title fragment, or author + topic (3-300 chars)
format	string	'bibtex' adds a ready-to-cite BibTeX entry per paper
year_from	integer	Only papers published in or after this year
open_access_only	boolean	Only papers with a direct open-access PDF link

Output

Schema · Response

{
  "type": "json",
  "example": {
    "note": "Merged and deduplicated across OpenAlex, Crossref and arXiv; citations from the richest source; pdf_url is a direct open-access link where one exists.",
    "query": "attention is all you need transformer",
    "papers": [
      {
        "doi": "10.48550/arxiv.1706.03762",
        "url": "https://doi.org/10.48550/arxiv.1706.03762",
        "year": 2017,
        "title": "Attention Is All You Need",
        "venue": "Neural Information Processing Systems",
        "bibtex": "@article{vaswani2017,\n  title = {Attention Is All You Need},\n  author = {Ashish Vaswani and Noam Shazeer and Niki Parmar},\n  year = {2017}\n}",
        "authors": [
          "Ashish Vaswani",
          "Noam Shazeer",
          "Niki Parmar"
        ],
        "pdf_url": "https://arxiv.org/pdf/1706.03762",
        "sources": [
          "openalex",
          "arxiv"
        ],
        "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
        "citations": 100000
      }
    ],
    "sources": {
      "arxiv": {
        "ok": true,
        "results": 15
      },
      "crossref": {
        "ok": true,
        "results": 15
      },
      "openalex": {
        "ok": true,
        "results": 15
      }
    },
    "result_count": 5
  }
}