arXiv to BibTeX Citation Generator

x402

arXiv to BibTeX Citation Generator is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).

Fetches paper metadata from the arXiv API and generates a properly-formatted BibTeX citation entry with an auto-derived or custom cite key.

Price / call$0.002

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How this service works

arXiv to BibTeX / arxiv citation generator / paper bibtex / LaTeX citation builder / academic citation lookup. Pulls a paper's title, authors, year, abstract, and DOI from the arXiv API and generates a properly-formatted BibTeX entry with an auto-derived cite key (or your own). Free public arXiv API. Pure XML parse — no upstream auth.

Output

Returns a fully-formatted BibTeX entry including fields like @article, title, author list, year, abstract, eprint ID, and DOI (when available), along with the derived or supplied cite key.

Example use cases

Example prompt

“Can you generate a BibTeX citation entry for the arXiv paper 2301.07041? Use the cite key 'vaswani2023attention' if possible.”

Technical details

Endpoint

POSThttps://x402-deployer.x402-deployer.workers.dev/arxiv-bibtex

When to prefer

Use this endpoint when you need a properly-formatted BibTeX entry for an arXiv paper and want to avoid manually copying metadata. Ideal for automating bibliography management in LaTeX workflows, research pipelines, or academic writing tools. Prefer this over manual lookup when you have an arXiv ID and need a cite-key-ready .bib entry immediately.

Side effects

Invalid or non-existent arXiv ID returns an error or empty result
arXiv API temporarily unavailable causes upstream fetch failure
Malformed arXiv ID format (e.g. missing version suffix) may return unexpected results
Paper with no DOI will omit the DOI field from BibTeX output

Input

{
  "input": {
    "body": {
      "arxiv_id": "1706.03762",
      "cite_key": "vaswani2017attention"
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  }
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "doi": null,
  "year": "2017",
  "title": "Attention Is All You Need",
  "bibtex": "@article{vaswani2017attention,\n  author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},\n  title = {Attention Is All You Need},\n  year = {2017},\n  eprint = {1706.03762},\n  archivePrefix = {arXiv},\n  url = {https://arxiv.org/abs/1706.03762},\n}",
  "source": "https://export.arxiv.org",
  "authors": [
    "Ashish Vaswani",
    "Noam Shazeer",
    "Niki Parmar",
    "Jakob Uszkoreit",
    "Llion Jones",
    "Aidan N. Gomez",
    "Lukasz Kaiser",
    "Illia Polosukhin"
  ],
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.",
  "arxiv_id": "1706.03762",
  "cite_key": "vaswani2017attention"
}

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How this service works

Output

Returns a fully-formatted BibTeX entry including fields like @article, title, author list, year, abstract, eprint ID, and DOI (when available), along with the derived or supplied cite key.

Technical details

Endpoint

POSThttps://x402-deployer.x402-deployer.workers.dev/arxiv-bibtex

When to prefer

Side effects

Invalid or non-existent arXiv ID returns an error or empty result
arXiv API temporarily unavailable causes upstream fetch failure
Malformed arXiv ID format (e.g. missing version suffix) may return unexpected results
Paper with no DOI will omit the DOI field from BibTeX output

Input

{
  "input": {
    "body": {
      "arxiv_id": "1706.03762",
      "cite_key": "vaswani2017attention"
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  }
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "doi": null,
  "year": "2017",
  "title": "Attention Is All You Need",
  "bibtex": "@article{vaswani2017attention,\n  author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},\n  title = {Attention Is All You Need},\n  year = {2017},\n  eprint = {1706.03762},\n  archivePrefix = {arXiv},\n  url = {https://arxiv.org/abs/1706.03762},\n}",
  "source": "https://export.arxiv.org",
  "authors": [
    "Ashish Vaswani",
    "Noam Shazeer",
    "Niki Parmar",
    "Jakob Uszkoreit",
    "Llion Jones",
    "Aidan N. Gomez",
    "Lukasz Kaiser",
    "Illia Polosukhin"
  ],
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.",
  "arxiv_id": "1706.03762",
  "cite_key": "vaswani2017attention"
}