Features • Quick Start • 📖 Advanced Usage • 🗺️ Roadmap • 🤝 Contributing

OneCite is a command-line tool and Python library for citation management. It accepts DOIs, paper titles, arXiv IDs, and mixed inputs, and outputs formatted bibliographic entries.

Researchers frequently accumulate reference lists in ad-hoc formats—DOIs copied from browser tabs, arXiv IDs from paper PDFs, titles typed by hand, and BibTeX fragments from various sources. Cleaning these into a consistent, complete .bib file is tedious and error-prone. However, in the era of Generative AI, compiling a bibliography is no longer just about formatting—it is about verification. OneCite takes raw, unverified text and cross-checks every single entry against canonical academic databases (CrossRef, PubMed, arXiv, Semantic Scholar, etc.). By serving as a deterministic truth-detector at the API-lookup layer, OneCite acts as the firewall between AI outputs and your manuscript.

Features

🌐 Data Sources

Quick Start

Install and try OneCite in a few steps.

1. Installation

# Recommended: Install from PyPI
pip install onecite

2. Create an Input File

Create a file named references.txt with your mixed-format references:

# references.txt
# Add blank lines between entries to avoid misidentification

10.1038/nature14539

Attention is all you need, Vaswani et al., NIPS 2017

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

https://github.com/tensorflow/tensorflow

10.5281/zenodo.3233118

arXiv:2103.00020

Smith, J. (2020). Neural Architecture Search. PhD Thesis. Stanford University.

3. Run OneCite

Execute the command to process your file and generate a clean .bib output.

onecite process references.txt -o results.bib --quiet

4. View Output

Your results.bib file now contains entries of different types.

@article{LeCun2015Deep,
  doi = "10.1038/nature14539",
  title = "Deep learning",
  author = "LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey",
  journal = "Nature",
  year = 2015,
  volume = 521,
  number = 7553,
  pages = "436-444",
  publisher = "Springer Science and Business Media LLC",
  url = "https://doi.org/10.1038/nature14539",
  type = "journal-article",
  abstract = "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction...",
}
@inproceedings{Vaswani2017Attention,
  arxiv = "1706.03762",
  title = "Attention Is All You Need",
  author = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia",
  year = 2017,
  booktitle = "Advances in Neural Information Processing Systems (NeurIPS)",
  url = "https://arxiv.org/abs/1706.03762",
}
# ... and 5 more entries ...

📖 Advanced Usage

onecite process "10.1038/nature14539"
onecite process "Attention is all you need, Vaswani et al., NIPS 2017"
echo "10.1038/nature14539" | onecite process -

onecite process ambiguous.txt --interactive

Found multiple possible matches for "Deep learning Hinton":
1. Deep learning
   Authors: LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
   Journal: Nature, 2015
   DOI: 10.1038/nature14539

2. Deep belief networks
   Authors: Hinton, Geoffrey E.
   Journal: Scholarpedia, 2009
   DOI: 10.4249/scholarpedia.5947

Please select (1-2, 0=skip): 1
Selected: Deep learning

from onecite import process_references

# A callback can be used for non-interactive selection (e.g., always choose the best match)
def auto_select_callback(candidates):
    return 0 # Index of the best candidate

result = process_references(
    input_content="Deep learning review\nLeCun, Bengio, Hinton\nNature 2015",
    input_type="txt",
    template_name="journal_article_full",
    output_format="bibtex",
    interactive_callback=auto_select_callback
)

print('\n\n'.join(result['results']))

🗺️ Roadmap

• OneCite Skill — Skill package for AI coding agents (Claude Code, Windsurf, etc.).
• Benchmarking — Public benchmark suite and leaderboard for citation extraction/verification accuracy.

🤝 Contributing

Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

Disclosure

Development was assisted by standard productivity tools including Generative AI for streamlining implementation details. All output was verified and integrated by the maintainer, and no LLMs are used by the package at runtime.