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.
| Feature | Description |
|---|---|
| Fuzzy Matching | Match references against multiple academic databases even from incomplete or inaccurate info. |
| Multiple Formats | Input .txt/.bib → Output BibTeX. |
| 4-stage Pipeline | A 4-stage process (clean → query → validate → format) to produce consistent output. |
| Field Completion | Enrich entries by filling in missing fields like journal, volume, pages, authors, and abstract. |
| 🎓 7+ Citation Types | Handles journal articles, conference papers, books, software, datasets, theses, and preprints. |
| Multi-Source Lookup | Queries CrossRef, arXiv, PubMed, Semantic Scholar, Google Books, and others for every entry. |
| Many Identifier Types | Accepts DOI, PMID, arXiv ID, ISBN, GitHub URL, Zenodo DOI, or plain text queries. |
| 🎛️ Interactive Mode | Manually select the correct entry when multiple potential matches are found. |
| Custom Templates | YAML-based presets that provide a fallback BibTeX entry type when auto-detection is inconclusive. |
Install and try OneCite in a few steps.
# Recommended: Install from PyPI
pip install oneciteCreate 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.
Execute the command to process your file and generate a clean .bib output.
onecite process references.txt -o results.bib --quietYour results.bib file now contains entries of different types.
View Complete Output (results.bib)
@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 ...Direct String and Stdin Input
onecite process "10.1038/nature14539"
onecite process "Attention is all you need, Vaswani et al., NIPS 2017"
echo "10.1038/nature14539" | onecite process -Interactive Disambiguation
For ambiguous entries, use the --interactive flag to manually select the correct match and ensure accuracy.
Command:
onecite process ambiguous.txt --interactiveExample Interaction:
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
🐍 Use as a Python Library
Use OneCite directly in your Python scripts.
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']))- OneCite Skill — Skill package for AI coding agents (Claude Code, Windsurf, etc.).
Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
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.
OneCite
