Features • Quick Start • 📖 Advanced Usage • 🤝 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.
OneCite solves this by accepting any mix of identifiers and text queries and automatically resolving them to structured BibTeX through a pipeline of academic APIs (CrossRef, arXiv, PubMed, Semantic Scholar, and others). It is designed for researchers who work primarily in the terminal, use LaTeX, and want a lightweight, scriptable tool — not a full reference manager.
When to use OneCite vs. alternatives:
| Tool | Best for |
|---|---|
| OneCite | One-shot conversion of messy reference lists to BibTeX in a terminal/script |
| Zotero | Long-term reference management, GUI-based, browser integration |
| CrossRef API directly | When you have clean DOIs and want canonical metadata |
| doi2bib | Single DOI → BibTeX conversion, no fuzzy matching |
| 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, and authors. |
| 🎓 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",
}
@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,
journal = "arXiv preprint",
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']))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
