citeget

Find, acquire, and manage academic references — for AI agents and humans.

citeget automates the tedious work of tracking down PDFs for academic papers. Point it at a document with a references section, and it will try every available source — direct URLs, arxiv, Library Genesis, Sci-Hub — to download each one.

It also handles the more general case: any list of URLs (blog posts, docs, specs, product pages) saved as Markdown or PDF — useful when references aren't peer-reviewed papers. See citeget fetch below.

Install

pip install citeget
python -m playwright install chromium   # one-time browser setup

Quick start

CLI

# Search for papers
citeget search "graph theory" --topic articles

# Download top results
citeget download "python programming" --download-dir ~/papers --max-downloads 3

# Or get one copy of a *specific* book: ranked against title + author,
# validated as a complete book, next candidate tried if it isn't one
citeget get-book "Crossing the Chasm" --authors "Geoffrey A. Moore" --download-dir ~/books

# Check which libgen mirrors are healthy today
citeget check-mirrors

# Acquire all references from a document (academic mode)
citeget acquire my_paper.md

# Fetch arbitrary URLs — markdown by default, pdf if you ask for it
citeget fetch "https://example.com/article" --output-dir ~/Downloads/refs
citeget fetch refs.md --prefer md   # accepts a file with URLs in any form

The acquire command reads the references section, resolves a working directory, and downloads every reference it can find:

$ citeget acquire paper.md
Parsed 34 references
Work dir: paper -- acquired_references/

Skipping 5 already-downloaded reference(s):
  [1] Efficiently modeling long sequences...
  [3] High-speed parallel architectures...
        -> (To re-download, rename or move the existing file.)

[1/29] Ref [2]: A two-step computation of cyclic redundancy code...
  SUCCESS (libgen_articles) -> references/A two-step computation... (Glaise, 1997).pdf
[2/29] Ref [4]: High-speed parallel LFSR architectures...
  SUCCESS (libgen_articles) -> references/High-speed parallel... (Hu et al., 2017).pdf
...

Acquired: 30/34
Output: paper -- acquired_references/

Output files:

• references/ — downloaded PDFs, named {title} ({authors}, {year}).pdf
• references.md — all acquired references with clickable local links
• {datetime}_missed_references.md — what couldn't be found and why
• {datetime}__acquisition_log.txt — every search attempt (TSV)

Python API

from citeget import search, search_and_download

# Search and get metadata
results = search("machine learning", topic="articles")
for r in results[:3]:
    print(f"{r['title'][:60]}  ({r['year']})")

# One-shot search + download
search_and_download("python programming", download_dir="~/papers", max_downloads=5)

Getting a specific book

search_and_download takes libgen's own ordering, which for a known title is usually the same book in several formats, interleaved with summaries and download-spam listings. When you want one copy of one book, use get_book:

from citeget import get_book

path = get_book(
    "Crossing the Chasm", authors="Geoffrey A. Moore", download_dir="~/books"
)

It ranks every result against the title and author you asked for, downloads the best candidate, checks that what arrived is a complete book, and falls through to the next candidate if it isn't. The pieces are usable on their own:

from citeget import search, rank_results, download_best, validate_download

results = search("Crossing the Chasm Moore")
ranked = rank_results(results, title="Crossing the Chasm", authors="Geoffrey A. Moore")
ranked[0].score, ranked[0].is_decoy, ranked[0].title_match

path = download_best(
    results,
    title="Crossing the Chasm",
    authors="Geoffrey A. Moore",
    download_dir="~/books",
)

Every knob is a keyword argument with a sensible default — format preference, language, scoring weights, size bounds, the decoy pattern:

from citeget import get_book, ScoreWeights

get_book(
    "Some Book",
    authors="A. Author",
    format_preference=("epub", "pdf"),  # e-reader workflow
    weights=ScoreWeights(author=5.0),
)  # trust the author field more

Download validation

Nothing about a successful transfer says the bytes are a document. Every download path checks magic bytes, rejects HTML pages, enforces a realistic size floor, and compares against the size libgen advertised — so a captcha wall or a truncated transfer is a failure, not a book. Files are written to a temporary path and moved into place only after they validate, so a failed attempt never leaves a stub that a later run reads back as a cached success.

Books get an extra tier of checking, because libgen catalogues excerpts, front-matter samples and reviews under the full work's title — files with valid PDF magic bytes, several megabytes in size, and 17 pages long:

from citeget import download_one, validate_download, BOOK_POLICY

download_one(result, download_dir="~/books", policy=BOOK_POLICY)  # page-count check on
validate_download("suspect.pdf", policy=BOOK_POLICY)  # check one by hand

BOOK_POLICY is opt-in rather than the default because an 80-page floor is right for a book and badly wrong for a journal article. get_book uses it automatically. Build your own with dataclasses.replace:

from dataclasses import replace
from citeget import BOOK_POLICY, ValidationPolicy

lenient = replace(BOOK_POLICY, min_pages=40)

For bulk reference acquisition:

from citeget import (
    parse_references_section,
    resolve_work_dir,
    acquire_all_references,
    write_references_md,
    write_missed_references_md,
)

# Parse references from any text
refs = parse_references_section(my_paper_text)

# Resolve working directory (auto-derived from filename)
work_dir = resolve_work_dir(reference_file="paper.md")

# Acquire — tries direct URL → libgen → arxiv → sci-hub
successes, failures, log = acquire_all_references(
    refs,
    download_dir=work_dir / "references",
    work_dir=work_dir,
)

# Write output files
write_references_md(successes, work_dir / "references", work_dir / "references.md")

AI agent usage (Claude Code skills)

citeget ships with Claude Code skills — structured prompts that let an AI agent use the tools interactively. The skills live in .claude/skills/ inside this repository.

To use in Claude Code, either work in the citeget project directory (skills are auto-discovered), or copy the skill folders into your project's .claude/skills/ directory. Then invoke them by name:

> /acquire-references my_paper.md
> /research-topic "linear recurrence substitution"
> /review-article draft.md ieee_software
> /check-submission-fit draft.md
> /format-for-journal draft.md cacm_practice
> /prepare-submission draft.md ieee_software

To use skills in other systems, the SKILL.md files are self-contained markdown documents that describe the workflow, tools needed, and expected output. Any AI agent system that supports tool-use prompts can consume them — read the SKILL.md file and include it in your system prompt alongside the relevant tool definitions. The skills call into citeget's Python API, so the agent needs access to a Python environment with citeget installed.

Available skills:

Acquisition strategy

For each reference, citeget tries these sources in order:

1. Direct URL — if the reference includes an arxiv, OpenReview, or other direct link, download the PDF.
2. Library Genesis — search by title with progressively adjusted specificity (full title → title + author → short title → author + year).
3. Arxiv API — structured search by author + title keywords.
4. Sci-Hub — DOI lookup via Crossref, then Sci-Hub download.
5. Fetch fallback — if no PDF is reachable but the reference has a URL, the page is fetched and saved as Markdown. Catches non-paper references (blog posts, docs, product pages). Disable with fetch_fallback=False.

Files are named in APA 7 citation style: {title} ({authors_apa7}, {year}).pdf — e.g., Retiming synchronous circuitry (Leiserson & Saxe, 1991).pdf

Author parsing handles libgen's mixed conventions — "Moore, Geoffrey A.", "Edward R. Tufte", "Chris Voss & Tahl Raz", and role markers like "(author)" — via citeget.names, which is also what result ranking matches authors with, so the two never disagree.

General-purpose fetch

Not all "references" are papers. For lists of arbitrary web URLs, use citeget fetch (or citeget.fetch()) — it accepts a URL, a list, a file of URLs, or prose with embedded URLs, and saves each one as Markdown (default), PDF, or original bytes.

from citeget import fetch

# Pass anything — citeget figures out what URLs are in there
results = fetch(
    "/path/to/links.md",
    output_dir="~/Downloads/refs",
    prefer="md",  # "md" (default), "pdf", "original", or "auto"
)
for r in results:
    print(r.status, r.format, r.output_file)

URL parsing recognizes markdown links [anchor](url), reference-style [1] ... https://url citations, and bare URLs in prose. Filenames are inferred from anchor text → URL path → domain hash.

PDF rendering is opt-in (HTML→PDF needs wkhtmltopdf):

pip install 'citeget[fetch]'
brew install wkhtmltopdf      # or apt-get install wkhtmltopdf

Without it, --prefer pdf quietly falls back to Markdown.

Article publication toolkit

Beyond reference acquisition, citeget includes tools for the full publication workflow. These are primarily used through Claude Code skills, backed by machine-readable journal profiles in citeget/article_pub/data/journal_profiles.json.

Supported journals: IEEE Software, CACM (Practice/Research/Viewpoints), IEEE TSE, ACM Queue.

Standalone scripts in citeget/article_pub/scripts/:

# Check article against journal requirements
python -m citeget.article_pub.scripts.check_article draft.md ieee_software

# Word count with section breakdown
python -m citeget.article_pub.scripts.word_count draft.md --breakdown

# Reference consistency check
python -m citeget.article_pub.scripts.extract_references draft.md

Mirrors

Libgen mirror domains rotate, so the shipped default list goes stale on its own schedule. citeget tries each mirror in order, retries one that merely timed out before failing over, and tells you which of the two failure modes happened if they all fail.

When searches start failing, ask which mirrors are alive:

citeget check-mirrors

It prints a health table and a ready-to-use override for the working ones:

export CITEGET_LIBGEN_MIRRORS='https://libgen.vg,https://libgen.la'
# or a single mirror
export CITEGET_LIBGEN_BASE_URL='https://libgen.la'

Or pass base_url= / mirrors= to search() and friends. Only libgen.vg-family mirrors (the JS #tablelibgen layout) are compatible with the parser; the older libgen.is/.rs/.st forks use different HTML.

Empty results are not always real

Libgen serves a blank result set for queries that do have matches — measured at roughly one run in four for one such query, with the same query returning 0 results and then 21 a minute apart. citeget defends in three layers: each mirror is retried, two mirrors must agree before search() returns [], and get_book re-runs the whole search once more. Result counts also vary legitimately by mirror (21 vs 9 for the same query), so a lower-than-expected count is normal.

If you are building on search() directly, treat one empty answer as weak evidence and ask again.

How it works

Library Genesis renders search results via JavaScript, so citeget uses Playwright (headless Chromium) to load pages. Ad domains are blocked for speed. Downloads use session keys extracted from intermediate pages — those keys are single-use, so a download is claimed on the first attempt or not at all.

The acquisition log records every attempt in TSV format, making it easy to audit what was tried, what matched, and what failed.