# lukasschwab/arxiv.py

Python wrapper for the arXiv API

Repository: https://github.com/lukasschwab/arxiv.py
Canonical: https://ross.abutalabs.com/products/arxivpy
Language: Python
License: MIT
License Family: permissive
Topics: arxiv, arxiv-api, python-wrapper, pdf
Last push: 2026-07-31T23:41:49+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 83, longevity 100
- inputs: {"age_days": 3934, "days_push": 33, "days_rel": 33, "gap_med": 45.5, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1541, forks 151 (observed 2026-08-28T04:05:00.585582+00:00)

## What it is
A Python client library wrapping the arXiv API for searching and retrieving academic paper metadata, PDFs, and source files. It provides a simple Search/Client interface with pagination, retries, and rate-limit handling.

## Use cases
- search arxiv for papers matching a keyword
- download pdfs of arxiv papers programmatically
- fetch paper metadata by arxiv id
- build a literature review dataset from arxiv
- monitor new arxiv submissions in a research area
- feed arxiv papers into an llm or rag pipeline

## When to choose
- you need programmatic access to arXiv search results in Python
- you want to bulk-download paper PDFs or source tarballs
- you need a maintained wrapper with retry and pagination handling

## When to avoid
- you need full-text search of paper contents rather than arXiv metadata
- you work with other academic APIs like Semantic Scholar or Crossref
- you need a non-Python language

## Facets
- artifact type: library
- maturity: stable
- function: http-client, search-engine, pdf, sdk
- domain: apis, developer-tools, data-science
- platform: python, cross-platform
- tags: arxiv, academic-papers, api-wrapper, research, metadata, natural-language-processing

## Member repositories
- lukasschwab/arxiv.py (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.585582+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:47.986006+00:00, confidence not recorded.
  - readme: https://github.com/lukasschwab/arxiv.py (fetched 2026-08-28T04:05:00.585582+00:00, sha f9d4fc53bcb3)
- Data as of 2026-08-30T08:39:29.467469+00:00.