LLM-Assisted Title/Abstract Screening for Systematic Reviews R package screenllm version 0.1.0 A turn-key workflow for LLM-assisted systematic-review screening. The package ranks a corpus of titles and abstracts with an ensemble of open-source large language models served locally by 'Ollama', then applies the SAFE stopping rule to identify the records a human should screen. Defaults match the four-LLM mean ensemble and the SAFE configuration recommended by Spillias et al. (2026). A companion 'Shiny' app walks the human reviewer through the records above the stopping point. Complementary to the 'AIscreenR' package of Vembye et al. (2025) <doi:10.1037/met0000769>, which targets cloud-hosted 'GPT' models via the 'OpenAI' API; 'screenllm' targets locally-served open-weights ensembles with an integrated stopping rule. Software Depends: R (>= 4.1.0) Imports: cli, digest, fs, jsonlite, tibble, dplyr, rlang, httr2, glue Suggests: shiny, bslib, DT, callr, later, writexl, readxl, testthat (>= 3.0.0), knitr, rmarkdown, readr, withr, mockery, stringdist Scott Spillias <scott.spillias@csiro.au> Comprehensive R Archive Network (CRAN) Scott Spillias [aut, cre], Laura Avila Turriago [aut], Christopher Brown [aut], Ariane Easton [aut], Jack Roberts [aut], Michael Sievers [aut], Steve Swearer [aut], Andrew Taylor [aut], Brigette Wright [aut], Valeriya Komyakova [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=screenllm/LICENSE) 2026-09-24 application/tgz https://CRAN.R-project.org/package=screenllm doi:10.32614/CRAN.package.screenllm