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Systematic reviews and meta-analyses require rigorous literature screening, a process highly dependent on the availability of accurate abstracts for each bibliographic record. However, researchers often encounter a bottleneck during the discovery phase: some academic search engines, such as Google Scholar, routinely truncate abstracts in their bibliographic records. ris-enrich is an open-source Python package designed to resolve this data loss. It parses .ris files and cascades searches across four major open academic databases (Semantic Scholar, OpenAlex, Europe PMC, and Crossref) to retrieve and append the full-text abstracts, utilizing strict NFKC Unicode normalization and fuzzy string matching to prevent false-positive data contamination. Our case study demonstrates that ris-enrich can recover complete abstracts for 21% of records across six languages, significantly enhancing the efficiency and accuracy of title-and-abstract screening in evidence synthesis workflows. By automating this enrichment process, ris-enrich empowers researchers to conduct more comprehensive and inclusive literature reviews, ultimately advancing the quality of systematic reviews and meta-analyses in ecology, evolution, psychology, and beyond.