Narrative review examines gene editing technologies and gRNA design tools in legume crops.

Legumes are among the most important protein-rich crops in global agri-food systems. To meet the rising protein demand of a growing population, significant efforts are underway to enhance legume yield, nutritional quality, and resilience to environmental stresses through the manipulation of key genetic traits. Advanced technologies-including genetic engineering, gene editing, genomic selection, next-generation sequencing, single-cell genomics, and multi-omics-are accelerating legume improvement due to their high precision and efficiency. This review focuses on major gene-editing technologies, namely, CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9), TALENs (Transcription Activator-Like Effector Nucleases), ZFNs (Zinc Finger Nucleases), base editing (BE), and prime editing (PE), and their applications in key legume crops such as soybean (Glycine max), cowpea (Vigna unguiculata), chickpea (Cicer arietinum), groundnut (Arachis hypogaea), pea (Pisum sativum), barrel clover (Medicago truncatula), alfalfa (Medicago sativa), and Lotus japonicus. Among these platforms, CRISPR/Cas9 is the most widely adopted in legumes, largely due to its simplicity, versatility, and dependence on accurate genome sequence information and guide RNA (gRNA) design. Advances in next-generation sequencing and the growing availability of intuitive online gRNA design tools have streamlined CRISPR workflows, improving accessibility and precision. The present review indicates that CRISPR-P is the most used gRNA design tool in legume research, likely due to its early development for plant systems and integrated off-target prediction features. Therefore, alongside reviewing gene-editing applications, we emphasized the critical role of robust gRNA design tools as a foundation for successful genome editing. Future integration of artificial intelligence and large language models is expected to further enhance target prediction accuracy, minimize off-target effects, and enable more precise genome-editing strategies in legumes.