Urolithiasis is a common urological disease with increasing incidence and a high recurrence rate, whose etiology is not fully understood. The application of sequencing and culturomics has revealed that urolithiasis is closely related to the urinary microbiome (urobiome), shedding new light on the pathogenesis of stone formation. In this study, we recruited 30 patients with unilateral stones and collected their renal pelvis urine from both sides. Then, we performed 2bRAD-M, a novel sequencing technique that provides precise microbial identification at the species level, to characterize the renal pelvis urobiome of unilateral stone formers in the both sides. We first found that the urobiome in the stone side could be divided into two clusters (Stone1 and Stone2) based on distance algorithms. Stone2 harbored higher microbial richness and diversity compared to Stone1. The genera Cupriavidus and Sphingomonas were overrepresented in Stone1, whereas Acinetobacter and Pseudomonas were overrepresented in Stone2. Meanwhile, differential species were identified between Stone1 and Stone2. We further constructed a random forest model to discriminate two clusters which achieved a powerful diagnostic potential. Moreover, the urobiome of the non-stone side (Control1/2) was compared with that of the stone side (Stone1/2). Stone1 and Control1 showed different microbial community distributions, while Stone2 was similar to Control2 based on diversity analysis. We also identified differentially abundant species among all groups. We assumed that there might be different mechanisms of how microbiota contribute to stone formation in two clusters. Our findings might assist in the selection of suitable medical treatments for urolithiasis.