Subjective perception of urban streets enables subjective quantitative evaluation of street environments. Aiming at the spatio-temporal dynamics of subjective street perception and the variation in standards across cities, the existing models based on static image training are difficult to capture the dynamics of subjective perception. In this paper, we design an LLM-Based Intelligent Agent for Subjective Perception of Urban Streets (SPUS-Agent), which includes two operational stages: Multidimensional subjective perception benchmarking and subjective perception prediction. Among them, in the subjective perception benchmarking stage, the SPUS-Agent automatically collects street view training images on an electronic map and employs a comparative learning approach using a large model to obtain the benchmark. In the subjective perception prediction stage, the SPUS-Agent can move to any location in the city and obtain the subjective perceptual score of the location by collecting the current street view images and comparing them with the perception benchmark. Experimental validation demonstrates the SPUS-Agent’s significant predictive capability (accuracy: 92.4%) and deployment feasibility in urban perception tasks.