The current research aims to identify learning agility profiles among teachers and students at STEM school in Zagazig, and classify them in homogeneous profiles of learning agility, and to explain differences in profiles among teachers based on gender, experience, and among students based on gender, grade level. The sample of teachers consisted of 41 individuals, and the sample of students consisted of 300 individuals from STEM School. The researcher translated and adapted two scales to measure learning agility: the Marmara Learning Agility Scale (MLAS) by Yazıcı & Özgenel (2020) for measuring teachers' learning agility, and the scale by Sung (2021) for measuring students' learning agility. Using means, cluster analysis, and the chi-square test, the results showed that the most common learning agility dimensions among teachers, according to gender and experience, were "Results Agility," "People Agility," and "Self-Awareness Agility," followed by "Change Agility" and "Mental Agility." On the other hand, among students, the most common dimensions of learning agility varied according to gender and grade level, resulting in different profiles of learning agility. There were two clusters representing different profiles of learning agility among teachers' sample: The first cluster was characterized by a low level of learning agility dimensions and included 18 teachers, representing 43.9% of the sample. The second cluster was characterized by a high level of these dimensions and included 23 teachers, representing 56.09% of the sample. Similarly, among students, the first cluster was characterized by a high level of learning agility dimensions (learning responsibility and Reflecting experience) and a low level in other dimensions. It included 191 students, representing 63.66% of the sample. The second cluster was characterized by a high level of learning agility dimensions, except for Change Adaptability dimension, and included 109 students, representing 36.33% of the sample. The results indicated a correlation between teachers' belonging to one of the two clusters and their experience, while there was no correlation between teachers' and students' belonging to one of the clusters and their gender. Additionally, there was no correlation between students' belonging to one of the two clusters and their grade level. Key Words: Profiles, Learning Agility, STEM School, Cluster Analysis