LLM-based AI therapists largely assume cooperative clients, despite real-world therapeutic interactions often involving resistant behaviors: withdrawn, deflecting, denying, or hostile. We propose a resistance-aware AI therapist combining (i) a multi-channel resistance detector that classifies each client turn into one of five types (R0–R4), (ii) a stabilized policy selector that maps a detected resistance to an evidence-based therapeutic strategy, and (iii) a strategy-grounded response generator. Across 800 simulated sessions over ten DSM-5 disorder profiles, the detector reaches 85% accuracy and 85.5% macro F1. Within the simulated evaluation, the adaptive policy achieves resistance-reduction rates of 70–100% across resistance types, whereas each static policy yields zero reduction for at least one type. The adaptive policy also maintains resistance-reduction performance across ten simulated DSM-5 disorder profiles. Our dataset and implementation are fully open-sourced.