We present EvoLiveDJ, a framework that unifies large language model (LLM)–driven music generation with interactive evolutionary feedback in a live coding context. The system uses an LLM to generate and iteratively refine Strudel code, while audience listening behaviour serves as a real-time fitness signal that guides selection. Each generation produces multiple musical variants that are played in parallel and selectively bred through LLM-guided semantic crossover and multi-scale mutation informed by audience preference and the model’s own critique. Analysis of captured generations reveals coherent evolutionary dynamics in symbolic musical code, including the emergence of stable and diverging lineages, multi-level mutation processes, and role-aware inheritance across rhythmic, melodic, and timbral structures. This hybrid approach combines the knowledge-driven musical competence of LLMs with the exploratory power of evolutionary search, enabling a continuous creative dialogue between AI and audience. The results point toward a new agentic paradigm of AI in music, which actively collaborates, self-evaluates, and evolves musical ideas in real time with human co-creators.