MiniOneRec provides an end-to-end workflow for generative recommendation using LLMs, spanning SID construction, supervised fine-tuning (SFT), and recommendation-oriented reinforcement learning (RL). It enables training language models to directly generate recommendation items rather than using traditional ranking paradigms. The framework supports constrained decoding and evaluation metrics specific to generative recommendation tasks.