  Large Language Models (LLM) have shown encouraging progress in multimodal
understanding and generation tasks. However, how to design a human-aligned and
interpretable melody composition system is still under-explored. To solve this
problem, we propose ByteComposer, an agent framework emulating a human's
creative pipeline in four separate steps : "Conception Analysis - Draft
Composition - Self-Evaluation and Modification - Aesthetic Selection". This
framework seamlessly blends the interactive and knowledge-understanding
features of LLMs with existing symbolic music generation models, thereby
achieving a melody composition agent comparable to human creators. We conduct
extensive experiments on GPT4 and several open-source large language models,
which substantiate our framework's effectiveness. Furthermore, professional
music composers were engaged in multi-dimensional evaluations, the final
results demonstrated that across various facets of music composition,
ByteComposer agent attains the level of a novice melody composer.
