The rapid emergence of large language model (LLM) technology presents significant opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build a multi-agent system to handle fundamental tasks involved throughout the chemical synthesis development process. The multi-agent system comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application was built with the multi-agent system as the backend to allow chemist users to interact with experimental platforms and analyze results via natural language, thus, requiring zero-coding skills to allow easy access for all chemists. We demonstrated this multi-agent system on the development of a recently developed copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, and this LLM multi-agent copiloted end-to-end reaction development process includes: literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. This work showcases the trilogy among chemist users, LLM-based agents, and automated experimental platforms to reform the traditional expert-centric and labor-intensive reaction development workflow.