With the set of LLM-based agents developed above, we created a web application to allow users to access them using natural language in a centralized manner, such that no coding was required during the synthesis reaction development (Fig. ). After agents receive prompts and related reference documents from the users describing the chemical task, they will analyze the requests and infer the appropriate responses or solutions through in-context learning . If necessary, they would employ external tools to enhance their capability to respond information out of the scope of the LLM knowledge itself, including Python interpreter, academic database search, and self-driven reaction optimization algorithms. In addition, there is a chain-of-thought mechanism to allow agents to interact with these tools step-by-step, thus maximizing their reasoning capability. Despite the advanced intelligence of GPT-4 model used for these agents, human chemists are still necessary in the decision- making loop, responsible for evaluating the correctness and completeness of agents' responses and deciding whether to directly implement their suggestions or further communicate with them to tweak the responses.