The ChatMolData agent is composed of three critical components: the LLM, a toolset, and a set of prompts. The LLM functions as the planner and the brain of the agent, similar to how a central processing unit (CPU) operates in a computer system . It is responsible for managing and evaluating processes, determining the appropriate tools to use, and making decisions based on the provided prompts and inputs. In this setup, we have utilized OpenAI's GPT-4 and GPT-3.5 as the LLM , leveraging its advanced capabilities in reasoning, planning, and interacting with the environment to perform complex tasks beyond mere text generation. As for the toolset, it is designed to handle a wide array of molecular data processing tasks, which consists of five modules: Literature, Molecule, Database, Search, and Dataframe (depicted in Figure ). Each of these modules includes various tools specifically tailored to process different forms of molecular data, ensuring that the agent can proficiently extract, analyze, and manipulate data from diverse source. As far as we know, one of the major advantages of agents is their ability to continuously iterate. The toolset of the agent can be modified or updated at any time based on user needs and technological advancements. Here, we showcase the representative tools of the ChatMolData in Method section. Regarding prompts, it plays a crucial role in guiding the LLM's actions . They serve as references, helping the LLM to think, reason, and decide on the appropriate course of action . By structuring the LLM's interaction with the toolset and the data, prompts ensure that the agent can systematically approach and solve complex tasks. The details of the prompt are shown in Supplementary Information Note 1. The workflow of ChatMolData draws inspiration from the ReAct and MRKL methodologies, integrating the Chain of Thought process with tool usage to address intricate problems. The agent processes tasks using a structured sequence of Thought, ActionN (N can be an integer starting from one, representing the sequence of actions), Action Input, and Observation. The architecture and operation mechanism are depicted in Figure . The input to the agent can be multiple types of molecular files coupled with a natural language task description provided by the user as illustrated in Figure . The agent begins by engaging in the Thought step, where it contemplates the task at hand. It then proposes the first tool from the toolset to deal with the problem in the Action1 step, accompanied by Action Input describing the input of this action. Once the tool is executed, the result is returned as an Observation. After completing each loop of Thought, ActionN (N stands for the order of actions) and Observation, the agent reassesses the situation and determines the next loop as shown in Figure . This could involve continuing the loop by invoking other tools or concluding the process by delivering the final answer along with the processed molecular data file. This iterative approach allows the ChatMolData agent to systematically break down complex tasks into manageable steps, ensuring accuracy and efficiency in its operations. Figure demonstrates that ChatMolData facilitate the conversion between structured & unstructured data in molecular science, as well as molecular structures and properties. Finally, depicted in Figure , the outputs of ChatMolData contains answers as well as files by molecular data processing. In the following sections, we will demonstrate that ChatMolData can execute four types of tasks for molecular data processing, including database-related, molecular data structuring, prediction & visualization and structure searching.