When a user, regardless of their programming or database experience, seeks to process molecular data from a database based on complex relationships, ChatMolData utilizes multiple tools within its Database module to achieve multi-step data extraction or storage. Figure illustrates an example of retrieving molecular data from the ChEMBL database . As shown in Figure , upon receiving the question, ChatMolData begins its thought process. The agent first identifies that the retrieving task does not include a ChEMBL ID (It is a unique ID that has been assigned to compounds, targets, assays in ChEMBL.) in the query. Consequently, the agent selects the Target2ID tool as Action1, attempting to replace the target abbreviation in the query with the corresponding ChEMBL ID. In Action1, the Action Input is the LLM-processed textual input of the retrieval request. Subsequently, as shown in Figure , the Target2ID tool extracts the target abbreviation (CDK2 and CDK5 in this case) from the input above, then generates a natural language instruction to retrieve the ChEMBL IDs. This natural language command is converted into SQL language (create_sql_query_chain function in LangChain is used here ), then executed to obtain the ChEMBL IDs (CHEMBL301 and CHEMBL4036 here) corresponding to the target abbreviations. As for the Observation in the first cycle, the retrieval text with the target abbreviations is replaced by the ChEMBL IDs in the original query text as illustrated in Figure . In Action2, the agent proceeds to the second cycle, where, after the second thought process, it selects the Text2SQL2CSV tool. As shown in Figure , the output of the Observation from the previous step is utilized as the Action Input. The Text2SQL2CSV tool first converts the natural language into SQL language. After executing the SQL command, the Text2SQL2CSV tool generates the CSV file shown on the left side of Figure , containing the simplified molecular input line entry system (SMILES) and IC50 information for two targets. In the next step, during the Thought process, the agent notices that the generated CSV only includes the molecular SMILES representation, other than directly molecular structure. Therefore, the agent employs the CSV2Structure_image tool to convert the SMILES into images representing molecular structures . As depicted in Figure , the generated images are saved in a new XLSX file, as well as the previous information in the last step. After executing these three Actions, the agent determines in the Thought process that the required molecular data has been generated, and thus it ends the cycle. At last, the agent provides the location of the molecular data file in the final answer, completing the entire automated reasoning process. For experimenters, handling such molecular data extraction poses three main challenges: firstly, experimenters are accustomed to using target abbreviations as keywords for querying molecular structure and property data. However, in an SQL database, target abbreviations may lead to incorrect data retrieval; secondly, compared to single-target molecular activity data extraction, extracting multi-target selectivity data is more complicated. SQL language is the fastest and most convenient method for the retrieval of selectivity information , yet most experimenters lack the foundational knowledge of SQL scripting; thirdly, the molecular structures extracted from databases traditionally are represented as SMILES, a format that is not intuitive for visualizing structural differences, increasing the manual conversion workload for users. After the user inputs the natural language description of the molecular data extraction task, ChatMolData automatically overcomes these three challenges by using the agent's built-in Target2ID, Text2SQL2CSV, and CSV2structure_image tools. As a result, users receive accurate and visually multimodal molecular data, significantly lowering the barriers to completing complex molecular data extraction tasks. Besides operations in SQL databases, we also conducted data processing operations on a QM9 , a non-SQL database. As shown in Supplementary Information Note 2, structures and electronic properties are retrieved automatically. Regarding the diverse functions of database-related operations, ChatMolData receives natural language command and loads molecular data into the database. As illustrated in Supplementary Information Note 3, the molecular information originally in a CSV file is inserted into the private database. Accordingly, the above examples show that experimenters without programming or database background can use ChatMolData to perform a variety of database processing tasks using simple natural language commands, which facilitating the adaptation to big data for molecular science.