Large language models (LLMs) have achieved significant success in general artificial intelligence, demonstrating remarkable capabilities in tasks ranging from natural language processing to complex problem-solving across various domains . However, in specialized fields such as medical and natural science, LLMs face certain limitations, including occasional inaccuracies or "hallucinations" when dealing with domain-specific knowledge . The construction of LLM-powered agents presents a promising solution to these challenges by integrating LLMs with specialized tools and well-designed prompts that enhance their performance and reliability in specific tasks . In recent years, there has been considerable progress in applying LLM-based agents to the natural sciences, particularly in fields like chemistry, biology, and materials science . These agents have shown potential in autonomous design of chemical experiment, automated multi-omics analysis, high-fidelity materials knowledge retrieval and so on . However, most of these agents encompass a single modality, based solely on text-based inputs and outputs. This limitation creates a barrier for experimenters who need to process and analyze various types of data that go beyond simple textual information. In the field of molecular science, these kinds of data are stored in various formats, including databases, PDF or comma-separated values (CSV) files, molecular structure files (CIF, MOL and etc.) and images. Although a variety of APIs and software programs are available to process different type of molecular data , the steep learning curve and the need for programming expertise often hinder their effective use. Hence, it gives rise to a disconnection between experimenters and algorithm developers. Concurrently, there is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain, which referred to as large multimodal agents (LMA) as well . For instance, GPT-Driver makes use of the OpenAI GPT-3.5 model to tackle the heterogeneous planner inputs, finally generating safe and comfortable driving trajectories . Additionally, in order to improve comprehension of the video content, several video understanding agents are proposed to recognize objects, actions, and scenes from video data . Last but not the least, some agents are designed to create and modify images, under the user's flexible requirements . Accordingly, these LMA-related studies have introduced the concept of sub-task tools designed to handle sophisticated data types, thereby enhancing the agents' capabilities to process diverse forms of information or data, particular visual or auditory data one . Inspired by these advancements in multimodal LLM agents, we have developed a new multimodal agent, named ChatMolData, designed to bridge the gap between experimenters and algorithm developers. ChatMolData is shown to address various challenges in molecular data processing, with inputs and outputs in multiple forms including various types of molecular data as well as natural language description. These challenges include but are not limited to: extracting and loading molecular data from natural language, structuring molecular data from literatures, visualizing molecular data mining results, and conducting substructure searches within molecular datasets. The performance of the agent is also evaluated and discussed under different variable conditions, which potentially provide inspiration for the LMAs design for data processing in various fields.