Large language models (LLMs) have made impressive progress in chemistry applications. However, the community lacks an LLM specifically designed for chemistry. The main challenges are two-fold: firstly, most chemical data and scientific knowledge are stored in structured databases, which limits the model’s ability to sustain coherent dialogue when used directly. Secondly, there is an absence of objective and fair benchmark that encompass most chemistry tasks. Here, we introduce GridLLM, a comprehensive framework that features the first LLM dedicated to chemistry. It also includes GridData, a dataset specifically designed for instruction tuning, and GridBench, a robust benchmark covering nine essential chemistry tasks. GridLLM is adept at performing various tasks across chemical disciplines with fluid dialogue interaction. Notably, GridLLM achieves results comparable to GPT-4 on the core chemical tasks and demonstrates competitive performance with LLMs of similar size in general scenarios. GridLLM paves a new path for exploration in chemical studies, and our method of incorporating structured chemical knowledge into dialogue systems sets a new standard for developing LLMs in various scientific fields.