The integration of large language models (LLMs) in reticular chemistry -the branch of chemistry concerned with the synthesis of extended crystalline materials connected by strong bonds -is transforming the way laboratory research is being conducted. Recent studies have demonstrated that LLMs can be applied across a variety of different tasks in reticular chemistry, such as, (i) assisting in the design and optimization of synthesis conditions for metalorganic frameworks (MOFs) 2 , (ii) enabling the highthroughput extraction of scientific insights, including graphical data such as X-ray diffraction patterns and adsorption isotherms 3 , (iii) editing and generating new linker chemistries to design MOFs for applications such as water harvesting , and (iv) generating novel MOF structures given specific prompts or desired performance criteria .