SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation
This work addresses the problem of automating spreadsheet tasks for users without scripting or formula knowledge, representing an incremental improvement through a novel multi-agent approach.
The authors tackled spreadsheet automation by developing SheetMind, an LLM-powered multi-agent framework that interprets natural language instructions to perform tasks in Google Sheets, achieving an 80% success rate on single-step tasks and about 70% on multi-step instructions.
We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. The system comprises three specialized agents: a Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user's original intent. Integrated into Google Sheets via a Workspace extension, SheetMind supports real-time interaction without requiring scripting or formula knowledge. Experiments on benchmark datasets demonstrate an 80 percent success rate on single step tasks and approximately 70 percent on multi step instructions, outperforming ablated and baseline variants. Our results highlight the effectiveness of multi agent decomposition and grammar based execution for bridging natural language and spreadsheet functionalities.