SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation

We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions.

In this paper, we introduce a hierarchical agentic system consisting of three specialized agents: 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.

We evaluate SheetMind on the 221-task SheetCopilot Benchmark with GPT-3.5-Turbo.

SheetMind achieved 100% execution success and 54.8% functional correctness, exceeding SheetCopilot (44.3%) while maintaining perfect execution reliability.

We also conduct ablation study on a separately curated dataset to confirm that the full three-agent configuration consistently outperforms all partial variants.

Lastly, we integrate our system into Google Sheets via a Workspace extension.