SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction

Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction: 12 upvotes on Hugging Face Daily Papers, #26 of 39 papers on 2025-09-03. Day-by-day upvote history.

Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided correction loop. Unlike prior systems that rely only on execution-based static correction, we introduce taxonomy-guided dynamic error modification informed by in-context learning. SQL-of-Thought achieves state-of-the-art results on the Spider dataset and its variants, combining guided error taxonomy with reasoning-based query planning.

Paper page on Hugging Face · arXiv

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