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

• URL: http://arxiv.org/abs/2509.00581v2
• Date: Sun, 28 Sep 2025 05:12:42 GMT
• Title: SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
• Authors: Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler,
• Abstract summary: In-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-context systems.<n>We propose a multi-agent framework that decomposes the Text2 task into schema linking, subproblem identification, query plan generation, and a guided correction loop.
• Score: 13.793886767052905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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