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    <abstract>Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/LLM-Cube/Squrve.</abstract>
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%0 Conference Proceedings
%T Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks
%A Wang, Yihan
%A Liu, Peiyu
%A Chen, Runyu
%A Pu, Jiaxing
%A Xu, Wei
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F wang-etal-2026-squrve
%X Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/LLM-Cube/Squrve.
%U https://aclanthology.org/2026.acl-demo.16/
%P 157-166

Markdown (Informal)

[Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks](https://aclanthology.org/2026.acl-demo.16/) (Wang et al., ACL 2026)

• Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks (Wang et al., ACL 2026)

ACL

• Yihan Wang, Peiyu Liu, Runyu Chen, Jiaxing Pu, and Wei Xu. 2026. Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 157–166, San Diego, California, United States. Association for Computational Linguistics.