# From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL

arXiv: https://arxiv.org/abs/2510.21045 · HTML: https://mlsanity.com/paper/2510.21045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)
- [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023)
- [Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning](https://mlsanity.com/paper/2405.20535/) (2024)
- [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024)
- [An Autonomous GIS Agent Framework for Geospatial Data Retrieval](https://mlsanity.com/paper/2407.21024/) (2024)
- [MapQA: Open-domain Geospatial Question Answering on Map Data](https://mlsanity.com/paper/2503.07871/) (2025)
- [GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS](https://mlsanity.com/paper/2503.23633/) (2025)
- [Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions](https://mlsanity.com/paper/2502.18470/) (2025)
- [Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap](https://mlsanity.com/paper/2308.16060/) (2023)
- [GeoSQL-Eval: First Evaluation of LLMs on PostGIS-Based NL2GeoSQL Queries](https://mlsanity.com/paper/2509.25264/) (2025)

## Built on by (most cited first, 2 of 2)

- [Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models](https://mlsanity.com/paper/2603.20670/) (2026)
- [Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions](https://mlsanity.com/paper/2603.23375/) (2026)

## Abstract (arXiv)

The complexity of SQL and the spatial semantics of PostGIS create barriers for non-experts working with spatial data. Although large language models can translate natural language into SQL, spatial Text-to-SQL is more error-prone than general Text-to-SQL because it must resolve geographic intent, schema ambiguity, geometry-bearing tables and columns, spatial function choice, and coordinate reference system and measurement assumptions. We introduce a multi-agent framework that addresses these coupled challenges through staged interpretation, schema grounding, logical planning, SQL generation, and execution-based review. The framework is supported by a knowledge base with programmatic schema profiling, semantic enrichment, and embedding-based retrieval. We evaluated the framework on the non-spatial KaggleDBQA benchmark and on SpatialQueryQA, a new multi-level and coverage-oriented benchmark with diverse geometry types, workload categories, and spatial operations. On KaggleDBQA, the system reached 81.2% accuracy, 221 of 272 questions, after reviewer corrections. On SpatialQueryQA, the system achieved 87.7% accuracy, 79 of 90, compared with 76.7% without the review stage. These results show that decomposing the task into specialized but tightly coupled agents improves robustness, especially for spatially sensitive queries. The study improves access to spatial analysis and provides a practical step toward more reliable spatial Text-to-SQL systems and autonomous GIS.