An Iterative LangGraph Agent for Text-to-SQL: Natural Language Access to the Chicago Crime Database
Non-technical stakeholders frequently cannot write the SQL needed to extract insights from operational databases. We built and evaluated a Text-to-SQL agent that closes this gap end to end: a six-node LangGraph StateGraph checks question relevance, fetches the live schema, generates PostgreSQL, validates it with a dry run, retries on failure, executes the query, and narrates the result set in plain English. The agent uses prompt engineering only; no model was fine-tuned. We evaluated it on the Chicago Crime dataset (approximately 8.5 million records, 22 attributes) against a hand-built benchmark of 100 natural language questions with ground-truth SQL, stratified into 30 Easy, 40 Medium an...
| Authors | Vigneshwar Ravi Rao, Rupesh Swarnakar, Fayeq Jeelani Syed† |
|---|---|
| Primary category | cs.CL |
| Published | 19 September 2026 |
| Last updated at source | 14 October 2026 |
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