Hugging Face smolagents enables self-correcting Text-to-SQL agents using ReAct pattern

Hugging Face's smolagents framework offers an alternative to traditional single-step text-to-SQL pipelines, which can silently return incorrect results without any visible error. The framework uses a ReAct (Reasoning + Acting) pattern, where a CodeAgent iteratively generates, executes, and self-corrects SQL queries rather than blindly translating natural language in one pass. The agent uses SQLAlchemy for an in-memory SQLite database and connects to Hugging Face-hosted models via the InferenceClientModel interface. Table schema information is embedded directly in the SQL tool's docstring, allowing the agent to dynamically adapt its understanding when the database structure changes without retraining. For more complex queries involving multi-table joins, switching to a more capable model such as Qwen3-Next-80B-A3B-Thinking significantly improves accuracy over simpler models like Llama-3.1-8B-Instruct.