DBGPT leverages large language models to understand user intent expressed in natural language, automatically generate accurate SQL statements, and run them against supported databases such as MySQL, PostgreSQL, SQLite, and more. It returns structured results along with charts, enabling data analysts, developers, and business users to gain insights quickly. Schema exploration, query tuning suggestions, and integration with dashboards make DBGPT a comprehensive tool for data-driven decision making.

DataWhisper uses a modular agent-based architecture to parse natural language questions, generate precise SQL queries, and execute them across diverse database systems. It incorporates conversational AI agents that handle context, error checking, and optimization, enabling users to retrieve insights without writing SQL manually. With a plugin interface, DataWhisper can integrate custom parsers, database drivers, and LLM backends, making it extensible for enterprise analytics, reporting, and interactive data-driven applications. It simplifies workflows by automating repetitive tasks, supports multiple SQL dialects including MySQL, PostgreSQL, and SQLite, and logs query histories for audit compliance. Agents communicate with mainstream LLM APIs, offer error handling and real-time feedback, and can be integrated into web services or chatbots via RESTful endpoints.