DataScribe: An AI-Powered Multi-Agent System for Natural Language Exploration of Relational Databases

This article introduces DataScribe, a multi-agent system that uses artificial intelligence to simplify and demote access to relational databases in natural language. Direct interaction with databases demands knowledge of Structured Query Language (SQL) and intricate schema structures from the user, which restricts users who are not familiar with technology. DataScribe removes this obstacle by using Large Language Models (LLMs) and modularity structured into three intelligent agents:Discovery Agent – Automatically extracts and visualizes database schemas based on graph-based methods.Planner Agent – Generates query execution plans from natural language requests.Inference Agent – Transforms natural language queries into correct SQL commands based on LLMs accessed via the Groq API.The platform allows the user to query databases like SQLite without manually writing SQL. Through the combination of natural language comprehension, schema interpretation, and real-time SQL generation, DataScribe offers a simple and intuitive method of dealing with structured data.This work highlights the significance of multi-agent design, schema visualization, and high-speed language model inference in closing the loop between human language and structured database querying.The paper is accompanied by architectural diagrams, algorithmic descriptions, and real-world scenarios to illustrate the efficacy of the system. DataScribe is an important milestone toward open, inclusive, and smart data access to users from all walks of life.