# SqlAgent: An LLM-based Intelligent System for Natural Language to SQL Conversion

> An open-source natural language to SQL tool that allows non-technical users to query databases using everyday language, with built-in multiple security verification mechanisms.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-09T06:15:58.000Z
- 最近活动: 2026-05-09T06:21:22.901Z
- 热度: 148.9
- 关键词: 自然语言处理, SQL生成, 大语言模型, 数据库查询, NLP2SQL, MySQL, 开源工具
- 页面链接: https://www.zingnex.cn/en/forum/thread/sqlagent-sql
- Canonical: https://www.zingnex.cn/forum/thread/sqlagent-sql
- Markdown 来源: floors_fallback

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## [Introduction] SqlAgent: An Open-Source LLM-based Intelligent System for Natural Language to SQL Conversion

SqlAgent is an open-source natural language to SQL intelligent system built on large language models (LLMs). Its core goal is to lower the technical barrier to database querying, enabling non-technical users (such as business personnel and product managers) to query databases and obtain information using everyday language. The system has built-in multiple security verification mechanisms, supports databases like MySQL and various LLM services, and provides a practical tool for data query democratization.

## Background: Technical Barriers in Database Querying and LLM's Potential to Break Through

Traditional SQL querying requires mastering specific syntax and understanding table structure relationships, which has an extremely high learning cost for non-technical users. With the rapid improvement of LLM's natural language processing capabilities, new possibilities have emerged to solve this problem. The SqlAgent project was born based on this trend, aiming to automatically convert everyday language into accurate SQL query statements through intelligent natural language understanding.

## Detailed Explanation of Core Functions and Technical Features

### Natural Language to SQL Capability
Generates precise SQL code by calling LLMs using carefully designed prompt templates combined with database schema information.
### Automatic Schema Loading and Context Awareness
Automatically extracts table names/comments, column information (name, type, primary key), and foreign key relationships between tables from the database's INFORMATION_SCHEMA to improve the accuracy of SQL generation.
### Multiple Security Verifications
- Only allows SELECT queries; prohibits dangerous operations like DROP/TRUNCATE;
- Syntax validity check;
- Query timeout control;
- Result set size limit.
### High-Performance Connection Pool
Uses DBUtils.PooledDB to implement efficient connection pool management, supports high-concurrency scenarios, and ensures stable performance.

## Technical Architecture and Implementation Details

SqlAgent uses a modular Python architecture, with core components including:
### Schema Data Model and Loader
Defines database structure models, automatically extracts table structures and relationships from metadata, and is easily extensible to support new database types.
### Core Agent Class (nl2sql_agent.py)
Coordinates the entire process: Schema formatting → Prompt construction → LLM calling → SQL extraction → Security verification → Query execution.
### Prompt Template Design
Designed a dedicated template for MySQL (prompts/mysql.txt), which can be customized to adapt to different database types.

## Usage Scenarios and Practical Value

SqlAgent is suitable for multiple scenarios:
- **Business Data Analysis**: Non-technical users can directly query business data without development assistance;
- **Rapid Prototype Verification**: Quickly verify data query requirements in the early stages of development;
- **Education and Training**: Helps SQL beginners understand the correspondence between natural language and SQL;
- **Internal Tool Integration**: Embedded into enterprise data analysis platforms to improve efficiency.

## Current Support and Future Plans

Currently fully supports MySQL databases; future plans include expanding support to mainstream databases like PostgreSQL and SQLite.
In terms of LLM services, it is compatible with all services that follow the OpenAI API format (such as OpenAI official, Tongyi Qianwen, DeepSeek, etc.), and users can choose as needed.

## Project Significance and Industry Outlook

SqlAgent represents an innovative application of LLMs in the field of database querying, lowering the threshold for data querying and promoting data democratization (enabling non-technical users to independently access data). As LLM capabilities improve, such natural language interaction tools will become more intelligent and practical. Its open-source release provides a valuable reference implementation for this field, which is worthy of attention from developers and researchers.