956501819

956501819 / sql-agent-kit

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这是一个基于大语言模型的 Text-to-SQL 工具，支持： 1. 多种 LLM（OpenAI、Qwen、硅基流动） 2. SQL 验证和安全检查 3. Schema 管理和语义注释 4. Few-shot 学习 5. 置信度评估 6. 查询执行和结果格式化

15
1
100% credibility
Found Apr 24, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A web-based toolkit that translates everyday questions into safe database queries, displays results with charts and summaries, and includes tools for customization and history tracking.

How It Works

1
🔍 Discover the helper

You find a handy tool that lets you chat with your database using simple questions, like asking a friend about your sales data.

2
🚀 Launch the web page

Open the easy web app right on your computer with one simple start.

3
🔗 Connect your data and AI

Point it to where your information lives and pick a smart thinking service, entering just a few details like addresses and passcodes.

4
📋 Pick and describe tables

Choose which data areas to explore and add plain notes explaining what each part means, like 'this is customer names'.

5
💬 Ask your question

Type a natural question like 'Show top products last month' and feel the excitement as it understands you perfectly.

6
📊 View results instantly

See the clever question it created for your data, plus tables, charts, and a helpful summary of key insights.

🎉 Master your data

Now you effortlessly get answers and discoveries from your information whenever you need, like magic without the hassle.

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Star Growth

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AI-Generated Review

What is sql-agent-kit?

sql-agent-kit is a Python text-to-sql kit that converts natural language questions into safe, executable SQL queries against your database. It handles schema management with semantic annotations, few-shot examples for better accuracy, confidence scoring to flag risky queries, and automatic retries on errors. Developers get a ready-to-use agent via a simple factory function, plus a Gradio web UI for testing queries, viewing results as tables or charts, and managing configs like LLM providers (OpenAI, Qwen, SiliconFlow) and table whitelists.

Why is it gaining traction?

This text to sql agent stands out with production guards like SQL safety checks, table whitelists, and low-confidence query halts, reducing hallucination risks in real databases. The multi-agent LangGraph pipeline adds planner, chart generator, and summarizer nodes for end-to-end analysis, while few-shot storage and query logging make it evolve with use. Easy YAML-based schema annotations and .env setup hook devs building text to sql github projects or internal text-to-sql chatbots.

Who should use this?

BI analysts querying sales data without memorizing schemas, backend devs prototyping text-to-sql LLM agents, or product teams embedding natural language SQL in apps like dashboards. Ideal for MySQL/PostgreSQL/SQLite setups where non-experts need quick insights on orders, products, or users.

Verdict

Grab it for text-to-sql prototypes—solid safety and multi-LLM support make it practical despite 15 stars and 1.0% credibility score signaling early maturity. Lacks extensive tests and docs, so audit before prod; pair with your own DB sample for fast wins.

(198 words)

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