ggaytan901

ggaytan901 / agentic-db-query

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Natural language to SQL in TypeScript — LLM text-to-SQL agent with schema injection, validation, Vercel AI SDK, REST API & CLI.

natural-language-query nodejs sql-agent text-to-sql typescript
20
0
100% credibility
Found Apr 18, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

A TypeScript library that converts natural language questions into safe, executable SQL queries for PostgreSQL, SQLite, and MySQL databases using AI language models.

How It Works

1
🔍 Discover the helper

You find a handy tool online that lets you chat with your database using everyday words instead of tricky commands.

2
💻 Get it ready

You download and prepare the tool on your computer so it's all set up for use.

3
🔗 Connect your data

You link the tool to your database, like telling it where your information is stored.

4
🧠 Add smart helper

You connect a thinking service so the tool can understand and answer your questions wisely.

5
❓ Ask in plain talk

You type a simple question like 'How many customers joined last month?' and hit go.

6
✨ See magic results

It instantly figures out the right way to fetch your data, runs it safely, and shows the answers with a clear explanation.

🎉 Explore freely

Now you easily get insights from your data anytime, feeling empowered without needing expert skills.

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Star Growth

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AI-Generated Review

What is agentic-db-query?

agentic-db-query is a TypeScript agent that turns natural language questions into SQL queries against your live PostgreSQL, MySQL, or SQLite database. It pulls schema details like tables, columns, and foreign keys, generates safe SQL via Vercel AI SDK (OpenAI or Anthropic models), validates it, executes with row limits and timeouts, and retries on errors. Users get results, explanations, and SQL via a simple library API, REST endpoint at POST /query, or interactive CLI.

Why is it gaining traction?

It beats basic natural language processing tools by injecting real schema for precise queries, adding session context for follow-ups, and offering explain mode or streaming without execution. Self-healing retries feed DB errors back to the LLM, while read-only safeguards prevent accidents. The Docker compose setup and .env config hook devs needing quick agentic natural language to SQL without heavy setup.

Who should use this?

Support engineers building ad-hoc query dashboards, backend devs prototyping BI tools, or data teams enabling natural language postgres github access for non-SQL users. Perfect for internal admin lookups or teaching natural language understanding via SQL examples.

Verdict

Early maturity at 20 stars and 1.0% credibility score, but thorough docs, CLI/API convenience, and test suite make it a low-risk trial for agentic workflows. Fork or contribute if natural language processing fits your stack—run on staging DBs first.

(178 words)

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