Open-source NL-to-SQL
for developers.

Your LLM generates validated, schema-grounded SQL. Your app reviews, approves, logs, and runs it. BYO model, database, embedder, and vector store.

Quickstart How it works

$ npx askdb init

# OR: npm install askdb

Next: Quickstart Install options GitHub

Postgres · MySQL · SQLite · SQL ServerOpen source · Apache 2.0SQL returned · Your app executesNo data leaves your stack

Why this toolkit exists

NL-to-SQL isn’t a new problem. Academic research goes back to LUNAR in 1972, and LLM-based generation has been good enough since Codex in 2021. The hard part in production isn’t the LLM call — it’s capturing the business context behind your schema, getting the right enrichment into the prompt, wiring up RAG when the schema is too big to fit, and repeating that work for every new project. AskDB is the toolkit for that part.

What makes AskDB different

Your data never touches the model

AskDB sends the question and your schema artifact — never your rows, your credentials, or your query results. The model writes SQL blind to your data.

Reviewed schema, your app runs the SQL

Your team authors and reviews the schema context. AskDB returns SQL — your application chooses whether to log it, approve it, or run it.

Safety guardrails on every query

Every generated query is parsed, scoped, and rejected if it violates the rules your schema declares — read-only, tenant filters, sensitive columns.

You own the infrastructure and the context

AskDB is a library you wire into your stack. Your model key, your database, your vector store. The schema artifact is a file you commit, not a black box.

How it works

Init

Run askdb init to scaffold askdb.config.ts — your provider, database, and model settings, checked in like code.
Introspect

AskDB reads your database (or a Prisma schema file) into a schema artifact: tables, columns, types, and relationships on disk.
Enrich

In Studio, add the descriptions, aliases, business concepts, and sensitive markers that make generation reliable. Test questions as you go.
Use

Call ask() from your app — or POST to the HTTP API. AskDB returns validated SQL; your application logs it, approves it, and runs it through your own pool.

Embed it in your app

A few lines of TypeScript: load a schema, ask a question, run the SQL through your own connection pool.

Config-driven (@askdb/client)
Direct (Vercel AI SDK)

import { createAskDb } from "@askdb/client";
import { bootstrapAskDbEnv, getAskDbRuntimeConfig } from "@askdb/config";
import { openaiProvider } from "@askdb/ai-openai";
import { Pool } from "pg";

// Resolve schema, model, and dialect from askdb.config.ts — same config the CLI and Studio use.
bootstrapAskDbEnv({ cwd: process.cwd() });
const askdb = createAskDb({
  config: getAskDbRuntimeConfig(),
  providers: [openaiProvider],
  schema: { path: "./my-app.schema" }, // or set host.schemaPath in config and omit
});
const pool = new Pool({ connectionString: process.env.DATABASE_URL });

const { sql } = await askdb.ask("Which customers signed up last week?");

// Log or approve `sql` here, then run it through your own pool.
const result = await pool.query(sql);

import { ask, loadSchema } from "@askdb/core";
import { openai } from "@ai-sdk/openai";
import { Pool } from "pg";

// Load schema once at startup — no config file needed.
const schema = loadSchema("./my-app.schema");
const model = openai("gpt-4o-mini"); // any Vercel AI SDK LanguageModel
const pool = new Pool({ connectionString: process.env.DATABASE_URL });

const { sql } = await ask({
  question: "Which customers signed up last week?",
  schema,
  model,
  dialect: "postgres",
});

// Log or approve `sql` here, then run it through your own pool.
const result = await pool.query(sql);

Works with the database you already have

PostgreSQL

@askdb/postgres

Reference dialect — new features land here first.

MySQL

@askdb/mysql

First-class dialect and introspection.

SQLite

@askdb/sqlite

First-class dialect — embedded and file-backed, great for dev and tests.

SQL Server

@askdb/sqlserver

First-class T-SQL dialect and introspection.

What the model sees — and what it doesn’t

The model sees

What crosses the boundary

The user's question
Your schema artifact (tables, columns, descriptions you authored)
Reasoning and SQL it generates

Stays in your stack

What never leaves

Your database rows
Database credentials and connection strings
Query results returned to your app
User identifiers, session tokens, anything you don't put in the prompt

Open-source NL-to-SQLfor developers.

Install AskDB