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24K Labs Text-to-SQL
x402
24K Labs Text-to-SQL is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Converts a plain-English question or request into a SQL query, optionally using a provided schema for accurate table joins.
Price / call$0.02
Success—
StatusUnknown
How this service works
Convert a plain-English request into a SQL query; pass an optional schema for accurate joins.
Output
Returns a SQL query string corresponding to the plain-English input, structured to be syntactically correct and (when a schema is provided) accurately referencing real table and column names with proper JOINs, WHERE clauses, and aggregations.
Example use cases
Non-technical analyst querying a database
“I need a SQL query that finds the top 10 products by total revenue this quarter — my schema has a products table (id, name, price) and an orders table (id, product_id, quantity, created_at). Can you generate that SQL for me?”
Developer prototyping a data pipeline
“Write me a SQL query that joins users and subscriptions to find all users whose subscription expired in the last 7 days but haven't logged in since. My tables: users(id, email, last_login), subscriptions(id, user_id, expires_at).”
Business intelligence quick lookup
“Convert this to SQL: 'list all orders where the total is above $500 and the shipping country is not the US, sorted by order date descending.' No schema needed, just standard e-commerce table names.”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/text-to-sqlWhen to prefer
Choose this endpoint when you need to translate natural language questions into executable SQL on demand, especially when you can supply a database schema for accurate join and column resolution. Ideal for building no-code data tools, agent-driven analytics, or rapid prototyping where writing SQL manually would slow down the workflow. Prefer over general LLM completions when you want a dedicated, cost-predictable SQL generation microservice.
Side effects
- Ambiguous natural language input results in a plausible but incorrect query
- Without a schema, table/column names may be guessed incorrectly
- Complex multi-step queries may produce simplified or incomplete SQL
- Dialect-specific syntax (PostgreSQL vs MySQL vs SQLite) may not match target database if not specified
- Malformed or incomplete schema input may cause join inference errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| schema | string | |
| dialect | string | |
| request | string |
Output
Schema · Response
{
"type": "json",
"example": {
"sql": "SELECT ..."
}
}Tags
x402
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