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24K Labs Structured Text Extraction
x402
24K Labs Structured Text Extraction is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).
Extracts specified fields from unstructured text and returns them as a structured JSON object matching a requested schema or field list.
Price / call$0.01
Success—
StatusUnknown
How this service works
Extract fields from unstructured text into JSON matching a requested shape/field list.
Output
A JSON object whose keys correspond to the requested fields or schema shape, with values populated from the unstructured input text. Fields not found in the source text may be returned as null or omitted depending on the service behavior.
Example use cases
Invoice data extraction pipeline
“Extract the vendor name, invoice number, due date, line items, and total amount from this invoice text and return it as structured JSON: 'Invoice #INV-2047 from Acme Supplies, due April 30, 2025. Items: 10x Widget A @ $5.00, 2x Widget B @ $20.00. Total: $90.00.'”
Resume parser for hiring tool
“Parse this resume snippet into structured JSON with fields for full name, email, phone, years of experience, and top three skills: 'Jane Doe | jane@email.com | 555-123-4567 | 8 years in software engineering | Python, Go, Kubernetes.'”
Support ticket field extraction
“From this customer support message, extract the product name, issue category, severity level, and contact email as a JSON object: 'My BlastPro X200 won't turn on after the last firmware update. This is critical — please contact me at bob@example.com.'”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/extract-structuredWhen to prefer
Choose this endpoint when you need to convert free-form, unstructured text into a predictable JSON structure with specific named fields — especially in pipelines where downstream code expects typed data. It is ideal for parsing emails, invoices, resumes, support tickets, or any natural language input where you already know the target schema. Prefer it over general LLM calls when you want a consistent, cost-predictable, single-purpose extraction with a defined per-call price.
Side effects
- Requested fields not present in the input text — returns null values or empty fields
- Ambiguous or conflicting values in text leading to incorrect extraction
- Malformed input text causing parsing errors
- Schema shape too complex or deeply nested for reliable extraction
- Rate limiting or payment failure via x402 protocol
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| fields | string |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {}
}
}Tags
x402
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