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WorxAI Gabriel Synthetic Dataset Generator
x402
WorxAI Gabriel Synthetic Dataset Generator is a paid API for AI agents from worxai.tech, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Generates synthetic dataset rows on demand from a free-text description of the data need, returning rows directly in the response
Price / call$0.05
Success—
StatusUnknown
How this service works
Send any free-text `need` describing the dataset you want — Gabriel matches it to an exact schema (built-in or inferred, never a miss) and returns the generated rows directly in this response, no second fetch. Price scales with row count ($0.01/row on the API channel). Pay from any linked region below. Every agent (by `wallet` param, or by IP if omitted) gets 50 free rows total — spread across up to 5 calls — before payment is required.
Output
Returns generated dataset rows directly in the API response as structured JSON, with the inferred or matched schema applied. The number of rows scales with the request; pricing is $0.01 per row with the first 50 rows free per agent wallet (or IP). No second fetch is required — all data is included in the initial response.
Example use cases
Synthetic ML training data generation
“I need 100 rows of synthetic customer churn data with fields like customer ID, subscription length, monthly spend, support tickets, and whether they churned — generate it for me now.”
Prototype API response mocking
“Can you generate 30 rows of fake user profile data including name, email, age, country, and account tier so I can populate my demo app without a real database?”
Agent trace data for testing
“Generate 15 rows of synthetic x402 agent payment trace data — include fields like agent wallet, endpoint called, amount paid, timestamp, and success status — so I can test my analytics dashboard.”
Technical details
Endpoint
GEThttps://worxai.tech/api/x402/v2/gabrielWhen to prefer
Choose this endpoint when you need on-demand synthetic tabular data described in plain English and want the generated rows immediately in a single API call — no polling, no separate fetch. It is especially well-suited for AI agents that need realistic mock data for prototyping, testing, or ML training without access to a real database, and when schema inference from a natural language description is preferred over manually specifying a schema. Prefer alternatives if you need to retrieve a pre-existing dataset by catalog ID or require real (non-synthetic) data.
Side effects
- Ambiguous or too-vague 'need' description may result in a mismatched or overly generic schema
- Exceeding the 50 free row quota without a linked wallet or payment triggers a 402 Payment Required response
- Requesting a very large row count may increase latency and cost significantly
- Invalid or unsupported query parameters return a 400 error
- If the inferred schema cannot be matched to any built-in or derivable pattern, an error or fallback schema is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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