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agentbit.app Text-to-JSON Extractor
x402
agentbit.app Text-to-JSON Extractor is a paid API for AI agents from agentbit.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Converts unstructured text into a validated JSON object using a provided schema or plain-English extraction instructions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Turn messy text into clean JSON. Pass text plus a field `schema` or plain `instructions`; get a validated JSON object back. Uses the provider JSON mode.
Output
A validated JSON object with the fields specified in the schema or derived from the plain-English instructions, populated with values extracted from the input text. The response conforms to JSON mode output, ensuring it is always parseable and structurally valid.
Example use cases
Parse invoice text into fields
“I have this raw text from a scanned invoice — can you extract the vendor name, invoice number, total amount, and due date as structured JSON? Here's the text: 'Invoice #4521 from Acme Supplies. Total due: $3,240.00. Please remit by March 15, 2025.'”
Structure survey free-text responses
“I've got a bunch of open-ended survey answers in plain text and I need to pull out the respondent's age, satisfaction level, and main feedback as JSON so I can load them into a database. Here's one: 'I'm 34 and overall pretty happy with the product, though shipping was way too slow.'”
Extract contact details from email signature
“Can you parse this email signature text and extract the person's full name, job title, company, phone number, and email address into a clean JSON object? Here's the signature: 'Jane Doe | Senior Account Manager | GlobalCorp Inc. | +1 (415) 555-0192 | jane.doe@globalcorp.com'”
Technical details
Endpoint
POSThttps://agentbit.app/v1/ai/extract?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have unstructured or semi-structured text and need a validated, schema-conforming JSON object back without writing custom parsing logic. It is particularly useful when the structure is defined at runtime (e.g. user-supplied schemas) or when plain-English instructions are more practical than formal schemas. Prefer this over general LLM calls when you need guaranteed JSON output with field-level extraction rather than a free-form narrative response.
Side effects
- Input text exceeds ~16 KB limit — request may be truncated or rejected
- No schema or instructions provided — extraction may be ambiguous or fail
- Requested fields not present in source text — missing fields returned as null or omitted
- Malformed schema object — may cause validation errors or incorrect extraction
- Text is too ambiguous for reliable extraction — low-confidence values may be returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Source text to extract from (required, up to ~16 KB). |
| schema | object | Optional map of field name → description of the value to extract. |
| instructions | string | Optional plain-English extraction instructions (used if no schema). |
Tags
x402
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