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NoJunk AI Text – Extract
x402
NoJunk AI Text – Extract is a paid API for AI agents from nojunk-ai-text.mick8511.workers.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-16).
Extracts named fields from text into strict JSON using LLM intelligence, returning null for any field not found in the source text and never inventing data.
Price / call$0.005
Success—
StatusUnknown
How this service works
Pay-per-call text intelligence for agents: summarize any text (paragraph, bullets or TL;DR, in any language), extract named fields into strict JSON (null for anything not in the text, nothing invented), or classify text into your own labels. Up to 24,000 characters per call. You are only charged when a valid result is returned.
Output
A JSON object with a `data` key containing each requested field mapped to its extracted value (or null if absent from the text), plus `model` (the LLM used), `confidence` (high/medium/low), `notes` (explanations for nulls or ambiguities), `usage` (prompt and completion token counts), and `generated_at` timestamp.
Example use cases
Structuring earnings report data
“Pull the company name, CEO, total revenue, net income, and revenue growth percentage out of this earnings press release text and give them to me as structured JSON, using null for anything not mentioned: [paste earnings text here].”
Extracting job posting details
“From this job posting text, extract the job title, company name, location, salary range, required years of experience, and application deadline into a clean JSON object — null for anything not stated.”
Parsing contract key terms
“Read this contract excerpt and extract the parties involved, effective date, contract value, payment terms, and termination clause into JSON fields — only what's actually written, nothing made up.”
Technical details
Endpoint
POSThttps://nojunk-ai-text.mick8511.workers.dev/extract?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need strict, hallucination-resistant field extraction — it returns null rather than guessing, which is critical for structured data pipelines. Prefer it over general-purpose LLM calls when you need consistent JSON schema output, pay-per-result pricing (no charge on failure), and up to 24,000 characters of context. Best for agents that need to parse documents, reports, or articles into database-ready records without post-processing cleanup.
Side effects
- Text exceeds 24,000 character limit — request rejected
- No fields specified or field list exceeds 20 items — validation error
- Payment not processed — 402 response, no charge
- LLM returns low-confidence result — confidence field reflects this
- Text is in an unsupported encoding or binary format — extraction fails
- Ambiguous field names lead to unexpected null returns — use descriptive field definitions object instead of plain array
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | The text to extract from (1 to 24000 characters) |
| fields | — | Fields to extract: an array of 1-20 field names, or an object {name: description} |
| instructions | string | Optional extra extraction instructions |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {
"ceo": "Jane Doe",
"company": "Acme Corp",
"revenue": "$4.2 billion",
"headquarters": null,
"revenue_growth_pct": 12
},
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"notes": "Headquarters is not mentioned in the text.",
"usage": {
"prompt_tokens": 260,
"completion_tokens": 48
},
"confidence": "high",
"generated_at": "2026-10-15T12:00:00.000Z"
}
}Tags
x402
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