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Pocket Network Structured Extraction
x402
Pocket Network Structured Extraction is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-01).
Extracts named fields from unstructured text using rule-based pattern matching and JSON Schema definitions, returning only what the text explicitly states.
Price / call$0.005
Success—
StatusUnknown
How this service works
Rule-based extraction of named fields from text; not a language model, it returns only what the text states. POST /v1/extract with {text, schema}: schema is a JSON Schema, a list of field names, or {fields: {name: regex}}. Built-in fields include email, phone, date, url, money and company; other fields come from "Label: value" lines. Pay per request in USDC; no account, no API key.
Output
A JSON object mapping each requested field name to the value extracted from the text, using only content explicitly present in the input. Built-in fields (email, phone, date, url, money, company) are recognized automatically; custom fields match 'Label: value' lines or user-supplied regex patterns. Fields not found in the text are omitted or returned as null.
Example use cases
Contact info from email body
“Extract the sender's email address, phone number, and company name from this email body: 'Hi, I'm Tom from BrightPath Inc. Reach me at tom@brightpath.com or 212-555-9090 for any follow-up.'”
Invoice field parsing
“From this invoice text, extract the invoice date, total amount, and any URLs mentioned: 'Invoice #4421 dated 2024-11-01. Total due: $3,450.00. Pay at https://pay.vendor.com/inv4421.'”
Custom regex field extraction
“Parse this support ticket and extract the fields 'ticket_id', 'priority', and 'customer_email' — priority lines look like 'Priority: High' — from the text: 'Ticket-ID: 9821\nPriority: High\nCustomer-Email: joe@example.com\nIssue: Login not working.'”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/structured-extraction?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, rule-based field extraction from text and do not want an LLM to infer or hallucinate values — it returns only what the text explicitly states. Ideal for parsing contact info, invoices, receipts, or structured documents where field labels follow consistent patterns. Prefer it over LLM-based extraction when auditability, cost predictability, and strict literal fidelity matter more than semantic understanding.
Side effects
- Field not found in text — field is omitted or null if the pattern or label is absent
- Ambiguous or malformed schema — returns error if the schema argument cannot be interpreted
- Text too short or lacks structure — built-in heuristics may miss values in highly unstructured prose
- Regex pattern mismatch — custom regex fields return nothing if no substring matches
- Payment failure — request rejected if USDC payment is not included or insufficient
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | The text to extract from. |
| schema | — | A JSON Schema with properties, a list of field names, or {fields: {name: regex}}. |
Tags
x402
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