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Agent Extraction API – Named Entity Extraction
x402
Agent Extraction API – Named Entity Extraction is a paid API for AI agents from freetier-agent-api.pages.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-11-01).
Extracts named entities (people, organizations, locations, dates, money amounts, products) plus emails and URLs from input text up to 6,000 characters.
Price / call$0.005
Success—
StatusUnknown
How this service works
Invoice-to-JSON, on-chain tools (transaction explainer + payment check, wallet profile, contract ABI, ERC-20 token risk), company contacts and web-to-RAG for AI agents. Pay per call with x402 (USDC on Base). No signup, no API key.
Output
A JSON object with arrays for each entity type: people, organizations, locations, dates, money, products, emails, and urls. Each array contains strings of the literal text spans found in the input. All AI-extracted entities are verified to literally appear in the source text.
Example use cases
Extract entities from news article
“Here's a news snippet — can you find all the people, organizations, locations, dates, and dollar amounts mentioned in it? 'On 15 April 2025, Treasury Secretary John Smith met with executives from Goldman Sachs and JPMorgan in Washington D.C. to discuss a $500 billion stimulus plan.'”
Pull contact details from unstructured text
“I've got this messy block of text from a form submission and need you to extract any emails and URLs from it: 'Reach us at support@example.com or visit https://example.com for more info. Also cc hello@acme.org.'”
Identify parties and amounts in a contract excerpt
“Can you extract the organizations, people, and money amounts from this contract clause? 'This agreement is between Globex Corporation, represented by CEO Alice Brown, and Initech LLC, for a total payment of €750,000 due by 1 June 2025.'”
Technical details
Endpoint
GEThttps://freetier-agent-api.pages.dev/api/entity-extract?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, lightweight named entity recognition from short-to-medium text blobs without running your own NLP pipeline. It's ideal for agents that need to pull structured entity data (people, orgs, locations, dates, money, products, emails, URLs) from unstructured text in a single API call at low cost ($0.005 USDC). Prefer alternatives if you need entity extraction from very long documents (>6,000 chars), need relationship extraction, or require higher-accuracy large-model NER.
Side effects
- Text exceeds 6,000 character limit — request may be rejected or truncated
- No entities found — all arrays return empty
- Ambiguous entities may be missed or miscategorized (e.g. a person's name mistaken for a product)
- Payment failure via x402 protocol if USDC balance is insufficient
- Regex-based email/URL extraction may miss malformed or unusual formats
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"urls": [],
"dates": [
"3 March 2026"
],
"money": [
"$2 million"
],
"emails": [],
"people": [
"Jane Doe"
],
"products": [],
"locations": [
"Berlin"
],
"organizations": [
"Acme Corp",
"Globex"
]
}
}Tags
x402
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