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VerityLayer Hallucination Verification Engine
x402
VerityLayer Hallucination Verification Engine is a paid API for AI agents from api.veritylayer.dev, paid per call via x402, $0.25/call, status unknown (last checked 2026-09-13).
Verifies a claim or AI-generated statement against authoritative sources and returns a verdict, evidence, reasoning, and confidence score to catch hallucinations before they reach users.
Price / call$0.25
Success—
StatusUnknown
How this service works
VerityLayer verifies whether a factual claim is true — fact-check, verify a claim, is this true. Independent, fail-closed verification for AI agents: returns a verdict (supported/unsupported/uncertain), a calibrated 0-1 confidence, reasoning, and evidence; abstains rather than guess. Every verdict includes an Ed25519-signed receipt — portable cryptographic proof of what was verified, free re-verification at /receipt/verify. Tier: grounded — live web citations (Sonnet, default).
Output
A JSON object containing: 'verdict' (e.g. 'supported', 'unsupported', 'uncertain'), 'evidence' (array of authoritative source citations supporting or refuting the claim), 'reasoning' (a human-readable explanation of how the verdict was reached), and 'confidence' (a 0–1 numeric score indicating certainty of the verdict).
Example use cases
Hallucination guard for RAG chatbot
“My RAG assistant just told a customer that our refund policy allows 60-day returns — fact-check that claim against our documentation and tell me whether it's supported, what evidence backs it up, and how confident you are.”
Citation verification for AI research tool
“This AI-generated literature review cites 'Smith et al. 2019, Nature — CRISPR off-target effects reduced by 94%' — can you verify whether that citation is real and accurately described before I include it in my report?”
Legal brief hallucination check
“Before I file this, check whether the case law cited here — 'Mata v. Avianca, 2023, ruling that AI-generated citations must be disclosed' — is a real case with accurate details, and give me your confidence level.”
Technical details
Endpoint
POSThttps://api.veritylayer.dev/verifyWhen to prefer
Choose VerityLayer when you need a fail-closed, independent fact-checking layer specifically designed to catch LLM hallucinations — especially fabricated citations, fake statistics, or unsupported claims — before they reach end users. It is particularly well-suited for legal, medical, research, or customer-facing AI applications where a single hallucination can cause serious harm. Prefer this over general web search or RAG retrieval when you need a structured verdict with evidence and confidence rather than raw search results.
Side effects
- Claim too vague or ambiguous for authoritative lookup — may return low-confidence result
- No authoritative sources found for niche or very recent claims — may return 'uncertain' verdict
- Overly long or compound claims may reduce accuracy — works best with atomic, single-fact statements
- Network or upstream source availability issues may cause timeouts or errors
- Rate limits or payment failures may result in 4xx/402 responses
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| claim | string | |
| context | string |
Output
Schema · Response
{
"type": "json",
"example": {
"verdict": "supported",
"evidence": [
"Encyclopaedia Britannica — Eiffel Tower, Paris, France."
],
"reasoning": "Multiple authoritative sources place the Eiffel Tower on the Champ de Mars in Paris, France.",
"confidence": 0.97
}
}Tags
x402
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