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LLM Response Citation Extractor
x402
LLM Response Citation Extractor is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Extracts and identifies citations, references, and sourced claims from LLM-generated text responses
Price / call$0.005
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing an analysis_json field with extracted citation data, along with a status field indicating success, warnings about verifying caller-supplied data, a service_id, and a full receipt with payment and execution metadata including requestId, executionId, and a resultSha256 hash for integrity verification.
Example use cases
Research report citation audit
“I just got this 2,000-word AI-written research report and I need to know every source it cites — can you extract all citations and references from it so I can verify them?”
RAG pipeline source attribution
“My RAG system returned this answer to a user question and I need to pull out all the document citations it included so I can show the user where the information came from.”
Fact-checking AI news summary
“Here's an AI-generated news summary — can you extract every citation or source reference in it so I can cross-check the claims against the original articles?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/llm_response_citation_extract/invokeWhen to prefer
Choose this endpoint when you need to programmatically extract citations and source references from LLM-generated text at scale via a pay-per-call API, especially in RAG pipelines, research workflows, or fact-checking agents where attribution and source traceability matter. Prefer it over manual parsing when you need structured, machine-readable citation output from free-form AI responses.
Side effects
- Malformed or non-JSON request_json input returns a validation error
- LLM response text with no citations returns empty citation list with warnings
- Input exceeding 65,536 character limit is rejected
- Ambiguous in-text references may be missed or misidentified
- Payment failure via x402 protocol prevents execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "llm_response_citation_extract",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "llm_response_citation_extract",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "5000",
"resultSha256": "c528ef1141727c7a84ef895b9168746d0c4cc53d622f123a7aa308650fe00a39",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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