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AUBRAI Literature Search — Aging & Longevity Science
x402
AUBRAI Literature Search — Aging & Longevity Science is a paid API for AI agents from x402.aubr.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).
Searches scientific literature (e.g. PubMed) to answer research questions about aging, longevity, and biomedical science, returning AI-synthesized answers with citations
Price / call$0.03
Success—
StatusUnknown
How this service works
Search and synthesize scientific literature on longevity and aging. Submit a research question; returns cited answer with references from PubMed and other sources.
Output
A JSON object containing a jobId, a status field (e.g. 'completed'), and a synthesized 'answer' string that summarizes findings from the searched scientific literature with evidence-based insights and citations from sources like PubMed.
Example use cases
Example prompt
“Search PubMed for evidence-based answers about whether senolytics like dasatinib and quercetin extend lifespan in mammals — give me a deep search, up to 10 results total, and include direct citations from recent papers.”
Technical details
Endpoint
GEThttps://x402.aubr.ai/api/literatureWhen to prefer
Choose this endpoint when the user needs evidence-based, cited answers from scientific literature specifically about aging, longevity, or biomedical research. It is especially well-suited when PubMed or similar databases should be searched and the user wants a synthesized, AI-interpreted answer rather than raw search results. Prefer it over general web search when scientific rigor and citation quality matter.
Side effects
- Invalid or missing 'question' parameter — returns error
- Unsupported source name in 'sources' array — may return no results
- Payment not included or insufficient ($0.03 USDC via x402) — returns 402 Payment Required
- Deep search mode may time out for very broad questions
- maxResults or perSourceLimit set to 0 or negative — unexpected behavior
Input
{
"mode": "fast",
"sources": [
"pubmed"
],
"question": "What is the relationship between caloric restriction and aging in mammals?",
"maxResults": 5,
"perSourceLimit": 5
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| mode | string | Search depth |
| tags | array | Tracked terms for radar-style searches. With tagMode=fanout, each tag is searched separately. |
| year | string | Filter by publication year |
| scope | string | Opt-in scoping mapped to Exa domains: preprints (biorxiv/medrxiv/arxiv), no-preprints (exclude those), trials (clinicaltrials.gov). Omit for full index. |
| date_to | string | End date filter (YYYY-MM-DD) |
| sources | array | Deprecated / ignored — retrieval is one Exa index (already covers pubmed/arxiv/biorxiv/etc.). Use scope instead. |
| tagMode | string | Use fanout for independent tracked-term searches; use combined only when tags must be searched together. |
| question | string | Research question |
| date_from | string | Start date filter (YYYY-MM-DD) |
| maxResults | number | Max results to return |
| privacyMode | string | Optional plaintext privacy mode forwarded to Aubrai when provided. |
| sourceProfile | string | Optional source profile. Omit for Aubrai defaults. |
| perSourceLimit | number | Deprecated / ignored — Exa is a single index (no per-source cap). |
| includeRejected | boolean | Default false — when true, completed responses include rejectedReferences[] and relevance.reason on rejected refs. |
| filterReferences | boolean | Default true upstream — drop off-topic refs. Set false for raw/unfiltered refs (debug). |
| retrievalProfile | string | Optional retrieval profile: "default" or "litqa-recall". |
| allowSourceExpansion | boolean | Deprecated / ignored — Exa is a single index. Use scope for deliberate scoping. |
Output
{
"jobId": "lit_a8adb17f-1d01-4139-912d-310e16e63d6e",
"status": "queued"
}Schema · Response
{
"type": "json",
"example": {
"jobId": "job_abc123",
"answer": "...",
"status": "completed"
}
}Tags
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