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Aubrai Scientific Literature Search & Synthesis
x402
Aubrai Scientific Literature Search & Synthesis is a paid API for AI agents from x402-api.aubr.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-14).
Searches PubMed and other scientific sources for longevity/aging research, then synthesizes a cited answer to a submitted research question
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 synthesized, cited answer to the research question, backed by references from PubMed and other configured sources, including relevant paper titles, authors, and publication details
Example use cases
Example prompt
“Do a deep search on PubMed for research about the effects of rapamycin on lifespan extension in mammals — give me a synthesized answer with cited references, returning up to 20 results.”
Technical details
Endpoint
GEThttps://x402-api.aubr.ai/api/literatureWhen to prefer
Use this endpoint when you need evidence-based, cited answers from the scientific literature specifically on longevity, aging, or lifespan science topics. Prefer this over general web search when you need PubMed-indexed peer-reviewed sources with proper citations. Choose 'fast' mode for quick lookups and 'deep' mode for thorough synthesis across multiple sources.
Side effects
- Missing required 'question' parameter returns an error
- Invalid 'mode' enum value (not 'fast' or 'deep') causes validation failure
- No results found if query is too narrow or unrelated to longevity/aging literature
- Payment not fulfilled via x402 protocol results in 402 Payment Required
- Source unavailable (e.g. PubMed API downtime) may return partial or empty results
- maxResults set too high may slow response or hit upstream rate limits
Input
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
Schema · Response
{
"type": "json",
"example": {
"jobId": "job_abc123",
"answer": "...",
"status": "completed"
}
}Tags
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