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Smart Research Search
x402
Smart Research Search is a paid API for AI agents from api.kadec0.xyz, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-13).
Auto-routed academic paper search that either grounds a broad field term against OpenAlex taxonomy or runs hybrid retrieval with SPECTER2 reranking for natural-language research questions.
Price / call$0.02
Success—
StatusUnknown
How this service works
Smart, auto-routed research paper search. A broad field term is grounded against the OpenAlex taxonomy and returned as a coherent field cluster; a natural-language question runs hybrid retrieval over OpenAlex and Semantic Scholar with reciprocal rank fusion and a SPECTER2 embedding rerank. The response exposes the routing decision, and each paper carries title, authors, year, venue, citation count, DOI, and a relevance score. Keyless.
Output
Returns a JSON object with the routing decision used (field-grounding vs hybrid retrieval), and a ranked list of up to 40 papers each containing title, authors, publication year, venue, citation count, DOI, and a relevance score.
Example use cases
Literature review for emerging research topic
“Find me the top 20 papers on protein structure prediction using deep learning — use semantic reranking so I get the most relevant ones, not just keyword matches.”
Exploring a broad scientific field
“I want to understand what subfields and key papers exist under 'computational neuroscience' — treat it as a field term and show me the main clusters of research.”
Competitive landscape scan for a thesis
“Search for academic papers asking 'how does sleep deprivation affect working memory consolidation?' and give me up to 15 results ranked by relevance so I can build my literature review.”
Technical details
Endpoint
GEThttps://api.kadec0.xyz/v1/smart-searchWhen to prefer
Choose this endpoint when you need intelligent query routing — it automatically decides whether to treat input as a broad field taxonomy lookup or a semantic natural-language question, then applies SPECTER2 reranking for the latter. Prefer it over plain academic search when query intent is ambiguous or when you want the highest relevance quality through hybrid retrieval and reranking, rather than simple keyword matching.
Side effects
- Missing required 'q' parameter returns a 400 error
- Ambiguous query that doesn't clearly map to field term or question may produce lower-quality routing
- Very niche topics with sparse OpenAlex/Semantic Scholar coverage return few or no results
- maxItems out of range (>40 or <1) may cause validation errors
- Network timeout if downstream academic APIs are slow
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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