Research Row
research_rowClaygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| entity | Yes | The row/company to research. At least one of name/domain required. | |
| sources | No | Where to look: "web" (company site only), "catalog" (entity_profile only), "both" (default). | |
| max_cost | No | Cost ceiling in US dollars for this one row. Default 0.02, max 0.50, minimum viable 0.002 (below that the call is refused rather than silently exceeding it). | |
| question | Yes | The question to answer about this company, in natural language, e.g. "Does this company sell to healthcare, and who is the head of sales?" | |
| output_schema | Yes | JSON Schema object naming the fields to extract, e.g. {"properties":{"sells_to_healthcare":{"type":"boolean"},"head_of_sales":{"type":"string"}}}. Up to 20 fields. |