search agent
Changed onAsk a question and get an answer. This is meant for quick, relatively scoped one-off questions, like "What is the NAICS code for this company...". For more complex questions, use the enrich/ endpoint.
This uses the same AI + web-tool loop that fills a single agent-mode cell in an enrichment (web search, web scraping, PDF reading, HTTP requests), with no enrichment setup.
The run is processed in the background while this request long-polls up to wait seconds (default 50) — most runs finish in time and return the answer inline in output.result. If the agent is still working when the budget elapses, the response comes back with status: processing and output: null; poll GET /runs/{id}/result (it long-polls too) until the run is terminal.
Pass output_schema (a JSON Schema object) to get output.result back as a structured object matching your schema instead of free text. Unanswerable questions return the string "not found".
Quick example
curl -X POST https://api.riveterhq.com/v1/search_agent \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Who is the current CEO of Anthropic, and when did they take the role?"}'
Credit costs
- 1 credit per call (same as one agent-mode enrichment cell), charged when the agent completes. Failed runs are not charged.
Request
- Base URLhttps://api.riveterhq.com/v1
- AuthHTTP bearer
- TypeScriptA function with typed inputs. Its response is checked against the schema.
Request body
Response
estimate_cost only — the flat per-call price; nothing was started
Example response
{
"estimate_cost": true,
"type": "enrichment",
"credit_detail": {
"estimate": {
"minimum": 0,
"maximum": 1920,
"charged_upfront": false
},
"credits_used": 0,
"credits_remaining": 5000,
"sufficient_credits": true
}
}Changes
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