Answer

Answer runs your question on Google, then Claude Haiku 5.5 (Anthropic) writes a concise answer that cites the numbered sources with [n] markers. depth: "fast" answers from the results page (snippets, answer box, knowledge panel); depth: "standard" (the default) also reads the top num_sources pages and answers from their query-ranked passages. Sources are untrusted: instructions inside a page are ignored.

Billing: the search part is priced like /v1/search with include_content: 1 credit plus 1 per page read, with the usual cache rules. The model adds 2 credits when it answers from the results page and 4 when it reads pages, so a fast answer costs 3 credits and a standard one 8 with 3 pages (6–10 for 1–5 pages). meta.credits splits the two parts. When the model fails, the model part is refunded and the call returns 503 ai_unavailable; the message says what the search cost. A search with nothing to answer from returns answer: null with a meta.warnings entry, and the model part is not charged.

Streaming: stream: true returns text/event-stream with these events, in order: results ({request, results}), delta ({text}, repeated), then done ({answer, citations, meta}) or error ({error}). Billing headers on a stream carry the search part only; the final meta has the total.

Structured output: with output_schema (a JSON Schema, at most 16 KB) the answer is a JSON value in output and answer is null. Fields that hold claims keep their [n] markers. It can't be combined with stream. See Cited answers.

Request body

Example request

Authenticate with Authorization: Bearer $SERPKITE_API_KEY (GET requests may pass ?api_key= instead). The official SDKs read SERPKITE_API_KEY for you. See Authentication.

curl https://api.serpkite.com/v1/answer \
  -H "Authorization: Bearer $SERPKITE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"q":"what is retrieval-augmented generation","depth":"standard","num_sources":3}'

Response fields

Every billed response also carries credit and latency headers (X-Credits-Used, X-Credits-Remaining, X-Request-Id…).

Example response

{
  "request": {
    "endpoint": "answer",
    "engine": "google",
    "q": "what is retrieval-augmented generation",
    "country": "us",
    "language": "en",
    "depth": "standard",
    "num_sources": 3
  },
  "answer": "Retrieval-augmented generation (RAG) grounds a language model's answer in documents retrieved at query time [1], which reduces hallucinations and keeps answers current without retraining [2].",
  "citations": [
    {
      "index": 1,
      "title": "Retrieval-augmented generation",
      "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
      "domain": "en.wikipedia.org"
    },
    {
      "index": 2,
      "title": "What is retrieval-augmented generation (RAG)?",
      "link": "https://www.example.com/blog/what-is-rag",
      "domain": "example.com"
    }
  ],
  "results": [
    {
      "index": 1,
      "title": "Retrieval-augmented generation",
      "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
      "domain": "en.wikipedia.org"
    },
    {
      "index": 2,
      "title": "What is retrieval-augmented generation (RAG)?",
      "link": "https://www.example.com/blog/what-is-rag",
      "domain": "example.com"
    }
  ],
  "meta": {
    "request_id": "req_01J8ZK4M6Q2V7",
    "credits_used": 8,
    "cached": false,
    "engine": "google",
    "latency_ms": 4410,
    "parse_quality": "ok",
    "resolved_urls": true,
    "credits": {
      "search": 4,
      "answer": 4
    },
    "llm": {
      "provider": "anthropic",
      "model": "claude-haiku-5-5",
      "input_tokens": 3120,
      "output_tokens": 88
    }
  }
}

Errors

Errors use one shape: {"error":{"code","message","request_id"}}. Errors are never billed. Full list in Errors.

Notes

• include_content, highlights, include_answer, format and fields don't apply here (400); use depth and num_sources.
• To add an answer to a normal search response instead, send include_answer: true to /v1/search or /v1/news: +2 credits over the results page, +4 when include_content also read pages.
• The question and the source text are sent to Anthropic to write the answer, and a decision model (OpenAI) may check which sources help. Neither trains on API data, and SerpKite never logs prompts or page text. See Privacy and data retention.
• The remote MCP server has an answer tool.

Related

• Cited answers guide
• Search
• Research
• Answer API overview and pricing
• OpenAPI spec