First Query

Send a natural language query and handle cited live-web output.

Start with one specific question and a small source limit.

const response = await fetch("https://api-onchain.optimai.network/v1/search", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.OPTIMAI_API_KEY ?? "",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    query: "What changed in AI agent retrieval this week?",
    limit: 5,
  }),
})

if (!response.ok) {
  const message = await response.text()
  throw new Error(`OptimAI Search failed: ${response.status} ${message}`)
}

const result = await response.json()

Minimal response renderer

console.log(result.answer ?? "No answer returned")

for (const source of result.sources ?? []) {
  console.log(`- ${source.title ?? source.url}: ${source.url}`)
}

What to inspect

answer: the cited live-web response.
sources: URLs used to support the answer.
metadata: request timing, tracking ID, and node context.

UI behavior

Show sources close to the answer. The product promise is trust, so the evidence should not be hidden behind a secondary screen.

Keep the original query and the source list together in logs. When a user asks "why did the answer say that?", the source URLs are the fastest path to debug the result.