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Answer & search monitoring API

AI answers, as data.

One API returns the live answers real users get from ChatGPT — every source, citation and product card parsed into JSON. Targeted to any country, down to US states.

no card · sync or async · webhooks

request
$ curl -X POST https://api.answerline.dev/v1/monitor/chatgpt \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"prompt": "Best CRM for small agencies", "country": "US"}'
200 · application/json

  "success" true
  "result" 
    "text" "For small agencies, the most recommended options are…"
    "sources" 
       "position" 1 "url" "https://…" "label" "The 9 best CRMs" 
    
    "searchQueries" "best crm for agencies 2026"
    "entities"  "type" "brand" "name" "…" 
  
8engines behind one schema
250countries, plus US states
500tasks per batch call
17documented endpoints

Works with  TypeScript SDK · Python SDK · MCP server · LangChain · n8n · Zapier

Engines

Every answer engine, one schema

Why it matters

The model API is not what users see

Provider APIs return a completion. The consumer apps answer differently — they search the web, cite pages and show product cards. We capture the real experience, so you measure what customers actually read.

Provider API response

 "choices"  "message"  "content" "For small agencies, the most…" 
]
no cited sources no search fan-out no product cards not the UI answers

What the user saw

"For small agencies, the most recommended options are HubSpot, Monday and Notion…"

1thebrandjournal.com — The 9 best CRMs for agencies
2reddit.com — r/agency: what CRM do you run?
3hubspot.com — CRM for agencies

Integrate

Call it from anywhere

$ curl -X POST https://api.answerline.dev/v1/monitor/chatgpt \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"prompt": "Best CRM for small agencies", "country": "US"}'
from answerline import Client

client = Client(api_key="sk_…")
res = client.monitor.chatgpt(
    prompt="Best CRM for small agencies", country="US"
)
print(res["result"]["text"])
import { Client } from "@answerline/sdk";

const client = new Client({ apiKey: "sk_…" });
const res = await client.monitor.chatgpt({
  prompt: "Best CRM for small agencies", country: "US",
});
console.log(res.result.text);

Every response carries

  • text / markdown — the answer itself
  • sources[] — every cited page, in order
  • citationPills[] — inline citation groups
  • searchQueries[] — the engine's query fan-out
  • entities[] — brands, products, places named
  • shoppingCards[], ads[] — commerce blocks

Full field list in the API reference.

Agents

Give your agent the data

The hosted MCP server exposes every engine as a tool. One command and Claude, Cursor or any MCP client can ask ChatGPT or search Google mid-task.

mcp
$ claude mcp add --transport http answerline \
    https://mcp.answerline.dev/mcp \
    --header "Authorization: Bearer $API_KEY"

One tool per engine, plus a credits tool. MCP guide

How it works

Prompt in, structured data out

Send a prompt

Pick an engine, a prompt and a country. Call it synchronously or queue thousands of tasks at once.

We run it live

The prompt runs against the real consumer product from the location you chose, and retries automatically if it has to.

Get structured JSON

Answer text, markdown, sources, search queries, shopping cards and entities, in the same shape every time.

Pay for successes

Credits are charged only when a result is delivered. Failed runs cost nothing.

Built for volume

Infrastructure, not a dashboard

Async batches

Submit up to 500 tasks per call with priorities, idempotency keys and webhooks — poll or get pushed results.

Fair queues

Every account has its own concurrency, so a large batch from someone else never slows yours.

Geo-targeted

250 countries for every engine, US states for AI assistants, city-level locations for Google.

Works with your tools

REST with an OpenAPI spec, a hosted MCP server for agents, SDKs for Python and TypeScript, n8n and Zapier.

Pricing

Credits, not surprises

Failed requests cost nothing. All 10 tiers and per-engine credit costs →

Pull your first answer in under a minute

500 free credits every month. No card, no sales call.

Questions

Is this the official OpenAI or Google API?

No. Official model APIs don't return what the consumer apps show users: the web-search answers, citations, shopping cards and ads. This API returns what people actually see.

How long does a request take?

AI engines usually answer in 30–45 seconds, Google Search much faster. Use async tasks and webhooks for large volumes.

What happens if a request fails?

It is retried automatically. If it still fails, you get an error and no credits are charged.

Do unused credits roll over?

No. Credits reset at the start of each billing cycle.