Grok scraper API

Request

curl -X POST "https://api.answerline.dev/v1/monitor/grok" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"Which electric car brands do people recommend?","country":"US"}'

import { Client } from "@answerline/sdk";

const client = new Client({ apiKey: process.env.API_KEY! });
const answer = await client.monitor.grok({
  "prompt": "Which electric car brands do people recommend?",
  "country": "US"
});

import os
from answerline import Client

with Client(os.environ["API_KEY"]) as client:
    answer = client.monitor.grok(
        prompt="Which electric car brands do people recommend?",
        country="US",
    )

Every request option is on the Grok reference page.

What you get back

Full field definitions are on the Grok reference page.

The Grok app answer versus xAI’s API

This endpoint returns the answer a user gets from the consumer Grok app, with the sources it cited (web pages and X posts), the searches it ran and the model identifier.

xAI’s developer documentation (checked 2026-09-17, docs.x.ai) describes search tools for API calls: a web search tool that lets Grok search the web in real time and browse pages, an X search tool, and domain filters (allowed_domains, excluded_domains). With the API you choose the model, the instructions and which tools and domains are allowed.

Those settings shape what your call cites, so an API run tells you what Grok cites under your configuration. The app answer shows what it cites for the public.

Grok also cites X posts alongside web pages. Brands whose reputation lives in posts and threads need to know which posts and authors the public answer draws on, and sources[].creator makes that attributable. Run the same prompt set through ChatGPT or Gemini and you get each consumer product’s answer in one schema family, so share-of-voice numbers compare directly.

An annotated response

The excerpt follows the result schema for POST /v1/monitor/grok. Values are illustrative; the request set include.markdown to true.

{
  "success": true,
  "result": {
    "text": "Recommendations vary by budget. Owners frequently mention...",
    "markdown": "Recommendations vary by budget.\n\n- **Range:** owners frequently mention...",
    // Web pages first, then X posts.
    "sources": [
      {
        "position": 1,
        "url": "https://www.example-ev-review.com/long-range-evs-2026",
        "label": "Longest-range EVs of 2026",
        "description": "Range test results for...",
        "preview": "We drove twelve EVs until empty..."
      },
      {
        "position": 2,
        "url": "https://x.com/example_owner/status/1900000000000000000",
        "label": "Example Owner",          // the author's display name
        "preview": "Six months in, charging on the road has been...",  // the post text
        "creator": "example_owner"        // the handle
      }
    ],
    // The searches Grok ran on the web and on X. Absent when there are none.
    "searchQueries": ["electric car owner recommendations 2026"],
    // Nullable model identifier.
    "model": "grok-example"
  }
}

result.provenance reads searchMode engine-default (all fields).

Notes for this engine

• Grok shows no citation chips, so citationPills is always empty. Count citations from sources.
• A US state is accepted and answered for the whole US; only ChatGPT and Gemini answer from inside a state (states).
• sources[].position is a number. Every other source field but url is optional. siteName, metadataTitle, searchEngineText, image and favicon are in the contract but empty on the answers served today.
• Citation chips are removed from text and markdown.
• model is nullable. Record it with each run so you can segment results when the served model changes.
• To split web pages from X posts, check for creator or classify each sources[].url by host (x.com).

When answers cite posts, the set of sources can change within hours. Compare runs from the same time window, and keep the period in the idempotency key so each window is a separate sample. Within a window, run each prompt several times and report how often a source or claim appears; see sampling vs census.

description, preview, creator and the other enrichment fields are optional. Don’t make them required columns.

Substring checks flag negations (“no recall”) as hits. Use them to route answers to review, not as the final classification.

Related: the best Grok scrapers compared, every request field, brand protection, monitoring Grok, brand crisis AI monitoring, news monitoring, threat intelligence, AI visibility tracking.

From the blog

Docs

• Quickstart
• API reference
• Engine features
• Async tasks and batches
• Webhooks
• Coverage
• Pricing and failures
• Rate limits

Other engines