Scrape ChatGPT answers, citations and sources to JSON with Node.js and JavaScript. Track brand visibility in ChatGPT. No OpenAI API key. Built on the Bright Data ChatGPT Scraper API. New accounts get 5,000 free credits a month.
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ChatGPT answers as JSON, in JavaScript: send a prompt, get back the answer ChatGPT shows in its own interface, as text, Markdown and HTML, with the sites it cited, by country, with web search and follow-up prompts. Built for ChatGPT visibility tracking and GEO: which brands ChatGPT mentions and recommends, and which sources it cites. No OpenAI API key, no browser, no dependencies. Built on the Bright Data ChatGPT Scraper API.
Also here: a one-command CLI for a list of prompts, and the Bright Data CLI, which needs no JavaScript at all.
Node 20 or newer, and nothing to install: this is the built-in fetch. Save
it as a .mjs file, since it uses top-level await.
export BRIGHTDATA_API_TOKEN=YOUR_API_KEYGet a token from the Bright Data control panel. No account yet? Create one; new accounts get 5,000 free credits a month.
const API = "https://api.brightdata.com/datasets/v3";
const HEADERS = {
Authorization: `Bearer ${process.env.BRIGHTDATA_API_TOKEN}`,
"Content-Type": "application/json",
};
async function call(path, body) {
const init = body === undefined ? { headers: HEADERS } : { method: "POST", headers: HEADERS, body: JSON.stringify(body) };
const response = await fetch(API + path, init);
if (!response.ok) throw new Error(`HTTP ${response.status}: ${await response.text()}`);
return response.json();
}
const prompt = {
url: "https://chatgpt.com/",
prompt: "Best CRM software for small businesses",
country: "US",
web_search: true,
};
const job = await call("/trigger?dataset_id=gd_m7aof0k82r803d5bjm&include_errors=true", [prompt]);
while (!["ready", "failed"].includes((await call(`/progress/${job.snapshot_id}`)).status)) {
await new Promise((done) => setTimeout(done, 10_000));
}
const [answer] = await call(`/snapshot/${job.snapshot_id}?format=json`);
if (answer.error) throw new Error(answer.error);
console.log(answer.answer_text.slice(0, 120));
for (const citation of (answer.citations ?? []).slice(0, 3)) console.log("cited:", citation.url);The best CRM software for a small business depends on whether you need to manage customers, track sales, automate follow
cited: https://www.hubspot.com/products/crm?utm_source=chatgpt.com
cited: https://www.zoho.com/crm/?utm_source=chatgpt.com
cited: https://www.pipedrive.com/?utm_source=chatgpt.com
Expect one to five minutes: the API asks ChatGPT and the loop waits for the answer. One run with web search and sources took eleven. Billed per answer.
Three things in that snippet are not optional.
url must be https://chatgpt.com/. The scraper requires it on every input;
the prompt goes in prompt.
web_search: true asks ChatGPT to search the web, which is what usually
brings citations. Without it, citations can come back null rather than an
empty list, so the loop guards for that.
include_errors=true makes the API report a failed prompt as a row with an
error key. The API default is off.
The Bright Data JavaScript SDK wraps this scraper, but its input type has no require_sources, so it cannot ask ChatGPT to cite. Hence the
plain HTTP, here and in this package.
The ChatGPT Scraper API needs no OpenAI account, no OpenAI API key and no
ChatGPT login. The only credential is a Bright Data API key, set as
BRIGHTDATA_API_TOKEN. This package and its command also find the key stored
by bdata login.
It returns what a person sees in ChatGPT itself, not what the OpenAI API returns: the answer with its citations, links, shopping and map blocks, as ChatGPT renders them for that country.
The command in this repo runs the quickstart for several prompts at once and writes one JSON file.
npm install -g github:brightdata/chatgpt-scraper-node
chatgpt-scraper "Best CRM software for small businesses" "Best project management tool for startups" --web-searchAsking ChatGPT 2 prompt(s) from US, with web search
Usually one to five minutes each. Billed per answer, 5,000 free credits a month.
asking "Best CRM software for small businesses"...
got "Best CRM software for small businesses": 3,335 characters, 11 citations
asking "Best project management tool for startups"...
got "Best project management tool for startups": 4,842 characters, 9 citations
Saved 2 answers as JSON to chatgpt.json
In a terminal the asking lines are replaced by a spinner and a running
clock, updating in place, so you can see it is working and how long it has
been going.
--country CC two-letter country ChatGPT answers from, default US
--web-search ask ChatGPT to search the web before answering
--out PATH output file, default chatgpt.json
The command finds the token in BRIGHTDATA_API_TOKEN, or, if you ran
npx -p @brightdata/cli bdata login once, in the stored login, for you and
for any coding agent in that terminal. It does not read a .env file; Node
loads one for it with node --env-file=.env.
Import it instead of running it, for ok and error per prompt instead of
raw rows. This package is ESM, so use import, not require. scrape never rejects
for one bad prompt; check ok before reading answer:
import { scrape } from "@brightdata/chatgpt-scraper-node";
for (const outcome of await scrape(["Best running shoes for beginners", "Best free photo editing app"])) {
if (outcome.ok) console.log(`${outcome.prompt}: ${outcome.answer.answer_text.slice(0, 60)}`);
else console.log(`${outcome.prompt} failed: ${outcome.error}`);
}Best running shoes for beginners: If you're just getting into running, I recommend shoes that
Best free photo editing app: Best overall Snapseed – Best free photo editing app Snapseed
There is one ChatGPT scraper, documented in the
ChatGPT Scraper API docs.
Every snippet below is complete: paste it as is after the npm install
above. Every one of them runs in Actions each Monday, a smaller check runs
every other day, and the badge at the top is the latest result.
| you want | how |
|---|---|
| ChatGPT's answer to prompts | ask(["prompt one", "prompt two"], { country: "US" }) |
| the sites ChatGPT cites | ask(prompt, { web_search: true, require_sources: true }) |
| a follow-up in the same chat | ask(prompt, { additional_prompt: "..." }) |
| the answer from another country | ask(prompt, { country: "DE" }) |
| the answer as Markdown | answer_text_markdown on any answer |
ask takes one prompt or an array. An array is one job, not one per prompt.
Each answer is one record, and a prompt the API could not answer comes back
as a row with an error key. Any other input the API accepts goes in as the
API names it, such as index.
import { ask } from "@brightdata/chatgpt-scraper-node";
const [answer] = await ask("Best project management software for remote teams", { web_search: true, require_sources: true });
const domains = (answer.citations ?? []).map((c) => new URL(c.url).host);
const unique = [...new Set(domains)].sort();
console.log(domains.length, "citations from", unique.length, "sites:");
for (const domain of unique.slice(0, 5)) console.log(" ", domain, domains.filter((d) => d === domain).length);9 citations from 8 sites:
asana.com 2
basecamp.com 1
clickup.com 1
monday.com 1
thedigitalprojectmanager.com 1
require_sources asks ChatGPT to cite, and web_search makes it look.
citations holds each cited url with its title; links_attached holds the
links inside the answer text. Asking the same prompt every day is how GEO
tools track share of citations over time.
import { ask } from "@brightdata/chatgpt-scraper-node";
const [answer] = await ask("Name three popular sourdough bread recipes", {
additional_prompt: "Which of them is easiest for a beginner? One sentence.",
});
console.log("first: ", answer.answer_text.slice(0, 80));
console.log("second:", answer.additional_answer_text.slice(0, 80));first: Here are three popular sourdough bread recipes worth trying: 1. Classic Artisan
second: The classic artisan country sourdough is easiest for a beginner because it uses
The follow-up runs in the same chat, so it can refer to the first answer. Its
answer lands in additional_answer_text, with its own
citations_additional_prompt.
import { ask } from "@brightdata/chatgpt-scraper-node";
for (const country of ["US", "DE"]) {
const [answer] = await ask("Which supermarket chain has the cheapest groceries?", { country, web_search: true });
console.log(country, "|", answer.answer_text.slice(0, 90));
}US | Aldi is generally the cheapest traditional supermarket chain in the U.S. , with Lidl a ver
DE | If you're shopping in Germany , my recommendation is ALDI or Lidl for consistently afforda
country is the two-letter country ChatGPT answers from. The US answer led
with Aldi; the German one with ALDI and Lidl. geolocation is also accepted, but on
9 Nov 2026 coordinates for Midtown Manhattan got an answer about Houston that
began "Since your precise location isn't available", so this package does not
document it.
import { writeFile } from "node:fs/promises";
import { ask } from "@brightdata/chatgpt-scraper-node";
const [answer] = await ask("Explain what a REST API is, with one short code example");
const markdown = answer.answer_text_markdown;
await writeFile("answer.md", markdown, "utf8");
const lines = markdown.split("\n");
const headings = lines.filter((line, i) => /^-+$/.test(lines[i + 1] ?? "") && line.trim());
console.log(markdown.length, "characters saved to answer.md; headings:", headings);1900 characters saved to answer.md; headings: [
'How does a REST API work?',
'Short code example',
'Key concept to remember'
]
answer_text_markdown is converted from the rendered page: headings are
underlined with dashes rather than written with #, tables are pipe tables,
and code is indented four spaces under a line naming the language, not
fenced. answer_text is the same answer as plain text, and answer_html the
full page, about 1.4 MB.
For a long list of prompts, do not block a process for an hour. Trigger, keep the snapshot id, fetch when ready. Snapshots stay downloadable for 30 days.
import { CHATGPT, fetchSnapshot, status, trigger } from "@brightdata/chatgpt-scraper-node";
const item = { url: "https://chatgpt.com/", prompt: "What is the capital of Australia?", country: "US" };
const snapshot = await trigger(CHATGPT, [item]);
console.log("snapshot:", snapshot);
let state;
while (!["ready", "failed"].includes((state = await status(snapshot)))) {
await new Promise((done) => setTimeout(done, 10_000));
}
console.log("status:", state);
console.log("fetched:", (await fetchSnapshot(snapshot))[0].answer_text.slice(0, 60));snapshot: sd_mw9canbkoi5uoia7a
status: ready
fetched: The capital of Australia is Canberra . 🇦🇺 It is located in
The input fields the live API accepted on 2026-11-09, for dataset
gd_m7aof0k82r803d5bjm:
| input | required | what it does |
|---|---|---|
url |
yes | always https://chatgpt.com/ |
prompt |
yes | the question |
country |
no | two-letter country ChatGPT answers from |
web_search |
no | true asks ChatGPT to search the web |
require_sources |
no | true asks ChatGPT to cite sources |
additional_prompt |
no | a follow-up in the same chat |
index |
no | your own number, returned on the record |
geolocation |
no | "lat,long"; not honoured in our tests |
bdata pipelines info gd_m7aof0k82r803d5bjm collect_by_url prints the input
fields the API validates.
The fields most people want from an answer:
prompt answer_text answer_text_markdown citations links_attached country web_search_triggered
web_search_triggered does not follow the web_search input: on
9 Nov 2026 it read false for an answer with ten citations and true for a
prompt sent without web_search. model was null on every record. The code
hardcodes no field list. Whatever the API returns lands in each record, and
in the command's file.
Regenerated every week from the published schema,
scrapers-full.json, so they cannot go stale. A record
carries the fields that apply to it, plus timestamp and input; the
sample file's first answer has 33 keys.
All 32 ChatGPT answer fields, gd_m7aof0k82r803d5bjm
| field | type | description |
|---|---|---|
url |
url | ChatGPT page URL |
prompt |
text | The input prompt |
answer_html |
text | The answer of ChatGPT search |
answer_text |
text | Answer text in plain text |
links_attached |
array | Hyperlinks included in the response text, along with their position and linked words. |
citations |
array | Sources directly referenced in the answer, often linked to specific facts or claims. |
country |
country | Country from which to perform the search |
is_map |
boolean | Indication whether a map package result was generated |
references |
array | Links grouped under the References section |
shopping |
array | Product recommendation cards from ChatGPT's Shopping panel |
shopping_visible |
boolean | Whether the Shopping panel appeared for this answer |
index |
number | The index column lets you assign a unique ID to each crawl request for easy tracking. |
answer_text_markdown |
html2markdown | Answer text in markdown format. |
web_search_triggered |
boolean | Was a web search triggered by ChatGPT when returning the answer |
additional_prompt |
text | The additional_prompt is a follow-up input sent after receiving the first answer, aiming to clarify, expand, or refine the initial response. The system processes this prompt and returns the result as additional_answer_text. |
additional_answer_text |
text | The additional_answer_text is the system's response to the additional_prompt, which follows the initial interaction. It provides additional information, clarification, or a refined answer based on the user's follow-up input. |
map |
array | Map package result |
search_sources |
array | A list of external web pages ChatGPT used to gather information, usually shown at the bottom of the response. |
search_sources_more |
array | A list of external web pages ChatGPT used to gather information, usually shown at the bottom of the response (from "More" partition). |
response_raw |
text | Raw data of ChatGPT's response |
response_raw_additional_prompt |
text | Raw data of ChatGPT's response for additional prompt. |
answer_section_html |
text | The answer's section html |
model |
text | Model used for answer |
web_search_query |
array | Model-generated web search queries (exact strings, in execution order) |
prompt_sent_at |
date | exact prompt timestamp |
answer_text_raw |
text | Original answer_text as extracted, without trimming or whitespace normalization (may include leading/trailing spaces and newline characters). |
business_locations |
array | Please consider adding new fields, attaching example of JSON we can get for each business location mentioned in answer. Array of business location entities returned when the answer includes local business results. Each entry contains the business ID, coordinates, name, category, address, contact info, ratings, hours, images, and open/closed status. |
ads |
array | Array of advertisement's card data. |
additional_answer_text_markdown |
html2markdown | additional_answer_text_markdown in markdown format. |
questionnaire |
array | Questionnaire |
shopping_additional_prompt |
array | Product recommendation cards from ChatGPT's Shopping panel from results of additional prompt |
citations_additional_prompt |
array | Sources directly referenced in the answer of additional prompt, often linked to specific facts or claims. |
The start of a real output file, from chatgpt-scraper "Best CRM software for small businesses"
Each line is cut at 160 characters here; answer_html alone is a 1.4 MB line.
{
"generated_at": "2026-11-09T14:12:26.652Z",
"country": "US",
"prompts": [
{
"prompt": "Best CRM software for small businesses",
"answer": {
"url": "https://chatgpt.com/?q=Best%20CRM%20software%20for%20small%20businesses",
"prompt": "Best CRM software for small businesses",
"answer_html": "<html class=\"x108lcm5 xzx5cyk xojncy5 xy3g5xu x1nktip0 xwbwmuu x1it3fuk xh8yej3 xuu8av0 xeuugli xgs08xk x1javzw2 xfqjyog x1rob05q xvmah...
"answer_text": "The best CRM software for a small business depends on whether you primarily need to manage customers, track sales leads, automate follow...
"links_attached": [
{
"url": "https://www.hubspot.com/products/crm?utm_source=chatgpt.com",
"text": "HubSpot website",
"position": 1
},
{
"url": "https://www.zoho.com/crm/?utm_source=chatgpt.com",
"text": "Zoho website",
...The whole file, one answer with every field, is examples/sample_output.json.
| you see | what it means |
|---|---|
API token required but not found. |
Exit 2, before any request. Set the token, or run bdata login. |
failed "prompt": ... |
Exit 1. The API returned an error row for that prompt instead of an answer. The message is the API's. |
failed "prompt": timeout: snapshot ... not ready after 900s |
Exit 1. Run it again, or fetch the snapshot later with fetchSnapshot(). |
failed "prompt": HTTP 401: ... |
The token is set but wrong. |
Any failure exits 1, so a run is safe to gate a script on.
From the functions, the same conditions look like this:
| you see | what it means |
|---|---|
TokenMissing |
No BRIGHTDATA_API_TOKEN and no bdata login. |
ApiError: HTTP 400: ...validation_error... |
An input the API does not accept. The message names the field. |
a row with an error key |
The API's answer for one prompt. The other rows are fine. |
ApiError: timeout: ... |
The job outlived the 900-second wait. Pass { timeoutMs } to run, or use trigger and fetch later. |
No JavaScript, nothing installed. Paste both lines; the first opens a browser
once, or use bdata login --device over SSH and in CI:
npx -p @brightdata/cli bdata login
npx -p @brightdata/cli bdata pipelines run gd_m7aof0k82r803d5bjm collect_by_url --url "https://chatgpt.com/" --prompt "Best CRM software for small businesses" --country US --pretty1 record, 19 fields, 75 seconds on 9 Nov 2026. The CLI leaves out fields that
are null. The record is about 3 MB of JSON, mostly answer_html.
npx skills add brightdata/skills teaches Claude Code, Cursor and Codex these
commands and the docs, so plain language works afterwards. Full guide:
Bright Data for your coding agent.
No terminal, for a hosted assistant? The
Bright Data MCP server
runs any pre-built scraper, ChatGPT included, through search_scrapers and
run_scraper in its scrapers group:
https://mcp.brightdata.com/mcp?token=YOUR_API_TOKEN&groups=scrapers
An agent can also open the account itself, no signup form: agent registration. Everything else Bright Data connects to, from LangChain to Zapier and n8n: integrations.
New Bright Data accounts get 5,000 free credits a month. The ChatGPT scraper bills per record, one per answer. Pricing lists the rates.
Bugs in this repo: open an issue, and CONTRIBUTING.md says what to put in it. Anything about the API, your account or your credits: Bright Data support.
The same ChatGPT scraper is available for Python: brightdata/chatgpt-scraper.
Other Node.js scrapers built the same way, with live-verified examples: