Gemini Scraper

Collect Google Gemini answers through the Scrapeless LLM Chat Scraper API, including Markdown responses and structured citation metadata such as titles, URLs, snippets, and highlights, without reverse-engineering the Gemini UI, maintaining browsers, or building your own anti-blocking stack.

Use this repo when you need a repeatable way to monitor Gemini answers for GEO and AI search visibility, compare prompts across regions, audit cited sources, or pipe AI responses into analytics and automation workflows.

• Full documentation: https://docs.scrapeless.com/en/llm-chat-scraper/quickstart/introduction/
• Get your x-api-token: https://app.scrapeless.com/passport/login?redirect=/quick-start
• API endpoint: POST https://api.scrapeless.com/api/v2/scraper/execute

How it works

Send a single POST request to the Scrapeless endpoint with your API token in the x-api-token header. The body specifies the actor (scraper.gemini) and an input object with your prompt and options. The API runs the query and returns the structured result in task_result.

POST https://api.scrapeless.com/api/v2/scraper/execute
Content-Type: application/json
x-api-token: <YOUR_API_TOKEN>

Quick start (curl)

curl 'https://api.scrapeless.com/api/v2/scraper/execute' \
  --header 'Content-Type: application/json' \
  --header 'x-api-token: YOUR_API_TOKEN' \
  --data '{
    "actor": "scraper.gemini",
    "input": {
      "prompt": "Recommended attractions in New York",
      "country": "US"
    }
  }'

To receive the result asynchronously, add a webhook object:

"webhook": { "url": "https://www.your-webhook.com" }

Request parameters

The request body has three top-level fields: actor (always scraper.gemini), input (below), and an optional webhook.

Parameter (input.*)	Type	Required	Description
prompt	string	Yes	Prompt to send to Gemini.
country	string	Yes	Country / region code (e.g. US, JP).

Response

A successful call returns a status envelope; the scraped data lives in task_result:

{
  "status": "success",
  "task_id": "e705743d-da2e-4163-9ccd-eef62529ff72",
  "task_result": {
    "prompt": "Recommended attractions in New York",
    "result_text": "...markdown answer...",
    "citations": [
      {
        "favicon": "https://.../favicon.ico",
        "highlights": ["..."],
        "snippet": "...",
        "title": "...",
        "url": "https://...",
        "website_name": "example.com"
      }
    ]
  }
}

Top-level fields

Field	Type	Description
status	string	Request status, e.g. success.
task_id	string	Unique identifier for the task.
task_result	object	Scraped result (fields below).

task_result fields

Field	Type	Description
result_text	string	Markdown response from Gemini.
prompt	string	Original prompt.
citations	array	Citation metadata (fields below).
citations.favicon	string	Favicon URL.
citations.highlights	array	Highlighted snippets from the source text.
citations.snippet	string	Citation snippet.
citations.title	string	Title of the cited page.
citations.url	string	URL of the cited page.
citations.website_name	string	Website name.

For the complete field list, see the official documentation.

Code examples

Ready-to-run examples live in examples/:

Language	File	Run
Python	example.py	pip install requests && python example.py
Node.js	example.js	node example.js (Node 18+)
Go	example.go	go run example.go
Java	Example.java	java Example.java (Java 11+)
PHP	example.php	php example.php

All examples read the token from the SCRAPELESS_API_TOKEN environment variable:

export SCRAPELESS_API_TOKEN="your_api_token"

Practical use cases

AI answer monitoring

Track how Gemini responds to your brand, product category, documentation topics, or competitor prompts. Store the Markdown answer and citations so your team can measure AI visibility over time.

GEO and SEO research

Run the same prompt across countries to compare which sources Gemini cites, how recommendations change by region, and where your content appears in AI-generated answers.

Competitor intelligence

Collect structured Gemini answers for competitor names, feature comparisons, pricing questions, and "best tool for..." prompts. Use the output to identify messaging gaps and content opportunities.

Dataset and workflow automation

Pipe Gemini answers into internal dashboards, knowledge-base QA systems, spreadsheets, data warehouses, or alerting workflows through the synchronous API response or webhook callback.

Why use Scrapeless for Gemini scraping?

Benefit	What it means for your team
One unified API	Query Gemini through the same Scrapeless LLM Chat Scraper workflow used for other AI answer engines.
Structured output	Receive Markdown answers, prompts, and rich citation metadata (titles, URLs, snippets, highlights, favicons) in a developer-friendly response.
Less maintenance	Avoid building browser automation, UI selectors, proxy rotation, retries, and anti-blocking logic yourself.
Region-aware analysis	Use country inputs to compare localized AI answers and source citations.
Production integration	Use API tokens, webhooks, and language examples to connect Gemini data to real applications quickly.

FAQ

What is Gemini Scraper?

Gemini Scraper is a Scrapeless LLM Chat Scraper actor that sends prompts to Google Gemini and returns structured answer data, including the Markdown response, the original prompt, and rich citation metadata.

Do I need to run a browser or proxy pool?

No. This repo shows how to call the Scrapeless API. Scrapeless handles the scraping workflow behind the API, so your application only needs to send requests and process the returned data.

What data does Gemini Scraper return?

Each successful call returns result_text (the Markdown answer from Gemini), the original prompt, and a citations array. Every citation includes the source title, url, snippet, highlights, favicon, and website_name, so you can audit and attribute the sources Gemini used.

Can I get results asynchronously?

Yes. Add a webhook object with your callback URL to receive results asynchronously when the task completes.

Is this suitable for AI search visibility monitoring?

Yes. The response includes AI-generated Markdown and structured citations, which makes it useful for GEO analysis, brand monitoring, source tracking, and competitive research.

What should I consider before using AI scraping in production?

Make sure your use case complies with applicable laws, platform terms, privacy requirements, and your organization's data policies. Avoid collecting sensitive, private, or unauthorized information.

Learn more

• Scrapeless LLM Chat Scraper documentation
• Supported LLM Chat Scraper actors
• Scrapeless dashboard
• Scrapeless website

Contact us

Need help building a Gemini monitoring workflow or scaling AI answer collection?

• Join our Discord.
• Contact us on Telegram.
• For repo-specific issues or improvements, open an issue or pull request in this repository.