Copilot Scraper

Collect Microsoft Copilot answers through the Scrapeless LLM Chat Scraper API, including Markdown responses, outbound links, and source citations, without reverse-engineering the Copilot UI, maintaining browsers, or building your own anti-blocking stack.

Use this repo when you need a repeatable way to monitor Copilot 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.copilot) 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.copilot",
    "input": {
      "prompt": "Recommended attractions in New York",
      "country": "US",
      "mode": "search"
    }
  }'

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.copilot), input (below), and an optional webhook.

Parameter (input.*)	Type	Required	Description
prompt	string	Yes	Prompt to send to Copilot.
country	string	Yes	Country / region code (e.g. US, JP).
mode	string	Yes	Mode to run: search, smart, chat (Quick response), reasoning (Think Deeper), or study (Study and learn).

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...",
    "mode": "search",
    "links": [],
    "citations": [
      { "title": "...", "url": "https://..." }
    ]
  }
}

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 Copilot.
prompt	string	Original prompt.
mode	string	Mode used: search, smart, chat, reasoning, or study.
links	array	All outbound links returned by Copilot.
citations	array	Citation objects extracted from the response (title, url).
citations.title	string	Title of the cited source.
citations.url	string	URL of the cited source.

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 Copilot 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 and modes to compare which sources Copilot cites, how recommendations change by region, and where your content appears in AI-generated answers.

Competitor intelligence

Collect structured Copilot 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 Copilot 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 Copilot scraping?

Benefit	What it means for your team
One unified API	Query Copilot through the same Scrapeless LLM Chat Scraper workflow used for other AI answer engines.
Structured output	Receive Markdown answers, links, citations, prompts, and mode metadata 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 Copilot data to real applications quickly.

FAQ

What is Copilot Scraper?

Copilot Scraper is a Scrapeless LLM Chat Scraper actor that sends prompts to Microsoft Copilot and returns structured answer data, including the Markdown response, links, citations, prompt, and mode.

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.

Which Copilot modes are supported?

The current request schema supports search, smart, chat, reasoning, and study. Check the official documentation for the latest supported options before deploying a production workflow.

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, outbound links, and 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 Copilot 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.