Scrapeless Weekly Changelog: August 10–16, 2026 Updates

TL;DR:

• ChatGPT Scraper now returns results more reliably. The update also improves the consistency of advertising-data capture when ads are available.
• Copilot collection is more stable. Backend processing changes improve successful-result delivery without changing the customer-facing workflow.
• Three AI engines now support fanout data. ChatGPT, Gemini, and Perplexity responses can include the new data without an additional field-level charge.
• Developer documentation is clearer. The n8n integration guide and the custom browser fingerprint userAgent reference were updated.

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AI-answer pipelines depend on consistent collection and response fields that downstream systems can process directly. During August 10–16, Scrapeless improved ChatGPT and Copilot collection, added fanout data support across three AI engines, and expanded two developer documentation areas.

🌟 Top Updates

ChatGPT Scraper Reliability and Ad Capture Improved

scraper.chatgpt now delivers a higher rate of successful results and more consistent advertising-data capture. The request format remains unchanged, so existing integrations can benefit from the service update without modifying their current actor calls.

Advertising output remains conditional. Whether an answer contains ads depends on the prompt and the response context, so applications should continue to treat advertising fields as optional.

Copilot Collection Is More Stable

scraper.copilot received processing improvements that increase stability and successful-result delivery. The work is service-side and does not introduce a new customer-facing parameter or migration requirement.

These changes are useful for recurring answer monitoring and other automated pipelines where incomplete results can interrupt downstream analysis.

💫 Fanout Data Across ChatGPT, Gemini, and Perplexity

Fanout data is now supported in responses from scraper.chatgpt, scraper.gemini, and scraper.perplexity. The new data is available without an additional field-level charge.

Teams can pass fanout data into their existing storage, analysis, or monitoring layers when the field is present. Integrations should treat it as conditional and verify that the field exists before processing it.

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⬆️ Documentation Updates

Updated n8n Integration Guide

The Scrapeless n8n documentation has been refreshed to make visual workflow setup easier to follow. The guide covers how Scrapeless modules fit into node-based data workflows and links into the current setup flow.

This update supports teams that connect collection steps with schedules, transformations, storage, and notifications in n8n.

Clearer Custom Fingerprint User-Agent Guidance

The custom fingerprint documentation now explains the userAgent parameter more clearly.

The parameter defines the browser User-Agent string used in the HTTP request header. The same browser identity is exposed through navigator.userAgent, as described by the Navigator userAgent reference. Developers comparing header and browser-visible values can also consult the User-Agent header reference and the HTML Navigator standard.

The updated Scrapeless page documents the parameter name, supported type, example values, and its role within the wider fingerprint configuration.

👀 What This Means for Developers

This week’s changes improve the collection layer and the integration layer together. ChatGPT and Copilot workflows receive more dependable results, while fanout data adds another response dimension across ChatGPT, Gemini, and Perplexity. Updated n8n and fingerprint documentation reduces ambiguity during setup.

Explore the LLM Chat Scraper, review the existing n8n LLM Scraper workflow, or check Scrapeless pricing when planning a production workload.

Conclusion

The August 10–16 release strengthens AI-answer collection across ChatGPT and Copilot, makes fanout data available across three supported engines, and gives developers clearer setup references for n8n and browser fingerprints.

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