DeepResearch approaches paywalled or restricted content by prioritizing compliance with legal and ethical standards while maximizing access to publicly available information. The system does not attempt to bypass paywalls, circumvent login requirements, or access content through unauthorized means like scraping protected pages. Instead, it focuses on aggregating data from open-access sources, cached versions (where legally permissible), or publicly indexed summaries. For example, if a research paper is behind a paywall, DeepResearch might surface a preprint version from repositories like arXiv or link to a publicly available abstract, avoiding unauthorized access to the full text.
To handle restricted content, DeepResearch relies on user-provided credentials or integrations with institutional access systems when explicitly authorized. For instance, if a user has subscriptions to specific journals or databases, they can configure the tool to use their credentials via secure API keys or browser extensions. This allows DeepResearch to access paywalled content legally, similar to how a library proxy service works. However, this requires explicit user input and adherence to the terms of service of the target platforms. The tool does not store or share credentials, ensuring compliance with data privacy regulations.
Finally, DeepResearch employs detection mechanisms to identify paywalled pages during web crawling. It looks for common indicators like subscription prompts, login forms, or HTTP status codes that signal restricted access. When such content is encountered, the system either excludes it from results or flags it as requiring user authentication. This approach balances utility with ethical constraints, ensuring the tool remains a reliable resource for developers and researchers without violating copyright laws or platform policies. For example, a query for a news article might return a snippet from a non-paywalled summary or direct the user to the publisher’s site for full access.