Automate the discovery and organization of scholarly papers from Bright Data and n8n to support faster literature reviews.
The AI agent uses Bright Data to access scholarly databases and journals, scraping new papers based on your topics. It extracts metadata including titles, authors, abstracts, and citations, and writes structured rows to Google Sheets. The sheet centralizes sources and updates automatically, enabling quick filtering, citation gathering, and literature reviews.
Executes end-to-end paper collection and organization in one lightweight AI agent.
Ingests tracking criteria (topics, journals, authors, and update frequency).
Scrapes academic sources through Bright Data to collect new papers.
Parses and extracts metadata (titles, authors, abstracts, citations).
Deduplicates and normalizes records across sources.
Updates Google Sheets with new entries and metadata fields.
Notifies you when new papers meet your criteria.
Before: you struggle with scattered sources, repeated papers, and manual tracking. After: you have a centralized, up-to-date repository with clean metadata and automatic updates.
A simple, three-step flow lets you configure topics and sources, then automate collection and storage.
Set topics, journals, authors, and update frequency in the AI agent.
Bright Data navigates sources while the AI agent extracts titles, authors, abstracts, and citations.
Save data to Google Sheets and alert you to new entries.
One supporting sentence with short explanation.
Scenario: A PhD student wants to monitor AI ethics literature. The AI agent is configured with topics covering ethics, fairness, and transparency, and it runs weekly. Over one week, it collects 12 new papers, stores structured metadata in Google Sheets, and sends a digest to the research group.
One supporting sentence.
Need up-to-date papers to inform experiments and reviews.
Support literature reviews and theses with current references.
Maintain a live bibliography for ongoing projects.
Create topic-specific bibliographies and reading lists.
Identify emerging trends and key authors.
Provide current references for courses and syllabi.
One supporting sentence with short explanation.
Provides proxy-based access to academic sources; the AI agent uses it to fetch papers without blocks.
Orchestrates the AI agent flow from sources to storage and notifications.
Stores structured metadata and supports collaborative review and filtering.
One supporting sentence with short explanation.
One supporting sentence with short explanation.
The AI agent uses Bright Data to access publicly available scholarly databases and journals. It can target topics, journals, and authors you specify, and you should ensure you have permission to access the sources. It respects site terms and robots.txt where possible, and you can exclude sources if needed. If a site blocks access or restricts content, you can adjust configuration to stay compliant. Maintain awareness of each site's terms when configuring your topics.
You define a schedule (daily, weekly, or custom) in the AI agent. It triggers scrapes at the configured cadence and fetches only new or updated papers since the last run. Runs are logged with status and results to help you audit the workflow. You can pause, adjust frequency, or pause individual sources without affecting the whole setup. The cadence should balance timely updates with source load considerations.
Compliance depends on the site and your authorization. The AI agent is designed to respect robots.txt and terms where possible, and Bright Data provides access paths intended to be compliant. You should review each target site’s terms and ensure your use aligns with legal and institutional policies. If a site disallows scraping, exclude it from configuration. For paywalled content, ensure you have proper access rights before retrieval.
Yes. The AI agent supports configurable metadata fields such as title, authors, abstract, publication date, journal, DOI, and citations. Fields can be added, removed, or renamed in the source configuration and the Google Sheets template. It normalizes formats to keep data consistent across sources. You can export the fields you need for downstream workflows.
The AI agent performs deduplication by matching titles, DOIs, and author lists across sources. Duplicates are merged or flagged to prevent multiple rows for the same paper. If a paper appears with updated metadata, the existing entry is enriched instead of creating a new one. You can tune deduplication sensitivity to balance precision and recall.
Google Sheets data can be exported as CSV or pushed to compatible reference managers and databases. The AI agent can be extended with post-processing steps to move data to your preferred tools. For deeper automation, connectors can be added to trigger external workflows. Exports can be scheduled or run on demand.
Data remains under your Google account permissions and within your Bright Data configuration. Access is controlled by account policies and sharing settings. You should enable strongest available protections for sensitive work, including restricted sharing and audit logs. If needed, review data retention policies and encryption options provided by your services.
Automate the discovery and organization of scholarly papers from Bright Data and n8n to support faster literature reviews.
