ResearchRabbit

• Citation-based literature mapping (earlier, later, similar papers)
• Interactive network visualizations of papers and authors
• Collections (curated sets) of papers for thematic grouping
• Recommendation engine that suggests related works based on your collection
• Import via DOI, URL, or paper metadata to seed collections
• Alerts / digests for new relevant research
• Collaboration features: share collections, leave comments
• Integration / interoperability (import from reference managers)
• Explore author networks and co-author relationships
• Trend and connection exploration to reveal clusters in research fields

ResearchRabbit is a cutting-edge AI-assisted literature discovery and visualization platform built for academics, students, and researchers seeking to navigate the vast landscape of scientific publication more effectively. At its core, ResearchRabbit helps you start from one or more “seed” papers and then uncovers related works—those that preceded, followed, or are similar in theme—thereby enabling you to expand your knowledge contextually, not just through keyword searches.

Once you supply a seed paper (via title, DOI, or URL), the system builds a visual map of related works and authors. These network graphs help you see connections among papers, detect clusters of ideas, and trace how ideas evolve over time. Rather than manually sifting through citations in isolation, you can traverse these visual relationships and discover potentially overlooked works. As you build more in your collection, the recommendation engine refines its suggestions, offering papers aligned with your interest profile.

ResearchRabbit also supports collaborative and organizational features. You can group papers into themed collections, share those with peers, and make annotations or comments. The tool sends you alerts or digests about new publications that align with your collections—so you stay updated without manually checking every journal. Because the platform emphasizes visual exploration, you may stumble on research paths you hadn’t thought to pursue before.

One of its distinguishing aspects is that it is offered free to researchers, giving high value to academics regardless of budget constraints. There is no paid tier, and the platform is committed to remaining a no-cost resource for scholarly discovery. The free nature, combined with powerful visualization tools and AI recommendations, makes it a compelling choice for mapping literature landscapes.

That said, some limitations are noted by users and librarians: the underlying citation data source (Microsoft Academic Graph) was retired in 2021, so more recent publications may be underrepresented. Users often use ResearchRabbit in tandem with tools like Google Scholar, library databases, or domain-specific repositories to fill in gaps. But as a visual, exploratory discovery companion, ResearchRabbit accelerates the process of seeing connections, spotting research gaps, and building a stronger literature foundation for academic projects.