research-agent

Automate personalized research paper discovery by analyzing your GitHub repositories and Overleaf projects to filter new arXiv, Semantic Scholar, and SSRN publications, generating structured memos explaining each paper's relevance to your specific research context.

Yes, you can filter new arXiv publications by deriving your specific research context directly from your GitHub repositories and Overleaf projects, ensuring the discovered papers match your active development and writing strands.

Prevent late-discovery of competing research papers by using an early-warning system that performs parallel analysis and novelty checking against your research context, identifying potential citations or competing approaches before you diverge.

Yes, you need Python 3, OpenClaw, and triage tools installed to execute the automated literature review process, which handles parallel retrieval, novelty checking, and local overlap assessment for new publications.

Automated literature review handles local overlap assessment by performing parallel analysis during retrieval, checking new publications against your existing research context to evaluate novelty and identify deep connections or useful methods.

Limitations include dependency on the publication indexing frequency of Semantic Scholar and SSRN, requiring Python 3 and OpenClaw execution environments, and needing explicit GitHub or Overleaf inputs to accurately derive your research context.