HJCheng0602 / paperwise

What is paperwise?

Paperwise is a Python CLI tool for deep-reading research papers on GitHub, turning raw PDFs or Arxiv IDs into LLM-powered reports that break down problems, methods, experiments, comparisons, limitations, and insights. It builds a local vector knowledge base for semantic searches across your papers and generates interactive knowledge graphs to visualize connections. Developers get structured outputs like detailed Markdown reports and domain surveys from queries, solving the pain of manual lit reviews.

Why is it gaining traction?

It stands out with dead-simple commands like `rh read --arxiv 2310.01234` for instant deep-reading reports or `rh survey --query "RAG"` for overviews of top papers, plus KB search (`rh kb search "contrastive learning"`) and graph exports. Multi-LLM support (Anthropic, OpenAI, DeepSeek, Qwen), precise cost tracking, and caching keep runs cheap and fast—unlike scattered scripts or manual Notion dumps. The pipeline feels natural for paper deep reading skills, delivering graph and knowledge insights without setup hassle.

Who should use this?

AI researchers grinding through Arxiv for surveys, PhD students needing quick lit reviews with comparisons, or engineers scouting papers for implementation ideas. Ideal for anyone building a personal research KB, like ML devs tracking trends in RAG or diffusion models. Skip if you prefer paperlike iPad scribbles over automated pipelines.

Verdict

Try it if you're drowning in papers—early traction with 18 stars shows niche appeal, but 1.0% credibility score flags immaturity like sparse docs and no tests. Solid prototype for Python tinkerers; fork and harden it for production.