Drop in a research question. Agents search papers, extract findings, and assemble a knowledge graph.

Hi everyone, I'm Flynn, and I built Arbor because I know how painful literature review is firsthand. During my PhD in Biomedical Engineering, I spent countless hours searching for papers, cross-referencing findings, and trying to piece together the state of research on a topic. It was slow, fragmented, but a vital part of the research process. Arbor automates that entire workflow. You drop in a question, and AI agents: - Decompose it into focused sub-inquiries - Search across arXiv and Semantic Scholar in parallel - Screen papers for relevance and extract key findings - Synthesize everything into a structured overview The entire pipeline streams in real-time as an interactive knowledge graph, so you can watch the research unfold and explore any branch. The stack: React + TypeScript frontend, FastAPI backend, DeepSeek V3 for decomposition and synthesis, Gemini 2.0 Flash for paper screening, and GPT-4o-mini for content moderation. I'd love feedback on the research quality, the graph UX, or anything else. What would make this more useful for your workflow?

Congrats on launching Arbor. The system where agents search papers, pull out key findings, and assemble them into a knowledge tree from any research question is a smart approach. It gives users a clear structure for complex topics.

Good luck with it!

@nicklaunches appreciate it man! Love the support

Good luck with it!

@nicklaunches appreciate it man! Love the support

Arbor

Build a knowledge tree on any subject

Build a knowledge tree on any subject