YS0meone/Corvus
Multi-agent AI research system — finds academic papers via semantic search & citation snowballing, then answers questions over them using agentic RAG with self-reflection. Built with LangGraph, FastAPI, Celery, and Qdrant.
This tool helps researchers, academics, or anyone needing to deeply understand a new topic by automating the process of finding relevant academic papers and extracting answers from them. You input a research question or topic, and it provides a list of highly relevant academic papers, followed by concise answers to your questions, all grounded in the selected papers. It's designed for individuals who need to quickly get up to speed on scientific literature or specific research questions without sifting through countless articles manually.
Use this if you need to rapidly discover academic papers related to a topic and then get evidence-based answers to specific questions directly from those papers.
Not ideal if you're looking for general web search results or need to synthesize information from non-academic sources like news articles or blog posts.
Stars
84
Forks
10
Language
Python
License
MIT
Category
Last pushed
Feb 28, 2026
Commits (30d)
0
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