0verL1nk/PaperSage
📦 Open Source Project0verL1nk
An AI-powered research workbench featuring multi-agent workflows and hybrid RAG for deep academic paper analysis.
PaperSage serves as a sophisticated research assistant designed to bridge the gap between massive document repositories and actionable insights. At its core, the platform utilizes a multi-agent architecture orchestrated by LangGraph, allowing for complex reasoning patterns such as ReAct, Plan-Act, and RePlan. This enables the system to break down intricate research queries into manageable steps, ensuring that the AI does not just retrieve information but actively synthesizes it.
The technical stack integrates hybrid RAG (Retrieval-Augmented Generation) to balance keyword-based search with semantic vector similarity, significantly improving retrieval precision. Furthermore, the inclusion of long-term memory allows the agent to maintain context across multiple sessions, which is critical for long-term research projects. The user interface, built with Streamlit, provides a clean, interactive dashboard where users can upload papers, manage projects, and verify AI-generated answers through traceable evidence links. By automating the heavy lifting of literature synthesis, PaperSage allows researchers to focus on critical analysis rather than manual information extraction.
💡Highlights
- ├─Multi-agent ReAct & Plan-Act flows
- ├─Hybrid RAG with traceable evidence
- └─Long-term memory for research context
🎯For
- ├─Academic Researchers
- ├─Data Scientists
- └─Knowledge Workers