Inspiration
Academic research requires reading lengthy papers, understanding complex methodologies, finding important results, and exploring references. We wanted to make this process faster and easier for students and researchers. This inspired us to build ScholarPulse, an AI-powered research companion.
What it does
ScholarPulse analyzes academic papers and converts them into structured and easy-to-understand insights. It can generate summaries, identify key contributions, extract findings and research gaps, and analyze citation relationships using specialized AI agents.
How we built it
We built ScholarPulse as a web-based multi-agent AI application. The frontend provides an interactive interface, while the Python backend handles paper processing and AI-based analysis. An orchestrator coordinates specialized agents such as the Paper Analyzer, Summarizer, Insight Extractor, and Citation Tracker.
Challenges we ran into
We faced challenges in extracting meaningful information from academic papers, maintaining context during AI processing, coordinating multiple agents, integrating frontend and backend components, and configuring external APIs and environment variables.
Accomplishments that we're proud of
We successfully developed a unified research assistant that combines paper analysis, summarization, insight extraction, and citation intelligence. We are proud of creating a modular multi-agent architecture that can be extended with additional research capabilities.
What we learned
We learned how to build multi-agent AI applications, integrate LLM APIs, process academic documents, develop frontend-backend communication, design modular software architecture, and use Git and GitHub for collaborative development.
What's next for ScholarPulse: AI Research & Citation Assistant
We plan to add multi-paper comparison, interactive citation network visualization, research paper Q&A, PDF and Markdown report export, personal research libraries, and advanced academic search and filtering.
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