The AI Researcher agent acts as a virtual research assistant that automates key phases of scientific inquiry. It begins by accepting a user-defined topic and performing automated literature searches across online databases via integrated web search. It then extracts and summarizes the most relevant papers, highlights core findings, and identifies research gaps. Using these insights, the agent generates novel research questions and proposes experimental design outlines. The framework supports customizable task pipelines, allowing users to adjust search parameters, summarization depth, and idea generation strategies. All interactions occur through a simple command-line interface, leveraging Python scripts and OpenAI APIs. Researchers can review, refine, and export results to accelerate literature reviews and early-stage planning.

The Arxiv GPT Summarizer is a convenient tool designed for researchers and students looking to digest academic papers quickly. By using OpenAI’s language models, it generates summaries directly from the arXiv paper's title and abstract, inserting these insights into the webpage. This allows users to grasp the essential content of papers at a glance, making it ideal for anyone overwhelmed with reading loads of research literature.