Paper Summarizer is an AI-powered command-line application designed to process academic papers and produce concise, structured summaries. It leverages OpenAI’s GPT API to analyze documents, extracting essential sections such as abstract, introduction, methods, results, and conclusion. Users can customize summary length and choose output formats like markdown or plain text. The tool supports batch processing of multiple files, making it easy to integrate into existing research workflows. By condensing complex research into clear, digestible overviews, Paper Summarizer helps users quickly grasp core insights and improve productivity without sacrificing accuracy.

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.