Tech Research Agent operates by first receiving a research query, then dispatching web searches via Google Serp API. It crawls result URLs, extracts code samples and textual content, applies natural language processing for summarization, and builds a knowledge graph of key concepts. Using OpenAI GPT, it synthesizes findings into coherent technical reports in markdown format. It supports customization of search depth, summarization granularity, and output templates. With built-in caching and parallel processing, the agent accelerates large-scale literature reviews, API explorations, and competitive analysis, enabling users to quickly identify trends, best practices, and relevant code examples for technology evaluation.

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.

Jurn.ai is an AI-based academic research summarization tool. It helps researchers, students, and professionals in academia efficiently extract and summarize key information from academic papers. By using advanced algorithms, this tool identifies the vital points in an article, saving time and effort. With Jurn.ai, users can easily grasp the essence of numerous papers, facilitating a faster and more effective literature review process.