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.

Multi-Agent AI Researcher provides a modular, extensible framework where users can configure and deploy multiple AI agents to collaboratively tackle complex scientific inquiries. It includes a hypothesis generation agent that proposes research directions based on literature analysis, an experiment simulation agent that models and tests hypotheses, a data analysis agent that processes simulation outputs, and a drafting agent that compiles findings into structured research documents. With plugin support, users can incorporate custom models and data sources. The orchestrator manages agent interactions, logging each step for traceability. Ideal for automating repetitive tasks and accelerating R&D workflows, it ensures reproducibility and scalability across diverse research domains.