IRIS (Interactive Research Ideation System) is an AI-driven assistant that empowers researchers to rapidly prototype study ideas. Users input a research topic or domain, and IRIS produces tailored research questions, identifies key concepts, synthesizes relevant literature abstracts, and suggests experimental designs or methodological approaches. It organizes these insights into customizable workflows, supporting hypothesis development, data collection planning, and result interpretation frameworks. Through iterative chatting, IRIS refines outputs based on feedback, ensures alignment with research goals, and exports structured reports in formats like PDF, DOCX, or Markdown. By automating repetitive tasks and enhancing creative brainstorming, IRIS accelerates early-stage research across academia, R&D labs, and startups, fostering innovation and reducing time-to-insight.

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.

Virtual Scientists V2 serves as a modular AI agent framework tailored for scientific research. It defines multiple virtual scientists—Chemist, Physicist, Biologist, and Data Scientist—each equipped with domain-specific knowledge and tool integrations. These agents utilize LangChain to orchestrate API calls to sources like Semantic Scholar, ArXiv, and web search, enabling automated literature retrieval, contextual analysis, and data extraction. Users script tasks by specifying research objectives; agents autonomously gather papers, summarize methodologies and results, propose experimental protocols, generate hypotheses, and produce structured reports. The framework supports plugins for custom tools and workflows, promoting extensibility. By automating repetitive research tasks, Virtual Scientists V2 accelerates insight generation and reduces manual effort across multidisciplinary projects.