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.

Deep Research AI Agent is an open-source Python framework designed for conducting comprehensive research tasks. It leverages integrated web search, PDF ingestion, and NLP pipelines to discover relevant sources, parse technical documents, and extract structured insights. The agent chains requests through LangChain and OpenAI, enabling context-aware question answering, automated citation formatting, and multi-document summarization. Researchers can adjust search scopes, filter by publication date or domain, and output reports in markdown or JSON. This tool minimizes manual literature review time and ensures consistent, high-quality summaries across diverse research domains.