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 framework designed to automate every stage of the research process. By chaining web scraping modules, language model-based summarizers, and insight extraction pipelines, it gathers data from online articles, academic journals, and custom sources. It supports GPT-3.5, GPT-4, and other OpenAI models, allowing users to tailor question prompts and memory settings to their needs. After extracting key points and citations, it organizes information into comprehensive markdown or PDF reports. Researchers can extend its capabilities with plugins for database integration, API-based data retrieval, and custom analysis functions. This agent streamlines literature reviews, market research, and technical due diligence, reducing manual effort and ensuring consistent, high-quality outputs.