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.

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.

OpenWebResearcher acts as an autonomous web research assistant by orchestrating a pipeline of web crawling, data extraction, and AI-driven summarization. After configuration, the agent navigates target sites, identifies relevant content via heuristics or user-defined criteria, and retrieves structured data. It then employs large language models to analyze, filter, and distill key insights, generating bullet-point summaries or detailed reports. Users can customize scraping parameters, integrate custom plugins for specialized processing, and schedule recurring research tasks. The modular architecture lets developers extend capabilities with new parsers or output formats. Ideal for competitive intelligence, academic literature reviews, market analysis, and content monitoring, OpenWebResearcher reduces the time spent on manual data gathering and synthesis.