Seamless is an AI-driven tool designed to facilitate comprehensive literature reviews, primarily for scientific research. By tapping into the vast database of Semantic Scholar, it provides users with quick, reliable, and relevant scientific papers. Its intuitive interface and powerful AI capabilities make it an invaluable tool for researchers looking to enhance productivity and accuracy in their literature review process. From uncovering key studies to linking relevant research, Seamless aims to make the daunting task of literature review straightforward and efficient.

FreeThinker provides a modular architecture for defining AI agents that can autonomously execute tasks by leveraging large language models, memory modules, and external tools. Developers can configure agents via Python or YAML, plug in custom tools for web search, data processing, or API calls, and utilize built-in planning strategies. The framework handles step-by-step execution, context retention, and result aggregation so agents can operate hands-free on research, automation, or decision-support workflows.

Project Mariner utilizes cutting-edge AI technologies to automate the extraction and analysis of large datasets. By streamlining these processes, it enables organizations to save time and resources while improving accuracy. The AI agent supports various applications, making it adaptable to different industries, from research to business management. With its advanced algorithms, Mariner can handle complex data structures, providing valuable insights and facilitating better decision-making.

Powerful Auto Researcher is a Python-based AI agent framework designed to automate and accelerate research workflows. Users define topics or initial questions, and the agent iteratively generates follow-up questions, submits them to OpenAI models, and aggregates the responses. It supports customizable prompt templates, workflow chaining, and automated export to Markdown, JSON, or PDF. Integrated logging and result management enable reproducibility. This tool is ideal for academic literature reviews, competitive intelligence gathering, market research, and technical deep dives, reducing manual overhead and ensuring systematic coverage of research questions.