Agent Laboratory: Using LLMs as Research Assistants

First and foremost, Agent Laboratory is meant to assist you as the human researcher toward implementing your research ideas. You are the pilot. Agent Laboratory provides a structured framework that adapts to your computational resources, whether you're running it on a MacBook or on a GPU cluster. Agent Laboratory consists of specialized agents driven by large language models to support you through the entire research workflow—from conducting literature reviews and formulating plans to executing experiments and writing comprehensive reports. This system is not designed to replace your creativity but to complement it, enabling you to focus on ideation and critical thinking while automating repetitive and time-intensive tasks like coding and documentation. By accommodating varying levels of computational resources and human involvement, Agent Laboratory aims to accelerate scientific discovery and optimize your research productivity.

Agent Laboratory consists of three primary phases that systematically guide the research process: (1) Literature Review, (2) Experimentation, and (3) Report Writing. During each phase, specialized agents driven by LLMs collaborate to accomplish distinct objectives, integrating external tools like arXiv, Hugging Face, Python, and LaTeX to optimize outcomes. This structured workflow begins with the independent collection and analysis of relevant research papers, progresses through collaborative planning and data preparation, and results in automated experimentation and comprehensive report generation. Details on specific agent roles and their contributions across these phases are discussed in the paper. The modular design ensures compute flexibility, accommodating diverse resource availability while maintaining efficiency in generating high-quality research artifacts.

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