Agent Laboratory: Using LLM Agents as Research Assistants
Authors: name: Samuel Schmidgall, name: Yusheng Su, name: Ze Wang, name: Ximeng Sun, name: Jialian Wu, name: Xiaodong Yu, name: Jiang Liu, name: Zicheng Liu, name: Emad Barsoum

Historically, scientific discovery has been a lengthy and costly process,
demanding substantial time and resources from initial conception to final
results. To accelerate scientific discovery, reduce research costs, and improve
research quality, we introduce Agent Laboratory, an autonomous LLM-based
framework capable of completing the entire research process. This framework
accepts a human-provided research idea and progresses through three
stages--literature review, experimentation, and report writing to produce
comprehensive research outputs, including a code repository and a research
report, while enabling users to provide feedback and guidance at each stage. We
deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple
researchers to assess its quality by participating in a survey, providing human
feedback to guide the research process, and then evaluate the final paper. We
found that: (1) Agent Laboratory driven by o1-preview generates the best
research outcomes; (2) The generated machine learning code is able to achieve
state-of-the-art performance compared to existing methods; (3) Human
involvement, providing feedback at each stage, significantly improves the
overall quality of research; (4) Agent Laboratory significantly reduces
research expenses, achieving an 84% decrease compared to previous autonomous
research methods. We hope Agent Laboratory enables researchers to allocate more
effort toward creative ideation rather than low-level coding and writing,
ultimately accelerating scientific discovery.