title: Agent Laboratory: Using LLM Agents as Research Assistants
summary: 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.
paper_authors._id: 677f4d709e9ddbcae5ce5364
paper_authors.name: Samuel Schmidgall
paper_authors._id: 677f4d709e9ddbcae5ce5365
paper_authors.name: Yusheng Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:22:29.469Z
paper_authors._id: 632181ab2284fb8b5d39fc07
paper_authors.avatarUrl: /avatars/467c7982b6b9b25689ebf677926f6ce0.svg
paper_authors.fullname: Yusheng Su
paper_authors.type: user
paper_authors.user: yushengsu
paper_authors._id: 677f4d709e9ddbcae5ce5366
paper_authors.name: Ze Wang
paper_authors._id: 677f4d709e9ddbcae5ce5367
paper_authors.name: Ximeng Sun
paper_authors._id: 677f4d709e9ddbcae5ce5368
paper_authors.name: Jialian Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:24:01.650Z
paper_authors._id: 66a966af504d582c8c65a2ee
paper_authors.avatarUrl: /avatars/710df9f4637b9188c8d0d58d488caa9b.svg
paper_authors.fullname: Jialian Wu
paper_authors.type: user
paper_authors.user: jialianww
paper_authors._id: 677f4d709e9ddbcae5ce5369
paper_authors.name: Xiaodong Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:23:43.262Z
paper_authors._id: 66f31375a3a9e600302f25d7
paper_authors.avatarUrl: /avatars/c049575a5d17eeb8b76a5be5de7a5392.svg
paper_authors.fullname: Xiaodong Yu
paper_authors.type: user
paper_authors.user: xiaodongyu-amd
paper_authors._id: 677f4d709e9ddbcae5ce536a
paper_authors.name: Jiang Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:23:35.567Z
paper_authors._id: 6328885bb0910efc277f42a0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1663600974191-6328885bb0910efc277f42a0.jpeg
paper_authors.fullname: JiangLiu
paper_authors.type: user
paper_authors.user: JiangLiu
paper_authors._id: 677f4d709e9ddbcae5ce536b
paper_authors.name: Zicheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:23:28.447Z
paper_authors._id: 6569f6cb44ce94a70187f407
paper_authors.avatarUrl: /avatars/414ecc9739ed23f9f395bd7cfa65055f.svg
paper_authors.fullname: Zicheng Liu
paper_authors.type: user
paper_authors.user: ZCLiu35
paper_authors._id: 677f4d709e9ddbcae5ce536c
paper_authors.name: Emad Barsoum
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:23:21.569Z
paper_authors._id: 65adc9d086f88a686be41215
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65adc9d086f88a686be41215/xizVHuZPkE0Gu8_ulx0Fm.jpeg
paper_authors.fullname: Emad Barsoum
paper_authors.type: user
paper_authors.user: ebarsoum
paper_title: Agent Laboratory: Using LLM Agents as Research Assistants
paper_summary: 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.
paper_ai_keywords: LLM-based Agent Laboratory literature review experimentation report writing research outputs code repository research report state-of-the-art LLMs machine learning code state-of-the-art performance human feedback research expenses creative ideation low-level coding writing