title: TPTU: Task Planning and Tool Usage of Large Language Model-based AI
    Agents
summary: With recent advancements in natural language processing, Large Language
  Models (LLMs) have emerged as powerful tools for various real-world
  applications. Despite their prowess, the intrinsic generative abilities of LLMs
  may prove insufficient for handling complex tasks which necessitate a
  combination of task planning and the usage of external tools. In this paper, we
  first propose a structured framework tailored for LLM-based AI Agents and
  discuss the crucial capabilities necessary for tackling intricate problems.
  Within this framework, we design two distinct types of agents (i.e., one-step
  agent and sequential agent) to execute the inference process. Subsequently, we
  instantiate the framework using various LLMs and evaluate their Task Planning
  and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings
  and challenges, our goal is to provide a helpful resource for researchers and
  practitioners to leverage the power of LLMs in their AI applications. Our study
  emphasizes the substantial potential of these models, while also identifying
  areas that need more investigation and improvement.
paper_authors._id: 64d1ad3f8749302d8186fc88
paper_authors.name: Jingqing Ruan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T08:47:16.065Z
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paper_authors.avatarUrl: /avatars/9743b5a2b6f9b9990aa1cb06bd6eb5c6.svg
paper_authors.fullname: Jingqing Ruan
paper_authors.type: user
paper_authors.user: Amanda2023
paper_authors._id: 64d1ad3f8749302d8186fc89
paper_authors.name: Yihong Chen
paper_authors._id: 64d1ad3f8749302d8186fc8a
paper_authors.name: Bin Zhang
paper_authors._id: 64d1ad3f8749302d8186fc8b
paper_authors.name: Zhiwei Xu
paper_authors._id: 64d1ad3f8749302d8186fc8c
paper_authors.name: Tianpeng Bao
paper_authors._id: 64d1ad3f8749302d8186fc8d
paper_authors.name: Guoqing Du
paper_authors._id: 64d1ad3f8749302d8186fc8e
paper_authors.name: Shiwei Shi
paper_authors._id: 64d1ad3f8749302d8186fc8f
paper_authors.name: Hangyu Mao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-11-22T02:13:25.888Z
paper_authors._id: 655d62c5ea26da0aca92746d
paper_authors.avatarUrl: /avatars/74144ab0d0c5c23769cfc2f33ad87a7a.svg
paper_authors.fullname: Hangyu Mao
paper_authors.type: user
paper_authors.user: HangyuMao
paper_authors._id: 64d1ad3f8749302d8186fc90
paper_authors.name: Xingyu Zeng
paper_authors._id: 64d1ad3f8749302d8186fc91
paper_authors.name: Rui Zhao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-08-08T02:49:37.335Z
paper_authors._id: 6405d7cca577649430bcceb9
paper_authors.avatarUrl: /avatars/befc09d3bc82685c8e205dcadfec13c0.svg
paper_authors.fullname: Rui Zhao
paper_authors.type: user
paper_authors.user: Robert0812
paper_title: TPTU: Task Planning and Tool Usage of Large Language Model-based AI
    Agents
paper_summary: With recent advancements in natural language processing, Large Language
  Models (LLMs) have emerged as powerful tools for various real-world
  applications. Despite their prowess, the intrinsic generative abilities of LLMs
  may prove insufficient for handling complex tasks which necessitate a
  combination of task planning and the usage of external tools. In this paper, we
  first propose a structured framework tailored for LLM-based AI Agents and
  discuss the crucial capabilities necessary for tackling intricate problems.
  Within this framework, we design two distinct types of agents (i.e., one-step
  agent and sequential agent) to execute the inference process. Subsequently, we
  instantiate the framework using various LLMs and evaluate their Task Planning
  and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings
  and challenges, our goal is to provide a helpful resource for researchers and
  practitioners to leverage the power of LLMs in their AI applications. Our study
  emphasizes the substantial potential of these models, while also identifying
  areas that need more investigation and improvement.
paper_ai_summary: A structured framework for LLM-based AI Agents is proposed to enhance task planning and tool usage for complex real-world applications.
paper_ai_keywords: Large Language Models LLMs AI Agents task planning tool usage one-step agent sequential agent TPTU inference process