TPTU: Task Planning and Tool Usage of Large Language Model-based AI
  Agents
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.
64d1ad3f8749302d8186fc88
Jingqing Ruan
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2025-05-22T08:47:16.065Z
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Jingqing Ruan
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Amanda2023
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Yihong Chen
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Bin Zhang
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Zhiwei Xu
64d1ad3f8749302d8186fc8c
Tianpeng Bao
64d1ad3f8749302d8186fc8d
Guoqing Du
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Shiwei Shi
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Hangyu Mao
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2023-11-22T02:13:25.888Z
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Hangyu Mao
user
HangyuMao
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Xingyu Zeng
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Rui Zhao
extracted_confirmed
2025-06-08T11:28:31.622Z
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/avatars/befc09d3bc82685c8e205dcadfec13c0.svg
Rui Zhao
user
Robert0812
TPTU: Task Planning and Tool Usage of Large Language Model-based AI
  Agents
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.
A structured framework for LLM-based AI Agents is proposed to enhance task planning and tool usage for complex real-world applications.
Large Language Models LLMs AI Agents task planning tool usage one-step agent sequential agent TPTU inference process