title: ModelScope-Agent: Building Your Customizable Agent System with
    Open-source Large Language Models
summary: Large language models (LLMs) have recently demonstrated remarkable
  capabilities to comprehend human intentions, engage in reasoning, and design
  planning-like behavior. To further unleash the power of LLMs to accomplish
  complex tasks, there is a growing trend to build agent framework that equips
  LLMs, such as ChatGPT, with tool-use abilities to connect with massive external
  APIs. In this work, we introduce ModelScope-Agent, a general and customizable
  agent framework for real-world applications, based on open-source LLMs as
  controllers. It provides a user-friendly system library, with customizable
  engine design to support model training on multiple open-source LLMs, while
  also enabling seamless integration with both model APIs and common APIs in a
  unified way. To equip the LLMs with tool-use abilities, a comprehensive
  framework has been proposed spanning over tool-use data collection, tool
  retrieval, tool registration, memory control, customized model training, and
  evaluation for practical real-world applications. Finally, we showcase
  ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based
  on the ModelScope-Agent framework, which is able to connect open-source LLMs
  with more than 1000 public AI models and localized community knowledge in
  ModelScope. The ModelScope-Agent
  libraryhttps://github.com/modelscope/modelscope-agent and online
  demohttps://modelscope.cn/studios/damo/ModelScopeGPT/summary are now
  publicly available.
paper_authors._id: 64f7eafcd04a890f535d701b
paper_authors.name: Chenliang Li
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paper_authors.name: Hehong Chen
paper_authors._id: 64f7eafcd04a890f535d701d
paper_authors.name: Ming Yan
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paper_authors.fullname: Ming
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paper_authors.user: MingYan123
paper_authors._id: 64f7eafcd04a890f535d701e
paper_authors.name: Weizhou Shen
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paper_authors.fullname: Weizhou Shen
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paper_authors.user: shenwzh3
paper_authors._id: 64f7eafcd04a890f535d701f
paper_authors.name: Haiyang Xu
paper_authors.status: admin_assigned
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paper_authors.fullname: xuhaiyang
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paper_authors.user: xhyandwyy
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paper_authors.name: Zhikai Wu
paper_authors._id: 64f7eafcd04a890f535d7021
paper_authors.name: Zhicheng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-06T11:28:27.111Z
paper_authors._id: 6153b6dea926b86b4f55cce5
paper_authors.avatarUrl: /avatars/0d1f699fd6f1f9ce5aeedb6334030763.svg
paper_authors.fullname: Zhicheng Zhang
paper_authors.type: user
paper_authors.user: zzclynn
paper_authors._id: 64f7eafcd04a890f535d7022
paper_authors.name: Wenmeng Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-06T11:28:35.686Z
paper_authors._id: 623c6253389748c9f72ca287
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1654828369523-623c6253389748c9f72ca287.jpeg
paper_authors.fullname: wenmeng zhou
paper_authors.type: user
paper_authors.user: wenmengzhou
paper_authors._id: 64f7eafcd04a890f535d7023
paper_authors.name: Yingda Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Yingda Chen
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paper_authors.user: Yingda
paper_authors._id: 64f7eafcd04a890f535d7024
paper_authors.name: Chen Cheng
paper_authors._id: 64f7eafcd04a890f535d7025
paper_authors.name: Hongzhu Shi
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paper_authors.fullname: Hongzhu Shi
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paper_authors.name: Ji Zhang
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paper_authors.name: Fei Huang
paper_authors._id: 64f7eafcd04a890f535d7028
paper_authors.name: Jingren Zhou
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paper_authors.type: user
paper_authors.user: Jingren
paper_title: ModelScope-Agent: Building Your Customizable Agent System with
    Open-source Large Language Models
paper_summary: Large language models (LLMs) have recently demonstrated remarkable
  capabilities to comprehend human intentions, engage in reasoning, and design
  planning-like behavior. To further unleash the power of LLMs to accomplish
  complex tasks, there is a growing trend to build agent framework that equips
  LLMs, such as ChatGPT, with tool-use abilities to connect with massive external
  APIs. In this work, we introduce ModelScope-Agent, a general and customizable
  agent framework for real-world applications, based on open-source LLMs as
  controllers. It provides a user-friendly system library, with customizable
  engine design to support model training on multiple open-source LLMs, while
  also enabling seamless integration with both model APIs and common APIs in a
  unified way. To equip the LLMs with tool-use abilities, a comprehensive
  framework has been proposed spanning over tool-use data collection, tool
  retrieval, tool registration, memory control, customized model training, and
  evaluation for practical real-world applications. Finally, we showcase
  ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based
  on the ModelScope-Agent framework, which is able to connect open-source LLMs
  with more than 1000 public AI models and localized community knowledge in
  ModelScope. The ModelScope-Agent
  libraryhttps://github.com/modelscope/modelscope-agent and online
  demohttps://modelscope.cn/studios/damo/ModelScopeGPT/summary are now
  publicly available.
paper_ai_summary: A customizable agent framework, ModelScope-Agent, equips large language models with tool-use capabilities for real-world applications by integrating them with extensive external APIs and supporting model training on multiple open-source models.
paper_ai_keywords: large language models LLMs agent framework ChatGPT tool-use abilities API integration tool-use data collection tool retrieval tool registration memory control customized model training evaluation intelligent assistant ModelScopeGPT