ModelScope-Agent: Building Your Customizable Agent System with
  Open-source Large Language Models
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.
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Chenliang Li
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2023-09-06T11:26:02.596Z
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li
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chenliangli
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Hehong Chen
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Ming Yan
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Ming
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MingYan123
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Weizhou Shen
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Weizhou Shen
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shenwzh3
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Haiyang Xu
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xuhaiyang
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xhyandwyy
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Zhikai Wu
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Zhicheng Zhang
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Zhicheng Zhang
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Wenmeng Zhou
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2023-09-06T11:28:35.686Z
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wenmeng zhou
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wenmengzhou
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Yingda Chen
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Yingda Chen
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Yingda
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Chen Cheng
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Hongzhu Shi
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Hongzhu Shi
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vivianvolvo
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Ji Zhang
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Fei Huang
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Jingren Zhou
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Zhou
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Jingren
ModelScope-Agent: Building Your Customizable Agent System with
  Open-source Large Language Models
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.
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.
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