ModelScope-Agent: Building Your Customizable Agent System with
  Open-source Large Language Models
Chenliang Li Hehong Chen Ming Yan Weizhou Shen Haiyang Xu Zhikai Wu Zhicheng Zhang Wenmeng Zhou Yingda Chen Chen Cheng Hongzhu Shi Ji Zhang Fei Huang Jingren Zhou
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
library\footnote{https://github.com/modelscope/modelscope-agent} and online
demo\footnote{https://modelscope.cn/studios/damo/ModelScopeGPT/summary} are now
publicly available.