  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.
