ModelScope-Agent
M
Modelscope Agent
Overview :
ModelScope-Agent is a customizable and extensible agent framework equipped with role-playing, large language model invocation, tool utilization, planning, and memory capabilities. It simplifies the implementation of agent applications by providing rich model and tool interfaces. Its unified interface and high extensibility, coupled with low coupling, allow developers to easily utilize built-in tools, LLMs, and memory components without being bound to higher-level agents.
Target Users :
ModelScope-Agent is designed for developers looking to build and customize agent systems, particularly those who need to leverage large language models and tools to automate complex tasks. It caters to enterprises and developers who require highly customized solutions, as well as teams seeking to enhance efficiency and innovation through technology.
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Use Cases
Constructing a Facechain Agent capable of generating personalized portraits using ModelScope-Agent.
Integrating a Story Agent for generating illustrations for storybooks in a local version.
Utilizing the local version of ModelScope GPT, run through gradio, to achieve local deployment and usage.
Features
Simple agent implementation: Achieve agent applications by simply specifying role instructions, LLM names, and tool name lists.
Rich models and tools: The framework integrates numerous LLM interfaces, including Dashscope, Modelscope model interfaces, and OpenAI model interfaces.
Unified interface and high extensibility: The framework features a clear tool and LLM registration mechanism, facilitating the expansion of diverse agent applications.
Low coupling: Developers can readily use built-in tools, LLMs, and memory components without being tied to higher-level agents.
Support for mobile agents: Mobile-Agent-V2 based on the Android Adb environment provides a broader range of application scenarios.
User-friendly assistant API: Offers a tool API for executing utilities within isolated and secure containers.
How to Use
Clone the repository and install dependencies: `cd modelscope-agent && pip install -r requirements.txt`
Utilize ModelScope Notebook, download and upload the demo file to the GPU, and execute the demo notebook in sequence.
Initialize a RolePlay object, configure the LLM and tool lists, and run the agent to complete specific tasks.
Customize the agent's workflow as needed, including message/prompt generation, invoking LLM(s), and invoking tools based on LLM results.
Implement custom agents and LLM configurations by inheriting from the base classes Agent and LLM.
Execute specific functionalities using built-in tools or custom tools based on the agent's requirements.
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