modelscope-agent

Introduction to ModelScope-Agent

ModelScope-Agent is a cutting-edge and adaptable framework designed for creating customizable agent systems using open-source large language models (LLMs). It offers a straightforward approach to implementing single agents, each equipped with capabilities for role-playing, LLM invocation, tool usage, planning, and memory management. Here's a breakdown of what this remarkable framework offers:

Simple Agent Implementation: Users can effortlessly create an agent by specifying role instructions, LLM names, and a list of tools. The framework automates the workflow arrangement for tool usage, planning, and memory management.
Diverse Models and Tools: The framework provides access to various LLM interfaces, including Dashscope and Modelscope, alongside OpenAI model interfaces. It also includes several built-in tools such as code interpreters, weather queries, text-to-image conversion, and web browsing. This vast array simplifies the customization of specialized agents.
Unified Interface and High Scalability: With a clear tool and LLM registration mechanism, users can easily expand their agent applications to include more diverse functionalities.
Low Coupling: Developers can utilize built-in tools, LLMs, memory, and other components without being tied to higher-level agents.

Recent Updates

Here are some exciting recent developments in the ModelScope-Agent project:

CodexGraph: A new graph-based code generation tool called CodexGraph was released on August 8, 2024. It's effective for various code-related tasks.
Data Science Assistant: A reliable Data Science Assistant now runs on ModelScope-Agent, offering high efficiency.
Parallel Tool Calling: This feature is now available on the ModelScope-Agent-Server, enhancing performance.
Hybrid Searching: RAG flow updates now enable hybrid searching of knowledge using different strategies and modalities.
Mobile-Agent-V2 Support: This feature supports the Android ADB environment for mobile applications.
Multi-role Room Chat: Multi-role interactions are now supported, enhancing communication dynamics.

Installation

To get started with ModelScope-Agent, you need to clone the repository and install the necessary dependencies:

git clone https://github.com/modelscope/modelscope-agent.git
cd modelscope-agent && pip install -r requirements.txt

Alternatively, for a more seamless setup, the ModelScope Notebook offers a free-tier environment to run applications like FaceChain with minimal effort.

Quickstart

Using ModelScope-Agent is intuitive. By initializing a RolePlay object with specific tasks and leveraging various tools, users can easily fulfill their needs. For instance, configuring an agent to query the weather and generate related visual content is just a matter of setting up appropriate variables and running the program.

Key Components

Agent

An agent in ModelScope comprises:

LLM: A large language model to process inputs and execute tool calls.
Function List: A list of tools available for use by the agent.

Agents are initialized with various configurations and can incorporate custom workflows and components.

LLM

Large language models form the core of the interaction experience, determining the quality of outcomes. Users can select from providers like Dashscope, Zhipu, or OpenAI, or customize their models.

Tool

ModelScope-Agent includes a variety of built-in tools for tasks ranging from image generation to weather forecasting. Users can also create custom tools for specific needs.

Community Engagement

ModelScope-Agent thrives on community contributions. You're encouraged to share your developments or report any issues. For any inquiries, you can reach out via email or join the communication group for ongoing support.

Conclusion

ModelScope-Agent represents an innovative step forward in customizable agent systems. Its user-friendly design and comprehensive features make it an invaluable tool for developers standing at the intersection of AI and practical applications.