Autogen: LLM with multiple agents

What’s New: AutoGen is an innovative framework designed to facilitate the development of large language model (LLM) applications that utilize multiple conversational agents. It simplifies the orchestration and automation of complex LLM workflows and provides support for diverse conversation patterns. By integrating various agents, including LLMs, human inputs, and tools, AutoGen offers a versatile platform for creating next-generation LLM applications.

Why Does It Matter: AutoGen’s ability to enable multi-agent conversations is significant for the development of intelligent systems. It allows for the creation of applications that can collectively perform tasks autonomously or with human feedback. This framework is particularly valuable for applications that require interaction with tools via code. With customizable agents and seamless human participation, AutoGen opens the door to a wide range of applications in domains like autonomous driving, computer vision, and more.

How It Works: AutoGen leverages a multi-agent conversation framework, offering customizable and conversable agents. These agents can integrate LLMs, tools, and human input. By automating chat among these agents, complex tasks can be efficiently performed. Users can customize the agents to meet specific application requirements, choose LLMs, specify types of human input, and determine the tools to employ. AutoGen also provides enhanced LLM inference, allowing users to optimize generations, perform tuning, and more.

Features:

• Multi-agent conversations: AutoGen agents can communicate with each other, enabling the development of applications that require interaction between multiple agents.
• Customization: AutoGen agents can be tailored to meet specific application needs, allowing users to choose LLMs, human input types, and tools.
• Human participation: AutoGen seamlessly integrates human input, providing the flexibility for users to participate in the conversation between agents.
• Enhanced LLM inference: The framework offers advanced functionalities like tuning, caching, error handling, and templating for optimizing LLM generations.

AutoGen simplifies the development of next-generation LLM applications by providing a powerful and versatile platform for multi-agent conversations.

More:

In addition to these features, AutoGen is a product of collaborative research studies from Microsoft, Penn State University, and the University of Washington. It empowers developers to build intelligent systems that can tackle complex tasks through conversation and interaction with LLMs, tools, and human input. AutoGen’s documentation provides comprehensive information, research, and guidance for those interested in leveraging this framework for their projects. The framework’s citation is also available for reference, making it a valuable resource for researchers and developers in the field of large language models. Contributions and suggestions are welcomed, and users can get involved in the development process by following the provided guidelines and the Microsoft Open Source Code of Conduct. AutoGen represents an important advancement in the field of intelligent systems and opens up new possibilities for applications in various domains.