AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalization. With this framework, users can build LLM workflows. The agent modularity and conversation-based programming simplifies development and enables reuse for developers. End-users benefit from multiple agents independently learning and collaborating on their behalf, enabling them to accomplish more with less work. Benefits of the multi agent approach with AutoGen include agents that can be backed by various LLM configurations; native support for a generic form of tool usage through code generation and execution; and, a special agent, the Human Proxy Agent that enables easy integration of human feedback and involvement at different levels.
Easily build LLM workflows
With AutoGen, building a complex multi-agent conversation system boils down to:
- Defining a set of agents with specialized capabilities and roles.
- Defining the interaction behavior between agents, i.e., what to reply when an agent receives messages from another agent.
AutoGen is an open-source, community-driven project under active development (as a spinoff from FLAML, a fast library for automated machine learning and tuning), which encourages contributions from individuals of all backgrounds. Many Microsoft Research collaborators have made great contributions to this project, including academic contributors like Pennsylvania State University and the University of Washington, and product teams like Microsoft Fabric and ML.NET. AutoGen aims to provide an effective and easy-to-use framework for developers to build next-generation applications, and already demonstrates promising opportunities to build creative applications and provide a large space for innovation.