AutoGen


Microsoft AutoGen is an open-source toolkit for building autonomous multi-agent systems. It provides a flexible and scalable framework for creating agent-based applications that can interact, collaborate, and learn from each other. AutoGen simplifies the development of complex agent architectures by offering high-level abstractions, predefined agent types, and communication patterns, enabling developers to focus on agent behaviors and interactions.

Microsoft AutoGen offers two primary APIs: AgentChat and Core. Each serves distinct purposes and caters to different levels of application complexity and control. Below is a comparison of their key features:

Aspect AgentChat Core
Purpose Provides a high-level framework for building interactive agent applications with preset agents, facilitating quick development. Offers a foundational, unopinionated, and flexible API for creating scalable, event-driven agent workflows, granting developers full control over agent behaviors and interactions.
Complexity Simplifies development by offering predefined agents and configurations, making it suitable for straightforward applications. Requires more detailed setup and configuration, ideal for complex applications needing customized agent behaviors and interactions.
Customization Allows for some customization through preset agents and configurations but is primarily designed for rapid application development. Enables extensive customization, allowing developers to define unique agent types, message handlers, and communication patterns tailored to specific application needs.
Scalability Suitable for applications with a limited number of agents and simpler interaction patterns. Designed to support scalable applications, capable of managing numerous agents with complex interaction patterns, including distributed deployments.
Use Cases Ideal for quickly setting up applications that require basic agent interactions, such as simple chatbots or assistants. Best suited for applications that demand intricate agent workflows, advanced message routing, and fine-grained control over agent lifecycles, such as large-scale multi-agent systems.
Learning Curve Lower learning curve due to high-level abstractions and preset configurations, enabling faster development. Steeper learning curve owing to its foundational nature, requiring a deeper understanding of agent-based architectures and event-driven programming.
Integration Integrates with the Core API, utilizing its runtime environment for message handling and agent management, but abstracts many complexities. Serves as the underlying framework upon which AgentChat is built, providing the essential components for agent communication, lifecycle management, and message routing.
Examples Creating a simple assistant agent that can perform tasks using predefined tools. Implementing a distributed multi-agent system where agents have specialized roles and communicate through custom message types and handlers.

Hands-On


Distributed by an MIT license. This hands-on lab was developed by Microsoft AI GBB (Global Black Belt).