AutoGen

AutoGen

https://microsoft.github.io/autogen/0.2/docs/Getting-Started

 

AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.

Main Features

• AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
• It supports diverse conversation patterns for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy, the number of agents, and agent conversation topology.
• It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.

AutoGen is powered by collaborative research studies from Microsoft, Penn State University, and University of Washington.

 

Agents

https://microsoft.github.io/autogen/0.2/docs/tutorial

Agents

In AutoGen, an agent is an entity that can send and receive messages to and from other agents in its environment. An agent can be powered by models (such as a large language model like GPT-4), code executors (such as an IPython kernel), human, or a combination of these and other pluggable and customizable components.

An example of such agents is the built-in ConversableAgent which supports the following components:

1. A list of LLMs
2. A code executor
3. A function and tool executor
4. A component for keeping human-in-the-loop