AutoGen
AutoGen by Microsoft is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. AutoGen is powered by collaborative research studies from Microsoft, Penn State University, and the University of Washington. 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. AutoGen 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. AutoGen provides a collection of working systems with different complexities that span a wide range of applications from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns. AutoGen provides a drop-in replacement of openai.Completion or openai.ChatCompletion as an enhanced inference API. It allows easy performance tuning, utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
Use Case: LLM Tools ML Framework Open Source