AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
It provides a generic infrastructure for developers to create diverse LLM applications, addressing the need for flexible and scalable multi-agent systems, though it is incremental as it builds on existing agent-based approaches.
AutoGen is a framework for building LLM applications using multi-agent conversations, enabling customizable agents that combine LLMs, human inputs, and tools to accomplish tasks across domains like mathematics and coding, with empirical studies showing its effectiveness.
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.