How can I build robust, collaborative LLM agents?
Autono: A ReAct-Based Highly Robust Autonomous Agent Framework
This paper introduces Autono, a new framework for building robust, multi-agent AI systems for complex tasks. It uses the ReAct (Reasoning + Acting) paradigm, allowing agents to dynamically adapt their actions based on past experiences and real-time feedback.
Key features for LLM-based multi-agent systems include: dynamic action generation, a "timely abandonment" strategy to prevent agents from getting stuck on unproductive tasks, a memory sharing mechanism for collaboration, and compatibility with the Model Context Protocol (MCP) for standardized communication with external tools and LLMs. These features aim to improve robustness, flexibility, and efficiency compared to existing frameworks like LangChain and AutoGen.