How can I build faster, more efficient LLM multi-agent systems?
DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems
DynTaskMAS is a new framework for coordinating multiple AI agents working together on complex tasks, particularly using Large Language Models (LLMs). It dynamically breaks down big jobs into smaller subtasks, arranges them in a graph showing which tasks depend on others, and then efficiently distributes these subtasks to different LLM agents working in parallel. This asynchronous and parallel execution improves efficiency, especially for resource-intensive LLMs, by better managing context sharing and adapting to changing needs throughout the task's lifecycle. A key feature is its ability to handle complex, ever-changing tasks and maintain meaningful communication between specialized agents. Experiments show significant improvements in execution time, resource usage, and scalability compared to traditional methods.