  Large language models (LLMs) have enabled remarkable advances in automated
task-solving with multi-agent systems. However, most existing LLM-based
multi-agent approaches rely on predefined agents to handle simple tasks,
limiting the adaptability of multi-agent collaboration to different scenarios.
Therefore, we introduce AutoAgents, an innovative framework that adaptively
generates and coordinates multiple specialized agents to build an AI team
according to different tasks. Specifically, AutoAgents couples the relationship
between tasks and roles by dynamically generating multiple required agents
based on task content and planning solutions for the current task based on the
generated expert agents. Multiple specialized agents collaborate with each
other to efficiently accomplish tasks. Concurrently, an observer role is
incorporated into the framework to reflect on the designated plans and agents'
responses and improve upon them. Our experiments on various benchmarks
demonstrate that AutoAgents generates more coherent and accurate solutions than
the existing multi-agent methods. This underscores the significance of
assigning different roles to different tasks and of team cooperation, offering
new perspectives for tackling complex tasks. The repository of this project is
available at https://github.com/Link-AGI/AutoAgents.
