Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited considerable capability in the realm of natural language processing (NLP) with world knowledge. Benefiting from ultra-large-scale training corpora, a single LLM can manage typical NLP tasks competently. However, its performance in executing complex tasks is still confined by the limitations of its internal representation. To push this boundary further, we introduce Corex, a suite of novel general-purpose strategies that transform LLMs into autonomous agents, pioneering multi-agent collaborations for task-solving. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Discuss, Review, and Retrieve modes, which collectively work towards enhancing the reasoning process. These paradigms foster task-agnostic approaches that enable LLM-based agents to “think outside the box,” thereby overcoming common errors and providing better solutions. Through extensive experiments across four different types of reasoning tasks, we demonstrate that orchestrating multiple agents to work in concert yields better results compared to existing strong methods. Further analysis reveals the cost-effectiveness of Corex, while also exploring synergies between models of various scales and promoting annotation efficiency.