Rethinking Multi-Agent Workflows
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This paper challenges the assumption that complex tasks require multiple specialized AI agents, demonstrating that a single LLM agent through iterative dialogue can match the performance of homogeneous multi-agent workflows while gaining efficiency from KV cache reuse. - **Single-agent hypothesis:** The research tests whether multi-agent systems truly require multiple agents or if a single agent engaging in multi-turn conversations can replicate their performance, finding the latter holds across diverse benchmarks. - **Comprehensive evaluation:** Testing across seven benchmarks spanning coding, math, QA, domain reasoning, and planning tasks demonstrates the single-agent approach consistently matches multi-agent performance. - **OneFlow algorithm:** The paper introduces OneFlow, an algorithm that automatically optimizes workflows for single-agent execution, enabling practitioners to simplify complex multi-agent architectures without sacrificing capability. - **Efficiency through KV cache reuse:** Single-agent implementations gain substantial efficiency advantages by reusing key-value caches across conversation turns, reducing inference costs compared to multi-agent orchestration overhead. - **Future directions:** The work identifies that truly heterogeneous systems using different specialized LLMs remain an open research opportunity, as current multi-agent benefits may only emerge when agents have genuinely different capabilities.
Single-agent hypothesis: The research tests whether multi-agent systems truly require multiple agents or if a single agent engaging in multi-turn conversations can replicate their performance, finding that the latter holds across diverse benchmarks.
Comprehensive evaluation: Testing across seven benchmarks spanning coding, math, QA, domain reasoning, and planning tasks demonstrates that the single-agent approach consistently matches multi-agent performance.
OneFlow algorithm: The paper introduces OneFlow, an algorithm that automatically optimizes workflows for single-agent execution, enabling practitioners to simplify complex multi-agent architectures without sacrificing capability.
Efficiency through KV cache reuse: Single-agent implementations gain substantial efficiency advantages by reusing key-value caches across conversation turns, reducing inference costs compared to multi-agent orchestration overhead.
Future directions: The work identifies that truly heterogeneous systems using different specialized LLMs remain an open research opportunity, as current multi-agent benefits may only emerge when agents have genuinely different capabilities.
Abstract
Recent advances in LLM-based multi-agent systems (MAS) show that workflows composed of multiple LLM agents with distinct roles, tools, and communication patterns can outperform single-LLM baselines on complex tasks. However, most frameworks are homogeneous, where all agents share the same base LLM and differ only in prompts, tools, and positions in the workflow. This raises the question of whether such workflows can be simulated by a single agent through multi-turn conversations. We investigate this across seven benchmarks spanning coding, mathematics, general question answering, domain-specific reasoning, and real-world planning and tool use. Our results show that a single agent can reach the performance of homogeneous workflows with an efficiency advantage from KV cache reuse, and can even match the performance of an automatically optimized heterogeneous workflow. Building on this finding, we propose \textbf{OneFlow}, an algorithm that automatically tailors workflows for single-agent execution, reducing inference costs compared to existing automatic multi-agent design frameworks without trading off accuracy. These results position the single-LLM implementation of multi-agent workflows as a strong baseline for MAS research. We also note that single-LLM methods cannot capture heterogeneous workflows due to the lack of KV cache sharing across different LLMs, highlighting future opportunities in developing \textit{truly} heterogeneous multi-agent systems.
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