CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs

• URL: http://arxiv.org/abs/2507.03254v1
• Date: Fri, 04 Jul 2025 02:20:19 GMT
• Title: CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs
• Authors: Bruce Yang, Xinfeng He, Huan Gao, Yifan Cao, Xiaofan Li, David Hsu,
• Abstract summary: We introduce CodeAgents, a prompting framework that codifies multi-agent reasoning and enables structured, token-efficient planning in multi-agent systems.<n>Results show consistent improvements in planning performance, with absolute gains of 3-36 percentage points over natural language prompting baselines.
• Score: 16.234259194402163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective prompt design is essential for improving the planning capabilities of large language model (LLM)-driven agents. However, existing structured prompting strategies are typically limited to single-agent, plan-only settings, and often evaluate performance solely based on task accuracy - overlooking critical factors such as token efficiency, modularity, and scalability in multi-agent environments. To address these limitations, we introduce CodeAgents, a prompting framework that codifies multi-agent reasoning and enables structured, token-efficient planning in multi-agent systems. In CodeAgents, all components of agent interaction - Task, Plan, Feedback, system roles, and external tool invocations - are codified into modular pseudocode enriched with control structures (e.g., loops, conditionals), boolean logic, and typed variables. This design transforms loosely connected agent plans into cohesive, interpretable, and verifiable multi-agent reasoning programs. We evaluate the proposed framework across three diverse benchmarks - GAIA, HotpotQA, and VirtualHome - using a range of representative LLMs. Results show consistent improvements in planning performance, with absolute gains of 3-36 percentage points over natural language prompting baselines. On VirtualHome, our method achieves a new state-of-the-art success rate of 56%. In addition, our approach reduces input and output token usage by 55-87% and 41-70%, respectively, underscoring the importance of token-aware evaluation metrics in the development of scalable multi-agent LLM systems. The code and resources are available at: https://anonymous.4open.science/r/CodifyingAgent-5A86

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