Multi-agent systems built on Large Language Models (LLMs) show exceptional promise for complex collaborative problem-solving, yet they face fundamental challenges stemming from context window limitations that impair memory consistency, role adherence, and procedural integrity. This paper introduces Intrinsic Memory Agents, a novel framework that addresses these limitations through agent-specific memories that evolve intrinsically with agent outputs. Specifically, our method maintains role-aligned memory that preserves specialized perspectives while focusing on task-relevant information. Our approach utilises a generic memory template applicable to new problems without the need to hand-craft specific memory prompts. We benchmark our approach on the PDDL, FEVER, and ALFWorld datasets, comparing its performance to existing state-of-the-art multi-agentic memory approaches and showing state-of-the-art or comparable performance across all three, with the highest consistency. An additional evaluation is performed on a complex data pipeline design task, and we demonstrate that our approach produces higher quality designs across 5 metrics: scalability, reliability, usability, cost-effectiveness, and documentation, plus additional qualitative evidence of the improvements. Our findings suggest that addressing memory limitations through intrinsic approaches can improve the capabilities of multi-agent LLM systems on structured planning tasks.

翻译：基于大型语言模型（LLM）构建的多智能体系统在复杂协同问题解决方面展现出卓越潜力，但其面临的根本挑战源于上下文窗口限制，这种限制会损害记忆一致性、角色遵循度和流程完整性。本文提出内在记忆智能体这一新颖框架，通过随智能体输出内在演化的专属记忆机制来解决这些限制。具体而言，我们的方法维护角色对齐的记忆系统，在专注于任务相关信息的同时保留专业视角。该框架采用通用记忆模板，可适用于新问题而无需手动设计特定记忆提示。我们在PDDL、FEVER和ALFWorld数据集上对本方法进行基准测试，与现有最先进的多智能体记忆方法进行比较，结果显示在全部三个数据集上均达到最优或相当的性能水平，且具有最高的一致性。此外，我们在复杂数据管道设计任务上进行了补充评估，证明本方法在可扩展性、可靠性、可用性、成本效益和文档质量这五项指标上均能生成更高质量的设计方案，并提供了改进效果的定性证据。我们的研究结果表明，通过内在方法解决记忆限制能够提升多智能体LLM系统在结构化规划任务中的能力。