Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory

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 framework that addresses these limitations through agent-specific memories that evolve intrinsically with agent outputs, maintaining role-aligned memory that preserves specialised 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 evaluate it against six published memory designs and a no-memory control on seven benchmarks datasets spanning knowledge reasoning (FEVER, HotpotQA), embodied action (ALFWorld, SciWorld, BabyAI) and planning (PDDL), with ten seeds per condition. Intrinsic memory is strongest on knowledge reasoning, taking the top three places on both FEVER and HotpotQA. 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.