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This research introduces Agentic Memory (AgeMem), a unified framework that integrates long-term and short-term memory management directly into an LLM agent's policy via tool-based actions. By employing progressive reinforcement learning, the system enables agents to autonomously optimize context usage for complex, multi-step reasoning tasks without relying on external heuristics. The approach demonstrates significant improvements in task performance and memory quality across various backbones, addressing a core bottleneck in current agentic systems.
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
arXiv:2601.01885v3 Announce Type: replace Abstract: Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. In this paper, we propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy. AgeMem exposes memory operations as tool-based actions, enabling the LLM agent to autonomously decide what and when to store, retrieve, update, summarize, or discard information. To train such unified behaviors, we propose a three-stage progressive reinforcement learning strategy and design a step-wise GRPO to address sparse and discontinuous rewards induced by memory operations. Experiments on five long-horizon benchmarks demonstrate that AgeMem consistently outperforms strong memory-augmented baselines across multiple LLM backbones, achieving improved task performance, higher-quality long-term memory, and more efficient context usage.
Abstract:Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. In this paper, we propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy. AgeMem exposes memory operations as tool-based actions, enabling the LLM agent to autonomously decide what and when to store, retrieve, update, summarize, or discard information. To train such unified behaviors, we propose a three-stage progressive reinforcement learning strategy and design a step-wise GRPO to address sparse and discontinuous rewards induced by memory operations. Experiments on five long-horizon benchmarks demonstrate that AgeMem consistently outperforms strong memory-augmented baselines across multiple LLM backbones, achieving improved task performance, higher-quality long-term memory, and more efficient context usage.
ACL'26 SAC Highlight. The code is available at this https URL
Computation and Language (cs.CL)
arXiv:2601.01885 [cs.CL]
(or arXiv:2601.01885v3 [cs.CL] for this version)
Full text under the publisher's open licence · 174 words
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