Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, yet their efficacy is constrained by a fundamental memory limitation: a static context window that resets with each interaction. This prevents them from accumulating experience and adapting to dynamic, long-term tasks. To address the limitations of long-term memory in agentic LLMs, this work introduces a neuro-inspired framework with two key contributions. First, we propose \textbf{ARTEM} (Agentic Retrieval with Temporal-Episodic Memory), a system that organizes experiences into structured events and manages utility-based memory consolidation. Second, we extend this framework with a distinct governance component, \textbf{Value-driven ARTEM}, that validates candidate outputs against core principles before finalization. Together, these components equip LLM agents with continual learning, adaptive reasoning, and robust value-aligned decision-making. Looking forward, we outline future directions including dynamic memory adaptation, memory decay mechanisms, and applications in interactive multi-agent environments.

Current large language models (LLMs) exhibit significant deficiencies in episodic memory tasks including encoding, storing, and retrieving specific information from temporally dependent events over a long period of time. Recent approaches to handle memory tasks in LLMs, such as in-context learning, retrieval-augmented generation (RAG), and fine-tuning, may resolve the long-term retention issues, but are still inadequate to handle tasks requiring chronological awareness of the stored information. We introduce Agentic Retrieval with Temporal-Episodic Memory (ARTEM), a hybrid LLM-based agent architecture integrating LLMs with a self-organizing neural network named Spatial-Temporal Episodic Memory (STEM), designed to handle episodic memory tasks. Our approach employs LLMs for event extraction from the inputs to represent temporal, spatial, entitative, and semantic information that may facilitate future retrieval, aside from generating outputs or direct responses. The extracted events can then be encoded vectorially and stored in a fast and stable manner in the episodic memory through an instance-based incremental learning in STEM. STEM supports precise episodes retrieval and helps reduce computational overhead in generating the appropriate responses by LLMs. Evaluation on standardized episodic memory benchmarks across four tasks—partial cue retrieval, epistemic uncertainty detection, recent event identification, and chronological recall—demonstrates superior performance of ARTEM compared to in-context learning, RAG, and fine-tuning in various popular LLMs.