"Large Language Models (LLMs) enable fluent, natural conversations, but most applications built on top of them remain fundamentally stateless. Each interaction starts from scratch, with no durable understanding of the user beyond the current prompt. This becomes a problem quickly. A customer support bot that forgets past orders or a personal assistant that repeatedly asks for preferences delivers an experience that feels disconnected and inefficient."
"How LLMs store context LLMs maintain context by conditioning each response on the messages provided in the current request. In most applications, this takes the form of a conversation history passed as an ordered array of messages, where each message includes a role (system, user, or assistant) and associated text. The model generates its next response by attending to this entire sequence."
Long-term memory enables LLM-powered systems to be stateful and retain user-specific information across sessions, improving personalization and efficiency. Conversation history alone does not scale because token limits and external context exceed single-session prompts. Memory systems must decide what to store, how to index and embed data, and when to retrieve or summarize information for the model. Different libraries adopt different memory-management strategies, affecting consistency, latency, developer ergonomics, and privacy. Effective long-term memory design balances relevance, retrieval cost, and user control to maintain context without overwhelming the model or compromising sensitive data.
Read at LogRocket Blog
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