"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."
"System messages are typically used to establish the assistant's role or high-level behavior at the start of the conversation, while user and assistant messages capture the ongoing exchange. This approach works well for short-lived, single-session interactions, where all relevant context can be included directly in the prompt. However, conversation history alone does not scale. As interactions grow longer or relevant context exists outside the current session, passing the full message history becomes inefficient and error-prone, setting the stage for approaches li"
LLMs enable fluent conversational experiences but most deployed applications remain stateless, losing durable understanding of users between interactions. Statelessness creates poor user experiences in scenarios like customer support or personal assistants that repeatedly forget orders or preferences. LLMs maintain context by conditioning each response on messages provided in the current request, typically using an ordered array of system, user, and assistant messages. System messages set role and behavior while user and assistant messages record the exchange. Passing full conversation history does not scale as interactions lengthen or relevant context lives outside the session. Long-term memory is therefore essential, and open-source solutions such as mem0 and Supermemory pursue fundamentally different memory-management approaches while using the same underlying LLMs.
Read at LogRocket Blog
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