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Recent conversation turns are retained and replayed so the model has context for the next turn — as a full transcript, a fixed-size window of the last N turns, or a running summary once the raw history outgrows the… Typical conditions for use: User context must be maintained across multiple turns; References to prior statements are expected. The central trad…
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Conversational memory retains recent dialog turns and replays them before the next model call so an assistant can stay connected across multiple turns.
In the Multi-Agent Systems source, conversational memory is presented as the production layer of an agent's memory. The running message history is reloaded before each reply as a full transcript, a fixed window of the last N turns, or a summary. This addresses the core problem of limited context windows: without stored history, LLMs quickly lose continuity in multi-turn dialogs.
Think of it as a context buffer with a budget cap. New turns are collected, then written back into the prompt as the relevant slice before the next model call. When the window fills up, the system either trims with a sliding window or compresses older history. Durable facts should move into structured storage so they do not disappear when text is shortened.
New messages are held temporarily before being reintroduced as context.
A temporary working state keeps intermediate steps and open tasks for a single run only.
Completed interactions are stored as retrievable episodes instead of being carried in full every time.
The limited context window is treated like scarce working space where information is deliberately paged in and out.
Stored context can be manipulated or corrupted and mislead later responses.
Conversational memory is useful when an assistant must preserve preferences, references, or open tasks across turns, such as in support, planning, or agent workflows. It is less suitable for strictly stateless tasks or when retention is legally or operationally sensitive. More context improves continuity, but it also raises token cost, latency, privacy burden, and the risk that older material crowds out the window.
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