Unverified50% confidenceSolutionExact time
Common strategies for handling extremely long conversation history include a sliding window that retains the most recent several turns, generating summaries of earlier history and putting them back into the context, and scoring information to keep only the important parts.
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
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Related Claims
UnverifiedStarting a new session in a timely manner during long conversations can avoid the unlimited accumulation of history and thereby optimize token usage.78% similarUnverifiedShort-term memory generally retains the raw content of the most recent 5–10 conversation turns and triggers a compression summary when the threshold is exceeded.76% similarUnverifiedTwo default practices for long-term memory: extraction should be placed asynchronously outside the reply path (because the facts of this conversation are already in this session's history); take a snapshot of memory at the start of each conversation (the memory block is placed before the history, and updating it midway would break the cache).75% similarUnverified长对话场景下上下文窗口会携带全部历史消息，Token消耗呈线性增长74% similarUnverifiedThe context management layer handles information compression, history trimming, and memory retrieval during long tasks to avoid exceeding the model's context window74% similar
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