Unverified50% confidenceFactExact time
The context window is limited, and multi-turn conversations and historical tool-call results quickly fill it up; common approaches include sliding windows, summary compression and external memory storage.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
AI Agent Engineer Practical Guide: From Getting the Model Running to System Deployment
bilibiliLLM应用开发10/19/2026
Related Entities
Related Claims
UnverifiedCommon 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.77% similarUnverifiedCompared to stuffing all knowledge into the prompt, which is constrained by the context window, the references directory approach is more flexible and scalable and avoids attention dilution caused by information overload.75% similarUnverifiedThe context management layer handles information compression, history trimming, and memory retrieval during long tasks to avoid exceeding the model's context window75% similarUnverifiedWindowed buffer memory is the simplest to implement but prone to losing early information, while vector retrieval supports longer history lookups but introduces additional embedding computation overhead.74% similarUnverifiedWindow Buffer Memory avoids exceeding the model's token limit by retaining only the most recent N rounds of conversation.72% similar
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