Unverified50% confidenceFactExact time
When a long session fills the context window, the agent summarizes everything so far, discards the original history, and continues from the summary, at the cost that details may be lost, causing long runs to sometimes go off track.
1
Sources
50%
Confidence
Long-term
Relevance
11/1/2026
First Seen
Sources
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.75% similarUnverifiedEngineering strategies for dealing with context forgetting include compressing conversation history via summarization, using external memory storage (such as vector databases Pinecone and Weaviate), and clearing already-read messages.73% similarUnverifiedThe 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.73% 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.72% similarUnverifiedThe memory summarization mechanism periodically compresses multiple historical interactions into structured short summaries, reducing token consumption while preserving core insights.72% similar
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