Conversation Memory
Conversation memory is saved context from previous interactions that an AI system may use to maintain continuity in later responses.
Category
Agent Memory Systems
These ideas describe how agent applications keep useful context available while controlling what is retrieved or summarized.
How to recognize this theme
AI agent terms for storing, retrieving, and condensing context.
In a daily board, this category groups terms by their shared role. Look for four cards that describe the same mechanism, risk area, or workflow rather than four words that merely sound similar.
Educational context
These entries are vocabulary notes for learning. They are not project endorsements, token recommendations, exchange rankings, or trading signals.
Vector Store
A vector store is a database optimized for saving and searching embeddings so applications can retrieve semantically related information.
Retrieval Policy
A retrieval policy defines what information an AI application should search for, rank, include, or exclude before generating an answer.
Memory Summarization
Memory summarization condenses older conversation or task history into shorter notes that preserve useful context for future model calls.