Agent Memory Systems: From Context to Persistent Storage › Key Parameters
Michael BrenndoerferFebruary 5, 20261 min read
Language AI HandbookAll chapters Part 48 of 67Tool Use and Agents
Agent Memory Systems: From Context to Persistent Storage › Key ParametersOne section · Sections in this chapter9 / 12 ↓
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The key parameters for the AgentMemory system are:
- capacity: Maximum number of memories stored in long-term memory. When exceeded, the system evicts the lowest-scoring memories based on recency and importance together with access frequency.
- context_size: Number of recent items maintained in working memory before consolidation into long-term storage.
- alpha: Weight for semantic similarity in retrieval scoring. Higher values favor conceptually relevant memories regardless of age.
- beta: Weight for recency in retrieval scoring. Higher values favor recent memories, appropriate for fast-moving conversational contexts.
- gamma: Weight for importance in retrieval scoring. Higher values raise the scores of memories marked as significant, which helps preserve necessary decisions.
- lambda (decay rate): Controls how quickly memories lose their recency score. Higher values create faster decay, suitable for rapidly changing domains.
These weights sum to 1.0 and together determine the retrieval "personality" of the agent. A conservative, accuracy-focused agent might use high alpha with slow decay. A context-sensitive assistant might use high beta to stay focused on the current conversational thread. An agent managing high-stakes decisions might use high gamma to ensure that previously flagged necessary constraints always surface when relevant.