Agent Memory on Valkey

Agent-memory retrieval with Valkey Search requires balancing vector similarity with metadata such as recency and task outcomes, since filters influence not only which memories qualify but also how searches are executed. Valkey’s query planner chooses between an exact pre-filtered scan for filters estimated to match 0.1% or fewer indexed vectors and inline filtering during HNSW traversal for broader matches, making index size, tenant scoping, and filter selectivity important performance considerations. Because the threshold is relatively strict and not normally configurable, the author recommends structuring indexes and key prefixes to isolate tenants or other logical namespaces, reducing qualifying sets and enabling fast exact searches. The discussion also highlights that deleted, expired, or evicted vectors remain as stranded nodes in the HNSW graph until the index is rebuilt, while in-place vector updates are comparatively inexpensive. To avoid graph bloat caused by creating TTL-based keys for every completed task, agent memories should use stable task-derived keys and be updated over time, with query-time timestamp ranges serving as the main recency mechanism and TTLs used more conservatively for storage control.

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