Vector Memory

Problem

An append-only log of everything the agent has seen grows unboundedly and quickly becomes too large to search by linear scan. Without a semantic retrieval layer, the agent has no way to find the relevant past, because keyword search misses paraphrase and chronological recency misses older but topically relevant items. The team needs a memory store that supports similarity queries against an embedding of the current context, so that the agent can pull back exactly the items it should be thinking about now.

Solution

Each memory item is embedded and indexed. At query time, embed the query (or a summary of current state), retrieve top-k most similar memories, prepend to context. Optional decay (boost recent, age old) and salience weighting.

When to use

• Long-running agents accumulate facts whose relevance is best judged by similarity.
• Append-only logs would otherwise grow unboundedly without retrieval.
• An embedding model and vector index can be deployed and maintained.