Vector Memory for Agents

The right mental model is thinking of context windows as RAM and vector databases as hard drives. Agents need both short term and long term memory to be useful.

Without persistent memory, every conversation starts from zero. Users explain their problem again. The agent suggests the same solution that didn't work last time. It's frustrating for everyone.

Vector memory makes a dramatic difference in agent quality. Every interaction gets embedded and stored. When users return, the agent remembers everything. Not just keywords but actual understanding of context.

The retrieval strategy matters more than the storage. You need semantic similarity but also temporal relevance and interaction frequency. The system has to learn what's worth remembering.

Storage is cheap and retrieval is fast with good indexing. Sub 100ms easily. But the impact on user experience is huge. People feel heard. They feel understood.

The patterns emerging look surprisingly like human memory. Important stuff gets reinforced. Irrelevant stuff fades. We're accidentally recreating how brains work.