Long-term agent memory: how it works and when to use it
Long-term agent memory, gives agents durable, per-(agent, user) memory recalled by relevance blended with recency, so an assistant remembers across sessions. Here's how Crowkis does it and why it matters for cost and safety.
The cheapest token is the one you never spend twice. Long-term agent memory is how Crowkis gives agents durable, per-(agent, user) memory recalled by relevance blended with recency, so an assistant remembers across sessions.
How it works
Crowkis gives agents durable, per-(agent, user) memory recalled by relevance blended with recency, so an assistant remembers across sessions. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
CMEMSET support u_42 "prefers email over phone" CMEMGET support u_42 "how to contact them?"
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Long-term agent memory is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Community edition ships at full power, free to run.
Infrastructure earns the critical path one boring, verifiable feature at a time.