Knowledge-graph memory: how it works and when to use it
Knowledge-graph memory, stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess. Here's how Crowkis does it and why it matters for cost and safety.
Every reworded repeat of a question you've already answered is a full-price model call. Knowledge-graph memory is how Crowkis stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess.
How it works
Crowkis stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess. 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.
CMEMLINK support u_42 acme employs alice CMEMGRAPH support u_42 acme HOPS 2
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Knowledge-graph 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. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.
Infrastructure earns the critical path one boring, verifiable feature at a time.