Tool-result caching (CTOOLSET): how it works and when to use it
Tool-result caching (CTOOLSET), caches a deterministic tool call keyed by tool plus exact args, so a swarm's duplicate lookups become one call. Here's how Crowkis does it and why it matters for cost and safety.
Your users ask the same things all day, phrased a hundred different ways. Tool-result caching (CTOOLSET) is how Crowkis caches a deterministic tool call keyed by tool plus exact args, so a swarm's duplicate lookups become one call.
How it works
Crowkis caches a deterministic tool call keyed by tool plus exact args, so a swarm's duplicate lookups become one call. 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.
CTOOLGET weather '{"city":"Berlin"}'Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Tool-result caching (CTOOLSET) 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. Runs self-hosted with zero egress, nothing leaves your machine.
Infrastructure earns the critical path one boring, verifiable feature at a time.