guidesApril 16, 2026· 4 min read
Cache Instructor in your multi-agent system with Crowkis
Building multi-agent systems on Instructor? Add a semantic cache so a swarm of agents asking overlapping questions stop costing full price.
Multi-agent systems built on Instructor share one problem: a swarm of agents asking overlapping questions. Each repeat is a full-price Instructor call for an answer you already have.
Put a semantic cache in front. Point Instructor's base URL at the Crowkis OpenAI-compatible gateway, or wrap the call in get-or-compute, and reworded repeats are served from cache, no rewrite of your multi-agent systems logic.
Instructor + Crowkis
base_url = "http://127.0.0.1:6380/v1" # Crowkis gateway, semantic cache in front
In plain words: The multi-agent systems keep working exactly as before; the repeats just stop hitting the model.
On repetitive traffic this cuts costs up to 60-70% on repetitive workloads, and every hit carries a confidence score so reuse stays safe. Runs self-hosted with zero egress, nothing leaves your machine.