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guidesJune 22, 2026· 4 min read

Cache LiteLLM in your HR assistant with Crowkis

Building HR assistants on LiteLLM? Add a semantic cache so the same policy questions from every employee stop costing full price.

HR assistants built on LiteLLM share one problem: the same policy questions from every employee. Each repeat is a full-price LiteLLM call for an answer you already have.

Put a semantic cache in front. Point LiteLLM'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 HR assistants logic.

LiteLLM + Crowkis
base_url = "http://127.0.0.1:6380/v1"   # Crowkis gateway, semantic cache in front
In plain words: The HR assistants 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.