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guidesApril 13, 2026· 4 min read

Cache LlamaIndex in your research assistant with Crowkis

Building research assistants on LlamaIndex? Add a semantic cache so overlapping literature and summary questions stop costing full price.

Research assistants built on LlamaIndex share one problem: overlapping literature and summary questions. Each repeat is a full-price LlamaIndex call for an answer you already have.

Put a semantic cache in front. Point LlamaIndex'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 research assistants logic.

LlamaIndex + Crowkis
base_url = "http://127.0.0.1:6380/v1"   # Crowkis gateway, semantic cache in front
In plain words: The research 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. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.