When using gemini through the LiveKit inference API, is implicit caching enabled by default or is there something that I need to configure in order to ensure that's turned on?

Thanks for reaching out, James. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

I want to be able to leverage LLM caching of context (like system prompt) to not always have to pass additional tokens with the system prompt to generate responses via the LLM

Thanks — you're asking whether you can cache the system prompt/context for Gemini so you don't resend those tokens every request. I can help confirm support and give an implementation pattern, but I need a few details first: • Which exact Gemini model and provider are you using (e.g.,

gemini-pro

,

gemini-1.5

, or other)? • Which LiveKit interface are you calling (Inference REST API, Node SDK, or Python SDK)? Please share a short request snippet or JSON payload if possible. • Do you want to cache only the system prompt or the full conversation context? • Any preferences for cache scope and storage (per-user vs global, in-memory vs Redis, TTL)? If you prefer, tell me two lines: model + SDK and whether system-only or full-context caching. I’ll confirm whether LiveKit supports

prompt_cache_key

(or equivalent) for your setup and provide a concrete config/code example or a recommended workaround. Sources: Kimi LLM | LiveKit Documentation | Understand and Improve Agent Latency | LiveKit, with interactive elements

gemini-2.5-flash

we're using the python SDK only the system prompt for now would need per session cache scope

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!