Prompt caching

LLM SDK 2.6.0-beta.2 forwards OpenAI Responses cache options. It does not yet provide unified cache controls, normalized cache-read/write usage, or verified cache-aware pricing across providers. This is provider prompt caching, not an application cache of generated answers.

OpenAI Responses options

Configure only the options supported by your selected model and endpoint:

import { generateText } from "@yourgpt/llm-sdk";
import { openai } from "@yourgpt/llm-sdk/openai";

const model = openai("gpt-5.2", {
  api: "responses",
  responses: {
    store: false,
    promptCacheKey: "support-assistant-v2",
    promptCacheRetention: "in_memory",
  },
});

const result = await generateText({
  model,
  system: "You answer questions about our product.",
  prompt: "How can I change my settings?",
});

The legacy retention values are in_memory and 24h. Newer endpoints also accept the SDK's promptCacheOptions shape:

const model = openai("gpt-6-luna", {
  api: "responses",
  responses: {
    promptCacheOptions: { mode: "implicit", ttl: "30m" },
  },
});

mode can be implicit or explicit; the exposed TTL is 30m. Treat these as alternative model-specific configurations, not options to combine indiscriminately. Consult OpenAI's prompt caching guide for model support, minimum prompt sizes, retention, and provider-side behavior. Short examples do not demonstrate a cache hit.

The same options can be supplied through Runtime's config.openai.responses. See Responses configuration. Use application-scoped cache keys; never put credentials or private prompt text into a cache-key label.

Usage and validation limits

OpenAI Responses raw usage is retained in result.providerMetadata?.openai?.response?.usage. The normalized result.usage still contains only promptTokens, completionTokens, and totalTokens; it has no normalized cache-read or cache-write counters. Do not calculate guaranteed savings from these three totals.

Request forwarding has fixture coverage. The beta release did not certify live cache hits, retention, or cost savings. OpenAI Chat-specific cache controls, Anthropic cache breakpoints/TTL, Google explicit cache resources, and consistent usage/cost reporting remain follow-ups in #114 and #104. A provider automatically caching requests does not mean the SDK exposes all its controls.