> ## Documentation Index > Fetch the complete documentation index at: https://trigger.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Prompt caching > Cache the stable prefix of your agent's prompt with Anthropic prompt caching to cut token cost and latency on every turn. **Prompt caching lets a provider reuse the unchanged prefix of your prompt across requests, billing it at a fraction of the input price and skipping re-processing.** With Anthropic, cache reads cost \~10% of base input tokens, so a long, stable system prompt or a growing conversation history pays full price once and reads cheaply on every turn after. Caching is a **byte-exact prefix match**: any change in the prefix invalidates everything after it. A multi-turn agent is the ideal case — the system prompt, tools, and earlier turns are identical turn over turn, so the cacheable prefix only grows. `chat.agent` is built to keep that prefix stable across turns, suspends, and resumes; this page shows how to place the cache breakpoints and verify they're hitting. Caching is provider-specific. This guide covers Anthropic (`@ai-sdk/anthropic`), where you opt in per breakpoint with `providerOptions.anthropic.cacheControl`. Other providers cache differently, and most cache automatically — see [Other providers](#other-providers). ## What you cache, and where A request renders as `tools` → `system` → `messages`. There are three prefix regions worth caching, in order: | Region | How to cache it | Stability | | ----------------------- | --------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- | | System prompt (+ tools) | `cacheControl` / `systemProviderOptions` on `chat.toStreamTextOptions()`, or `providerOptions` on `chat.prompt.set()` | Set once, never changes — the highest-value target | | Conversation history | `prepareMessages` adds a breakpoint to the last message | Grows append-only across turns | | Tool definitions | Stable as long as your tool set doesn't change between turns | Render at position 0 — changing them invalidates everything | `chat.agent` preserves `providerOptions` through message persistence and rehydration, so a breakpoint you place survives a suspend/resume or a page refresh. The recommended way to place message breakpoints is `prepareMessages` (below) rather than baking `cacheControl` into stored messages — `prepareMessages` runs on every prompt-assembly path, including after compaction, so the breakpoint is always in the right place. ## Cache the system prompt The system prompt (your `chat.prompt` text plus any skills preamble) is usually the largest stable block, so it's the first thing to cache. `chat.toStreamTextOptions()` returns `system` as a plain string by default; opt into caching and it returns a structured system message carrying the cache breakpoint instead. System-prompt caching needs AI SDK v6 or later, where the `system` parameter accepts a structured message. On AI SDK v5 `system` is a plain string, so these options won't apply a breakpoint to the system block — cache the conversation via `prepareMessages` instead. Three ways to opt in, depending on where you'd rather express it. **`cacheControl` at the `streamText` call site** — the Anthropic-flavored one-liner: ```ts /trigger/chat.ts theme={"theme":"css-variables"} import { chat } from "@trigger.dev/sdk/ai"; import { streamText } from "ai"; import { anthropic } from "@ai-sdk/anthropic"; export const myChat = chat.agent({ id: "my-chat", onChatStart: async () => { chat.prompt.set(SYSTEM_PROMPT); // a large, stable instruction block }, run: async ({ messages, signal }) => { return streamText({ model: anthropic("claude-sonnet-4-6"), // Caches the system block with a 5-minute breakpoint. ...chat.toStreamTextOptions({ cacheControl: { type: "ephemeral" } }), messages, abortSignal: signal, }); }, }); ``` **`systemProviderOptions`** is the provider-agnostic form — pass the raw `providerOptions` so it composes with any provider: ```ts /trigger/chat.ts theme={"theme":"css-variables"} return streamText({ model: anthropic("claude-sonnet-4-6"), ...chat.toStreamTextOptions({ systemProviderOptions: { anthropic: { cacheControl: { type: "ephemeral" } } }, }), messages, abortSignal: signal, }); ``` **`providerOptions` on `chat.prompt.set()`** co-locates the intent with where the prompt is defined. It carries through to `toStreamTextOptions()` with no call-site change: ```ts /trigger/chat.ts theme={"theme":"css-variables"} onChatStart: async () => { chat.prompt.set(SYSTEM_PROMPT, { providerOptions: { anthropic: { cacheControl: { type: "ephemeral" } } }, }); }, run: async ({ messages, signal }) => { return streamText({ model: anthropic("claude-sonnet-4-6"), ...chat.toStreamTextOptions(), // already cached messages, abortSignal: signal, }); }, ``` If more than one is set, the call-site option wins: `systemProviderOptions` overrides `cacheControl`, and both override `chat.prompt.set`'s `providerOptions`. There's no deep merge — the most specific option replaces the rest. Use the 1-hour cache for prefixes that sit idle longer than 5 minutes between turns: `cacheControl: { type: "ephemeral", ttl: "1h" }`. Writes cost more (2× vs 1.25×), so it pays off only when reads span the longer window. ## Cache the conversation history Place a breakpoint on the last message and the entire conversation prefix up to that point is cached, so the next turn reads it back instead of re-processing it. Do this in [`prepareMessages`](/ai-chat/reference#chatagentoptions) — it transforms model messages once, and `chat.agent` applies it on every path that builds a prompt (each turn, and both compaction rebuild paths), so the breakpoint always lands on the real last message. ```ts /trigger/chat.ts theme={"theme":"css-variables"} export const myChat = chat.agent({ id: "my-chat", prepareMessages: async ({ messages }) => { if (messages.length === 0) return messages; const last = messages[messages.length - 1]; return [ ...messages.slice(0, -1), { ...last, providerOptions: { ...last.providerOptions, anthropic: { cacheControl: { type: "ephemeral" } }, }, }, ]; }, run: async ({ messages, signal }) => { return streamText({ model: anthropic("claude-sonnet-4-6"), ...chat.toStreamTextOptions({ cacheControl: { type: "ephemeral" } }), messages, abortSignal: signal, }); }, }); ``` The system breakpoint and the conversation breakpoint compose: the system block is cached once for the life of the chat, and each turn extends the cached message prefix. Anthropic allows **at most 4** cache breakpoints per request, and a prefix must be at least \~1024 tokens (model-dependent) to cache at all — shorter prefixes silently don't cache. One system breakpoint plus one rolling message breakpoint is the typical setup and leaves headroom. This rolling-breakpoint pattern composes with [Head Start](/ai-chat/fast-starts#head-start). On a head-start handover, the first turn's pending tool call is handed to the agent as a tool-approval round whose trailing `tool` message must reach `streamText` untouched for that call to execute. `chat.agent` preserves that tail across `prepareMessages` automatically, so rewriting the last message here is safe: on a resume turn the breakpoint just lands on the next user or assistant message instead of the transient approval row. ## Caching and compaction Compaction rewrites the conversation prefix — it replaces earlier turns with a summary — so it necessarily invalidates the cached message prefix at that point. That's a one-time reset, not a regression: because `prepareMessages` also runs on the compaction rebuild and result paths, the new (shorter) prefix gets a fresh breakpoint and re-warms on the next turn. Your system-prompt cache is unaffected — compaction never touches the system block. See [Compaction](/ai-chat/compaction) for how the summary is produced. ## Other providers Caching is provider-specific, and most providers don't use per-block breakpoints at all: * **OpenAI** and **Google Gemini** cache automatically. OpenAI caches any prompt prefix over 1024 tokens; Gemini 2.5 caches implicitly (1024 tokens on Flash, 2048 on Pro). Neither needs a breakpoint, so the system-caching options above are a no-op for them — `chat.agent` already gives automatic caching exactly what it needs: a byte-stable prefix that only grows across turns. Keep the system prompt frozen and the prefix over the model's minimum and reads happen on their own. (OpenAI's optional `providerOptions.openai.promptCacheKey` improves hit-routing across requests; it's a top-level option, not a system-block breakpoint.) * **Anthropic** and **Amazon Bedrock** take an explicit breakpoint on the system block — Anthropic via `cacheControl`, Bedrock via `cachePoint`. Both go through the provider-agnostic `systemProviderOptions`: ```ts /trigger/chat.ts theme={"theme":"css-variables"} // Amazon Bedrock return streamText({ ...chat.toStreamTextOptions({ systemProviderOptions: { bedrock: { cachePoint: { type: "default" } } }, }), messages, }); ``` The `cacheControl` shorthand is Anthropic-only; `systemProviderOptions` (and `chat.prompt.set`'s `providerOptions`) is the form to reach for on any other breakpoint-based provider. Usage reporting is normalized. Each provider reports cache tokens under its own provider-specific field, but the AI SDK maps them into the same `inputTokenDetails.cacheReadTokens` / `cacheWriteTokens` that `previousTurnUsage` and `totalUsage` carry and the dashboard shows — so the [verify step](#verify-caching-is-working) is the same regardless of provider. ## Verify caching is working The turn's usage carries cache token counts. `chat.agent` accumulates them across turns and hands them to `run` as `previousTurnUsage` (last turn) and `totalUsage` (whole chat), both `LanguageModelUsage`: ```ts /trigger/chat.ts theme={"theme":"css-variables"} run: async ({ messages, signal, previousTurnUsage }) => { // After turn 1, cacheReadTokens should be > 0 on a stable prefix. console.log("cache read", previousTurnUsage?.inputTokenDetails?.cacheReadTokens); console.log("cache write", previousTurnUsage?.inputTokenDetails?.cacheWriteTokens); return streamText({ model: anthropic("claude-sonnet-4-6"), ...chat.toStreamTextOptions({ cacheControl: { type: "ephemeral" } }), messages, abortSignal: signal, }); }, ``` The first turn writes the cache (`cacheWriteTokens > 0`, `cacheReadTokens` is 0). Every turn after, on an unchanged prefix, reads it (`cacheReadTokens > 0`). The dashboard surfaces the same numbers on the AI span as **Cache write** and **Cache read**, so you can confirm hits per run without logging. If `cacheReadTokens` stays 0 across turns with an identical prefix, a silent invalidator is shifting the bytes — see below. Anything that changes the prefix between turns silently kills the cache. Keep the system prompt **byte-stable** — never interpolate a timestamp, request ID, or per-turn value into `chat.prompt`. Don't change the **model** or the **tool set** mid-conversation (tools render at position 0, so adding one invalidates everything after). Inject dynamic per-turn context as a late message via [pending messages](/ai-chat/pending-messages) or [background injection](/ai-chat/background-injection), not into the cached prefix. ## Next steps Keep long conversations within token limits — and re-warm the cache after. Cut cold-start latency so a cached prefix is the only thing between a message and a reply. Full option surface, including `prepareMessages` and `toStreamTextOptions`. The three ways to build a chat backend and when to reach for each.