---
title: "Prompt caching"
sidebarTitle: "Prompt caching"
description: "Cache the stable prefix of your agent's prompt with Anthropic prompt caching to cut token cost and latency on every turn."
---

import RcBanner from "/snippets/ai-chat-rc-banner.mdx";

<RcBanner />

**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.

<Note>
  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.
</Note>

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
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
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
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.

<Note>
  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.
</Note>

## 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
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.

<Note>
  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.
</Note>

<Note>
  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.
</Note>

## 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
// 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
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.

<Warning>
  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.
</Warning>

## Next steps

<CardGroup cols={2}>
  <Card title="Compaction" icon="compress" href="/ai-chat/compaction">
    Keep long conversations within token limits — and re-warm the cache after.
  </Card>
  <Card title="Fast starts" icon="bolt" href="/ai-chat/fast-starts">
    Cut cold-start latency so a cached prefix is the only thing between a message and a reply.
  </Card>
  <Card title="chat.agent reference" icon="book" href="/ai-chat/reference#chatagentoptions">
    Full option surface, including `prepareMessages` and `toStreamTextOptions`.
  </Card>
  <Card title="Building agents: backend" icon="server" href="/ai-chat/backend">
    The three ways to build a chat backend and when to reach for each.
  </Card>
</CardGroup>
