",
middleware: [bedrockPromptCachingMiddleware({ ttl: "5m" })], // [!code highlight]
});
```
Whether to apply prompt caching.
Time to live for cached content. Valid values: `'5m'` or `'1h'`. Note that Amazon Nova models only support `'5m'`.
Minimum number of messages before caching starts. The system prompt counts as one message.
Behavior when using unsupported models. Options: `'ignore'`, `'warn'`, or `'raise'`.
The middleware caches content up to and including the latest message in each request. On subsequent requests within the TTL window (5 minutes or 1 hour), previously seen content is retrieved from cache rather than reprocessed, reducing costs and latency.
**How it works:**
1. First request: System prompt, tools, and the user message are sent to the API and cached
2. Second request: The cached content is retrieved from cache. Only the new message needs to be processed
3. This pattern continues for each turn, with each request reusing the cached conversation history
Prompt caching reduces API costs by caching tokens, but does **not** provide conversation memory. To persist conversation history across invocations, use a [checkpointer](/oss/javascript/langgraph/persistence#checkpointer-libraries) like `MemorySaver`.
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createAgent, bedrockPromptCachingMiddleware, AIMessage, HumanMessage, tool } from "langchain";
import { ChatBedrockConverse } from "@langchain/aws";
import { z } from "zod";
const getWeather = tool(
async ({ city }) => `The weather in ${city} is sunny and 72F.`,
{
name: "get_weather",
description: "Get the current weather for a city.",
schema: z.object({ city: z.string() }),
}
);
// System prompt must exceed 1,024 tokens for caching to take effect
const LONG_PROMPT =
"You are a helpful weather assistant with deep expertise in meteorology, " +
"climate science, and atmospheric phenomena. When answering questions about " +
"weather, provide accurate and up-to-date information. " +
"You should always strive to give the most helpful response possible. ".repeat(85);
const agent = createAgent({
model: new ChatBedrockConverse({ model: "us.anthropic.claude-sonnet-4-5-20250929-v1:0" }),
systemPrompt: LONG_PROMPT,
tools: [getWeather],
middleware: [bedrockPromptCachingMiddleware({ ttl: "5m" })], // [!code highlight]
});
// First invocation: writes the cache (system prompt, tool definitions, and message)
let response = await agent.invoke({
messages: [new HumanMessage("What is the weather in Miami?")],
});
const last = response.messages.at(-1);
console.log(last?.content);
// Check cache token usage
if (AIMessage.isInstance(last)) {
const details = last.usage_metadata?.input_token_details;
if (details) {
console.log(`Cache read: ${details.cache_read ?? 0}, Cache write: ${details.cache_creation ?? 0}`);
}
}
// Second invocation within the TTL: reuses the cached system prompt and tool definitions
response = await agent.invoke({
messages: [new HumanMessage("How about Seattle?")],
});
console.log(response.messages.at(-1)?.content);
```
### Model-specific behavior
The middleware handles differences between model families automatically:
| Feature | ChatBedrockConverse (Anthropic) | ChatBedrockConverse (Nova) |
| ----------------------- | :-----------------------------: | :-------------------------------: |
| System prompt caching | ✅ | ✅ |
| Tool definition caching | ✅ | ❌ |
| Message caching | ✅ | ✅ (excludes tool result messages) |
| Extended TTL (`1h`) | ✅ | ❌ |
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/middleware/aws.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).