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Middleware specifically designed for Anthropic’s Claude models. Learn more about middleware.
MiddlewareDescription
Prompt cachingReduce costs by caching repetitive prompt prefixes

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Prompt caching

Reduce costs and latency by caching static or repetitive prompt content (like system prompts, tool definitions, and conversation history) on Anthropic’s servers. This middleware implements a conversational caching strategy that places cache breakpoints after the most recent message, allowing the entire conversation history (including the latest user message) to be cached and reused in subsequent API calls. Prompt caching is useful for the following:
  • Applications with long, static system prompts that don’t change between requests
  • Agents with many tool definitions that remain constant across invocations
  • Conversations where early message history is reused across multiple turns
  • High-volume deployments where reducing API costs and latency is critical
Learn more about Anthropic prompt caching strategies and limitations.
import { createAgent, anthropicPromptCachingMiddleware } from "langchain";

const agent = createAgent({
  model: "claude-sonnet-4-5-20250929",
  prompt: "<Your long system prompt here>",
  middleware: [anthropicPromptCachingMiddleware({ ttl: "5m" })],
});
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ttl
string
default:"5m"
Time to live for cached content. Valid values: '5m' or '1h'
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, significantly reducing costs and latency.How it works:
  1. First request: System prompt, tools, and the user message “Hi, my name is Bob” are sent to the API and cached
  2. Second request: The cached content (system prompt, tools, and first message) is retrieved from cache. Only the new message “What’s my name?” needs to be processed, plus the model’s response from the first request
  3. This pattern continues for each turn, with each request reusing the cached conversation history
import { createAgent, HumanMessage, anthropicPromptCachingMiddleware } from "langchain";

const LONG_PROMPT = `
Please be a helpful assistant.

<Lots more context ...>
`;

const agent = createAgent({
  model: "claude-sonnet-4-5-20250929",
  prompt: LONG_PROMPT,
  middleware: [anthropicPromptCachingMiddleware({ ttl: "5m" })],
});

// First invocation: Creates cache with system prompt, tools, and "Hi, my name is Bob"
await agent.invoke({
  messages: [new HumanMessage("Hi, my name is Bob")]
});

// Second invocation: Reuses cached system prompt, tools, and previous messages
// Only processes the new message "What's my name?" and the previous AI response
const result = await agent.invoke({
  messages: [new HumanMessage("What's my name?")]
});
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