Anthropic integration | LangChain 한국어

Middleware specifically designed for Anthropic's Claude models. Learn more about middleware.

Middleware	Description
Prompt caching	Reduce costs by caching repetitive prompt prefixes

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 explicit cache breakpoints on the system message, tool definitions, and the most recent user message, allowing the entire conversation history 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

Tip

For simpler use cases, you can also use prompt caching on the chat model by passing cache_control at invocation time without middleware. The middleware is recommended when you need explicit control over cache breakpoints on system prompts and tool definitions.

Info

Learn more about Anthropic prompt caching strategies and limitations.

import { createAgent, anthropicPromptCachingMiddleware } from "langchain";

const agent = createAgent({
  model: "claude-sonnet-4-6",
  prompt: "<Your long system prompt here>",
  middleware: [anthropicPromptCachingMiddleware({ ttl: "5m" })],
});

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:

First request: System prompt, tools, and the user message "Hi, my name is Bob" are sent to the API and cached
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
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-4-6",
  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?")]
});

Configuration options

Full example

How it works:

First request: System prompt, tools, and the user message "Hi, my name is Bob" are sent to the API and cached
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
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-4-6",
  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?")]
});