Configuration options

缓存类型。对于 ChatBedrock，目前仅支持 'ephemeral' 。对于 ChatBedrockConverse，此值被忽略，因为 Converse API 始终使用 "default" 缓存类型。

缓存内容的生存时间。有效值： '5m' or '1h'。请注意，Amazon Nova 模型仅支持 '5m'.

开始缓存前的最小消息数。

使用不支持的模型时的行为。选项： 'ignore', 'warn', or 'raise'.

Full example

中间件将每个请求中的内容缓存到最新消息为止。在 TTL 窗口内（5 分钟或 1 小时）的后续请求中，之前看到的内容从缓存中检索，而不是重新处理，从而降低成本和延迟。

工作原理： 1. 第一个请求：系统提示词、工具和用户消息被发送到 API 并缓存 2. 第二个请求：从缓存中检索缓存的内容。只需要处理新消息 3. 每个轮次都延续此模式，每个请求都重用缓存的对话历史

from langchain_aws import ChatBedrockConverse
from langchain_aws.middleware.prompt_caching import BedrockPromptCachingMiddleware
from langchain.agents import create_agent
from langchain_core.runnables import RunnableConfig
from langchain.messages import HumanMessage
from langchain.tools import tool
from langgraph.checkpoint.memory import MemorySaver


@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny and 72F."


# System prompt must exceed 1,024 tokens for caching to take effect
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. " * 85
)

agent = create_agent(
    model=ChatBedrockConverse(model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"),
    system_prompt=LONG_PROMPT,
    tools=[get_weather],
    middleware=[BedrockPromptCachingMiddleware(ttl="5m")], # [!code highlight]
    checkpointer=MemorySaver(),  # Persists conversation history
)

# Use a thread_id to maintain conversation state
config: RunnableConfig = {"configurable": {"thread_id": "user-123"}}

# First invocation: Creates cache with system prompt, tools, and user message
response = agent.invoke(
    {"messages": [HumanMessage("What is the weather in Miami?")]}, config=config
)

last_msg = response["messages"][-1]
print(last_msg.content)

# Check cache token usage
um = last_msg.usage_metadata
if um:
    details = um.get("input_token_details", {})
    cache_read = details.get("cache_read", 0) or 0
    cache_write = details.get("cache_creation", 0) or 0
    print(f"Cache read: {cache_read}, Cache write: {cache_write}")

# Second invocation: Reuses cached system prompt, tools, and previous messages
response = agent.invoke(
    {"messages": [HumanMessage("How about Seattle?")]}, config=config
)
print(response["messages"][-1].content)