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Middleware specifically designed for models hosted on AWS Bedrock. Learn more about middleware.
MiddlewareDescription
Prompt cachingReduce costs by caching repetitive prompt prefixes

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Prompt caching

Reduce inference latency and input token costs by caching frequently reused prompt prefixes on Amazon Bedrock. This middleware automatically places cache checkpoints after the system prompt, tool definitions, and the most recent message so that the model can skip recomputation of previously seen content on subsequent requests. Prompt caching is useful for the following:
  • Multi-turn conversations with long, consistent system prompts
  • Agents with many tool definitions that remain constant across invocations
  • Document-based Q&A where users ask multiple questions over the same uploaded context
  • Batch processing workloads with repeated static content
Supported models:
  • Anthropic Claude
  • Amazon Nova
Learn more about AWS Bedrock prompt caching strategies and limitations. Cached content must exceed 1,024 tokens for a cache checkpoint to take effect, sometimes more depending on model. See supported models, regions, and limits.
API reference: BedrockPromptCachingMiddleware
ChatBedrockConverse
from langchain_aws import ChatBedrockConverse
from langchain_aws.middleware.prompt_caching import BedrockPromptCachingMiddleware
from langchain.agents import create_agent

agent = create_agent(
    model=ChatBedrockConverse(model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"),
    system_prompt="<Your long system prompt here>",
    middleware=[BedrockPromptCachingMiddleware(ttl="1h")],
)
ChatBedrock
from langchain_aws import ChatBedrock
from langchain_aws.middleware.prompt_caching import BedrockPromptCachingMiddleware
from langchain.agents import create_agent

agent = create_agent(
    model=ChatBedrock(model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"),
    system_prompt="<Your long system prompt here>",
    middleware=[BedrockPromptCachingMiddleware(ttl="5m")],
)
​
type
string
default:"ephemeral"
Cache type. For ChatBedrock, only 'ephemeral' is currently supported. For ChatBedrockConverse, this value is ignored as the Converse API always uses "default" cache type.
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ttl
string
default:"5m"
Time to live for cached content. Valid values: '5m' or '1h'. Note that Amazon Nova models only support '5m'.
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min_messages_to_cache
number
default:"0"
Minimum number of messages before caching starts.
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unsupported_model_behavior
string
default:"warn"
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 like MemorySaver.
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")],
    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)

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Model-specific behavior

The middleware handles differences between APIs and model families automatically:
FeatureChatBedrockConverse (Anthropic)ChatBedrockConverse (Nova)ChatBedrock (Anthropic)
System prompt caching✅✅✅
Tool definition caching✅❌✅
Message caching✅✅ (excludes tool result messages)✅
Extended TTL (1h)✅❌✅
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