text: capability, and blazing-fast performance.
  With Momento Cache, you grab the SDK, you get an end point, input a few lines into your code, and you're off and running.This page covers how to use the Momento ecosystem within LangChain.Installation and Setupâ€‹Sign up for a free account here and get an auth tokenInstall the Momento Python SDK with pip install momentoCacheâ€‹The Cache wrapper allows for Momento to be used as a serverless, distributed, low-latency cache for LLM prompts and responses.The standard cache is the go-to use case for Momento users in any environment.Import the cache as follows:from langchain.cache import MomentoCacheAnd set up like so:from datetime import timedeltafrom momento import CacheClient, Configurations, CredentialProviderimport langchain# Instantiate the Momento clientcache_client = CacheClient(    Configurations.Laptop.v1(),    CredentialProvider.from_environment_variable("MOMENTO_AUTH_TOKEN"),    default_ttl=timedelta(days=1))# Choose a Momento cache name of your choicecache_name = "langchain"# Instantiate the LLM cachelangchain.llm_cache = MomentoCache(cache_client, cache_name)Memoryâ€‹Momento can be used as a distributed memory store for LLMs.Chat Message History Memoryâ€‹See this notebook for a walkthrough of how to use Momento as a memory store for chat message history.PreviousModern TreasuryNextMotherduckInstallation and SetupCacheMemoryChat Message History MemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.