langchain/docs/extras/ecosystem/integrations/momento.mdx

# Momento

>[Momento Cache](https://docs.momentohq.com/) is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero 
> 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](https://gomomento.com) ecosystem within LangChain.

## Installation and Setup

- Sign up for a free account [here](https://docs.momentohq.com/getting-started) and get an auth token
- Install the Momento Python SDK with `pip install momento`


## Cache

The Cache wrapper allows for [Momento](https://gomomento.com) 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](https://gomomento.com) users in any environment.

Import the cache as follows:

```python
from langchain.cache import MomentoCache
```

And set up like so:

```python
from datetime import timedelta
from momento import CacheClient, Configurations, CredentialProvider
import langchain

# Instantiate the Momento client
cache_client = CacheClient(
    Configurations.Laptop.v1(),
    CredentialProvider.from_environment_variable("MOMENTO_AUTH_TOKEN"),
    default_ttl=timedelta(days=1))

# Choose a Momento cache name of your choice
cache_name = "langchain"

# Instantiate the LLM cache
langchain.llm_cache = MomentoCache(cache_client, cache_name)
```

## Memory

Momento can be used as a distributed memory store for LLMs.

### Chat Message History Memory

See [this notebook](/docs/modules/memory/integrations/momento_chat_message_history.html) for a walkthrough of how to use Momento as a memory store for chat message history.