How to cache chat model responses
Prerequisites
This guide assumes familiarity with the following concepts:
LangChain provides an optional caching layer forchat models. This is useful for two main reasons:
- It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times. This is especially useful during app development.
- It can speed up your application by reducing the number of API calls you make to the LLM provider.
This guide will walk you through how to enable this in your apps.
Selectchat model:
pip install -qU "langchain[google-genai]"
import getpass
import os
ifnot os.environ.get("GOOGLE_API_KEY"):
os.environ["GOOGLE_API_KEY"]= getpass.getpass("Enter API key for Google Gemini: ")
from langchain.chat_modelsimport init_chat_model
llm= init_chat_model("gemini-2.0-flash", model_provider="google_genai")
# <!-- ruff: noqa: F821 -->
from langchain_core.globalsimport set_llm_cache
API Reference:set_llm_cache
In Memory Cache
This is an ephemeral cache that stores model calls in memory. It will be wiped when your environment restarts, and is not shared across processes.
%%time
from langchain_core.cachesimport InMemoryCache
set_llm_cache(InMemoryCache())
# The first time, it is not yet in cache, so it should take longer
llm.invoke("Tell me a joke")
API Reference:InMemoryCache
CPU times: user 645 ms, sys: 214 ms, total: 859 ms
Wall time: 829 ms
AIMessage(content="Why don't scientists trust atoms?\n\nBecause they make up everything!", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 11, 'total_tokens': 24}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-b6836bdd-8c30-436b-828f-0ac5fc9ab50e-0')
%%time
# The second time it is, so it goes faster
llm.invoke("Tell me a joke")
CPU times: user 822 µs, sys: 288 µs, total: 1.11 ms
Wall time: 1.06 ms
AIMessage(content="Why don't scientists trust atoms?\n\nBecause they make up everything!", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 11, 'total_tokens': 24}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-b6836bdd-8c30-436b-828f-0ac5fc9ab50e-0')
SQLite Cache
This cache implementation uses aSQLite database to store responses, and will last across process restarts.
!rm.langchain.db
# We can do the same thing with a SQLite cache
from langchain_community.cacheimport SQLiteCache
set_llm_cache(SQLiteCache(database_path=".langchain.db"))
API Reference:SQLiteCache
%%time
# The first time, it is not yet in cache, so it should take longer
llm.invoke("Tell me a joke")
CPU times: user 9.91 ms, sys: 7.68 ms, total: 17.6 ms
Wall time: 657 ms
AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field!', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 11, 'total_tokens': 28}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-39d9e1e8-7766-4970-b1d8-f50213fd94c5-0')
%%time
# The second time it is, so it goes faster
llm.invoke("Tell me a joke")
CPU times: user 52.2 ms, sys: 60.5 ms, total: 113 ms
Wall time: 127 ms
AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field!', id='run-39d9e1e8-7766-4970-b1d8-f50213fd94c5-0')
Next steps
You've now learned how to cache model responses to save time and money.
Next, check out the other how-to guides chat models in this section, likehow to get a model to return structured output orhow to create your own custom chat model.