LLM Caching

AutoGen supports caching API requests so that they can be reused when the same request is issued. This is useful when repeating or continuing experiments for reproducibility and cost saving.

Since version 0.2.8, a configurable context manager allows you to easily configure LLM cache, using either DiskCache, RedisCache, or Cosmos DB Cache. All agents inside the context manager will use the same cache.

from autogen import Cache

# Use Redis as cache
with Cache.redis(redis_url="redis://localhost:6379/0") as cache:
    user.initiate_chat(assistant, message=coding_task, cache=cache)

# Use DiskCache as cache
with Cache.disk() as cache:
    user.initiate_chat(assistant, message=coding_task, cache=cache)

# Use Azure Cosmos DB as cache
with Cache.cosmos_db(connection_string="your_connection_string", database_id="your_database_id", container_id="your_container_id") as cache:
    user.initiate_chat(assistant, message=coding_task, cache=cache)

The cache can also be passed directly to the model client’s create call.

client = OpenAIWrapper(...)
with Cache.disk() as cache:
    client.create(..., cache=cache)

​

Controlling the seed

You can vary the cache_seed parameter to get different LLM output while still using cache.

# Setting the cache_seed to 1 will use a different cache from the default one
# and you will see different output.
with Cache.disk(cache_seed=1) as cache:
    user.initiate_chat(assistant, message=coding_task, cache=cache)

​

Cache path

By default DiskCache uses .cache for storage. To change the cache directory, set cache_path_root:

with Cache.disk(cache_path_root="/tmp/autogen_cache") as cache:
    user.initiate_chat(assistant, message=coding_task, cache=cache)

​

Disabling cache

For backward compatibility, DiskCache is on by default with cache_seed set to 41. To disable caching completely, set cache_seed to None in the llm_config of the agent.

assistant = AssistantAgent(
    "coding_agent",
    llm_config={
        "cache_seed": None,
        "config_list": OAI_CONFIG_LIST,
        "max_tokens": 1024,
    },
)

​

Difference between cache_seed and OpenAI’s seed parameter

OpenAI v1.1 introduced a new parameter seed. The difference between AutoGen’s cache_seed and OpenAI’s seed is AutoGen uses an explicit request cache to guarantee the exactly same output is produced for the same input and when cache is hit, no OpenAI API call will be made. OpenAI’s seed is a best-effort deterministic sampling with no guarantee of determinism. When using OpenAI’s seed with cache_seed set to None, even for the same input, an OpenAI API call will be made and there is no guarantee for getting exactly the same output.