Using Llama Stack as a Library

Setup Llama Stack without a Server

If you are planning to use an external service for Inference (even Ollama or TGI counts as external), it is often easier to use Llama Stack as a library. This avoids the overhead of setting up a server.

# setup
uv pip install llama-stack
llama stack build --template ollama --image-type venv

from llama_stack.distribution.library_client import LlamaStackAsLibraryClient

client = LlamaStackAsLibraryClient(
    "ollama",
    # provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
    provider_data={"tavily_search_api_key": os.environ["TAVILY_SEARCH_API_KEY"]},
)
client.initialize()

This will parse your config and set up any inline implementations and remote clients needed for your implementation.

Then, you can access the APIs like models and inference on the client and call their methods directly:

response = client.models.list()

If you’ve created a custom distribution, you can also use the run.yaml configuration file directly:

client = LlamaStackAsLibraryClient(config_path)
client.initialize()