GitHub - meta-llama/llama-stack: Composable building blocks to build Llama Apps (original) (raw)

Llama Stack

Quick Start | Documentation | Colab Notebook

✨🎉 Llama 4 Support 🎉✨

We released Version 0.2.0 with support for the Llama 4 herd of models released by Meta.

👋 Click here to see how to run Llama 4 models on Llama Stack

Note you need 8xH100 GPU-host to run these models

pip install -U llama_stack

MODEL="Llama-4-Scout-17B-16E-Instruct"

get meta url from llama.com

llama model download --source meta --model-id $MODEL --meta-url

start a llama stack server

INFERENCE_MODEL=meta-llama/$MODEL llama stack build --run --template meta-reference-gpu

install client to interact with the server

pip install llama-stack-client

CLI

Run a chat completion

llama-stack-client --endpoint http://localhost:8321
inference chat-completion
--model-id meta-llama/$MODEL
--message "write a haiku for meta's llama 4 models"

ChatCompletionResponse( completion_message=CompletionMessage(content="Whispers in code born\nLlama's gentle, wise heartbeat\nFuture's soft unfold", role='assistant', stop_reason='end_of_turn', tool_calls=[]), logprobs=None, metrics=[Metric(metric='prompt_tokens', value=21.0, unit=None), Metric(metric='completion_tokens', value=28.0, unit=None), Metric(metric='total_tokens', value=49.0, unit=None)] )

Python SDK

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url=f"http://localhost:8321")

model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct" prompt = "Write a haiku about coding"

print(f"User> {prompt}") response = client.inference.chat_completion( model_id=model_id, messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}, ], ) print(f"Assistant> {response.completion_message.content}")

As more providers start supporting Llama 4, you can use them in Llama Stack as well. We are adding to the list. Stay tuned!

🚀 One-Line Installer 🚀

To try Llama Stack locally, run:

curl -LsSf https://github.com/meta-llama/llama-stack/raw/main/install.sh | sh

Overview

Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides

• Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
• Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
• Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
• Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
• Standalone applications as examples for how to build production-grade AI applications with Llama Stack.

Llama Stack Benefits

• Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
• Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
• Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.

By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications.

API Providers

Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.

Distributions

A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider implementations for each API component. Distributions make it easy to get started with a specific deployment scenario - you can begin with a local development setup (eg. ollama) and seamlessly transition to production (eg. Fireworks) without changing your application code. Here are some of the distributions we support:

Documentation

Please checkout our Documentation page for more details.

• CLI references
  • llama (server-side) CLI Reference: Guide for using the llama CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution.
  • llama (client-side) CLI Reference: Guide for using the llama-stack-client CLI, which allows you to query information about the distribution.
• Getting Started
  • Quick guide to start a Llama Stack server.
  • Jupyter notebook to walk-through how to use simple text and vision inference llama_stack_client APIs
  • The complete Llama Stack lesson Colab notebook of the new Llama 3.2 course on Deeplearning.ai.
  • A Zero-to-Hero Guide that guide you through all the key components of llama stack with code samples.
• Contributing
  • Adding a new API Provider to walk-through how to add a new API provider.

Llama Stack Client SDKs

Language	Client SDK	Package
Python	llama-stack-client-python
Swift	llama-stack-client-swift
Typescript	llama-stack-client-typescript
Kotlin	llama-stack-client-kotlin

Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from python, typescript, swift, and kotlin programming languages to quickly build your applications.

You can find more example scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo.