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Composable building blocks to build Llama Apps https://llama-stack.readthedocs.io

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Alina Ryan c02464b635 fix: Clarify `llama model prompt-format` help text (#1010 ) # What does this PR do? Updates the help text for the `llama model prompt-format` command to clarify that users should provide a specific model name (e.g., Llama3.1-8B, Llama3.2-11B-Vision), not a model family. Removes the default value and field for `--model-name` to prevent users from mistakenly thinking a model family name is acceptable. Adds guidance to run `llama model list` to view valid model names. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Output of `llama model prompt-format -h` Before: ``` (venv) alina@fedora:~/dev/llama/llama-stack$ llama model prompt-format -h usage: llama model prompt-format [-h] [-m MODEL_NAME] Show llama model message formats options: -h, --help show this help message and exit -m MODEL_NAME, --model-name MODEL_NAME Model Family (llama3_1, llama3_X, etc.) Example: llama model prompt-format <options> (venv) alina@fedora:~/dev/llama/llama-stack$ llama model prompt-format --model-name llama3_1 usage: llama model prompt-format [-h] [-m MODEL_NAME] llama model prompt-format: error: llama3_1 is not a valid Model. Choose one from -- Llama3.1-8B Llama3.1-70B Llama3.1-405B Llama3.1-8B-Instruct Llama3.1-70B-Instruct Llama3.1-405B-Instruct Llama3.2-1B Llama3.2-3B Llama3.2-1B-Instruct Llama3.2-3B-Instruct Llama3.2-11B-Vision Llama3.2-90B-Vision Llama3.2-11B-Vision-Instruct Llama3.2-90B-Vision-Instruct ``` Output of `llama model prompt-format -h` After: ``` (venv) alina@fedora:~/dev/llama/llama-stack$ llama model prompt-format -h usage: llama model prompt-format [-h] [-m MODEL_NAME] Show llama model message formats options: -h, --help show this help message and exit -m MODEL_NAME, --model-name MODEL_NAME Example: Llama3.1-8B or Llama3.2-11B-Vision, etc (Run `llama model list` to see a list of valid model names) Example: llama model prompt-format <options> ``` Signed-off-by: Alina Ryan <aliryan@redhat.com>		2025-03-13 20:47:09 -04:00
.cursor/rules	test: first unit test for resolver (#1475 )	2025-03-07 10:20:51 -08:00
.github	build(deps): bump astral-sh/setup-uv from 4 to 5 (#1620 )	2025-03-13 16:40:15 -04:00
distributions	fix: passthrough provider template + fix (#1612 )	2025-03-13 09:44:26 -07:00
docs	docs: Add OpenAI, Anthropic, Gemini to inference API providers table (#1622 )	2025-03-13 15:28:52 -07:00
llama_stack	fix: Clarify `llama model prompt-format` help text (#1010 )	2025-03-13 20:47:09 -04:00
rfcs	chore: remove straggler references to llama-models (#1345 )	2025-03-01 14:26:03 -08:00
scripts	fix: clean up detailed history for CHANGELOG (#1494 )	2025-03-07 14:03:54 -08:00
tests	feat: add provider API for listing and inspecting provider info (#1429 )	2025-03-13 15:07:21 -07:00
.gitignore	build: remove .python-version (#1513 )	2025-03-12 20:08:24 -07:00
.pre-commit-config.yaml	chore: Always check that git merge conflict markers are not present (#1610 )	2025-03-13 13:19:44 -07:00
.readthedocs.yaml	first version of readthedocs (#278 )	2024-10-22 10:15:58 +05:30
CHANGELOG.md	docs: Add v0.1.6 release notes to changelog (#1506 )	2025-03-08 16:20:08 -08:00
CODE_OF_CONDUCT.md	Initial commit	2024-07-23 08:32:33 -07:00
CONTRIBUTING.md	build: remove .python-version (#1513 )	2025-03-12 20:08:24 -07:00
LICENSE	Update LICENSE (#47 )	2024-08-29 07:39:50 -07:00
MANIFEST.in	build: include .md (#1482 )	2025-03-07 12:10:52 -08:00
pyproject.toml	refactor: move all datetime.now() calls to UTC (#1589 )	2025-03-13 15:34:53 -07:00
README.md	docs: Add OpenAI, Anthropic, Gemini to API providers table (#1617 )	2025-03-13 15:47:58 -04:00
requirements.txt	fix: include jinja2 as a core llama-stack dependency (#1529 )	2025-03-10 14:59:11 -07:00
SECURITY.md	Create SECURITY.md	2024-10-08 13:30:40 -04:00
uv.lock	build: revamp "test" dependencies from pyproject (#1468 )	2025-03-10 15:43:16 -07:00

README.md

Llama Stack

Quick Start | Documentation | Colab Notebook

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.

API Provider Builder	Environments	Agents	Inference	Memory	Safety	Telemetry
Meta Reference	Single Node	✅	✅	✅	✅	✅
SambaNova	Hosted		✅
Cerebras	Hosted		✅
Fireworks	Hosted	✅	✅	✅
AWS Bedrock	Hosted		✅		✅
Together	Hosted	✅	✅		✅
Groq	Hosted		✅
Ollama	Single Node		✅
TGI	Hosted and Single Node		✅
NVIDIA NIM	Hosted and Single Node		✅
Chroma	Single Node			✅
PG Vector	Single Node			✅
PyTorch ExecuTorch	On-device iOS	✅	✅
vLLM	Hosted and Single Node		✅
OpenAI	Hosted		✅
Anthropic	Hosted		✅
Gemini	Hosted		✅

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:

Distribution	Llama Stack Docker	Start This Distribution
Meta Reference	llamastack/distribution-meta-reference-gpu	Guide
Meta Reference Quantized	llamastack/distribution-meta-reference-quantized-gpu	Guide
SambaNova	llamastack/distribution-sambanova	Guide
Cerebras	llamastack/distribution-cerebras	Guide
Ollama	llamastack/distribution-ollama	Guide
TGI	llamastack/distribution-tgi	Guide
Together	llamastack/distribution-together	Guide
Fireworks	llamastack/distribution-fireworks	Guide
vLLM	llamastack/distribution-remote-vllm	Guide

Installation

You have two ways to install this repository:

Install as a package: You can install the repository directly from PyPI by running the following command:
```
pip install llama-stack
```
Install from source: If you prefer to install from the source code, we recommend using uv. Then, run the following commands:
```
 git clone git@github.com:meta-llama/llama-stack.git
 cd llama-stack

 uv sync
 uv pip install -e .
```

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.