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

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Sébastien Han 7cf1e24c4e feat(logging): implement category-based logging (#1362 ) # What does this PR do? This commit introduces a new logging system that allows loggers to be assigned a category while retaining the logger name based on the file name. The log format includes both the logger name and the category, producing output like: ``` INFO 2025-03-03 21:44:11,323 llama_stack.distribution.stack:103 [core]: Tool_groups: builtin::websearch served by tavily-search ``` Key features include: - Category-based logging: Loggers can be assigned a category (e.g., "core", "server") when programming. The logger can be loaded like this: `logger = get_logger(name=__name__, category="server")` - Environment variable control: Log levels can be configured per-category using the `LLAMA_STACK_LOGGING` environment variable. For example: `LLAMA_STACK_LOGGING="server=DEBUG;core=debug"` enables DEBUG level for the "server" and "core" categories. - `LLAMA_STACK_LOGGING="all=debug"` sets DEBUG level globally for all categories and third-party libraries. This provides fine-grained control over logging levels while maintaining a clean and informative log format. The formatter uses the rich library which provides nice colors better stack traces like so: ``` ERROR 2025-03-03 21:49:37,124 asyncio:1758 [uncategorized]: unhandled exception during asyncio.run() shutdown task: <Task finished name='Task-16' coro=<handle_signal.<locals>.shutdown() done, defined at /Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py:146> exception=UnboundLocalError("local variable 'loop' referenced before assignment")> ╭────────────────────────────────────── Traceback (most recent call last) ───────────────────────────────────────╮ │ /Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py:178 in shutdown │ │ │ │ 175 │ │ except asyncio.CancelledError: │ │ 176 │ │ │ pass │ │ 177 │ │ finally: │ │ ❱ 178 │ │ │ loop.stop() │ │ 179 │ │ │ 180 │ loop = asyncio.get_running_loop() │ │ 181 │ loop.create_task(shutdown()) │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ UnboundLocalError: local variable 'loop' referenced before assignment ``` Co-authored-by: Ashwin Bharambe <@ashwinb> Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan ``` python -m llama_stack.distribution.server.server --yaml-config ./llama_stack/templates/ollama/run.yaml INFO 2025-03-03 21:55:35,918 __main__:365 [server]: Using config file: llama_stack/templates/ollama/run.yaml INFO 2025-03-03 21:55:35,925 __main__:378 [server]: Run configuration: INFO 2025-03-03 21:55:35,928 __main__:380 [server]: apis: - agents ``` [//]: # (## Documentation) --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>		2025-03-07 11:34:30 -08:00
.cursor/rules	test: first unit test for resolver (#1475 )	2025-03-07 10:20:51 -08:00
.github	ci: add Github workflow which runs unittests in PR (#1442 )	2025-03-05 21:23:28 -05:00
distributions	feat: open benchmark template and doc (#1465 )	2025-03-07 10:37:55 -08:00
docs	feat(agent): plain function as client tool (#1479 )	2025-03-07 11:10:07 -08:00
llama_stack	feat(logging): implement category-based logging (#1362 )	2025-03-07 11:34:30 -08:00
rfcs	chore: remove straggler references to llama-models (#1345 )	2025-03-01 14:26:03 -08:00
scripts	ci: Add script to generate changelog (#1463 )	2025-03-07 12:45:08 -05:00
tests	feat(logging): implement category-based logging (#1362 )	2025-03-07 11:34:30 -08:00
.gitignore	chore: add pytest-report.xml to gitignore (#1473 )	2025-03-07 10:41:22 -08:00
.pre-commit-config.yaml	chore: remove dependency on llama_models completely (#1344 )	2025-03-01 12:48:08 -08:00
.python-version	build: hint on Python version for uv venv (#1172 )	2025-02-25 10:37:45 -05:00
.readthedocs.yaml	first version of readthedocs (#278 )	2024-10-22 10:15:58 +05:30
CHANGELOG.md	ci: Add script to generate changelog (#1463 )	2025-03-07 12:45:08 -05:00
CODE_OF_CONDUCT.md	Initial commit	2024-07-23 08:32:33 -07:00
CONTRIBUTING.md	chore: Make README code blocks more easily copy pastable (#1420 )	2025-03-05 09:11:01 -08:00
LICENSE	Update LICENSE (#47 )	2024-08-29 07:39:50 -07:00
MANIFEST.in	ci: add Github workflow which runs unittests in PR (#1442 )	2025-03-05 21:23:28 -05:00
pyproject.toml	feat(logging): implement category-based logging (#1362 )	2025-03-07 11:34:30 -08:00
README.md	chore: remove dependency on llama_models completely (#1344 )	2025-03-01 12:48:08 -08:00
requirements.txt	chore: remove dependency on llama_models completely (#1344 )	2025-03-01 12:48:08 -08:00
SECURITY.md	Create SECURITY.md	2024-10-08 13:30:40 -04:00
uv.lock	fix(security): Bump jinja2 to >=3.1.6 (#1461 )	2025-03-07 10:38:39 -08:00

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		✅

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.