# What does this PR do? Currently there is no shutdown method implemented for the `ProviderImpl` class This leads to the following warning ```shell INFO: Waiting for application shutdown. INFO 2025-03-17 17:25:13,280 __main__:145 server: Shutting down INFO 2025-03-17 17:25:13,282 __main__:129 server: Shutting down ModelsRoutingTable INFO 2025-03-17 17:25:13,284 __main__:129 server: Shutting down DatasetsRoutingTable INFO 2025-03-17 17:25:13,286 __main__:129 server: Shutting down DatasetIORouter INFO 2025-03-17 17:25:13,287 __main__:129 server: Shutting down TelemetryAdapter INFO 2025-03-17 17:25:13,288 __main__:129 server: Shutting down InferenceRouter INFO 2025-03-17 17:25:13,290 __main__:129 server: Shutting down ShieldsRoutingTable INFO 2025-03-17 17:25:13,291 __main__:129 server: Shutting down SafetyRouter INFO 2025-03-17 17:25:13,292 __main__:129 server: Shutting down VectorDBsRoutingTable INFO 2025-03-17 17:25:13,293 __main__:129 server: Shutting down VectorIORouter INFO 2025-03-17 17:25:13,294 __main__:129 server: Shutting down ToolGroupsRoutingTable INFO 2025-03-17 17:25:13,295 __main__:129 server: Shutting down ToolRuntimeRouter INFO 2025-03-17 17:25:13,296 __main__:129 server: Shutting down MetaReferenceAgentsImpl INFO 2025-03-17 17:25:13,297 __main__:129 server: Shutting down ScoringFunctionsRoutingTable INFO 2025-03-17 17:25:13,298 __main__:129 server: Shutting down ScoringRouter INFO 2025-03-17 17:25:13,299 __main__:129 server: Shutting down BenchmarksRoutingTable INFO 2025-03-17 17:25:13,300 __main__:129 server: Shutting down EvalRouter INFO 2025-03-17 17:25:13,301 __main__:129 server: Shutting down DistributionInspectImpl INFO 2025-03-17 17:25:13,303 __main__:129 server: Shutting down ProviderImpl WARNING 2025-03-17 17:25:13,304 __main__:134 server: No shutdown method for ProviderImpl INFO: Application shutdown complete. INFO: Finished server process [1] ``` ## Test Plan Start a server and shut it down Signed-off-by: Nathan Weinberg <nweinber@redhat.com> |
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distributions | ||
docs | ||
llama_stack | ||
rfcs | ||
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tests | ||
.gitignore | ||
.pre-commit-config.yaml | ||
.readthedocs.yaml | ||
CHANGELOG.md | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
LICENSE | ||
MANIFEST.in | ||
pyproject.toml | ||
README.md | ||
requirements.txt | ||
SECURITY.md | ||
uv.lock |
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.
- llama (server-side) CLI Reference: Guide for using the
- 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.