Composable building blocks to build Llama Apps
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Nathan Weinberg e48af78b76
fix: add shutdown method for ProviderImpl (#1670)
# 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>
2025-03-17 14:55:40 -07:00
.cursor/rules test: first unit test for resolver (#1475) 2025-03-07 10:20:51 -08:00
.github chore: Add ./scripts/unit-tests.sh (#1515) 2025-03-13 20:25:15 -07:00
distributions feat: added nvidia as safety provider (#1248) 2025-03-17 14:39:23 -07:00
docs feat: added nvidia as safety provider (#1248) 2025-03-17 14:39:23 -07:00
llama_stack fix: add shutdown method for ProviderImpl (#1670) 2025-03-17 14:55:40 -07:00
rfcs chore: remove straggler references to llama-models (#1345) 2025-03-01 14:26:03 -08:00
scripts chore: Add ./scripts/unit-tests.sh (#1515) 2025-03-13 20:25:15 -07:00
tests fix: sleep between tests oof 2025-03-14 14:45:37 -07:00
.gitignore build: remove .python-version (#1513) 2025-03-12 20:08:24 -07:00
.pre-commit-config.yaml chore: Add pre-commit check to sync api spec docs (#1609) 2025-03-14 09:20:49 -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 chore: Add ./scripts/unit-tests.sh (#1515) 2025-03-13 20:25:15 -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 feat: added nvidia as safety provider (#1248) 2025-03-17 14:39:23 -07:00
README.md docs: Add OpenAI, Anthropic, Gemini to API providers table (#1617) 2025-03-13 15:47:58 -04:00
requirements.txt ci: Bump version to 0.1.7 2025-03-14 15:21:26 -07:00
SECURITY.md Create SECURITY.md 2024-10-08 13:30:40 -04:00
uv.lock ci: Bump version to 0.1.7 2025-03-14 15:21:26 -07:00

Llama Stack

PyPI version PyPI - Downloads License Discord Unit

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

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.

Llama Stack Client SDKs

Language Client SDK Package
Python llama-stack-client-python PyPI version
Swift llama-stack-client-swift Swift Package Index
Typescript llama-stack-client-typescript NPM version
Kotlin llama-stack-client-kotlin Maven version

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.