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Composable building blocks to build Llama Apps

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Ben Browning c64f0d5888 fix: Get builtin tool calling working in remote-vllm (#1236 ) # What does this PR do? This PR makes a couple of changes required to get the test `tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search` passing on the remote-vllm provider. First, we adjust agent_instance to also pass in the description and parameters of builtin tools. We need these parameters so we can pass the tool's expected parameters into vLLM. The meta-reference implementations may not have needed these for builtin tools, as they are able to take advantage of the Llama-model specific support for certain builtin tools. However, with vLLM, our server-side chat templates for tool calling treat all tools the same and don't separate out Llama builtin vs custom tools. So, we need to pass the full set of parameter definitions and list of required parameters for builtin tools as well. Next, we adjust the vllm streaming chat completion code to fix up some edge cases where it was returning an extra ChatCompletionResponseEvent with an empty ToolCall with empty string call_id, tool_name, and arguments properties. This is a bug discovered after the above fix, where after a successful tool invocation we were sending extra chunks back to the client with these empty ToolCalls. ## Test Plan With these changes, the following test that previously failed now passes: ``` VLLM_URL="http://localhost:8000/v1" \ INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ LLAMA_STACK_CONFIG=remote-vllm \ python -m pytest -v \ tests/client-sdk/agents/test_agents.py::test_builtin_tool_web_search \ --inference-model "meta-llama/Llama-3.2-3B-Instruct" ``` Additionally, I ran the remote-vllm client-sdk and provider inference tests as below to ensure they all still passed with this change: ``` VLLM_URL="http://localhost:8000/v1" \ INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ LLAMA_STACK_CONFIG=remote-vllm \ python -m pytest -v \ tests/client-sdk/inference/test_text_inference.py \ --inference-model "meta-llama/Llama-3.2-3B-Instruct" ``` ``` VLLM_URL="http://localhost:8000/v1" \ python -m pytest -s -v \ llama_stack/providers/tests/inference/test_text_inference.py \ --providers "inference=vllm_remote" ``` [//]: # (## Documentation) Signed-off-by: Ben Browning <bbrownin@redhat.com>		2025-02-26 15:25:47 -05:00
.github	ci: improve GitHub Actions workflow for website builds (#1151 )	2025-02-20 21:37:37 -08:00
distributions	feat: add (openai, anthropic, gemini) providers via litellm (#1267 )	2025-02-25 22:07:33 -08:00
docs	fix: the pre-commit new line issue (#1272 )	2025-02-26 04:25:41 -05:00
llama_stack	fix: Get builtin tool calling working in remote-vllm (#1236 )	2025-02-26 15:25:47 -05:00
rfcs	docs: Fix url to the llama-stack-spec yaml/html files (#1081 )	2025-02-13 12:39:26 -08:00
tests/client-sdk	feat: add (openai, anthropic, gemini) providers via litellm (#1267 )	2025-02-25 22:07:33 -08:00
.gitignore	github: ignore non-hidden python virtual environments (#939 )	2025-02-03 11:53:05 -08:00
.gitmodules	chore: removed executorch submodule (#1265 )	2025-02-25 21:57:21 -08:00
.pre-commit-config.yaml	build: Merge redundant "files" field for codegen check in .pre-commit-config.yaml (#1261 )	2025-02-25 20:56:22 -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
CODE_OF_CONDUCT.md	Initial commit	2024-07-23 08:32:33 -07:00
CONTRIBUTING.md	docs: Add missing uv command and clarify website rebuild (#1199 )	2025-02-21 11:29:32 -05:00
LICENSE	Update LICENSE (#47 )	2024-08-29 07:39:50 -07:00
MANIFEST.in	feat: completing text /chat-completion and /completion tests (#1223 )	2025-02-25 11:37:04 -08:00
pyproject.toml	fix: resolve type hint issues and import dependencies (#1176 )	2025-02-25 11:06:47 -08:00
README.md	docs: Simplify installation guide with `uv` (#1196 )	2025-02-20 21:05:47 -08:00
requirements.txt	fix: pre-commit updates (#1243 )	2025-02-24 17:20:29 -08:00
SECURITY.md	Create SECURITY.md	2024-10-08 13:30:40 -04:00
uv.lock	fix: pre-commit updates (#1243 )	2025-02-24 17:20:29 -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.