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

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Dinesh Yeduguru b8535417e0 feat: record token usage for inference API (#1300 ) # What does this PR do? Inference router computes the token usage related metrics for all providers and returns the metrics as part of response and also logs to telemetry. ## Test Plan LLAMA_STACK_DISABLE_VERSION_CHECK=true llama stack run ~/.llama/distributions/fireworks/fireworks-run.yaml ``` curl --request POST \ --url http://localhost:8321/v1/inference/chat-completion \ --header 'content-type: application/json' \ --data '{ "model_id": "meta-llama/Llama-3.1-70B-Instruct", "messages": [ { "role": "user", "content": { "type": "text", "text": "where do humans live" } } ], "stream": false }' \| jq . { "metrics": [ { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770903Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "prompt_tokens", "value": 10, "unit": "tokens" }, { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770916Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "completion_tokens", "value": 411, "unit": "tokens" }, { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770919Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "total_tokens", "value": 421, "unit": "tokens" } ], "completion_message": { "role": "assistant", "content": "Humans live in various parts of the world, inhabiting almost every continent, country, and region. Here's a breakdown of where humans live:\n\n1. Continents: Humans inhabit all seven continents:\n\t* Africa\n\t* Antarctica (research stations only)\n\t* Asia\n\t* Australia\n\t* Europe\n\t* North America\n\t* South America\n2. Countries: There are 196 countries recognized by the United Nations, and humans live in almost all of them.\n3. Regions: Humans live in diverse regions, including:\n\t* Deserts (e.g., Sahara, Mojave)\n\t* Forests (e.g., Amazon, Congo)\n\t* Grasslands (e.g., Prairies, Steppes)\n\t* Mountains (e.g., Himalayas, Andes)\n\t* Oceans (e.g., coastal areas, islands)\n\t* Tundras (e.g., Arctic, sub-Arctic)\n4. Cities and towns: Many humans live in urban areas, such as cities and towns, which are often located near:\n\t* Coastlines\n\t* Rivers\n\t* Lakes\n\t* Mountains\n5. Rural areas: Some humans live in rural areas, such as:\n\t* Villages\n\t* Farms\n\t* Countryside\n6. Islands: Humans inhabit many islands, including:\n\t* Tropical islands (e.g., Hawaii, Maldives)\n\t* Arctic islands (e.g., Greenland, Iceland)\n\t* Continental islands (e.g., Great Britain, Ireland)\n7. Extreme environments: Humans also live in extreme environments, such as:\n\t* High-altitude areas (e.g., Tibet, Andes)\n\t* Low-altitude areas (e.g., Death Valley, Dead Sea)\n\t* Areas with extreme temperatures (e.g., Arctic, Sahara)\n\nOverall, humans have adapted to live in a wide range of environments and ecosystems around the world.", "stop_reason": "end_of_turn", "tool_calls": [] }, "logprobs": null } ``` ``` LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/integration/inference ======================================================================== short test summary info ========================================================================= FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-True] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-False] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_non_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f... FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f... ========================================================= 4 failed, 16 passed, 23 xfailed, 17 warnings in 44.36s ========================================================= ```		2025-03-05 12:41:45 -08:00
.github	chore: Update CODEOWNERS (#1407 )	2025-03-04 21:48:24 -08:00
distributions	fix: precommits ugh why wont they run correctly because they dont have the right dependencies	2025-02-27 15:02:04 -08:00
docs	feat: effective agent workflow notebook (#1372 )	2025-03-05 11:53:25 -08:00
llama_stack	feat: record token usage for inference API (#1300 )	2025-03-05 12:41:45 -08:00
rfcs	chore: remove straggler references to llama-models (#1345 )	2025-03-01 14:26:03 -08:00
tests	fix: Gracefully handle no choices in remote vLLM response (#1424 )	2025-03-05 15:07:54 -05:00
.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	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
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	feat: completing text /chat-completion and /completion tests (#1223 )	2025-02-25 11:37:04 -08:00
pyproject.toml	fix: update version and fix docs release notes link	2025-03-03 11:48:57 -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: update version and fix docs release notes link	2025-03-03 11:48:57 -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.