commit a95d2b15b83057e194cf69e57a03deeeeeadd7c2 Author: m-misiura <mmisiura@redhat.com> Date: Mon Mar 24 14:33:50 2025 +0000 🚧 working on the config file so that it is inheriting from pydantic base models commit 0546379f817e37bca030247b48c72ce84899a766 Author: m-misiura <mmisiura@redhat.com> Date: Mon Mar 24 09:14:31 2025 +0000 🚧 dealing with ruff checks commit 8abe39ee4cb4b8fb77c7252342c4809fa6ddc432 Author: m-misiura <mmisiura@redhat.com> Date: Mon Mar 24 09:03:18 2025 +0000 🚧 dealing with mypy errors in `base.py` commit 045f833e79c9a25af3d46af6c8896da91a0e6e62 Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 17:31:25 2025 +0000 🚧 fixing mypy errors in content.py commit a9c1ee4e92ad1b5db89039317555cd983edbde65 Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 17:09:02 2025 +0000 🚧 fixing mypy errors in chat.py commit 69e8ddc2f8a4e13cecbab30272fd7d685d7864ec Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 16:57:28 2025 +0000 🚧 fixing mypy errors commit 56739d69a145c55335ac2859ecbe5b43d556e3b1 Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 14:01:03 2025 +0000 🚧 fixing mypy errors in `__init__.py` commit 4d2e3b55c4102ed75d997c8189847bbc5524cb2c Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 12:58:06 2025 +0000 🚧 ensuring routing_tables.py do not fail the ci commit c0cc7b4b09ef50d5ec95fdb0a916c7ed228bf366 Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 12:09:24 2025 +0000 🐛 fixing linter problems commit 115a50211b604feb4106275204fe7f863da865f6 Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 11:47:04 2025 +0000 🐛 fixing ruff errors commit 29b5bfaabc77a35ea036b57f75fded711228dbbf Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 11:33:31 2025 +0000 🎨 automatic ruff fixes commit 7c5a334c7d4649c2fc297993f89791c1e5643e5b Author: m-misiura <mmisiura@redhat.com> Date: Fri Mar 21 11:15:02 2025 +0000 Squashed commit of the following: commit e671aae5bcd4ea57d601ee73c9e3adf5e223e830 Merge: b0dd9a4f |
||
---|---|---|
.github | ||
distributions | ||
docs | ||
llama_stack | ||
rfcs | ||
scripts | ||
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 |
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.