Quick Start | Documentation | Zero-to-Hero Guide
Llama Stack defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.
Our goal is to provide pre-packaged implementations which can be operated in a variety of deployment environments: developers start iterating with Desktops or their mobile devices and can seamlessly transition to on-prem or public cloud deployments. At every point in this transition, the same set of APIs and the same developer experience is available.
⚠️ Note The Stack APIs are rapidly improving, but still very much work in progress and we invite feedback as well as direct contributions.
We have working implementations of the following APIs today:
- Inference
- Safety
- Memory
- Agents
- Eval
- Telemetry
Alongside these APIs, we also related APIs for operating with associated resources (see Concepts):
- Models
- Shields
- Memory Banks
- Eval Tasks
- Datasets
- Scoring Functions
We are also working on the following APIs which will be released soon:
- Post Training
- Synthetic Data Generation
- Reward Scoring
Each of the APIs themselves is a collection of REST endpoints.
Unlike other frameworks, Llama Stack is built with a service-oriented, REST API-first approach. Such a design not only allows for seamless transitions from a local to remote deployments, but also forces the design to be more declarative. We believe this restriction can result in a much simpler, robust developer experience. This will necessarily trade-off against expressivity however if we get the APIs right, it can lead to a very powerful platform.
We expect the set of APIs we design to be composable. An Agent abstractly depends on { Inference, Memory, Safety } APIs but does not care about the actual implementation details. Safety itself may require model inference and hence can depend on the Inference API.
We expect to provide turnkey solutions for popular deployment scenarios. It should be easy to deploy a Llama Stack server on AWS or on a private data center. Either of these should allow a developer to get started with powerful agentic apps, model evaluations or fine-tuning services in a matter of minutes. They should all result in the same uniform observability and developer experience.
As a Meta initiated project, we have started by explicitly focusing on Meta's Llama series of models. Supporting the broad set of open models is no easy task and we want to start with models we understand best.
There is a vibrant ecosystem of Providers which provide efficient inference or scalable vector stores or powerful observability solutions. We want to make sure it is easy for developers to pick and choose the best implementations for their use cases. We also want to make sure it is easy for new Providers to onboard and participate in the ecosystem.
Additionally, we have designed every element of the Stack such that APIs as well as Resources (like Models) can be federated.
| API Provider Builder | Environments | Agents | Inference | Memory | Safety | Telemetry |
|---|---|---|---|---|---|---|
| Meta Reference | Single Node | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| Cerebras | Hosted | ✔️ | ||||
| Fireworks | Hosted | ✔️ | ✔️ | ✔️ | ||
| AWS Bedrock | Hosted | ✔️ | ✔️ | |||
| Together | 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 | ✔️ |
| 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 |
| 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 |
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, make sure you have conda installed. Then, follow these steps:
mkdir -p ~/local cd ~/local git clone [email protected]:meta-llama/llama-stack.git conda create -n stack python=3.10 conda activate stack cd llama-stack pip install -e .
Please checkout our Documentation page for more details.
- CLI reference
- Guide using
llamaCLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution.
- Guide using
- 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.
| Language | Client SDK | Package |
|---|---|---|
| Python | llama-stack-client-python |  |
| Swift | llama-stack-client-swift |  |
| Node | llama-stack-client-node |  |
| Kotlin | llama-stack-client-kotlin |  |
Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from python, node, 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.
