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- Added new template `dell` and its documentation - Update docs - [minor] uv fix i came across - codegen for all templates Tested with ```bash export INFERENCE_PORT=8181 export DEH_URL=http://0.0.0.0:$INFERENCE_PORT export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct export CHROMADB_HOST=localhost export CHROMADB_PORT=6601 export CHROMA_URL=[http://$CHROMADB_HOST:$CHROMADB_PORT](about:blank) export CUDA_VISIBLE_DEVICES=0 export LLAMA_STACK_PORT=8321 # build the stack template llama stack build --template=dell # start the TGI inference server podman run --rm -it --network host -v $HOME/.cache/huggingface:/data -e HF_TOKEN=$HF_TOKEN -p $INFERENCE_PORT:$INFERENCE_PORT --gpus $CUDA_VISIBLE_DEVICES [ghcr.io/huggingface/text-generation-inference](http://ghcr.io/huggingface/text-generation-inference) --dtype bfloat16 --usage-stats off --sharded false --cuda-memory-fraction 0.7 --model-id $INFERENCE_MODEL --port $INFERENCE_PORT --hostname 0.0.0.0 # start chroma-db for vector-io ( aka RAG ) podman run --rm -it --network host --name chromadb -v .:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest --port $CHROMADB_PORT --host $(hostname) # build docker llama stack build --template=dell --image-type=container # run llama stack server ( via docker ) podman run -it \ --network host \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ # NOTE: mount the llama-stack / llama-model directories if testing local changes -v /home/hjshah/git/llama-stack:/app/llama-stack-source -v /home/hjshah/git/llama-models:/app/llama-models-source \ localhost/distribution-dell:dev \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env DEH_URL=$DEH_URL \ --env CHROMA_URL=$CHROMA_URL # test the server cd <PATH_TO_LLAMA_STACK_REPO> LLAMA_STACK_BASE_URL=http://0.0.0.0:$LLAMA_STACK_PORT pytest -s -v tests/client-sdk/agents/test_agents.py ``` --------- Co-authored-by: Hardik Shah <hjshah@fb.com> |
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| .gitignore | ||
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| .pre-commit-config.yaml | ||
| .readthedocs.yaml | ||
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| 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 defines and standardizes the core building blocks that simplify AI application development. It codified 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 implementations of the different APIs in different environments like 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 choice.
- 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 to developers started easily,
| 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, make sure you have conda installed. Then, follow these steps:
mkdir -p ~/local cd ~/local git clone git@github.com:meta-llama/llama-stack.git conda create -n stack python=3.10 conda activate stack cd llama-stack pip install -e .
Documentation
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.
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 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.