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# What does this PR do? Update README and other documentation ## Before submitting - [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
129 lines
13 KiB
Markdown
129 lines
13 KiB
Markdown
# Llama Stack
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[](https://pypi.org/project/llama_stack/)
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[](https://pypi.org/project/llama-stack/)
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[](https://discord.gg/llama-stack)
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[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb)
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Llama Stack defines and standardizes the core building blocks needed to bring generative AI applications to market. It provides a unified set of APIs with implementations from leading service providers, enabling seamless transitions between development and production environments.
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We focus on making it easy to build production applications with the Llama model family - from the latest Llama 3.3 to specialized models like Llama Guard for safety.
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<div style="text-align: center;">
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<img
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src="https://github.com/user-attachments/assets/33d9576d-95ea-468d-95e2-8fa233205a50"
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width="480"
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title="Llama Stack"
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alt="Llama Stack"
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/>
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</div>
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## Key Features
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- **Unified API Layer** for:
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- Inference: Run LLM models efficiently
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- Safety: Apply content filtering and safety policies
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- DatasetIO: Store and retrieve knowledge for RAG
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- Agents: Build multi-step agentic workflows
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- Evaluation: Test and improve model and agent quality
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- Telemetry: Collect and analyze usage data and complex agentic traces
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- Post Training ( Coming Soon ): Fine tune models for specific use cases
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- **Rich Provider Ecosystem**
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- Local Development: Meta's Reference,Ollama, vLLM, TGI
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- Self-hosted: Chroma, pgvector, Nvidia NIM
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- Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras
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- On-device: iOS and Android support
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- **Built for Production**
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- Pre-packaged distributions for common deployment scenarios
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- Comprehensive evaluation capabilities
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- Full observability and monitoring
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- Provider federation and fallback
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## Supported Llama Stack Implementations
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### API Providers
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| **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** |
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|:------------------------------------------------------------------------------------------:|:----------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
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| Meta Reference | Single Node | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
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| Cerebras | Hosted | | :heavy_check_mark: | | | |
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| Fireworks | Hosted | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
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| AWS Bedrock | Hosted | | :heavy_check_mark: | | :heavy_check_mark: | |
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| Together | Hosted | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | |
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| Groq | Hosted | | :heavy_check_mark: | | | |
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| Ollama | Single Node | | :heavy_check_mark: | | | |
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| TGI | Hosted and Single Node | | :heavy_check_mark: | | | |
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| NVIDIA NIM | Hosted and Single Node | | :heavy_check_mark: | | | |
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| Chroma | Single Node | | | :heavy_check_mark: | | |
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| PG Vector | Single Node | | | :heavy_check_mark: | | |
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| PyTorch ExecuTorch | On-device iOS | :heavy_check_mark: | :heavy_check_mark: | | | |
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| vLLM | Hosted and Single Node | | :heavy_check_mark: | | | |
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### Distributions
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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:
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| **Distribution** | **Llama Stack Docker** | Start This Distribution |
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|:---------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------:|
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| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) |
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| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
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| Cerebras | [llamastack/distribution-cerebras](https://hub.docker.com/repository/docker/llamastack/distribution-cerebras/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/cerebras.html) |
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| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/ollama.html) |
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| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/tgi.html) |
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| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/together.html) |
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| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/fireworks.html) |
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| vLLM | [llamastack/distribution-remote-vllm](https://hub.docker.com/repository/docker/llamastack/distribution-remote-vllm/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/remote-vllm.html) |
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## Installation
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You have two ways to install this repository:
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1. **Install as a package**:
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You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command:
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```bash
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pip install llama-stack
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```
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2. **Install from source**:
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If you prefer to install from the source code, make sure you have [conda installed](https://docs.conda.io/projects/conda/en/stable).
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Then, follow these steps:
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```bash
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mkdir -p ~/local
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cd ~/local
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git clone git@github.com:meta-llama/llama-stack.git
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conda create -n stack python=3.10
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conda activate stack
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cd llama-stack
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pip install -e .
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```
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## Documentation
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Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details.
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* [CLI reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html)
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* Guide using `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution.
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* [Getting Started](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html)
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* Quick guide to start a Llama Stack server.
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* [Jupyter notebook](./docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb) to walk-through how to use simple text and vision inference llama_stack_client APIs
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* The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack).
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* A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples.
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* [Contributing](CONTRIBUTING.md)
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* [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) to walk-through how to add a new API provider.
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## Llama Stack Client SDKs
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| **Language** | **Client SDK** | **Package** |
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| :----: | :----: | :----: |
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| Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [](https://pypi.org/project/llama_stack_client/)
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| Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift) | [](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift)
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| Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [](https://npmjs.org/package/llama-stack-client)
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| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin)
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Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [node](https://github.com/meta-llama/llama-stack-client-node), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications.
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You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.
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