# What does this PR do? This is the second attempt to switch to system packages by default. Now with a hack to detect conda environment - in which case conda image-type is used. Note: Conda will only be used when --image-name is unset *and* CONDA_DEFAULT_ENV is set. This means that users without conda will correctly fall back to using system packages when no --image-* arguments are passed at all. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Uses virtualenv: ``` $ llama stack build --template ollama --image-type venv $ llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml [...] Using virtual environment: /home/ec2-user/src/llama-stack/schedule/.local [...] ``` Uses system packages (virtualenv already initialized): ``` $ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml [...] INFO 2025-03-27 20:46:22,882 llama_stack.cli.stack.run:142 server: No image type or image name provided. Assuming environment packages. [...] ``` Attempt to run from environment packages without necessary packages installed: ``` $ python -m venv barebones $ . ./barebones/bin/activate $ pip install -e . # to install llama command $ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml [...] ModuleNotFoundError: No module named 'fastapi' ``` ^ failed as expected because the environment doesn't have necessary packages installed. Now install some packages in the new environment: ``` $ pip install fastapi opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp aiosqlite ollama openai datasets faiss-cpu mcp autoevals $ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml [...] Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) ``` Now see if setting CONDA_DEFAULT_ENV will change what happens by default: ``` $ export CONDA_DEFAULT_ENV=base $ llama stack run ~/.llama/distributions/ollama/ollama-run.yaml [...] Using conda environment: base Conda environment base does not exist. [...] ``` --------- Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com> |
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CHANGELOG.md | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
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pyproject.toml | ||
README.md | ||
requirements.txt | ||
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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.