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refactor(build): rework CLI commands and build process (1/2) (#2974) 
# What does this PR do?

This PR does a few things outlined in #2878 namely:
1. adds `llama stack list-deps` a command which simply takes the build
logic and instead of executing one of the `build_...` scripts, it
displays all of the providers' dependencies using the `module` and `uv`.
2. deprecated `llama stack build` in favor of `llama stack list-deps`
3. updates all tests to use `list-deps` alongside `build`.

PR 2/2 will migrate `llama stack run`'s default behavior to be `llama
stack build --run` and use the new `list-deps` command under the hood
before running the server.

examples of `llama stack list-deps starter`

```
llama stack list-deps starter --format json
{
  "name": "starter",
  "description": "Quick start template for running Llama Stack with several popular providers. This distribution is intended for CPU-only environments.",
  "apis": [
    {
      "api": "inference",
      "provider": "remote::cerebras"
    },
    {
      "api": "inference",
      "provider": "remote::ollama"
    },
    {
      "api": "inference",
      "provider": "remote::vllm"
    },
    {
      "api": "inference",
      "provider": "remote::tgi"
    },
    {
      "api": "inference",
      "provider": "remote::fireworks"
    },
    {
      "api": "inference",
      "provider": "remote::together"
    },
    {
      "api": "inference",
      "provider": "remote::bedrock"
    },
    {
      "api": "inference",
      "provider": "remote::nvidia"
    },
    {
      "api": "inference",
      "provider": "remote::openai"
    },
    {
      "api": "inference",
      "provider": "remote::anthropic"
    },
    {
      "api": "inference",
      "provider": "remote::gemini"
    },
    {
      "api": "inference",
      "provider": "remote::vertexai"
    },
    {
      "api": "inference",
      "provider": "remote::groq"
    },
    {
      "api": "inference",
      "provider": "remote::sambanova"
    },
    {
      "api": "inference",
      "provider": "remote::azure"
    },
    {
      "api": "inference",
      "provider": "inline::sentence-transformers"
    },
    {
      "api": "vector_io",
      "provider": "inline::faiss"
    },
    {
      "api": "vector_io",
      "provider": "inline::sqlite-vec"
    },
    {
      "api": "vector_io",
      "provider": "inline::milvus"
    },
    {
      "api": "vector_io",
      "provider": "remote::chromadb"
    },
    {
      "api": "vector_io",
      "provider": "remote::pgvector"
    },
    {
      "api": "files",
      "provider": "inline::localfs"
    },
    {
      "api": "safety",
      "provider": "inline::llama-guard"
    },
    {
      "api": "safety",
      "provider": "inline::code-scanner"
    },
    {
      "api": "agents",
      "provider": "inline::meta-reference"
    },
    {
      "api": "telemetry",
      "provider": "inline::meta-reference"
    },
    {
      "api": "post_training",
      "provider": "inline::torchtune-cpu"
    },
    {
      "api": "eval",
      "provider": "inline::meta-reference"
    },
    {
      "api": "datasetio",
      "provider": "remote::huggingface"
    },
    {
      "api": "datasetio",
      "provider": "inline::localfs"
    },
    {
      "api": "scoring",
      "provider": "inline::basic"
    },
    {
      "api": "scoring",
      "provider": "inline::llm-as-judge"
    },
    {
      "api": "scoring",
      "provider": "inline::braintrust"
    },
    {
      "api": "tool_runtime",
      "provider": "remote::brave-search"
    },
    {
      "api": "tool_runtime",
      "provider": "remote::tavily-search"
    },
    {
      "api": "tool_runtime",
      "provider": "inline::rag-runtime"
    },
    {
      "api": "tool_runtime",
      "provider": "remote::model-context-protocol"
    },
    {
      "api": "batches",
      "provider": "inline::reference"
    }
  ],
  "pip_dependencies": [
    "pandas",
    "opentelemetry-exporter-otlp-proto-http",
    "matplotlib",
    "opentelemetry-sdk",
    "sentence-transformers",
    "datasets",
    "pymilvus[milvus-lite]>=2.4.10",
    "codeshield",
    "scipy",
    "torchvision",
    "tree_sitter",
    "h11>=0.16.0",
    "aiohttp",
    "pymongo",
    "tqdm",
    "pythainlp",
    "pillow",
    "torch",
    "emoji",
    "grpcio>=1.67.1,<1.71.0",
    "fireworks-ai",
    "langdetect",
    "psycopg2-binary",
    "asyncpg",
    "redis",
    "together",
    "torchao>=0.12.0",
    "openai",
    "sentencepiece",
    "aiosqlite",
    "google-cloud-aiplatform",
    "faiss-cpu",
    "numpy",
    "sqlite-vec",
    "nltk",
    "scikit-learn",
    "mcp>=1.8.1",
    "transformers",
    "boto3",
    "huggingface_hub",
    "ollama",
    "autoevals",
    "sqlalchemy[asyncio]",
    "torchtune>=0.5.0",
    "chromadb-client",
    "pypdf",
    "requests",
    "anthropic",
    "chardet",
    "aiosqlite",
    "fastapi",
    "fire",
    "httpx",
    "uvicorn",
    "opentelemetry-sdk",
    "opentelemetry-exporter-otlp-proto-http"
  ]
}
```

<img width="1500" height="420" alt="Screenshot 2025-10-16 at 5 53 03 PM"
src="https://github.com/user-attachments/assets/765929fb-93e2-44d7-9c3d-8918b70fc721"
/>

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>
2025-10-17 19:52:14 -07:00
..
cli chore(rename): move llama_stack.distribution to llama_stack.core (#2975) 2025-07-30 23:30:53 -07:00
conversations feat: Add OpenAI Conversations API (#3429) 2025-10-03 08:47:18 -07:00
core feat: Enable setting a default embedding model in the stack (#3803) 2025-10-14 18:25:13 -07:00
distribution refactor(build): rework CLI commands and build process (1/2) (#2974) 2025-10-17 19:52:14 -07:00
files chore(files tests): update files integration tests and fix inline::localfs (#3195) 2025-08-20 14:22:40 -04:00
models feat(tools)!: substantial clean up of "Tool" related datatypes (#3627) 2025-10-02 15:12:03 -07:00
prompts/prompts feat: Adding OpenAI Prompts API (#3319) 2025-09-08 11:05:13 -04:00
providers fix(openai_mixin): no yelling for model listing if API keys are not provided (#3826) 2025-10-16 10:12:13 -07:00
rag feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794) 2025-10-12 19:01:52 -07:00
registry refactor: replace default all-MiniLM-L6-v2 embedding model by nomic-embed-text-v1.5 in Llama Stack (#3183) 2025-10-14 10:44:20 -04:00
server fix(models)!: always prefix models with provider_id when registering (#3822) 2025-10-16 06:47:39 -07:00
tools feat(tools)!: substantial clean up of "Tool" related datatypes (#3627) 2025-10-02 15:12:03 -07:00
utils feat: Allow :memory: for kvstore (#3696) 2025-10-13 11:19:27 +02:00
__init__.py chore: Add fixtures to conftest.py (#2067) 2025-05-06 13:57:48 +02:00
conftest.py fix(tests): reduce some test noise (#3825) 2025-10-16 09:52:16 -07:00
fixtures.py chore(rename): move llama_stack.distribution to llama_stack.core (#2975) 2025-07-30 23:30:53 -07:00
README.md test: Measure and track code coverage (#2636) 2025-07-18 18:08:36 +02:00

README.md

Llama Stack Unit Tests

Unit Tests

Unit tests verify individual components and functions in isolation. They are fast, reliable, and don't require external services.

Prerequisites

  1. Python Environment: Ensure you have Python 3.12+ installed
  2. uv Package Manager: Install uv if not already installed

You can run the unit tests by running:

./scripts/unit-tests.sh [PYTEST_ARGS]

Any additional arguments are passed to pytest. For example, you can specify a test directory, a specific test file, or any pytest flags (e.g., -vvv for verbosity). If no test directory is specified, it defaults to "tests/unit", e.g:

./scripts/unit-tests.sh tests/unit/registry/test_registry.py -vvv

If you'd like to run for a non-default version of Python (currently 3.12), pass PYTHON_VERSION variable as follows:

source .venv/bin/activate
PYTHON_VERSION=3.13 ./scripts/unit-tests.sh

Test Configuration

  • Test Discovery: Tests are automatically discovered in the tests/unit/ directory
  • Async Support: Tests use --asyncio-mode=auto for automatic async test handling
  • Coverage: Tests generate coverage reports in htmlcov/ directory
  • Python Version: Defaults to Python 3.12, but can be overridden with PYTHON_VERSION environment variable

Coverage Reports

After running tests, you can view coverage reports:

# Open HTML coverage report in browser
open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux
start htmlcov/index.html  # Windows