llama-stack-mirror/tests/integration/README.md

Llama Stack Integration Tests

We use pytest for parameterizing and running tests. You can see all options with:

cd tests/integration

# this will show a long list of options, look for "Custom options:"
pytest --help

Here are the most important options:

• --stack-config: specify the stack config to use. You have four ways to point to a stack:
  • server:<config> - automatically start a server with the given config (e.g., server:fireworks). This provides one-step testing by auto-starting the server if the port is available, or reusing an existing server if already running.
  • server:<config>:<port> - same as above but with a custom port (e.g., server:together:8322)
  • a URL which points to a Llama Stack distribution server
  • a template (e.g., fireworks, together) or a path to a run.yaml file
  • a comma-separated list of api=provider pairs, e.g. inference=fireworks,safety=llama-guard,agents=meta-reference. This is most useful for testing a single API surface.
• --env: set environment variables, e.g. --env KEY=value. this is a utility option to set environment variables required by various providers.

Model parameters can be influenced by the following options:

• --text-model: comma-separated list of text models.
• --vision-model: comma-separated list of vision models.
• --embedding-model: comma-separated list of embedding models.
• --safety-shield: comma-separated list of safety shields.
• --judge-model: comma-separated list of judge models.
• --embedding-dimension: output dimensionality of the embedding model to use for testing. Default: 384

Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified.

Examples

Testing against a Server

Run all text inference tests by auto-starting a server with the fireworks config:

pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=server:fireworks \
   --text-model=meta-llama/Llama-3.1-8B-Instruct

Run tests with auto-server startup on a custom port:

pytest -s -v tests/integration/inference/ \
   --stack-config=server:together:8322 \
   --text-model=meta-llama/Llama-3.1-8B-Instruct

Run multiple test suites with auto-server (eliminates manual server management):

# Auto-start server and run all integration tests
export FIREWORKS_API_KEY=<your_key>

pytest -s -v tests/integration/inference/ tests/integration/safety/ tests/integration/agents/ \
   --stack-config=server:fireworks \
   --text-model=meta-llama/Llama-3.1-8B-Instruct

Testing with Library Client

Run all text inference tests with the together distribution:

pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=together \
   --text-model=meta-llama/Llama-3.1-8B-Instruct

Run all text inference tests with the together distribution and meta-llama/Llama-3.1-8B-Instruct:

pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=together \
   --text-model=meta-llama/Llama-3.1-8B-Instruct

Running all inference tests for a number of models:

TEXT_MODELS=meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-70B-Instruct
VISION_MODELS=meta-llama/Llama-3.2-11B-Vision-Instruct
EMBEDDING_MODELS=all-MiniLM-L6-v2
export TOGETHER_API_KEY=<together_api_key>

pytest -s -v tests/integration/inference/ \
   --stack-config=together \
   --text-model=$TEXT_MODELS \
   --vision-model=$VISION_MODELS \
   --embedding-model=$EMBEDDING_MODELS

Same thing but instead of using the distribution, use an adhoc stack with just one provider (fireworks for inference):

export FIREWORKS_API_KEY=<fireworks_api_key>

pytest -s -v tests/integration/inference/ \
   --stack-config=inference=fireworks \
   --text-model=$TEXT_MODELS \
   --vision-model=$VISION_MODELS \
   --embedding-model=$EMBEDDING_MODELS

Running Vector IO tests for a number of embedding models:

EMBEDDING_MODELS=all-MiniLM-L6-v2

pytest -s -v tests/integration/vector_io/ \
   --stack-config=inference=sentence-transformers,vector_io=sqlite-vec \
   --embedding-model=$EMBEDDING_MODELS