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

Integration Testing Guide

Integration tests verify complete workflows across different providers using Llama Stack's record-replay system.

Quick Start

# Run all integration tests with existing recordings
LLAMA_STACK_TEST_INFERENCE_MODE=replay \
  LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
  uv run --group test \
  pytest -sv tests/integration/ --stack-config=starter

Configuration Options

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:starter). 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:starter:8322)
  • a URL which points to a Llama Stack distribution server
  • a distribution name (e.g., starter) or a path to a run.yaml file
  • a comma-separated list of api=provider pairs, e.g. inference=ollama,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 starter config:

OLLAMA_URL=http://localhost:11434 \
  pytest -s -v tests/integration/inference/test_text_inference.py \
   --stack-config=server:starter \
   --text-model=ollama/llama3.2:3b-instruct-fp16 \
   --embedding-model=sentence-transformers/all-MiniLM-L6-v2

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

OLLAMA_URL=http://localhost:11434 \
  pytest -s -v tests/integration/inference/ \
   --stack-config=server:starter:8322 \
   --text-model=ollama/llama3.2:3b-instruct-fp16 \
   --embedding-model=sentence-transformers/all-MiniLM-L6-v2

Testing with Library Client

The library client constructs the Stack "in-process" instead of using a server. This is useful during the iterative development process since you don't need to constantly start and stop servers.

You can do this by simply using --stack-config=starter instead of --stack-config=server:starter.

Using ad-hoc distributions

Sometimes, you may want to make up a distribution on the fly. This is useful for testing a single provider or a single API or a small combination of providers. You can do so by specifying a comma-separated list of api=provider pairs to the --stack-config option, e.g. inference=remote::ollama,safety=inline::llama-guard,agents=inline::meta-reference.

pytest -s -v tests/integration/inference/ \
   --stack-config=inference=remote::ollama,safety=inline::llama-guard,agents=inline::meta-reference \
   --text-model=$TEXT_MODELS \
   --vision-model=$VISION_MODELS \
   --embedding-model=$EMBEDDING_MODELS

Another example: Running Vector IO tests for embedding models:

pytest -s -v tests/integration/vector_io/ \
   --stack-config=inference=inline::sentence-transformers,vector_io=inline::sqlite-vec \
   --embedding-model=sentence-transformers/all-MiniLM-L6-v2

Recording Modes

The testing system supports three modes controlled by environment variables:

REPLAY Mode (Default)

Uses cached responses instead of making API calls:

pytest tests/integration/

RECORD Mode

Captures API interactions for later replay:

LLAMA_STACK_TEST_INFERENCE_MODE=record \
pytest tests/integration/inference/test_new_feature.py

LIVE Mode

Tests make real API calls (but not recorded):

LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/

By default, the recording directory is tests/integration/recordings. You can override this by setting the LLAMA_STACK_TEST_RECORDING_DIR environment variable.

Managing Recordings

Viewing Recordings

# See what's recorded
sqlite3 recordings/index.sqlite "SELECT endpoint, model, timestamp FROM recordings;"

# Inspect specific response
cat recordings/responses/abc123.json | jq '.'

Re-recording Tests

Remote Re-recording (Recommended)

Use the automated workflow script for easier re-recording:

./scripts/github/schedule-record-workflow.sh --test-subdirs "inference,agents"

See the main testing guide for full details.

Local Re-recording

# Re-record specific tests
LLAMA_STACK_TEST_INFERENCE_MODE=record \
pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py

Note that when re-recording tests, you must use a Stack pointing to a server (i.e., server:starter). This subtlety exists because the set of tests run in server are a superset of the set of tests run in the library client.

Writing Tests

Basic Test Pattern

def test_basic_completion(llama_stack_client, text_model_id):
    response = llama_stack_client.inference.completion(
        model_id=text_model_id,
        content=CompletionMessage(role="user", content="Hello"),
    )

    # Test structure, not AI output quality
    assert response.completion_message is not None
    assert isinstance(response.completion_message.content, str)
    assert len(response.completion_message.content) > 0

Provider-Specific Tests

def test_asymmetric_embeddings(llama_stack_client, embedding_model_id):
    if embedding_model_id not in MODELS_SUPPORTING_TASK_TYPE:
        pytest.skip(f"Model {embedding_model_id} doesn't support task types")

    query_response = llama_stack_client.inference.embeddings(
        model_id=embedding_model_id,
        contents=["What is machine learning?"],
        task_type="query",
    )

    assert query_response.embeddings is not None