chore(misc): make tests and starter faster (#3042)

A bunch of miscellaneous cleanup focusing on tests, but ended up
speeding up starter distro substantially.

- Pulled llama stack client init for tests into `pytest_sessionstart` so
it does not clobber output
- Profiling of that told me where we were doing lots of heavy imports
for starter, so lazied them
- starter now starts 20seconds+ faster on my Mac
- A few other smallish refactors for `compat_client`

docs/source/providers/files/inline_localfs.md

@@ -8,7 +8,7 @@ Local filesystem-based file storage provider for managing files and documents lo
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `storage_dir` | `<class 'str'>` | No | PydanticUndefined | Directory to store uploaded files |
+| `storage_dir` | `<class 'str'>` | No |  | Directory to store uploaded files |
 | `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
 | `ttl_secs` | `<class 'int'>` | No | 31536000 |  |
 

docs/source/providers/inference/remote_hf_endpoint.md

@@ -8,7 +8,7 @@ HuggingFace Inference Endpoints provider for dedicated model serving.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `endpoint_name` | `<class 'str'>` | No | PydanticUndefined | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
+| `endpoint_name` | `<class 'str'>` | No |  | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
 | `api_token` | `pydantic.types.SecretStr \| None` | No |  | Your Hugging Face user access token (will default to locally saved token if not provided) |
 
 ## Sample Configuration

docs/source/providers/inference/remote_hf_serverless.md

@@ -8,7 +8,7 @@ HuggingFace Inference API serverless provider for on-demand model inference.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `huggingface_repo` | `<class 'str'>` | No | PydanticUndefined | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
+| `huggingface_repo` | `<class 'str'>` | No |  | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
 | `api_token` | `pydantic.types.SecretStr \| None` | No |  | Your Hugging Face user access token (will default to locally saved token if not provided) |
 
 ## Sample Configuration

docs/source/providers/inference/remote_tgi.md

@@ -8,7 +8,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `url` | `<class 'str'>` | No | PydanticUndefined | The URL for the TGI serving endpoint |
+| `url` | `<class 'str'>` | No |  | The URL for the TGI serving endpoint |
 
 ## Sample Configuration
 

docs/source/providers/post_training/inline_huggingface.md

@@ -27,7 +27,7 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
 | `dpo_beta` | `<class 'float'>` | No | 0.1 |  |
 | `use_reference_model` | `<class 'bool'>` | No | True |  |
 | `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid |  |
-| `dpo_output_dir` | `<class 'str'>` | No | ./checkpoints/dpo |  |
+| `dpo_output_dir` | `<class 'str'>` | No |  |  |
 
 ## Sample Configuration
 
@@ -35,6 +35,7 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
 checkpoint_format: huggingface
 distributed_backend: null
 device: cpu
+dpo_output_dir: ~/.llama/dummy/dpo_output
 
 ```
 

docs/source/providers/vector_io/inline_chromadb.md

@@ -41,7 +41,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `db_path` | `<class 'str'>` | No |  |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_milvus.md

@@ -10,7 +10,7 @@ Please refer to the remote provider documentation.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `db_path` | `<class 'str'>` | No |  |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 

docs/source/providers/vector_io/inline_qdrant.md

@@ -50,7 +50,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `path` | `<class 'str'>` | No |  |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_sqlite-vec.md

@@ -205,7 +205,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
+| `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_sqlite_vec.md

@@ -10,7 +10,7 @@ Please refer to the sqlite-vec provider documentation.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
+| `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration

docs/source/providers/vector_io/remote_chromadb.md

@@ -40,7 +40,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `url` | `str \| None` | No | PydanticUndefined |  |
+| `url` | `str \| None` | No |  |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration

docs/source/providers/vector_io/remote_milvus.md

@@ -111,8 +111,8 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `uri` | `<class 'str'>` | No | PydanticUndefined | The URI of the Milvus server |
-| `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
+| `uri` | `<class 'str'>` | No |  | The URI of the Milvus server |
+| `token` | `str \| None` | No |  | The token of the Milvus server |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 | `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |

llama_stack/distributions/ci-tests/run.yaml

@@ -154,6 +154,7 @@ providers:
       checkpoint_format: huggingface
       distributed_backend: null
       device: cpu
+      dpo_output_dir: ~/.llama/distributions/ci-tests/dpo_output
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference

llama_stack/distributions/starter/run.yaml

@@ -154,6 +154,7 @@ providers:
       checkpoint_format: huggingface
       distributed_backend: null
       device: cpu
+      dpo_output_dir: ~/.llama/distributions/starter/dpo_output
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference

llama_stack/providers/inline/datasetio/localfs/datasetio.py

@@ -5,8 +5,6 @@
 # the root directory of this source tree.
 from typing import Any
 
-import pandas
-
 from llama_stack.apis.common.responses import PaginatedResponse
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Dataset
@@ -44,6 +42,8 @@ class PandasDataframeDataset:
         if self.dataset_def.source.type == "uri":
             self.df = await get_dataframe_from_uri(self.dataset_def.source.uri)
         elif self.dataset_def.source.type == "rows":
+            import pandas
+
             self.df = pandas.DataFrame(self.dataset_def.source.rows)
         else:
             raise ValueError(f"Unsupported dataset source type: {self.dataset_def.source.type}")
@@ -103,6 +103,8 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         return paginate_records(records, start_index, limit)
 
     async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
+        import pandas
+
         dataset_def = self.dataset_infos[dataset_id]
         dataset_impl = PandasDataframeDataset(dataset_def)
         await dataset_impl.load()

llama_stack/providers/inline/post_training/huggingface/config.py

@@ -71,8 +71,13 @@ class HuggingFacePostTrainingConfig(BaseModel):
     dpo_beta: float = 0.1
     use_reference_model: bool = True
     dpo_loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"] = "sigmoid"
-    dpo_output_dir: str = "./checkpoints/dpo"
+    dpo_output_dir: str
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
-        return {"checkpoint_format": "huggingface", "distributed_backend": None, "device": "cpu"}
+        return {
+            "checkpoint_format": "huggingface",
+            "distributed_backend": None,
+            "device": "cpu",
+            "dpo_output_dir": __distro_dir__ + "/dpo_output",
+        }

llama_stack/providers/inline/post_training/huggingface/post_training.py

@@ -22,15 +22,8 @@ from llama_stack.apis.post_training import (
 from llama_stack.providers.inline.post_training.huggingface.config import (
     HuggingFacePostTrainingConfig,
 )
-from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
-    HFFinetuningSingleDevice,
-)
-from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
-    HFDPOAlignmentSingleDevice,
-)
 from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
 from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
-from llama_stack.schema_utils import webmethod
 
 
 class TrainingArtifactType(Enum):
@@ -85,6 +78,10 @@ class HuggingFacePostTrainingImpl:
         algorithm_config: AlgorithmConfig | None = None,
     ) -> PostTrainingJob:
         async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
+            from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
+                HFFinetuningSingleDevice,
+            )
+
             on_log_message_cb("Starting HF finetuning")
 
             recipe = HFFinetuningSingleDevice(
@@ -124,6 +121,10 @@ class HuggingFacePostTrainingImpl:
         logger_config: dict[str, Any],
     ) -> PostTrainingJob:
         async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
+            from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
+                HFDPOAlignmentSingleDevice,
+            )
+
             on_log_message_cb("Starting HF DPO alignment")
 
             recipe = HFDPOAlignmentSingleDevice(
@@ -168,7 +169,6 @@ class HuggingFacePostTrainingImpl:
         data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
         return data[0] if data else None
 
-    @webmethod(route="/post-training/job/status")
     async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
         job = self._scheduler.get_job(job_uuid)
 
@@ -195,16 +195,13 @@ class HuggingFacePostTrainingImpl:
             resources_allocated=self._get_resources_allocated(job),
         )
 
-    @webmethod(route="/post-training/job/cancel")
     async def cancel_training_job(self, job_uuid: str) -> None:
         self._scheduler.cancel(job_uuid)
 
-    @webmethod(route="/post-training/job/artifacts")
     async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
         job = self._scheduler.get_job(job_uuid)
         return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))
 
-    @webmethod(route="/post-training/jobs", method="GET")
     async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
         return ListPostTrainingJobsResponse(
             data=[PostTrainingJob(job_uuid=job.id) for job in self._scheduler.get_jobs()]

llama_stack/providers/inline/post_training/torchtune/post_training.py

@@ -23,12 +23,8 @@ from llama_stack.apis.post_training import (
 from llama_stack.providers.inline.post_training.torchtune.config import (
     TorchtunePostTrainingConfig,
 )
-from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
-    LoraFinetuningSingleDevice,
-)
 from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
 from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
-from llama_stack.schema_utils import webmethod
 
 
 class TrainingArtifactType(Enum):
@@ -84,6 +80,10 @@ class TorchtunePostTrainingImpl:
         if isinstance(algorithm_config, LoraFinetuningConfig):
 
             async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
+                from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
+                    LoraFinetuningSingleDevice,
+                )
+
                 on_log_message_cb("Starting Lora finetuning")
 
                 recipe = LoraFinetuningSingleDevice(
@@ -144,7 +144,6 @@ class TorchtunePostTrainingImpl:
         data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
         return data[0] if data else None
 
-    @webmethod(route="/post-training/job/status")
     async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
         job = self._scheduler.get_job(job_uuid)
 
@@ -171,11 +170,9 @@ class TorchtunePostTrainingImpl:
             resources_allocated=self._get_resources_allocated(job),
         )
 
-    @webmethod(route="/post-training/job/cancel")
     async def cancel_training_job(self, job_uuid: str) -> None:
         self._scheduler.cancel(job_uuid)
 
-    @webmethod(route="/post-training/job/artifacts")
     async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
         job = self._scheduler.get_job(job_uuid)
         return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))

llama_stack/providers/remote/datasetio/huggingface/huggingface.py

@@ -6,8 +6,6 @@
 from typing import Any
 from urllib.parse import parse_qs, urlparse
 
-import datasets as hf_datasets
-
 from llama_stack.apis.common.responses import PaginatedResponse
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Dataset
@@ -73,6 +71,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         start_index: int | None = None,
         limit: int | None = None,
     ) -> PaginatedResponse:
+        import datasets as hf_datasets
+
         dataset_def = self.dataset_infos[dataset_id]
         path, params = parse_hf_params(dataset_def)
         loaded_dataset = hf_datasets.load_dataset(path, **params)
@@ -81,6 +81,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         return paginate_records(records, start_index, limit)
 
     async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
+        import datasets as hf_datasets
+
         dataset_def = self.dataset_infos[dataset_id]
         path, params = parse_hf_params(dataset_def)
         loaded_dataset = hf_datasets.load_dataset(path, **params)

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -112,7 +112,8 @@ class OllamaInferenceAdapter(
     @property
     def openai_client(self) -> AsyncOpenAI:
         if self._openai_client is None:
-            self._openai_client = AsyncOpenAI(base_url=f"{self.config.url}/v1", api_key="ollama")
+            url = self.config.url.rstrip("/")
+            self._openai_client = AsyncOpenAI(base_url=f"{url}/v1", api_key="ollama")
         return self._openai_client
 
     async def initialize(self) -> None:

llama_stack/providers/utils/datasetio/url_utils.py

@@ -9,12 +9,12 @@ import base64
 import io
 from urllib.parse import unquote
 
-import pandas
-
 from llama_stack.providers.utils.memory.vector_store import parse_data_url
 
 
 async def get_dataframe_from_uri(uri: str):
+    import pandas
+
     df = None
     if uri.endswith(".csv"):
         # Moving to its own thread to avoid io from blocking the eventloop

scripts/provider_codegen.py

@@ -10,6 +10,7 @@ import sys
 from pathlib import Path
 from typing import Any
 
+from pydantic_core import PydanticUndefined
 from rich.progress import Progress, SpinnerColumn, TextColumn
 
 from llama_stack.core.distribution import get_provider_registry
@@ -59,6 +60,8 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
         if hasattr(config_class, "model_fields"):
             for field_name, field in config_class.model_fields.items():
                 field_type = str(field.annotation) if field.annotation else "Any"
+
+                # this string replace is ridiculous
                 field_type = field_type.replace("typing.", "").replace("Optional[", "").replace("]", "")
                 field_type = field_type.replace("Annotated[", "").replace("FieldInfo(", "").replace(")", "")
                 field_type = field_type.replace("llama_stack.apis.inference.inference.", "")
@@ -77,7 +80,7 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
                                 default_value = f"~/.llama/{path_part}"
                     except Exception:
                         default_value = ""
-                elif field.default is None:
+                elif field.default is None or field.default is PydanticUndefined:
                     default_value = ""
 
                 field_info = {
@@ -255,7 +258,7 @@ def process_provider_registry(progress, change_tracker: ChangedPathTracker) -> N
             change_tracker.add_paths(doc_output_dir)
 
             index_content = []
-            index_content.append(f"# {api_name.title()} \n")
+            index_content.append(f"# {api_name.title()}\n")
             index_content.append("## Overview\n")
 
             index_content.append(

tests/integration/agents/test_openai_responses.py

@@ -9,12 +9,6 @@ from openai import BadRequestError, OpenAI
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
 
 
-@pytest.fixture
-def openai_client(client_with_models):
-    base_url = f"{client_with_models.base_url}/v1/openai/v1"
-    return OpenAI(base_url=base_url, api_key="bar")
-
-
 @pytest.mark.parametrize(
     "stream",
     [
@@ -41,15 +35,14 @@ def openai_client(client_with_models):
         ],
     ],
 )
-def test_responses_store(openai_client, client_with_models, text_model_id, stream, tools):
-    if isinstance(client_with_models, LlamaStackAsLibraryClient):
-        pytest.skip("OpenAI responses are not supported when testing with library client yet.")
+def test_responses_store(compat_client, text_model_id, stream, tools):
+    if not isinstance(compat_client, OpenAI):
+        pytest.skip("OpenAI client is required until responses.delete() exists in llama-stack-client")
 
-    client = openai_client
     message = "What's the weather in Tokyo?" + (
         " YOU MUST USE THE get_weather function to get the weather." if tools else ""
     )
-    response = client.responses.create(
+    response = compat_client.responses.create(
         model=text_model_id,
         input=[
             {
@@ -78,14 +71,8 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
         if output_type == "message":
             content = response.output[0].content[0].text
 
-    # list responses - use the underlying HTTP client for endpoints not in SDK
-    list_response = client._client.get("/responses")
-    assert list_response.status_code == 200
-    data = list_response.json()["data"]
-    assert response_id in [r["id"] for r in data]
-
     # test retrieve response
-    retrieved_response = client.responses.retrieve(response_id)
+    retrieved_response = compat_client.responses.retrieve(response_id)
     assert retrieved_response.id == response_id
     assert retrieved_response.model == text_model_id
     assert retrieved_response.output[0].type == output_type, retrieved_response
@@ -93,23 +80,19 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
         assert retrieved_response.output[0].content[0].text == content
 
     # Delete the response
-    delete_response = client.responses.delete(response_id)
+    delete_response = compat_client.responses.delete(response_id)
     assert delete_response is None
 
     with pytest.raises(BadRequestError):
-        client.responses.retrieve(response_id)
+        compat_client.responses.retrieve(response_id)
 
 
-def test_list_response_input_items(openai_client, client_with_models, text_model_id):
+def test_list_response_input_items(compat_client, text_model_id):
     """Test the new list_openai_response_input_items endpoint."""
-    if isinstance(client_with_models, LlamaStackAsLibraryClient):
-        pytest.skip("OpenAI responses are not supported when testing with library client yet.")
-
-    client = openai_client
     message = "What is the capital of France?"
 
     # Create a response first
-    response = client.responses.create(
+    response = compat_client.responses.create(
         model=text_model_id,
         input=[
             {
@@ -123,7 +106,7 @@ def test_list_response_input_items(openai_client, client_with_models, text_model
     response_id = response.id
 
     # Test the new list input items endpoint
-    input_items_response = client.responses.input_items.list(response_id=response_id)
+    input_items_response = compat_client.responses.input_items.list(response_id=response_id)
 
     # Verify the structure follows OpenAI API spec
     assert input_items_response.object == "list"

tests/integration/conftest.py

@@ -9,12 +9,15 @@ import os
 import platform
 import textwrap
 import time
+import warnings
 
 import pytest
 from dotenv import load_dotenv
 
 from llama_stack.log import get_logger
 
+from .fixtures.common import instantiate_llama_stack_client
+
 logger = get_logger(__name__, category="tests")
 
 
@@ -27,6 +30,20 @@ def pytest_runtest_makereport(item, call):
         item.was_xfail = getattr(report, "wasxfail", False)
 
 
+def pytest_sessionstart(session):
+    # stop macOS from complaining about duplicate OpenMP libraries
+    os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+    # pull client instantiation to session start so all the complex logs during initialization
+    # don't clobber the test one-liner outputs
+    print("instantiating llama_stack_client")
+    start_time = time.time()
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        session._llama_stack_client = instantiate_llama_stack_client(session)
+    print(f"llama_stack_client instantiated in {time.time() - start_time:.3f}s")
+
+
 def pytest_runtest_teardown(item):
     # Check if the test actually ran and passed or failed, but was not skipped or an expected failure (xfail)
     outcome = getattr(item, "execution_outcome", None)

tests/integration/fixtures/common.py

@@ -82,8 +82,7 @@ def wait_for_server_ready(base_url: str, timeout: int = 30, process: subprocess.
     return False
 
 
-@pytest.fixture(scope="session")
-def provider_data():
+def get_provider_data():
     # TODO: this needs to be generalized so each provider can have a sample provider data just
     # like sample run config on which we can do replace_env_vars()
     keymap = {
@@ -178,8 +177,14 @@ def skip_if_no_model(request):
 
 
 @pytest.fixture(scope="session")
-def llama_stack_client(request, provider_data):
-    config = request.config.getoption("--stack-config")
+def llama_stack_client(request):
+    client = request.session._llama_stack_client
+    assert client is not None, "llama_stack_client not found in session cache"
+    return client
+
+
+def instantiate_llama_stack_client(session):
+    config = session.config.getoption("--stack-config")
     if not config:
         config = get_env_or_fail("LLAMA_STACK_CONFIG")
 
@@ -212,13 +217,13 @@ def llama_stack_client(request, provider_data):
             print(f"Server is ready at {base_url}")
 
             # Store process for potential cleanup (pytest will handle termination at session end)
-            request.session._llama_stack_server_process = server_process
+            session._llama_stack_server_process = server_process
         else:
             print(f"Port {port} is already in use, assuming server is already running...")
 
         return LlamaStackClient(
             base_url=base_url,
-            provider_data=provider_data,
+            provider_data=get_provider_data(),
             timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")),
         )
 
@@ -228,7 +233,7 @@ def llama_stack_client(request, provider_data):
         if parsed_url.scheme and parsed_url.netloc:
             return LlamaStackClient(
                 base_url=config,
-                provider_data=provider_data,
+                provider_data=get_provider_data(),
             )
     except Exception:
         # If URL parsing fails, treat as non-URL config
@@ -243,7 +248,7 @@ def llama_stack_client(request, provider_data):
 
     client = LlamaStackAsLibraryClient(
         config,
-        provider_data=provider_data,
+        provider_data=get_provider_data(),
         skip_logger_removal=True,
     )
     if not client.initialize():
@@ -258,8 +263,17 @@ def openai_client(client_with_models):
     return OpenAI(base_url=base_url, api_key="fake")
 
 
-@pytest.fixture(params=["openai_client", "llama_stack_client"])
-def compat_client(request):
+@pytest.fixture(params=["openai_client", "client_with_models"])
+def compat_client(request, client_with_models):
+    if isinstance(client_with_models, LlamaStackAsLibraryClient):
+        # OpenAI client expects a server, so unless we also rewrite OpenAI client's requests
+        # to go via the Stack library client (which itself rewrites requests to be served inline),
+        # we cannot do this.
+        #
+        # This means when we are using Stack as a library, we will test only via the Llama Stack client.
+        # When we are using a server setup, we can exercise both OpenAI and Llama Stack clients.
+        pytest.skip("(OpenAI) Compat client cannot be used with Stack library client")
+
     return request.getfixturevalue(request.param)
 
 

tests/integration/inference/test_openai_completion.py

@@ -6,9 +6,6 @@
 
 
 import pytest
-from openai import OpenAI
-
-from llama_stack.core.library_client import LlamaStackAsLibraryClient
 
 from ..test_cases.test_case import TestCase
 
@@ -59,9 +56,6 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id):
 
 
 def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id):
-    if isinstance(client_with_models, LlamaStackAsLibraryClient):
-        pytest.skip("OpenAI chat completions are not supported when testing with library client yet.")
-
     provider = provider_from_model(client_with_models, model_id)
     if provider.provider_type in (
         "inline::meta-reference",
@@ -90,17 +84,6 @@ def skip_if_provider_isnt_openai(client_with_models, model_id):
         )
 
 
-@pytest.fixture
-def openai_client(client_with_models):
-    base_url = f"{client_with_models.base_url}/v1/openai/v1"
-    return OpenAI(base_url=base_url, api_key="bar")
-
-
-@pytest.fixture(params=["openai_client", "llama_stack_client"])
-def compat_client(request):
-    return request.getfixturevalue(request.param)
-
-
 @pytest.mark.parametrize(
     "test_case",
     [

tests/integration/recordings/responses/4a3a4447b16b.json

@@ -14,7 +14,7 @@
         "models": [
           {
             "model": "nomic-embed-text:latest",
-            "modified_at": "2025-08-04T15:54:50.584357-07:00",
+            "modified_at": "2025-08-05T14:04:07.946926-07:00",
             "digest": "0a109f422b47e3a30ba2b10eca18548e944e8a23073ee3f3e947efcf3c45e59f",
             "size": 274302450,
             "details": {
@@ -28,9 +28,41 @@
               "quantization_level": "F16"
             }
           },
+          {
+            "model": "llama3.2-vision:11b",
+            "modified_at": "2025-07-30T18:45:02.517873-07:00",
+            "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e",
+            "size": 7816589186,
+            "details": {
+              "parent_model": "",
+              "format": "gguf",
+              "family": "mllama",
+              "families": [
+                "mllama"
+              ],
+              "parameter_size": "10.7B",
+              "quantization_level": "Q4_K_M"
+            }
+          },
+          {
+            "model": "llama3.2-vision:latest",
+            "modified_at": "2025-07-29T20:18:47.920468-07:00",
+            "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e",
+            "size": 7816589186,
+            "details": {
+              "parent_model": "",
+              "format": "gguf",
+              "family": "mllama",
+              "families": [
+                "mllama"
+              ],
+              "parameter_size": "10.7B",
+              "quantization_level": "Q4_K_M"
+            }
+          },
           {
             "model": "llama-guard3:1b",
-            "modified_at": "2025-08-01T15:46:28.963517-07:00",
+            "modified_at": "2025-07-25T14:39:44.978630-07:00",
             "digest": "494147e06bf99e10dbe67b63a07ac81c162f18ef3341aa3390007ac828571b3b",
             "size": 1600181919,
             "details": {
@@ -46,7 +78,7 @@
           },
           {
             "model": "all-minilm:l6-v2",
-            "modified_at": "2025-07-29T15:07:06.295748-07:00",
+            "modified_at": "2025-07-24T15:15:11.129290-07:00",
             "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
             "size": 45960996,
             "details": {
@@ -61,26 +93,10 @@
             }
           },
           {
-            "model": "all-minilm:latest",
-            "modified_at": "2025-06-04T12:06:43.990073-07:00",
-            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
-            "size": 45960996,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "bert",
-              "families": [
-                "bert"
-              ],
-              "parameter_size": "23M",
-              "quantization_level": "F16"
-            }
-          },
-          {
-            "model": "llama3.1:8b-instruct-fp16",
-            "modified_at": "2025-02-14T15:23:24.865395-08:00",
-            "digest": "4aacac4194543ff7f70dab3f2ebc169c132d5319bb36f7a7e99c4ff525ebcc09",
-            "size": 16068910253,
+            "model": "llama3.2:1b",
+            "modified_at": "2025-07-17T22:02:24.953208-07:00",
+            "digest": "baf6a787fdffd633537aa2eb51cfd54cb93ff08e28040095462bb63daf552878",
+            "size": 1321098329,
             "details": {
               "parent_model": "",
               "format": "gguf",
@@ -88,13 +104,45 @@
               "families": [
                 "llama"
               ],
-              "parameter_size": "8.0B",
+              "parameter_size": "1.2B",
+              "quantization_level": "Q8_0"
+            }
+          },
+          {
+            "model": "all-minilm:latest",
+            "modified_at": "2025-06-03T16:50:10.946583-07:00",
+            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
+            "size": 45960996,
+            "details": {
+              "parent_model": "",
+              "format": "gguf",
+              "family": "bert",
+              "families": [
+                "bert"
+              ],
+              "parameter_size": "23M",
               "quantization_level": "F16"
             }
           },
+          {
+            "model": "llama3.2:3b",
+            "modified_at": "2025-05-01T11:15:23.797447-07:00",
+            "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
+            "size": 2019393189,
+            "details": {
+              "parent_model": "",
+              "format": "gguf",
+              "family": "llama",
+              "families": [
+                "llama"
+              ],
+              "parameter_size": "3.2B",
+              "quantization_level": "Q4_K_M"
+            }
+          },
           {
             "model": "llama3.2:3b-instruct-fp16",
-            "modified_at": "2025-01-21T13:46:43.514008-08:00",
+            "modified_at": "2025-04-30T15:33:48.939665-07:00",
             "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d",
             "size": 6433703586,
             "details": {

tests/integration/recordings/responses/d0ac68cbde69.json

@@ -16,9 +16,9 @@
             "model": "llama3.2:3b-instruct-fp16",
             "name": "llama3.2:3b-instruct-fp16",
             "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d",
-            "expires_at": "2025-08-04T16:00:57.955349-07:00",
-            "size": 8581748736,
-            "size_vram": 8581748736,
+            "expires_at": "2025-08-05T14:12:18.480323-07:00",
+            "size": 7919570944,
+            "size_vram": 7919570944,
             "details": {
               "parent_model": "",
               "format": "gguf",
@@ -29,6 +29,24 @@
               "parameter_size": "3.2B",
               "quantization_level": "F16"
             }
+          },
+          {
+            "model": "all-minilm:l6-v2",
+            "name": "all-minilm:l6-v2",
+            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
+            "expires_at": "2025-08-05T14:10:20.883978-07:00",
+            "size": 590204928,
+            "size_vram": 590204928,
+            "details": {
+              "parent_model": "",
+              "format": "gguf",
+              "family": "bert",
+              "families": [
+                "bert"
+              ],
+              "parameter_size": "23M",
+              "quantization_level": "F16"
+            }
           }
         ]
       }

tests/integration/recordings/responses/da531c71e64f.json

@@ -0,0 +1,421 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/embeddings",
+    "headers": {},
+    "body": {
+      "model": "all-minilm:l6-v2",
+      "input": [
+        "Python programming language"
+      ]
+    },
+    "endpoint": "/api/embeddings",
+    "model": "all-minilm:l6-v2"
+  },
+  "response": {
+    "body": {
+      "__type__": "ollama._types.EmbedResponse",
+      "__data__": {
+        "model": "all-minilm:l6-v2",
+        "created_at": null,
+        "done": null,
+        "done_reason": null,
+        "total_duration": 105895041,
+        "load_duration": 91634666,
+        "prompt_eval_count": 3,
+        "prompt_eval_duration": null,
+        "eval_count": null,
+        "eval_duration": null,
+        "embeddings": [
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tests/integration/recordings/responses/dbc41d2417e1.json

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tests/integration/recordings/responses/f1ea938b0b0d.json

@@ -0,0 +1,56 @@
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+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Hello, world!"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-796",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 1754422173,
+        "model": "llama3.2:3b-instruct-fp16",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 26,
+          "prompt_tokens": 29,
+          "total_tokens": 55,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/vector_io/test_openai_vector_stores.py

@@ -11,10 +11,8 @@ from io import BytesIO
 import pytest
 from llama_stack_client import BadRequestError, LlamaStackClient
 from openai import BadRequestError as OpenAIBadRequestError
-from openai import OpenAI
 
 from llama_stack.apis.vector_io import Chunk
-from llama_stack.core.library_client import LlamaStackAsLibraryClient
 
 logger = logging.getLogger(__name__)
 
@@ -69,19 +67,6 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
     )
 
 
-@pytest.fixture
-def openai_client(client_with_models):
-    base_url = f"{client_with_models.base_url}/v1/openai/v1"
-    return OpenAI(base_url=base_url, api_key="fake")
-
-
-@pytest.fixture(params=["openai_client", "llama_stack_client"])
-def compat_client(request, client_with_models):
-    if request.param == "openai_client" and isinstance(client_with_models, LlamaStackAsLibraryClient):
-        pytest.skip("OpenAI client tests not supported with library client")
-    return request.getfixturevalue(request.param)
-
-
 @pytest.fixture(scope="session")
 def sample_chunks():
     return [