Merge branch 'main' into use-openai-for-databricks

benchmarking/k8s-benchmark/apply.sh

@@ -17,11 +17,8 @@ export POSTGRES_PASSWORD=llamastack
 export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
 export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
 
-export MOCK_INFERENCE_MODEL=mock-inference
-
-export MOCK_INFERENCE_URL=openai-mock-service:8080
-
 export BENCHMARK_INFERENCE_MODEL=$INFERENCE_MODEL
+export LLAMA_STACK_WORKERS=4
 
 set -euo pipefail
 set -x

benchmarking/k8s-benchmark/stack-configmap.yaml

@@ -5,6 +5,7 @@ data:
     image_name: kubernetes-benchmark-demo
     apis:
     - agents
+    - files
     - inference
     - files
     - safety
@@ -23,6 +24,14 @@ data:
       - provider_id: sentence-transformers
         provider_type: inline::sentence-transformers
         config: {}
+      files:
+      - provider_id: meta-reference-files
+        provider_type: inline::localfs
+        config:
+          storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
+          metadata_store:
+            type: sqlite
+            db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
       vector_io:
       - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
         provider_type: remote::chromadb

benchmarking/k8s-benchmark/stack-k8s.yaml.template

@@ -52,9 +52,20 @@ spec:
           value: http://vllm-server-safety.default.svc.cluster.local:8001/v1
         - name: VLLM_TLS_VERIFY
           value: "false"
-        command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8323"]
+        - name: LLAMA_STACK_LOGGING
+          value: "all=WARNING"
+        - name: LLAMA_STACK_CONFIG
+          value: "/etc/config/stack_run_config.yaml"
+        - name: LLAMA_STACK_WORKERS
+          value: "${LLAMA_STACK_WORKERS}"
+        command: ["uvicorn", "llama_stack.core.server.server:create_app", "--host", "0.0.0.0", "--port", "8323", "--workers", "$LLAMA_STACK_WORKERS", "--factory"]
         ports:
           - containerPort: 8323
+        resources:
+          requests:
+            cpu: "${LLAMA_STACK_WORKERS}"
+          limits:
+            cpu: "${LLAMA_STACK_WORKERS}"
         volumeMounts:
           - name: llama-storage
             mountPath: /root/.llama

docs/source/getting_started/detailed_tutorial.md

@@ -460,10 +460,12 @@ client = LlamaStackClient(base_url="http://localhost:8321")
 embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
 embedding_model = embed_lm.identifier
 vector_db_id = f"v{uuid.uuid4().hex}"
-client.vector_dbs.register(
+# The VectorDB API is deprecated; the server now returns its own authoritative ID.
+# We capture the correct ID from the response's .identifier attribute.
+vector_db_id = client.vector_dbs.register(
     vector_db_id=vector_db_id,
     embedding_model=embedding_model,
-)
+).identifier
 
 # Create Documents
 urls = [

docs/source/providers/vector_io/remote_milvus.md

@@ -23,7 +23,13 @@ To use Milvus in your Llama Stack project, follow these steps:
 
 ## Installation
 
-You can install Milvus using pymilvus:
+If you want to use inline Milvus, you can install:
+
+```bash
+pip install pymilvus[milvus-lite]
+```
+
+If you want to use remote Milvus, you can install:
 
 ```bash
 pip install pymilvus

llama_stack/core/datatypes.py

@@ -121,10 +121,6 @@ class AutoRoutedProviderSpec(ProviderSpec):
         default=None,
     )
 
-    @property
-    def pip_packages(self) -> list[str]:
-        raise AssertionError("Should not be called on AutoRoutedProviderSpec")
-
 
 # Example: /models, /shields
 class RoutingTableProviderSpec(ProviderSpec):

llama_stack/core/distribution.py

@@ -16,11 +16,10 @@ from llama_stack.core.datatypes import BuildConfig, DistributionSpec
 from llama_stack.core.external import load_external_apis
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 logger = get_logger(name=__name__, category="core")
@@ -77,27 +76,12 @@ def providable_apis() -> list[Api]:
 
 
 def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec:
-    adapter = AdapterSpec(**spec_data["adapter"])
+    spec = RemoteProviderSpec(api=api, provider_type=f"remote::{spec_data['adapter_type']}", **spec_data)
-    spec = remote_provider_spec(
-        api=api,
-        adapter=adapter,
-        api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])],
-    )
     return spec
 
 
 def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec:
-    spec = InlineProviderSpec(
+    spec = InlineProviderSpec(api=api, provider_type=f"inline::{provider_name}", **spec_data)
-        api=api,
-        provider_type=f"inline::{provider_name}",
-        pip_packages=spec_data.get("pip_packages", []),
-        module=spec_data["module"],
-        config_class=spec_data["config_class"],
-        api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])],
-        optional_api_dependencies=[Api(dep) for dep in spec_data.get("optional_api_dependencies", [])],
-        provider_data_validator=spec_data.get("provider_data_validator"),
-        container_image=spec_data.get("container_image"),
-    )
     return spec
 
 

llama_stack/core/library_client.py

@@ -40,7 +40,7 @@ from llama_stack.core.request_headers import (
 from llama_stack.core.resolver import ProviderRegistry
 from llama_stack.core.server.routes import RouteImpls, find_matching_route, initialize_route_impls
 from llama_stack.core.stack import (
-    construct_stack,
+    Stack,
     get_stack_run_config_from_distro,
     replace_env_vars,
 )
@@ -252,7 +252,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
 
         try:
             self.route_impls = None
-            self.impls = await construct_stack(self.config, self.custom_provider_registry)
+
+            stack = Stack(self.config, self.custom_provider_registry)
+            await stack.initialize()
+            self.impls = stack.impls
         except ModuleNotFoundError as _e:
             cprint(_e.msg, color="red", file=sys.stderr)
             cprint(
@@ -289,6 +292,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
             )
             raise _e
 
+        assert self.impls is not None
         if Api.telemetry in self.impls:
             setup_logger(self.impls[Api.telemetry])
 

llama_stack/core/server/server.py

@@ -6,6 +6,7 @@
 
 import argparse
 import asyncio
+import concurrent.futures
 import functools
 import inspect
 import json
@@ -50,17 +51,15 @@ from llama_stack.core.request_headers import (
     request_provider_data_context,
     user_from_scope,
 )
-from llama_stack.core.resolver import InvalidProviderError
 from llama_stack.core.server.routes import (
     find_matching_route,
     get_all_api_routes,
     initialize_route_impls,
 )
 from llama_stack.core.stack import (
+    Stack,
     cast_image_name_to_string,
-    construct_stack,
     replace_env_vars,
-    shutdown_stack,
     validate_env_pair,
 )
 from llama_stack.core.utils.config import redact_sensitive_fields
@@ -156,21 +155,34 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro
         )
 
 
-async def shutdown(app):
+class StackApp(FastAPI):
-    """Initiate a graceful shutdown of the application.
-
-    Handled by the lifespan context manager. The shutdown process involves
-    shutting down all implementations registered in the application.
     """
-    await shutdown_stack(app.__llama_stack_impls__)
+    A wrapper around the FastAPI application to hold a reference to the Stack instance so that we can
+    start background tasks (e.g. refresh model registry periodically) from the lifespan context manager.
+    """
+
+    def __init__(self, config: StackRunConfig, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.stack: Stack = Stack(config)
+
+        # This code is called from a running event loop managed by uvicorn so we cannot simply call
+        # asyncio.run() to initialize the stack. We cannot await either since this is not an async
+        # function.
+        # As a workaround, we use a thread pool executor to run the initialize() method
+        # in a separate thread.
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(asyncio.run, self.stack.initialize())
+            future.result()
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI):
+async def lifespan(app: StackApp):
     logger.info("Starting up")
+    assert app.stack is not None
+    app.stack.create_registry_refresh_task()
     yield
     logger.info("Shutting down")
-    await shutdown(app)
+    await app.stack.shutdown()
 
 
 def is_streaming_request(func_name: str, request: Request, **kwargs):
@@ -386,73 +398,61 @@ class ClientVersionMiddleware:
         return await self.app(scope, receive, send)
 
 
-def main(args: argparse.Namespace | None = None):
+def create_app(
-    """Start the LlamaStack server."""
+    config_file: str | None = None,
-    parser = argparse.ArgumentParser(description="Start the LlamaStack server.")
+    env_vars: list[str] | None = None,
+) -> StackApp:
+    """Create and configure the FastAPI application.
 
-    add_config_distro_args(parser)
+    Args:
-    parser.add_argument(
+        config_file: Path to config file. If None, uses LLAMA_STACK_CONFIG env var or default resolution.
-        "--port",
+        env_vars: List of environment variables in KEY=value format.
-        type=int,
+        disable_version_check: Whether to disable version checking. If None, uses LLAMA_STACK_DISABLE_VERSION_CHECK env var.
-        default=int(os.getenv("LLAMA_STACK_PORT", 8321)),
-        help="Port to listen on",
-    )
-    parser.add_argument(
-        "--env",
-        action="append",
-        help="Environment variables in KEY=value format. Can be specified multiple times.",
-    )
 
-    # Determine whether the server args are being passed by the "run" command, if this is the case
+    Returns:
-    # the args will be passed as a Namespace object to the main function, otherwise they will be
+        Configured StackApp instance.
-    # parsed from the command line
+    """
-    if args is None:
+    config_file = config_file or os.getenv("LLAMA_STACK_CONFIG")
-        args = parser.parse_args()
+    if config_file is None:
+        raise ValueError("No config file provided and LLAMA_STACK_CONFIG env var is not set")
 
-    config_or_distro = get_config_from_args(args)
+    config_file = resolve_config_or_distro(config_file, Mode.RUN)
-    config_file = resolve_config_or_distro(config_or_distro, Mode.RUN)
 
+    # Load and process configuration
     logger_config = None
     with open(config_file) as fp:
         config_contents = yaml.safe_load(fp)
         if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
             logger_config = LoggingConfig(**cfg)
         logger = get_logger(name=__name__, category="core::server", config=logger_config)
-        if args.env:
+
-            for env_pair in args.env:
+        if env_vars:
+            for env_pair in env_vars:
                 try:
                     key, value = validate_env_pair(env_pair)
-                    logger.info(f"Setting CLI environment variable {key} => {value}")
+                    logger.info(f"Setting environment variable {key} => {value}")
                     os.environ[key] = value
                 except ValueError as e:
                     logger.error(f"Error: {str(e)}")
-                    sys.exit(1)
+                    raise ValueError(f"Invalid environment variable format: {env_pair}") from e
+
         config = replace_env_vars(config_contents)
         config = StackRunConfig(**cast_image_name_to_string(config))
 
     _log_run_config(run_config=config)
 
-    app = FastAPI(
+    app = StackApp(
         lifespan=lifespan,
         docs_url="/docs",
         redoc_url="/redoc",
         openapi_url="/openapi.json",
+        config=config,
     )
 
     if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"):
         app.add_middleware(ClientVersionMiddleware)
 
-    try:
+    impls = app.stack.impls
-        # Create and set the event loop that will be used for both construction and server runtime
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-
-        # Construct the stack in the persistent event loop
-        impls = loop.run_until_complete(construct_stack(config))
-
-    except InvalidProviderError as e:
-        logger.error(f"Error: {str(e)}")
-        sys.exit(1)
 
     if config.server.auth:
         logger.info(f"Enabling authentication with provider: {config.server.auth.provider_config.type.value}")
@@ -553,9 +553,54 @@ def main(args: argparse.Namespace | None = None):
     app.exception_handler(RequestValidationError)(global_exception_handler)
     app.exception_handler(Exception)(global_exception_handler)
 
-    app.__llama_stack_impls__ = impls
     app.add_middleware(TracingMiddleware, impls=impls, external_apis=external_apis)
 
+    return app
+
+
+def main(args: argparse.Namespace | None = None):
+    """Start the LlamaStack server."""
+    parser = argparse.ArgumentParser(description="Start the LlamaStack server.")
+
+    add_config_distro_args(parser)
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=int(os.getenv("LLAMA_STACK_PORT", 8321)),
+        help="Port to listen on",
+    )
+    parser.add_argument(
+        "--env",
+        action="append",
+        help="Environment variables in KEY=value format. Can be specified multiple times.",
+    )
+
+    # Determine whether the server args are being passed by the "run" command, if this is the case
+    # the args will be passed as a Namespace object to the main function, otherwise they will be
+    # parsed from the command line
+    if args is None:
+        args = parser.parse_args()
+
+    config_or_distro = get_config_from_args(args)
+
+    try:
+        app = create_app(
+            config_file=config_or_distro,
+            env_vars=args.env,
+        )
+    except Exception as e:
+        logger.error(f"Error creating app: {str(e)}")
+        sys.exit(1)
+
+    config_file = resolve_config_or_distro(config_or_distro, Mode.RUN)
+    with open(config_file) as fp:
+        config_contents = yaml.safe_load(fp)
+        if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
+            logger_config = LoggingConfig(**cfg)
+        else:
+            logger_config = None
+        config = StackRunConfig(**cast_image_name_to_string(replace_env_vars(config_contents)))
+
     import uvicorn
 
     # Configure SSL if certificates are provided
@@ -593,7 +638,6 @@ def main(args: argparse.Namespace | None = None):
     if ssl_config:
         uvicorn_config.update(ssl_config)
 
-    # Run uvicorn in the existing event loop to preserve background tasks
     # We need to catch KeyboardInterrupt because uvicorn's signal handling
     # re-raises SIGINT signals using signal.raise_signal(), which Python
     # converts to KeyboardInterrupt. Without this catch, we'd get a confusing
@@ -604,13 +648,9 @@ def main(args: argparse.Namespace | None = None):
     # Another approach would be to ignore SIGINT entirely - let uvicorn handle it through its own
     # signal handling but this is quite intrusive and not worth the effort.
     try:
-        loop.run_until_complete(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve())
+        asyncio.run(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve())
     except (KeyboardInterrupt, SystemExit):
         logger.info("Received interrupt signal, shutting down gracefully...")
-    finally:
-        if not loop.is_closed():
-            logger.debug("Closing event loop")
-            loop.close()
 
 
 def _log_run_config(run_config: StackRunConfig):

llama_stack/core/stack.py

@@ -315,11 +315,15 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf
     impls[Api.prompts] = prompts_impl
 
 
+class Stack:
+    def __init__(self, run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None):
+        self.run_config = run_config
+        self.provider_registry = provider_registry
+        self.impls = None
+
     # Produces a stack of providers for the given run config. Not all APIs may be
     # asked for in the run config.
-async def construct_stack(
+    async def initialize(self):
-    run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None
-) -> dict[Api, Any]:
         if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ:
             from llama_stack.testing.inference_recorder import setup_inference_recording
 
@@ -329,24 +333,28 @@ async def construct_stack(
                 TEST_RECORDING_CONTEXT.__enter__()
                 logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
 
-    dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name)
+        dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name)
-    policy = run_config.server.auth.access_policy if run_config.server.auth else []
+        policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else []
         impls = await resolve_impls(
-        run_config, provider_registry or get_provider_registry(run_config), dist_registry, policy
+            self.run_config, self.provider_registry or get_provider_registry(self.run_config), dist_registry, policy
         )
 
         # Add internal implementations after all other providers are resolved
-    add_internal_implementations(impls, run_config)
+        add_internal_implementations(impls, self.run_config)
 
         if Api.prompts in impls:
             await impls[Api.prompts].initialize()
 
-    await register_resources(run_config, impls)
+        await register_resources(self.run_config, impls)
 
         await refresh_registry_once(impls)
+        self.impls = impls
+
+    def create_registry_refresh_task(self):
+        assert self.impls is not None, "Must call initialize() before starting"
 
         global REGISTRY_REFRESH_TASK
-    REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(impls))
+        REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(self.impls))
 
         def cb(task):
             import traceback
@@ -360,11 +368,9 @@ async def construct_stack(
                 logger.debug("Model refresh task completed")
 
         REGISTRY_REFRESH_TASK.add_done_callback(cb)
-    return impls
 
-
+    async def shutdown(self):
-async def shutdown_stack(impls: dict[Api, Any]):
+        for impl in self.impls.values():
-    for impl in impls.values():
             impl_name = impl.__class__.__name__
             logger.info(f"Shutting down {impl_name}")
             try:

llama_stack/distributions/starter/starter.py

@@ -78,12 +78,12 @@ def get_remote_inference_providers() -> list[Provider]:
     remote_providers = [
         provider
         for provider in available_providers()
-        if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS
+        if isinstance(provider, RemoteProviderSpec) and provider.adapter_type in ENABLED_INFERENCE_PROVIDERS
     ]
 
     inference_providers = []
     for provider_spec in remote_providers:
-        provider_type = provider_spec.adapter.adapter_type
+        provider_type = provider_spec.adapter_type
 
         if provider_type in INFERENCE_PROVIDER_IDS:
             provider_id = INFERENCE_PROVIDER_IDS[provider_type]

llama_stack/providers/datatypes.py

@@ -131,6 +131,15 @@ class ProviderSpec(BaseModel):
  """,
     )
 
+    pip_packages: list[str] = Field(
+        default_factory=list,
+        description="The pip dependencies needed for this implementation",
+    )
+
+    provider_data_validator: str | None = Field(
+        default=None,
+    )
+
     is_external: bool = Field(default=False, description="Notes whether this provider is an external provider.")
 
     # used internally by the resolver; this is a hack for now
@@ -145,45 +154,8 @@ class RoutingTable(Protocol):
     async def get_provider_impl(self, routing_key: str) -> Any: ...
 
 
-# TODO: this can now be inlined into RemoteProviderSpec
-@json_schema_type
-class AdapterSpec(BaseModel):
-    adapter_type: str = Field(
-        ...,
-        description="Unique identifier for this adapter",
-    )
-    module: str = Field(
-        default_factory=str,
-        description="""
-Fully-qualified name of the module to import. The module is expected to have:
-
- - `get_adapter_impl(config, deps)`: returns the adapter implementation
-""",
-    )
-    pip_packages: list[str] = Field(
-        default_factory=list,
-        description="The pip dependencies needed for this implementation",
-    )
-    config_class: str = Field(
-        description="Fully-qualified classname of the config for this provider",
-    )
-    provider_data_validator: str | None = Field(
-        default=None,
-    )
-    description: str | None = Field(
-        default=None,
-        description="""
-A description of the provider. This is used to display in the documentation.
-""",
-    )
-
-
 @json_schema_type
 class InlineProviderSpec(ProviderSpec):
-    pip_packages: list[str] = Field(
-        default_factory=list,
-        description="The pip dependencies needed for this implementation",
-    )
     container_image: str | None = Field(
         default=None,
         description="""
@@ -191,10 +163,6 @@ The container image to use for this implementation. If one is provided, pip_pack
 If a provider depends on other providers, the dependencies MUST NOT specify a container image.
 """,
     )
-    # module field is inherited from ProviderSpec
-    provider_data_validator: str | None = Field(
-        default=None,
-    )
     description: str | None = Field(
         default=None,
         description="""
@@ -223,10 +191,15 @@ class RemoteProviderConfig(BaseModel):
 
 @json_schema_type
 class RemoteProviderSpec(ProviderSpec):
-    adapter: AdapterSpec = Field(
+    adapter_type: str = Field(
+        ...,
+        description="Unique identifier for this adapter",
+    )
+
+    description: str | None = Field(
+        default=None,
         description="""
-If some code is needed to convert the remote responses into Llama Stack compatible
+A description of the provider. This is used to display in the documentation.
-API responses, specify the adapter here.
 """,
     )
 
@@ -234,33 +207,6 @@ API responses, specify the adapter here.
     def container_image(self) -> str | None:
         return None
 
-    # module field is inherited from ProviderSpec
-
-    @property
-    def pip_packages(self) -> list[str]:
-        return self.adapter.pip_packages
-
-    @property
-    def provider_data_validator(self) -> str | None:
-        return self.adapter.provider_data_validator
-
-
-def remote_provider_spec(
-    api: Api,
-    adapter: AdapterSpec,
-    api_dependencies: list[Api] | None = None,
-    optional_api_dependencies: list[Api] | None = None,
-) -> RemoteProviderSpec:
-    return RemoteProviderSpec(
-        api=api,
-        provider_type=f"remote::{adapter.adapter_type}",
-        config_class=adapter.config_class,
-        module=adapter.module,
-        adapter=adapter,
-        api_dependencies=api_dependencies or [],
-        optional_api_dependencies=optional_api_dependencies or [],
-    )
-
 
 class HealthStatus(StrEnum):
     OK = "OK"

llama_stack/providers/inline/files/localfs/files.py

@@ -44,7 +44,7 @@ class LocalfsFilesImpl(Files):
         storage_path.mkdir(parents=True, exist_ok=True)
 
         # Initialize SQL store for metadata
-        self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.config.metadata_store))
+        self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.config.metadata_store), self.policy)
         await self.sql_store.create_table(
             "openai_files",
             {
@@ -74,7 +74,7 @@ class LocalfsFilesImpl(Files):
         if not self.sql_store:
             raise RuntimeError("Files provider not initialized")
 
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
         if not row:
             raise ResourceNotFoundError(file_id, "File", "client.files.list()")
 
@@ -150,7 +150,6 @@ class LocalfsFilesImpl(Files):
 
         paginated_result = await self.sql_store.fetch_all(
             table="openai_files",
-            policy=self.policy,
             where=where_conditions if where_conditions else None,
             order_by=[("created_at", order.value)],
             cursor=("id", after) if after else None,

llama_stack/providers/registry/datasetio.py

@@ -6,11 +6,10 @@
 
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 
@@ -25,10 +24,10 @@ def available_providers() -> list[ProviderSpec]:
             api_dependencies=[],
             description="Local filesystem-based dataset I/O provider for reading and writing datasets to local storage.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.datasetio,
-            adapter=AdapterSpec(
             adapter_type="huggingface",
+            provider_type="remote::huggingface",
             pip_packages=[
                 "datasets>=4.0.0",
             ],
@@ -36,17 +35,15 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig",
             description="HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.datasetio,
-            adapter=AdapterSpec(
             adapter_type="nvidia",
+            provider_type="remote::nvidia",
+            module="llama_stack.providers.remote.datasetio.nvidia",
+            config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig",
             pip_packages=[
                 "datasets>=4.0.0",
             ],
-                module="llama_stack.providers.remote.datasetio.nvidia",
-                config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig",
             description="NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform.",
         ),
-        ),
     ]

llama_stack/providers/registry/eval.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 
-from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
+from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
 
 
 def available_providers() -> list[ProviderSpec]:
@@ -25,17 +25,16 @@ def available_providers() -> list[ProviderSpec]:
             ],
             description="Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.eval,
-            adapter=AdapterSpec(
             adapter_type="nvidia",
             pip_packages=[
                 "requests",
             ],
+            provider_type="remote::nvidia",
             module="llama_stack.providers.remote.eval.nvidia",
             config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig",
             description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.",
-            ),
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,

llama_stack/providers/registry/files.py

@@ -4,13 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.datatypes import (
+from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
-    AdapterSpec,
-    Api,
-    InlineProviderSpec,
-    ProviderSpec,
-    remote_provider_spec,
-)
 from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages
 
 
@@ -25,14 +19,13 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig",
             description="Local filesystem-based file storage provider for managing files and documents locally.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.files,
-            adapter=AdapterSpec(
+            provider_type="remote::s3",
             adapter_type="s3",
             pip_packages=["boto3"] + sql_store_pip_packages,
             module="llama_stack.providers.remote.files.s3",
             config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig",
             description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.",
         ),
-        ),
     ]

llama_stack/providers/registry/inference.py

@@ -6,11 +6,10 @@
 
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 META_REFERENCE_DEPS = [
@@ -49,10 +48,10 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",
             description="Sentence Transformers inference provider for text embeddings and similarity search.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="cerebras",
+            provider_type="remote::cerebras",
             pip_packages=[
                 "cerebras_cloud_sdk",
             ],
@@ -60,62 +59,56 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
             description="Cerebras inference provider for running models on Cerebras Cloud platform.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="ollama",
+            provider_type="remote::ollama",
             pip_packages=["ollama", "aiohttp", "h11>=0.16.0"],
             config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
             module="llama_stack.providers.remote.inference.ollama",
             description="Ollama inference provider for running local models through the Ollama runtime.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="vllm",
+            provider_type="remote::vllm",
             pip_packages=[],
             module="llama_stack.providers.remote.inference.vllm",
             config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
             provider_data_validator="llama_stack.providers.remote.inference.vllm.VLLMProviderDataValidator",
             description="Remote vLLM inference provider for connecting to vLLM servers.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="tgi",
+            provider_type="remote::tgi",
             pip_packages=["huggingface_hub", "aiohttp"],
             module="llama_stack.providers.remote.inference.tgi",
             config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
             description="Text Generation Inference (TGI) provider for HuggingFace model serving.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="hf::serverless",
+            provider_type="remote::hf::serverless",
             pip_packages=["huggingface_hub", "aiohttp"],
             module="llama_stack.providers.remote.inference.tgi",
             config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
             description="HuggingFace Inference API serverless provider for on-demand model inference.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
+            provider_type="remote::hf::endpoint",
             adapter_type="hf::endpoint",
             pip_packages=["huggingface_hub", "aiohttp"],
             module="llama_stack.providers.remote.inference.tgi",
             config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
             description="HuggingFace Inference Endpoints provider for dedicated model serving.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="fireworks",
+            provider_type="remote::fireworks",
             pip_packages=[
                 "fireworks-ai<=0.17.16",
             ],
@@ -124,11 +117,10 @@ def available_providers() -> list[ProviderSpec]:
             provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
             description="Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="together",
+            provider_type="remote::together",
             pip_packages=[
                 "together",
             ],
@@ -137,85 +129,82 @@ def available_providers() -> list[ProviderSpec]:
             provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
             description="Together AI inference provider for open-source models and collaborative AI development.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="bedrock",
+            provider_type="remote::bedrock",
             pip_packages=["boto3"],
             module="llama_stack.providers.remote.inference.bedrock",
             config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
             description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="databricks",
+            provider_type="remote::databricks",
             pip_packages=["databricks-sdk"],
             module="llama_stack.providers.remote.inference.databricks",
             config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             description="Databricks inference provider for running models on Databricks' unified analytics platform.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="nvidia",
+            provider_type="remote::nvidia",
             pip_packages=[],
             module="llama_stack.providers.remote.inference.nvidia",
             config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
             description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="runpod",
+            provider_type="remote::runpod",
             pip_packages=[],
             module="llama_stack.providers.remote.inference.runpod",
             config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig",
             description="RunPod inference provider for running models on RunPod's cloud GPU platform.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="openai",
+            provider_type="remote::openai",
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.openai",
             config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig",
             provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator",
             description="OpenAI inference provider for accessing GPT models and other OpenAI services.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="anthropic",
+            provider_type="remote::anthropic",
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.anthropic",
             config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig",
             provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator",
             description="Anthropic inference provider for accessing Claude models and Anthropic's AI services.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="gemini",
-                pip_packages=["litellm"],
+            provider_type="remote::gemini",
+            pip_packages=[
+                "litellm",
+            ],
             module="llama_stack.providers.remote.inference.gemini",
             config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
             provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator",
             description="Google Gemini inference provider for accessing Gemini models and Google's AI services.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="vertexai",
-                pip_packages=["litellm", "google-cloud-aiplatform"],
+            provider_type="remote::vertexai",
+            pip_packages=[
+                "litellm",
+                "google-cloud-aiplatform",
+            ],
             module="llama_stack.providers.remote.inference.vertexai",
             config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig",
             provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator",
@@ -240,65 +229,63 @@ Available Models:
 - vertex_ai/gemini-2.5-flash
 - vertex_ai/gemini-2.5-pro""",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="groq",
-                pip_packages=["litellm"],
+            provider_type="remote::groq",
+            pip_packages=[
+                "litellm",
+            ],
             module="llama_stack.providers.remote.inference.groq",
             config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
             provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator",
             description="Groq inference provider for ultra-fast inference using Groq's LPU technology.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="llama-openai-compat",
+            provider_type="remote::llama-openai-compat",
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.llama_openai_compat",
             config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig",
             provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator",
             description="Llama OpenAI-compatible provider for using Llama models with OpenAI API format.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="sambanova",
-                pip_packages=["litellm"],
+            provider_type="remote::sambanova",
+            pip_packages=[
+                "litellm",
+            ],
             module="llama_stack.providers.remote.inference.sambanova",
             config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
             provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator",
             description="SambaNova inference provider for running models on SambaNova's dataflow architecture.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="passthrough",
+            provider_type="remote::passthrough",
             pip_packages=[],
             module="llama_stack.providers.remote.inference.passthrough",
             config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig",
             provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator",
             description="Passthrough inference provider for connecting to any external inference service not directly supported.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
             adapter_type="watsonx",
+            provider_type="remote::watsonx",
             pip_packages=["ibm_watsonx_ai"],
             module="llama_stack.providers.remote.inference.watsonx",
             config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig",
             provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator",
             description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.inference,
-            adapter=AdapterSpec(
+            provider_type="remote::azure",
             adapter_type="azure",
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.azure",
@@ -310,5 +297,4 @@ Provider documentation
 https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
 """,
         ),
-        ),
     ]

llama_stack/providers/registry/post_training.py

@@ -7,7 +7,7 @@
 
 from typing import cast
 
-from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
+from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
 
 # We provide two versions of these providers so that distributions can package the appropriate version of torch.
 # The CPU version is used for distributions that don't have GPU support -- they result in smaller container images.
@@ -57,14 +57,13 @@ def available_providers() -> list[ProviderSpec]:
             ],
             description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.post_training,
-            adapter=AdapterSpec(
             adapter_type="nvidia",
+            provider_type="remote::nvidia",
             pip_packages=["requests", "aiohttp"],
             module="llama_stack.providers.remote.post_training.nvidia",
             config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
             description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.",
         ),
-        ),
     ]

llama_stack/providers/registry/safety.py

@@ -6,11 +6,10 @@
 
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 
@@ -48,35 +47,32 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.safety.code_scanner.CodeScannerConfig",
             description="Code Scanner safety provider for detecting security vulnerabilities and unsafe code patterns.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.safety,
-            adapter=AdapterSpec(
             adapter_type="bedrock",
+            provider_type="remote::bedrock",
             pip_packages=["boto3"],
             module="llama_stack.providers.remote.safety.bedrock",
             config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig",
             description="AWS Bedrock safety provider for content moderation using AWS's safety services.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.safety,
-            adapter=AdapterSpec(
             adapter_type="nvidia",
+            provider_type="remote::nvidia",
             pip_packages=["requests"],
             module="llama_stack.providers.remote.safety.nvidia",
             config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig",
             description="NVIDIA's safety provider for content moderation and safety filtering.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.safety,
-            adapter=AdapterSpec(
             adapter_type="sambanova",
+            provider_type="remote::sambanova",
             pip_packages=["litellm", "requests"],
             module="llama_stack.providers.remote.safety.sambanova",
             config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig",
             provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator",
             description="SambaNova's safety provider for content moderation and safety filtering.",
         ),
-        ),
     ]

llama_stack/providers/registry/tool_runtime.py

@@ -6,11 +6,10 @@
 
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 
@@ -35,59 +34,54 @@ def available_providers() -> list[ProviderSpec]:
             api_dependencies=[Api.vector_io, Api.inference, Api.files],
             description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.",
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
             api=Api.tool_runtime,
-            adapter=AdapterSpec(
             adapter_type="brave-search",
+            provider_type="remote::brave-search",
             module="llama_stack.providers.remote.tool_runtime.brave_search",
             config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig",
             pip_packages=["requests"],
             provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator",
             description="Brave Search tool for web search capabilities with privacy-focused results.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.tool_runtime,
-            adapter=AdapterSpec(
             adapter_type="bing-search",
+            provider_type="remote::bing-search",
             module="llama_stack.providers.remote.tool_runtime.bing_search",
             config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig",
             pip_packages=["requests"],
             provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator",
             description="Bing Search tool for web search capabilities using Microsoft's search engine.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.tool_runtime,
-            adapter=AdapterSpec(
             adapter_type="tavily-search",
+            provider_type="remote::tavily-search",
             module="llama_stack.providers.remote.tool_runtime.tavily_search",
             config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig",
             pip_packages=["requests"],
             provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator",
             description="Tavily Search tool for AI-optimized web search with structured results.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.tool_runtime,
-            adapter=AdapterSpec(
             adapter_type="wolfram-alpha",
+            provider_type="remote::wolfram-alpha",
             module="llama_stack.providers.remote.tool_runtime.wolfram_alpha",
             config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig",
             pip_packages=["requests"],
             provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator",
             description="Wolfram Alpha tool for computational knowledge and mathematical calculations.",
         ),
-        ),
+        RemoteProviderSpec(
-        remote_provider_spec(
             api=Api.tool_runtime,
-            adapter=AdapterSpec(
             adapter_type="model-context-protocol",
+            provider_type="remote::model-context-protocol",
             module="llama_stack.providers.remote.tool_runtime.model_context_protocol",
             config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig",
             pip_packages=["mcp>=1.8.1"],
             provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator",
             description="Model Context Protocol (MCP) tool for standardized tool calling and context management.",
         ),
-        ),
     ]

llama_stack/providers/registry/vector_io.py

@@ -6,11 +6,10 @@
 
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 
@@ -300,13 +299,15 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
 Please refer to the sqlite-vec provider documentation.
 """,
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
-            Api.vector_io,
+            api=Api.vector_io,
-            AdapterSpec(
             adapter_type="chromadb",
+            provider_type="remote::chromadb",
             pip_packages=["chromadb-client"],
             module="llama_stack.providers.remote.vector_io.chroma",
             config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
+            api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 [Chroma](https://www.trychroma.com/) is an inline and remote vector
 database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
@@ -341,9 +342,6 @@ pip install chromadb
 See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
 """,
         ),
-            api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
-        ),
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::chromadb",
@@ -387,13 +385,15 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 
 """,
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
-            Api.vector_io,
+            api=Api.vector_io,
-            AdapterSpec(
             adapter_type="pgvector",
+            provider_type="remote::pgvector",
             pip_packages=["psycopg2-binary"],
             module="llama_stack.providers.remote.vector_io.pgvector",
             config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
+            api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 [PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
@@ -496,17 +496,16 @@ docker pull pgvector/pgvector:pg17
 See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general.
 """,
         ),
-            api_dependencies=[Api.inference],
+        RemoteProviderSpec(
-            optional_api_dependencies=[Api.files],
+            api=Api.vector_io,
-        ),
-        remote_provider_spec(
-            Api.vector_io,
-            AdapterSpec(
             adapter_type="weaviate",
+            provider_type="remote::weaviate",
             pip_packages=["weaviate-client"],
             module="llama_stack.providers.remote.vector_io.weaviate",
             config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
             provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
+            api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 [Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
 It allows you to store and query vectors directly within a Weaviate database.
@@ -539,9 +538,6 @@ To install Weaviate see the [Weaviate quickstart documentation](https://weaviate
 See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
 """,
         ),
-            api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
-        ),
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::qdrant",
@@ -594,27 +590,28 @@ docker pull qdrant/qdrant
 See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
 """,
         ),
-        remote_provider_spec(
+        RemoteProviderSpec(
-            Api.vector_io,
+            api=Api.vector_io,
-            AdapterSpec(
             adapter_type="qdrant",
+            provider_type="remote::qdrant",
             pip_packages=["qdrant-client"],
             module="llama_stack.providers.remote.vector_io.qdrant",
             config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
+            api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 Please refer to the inline provider documentation.
 """,
         ),
-            api_dependencies=[Api.inference],
+        RemoteProviderSpec(
-            optional_api_dependencies=[Api.files],
+            api=Api.vector_io,
-        ),
-        remote_provider_spec(
-            Api.vector_io,
-            AdapterSpec(
             adapter_type="milvus",
+            provider_type="remote::milvus",
             pip_packages=["pymilvus>=2.4.10"],
             module="llama_stack.providers.remote.vector_io.milvus",
             config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
+            api_dependencies=[Api.inference],
+            optional_api_dependencies=[Api.files],
             description="""
 [Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly within a Milvus database.
@@ -636,7 +633,13 @@ To use Milvus in your Llama Stack project, follow these steps:
 
 ## Installation
 
-You can install Milvus using pymilvus:
+If you want to use inline Milvus, you can install:
+
+```bash
+pip install pymilvus[milvus-lite]
+```
+
+If you want to use remote Milvus, you can install:
 
 ```bash
 pip install pymilvus
@@ -807,13 +810,10 @@ See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for m
 For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).
 """,
         ),
-            api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
-        ),
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::milvus",
-            pip_packages=["pymilvus>=2.4.10"],
+            pip_packages=["pymilvus[milvus-lite]>=2.4.10"],
             module="llama_stack.providers.inline.vector_io.milvus",
             config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
             api_dependencies=[Api.inference],

llama_stack/providers/remote/files/s3/files.py

@@ -137,7 +137,7 @@ class S3FilesImpl(Files):
         where: dict[str, str | dict] = {"id": file_id}
         if not return_expired:
             where["expires_at"] = {">": self._now()}
-        if not (row := await self.sql_store.fetch_one("openai_files", policy=self.policy, where=where)):
+        if not (row := await self.sql_store.fetch_one("openai_files", where=where)):
             raise ResourceNotFoundError(file_id, "File", "files.list()")
         return row
 
@@ -164,7 +164,7 @@ class S3FilesImpl(Files):
         self._client = _create_s3_client(self._config)
         await _create_bucket_if_not_exists(self._client, self._config)
 
-        self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store))
+        self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store), self.policy)
         await self._sql_store.create_table(
             "openai_files",
             {
@@ -268,7 +268,6 @@ class S3FilesImpl(Files):
 
         paginated_result = await self.sql_store.fetch_all(
             table="openai_files",
-            policy=self.policy,
             where=where_conditions,
             order_by=[("created_at", order.value)],
             cursor=("id", after) if after else None,

llama_stack/providers/utils/inference/inference_store.py

@@ -54,7 +54,7 @@ class InferenceStore:
 
     async def initialize(self):
         """Create the necessary tables if they don't exist."""
-        self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config))
+        self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config), self.policy)
         await self.sql_store.create_table(
             "chat_completions",
             {
@@ -202,7 +202,6 @@ class InferenceStore:
             order_by=[("created", order.value)],
             cursor=("id", after) if after else None,
             limit=limit,
-            policy=self.policy,
         )
 
         data = [
@@ -229,7 +228,6 @@ class InferenceStore:
         row = await self.sql_store.fetch_one(
             table="chat_completions",
             where={"id": completion_id},
-            policy=self.policy,
         )
 
         if not row:

llama_stack/providers/utils/responses/responses_store.py

@@ -28,8 +28,7 @@ class ResponsesStore:
             sql_store_config = SqliteSqlStoreConfig(
                 db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
             )
-        self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config))
+        self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config), policy)
-        self.policy = policy
 
     async def initialize(self):
         """Create the necessary tables if they don't exist."""
@@ -87,7 +86,6 @@ class ResponsesStore:
             order_by=[("created_at", order.value)],
             cursor=("id", after) if after else None,
             limit=limit,
-            policy=self.policy,
         )
 
         data = [OpenAIResponseObjectWithInput(**row["response_object"]) for row in paginated_result.data]
@@ -105,7 +103,6 @@ class ResponsesStore:
         row = await self.sql_store.fetch_one(
             "openai_responses",
             where={"id": response_id},
-            policy=self.policy,
         )
 
         if not row:
@@ -116,7 +113,7 @@ class ResponsesStore:
         return OpenAIResponseObjectWithInput(**row["response_object"])
 
     async def delete_response_object(self, response_id: str) -> OpenAIDeleteResponseObject:
-        row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id}, policy=self.policy)
+        row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id})
         if not row:
             raise ValueError(f"Response with id {response_id} not found")
         await self.sql_store.delete("openai_responses", where={"id": response_id})

llama_stack/providers/utils/sqlstore/authorized_sqlstore.py

@@ -53,13 +53,15 @@ class AuthorizedSqlStore:
     access control policies, user attribute capture, and SQL filtering optimization.
     """
 
-    def __init__(self, sql_store: SqlStore):
+    def __init__(self, sql_store: SqlStore, policy: list[AccessRule]):
         """
         Initialize the authorization layer.
 
         :param sql_store: Base SqlStore implementation to wrap
+        :param policy: Access control policy to use for authorization
         """
         self.sql_store = sql_store
+        self.policy = policy
         self._detect_database_type()
         self._validate_sql_optimized_policy()
 
@@ -117,14 +119,13 @@ class AuthorizedSqlStore:
     async def fetch_all(
         self,
         table: str,
-        policy: list[AccessRule],
         where: Mapping[str, Any] | None = None,
         limit: int | None = None,
         order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
         cursor: tuple[str, str] | None = None,
     ) -> PaginatedResponse:
         """Fetch all rows with automatic access control filtering."""
-        access_where = self._build_access_control_where_clause(policy)
+        access_where = self._build_access_control_where_clause(self.policy)
         rows = await self.sql_store.fetch_all(
             table=table,
             where=where,
@@ -146,7 +147,7 @@ class AuthorizedSqlStore:
                 str(record_id), table, User(principal=stored_owner_principal, attributes=stored_access_attrs)
             )
 
-            if is_action_allowed(policy, Action.READ, sql_record, current_user):
+            if is_action_allowed(self.policy, Action.READ, sql_record, current_user):
                 filtered_rows.append(row)
 
         return PaginatedResponse(
@@ -157,14 +158,12 @@ class AuthorizedSqlStore:
     async def fetch_one(
         self,
         table: str,
-        policy: list[AccessRule],
         where: Mapping[str, Any] | None = None,
         order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None,
     ) -> dict[str, Any] | None:
         """Fetch one row with automatic access control checking."""
         results = await self.fetch_all(
             table=table,
-            policy=policy,
             where=where,
             limit=1,
             order_by=order_by,

tests/external/kaze.yaml

@@ -1,4 +1,3 @@
-adapter:
 adapter_type: kaze
 pip_packages: ["tests/external/llama-stack-provider-kaze"]
 config_class: llama_stack_provider_kaze.config.KazeProviderConfig

tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py

@@ -6,7 +6,7 @@
 
 from typing import Protocol
 
-from llama_stack.providers.datatypes import AdapterSpec, Api, ProviderSpec, RemoteProviderSpec
+from llama_stack.providers.datatypes import Api, ProviderSpec, RemoteProviderSpec
 from llama_stack.schema_utils import webmethod
 
 
@@ -16,12 +16,9 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.weather,
             provider_type="remote::kaze",
             config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            adapter=AdapterSpec(
             adapter_type="kaze",
             module="llama_stack_provider_kaze",
             pip_packages=["llama_stack_provider_kaze"],
-                config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            ),
         ),
     ]
 

tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py

@@ -57,7 +57,7 @@ def authorized_store(backend_config):
     config = config_func()
 
     base_sqlstore = sqlstore_impl(config)
-    authorized_store = AuthorizedSqlStore(base_sqlstore)
+    authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
 
     yield authorized_store
 
@@ -106,7 +106,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         await authorized_store.insert(table_name, {"id": "1", "data": "public_data"})
 
         # Test fetching with no user - should not error on JSON comparison
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 1
         assert result.data[0]["id"] == "1"
         assert result.data[0]["access_attributes"] is None
@@ -119,7 +119,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         await authorized_store.insert(table_name, {"id": "2", "data": "admin_data"})
 
         # Fetch all - admin should see both
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 2
 
         # Test with non-admin user
@@ -127,7 +127,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
         mock_get_authenticated_user.return_value = regular_user
 
         # Should only see public record
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
         assert len(result.data) == 1
         assert result.data[0]["id"] == "1"
 
@@ -156,7 +156,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
 
         # Now test with the multi-user who has both roles=admin and teams=dev
         mock_get_authenticated_user.return_value = multi_user
-        result = await authorized_store.fetch_all(table_name, policy=default_policy())
+        result = await authorized_store.fetch_all(table_name)
 
         # Should see:
         # - public record (1) - no access_attributes
@@ -217,21 +217,24 @@ async def test_user_ownership_policy(mock_get_authenticated_user, authorized_sto
             ),
         ]
 
+        # Create a new authorized store with the owner-only policy
+        owner_only_store = AuthorizedSqlStore(authorized_store.sql_store, owner_only_policy)
+
         # Test user1 access - should only see their own record
         mock_get_authenticated_user.return_value = user1
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 1, f"Expected user1 to see 1 record, got {len(result.data)}"
         assert result.data[0]["id"] == "1", f"Expected user1's record, got {result.data[0]['id']}"
 
         # Test user2 access - should only see their own record
         mock_get_authenticated_user.return_value = user2
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 1, f"Expected user2 to see 1 record, got {len(result.data)}"
         assert result.data[0]["id"] == "2", f"Expected user2's record, got {result.data[0]['id']}"
 
         # Test with anonymous user - should see no records
         mock_get_authenticated_user.return_value = None
-        result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
+        result = await owner_only_store.fetch_all(table_name)
         assert len(result.data) == 0, f"Expected anonymous user to see 0 records, got {len(result.data)}"
 
     finally:

tests/unit/distribution/test_distribution.py

@@ -66,7 +66,6 @@ def base_config(tmp_path):
 def provider_spec_yaml():
     """Common provider spec YAML for testing."""
     return """
-adapter:
 adapter_type: test_provider
 config_class: test_provider.config.TestProviderConfig
 module: test_provider
@@ -182,9 +181,9 @@ class TestProviderRegistry:
         assert Api.inference in registry
         assert "remote::test_provider" in registry[Api.inference]
         provider = registry[Api.inference]["remote::test_provider"]
-        assert provider.adapter.adapter_type == "test_provider"
+        assert provider.adapter_type == "test_provider"
-        assert provider.adapter.module == "test_provider"
+        assert provider.module == "test_provider"
-        assert provider.adapter.config_class == "test_provider.config.TestProviderConfig"
+        assert provider.config_class == "test_provider.config.TestProviderConfig"
         assert Api.safety in provider.api_dependencies
 
     def test_external_inline_providers(self, api_directories, mock_providers, base_config, inline_provider_spec_yaml):
@@ -246,7 +245,6 @@ class TestProviderRegistry:
         """Test handling of malformed remote provider spec (missing required fields)."""
         remote_dir, _ = api_directories
         malformed_spec = """
-adapter:
 adapter_type: test_provider
   # Missing required fields
 api_dependencies:
@@ -270,7 +268,7 @@ pip_packages:
         with open(inline_dir / "malformed.yaml", "w") as f:
             f.write(malformed_spec)
 
-        with pytest.raises(KeyError) as exc_info:
+        with pytest.raises(ValidationError) as exc_info:
             get_provider_registry(base_config)
         assert "config_class" in str(exc_info.value)
 

tests/unit/distribution/test_library_client_initialization.py

@@ -27,13 +27,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
+        class MockStack:
-            return mock_impls
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = LlamaStackAsLibraryClient("ci-tests")
@@ -46,13 +50,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
+        class MockStack:
-            return mock_impls
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = AsyncLlamaStackAsLibraryClient("ci-tests")
@@ -68,13 +76,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
+        class MockStack:
-            return mock_impls
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = LlamaStackAsLibraryClient("ci-tests")
@@ -90,13 +102,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
+        class MockStack:
-            return mock_impls
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         client = AsyncLlamaStackAsLibraryClient("ci-tests")
@@ -112,13 +128,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization:
         mock_impls = {}
         mock_route_impls = RouteImpls({})
 
-        async def mock_construct_stack(config, custom_provider_registry):
+        class MockStack:
-            return mock_impls
+            def __init__(self, config, custom_provider_registry=None):
+                self.impls = mock_impls
+
+            async def initialize(self):
+                pass
 
         def mock_initialize_route_impls(impls):
             return mock_route_impls
 
-        monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack)
+        monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack)
         monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls)
 
         sync_client = LlamaStackAsLibraryClient("ci-tests")

tests/unit/utils/test_authorized_sqlstore.py

@@ -26,7 +26,7 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        sqlstore = AuthorizedSqlStore(base_sqlstore)
+        sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         # Create table with access control
         await sqlstore.create_table(
@@ -56,24 +56,24 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic
         mock_get_authenticated_user.return_value = admin_user
 
         # Admin should see both documents
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1})
+        result = await sqlstore.fetch_all("documents", where={"id": 1})
         assert len(result.data) == 1
         assert result.data[0]["title"] == "Admin Document"
 
         # User should only see their document
         mock_get_authenticated_user.return_value = regular_user
 
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1})
+        result = await sqlstore.fetch_all("documents", where={"id": 1})
         assert len(result.data) == 0
 
-        result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 2})
+        result = await sqlstore.fetch_all("documents", where={"id": 2})
         assert len(result.data) == 1
         assert result.data[0]["title"] == "User Document"
 
-        row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 1})
+        row = await sqlstore.fetch_one("documents", where={"id": 1})
         assert row is None
 
-        row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 2})
+        row = await sqlstore.fetch_one("documents", where={"id": 2})
         assert row is not None
         assert row["title"] == "User Document"
 
@@ -88,7 +88,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user):
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        sqlstore = AuthorizedSqlStore(base_sqlstore)
+        sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         await sqlstore.create_table(
             table="resources",
@@ -144,7 +144,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user):
             user = User(principal=user_data["principal"], attributes=user_data["attributes"])
             mock_get_authenticated_user.return_value = user
 
-            sql_results = await sqlstore.fetch_all("resources", policy=policy)
+            sql_results = await sqlstore.fetch_all("resources")
             sql_ids = {row["id"] for row in sql_results.data}
             policy_ids = set()
             for scenario in test_scenarios:
@@ -174,7 +174,7 @@ async def test_authorized_store_user_attribute_capture(mock_get_authenticated_us
                 db_path=tmp_dir + "/" + db_name,
             )
         )
-        authorized_store = AuthorizedSqlStore(base_sqlstore)
+        authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
 
         await authorized_store.create_table(
             table="user_data",