feat: combine ProviderSpec datatypes (#3378)

# What does this PR do?

currently `RemoteProviderSpec` has an `AdapterSpec` embedded in it.
Remove `AdapterSpec`, and put its leftover fields into
`RemoteProviderSpec`.

Additionally, many of the fields were duplicated between
`InlineProviderSpec` and `RemoteProviderSpec`. Move these to
`ProviderSpec` so they are shared.

Fixup the distro codegen to use `RemoteProviderSpec` directly rather
than `remote_provider_spec` which took an AdapterSpec and returned a
full provider spec

## Test Plan

existing distro tests should pass.

Signed-off-by: Charlie Doern <cdoern@redhat.com>

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/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/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=[],
             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.
@@ -807,9 +804,6 @@ 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",

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/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)