feat: use SecretStr for inference provider auth credentials (#3724)

# What does this PR do?

use SecretStr for OpenAIMixin providers

- RemoteInferenceProviderConfig now has auth_credential: SecretStr
- the default alias is api_key (most common name)
- some providers override to use api_token (RunPod, vLLM, Databricks)
- some providers exclude it (Ollama, TGI, Vertex AI)

addresses #3517 

## Test Plan

ci w/ new tests

docs/docs/providers/inference/remote_anthropic.mdx

@@ -16,7 +16,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `str \| None` | No |  | API key for Anthropic models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_azure.mdx

@@ -23,7 +23,7 @@ https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Azure API key for Azure |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `api_base` | `<class 'pydantic.networks.HttpUrl'>` | No |  | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) |
 | `api_version` | `str \| None` | No |  | Azure API version for Azure (e.g., 2024-12-01-preview) |
 | `api_type` | `str \| None` | No | azure | Azure API type for Azure (e.g., azure) |

docs/docs/providers/inference/remote_cerebras.mdx

@@ -16,8 +16,8 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `base_url` | `<class 'str'>` | No | https://api.cerebras.ai | Base URL for the Cerebras API |
-| `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Cerebras API Key |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_databricks.mdx

@@ -16,8 +16,8 @@ Databricks inference provider for running models on Databricks' unified analytic
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_token` | `pydantic.types.SecretStr \| None` | No |  | The Databricks API token |
 | `url` | `str \| None` | No |  | The URL for the Databricks model serving endpoint |
-| `api_token` | `<class 'pydantic.types.SecretStr'>` | No |  | The Databricks API token |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_fireworks.mdx

@@ -16,8 +16,8 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://api.fireworks.ai/inference/v1 | The URL for the Fireworks server |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Fireworks.ai API Key |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_gemini.mdx

@@ -16,7 +16,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `str \| None` | No |  | API key for Gemini models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_groq.mdx

@@ -16,7 +16,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `str \| None` | No |  | The Groq API key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://api.groq.com | The URL for the Groq AI server |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_llama-openai-compat.mdx

@@ -16,7 +16,7 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `str \| None` | No |  | The Llama API key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `openai_compat_api_base` | `<class 'str'>` | No | https://api.llama.com/compat/v1/ | The URL for the Llama API server |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_nvidia.mdx

@@ -16,8 +16,8 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://integrate.api.nvidia.com | A base url for accessing the NVIDIA NIM |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The NVIDIA API key, only needed of using the hosted service |
 | `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |
 | `append_api_version` | `<class 'bool'>` | No | True | When set to false, the API version will not be appended to the base_url. By default, it is true. |
 

docs/docs/providers/inference/remote_openai.mdx

@@ -16,7 +16,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `str \| None` | No |  | API key for OpenAI models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `base_url` | `<class 'str'>` | No | https://api.openai.com/v1 | Base URL for OpenAI API |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_passthrough.mdx

@@ -16,8 +16,8 @@ Passthrough inference provider for connecting to any external inference service
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `url` | `<class 'str'>` | No |  | The URL for the passthrough endpoint |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | API Key for the passthrouth endpoint |
+| `url` | `<class 'str'>` | No |  | The URL for the passthrough endpoint |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_runpod.mdx

@@ -16,8 +16,8 @@ RunPod inference provider for running models on RunPod's cloud GPU platform.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_token` | `pydantic.types.SecretStr \| None` | No |  | The API token |
 | `url` | `str \| None` | No |  | The URL for the Runpod model serving endpoint |
-| `api_token` | `str \| None` | No |  | The API token |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_sambanova.mdx

@@ -16,8 +16,8 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://api.sambanova.ai/v1 | The URL for the SambaNova AI server |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The SambaNova cloud API Key |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_together.mdx

@@ -16,8 +16,8 @@ Together AI inference provider for open-source models and collaborative AI devel
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://api.together.xyz/v1 | The URL for the Together AI server |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Together AI API Key |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_vllm.mdx

@@ -16,9 +16,9 @@ Remote vLLM inference provider for connecting to vLLM servers.
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_token` | `pydantic.types.SecretStr \| None` | No |  | The API token |
 | `url` | `str \| None` | No |  | The URL for the vLLM model serving endpoint |
 | `max_tokens` | `<class 'int'>` | No | 4096 | Maximum number of tokens to generate. |
-| `api_token` | `str \| None` | No | fake | The API token |
 | `tls_verify` | `bool \| str` | No | True | Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file. |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_watsonx.mdx

@@ -16,8 +16,8 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The watsonx.ai API key |
 | `project_id` | `str \| None` | No |  | The watsonx.ai project ID |
 | `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |
 

llama_stack/providers/remote/inference/anthropic/anthropic.py

@@ -29,9 +29,6 @@ class AnthropicInferenceAdapter(OpenAIMixin):
     #     "voyage-multimodal-3": {"embedding_dimension": 1024, "context_length": 32000},
     # }
 
-    def get_api_key(self) -> str:
-        return self.config.api_key or ""
-
     def get_base_url(self):
         return "https://api.anthropic.com/v1"
 

llama_stack/providers/remote/inference/anthropic/config.py

@@ -21,11 +21,6 @@ class AnthropicProviderDataValidator(BaseModel):
 
 @json_schema_type
 class AnthropicConfig(RemoteInferenceProviderConfig):
-    api_key: str | None = Field(
-        default=None,
-        description="API key for Anthropic models",
-    )
-
     @classmethod
     def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {

llama_stack/providers/remote/inference/azure/azure.py

@@ -16,9 +16,6 @@ class AzureInferenceAdapter(OpenAIMixin):
 
     provider_data_api_key_field: str = "azure_api_key"
 
-    def get_api_key(self) -> str:
-        return self.config.api_key.get_secret_value()
-
     def get_base_url(self) -> str:
         """
         Get the Azure API base URL.

llama_stack/providers/remote/inference/azure/config.py

@@ -32,9 +32,6 @@ class AzureProviderDataValidator(BaseModel):
 
 @json_schema_type
 class AzureConfig(RemoteInferenceProviderConfig):
-    api_key: SecretStr = Field(
-        description="Azure API key for Azure",
-    )
     api_base: HttpUrl = Field(
         description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)",
     )

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -15,9 +15,6 @@ from .config import CerebrasImplConfig
 class CerebrasInferenceAdapter(OpenAIMixin):
     config: CerebrasImplConfig
 
-    def get_api_key(self) -> str:
-        return self.config.api_key.get_secret_value()
-
     def get_base_url(self) -> str:
         return urljoin(self.config.base_url, "v1")
 

llama_stack/providers/remote/inference/cerebras/config.py

@@ -7,7 +7,7 @@
 import os
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -21,10 +21,6 @@ class CerebrasImplConfig(RemoteInferenceProviderConfig):
         default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL),
         description="Base URL for the Cerebras API",
     )
-    api_key: SecretStr = Field(
-        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),  # type: ignore[arg-type]
-        description="Cerebras API Key",
-    )
 
     @classmethod
     def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY:=}", **kwargs) -> dict[str, Any]:

llama_stack/providers/remote/inference/databricks/config.py

@@ -18,8 +18,9 @@ class DatabricksImplConfig(RemoteInferenceProviderConfig):
         default=None,
         description="The URL for the Databricks model serving endpoint",
     )
-    api_token: SecretStr = Field(
-        default=SecretStr(None),  # type: ignore[arg-type]
+    auth_credential: SecretStr | None = Field(
+        default=None,
+        alias="api_token",
         description="The Databricks API token",
     )
 

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -27,9 +27,6 @@ class DatabricksInferenceAdapter(OpenAIMixin):
         "databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
     }
 
-    def get_api_key(self) -> str:
-        return self.config.api_token.get_secret_value()
-
     def get_base_url(self) -> str:
         return f"{self.config.url}/serving-endpoints"
 

llama_stack/providers/remote/inference/fireworks/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -18,10 +18,6 @@ class FireworksImplConfig(RemoteInferenceProviderConfig):
         default="https://api.fireworks.ai/inference/v1",
         description="The URL for the Fireworks server",
     )
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="The Fireworks.ai API Key",
-    )
 
     @classmethod
     def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY:=}", **kwargs) -> dict[str, Any]:

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -23,8 +23,5 @@ class FireworksInferenceAdapter(OpenAIMixin):
 
     provider_data_api_key_field: str = "fireworks_api_key"
 
-    def get_api_key(self) -> str:
-        return self.config.api_key.get_secret_value() if self.config.api_key else None  # type: ignore[return-value]
-
     def get_base_url(self) -> str:
         return "https://api.fireworks.ai/inference/v1"

llama_stack/providers/remote/inference/gemini/config.py

@@ -21,11 +21,6 @@ class GeminiProviderDataValidator(BaseModel):
 
 @json_schema_type
 class GeminiConfig(RemoteInferenceProviderConfig):
-    api_key: str | None = Field(
-        default=None,
-        description="API key for Gemini models",
-    )
-
     @classmethod
     def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -17,8 +17,5 @@ class GeminiInferenceAdapter(OpenAIMixin):
         "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
     }
 
-    def get_api_key(self) -> str:
-        return self.config.api_key or ""
-
     def get_base_url(self):
         return "https://generativelanguage.googleapis.com/v1beta/openai/"

llama_stack/providers/remote/inference/groq/config.py

@@ -21,12 +21,6 @@ class GroqProviderDataValidator(BaseModel):
 
 @json_schema_type
 class GroqConfig(RemoteInferenceProviderConfig):
-    api_key: str | None = Field(
-        # The Groq client library loads the GROQ_API_KEY environment variable by default
-        default=None,
-        description="The Groq API key",
-    )
-
     url: str = Field(
         default="https://api.groq.com",
         description="The URL for the Groq AI server",

llama_stack/providers/remote/inference/groq/groq.py

@@ -14,8 +14,5 @@ class GroqInferenceAdapter(OpenAIMixin):
 
     provider_data_api_key_field: str = "groq_api_key"
 
-    def get_api_key(self) -> str:
-        return self.config.api_key or ""
-
     def get_base_url(self) -> str:
         return f"{self.config.url}/openai/v1"

llama_stack/providers/remote/inference/llama_openai_compat/config.py

@@ -21,11 +21,6 @@ class LlamaProviderDataValidator(BaseModel):
 
 @json_schema_type
 class LlamaCompatConfig(RemoteInferenceProviderConfig):
-    api_key: str | None = Field(
-        default=None,
-        description="The Llama API key",
-    )
-
     openai_compat_api_base: str = Field(
         default="https://api.llama.com/compat/v1/",
         description="The URL for the Llama API server",

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -21,9 +21,6 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
     Llama API Inference Adapter for Llama Stack.
     """
 
-    def get_api_key(self) -> str:
-        return self.config.api_key or ""
-
     def get_base_url(self) -> str:
         """
         Get the base URL for OpenAI mixin.

llama_stack/providers/remote/inference/nvidia/config.py

@@ -7,7 +7,7 @@
 import os
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -40,10 +40,6 @@ class NVIDIAConfig(RemoteInferenceProviderConfig):
         default_factory=lambda: os.getenv("NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"),
         description="A base url for accessing the NVIDIA NIM",
     )
-    api_key: SecretStr | None = Field(
-        default_factory=lambda: SecretStr(os.getenv("NVIDIA_API_KEY")),
-        description="The NVIDIA API key, only needed of using the hosted service",
-    )
     timeout: int = Field(
         default=60,
         description="Timeout for the HTTP requests",

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -49,7 +49,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
         logger.info(f"Initializing NVIDIAInferenceAdapter({self.config.url})...")
 
         if _is_nvidia_hosted(self.config):
-            if not self.config.api_key:
+            if not self.config.auth_credential:
                 raise RuntimeError(
                     "API key is required for hosted NVIDIA NIM. Either provide an API key or use a self-hosted NIM."
                 )
@@ -60,7 +60,13 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
 
         :return: The NVIDIA API key
         """
-        return self.config.api_key.get_secret_value() if self.config.api_key else "NO KEY"
+        if self.config.auth_credential:
+            return self.config.auth_credential.get_secret_value()
+
+        if not _is_nvidia_hosted(self.config):
+            return "NO KEY REQUIRED"
+
+        return None
 
     def get_base_url(self) -> str:
         """

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

@@ -6,12 +6,16 @@
 
 from typing import Any
 
+from pydantic import Field, SecretStr
+
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 
 DEFAULT_OLLAMA_URL = "http://localhost:11434"
 
 
 class OllamaImplConfig(RemoteInferenceProviderConfig):
+    auth_credential: SecretStr | None = Field(default=None, exclude=True)
+
     url: str = DEFAULT_OLLAMA_URL
 
     @classmethod

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

@@ -59,7 +59,7 @@ class OllamaInferenceAdapter(OpenAIMixin):
         return self._clients[loop]
 
     def get_api_key(self):
-        return "NO_KEY"
+        return "NO KEY REQUIRED"
 
     def get_base_url(self):
         return self.config.url.rstrip("/") + "/v1"

llama_stack/providers/remote/inference/openai/config.py

@@ -21,10 +21,6 @@ class OpenAIProviderDataValidator(BaseModel):
 
 @json_schema_type
 class OpenAIConfig(RemoteInferenceProviderConfig):
-    api_key: str | None = Field(
-        default=None,
-        description="API key for OpenAI models",
-    )
     base_url: str = Field(
         default="https://api.openai.com/v1",
         description="Base URL for OpenAI API",

llama_stack/providers/remote/inference/openai/openai.py

@@ -29,9 +29,6 @@ class OpenAIInferenceAdapter(OpenAIMixin):
         "text-embedding-3-large": {"embedding_dimension": 3072, "context_length": 8192},
     }
 
-    def get_api_key(self) -> str:
-        return self.config.api_key or ""
-
     def get_base_url(self) -> str:
         """
         Get the OpenAI API base URL.

llama_stack/providers/remote/inference/runpod/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import Field
+from pydantic import Field, SecretStr
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -18,8 +18,9 @@ class RunpodImplConfig(RemoteInferenceProviderConfig):
         default=None,
         description="The URL for the Runpod model serving endpoint",
     )
-    api_token: str | None = Field(
+    auth_credential: SecretStr | None = Field(
         default=None,
+        alias="api_token",
         description="The API token",
     )
 

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -24,10 +24,6 @@ class RunpodInferenceAdapter(OpenAIMixin):
 
     config: RunpodImplConfig
 
-    def get_api_key(self) -> str:
-        """Get API key for OpenAI client."""
-        return self.config.api_token
-
     def get_base_url(self) -> str:
         """Get base URL for OpenAI client."""
         return self.config.url

llama_stack/providers/remote/inference/sambanova/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field, SecretStr
+from pydantic import BaseModel, Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -25,10 +25,6 @@ class SambaNovaImplConfig(RemoteInferenceProviderConfig):
         default="https://api.sambanova.ai/v1",
         description="The URL for the SambaNova AI server",
     )
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="The SambaNova cloud API Key",
-    )
 
     @classmethod
     def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY:=}", **kwargs) -> dict[str, Any]:

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -19,9 +19,6 @@ class SambaNovaInferenceAdapter(OpenAIMixin):
     SambaNova Inference Adapter for Llama Stack.
     """
 
-    def get_api_key(self) -> str:
-        return self.config.api_key.get_secret_value() if self.config.api_key else ""
-
     def get_base_url(self) -> str:
         """
         Get the base URL for OpenAI mixin.

llama_stack/providers/remote/inference/tgi/config.py

@@ -13,6 +13,8 @@ from llama_stack.schema_utils import json_schema_type
 
 @json_schema_type
 class TGIImplConfig(RemoteInferenceProviderConfig):
+    auth_credential: SecretStr | None = Field(default=None, exclude=True)
+
     url: str = Field(
         description="The URL for the TGI serving endpoint",
     )

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -30,7 +30,7 @@ class _HfAdapter(OpenAIMixin):
     overwrite_completion_id = True  # TGI always returns id=""
 
     def get_api_key(self):
-        return self.api_key.get_secret_value()
+        return "NO KEY REQUIRED"
 
     def get_base_url(self):
         return self.url

llama_stack/providers/remote/inference/together/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -18,10 +18,6 @@ class TogetherImplConfig(RemoteInferenceProviderConfig):
         default="https://api.together.xyz/v1",
         description="The URL for the Together AI server",
     )
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="The Together AI API Key",
-    )
 
     @classmethod
     def sample_run_config(cls, **kwargs) -> dict[str, Any]:

llama_stack/providers/remote/inference/together/together.py

@@ -39,15 +39,12 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
 
     provider_data_api_key_field: str = "together_api_key"
 
-    def get_api_key(self):
-        return self.config.api_key.get_secret_value() if self.config.api_key else None
-
     def get_base_url(self):
         return BASE_URL
 
     def _get_client(self) -> AsyncTogether:
         together_api_key = None
-        config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
+        config_api_key = self.config.auth_credential.get_secret_value() if self.config.auth_credential else None
         if config_api_key:
             together_api_key = config_api_key
         else:

llama_stack/providers/remote/inference/vertexai/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -25,6 +25,8 @@ class VertexAIProviderDataValidator(BaseModel):
 
 @json_schema_type
 class VertexAIConfig(RemoteInferenceProviderConfig):
+    auth_credential: SecretStr | None = Field(default=None, exclude=True)
+
     project: str = Field(
         description="Google Cloud project ID for Vertex AI",
     )

llama_stack/providers/remote/inference/vllm/config.py

@@ -6,7 +6,7 @@
 
 from pathlib import Path
 
-from pydantic import Field, field_validator
+from pydantic import Field, SecretStr, field_validator
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -22,8 +22,9 @@ class VLLMInferenceAdapterConfig(RemoteInferenceProviderConfig):
         default=4096,
         description="Maximum number of tokens to generate.",
     )
-    api_token: str | None = Field(
-        default="fake",
+    auth_credential: SecretStr | None = Field(
+        default=None,
+        alias="api_token",
         description="The API token",
     )
     tls_verify: bool | str = Field(

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -38,8 +38,10 @@ class VLLMInferenceAdapter(OpenAIMixin):
 
     provider_data_api_key_field: str = "vllm_api_token"
 
-    def get_api_key(self) -> str:
-        return self.config.api_token or ""
+    def get_api_key(self) -> str | None:
+        if self.config.auth_credential:
+            return self.config.auth_credential.get_secret_value()
+        return "NO KEY REQUIRED"
 
     def get_base_url(self) -> str:
         """Get the base URL from config."""

llama_stack/providers/remote/inference/watsonx/config.py

@@ -7,7 +7,7 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, ConfigDict, Field, SecretStr
+from pydantic import BaseModel, ConfigDict, Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -27,14 +27,6 @@ class WatsonXConfig(RemoteInferenceProviderConfig):
         default_factory=lambda: os.getenv("WATSONX_BASE_URL", "https://us-south.ml.cloud.ibm.com"),
         description="A base url for accessing the watsonx.ai",
     )
-    # This seems like it should be required, but none of the other remote inference
-    # providers require it, so this is optional here too for consistency.
-    # The OpenAIConfig uses default=None instead, so this is following that precedent.
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="The watsonx.ai API key",
-    )
-    # As above, this is optional here too for consistency.
     project_id: str | None = Field(
         default=None,
         description="The watsonx.ai project ID",

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -22,7 +22,7 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="watsonx",
-            api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
+            api_key_from_config=config.auth_credential.get_secret_value() if config.auth_credential else None,
             provider_data_api_key_field="watsonx_api_key",
         )
         self.available_models = None

llama_stack/providers/utils/bedrock/config.py

@@ -12,6 +12,7 @@ from llama_stack.providers.utils.inference.model_registry import RemoteInference
 
 
 class BedrockBaseConfig(RemoteInferenceProviderConfig):
+    auth_credential: None = Field(default=None, exclude=True)
     aws_access_key_id: str | None = Field(
         default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"),
         description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID",

llama_stack/providers/utils/inference/model_registry.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.apis.common.errors import UnsupportedModelError
 from llama_stack.apis.models import ModelType
@@ -28,6 +28,11 @@ class RemoteInferenceProviderConfig(BaseModel):
         default=False,
         description="Whether to refresh models periodically from the provider",
     )
+    auth_credential: SecretStr | None = Field(
+        default=None,
+        description="Authentication credential for the provider",
+        alias="api_key",
+    )
 
 
 # TODO: this class is more confusing than useful right now. We need to make it

llama_stack/providers/utils/inference/openai_mixin.py

@@ -40,7 +40,6 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
     This class handles direct OpenAI API calls using the AsyncOpenAI client.
 
     This is an abstract base class that requires child classes to implement:
-    - get_api_key(): Method to retrieve the API key
     - get_base_url(): Method to retrieve the OpenAI-compatible API base URL
 
     The behavior of this class can be customized by child classes in the following ways:
@@ -87,17 +86,15 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
     # Optional field name in provider data to look for API key, which takes precedence
     provider_data_api_key_field: str | None = None
 
-    @abstractmethod
-    def get_api_key(self) -> str:
+    def get_api_key(self) -> str | None:
         """
         Get the API key.
 
-        This method must be implemented by child classes to provide the API key
-        for authenticating with the OpenAI API or compatible endpoints.
-
-        :return: The API key as a string
+        :return: The API key as a string, or None if not set
         """
-        pass
+        if self.config.auth_credential is None:
+            return None
+        return self.config.auth_credential.get_secret_value()
 
     @abstractmethod
     def get_base_url(self) -> str:
@@ -176,13 +173,11 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
             if provider_data and getattr(provider_data, self.provider_data_api_key_field, None):
                 api_key = getattr(provider_data, self.provider_data_api_key_field)
 
-            if not api_key:  # TODO: let get_api_key return None
-                raise ValueError(
-                    "API key is not set. Please provide a valid API key in the "
-                    "provider data header, e.g. x-llamastack-provider-data: "
-                    f'{{"{self.provider_data_api_key_field}": "<API_KEY>"}}, '
-                    "or in the provider config."
-                )
+        if not api_key:
+            message = "API key not provided."
+            if self.provider_data_api_key_field:
+                message += f' Please provide a valid API key in the provider data header, e.g. x-llamastack-provider-data: {{"{self.provider_data_api_key_field}": "<API_KEY>"}}.'
+            raise ValueError(message)
 
         return AsyncOpenAI(
             api_key=api_key,

scripts/provider_codegen.py

@@ -76,6 +76,8 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
         fields_info = {}
         if hasattr(config_class, "model_fields"):
             for field_name, field in config_class.model_fields.items():
+                if getattr(field, "exclude", False):
+                    continue
                 field_type = str(field.annotation) if field.annotation else "Any"
 
                 # this string replace is ridiculous
@@ -106,7 +108,10 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
                     "default": default_value,
                     "required": field.default is None and not field.is_required,
                 }
-                fields_info[field_name] = field_info
+
+                # Use alias if available, otherwise use the field name
+                display_name = field.alias if field.alias else field_name
+                fields_info[display_name] = field_info
 
         if accepts_extra_config:
             config_description = "Additional configuration options that will be forwarded to the underlying provider"

tests/unit/providers/utils/inference/test_openai_mixin.py

@@ -720,7 +720,7 @@ class TestOpenAIMixinProviderDataApiKey:
     ):
         """Test that ValueError is raised when provider data exists but doesn't have required key"""
         with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"wrong_key": "some-value"})}):
-            with pytest.raises(ValueError, match="API key is not set"):
+            with pytest.raises(ValueError, match="API key not provided"):
                 _ = mixin_with_provider_data_field_and_none_api_key.client
 
     def test_error_message_includes_correct_field_names(self, mixin_with_provider_data_field_and_none_api_key):

tests/unit/providers/utils/inference/test_remote_inference_provider_config.py

@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import pytest
+
+from llama_stack.core.stack import replace_env_vars
+from llama_stack.providers.remote.inference.anthropic import AnthropicConfig
+from llama_stack.providers.remote.inference.azure import AzureConfig
+from llama_stack.providers.remote.inference.bedrock import BedrockConfig
+from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig
+from llama_stack.providers.remote.inference.databricks import DatabricksImplConfig
+from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
+from llama_stack.providers.remote.inference.gemini import GeminiConfig
+from llama_stack.providers.remote.inference.groq import GroqConfig
+from llama_stack.providers.remote.inference.llama_openai_compat import LlamaCompatConfig
+from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
+from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
+from llama_stack.providers.remote.inference.openai import OpenAIConfig
+from llama_stack.providers.remote.inference.runpod import RunpodImplConfig
+from llama_stack.providers.remote.inference.sambanova import SambaNovaImplConfig
+from llama_stack.providers.remote.inference.tgi import TGIImplConfig
+from llama_stack.providers.remote.inference.together import TogetherImplConfig
+from llama_stack.providers.remote.inference.vertexai import VertexAIConfig
+from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
+from llama_stack.providers.remote.inference.watsonx import WatsonXConfig
+
+
+class TestRemoteInferenceProviderConfig:
+    @pytest.mark.parametrize(
+        "config_cls,alias_name,env_name,extra_config",
+        [
+            (AnthropicConfig, "api_key", "ANTHROPIC_API_KEY", {}),
+            (AzureConfig, "api_key", "AZURE_API_KEY", {"api_base": "HTTP://FAKE"}),
+            (BedrockConfig, None, None, {}),
+            (CerebrasImplConfig, "api_key", "CEREBRAS_API_KEY", {}),
+            (DatabricksImplConfig, "api_token", "DATABRICKS_TOKEN", {}),
+            (FireworksImplConfig, "api_key", "FIREWORKS_API_KEY", {}),
+            (GeminiConfig, "api_key", "GEMINI_API_KEY", {}),
+            (GroqConfig, "api_key", "GROQ_API_KEY", {}),
+            (LlamaCompatConfig, "api_key", "LLAMA_API_KEY", {}),
+            (NVIDIAConfig, "api_key", "NVIDIA_API_KEY", {}),
+            (OllamaImplConfig, None, None, {}),
+            (OpenAIConfig, "api_key", "OPENAI_API_KEY", {}),
+            (RunpodImplConfig, "api_token", "RUNPOD_API_TOKEN", {}),
+            (SambaNovaImplConfig, "api_key", "SAMBANOVA_API_KEY", {}),
+            (TGIImplConfig, None, None, {"url": "FAKE"}),
+            (TogetherImplConfig, "api_key", "TOGETHER_API_KEY", {}),
+            (VertexAIConfig, None, None, {"project": "FAKE", "location": "FAKE"}),
+            (VLLMInferenceAdapterConfig, "api_token", "VLLM_API_TOKEN", {}),
+            (WatsonXConfig, "api_key", "WATSONX_API_KEY", {}),
+        ],
+    )
+    def test_provider_config_auth_credentials(self, monkeypatch, config_cls, alias_name, env_name, extra_config):
+        """Test that the config class correctly maps the alias to auth_credential."""
+        secret_value = config_cls.__name__
+
+        if alias_name is None:
+            pytest.skip("No alias name provided for this config class.")
+
+        config = config_cls(**{alias_name: secret_value, **extra_config})
+        assert config.auth_credential is not None
+        assert config.auth_credential.get_secret_value() == secret_value
+
+        schema = config_cls.model_json_schema()
+        assert alias_name in schema["properties"]
+        assert "auth_credential" not in schema["properties"]
+
+        if env_name:
+            monkeypatch.setenv(env_name, secret_value)
+            sample_config = config_cls.sample_run_config()
+            expanded_config = replace_env_vars(sample_config)
+            config_from_sample = config_cls(**{**expanded_config, **extra_config})
+            assert config_from_sample.auth_credential is not None
+            assert config_from_sample.auth_credential.get_secret_value() == secret_value