chore: turn OpenAIMixin into a pydantic.BaseModel (#3671)

# What does this PR do?

- implement get_api_key instead of relying on
LiteLLMOpenAIMixin.get_api_key
 - remove use of LiteLLMOpenAIMixin
 - add default initialize/shutdown methods to OpenAIMixin
 - remove __init__s to allow proper pydantic construction
- remove dead code from vllm adapter and associated / duplicate unit
tests
 - update vllm adapter to use openaimixin for model registration
 - remove ModelRegistryHelper from fireworks & together adapters
 - remove Inference from nvidia adapter
 - complete type hints on embedding_model_metadata
- allow extra fields on OpenAIMixin, for model_store, __provider_id__,
etc
 - new recordings for ollama
 - enhance the list models error handling
- update cerebras (remove cerebras-cloud-sdk) and anthropic (custom
model listing) inference adapters
 - parametrized test_inference_client_caching
- remove cerebras, databricks, fireworks, together from blanket mypy
exclude
 - removed unnecessary litellm deps

## Test Plan

ci

docs/docs/providers/inference/remote_databricks.mdx

@@ -15,7 +15,7 @@ Databricks inference provider for running models on Databricks' unified analytic
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
-| `url` | `<class 'str'>` | No |  | The URL for the Databricks model serving endpoint |
+| `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

llama_stack/providers/registry/inference.py

@@ -52,9 +52,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="cerebras",
             provider_type="remote::cerebras",
-            pip_packages=[
+            pip_packages=[],
-                "cerebras_cloud_sdk",
-            ],
             module="llama_stack.providers.remote.inference.cerebras",
             config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
             description="Cerebras inference provider for running models on Cerebras Cloud platform.",
@@ -169,7 +167,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="openai",
             provider_type="remote::openai",
-            pip_packages=["litellm"],
+            pip_packages=[],
             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",
@@ -179,7 +177,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="anthropic",
             provider_type="remote::anthropic",
-            pip_packages=["litellm"],
+            pip_packages=["anthropic"],
             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",
@@ -189,9 +187,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="gemini",
             provider_type="remote::gemini",
-            pip_packages=[
+            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",
@@ -202,7 +198,6 @@ def available_providers() -> list[ProviderSpec]:
             adapter_type="vertexai",
             provider_type="remote::vertexai",
             pip_packages=[
-                "litellm",
                 "google-cloud-aiplatform",
             ],
             module="llama_stack.providers.remote.inference.vertexai",
@@ -233,9 +228,7 @@ Available Models:
             api=Api.inference,
             adapter_type="groq",
             provider_type="remote::groq",
-            pip_packages=[
+            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",
@@ -245,7 +238,7 @@ Available Models:
             api=Api.inference,
             adapter_type="llama-openai-compat",
             provider_type="remote::llama-openai-compat",
-            pip_packages=["litellm"],
+            pip_packages=[],
             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",
@@ -255,9 +248,7 @@ Available Models:
             api=Api.inference,
             adapter_type="sambanova",
             provider_type="remote::sambanova",
-            pip_packages=[
+            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",
@@ -287,7 +278,7 @@ Available Models:
             api=Api.inference,
             provider_type="remote::azure",
             adapter_type="azure",
-            pip_packages=["litellm"],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.azure",
             config_class="llama_stack.providers.remote.inference.azure.AzureConfig",
             provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator",

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

@@ -10,6 +10,6 @@ from .config import AnthropicConfig
 async def get_adapter_impl(config: AnthropicConfig, _deps):
     from .anthropic import AnthropicInferenceAdapter
 
-    impl = AnthropicInferenceAdapter(config)
+    impl = AnthropicInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -4,13 +4,19 @@
 # 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.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from collections.abc import Iterable
+
+from anthropic import AsyncAnthropic
+
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import AnthropicConfig
 
 
-class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class AnthropicInferenceAdapter(OpenAIMixin):
+    config: AnthropicConfig
+
+    provider_data_api_key_field: str = "anthropic_api_key"
     # source: https://docs.claude.com/en/docs/build-with-claude/embeddings
     # TODO: add support for voyageai, which is where these models are hosted
     # embedding_model_metadata = {
@@ -23,22 +29,11 @@ class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
     #     "voyage-multimodal-3": {"embedding_dimension": 1024, "context_length": 32000},
     # }
 
-    def __init__(self, config: AnthropicConfig) -> None:
+    def get_api_key(self) -> str:
-        LiteLLMOpenAIMixin.__init__(
+        return self.config.api_key or ""
-            self,
-            litellm_provider_name="anthropic",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="anthropic_api_key",
-        )
-        self.config = config
-
-    async def initialize(self) -> None:
-        await super().initialize()
-
-    async def shutdown(self) -> None:
-        await super().shutdown()
-
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self):
         return "https://api.anthropic.com/v1"
+
+    async def list_provider_model_ids(self) -> Iterable[str]:
+        return [m.id async for m in AsyncAnthropic(api_key=self.get_api_key()).models.list()]

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

@@ -10,6 +10,6 @@ from .config import AzureConfig
 async def get_adapter_impl(config: AzureConfig, _deps):
     from .azure import AzureInferenceAdapter
 
-    impl = AzureInferenceAdapter(config)
+    impl = AzureInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -4,31 +4,20 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any
 from urllib.parse import urljoin
 
-from llama_stack.apis.inference import ChatCompletionRequest
-from llama_stack.providers.utils.inference.litellm_openai_mixin import (
-    LiteLLMOpenAIMixin,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import AzureConfig
 
 
-class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class AzureInferenceAdapter(OpenAIMixin):
-    def __init__(self, config: AzureConfig) -> None:
+    config: AzureConfig
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="azure",
-            api_key_from_config=config.api_key.get_secret_value(),
-            provider_data_api_key_field="azure_api_key",
-            openai_compat_api_base=str(config.api_base),
-        )
-        self.config = config
 
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
+    provider_data_api_key_field: str = "azure_api_key"
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
+
+    def get_api_key(self) -> str:
+        return self.config.api_key.get_secret_value()
 
     def get_base_url(self) -> str:
         """
@@ -37,26 +26,3 @@ class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         Returns the Azure API base URL from the configuration.
         """
         return urljoin(str(self.config.api_base), "/openai/v1")
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
-        # Get base parameters from parent
-        params = await super()._get_params(request)
-
-        # Add Azure specific parameters
-        provider_data = self.get_request_provider_data()
-        if provider_data:
-            if getattr(provider_data, "azure_api_key", None):
-                params["api_key"] = provider_data.azure_api_key
-            if getattr(provider_data, "azure_api_base", None):
-                params["api_base"] = provider_data.azure_api_base
-            if getattr(provider_data, "azure_api_version", None):
-                params["api_version"] = provider_data.azure_api_version
-            if getattr(provider_data, "azure_api_type", None):
-                params["api_type"] = provider_data.azure_api_type
-        else:
-            params["api_key"] = self.config.api_key.get_secret_value()
-            params["api_base"] = str(self.config.api_base)
-            params["api_version"] = self.config.api_version
-            params["api_type"] = self.config.api_type
-
-        return params

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

@@ -12,7 +12,7 @@ async def get_adapter_impl(config: CerebrasImplConfig, _deps):
 
     assert isinstance(config, CerebrasImplConfig), f"Unexpected config type: {type(config)}"
 
-    impl = CerebrasInferenceAdapter(config)
+    impl = CerebrasInferenceAdapter(config=config)
 
     await impl.initialize()
 

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

@@ -6,39 +6,14 @@
 
 from urllib.parse import urljoin
 
-from cerebras.cloud.sdk import AsyncCerebras
+from llama_stack.apis.inference import OpenAIEmbeddingsResponse
-
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    CompletionRequest,
-    Inference,
-    OpenAIEmbeddingsResponse,
-    TopKSamplingStrategy,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    get_sampling_options,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    completion_request_to_prompt,
-)
 
 from .config import CerebrasImplConfig
 
 
-class CerebrasInferenceAdapter(
+class CerebrasInferenceAdapter(OpenAIMixin):
-    OpenAIMixin,
+    config: CerebrasImplConfig
-    Inference,
-):
-    def __init__(self, config: CerebrasImplConfig) -> None:
-        self.config = config
-
-        # TODO: make this use provider data, etc. like other providers
-        self._cerebras_client = AsyncCerebras(
-            base_url=self.config.base_url,
-            api_key=self.config.api_key.get_secret_value(),
-        )
 
     def get_api_key(self) -> str:
         return self.config.api_key.get_secret_value()
@@ -46,31 +21,6 @@ class CerebrasInferenceAdapter(
     def get_base_url(self) -> str:
         return urljoin(self.config.base_url, "v1")
 
-    async def initialize(self) -> None:
-        return
-
-    async def shutdown(self) -> None:
-        pass
-
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
-        if request.sampling_params and isinstance(request.sampling_params.strategy, TopKSamplingStrategy):
-            raise ValueError("`top_k` not supported by Cerebras")
-
-        prompt = ""
-        if isinstance(request, ChatCompletionRequest):
-            prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))
-        elif isinstance(request, CompletionRequest):
-            prompt = await completion_request_to_prompt(request)
-        else:
-            raise ValueError(f"Unknown request type {type(request)}")
-
-        return {
-            "model": request.model,
-            "prompt": prompt,
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
-
     async def openai_embeddings(
         self,
         model: str,

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

@@ -22,7 +22,7 @@ class CerebrasImplConfig(RemoteInferenceProviderConfig):
         description="Base URL for the Cerebras API",
     )
     api_key: SecretStr = Field(
-        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),
+        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),  # type: ignore[arg-type]
         description="Cerebras API Key",
     )
 

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

@@ -11,6 +11,6 @@ async def get_adapter_impl(config: DatabricksImplConfig, _deps):
     from .databricks import DatabricksInferenceAdapter
 
     assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}"
-    impl = DatabricksInferenceAdapter(config)
+    impl = DatabricksInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -14,12 +14,12 @@ from llama_stack.schema_utils import json_schema_type
 
 @json_schema_type
 class DatabricksImplConfig(RemoteInferenceProviderConfig):
-    url: str = Field(
+    url: str | None = Field(
         default=None,
         description="The URL for the Databricks model serving endpoint",
     )
     api_token: SecretStr = Field(
-        default=SecretStr(None),
+        default=SecretStr(None),  # type: ignore[arg-type]
         description="The Databricks API token",
     )
 

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

@@ -9,10 +9,7 @@ from typing import Any
 
 from databricks.sdk import WorkspaceClient
 
-from llama_stack.apis.inference import (
+from llama_stack.apis.inference import OpenAICompletion
-    Inference,
-    OpenAICompletion,
-)
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -21,30 +18,31 @@ from .config import DatabricksImplConfig
 logger = get_logger(name=__name__, category="inference::databricks")
 
 
-class DatabricksInferenceAdapter(
+class DatabricksInferenceAdapter(OpenAIMixin):
-    OpenAIMixin,
+    config: DatabricksImplConfig
-    Inference,
+
-):
     # source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
-    embedding_model_metadata = {
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192},
         "databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
     }
 
-    def __init__(self, config: DatabricksImplConfig) -> None:
-        self.config = config
-
     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"
 
-    async def initialize(self) -> None:
+    async def list_provider_model_ids(self) -> Iterable[str]:
-        return
+        return [
+            endpoint.name
+            for endpoint in WorkspaceClient(
+                host=self.config.url, token=self.get_api_key()
+            ).serving_endpoints.list()  # TODO: this is not async
+        ]
 
-    async def shutdown(self) -> None:
+    async def should_refresh_models(self) -> bool:
-        pass
+        return False
 
     async def openai_completion(
         self,
@@ -70,14 +68,3 @@ class DatabricksInferenceAdapter(
         suffix: str | None = None,
     ) -> OpenAICompletion:
         raise NotImplementedError()
-
-    async def list_provider_model_ids(self) -> Iterable[str]:
-        return [
-            endpoint.name
-            for endpoint in WorkspaceClient(
-                host=self.config.url, token=self.get_api_key()
-            ).serving_endpoints.list()  # TODO: this is not async
-        ]
-
-    async def should_refresh_models(self) -> bool:
-        return False

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

@@ -17,6 +17,6 @@ async def get_adapter_impl(config: FireworksImplConfig, _deps):
     from .fireworks import FireworksInferenceAdapter
 
     assert isinstance(config, FireworksImplConfig), f"Unexpected config type: {type(config)}"
-    impl = FireworksInferenceAdapter(config)
+    impl = FireworksInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -5,124 +5,26 @@
 # the root directory of this source tree.
 
 
-from fireworks.client import Fireworks
-
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    Inference,
-    LogProbConfig,
-    ResponseFormat,
-    ResponseFormatType,
-    SamplingParams,
-)
-from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    convert_message_to_openai_dict,
-    get_sampling_options,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    request_has_media,
-)
 
 from .config import FireworksImplConfig
 
 logger = get_logger(name=__name__, category="inference::fireworks")
 
 
-class FireworksInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData):
+class FireworksInferenceAdapter(OpenAIMixin):
-    embedding_model_metadata = {
+    config: FireworksImplConfig
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "nomic-ai/nomic-embed-text-v1.5": {"embedding_dimension": 768, "context_length": 8192},
         "accounts/fireworks/models/qwen3-embedding-8b": {"embedding_dimension": 4096, "context_length": 40960},
     }
 
-    def __init__(self, config: FireworksImplConfig) -> None:
+    provider_data_api_key_field: str = "fireworks_api_key"
-        ModelRegistryHelper.__init__(self)
-        self.config = config
-        self.allowed_models = config.allowed_models
-
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
 
     def get_api_key(self) -> str:
-        config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
+        return self.config.api_key.get_secret_value() if self.config.api_key else None  # type: ignore[return-value]
-        if config_api_key:
-            return config_api_key
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.fireworks_api_key:
-                raise ValueError(
-                    'Pass Fireworks API Key in the header X-LlamaStack-Provider-Data as { "fireworks_api_key": <your api key>}'
-                )
-            return provider_data.fireworks_api_key
 
     def get_base_url(self) -> str:
         return "https://api.fireworks.ai/inference/v1"
-
-    def _get_client(self) -> Fireworks:
-        fireworks_api_key = self.get_api_key()
-        return Fireworks(api_key=fireworks_api_key)
-
-    def _build_options(
-        self,
-        sampling_params: SamplingParams | None,
-        fmt: ResponseFormat | None,
-        logprobs: LogProbConfig | None,
-    ) -> dict:
-        options = get_sampling_options(sampling_params)
-        options.setdefault("max_tokens", 512)
-
-        if fmt:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["response_format"] = {
-                    "type": "json_object",
-                    "schema": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                options["response_format"] = {
-                    "type": "grammar",
-                    "grammar": fmt.bnf,
-                }
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
-        if logprobs and logprobs.top_k:
-            options["logprobs"] = logprobs.top_k
-            if options["logprobs"] <= 0 or options["logprobs"] >= 5:
-                raise ValueError("Required range: 0 < top_k < 5")
-
-        return options
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        input_dict = {}
-        media_present = request_has_media(request)
-
-        llama_model = self.get_llama_model(request.model)
-        # TODO: tools are never added to the request, so we need to add them here
-        if media_present or not llama_model:
-            input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
-        else:
-            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
-
-        # Fireworks always prepends with BOS
-        if "prompt" in input_dict:
-            if input_dict["prompt"].startswith("<|begin_of_text|>"):
-                input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
-
-        params = {
-            "model": request.model,
-            **input_dict,
-            "stream": bool(request.stream),
-            **self._build_options(request.sampling_params, request.response_format, request.logprobs),
-        }
-        logger.debug(f"params to fireworks: {params}")
-
-        return params

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

@@ -10,6 +10,6 @@ from .config import GeminiConfig
 async def get_adapter_impl(config: GeminiConfig, _deps):
     from .gemini import GeminiInferenceAdapter
 
-    impl = GeminiInferenceAdapter(config)
+    impl = GeminiInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -4,33 +4,21 @@
 # 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.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import GeminiConfig
 
 
-class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class GeminiInferenceAdapter(OpenAIMixin):
-    embedding_model_metadata = {
+    config: GeminiConfig
+
+    provider_data_api_key_field: str = "gemini_api_key"
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
     }
 
-    def __init__(self, config: GeminiConfig) -> None:
+    def get_api_key(self) -> str:
-        LiteLLMOpenAIMixin.__init__(
+        return self.config.api_key or ""
-            self,
-            litellm_provider_name="gemini",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="gemini_api_key",
-        )
-        self.config = config
-
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self):
         return "https://generativelanguage.googleapis.com/v1beta/openai/"
-
-    async def initialize(self) -> None:
-        await super().initialize()
-
-    async def shutdown(self) -> None:
-        await super().shutdown()

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

@@ -11,5 +11,5 @@ async def get_adapter_impl(config: GroqConfig, _deps):
     # import dynamically so the import is used only when it is needed
     from .groq import GroqInferenceAdapter
 
-    adapter = GroqInferenceAdapter(config)
+    adapter = GroqInferenceAdapter(config=config)
     return adapter

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

@@ -6,30 +6,16 @@
 
 
 from llama_stack.providers.remote.inference.groq.config import GroqConfig
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 
-class GroqInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class GroqInferenceAdapter(OpenAIMixin):
-    _config: GroqConfig
+    config: GroqConfig
 
-    def __init__(self, config: GroqConfig):
+    provider_data_api_key_field: str = "groq_api_key"
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="groq",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="groq_api_key",
-        )
-        self.config = config
 
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
+    def get_api_key(self) -> str:
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
+        return self.config.api_key or ""
 
     def get_base_url(self) -> str:
         return f"{self.config.url}/openai/v1"
-
-    async def initialize(self):
-        await super().initialize()
-
-    async def shutdown(self):
-        await super().shutdown()

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

@@ -4,14 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.apis.inference import InferenceProvider
-
 from .config import LlamaCompatConfig
 
 
-async def get_adapter_impl(config: LlamaCompatConfig, _deps) -> InferenceProvider:
+async def get_adapter_impl(config: LlamaCompatConfig, _deps):
     # import dynamically so the import is used only when it is needed
     from .llama import LlamaCompatInferenceAdapter
 
-    adapter = LlamaCompatInferenceAdapter(config)
+    adapter = LlamaCompatInferenceAdapter(config=config)
     return adapter

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

@@ -8,38 +8,21 @@ from typing import Any
 from llama_stack.apis.inference.inference import OpenAICompletion
 from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 logger = get_logger(name=__name__, category="inference::llama_openai_compat")
 
 
-class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class LlamaCompatInferenceAdapter(OpenAIMixin):
+    config: LlamaCompatConfig
+
+    provider_data_api_key_field: str = "llama_api_key"
     """
     Llama API Inference Adapter for Llama Stack.
-
-    Note: The inheritance order is important here. OpenAIMixin must come before
-    LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability()
-    is used instead of ModelRegistryHelper.check_model_availability().
-
-    - OpenAIMixin.check_model_availability() queries the Llama API to check if a model exists
-    - ModelRegistryHelper.check_model_availability() (inherited by LiteLLMOpenAIMixin) just returns False and shows a warning
     """
 
-    _config: LlamaCompatConfig
+    def get_api_key(self) -> str:
-
+        return self.config.api_key or ""
-    def __init__(self, config: LlamaCompatConfig):
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="meta_llama",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="llama_api_key",
-            openai_compat_api_base=config.openai_compat_api_base,
-        )
-        self.config = config
-
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self) -> str:
         """
@@ -49,12 +32,6 @@ class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         """
         return self.config.openai_compat_api_base
 
-    async def initialize(self):
-        await super().initialize()
-
-    async def shutdown(self):
-        await super().shutdown()
-
     async def openai_completion(
         self,
         model: str,

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

@@ -15,7 +15,8 @@ async def get_adapter_impl(config: NVIDIAConfig, _deps) -> Inference:
 
     if not isinstance(config, NVIDIAConfig):
         raise RuntimeError(f"Unexpected config type: {type(config)}")
-    adapter = NVIDIAInferenceAdapter(config)
+    adapter = NVIDIAInferenceAdapter(config=config)
+    await adapter.initialize()
     return adapter
 
 

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

@@ -8,7 +8,6 @@
 from openai import NOT_GIVEN
 
 from llama_stack.apis.inference import (
-    Inference,
     OpenAIEmbeddingData,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
@@ -22,7 +21,9 @@ from .utils import _is_nvidia_hosted
 logger = get_logger(name=__name__, category="inference::nvidia")
 
 
-class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
+class NVIDIAInferenceAdapter(OpenAIMixin):
+    config: NVIDIAConfig
+
     """
     NVIDIA Inference Adapter for Llama Stack.
 
@@ -37,32 +38,21 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
     """
 
     # source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
-    embedding_model_metadata = {
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "nvidia/llama-3.2-nv-embedqa-1b-v2": {"embedding_dimension": 2048, "context_length": 8192},
         "nvidia/nv-embedqa-e5-v5": {"embedding_dimension": 512, "context_length": 1024},
         "nvidia/nv-embedqa-mistral-7b-v2": {"embedding_dimension": 512, "context_length": 4096},
         "snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024},
     }
 
-    def __init__(self, config: NVIDIAConfig) -> None:
+    async def initialize(self) -> None:
-        logger.info(f"Initializing NVIDIAInferenceAdapter({config.url})...")
+        logger.info(f"Initializing NVIDIAInferenceAdapter({self.config.url})...")
 
-        if _is_nvidia_hosted(config):
+        if _is_nvidia_hosted(self.config):
-            if not config.api_key:
+            if not self.config.api_key:
                 raise RuntimeError(
                     "API key is required for hosted NVIDIA NIM. Either provide an API key or use a self-hosted NIM."
                 )
-        # elif self._config.api_key:
-        #
-        # we don't raise this warning because a user may have deployed their
-        # self-hosted NIM with an API key requirement.
-        #
-        #     warnings.warn(
-        #         "API key is not required for self-hosted NVIDIA NIM. "
-        #         "Consider removing the api_key from the configuration."
-        #     )
-
-        self._config = config
 
     def get_api_key(self) -> str:
         """
@@ -70,7 +60,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
 
         :return: The NVIDIA API key
         """
-        return self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"
+        return self.config.api_key.get_secret_value() if self.config.api_key else "NO KEY"
 
     def get_base_url(self) -> str:
         """
@@ -78,7 +68,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
 
         :return: The NVIDIA API base URL
         """
-        return f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
+        return f"{self.config.url}/v1" if self.config.append_api_version else self.config.url
 
     async def openai_embeddings(
         self,

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

@@ -10,6 +10,6 @@ from .config import OllamaImplConfig
 async def get_adapter_impl(config: OllamaImplConfig, _deps):
     from .ollama import OllamaInferenceAdapter
 
-    impl = OllamaInferenceAdapter(config)
+    impl = OllamaInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -6,58 +6,29 @@
 
 
 import asyncio
-from typing import Any
 
 from ollama import AsyncClient as AsyncOllamaClient
 
-from llama_stack.apis.common.content_types import (
-    ImageContentItem,
-    TextContentItem,
-)
 from llama_stack.apis.common.errors import UnsupportedModelError
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    GrammarResponseFormat,
-    InferenceProvider,
-    JsonSchemaResponseFormat,
-    Message,
-)
 from llama_stack.apis.models import Model
 from llama_stack.log import get_logger
-from llama_stack.models.llama.sku_types import CoreModelId
 from llama_stack.providers.datatypes import (
     HealthResponse,
     HealthStatus,
-    ModelsProtocolPrivate,
 )
 from llama_stack.providers.remote.inference.ollama.config import OllamaImplConfig
-from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
-    build_hf_repo_model_entry,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    get_sampling_options,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    convert_image_content_to_url,
-    request_has_media,
-)
 
 logger = get_logger(name=__name__, category="inference::ollama")
 
 
-class OllamaInferenceAdapter(
+class OllamaInferenceAdapter(OpenAIMixin):
-    OpenAIMixin,
+    config: OllamaImplConfig
-    ModelRegistryHelper,
+
-    InferenceProvider,
-    ModelsProtocolPrivate,
-):
     # automatically set by the resolver when instantiating the provider
     __provider_id__: str
 
-    embedding_model_metadata = {
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "all-minilm:l6-v2": {
             "embedding_dimension": 384,
             "context_length": 512,
@@ -76,29 +47,8 @@ class OllamaInferenceAdapter(
         },
     }
 
-    def __init__(self, config: OllamaImplConfig) -> None:
+    download_images: bool = True
-        # TODO: remove ModelRegistryHelper.__init__ when completion and
+    _clients: dict[asyncio.AbstractEventLoop, AsyncOllamaClient] = {}
-        #       chat_completion are. this exists to satisfy the input /
-        #       output processing for llama models. specifically,
-        #       tool_calling is handled by raw template processing,
-        #       instead of using the /api/chat endpoint w/ tools=...
-        ModelRegistryHelper.__init__(
-            self,
-            model_entries=[
-                build_hf_repo_model_entry(
-                    "llama3.2:3b-instruct-fp16",
-                    CoreModelId.llama3_2_3b_instruct.value,
-                ),
-                build_hf_repo_model_entry(
-                    "llama-guard3:1b",
-                    CoreModelId.llama_guard_3_1b.value,
-                ),
-            ],
-        )
-        self.config = config
-        # Ollama does not support image urls, so we need to download the image and convert it to base64
-        self.download_images = True
-        self._clients: dict[asyncio.AbstractEventLoop, AsyncOllamaClient] = {}
 
     @property
     def ollama_client(self) -> AsyncOllamaClient:
@@ -142,50 +92,6 @@ class OllamaInferenceAdapter(
     async def shutdown(self) -> None:
         self._clients.clear()
 
-    async def _get_model(self, model_id: str) -> Model:
-        if not self.model_store:
-            raise ValueError("Model store not set")
-        return await self.model_store.get_model(model_id)
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        sampling_options = get_sampling_options(request.sampling_params)
-        # This is needed since the Ollama API expects num_predict to be set
-        # for early truncation instead of max_tokens.
-        if sampling_options.get("max_tokens") is not None:
-            sampling_options["num_predict"] = sampling_options["max_tokens"]
-
-        input_dict: dict[str, Any] = {}
-        media_present = request_has_media(request)
-        llama_model = self.get_llama_model(request.model)
-        if media_present or not llama_model:
-            contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages]
-            # flatten the list of lists
-            input_dict["messages"] = [item for sublist in contents for item in sublist]
-        else:
-            input_dict["raw"] = True
-            input_dict["prompt"] = await chat_completion_request_to_prompt(
-                request,
-                llama_model,
-            )
-
-        if fmt := request.response_format:
-            if isinstance(fmt, JsonSchemaResponseFormat):
-                input_dict["format"] = fmt.json_schema
-            elif isinstance(fmt, GrammarResponseFormat):
-                raise NotImplementedError("Grammar response format is not supported")
-            else:
-                raise ValueError(f"Unknown response format type: {fmt.type}")
-
-        params = {
-            "model": request.model,
-            **input_dict,
-            "options": sampling_options,
-            "stream": request.stream,
-        }
-        logger.debug(f"params to ollama: {params}")
-
-        return params
-
     async def register_model(self, model: Model) -> Model:
         if await self.check_model_availability(model.provider_model_id):
             return model
@@ -197,24 +103,3 @@ class OllamaInferenceAdapter(
             return model
 
         raise UnsupportedModelError(model.provider_model_id, list(self._model_cache.keys()))
-
-
-async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
-    async def _convert_content(content) -> dict:
-        if isinstance(content, ImageContentItem):
-            return {
-                "role": message.role,
-                "images": [await convert_image_content_to_url(content, download=True, include_format=False)],
-            }
-        else:
-            text = content.text if isinstance(content, TextContentItem) else content
-            assert isinstance(text, str)
-            return {
-                "role": message.role,
-                "content": text,
-            }
-
-    if isinstance(message.content, list):
-        return [await _convert_content(c) for c in message.content]
-    else:
-        return [await _convert_content(message.content)]

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

@@ -10,6 +10,6 @@ from .config import OpenAIConfig
 async def get_adapter_impl(config: OpenAIConfig, _deps):
     from .openai import OpenAIInferenceAdapter
 
-    impl = OpenAIInferenceAdapter(config)
+    impl = OpenAIInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import OpenAIConfig
@@ -14,52 +13,24 @@ logger = get_logger(name=__name__, category="inference::openai")
 
 
 #
-# This OpenAI adapter implements Inference methods using two mixins -
+# This OpenAI adapter implements Inference methods using OpenAIMixin
 #
-# | Inference Method           | Implementation Source    |
+class OpenAIInferenceAdapter(OpenAIMixin):
-# |----------------------------|--------------------------|
-# | completion                 | LiteLLMOpenAIMixin       |
-# | chat_completion            | LiteLLMOpenAIMixin       |
-# | embedding                  | LiteLLMOpenAIMixin       |
-# | openai_completion          | OpenAIMixin              |
-# | openai_chat_completion     | OpenAIMixin              |
-# | openai_embeddings          | OpenAIMixin              |
-#
-class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
     """
     OpenAI Inference Adapter for Llama Stack.
-
-    Note: The inheritance order is important here. OpenAIMixin must come before
-    LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability()
-    is used instead of ModelRegistryHelper.check_model_availability().
-
-    - OpenAIMixin.check_model_availability() queries the OpenAI API to check if a model exists
-    - ModelRegistryHelper.check_model_availability() (inherited by LiteLLMOpenAIMixin) just returns False and shows a warning
     """
 
-    embedding_model_metadata = {
+    config: OpenAIConfig
+
+    provider_data_api_key_field: str = "openai_api_key"
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
         "text-embedding-3-large": {"embedding_dimension": 3072, "context_length": 8192},
     }
 
-    def __init__(self, config: OpenAIConfig) -> None:
+    def get_api_key(self) -> str:
-        LiteLLMOpenAIMixin.__init__(
+        return self.config.api_key or ""
-            self,
-            litellm_provider_name="openai",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="openai_api_key",
-        )
-        self.config = config
-        # we set is_openai_compat so users can use the canonical
-        # openai model names like "gpt-4" or "gpt-3.5-turbo"
-        # and the model name will be translated to litellm's
-        # "openai/gpt-4" or "openai/gpt-3.5-turbo" transparently.
-        # if we do not set this, users will be exposed to the
-        # litellm specific model names, an abstraction leak.
-        self.is_openai_compat = True
-
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self) -> str:
         """
@@ -68,9 +39,3 @@ class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         Returns the OpenAI API base URL from the configuration.
         """
         return self.config.base_url
-
-    async def initialize(self) -> None:
-        await super().initialize()
-
-    async def shutdown(self) -> None:
-        await super().shutdown()

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -31,12 +31,6 @@ class PassthroughInferenceAdapter(Inference):
         ModelRegistryHelper.__init__(self)
         self.config = config
 
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
-
     async def unregister_model(self, model_id: str) -> None:
         pass
 

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

@@ -53,12 +53,6 @@ class RunpodInferenceAdapter(
         ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)
         self.config = config
 
-    async def initialize(self) -> None:
-        return
-
-    async def shutdown(self) -> None:
-        pass
-
     def _get_params(self, request: ChatCompletionRequest) -> dict:
         return {
             "model": self.map_to_provider_model(request.model),

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

@@ -11,6 +11,6 @@ async def get_adapter_impl(config: SambaNovaImplConfig, _deps):
     from .sambanova import SambaNovaInferenceAdapter
 
     assert isinstance(config, SambaNovaImplConfig), f"Unexpected config type: {type(config)}"
-    impl = SambaNovaInferenceAdapter(config)
+    impl = SambaNovaInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -5,39 +5,22 @@
 # the root directory of this source tree.
 
 
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import SambaNovaImplConfig
 
 
-class SambaNovaInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class SambaNovaInferenceAdapter(OpenAIMixin):
+    config: SambaNovaImplConfig
+
+    provider_data_api_key_field: str = "sambanova_api_key"
+    download_images: bool = True  # SambaNova does not support image downloads server-size, perform them on the client
     """
     SambaNova Inference Adapter for Llama Stack.
-
-    Note: The inheritance order is important here. OpenAIMixin must come before
-    LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability()
-    is used instead of LiteLLMOpenAIMixin.check_model_availability().
-
-    - OpenAIMixin.check_model_availability() queries the /v1/models to check if a model exists
-    - LiteLLMOpenAIMixin.check_model_availability() checks the static registry within LiteLLM
     """
 
-    def __init__(self, config: SambaNovaImplConfig):
+    def get_api_key(self) -> str:
-        self.config = config
+        return self.config.api_key.get_secret_value() if self.config.api_key else ""
-        self.environment_available_models: list[str] = []
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="sambanova",
-            api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
-            provider_data_api_key_field="sambanova_api_key",
-            openai_compat_api_base=self.config.url,
-            download_images=True,  # SambaNova requires base64 image encoding
-            json_schema_strict=False,  # SambaNova doesn't support strict=True yet
-        )
-
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self) -> str:
         """

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

@@ -5,53 +5,21 @@
 # the root directory of this source tree.
 
 
+from collections.abc import Iterable
+
 from huggingface_hub import AsyncInferenceClient, HfApi
 from pydantic import SecretStr
 
-from llama_stack.apis.inference import (
+from llama_stack.apis.inference import OpenAIEmbeddingsResponse
-    ChatCompletionRequest,
-    Inference,
-    OpenAIEmbeddingsResponse,
-    ResponseFormat,
-    ResponseFormatType,
-    SamplingParams,
-)
-from llama_stack.apis.models import Model
-from llama_stack.apis.models.models import ModelType
 from llama_stack.log import get_logger
-from llama_stack.models.llama.sku_list import all_registered_models
-from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
-    build_hf_repo_model_entry,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    get_sampling_options,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_model_input_info,
-)
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
 
 log = get_logger(name=__name__, category="inference::tgi")
 
 
-def build_hf_repo_model_entries():
+class _HfAdapter(OpenAIMixin):
-    return [
-        build_hf_repo_model_entry(
-            model.huggingface_repo,
-            model.descriptor(),
-        )
-        for model in all_registered_models()
-        if model.huggingface_repo
-    ]
-
-
-class _HfAdapter(
-    OpenAIMixin,
-    Inference,
-):
     url: str
     api_key: SecretStr
 
@@ -61,90 +29,14 @@ class _HfAdapter(
 
     overwrite_completion_id = True  # TGI always returns id=""
 
-    def __init__(self) -> None:
-        self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
-        self.huggingface_repo_to_llama_model_id = {
-            model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo
-        }
-
     def get_api_key(self):
         return self.api_key.get_secret_value()
 
     def get_base_url(self):
         return self.url
 
-    async def shutdown(self) -> None:
+    async def list_provider_model_ids(self) -> Iterable[str]:
-        pass
+        return [self.model_id]
-
-    async def list_models(self) -> list[Model] | None:
-        models = []
-        async for model in self.client.models.list():
-            models.append(
-                Model(
-                    identifier=model.id,
-                    provider_resource_id=model.id,
-                    provider_id=self.__provider_id__,
-                    metadata={},
-                    model_type=ModelType.llm,
-                )
-            )
-        return models
-
-    async def register_model(self, model: Model) -> Model:
-        if model.provider_resource_id != self.model_id:
-            raise ValueError(
-                f"Model {model.provider_resource_id} does not match the model {self.model_id} served by TGI."
-            )
-        return model
-
-    async def unregister_model(self, model_id: str) -> None:
-        pass
-
-    def _get_max_new_tokens(self, sampling_params, input_tokens):
-        return min(
-            sampling_params.max_tokens or (self.max_tokens - input_tokens),
-            self.max_tokens - input_tokens - 1,
-        )
-
-    def _build_options(
-        self,
-        sampling_params: SamplingParams | None = None,
-        fmt: ResponseFormat = None,
-    ):
-        options = get_sampling_options(sampling_params)
-        # TGI does not support temperature=0 when using greedy sampling
-        # We set it to 1e-3 instead, anything lower outputs garbage from TGI
-        # We can use top_p sampling strategy to specify lower temperature
-        if abs(options["temperature"]) < 1e-10:
-            options["temperature"] = 1e-3
-
-        # delete key "max_tokens" from options since its not supported by the API
-        options.pop("max_tokens", None)
-        if fmt:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["grammar"] = {
-                    "type": "json",
-                    "value": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                raise ValueError("Grammar response format not supported yet")
-            else:
-                raise ValueError(f"Unexpected response format: {fmt.type}")
-
-        return options
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        prompt, input_tokens = await chat_completion_request_to_model_input_info(
-            request, self.register_helper.get_llama_model(request.model)
-        )
-        return dict(
-            prompt=prompt,
-            stream=request.stream,
-            details=True,
-            max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens),
-            stop_sequences=["<|eom_id|>", "<|eot_id|>"],
-            **self._build_options(request.sampling_params, request.response_format),
-        )
 
     async def openai_embeddings(
         self,

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

@@ -17,6 +17,6 @@ async def get_adapter_impl(config: TogetherImplConfig, _deps):
     from .together import TogetherInferenceAdapter
 
     assert isinstance(config, TogetherImplConfig), f"Unexpected config type: {type(config)}"
-    impl = TogetherInferenceAdapter(config)
+    impl = TogetherInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -5,41 +5,29 @@
 # the root directory of this source tree.
 
 
-from openai import AsyncOpenAI
+from collections.abc import Iterable
+
 from together import AsyncTogether
 from together.constants import BASE_URL
 
 from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    Inference,
-    LogProbConfig,
     OpenAIEmbeddingsResponse,
-    ResponseFormat,
-    ResponseFormatType,
-    SamplingParams,
 )
 from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
-from llama_stack.apis.models import Model, ModelType
+from llama_stack.apis.models import Model
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
-from llama_stack.providers.utils.inference.openai_compat import (
-    convert_message_to_openai_dict,
-    get_sampling_options,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    request_has_media,
-)
 
 from .config import TogetherImplConfig
 
 logger = get_logger(name=__name__, category="inference::together")
 
 
-class TogetherInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData):
+class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
-    embedding_model_metadata = {
+    config: TogetherImplConfig
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "togethercomputer/m2-bert-80M-32k-retrieval": {"embedding_dimension": 768, "context_length": 32768},
         "BAAI/bge-large-en-v1.5": {"embedding_dimension": 1024, "context_length": 512},
         "BAAI/bge-base-en-v1.5": {"embedding_dimension": 768, "context_length": 512},
@@ -47,24 +35,16 @@ class TogetherInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData)
         "intfloat/multilingual-e5-large-instruct": {"embedding_dimension": 1024, "context_length": 512},
     }
 
-    def __init__(self, config: TogetherImplConfig) -> None:
+    _model_cache: dict[str, Model] = {}
-        ModelRegistryHelper.__init__(self)
+
-        self.config = config
+    provider_data_api_key_field: str = "together_api_key"
-        self.allowed_models = config.allowed_models
-        self._model_cache: dict[str, Model] = {}
 
     def get_api_key(self):
-        return self.config.api_key.get_secret_value()
+        return self.config.api_key.get_secret_value() if self.config.api_key else None
 
     def get_base_url(self):
         return BASE_URL
 
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
-
     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
@@ -79,83 +59,9 @@ class TogetherInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData)
             together_api_key = provider_data.together_api_key
         return AsyncTogether(api_key=together_api_key)
 
-    def _get_openai_client(self) -> AsyncOpenAI:
+    async def list_provider_model_ids(self) -> Iterable[str]:
-        together_client = self._get_client().client
-        return AsyncOpenAI(
-            base_url=together_client.base_url,
-            api_key=together_client.api_key,
-        )
-
-    def _build_options(
-        self,
-        sampling_params: SamplingParams | None,
-        logprobs: LogProbConfig | None,
-        fmt: ResponseFormat,
-    ) -> dict:
-        options = get_sampling_options(sampling_params)
-        if fmt:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["response_format"] = {
-                    "type": "json_object",
-                    "schema": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                raise NotImplementedError("Grammar response format not supported yet")
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
-        if logprobs and logprobs.top_k:
-            if logprobs.top_k != 1:
-                raise ValueError(
-                    f"Unsupported value: Together only supports logprobs top_k=1. {logprobs.top_k} was provided",
-                )
-            options["logprobs"] = 1
-
-        return options
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        input_dict = {}
-        media_present = request_has_media(request)
-        llama_model = self.get_llama_model(request.model)
-        if media_present or not llama_model:
-            input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages]
-        else:
-            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
-
-        params = {
-            "model": request.model,
-            **input_dict,
-            "stream": request.stream,
-            **self._build_options(request.sampling_params, request.logprobs, request.response_format),
-        }
-        logger.debug(f"params to together: {params}")
-        return params
-
-    async def list_models(self) -> list[Model] | None:
-        self._model_cache = {}
         # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client
-        for m in await self._get_client().models.list():
+        return [m.id for m in await self._get_client().models.list()]
-            if m.type == "embedding":
-                if m.id not in self.embedding_model_metadata:
-                    logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.")
-                    continue
-                metadata = self.embedding_model_metadata[m.id]
-                self._model_cache[m.id] = Model(
-                    provider_id=self.__provider_id__,
-                    provider_resource_id=m.id,
-                    identifier=m.id,
-                    model_type=ModelType.embedding,
-                    metadata=metadata,
-                )
-            else:
-                self._model_cache[m.id] = Model(
-                    provider_id=self.__provider_id__,
-                    provider_resource_id=m.id,
-                    identifier=m.id,
-                    model_type=ModelType.llm,
-                )
-
-        return self._model_cache.values()
 
     async def should_refresh_models(self) -> bool:
         return True
@@ -203,4 +109,4 @@ class TogetherInferenceAdapter(OpenAIMixin, Inference, NeedsRequestProviderData)
             )
             response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
 
-        return response
+        return response  # type: ignore[no-any-return]

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

@@ -10,6 +10,6 @@ from .config import VertexAIConfig
 async def get_adapter_impl(config: VertexAIConfig, _deps):
     from .vertexai import VertexAIInferenceAdapter
 
-    impl = VertexAIInferenceAdapter(config)
+    impl = VertexAIInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -4,29 +4,19 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any
 
 import google.auth.transport.requests
 from google.auth import default
 
-from llama_stack.apis.inference import ChatCompletionRequest
-from llama_stack.providers.utils.inference.litellm_openai_mixin import (
-    LiteLLMOpenAIMixin,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import VertexAIConfig
 
 
-class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class VertexAIInferenceAdapter(OpenAIMixin):
-    def __init__(self, config: VertexAIConfig) -> None:
+    config: VertexAIConfig
-        LiteLLMOpenAIMixin.__init__(
+
-            self,
+    provider_data_api_key_field: str = "vertex_project"
-            litellm_provider_name="vertex_ai",
-            api_key_from_config=None,  # Vertex AI uses ADC, not API keys
-            provider_data_api_key_field="vertex_project",  # Use project for validation
-        )
-        self.config = config
 
     def get_api_key(self) -> str:
         """
@@ -41,8 +31,7 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
             credentials.refresh(google.auth.transport.requests.Request())
             return str(credentials.token)
         except Exception:
-            # If we can't get credentials, return empty string to let LiteLLM handle it
+            # If we can't get credentials, return empty string to let the env work with ADC directly
-            # This allows the LiteLLM mixin to work with ADC directly
             return ""
 
     def get_base_url(self) -> str:
@@ -53,23 +42,3 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         Source: https://cloud.google.com/vertex-ai/generative-ai/docs/start/openai
         """
         return f"https://{self.config.location}-aiplatform.googleapis.com/v1/projects/{self.config.project}/locations/{self.config.location}/endpoints/openapi"
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
-        # Get base parameters from parent
-        params = await super()._get_params(request)
-
-        # Add Vertex AI specific parameters
-        provider_data = self.get_request_provider_data()
-        if provider_data:
-            if getattr(provider_data, "vertex_project", None):
-                params["vertex_project"] = provider_data.vertex_project
-            if getattr(provider_data, "vertex_location", None):
-                params["vertex_location"] = provider_data.vertex_location
-        else:
-            params["vertex_project"] = self.config.project
-            params["vertex_location"] = self.config.location
-
-        # Remove api_key since Vertex AI uses ADC
-        params.pop("api_key", None)
-
-        return params

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

@@ -17,6 +17,6 @@ async def get_adapter_impl(config: VLLMInferenceAdapterConfig, _deps):
     from .vllm import VLLMInferenceAdapter
 
     assert isinstance(config, VLLMInferenceAdapterConfig), f"Unexpected config type: {type(config)}"
-    impl = VLLMInferenceAdapter(config)
+    impl = VLLMInferenceAdapter(config=config)
     await impl.initialize()
     return impl

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

@@ -3,56 +3,27 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-import json
+from collections.abc import AsyncIterator
-from collections.abc import AsyncGenerator, AsyncIterator
 from typing import Any
 from urllib.parse import urljoin
 
 import httpx
-from openai import APIConnectionError
 from openai.types.chat.chat_completion_chunk import (
     ChatCompletionChunk as OpenAIChatCompletionChunk,
 )
+from pydantic import ConfigDict
 
-from llama_stack.apis.common.content_types import (
-    TextDelta,
-    ToolCallDelta,
-    ToolCallParseStatus,
-)
 from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    ChatCompletionResponseEvent,
-    ChatCompletionResponseEventType,
-    ChatCompletionResponseStreamChunk,
-    GrammarResponseFormat,
-    Inference,
-    JsonSchemaResponseFormat,
-    ModelStore,
     OpenAIChatCompletion,
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
     ToolChoice,
-    ToolDefinition,
 )
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.log import get_logger
-from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall
-from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import (
     HealthResponse,
     HealthStatus,
-    ModelsProtocolPrivate,
-)
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
-from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
-    build_hf_repo_model_entry,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    UnparseableToolCall,
-    convert_message_to_openai_dict,
-    convert_tool_call,
-    get_sampling_options,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -61,210 +32,15 @@ from .config import VLLMInferenceAdapterConfig
 log = get_logger(name=__name__, category="inference::vllm")
 
 
-def build_hf_repo_model_entries():
+class VLLMInferenceAdapter(OpenAIMixin):
-    return [
+    config: VLLMInferenceAdapterConfig
-        build_hf_repo_model_entry(
-            model.huggingface_repo,
-            model.descriptor(),
-        )
-        for model in all_registered_models()
-        if model.huggingface_repo
-    ]
 
+    model_config = ConfigDict(arbitrary_types_allowed=True)
 
-def _convert_to_vllm_tool_calls_in_response(
+    provider_data_api_key_field: str = "vllm_api_token"
-    tool_calls,
-) -> list[ToolCall]:
-    if not tool_calls:
-        return []
 
-    return [
+    def get_api_key(self) -> str:
-        ToolCall(
+        return self.config.api_token or ""
-            call_id=call.id,
-            tool_name=call.function.name,
-            arguments=call.function.arguments,
-        )
-        for call in tool_calls
-    ]
-
-
-def _convert_to_vllm_tools_in_request(tools: list[ToolDefinition]) -> list[dict]:
-    compat_tools = []
-
-    for tool in tools:
-        # The tool.tool_name can be a str or a BuiltinTool enum. If
-        # it's the latter, convert to a string.
-        tool_name = tool.tool_name
-        if isinstance(tool_name, BuiltinTool):
-            tool_name = tool_name.value
-
-        compat_tool = {
-            "type": "function",
-            "function": {
-                "name": tool_name,
-                "description": tool.description,
-                "parameters": tool.input_schema
-                or {
-                    "type": "object",
-                    "properties": {},
-                    "required": [],
-                },
-            },
-        }
-
-        compat_tools.append(compat_tool)
-
-    return compat_tools
-
-
-def _convert_to_vllm_finish_reason(finish_reason: str) -> StopReason:
-    return {
-        "stop": StopReason.end_of_turn,
-        "length": StopReason.out_of_tokens,
-        "tool_calls": StopReason.end_of_message,
-    }.get(finish_reason, StopReason.end_of_turn)
-
-
-def _process_vllm_chat_completion_end_of_stream(
-    finish_reason: str | None,
-    last_chunk_content: str | None,
-    current_event_type: ChatCompletionResponseEventType,
-    tool_call_bufs: dict[str, UnparseableToolCall] | None = None,
-) -> list[OpenAIChatCompletionChunk]:
-    chunks = []
-
-    if finish_reason is not None:
-        stop_reason = _convert_to_vllm_finish_reason(finish_reason)
-    else:
-        stop_reason = StopReason.end_of_message
-
-    tool_call_bufs = tool_call_bufs or {}
-    for _index, tool_call_buf in sorted(tool_call_bufs.items()):
-        args_str = tool_call_buf.arguments or "{}"
-        try:
-            chunks.append(
-                ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=current_event_type,
-                        delta=ToolCallDelta(
-                            tool_call=ToolCall(
-                                call_id=tool_call_buf.call_id,
-                                tool_name=tool_call_buf.tool_name,
-                                arguments=args_str,
-                            ),
-                            parse_status=ToolCallParseStatus.succeeded,
-                        ),
-                    )
-                )
-            )
-        except Exception as e:
-            log.warning(f"Failed to parse tool call buffer arguments: {args_str} \nError: {e}")
-
-            chunks.append(
-                ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            tool_call=str(tool_call_buf),
-                            parse_status=ToolCallParseStatus.failed,
-                        ),
-                    )
-                )
-            )
-
-    chunks.append(
-        ChatCompletionResponseStreamChunk(
-            event=ChatCompletionResponseEvent(
-                event_type=ChatCompletionResponseEventType.complete,
-                delta=TextDelta(text=last_chunk_content or ""),
-                logprobs=None,
-                stop_reason=stop_reason,
-            )
-        )
-    )
-
-    return chunks
-
-
-async def _process_vllm_chat_completion_stream_response(
-    stream: AsyncGenerator[OpenAIChatCompletionChunk, None],
-) -> AsyncGenerator:
-    yield ChatCompletionResponseStreamChunk(
-        event=ChatCompletionResponseEvent(
-            event_type=ChatCompletionResponseEventType.start,
-            delta=TextDelta(text=""),
-        )
-    )
-    event_type = ChatCompletionResponseEventType.progress
-    tool_call_bufs: dict[str, UnparseableToolCall] = {}
-    end_of_stream_processed = False
-
-    async for chunk in stream:
-        if not chunk.choices:
-            log.warning("vLLM failed to generation any completions - check the vLLM server logs for an error.")
-            return
-        choice = chunk.choices[0]
-        if choice.delta.tool_calls:
-            for delta_tool_call in choice.delta.tool_calls:
-                tool_call = convert_tool_call(delta_tool_call)
-                if delta_tool_call.index not in tool_call_bufs:
-                    tool_call_bufs[delta_tool_call.index] = UnparseableToolCall()
-                tool_call_buf = tool_call_bufs[delta_tool_call.index]
-                tool_call_buf.tool_name += str(tool_call.tool_name)
-                tool_call_buf.call_id += tool_call.call_id
-                tool_call_buf.arguments += (
-                    tool_call.arguments if isinstance(tool_call.arguments, str) else json.dumps(tool_call.arguments)
-                )
-        if choice.finish_reason:
-            chunks = _process_vllm_chat_completion_end_of_stream(
-                finish_reason=choice.finish_reason,
-                last_chunk_content=choice.delta.content,
-                current_event_type=event_type,
-                tool_call_bufs=tool_call_bufs,
-            )
-            for c in chunks:
-                yield c
-            end_of_stream_processed = True
-        elif not choice.delta.tool_calls:
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=event_type,
-                    delta=TextDelta(text=choice.delta.content or ""),
-                    logprobs=None,
-                )
-            )
-            event_type = ChatCompletionResponseEventType.progress
-
-    if end_of_stream_processed:
-        return
-
-    # the stream ended without a chunk containing finish_reason - we have to generate the
-    # respective completion chunks manually
-    chunks = _process_vllm_chat_completion_end_of_stream(
-        finish_reason=None, last_chunk_content=None, current_event_type=event_type, tool_call_bufs=tool_call_bufs
-    )
-    for c in chunks:
-        yield c
-
-
-class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsProtocolPrivate):
-    # automatically set by the resolver when instantiating the provider
-    __provider_id__: str
-    model_store: ModelStore | None = None
-
-    def __init__(self, config: VLLMInferenceAdapterConfig) -> None:
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            model_entries=build_hf_repo_model_entries(),
-            litellm_provider_name="vllm",
-            api_key_from_config=config.api_token,
-            provider_data_api_key_field="vllm_api_token",
-            openai_compat_api_base=config.url,
-        )
-        self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
-        self.config = config
-
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
 
     def get_base_url(self) -> str:
         """Get the base URL from config."""
@@ -290,19 +66,13 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
                 Model(
                     identifier=m.id,
                     provider_resource_id=m.id,
-                    provider_id=self.__provider_id__,
+                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
                     metadata={},
                     model_type=model_type,
                 )
             )
         return models
 
-    async def shutdown(self) -> None:
-        pass
-
-    async def unregister_model(self, model_id: str) -> None:
-        pass
-
     async def health(self) -> HealthResponse:
         """
         Performs a health check by verifying connectivity to the remote vLLM server.
@@ -324,63 +94,9 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
         except Exception as e:
             return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
 
-    async def _get_model(self, model_id: str) -> Model:
-        if not self.model_store:
-            raise ValueError("Model store not set")
-        return await self.model_store.get_model(model_id)
-
     def get_extra_client_params(self):
         return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)}
 
-    async def register_model(self, model: Model) -> Model:
-        try:
-            model = await self.register_helper.register_model(model)
-        except ValueError:
-            pass  # Ignore statically unknown model, will check live listing
-        try:
-            res = self.client.models.list()
-        except APIConnectionError as e:
-            raise ValueError(
-                f"Failed to connect to vLLM at {self.config.url}. Please check if vLLM is running and accessible at that URL."
-            ) from e
-        available_models = [m.id async for m in res]
-        if model.provider_resource_id not in available_models:
-            raise ValueError(
-                f"Model {model.provider_resource_id} is not being served by vLLM. "
-                f"Available models: {', '.join(available_models)}"
-            )
-        return model
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        options = get_sampling_options(request.sampling_params)
-        if "max_tokens" not in options:
-            options["max_tokens"] = self.config.max_tokens
-
-        input_dict: dict[str, Any] = {}
-        # Only include the 'tools' param if there is any. It can break things if an empty list is sent to the vLLM.
-        if isinstance(request, ChatCompletionRequest) and request.tools:
-            input_dict = {"tools": _convert_to_vllm_tools_in_request(request.tools)}
-
-        input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
-
-        if fmt := request.response_format:
-            if isinstance(fmt, JsonSchemaResponseFormat):
-                input_dict["extra_body"] = {"guided_json": fmt.json_schema}
-            elif isinstance(fmt, GrammarResponseFormat):
-                raise NotImplementedError("Grammar response format not supported yet")
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
-        if request.logprobs and request.logprobs.top_k:
-            input_dict["logprobs"] = request.logprobs.top_k
-
-        return {
-            "model": request.model,
-            **input_dict,
-            "stream": request.stream,
-            **options,
-        }
-
     async def openai_chat_completion(
         self,
         model: str,

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

@@ -65,12 +65,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
 
         self._project_id = self._config.project_id
 
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
-
     def _get_client(self, model_id) -> Model:
         config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None
         config_url = self._config.url

llama_stack/providers/utils/inference/openai_mixin.py

@@ -11,6 +11,7 @@ from collections.abc import AsyncIterator, Iterable
 from typing import Any
 
 from openai import NOT_GIVEN, AsyncOpenAI
+from pydantic import BaseModel, ConfigDict
 
 from llama_stack.apis.inference import (
     Model,
@@ -26,14 +27,14 @@ from llama_stack.apis.inference import (
 from llama_stack.apis.models import ModelType
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import ModelsProtocolPrivate
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
 from llama_stack.providers.utils.inference.prompt_adapter import localize_image_content
 
 logger = get_logger(name=__name__, category="providers::utils")
 
 
-class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
+class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
     """
     Mixin class that provides OpenAI-specific functionality for inference providers.
     This class handles direct OpenAI API calls using the AsyncOpenAI client.
@@ -42,12 +43,25 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
     - 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:
+    - overwrite_completion_id: If True, overwrites the 'id' field in OpenAI responses
+    - download_images: If True, downloads images and converts to base64 for providers that require it
+    - embedding_model_metadata: A dictionary mapping model IDs to their embedding metadata
+    - provider_data_api_key_field: Optional field name in provider data to look for API key
+    - list_provider_model_ids: Method to list available models from the provider
+    - get_extra_client_params: Method to provide extra parameters to the AsyncOpenAI client
+
     Expected Dependencies:
     - self.model_store: Injected by the Llama Stack distribution system at runtime.
       This provides model registry functionality for looking up registered models.
       The model_store is set in routing_tables/common.py during provider initialization.
     """
 
+    # Allow extra fields so the routing infra can inject model_store, __provider_id__, etc.
+    model_config = ConfigDict(extra="allow")
+
+    config: RemoteInferenceProviderConfig
+
     # Allow subclasses to control whether to overwrite the 'id' field in OpenAI responses
     # is overwritten with a client-side generated id.
     #
@@ -73,9 +87,6 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
     # Optional field name in provider data to look for API key, which takes precedence
     provider_data_api_key_field: str | None = None
 
-    # automatically set by the resolver when instantiating the provider
-    __provider_id__: str
-
     @abstractmethod
     def get_api_key(self) -> str:
         """
@@ -123,6 +134,26 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
         """
         return [m.id async for m in self.client.models.list()]
 
+    async def initialize(self) -> None:
+        """
+        Initialize the OpenAI mixin.
+
+        This method provides a default implementation that does nothing.
+        Subclasses can override this method to perform initialization tasks
+        such as setting up clients, validating configurations, etc.
+        """
+        pass
+
+    async def shutdown(self) -> None:
+        """
+        Shutdown the OpenAI mixin.
+
+        This method provides a default implementation that does nothing.
+        Subclasses can override this method to perform cleanup tasks
+        such as closing connections, releasing resources, etc.
+        """
+        pass
+
     @property
     def client(self) -> AsyncOpenAI:
         """
@@ -383,7 +414,7 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
 
     async def register_model(self, model: Model) -> Model:
         if not await self.check_model_availability(model.provider_model_id):
-            raise ValueError(f"Model {model.provider_model_id} is not available from provider {self.__provider_id__}")
+            raise ValueError(f"Model {model.provider_model_id} is not available from provider {self.__provider_id__}")  # type: ignore[attr-defined]
         return model
 
     async def unregister_model(self, model_id: str) -> None:
@@ -399,17 +430,23 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
         """
         self._model_cache = {}
 
-        # give subclasses a chance to provide custom model listing
+        try:
             iterable = await self.list_provider_model_ids()
+        except Exception as e:
+            logger.error(f"{self.__class__.__name__}.list_provider_model_ids() failed with: {e}")
+            raise
         if not hasattr(iterable, "__iter__"):
             raise TypeError(
                 f"Failed to list models: {self.__class__.__name__}.list_provider_model_ids() must return an iterable of "
-                f"strings or None, but returned {type(iterable).__name__}"
+                f"strings, but returned {type(iterable).__name__}"
             )
+
         provider_models_ids = list(iterable)
         logger.info(f"{self.__class__.__name__}.list_provider_model_ids() returned {len(provider_models_ids)} models")
 
         for provider_model_id in provider_models_ids:
+            if not isinstance(provider_model_id, str):
+                raise ValueError(f"Model ID {provider_model_id} from list_provider_model_ids() is not a string")
             if self.allowed_models and provider_model_id not in self.allowed_models:
                 logger.info(f"Skipping model {provider_model_id} as it is not in the allowed models list")
                 continue
@@ -445,3 +482,29 @@ class OpenAIMixin(ModelsProtocolPrivate, NeedsRequestProviderData, ABC):
 
     async def should_refresh_models(self) -> bool:
         return False
+
+    #
+    # The model_dump implementations are to avoid serializing the extra fields,
+    # e.g. model_store, which are not pydantic.
+    #
+
+    def _filter_fields(self, **kwargs):
+        """Helper to exclude extra fields from serialization."""
+        # Exclude any extra fields stored in __pydantic_extra__
+        if hasattr(self, "__pydantic_extra__") and self.__pydantic_extra__:
+            exclude = kwargs.get("exclude", set())
+            if not isinstance(exclude, set):
+                exclude = set(exclude) if exclude else set()
+            exclude.update(self.__pydantic_extra__.keys())
+            kwargs["exclude"] = exclude
+        return kwargs
+
+    def model_dump(self, **kwargs):
+        """Override to exclude extra fields from serialization."""
+        kwargs = self._filter_fields(**kwargs)
+        return super().model_dump(**kwargs)
+
+    def model_dump_json(self, **kwargs):
+        """Override to exclude extra fields from JSON serialization."""
+        kwargs = self._filter_fields(**kwargs)
+        return super().model_dump_json(**kwargs)

pyproject.toml

@@ -278,14 +278,10 @@ exclude = [
     "^llama_stack/providers/remote/datasetio/huggingface/",
     "^llama_stack/providers/remote/datasetio/nvidia/",
     "^llama_stack/providers/remote/inference/bedrock/",
-    "^llama_stack/providers/remote/inference/cerebras/",
-    "^llama_stack/providers/remote/inference/databricks/",
-    "^llama_stack/providers/remote/inference/fireworks/",
     "^llama_stack/providers/remote/inference/nvidia/",
     "^llama_stack/providers/remote/inference/passthrough/",
     "^llama_stack/providers/remote/inference/runpod/",
     "^llama_stack/providers/remote/inference/tgi/",
-    "^llama_stack/providers/remote/inference/together/",
     "^llama_stack/providers/remote/inference/watsonx/",
     "^llama_stack/providers/remote/safety/bedrock/",
     "^llama_stack/providers/remote/safety/nvidia/",

tests/integration/agents/recordings/000506671ad4807d1bf577100f7af1cc99d782d2c6eb32892c3f6f7c6157bf93.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool_infinite_loop[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Tool' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Get the boiling point of polyjuice with a tool call.\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() missing 1 required positional argument: 'liquid_name'\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Tool message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-000506671ad4",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 0,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 422,
+          "total_tokens": 424,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/agents/recordings/06fbbb88ed5ed37e25609755e1cb348578dbddb2c8b76dafa5025bb3068c94ea.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Tool' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Tool message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-06fbbb88ed5e",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 0,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 421,
+          "total_tokens": 423,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/agents/recordings/176bcef706a9e6f02e47d884df602092bd43906c19747790e7a4ad3aab7ef9f3.json

@@ -0,0 +1,104 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_create_turn_response[ollama/llama3.2:3b-instruct-fp16-client_tools1]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "Call get_boiling_point_with_metadata tool and answer What is the boiling point of polyjuice?"
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point_with_metadata",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit"
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-176bcef706a9",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_wxinam9c",
+                    "function": {
+                      "arguments": "{}",
+                      "name": "get_boiling_point_with_metadata"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-176bcef706a9",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/agents/recordings/234603185e852680100f4b9141949e514dd0432d3bb509ad433d04e47baadaf6.json

@@ -0,0 +1,104 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "What is the boiling point of the liquid polyjuice in celsius?"
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit."
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-234603185e85",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_fkdqo820",
+                    "function": {
+                      "arguments": "{\"liquid\":\"polyjuice\",\"unit\":\"celsius\"}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-234603185e85",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/agents/recordings/2e343d7d4768981d6b7d8140376d35d7fecc83ba9f849beb399dad0e548e1515.json

@@ -0,0 +1,716 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_tool_choice_get_boiling_point[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
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+}

tests/integration/agents/recordings/4bf877c72dee9d16e9a4276f3163566d8ad3d8844106981a20d06cde94a2ac3b.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_tool_choice_get_boiling_point[ollama/llama3.2:3b-instruct-fp16]",
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+    "headers": {},
+    "body": {
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+      "messages": [
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+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\nAssistant: I was unable to find the boiling point of polyjuice in my search. Can I help you with something else?\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
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+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
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+          "completion_tokens": 2,
+          "prompt_tokens": 447,
+          "total_tokens": 449,
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+    "is_streaming": false
+  }
+}

tests/integration/agents/recordings/525612e8c7f35ca56ff30f0af8be3eff5dfbdd3a3c34f6e3024147936f543ae1.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool_infinite_loop[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Get the boiling point of polyjuice with a tool call.\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() missing 1 required positional argument: 'liquid_name'\n\nAssistant: I apologize for the error. It seems that the `get_boiling_point` tool requires a liquid name as an argument.\n\nTo provide the boiling point of polyjuice, I'll need to know that polyjuice is not a real substance and its boiling point cannot be found in my database. However, if you meant to ask about Polyjuice Potion from the Harry Potter series, I can tell you that it's a fictional potion.\n\nIf you could provide more context or clarify which polyjuice you are referring to, I'll do my best to assist you with your question.\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
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+    "endpoint": "/v1/chat/completions",
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+        "system_fingerprint": "fp_ollama",
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+    "is_streaming": false
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tests/integration/agents/recordings/5af3e74e91e5eb78e8b36b1c21c228f903d101a4c2af4838a1f2b339fdaf52cd.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_tool_choice_required[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Tool' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Tool message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
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+    "endpoint": "/v1/chat/completions",
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tests/integration/agents/recordings/73daf748041faa7ccfbcd7a9595d546cccf751b81bb8c26c2c284309fdb61968.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_agent_simple[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Give me a sentence that contains the word: hello\n\nAssistant: The friendly receptionist greeted us with a warm \"hello\" as we walked into the office.\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
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+      ],
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+    "endpoint": "/v1/chat/completions",
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tests/integration/agents/recordings/79babad60ca39d81716188f8db60d88cb1f06211209581b9f2ee3a24b8ddde8c.json

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+{
+  "test_id": "tests/integration/agents/test_agents.py::test_agent_simple[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
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+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Give me a sentence that contains the word: hello\n\nAssistant: The friendly receptionist greeted us with a warm \"hello\" as we walked into the office.\n\nUser: Tell me how to make a bomb\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST User message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
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tests/integration/agents/recordings/7db9a6dcf157b5a308ae53f47f6cc81337b14541b7b94edd8d4e1bbc1c1b89be.json

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+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
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+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "Call get_boiling_point tool and answer What is the boiling point of polyjuice?"
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+      "temperature": 0.0001,
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tests/integration/agents/recordings/8be9dbb13d67d31e8f8bc2a334f2eb6045cd13785f33d43f6c4a3813ec45e7c5.json

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+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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tests/integration/agents/recordings/b28ac251c5c5e13806b43e6f4f66f516595677e02714a31d452ec4bc7a963b56.json

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tests/integration/agents/recordings/b665199ffbc58f4bcb3081247c92d49578f356e245905025f87977a94a7ae5af.json

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tests/integration/agents/recordings/ce551aa63ba8295ddd92a55d665c3733181210ef7a3b9ae831185247ffeb09d5.json

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tests/integration/agents/recordings/daf33ab8430b09f825c6718f8f389eced3b2cd3fddcb3450b46a7f4c89ca1706.json

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tests/integration/agents/recordings/e04f133c751c8cb6833ffa793cf3390930ee9d89af70ee81b7184c8190de247c.json

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+        "__data__": {
+          "id": "rec-e04f133c751c",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "stop",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
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+}

tests/integration/agents/recordings/e61f5ae8e721820f88a73c53c3ad43359b1fbdd1883178f3d5273d79d0dcde09.json

@@ -0,0 +1,104 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool_infinite_loop[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant Always respond with tool calls no matter what. "
+        },
+        {
+          "role": "user",
+          "content": "Get the boiling point of polyjuice with a tool call."
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit."
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-e61f5ae8e721",
+          "choices": [
+            {
+              "delta": {
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+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_91hje6b1",
+                    "function": {
+                      "arguments": "{}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
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+              "finish_reason": null,
+              "index": 0,
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+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
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+      },
+      {
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+        "__data__": {
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+    "is_streaming": true
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+}

tests/integration/agents/recordings/f4cf516f3450fe5cf567283d65718c946335022942b027bc3738968e34e6c394.json

@@ -0,0 +1,58 @@
+{
+  "test_id": "tests/integration/agents/test_agents.py::test_custom_tool[ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\nAssistant: I was unable to find the boiling point of polyjuice in my search. Can I help you with something else?\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
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+  "response": {
+    "body": {
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+      "__data__": {
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+        "choices": [
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+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
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+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 0,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 447,
+          "total_tokens": 449,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/common/recordings/7d1040b8443915a13200de1dd5dfad11ebbe4d6cbc3485d87c680e79e4cd1a09.json

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+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "What is 2 + 2?"
+        },
+        {
+          "role": "assistant",
+          "content": "The answer to 2 + 2 is:\n\n4"
+        },
+        {
+          "role": "user",
+          "content": "Tell me a short joke"
+        }
+      ],
+      "max_tokens": 0,
+      "stream": true
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
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+    "is_streaming": true
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+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-3255f444.json

@@ -0,0 +1,232 @@
+{
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tests/integration/recordings/responses/0d3602bdeb33.json

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+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-759",
+          "choices": [
+            {
+              "delta": {
+                "content": " else",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514982,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-759",
+          "choices": [
+            {
+              "delta": {
+                "content": "?",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514982,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-759",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "stop",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514982,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/11675efe359b.json

@@ -0,0 +1,57 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\nAssistant: I was unable to find the boiling point of polyjuice in my search. Can I help you with something else?\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-774",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 1759514987,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 447,
+          "total_tokens": 449,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/1b08c8e14202.json

@@ -0,0 +1,103 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant Always respond with tool calls no matter what. "
+        },
+        {
+          "role": "user",
+          "content": "Get the boiling point of polyjuice with a tool call."
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit."
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-707",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_laifztfo",
+                    "function": {
+                      "arguments": "{}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514973,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-707",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514973,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/31a87d74ea98.json

@@ -0,0 +1,108 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "What is the boiling point of the liquid polyjuice in celsius?"
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": {
+        "type": "function",
+        "function": {
+          "name": "get_boiling_point"
+        }
+      },
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit."
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-269",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_m61820zt",
+                    "function": {
+                      "arguments": "{\"liquid\":\"polyjuice\",\"unit\":\"celsius\"}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514985,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-269",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514985,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/3e8248d253fd.json

@@ -0,0 +1,170 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant."
+        },
+        {
+          "role": "user",
+          "content": "Say hi to the world. Use tools to do so."
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "id": "call_bhtxlmzm",
+              "type": "function",
+              "function": {
+                "name": "greet_everyone",
+                "arguments": "{\"url\":\"world\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_bhtxlmzm",
+          "content": [
+            {
+              "type": "text",
+              "text": "Hello, world!"
+            }
+          ]
+        },
+        {
+          "role": "assistant",
+          "content": "I'm able to \"speak\" to you through this chat platform, hello! Would you like me to repeat anything or provide assistance with something else?"
+        },
+        {
+          "role": "user",
+          "content": "What is the boiling point of polyjuice? Use tools to answer."
+        }
+      ],
+      "max_tokens": 0,
+      "stream": true,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "greet_everyone",
+            "parameters": {
+              "properties": {
+                "url": {
+                  "title": "Url",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "url"
+              ],
+              "title": "greet_everyoneArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        ",
+            "parameters": {
+              "properties": {
+                "liquid_name": {
+                  "title": "Liquid Name",
+                  "type": "string"
+                },
+                "celsius": {
+                  "default": true,
+                  "title": "Celsius",
+                  "type": "boolean"
+                }
+              },
+              "required": [
+                "liquid_name"
+              ],
+              "title": "get_boiling_pointArguments",
+              "type": "object"
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-515",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_li57r4tl",
+                    "function": {
+                      "arguments": "{\"celsius\":\"true\",\"liquid_name\":\"polyjuice\"}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-515",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/864a270ca97b.json

@@ -0,0 +1,57 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Tool' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Get the boiling point of polyjuice with a tool call.\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() missing 1 required positional argument: 'liquid_name'\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Tool message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-394",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 1759514973,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 422,
+          "total_tokens": 424,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/b63c1324a814.json

@@ -0,0 +1,57 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Get the boiling point of polyjuice with a tool call.\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() missing 1 required positional argument: 'liquid_name'\n\nAssistant: I apologize for the error. It seems that the `get_boiling_point` tool requires a liquid name as an argument.\n\nTo provide the boiling point of polyjuice, I'll need to know that polyjuice is not a real substance and its boiling point cannot be found in my database. However, if you meant to ask about Polyjuice Potion from the Harry Potter series, I can tell you that it's a fictional potion.\n\nIf you could provide more context or clarify which polyjuice you are referring to, I'll do my best to assist you with your question.\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama-guard3:1b"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "chatcmpl-515",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "safe",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": null
+            }
+          }
+        ],
+        "created": 1759514975,
+        "model": "llama-guard3:1b",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": "fp_ollama",
+        "usage": {
+          "completion_tokens": 2,
+          "prompt_tokens": 542,
+          "total_tokens": 544,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/c23c11b48503.json

@@ -0,0 +1,57 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama-guard3:1b",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Task: Check if there is unsafe content in 'Tool' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\nTool: Error when running tool: get_boiling_point() got an unexpected keyword argument 'liquid'\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Tool message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
+        }
+      ],
+      "stream": false,
+      "temperature": 0.0
+    },
+    "endpoint": "/v1/chat/completions",
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+}

tests/integration/recordings/responses/c315ffba4f17.json

@@ -0,0 +1,715 @@
+{
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+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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+}

tests/integration/recordings/responses/c8632717f6b8.json

@@ -0,0 +1,103 @@
+{
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tests/integration/recordings/responses/ccdce45aab2c.json

@@ -0,0 +1,103 @@
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tests/integration/recordings/responses/d9c3bf231670.json

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+              "title": "greet_everyoneArguments",
+              "type": "object"
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+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
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+          ],
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+          "object": "chat.completion.chunk",
+          "service_tier": null,
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+          "usage": null
+        }
+      },
+      {
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+        "__data__": {
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+        }
+      },
+      {
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+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
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+          ],
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+        }
+      },
+      {
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+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
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+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
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+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
+            {
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+                "content": " with",
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+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
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+              },
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+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
+            {
+              "delta": {
+                "content": " else",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
+            {
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+              },
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+              "index": 0,
+              "logprobs": null
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+          ],
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+          "model": "llama3.2:3b-instruct-fp16",
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+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-770",
+          "choices": [
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+                "function_call": null,
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+                "role": "assistant",
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+              },
+              "finish_reason": "stop",
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+              "logprobs": null
+            }
+          ],
+          "created": 1759515074,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/db9689e2cf53.json

@@ -0,0 +1,103 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "Call get_boiling_point_with_metadata tool and answer What is the boiling point of polyjuice?"
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point_with_metadata",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit"
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-178",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_9vy3xwac",
+                    "function": {
+                      "arguments": "{}",
+                      "name": "get_boiling_point_with_metadata"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515075,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-178",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759515075,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/eac12959a803.json

@@ -0,0 +1,103 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant"
+        },
+        {
+          "role": "user",
+          "content": "Call get_boiling_point tool and answer What is the boiling point of polyjuice?"
+        }
+      ],
+      "max_tokens": 512,
+      "stream": true,
+      "temperature": 0.0001,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "Returns the boiling point of a liquid in Celcius or Fahrenheit."
+          }
+        }
+      ],
+      "top_p": 0.9
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-367",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_swism1x1",
+                    "function": {
+                      "arguments": "{}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514987,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-367",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1759514987,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/suites.py

@@ -131,6 +131,27 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
             "embedding_model": "fireworks/accounts/fireworks/models/qwen3-embedding-8b",
         },
     ),
+    "anthropic": Setup(
+        name="anthropic",
+        description="Anthropic Claude models",
+        defaults={
+            "text_model": "anthropic/claude-3-5-haiku-20241022",
+        },
+    ),
+    "llama-api": Setup(
+        name="llama-openai-compat",
+        description="Llama models from https://api.llama.com",
+        defaults={
+            "text_model": "llama_openai_compat/Llama-3.3-8B-Instruct",
+        },
+    ),
+    "groq": Setup(
+        name="groq",
+        description="Groq models",
+        defaults={
+            "text_model": "groq/llama-3.3-70b-versatile",
+        },
+    ),
 }
 
 

tests/integration/tool_runtime/recordings/4d04859cb4d87fe1c3df23b0a9f49cb9fe05b2b91ee425f5ec97332297451220.json

@@ -0,0 +1,171 @@
+{
+  "test_id": "tests/integration/tool_runtime/test_mcp.py::test_mcp_invocation[txt=ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant."
+        },
+        {
+          "role": "user",
+          "content": "Say hi to the world. Use tools to do so."
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "id": "call_ncf4rv01",
+              "type": "function",
+              "function": {
+                "name": "greet_everyone",
+                "arguments": "{\"url\":\"world\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_ncf4rv01",
+          "content": [
+            {
+              "type": "text",
+              "text": "Hello, world!"
+            }
+          ]
+        },
+        {
+          "role": "assistant",
+          "content": "<|python_tag|>{\"type\": \"function\", \"name\": \"print_hello_world\", \"parameters\": {}}"
+        },
+        {
+          "role": "user",
+          "content": "What is the boiling point of polyjuice? Use tools to answer."
+        }
+      ],
+      "max_tokens": 0,
+      "stream": true,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "greet_everyone",
+            "parameters": {
+              "properties": {
+                "url": {
+                  "title": "Url",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "url"
+              ],
+              "title": "greet_everyoneArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        ",
+            "parameters": {
+              "properties": {
+                "liquid_name": {
+                  "title": "Liquid Name",
+                  "type": "string"
+                },
+                "celsius": {
+                  "default": true,
+                  "title": "Celsius",
+                  "type": "boolean"
+                }
+              },
+              "required": [
+                "liquid_name"
+              ],
+              "title": "get_boiling_pointArguments",
+              "type": "object"
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "llama3.2:3b-instruct-fp16"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
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+                "content": "",
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+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_bjfvf9mr",
+                    "function": {
+                      "arguments": "{\"celsius\":\"true\",\"liquid_name\":\"polyjuice\"}",
+                      "name": "get_boiling_point"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
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+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-4d04859cb4d8",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": "tool_calls",
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "llama3.2:3b-instruct-fp16",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": "fp_ollama",
+          "usage": null
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/tool_runtime/recordings/854e45e9fdcadf1ddc67b43b006f287a66185d674f8945ebd3dea4b40d8f1e4e.json

@@ -0,0 +1,647 @@
+{
+  "test_id": "tests/integration/tool_runtime/test_mcp.py::test_mcp_invocation[txt=ollama/llama3.2:3b-instruct-fp16]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b-instruct-fp16",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant."
+        },
+        {
+          "role": "user",
+          "content": "Say hi to the world. Use tools to do so."
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "id": "call_ncf4rv01",
+              "type": "function",
+              "function": {
+                "name": "greet_everyone",
+                "arguments": "{\"url\":\"world\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_ncf4rv01",
+          "content": [
+            {
+              "type": "text",
+              "text": "Hello, world!"
+            }
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tests/integration/vector_io/recordings/01a319ad65beb355edeaac4d39e620e83bfed1bbcc573b80c3117009b0c12c4a.json

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+        "Python is a high-level programming language with code readability and fewer lines than C++ or Java"
+      ],
+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
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tests/integration/vector_io/recordings/02630ecc0d941bf6f878e89f4753d30018fbd020289803e4d1df6d0a0b8eaf64.json

@@ -0,0 +1,807 @@
+{
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+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "This is a test file 0"
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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tests/integration/vector_io/recordings/0838ee102f6ac2da4e71334cf0b3aadd9761b4374ae7e1f2deedff962992115d.json

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tests/integration/vector_io/recordings/17df655a22bff78d2c1dec1f1a9fc77b767ddab6681d09e64cfa4b13f8bd404c.json

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+      "input": [
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
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tests/integration/vector_io/recordings/2d32599c6c245e619d24d5bd696c184d8046898e35302c65492cd0b1c4238f2f.json

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tests/integration/vector_io/recordings/2e91f995cc19c8d208eb82092cec184c9c101334e13288d4b7fe6780eadedcae.json

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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
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tests/integration/vector_io/recordings/321f0309725942dbcad18528f40cc6fe2feb7c33b9bb11cad105bc1540b36e6c.json

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+  "request": {
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+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
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+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+}

tests/integration/vector_io/recordings/338b25115a6ae999bd83750922367d6f00c5934bd54ca9c089ecf8d511ebda94.json

@@ -0,0 +1,807 @@
+{
+  "test_id": "tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_attach_file[client_with_models-ollama/llama3.2:3b-instruct-fp16-None-ollama/all-minilm:l6-v2-None-384]",
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "The secret string is foobazbar."
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
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+    "endpoint": "/v1/embeddings",
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