feat: add (openai, anthropic, gemini) providers via litellm (#1267)

# What does this PR do?

This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.

We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)

## Test Plan

```bash
#!/bin/bash

args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
    LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
        --embedding-model=all-MiniLM-L6-v2 \
        --vision-inference-model="" \
        --inference-model=$model "${args[@]}"
done
```

distributions/dependencies.json

@@ -136,6 +136,42 @@
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
   ],
+  "dev": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "fastapi",
+    "fire",
+    "fireworks-ai",
+    "httpx",
+    "litellm",
+    "matplotlib",
+    "mcp",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pymongo",
+    "pypdf",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "sqlite-vec",
+    "tqdm",
+    "transformers",
+    "uvicorn",
+    "sentence-transformers --no-deps",
+    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
+  ],
   "fireworks": [
     "aiosqlite",
     "autoevals",

llama_stack/providers/registry/inference.py

@@ -207,6 +207,33 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="openai",
+                pip_packages=["litellm"],
+                module="llama_stack.providers.remote.inference.openai",
+                config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig",
+            ),
+        ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="anthropic",
+                pip_packages=["litellm"],
+                module="llama_stack.providers.remote.inference.anthropic",
+                config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig",
+            ),
+        ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="gemini",
+                pip_packages=["litellm"],
+                module="llama_stack.providers.remote.inference.gemini",
+                config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
+            ),
+        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(

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

@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .config import AnthropicConfig
+
+
+class AnthropicProviderDataValidator(BaseModel):
+    anthropic_api_key: Optional[str] = None
+
+
+async def get_adapter_impl(config: AnthropicConfig, _deps):
+    from .anthropic import AnthropicInferenceAdapter
+
+    impl = AnthropicInferenceAdapter(config)
+    await impl.initialize()
+    return impl

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

@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+
+from .config import AnthropicConfig
+from .models import MODEL_ENTRIES
+
+
+class AnthropicInferenceAdapter(LiteLLMOpenAIMixin):
+    def __init__(self, config: AnthropicConfig) -> None:
+        LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
+        self.config = config
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass

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

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class AnthropicConfig(BaseModel):
+    api_key: Optional[str] = Field(
+        default=None,
+        description="API key for Anthropic models",
+    )
+
+    @classmethod
+    def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> Dict[str, Any]:
+        return {
+            "api_key": api_key,
+        }

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

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.providers.utils.inference.model_registry import (
+    ProviderModelEntry,
+)
+
+LLM_MODEL_IDS = [
+    "anthropic/claude-3-5-sonnet-latest",
+    "anthropic/claude-3-7-sonnet-latest",
+    "anthropic/claude-3-5-haiku-latest",
+]
+
+
+MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + [
+    ProviderModelEntry(
+        provider_model_id="anthropic/voyage-3",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 1024, "context_length": 32000},
+    ),
+    ProviderModelEntry(
+        provider_model_id="anthropic/voyage-3-lite",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 512, "context_length": 32000},
+    ),
+    ProviderModelEntry(
+        provider_model_id="anthropic/voyage-code-3",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 1024, "context_length": 32000},
+    ),
+]

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

@@ -23,8 +23,8 @@ class FireworksImplConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> Dict[str, Any]:
         return {
             "url": "https://api.fireworks.ai/inference/v1",
-            "api_key": "${env.FIREWORKS_API_KEY}",
+            "api_key": api_key,
         }

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

@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .config import GeminiConfig
+
+
+class GeminiProviderDataValidator(BaseModel):
+    gemini_api_key: Optional[str] = None
+
+
+async def get_adapter_impl(config: GeminiConfig, _deps):
+    from .gemini import GeminiInferenceAdapter
+
+    impl = GeminiInferenceAdapter(config)
+    await impl.initialize()
+    return impl

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

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class GeminiConfig(BaseModel):
+    api_key: Optional[str] = Field(
+        default=None,
+        description="API key for Gemini models",
+    )
+
+    @classmethod
+    def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY}", **kwargs) -> Dict[str, Any]:
+        return {
+            "api_key": api_key,
+        }

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

@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+
+from .config import GeminiConfig
+from .models import MODEL_ENTRIES
+
+
+class GeminiInferenceAdapter(LiteLLMOpenAIMixin):
+    def __init__(self, config: GeminiConfig) -> None:
+        LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
+        self.config = config
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass

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

@@ -0,0 +1,24 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.providers.utils.inference.model_registry import (
+    ProviderModelEntry,
+)
+
+LLM_MODEL_IDS = [
+    "gemini/gemini-1.5-flash",
+    "gemini/gemini-1.5-pro",
+]
+
+
+MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + [
+    ProviderModelEntry(
+        provider_model_id="gemini/text-embedding-004",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 768, "context_length": 2048},
+    ),
+]

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

@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .config import OpenAIConfig
+
+
+class OpenAIProviderDataValidator(BaseModel):
+    openai_api_key: Optional[str] = None
+
+
+async def get_adapter_impl(config: OpenAIConfig, _deps):
+    from .openai import OpenAIInferenceAdapter
+
+    impl = OpenAIInferenceAdapter(config)
+    await impl.initialize()
+    return impl

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

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class OpenAIConfig(BaseModel):
+    api_key: Optional[str] = Field(
+        default=None,
+        description="API key for OpenAI models",
+    )
+
+    @classmethod
+    def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY}", **kwargs) -> Dict[str, Any]:
+        return {
+            "api_key": api_key,
+        }

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

@@ -0,0 +1,30 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.providers.utils.inference.model_registry import (
+    ProviderModelEntry,
+)
+
+LLM_MODEL_IDS = [
+    "openai/gpt-4o",
+    "openai/gpt-4o-mini",
+    "openai/chatgpt-4o-latest",
+]
+
+
+MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + [
+    ProviderModelEntry(
+        provider_model_id="openai/text-embedding-3-small",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 1536},
+    ),
+    ProviderModelEntry(
+        provider_model_id="openai/text-embedding-3-large",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 3072},
+    ),
+]

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

@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+
+from .config import OpenAIConfig
+from .models import MODEL_ENTRIES
+
+
+class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
+    def __init__(self, config: OpenAIConfig) -> None:
+        LiteLLMOpenAIMixin.__init__(self, MODEL_ENTRIES)
+        self.config = config
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass

llama_stack/providers/tests/test_cases/inference/chat_completion.json

@@ -40,7 +40,7 @@
   "tool_calling": {
     "data": {
       "messages": [
-        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "system", "content": "Pretend you are a weather assistant."},
         {"role": "user", "content": "What's the weather like in San Francisco?"}
       ],
       "tools": [
@@ -65,7 +65,7 @@
       "messages": [
         {
           "role": "system",
-          "content": "You are a helpful assistant."
+          "content": "Pretend you are a weather assistant."
         },
         {
           "role": "user",

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -0,0 +1,171 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator, AsyncIterator, List, Optional, Union
+
+import litellm
+
+from llama_stack.apis.common.content_types import (
+    InterleavedContent,
+    InterleavedContentItem,
+)
+from llama_stack.apis.inference import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChunk,
+    EmbeddingsResponse,
+    EmbeddingTaskType,
+    Inference,
+    JsonSchemaResponseFormat,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    SamplingParams,
+    TextTruncation,
+    ToolChoice,
+    ToolConfig,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_stack.apis.models.models import Model
+from llama_stack.providers.utils.inference.model_registry import (
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    convert_message_to_openai_dict_new,
+    convert_openai_chat_completion_choice,
+    convert_openai_chat_completion_stream,
+    convert_tooldef_to_openai_tool,
+    get_sampling_options,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    interleaved_content_as_str,
+)
+
+
+class LiteLLMOpenAIMixin(
+    ModelRegistryHelper,
+    Inference,
+):
+    def __init__(self, model_entries) -> None:
+        self.model_entries = model_entries
+        ModelRegistryHelper.__init__(self, model_entries)
+
+    async def register_model(self, model: Model) -> Model:
+        model_id = self.get_provider_model_id(model.provider_resource_id)
+        if model_id is None:
+            raise ValueError(f"Unsupported model: {model.provider_resource_id}")
+        return model
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedContent,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        raise NotImplementedError("LiteLLM does not support completion requests")
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+        tool_config: Optional[ToolConfig] = None,
+    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+        model = await self.model_store.get_model(model_id)
+        request = ChatCompletionRequest(
+            model=model.provider_resource_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+            tool_config=tool_config,
+        )
+
+        params = await self._get_params(request)
+
+        # unfortunately, we need to use synchronous litellm.completion here because litellm
+        # caches various httpx.client objects in a non-eventloop aware manner
+        response = litellm.completion(**params)
+        if stream:
+            return self._stream_chat_completion(response)
+        else:
+            return convert_openai_chat_completion_choice(response.choices[0])
+
+    async def _stream_chat_completion(
+        self, response: litellm.ModelResponse
+    ) -> AsyncIterator[ChatCompletionResponseStreamChunk]:
+        async def _stream_generator():
+            for chunk in response:
+                yield chunk
+
+        async for chunk in convert_openai_chat_completion_stream(
+            _stream_generator(), enable_incremental_tool_calls=True
+        ):
+            yield chunk
+
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
+        input_dict = {}
+
+        input_dict["messages"] = [await convert_message_to_openai_dict_new(m) for m in request.messages]
+        if fmt := request.response_format:
+            if not isinstance(fmt, JsonSchemaResponseFormat):
+                raise ValueError(
+                    f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
+                )
+
+            fmt = fmt.json_schema
+            name = fmt["title"]
+            del fmt["title"]
+            fmt["additionalProperties"] = False
+            input_dict["response_format"] = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": name,
+                    "schema": fmt,
+                    "strict": True,
+                },
+            }
+        if request.tools:
+            input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
+            if request.tool_config.tool_choice:
+                input_dict["tool_choice"] = request.tool_config.tool_choice.value
+
+        return {
+            "model": request.model,
+            **input_dict,
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[str] | List[InterleavedContentItem],
+        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        output_dimension: Optional[int] = None,
+        task_type: Optional[EmbeddingTaskType] = None,
+    ) -> EmbeddingsResponse:
+        model = await self.model_store.get_model(model_id)
+
+        response = litellm.embedding(
+            model=model.provider_resource_id,
+            input=[interleaved_content_as_str(content) for content in contents],
+        )
+
+        embeddings = [data["embedding"] for data in response["data"]]
+        return EmbeddingsResponse(embeddings=embeddings)

llama_stack/templates/ci-tests/ci_tests.py

@@ -57,17 +57,6 @@ def get_distribution_template() -> DistributionTemplate:
         config=SentenceTransformersInferenceConfig.sample_run_config(),
     )
 
-    core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
-    default_models = [
-        ModelInput(
-            model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
-            provider_model_id=m.provider_model_id,
-            provider_id="fireworks",
-            metadata=m.metadata,
-            model_type=m.model_type,
-        )
-        for m in MODEL_ENTRIES
-    ]
     default_tool_groups = [
         ToolGroupInput(
             toolgroup_id="builtin::websearch",
@@ -82,6 +71,16 @@ def get_distribution_template() -> DistributionTemplate:
             provider_id="code-interpreter",
         ),
     ]
+    core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
+    default_models = [
+        ModelInput(
+            model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
+            provider_id="fireworks",
+            model_type=m.model_type,
+            metadata=m.metadata,
+        )
+        for m in MODEL_ENTRIES
+    ]
     embedding_model = ModelInput(
         model_id="all-MiniLM-L6-v2",
         provider_id="sentence-transformers",
@@ -98,7 +97,7 @@ def get_distribution_template() -> DistributionTemplate:
         container_image=None,
         template_path=None,
         providers=providers,
-        default_models=default_models,
+        default_models=default_models + [embedding_model],
         run_configs={
             "run.yaml": RunConfigSettings(
                 provider_overrides={

llama_stack/templates/ci-tests/run.yaml

@@ -93,59 +93,48 @@ models:
 - metadata: {}
   model_id: meta-llama/Llama-3.1-8B-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.1-70B-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.2-1B-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p2-1b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.2-3B-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.3-70B-Instruct
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-Guard-3-8B
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-guard-3-8b
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-Guard-3-11B-Vision
   provider_id: fireworks
-  provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
   model_type: llm
 - metadata:
     embedding_dimension: 768
     context_length: 8192
   model_id: nomic-ai/nomic-embed-text-v1.5
   provider_id: fireworks
-  provider_model_id: nomic-ai/nomic-embed-text-v1.5
   model_type: embedding
 - metadata:
     embedding_dimension: 384

llama_stack/templates/dev/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .dev import get_distribution_template  # noqa: F401

llama_stack/templates/dev/build.yaml

@@ -0,0 +1,36 @@
+version: '2'
+distribution_spec:
+  description: Distribution for running e2e tests in CI
+  providers:
+    inference:
+    - remote::openai
+    - remote::fireworks
+    - remote::anthropic
+    - remote::gemini
+    - inline::sentence-transformers
+    vector_io:
+    - inline::sqlite-vec
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+    eval:
+    - inline::meta-reference
+    datasetio:
+    - remote::huggingface
+    - inline::localfs
+    scoring:
+    - inline::basic
+    - inline::llm-as-judge
+    - inline::braintrust
+    tool_runtime:
+    - remote::brave-search
+    - remote::tavily-search
+    - inline::code-interpreter
+    - inline::rag-runtime
+    - remote::model-context-protocol
+image_type: conda

llama_stack/templates/dev/dev.py

@@ -0,0 +1,174 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Tuple
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.distribution.datatypes import (
+    ModelInput,
+    Provider,
+    ShieldInput,
+    ToolGroupInput,
+)
+from llama_stack.models.llama.sku_list import all_registered_models
+from llama_stack.providers.inline.inference.sentence_transformers import (
+    SentenceTransformersInferenceConfig,
+)
+from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
+from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
+from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
+from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
+from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
+from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
+from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
+
+
+def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
+    # in this template, we allow each API key to be optional
+    providers = [
+        (
+            "openai",
+            OPENAI_MODEL_ENTRIES,
+            OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
+        ),
+        (
+            "fireworks",
+            FIREWORKS_MODEL_ENTRIES,
+            FireworksImplConfig.sample_run_config(api_key="${env.FIREWORKS_API_KEY:}"),
+        ),
+        (
+            "anthropic",
+            ANTHROPIC_MODEL_ENTRIES,
+            AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
+        ),
+        (
+            "gemini",
+            GEMINI_MODEL_ENTRIES,
+            GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
+        ),
+    ]
+    inference_providers = []
+    default_models = []
+    core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
+    for provider_id, model_entries, config in providers:
+        inference_providers.append(
+            Provider(
+                provider_id=provider_id,
+                provider_type=f"remote::{provider_id}",
+                config=config,
+            )
+        )
+        default_models.extend(
+            ModelInput(
+                model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
+                provider_model_id=m.provider_model_id,
+                provider_id=provider_id,
+                model_type=m.model_type,
+                metadata=m.metadata,
+            )
+            for m in model_entries
+        )
+    return inference_providers, default_models
+
+
+def get_distribution_template() -> DistributionTemplate:
+    providers = {
+        "inference": [
+            "remote::openai",
+            "remote::fireworks",
+            "remote::anthropic",
+            "remote::gemini",
+            "inline::sentence-transformers",
+        ],
+        "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+        "tool_runtime": [
+            "remote::brave-search",
+            "remote::tavily-search",
+            "inline::code-interpreter",
+            "inline::rag-runtime",
+            "remote::model-context-protocol",
+        ],
+    }
+    name = "dev"
+
+    vector_io_provider = Provider(
+        provider_id="sqlite-vec",
+        provider_type="inline::sqlite-vec",
+        config=SQLiteVectorIOConfig.sample_run_config(f"distributions/{name}"),
+    )
+    embedding_provider = Provider(
+        provider_id="sentence-transformers",
+        provider_type="inline::sentence-transformers",
+        config=SentenceTransformersInferenceConfig.sample_run_config(),
+    )
+
+    default_tool_groups = [
+        ToolGroupInput(
+            toolgroup_id="builtin::websearch",
+            provider_id="tavily-search",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::rag",
+            provider_id="rag-runtime",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::code_interpreter",
+            provider_id="code-interpreter",
+        ),
+    ]
+    embedding_model = ModelInput(
+        model_id="all-MiniLM-L6-v2",
+        provider_id=embedding_provider.provider_id,
+        model_type=ModelType.embedding,
+        metadata={
+            "embedding_dimension": 384,
+        },
+    )
+    inference_providers, default_models = get_inference_providers()
+
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Distribution for running e2e tests in CI",
+        container_image=None,
+        template_path=None,
+        providers=providers,
+        default_models=[],
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": inference_providers + [embedding_provider],
+                    "vector_io": [vector_io_provider],
+                },
+                default_models=default_models + [embedding_model],
+                default_tool_groups=default_tool_groups,
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+            ),
+        },
+        run_config_env_vars={
+            "LLAMA_STACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "FIREWORKS_API_KEY": (
+                "",
+                "Fireworks API Key",
+            ),
+            "OPENAI_API_KEY": (
+                "",
+                "OpenAI API Key",
+            ),
+        },
+    )

llama_stack/templates/dev/run.yaml

@@ -0,0 +1,261 @@
+version: '2'
+image_name: dev
+apis:
+- agents
+- datasetio
+- eval
+- inference
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:}
+  - provider_id: fireworks
+    provider_type: remote::fireworks
+    config:
+      url: https://api.fireworks.ai/inference/v1
+      api_key: ${env.FIREWORKS_API_KEY:}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
+  vector_io:
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/sqlite_vec.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db}
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config: {}
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config: {}
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+    config: {}
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+    config: {}
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:}
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: code-interpreter
+    provider_type: inline::code-interpreter
+    config: {}
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+    config: {}
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+    config: {}
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/registry.db
+models:
+- metadata: {}
+  model_id: openai/gpt-4o
+  provider_id: openai
+  provider_model_id: openai/gpt-4o
+  model_type: llm
+- metadata: {}
+  model_id: openai/gpt-4o-mini
+  provider_id: openai
+  provider_model_id: openai/gpt-4o-mini
+  model_type: llm
+- metadata: {}
+  model_id: openai/chatgpt-4o-latest
+  provider_id: openai
+  provider_model_id: openai/chatgpt-4o-latest
+  model_type: llm
+- metadata:
+    embedding_dimension: 1536
+  model_id: openai/text-embedding-3-small
+  provider_id: openai
+  provider_model_id: openai/text-embedding-3-small
+  model_type: embedding
+- metadata:
+    embedding_dimension: 3072
+  model_id: openai/text-embedding-3-large
+  provider_id: openai
+  provider_model_id: openai/text-embedding-3-large
+  model_type: embedding
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-70B-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-1B-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p2-1b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-3B-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-Guard-3-8B
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-guard-3-8b
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-Guard-3-11B-Vision
+  provider_id: fireworks
+  provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+    context_length: 8192
+  model_id: nomic-ai/nomic-embed-text-v1.5
+  provider_id: fireworks
+  provider_model_id: nomic-ai/nomic-embed-text-v1.5
+  model_type: embedding
+- metadata: {}
+  model_id: anthropic/claude-3-5-sonnet-latest
+  provider_id: anthropic
+  provider_model_id: anthropic/claude-3-5-sonnet-latest
+  model_type: llm
+- metadata: {}
+  model_id: anthropic/claude-3-7-sonnet-latest
+  provider_id: anthropic
+  provider_model_id: anthropic/claude-3-7-sonnet-latest
+  model_type: llm
+- metadata: {}
+  model_id: anthropic/claude-3-5-haiku-latest
+  provider_id: anthropic
+  provider_model_id: anthropic/claude-3-5-haiku-latest
+  model_type: llm
+- metadata:
+    embedding_dimension: 1024
+    context_length: 32000
+  model_id: anthropic/voyage-3
+  provider_id: anthropic
+  provider_model_id: anthropic/voyage-3
+  model_type: embedding
+- metadata:
+    embedding_dimension: 512
+    context_length: 32000
+  model_id: anthropic/voyage-3-lite
+  provider_id: anthropic
+  provider_model_id: anthropic/voyage-3-lite
+  model_type: embedding
+- metadata:
+    embedding_dimension: 1024
+    context_length: 32000
+  model_id: anthropic/voyage-code-3
+  provider_id: anthropic
+  provider_model_id: anthropic/voyage-code-3
+  model_type: embedding
+- metadata: {}
+  model_id: gemini/gemini-1.5-flash
+  provider_id: gemini
+  provider_model_id: gemini/gemini-1.5-flash
+  model_type: llm
+- metadata: {}
+  model_id: gemini/gemini-1.5-pro
+  provider_id: gemini
+  provider_model_id: gemini/gemini-1.5-pro
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+    context_length: 2048
+  model_id: gemini/text-embedding-004
+  provider_id: gemini
+  provider_model_id: gemini/text-embedding-004
+  model_type: embedding
+- metadata:
+    embedding_dimension: 384
+  model_id: all-MiniLM-L6-v2
+  provider_id: sentence-transformers
+  model_type: embedding
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
+- toolgroup_id: builtin::code_interpreter
+  provider_id: code-interpreter
+server:
+  port: 8321

tests/client-sdk/conftest.py

@@ -116,12 +116,14 @@ def client_with_models(llama_stack_client, text_model_id, vision_model_id, embed
     providers = [p for p in client.providers.list() if p.api == "inference"]
     assert len(providers) > 0, "No inference providers found"
     inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
-    if text_model_id:
+
+    model_ids = [m.identifier for m in client.models.list()]
+    if text_model_id and text_model_id not in model_ids:
         client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
-    if vision_model_id:
+    if vision_model_id and vision_model_id not in model_ids:
         client.models.register(model_id=vision_model_id, provider_id=inference_providers[0])
 
-    if embedding_model_id and embedding_dimension:
+    if embedding_model_id and embedding_dimension and embedding_model_id not in model_ids:
         # try to find a provider that supports embeddings, if sentence-transformers is not available
         selected_provider = None
         for p in providers:

tests/client-sdk/inference/test_text_inference.py

@@ -19,6 +19,16 @@ PROVIDER_TOOL_PROMPT_FORMAT = {
 PROVIDER_LOGPROBS_TOP_K = {"remote::together", "remote::fireworks", "remote::vllm"}
 
 
+def skip_if_model_doesnt_support_completion(client_with_models, model_id):
+    models = {m.identifier: m for m in client_with_models.models.list()}
+    provider_id = models[model_id].provider_id
+    providers = {p.provider_id: p for p in client_with_models.providers.list()}
+    provider = providers[provider_id]
+    print(f"Provider: {provider.provider_type} for model {model_id}")
+    if provider.provider_type in ("remote::openai", "remote::anthropic", "remote::gemini"):
+        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support completion")
+
+
 @pytest.fixture(scope="session")
 def provider_tool_format(inference_provider_type):
     return (
@@ -35,6 +45,7 @@ def provider_tool_format(inference_provider_type):
     ],
 )
 def test_text_completion_non_streaming(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     tc = TestCase(test_case)
 
     response = client_with_models.inference.completion(
@@ -56,6 +67,7 @@ def test_text_completion_non_streaming(client_with_models, text_model_id, test_c
     ],
 )
 def test_text_completion_streaming(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     tc = TestCase(test_case)
 
     response = client_with_models.inference.completion(
@@ -79,6 +91,7 @@ def test_text_completion_streaming(client_with_models, text_model_id, test_case)
     ],
 )
 def test_text_completion_log_probs_non_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
         pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
 
@@ -107,6 +120,7 @@ def test_text_completion_log_probs_non_streaming(client_with_models, text_model_
     ],
 )
 def test_text_completion_log_probs_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
         pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
 
@@ -139,6 +153,8 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id,
     ],
 )
 def test_text_completion_structured_output(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
+
     class AnswerFormat(BaseModel):
         name: str
         year_born: str
@@ -237,9 +253,7 @@ def test_text_chat_completion_with_tool_calling_and_non_streaming(
         tool_prompt_format=tool_prompt_format,
         stream=False,
     )
-    # No content is returned for the system message since we expect the
+    # some models can return content for the response in addition to the tool call
-    # response to be a tool call
-    assert response.completion_message.content == ""
     assert response.completion_message.role == "assistant"
 
     assert len(response.completion_message.tool_calls) == 1