feat: add api.llama provider, llama-guard-4 model (#2058)

This PR adds a llama-stack inference provider for `api.llama.com`, as
well as adds entries for Llama-Guard-4 and updated Prompt-Guard models.

llama_stack/cli/download.py

@@ -460,15 +460,17 @@ def run_download_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser):
         from llama_stack.models.llama.sku_list import llama_meta_net_info, resolve_model
 
         from .model.safety_models import (
-            prompt_guard_download_info,
+            prompt_guard_download_info_map,
-            prompt_guard_model_sku,
+            prompt_guard_model_sku_map,
         )
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_sku_map = prompt_guard_model_sku_map()
+        prompt_guard_download_info_map = prompt_guard_download_info_map()
+
         for model_id in model_ids:
-            if model_id == prompt_guard.model_id:
+            if model_id in prompt_guard_model_sku_map.keys():
-                model = prompt_guard
+                model = prompt_guard_model_sku_map[model_id]
-                info = prompt_guard_download_info()
+                info = prompt_guard_download_info_map[model_id]
             else:
                 model = resolve_model(model_id)
                 if model is None:

llama_stack/cli/model/describe.py

@@ -36,11 +36,11 @@ class ModelDescribe(Subcommand):
         )
 
     def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_sku_map
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_map = prompt_guard_model_sku_map()
-        if args.model_id == prompt_guard.model_id:
+        if args.model_id in prompt_guard_model_map.keys():
-            model = prompt_guard
+            model = prompt_guard_model_map[args.model_id]
         else:
             model = resolve_model(args.model_id)
 

llama_stack/cli/model/list.py

@@ -84,7 +84,7 @@ class ModelList(Subcommand):
         )
 
     def _run_model_list_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_skus
 
         if args.downloaded:
             return _run_model_list_downloaded_cmd()
@@ -96,7 +96,7 @@ class ModelList(Subcommand):
         ]
 
         rows = []
-        for model in all_registered_models() + [prompt_guard_model_sku()]:
+        for model in all_registered_models() + prompt_guard_model_skus():
             if not args.show_all and not model.is_featured:
                 continue
 

llama_stack/cli/model/remove.py

@@ -42,11 +42,12 @@ class ModelRemove(Subcommand):
         )
 
     def _run_model_remove_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_sku_map
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_map = prompt_guard_model_sku_map()
-        if args.model == prompt_guard.model_id:
+
-            model = prompt_guard
+        if args.model in prompt_guard_model_map.keys():
+            model = prompt_guard_model_map[args.model]
         else:
             model = resolve_model(args.model)
 

llama_stack/cli/model/safety_models.py

@@ -15,11 +15,11 @@ from llama_stack.models.llama.sku_types import CheckpointQuantizationFormat
 class PromptGuardModel(BaseModel):
     """Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed."""
 
-    model_id: str = "Prompt-Guard-86M"
+    model_id: str
+    huggingface_repo: str
     description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon."
     is_featured: bool = False
-    huggingface_repo: str = "meta-llama/Prompt-Guard-86M"
+    max_seq_length: int = 512
-    max_seq_length: int = 2048
     is_instruct_model: bool = False
     quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16
     arch_args: Dict[str, Any] = Field(default_factory=dict)
@@ -30,13 +30,28 @@ class PromptGuardModel(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-def prompt_guard_model_sku():
+def prompt_guard_model_skus():
-    return PromptGuardModel()
+    return [
+        PromptGuardModel(model_id="Prompt-Guard-86M", huggingface_repo="meta-llama/Prompt-Guard-86M"),
+        PromptGuardModel(
+            model_id="Llama-Prompt-Guard-2-86M",
+            huggingface_repo="meta-llama/Llama-Prompt-Guard-2-86M",
+        ),
+        PromptGuardModel(
+            model_id="Llama-Prompt-Guard-2-22M",
+            huggingface_repo="meta-llama/Llama-Prompt-Guard-2-22M",
+        ),
+    ]
 
 
-def prompt_guard_download_info():
+def prompt_guard_model_sku_map() -> Dict[str, Any]:
-    return LlamaDownloadInfo(
+    return {model.model_id: model for model in prompt_guard_model_skus()}
-        folder="Prompt-Guard",
+
+
+def prompt_guard_download_info_map() -> Dict[str, LlamaDownloadInfo]:
+    return {
+        model.model_id: LlamaDownloadInfo(
+            folder="Prompt-Guard" if model.model_id == "Prompt-Guard-86M" else model.model_id,
             files=[
                 "model.safetensors",
                 "special_tokens_map.json",
@@ -45,3 +60,5 @@ def prompt_guard_download_info():
             ],
             pth_size=1,
         )
+        for model in prompt_guard_model_skus()
+    }

llama_stack/models/llama/sku_list.py

@@ -792,6 +792,13 @@ def llama3_3_instruct_models() -> List[Model]:
 @lru_cache
 def safety_models() -> List[Model]:
     return [
+        Model(
+            core_model_id=CoreModelId.llama_guard_4_12b,
+            description="Llama Guard v4 12b system safety model",
+            huggingface_repo="meta-llama/Llama-Guard-4-12B",
+            arch_args={},
+            pth_file_count=1,
+        ),
         Model(
             core_model_id=CoreModelId.llama_guard_3_11b_vision,
             description="Llama Guard v3 11b vision system safety model",

llama_stack/models/llama/sku_types.py

@@ -81,6 +81,7 @@ class CoreModelId(Enum):
     llama_guard_2_8b = "Llama-Guard-2-8B"
     llama_guard_3_11b_vision = "Llama-Guard-3-11B-Vision"
     llama_guard_3_1b = "Llama-Guard-3-1B"
+    llama_guard_4_12b = "Llama-Guard-4-12B"
 
 
 def is_multimodal(model_id) -> bool:
@@ -148,6 +149,7 @@ def model_family(model_id) -> ModelFamily:
         CoreModelId.llama_guard_2_8b,
         CoreModelId.llama_guard_3_11b_vision,
         CoreModelId.llama_guard_3_1b,
+        CoreModelId.llama_guard_4_12b,
     ]:
         return ModelFamily.safety
     else:
@@ -225,5 +227,7 @@ class Model(BaseModel):
             CoreModelId.llama_guard_3_1b,
         ]:
             return 131072
+        elif self.core_model_id == CoreModelId.llama_guard_4_12b:
+            return 8192
         else:
             raise ValueError(f"Unknown max_seq_len for {self.core_model_id}")

llama_stack/providers/registry/inference.py

@@ -227,6 +227,16 @@ def available_providers() -> List[ProviderSpec]:
                 provider_data_validator="llama_stack.providers.remote.inference.fireworks_openai_compat.config.FireworksProviderDataValidator",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="llama-openai-compat",
+                pip_packages=["litellm"],
+                module="llama_stack.providers.remote.inference.llama_openai_compat",
+                config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig",
+                provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator",
+            ),
+        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(

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

@@ -0,0 +1,17 @@
+# 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.inference import Inference
+
+from .config import LlamaCompatConfig
+
+
+async def get_adapter_impl(config: LlamaCompatConfig, _deps) -> Inference:
+    # import dynamically so the import is used only when it is needed
+    from .llama import LlamaCompatInferenceAdapter
+
+    adapter = LlamaCompatInferenceAdapter(config)
+    return adapter

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

@@ -0,0 +1,38 @@
+# 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
+
+
+class LlamaProviderDataValidator(BaseModel):
+    llama_api_key: Optional[str] = Field(
+        default=None,
+        description="API key for api.llama models",
+    )
+
+
+@json_schema_type
+class LlamaCompatConfig(BaseModel):
+    api_key: Optional[str] = Field(
+        default=None,
+        description="The Llama API key",
+    )
+
+    openai_compat_api_base: str = Field(
+        default="https://api.llama.com/compat/v1/",
+        description="The URL for the Llama API server",
+    )
+
+    @classmethod
+    def sample_run_config(cls, api_key: str = "${env.LLAMA_API_KEY}", **kwargs) -> Dict[str, Any]:
+        return {
+            "openai_compat_api_base": "https://api.llama.com/compat/v1/",
+            "api_key": api_key,
+        }

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

@@ -0,0 +1,34 @@
+# 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.remote.inference.llama_openai_compat.config import (
+    LlamaCompatConfig,
+)
+from llama_stack.providers.utils.inference.litellm_openai_mixin import (
+    LiteLLMOpenAIMixin,
+)
+
+from .models import MODEL_ENTRIES
+
+
+class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin):
+    _config: LlamaCompatConfig
+
+    def __init__(self, config: LlamaCompatConfig):
+        LiteLLMOpenAIMixin.__init__(
+            self,
+            model_entries=MODEL_ENTRIES,
+            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
+
+    async def initialize(self):
+        await super().initialize()
+
+    async def shutdown(self):
+        await super().shutdown()

llama_stack/providers/remote/inference/llama_openai_compat/models.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 llama_stack.models.llama.sku_types import CoreModelId
+from llama_stack.providers.utils.inference.model_registry import (
+    build_hf_repo_model_entry,
+)
+
+MODEL_ENTRIES = [
+    build_hf_repo_model_entry(
+        "Llama-3.3-70B-Instruct",
+        CoreModelId.llama3_3_70b_instruct.value,
+    ),
+    build_hf_repo_model_entry(
+        "Llama-4-Scout-17B-16E-Instruct-FP8",
+        CoreModelId.llama4_scout_17b_16e_instruct.value,
+    ),
+    build_hf_repo_model_entry(
+        "Llama-4-Maverick-17B-128E-Instruct-FP8",
+        CoreModelId.llama4_maverick_17b_128e_instruct.value,
+    ),
+]

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -90,6 +90,9 @@ class LiteLLMOpenAIMixin(
             raise ValueError(f"Unsupported model: {model.provider_resource_id}")
         return model
 
+    def get_litellm_model_name(self, model_id: str) -> str:
+        return "openai/" + model_id if self.is_openai_compat else model_id
+
     async def completion(
         self,
         model_id: str,
@@ -130,8 +133,7 @@ class LiteLLMOpenAIMixin(
         )
 
         params = await self._get_params(request)
-        if self.is_openai_compat:
+        params["model"] = self.get_litellm_model_name(params["model"])
-            params["model"] = "openai/" + params["model"]
 
         logger.debug(f"params to litellm (openai compat): {params}")
         # unfortunately, we need to use synchronous litellm.completion here because litellm
@@ -220,21 +222,23 @@ class LiteLLMOpenAIMixin(
                     else request.tool_config.tool_choice
                 )
 
+        return {
+            "model": request.model,
+            "api_key": self.get_api_key(),
+            "api_base": self.api_base,
+            **input_dict,
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    def get_api_key(self) -> str:
         provider_data = self.get_request_provider_data()
         key_field = self.provider_data_api_key_field
         if provider_data and getattr(provider_data, key_field, None):
             api_key = getattr(provider_data, key_field)
         else:
             api_key = self.api_key_from_config
-
+        return api_key
-        return {
-            "model": request.model,
-            "api_key": api_key,
-            "api_base": self.api_base,
-            **input_dict,
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
 
     async def embeddings(
         self,
@@ -247,7 +251,7 @@ class LiteLLMOpenAIMixin(
         model = await self.model_store.get_model(model_id)
 
         response = litellm.embedding(
-            model=model.provider_resource_id,
+            model=self.get_litellm_model_name(model.provider_resource_id),
             input=[interleaved_content_as_str(content) for content in contents],
         )
 
@@ -278,7 +282,7 @@ class LiteLLMOpenAIMixin(
     ) -> OpenAICompletion:
         model_obj = await self.model_store.get_model(model)
         params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
             prompt=prompt,
             best_of=best_of,
             echo=echo,
@@ -297,6 +301,8 @@ class LiteLLMOpenAIMixin(
             user=user,
             guided_choice=guided_choice,
             prompt_logprobs=prompt_logprobs,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
         )
         return await litellm.atext_completion(**params)
 
@@ -328,7 +334,7 @@ class LiteLLMOpenAIMixin(
     ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
         model_obj = await self.model_store.get_model(model)
         params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
             messages=messages,
             frequency_penalty=frequency_penalty,
             function_call=function_call,
@@ -351,6 +357,8 @@ class LiteLLMOpenAIMixin(
             top_logprobs=top_logprobs,
             top_p=top_p,
             user=user,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
         )
         return await litellm.acompletion(**params)
 

llama_stack/providers/utils/inference/openai_compat.py

@@ -638,10 +638,13 @@ async def convert_message_to_openai_dict_new(
             )
             for tool in message.tool_calls
         ]
+        params = {}
+        if tool_calls:
+            params["tool_calls"] = tool_calls
         out = OpenAIChatCompletionAssistantMessage(
             role="assistant",
             content=await _convert_message_content(message.content),
-            tool_calls=tool_calls or None,
+            **params,
         )
     elif isinstance(message, ToolResponseMessage):
         out = OpenAIChatCompletionToolMessage(

llama_stack/templates/dependencies.json

@@ -344,6 +344,45 @@
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
   ],
+  "llama_api": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "emoji",
+    "fastapi",
+    "fire",
+    "httpx",
+    "langdetect",
+    "litellm",
+    "matplotlib",
+    "mcp",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pymongo",
+    "pypdf",
+    "pythainlp",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "sqlite-vec",
+    "tqdm",
+    "transformers",
+    "tree_sitter",
+    "uvicorn",
+    "sentence-transformers --no-deps",
+    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
+  ],
   "meta-reference-gpu": [
     "accelerate",
     "aiosqlite",

llama_stack/templates/llama_api/__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 .llama_api import get_distribution_template  # noqa: F401

llama_stack/templates/llama_api/build.yaml

@@ -0,0 +1,33 @@
+version: '2'
+distribution_spec:
+  description: Distribution for running e2e tests in CI
+  providers:
+    inference:
+    - remote::llama-openai-compat
+    - 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/llama_api/llama_api.py

@@ -0,0 +1,159 @@
+# 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.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.llama_openai_compat.config import (
+    LlamaCompatConfig,
+)
+from llama_stack.providers.remote.inference.llama_openai_compat.models import (
+    MODEL_ENTRIES as LLLAMA_MODEL_ENTRIES,
+)
+from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
+from llama_stack.providers.remote.vector_io.pgvector.config import (
+    PGVectorVectorIOConfig,
+)
+from llama_stack.templates.template import (
+    DistributionTemplate,
+    RunConfigSettings,
+    get_model_registry,
+)
+
+
+def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
+    # in this template, we allow each API key to be optional
+    providers = [
+        (
+            "llama-openai-compat",
+            LLLAMA_MODEL_ENTRIES,
+            LlamaCompatConfig.sample_run_config(api_key="${env.LLAMA_API_KEY:}"),
+        ),
+    ]
+    inference_providers = []
+    available_models = {}
+    for provider_id, model_entries, config in providers:
+        inference_providers.append(
+            Provider(
+                provider_id=provider_id,
+                provider_type=f"remote::{provider_id}",
+                config=config,
+            )
+        )
+        available_models[provider_id] = model_entries
+    return inference_providers, available_models
+
+
+def get_distribution_template() -> DistributionTemplate:
+    inference_providers, available_models = get_inference_providers()
+    providers = {
+        "inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
+        "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+        "tool_runtime": [
+            "remote::brave-search",
+            "remote::tavily-search",
+            "inline::code-interpreter",
+            "inline::rag-runtime",
+            "remote::model-context-protocol",
+        ],
+    }
+    name = "llama_api"
+
+    vector_io_providers = [
+        Provider(
+            provider_id="sqlite-vec",
+            provider_type="inline::sqlite-vec",
+            config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_CHROMADB+chromadb}",
+            provider_type="remote::chromadb",
+            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_PGVECTOR+pgvector}",
+            provider_type="remote::pgvector",
+            config=PGVectorVectorIOConfig.sample_run_config(
+                db="${env.PGVECTOR_DB:}",
+                user="${env.PGVECTOR_USER:}",
+                password="${env.PGVECTOR_PASSWORD:}",
+            ),
+        ),
+    ]
+    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,
+        },
+    )
+
+    default_models = get_model_registry(available_models)
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Distribution for running e2e tests in CI",
+        container_image=None,
+        template_path=None,
+        providers=providers,
+        available_models_by_provider=available_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": inference_providers + [embedding_provider],
+                    "vector_io": vector_io_providers,
+                },
+                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": (
+                "8321",
+                "Port for the Llama Stack distribution server",
+            ),
+        },
+    )

llama_stack/templates/llama_api/run.yaml

@@ -0,0 +1,167 @@
+version: '2'
+image_name: llama_api
+apis:
+- agents
+- datasetio
+- eval
+- inference
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: llama-openai-compat
+    provider_type: remote::llama-openai-compat
+    config:
+      openai_compat_api_base: https://api.llama.com/compat/v1/
+      api_key: ${env.LLAMA_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/llama_api}/sqlite_vec.db
+  - provider_id: ${env.ENABLE_CHROMADB+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:}
+  - provider_id: ${env.ENABLE_PGVECTOR+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:localhost}
+      port: ${env.PGVECTOR_PORT:5432}
+      db: ${env.PGVECTOR_DB:}
+      user: ${env.PGVECTOR_USER:}
+      password: ${env.PGVECTOR_PASSWORD:}
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/llama_api/trace_store.db}
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/meta_reference_eval.db
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/huggingface_datasetio.db
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/localfs_datasetio.db
+  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/llama_api}/registry.db
+models:
+- metadata: {}
+  model_id: Llama-3.3-70B-Instruct
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-3.3-70B-Instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-3.3-70B-Instruct
+  model_type: llm
+- metadata: {}
+  model_id: Llama-4-Scout-17B-16E-Instruct-FP8
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-4-Scout-17B-16E-Instruct-FP8
+  model_type: llm
+- metadata: {}
+  model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct
+  provider_id: llama-openai-compat
+  provider_model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
+  model_type: llm
+- 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

pyproject.toml

@@ -321,6 +321,7 @@ exclude = [
     "^llama_stack/strong_typing/serializer\\.py$",
     "^llama_stack/templates/dev/dev\\.py$",
     "^llama_stack/templates/groq/groq\\.py$",
+    "^llama_stack/templates/llama_api/llama_api\\.py$",
     "^llama_stack/templates/sambanova/sambanova\\.py$",
     "^llama_stack/templates/template\\.py$",
 ]