Add dana agent provider stub

- Register inline::dana provider in agents registry
- Add stub implementation with get_provider_impl function
- Add DanaAgentConfig with sample_run_config
- Include dana provider in starter, starter-gpu, and ci-tests distributions
- All agent methods currently raise NotImplementedError until implementation

src/llama_stack/distributions/ci-tests/build.yaml

@@ -34,6 +34,7 @@ distribution_spec:
     - provider_type: inline::code-scanner
     agents:
     - provider_type: inline::meta-reference
+    - provider_type: inline::dana
     post_training:
     - provider_type: inline::torchtune-cpu
     eval:

src/llama_stack/distributions/ci-tests/run.yaml

@@ -171,6 +171,8 @@ providers:
           backend: sql_default
           max_write_queue_size: 10000
           num_writers: 4
+  - provider_id: dana
+    provider_type: inline::dana
   post_training:
   - provider_id: torchtune-cpu
     provider_type: inline::torchtune-cpu

src/llama_stack/distributions/starter-gpu/build.yaml

@@ -35,6 +35,7 @@ distribution_spec:
     - provider_type: inline::code-scanner
     agents:
     - provider_type: inline::meta-reference
+    - provider_type: inline::dana
     post_training:
     - provider_type: inline::huggingface-gpu
     eval:

src/llama_stack/distributions/starter-gpu/run.yaml

@@ -171,6 +171,8 @@ providers:
           backend: sql_default
           max_write_queue_size: 10000
           num_writers: 4
+  - provider_id: dana
+    provider_type: inline::dana
   post_training:
   - provider_id: huggingface-gpu
     provider_type: inline::huggingface-gpu

src/llama_stack/distributions/starter/build.yaml

@@ -35,6 +35,7 @@ distribution_spec:
     - provider_type: inline::code-scanner
     agents:
     - provider_type: inline::meta-reference
+    - provider_type: inline::dana
     post_training:
     - provider_type: inline::torchtune-cpu
     eval:

src/llama_stack/distributions/starter/run.yaml

@@ -171,6 +171,8 @@ providers:
           backend: sql_default
           max_write_queue_size: 10000
           num_writers: 4
+  - provider_id: dana
+    provider_type: inline::dana
   post_training:
   - provider_id: torchtune-cpu
     provider_type: inline::torchtune-cpu

src/llama_stack/distributions/starter/starter.py

@@ -126,7 +126,10 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
             BuildProvider(provider_type="inline::llama-guard"),
             BuildProvider(provider_type="inline::code-scanner"),
         ],
-        "agents": [BuildProvider(provider_type="inline::meta-reference")],
+        "agents": [
+            BuildProvider(provider_type="inline::meta-reference"),
+            BuildProvider(provider_type="inline::dana"),
+        ],
         "post_training": [BuildProvider(provider_type="inline::torchtune-cpu")],
         "eval": [BuildProvider(provider_type="inline::meta-reference")],
         "datasetio": [

src/llama_stack/providers/inline/agents/dana/__init__.py

@@ -0,0 +1,31 @@
+from typing import Any
+
+from llama_stack.core.datatypes import AccessRule, Api
+
+from .config import DanaAgentConfig
+
+
+async def get_provider_impl(
+    config: DanaAgentConfig,
+    deps: dict[Api, Any],
+    policy: list[AccessRule],
+    telemetry_enabled: bool = False,
+):
+    from .agents import DanaAgentsImpl
+
+    impl = DanaAgentsImpl(
+        config,
+        deps[Api.inference],
+        deps[Api.vector_io],
+        deps[Api.safety],
+        deps[Api.tool_runtime],
+        deps[Api.tool_groups],
+        deps[Api.conversations],
+        policy,
+        telemetry_enabled,
+    )
+    await impl.initialize()
+    return impl
+
+
+__all__ = ["DanaAgentConfig", "get_provider_impl"]

src/llama_stack/providers/inline/agents/dana/agents.py

@@ -0,0 +1,207 @@
+# 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 collections.abc import AsyncIterator
+from typing import Any
+
+from llama_stack.apis.agents import (
+    Agent,
+    AgentConfig,
+    AgentCreateResponse,
+    AgentSessionCreateResponse,
+    AgentStepResponse,
+    AgentToolGroup,
+    Document,
+    ListOpenAIResponseInputItem,
+    ListOpenAIResponseObject,
+    OpenAIDeleteResponseObject,
+    OpenAIResponseInput,
+    OpenAIResponseInputTool,
+    OpenAIResponseObject,
+    OpenAIResponseObjectStream,
+    OpenAIResponsePrompt,
+    OpenAIResponseText,
+    Order,
+    Session,
+    Turn,
+)
+from llama_stack.apis.agents.agents import ResponseGuardrail
+from llama_stack.apis.common.responses import PaginatedResponse
+from llama_stack.apis.conversations import Conversations
+from llama_stack.apis.inference import ToolConfig, ToolResponse, ToolResponseMessage, UserMessage
+from llama_stack.apis.safety import Safety
+from llama_stack.apis.tools import ToolGroups, ToolRuntime
+from llama_stack.apis.vector_io import VectorIO
+from llama_stack.core.datatypes import AccessRule
+from llama_stack.log import get_logger
+
+from .config import DanaAgentConfig
+
+logger = get_logger(name=__name__, category="agents::dana")
+
+
+class DanaAgentsImpl:
+    """Dana agent system implementation (stub)."""
+
+    def __init__(
+        self,
+        config: DanaAgentConfig,
+        inference_api: Any,  # Inference
+        vector_io_api: VectorIO,
+        safety_api: Safety,
+        tool_runtime_api: ToolRuntime,
+        tool_groups_api: ToolGroups,
+        conversations_api: Conversations,
+        policy: list[AccessRule],
+        telemetry_enabled: bool = False,
+    ):
+        self.config = config
+        self.inference_api = inference_api
+        self.vector_io_api = vector_io_api
+        self.safety_api = safety_api
+        self.tool_runtime_api = tool_runtime_api
+        self.tool_groups_api = tool_groups_api
+        self.conversations_api = conversations_api
+        self.policy = policy
+        self.telemetry_enabled = telemetry_enabled
+
+    async def initialize(self) -> None:
+        """Initialize the provider."""
+        pass
+
+    async def create_agent(self, agent_config: AgentConfig) -> AgentCreateResponse:
+        """Create an agent with the given configuration."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def create_agent_turn(
+        self,
+        agent_id: str,
+        session_id: str,
+        messages: list[UserMessage | ToolResponseMessage],
+        stream: bool | None = False,
+        documents: list[Document] | None = None,
+        toolgroups: list[AgentToolGroup] | None = None,
+        tool_config: ToolConfig | None = None,
+    ) -> Turn | AsyncIterator[Any]:
+        """Create a new turn for an agent."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def resume_agent_turn(
+        self,
+        agent_id: str,
+        session_id: str,
+        turn_id: str,
+        tool_responses: list[ToolResponse],
+        stream: bool | None = False,
+    ) -> Turn | AsyncIterator[Any]:
+        """Resume an agent turn with executed tool call responses."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def get_agents_turn(self, agent_id: str, session_id: str, turn_id: str) -> Turn:
+        """Retrieve an agent turn by its ID."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def get_agents_step(
+        self,
+        agent_id: str,
+        session_id: str,
+        turn_id: str,
+        step_id: str,
+    ) -> AgentStepResponse:
+        """Retrieve an agent step by its ID."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def create_agent_session(
+        self,
+        agent_id: str,
+        session_name: str,
+    ) -> AgentSessionCreateResponse:
+        """Create a new session for an agent."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def get_agents_session(
+        self,
+        session_id: str,
+        agent_id: str,
+        turn_ids: list[str] | None = None,
+    ) -> Session:
+        """Retrieve an agent session by its ID."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def delete_agents_session(self, session_id: str, agent_id: str) -> None:
+        """Delete an agent session by its ID and its associated turns."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def delete_agent(self, agent_id: str) -> None:
+        """Delete an agent by its ID and its associated sessions and turns."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def list_agents(self, start_index: int | None = None, limit: int | None = None) -> PaginatedResponse:
+        """List all agents."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def get_agent(self, agent_id: str) -> Agent:
+        """Describe an agent by its ID."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def list_agent_sessions(
+        self,
+        agent_id: str,
+        start_index: int | None = None,
+        limit: int | None = None,
+    ) -> PaginatedResponse:
+        """List all session(s) of a given agent."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def get_openai_response(self, response_id: str) -> OpenAIResponseObject:
+        """Get a model response."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def create_openai_response(
+        self,
+        input: str | list[OpenAIResponseInput],
+        model: str,
+        prompt: OpenAIResponsePrompt | None = None,
+        instructions: str | None = None,
+        previous_response_id: str | None = None,
+        conversation: str | None = None,
+        store: bool | None = True,
+        stream: bool | None = False,
+        temperature: float | None = None,
+        text: OpenAIResponseText | None = None,
+        tools: list[OpenAIResponseInputTool] | None = None,
+        include: list[str] | None = None,
+        max_infer_iters: int | None = 10,
+        guardrails: list[ResponseGuardrail] | None = None,
+    ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
+        """Create a model response."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def list_openai_responses(
+        self,
+        after: str | None = None,
+        limit: int | None = 50,
+        model: str | None = None,
+        order: Order | None = Order.desc,
+    ) -> ListOpenAIResponseObject:
+        """List all responses."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def list_openai_response_input_items(
+        self,
+        response_id: str,
+        after: str | None = None,
+        before: str | None = None,
+        include: list[str] | None = None,
+        limit: int | None = 20,
+        order: Order | None = Order.desc,
+    ) -> ListOpenAIResponseInputItem:
+        """List input items."""
+        raise NotImplementedError("Dana agent implementation is not yet available")
+
+    async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
+        """Delete a response."""
+        raise NotImplementedError("Dana agent implementation is not yet available")

src/llama_stack/providers/inline/agents/dana/config.py

@@ -0,0 +1,9 @@
+from typing import Any
+
+from pydantic import BaseModel
+
+
+class DanaAgentConfig(BaseModel):
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
+        return {}

src/llama_stack/providers/registry/agents.py

@@ -38,4 +38,20 @@ def available_providers() -> list[ProviderSpec]:
             ],
             description="Meta's reference implementation of an agent system that can use tools, access vector databases, and perform complex reasoning tasks.",
         ),
+        InlineProviderSpec(
+            api=Api.agents,
+            provider_type="inline::dana",
+            pip_packages=["dana>=0.1.0"],
+            module="llama_stack.providers.inline.agents.dana",
+            config_class="llama_stack.providers.inline.agents.dana.DanaAgentConfig",
+            api_dependencies=[
+                Api.inference,
+                Api.safety,
+                Api.vector_io,
+                Api.tool_runtime,
+                Api.tool_groups,
+                Api.conversations,
+            ],
+            description="Dana agent system featuring STAR Loop (See-Think-Act-Reflect), deterministic workflows, and continuous learning via LoRA fine-tuning.",
+        ),
     ]