Merge remote-tracking branch 'upstream/main' into feat/gunicorn-production-server

src/llama_stack/apis/agents/openai_responses.py

@@ -4,6 +4,7 @@
 # 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 Sequence
 from typing import Annotated, Any, Literal
 
 from pydantic import BaseModel, Field, model_validator
@@ -202,7 +203,7 @@ class OpenAIResponseMessage(BaseModel):
     scenarios.
     """
 
-    content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]
+    content: str | Sequence[OpenAIResponseInputMessageContent] | Sequence[OpenAIResponseOutputMessageContent]
     role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
     type: Literal["message"] = "message"
 
@@ -254,10 +255,10 @@ class OpenAIResponseOutputMessageFileSearchToolCall(BaseModel):
     """
 
     id: str
-    queries: list[str]
+    queries: Sequence[str]
     status: str
     type: Literal["file_search_call"] = "file_search_call"
-    results: list[OpenAIResponseOutputMessageFileSearchToolCallResults] | None = None
+    results: Sequence[OpenAIResponseOutputMessageFileSearchToolCallResults] | None = None
 
 
 @json_schema_type
@@ -597,7 +598,7 @@ class OpenAIResponseObject(BaseModel):
     id: str
     model: str
     object: Literal["response"] = "response"
-    output: list[OpenAIResponseOutput]
+    output: Sequence[OpenAIResponseOutput]
     parallel_tool_calls: bool = False
     previous_response_id: str | None = None
     prompt: OpenAIResponsePrompt | None = None
@@ -607,7 +608,7 @@ class OpenAIResponseObject(BaseModel):
     # before the field was added. New responses will have this set always.
     text: OpenAIResponseText = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text"))
     top_p: float | None = None
-    tools: list[OpenAIResponseTool] | None = None
+    tools: Sequence[OpenAIResponseTool] | None = None
     truncation: str | None = None
     usage: OpenAIResponseUsage | None = None
     instructions: str | None = None
@@ -1315,7 +1316,7 @@ class ListOpenAIResponseInputItem(BaseModel):
     :param object: Object type identifier, always "list"
     """
 
-    data: list[OpenAIResponseInput]
+    data: Sequence[OpenAIResponseInput]
     object: Literal["list"] = "list"
 
 
@@ -1326,7 +1327,7 @@ class OpenAIResponseObjectWithInput(OpenAIResponseObject):
     :param input: List of input items that led to this response
     """
 
-    input: list[OpenAIResponseInput]
+    input: Sequence[OpenAIResponseInput]
 
     def to_response_object(self) -> OpenAIResponseObject:
         """Convert to OpenAIResponseObject by excluding input field."""
@@ -1344,7 +1345,7 @@ class ListOpenAIResponseObject(BaseModel):
     :param object: Object type identifier, always "list"
     """
 
-    data: list[OpenAIResponseObjectWithInput]
+    data: Sequence[OpenAIResponseObjectWithInput]
     has_more: bool
     first_id: str
     last_id: str

src/llama_stack/apis/vector_io/vector_io.py

@@ -8,7 +8,6 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-import uuid
 from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
 from fastapi import Body
@@ -18,7 +17,6 @@ from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.version import LLAMA_STACK_API_V1
 from llama_stack.core.telemetry.trace_protocol import trace_protocol
-from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
 from llama_stack.schema_utils import json_schema_type, webmethod
 from llama_stack.strong_typing.schema import register_schema
 
@@ -61,38 +59,19 @@ class Chunk(BaseModel):
     """
     A chunk of content that can be inserted into a vector database.
     :param content: The content of the chunk, which can be interleaved text, images, or other types.
-    :param embedding: Optional embedding for the chunk. If not provided, it will be computed later.
+    :param chunk_id: Unique identifier for the chunk. Must be provided explicitly.
     :param metadata: Metadata associated with the chunk that will be used in the model context during inference.
-    :param stored_chunk_id: The chunk ID that is stored in the vector database. Used for backend functionality.
+    :param embedding: Optional embedding for the chunk. If not provided, it will be computed later.
     :param chunk_metadata: Metadata for the chunk that will NOT be used in the context during inference.
         The `chunk_metadata` is required backend functionality.
     """
 
     content: InterleavedContent
+    chunk_id: str
     metadata: dict[str, Any] = Field(default_factory=dict)
     embedding: list[float] | None = None
-    # The alias parameter serializes the field as "chunk_id" in JSON but keeps the internal name as "stored_chunk_id"
-    stored_chunk_id: str | None = Field(default=None, alias="chunk_id")
     chunk_metadata: ChunkMetadata | None = None
 
-    model_config = {"populate_by_name": True}
-
-    def model_post_init(self, __context):
-        # Extract chunk_id from metadata if present
-        if self.metadata and "chunk_id" in self.metadata:
-            self.stored_chunk_id = self.metadata.pop("chunk_id")
-
-    @property
-    def chunk_id(self) -> str:
-        """Returns the chunk ID, which is either an input `chunk_id` or a generated one if not set."""
-        if self.stored_chunk_id:
-            return self.stored_chunk_id
-
-        if "document_id" in self.metadata:
-            return generate_chunk_id(self.metadata["document_id"], str(self.content))
-
-        return generate_chunk_id(str(uuid.uuid4()), str(self.content))
-
     @property
     def document_id(self) -> str | None:
         """Returns the document_id from either metadata or chunk_metadata, with metadata taking precedence."""

src/llama_stack/core/routing_tables/models.py

@@ -13,6 +13,8 @@ from llama_stack.core.datatypes import (
     ModelWithOwner,
     RegistryEntrySource,
 )
+from llama_stack.core.request_headers import PROVIDER_DATA_VAR, NeedsRequestProviderData
+from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl, lookup_model
@@ -42,11 +44,90 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
 
             await self.update_registered_models(provider_id, models)
 
+    async def _get_dynamic_models_from_provider_data(self) -> list[Model]:
+        """
+        Fetch models from providers that have credentials in the current request's provider_data.
+
+        This allows users to see models available to them from providers that require
+        per-request API keys (via X-LlamaStack-Provider-Data header).
+
+        Returns models with fully qualified identifiers (provider_id/model_id) but does NOT
+        cache them in the registry since they are user-specific.
+        """
+        provider_data = PROVIDER_DATA_VAR.get()
+        if not provider_data:
+            return []
+
+        dynamic_models = []
+
+        for provider_id, provider in self.impls_by_provider_id.items():
+            # Check if this provider supports provider_data
+            if not isinstance(provider, NeedsRequestProviderData):
+                continue
+
+            # Check if provider has a validator (some providers like ollama don't need per-request credentials)
+            spec = getattr(provider, "__provider_spec__", None)
+            if not spec or not getattr(spec, "provider_data_validator", None):
+                continue
+
+            # Validate provider_data silently - we're speculatively checking all providers
+            # so validation failures are expected when user didn't provide keys for this provider
+            try:
+                validator = instantiate_class_type(spec.provider_data_validator)
+                validator(**provider_data)
+            except Exception:
+                # User didn't provide credentials for this provider - skip silently
+                continue
+
+            # Validation succeeded! User has credentials for this provider
+            # Now try to list models
+            try:
+                models = await provider.list_models()
+                if not models:
+                    continue
+
+                # Ensure models have fully qualified identifiers with provider_id prefix
+                for model in models:
+                    # Only add prefix if model identifier doesn't already have it
+                    if not model.identifier.startswith(f"{provider_id}/"):
+                        model.identifier = f"{provider_id}/{model.provider_resource_id}"
+
+                    dynamic_models.append(model)
+
+                logger.debug(f"Fetched {len(models)} models from provider {provider_id} using provider_data")
+
+            except Exception as e:
+                logger.debug(f"Failed to list models from provider {provider_id} with provider_data: {e}")
+                continue
+
+        return dynamic_models
+
     async def list_models(self) -> ListModelsResponse:
-        return ListModelsResponse(data=await self.get_all_with_type("model"))
+        # Get models from registry
+        registry_models = await self.get_all_with_type("model")
+
+        # Get additional models available via provider_data (user-specific, not cached)
+        dynamic_models = await self._get_dynamic_models_from_provider_data()
+
+        # Combine, avoiding duplicates (registry takes precedence)
+        registry_identifiers = {m.identifier for m in registry_models}
+        unique_dynamic_models = [m for m in dynamic_models if m.identifier not in registry_identifiers]
+
+        return ListModelsResponse(data=registry_models + unique_dynamic_models)
 
     async def openai_list_models(self) -> OpenAIListModelsResponse:
-        models = await self.get_all_with_type("model")
+        # Get models from registry
+        registry_models = await self.get_all_with_type("model")
+
+        # Get additional models available via provider_data (user-specific, not cached)
+        dynamic_models = await self._get_dynamic_models_from_provider_data()
+
+        # Combine, avoiding duplicates (registry takes precedence)
+        registry_identifiers = {m.identifier for m in registry_models}
+        unique_dynamic_models = [m for m in dynamic_models if m.identifier not in registry_identifiers]
+
+        all_models = registry_models + unique_dynamic_models
+
         openai_models = [
             OpenAIModel(
                 id=model.identifier,
@@ -54,7 +135,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
                 created=int(time.time()),
                 owned_by="llama_stack",
             )
-            for model in models
+            for model in all_models
         ]
         return OpenAIListModelsResponse(data=openai_models)
 

src/llama_stack/core/stack.py

@@ -14,6 +14,7 @@ from typing import Any
 import yaml
 
 from llama_stack.apis.agents import Agents
+from llama_stack.apis.batches import Batches
 from llama_stack.apis.benchmarks import Benchmarks
 from llama_stack.apis.conversations import Conversations
 from llama_stack.apis.datasetio import DatasetIO
@@ -63,6 +64,7 @@ class LlamaStack(
     Providers,
     Inference,
     Agents,
+    Batches,
     Safety,
     SyntheticDataGeneration,
     Datasets,

src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -11,6 +11,7 @@ import uuid
 import warnings
 from collections.abc import AsyncGenerator
 from datetime import UTC, datetime
+from typing import Any, cast
 
 import httpx
 
@@ -125,12 +126,12 @@ class ChatAgent(ShieldRunnerMixin):
         )
 
     def turn_to_messages(self, turn: Turn) -> list[Message]:
-        messages = []
+        messages: list[Message] = []
 
         # NOTE: if a toolcall response is in a step, we do not add it when processing the input messages
         tool_call_ids = set()
         for step in turn.steps:
-            if step.step_type == StepType.tool_execution.value:
+            if step.step_type == StepType.tool_execution.value and isinstance(step, ToolExecutionStep):
                 for response in step.tool_responses:
                     tool_call_ids.add(response.call_id)
 
@@ -149,9 +150,9 @@ class ChatAgent(ShieldRunnerMixin):
             messages.append(msg)
 
         for step in turn.steps:
-            if step.step_type == StepType.inference.value:
+            if step.step_type == StepType.inference.value and isinstance(step, InferenceStep):
                 messages.append(step.model_response)
-            elif step.step_type == StepType.tool_execution.value:
+            elif step.step_type == StepType.tool_execution.value and isinstance(step, ToolExecutionStep):
                 for response in step.tool_responses:
                     messages.append(
                         ToolResponseMessage(
@@ -159,8 +160,8 @@ class ChatAgent(ShieldRunnerMixin):
                             content=response.content,
                         )
                     )
-            elif step.step_type == StepType.shield_call.value:
-                if step.violation:
+            elif step.step_type == StepType.shield_call.value and isinstance(step, ShieldCallStep):
+                if step.violation and step.violation.user_message:
                     # CompletionMessage itself in the ShieldResponse
                     messages.append(
                         CompletionMessage(
@@ -174,7 +175,7 @@ class ChatAgent(ShieldRunnerMixin):
         return await self.storage.create_session(name)
 
     async def get_messages_from_turns(self, turns: list[Turn]) -> list[Message]:
-        messages = []
+        messages: list[Message] = []
         if self.agent_config.instructions != "":
             messages.append(SystemMessage(content=self.agent_config.instructions))
 
@@ -231,7 +232,9 @@ class ChatAgent(ShieldRunnerMixin):
 
         steps = []
         messages = await self.get_messages_from_turns(turns)
+
         if is_resume:
+            assert isinstance(request, AgentTurnResumeRequest)
             tool_response_messages = [
                 ToolResponseMessage(call_id=x.call_id, content=x.content) for x in request.tool_responses
             ]
@@ -252,42 +255,52 @@ class ChatAgent(ShieldRunnerMixin):
             in_progress_tool_call_step = await self.storage.get_in_progress_tool_call_step(
                 request.session_id, request.turn_id
             )
-            now = datetime.now(UTC).isoformat()
+            now_dt = datetime.now(UTC)
             tool_execution_step = ToolExecutionStep(
                 step_id=(in_progress_tool_call_step.step_id if in_progress_tool_call_step else str(uuid.uuid4())),
                 turn_id=request.turn_id,
                 tool_calls=(in_progress_tool_call_step.tool_calls if in_progress_tool_call_step else []),
                 tool_responses=request.tool_responses,
-                completed_at=now,
-                started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now),
+                completed_at=now_dt,
+                started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now_dt),
             )
             steps.append(tool_execution_step)
             yield AgentTurnResponseStreamChunk(
                 event=AgentTurnResponseEvent(
                     payload=AgentTurnResponseStepCompletePayload(
-                        step_type=StepType.tool_execution.value,
+                        step_type=StepType.tool_execution,
                         step_id=tool_execution_step.step_id,
                         step_details=tool_execution_step,
                     )
                 )
             )
-            input_messages = last_turn.input_messages
+            # Cast needed due to list invariance - last_turn.input_messages is the right type
+            input_messages = last_turn.input_messages  # type: ignore[assignment]
 
-            turn_id = request.turn_id
+            actual_turn_id = request.turn_id
             start_time = last_turn.started_at
         else:
+            assert isinstance(request, AgentTurnCreateRequest)
             messages.extend(request.messages)
-            start_time = datetime.now(UTC).isoformat()
-            input_messages = request.messages
+            start_time = datetime.now(UTC)
+            # Cast needed due to list invariance - request.messages is the right type
+            input_messages = request.messages  # type: ignore[assignment]
+            # Use the generated turn_id from beginning of function
+            actual_turn_id = turn_id if turn_id else str(uuid.uuid4())
 
         output_message = None
+        req_documents = request.documents if isinstance(request, AgentTurnCreateRequest) and not is_resume else None
+        req_sampling = (
+            self.agent_config.sampling_params if self.agent_config.sampling_params is not None else SamplingParams()
+        )
+
         async for chunk in self.run(
             session_id=request.session_id,
-            turn_id=turn_id,
+            turn_id=actual_turn_id,
             input_messages=messages,
-            sampling_params=self.agent_config.sampling_params,
+            sampling_params=req_sampling,
             stream=request.stream,
-            documents=request.documents if not is_resume else None,
+            documents=req_documents,
         ):
             if isinstance(chunk, CompletionMessage):
                 output_message = chunk
@@ -295,20 +308,23 @@ class ChatAgent(ShieldRunnerMixin):
 
             assert isinstance(chunk, AgentTurnResponseStreamChunk), f"Unexpected type {type(chunk)}"
             event = chunk.event
-            if event.payload.event_type == AgentTurnResponseEventType.step_complete.value:
-                steps.append(event.payload.step_details)
+            if event.payload.event_type == AgentTurnResponseEventType.step_complete.value and hasattr(
+                event.payload, "step_details"
+            ):
+                step_details = event.payload.step_details
+                steps.append(step_details)
 
             yield chunk
 
         assert output_message is not None
 
         turn = Turn(
-            turn_id=turn_id,
+            turn_id=actual_turn_id,
             session_id=request.session_id,
-            input_messages=input_messages,
+            input_messages=input_messages,  # type: ignore[arg-type]
             output_message=output_message,
             started_at=start_time,
-            completed_at=datetime.now(UTC).isoformat(),
+            completed_at=datetime.now(UTC),
             steps=steps,
         )
         await self.storage.add_turn_to_session(request.session_id, turn)
@@ -345,9 +361,9 @@ class ChatAgent(ShieldRunnerMixin):
         # return a "final value" for the `yield from` statement. we simulate that by yielding a
         # final boolean (to see whether an exception happened) and then explicitly testing for it.
 
-        if len(self.input_shields) > 0:
+        if self.input_shields:
             async for res in self.run_multiple_shields_wrapper(
-                turn_id, input_messages, self.input_shields, "user-input"
+                turn_id, cast(list[OpenAIMessageParam], input_messages), self.input_shields, "user-input"
             ):
                 if isinstance(res, bool):
                     return
@@ -374,9 +390,9 @@ class ChatAgent(ShieldRunnerMixin):
         # for output shields run on the full input and output combination
         messages = input_messages + [final_response]
 
-        if len(self.output_shields) > 0:
+        if self.output_shields:
             async for res in self.run_multiple_shields_wrapper(
-                turn_id, messages, self.output_shields, "assistant-output"
+                turn_id, cast(list[OpenAIMessageParam], messages), self.output_shields, "assistant-output"
             ):
                 if isinstance(res, bool):
                     return
@@ -388,7 +404,7 @@ class ChatAgent(ShieldRunnerMixin):
     async def run_multiple_shields_wrapper(
         self,
         turn_id: str,
-        messages: list[Message],
+        messages: list[OpenAIMessageParam],
         shields: list[str],
         touchpoint: str,
     ) -> AsyncGenerator:
@@ -402,12 +418,12 @@ class ChatAgent(ShieldRunnerMixin):
                 return
 
             step_id = str(uuid.uuid4())
-            shield_call_start_time = datetime.now(UTC).isoformat()
+            shield_call_start_time = datetime.now(UTC)
             try:
                 yield AgentTurnResponseStreamChunk(
                     event=AgentTurnResponseEvent(
                         payload=AgentTurnResponseStepStartPayload(
-                            step_type=StepType.shield_call.value,
+                            step_type=StepType.shield_call,
                             step_id=step_id,
                             metadata=dict(touchpoint=touchpoint),
                         )
@@ -419,14 +435,14 @@ class ChatAgent(ShieldRunnerMixin):
                 yield AgentTurnResponseStreamChunk(
                     event=AgentTurnResponseEvent(
                         payload=AgentTurnResponseStepCompletePayload(
-                            step_type=StepType.shield_call.value,
+                            step_type=StepType.shield_call,
                             step_id=step_id,
                             step_details=ShieldCallStep(
                                 step_id=step_id,
                                 turn_id=turn_id,
                                 violation=e.violation,
                                 started_at=shield_call_start_time,
-                                completed_at=datetime.now(UTC).isoformat(),
+                                completed_at=datetime.now(UTC),
                             ),
                         )
                     )
@@ -443,14 +459,14 @@ class ChatAgent(ShieldRunnerMixin):
             yield AgentTurnResponseStreamChunk(
                 event=AgentTurnResponseEvent(
                     payload=AgentTurnResponseStepCompletePayload(
-                        step_type=StepType.shield_call.value,
+                        step_type=StepType.shield_call,
                         step_id=step_id,
                         step_details=ShieldCallStep(
                             step_id=step_id,
                             turn_id=turn_id,
                             violation=None,
                             started_at=shield_call_start_time,
-                            completed_at=datetime.now(UTC).isoformat(),
+                            completed_at=datetime.now(UTC),
                         ),
                     )
                 )
@@ -496,21 +512,22 @@ class ChatAgent(ShieldRunnerMixin):
                     else:
                         self.tool_name_to_args[tool_name]["vector_store_ids"].append(session_info.vector_store_id)
 
-        output_attachments = []
+        output_attachments: list[Attachment] = []
 
         n_iter = await self.storage.get_num_infer_iters_in_turn(session_id, turn_id) or 0
 
         # Build a map of custom tools to their definitions for faster lookup
         client_tools = {}
-        for tool in self.agent_config.client_tools:
-            client_tools[tool.name] = tool
+        if self.agent_config.client_tools:
+            for tool in self.agent_config.client_tools:
+                client_tools[tool.name] = tool
         while True:
             step_id = str(uuid.uuid4())
-            inference_start_time = datetime.now(UTC).isoformat()
+            inference_start_time = datetime.now(UTC)
             yield AgentTurnResponseStreamChunk(
                 event=AgentTurnResponseEvent(
                     payload=AgentTurnResponseStepStartPayload(
-                        step_type=StepType.inference.value,
+                        step_type=StepType.inference,
                         step_id=step_id,
                     )
                 )
@@ -538,7 +555,7 @@ class ChatAgent(ShieldRunnerMixin):
                     else:
                         return value
 
-                def _add_type(openai_msg: dict) -> OpenAIMessageParam:
+                def _add_type(openai_msg: Any) -> OpenAIMessageParam:
                     # Serialize any nested Pydantic models to plain dicts
                     openai_msg = _serialize_nested(openai_msg)
 
@@ -588,7 +605,7 @@ class ChatAgent(ShieldRunnerMixin):
                     messages=openai_messages,
                     tools=openai_tools if openai_tools else None,
                     tool_choice=tool_choice,
-                    response_format=self.agent_config.response_format,
+                    response_format=self.agent_config.response_format,  # type: ignore[arg-type]
                     temperature=temperature,
                     top_p=top_p,
                     max_tokens=max_tokens,
@@ -598,7 +615,8 @@ class ChatAgent(ShieldRunnerMixin):
 
                 # Convert OpenAI stream back to Llama Stack format
                 response_stream = convert_openai_chat_completion_stream(
-                    openai_stream, enable_incremental_tool_calls=True
+                    openai_stream,  # type: ignore[arg-type]
+                    enable_incremental_tool_calls=True,
                 )
 
                 async for chunk in response_stream:
@@ -620,7 +638,7 @@ class ChatAgent(ShieldRunnerMixin):
                             yield AgentTurnResponseStreamChunk(
                                 event=AgentTurnResponseEvent(
                                     payload=AgentTurnResponseStepProgressPayload(
-                                        step_type=StepType.inference.value,
+                                        step_type=StepType.inference,
                                         step_id=step_id,
                                         delta=delta,
                                     )
@@ -633,7 +651,7 @@ class ChatAgent(ShieldRunnerMixin):
                             yield AgentTurnResponseStreamChunk(
                                 event=AgentTurnResponseEvent(
                                     payload=AgentTurnResponseStepProgressPayload(
-                                        step_type=StepType.inference.value,
+                                        step_type=StepType.inference,
                                         step_id=step_id,
                                         delta=delta,
                                     )
@@ -651,7 +669,9 @@ class ChatAgent(ShieldRunnerMixin):
                     output_attr = json.dumps(
                         {
                             "content": content,
-                            "tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls],
+                            "tool_calls": [
+                                json.loads(t.model_dump_json()) for t in tool_calls if isinstance(t, ToolCall)
+                            ],
                         }
                     )
                     span.set_attribute("output", output_attr)
@@ -667,16 +687,18 @@ class ChatAgent(ShieldRunnerMixin):
             if tool_calls:
                 content = ""
 
+            # Filter out string tool calls for CompletionMessage (only keep ToolCall objects)
+            valid_tool_calls = [t for t in tool_calls if isinstance(t, ToolCall)]
             message = CompletionMessage(
                 content=content,
                 stop_reason=stop_reason,
-                tool_calls=tool_calls,
+                tool_calls=valid_tool_calls if valid_tool_calls else None,
             )
 
             yield AgentTurnResponseStreamChunk(
                 event=AgentTurnResponseEvent(
                     payload=AgentTurnResponseStepCompletePayload(
-                        step_type=StepType.inference.value,
+                        step_type=StepType.inference,
                         step_id=step_id,
                         step_details=InferenceStep(
                             # somewhere deep, we are re-assigning message or closing over some
@@ -686,13 +708,14 @@ class ChatAgent(ShieldRunnerMixin):
                             turn_id=turn_id,
                             model_response=copy.deepcopy(message),
                             started_at=inference_start_time,
-                            completed_at=datetime.now(UTC).isoformat(),
+                            completed_at=datetime.now(UTC),
                         ),
                     )
                 )
             )
 
-            if n_iter >= self.agent_config.max_infer_iters:
+            max_iters = self.agent_config.max_infer_iters if self.agent_config.max_infer_iters is not None else 10
+            if n_iter >= max_iters:
                 logger.info(f"done with MAX iterations ({n_iter}), exiting.")
                 # NOTE: mark end_of_turn to indicate to client that we are done with the turn
                 # Do not continue the tool call loop after this point
@@ -705,14 +728,16 @@ class ChatAgent(ShieldRunnerMixin):
                 yield message
                 break
 
-            if len(message.tool_calls) == 0:
+            if not message.tool_calls or len(message.tool_calls) == 0:
                 if stop_reason == StopReason.end_of_turn:
                     # TODO: UPDATE RETURN TYPE TO SEND A TUPLE OF (MESSAGE, ATTACHMENTS)
                     if len(output_attachments) > 0:
                         if isinstance(message.content, list):
-                            message.content += output_attachments
+                            # List invariance - attachments are compatible at runtime
+                            message.content += output_attachments  # type: ignore[arg-type]
                         else:
-                            message.content = [message.content] + output_attachments
+                            # List invariance - attachments are compatible at runtime
+                            message.content = [message.content] + output_attachments  # type: ignore[assignment]
                     yield message
                 else:
                     logger.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}")
@@ -725,11 +750,12 @@ class ChatAgent(ShieldRunnerMixin):
                 non_client_tool_calls = []
 
                 # Separate client and non-client tool calls
-                for tool_call in message.tool_calls:
-                    if tool_call.tool_name in client_tools:
-                        client_tool_calls.append(tool_call)
-                    else:
-                        non_client_tool_calls.append(tool_call)
+                if message.tool_calls:
+                    for tool_call in message.tool_calls:
+                        if tool_call.tool_name in client_tools:
+                            client_tool_calls.append(tool_call)
+                        else:
+                            non_client_tool_calls.append(tool_call)
 
                 # Process non-client tool calls first
                 for tool_call in non_client_tool_calls:
@@ -737,7 +763,7 @@ class ChatAgent(ShieldRunnerMixin):
                     yield AgentTurnResponseStreamChunk(
                         event=AgentTurnResponseEvent(
                             payload=AgentTurnResponseStepStartPayload(
-                                step_type=StepType.tool_execution.value,
+                                step_type=StepType.tool_execution,
                                 step_id=step_id,
                             )
                         )
@@ -746,7 +772,7 @@ class ChatAgent(ShieldRunnerMixin):
                     yield AgentTurnResponseStreamChunk(
                         event=AgentTurnResponseEvent(
                             payload=AgentTurnResponseStepProgressPayload(
-                                step_type=StepType.tool_execution.value,
+                                step_type=StepType.tool_execution,
                                 step_id=step_id,
                                 delta=ToolCallDelta(
                                     parse_status=ToolCallParseStatus.in_progress,
@@ -766,7 +792,7 @@ class ChatAgent(ShieldRunnerMixin):
                         if self.telemetry_enabled
                         else {},
                     ) as span:
-                        tool_execution_start_time = datetime.now(UTC).isoformat()
+                        tool_execution_start_time = datetime.now(UTC)
                         tool_result = await self.execute_tool_call_maybe(
                             session_id,
                             tool_call,
@@ -796,14 +822,14 @@ class ChatAgent(ShieldRunnerMixin):
                                 )
                             ],
                             started_at=tool_execution_start_time,
-                            completed_at=datetime.now(UTC).isoformat(),
+                            completed_at=datetime.now(UTC),
                         )
 
                         # Yield the step completion event
                         yield AgentTurnResponseStreamChunk(
                             event=AgentTurnResponseEvent(
                                 payload=AgentTurnResponseStepCompletePayload(
-                                    step_type=StepType.tool_execution.value,
+                                    step_type=StepType.tool_execution,
                                     step_id=step_id,
                                     step_details=tool_execution_step,
                                 )
@@ -833,7 +859,7 @@ class ChatAgent(ShieldRunnerMixin):
                             turn_id=turn_id,
                             tool_calls=client_tool_calls,
                             tool_responses=[],
-                            started_at=datetime.now(UTC).isoformat(),
+                            started_at=datetime.now(UTC),
                         ),
                     )
 
@@ -868,19 +894,20 @@ class ChatAgent(ShieldRunnerMixin):
 
         toolgroup_to_args = toolgroup_to_args or {}
 
-        tool_name_to_def = {}
+        tool_name_to_def: dict[str, ToolDefinition] = {}
         tool_name_to_args = {}
 
-        for tool_def in self.agent_config.client_tools:
-            if tool_name_to_def.get(tool_def.name, None):
-                raise ValueError(f"Tool {tool_def.name} already exists")
+        if self.agent_config.client_tools:
+            for tool_def in self.agent_config.client_tools:
+                if tool_name_to_def.get(tool_def.name, None):
+                    raise ValueError(f"Tool {tool_def.name} already exists")
 
-            # Use input_schema from ToolDef directly
-            tool_name_to_def[tool_def.name] = ToolDefinition(
-                tool_name=tool_def.name,
-                description=tool_def.description,
-                input_schema=tool_def.input_schema,
-            )
+                # Use input_schema from ToolDef directly
+                tool_name_to_def[tool_def.name] = ToolDefinition(
+                    tool_name=tool_def.name,
+                    description=tool_def.description,
+                    input_schema=tool_def.input_schema,
+                )
         for toolgroup_name_with_maybe_tool_name in agent_config_toolgroups:
             toolgroup_name, input_tool_name = self._parse_toolgroup_name(toolgroup_name_with_maybe_tool_name)
             tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name)
@@ -908,15 +935,17 @@ class ChatAgent(ShieldRunnerMixin):
                     else:
                         identifier = None
 
-                if tool_name_to_def.get(identifier, None):
-                    raise ValueError(f"Tool {identifier} already exists")
                 if identifier:
-                    tool_name_to_def[identifier] = ToolDefinition(
-                        tool_name=identifier,
+                    # Convert BuiltinTool to string for dictionary key
+                    identifier_str = identifier.value if isinstance(identifier, BuiltinTool) else identifier
+                    if tool_name_to_def.get(identifier_str, None):
+                        raise ValueError(f"Tool {identifier_str} already exists")
+                    tool_name_to_def[identifier_str] = ToolDefinition(
+                        tool_name=identifier_str,
                         description=tool_def.description,
                         input_schema=tool_def.input_schema,
                     )
-                    tool_name_to_args[identifier] = toolgroup_to_args.get(toolgroup_name, {})
+                    tool_name_to_args[identifier_str] = toolgroup_to_args.get(toolgroup_name, {})
 
         self.tool_defs, self.tool_name_to_args = (
             list(tool_name_to_def.values()),
@@ -966,14 +995,17 @@ class ChatAgent(ShieldRunnerMixin):
         except json.JSONDecodeError as e:
             raise ValueError(f"Failed to parse arguments for tool call: {tool_call.arguments}") from e
 
-        result = await self.tool_runtime_api.invoke_tool(
-            tool_name=tool_name_str,
-            kwargs={
-                "session_id": session_id,
-                # get the arguments generated by the model and augment with toolgroup arg overrides for the agent
-                **args,
-                **self.tool_name_to_args.get(tool_name_str, {}),
-            },
+        result = cast(
+            ToolInvocationResult,
+            await self.tool_runtime_api.invoke_tool(
+                tool_name=tool_name_str,
+                kwargs={
+                    "session_id": session_id,
+                    # get the arguments generated by the model and augment with toolgroup arg overrides for the agent
+                    **args,
+                    **self.tool_name_to_args.get(tool_name_str, {}),
+                },
+            ),
         )
         logger.debug(f"tool call {tool_name_str} completed with result: {result}")
         return result
@@ -1017,7 +1049,7 @@ def _interpret_content_as_attachment(
         snippet = match.group(1)
         data = json.loads(snippet)
         return Attachment(
-            url=URL(uri="file://" + data["filepath"]),
+            content=URL(uri="file://" + data["filepath"]),
             mime_type=data["mimetype"],
         )
 

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

@@ -21,6 +21,7 @@ from llama_stack.apis.agents import (
     Document,
     ListOpenAIResponseInputItem,
     ListOpenAIResponseObject,
+    OpenAIDeleteResponseObject,
     OpenAIResponseInput,
     OpenAIResponseInputTool,
     OpenAIResponseObject,
@@ -141,7 +142,7 @@ class MetaReferenceAgentsImpl(Agents):
             persistence_store=(
                 self.persistence_store if agent_info.enable_session_persistence else self.in_memory_store
             ),
-            created_at=agent_info.created_at,
+            created_at=agent_info.created_at.isoformat(),
             policy=self.policy,
             telemetry_enabled=self.telemetry_enabled,
         )
@@ -163,9 +164,9 @@ class MetaReferenceAgentsImpl(Agents):
         agent_id: str,
         session_id: str,
         messages: list[UserMessage | ToolResponseMessage],
-        toolgroups: list[AgentToolGroup] | None = None,
-        documents: list[Document] | None = None,
         stream: bool | None = False,
+        documents: list[Document] | None = None,
+        toolgroups: list[AgentToolGroup] | None = None,
         tool_config: ToolConfig | None = None,
     ) -> AsyncGenerator:
         request = AgentTurnCreateRequest(
@@ -221,6 +222,8 @@ class MetaReferenceAgentsImpl(Agents):
     async def get_agents_turn(self, agent_id: str, session_id: str, turn_id: str) -> Turn:
         agent = await self._get_agent_impl(agent_id)
         turn = await agent.storage.get_session_turn(session_id, turn_id)
+        if turn is None:
+            raise ValueError(f"Turn {turn_id} not found in session {session_id}")
         return turn
 
     async def get_agents_step(self, agent_id: str, session_id: str, turn_id: str, step_id: str) -> AgentStepResponse:
@@ -232,13 +235,15 @@ class MetaReferenceAgentsImpl(Agents):
 
     async def get_agents_session(
         self,
-        agent_id: str,
         session_id: str,
+        agent_id: str,
         turn_ids: list[str] | None = None,
     ) -> Session:
         agent = await self._get_agent_impl(agent_id)
 
         session_info = await agent.storage.get_session_info(session_id)
+        if session_info is None:
+            raise ValueError(f"Session {session_id} not found")
         turns = await agent.storage.get_session_turns(session_id)
         if turn_ids:
             turns = [turn for turn in turns if turn.turn_id in turn_ids]
@@ -249,7 +254,7 @@ class MetaReferenceAgentsImpl(Agents):
             started_at=session_info.started_at,
         )
 
-    async def delete_agents_session(self, agent_id: str, session_id: str) -> None:
+    async def delete_agents_session(self, session_id: str, agent_id: str) -> None:
         agent = await self._get_agent_impl(agent_id)
 
         # Delete turns first, then the session
@@ -302,7 +307,7 @@ class MetaReferenceAgentsImpl(Agents):
         agent = Agent(
             agent_id=agent_id,
             agent_config=chat_agent.agent_config,
-            created_at=chat_agent.created_at,
+            created_at=datetime.fromisoformat(chat_agent.created_at),
         )
         return agent
 
@@ -323,6 +328,7 @@ class MetaReferenceAgentsImpl(Agents):
         self,
         response_id: str,
     ) -> OpenAIResponseObject:
+        assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
         return await self.openai_responses_impl.get_openai_response(response_id)
 
     async def create_openai_response(
@@ -342,7 +348,8 @@ class MetaReferenceAgentsImpl(Agents):
         max_infer_iters: int | None = 10,
         guardrails: list[ResponseGuardrail] | None = None,
     ) -> OpenAIResponseObject:
-        return await self.openai_responses_impl.create_openai_response(
+        assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
+        result = await self.openai_responses_impl.create_openai_response(
             input,
             model,
             prompt,
@@ -358,6 +365,7 @@ class MetaReferenceAgentsImpl(Agents):
             max_infer_iters,
             guardrails,
         )
+        return result  # type: ignore[no-any-return]
 
     async def list_openai_responses(
         self,
@@ -366,6 +374,7 @@ class MetaReferenceAgentsImpl(Agents):
         model: str | None = None,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseObject:
+        assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
         return await self.openai_responses_impl.list_openai_responses(after, limit, model, order)
 
     async def list_openai_response_input_items(
@@ -377,9 +386,11 @@ class MetaReferenceAgentsImpl(Agents):
         limit: int | None = 20,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseInputItem:
+        assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
         return await self.openai_responses_impl.list_openai_response_input_items(
             response_id, after, before, include, limit, order
         )
 
-    async def delete_openai_response(self, response_id: str) -> None:
+    async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
+        assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
         return await self.openai_responses_impl.delete_openai_response(response_id)

src/llama_stack/providers/inline/agents/meta_reference/persistence.py

@@ -6,12 +6,14 @@
 
 import json
 import uuid
+from dataclasses import dataclass
 from datetime import UTC, datetime
 
 from llama_stack.apis.agents import AgentConfig, Session, ToolExecutionStep, Turn
 from llama_stack.apis.common.errors import SessionNotFoundError
 from llama_stack.core.access_control.access_control import AccessDeniedError, is_action_allowed
-from llama_stack.core.access_control.datatypes import AccessRule
+from llama_stack.core.access_control.conditions import User as ProtocolUser
+from llama_stack.core.access_control.datatypes import AccessRule, Action
 from llama_stack.core.datatypes import User
 from llama_stack.core.request_headers import get_authenticated_user
 from llama_stack.log import get_logger
@@ -33,6 +35,15 @@ class AgentInfo(AgentConfig):
     created_at: datetime
 
 
+@dataclass
+class SessionResource:
+    """Concrete implementation of ProtectedResource for session access control."""
+
+    type: str
+    identifier: str
+    owner: ProtocolUser  # Use the protocol type for structural compatibility
+
+
 class AgentPersistence:
     def __init__(self, agent_id: str, kvstore: KVStore, policy: list[AccessRule]):
         self.agent_id = agent_id
@@ -53,8 +64,15 @@ class AgentPersistence:
             turns=[],
             identifier=name,  # should this be qualified in any way?
         )
-        if not is_action_allowed(self.policy, "create", session_info, user):
-            raise AccessDeniedError("create", session_info, user)
+        # Only perform access control if we have an authenticated user
+        if user is not None and session_info.identifier is not None:
+            resource = SessionResource(
+                type=session_info.type,
+                identifier=session_info.identifier,
+                owner=user,
+            )
+            if not is_action_allowed(self.policy, Action.CREATE, resource, user):
+                raise AccessDeniedError(Action.CREATE, resource, user)
 
         await self.kvstore.set(
             key=f"session:{self.agent_id}:{session_id}",
@@ -62,7 +80,7 @@ class AgentPersistence:
         )
         return session_id
 
-    async def get_session_info(self, session_id: str) -> AgentSessionInfo:
+    async def get_session_info(self, session_id: str) -> AgentSessionInfo | None:
         value = await self.kvstore.get(
             key=f"session:{self.agent_id}:{session_id}",
         )
@@ -83,7 +101,22 @@ class AgentPersistence:
         if not hasattr(session_info, "access_attributes") and not hasattr(session_info, "owner"):
             return True
 
-        return is_action_allowed(self.policy, "read", session_info, get_authenticated_user())
+        # Get current user - if None, skip access control (e.g., in tests)
+        user = get_authenticated_user()
+        if user is None:
+            return True
+
+        # Access control requires identifier and owner to be set
+        if session_info.identifier is None or session_info.owner is None:
+            return True
+
+        # At this point, both identifier and owner are guaranteed to be non-None
+        resource = SessionResource(
+            type=session_info.type,
+            identifier=session_info.identifier,
+            owner=session_info.owner,
+        )
+        return is_action_allowed(self.policy, Action.READ, resource, user)
 
     async def get_session_if_accessible(self, session_id: str) -> AgentSessionInfo | None:
         """Get session info if the user has access to it. For internal use by sub-session methods."""

src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -91,7 +91,8 @@ class OpenAIResponsesImpl:
         input: str | list[OpenAIResponseInput],
         previous_response: _OpenAIResponseObjectWithInputAndMessages,
     ):
-        new_input_items = previous_response.input.copy()
+        # Convert Sequence to list for mutation
+        new_input_items = list(previous_response.input)
         new_input_items.extend(previous_response.output)
 
         if isinstance(input, str):
@@ -107,7 +108,7 @@ class OpenAIResponsesImpl:
         tools: list[OpenAIResponseInputTool] | None,
         previous_response_id: str | None,
         conversation: str | None,
-    ) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam]]:
+    ) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam], ToolContext]:
         """Process input with optional previous response context.
 
         Returns:
@@ -208,6 +209,9 @@ class OpenAIResponsesImpl:
         messages: list[OpenAIMessageParam],
     ) -> None:
         new_input_id = f"msg_{uuid.uuid4()}"
+        # Type input_items_data as the full OpenAIResponseInput union to avoid list invariance issues
+        input_items_data: list[OpenAIResponseInput] = []
+
         if isinstance(input, str):
             # synthesize a message from the input string
             input_content = OpenAIResponseInputMessageContentText(text=input)
@@ -219,7 +223,6 @@ class OpenAIResponsesImpl:
             input_items_data = [input_content_item]
         else:
             # we already have a list of messages
-            input_items_data = []
             for input_item in input:
                 if isinstance(input_item, OpenAIResponseMessage):
                     # These may or may not already have an id, so dump to dict, check for id, and add if missing
@@ -251,7 +254,7 @@ class OpenAIResponsesImpl:
         tools: list[OpenAIResponseInputTool] | None = None,
         include: list[str] | None = None,
         max_infer_iters: int | None = 10,
-        guardrails: list[ResponseGuardrailSpec] | None = None,
+        guardrails: list[str | ResponseGuardrailSpec] | None = None,
     ):
         stream = bool(stream)
         text = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) if text is None else text
@@ -289,16 +292,19 @@ class OpenAIResponsesImpl:
             failed_response = None
 
             async for stream_chunk in stream_gen:
-                if stream_chunk.type in {"response.completed", "response.incomplete"}:
-                    if final_response is not None:
-                        raise ValueError(
-                            "The response stream produced multiple terminal responses! "
-                            f"Earlier response from {final_event_type}"
-                        )
-                    final_response = stream_chunk.response
-                    final_event_type = stream_chunk.type
-                elif stream_chunk.type == "response.failed":
-                    failed_response = stream_chunk.response
+                match stream_chunk.type:
+                    case "response.completed" | "response.incomplete":
+                        if final_response is not None:
+                            raise ValueError(
+                                "The response stream produced multiple terminal responses! "
+                                f"Earlier response from {final_event_type}"
+                            )
+                        final_response = stream_chunk.response
+                        final_event_type = stream_chunk.type
+                    case "response.failed":
+                        failed_response = stream_chunk.response
+                    case _:
+                        pass  # Other event types don't have .response
 
             if failed_response is not None:
                 error_message = (
@@ -326,6 +332,11 @@ class OpenAIResponsesImpl:
         max_infer_iters: int | None = 10,
         guardrail_ids: list[str] | None = None,
     ) -> AsyncIterator[OpenAIResponseObjectStream]:
+        # These should never be None when called from create_openai_response (which sets defaults)
+        # but we assert here to help mypy understand the types
+        assert text is not None, "text must not be None"
+        assert max_infer_iters is not None, "max_infer_iters must not be None"
+
         # Input preprocessing
         all_input, messages, tool_context = await self._process_input_with_previous_response(
             input, tools, previous_response_id, conversation
@@ -368,16 +379,19 @@ class OpenAIResponsesImpl:
         final_response = None
         failed_response = None
 
-        output_items = []
+        # Type as ConversationItem to avoid list invariance issues
+        output_items: list[ConversationItem] = []
         async for stream_chunk in orchestrator.create_response():
-            if stream_chunk.type in {"response.completed", "response.incomplete"}:
-                final_response = stream_chunk.response
-            elif stream_chunk.type == "response.failed":
-                failed_response = stream_chunk.response
-
-            if stream_chunk.type == "response.output_item.done":
-                item = stream_chunk.item
-                output_items.append(item)
+            match stream_chunk.type:
+                case "response.completed" | "response.incomplete":
+                    final_response = stream_chunk.response
+                case "response.failed":
+                    failed_response = stream_chunk.response
+                case "response.output_item.done":
+                    item = stream_chunk.item
+                    output_items.append(item)
+                case _:
+                    pass  # Other event types
 
             # Store and sync before yielding terminal events
             # This ensures the storage/syncing happens even if the consumer breaks after receiving the event
@@ -410,7 +424,8 @@ class OpenAIResponsesImpl:
         self, conversation_id: str, input: str | list[OpenAIResponseInput] | None, output_items: list[ConversationItem]
     ) -> None:
         """Sync content and response messages to the conversation."""
-        conversation_items = []
+        # Type as ConversationItem union to avoid list invariance issues
+        conversation_items: list[ConversationItem] = []
 
         if isinstance(input, str):
             conversation_items.append(

src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -111,7 +111,7 @@ class StreamingResponseOrchestrator:
         text: OpenAIResponseText,
         max_infer_iters: int,
         tool_executor,  # Will be the tool execution logic from the main class
-        instructions: str,
+        instructions: str | None,
         safety_api,
         guardrail_ids: list[str] | None = None,
         prompt: OpenAIResponsePrompt | None = None,
@@ -128,7 +128,9 @@ class StreamingResponseOrchestrator:
         self.prompt = prompt
         self.sequence_number = 0
         # Store MCP tool mapping that gets built during tool processing
-        self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = ctx.tool_context.previous_tools or {}
+        self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = (
+            ctx.tool_context.previous_tools if ctx.tool_context else {}
+        )
         # Track final messages after all tool executions
         self.final_messages: list[OpenAIMessageParam] = []
         # mapping for annotations
@@ -229,7 +231,8 @@ class StreamingResponseOrchestrator:
                 params = OpenAIChatCompletionRequestWithExtraBody(
                     model=self.ctx.model,
                     messages=messages,
-                    tools=self.ctx.chat_tools,
+                    # Pydantic models are dict-compatible but mypy treats them as distinct types
+                    tools=self.ctx.chat_tools,  # type: ignore[arg-type]
                     stream=True,
                     temperature=self.ctx.temperature,
                     response_format=response_format,
@@ -272,7 +275,12 @@ class StreamingResponseOrchestrator:
 
                 # Handle choices with no tool calls
                 for choice in current_response.choices:
-                    if not (choice.message.tool_calls and self.ctx.response_tools):
+                    has_tool_calls = (
+                        isinstance(choice.message, OpenAIAssistantMessageParam)
+                        and choice.message.tool_calls
+                        and self.ctx.response_tools
+                    )
+                    if not has_tool_calls:
                         output_messages.append(
                             await convert_chat_choice_to_response_message(
                                 choice,
@@ -722,7 +730,10 @@ class StreamingResponseOrchestrator:
                                 )
 
                             # Accumulate arguments for final response (only for subsequent chunks)
-                            if not is_new_tool_call:
+                            if not is_new_tool_call and response_tool_call is not None:
+                                # Both should have functions since we're inside the tool_call.function check above
+                                assert response_tool_call.function is not None
+                                assert tool_call.function is not None
                                 response_tool_call.function.arguments = (
                                     response_tool_call.function.arguments or ""
                                 ) + tool_call.function.arguments
@@ -747,10 +758,13 @@ class StreamingResponseOrchestrator:
         for tool_call_index in sorted(chat_response_tool_calls.keys()):
             tool_call = chat_response_tool_calls[tool_call_index]
             # Ensure that arguments, if sent back to the inference provider, are not None
-            tool_call.function.arguments = tool_call.function.arguments or "{}"
+            if tool_call.function:
+                tool_call.function.arguments = tool_call.function.arguments or "{}"
             tool_call_item_id = tool_call_item_ids[tool_call_index]
-            final_arguments = tool_call.function.arguments
-            tool_call_name = chat_response_tool_calls[tool_call_index].function.name
+            final_arguments: str = tool_call.function.arguments or "{}" if tool_call.function else "{}"
+            func = chat_response_tool_calls[tool_call_index].function
+
+            tool_call_name = func.name if func else ""
 
             # Check if this is an MCP tool call
             is_mcp_tool = tool_call_name and tool_call_name in self.mcp_tool_to_server
@@ -894,12 +908,11 @@ class StreamingResponseOrchestrator:
 
             self.sequence_number += 1
             if tool_call.function.name and tool_call.function.name in self.mcp_tool_to_server:
-                item = OpenAIResponseOutputMessageMCPCall(
+                item: OpenAIResponseOutput = OpenAIResponseOutputMessageMCPCall(
                     arguments="",
                     name=tool_call.function.name,
                     id=matching_item_id,
                     server_label=self.mcp_tool_to_server[tool_call.function.name].server_label,
-                    status="in_progress",
                 )
             elif tool_call.function.name == "web_search":
                 item = OpenAIResponseOutputMessageWebSearchToolCall(
@@ -1008,7 +1021,7 @@ class StreamingResponseOrchestrator:
                 description=tool.description,
                 input_schema=tool.input_schema,
             )
-            return convert_tooldef_to_openai_tool(tool_def)
+            return convert_tooldef_to_openai_tool(tool_def)  # type: ignore[return-value]  # Returns dict but ChatCompletionToolParam expects TypedDict
 
         # Initialize chat_tools if not already set
         if self.ctx.chat_tools is None:
@@ -1016,7 +1029,7 @@ class StreamingResponseOrchestrator:
 
         for input_tool in tools:
             if input_tool.type == "function":
-                self.ctx.chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump()))
+                self.ctx.chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump()))  # type: ignore[typeddict-item,arg-type]  # Dict compatible with FunctionDefinition
             elif input_tool.type in WebSearchToolTypes:
                 tool_name = "web_search"
                 # Need to access tool_groups_api from tool_executor
@@ -1055,8 +1068,8 @@ class StreamingResponseOrchestrator:
                 if isinstance(mcp_tool.allowed_tools, list):
                     always_allowed = mcp_tool.allowed_tools
                 elif isinstance(mcp_tool.allowed_tools, AllowedToolsFilter):
-                    always_allowed = mcp_tool.allowed_tools.always
-                    never_allowed = mcp_tool.allowed_tools.never
+                    # AllowedToolsFilter only has tool_names field (not allowed/disallowed)
+                    always_allowed = mcp_tool.allowed_tools.tool_names
 
             # Call list_mcp_tools
             tool_defs = None
@@ -1088,7 +1101,7 @@ class StreamingResponseOrchestrator:
                     openai_tool = convert_tooldef_to_chat_tool(t)
                     if self.ctx.chat_tools is None:
                         self.ctx.chat_tools = []
-                    self.ctx.chat_tools.append(openai_tool)
+                    self.ctx.chat_tools.append(openai_tool)  # type: ignore[arg-type]  # Returns dict but ChatCompletionToolParam expects TypedDict
 
                     # Add to MCP tool mapping
                     if t.name in self.mcp_tool_to_server:
@@ -1120,13 +1133,17 @@ class StreamingResponseOrchestrator:
         self, output_messages: list[OpenAIResponseOutput]
     ) -> AsyncIterator[OpenAIResponseObjectStream]:
         # Handle all mcp tool lists from previous response that are still valid:
-        for tool in self.ctx.tool_context.previous_tool_listings:
-            async for evt in self._reuse_mcp_list_tools(tool, output_messages):
-                yield evt
-        # Process all remaining tools (including MCP tools) and emit streaming events
-        if self.ctx.tool_context.tools_to_process:
-            async for stream_event in self._process_new_tools(self.ctx.tool_context.tools_to_process, output_messages):
-                yield stream_event
+        # tool_context can be None when no tools are provided in the response request
+        if self.ctx.tool_context:
+            for tool in self.ctx.tool_context.previous_tool_listings:
+                async for evt in self._reuse_mcp_list_tools(tool, output_messages):
+                    yield evt
+            # Process all remaining tools (including MCP tools) and emit streaming events
+            if self.ctx.tool_context.tools_to_process:
+                async for stream_event in self._process_new_tools(
+                    self.ctx.tool_context.tools_to_process, output_messages
+                ):
+                    yield stream_event
 
     def _approval_required(self, tool_name: str) -> bool:
         if tool_name not in self.mcp_tool_to_server:
@@ -1220,7 +1237,7 @@ class StreamingResponseOrchestrator:
             openai_tool = convert_tooldef_to_openai_tool(tool_def)
             if self.ctx.chat_tools is None:
                 self.ctx.chat_tools = []
-            self.ctx.chat_tools.append(openai_tool)
+            self.ctx.chat_tools.append(openai_tool)  # type: ignore[arg-type]  # Returns dict but ChatCompletionToolParam expects TypedDict
 
         mcp_list_message = OpenAIResponseOutputMessageMCPListTools(
             id=f"mcp_list_{uuid.uuid4()}",

src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py

@@ -7,6 +7,7 @@
 import asyncio
 import json
 from collections.abc import AsyncIterator
+from typing import Any
 
 from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseInputToolFileSearch,
@@ -22,6 +23,7 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseObjectStreamResponseWebSearchCallSearching,
     OpenAIResponseOutputMessageFileSearchToolCall,
     OpenAIResponseOutputMessageFileSearchToolCallResults,
+    OpenAIResponseOutputMessageMCPCall,
     OpenAIResponseOutputMessageWebSearchToolCall,
 )
 from llama_stack.apis.common.content_types import (
@@ -67,7 +69,7 @@ class ToolExecutor:
     ) -> AsyncIterator[ToolExecutionResult]:
         tool_call_id = tool_call.id
         function = tool_call.function
-        tool_kwargs = json.loads(function.arguments) if function.arguments else {}
+        tool_kwargs = json.loads(function.arguments) if function and function.arguments else {}
 
         if not function or not tool_call_id or not function.name:
             yield ToolExecutionResult(sequence_number=sequence_number)
@@ -84,7 +86,16 @@ class ToolExecutor:
         error_exc, result = await self._execute_tool(function.name, tool_kwargs, ctx, mcp_tool_to_server)
 
         # Emit completion events for tool execution
-        has_error = error_exc or (result and ((result.error_code and result.error_code > 0) or result.error_message))
+        has_error = bool(
+            error_exc
+            or (
+                result
+                and (
+                    ((error_code := getattr(result, "error_code", None)) and error_code > 0)
+                    or getattr(result, "error_message", None)
+                )
+            )
+        )
         async for event_result in self._emit_completion_events(
             function.name, ctx, sequence_number, output_index, item_id, has_error, mcp_tool_to_server
         ):
@@ -101,7 +112,9 @@ class ToolExecutor:
             sequence_number=sequence_number,
             final_output_message=output_message,
             final_input_message=input_message,
-            citation_files=result.metadata.get("citation_files") if result and result.metadata else None,
+            citation_files=(
+                metadata.get("citation_files") if result and (metadata := getattr(result, "metadata", None)) else None
+            ),
         )
 
     async def _execute_knowledge_search_via_vector_store(
@@ -188,8 +201,9 @@ class ToolExecutor:
 
             citation_files[file_id] = filename
 
+        # Cast to proper InterleavedContent type (list invariance)
         return ToolInvocationResult(
-            content=content_items,
+            content=content_items,  # type: ignore[arg-type]
             metadata={
                 "document_ids": [r.file_id for r in search_results],
                 "chunks": [r.content[0].text if r.content else "" for r in search_results],
@@ -209,51 +223,60 @@ class ToolExecutor:
     ) -> AsyncIterator[ToolExecutionResult]:
         """Emit progress events for tool execution start."""
         # Emit in_progress event based on tool type (only for tools with specific streaming events)
-        progress_event = None
         if mcp_tool_to_server and function_name in mcp_tool_to_server:
             sequence_number += 1
-            progress_event = OpenAIResponseObjectStreamResponseMcpCallInProgress(
-                item_id=item_id,
-                output_index=output_index,
+            yield ToolExecutionResult(
+                stream_event=OpenAIResponseObjectStreamResponseMcpCallInProgress(
+                    item_id=item_id,
+                    output_index=output_index,
+                    sequence_number=sequence_number,
+                ),
                 sequence_number=sequence_number,
             )
         elif function_name == "web_search":
             sequence_number += 1
-            progress_event = OpenAIResponseObjectStreamResponseWebSearchCallInProgress(
-                item_id=item_id,
-                output_index=output_index,
+            yield ToolExecutionResult(
+                stream_event=OpenAIResponseObjectStreamResponseWebSearchCallInProgress(
+                    item_id=item_id,
+                    output_index=output_index,
+                    sequence_number=sequence_number,
+                ),
                 sequence_number=sequence_number,
             )
         elif function_name == "knowledge_search":
             sequence_number += 1
-            progress_event = OpenAIResponseObjectStreamResponseFileSearchCallInProgress(
-                item_id=item_id,
-                output_index=output_index,
+            yield ToolExecutionResult(
+                stream_event=OpenAIResponseObjectStreamResponseFileSearchCallInProgress(
+                    item_id=item_id,
+                    output_index=output_index,
+                    sequence_number=sequence_number,
+                ),
                 sequence_number=sequence_number,
             )
 
-        if progress_event:
-            yield ToolExecutionResult(stream_event=progress_event, sequence_number=sequence_number)
-
         # For web search, emit searching event
         if function_name == "web_search":
             sequence_number += 1
-            searching_event = OpenAIResponseObjectStreamResponseWebSearchCallSearching(
-                item_id=item_id,
-                output_index=output_index,
+            yield ToolExecutionResult(
+                stream_event=OpenAIResponseObjectStreamResponseWebSearchCallSearching(
+                    item_id=item_id,
+                    output_index=output_index,
+                    sequence_number=sequence_number,
+                ),
                 sequence_number=sequence_number,
             )
-            yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number)
 
         # For file search, emit searching event
         if function_name == "knowledge_search":
             sequence_number += 1
-            searching_event = OpenAIResponseObjectStreamResponseFileSearchCallSearching(
-                item_id=item_id,
-                output_index=output_index,
+            yield ToolExecutionResult(
+                stream_event=OpenAIResponseObjectStreamResponseFileSearchCallSearching(
+                    item_id=item_id,
+                    output_index=output_index,
+                    sequence_number=sequence_number,
+                ),
                 sequence_number=sequence_number,
             )
-            yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number)
 
     async def _execute_tool(
         self,
@@ -261,7 +284,7 @@ class ToolExecutor:
         tool_kwargs: dict,
         ctx: ChatCompletionContext,
         mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
-    ) -> tuple[Exception | None, any]:
+    ) -> tuple[Exception | None, Any]:
         """Execute the tool and return error exception and result."""
         error_exc = None
         result = None
@@ -284,9 +307,13 @@ class ToolExecutor:
                         kwargs=tool_kwargs,
                     )
             elif function_name == "knowledge_search":
-                response_file_search_tool = next(
-                    (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)),
-                    None,
+                response_file_search_tool = (
+                    next(
+                        (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)),
+                        None,
+                    )
+                    if ctx.response_tools
+                    else None
                 )
                 if response_file_search_tool:
                     # Use vector_stores.search API instead of knowledge_search tool
@@ -322,35 +349,34 @@ class ToolExecutor:
         mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
     ) -> AsyncIterator[ToolExecutionResult]:
         """Emit completion or failure events for tool execution."""
-        completion_event = None
-
         if mcp_tool_to_server and function_name in mcp_tool_to_server:
             sequence_number += 1
             if has_error:
-                completion_event = OpenAIResponseObjectStreamResponseMcpCallFailed(
+                mcp_failed_event = OpenAIResponseObjectStreamResponseMcpCallFailed(
                     sequence_number=sequence_number,
                 )
+                yield ToolExecutionResult(stream_event=mcp_failed_event, sequence_number=sequence_number)
             else:
-                completion_event = OpenAIResponseObjectStreamResponseMcpCallCompleted(
+                mcp_completed_event = OpenAIResponseObjectStreamResponseMcpCallCompleted(
                     sequence_number=sequence_number,
                 )
+                yield ToolExecutionResult(stream_event=mcp_completed_event, sequence_number=sequence_number)
         elif function_name == "web_search":
             sequence_number += 1
-            completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted(
+            web_completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted(
                 item_id=item_id,
                 output_index=output_index,
                 sequence_number=sequence_number,
             )
+            yield ToolExecutionResult(stream_event=web_completion_event, sequence_number=sequence_number)
         elif function_name == "knowledge_search":
             sequence_number += 1
-            completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted(
+            file_completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted(
                 item_id=item_id,
                 output_index=output_index,
                 sequence_number=sequence_number,
             )
-
-        if completion_event:
-            yield ToolExecutionResult(stream_event=completion_event, sequence_number=sequence_number)
+            yield ToolExecutionResult(stream_event=file_completion_event, sequence_number=sequence_number)
 
     async def _build_result_messages(
         self,
@@ -360,21 +386,18 @@ class ToolExecutor:
         tool_kwargs: dict,
         ctx: ChatCompletionContext,
         error_exc: Exception | None,
-        result: any,
+        result: Any,
         has_error: bool,
         mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None,
-    ) -> tuple[any, any]:
+    ) -> tuple[Any, Any]:
         """Build output and input messages from tool execution results."""
         from llama_stack.providers.utils.inference.prompt_adapter import (
             interleaved_content_as_str,
         )
 
         # Build output message
+        message: Any
         if mcp_tool_to_server and function.name in mcp_tool_to_server:
-            from llama_stack.apis.agents.openai_responses import (
-                OpenAIResponseOutputMessageMCPCall,
-            )
-
             message = OpenAIResponseOutputMessageMCPCall(
                 id=item_id,
                 arguments=function.arguments,
@@ -383,10 +406,14 @@ class ToolExecutor:
             )
             if error_exc:
                 message.error = str(error_exc)
-            elif (result and result.error_code and result.error_code > 0) or (result and result.error_message):
-                message.error = f"Error (code {result.error_code}): {result.error_message}"
-            elif result and result.content:
-                message.output = interleaved_content_as_str(result.content)
+            elif (result and (error_code := getattr(result, "error_code", None)) and error_code > 0) or (
+                result and getattr(result, "error_message", None)
+            ):
+                ec = getattr(result, "error_code", "unknown")
+                em = getattr(result, "error_message", "")
+                message.error = f"Error (code {ec}): {em}"
+            elif result and (content := getattr(result, "content", None)):
+                message.output = interleaved_content_as_str(content)
         else:
             if function.name == "web_search":
                 message = OpenAIResponseOutputMessageWebSearchToolCall(
@@ -401,17 +428,17 @@ class ToolExecutor:
                     queries=[tool_kwargs.get("query", "")],
                     status="completed",
                 )
-                if result and "document_ids" in result.metadata:
+                if result and (metadata := getattr(result, "metadata", None)) and "document_ids" in metadata:
                     message.results = []
-                    for i, doc_id in enumerate(result.metadata["document_ids"]):
-                        text = result.metadata["chunks"][i] if "chunks" in result.metadata else None
-                        score = result.metadata["scores"][i] if "scores" in result.metadata else None
+                    for i, doc_id in enumerate(metadata["document_ids"]):
+                        text = metadata["chunks"][i] if "chunks" in metadata else None
+                        score = metadata["scores"][i] if "scores" in metadata else None
                         message.results.append(
                             OpenAIResponseOutputMessageFileSearchToolCallResults(
                                 file_id=doc_id,
                                 filename=doc_id,
-                                text=text,
-                                score=score,
+                                text=text if text is not None else "",
+                                score=score if score is not None else 0.0,
                                 attributes={},
                             )
                         )
@@ -421,27 +448,32 @@ class ToolExecutor:
                 raise ValueError(f"Unknown tool {function.name} called")
 
         # Build input message
-        input_message = None
-        if result and result.content:
-            if isinstance(result.content, str):
-                content = result.content
-            elif isinstance(result.content, list):
-                content = []
-                for item in result.content:
+        input_message: OpenAIToolMessageParam | None = None
+        if result and (result_content := getattr(result, "content", None)):
+            # all the mypy contortions here are still unsatisfactory with random Any typing
+            if isinstance(result_content, str):
+                msg_content: str | list[Any] = result_content
+            elif isinstance(result_content, list):
+                content_list: list[Any] = []
+                for item in result_content:
+                    part: Any
                     if isinstance(item, TextContentItem):
                         part = OpenAIChatCompletionContentPartTextParam(text=item.text)
                     elif isinstance(item, ImageContentItem):
                         if item.image.data:
-                            url = f"data:image;base64,{item.image.data}"
+                            url_value = f"data:image;base64,{item.image.data}"
                         else:
-                            url = item.image.url
-                        part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url))
+                            url_value = str(item.image.url) if item.image.url else ""
+                        part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url_value))
                     else:
                         raise ValueError(f"Unknown result content type: {type(item)}")
-                    content.append(part)
+                    content_list.append(part)
+                msg_content = content_list
             else:
-                raise ValueError(f"Unknown result content type: {type(result.content)}")
-            input_message = OpenAIToolMessageParam(content=content, tool_call_id=tool_call_id)
+                raise ValueError(f"Unknown result content type: {type(result_content)}")
+            # OpenAIToolMessageParam accepts str | list[TextParam] but we may have images
+            # This is runtime-safe as the API accepts it, but mypy complains
+            input_message = OpenAIToolMessageParam(content=msg_content, tool_call_id=tool_call_id)  # type: ignore[arg-type]
         else:
             text = str(error_exc) if error_exc else "Tool execution failed"
             input_message = OpenAIToolMessageParam(content=text, tool_call_id=tool_call_id)

src/llama_stack/providers/inline/agents/meta_reference/responses/types.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 from dataclasses import dataclass
+from typing import cast
 
 from openai.types.chat import ChatCompletionToolParam
 from pydantic import BaseModel
@@ -100,17 +101,19 @@ class ToolContext(BaseModel):
                 if isinstance(tool, OpenAIResponseToolMCP):
                     previous_tools_by_label[tool.server_label] = tool
             # collect tool definitions which are the same in current and previous requests:
-            tools_to_process = []
+            tools_to_process: list[OpenAIResponseInputTool] = []
             matched: dict[str, OpenAIResponseInputToolMCP] = {}
-            for tool in self.current_tools:
+            # Mypy confuses OpenAIResponseInputTool (Input union) with OpenAIResponseTool (output union)
+            # which differ only in MCP type (InputToolMCP vs ToolMCP). Code is correct.
+            for tool in cast(list[OpenAIResponseInputTool], self.current_tools):  # type: ignore[assignment]
                 if isinstance(tool, OpenAIResponseInputToolMCP) and tool.server_label in previous_tools_by_label:
                     previous_tool = previous_tools_by_label[tool.server_label]
                     if previous_tool.allowed_tools == tool.allowed_tools:
                         matched[tool.server_label] = tool
                     else:
-                        tools_to_process.append(tool)
+                        tools_to_process.append(tool)  # type: ignore[arg-type]
                 else:
-                    tools_to_process.append(tool)
+                    tools_to_process.append(tool)  # type: ignore[arg-type]
             # tools that are not the same or were not previously defined need to be processed:
             self.tools_to_process = tools_to_process
             # for all matched definitions, get the mcp_list_tools objects from the previous output:
@@ -119,9 +122,11 @@ class ToolContext(BaseModel):
             ]
             # reconstruct the tool to server mappings that can be reused:
             for listing in self.previous_tool_listings:
+                # listing is OpenAIResponseOutputMessageMCPListTools which has tools: list[MCPListToolsTool]
                 definition = matched[listing.server_label]
-                for tool in listing.tools:
-                    self.previous_tools[tool.name] = definition
+                for mcp_tool in listing.tools:
+                    # mcp_tool is MCPListToolsTool which has a name: str field
+                    self.previous_tools[mcp_tool.name] = definition
 
     def available_tools(self) -> list[OpenAIResponseTool]:
         if not self.current_tools:
@@ -139,6 +144,8 @@ class ToolContext(BaseModel):
                     server_label=tool.server_label,
                     allowed_tools=tool.allowed_tools,
                 )
+            # Exhaustive check - all tool types should be handled above
+            raise AssertionError(f"Unexpected tool type: {type(tool)}")
 
         return [convert_tool(tool) for tool in self.current_tools]
 

src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py

@@ -7,6 +7,7 @@
 import asyncio
 import re
 import uuid
+from collections.abc import Sequence
 
 from llama_stack.apis.agents.agents import ResponseGuardrailSpec
 from llama_stack.apis.agents.openai_responses import (
@@ -71,14 +72,14 @@ async def convert_chat_choice_to_response_message(
 
     return OpenAIResponseMessage(
         id=message_id or f"msg_{uuid.uuid4()}",
-        content=[OpenAIResponseOutputMessageContentOutputText(text=clean_text, annotations=annotations)],
+        content=[OpenAIResponseOutputMessageContentOutputText(text=clean_text, annotations=list(annotations))],
         status="completed",
         role="assistant",
     )
 
 
 async def convert_response_content_to_chat_content(
-    content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]),
+    content: str | Sequence[OpenAIResponseInputMessageContent | OpenAIResponseOutputMessageContent],
 ) -> str | list[OpenAIChatCompletionContentPartParam]:
     """
     Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts.
@@ -88,7 +89,8 @@ async def convert_response_content_to_chat_content(
     if isinstance(content, str):
         return content
 
-    converted_parts = []
+    # Type with union to avoid list invariance issues
+    converted_parts: list[OpenAIChatCompletionContentPartParam] = []
     for content_part in content:
         if isinstance(content_part, OpenAIResponseInputMessageContentText):
             converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
@@ -158,9 +160,11 @@ async def convert_response_input_to_chat_messages(
                     ),
                 )
                 messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                # Output can be None, use empty string as fallback
+                output_content = input_item.output if input_item.output is not None else ""
                 messages.append(
                     OpenAIToolMessageParam(
-                        content=input_item.output,
+                        content=output_content,
                         tool_call_id=input_item.id,
                     )
                 )
@@ -172,7 +176,8 @@ async def convert_response_input_to_chat_messages(
             ):
                 # these are handled by the responses impl itself and not pass through to chat completions
                 pass
-            else:
+            elif isinstance(input_item, OpenAIResponseMessage):
+                # Narrow type to OpenAIResponseMessage which has content and role attributes
                 content = await convert_response_content_to_chat_content(input_item.content)
                 message_type = await get_message_type_by_role(input_item.role)
                 if message_type is None:
@@ -191,7 +196,8 @@ async def convert_response_input_to_chat_messages(
                         last_user_content = getattr(last_user_msg, "content", None)
                         if last_user_content == content:
                             continue  # Skip duplicate user message
-                messages.append(message_type(content=content))
+                # Dynamic message type call - different message types have different content expectations
+                messages.append(message_type(content=content))  # type: ignore[call-arg,arg-type]
         if len(tool_call_results):
             # Check if unpaired function_call_outputs reference function_calls from previous messages
             if previous_messages:
@@ -237,8 +243,11 @@ async def convert_response_text_to_chat_response_format(
     if text.format["type"] == "json_object":
         return OpenAIResponseFormatJSONObject()
     if text.format["type"] == "json_schema":
+        # Assert name exists for json_schema format
+        assert text.format.get("name"), "json_schema format requires a name"
+        schema_name: str = text.format["name"]  # type: ignore[assignment]
         return OpenAIResponseFormatJSONSchema(
-            json_schema=OpenAIJSONSchema(name=text.format["name"], schema=text.format["schema"])
+            json_schema=OpenAIJSONSchema(name=schema_name, schema=text.format["schema"])
         )
     raise ValueError(f"Unsupported text format: {text.format}")
 
@@ -251,7 +260,7 @@ async def get_message_type_by_role(role: str) -> type[OpenAIMessageParam] | None
         "assistant": OpenAIAssistantMessageParam,
         "developer": OpenAIDeveloperMessageParam,
     }
-    return role_to_type.get(role)
+    return role_to_type.get(role)  # type: ignore[return-value]  # Pydantic models use ModelMetaclass
 
 
 def _extract_citations_from_text(
@@ -320,7 +329,8 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[
 
     # Look up shields to get their provider_resource_id (actual model ID)
     model_ids = []
-    shields_list = await safety_api.routing_table.list_shields()
+    # TODO: list_shields not in Safety interface but available at runtime via API routing
+    shields_list = await safety_api.routing_table.list_shields()  # type: ignore[attr-defined]
 
     for guardrail_id in guardrail_ids:
         matching_shields = [shield for shield in shields_list.data if shield.identifier == guardrail_id]
@@ -337,7 +347,9 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[
         for result in response.results:
             if result.flagged:
                 message = result.user_message or "Content blocked by safety guardrails"
-                flagged_categories = [cat for cat, flagged in result.categories.items() if flagged]
+                flagged_categories = (
+                    [cat for cat, flagged in result.categories.items() if flagged] if result.categories else []
+                )
                 violation_type = result.metadata.get("violation_type", []) if result.metadata else []
 
                 if flagged_categories:
@@ -347,6 +359,9 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[
 
                 return message
 
+    # No violations found
+    return None
+
 
 def extract_guardrail_ids(guardrails: list | None) -> list[str]:
     """Extract guardrail IDs from guardrails parameter, handling both string IDs and ResponseGuardrailSpec objects."""

src/llama_stack/providers/inline/agents/meta_reference/safety.py

@@ -6,7 +6,7 @@
 
 import asyncio
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel
 from llama_stack.core.telemetry import tracing
 from llama_stack.log import get_logger
@@ -31,7 +31,7 @@ class ShieldRunnerMixin:
         self.input_shields = input_shields
         self.output_shields = output_shields
 
-    async def run_multiple_shields(self, messages: list[Message], identifiers: list[str]) -> None:
+    async def run_multiple_shields(self, messages: list[OpenAIMessageParam], identifiers: list[str]) -> None:
         async def run_shield_with_span(identifier: str):
             async with tracing.span(f"run_shield_{identifier}"):
                 return await self.safety_api.run_shield(

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

@@ -33,4 +33,5 @@ class AnthropicInferenceAdapter(OpenAIMixin):
         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()]
+        api_key = self._get_api_key_from_config_or_provider_data()
+        return [m.id async for m in AsyncAnthropic(api_key=api_key).models.list()]

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

@@ -33,10 +33,11 @@ class DatabricksInferenceAdapter(OpenAIMixin):
 
     async def list_provider_model_ids(self) -> Iterable[str]:
         # Filter out None values from endpoint names
+        api_token = self._get_api_key_from_config_or_provider_data()
         return [
             endpoint.name  # type: ignore[misc]
             for endpoint in WorkspaceClient(
-                host=self.config.url, token=self.get_api_key()
+                host=self.config.url, token=api_token
             ).serving_endpoints.list()  # TODO: this is not async
         ]
 

src/llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -128,7 +128,9 @@ class LiteLLMOpenAIMixin(
         return schema
 
     async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        input_dict = {}
+        from typing import Any
+
+        input_dict: dict[str, Any] = {}
 
         input_dict["messages"] = [
             await convert_message_to_openai_dict_new(m, download_images=self.download_images) for m in request.messages
@@ -139,30 +141,27 @@ class LiteLLMOpenAIMixin(
                     f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
                 )
 
-            fmt = fmt.json_schema
-            name = fmt["title"]
-            del fmt["title"]
-            fmt["additionalProperties"] = False
+            # Convert to dict for manipulation
+            fmt_dict = dict(fmt.json_schema)
+            name = fmt_dict["title"]
+            del fmt_dict["title"]
+            fmt_dict["additionalProperties"] = False
 
             # Apply additionalProperties: False recursively to all objects
-            fmt = self._add_additional_properties_recursive(fmt)
+            fmt_dict = self._add_additional_properties_recursive(fmt_dict)
 
             input_dict["response_format"] = {
                 "type": "json_schema",
                 "json_schema": {
                     "name": name,
-                    "schema": fmt,
+                    "schema": fmt_dict,
                     "strict": self.json_schema_strict,
                 },
             }
         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
-                    if isinstance(request.tool_config.tool_choice, ToolChoice)
-                    else request.tool_config.tool_choice
-                )
+            if request.tool_config and (tool_choice := request.tool_config.tool_choice):
+                input_dict["tool_choice"] = tool_choice.value if isinstance(tool_choice, ToolChoice) else tool_choice
 
         return {
             "model": request.model,
@@ -176,10 +175,10 @@ class LiteLLMOpenAIMixin(
     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
+        if provider_data and key_field and (api_key := getattr(provider_data, key_field, None)):
+            return str(api_key)  # type: ignore[no-any-return]  # getattr returns Any, can't narrow without runtime type inspection
+
+        api_key = self.api_key_from_config
         if not api_key:
             raise ValueError(
                 "API key is not set. Please provide a valid API key in the "
@@ -192,7 +191,13 @@ class LiteLLMOpenAIMixin(
         self,
         params: OpenAIEmbeddingsRequestWithExtraBody,
     ) -> OpenAIEmbeddingsResponse:
+        if not self.model_store:
+            raise ValueError("Model store is not initialized")
+
         model_obj = await self.model_store.get_model(params.model)
+        if model_obj.provider_resource_id is None:
+            raise ValueError(f"Model {params.model} has no provider_resource_id")
+        provider_resource_id = model_obj.provider_resource_id
 
         # Convert input to list if it's a string
         input_list = [params.input] if isinstance(params.input, str) else params.input
@@ -200,7 +205,7 @@ class LiteLLMOpenAIMixin(
         # Call litellm embedding function
         # litellm.drop_params = True
         response = litellm.embedding(
-            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            model=self.get_litellm_model_name(provider_resource_id),
             input=input_list,
             api_key=self.get_api_key(),
             api_base=self.api_base,
@@ -217,7 +222,7 @@ class LiteLLMOpenAIMixin(
 
         return OpenAIEmbeddingsResponse(
             data=data,
-            model=model_obj.provider_resource_id,
+            model=provider_resource_id,
             usage=usage,
         )
 
@@ -225,10 +230,16 @@ class LiteLLMOpenAIMixin(
         self,
         params: OpenAICompletionRequestWithExtraBody,
     ) -> OpenAICompletion:
+        if not self.model_store:
+            raise ValueError("Model store is not initialized")
+
         model_obj = await self.model_store.get_model(params.model)
+        if model_obj.provider_resource_id is None:
+            raise ValueError(f"Model {params.model} has no provider_resource_id")
+        provider_resource_id = model_obj.provider_resource_id
 
         request_params = await prepare_openai_completion_params(
-            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            model=self.get_litellm_model_name(provider_resource_id),
             prompt=params.prompt,
             best_of=params.best_of,
             echo=params.echo,
@@ -249,7 +260,8 @@ class LiteLLMOpenAIMixin(
             api_key=self.get_api_key(),
             api_base=self.api_base,
         )
-        return await litellm.atext_completion(**request_params)
+        # LiteLLM returns compatible type but mypy can't verify external library
+        return await litellm.atext_completion(**request_params)  # type: ignore[no-any-return]  # external lib lacks type stubs
 
     async def openai_chat_completion(
         self,
@@ -265,10 +277,16 @@ class LiteLLMOpenAIMixin(
             elif "include_usage" not in stream_options:
                 stream_options = {**stream_options, "include_usage": True}
 
+        if not self.model_store:
+            raise ValueError("Model store is not initialized")
+
         model_obj = await self.model_store.get_model(params.model)
+        if model_obj.provider_resource_id is None:
+            raise ValueError(f"Model {params.model} has no provider_resource_id")
+        provider_resource_id = model_obj.provider_resource_id
 
         request_params = await prepare_openai_completion_params(
-            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            model=self.get_litellm_model_name(provider_resource_id),
             messages=params.messages,
             frequency_penalty=params.frequency_penalty,
             function_call=params.function_call,
@@ -294,7 +312,8 @@ class LiteLLMOpenAIMixin(
             api_key=self.get_api_key(),
             api_base=self.api_base,
         )
-        return await litellm.acompletion(**request_params)
+        # LiteLLM returns compatible type but mypy can't verify external library
+        return await litellm.acompletion(**request_params)  # type: ignore[no-any-return]  # external lib lacks type stubs
 
     async def check_model_availability(self, model: str) -> bool:
         """

src/llama_stack/providers/utils/inference/openai_compat.py

@@ -161,8 +161,10 @@ def get_sampling_strategy_options(params: SamplingParams) -> dict:
     if isinstance(params.strategy, GreedySamplingStrategy):
         options["temperature"] = 0.0
     elif isinstance(params.strategy, TopPSamplingStrategy):
-        options["temperature"] = params.strategy.temperature
-        options["top_p"] = params.strategy.top_p
+        if params.strategy.temperature is not None:
+            options["temperature"] = params.strategy.temperature
+        if params.strategy.top_p is not None:
+            options["top_p"] = params.strategy.top_p
     elif isinstance(params.strategy, TopKSamplingStrategy):
         options["top_k"] = params.strategy.top_k
     else:
@@ -192,12 +194,12 @@ def get_sampling_options(params: SamplingParams | None) -> dict:
 
 def text_from_choice(choice) -> str:
     if hasattr(choice, "delta") and choice.delta:
-        return choice.delta.content
+        return choice.delta.content  # type: ignore[no-any-return]  # external OpenAI types lack precise annotations
 
     if hasattr(choice, "message"):
-        return choice.message.content
+        return choice.message.content  # type: ignore[no-any-return]  # external OpenAI types lack precise annotations
 
-    return choice.text
+    return choice.text  # type: ignore[no-any-return]  # external OpenAI types lack precise annotations
 
 
 def get_stop_reason(finish_reason: str) -> StopReason:
@@ -216,7 +218,7 @@ def convert_openai_completion_logprobs(
 ) -> list[TokenLogProbs] | None:
     if not logprobs:
         return None
-    if hasattr(logprobs, "top_logprobs"):
+    if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs:
         return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
 
     # Together supports logprobs with top_k=1 only. This means for each token position,
@@ -236,7 +238,7 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenA
     if isinstance(logprobs, float):
         # Adapt response from Together CompletionChoicesChunk
         return [TokenLogProbs(logprobs_by_token={text: logprobs})]
-    if hasattr(logprobs, "top_logprobs"):
+    if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs:
         return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
     return None
 
@@ -245,23 +247,24 @@ def process_completion_response(
     response: OpenAICompatCompletionResponse,
 ) -> CompletionResponse:
     choice = response.choices[0]
+    text = choice.text or ""
     # drop suffix <eot_id> if present and return stop reason as end of turn
-    if choice.text.endswith("<|eot_id|>"):
+    if text.endswith("<|eot_id|>"):
         return CompletionResponse(
             stop_reason=StopReason.end_of_turn,
-            content=choice.text[: -len("<|eot_id|>")],
+            content=text[: -len("<|eot_id|>")],
             logprobs=convert_openai_completion_logprobs(choice.logprobs),
         )
     # drop suffix <eom_id> if present and return stop reason as end of message
-    if choice.text.endswith("<|eom_id|>"):
+    if text.endswith("<|eom_id|>"):
         return CompletionResponse(
             stop_reason=StopReason.end_of_message,
-            content=choice.text[: -len("<|eom_id|>")],
+            content=text[: -len("<|eom_id|>")],
             logprobs=convert_openai_completion_logprobs(choice.logprobs),
         )
     return CompletionResponse(
-        stop_reason=get_stop_reason(choice.finish_reason),
-        content=choice.text,
+        stop_reason=get_stop_reason(choice.finish_reason or "stop"),
+        content=text,
         logprobs=convert_openai_completion_logprobs(choice.logprobs),
     )
 
@@ -272,10 +275,10 @@ def process_chat_completion_response(
 ) -> ChatCompletionResponse:
     choice = response.choices[0]
     if choice.finish_reason == "tool_calls":
-        if not choice.message or not choice.message.tool_calls:
+        if not hasattr(choice, "message") or not choice.message or not choice.message.tool_calls:  # type: ignore[attr-defined]  # OpenAICompatCompletionChoice is runtime duck-typed
             raise ValueError("Tool calls are not present in the response")
 
-        tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]
+        tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]  # type: ignore[attr-defined]  # OpenAICompatCompletionChoice is runtime duck-typed
         if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
             # If we couldn't parse a tool call, jsonify the tool calls and return them
             return ChatCompletionResponse(
@@ -287,9 +290,11 @@ def process_chat_completion_response(
             )
         else:
             # Otherwise, return tool calls as normal
+            # Filter to only valid ToolCall objects
+            valid_tool_calls = [tc for tc in tool_calls if isinstance(tc, ToolCall)]
             return ChatCompletionResponse(
                 completion_message=CompletionMessage(
-                    tool_calls=tool_calls,
+                    tool_calls=valid_tool_calls,
                     stop_reason=StopReason.end_of_turn,
                     # Content is not optional
                     content="",
@@ -299,7 +304,7 @@ def process_chat_completion_response(
 
     # TODO: This does not work well with tool calls for vLLM remote provider
     #   Ref: https://github.com/meta-llama/llama-stack/issues/1058
-    raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason))
+    raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason or "stop"))
 
     # NOTE: If we do not set tools in chat-completion request, we should not
     # expect the ToolCall in the response. Instead, we should return the raw
@@ -324,8 +329,8 @@ def process_chat_completion_response(
 
     return ChatCompletionResponse(
         completion_message=CompletionMessage(
-            content=raw_message.content,
-            stop_reason=raw_message.stop_reason,
+            content=raw_message.content,  # type: ignore[arg-type]  # decode_assistant_message returns Union[str, InterleavedContent]
+            stop_reason=raw_message.stop_reason or StopReason.end_of_turn,
             tool_calls=raw_message.tool_calls,
         ),
         logprobs=None,
@@ -448,7 +453,7 @@ async def process_chat_completion_stream_response(
             )
 
     # parse tool calls and report errors
-    message = decode_assistant_message(buffer, stop_reason)
+    message = decode_assistant_message(buffer, stop_reason or StopReason.end_of_turn)
 
     parsed_tool_calls = len(message.tool_calls) > 0
     if ipython and not parsed_tool_calls:
@@ -463,7 +468,7 @@ async def process_chat_completion_stream_response(
             )
         )
 
-    request_tools = {t.tool_name: t for t in request.tools}
+    request_tools = {t.tool_name: t for t in (request.tools or [])}
     for tool_call in message.tool_calls:
         if tool_call.tool_name in request_tools:
             yield ChatCompletionResponseStreamChunk(
@@ -525,7 +530,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
     }
 
     if hasattr(message, "tool_calls") and message.tool_calls:
-        result["tool_calls"] = []
+        tool_calls_list = []
         for tc in message.tool_calls:
             # The tool.tool_name can be a str or a BuiltinTool enum. If
             # it's the latter, convert to a string.
@@ -533,7 +538,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
             if isinstance(tool_name, BuiltinTool):
                 tool_name = tool_name.value
 
-            result["tool_calls"].append(
+            tool_calls_list.append(
                 {
                     "id": tc.call_id,
                     "type": "function",
@@ -543,6 +548,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
                     },
                 }
             )
+        result["tool_calls"] = tool_calls_list  # type: ignore[assignment]  # dict allows Any value, stricter type expected
     return result
 
 
@@ -608,7 +614,7 @@ async def convert_message_to_openai_dict_new(
                     ),
                 )
             elif isinstance(content_, list):
-                return [await impl(item) for item in content_]
+                return [await impl(item) for item in content_]  # type: ignore[misc]  # recursive list comprehension confuses mypy's type narrowing
             else:
                 raise ValueError(f"Unsupported content type: {type(content_)}")
 
@@ -620,7 +626,7 @@ async def convert_message_to_openai_dict_new(
         else:
             return [ret]
 
-    out: OpenAIChatCompletionMessage = None
+    out: OpenAIChatCompletionMessage
     if isinstance(message, UserMessage):
         out = OpenAIChatCompletionUserMessage(
             role="user",
@@ -636,7 +642,7 @@ async def convert_message_to_openai_dict_new(
                 ),
                 type="function",
             )
-            for tool in message.tool_calls
+            for tool in (message.tool_calls or [])
         ]
         params = {}
         if tool_calls:
@@ -644,18 +650,18 @@ async def convert_message_to_openai_dict_new(
         out = OpenAIChatCompletionAssistantMessage(
             role="assistant",
             content=await _convert_message_content(message.content),
-            **params,
+            **params,  # type: ignore[typeddict-item]  # tool_calls dict expansion conflicts with TypedDict optional field
         )
     elif isinstance(message, ToolResponseMessage):
         out = OpenAIChatCompletionToolMessage(
             role="tool",
             tool_call_id=message.call_id,
-            content=await _convert_message_content(message.content),
+            content=await _convert_message_content(message.content),  # type: ignore[typeddict-item]  # content union type incompatible with TypedDict str requirement
         )
     elif isinstance(message, SystemMessage):
         out = OpenAIChatCompletionSystemMessage(
             role="system",
-            content=await _convert_message_content(message.content),
+            content=await _convert_message_content(message.content),  # type: ignore[typeddict-item]  # content union type incompatible with TypedDict str requirement
         )
     else:
         raise ValueError(f"Unsupported message type: {type(message)}")
@@ -758,16 +764,16 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
     function = out["function"]
 
     if isinstance(tool.tool_name, BuiltinTool):
-        function["name"] = tool.tool_name.value
+        function["name"] = tool.tool_name.value  # type: ignore[index]  # dict value inferred as Any but mypy sees Collection[str]
     else:
-        function["name"] = tool.tool_name
+        function["name"] = tool.tool_name  # type: ignore[index]  # dict value inferred as Any but mypy sees Collection[str]
 
     if tool.description:
-        function["description"] = tool.description
+        function["description"] = tool.description  # type: ignore[index]  # dict value inferred as Any but mypy sees Collection[str]
 
     if tool.input_schema:
         # Pass through the entire JSON Schema as-is
-        function["parameters"] = tool.input_schema
+        function["parameters"] = tool.input_schema  # type: ignore[index]  # dict value inferred as Any but mypy sees Collection[str]
 
     # NOTE: OpenAI does not support output_schema, so we drop it here
     # It's stored in LlamaStack for validation and other provider usage
@@ -815,15 +821,15 @@ def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None
     tool_config = ToolConfig()
     if tool_choice:
         try:
-            tool_choice = ToolChoice(tool_choice)
+            tool_choice = ToolChoice(tool_choice)  # type: ignore[assignment]  # reassigning to enum narrows union but mypy can't track after exception
         except ValueError:
             pass
-        tool_config.tool_choice = tool_choice
+        tool_config.tool_choice = tool_choice  # type: ignore[assignment]  # ToolConfig.tool_choice accepts Union[ToolChoice, dict] but mypy tracks narrower type
     return tool_config
 
 
 def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]:
-    lls_tools = []
+    lls_tools: list[ToolDefinition] = []
     if not tools:
         return lls_tools
 
@@ -843,16 +849,16 @@ def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) ->
 
 
 def _convert_openai_request_response_format(
-    response_format: OpenAIResponseFormatParam = None,
+    response_format: OpenAIResponseFormatParam | None = None,
 ):
     if not response_format:
         return None
     # response_format can be a dict or a pydantic model
-    response_format = dict(response_format)
-    if response_format.get("type", "") == "json_schema":
+    response_format_dict = dict(response_format)  # type: ignore[arg-type]  # OpenAIResponseFormatParam union needs dict conversion
+    if response_format_dict.get("type", "") == "json_schema":
         return JsonSchemaResponseFormat(
-            type="json_schema",
-            json_schema=response_format.get("json_schema", {}).get("schema", ""),
+            type="json_schema",  # type: ignore[arg-type]  # Literal["json_schema"] incompatible with expected type
+            json_schema=response_format_dict.get("json_schema", {}).get("schema", ""),
         )
     return None
 
@@ -938,16 +944,15 @@ def _convert_openai_sampling_params(
 
     # Map an explicit temperature of 0 to greedy sampling
     if temperature == 0:
-        strategy = GreedySamplingStrategy()
+        sampling_params.strategy = GreedySamplingStrategy()
     else:
         # OpenAI defaults to 1.0 for temperature and top_p if unset
         if temperature is None:
             temperature = 1.0
         if top_p is None:
             top_p = 1.0
-        strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p)
+        sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p)  # type: ignore[assignment]  # SamplingParams.strategy union accepts this type
 
-    sampling_params.strategy = strategy
     return sampling_params
 
 
@@ -957,23 +962,24 @@ def openai_messages_to_messages(
     """
     Convert a list of OpenAIChatCompletionMessage into a list of Message.
     """
-    converted_messages = []
+    converted_messages: list[Message] = []
     for message in messages:
+        converted_message: Message
         if message.role == "system":
-            converted_message = SystemMessage(content=openai_content_to_content(message.content))
+            converted_message = SystemMessage(content=openai_content_to_content(message.content))  # type: ignore[arg-type]  # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
         elif message.role == "user":
-            converted_message = UserMessage(content=openai_content_to_content(message.content))
+            converted_message = UserMessage(content=openai_content_to_content(message.content))  # type: ignore[arg-type]  # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
         elif message.role == "assistant":
             converted_message = CompletionMessage(
-                content=openai_content_to_content(message.content),
-                tool_calls=_convert_openai_tool_calls(message.tool_calls),
+                content=openai_content_to_content(message.content),  # type: ignore[arg-type]  # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
+                tool_calls=_convert_openai_tool_calls(message.tool_calls) if message.tool_calls else [],  # type: ignore[arg-type]  # OpenAI tool_calls type incompatible with conversion function
                 stop_reason=StopReason.end_of_turn,
             )
         elif message.role == "tool":
             converted_message = ToolResponseMessage(
                 role="tool",
                 call_id=message.tool_call_id,
-                content=openai_content_to_content(message.content),
+                content=openai_content_to_content(message.content),  # type: ignore[arg-type]  # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
             )
         else:
             raise ValueError(f"Unknown role {message.role}")
@@ -990,9 +996,9 @@ def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionConten
         return [openai_content_to_content(c) for c in content]
     elif hasattr(content, "type"):
         if content.type == "text":
-            return TextContentItem(type="text", text=content.text)
+            return TextContentItem(type="text", text=content.text)  # type: ignore[attr-defined]  # Iterable narrowed by hasattr check but mypy doesn't track
         elif content.type == "image_url":
-            return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url)))
+            return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url)))  # type: ignore[attr-defined]  # Iterable narrowed by hasattr check but mypy doesn't track
         else:
             raise ValueError(f"Unknown content type: {content.type}")
     else:
@@ -1041,9 +1047,9 @@ def convert_openai_chat_completion_choice(
         completion_message=CompletionMessage(
             content=choice.message.content or "",  # CompletionMessage content is not optional
             stop_reason=_convert_openai_finish_reason(choice.finish_reason),
-            tool_calls=_convert_openai_tool_calls(choice.message.tool_calls),
+            tool_calls=_convert_openai_tool_calls(choice.message.tool_calls) if choice.message.tool_calls else [],  # type: ignore[arg-type]  # OpenAI tool_calls Optional type broadens union
         ),
-        logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)),
+        logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)),  # type: ignore[arg-type]  # getattr returns Any, can't narrow without inspection
     )
 
 
@@ -1070,7 +1076,7 @@ async def convert_openai_chat_completion_stream(
         choice = chunk.choices[0]  # assuming only one choice per chunk
 
         # we assume there's only one finish_reason in the stream
-        stop_reason = _convert_openai_finish_reason(choice.finish_reason) or stop_reason
+        stop_reason = _convert_openai_finish_reason(choice.finish_reason) if choice.finish_reason else stop_reason
         logprobs = getattr(choice, "logprobs", None)
 
         # if there's a tool call, emit an event for each tool in the list
@@ -1083,7 +1089,7 @@ async def convert_openai_chat_completion_stream(
                     event=ChatCompletionResponseEvent(
                         event_type=event_type,
                         delta=TextDelta(text=choice.delta.content),
-                        logprobs=_convert_openai_logprobs(logprobs),
+                        logprobs=_convert_openai_logprobs(logprobs),  # type: ignore[arg-type]  # logprobs type broadened from getattr result
                     )
                 )
 
@@ -1101,10 +1107,10 @@ async def convert_openai_chat_completion_stream(
                         event=ChatCompletionResponseEvent(
                             event_type=event_type,
                             delta=ToolCallDelta(
-                                tool_call=_convert_openai_tool_calls([tool_call])[0],
+                                tool_call=_convert_openai_tool_calls([tool_call])[0],  # type: ignore[arg-type, list-item]  # delta tool_call type differs from complete tool_call
                                 parse_status=ToolCallParseStatus.succeeded,
                             ),
-                            logprobs=_convert_openai_logprobs(logprobs),
+                            logprobs=_convert_openai_logprobs(logprobs),  # type: ignore[arg-type]  # logprobs type broadened from getattr result
                         )
                     )
             else:
@@ -1125,12 +1131,15 @@ async def convert_openai_chat_completion_stream(
                         if tool_call.function.name:
                             buffer["name"] = tool_call.function.name
                             delta = f"{buffer['name']}("
-                            buffer["content"] += delta
+                            if buffer["content"] is not None:
+                                buffer["content"] += delta
 
                         if tool_call.function.arguments:
                             delta = tool_call.function.arguments
-                            buffer["arguments"] += delta
-                            buffer["content"] += delta
+                            if buffer["arguments"] is not None and delta:
+                                buffer["arguments"] += delta
+                            if buffer["content"] is not None and delta:
+                                buffer["content"] += delta
 
                         yield ChatCompletionResponseStreamChunk(
                             event=ChatCompletionResponseEvent(
@@ -1139,7 +1148,7 @@ async def convert_openai_chat_completion_stream(
                                     tool_call=delta,
                                     parse_status=ToolCallParseStatus.in_progress,
                                 ),
-                                logprobs=_convert_openai_logprobs(logprobs),
+                                logprobs=_convert_openai_logprobs(logprobs),  # type: ignore[arg-type]  # logprobs type broadened from getattr result
                             )
                         )
         elif choice.delta.content:
@@ -1147,7 +1156,7 @@ async def convert_openai_chat_completion_stream(
                 event=ChatCompletionResponseEvent(
                     event_type=event_type,
                     delta=TextDelta(text=choice.delta.content or ""),
-                    logprobs=_convert_openai_logprobs(logprobs),
+                    logprobs=_convert_openai_logprobs(logprobs),  # type: ignore[arg-type]  # logprobs type broadened from getattr result
                 )
             )
 
@@ -1155,7 +1164,8 @@ async def convert_openai_chat_completion_stream(
         logger.debug(f"toolcall_buffer[{idx}]: {buffer}")
         if buffer["name"]:
             delta = ")"
-            buffer["content"] += delta
+            if buffer["content"] is not None:
+                buffer["content"] += delta
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
                     event_type=event_type,
@@ -1168,16 +1178,16 @@ async def convert_openai_chat_completion_stream(
             )
 
             try:
-                tool_call = ToolCall(
-                    call_id=buffer["call_id"],
-                    tool_name=buffer["name"],
-                    arguments=buffer["arguments"],
+                parsed_tool_call = ToolCall(
+                    call_id=buffer["call_id"] or "",
+                    tool_name=buffer["name"] or "",
+                    arguments=buffer["arguments"] or "",
                 )
                 yield ChatCompletionResponseStreamChunk(
                     event=ChatCompletionResponseEvent(
                         event_type=ChatCompletionResponseEventType.progress,
                         delta=ToolCallDelta(
-                            tool_call=tool_call,
+                            tool_call=parsed_tool_call,  # type: ignore[arg-type]  # ToolCallDelta.tool_call accepts Union[str, ToolCall]
                             parse_status=ToolCallParseStatus.succeeded,
                         ),
                         stop_reason=stop_reason,
@@ -1189,7 +1199,7 @@ async def convert_openai_chat_completion_stream(
                     event=ChatCompletionResponseEvent(
                         event_type=ChatCompletionResponseEventType.progress,
                         delta=ToolCallDelta(
-                            tool_call=buffer["content"],
+                            tool_call=buffer["content"],  # type: ignore[arg-type]  # ToolCallDelta.tool_call accepts Union[str, ToolCall]
                             parse_status=ToolCallParseStatus.failed,
                         ),
                         stop_reason=stop_reason,
@@ -1250,7 +1260,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
         top_p: float | None = None,
         user: str | None = None,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        messages = openai_messages_to_messages(messages)
+        messages = openai_messages_to_messages(messages)  # type: ignore[assignment]  # converted from OpenAI to LlamaStack message format
         response_format = _convert_openai_request_response_format(response_format)
         sampling_params = _convert_openai_sampling_params(
             max_tokens=max_tokens,
@@ -1259,15 +1269,15 @@ class OpenAIChatCompletionToLlamaStackMixin:
         )
         tool_config = _convert_openai_request_tool_config(tool_choice)
 
-        tools = _convert_openai_request_tools(tools)
+        tools = _convert_openai_request_tools(tools)  # type: ignore[assignment]  # converted from OpenAI to LlamaStack tool format
         if tool_config.tool_choice == ToolChoice.none:
-            tools = []
+            tools = []  # type: ignore[assignment]  # empty list narrows return type but mypy tracks broader type
 
         outstanding_responses = []
         # "n" is the number of completions to generate per prompt
         n = n or 1
         for _i in range(0, n):
-            response = self.chat_completion(
+            response = self.chat_completion(  # type: ignore[attr-defined]  # mixin expects class to implement chat_completion
                 model_id=model,
                 messages=messages,
                 sampling_params=sampling_params,
@@ -1279,7 +1289,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
             outstanding_responses.append(response)
 
         if stream:
-            return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses)
+            return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses)  # type: ignore[no-any-return]  # mixin async generator return type too complex for mypy
 
         return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response(
             self, model, outstanding_responses
@@ -1295,14 +1305,16 @@ class OpenAIChatCompletionToLlamaStackMixin:
             response = await outstanding_response
             async for chunk in response:
                 event = chunk.event
-                finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason)
+                finish_reason = (
+                    _convert_stop_reason_to_openai_finish_reason(event.stop_reason) if event.stop_reason else None
+                )
 
                 if isinstance(event.delta, TextDelta):
                     text_delta = event.delta.text
                     delta = OpenAIChoiceDelta(content=text_delta)
                     yield OpenAIChatCompletionChunk(
                         id=id,
-                        choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)],
+                        choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)],  # type: ignore[arg-type]  # finish_reason Optional[str] incompatible with Literal union
                         created=int(time.time()),
                         model=model,
                         object="chat.completion.chunk",
@@ -1310,13 +1322,17 @@ class OpenAIChatCompletionToLlamaStackMixin:
                 elif isinstance(event.delta, ToolCallDelta):
                     if event.delta.parse_status == ToolCallParseStatus.succeeded:
                         tool_call = event.delta.tool_call
+                        if isinstance(tool_call, str):
+                            continue
 
                         # First chunk includes full structure
                         openai_tool_call = OpenAIChoiceDeltaToolCall(
                             index=0,
                             id=tool_call.call_id,
                             function=OpenAIChoiceDeltaToolCallFunction(
-                                name=tool_call.tool_name,
+                                name=tool_call.tool_name
+                                if isinstance(tool_call.tool_name, str)
+                                else tool_call.tool_name.value,  # type: ignore[arg-type]  # enum .value extraction on Union confuses mypy
                                 arguments="",
                             ),
                         )
@@ -1324,7 +1340,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
                         yield OpenAIChatCompletionChunk(
                             id=id,
                             choices=[
-                                OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
+                                OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)  # type: ignore[arg-type]  # finish_reason Optional[str] incompatible with Literal union
                             ],
                             created=int(time.time()),
                             model=model,
@@ -1341,7 +1357,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
                         yield OpenAIChatCompletionChunk(
                             id=id,
                             choices=[
-                                OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
+                                OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)  # type: ignore[arg-type]  # finish_reason Optional[str] incompatible with Literal union
                             ],
                             created=int(time.time()),
                             model=model,
@@ -1351,7 +1367,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
     async def _process_non_stream_response(
         self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]]
     ) -> OpenAIChatCompletion:
-        choices = []
+        choices: list[OpenAIChatCompletionChoice] = []
         for outstanding_response in outstanding_responses:
             response = await outstanding_response
             completion_message = response.completion_message
@@ -1360,14 +1376,14 @@ class OpenAIChatCompletionToLlamaStackMixin:
 
             choice = OpenAIChatCompletionChoice(
                 index=len(choices),
-                message=message,
+                message=message,  # type: ignore[arg-type]  # OpenAIChatCompletionMessage union incompatible with narrower Message type
                 finish_reason=finish_reason,
             )
-            choices.append(choice)
+            choices.append(choice)  # type: ignore[arg-type]  # OpenAIChatCompletionChoice type annotation mismatch
 
         return OpenAIChatCompletion(
             id=f"chatcmpl-{uuid.uuid4()}",
-            choices=choices,
+            choices=choices,  # type: ignore[arg-type]  # list[OpenAIChatCompletionChoice] union incompatible
             created=int(time.time()),
             model=model,
             object="chat.completion",

src/llama_stack/providers/utils/memory/vector_store.py

@@ -196,6 +196,7 @@ def make_overlapped_chunks(
         chunks.append(
             Chunk(
                 content=chunk,
+                chunk_id=chunk_id,
                 metadata=chunk_metadata,
                 chunk_metadata=backend_chunk_metadata,
             )

src/llama_stack/strong_typing/inspection.py

@@ -430,6 +430,32 @@ def _unwrap_generic_list(typ: type[list[T]]) -> type[T]:
     return list_type  # type: ignore[no-any-return]
 
 
+def is_generic_sequence(typ: object) -> bool:
+    "True if the specified type is a generic Sequence, i.e. `Sequence[T]`."
+    import collections.abc
+
+    typ = unwrap_annotated_type(typ)
+    return typing.get_origin(typ) is collections.abc.Sequence
+
+
+def unwrap_generic_sequence(typ: object) -> type:
+    """
+    Extracts the item type of a Sequence type.
+
+    :param typ: The Sequence type `Sequence[T]`.
+    :returns: The item type `T`.
+    """
+
+    return rewrap_annotated_type(_unwrap_generic_sequence, typ)  # type: ignore[arg-type]
+
+
+def _unwrap_generic_sequence(typ: object) -> type:
+    "Extracts the item type of a Sequence type (e.g. returns `T` for `Sequence[T]`)."
+
+    (sequence_type,) = typing.get_args(typ)  # unpack single tuple element
+    return sequence_type  # type: ignore[no-any-return]
+
+
 def is_generic_set(typ: object) -> TypeGuard[type[set]]:
     "True if the specified type is a generic set, i.e. `Set[T]`."
 

src/llama_stack/strong_typing/name.py

@@ -18,10 +18,12 @@ from .inspection import (
     TypeLike,
     is_generic_dict,
     is_generic_list,
+    is_generic_sequence,
     is_type_optional,
     is_type_union,
     unwrap_generic_dict,
     unwrap_generic_list,
+    unwrap_generic_sequence,
     unwrap_optional_type,
     unwrap_union_types,
 )
@@ -155,24 +157,28 @@ def python_type_to_name(data_type: TypeLike, force: bool = False) -> str:
     if metadata is not None:
         # type is Annotated[T, ...]
         arg = typing.get_args(data_type)[0]
-        return python_type_to_name(arg)
+        return python_type_to_name(arg, force=force)
 
     if force:
         # generic types
         if is_type_optional(data_type, strict=True):
-            inner_name = python_type_to_name(unwrap_optional_type(data_type))
+            inner_name = python_type_to_name(unwrap_optional_type(data_type), force=True)
             return f"Optional__{inner_name}"
         elif is_generic_list(data_type):
-            item_name = python_type_to_name(unwrap_generic_list(data_type))
+            item_name = python_type_to_name(unwrap_generic_list(data_type), force=True)
+            return f"List__{item_name}"
+        elif is_generic_sequence(data_type):
+            # Treat Sequence the same as List for schema generation purposes
+            item_name = python_type_to_name(unwrap_generic_sequence(data_type), force=True)
             return f"List__{item_name}"
         elif is_generic_dict(data_type):
             key_type, value_type = unwrap_generic_dict(data_type)
-            key_name = python_type_to_name(key_type)
-            value_name = python_type_to_name(value_type)
+            key_name = python_type_to_name(key_type, force=True)
+            value_name = python_type_to_name(value_type, force=True)
             return f"Dict__{key_name}__{value_name}"
         elif is_type_union(data_type):
             member_types = unwrap_union_types(data_type)
-            member_names = "__".join(python_type_to_name(member_type) for member_type in member_types)
+            member_names = "__".join(python_type_to_name(member_type, force=True) for member_type in member_types)
             return f"Union__{member_names}"
 
     # named system or user-defined type

src/llama_stack/strong_typing/schema.py

@@ -111,7 +111,7 @@ def get_class_property_docstrings(
 def docstring_to_schema(data_type: type) -> Schema:
     short_description, long_description = get_class_docstrings(data_type)
     schema: Schema = {
-        "title": python_type_to_name(data_type),
+        "title": python_type_to_name(data_type, force=True),
     }
 
     description = "\n".join(filter(None, [short_description, long_description]))
@@ -417,6 +417,10 @@ class JsonSchemaGenerator:
         if origin_type is list:
             (list_type,) = typing.get_args(typ)  # unpack single tuple element
             return {"type": "array", "items": self.type_to_schema(list_type)}
+        elif origin_type is collections.abc.Sequence:
+            # Treat Sequence the same as list for JSON schema (both are arrays)
+            (sequence_type,) = typing.get_args(typ)  # unpack single tuple element
+            return {"type": "array", "items": self.type_to_schema(sequence_type)}
         elif origin_type is dict:
             key_type, value_type = typing.get_args(typ)
             if not (key_type is str or key_type is int or is_type_enum(key_type)):