fix(mypy): part-03 completely resolve meta reference responses impl typing issues (#3951)

## Summary
Resolves all mypy errors in meta reference agent OpenAI responses
implementation by adding proper type narrowing, None checks, and
Sequence type support.

## Changes
- Fixed streaming.py, openai_responses.py, utils.py, tool_executor.py,
agent_instance.py
- Added Sequence type support to schema generator (ensures correct JSON
schema generation)
- Applied union type narrowing and None checks throughout

## Test plan
- All modified files pass mypy type checking (0 errors)
- Schema generator produces correct `type: array` for Sequence types

---------

Co-authored-by: Claude <noreply@anthropic.com>

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)):