Merge branch 'main' into IMPROVEMENT

Author: seratch

docs/agents.md

@@ -34,6 +34,7 @@ Agents are generic on their `context` type. Context is a dependency-injection to
 ```python
 @dataclass
 class UserContext:
+    name: str
     uid: str
     is_pro_user: bool
 

docs/ja/agents.md

@@ -38,6 +38,7 @@ agent = Agent(
 ```python
 @dataclass
 class UserContext:
+    name: str
     uid: str
     is_pro_user: bool
 

docs/scripts/generate_ref_files.py

@@ -31,6 +31,7 @@ def md_target(py_path: Path) -> Path:
     rel = py_path.relative_to(SRC_ROOT).with_suffix(".md")
     return DOCS_ROOT / rel
 
+
 def pretty_title(last_segment: str) -> str:
     """
     Convert a module/file segment like 'tool_context' to 'Tool Context'.
@@ -39,6 +40,7 @@ def pretty_title(last_segment: str) -> str:
     cleaned = last_segment.replace("_", " ").replace("-", " ")
     return capwords(cleaned)
 
+
 # ---- Main ------------------------------------------------------------
 
 

src/agents/extensions/models/litellm_model.py

@@ -45,6 +45,14 @@
 from ...usage import Usage
 
 
+class InternalChatCompletionMessage(ChatCompletionMessage):
+    """
+    An internal subclass to carry reasoning_content without modifying the original model.
+    """
+
+    reasoning_content: str
+
+
 class LitellmModel(Model):
     """This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI,
     Anthropic, Gemini, Mistral, and many other models.
@@ -364,13 +372,18 @@ def convert_message_to_openai(
             provider_specific_fields.get("refusal", None) if provider_specific_fields else None
         )
 
-        return ChatCompletionMessage(
+        reasoning_content = ""
+        if hasattr(message, "reasoning_content") and message.reasoning_content:
+            reasoning_content = message.reasoning_content
+
+        return InternalChatCompletionMessage(
             content=message.content,
             refusal=refusal,
             role="assistant",
             annotations=cls.convert_annotations_to_openai(message),
             audio=message.get("audio", None),  # litellm deletes audio if not present
             tool_calls=tool_calls,
+            reasoning_content=reasoning_content,
         )
 
     @classmethod

src/agents/models/chatcmpl_converter.py

@@ -36,6 +36,7 @@
     ResponseOutputRefusal,
     ResponseOutputText,
     ResponseReasoningItem,
+    ResponseReasoningItemParam,
 )
 from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
 from openai.types.responses.response_reasoning_item import Summary
@@ -210,6 +211,12 @@ def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam
             return cast(ResponseOutputMessageParam, item)
         return None
 
+    @classmethod
+    def maybe_reasoning_message(cls, item: Any) -> ResponseReasoningItemParam | None:
+        if isinstance(item, dict) and item.get("type") == "reasoning":
+            return cast(ResponseReasoningItemParam, item)
+        return None
+
     @classmethod
     def extract_text_content(
         cls, content: str | Iterable[ResponseInputContentParam]
@@ -459,7 +466,11 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
                     f"Encountered an item_reference, which is not supported: {item_ref}"
                 )
 
-            # 7) If we haven't recognized it => fail or ignore
+            # 7) reasoning message => not handled
+            elif cls.maybe_reasoning_message(item):
+                pass
+
+            # 8) If we haven't recognized it => fail or ignore
             else:
                 raise UserError(f"Unhandled item type or structure: {item}")
 

src/agents/tracing/processors.py

@@ -269,8 +269,7 @@ def _run(self):
 
     def _export_batches(self, force: bool = False):
         """Drains the queue and exports in batches. If force=True, export everything.
-        Otherwise, export up to `max_batch_size` repeatedly until the queue is empty or below a
-        certain threshold.
+        Otherwise, export up to `max_batch_size` repeatedly until the queue is completely empty.
         """
         while True:
             items_to_export: list[Span[Any] | Trace] = []