[pull] main from openai:main

src/agents/extensions/models/litellm_model.py

@@ -53,10 +53,11 @@
 
 class InternalChatCompletionMessage(ChatCompletionMessage):
     """
-    An internal subclass to carry reasoning_content without modifying the original model.
-    """
+    An internal subclass to carry reasoning_content and thinking_blocks without modifying the original model.
+    """  # noqa: E501
 
     reasoning_content: str
+    thinking_blocks: list[dict[str, Any]] | None = None
 
 
 class LitellmModel(Model):
@@ -401,6 +402,26 @@ def convert_message_to_openai(
         if hasattr(message, "reasoning_content") and message.reasoning_content:
             reasoning_content = message.reasoning_content
 
+        # Extract full thinking blocks including signatures (for Anthropic)
+        thinking_blocks: list[dict[str, Any]] | None = None
+        if hasattr(message, "thinking_blocks") and message.thinking_blocks:
+            # Convert thinking blocks to dict format for compatibility
+            thinking_blocks = []
+            for block in message.thinking_blocks:
+                if isinstance(block, dict):
+                    thinking_blocks.append(cast(dict[str, Any], block))
+                else:
+                    # Convert object to dict by accessing its attributes
+                    block_dict: dict[str, Any] = {}
+                    if hasattr(block, '__dict__'):
+                        block_dict = dict(block.__dict__.items())
+                    elif hasattr(block, 'model_dump'):
+                        block_dict = block.model_dump()
+                    else:
+                        # Last resort: convert to string representation
+                        block_dict = {"thinking": str(block)}
+                    thinking_blocks.append(block_dict)
+
         return InternalChatCompletionMessage(
             content=message.content,
             refusal=refusal,
@@ -409,6 +430,7 @@ def convert_message_to_openai(
             audio=message.get("audio", None),  # litellm deletes audio if not present
             tool_calls=tool_calls,
             reasoning_content=reasoning_content,
+            thinking_blocks=thinking_blocks,
         )
 
     @classmethod

src/agents/models/chatcmpl_converter.py

@@ -95,14 +95,24 @@ def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TRespon
 
         # Handle reasoning content if available
         if hasattr(message, "reasoning_content") and message.reasoning_content:
-            items.append(
-                ResponseReasoningItem(
-                    id=FAKE_RESPONSES_ID,
-                    summary=[Summary(text=message.reasoning_content, type="summary_text")],
-                    type="reasoning",
-                )
+            reasoning_item = ResponseReasoningItem(
+                id=FAKE_RESPONSES_ID,
+                summary=[Summary(text=message.reasoning_content, type="summary_text")],
+                type="reasoning",
             )
 
+            # Store full thinking blocks for Anthropic compatibility
+            if hasattr(message, "thinking_blocks") and message.thinking_blocks:
+                # Store thinking blocks in the reasoning item's content
+                # Convert thinking blocks to Content objects
+                from openai.types.responses.response_reasoning_item import Content
+                reasoning_item.content = [
+                    Content(text=str(block.get("thinking", "")), type="reasoning_text")
+                    for block in message.thinking_blocks
+                ]
+
+            items.append(reasoning_item)
+
         message_item = ResponseOutputMessage(
             id=FAKE_RESPONSES_ID,
             content=[],

src/agents/run.py

@@ -45,6 +45,7 @@
 )
 from .handoffs import Handoff, HandoffInputFilter, handoff
 from .items import (
+    HandoffCallItem,
     ItemHelpers,
     ModelResponse,
     RunItem,
@@ -60,7 +61,12 @@
 from .models.multi_provider import MultiProvider
 from .result import RunResult, RunResultStreaming
 from .run_context import RunContextWrapper, TContext
-from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent, RunItemStreamEvent
+from .stream_events import (
+    AgentUpdatedStreamEvent,
+    RawResponsesStreamEvent,
+    RunItemStreamEvent,
+    StreamEvent,
+)
 from .tool import Tool
 from .tracing import Span, SpanError, agent_span, get_current_trace, trace
 from .tracing.span_data import AgentSpanData
@@ -1095,14 +1101,19 @@ async def _run_single_turn_streamed(
             context_wrapper=context_wrapper,
             run_config=run_config,
             tool_use_tracker=tool_use_tracker,
+            event_queue=streamed_result._event_queue,
         )
 
-        if emitted_tool_call_ids:
-            import dataclasses as _dc
+        import dataclasses as _dc
+
+        # Filter out items that have already been sent to avoid duplicates
+        items_to_filter = single_step_result.new_step_items
 
-            filtered_items = [
+        if emitted_tool_call_ids:
+            # Filter out tool call items that were already emitted during streaming
+            items_to_filter = [
                 item
-                for item in single_step_result.new_step_items
+                for item in items_to_filter
                 if not (
                     isinstance(item, ToolCallItem)
                     and (
@@ -1114,15 +1125,17 @@ async def _run_single_turn_streamed(
                 )
             ]
 
-            single_step_result_filtered = _dc.replace(
-                single_step_result, new_step_items=filtered_items
-            )
+        # Filter out HandoffCallItem to avoid duplicates (already sent earlier)
+        items_to_filter = [
+            item for item in items_to_filter
+            if not isinstance(item, HandoffCallItem)
+        ]
 
-            RunImpl.stream_step_result_to_queue(
-                single_step_result_filtered, streamed_result._event_queue
-            )
-        else:
-            RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue)
+        # Create filtered result and send to queue
+        filtered_result = _dc.replace(
+            single_step_result, new_step_items=items_to_filter
+        )
+        RunImpl.stream_step_result_to_queue(filtered_result, streamed_result._event_queue)
         return single_step_result
 
     @classmethod
@@ -1207,6 +1220,7 @@ async def _get_single_step_result_from_response(
         context_wrapper: RunContextWrapper[TContext],
         run_config: RunConfig,
         tool_use_tracker: AgentToolUseTracker,
+        event_queue: asyncio.Queue[StreamEvent | QueueCompleteSentinel] | None = None,
     ) -> SingleStepResult:
         processed_response = RunImpl.process_model_response(
             agent=agent,
@@ -1218,6 +1232,15 @@ async def _get_single_step_result_from_response(
 
         tool_use_tracker.add_tool_use(agent, processed_response.tools_used)
 
+        # Send handoff items immediately for streaming, but avoid duplicates
+        if event_queue is not None and processed_response.new_items:
+            handoff_items = [
+                item for item in processed_response.new_items
+                if isinstance(item, HandoffCallItem)
+            ]
+            if handoff_items:
+                RunImpl.stream_step_items_to_queue(cast(list[RunItem], handoff_items), event_queue)
+
         return await RunImpl.execute_tools_and_side_effects(
             agent=agent,
             original_input=original_input,

tests/test_anthropic_thinking_blocks.py

@@ -0,0 +1,101 @@
+"""
+Test for Anthropic thinking blocks in conversation history.
+
+This test validates the fix for issue #1704:
+- Thinking blocks are properly preserved from Anthropic responses
+- Reasoning items are stored in session but not sent back in conversation history
+- Non-reasoning models are unaffected
+- Token usage is not increased for non-reasoning scenarios
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from agents.extensions.models.litellm_model import InternalChatCompletionMessage
+from agents.models.chatcmpl_converter import Converter
+
+
+def create_mock_anthropic_response_with_thinking() -> InternalChatCompletionMessage:
+    """Create a mock Anthropic response with thinking blocks (like real response)."""
+    message = InternalChatCompletionMessage(
+        role="assistant",
+        content="I'll check the weather in Paris for you.",
+        reasoning_content="I need to call the weather function for Paris",
+        thinking_blocks=[
+            {
+                "type": "thinking",
+                "thinking": "I need to call the weather function for Paris",
+                "signature": "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",  # noqa: E501
+            }
+        ],
+    )
+    return message
+
+
+def test_converter_skips_reasoning_items():
+    """
+    Unit test to verify that reasoning items are skipped when converting items to messages.
+    """
+    # Create test items including a reasoning item
+    test_items: list[dict[str, Any]] = [
+        {"role": "user", "content": "Hello"},
+        {
+            "id": "reasoning_123",
+            "type": "reasoning",
+            "summary": [{"text": "User said hello", "type": "summary_text"}],
+        },
+        {
+            "id": "msg_123",
+            "type": "message",
+            "role": "assistant",
+            "content": [{"type": "output_text", "text": "Hi there!"}],
+            "status": "completed",
+        },
+    ]
+
+    # Convert to messages
+    messages = Converter.items_to_messages(test_items)  # type: ignore[arg-type]
+
+    # Should have user message and assistant message, but no reasoning content
+    assert len(messages) == 2
+    assert messages[0]["role"] == "user"
+    assert messages[1]["role"] == "assistant"
+
+    # Verify no thinking blocks in assistant message
+    assistant_msg = messages[1]
+    content = assistant_msg.get("content")
+    if isinstance(content, list):
+        for part in content:
+            assert part.get("type") != "thinking"
+
+
+def test_reasoning_items_preserved_in_message_conversion():
+    """
+    Test that reasoning content and thinking blocks are properly extracted
+    from Anthropic responses and stored in reasoning items.
+    """
+    # Create mock message with thinking blocks
+    mock_message = create_mock_anthropic_response_with_thinking()
+
+    # Convert to output items
+    output_items = Converter.message_to_output_items(mock_message)
+
+    # Should have reasoning item, message item, and tool call items
+    reasoning_items = [
+        item for item in output_items if hasattr(item, "type") and item.type == "reasoning"
+    ]
+    assert len(reasoning_items) == 1
+
+    reasoning_item = reasoning_items[0]
+    assert reasoning_item.summary[0].text == "I need to call the weather function for Paris"
+
+    # Verify thinking blocks are stored if we preserve them
+    if (
+        hasattr(reasoning_item, "content")
+        and reasoning_item.content
+        and len(reasoning_item.content) > 0
+    ):
+        thinking_block = reasoning_item.content[0]
+        assert thinking_block.type == "reasoning_text"
+        assert thinking_block.text == "I need to call the weather function for Paris"

tests/test_stream_events.py

@@ -3,10 +3,12 @@
 
 import pytest
 
-from agents import Agent, Runner, function_tool
+from agents import Agent, HandoffCallItem, Runner, function_tool
+from agents.extensions.handoff_filters import remove_all_tools
+from agents.handoffs import handoff
 
 from .fake_model import FakeModel
-from .test_responses import get_function_tool_call, get_text_message
+from .test_responses import get_function_tool_call, get_handoff_tool_call, get_text_message
 
 
 @function_tool
@@ -52,3 +54,57 @@ async def test_stream_events_main():
     assert tool_call_start_time > 0, "tool_call_item was not observed"
     assert tool_call_end_time > 0, "tool_call_output_item was not observed"
     assert tool_call_start_time < tool_call_end_time, "Tool call ended before or equals it started?"
+
+
+@pytest.mark.asyncio
+async def test_stream_events_main_with_handoff():
+    @function_tool
+    async def foo(args: str) -> str:
+        return f"foo_result_{args}"
+
+    english_agent = Agent(
+        name="EnglishAgent",
+        instructions="You only speak English.",
+        model=FakeModel(),
+    )
+
+    model = FakeModel()
+    model.add_multiple_turn_outputs(
+        [
+            [
+                get_text_message("Hello"),
+                get_function_tool_call("foo", '{"args": "arg1"}'),
+                get_handoff_tool_call(english_agent),
+            ],
+            [get_text_message("Done")],
+        ]
+    )
+
+    triage_agent = Agent(
+        name="TriageAgent",
+        instructions="Handoff to the appropriate agent based on the language of the request.",
+        handoffs=[
+            handoff(english_agent, input_filter=remove_all_tools),
+        ],
+        tools=[foo],
+        model=model,
+    )
+
+    result = Runner.run_streamed(
+        triage_agent,
+        input="Start",
+    )
+
+    handoff_requested_seen = False
+    agent_switched_to_english = False
+
+    async for event in result.stream_events():
+        if event.type == "run_item_stream_event":
+            if isinstance(event.item, HandoffCallItem):
+                handoff_requested_seen = True
+        elif event.type == "agent_updated_stream_event":
+            if hasattr(event, "new_agent") and event.new_agent.name == "EnglishAgent":
+                agent_switched_to_english = True
+
+    assert handoff_requested_seen, "handoff_requested event not observed"
+    assert agent_switched_to_english, "Agent did not switch to EnglishAgent"