[Frontend][Responses API] Support reporting tool output tokens and fix...

[Frontend][Responses API] Support reporting tool output tokens and fix reasoning token count (#24285)
Signed-off-by: Ye (Charlotte) Qi <yeq@meta.com>

tests/entrypoints/test_context.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+from openai_harmony import StreamState
+
+from vllm.entrypoints.context import HarmonyContext, StreamingHarmonyContext
+from vllm.outputs import CompletionOutput, RequestOutput
+
+
+# Helper function for Python < 3.10 compatibility
+async def async_next(async_iterator):
+    """Compatibility function equivalent to Python 3.10's anext()."""
+    return await async_iterator.__anext__()
+
+
+def create_mock_request_output(
+    prompt_token_ids=None,
+    output_token_ids=None,
+    num_cached_tokens=0,
+    finished=True,
+):
+    """Helper function to create a mock RequestOutput object for testing."""
+    outputs = []
+    token_ids = output_token_ids if output_token_ids is not None else []
+    outputs = [
+        CompletionOutput(
+            index=0,
+            text="Test output",
+            token_ids=token_ids,
+            cumulative_logprob=0.0,
+            logprobs=None,
+            finish_reason=None,
+            stop_reason=None,
+        )
+    ]
+
+    return RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=prompt_token_ids,
+        prompt_logprobs=None,
+        outputs=outputs,
+        finished=finished,
+        num_cached_tokens=num_cached_tokens,
+    )
+
+
+async def generate_mock_outputs(num_turns,
+                                prompt_token_counts,
+                                output_token_counts,
+                                cached_token_counts=None):
+    """Generate a sequence of mock RequestOutput objects to simulate multiple
+    turns."""
+    if cached_token_counts is None:
+        cached_token_counts = [0] * num_turns
+
+    for i in range(num_turns):
+        # Create mock prompt token IDs and output token IDs
+        prompt_token_ids = list(range(1, prompt_token_counts[i] + 1))
+        output_token_ids = list(range(1, output_token_counts[i] + 1))
+
+        # Create and yield the RequestOutput
+        yield create_mock_request_output(
+            prompt_token_ids=prompt_token_ids,
+            output_token_ids=output_token_ids,
+            num_cached_tokens=cached_token_counts[i],
+        )
+
+
+@pytest.fixture
+def mock_parser():
+    """Set up a mock parser for tests."""
+    with patch("vllm.entrypoints.context.get_streamable_parser_for_assistant"
+               ) as mock_parser_factory:
+        # Create a mock parser object
+        parser = MagicMock()
+        parser.messages = []
+        parser.current_channel = None
+        parser.state = StreamState.EXPECT_START
+        mock_parser_factory.return_value = parser
+        yield parser
+
+
+def test_single_turn_token_counting():
+    """Test token counting behavior for a single turn."""
+    # Create a context
+    context = HarmonyContext(messages=[], available_tools=[])
+
+    # Create a mock RequestOutput with specific token counts
+    mock_output = create_mock_request_output(
+        prompt_token_ids=[1, 2, 3, 4, 5],  # 5 prompt tokens
+        output_token_ids=[6, 7, 8],  # 3 output tokens
+        num_cached_tokens=2,  # 2 cached tokens
+    )
+
+    # Append the output to the context
+    context.append_output(mock_output)
+
+    # Verify the token counts
+    assert context.num_prompt_tokens == 5
+    assert context.num_output_tokens == 3
+    assert context.num_cached_tokens == 2
+    assert context.num_tool_output_tokens == 0  # No tool tokens in first turn
+
+    # Verify internal state tracking
+    assert not context.is_first_turn
+    assert context.previous_turn.input_tokens == 5
+    assert context.previous_turn.output_tokens == 3
+
+
+@pytest.mark.asyncio
+async def test_multi_turn_token_counting():
+    """Test token counting behavior across multiple turns with tool output."""
+    # Create a context
+    context = HarmonyContext(messages=[], available_tools=["browser"])
+
+    # Simulate a conversation with 3 turns
+    # Turn 1: prefill 5, decode 3, tool 7
+    # Turn 2: prefill 15, cached 5, decode 4, tool 1
+    # Turn 3: prefill 20, cached 15, decode 5
+    prompt_token_counts = [5, 15, 20]
+    output_token_counts = [3, 4, 5]
+    cached_token_counts = [0, 5, 15]
+    mock_generator = generate_mock_outputs(3, prompt_token_counts,
+                                           output_token_counts,
+                                           cached_token_counts)
+
+    # First turn - initial prompt and response
+    mock_output1 = await async_next(mock_generator)
+    context.append_output(mock_output1)
+
+    # At this point, we should have 5 prompt tokens and 3 output tokens
+    assert context.num_prompt_tokens == 5
+    assert context.num_output_tokens == 3
+    assert context.num_tool_output_tokens == 0
+
+    # Second turn - after tool output
+    mock_output2 = await async_next(mock_generator)
+    context.append_output(mock_output2)
+    # Current prompt tokens (15) - last_turn_input_tokens (5) -
+    # last_turn_output_tokens (3) = 7
+    expected_tool_output = 7
+
+    assert context.num_prompt_tokens == 5 + 15
+    assert context.num_output_tokens == 3 + 4
+    assert context.num_tool_output_tokens == expected_tool_output
+    assert context.num_cached_tokens == 5
+
+    # Third turn - final response
+    mock_output3 = await async_next(mock_generator)
+    context.append_output(mock_output3)
+    # Additional tool output tokens from third turn:
+    # Current prompt (20) - last_turn_input_tokens (15) -
+    # last_turn_output_tokens (4) = 1
+    expected_tool_output = 7 + 1
+
+    assert context.num_prompt_tokens == 5 + 15 + 20
+    assert context.num_output_tokens == 3 + 4 + 5
+    assert context.num_tool_output_tokens == expected_tool_output
+    assert context.num_cached_tokens == 5 + 15
+
+
+def test_empty_output_tokens():
+    """Test behavior when RequestOutput has empty output tokens."""
+    context = HarmonyContext(messages=[], available_tools=[])
+
+    # Create a RequestOutput with empty output tokens
+    mock_output = create_mock_request_output(
+        prompt_token_ids=[1, 2, 3],  # 3 prompt tokens
+        output_token_ids=[],  # Empty output tokens list
+        num_cached_tokens=1,
+    )
+
+    context.append_output(mock_output)
+
+    # Should handle empty outputs gracefully
+    assert context.num_prompt_tokens == 3
+    assert context.num_output_tokens == 0  # No output tokens
+    assert context.num_cached_tokens == 1
+    assert context.num_tool_output_tokens == 0
+
+
+def test_missing_prompt_token_ids():
+    """Test behavior when RequestOutput has None prompt_token_ids."""
+    context = HarmonyContext(messages=[], available_tools=[])
+
+    mock_output = create_mock_request_output(
+        prompt_token_ids=None,  # No prompt token IDs
+        output_token_ids=[1, 2],  # 2 output tokens
+        num_cached_tokens=0,
+    )
+
+    # Logger.error will be called, but we don't need to check for warnings
+    # here Just ensure it doesn't raise an exception
+    context.append_output(mock_output)
+
+    # Should handle missing prompt tokens gracefully
+    assert context.num_prompt_tokens == 0
+    assert context.num_output_tokens == 2
+    assert context.num_cached_tokens == 0
+    assert context.num_tool_output_tokens == 0
+
+
+def test_reasoning_tokens_counting(mock_parser):
+    """Test that reasoning tokens are counted correctly."""
+    context = HarmonyContext(messages=[], available_tools=[])
+
+    # Mock parser to simulate reasoning channel
+    mock_parser.current_channel = "analysis"  # Reasoning channel
+
+    mock_output = create_mock_request_output(
+        prompt_token_ids=[1, 2, 3],
+        output_token_ids=[4, 5, 6, 7],  # 4 tokens, all in reasoning
+        num_cached_tokens=0,
+    )
+
+    context.append_output(mock_output)
+
+    # All output tokens should be counted as reasoning
+    assert context.num_reasoning_tokens == 4
+    assert context.num_output_tokens == 4
+
+
+def test_zero_tokens_edge_case():
+    """Test behavior with all zero token counts."""
+    context = HarmonyContext(messages=[], available_tools=[])
+
+    # Create a request with empty lists (not None) for both prompt and
+    # output tokens
+    mock_output = create_mock_request_output(
+        prompt_token_ids=[],  # Empty prompt tokens
+        output_token_ids=[],  # Empty output tokens
+        num_cached_tokens=0,
+    )
+
+    context.append_output(mock_output)
+
+    # All counts should be zero
+    assert context.num_prompt_tokens == 0
+    assert context.num_output_tokens == 0
+    assert context.num_cached_tokens == 0
+    assert context.num_tool_output_tokens == 0
+    assert context.num_reasoning_tokens == 0
+
+
+@pytest.mark.asyncio
+async def test_single_turn_no_tool_output():
+    """Test that first turn never generates tool output tokens."""
+    context = HarmonyContext(
+        messages=[],
+        available_tools=["browser"]  # Tools available
+    )
+
+    # Even with large prompt in first turn, no tool tokens should be counted
+    mock_output = create_mock_request_output(
+        prompt_token_ids=list(range(100)),  # 100 tokens
+        output_token_ids=[1, 2, 3],
+        num_cached_tokens=0,
+    )
+
+    context.append_output(mock_output)
+
+    # First turn should never have tool output tokens
+    assert context.num_tool_output_tokens == 0
+    assert context.is_first_turn is False  # Should be updated after first turn
+
+
+@pytest.mark.asyncio
+async def test_negative_tool_tokens_edge_case():
+    """Test edge case where calculation could result in negative tool
+    tokens. We should log an error and clamp the value to 0."""
+    # Use patch to check if logger.error was called
+    with patch("vllm.entrypoints.context.logger.error") as mock_log:
+        context = HarmonyContext(messages=[], available_tools=["browser"])
+
+        # First turn
+        mock_output1 = create_mock_request_output(
+            prompt_token_ids=list(range(10)),  # 10 tokens
+            output_token_ids=[1, 2, 3, 4, 5],  # 5 tokens
+        )
+        context.append_output(mock_output1)
+
+        # Second turn with fewer new tokens than previous output
+        # This could happen in edge cases with aggressive caching
+        mock_output2 = create_mock_request_output(
+            prompt_token_ids=list(range(12)),  # 12 tokens (only 2 new)
+            output_token_ids=[6, 7],  # 2 tokens
+        )
+        context.append_output(mock_output2)
+
+        # Calculated negative tool tokens (12 - 10 - 5 = -3) should be clamped
+        # to 0 and an error should be logged
+        assert context.num_tool_output_tokens == 0
+        assert context.num_prompt_tokens == 10 + 12
+        assert context.num_output_tokens == 5 + 2
+
+        # Verify the error was logged properly
+        mock_log.assert_called_once()
+
+        # Extract the actual log message and arguments from the call
+        args, _ = mock_log.call_args
+        log_message = args[0]
+
+        # Check for key parts of the message
+        assert "Negative tool output tokens calculated" in log_message
+        assert "-3" in str(args)  # Check that -3 is in the arguments
+
+
+@pytest.mark.asyncio
+async def test_streaming_multi_turn_token_counting(mock_parser):
+    """Test token counting for streaming multi-turn conversations.
+    
+    This test focuses on how StreamingHarmonyContext counts tokens in a 
+    multi-turn conversation with streaming (token-by-token) outputs and 
+    message boundaries.
+    """
+    # Create a streaming context
+    context = StreamingHarmonyContext(messages=[], available_tools=["browser"])
+
+    # Simulate three turns of conversation:
+    # Turn 1: stream tokens one by one, then finish the message
+    # Turn 2: new prompt, stream more tokens with a reasoning segment
+    # Turn 3: new prompt with tool output and cached tokens
+
+    # First turn: 3 tokens streamed one by one
+    # First token of first turn
+    context.append_output(
+        create_mock_request_output(
+            prompt_token_ids=[1, 2, 3],  # 3 prompt tokens
+            output_token_ids=[101],  # Single token
+            num_cached_tokens=0,
+            finished=False,  # Not end of message yet
+        ))
+
+    # Second token of first turn
+    context.append_output(
+        create_mock_request_output(
+            output_token_ids=[102],
+            finished=False,
+        ))
+
+    # Last token of first turn (finished=True signals end of message)
+    context.append_output(
+        create_mock_request_output(
+            output_token_ids=[103],
+            finished=True,  # End of message
+        ))
+
+    # Check token counts after first turn
+    assert context.num_prompt_tokens == 3  # Initial prompt tokens
+    assert context.num_output_tokens == 3  # Three output tokens
+    assert context.num_cached_tokens == 0
+    assert context.num_tool_output_tokens == 0  # No tool output in first turn
+    assert context.first_tok_of_message is True  # Ready for next message
+
+    # Second turn: reasoning tokens in analysis channel
+    mock_parser.current_channel = "analysis"  # Set to reasoning channel
+
+    # First token of second turn
+    context.append_output(
+        create_mock_request_output(
+            prompt_token_ids=[1, 2, 3, 101, 102, 103, 4,
+                              5],  # 8 tokens (includes previous)
+            output_token_ids=[201],
+            num_cached_tokens=3,  # Some tokens cached
+            finished=False,
+        ))
+
+    # More tokens in reasoning channel
+    context.append_output(
+        create_mock_request_output(
+            output_token_ids=[202],
+            finished=False,
+        ))
+
+    context.append_output(
+        create_mock_request_output(
+            output_token_ids=[203],
+            finished=True,  # End of reasoning message
+        ))
+
+    # Check counts after second turn (reasoning message)
+    assert context.num_prompt_tokens == 3 + 8  # Initial + second prompt
+    assert context.num_output_tokens == 3 + 3  # First turn + second turn
+    assert context.num_reasoning_tokens == 3  # All tokens in analysis channel
+    assert context.num_cached_tokens == 3  # Cached tokens from second turn
+
+    # Formula: this turn prompt tokens - last turn prompt - last turn output
+    expected_tool_tokens = 8 - 3 - 3  # = 2
+    assert context.num_tool_output_tokens == expected_tool_tokens
+
+    # Third turn: regular output channel
+    mock_parser.current_channel = "final"  # Switch back to regular channel
+
+    # Third turn (with more cached tokens)
+    context.append_output(
+        create_mock_request_output(
+            prompt_token_ids=[
+                1, 2, 3, 101, 102, 103, 4, 5, 201, 202, 203, 6, 7
+            ],  # 13 tokens
+            output_token_ids=[301],
+            num_cached_tokens=8,  # More cached tokens
+            finished=False,
+        ))
+
+    context.append_output(
+        create_mock_request_output(
+            output_token_ids=[302],
+            finished=True,
+        ))
+
+    # Final token counts check
+    assert context.num_prompt_tokens == 3 + 8 + 13  # All prompts
+    assert context.num_output_tokens == 3 + 3 + 2  # All outputs
+    assert context.num_reasoning_tokens == 3  # Unchanged from second turn
+    assert context.num_cached_tokens == 3 + 8  # Accumulated cached tokens
+
+    # Additional tool tokens from third turn
+    # Formula: this turn prompt - last turn prompt - last turn output
+    additional_tool_tokens = 13 - 8 - 3  # = 2
+    assert context.num_tool_output_tokens == expected_tool_tokens \
+        + additional_tool_tokens

vllm/entrypoints/context.py

@@ -3,7 +3,6 @@
 import json
 import logging
 from abc import ABC, abstractmethod
-from collections.abc import Sequence
 from contextlib import AsyncExitStack
 from typing import TYPE_CHECKING, Optional, Union
 
@@ -21,6 +20,23 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
+class TurnTokens:
+    """Tracks token counts for a single conversation turn."""
+
+    def __init__(self, input_tokens=0, output_tokens=0):
+        self.input_tokens = input_tokens
+        self.output_tokens = output_tokens
+
+    def reset(self):
+        """Reset counters for a new turn."""
+        self.input_tokens = 0
+        self.output_tokens = 0
+
+    def copy(self):
+        """Create a copy of this turn's token counts."""
+        return TurnTokens(self.input_tokens, self.output_tokens)
+
+
 class ConversationContext(ABC):
 
     @abstractmethod
@@ -92,52 +108,124 @@ class HarmonyContext(ConversationContext):
         self.num_init_messages = len(messages)
         self.num_prompt_tokens = 0
         self.num_output_tokens = 0
-        # TODO(woosuk): Implement the following fields.
         self.num_cached_tokens = 0
         self.num_reasoning_tokens = 0
+        self.num_tool_output_tokens = 0
 
-    def _update_num_prompt_tokens(self, output: RequestOutput):
-        if output.prompt_token_ids and len(output.prompt_token_ids) > 0:
-            # NOTE: with built-in tools, there might be multiple rounds in
-            # the conversation, with the full conversation being resent
-            # as new prompt each time. Hence the sum.
-            self.num_prompt_tokens += len(output.prompt_token_ids)
+        # Turn tracking - replaces multiple individual tracking variables
+        self.current_turn = TurnTokens()
+        self.previous_turn = TurnTokens()
+        self.is_first_turn = True
+        self.first_tok_of_message = True  # For streaming support
 
-    def _update_num_cached_tokens(self, output: RequestOutput):
-        if output.num_cached_tokens is not None:
-            #Similar to num_prompt_tokens
-            self.num_cached_tokens += output.num_cached_tokens
-
-    def _update_num_output_tokens(self, token_ids: Sequence[int]):
-        self.num_output_tokens += len(token_ids)
-
-    def _update_num_reasoning_tokens(self, token_ids: Sequence[int]):
-        # Count tokens that are part of reasoning content (analysis channel
-        # or tool-directed messages like python/browser calls)
-        is_analysis = self.parser.current_channel == "analysis"
-        is_tool_call = (self.parser.current_recipient is not None and
-                        (self.parser.current_recipient.startswith("python") or
-                         self.parser.current_recipient.startswith("browser.")))
-        if is_analysis or is_tool_call:
-            self.num_reasoning_tokens += len(token_ids)
+    def _update_num_reasoning_tokens(self):
+        # Count all analysis and commentary channels as reasoning tokens
+        if self.parser.current_channel in {"analysis", "commentary"}:
+            self.num_reasoning_tokens += 1
 
     def append_output(self, output) -> None:
         if isinstance(output, RequestOutput):
-            self._update_num_prompt_tokens(output)
-            self._update_num_cached_tokens(output)
             output_token_ids = output.outputs[0].token_ids
-            self._update_num_output_tokens(output_token_ids)
             self.parser = get_streamable_parser_for_assistant()
             for token_id in output_token_ids:
                 self.parser.process(token_id)
                 # Check if the current token is part of reasoning content
-                self._update_num_reasoning_tokens([token_id])
+                self._update_num_reasoning_tokens()
+            self._update_prefill_token_usage(output)
+            # Reset current turn output tokens for this turn
+            self.current_turn.output_tokens = 0
+            self._update_decode_token_usage(output)
+            # Move current turn to previous turn for next turn's calculations
+            self.previous_turn = self.current_turn.copy()
             output_msgs = self.parser.messages
         else:
             # Tool output.
             output_msgs = output
         self._messages.extend(output_msgs)
 
+    def _update_prefill_token_usage(self, output: RequestOutput) -> None:
+        """Update token usage statistics for the prefill phase of generation.
+        
+        The prefill phase processes the input prompt tokens. This method:
+        1. Counts the prompt tokens for this turn
+        2. Calculates tool output tokens for multi-turn conversations
+        3. Updates cached token counts
+        4. Tracks state for next turn calculations
+        
+        Tool output tokens are calculated as:
+        current_prompt_tokens - last_turn_prompt_tokens - 
+        last_turn_output_tokens
+        This represents tokens added between turns (typically tool responses).
+        
+        Args:
+            output: The RequestOutput containing prompt token information
+        """
+        if output.prompt_token_ids is not None:
+            this_turn_input_tokens = len(output.prompt_token_ids)
+        else:
+            this_turn_input_tokens = 0
+            logger.error(
+                "RequestOutput appended contains no prompt_token_ids.")
+
+        # Update current turn input tokens
+        self.current_turn.input_tokens = this_turn_input_tokens
+        self.num_prompt_tokens += this_turn_input_tokens
+
+        # Calculate tool tokens (except on first turn)
+        if self.is_first_turn:
+            self.is_first_turn = False
+        else:
+            # start counting tool after first turn
+            # tool tokens = this turn prefill - last turn prefill -
+            # last turn decode
+            this_turn_tool_tokens = (self.current_turn.input_tokens -
+                                     self.previous_turn.input_tokens -
+                                     self.previous_turn.output_tokens)
+
+            # Handle negative tool token counts (shouldn't happen in normal
+            # cases)
+            if this_turn_tool_tokens < 0:
+                logger.error(
+                    "Negative tool output tokens calculated: %d "
+                    "(current_input=%d, previous_input=%d, "
+                    "previous_output=%d). Setting to 0.",
+                    this_turn_tool_tokens, self.current_turn.input_tokens,
+                    self.previous_turn.input_tokens,
+                    self.previous_turn.output_tokens)
+                this_turn_tool_tokens = 0
+
+            self.num_tool_output_tokens += this_turn_tool_tokens
+
+        # Update cached tokens
+        if output.num_cached_tokens is not None:
+            self.num_cached_tokens += output.num_cached_tokens
+
+    def _update_decode_token_usage(self, output: RequestOutput) -> int:
+        """Update token usage statistics for the decode phase of generation.
+        
+        The decode phase processes the generated output tokens. This method:
+        1. Counts output tokens from all completion outputs
+        2. Updates the total output token count
+        3. Tracks tokens generated in the current turn
+        
+        In streaming mode, this is called for each token generated.
+        In non-streaming mode, this is called once with all output tokens.
+        
+        Args:
+            output: The RequestOutput containing generated token information
+            
+        Returns:
+            int: Number of output tokens processed in this call
+        """
+        updated_output_token_count = 0
+        if output.outputs:
+            for completion_output in output.outputs:
+                # only keep last round
+                updated_output_token_count += len(completion_output.token_ids)
+            self.num_output_tokens += updated_output_token_count
+            self.current_turn.output_tokens += updated_output_token_count
+        return updated_output_token_count
+
     @property
     def messages(self) -> list:
         return self._messages
@@ -231,8 +319,8 @@ class StreamingHarmonyContext(HarmonyContext):
             # append_output is called for each output token in streaming case,
             # so we only want to add the prompt tokens once for each message.
             if self.first_tok_of_message:
-                self._update_num_prompt_tokens(output)
-                self._update_num_cached_tokens(output)
+                self._update_prefill_token_usage(output)
+                self.current_turn.output_tokens = 0
             # Reset self.first_tok_of_message if needed:
             # if the current token is the last one of the current message
             # (finished=True), then the next token processed will mark the
@@ -240,9 +328,13 @@ class StreamingHarmonyContext(HarmonyContext):
             self.first_tok_of_message = output.finished
             for tok in output.outputs[0].token_ids:
                 self.parser.process(tok)
-            self._update_num_output_tokens(output.outputs[0].token_ids)
+            self._update_decode_token_usage(output)
+
+            # For streaming, update previous turn when message is complete
+            if output.finished:
+                self.previous_turn = self.current_turn.copy()
             # Check if the current token is part of reasoning content
-            self._update_num_reasoning_tokens(output.outputs[0].token_ids)
+            self._update_num_reasoning_tokens()
             self.last_tok = tok
         else:
             # Handle the case of tool output in direct message format

vllm/entrypoints/openai/protocol.py

@@ -1841,7 +1841,8 @@ class InputTokensDetails(OpenAIBaseModel):
 
 
 class OutputTokensDetails(OpenAIBaseModel):
-    reasoning_tokens: int
+    reasoning_tokens: int = 0
+    tool_output_tokens: int = 0
 
 
 class ResponseUsage(OpenAIBaseModel):

vllm/entrypoints/openai/serving_responses.py

@@ -460,7 +460,7 @@ class OpenAIServingResponses(OpenAIServing):
         if self.use_harmony:
             assert isinstance(context, HarmonyContext)
             output = self._make_response_output_items_with_harmony(context)
-            # TODO: these are all 0 for now!
+            num_tool_output_tokens = context.num_tool_output_tokens
         else:
             assert isinstance(context, SimpleContext)
             final_res = context.last_output
@@ -473,6 +473,8 @@ class OpenAIServingResponses(OpenAIServing):
 
             # Calculate usage.
             assert final_res.prompt_token_ids is not None
+            num_tool_output_tokens = 0
+
         assert isinstance(context, (SimpleContext, HarmonyContext))
         num_prompt_tokens = context.num_prompt_tokens
         num_generated_tokens = context.num_output_tokens
@@ -486,7 +488,8 @@ class OpenAIServingResponses(OpenAIServing):
             input_tokens_details=InputTokensDetails(
                 cached_tokens=num_cached_tokens),
             output_tokens_details=OutputTokensDetails(
-                reasoning_tokens=num_reasoning_tokens),
+                reasoning_tokens=num_reasoning_tokens,
+                tool_output_tokens=num_tool_output_tokens),
         )
         response = ResponsesResponse.from_request(
             request,