fix(anthropic): capture web_search_tool_result in streaming for multi-turn conversations

litellm/llms/anthropic/chat/handler.py

@@ -504,6 +504,14 @@ def __init__(
         self.accumulated_json: str = ""
         self.chunk_type: Literal["valid_json", "accumulated_json"] = "valid_json"
 
+        # Track current content block type to avoid emitting tool calls for non-tool blocks
+        # See: https://github.com/BerriAI/litellm/issues/17254
+        self.current_content_block_type: Optional[str] = None
+
+        # Accumulate web_search_tool_result blocks for multi-turn reconstruction
+        # See: https://github.com/BerriAI/litellm/issues/17737
+        self.web_search_results: List[Dict[str, Any]] = []
+
     def check_empty_tool_call_args(self) -> bool:
         """
         Check if the tool call block so far has been an empty string
@@ -553,18 +561,22 @@ def _content_block_delta_helper(self, chunk: dict) -> Tuple[
         if "text" in content_block["delta"]:
             text = content_block["delta"]["text"]
         elif "partial_json" in content_block["delta"]:
-            tool_use = cast(
-                ChatCompletionToolCallChunk,
-                {
-                    "id": None,
-                    "type": "function",
-                    "function": {
-                        "name": None,
-                        "arguments": content_block["delta"]["partial_json"],
+            # Only emit tool calls if we're in a tool_use or server_tool_use block
+            # web_search_tool_result blocks also have input_json_delta but should not be treated as tool calls
+            # See: https://github.com/BerriAI/litellm/issues/17254
+            if self.current_content_block_type in ("tool_use", "server_tool_use"):
+                tool_use = cast(
+                    ChatCompletionToolCallChunk,
+                    {
+                        "id": None,
+                        "type": "function",
+                        "function": {
+                            "name": None,
+                            "arguments": content_block["delta"]["partial_json"],
+                        },
+                        "index": self.tool_index,
                     },
-                    "index": self.tool_index,
-                },
-            )
+                )
         elif "citation" in content_block["delta"]:
             provider_specific_fields["citation"] = content_block["delta"]["citation"]
         elif (
@@ -674,6 +686,8 @@ def chunk_parser(self, chunk: dict) -> ModelResponseStream:  # noqa: PLR0915
 
                 content_block_start = self.get_content_block_start(chunk=chunk)
                 self.content_blocks = []  # reset content blocks when new block starts
+                # Track current content block type for filtering deltas
+                self.current_content_block_type = content_block_start["content_block"]["type"]
                 if content_block_start["content_block"]["type"] == "text":
                     text = content_block_start["content_block"]["text"]
                 elif content_block_start["content_block"]["type"] == "tool_use":
@@ -714,22 +728,38 @@ def chunk_parser(self, chunk: dict) -> ModelResponseStream:  # noqa: PLR0915
                         content_block_start=content_block_start,
                         provider_specific_fields=provider_specific_fields,
                     )
+                elif (
+                    content_block_start["content_block"]["type"]
+                    == "web_search_tool_result"
+                ):
+                    # Capture web_search_tool_result for multi-turn reconstruction
+                    # The full content comes in content_block_start, not in deltas
+                    # See: https://github.com/BerriAI/litellm/issues/17737
+                    self.web_search_results.append(
+                        content_block_start["content_block"]
+                    )
+                    provider_specific_fields["web_search_results"] = (
+                        self.web_search_results
+                    )
             elif type_chunk == "content_block_stop":
                 ContentBlockStop(**chunk)  # type: ignore
-                # check if tool call content block
-                is_empty = self.check_empty_tool_call_args()
-                if is_empty:
-                    tool_use = ChatCompletionToolCallChunk(
-                        id=None,  # type: ignore[typeddict-item]
-                        type="function",
-                        function=ChatCompletionToolCallFunctionChunk(
-                            name=None,  # type: ignore[typeddict-item]
-                            arguments="{}",
-                        ),
-                        index=self.tool_index,
-                    )
+                # check if tool call content block - only for tool_use and server_tool_use blocks
+                if self.current_content_block_type in ("tool_use", "server_tool_use"):
+                    is_empty = self.check_empty_tool_call_args()
+                    if is_empty:
+                        tool_use = ChatCompletionToolCallChunk(
+                            id=None,  # type: ignore[typeddict-item]
+                            type="function",
+                            function=ChatCompletionToolCallFunctionChunk(
+                                name=None,  # type: ignore[typeddict-item]
+                                arguments="{}",
+                            ),
+                            index=self.tool_index,
+                        )
                 # Reset response_format tool tracking when block stops
                 self.is_response_format_tool = False
+                # Reset current content block type
+                self.current_content_block_type = None
             elif type_chunk == "tool_result":
                 # Handle tool_result blocks (for tool search results with tool_reference)
                 # These are automatically handled by Anthropic API, we just pass them through

litellm/main.py

@@ -6750,6 +6750,36 @@ def stream_chunk_builder(  # noqa: PLR0915
             _choice = cast(Choices, response.choices[0])
             _choice.message.audio = processor.get_combined_audio_content(audio_chunks)
 
+        # Combine provider_specific_fields from streaming chunks (e.g., web_search_results, citations)
+        # See: https://github.com/BerriAI/litellm/issues/17737
+        provider_specific_chunks = [
+            chunk
+            for chunk in chunks
+            if len(chunk["choices"]) > 0
+            and "provider_specific_fields" in chunk["choices"][0]["delta"]
+            and chunk["choices"][0]["delta"]["provider_specific_fields"] is not None
+        ]
+
+        if len(provider_specific_chunks) > 0:
+            combined_provider_fields: Dict[str, Any] = {}
+            for chunk in provider_specific_chunks:
+                fields = chunk["choices"][0]["delta"]["provider_specific_fields"]
+                if isinstance(fields, dict):
+                    for key, value in fields.items():
+                        if key not in combined_provider_fields:
+                            combined_provider_fields[key] = value
+                        elif isinstance(value, list) and isinstance(
+                            combined_provider_fields[key], list
+                        ):
+                            # For lists like web_search_results, take the last (most complete) one
+                            combined_provider_fields[key] = value
+                        else:
+                            combined_provider_fields[key] = value
+
+            if combined_provider_fields:
+                _choice = cast(Choices, response.choices[0])
+                _choice.message.provider_specific_fields = combined_provider_fields
+
         completion_output = get_content_from_model_response(response)
 
         reasoning_tokens = processor.count_reasoning_tokens(response)

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py

@@ -532,3 +532,250 @@ def test_multiple_partial_chunks_accumulation():
     assert result3 is not None
     assert iterator.accumulated_json == ""
     assert result3.choices[0].delta.content == "Hello"
+
+
+def test_web_search_tool_result_no_extra_tool_calls():
+    """
+    Test that web_search_tool_result blocks don't emit tool call chunks.
+
+    This tests the fix for https://github.com/BerriAI/litellm/issues/17254
+    where streaming with Anthropic web search was adding trailing {} to tool call arguments.
+
+    The issue was that web_search_tool_result blocks have input_json_delta events with {}
+    that were incorrectly being converted to tool calls.
+    """
+    iterator = ModelResponseIterator(
+        streaming_response=MagicMock(), sync_stream=True, json_mode=False
+    )
+
+    # Simulate the streaming sequence:
+    # 1. server_tool_use block starts (web_search)
+    # 2. input_json_delta with the query
+    # 3. content_block_stop
+    # 4. web_search_tool_result block starts
+    # 5. input_json_delta with {} (this should NOT emit a tool call)
+    # 6. content_block_stop
+
+    chunks = [
+        # 1. server_tool_use block starts
+        {
+            "type": "content_block_start",
+            "index": 0,
+            "content_block": {
+                "type": "server_tool_use",
+                "id": "srvtoolu_01ABC123",
+                "name": "web_search",
+            },
+        },
+        # 2. input_json_delta with the query
+        {
+            "type": "content_block_delta",
+            "index": 0,
+            "delta": {"type": "input_json_delta", "partial_json": '{"query": "test"}'},
+        },
+        # 3. content_block_stop for server_tool_use
+        {"type": "content_block_stop", "index": 0},
+        # 4. web_search_tool_result block starts
+        {
+            "type": "content_block_start",
+            "index": 1,
+            "content_block": {
+                "type": "web_search_tool_result",
+                "tool_use_id": "srvtoolu_01ABC123",
+                "content": [],
+            },
+        },
+        # 5. input_json_delta with {} - this should NOT emit a tool call
+        {
+            "type": "content_block_delta",
+            "index": 1,
+            "delta": {"type": "input_json_delta", "partial_json": "{}"},
+        },
+        # 6. content_block_stop for web_search_tool_result
+        {"type": "content_block_stop", "index": 1},
+        # 7. Another web_search_tool_result with {} - also should NOT emit
+        {
+            "type": "content_block_start",
+            "index": 2,
+            "content_block": {
+                "type": "web_search_tool_result",
+                "tool_use_id": "srvtoolu_01ABC123",
+                "content": [],
+            },
+        },
+        {
+            "type": "content_block_delta",
+            "index": 2,
+            "delta": {"type": "input_json_delta", "partial_json": "{}"},
+        },
+        {"type": "content_block_stop", "index": 2},
+    ]
+
+    tool_calls_emitted = []
+    for chunk in chunks:
+        parsed = iterator.chunk_parser(chunk)
+        if parsed.choices and parsed.choices[0].delta.tool_calls:
+            for tc in parsed.choices[0].delta.tool_calls:
+                tool_calls_emitted.append(tc)
+
+    # Should have exactly 2 tool calls:
+    # 1. From content_block_start (server_tool_use) with id and name
+    # 2. From content_block_delta with the actual query
+    assert len(tool_calls_emitted) == 2, f"Expected 2 tool calls, got {len(tool_calls_emitted)}"
+
+    # First tool call should have the id and name
+    assert tool_calls_emitted[0]["id"] == "srvtoolu_01ABC123"
+    assert tool_calls_emitted[0]["function"]["name"] == "web_search"
+
+    # Second tool call should have the query arguments
+    assert tool_calls_emitted[1]["function"]["arguments"] == '{"query": "test"}'
+
+    # The {} chunks from web_search_tool_result should NOT have been emitted as tool calls
+
+
+def test_current_content_block_type_tracking():
+    """
+    Test that current_content_block_type is properly tracked and reset.
+    """
+    iterator = ModelResponseIterator(
+        streaming_response=MagicMock(), sync_stream=True, json_mode=False
+    )
+
+    # Initially should be None
+    assert iterator.current_content_block_type is None
+
+    # After server_tool_use block start
+    chunk1 = {
+        "type": "content_block_start",
+        "index": 0,
+        "content_block": {
+            "type": "server_tool_use",
+            "id": "srvtoolu_01ABC",
+            "name": "web_search",
+        },
+    }
+    iterator.chunk_parser(chunk1)
+    assert iterator.current_content_block_type == "server_tool_use"
+
+    # After content_block_stop
+    chunk2 = {"type": "content_block_stop", "index": 0}
+    iterator.chunk_parser(chunk2)
+    assert iterator.current_content_block_type is None
+
+    # After web_search_tool_result block start
+    chunk3 = {
+        "type": "content_block_start",
+        "index": 1,
+        "content_block": {
+            "type": "web_search_tool_result",
+            "tool_use_id": "srvtoolu_01ABC",
+            "content": [],
+        },
+    }
+    iterator.chunk_parser(chunk3)
+    assert iterator.current_content_block_type == "web_search_tool_result"
+
+    # After content_block_stop
+    chunk4 = {"type": "content_block_stop", "index": 1}
+    iterator.chunk_parser(chunk4)
+    assert iterator.current_content_block_type is None
+
+
+def test_web_search_tool_result_captured_in_provider_specific_fields():
+    """
+    Test that web_search_tool_result content is captured in provider_specific_fields.
+
+    This tests the fix for https://github.com/BerriAI/litellm/issues/17737
+    where streaming with Anthropic web search wasn't capturing web_search_tool_result
+    blocks, causing multi-turn conversations to fail.
+
+    The web_search_tool_result content comes ALL AT ONCE in content_block_start,
+    not in deltas, so we need to capture it there.
+    """
+    iterator = ModelResponseIterator(
+        streaming_response=MagicMock(), sync_stream=True, json_mode=False
+    )
+
+    # Simulate the streaming sequence with web_search_tool_result
+    chunks = [
+        # 1. message_start
+        {
+            "type": "message_start",
+            "message": {
+                "id": "msg_123",
+                "type": "message",
+                "role": "assistant",
+                "content": [],
+                "usage": {"input_tokens": 10, "output_tokens": 1},
+            },
+        },
+        # 2. server_tool_use block starts (web_search)
+        {
+            "type": "content_block_start",
+            "index": 0,
+            "content_block": {
+                "type": "server_tool_use",
+                "id": "srvtoolu_01ABC123",
+                "name": "web_search",
+            },
+        },
+        # 3. input_json_delta with the query
+        {
+            "type": "content_block_delta",
+            "index": 0,
+            "delta": {"type": "input_json_delta", "partial_json": '{"query": "otter facts"}'},
+        },
+        # 4. content_block_stop for server_tool_use
+        {"type": "content_block_stop", "index": 0},
+        # 5. web_search_tool_result block starts - THIS IS WHERE THE RESULTS ARE
+        {
+            "type": "content_block_start",
+            "index": 1,
+            "content_block": {
+                "type": "web_search_tool_result",
+                "tool_use_id": "srvtoolu_01ABC123",
+                "content": [
+                    {
+                        "type": "web_search_result",
+                        "url": "https://example.com/otters",
+                        "title": "Fun Otter Facts",
+                        "encrypted_content": "abc123encrypted",
+                    },
+                    {
+                        "type": "web_search_result",
+                        "url": "https://example.com/otters2",
+                        "title": "More Otter Facts",
+                        "encrypted_content": "def456encrypted",
+                    },
+                ],
+            },
+        },
+        # 6. content_block_stop for web_search_tool_result
+        {"type": "content_block_stop", "index": 1},
+    ]
+
+    web_search_results = None
+    for chunk in chunks:
+        parsed = iterator.chunk_parser(chunk)
+        if (
+            parsed.choices
+            and parsed.choices[0].delta.provider_specific_fields
+            and "web_search_results" in parsed.choices[0].delta.provider_specific_fields
+        ):
+            web_search_results = parsed.choices[0].delta.provider_specific_fields[
+                "web_search_results"
+            ]
+
+    # Verify web_search_results was captured
+    assert web_search_results is not None, "web_search_results should be captured"
+    assert len(web_search_results) == 1, "Should have 1 web_search_tool_result block"
+    assert (
+        web_search_results[0]["type"] == "web_search_tool_result"
+    ), "Block type should be web_search_tool_result"
+    assert (
+        web_search_results[0]["tool_use_id"] == "srvtoolu_01ABC123"
+    ), "tool_use_id should match"
+    assert len(web_search_results[0]["content"]) == 2, "Should have 2 search results"
+    assert (
+        web_search_results[0]["content"][0]["title"] == "Fun Otter Facts"
+    ), "First result title should match"