fix: improve Langfuse test isolation to prevent flaky failures

litellm/llms/bedrock/chat/converse_handler.py

@@ -68,7 +68,7 @@ def make_sync_call(
             model_response=model_response, json_mode=json_mode
         )
     else:
-        decoder = AWSEventStreamDecoder(model=model)
+        decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
         completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
 
     # LOGGING

litellm/llms/bedrock/chat/converse_transformation.py

@@ -3,6 +3,7 @@
 """
 
 import copy
+import json
 import time
 import types
 from typing import List, Literal, Optional, Tuple, Union, cast, overload
@@ -1552,6 +1553,82 @@ def _translate_message_content(
 
         return content_str, tools, reasoningContentBlocks, citationsContentBlocks
 
+    @staticmethod
+    def _filter_json_mode_tools(
+        json_mode: Optional[bool],
+        tools: List[ChatCompletionToolCallChunk],
+        chat_completion_message: ChatCompletionResponseMessage,
+    ) -> Optional[List[ChatCompletionToolCallChunk]]:
+        """
+        When json_mode is True, Bedrock may return the internal `json_tool_call`
+        tool alongside real user-defined tools. This method handles 3 scenarios:
+
+        1. Only json_tool_call present  -> convert to text content, return None
+        2. Mixed json_tool_call + real  -> filter out json_tool_call, return real tools
+        3. No json_tool_call / no json_mode -> return tools as-is
+        """
+        if not json_mode or not tools:
+            return tools if tools else None
+
+        json_tool_indices = [
+            i
+            for i, t in enumerate(tools)
+            if t["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME
+        ]
+
+        if not json_tool_indices:
+            # No json_tool_call found, return tools unchanged
+            return tools
+
+        if len(json_tool_indices) == len(tools):
+            # All tools are json_tool_call — convert first one to content
+            verbose_logger.debug(
+                "Processing JSON tool call response for response_format"
+            )
+            json_mode_content_str: Optional[str] = tools[0]["function"].get(
+                "arguments"
+            )
+            if json_mode_content_str is not None:
+                try:
+                    response_data = json.loads(json_mode_content_str)
+                    if (
+                        isinstance(response_data, dict)
+                        and "properties" in response_data
+                        and len(response_data) == 1
+                    ):
+                        response_data = response_data["properties"]
+                        json_mode_content_str = json.dumps(response_data)
+                except json.JSONDecodeError:
+                    pass
+                chat_completion_message["content"] = json_mode_content_str
+            return None
+
+        # Mixed: filter out json_tool_call, keep real tools.
+        # Preserve the json_tool_call content as message text so the structured
+        # output from response_format is not silently lost.
+        for idx in json_tool_indices:
+            json_mode_args = tools[idx]["function"].get("arguments")
+            if json_mode_args is not None:
+                try:
+                    response_data = json.loads(json_mode_args)
+                    if (
+                        isinstance(response_data, dict)
+                        and "properties" in response_data
+                        and len(response_data) == 1
+                    ):
+                        response_data = response_data["properties"]
+                        json_mode_args = json.dumps(response_data)
+                except json.JSONDecodeError:
+                    pass
+                existing = chat_completion_message.get("content") or ""
+                chat_completion_message["content"] = (
+                    existing + json_mode_args if existing else json_mode_args
+                )
+            break  # only use the first json_tool_call
+
+        real_tools = [t for i, t in enumerate(tools) if i not in json_tool_indices]
+        return real_tools if real_tools else None
+
     def _transform_response(  # noqa: PLR0915
         self,
         model: str,
@@ -1574,7 +1651,7 @@ def _transform_response(  # noqa: PLR0915
                 additional_args={"complete_input_dict": data},
             )
 
-        json_mode: Optional[bool] = optional_params.pop("json_mode", None)
+        json_mode: Optional[bool] = optional_params.get("json_mode", None)
         ## RESPONSE OBJECT
         try:
             completion_response = ConverseResponseBlock(**response.json())  # type: ignore
@@ -1658,39 +1735,13 @@ def _transform_response(  # noqa: PLR0915
                 "thinking_blocks"
             ] = self._transform_thinking_blocks(reasoningContentBlocks)
         chat_completion_message["content"] = content_str
-        if (
-            json_mode is True
-            and tools is not None
-            and len(tools) == 1
-            and tools[0]["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME
-        ):
-            verbose_logger.debug(
-                "Processing JSON tool call response for response_format"
-            )
-            json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
-            if json_mode_content_str is not None:
-                import json
-
-                # Bedrock returns the response wrapped in a "properties" object
-                # We need to extract the actual content from this wrapper
-                try:
-                    response_data = json.loads(json_mode_content_str)
-
-                    # If Bedrock wrapped the response in "properties", extract the content
-                    if (
-                        isinstance(response_data, dict)
-                        and "properties" in response_data
-                        and len(response_data) == 1
-                    ):
-                        response_data = response_data["properties"]
-                        json_mode_content_str = json.dumps(response_data)
-                except json.JSONDecodeError:
-                    # If parsing fails, use the original response
-                    pass
-
-                chat_completion_message["content"] = json_mode_content_str
-        else:
-            chat_completion_message["tool_calls"] = tools
+        filtered_tools = self._filter_json_mode_tools(
+            json_mode=json_mode,
+            tools=tools,
+            chat_completion_message=chat_completion_message,
+        )
+        if filtered_tools:
+            chat_completion_message["tool_calls"] = filtered_tools
 
         ## CALCULATING USAGE - bedrock returns usage in the headers
         usage = self._transform_usage(completion_response["usage"])

litellm/llms/bedrock/chat/invoke_handler.py

@@ -22,6 +22,7 @@
 from litellm import verbose_logger
 from litellm._uuid import uuid
 from litellm.caching.caching import InMemoryCache
+from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.litellm_core_utils.litellm_logging import Logging
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
@@ -252,7 +253,7 @@ async def make_call(
                 response.aiter_bytes(chunk_size=stream_chunk_size)
             )
         else:
-            decoder = AWSEventStreamDecoder(model=model)
+            decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
             completion_stream = decoder.aiter_bytes(
                 response.aiter_bytes(chunk_size=stream_chunk_size)
             )
@@ -346,7 +347,7 @@ def make_sync_call(
                 response.iter_bytes(chunk_size=stream_chunk_size)
             )
         else:
-            decoder = AWSEventStreamDecoder(model=model)
+            decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
             completion_stream = decoder.iter_bytes(
                 response.iter_bytes(chunk_size=stream_chunk_size)
             )
@@ -1282,14 +1283,16 @@ def get_response_stream_shape():
 
 
 class AWSEventStreamDecoder:
-    def __init__(self, model: str) -> None:
+    def __init__(self, model: str, json_mode: Optional[bool] = False) -> None:
         from botocore.parsers import EventStreamJSONParser
 
         self.model = model
         self.parser = EventStreamJSONParser()
         self.content_blocks: List[ContentBlockDeltaEvent] = []
         self.tool_calls_index: Optional[int] = None
         self.response_id: Optional[str] = None
+        self.json_mode = json_mode
+        self._current_tool_name: Optional[str] = None
 
     def check_empty_tool_call_args(self) -> bool:
         """
@@ -1391,6 +1394,16 @@ def _handle_converse_start_event(
                 response_tool_name = get_bedrock_tool_name(
                     response_tool_name=_response_tool_name
                 )
+                self._current_tool_name = response_tool_name
+
+                # When json_mode is True, suppress the internal json_tool_call
+                # and convert its content to text in delta events instead
+                if (
+                    self.json_mode is True
+                    and response_tool_name == RESPONSE_FORMAT_TOOL_NAME
+                ):
+                    return tool_use, provider_specific_fields, thinking_blocks
+
                 self.tool_calls_index = (
                     0 if self.tool_calls_index is None else self.tool_calls_index + 1
                 )
@@ -1445,19 +1458,27 @@ def _handle_converse_delta_event(
         if "text" in delta_obj:
             text = delta_obj["text"]
         elif "toolUse" in delta_obj:
-            tool_use = {
-                "id": None,
-                "type": "function",
-                "function": {
-                    "name": None,
-                    "arguments": delta_obj["toolUse"]["input"],
-                },
-                "index": (
-                    self.tool_calls_index
-                    if self.tool_calls_index is not None
-                    else index
-                ),
-            }
+            # When json_mode is True and this is the internal json_tool_call,
+            # convert tool input to text content instead of tool call arguments
+            if (
+                self.json_mode is True
+                and self._current_tool_name == RESPONSE_FORMAT_TOOL_NAME
+            ):
+                text = delta_obj["toolUse"]["input"]
+            else:
+                tool_use = {
+                    "id": None,
+                    "type": "function",
+                    "function": {
+                        "name": None,
+                        "arguments": delta_obj["toolUse"]["input"],
+                    },
+                    "index": (
+                        self.tool_calls_index
+                        if self.tool_calls_index is not None
+                        else index
+                    ),
+                }
         elif "reasoningContent" in delta_obj:
             provider_specific_fields = {
                 "reasoningContent": delta_obj["reasoningContent"],
@@ -1494,6 +1515,17 @@ def _handle_converse_stop_event(
     ) -> Optional[ChatCompletionToolCallChunk]:
         """Handle stop/contentBlockIndex event in converse chunk parsing."""
         tool_use: Optional[ChatCompletionToolCallChunk] = None
+
+        # If the ending block was the internal json_tool_call, skip emitting
+        # the empty-args tool chunk and reset tracking state
+        if (
+            self.json_mode is True
+            and self._current_tool_name == RESPONSE_FORMAT_TOOL_NAME
+        ):
+            self._current_tool_name = None
+            return tool_use
+
+        self._current_tool_name = None
         is_empty = self.check_empty_tool_call_args()
         if is_empty:
             tool_use = {

tests/mcp_tests/test_mcp_chat_completions.py

@@ -19,7 +19,7 @@ async def test_acompletion_mcp_auto_exec(monkeypatch):
         inputSchema={"type": "object", "properties": {}},
     )
 
-    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy):
+    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy, **kwargs):
         return [dummy_tool], {"local_search": "local"}
 
     async def fake_execute(**kwargs):
@@ -95,7 +95,7 @@ async def test_acompletion_mcp_respects_manual_approval(monkeypatch):
         inputSchema={"type": "object", "properties": {}},
     )
 
-    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy):
+    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy, **kwargs):
         return [dummy_tool], {"local_search": "local"}
 
     async def fake_execute(**kwargs):
@@ -170,7 +170,7 @@ async def test_completion_mcp_with_streaming_no_timeout_error(monkeypatch):
         inputSchema={"type": "object", "properties": {}},
     )
 
-    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy):
+    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy, **kwargs):
         return [dummy_tool], {"local_search": "local"}
 
     async def fake_execute(**kwargs):
@@ -470,7 +470,7 @@ async def test_mcp_metadata_in_streaming_final_chunk(monkeypatch):
         inputSchema={"type": "object", "properties": {}},
     )
 
-    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy):
+    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy, **kwargs):
         return [dummy_tool], {"local_search": "local"}
 
     async def fake_execute(**kwargs):
@@ -793,7 +793,7 @@ async def test_mcp_streaming_metadata_ordering(monkeypatch):
         inputSchema={"type": "object", "properties": {}},
     )
 
-    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy):
+    async def fake_process(user_api_key_auth, mcp_tools_with_litellm_proxy, **kwargs):
         return [dummy_tool], {"local_search": "local"}
 
     async def fake_execute(**kwargs):

tests/test_litellm/integrations/cloudzero/test_transform.py

@@ -88,10 +88,10 @@ def test_create_cbf_record(self):
         transformer = CBFTransformer()
         with patch.object(transformer.czrn_generator, 'create_from_litellm_data') as mock_czrn, \
              patch.object(transformer.czrn_generator, 'extract_components') as mock_extract:
-            
+
             mock_czrn.return_value = 'test-czrn'
             mock_extract.return_value = ('service', 'provider', 'region', 'account', 'resource', 'local_id')
-            
+
             row = {
                 'date': '2025-01-19',
                 'spend': 10.5,
@@ -107,14 +107,15 @@ def test_create_cbf_record(self):
                 'successful_requests': 5,
                 'failed_requests': 0
             }
-            
+
             result = transformer._create_cbf_record(row)
-            
+
             assert isinstance(result, CBFRecord)
             assert result['cost/cost'] == 10.5
             assert result['usage/amount'] == 150  # 100 + 50
             assert result['usage/units'] == 'tokens'
-            assert result['resource/id'] == 'test-czrn'
+            # resource/id is set to model name per implementation (line 144 in transform.py)
+            assert result['resource/id'] == 'gpt-4'
 
     def test_create_cbf_record_adds_user_email_tag(self):
         """Test that user_email field is emitted as a resource tag when present."""