feat: add streaming tool call events; fix provider id tracking; add tests and cassettes

Adds support for streaming tool call events with test coverage, fixes tool-stream ID tracking (including OpenAI-style tracking for Azure), improves Gemini tool calling + streaming tests, adds Anthropic tests, generates Azure cassettes, and fixes Azure cassette URIs.

lib/crewai/src/crewai/llms/base_llm.py

@@ -354,8 +354,17 @@ class BaseLLM(ABC):
         from_task: Task | None = None,
         from_agent: Agent | None = None,
         tool_call: dict[str, Any] | None = None,
+        call_type: LLMCallType | None = None,
     ) -> None:
-        """Emit stream chunk event."""
+        """Emit stream chunk event.
+
+        Args:
+            chunk: The text content of the chunk.
+            from_task: The task that initiated the call.
+            from_agent: The agent that initiated the call.
+            tool_call: Tool call information if this is a tool call chunk.
+            call_type: The type of LLM call (LLM_CALL or TOOL_CALL).
+        """
         if not hasattr(crewai_event_bus, "emit"):
             raise ValueError("crewai_event_bus does not have an emit method") from None
 
@@ -366,6 +375,7 @@ class BaseLLM(ABC):
                 tool_call=tool_call,
                 from_task=from_task,
                 from_agent=from_agent,
+                call_type=call_type,
             ),
         )
 

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -598,6 +598,8 @@ class AnthropicCompletion(BaseLLM):
         # (the SDK sets it internally)
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
+        current_tool_calls: dict[int, dict[str, Any]] = {}
+
         # Make streaming API call
         with self.client.messages.stream(**stream_params) as stream:
             for event in stream:
@@ -610,6 +612,55 @@ class AnthropicCompletion(BaseLLM):
                         from_agent=from_agent,
                     )
 
+                if event.type == "content_block_start":
+                    block = event.content_block
+                    if block.type == "tool_use":
+                        block_index = event.index
+                        current_tool_calls[block_index] = {
+                            "id": block.id,
+                            "name": block.name,
+                            "arguments": "",
+                            "index": block_index,
+                        }
+                        self._emit_stream_chunk_event(
+                            chunk="",
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            tool_call={
+                                "id": block.id,
+                                "function": {
+                                    "name": block.name,
+                                    "arguments": "",
+                                },
+                                "type": "function",
+                                "index": block_index,
+                            },
+                            call_type=LLMCallType.TOOL_CALL,
+                        )
+                elif event.type == "content_block_delta":
+                    if event.delta.type == "input_json_delta":
+                        block_index = event.index
+                        partial_json = event.delta.partial_json
+                        if block_index in current_tool_calls and partial_json:
+                            current_tool_calls[block_index]["arguments"] += partial_json
+                            self._emit_stream_chunk_event(
+                                chunk=partial_json,
+                                from_task=from_task,
+                                from_agent=from_agent,
+                                tool_call={
+                                    "id": current_tool_calls[block_index]["id"],
+                                    "function": {
+                                        "name": current_tool_calls[block_index]["name"],
+                                        "arguments": current_tool_calls[block_index][
+                                            "arguments"
+                                        ],
+                                    },
+                                    "type": "function",
+                                    "index": block_index,
+                                },
+                                call_type=LLMCallType.TOOL_CALL,
+                            )
+
             final_message: Message = stream.get_final_message()
 
         thinking_blocks: list[ThinkingBlock] = []
@@ -941,6 +992,8 @@ class AnthropicCompletion(BaseLLM):
 
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
+        current_tool_calls: dict[int, dict[str, Any]] = {}
+
         async with self.async_client.messages.stream(**stream_params) as stream:
             async for event in stream:
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
@@ -952,6 +1005,55 @@ class AnthropicCompletion(BaseLLM):
                         from_agent=from_agent,
                     )
 
+                if event.type == "content_block_start":
+                    block = event.content_block
+                    if block.type == "tool_use":
+                        block_index = event.index
+                        current_tool_calls[block_index] = {
+                            "id": block.id,
+                            "name": block.name,
+                            "arguments": "",
+                            "index": block_index,
+                        }
+                        self._emit_stream_chunk_event(
+                            chunk="",
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            tool_call={
+                                "id": block.id,
+                                "function": {
+                                    "name": block.name,
+                                    "arguments": "",
+                                },
+                                "type": "function",
+                                "index": block_index,
+                            },
+                            call_type=LLMCallType.TOOL_CALL,
+                        )
+                elif event.type == "content_block_delta":
+                    if event.delta.type == "input_json_delta":
+                        block_index = event.index
+                        partial_json = event.delta.partial_json
+                        if block_index in current_tool_calls and partial_json:
+                            current_tool_calls[block_index]["arguments"] += partial_json
+                            self._emit_stream_chunk_event(
+                                chunk=partial_json,
+                                from_task=from_task,
+                                from_agent=from_agent,
+                                tool_call={
+                                    "id": current_tool_calls[block_index]["id"],
+                                    "function": {
+                                        "name": current_tool_calls[block_index]["name"],
+                                        "arguments": current_tool_calls[block_index][
+                                            "arguments"
+                                        ],
+                                    },
+                                    "type": "function",
+                                    "index": block_index,
+                                },
+                                call_type=LLMCallType.TOOL_CALL,
+                            )
+
             final_message: Message = await stream.get_final_message()
 
         usage = self._extract_anthropic_token_usage(final_message)

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -674,7 +674,7 @@ class AzureCompletion(BaseLLM):
         self,
         update: StreamingChatCompletionsUpdate,
         full_response: str,
-        tool_calls: dict[str, dict[str, str]],
+        tool_calls: dict[int, dict[str, Any]],
         from_task: Any | None = None,
         from_agent: Any | None = None,
     ) -> str:
@@ -702,25 +702,45 @@ class AzureCompletion(BaseLLM):
                 )
 
             if choice.delta and choice.delta.tool_calls:
-                for tool_call in choice.delta.tool_calls:
+                for idx, tool_call in enumerate(choice.delta.tool_calls):
-                    call_id = tool_call.id or "default"
+                    if idx not in tool_calls:
-                    if call_id not in tool_calls:
+                        tool_calls[idx] = {
-                        tool_calls[call_id] = {
+                            "id": tool_call.id,
                             "name": "",
                             "arguments": "",
                         }
+                    elif tool_call.id and not tool_calls[idx]["id"]:
+                        tool_calls[idx]["id"] = tool_call.id
 
                     if tool_call.function and tool_call.function.name:
-                        tool_calls[call_id]["name"] = tool_call.function.name
+                        tool_calls[idx]["name"] = tool_call.function.name
                     if tool_call.function and tool_call.function.arguments:
-                        tool_calls[call_id]["arguments"] += tool_call.function.arguments
+                        tool_calls[idx]["arguments"] += tool_call.function.arguments
+
+                    self._emit_stream_chunk_event(
+                        chunk=tool_call.function.arguments
+                        if tool_call.function and tool_call.function.arguments
+                        else "",
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        tool_call={
+                            "id": tool_calls[idx]["id"],
+                            "function": {
+                                "name": tool_calls[idx]["name"],
+                                "arguments": tool_calls[idx]["arguments"],
+                            },
+                            "type": "function",
+                            "index": idx,
+                        },
+                        call_type=LLMCallType.TOOL_CALL,
+                    )
 
         return full_response
 
     def _finalize_streaming_response(
         self,
         full_response: str,
-        tool_calls: dict[str, dict[str, str]],
+        tool_calls: dict[int, dict[str, Any]],
         usage_data: dict[str, int],
         params: AzureCompletionParams,
         available_functions: dict[str, Any] | None = None,
@@ -804,7 +824,7 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle streaming chat completion."""
         full_response = ""
-        tool_calls: dict[str, dict[str, Any]] = {}
+        tool_calls: dict[int, dict[str, Any]] = {}
 
         usage_data = {"total_tokens": 0}
         for update in self.client.complete(**params):  # type: ignore[arg-type]
@@ -870,7 +890,7 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle streaming chat completion asynchronously."""
         full_response = ""
-        tool_calls: dict[str, dict[str, Any]] = {}
+        tool_calls: dict[int, dict[str, Any]] = {}
 
         usage_data = {"total_tokens": 0}
 

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -315,9 +315,7 @@ class BedrockCompletion(BaseLLM):
                 messages
             )
 
-            if not self._invoke_before_llm_call_hooks(
+            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                cast(list[LLMMessage], formatted_messages), from_agent
-            ):
                 raise ValueError("LLM call blocked by before_llm_call hook")
 
             # Prepare request body
@@ -361,7 +359,7 @@ class BedrockCompletion(BaseLLM):
 
             if self.stream:
                 return self._handle_streaming_converse(
-                    cast(list[LLMMessage], formatted_messages),
+                    formatted_messages,
                     body,
                     available_functions,
                     from_task,
@@ -369,7 +367,7 @@ class BedrockCompletion(BaseLLM):
                 )
 
             return self._handle_converse(
-                cast(list[LLMMessage], formatted_messages),
+                formatted_messages,
                 body,
                 available_functions,
                 from_task,
@@ -433,7 +431,7 @@ class BedrockCompletion(BaseLLM):
             )
 
             formatted_messages, system_message = self._format_messages_for_converse(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
             body: BedrockConverseRequestBody = {
@@ -687,8 +685,10 @@ class BedrockCompletion(BaseLLM):
     ) -> str:
         """Handle streaming converse API call with comprehensive event handling."""
         full_response = ""
-        current_tool_use = None
+        current_tool_use: dict[str, Any] | None = None
-        tool_use_id = None
+        tool_use_id: str | None = None
+        tool_use_index = 0
+        accumulated_tool_input = ""
 
         try:
             response = self.client.converse_stream(
@@ -709,9 +709,30 @@ class BedrockCompletion(BaseLLM):
 
                     elif "contentBlockStart" in event:
                         start = event["contentBlockStart"].get("start", {})
+                        content_block_index = event["contentBlockStart"].get(
+                            "contentBlockIndex", 0
+                        )
                         if "toolUse" in start:
-                            current_tool_use = start["toolUse"]
+                            tool_use_block = start["toolUse"]
+                            current_tool_use = cast(dict[str, Any], tool_use_block)
                             tool_use_id = current_tool_use.get("toolUseId")
+                            tool_use_index = content_block_index
+                            accumulated_tool_input = ""
+                            self._emit_stream_chunk_event(
+                                chunk="",
+                                from_task=from_task,
+                                from_agent=from_agent,
+                                tool_call={
+                                    "id": tool_use_id or "",
+                                    "function": {
+                                        "name": current_tool_use.get("name", ""),
+                                        "arguments": "",
+                                    },
+                                    "type": "function",
+                                    "index": tool_use_index,
+                                },
+                                call_type=LLMCallType.TOOL_CALL,
+                            )
                         logging.debug(
                             f"Tool use started in stream: {json.dumps(current_tool_use)} (ID: {tool_use_id})"
                         )
@@ -730,7 +751,23 @@ class BedrockCompletion(BaseLLM):
                         elif "toolUse" in delta and current_tool_use:
                             tool_input = delta["toolUse"].get("input", "")
                             if tool_input:
+                                accumulated_tool_input += tool_input
                                 logging.debug(f"Tool input delta: {tool_input}")
+                                self._emit_stream_chunk_event(
+                                    chunk=tool_input,
+                                    from_task=from_task,
+                                    from_agent=from_agent,
+                                    tool_call={
+                                        "id": tool_use_id or "",
+                                        "function": {
+                                            "name": current_tool_use.get("name", ""),
+                                            "arguments": accumulated_tool_input,
+                                        },
+                                        "type": "function",
+                                        "index": tool_use_index,
+                                    },
+                                    call_type=LLMCallType.TOOL_CALL,
+                                )
                     elif "contentBlockStop" in event:
                         logging.debug("Content block stopped in stream")
                         if current_tool_use and available_functions:
@@ -848,7 +885,7 @@ class BedrockCompletion(BaseLLM):
 
     async def _ahandle_converse(
         self,
-        messages: list[dict[str, Any]],
+        messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
         available_functions: Mapping[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1013,7 +1050,7 @@ class BedrockCompletion(BaseLLM):
 
     async def _ahandle_streaming_converse(
         self,
-        messages: list[dict[str, Any]],
+        messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1021,8 +1058,10 @@ class BedrockCompletion(BaseLLM):
     ) -> str:
         """Handle async streaming converse API call."""
         full_response = ""
-        current_tool_use = None
+        current_tool_use: dict[str, Any] | None = None
-        tool_use_id = None
+        tool_use_id: str | None = None
+        tool_use_index = 0
+        accumulated_tool_input = ""
 
         try:
             async_client = await self._ensure_async_client()
@@ -1044,9 +1083,30 @@ class BedrockCompletion(BaseLLM):
 
                     elif "contentBlockStart" in event:
                         start = event["contentBlockStart"].get("start", {})
+                        content_block_index = event["contentBlockStart"].get(
+                            "contentBlockIndex", 0
+                        )
                         if "toolUse" in start:
-                            current_tool_use = start["toolUse"]
+                            tool_use_block = start["toolUse"]
+                            current_tool_use = cast(dict[str, Any], tool_use_block)
                             tool_use_id = current_tool_use.get("toolUseId")
+                            tool_use_index = content_block_index
+                            accumulated_tool_input = ""
+                            self._emit_stream_chunk_event(
+                                chunk="",
+                                from_task=from_task,
+                                from_agent=from_agent,
+                                tool_call={
+                                    "id": tool_use_id or "",
+                                    "function": {
+                                        "name": current_tool_use.get("name", ""),
+                                        "arguments": "",
+                                    },
+                                    "type": "function",
+                                    "index": tool_use_index,
+                                },
+                                call_type=LLMCallType.TOOL_CALL,
+                            )
                             logging.debug(
                                 f"Tool use started in stream: {current_tool_use.get('name')} (ID: {tool_use_id})"
                             )
@@ -1065,7 +1125,23 @@ class BedrockCompletion(BaseLLM):
                         elif "toolUse" in delta and current_tool_use:
                             tool_input = delta["toolUse"].get("input", "")
                             if tool_input:
+                                accumulated_tool_input += tool_input
                                 logging.debug(f"Tool input delta: {tool_input}")
+                                self._emit_stream_chunk_event(
+                                    chunk=tool_input,
+                                    from_task=from_task,
+                                    from_agent=from_agent,
+                                    tool_call={
+                                        "id": tool_use_id or "",
+                                        "function": {
+                                            "name": current_tool_use.get("name", ""),
+                                            "arguments": accumulated_tool_input,
+                                        },
+                                        "type": "function",
+                                        "index": tool_use_index,
+                                    },
+                                    call_type=LLMCallType.TOOL_CALL,
+                                )
 
                     elif "contentBlockStop" in event:
                         logging.debug("Content block stopped in stream")
@@ -1174,7 +1250,7 @@ class BedrockCompletion(BaseLLM):
 
     def _format_messages_for_converse(
         self, messages: str | list[LLMMessage]
-    ) -> tuple[list[dict[str, Any]], str | None]:
+    ) -> tuple[list[LLMMessage], str | None]:
         """Format messages for Converse API following AWS documentation.
 
         Note: Returns dict[str, Any] instead of LLMMessage because Bedrock uses
@@ -1184,7 +1260,7 @@ class BedrockCompletion(BaseLLM):
         # Use base class formatting first
         formatted_messages = self._format_messages(messages)
 
-        converse_messages: list[dict[str, Any]] = []
+        converse_messages: list[LLMMessage] = []
         system_message: str | None = None
 
         for message in formatted_messages:

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import json
 import logging
 import os
 import re
@@ -24,7 +25,7 @@ try:
     from google import genai
     from google.genai import types
     from google.genai.errors import APIError
-    from google.genai.types import GenerateContentResponse, Schema
+    from google.genai.types import GenerateContentResponse
 except ImportError:
     raise ImportError(
         'Google Gen AI native provider not available, to install: uv add "crewai[google-genai]"'
@@ -434,12 +435,9 @@ class GeminiCompletion(BaseLLM):
             function_declaration = types.FunctionDeclaration(
                 name=name,
                 description=description,
+                parameters=parameters if parameters else None,
             )
 
-            # Add parameters if present - ensure parameters is a dict
-            if parameters and isinstance(parameters, Schema):
-                function_declaration.parameters = parameters
-
             gemini_tool = types.Tool(function_declarations=[function_declaration])
             gemini_tools.append(gemini_tool)
 
@@ -609,7 +607,7 @@ class GeminiCompletion(BaseLLM):
             candidate = response.candidates[0]
             if candidate.content and candidate.content.parts:
                 for part in candidate.content.parts:
-                    if hasattr(part, "function_call") and part.function_call:
+                    if part.function_call:
                         function_name = part.function_call.name
                         if function_name is None:
                             continue
@@ -645,17 +643,17 @@ class GeminiCompletion(BaseLLM):
         self,
         chunk: GenerateContentResponse,
         full_response: str,
-        function_calls: dict[str, dict[str, Any]],
+        function_calls: dict[int, dict[str, Any]],
         usage_data: dict[str, int],
         from_task: Any | None = None,
         from_agent: Any | None = None,
-    ) -> tuple[str, dict[str, dict[str, Any]], dict[str, int]]:
+    ) -> tuple[str, dict[int, dict[str, Any]], dict[str, int]]:
         """Process a single streaming chunk.
 
         Args:
             chunk: The streaming chunk response
             full_response: Accumulated response text
-            function_calls: Accumulated function calls
+            function_calls: Accumulated function calls keyed by sequential index
             usage_data: Accumulated usage data
             from_task: Task that initiated the call
             from_agent: Agent that initiated the call
@@ -678,22 +676,44 @@ class GeminiCompletion(BaseLLM):
             candidate = chunk.candidates[0]
             if candidate.content and candidate.content.parts:
                 for part in candidate.content.parts:
-                    if hasattr(part, "function_call") and part.function_call:
+                    if part.function_call:
-                        call_id = part.function_call.name or "default"
+                        call_index = len(function_calls)
-                        if call_id not in function_calls:
+                        call_id = f"call_{call_index}"
-                            function_calls[call_id] = {
+                        args_dict = (
-                                "name": part.function_call.name,
+                            dict(part.function_call.args)
-                                "args": dict(part.function_call.args)
+                            if part.function_call.args
-                                if part.function_call.args
+                            else {}
-                                else {},
+                        )
-                            }
+                        args_json = json.dumps(args_dict)
+
+                        function_calls[call_index] = {
+                            "id": call_id,
+                            "name": part.function_call.name,
+                            "args": args_dict,
+                        }
+
+                        self._emit_stream_chunk_event(
+                            chunk=args_json,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            tool_call={
+                                "id": call_id,
+                                "function": {
+                                    "name": part.function_call.name or "",
+                                    "arguments": args_json,
+                                },
+                                "type": "function",
+                                "index": call_index,
+                            },
+                            call_type=LLMCallType.TOOL_CALL,
+                        )
 
         return full_response, function_calls, usage_data
 
     def _finalize_streaming_response(
         self,
         full_response: str,
-        function_calls: dict[str, dict[str, Any]],
+        function_calls: dict[int, dict[str, Any]],
         usage_data: dict[str, int],
         contents: list[types.Content],
         available_functions: dict[str, Any] | None = None,
@@ -800,7 +820,7 @@ class GeminiCompletion(BaseLLM):
     ) -> str:
         """Handle streaming content generation."""
         full_response = ""
-        function_calls: dict[str, dict[str, Any]] = {}
+        function_calls: dict[int, dict[str, Any]] = {}
         usage_data = {"total_tokens": 0}
 
         # The API accepts list[Content] but mypy is overly strict about variance
@@ -878,7 +898,7 @@ class GeminiCompletion(BaseLLM):
     ) -> str:
         """Handle async streaming content generation."""
         full_response = ""
-        function_calls: dict[str, dict[str, Any]] = {}
+        function_calls: dict[int, dict[str, Any]] = {}
         usage_data = {"total_tokens": 0}
 
         # The API accepts list[Content] but mypy is overly strict about variance

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -521,7 +521,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str:
         """Handle streaming chat completion."""
         full_response = ""
-        tool_calls = {}
+        tool_calls: dict[int, dict[str, Any]] = {}
 
         if response_model:
             parse_params = {
@@ -591,17 +591,41 @@ class OpenAICompletion(BaseLLM):
 
             if chunk_delta.tool_calls:
                 for tool_call in chunk_delta.tool_calls:
-                    call_id = tool_call.id or "default"
+                    tool_index = tool_call.index if tool_call.index is not None else 0
-                    if call_id not in tool_calls:
+                    if tool_index not in tool_calls:
-                        tool_calls[call_id] = {
+                        tool_calls[tool_index] = {
+                            "id": tool_call.id,
                             "name": "",
                             "arguments": "",
+                            "index": tool_index,
                         }
+                    elif tool_call.id and not tool_calls[tool_index]["id"]:
+                        tool_calls[tool_index]["id"] = tool_call.id
 
                     if tool_call.function and tool_call.function.name:
-                        tool_calls[call_id]["name"] = tool_call.function.name
+                        tool_calls[tool_index]["name"] = tool_call.function.name
                     if tool_call.function and tool_call.function.arguments:
-                        tool_calls[call_id]["arguments"] += tool_call.function.arguments
+                        tool_calls[tool_index]["arguments"] += (
+                            tool_call.function.arguments
+                        )
+
+                    self._emit_stream_chunk_event(
+                        chunk=tool_call.function.arguments
+                        if tool_call.function and tool_call.function.arguments
+                        else "",
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        tool_call={
+                            "id": tool_calls[tool_index]["id"],
+                            "function": {
+                                "name": tool_calls[tool_index]["name"],
+                                "arguments": tool_calls[tool_index]["arguments"],
+                            },
+                            "type": "function",
+                            "index": tool_calls[tool_index]["index"],
+                        },
+                        call_type=LLMCallType.TOOL_CALL,
+                    )
 
         self._track_token_usage_internal(usage_data)
 
@@ -789,7 +813,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str:
         """Handle async streaming chat completion."""
         full_response = ""
-        tool_calls = {}
+        tool_calls: dict[int, dict[str, Any]] = {}
 
         if response_model:
             completion_stream: AsyncIterator[
@@ -870,17 +894,41 @@ class OpenAICompletion(BaseLLM):
 
             if chunk_delta.tool_calls:
                 for tool_call in chunk_delta.tool_calls:
-                    call_id = tool_call.id or "default"
+                    tool_index = tool_call.index if tool_call.index is not None else 0
-                    if call_id not in tool_calls:
+                    if tool_index not in tool_calls:
-                        tool_calls[call_id] = {
+                        tool_calls[tool_index] = {
+                            "id": tool_call.id,
                             "name": "",
                             "arguments": "",
+                            "index": tool_index,
                         }
+                    elif tool_call.id and not tool_calls[tool_index]["id"]:
+                        tool_calls[tool_index]["id"] = tool_call.id
 
                     if tool_call.function and tool_call.function.name:
-                        tool_calls[call_id]["name"] = tool_call.function.name
+                        tool_calls[tool_index]["name"] = tool_call.function.name
                     if tool_call.function and tool_call.function.arguments:
-                        tool_calls[call_id]["arguments"] += tool_call.function.arguments
+                        tool_calls[tool_index]["arguments"] += (
+                            tool_call.function.arguments
+                        )
+
+                    self._emit_stream_chunk_event(
+                        chunk=tool_call.function.arguments
+                        if tool_call.function and tool_call.function.arguments
+                        else "",
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        tool_call={
+                            "id": tool_calls[tool_index]["id"],
+                            "function": {
+                                "name": tool_calls[tool_index]["name"],
+                                "arguments": tool_calls[tool_index]["arguments"],
+                            },
+                            "type": "function",
+                            "index": tool_calls[tool_index]["index"],
+                        },
+                        call_type=LLMCallType.TOOL_CALL,
+                    )
 
         self._track_token_usage_internal(usage_data)
 

lib/crewai/tests/cassettes/llms/TestAnthropicToolCallStreaming.test_anthropic_streaming_emits_tool_call_events.yaml

@@ -0,0 +1,371 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the temperature
+      in San Francisco?"}],"model":"claude-3-5-haiku-latest","tools":[{"name":"get_current_temperature","description":"Get
+      the current temperature in a city.","input_schema":{"type":"object","properties":{"city":{"type":"string","description":"The
+      name of the city to get the temperature for."}},"required":["city"]}}],"stream":true}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '408'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      x-api-key:
+      - X-API-KEY-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.71.1
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+      x-stainless-stream-helper:
+      - messages
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: 'event: message_start
+
+        data: {"type":"message_start","message":{"model":"claude-3-5-haiku-20241022","id":"msg_01JCJXSfyzkcecJUydp157cS","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":351,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}}        }
+
+
+        event: content_block_start
+
+        data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}    }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"I"}      }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"''ll"}         }
+
+
+        event: ping
+
+        data: {"type": "ping"}
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        help"}         }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        you find out"}        }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        the current temperature in San"}        }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        Francisco. I"} }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"''ll"}             }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        use the get"}             }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"_current_temperature
+        function"}               }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        to"}           }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        retrieve"}            }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        this"}         }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"
+        information."}           }
+
+
+        event: content_block_stop
+
+        data: {"type":"content_block_stop","index":0}
+
+
+        event: content_block_start
+
+        data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01Lfr3kUnHMZApePPRWMv1uS","name":"get_current_temperature","input":{}}        }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":""}   }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"c"}          }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"ity\":"}      }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"
+        \"San Franci"}     }
+
+
+        event: content_block_delta
+
+        data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"sco\"}"}             }
+
+
+        event: content_block_stop
+
+        data: {"type":"content_block_stop","index":1             }
+
+
+        event: message_delta
+
+        data: {"type":"message_delta","delta":{"stop_reason":"tool_use","stop_sequence":null},"usage":{"input_tokens":351,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":85}}
+
+
+        event: message_stop
+
+        data: {"type":"message_stop"      }
+
+
+        '
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Cache-Control:
+      - no-cache
+      Connection:
+      - keep-alive
+      Content-Type:
+      - text/event-stream; charset=utf-8
+      Date:
+      - Mon, 05 Jan 2026 16:04:31 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-01-05T16:04:30Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '690'
+    status:
+      code: 200
+      message: OK
+- request:
+    body: "{\"max_tokens\":4096,\"messages\":[{\"role\":\"user\",\"content\":\"What
+      is the temperature in San Francisco?\"},{\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"I'll
+      help you find out the current temperature in San Francisco. I'll use the get_current_temperature
+      function to retrieve this information.\"},{\"type\":\"tool_use\",\"id\":\"toolu_01Lfr3kUnHMZApePPRWMv1uS\",\"name\":\"get_current_temperature\",\"input\":{\"city\":\"San
+      Francisco\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"toolu_01Lfr3kUnHMZApePPRWMv1uS\",\"content\":\"The
+      temperature in San Francisco is 72\xB0F\"}]}],\"model\":\"claude-3-5-haiku-latest\",\"stream\":true,\"tools\":[{\"name\":\"get_current_temperature\",\"description\":\"Get
+      the current temperature in a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\",\"description\":\"The
+      name of the city to get the temperature for.\"}},\"required\":[\"city\"]}}]}"
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '883'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      x-api-key:
+      - X-API-KEY-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.71.1
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"model\":\"claude-3-5-haiku-20241022\",\"id\":\"msg_01XbRN6xwSPSLv6pWtB15EZs\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":457,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":2,\"service_tier\":\"standard\"}}
+        \        }\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}
+        }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"The\"}
+        \     }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        current\"}              }\n\nevent: ping\ndata: {\"type\": \"ping\"}\n\nevent:
+        content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        temperature in San Francisco is\"}            }\n\nevent: content_block_delta\ndata:
+        {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        72\xB0F.\"}              }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        It\"}         }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        sounds\"}              }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        like a\"}  }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        pleasant\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        day!\"}    }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        Is\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        there anything else I can\"}          }\n\nevent: content_block_delta\ndata:
+        {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        help\"}          }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
+        you with?\"}            }\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0
+        \ }\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\",\"stop_sequence\":null},\"usage\":{\"input_tokens\":457,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"output_tokens\":33}
+        \  }\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"   }\n\n"
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Cache-Control:
+      - no-cache
+      Connection:
+      - keep-alive
+      Content-Type:
+      - text/event-stream; charset=utf-8
+      Date:
+      - Mon, 05 Jan 2026 16:04:33 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-01-05T16:04:32Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '532'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/llms/TestAzureToolCallStreaming.test_azure_streaming_emits_tool_call_events.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "What is the temperature in San
+      Francisco?"}], "stream": true, "tool_choice": "auto", "tools": [{"function":
+      {"name": "get_current_temperature", "description": "Get the current temperature
+      in a city.", "parameters": {"type": "object", "properties": {"city": {"type":
+      "string", "description": "The name of the city to get the temperature for."}},
+      "required": ["city"]}}, "type": "function"}], "stream_options": {"include_usage":
+      true}}'
+    headers:
+      Accept:
+      - application/json
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '476'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      api-key:
+      - X-API-KEY-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      extra-parameters:
+      - pass-through
+      x-ms-client-request-id:
+      - X-MS-CLIENT-REQUEST-ID-XXX
+    method: POST
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-02-15-preview
+  response:
+    body:
+      string: 'data: {"choices":[],"created":0,"id":"","model":"","object":"","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}]}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"content":null,"refusal":null,"role":"assistant","tool_calls":[{"function":{"arguments":"","name":"get_current_temperature"},"id":"call_e6RnREl4LBGp0PdkIf6bBioH","index":0,"type":"function"}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"a","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"{\""},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"scYzCqI","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"city"},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"gtrknf","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"\":\""},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"Fgf3u","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"San"},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"Y11NWOp","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"
+        Francisco"},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{"tool_calls":[{"function":{"arguments":"\"}"},"index":0}]},"finish_reason":null,"index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"21nwlWJ","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
+
+
+        data: {"choices":[{"content_filter_results":{},"delta":{},"finish_reason":"tool_calls","index":0,"logprobs":null}],"created":1767630292,"id":"chatcmpl-Cuhfwc9oYO2rZ1Y2xInKelrARv7iC","model":"gpt-4o-mini-2024-07-18","obfuscation":"lX7hrh76","object":"chat.completion.chunk","system_fingerprint":"fp_f97eff32c5","usage":null}
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lib/crewai/tests/llms/test_tool_call_streaming.py

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+"""Tests for tool call streaming events across LLM providers.
+
+These tests verify that when streaming is enabled and the LLM makes a tool call,
+the stream chunk events include proper tool call information with
+call_type=LLMCallType.TOOL_CALL.
+"""
+
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai.events.types.llm_events import LLMCallType, LLMStreamChunkEvent, ToolCall
+from crewai.llm import LLM
+
+
+@pytest.fixture
+def get_temperature_tool_schema() -> dict[str, Any]:
+    """Create a temperature tool schema for native function calling."""
+    return {
+        "type": "function",
+        "function": {
+            "name": "get_current_temperature",
+            "description": "Get the current temperature in a city.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city": {
+                        "type": "string",
+                        "description": "The name of the city to get the temperature for.",
+                    }
+                },
+                "required": ["city"],
+            },
+        },
+    }
+
+
+@pytest.fixture
+def mock_emit() -> MagicMock:
+    """Mock the event bus emit function."""
+    from crewai.events.event_bus import CrewAIEventsBus
+
+    with patch.object(CrewAIEventsBus, "emit") as mock:
+        yield mock
+
+
+def get_tool_call_events(mock_emit: MagicMock) -> list[LLMStreamChunkEvent]:
+    """Extract tool call streaming events from mock emit calls."""
+    tool_call_events = []
+    for call in mock_emit.call_args_list:
+        event = call[1].get("event") if len(call) > 1 else None
+        if isinstance(event, LLMStreamChunkEvent) and event.call_type == LLMCallType.TOOL_CALL:
+            tool_call_events.append(event)
+    return tool_call_events
+
+
+def get_all_stream_events(mock_emit: MagicMock) -> list[LLMStreamChunkEvent]:
+    """Extract all streaming events from mock emit calls."""
+    stream_events = []
+    for call in mock_emit.call_args_list:
+        event = call[1].get("event") if len(call) > 1 else None
+        if isinstance(event, LLMStreamChunkEvent):
+            stream_events.append(event)
+    return stream_events
+
+
+class TestOpenAIToolCallStreaming:
+    """Tests for OpenAI provider tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_openai_streaming_emits_tool_call_events(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that OpenAI streaming emits tool call events with correct call_type."""
+        llm = LLM(model="openai/gpt-4o-mini", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) > 0, "Should receive tool call streaming events"
+
+        first_tool_call_event = tool_call_events[0]
+        assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
+        assert first_tool_call_event.tool_call is not None
+        assert isinstance(first_tool_call_event.tool_call, ToolCall)
+        assert first_tool_call_event.tool_call.function is not None
+        assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
+        assert first_tool_call_event.tool_call.type == "function"
+        assert first_tool_call_event.tool_call.index >= 0
+
+
+class TestToolCallStreamingEventStructure:
+    """Tests for the structure and content of tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_tool_call_event_accumulates_arguments(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that tool call events accumulate arguments progressively."""
+        llm = LLM(model="openai/gpt-4o-mini", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) >= 2, "Should receive multiple tool call streaming events"
+
+        for evt in tool_call_events:
+            assert evt.tool_call is not None
+            assert evt.tool_call.function is not None
+
+    @pytest.mark.vcr()
+    def test_tool_call_events_have_consistent_tool_id(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that all events for the same tool call have the same tool ID."""
+        llm = LLM(model="openai/gpt-4o-mini", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) >= 1, "Should receive tool call streaming events"
+
+        if len(tool_call_events) > 1:
+            events_by_index: dict[int, list[LLMStreamChunkEvent]] = {}
+            for evt in tool_call_events:
+                if evt.tool_call is not None:
+                    idx = evt.tool_call.index
+                    if idx not in events_by_index:
+                        events_by_index[idx] = []
+                    events_by_index[idx].append(evt)
+
+            for idx, evts in events_by_index.items():
+                ids = [
+                    e.tool_call.id
+                    for e in evts
+                    if e.tool_call is not None and e.tool_call.id
+                ]
+                if ids:
+                    assert len(set(ids)) == 1, f"Tool call ID should be consistent for index {idx}"
+
+
+class TestMixedStreamingEvents:
+    """Tests for scenarios with both text and tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_streaming_distinguishes_text_and_tool_calls(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that streaming correctly distinguishes between text chunks and tool calls."""
+        llm = LLM(model="openai/gpt-4o-mini", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        all_events = get_all_stream_events(mock_emit)
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(all_events) >= 1, "Should receive streaming events"
+
+        for event in tool_call_events:
+            assert event.call_type == LLMCallType.TOOL_CALL
+            assert event.tool_call is not None
+
+
+class TestGeminiToolCallStreaming:
+    """Tests for Gemini provider tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_gemini_streaming_emits_tool_call_events(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that Gemini streaming emits tool call events with correct call_type."""
+        llm = LLM(model="gemini/gemini-2.0-flash", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) > 0, "Should receive tool call streaming events"
+
+        first_tool_call_event = tool_call_events[0]
+        assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
+        assert first_tool_call_event.tool_call is not None
+        assert isinstance(first_tool_call_event.tool_call, ToolCall)
+        assert first_tool_call_event.tool_call.function is not None
+        assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
+        assert first_tool_call_event.tool_call.type == "function"
+
+    @pytest.mark.vcr()
+    def test_gemini_streaming_multiple_tool_calls_unique_ids(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that Gemini streaming assigns unique IDs to multiple tool calls."""
+        llm = LLM(model="gemini/gemini-2.0-flash", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in Paris and London?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) >= 2, "Should receive at least 2 tool call events"
+
+        tool_ids = [
+            evt.tool_call.id
+            for evt in tool_call_events
+            if evt.tool_call is not None and evt.tool_call.id
+        ]
+        assert len(set(tool_ids)) >= 2, "Each tool call should have a unique ID"
+
+
+class TestAzureToolCallStreaming:
+    """Tests for Azure provider tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_azure_streaming_emits_tool_call_events(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that Azure streaming emits tool call events with correct call_type."""
+        llm = LLM(model="azure/gpt-4o-mini", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) > 0, "Should receive tool call streaming events"
+
+        first_tool_call_event = tool_call_events[0]
+        assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
+        assert first_tool_call_event.tool_call is not None
+        assert isinstance(first_tool_call_event.tool_call, ToolCall)
+        assert first_tool_call_event.tool_call.function is not None
+        assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
+        assert first_tool_call_event.tool_call.type == "function"
+
+
+class TestAnthropicToolCallStreaming:
+    """Tests for Anthropic provider tool call streaming events."""
+
+    @pytest.mark.vcr()
+    def test_anthropic_streaming_emits_tool_call_events(
+        self, get_temperature_tool_schema: dict[str, Any], mock_emit: MagicMock
+    ) -> None:
+        """Test that Anthropic streaming emits tool call events with correct call_type."""
+        llm = LLM(model="anthropic/claude-3-5-haiku-latest", stream=True)
+
+        llm.call(
+            messages=[
+                {"role": "user", "content": "What is the temperature in San Francisco?"},
+            ],
+            tools=[get_temperature_tool_schema],
+            available_functions={
+                "get_current_temperature": lambda city: f"The temperature in {city} is 72°F"
+            },
+        )
+
+        tool_call_events = get_tool_call_events(mock_emit)
+
+        assert len(tool_call_events) > 0, "Should receive tool call streaming events"
+
+        first_tool_call_event = tool_call_events[0]
+        assert first_tool_call_event.call_type == LLMCallType.TOOL_CALL
+        assert first_tool_call_event.tool_call is not None
+        assert isinstance(first_tool_call_event.tool_call, ToolCall)
+        assert first_tool_call_event.tool_call.function is not None
+        assert first_tool_call_event.tool_call.function.name == "get_current_temperature"
+        assert first_tool_call_event.tool_call.type == "function"