Merge branch 'main' into codex/codeql-coverage-fix

.github/codeql/codeql-config.yml

@@ -14,13 +14,18 @@ paths-ignore:
   - "lib/crewai/src/crewai/experimental/a2a/**"
 
 paths:
+  # Include GitHub Actions workflows/composite actions for CodeQL actions analysis
+  - ".github/workflows/**"
+  - ".github/actions/**"
   # Include all Python source code from workspace packages
   - "lib/crewai/src/**"
   - "lib/crewai-tools/src/**"
+  - "lib/crewai-files/src/**"
   - "lib/devtools/src/**"
   # Include tests (but exclude cassettes via paths-ignore)
   - "lib/crewai/tests/**"
   - "lib/crewai-tools/tests/**"
+  - "lib/crewai-files/tests/**"
   - "lib/devtools/tests/**"
 
 # Configure specific queries or packs if needed

.github/workflows/codeql.yml

@@ -69,7 +69,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v3
+      uses: github/codeql-action/init@v4
       with:
         languages: ${{ matrix.language }}
         build-mode: ${{ matrix.build-mode }}
@@ -98,6 +98,6 @@ jobs:
         exit 1
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v3
+      uses: github/codeql-action/analyze@v4
       with:
         category: "/language:${{matrix.language}}"

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

@@ -1696,99 +1696,6 @@ class OpenAICompletion(BaseLLM):
 
         return content
 
-    def _finalize_streaming_response(
-        self,
-        full_response: str,
-        tool_calls: dict[int, dict[str, Any]],
-        usage_data: dict[str, int],
-        params: dict[str, Any],
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-    ) -> str | list[dict[str, Any]]:
-        """Finalize a streaming response with usage tracking, tool call handling, and events.
-
-        Args:
-            full_response: The accumulated text response from the stream.
-            tool_calls: Accumulated tool calls from the stream, keyed by index.
-            usage_data: Token usage data from the stream.
-            params: The completion parameters containing messages.
-            available_functions: Available functions for tool calling.
-            from_task: Task that initiated the call.
-            from_agent: Agent that initiated the call.
-
-        Returns:
-            Tool calls list when tools were invoked without available_functions,
-            tool execution result when available_functions is provided,
-            or the text response string.
-        """
-        self._track_token_usage_internal(usage_data)
-
-        if tool_calls and not available_functions:
-            tool_calls_list = [
-                {
-                    "id": call_data["id"],
-                    "type": "function",
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": call_data["arguments"],
-                    },
-                    "index": call_data["index"],
-                }
-                for call_data in tool_calls.values()
-            ]
-            self._emit_call_completed_event(
-                response=tool_calls_list,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params["messages"],
-            )
-            return tool_calls_list
-
-        if tool_calls and available_functions:
-            for call_data in tool_calls.values():
-                function_name = call_data["name"]
-                arguments = call_data["arguments"]
-
-                if not function_name or not arguments:
-                    continue
-
-                if function_name not in available_functions:
-                    logging.warning(
-                        f"Function '{function_name}' not found in available functions"
-                    )
-                    continue
-
-                try:
-                    function_args = json.loads(arguments)
-                except json.JSONDecodeError as e:
-                    logging.error(f"Failed to parse streamed tool arguments: {e}")
-                    continue
-
-                result = self._handle_tool_execution(
-                    function_name=function_name,
-                    function_args=function_args,
-                    available_functions=available_functions,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-                if result is not None:
-                    return result
-
-        full_response = self._apply_stop_words(full_response)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
-            from_task=from_task,
-            from_agent=from_agent,
-            messages=params["messages"],
-        )
-
-        return full_response
-
     def _handle_streaming_completion(
         self,
         params: dict[str, Any],
@@ -1796,7 +1703,7 @@ class OpenAICompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | list[dict[str, Any]] | BaseModel:
+    ) -> str | BaseModel:
         """Handle streaming chat completion."""
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
@@ -1913,20 +1820,54 @@ class OpenAICompletion(BaseLLM):
                         response_id=response_id_stream,
                     )
 
-        result = self._finalize_streaming_response(
-            full_response=full_response,
-            tool_calls=tool_calls,
-            usage_data=usage_data,
-            params=params,
-            available_functions=available_functions,
+        self._track_token_usage_internal(usage_data)
+
+        if tool_calls and available_functions:
+            for call_data in tool_calls.values():
+                function_name = call_data["name"]
+                arguments = call_data["arguments"]
+
+                # Skip if function name is empty or arguments are empty
+                if not function_name or not arguments:
+                    continue
+
+                # Check if function exists in available functions
+                if function_name not in available_functions:
+                    logging.warning(
+                        f"Function '{function_name}' not found in available functions"
+                    )
+                    continue
+
+                try:
+                    function_args = json.loads(arguments)
+                except json.JSONDecodeError as e:
+                    logging.error(f"Failed to parse streamed tool arguments: {e}")
+                    continue
+
+                result = self._handle_tool_execution(
+                    function_name=function_name,
+                    function_args=function_args,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+                if result is not None:
+                    return result
+
+        full_response = self._apply_stop_words(full_response)
+
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
             from_task=from_task,
             from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], full_response, from_agent
         )
-        if isinstance(result, str):
-            return self._invoke_after_llm_call_hooks(
-                params["messages"], result, from_agent
-            )
-        return result
 
     async def _ahandle_completion(
         self,
@@ -2075,7 +2016,7 @@ class OpenAICompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | list[dict[str, Any]] | BaseModel:
+    ) -> str | BaseModel:
         """Handle async streaming chat completion."""
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
@@ -2201,16 +2142,51 @@ class OpenAICompletion(BaseLLM):
                         response_id=response_id_stream,
                     )
 
-        return self._finalize_streaming_response(
-            full_response=full_response,
-            tool_calls=tool_calls,
-            usage_data=usage_data,
-            params=params,
-            available_functions=available_functions,
+        self._track_token_usage_internal(usage_data)
+
+        if tool_calls and available_functions:
+            for call_data in tool_calls.values():
+                function_name = call_data["name"]
+                arguments = call_data["arguments"]
+
+                if not function_name or not arguments:
+                    continue
+
+                if function_name not in available_functions:
+                    logging.warning(
+                        f"Function '{function_name}' not found in available functions"
+                    )
+                    continue
+
+                try:
+                    function_args = json.loads(arguments)
+                except json.JSONDecodeError as e:
+                    logging.error(f"Failed to parse streamed tool arguments: {e}")
+                    continue
+
+                result = self._handle_tool_execution(
+                    function_name=function_name,
+                    function_args=function_args,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+                if result is not None:
+                    return result
+
+        full_response = self._apply_stop_words(full_response)
+
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
             from_task=from_task,
             from_agent=from_agent,
+            messages=params["messages"],
         )
 
+        return full_response
+
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
         return not self.is_o1_model

lib/crewai/tests/llms/openai/test_openai.py

@@ -1,7 +1,6 @@
 import os
 import sys
 import types
-from typing import Any
 from unittest.mock import patch, MagicMock
 import openai
 import pytest
@@ -1579,167 +1578,3 @@ def test_openai_structured_output_preserves_json_with_stop_word_patterns():
     assert "Action:" in result.action_taken
     assert "Observation:" in result.observation_result
     assert "Final Answer:" in result.final_answer
-
-
-def test_openai_streaming_returns_tool_calls_without_available_functions():
-    """Test that streaming returns tool calls list when available_functions is None.
-
-    This mirrors the non-streaming path where tool_calls are returned for
-    the executor to handle. Reproduces the bug where streaming with tool
-    calls would return empty text instead of tool_calls when
-    available_functions was not provided (as the crew executor does).
-    """
-    llm = LLM(model="openai/gpt-4o-mini", stream=True)
-
-    mock_chunk_1 = MagicMock()
-    mock_chunk_1.choices = [MagicMock()]
-    mock_chunk_1.choices[0].delta = MagicMock()
-    mock_chunk_1.choices[0].delta.content = None
-    mock_chunk_1.choices[0].delta.tool_calls = [MagicMock()]
-    mock_chunk_1.choices[0].delta.tool_calls[0].index = 0
-    mock_chunk_1.choices[0].delta.tool_calls[0].id = "call_abc123"
-    mock_chunk_1.choices[0].delta.tool_calls[0].function = MagicMock()
-    mock_chunk_1.choices[0].delta.tool_calls[0].function.name = "calculator"
-    mock_chunk_1.choices[0].delta.tool_calls[0].function.arguments = '{"expr'
-    mock_chunk_1.choices[0].finish_reason = None
-    mock_chunk_1.usage = None
-    mock_chunk_1.id = "chatcmpl-1"
-
-    mock_chunk_2 = MagicMock()
-    mock_chunk_2.choices = [MagicMock()]
-    mock_chunk_2.choices[0].delta = MagicMock()
-    mock_chunk_2.choices[0].delta.content = None
-    mock_chunk_2.choices[0].delta.tool_calls = [MagicMock()]
-    mock_chunk_2.choices[0].delta.tool_calls[0].index = 0
-    mock_chunk_2.choices[0].delta.tool_calls[0].id = None
-    mock_chunk_2.choices[0].delta.tool_calls[0].function = MagicMock()
-    mock_chunk_2.choices[0].delta.tool_calls[0].function.name = None
-    mock_chunk_2.choices[0].delta.tool_calls[0].function.arguments = 'ession": "1+1"}'
-    mock_chunk_2.choices[0].finish_reason = None
-    mock_chunk_2.usage = None
-    mock_chunk_2.id = "chatcmpl-1"
-
-    mock_chunk_3 = MagicMock()
-    mock_chunk_3.choices = [MagicMock()]
-    mock_chunk_3.choices[0].delta = MagicMock()
-    mock_chunk_3.choices[0].delta.content = None
-    mock_chunk_3.choices[0].delta.tool_calls = None
-    mock_chunk_3.choices[0].finish_reason = "tool_calls"
-    mock_chunk_3.usage = MagicMock()
-    mock_chunk_3.usage.prompt_tokens = 10
-    mock_chunk_3.usage.completion_tokens = 5
-    mock_chunk_3.id = "chatcmpl-1"
-
-    with patch.object(
-        llm.client.chat.completions, "create", return_value=iter([mock_chunk_1, mock_chunk_2, mock_chunk_3])
-    ):
-        result = llm.call(
-            messages=[{"role": "user", "content": "Calculate 1+1"}],
-            tools=[{
-                "type": "function",
-                "function": {
-                    "name": "calculator",
-                    "description": "Calculate expression",
-                    "parameters": {"type": "object", "properties": {"expression": {"type": "string"}}},
-                },
-            }],
-            available_functions=None,
-        )
-
-    assert isinstance(result, list), f"Expected list of tool calls, got {type(result)}: {result}"
-    assert len(result) == 1
-    assert result[0]["function"]["name"] == "calculator"
-    assert result[0]["function"]["arguments"] == '{"expression": "1+1"}'
-    assert result[0]["id"] == "call_abc123"
-    assert result[0]["type"] == "function"
-
-
-@pytest.mark.asyncio
-async def test_openai_async_streaming_returns_tool_calls_without_available_functions():
-    """Test that async streaming returns tool calls list when available_functions is None.
-
-    Same as the sync test but for the async path (_ahandle_streaming_completion).
-    """
-    llm = LLM(model="openai/gpt-4o-mini", stream=True)
-
-    mock_chunk_1 = MagicMock()
-    mock_chunk_1.choices = [MagicMock()]
-    mock_chunk_1.choices[0].delta = MagicMock()
-    mock_chunk_1.choices[0].delta.content = None
-    mock_chunk_1.choices[0].delta.tool_calls = [MagicMock()]
-    mock_chunk_1.choices[0].delta.tool_calls[0].index = 0
-    mock_chunk_1.choices[0].delta.tool_calls[0].id = "call_abc123"
-    mock_chunk_1.choices[0].delta.tool_calls[0].function = MagicMock()
-    mock_chunk_1.choices[0].delta.tool_calls[0].function.name = "calculator"
-    mock_chunk_1.choices[0].delta.tool_calls[0].function.arguments = '{"expr'
-    mock_chunk_1.choices[0].finish_reason = None
-    mock_chunk_1.usage = None
-    mock_chunk_1.id = "chatcmpl-1"
-
-    mock_chunk_2 = MagicMock()
-    mock_chunk_2.choices = [MagicMock()]
-    mock_chunk_2.choices[0].delta = MagicMock()
-    mock_chunk_2.choices[0].delta.content = None
-    mock_chunk_2.choices[0].delta.tool_calls = [MagicMock()]
-    mock_chunk_2.choices[0].delta.tool_calls[0].index = 0
-    mock_chunk_2.choices[0].delta.tool_calls[0].id = None
-    mock_chunk_2.choices[0].delta.tool_calls[0].function = MagicMock()
-    mock_chunk_2.choices[0].delta.tool_calls[0].function.name = None
-    mock_chunk_2.choices[0].delta.tool_calls[0].function.arguments = 'ession": "1+1"}'
-    mock_chunk_2.choices[0].finish_reason = None
-    mock_chunk_2.usage = None
-    mock_chunk_2.id = "chatcmpl-1"
-
-    mock_chunk_3 = MagicMock()
-    mock_chunk_3.choices = [MagicMock()]
-    mock_chunk_3.choices[0].delta = MagicMock()
-    mock_chunk_3.choices[0].delta.content = None
-    mock_chunk_3.choices[0].delta.tool_calls = None
-    mock_chunk_3.choices[0].finish_reason = "tool_calls"
-    mock_chunk_3.usage = MagicMock()
-    mock_chunk_3.usage.prompt_tokens = 10
-    mock_chunk_3.usage.completion_tokens = 5
-    mock_chunk_3.id = "chatcmpl-1"
-
-    class MockAsyncStream:
-        """Async iterator that mimics OpenAI's async streaming response."""
-
-        def __init__(self, chunks: list[Any]) -> None:
-            self._chunks = chunks
-            self._index = 0
-
-        def __aiter__(self) -> "MockAsyncStream":
-            return self
-
-        async def __anext__(self) -> Any:
-            if self._index >= len(self._chunks):
-                raise StopAsyncIteration
-            chunk = self._chunks[self._index]
-            self._index += 1
-            return chunk
-
-    async def mock_create(**kwargs: Any) -> MockAsyncStream:
-        return MockAsyncStream([mock_chunk_1, mock_chunk_2, mock_chunk_3])
-
-    with patch.object(
-        llm.async_client.chat.completions, "create", side_effect=mock_create
-    ):
-        result = await llm.acall(
-            messages=[{"role": "user", "content": "Calculate 1+1"}],
-            tools=[{
-                "type": "function",
-                "function": {
-                    "name": "calculator",
-                    "description": "Calculate expression",
-                    "parameters": {"type": "object", "properties": {"expression": {"type": "string"}}},
-                },
-            }],
-            available_functions=None,
-        )
-
-    assert isinstance(result, list), f"Expected list of tool calls, got {type(result)}: {result}"
-    assert len(result) == 1
-    assert result[0]["function"]["name"] == "calculator"
-    assert result[0]["function"]["arguments"] == '{"expression": "1+1"}'
-    assert result[0]["id"] == "call_abc123"
-    assert result[0]["type"] == "function"