fix: reject non-dict values in process_config with clear ValidationError

Fixes #4419. When an invalid type (e.g. a string) was passed for a field
that expects a Pydantic model like BaseAgent, the process_config function
would crash with AttributeError: 'str' object has no attribute 'get'.

Now process_config raises ValueError for non-dict inputs, which Pydantic
wraps into a proper ValidationError with a clear message like:
  'BaseAgent expected a mapping/dictionary, got str'

Co-Authored-By: João <joao@crewai.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,8 +65,6 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
-        - '3.13'
-        - '3.14'
     validations:
       required: true
   - type: input

.github/codeql/codeql-config.yml

@@ -14,18 +14,13 @@ 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/build-uv-cache.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout repository
@@ -32,10 +32,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
-      - name: Install system build dependencies
-        if: matrix.python-version == '3.14'
-        run: sudo apt-get update && sudo apt-get install -y libxml2-dev libxslt-dev libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libavfilter-dev libswscale-dev libswresample-dev libheif-dev
-
       - name: Install dependencies and populate cache
         run: |
           echo "Building global UV cache for Python ${{ matrix.python-version }}..."

.github/workflows/codeql.yml

@@ -69,7 +69,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v4
+      uses: github/codeql-action/init@v3
       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@v4
+      uses: github/codeql-action/analyze@v3
       with:
         category: "/language:${{matrix.language}}"

.github/workflows/linter.yml

@@ -26,15 +26,15 @@ jobs:
             ~/.cache/uv
             ~/.local/share/uv
             .venv
-          key: uv-main-py3.12-${{ hashFiles('uv.lock') }}
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
           restore-keys: |
-            uv-main-py3.12-
+            uv-main-py3.11-
 
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
           version: "0.8.4"
-          python-version: "3.12"
+          python-version: "3.11"
           enable-cache: false
 
       - name: Install dependencies
@@ -66,4 +66,4 @@ jobs:
             ~/.cache/uv
             ~/.local/share/uv
             .venv
-          key: uv-main-py3.12-${{ hashFiles('uv.lock') }}
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/tests.yml

@@ -13,8 +13,8 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
-        group: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -40,10 +40,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
-      - name: Install system build dependencies
-        if: matrix.python-version == '3.14'
-        run: sudo apt-get update && sudo apt-get install -y libxml2-dev libxslt-dev libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libavfilter-dev libswscale-dev libswresample-dev libheif-dev
-
       - name: Install the project
         run: uv sync --all-groups --all-extras
 
@@ -53,7 +49,7 @@ jobs:
           path: .test_durations_py*
           key: test-durations-py${{ matrix.python-version }}
 
-      - name: Run tests (group ${{ matrix.group }} of 16)
+      - name: Run tests (group ${{ matrix.group }} of 8)
         run: |
           PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
           DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}"
@@ -77,17 +73,17 @@ jobs:
 
           cd lib/crewai && uv run pytest \
             -vv \
-            --splits 16 \
+            --splits 8 \
             --group ${{ matrix.group }} \
             $DURATIONS_ARG \
             --durations=10 \
             --maxfail=3
 
-      - name: Run tool tests (group ${{ matrix.group }} of 16)
+      - name: Run tool tests (group ${{ matrix.group }} of 8)
         run: |
           cd lib/crewai-tools && uv run pytest \
             -vv \
-            --splits 16 \
+            --splits 8 \
             --group ${{ matrix.group }} \
             --durations=10 \
             --maxfail=3

.github/workflows/type-checker.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout code
@@ -39,10 +39,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
-      - name: Install system build dependencies
-        if: matrix.python-version == '3.14'
-        run: sudo apt-get update && sudo apt-get install -y libxml2-dev libxslt-dev libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libavfilter-dev libswscale-dev libswresample-dev libheif-dev
-
       - name: Install dependencies
         run: uv sync --all-groups --all-extras
 

.github/workflows/update-test-durations.yml

@@ -16,11 +16,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+        python-version: ['3.10', '3.11', '3.12', '3.13']
     env:
       OPENAI_API_KEY: fake-api-key
       PYTHONUNBUFFERED: 1
-
+    
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -44,10 +44,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
-      - name: Install system build dependencies
-        if: matrix.python-version == '3.14'
-        run: sudo apt-get update && sudo apt-get install -y libxml2-dev libxslt-dev libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libavfilter-dev libswscale-dev libswresample-dev libheif-dev
-
       - name: Install the project
         run: uv sync --all-groups --all-extras
 
@@ -72,4 +68,4 @@ jobs:
             ~/.cache/uv
             ~/.local/share/uv
             .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

lib/crewai-files/pyproject.toml

@@ -6,7 +6,7 @@ readme = "README.md"
 authors = [
     { name = "Greyson LaLonde", email = "greyson@crewai.com" }
 ]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10, <3.14"
 dependencies = [
     "Pillow~=10.4.0",
     "pypdf~=4.0.0",

lib/crewai-tools/pyproject.toml

@@ -6,7 +6,7 @@ readme = "README.md"
 authors = [
     { name = "João Moura", email = "joaomdmoura@gmail.com" },
 ]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10, <3.14"
 dependencies = [
     "lancedb~=0.5.4",
     "pytube~=15.0.0",
@@ -118,7 +118,7 @@ rag = [
     "lxml>=5.3.0,<5.4.0",  # Pin to avoid etree import issues in 5.4.0
 ]
 xml = [
-    "unstructured[local-inference, all-docs]>=0.17.2,<0.18.31"
+    "unstructured[local-inference, all-docs]>=0.17.2"
 ]
 oxylabs = [
     "oxylabs==2.0.0"

lib/crewai-tools/tests/tools/brave_search_tool_test.py

@@ -33,11 +33,8 @@ def test_brave_tool_search(mock_get, brave_tool):
     mock_get.return_value.json.return_value = mock_response
 
     result = brave_tool.run(query="test")
-    data = json.loads(result)
-    assert isinstance(data, list)
-    assert len(data) >= 1
-    assert data[0]["title"] == "Test Title"
-    assert data[0]["url"] == "http://test.com"
+    assert "Test Title" in result
+    assert "http://test.com" in result
 
 
 @patch("requests.get")

lib/crewai/pyproject.toml

@@ -6,7 +6,7 @@ readme = "README.md"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10, <3.14"
 dependencies = [
     # Core Dependencies
     "pydantic~=2.11.9",
@@ -14,7 +14,7 @@ dependencies = [
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
-    "regex~=2026.1.15",
+    "regex~=2024.9.11",
     # Telemetry and Monitoring
     "opentelemetry-api~=1.34.0",
     "opentelemetry-sdk~=1.34.0",
@@ -36,7 +36,7 @@ dependencies = [
     "json5~=0.10.0",
     "portalocker~=2.7.0",
     "pydantic-settings~=2.10.1",
-    "mcp~=1.26.0",
+    "mcp~=1.23.1",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
 ]

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10,<3.14"
 dependencies = [
     "crewai[tools]==1.9.3"
 ]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10,<3.14"
 dependencies = [
     "crewai[tools]==1.9.3"
 ]

lib/crewai/src/crewai/cli/templates/tool/pyproject.toml

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.9.3"
+    "crewai[tools]>=0.203.1"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/crew.py

@@ -187,7 +187,6 @@ class Crew(FlowTrackable, BaseModel):
     _task_output_handler: TaskOutputStorageHandler = PrivateAttr(
         default_factory=TaskOutputStorageHandler
     )
-    _kickoff_event_id: str | None = PrivateAttr(default=None)
 
     name: str | None = Field(default="crew")
     cache: bool = Field(default=True)
@@ -760,11 +759,7 @@ class Crew(FlowTrackable, BaseModel):
         except Exception as e:
             crewai_event_bus.emit(
                 self,
-                CrewKickoffFailedEvent(
-                    error=str(e),
-                    crew_name=self.name,
-                    started_event_id=self._kickoff_event_id,
-                ),
+                CrewKickoffFailedEvent(error=str(e), crew_name=self.name),
             )
             raise
         finally:
@@ -954,11 +949,7 @@ class Crew(FlowTrackable, BaseModel):
         except Exception as e:
             crewai_event_bus.emit(
                 self,
-                CrewKickoffFailedEvent(
-                    error=str(e),
-                    crew_name=self.name,
-                    started_event_id=self._kickoff_event_id,
-                ),
+                CrewKickoffFailedEvent(error=str(e), crew_name=self.name),
             )
             raise
         finally:
@@ -1533,7 +1524,6 @@ class Crew(FlowTrackable, BaseModel):
                 crew_name=self.name,
                 output=final_task_output,
                 total_tokens=self.token_usage.total_tokens,
-                started_event_id=self._kickoff_event_id,
             ),
         )
 

lib/crewai/src/crewai/crews/utils.py

@@ -265,9 +265,10 @@ def prepare_kickoff(
             normalized = {}
         normalized = before_callback(normalized)
 
-    started_event = CrewKickoffStartedEvent(crew_name=crew.name, inputs=normalized)
-    crew._kickoff_event_id = started_event.event_id
-    future = crewai_event_bus.emit(crew, started_event)
+    future = crewai_event_bus.emit(
+        crew,
+        CrewKickoffStartedEvent(crew_name=crew.name, inputs=normalized),
+    )
     if future is not None:
         try:
             future.result()

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/src/crewai/utilities/config.py

@@ -14,7 +14,16 @@ def process_config(
 
     Returns:
         The updated values dictionary.
+
+    Raises:
+        ValueError: If values is not a dictionary (e.g. when an invalid type
+            is passed for a field that expects a Pydantic model).
     """
+    if not isinstance(values, dict):
+        raise ValueError(
+            f"{model_class.__name__} expected a mapping/dictionary, "
+            f"got {type(values).__name__}"
+        )
     config = values.get("config", {})
     if not config:
         return values

lib/crewai/tests/cli/deploy/test_deploy_main.py

@@ -230,7 +230,7 @@ class TestDeployCommand(unittest.TestCase):
         [project]
         name = "test_project"
         version = "0.1.0"
-        requires-python = ">=3.10,<3.15"
+        requires-python = ">=3.10,<3.14"
         dependencies = ["crewai"]
         """,
     )
@@ -249,7 +249,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-    requires-python = ">=3.10,<3.15"
+    requires-python = ">=3.10,<3.14"
     dependencies = ["crewai"]
     """,
     )

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"

lib/crewai/tests/test_task.py

@@ -1748,3 +1748,50 @@ def test_async_execution_fails():
       with pytest.raises(RuntimeError, match="boom!"):
         execution = task.execute_async(agent=researcher)
         execution.result()
+
+
+def test_task_with_invalid_agent_string_raises_validation_error():
+    with pytest.raises(ValidationError) as exc_info:
+        Task(
+            description="Test task",
+            expected_output="Test output",
+            agent="not_an_agent",
+        )
+    assert "BaseAgent expected a mapping/dictionary, got str" in str(exc_info.value)
+
+
+def test_task_with_invalid_agent_int_raises_validation_error():
+    with pytest.raises(ValidationError) as exc_info:
+        Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=123,
+        )
+    assert "BaseAgent expected a mapping/dictionary, got int" in str(exc_info.value)
+
+
+def test_task_with_invalid_agent_list_raises_validation_error():
+    with pytest.raises(ValidationError) as exc_info:
+        Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=[1, 2, 3],
+        )
+    assert "BaseAgent expected a mapping/dictionary, got list" in str(exc_info.value)
+
+
+def test_task_with_none_agent_is_valid():
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=None,
+    )
+    assert task.agent is None
+
+
+def test_task_without_agent_is_valid():
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+    )
+    assert task.agent is None

lib/devtools/pyproject.toml

@@ -6,7 +6,7 @@ readme = "README.md"
 authors = [
     { name = "Greyson R. LaLonde", email = "greyson@crewai.com" },
 ]
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10, <3.14"
 classifiers = ["Private :: Do Not Upload"]
 private = true
 dependencies = [

pyproject.toml

@@ -1,7 +1,7 @@
 name = "crewai-workspace"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
-requires-python = ">=3.10,<3.15"
+requires-python = ">=3.10,<3.14"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
@@ -143,11 +143,6 @@ python_classes = "Test*"
 python_functions = "test_*"
 
 
-[tool.uv]
-constraint-dependencies = [
-    "onnxruntime<1.24; python_version < '3.11'",
-]
-
 [tool.uv.workspace]
 members = [
     "lib/crewai",