fix: reset messages and iterations in CrewAgentExecutor between task executions

When the same CrewAgentExecutor instance is reused across multiple
sequential tasks, messages and iterations were accumulating instead of
being reset. This caused duplicate system prompts, context window
explosion, and eventually crashes with empty LLM responses.

Reset self.messages and self.iterations at the start of invoke() and
ainvoke() to ensure each task execution starts with a clean slate.

Fixes #4319, #4389, #4415

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/agents/crew_agent_executor.py

@@ -210,6 +210,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Dictionary with agent output.
         """
+        self.messages = []
+        self.iterations = 0
         self._setup_messages(inputs)
 
         self._inject_multimodal_files(inputs)
@@ -987,6 +989,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Dictionary with agent output.
         """
+        self.messages = []
+        self.iterations = 0
         self._setup_messages(inputs)
 
         await self._ainject_multimodal_files(inputs)

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/tests/agents/test_executor_state_reset.py

@@ -0,0 +1,270 @@
+"""Tests for CrewAgentExecutor state reset between task executions.
+
+Verifies that messages and iterations are properly reset when invoke() or
+ainvoke() is called multiple times on the same executor instance, preventing
+context pollution across sequential tasks (issues #4319, #4389, #4415).
+"""
+
+import asyncio
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.agents.parser import AgentFinish
+
+
+@pytest.fixture
+def mock_llm() -> MagicMock:
+    llm = MagicMock()
+    llm.supports_stop_words.return_value = True
+    llm.stop = []
+    return llm
+
+
+@pytest.fixture
+def mock_agent() -> MagicMock:
+    agent = MagicMock()
+    agent.role = "Test Agent"
+    agent.key = "test_agent_key"
+    agent.verbose = False
+    agent.id = "test_agent_id"
+    return agent
+
+
+@pytest.fixture
+def mock_task() -> MagicMock:
+    task = MagicMock()
+    task.description = "Test task"
+    task.human_input = False
+    task.response_model = None
+    task.id = "test_task_id"
+    return task
+
+
+@pytest.fixture
+def mock_crew() -> MagicMock:
+    crew = MagicMock()
+    crew.verbose = False
+    crew._train = False
+    crew.id = "test_crew_id"
+    return crew
+
+
+@pytest.fixture
+def mock_tools_handler() -> MagicMock:
+    return MagicMock()
+
+
+@pytest.fixture
+def executor(
+    mock_llm: MagicMock,
+    mock_agent: MagicMock,
+    mock_task: MagicMock,
+    mock_crew: MagicMock,
+    mock_tools_handler: MagicMock,
+) -> CrewAgentExecutor:
+    return CrewAgentExecutor(
+        llm=mock_llm,
+        task=mock_task,
+        crew=mock_crew,
+        agent=mock_agent,
+        prompt={"system": "You are a helpful agent.", "user": "Task: {input} Tools: {tool_names} {tools}"},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=["Observation:"],
+        tools_description="",
+        tools_handler=mock_tools_handler,
+    )
+
+
+class TestCrewAgentExecutorStateReset:
+    """Tests that CrewAgentExecutor resets messages and iterations on each invoke."""
+
+    def test_invoke_resets_messages(self, executor: CrewAgentExecutor) -> None:
+        """Messages from a previous invoke must not leak into the next one."""
+        executor.messages = [
+            {"role": "system", "content": "old system prompt"},
+            {"role": "user", "content": "old task prompt"},
+            {"role": "assistant", "content": "old response"},
+        ]
+        executor.iterations = 7
+
+        with patch.object(
+            executor,
+            "_invoke_loop",
+            return_value=AgentFinish(thought="done", output="result", text="Final Answer: result"),
+        ):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        executor.invoke({"input": "new task", "tool_names": "", "tools": ""})
+
+        system_msgs = [m for m in executor.messages if m.get("role") == "system"]
+        assert len(system_msgs) == 1, (
+            f"Expected exactly 1 system message after invoke, got {len(system_msgs)}"
+        )
+
+    def test_invoke_resets_iterations(self, executor: CrewAgentExecutor) -> None:
+        """Iterations must be reset to 0 at the start of each invoke."""
+        executor.iterations = 42
+
+        with patch.object(
+            executor,
+            "_invoke_loop",
+            return_value=AgentFinish(thought="done", output="result", text="done"),
+        ):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        executor.invoke({"input": "task", "tool_names": "", "tools": ""})
+
+    def test_sequential_invokes_no_message_accumulation(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Calling invoke multiple times must not accumulate messages."""
+        message_counts: list[int] = []
+
+        original_invoke_loop = executor._invoke_loop
+
+        def capture_messages_then_finish() -> AgentFinish:
+            message_counts.append(len(executor.messages))
+            return AgentFinish(thought="done", output="result", text="done")
+
+        with patch.object(executor, "_invoke_loop", side_effect=capture_messages_then_finish):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        for i in range(3):
+                            executor.invoke(
+                                {"input": f"task {i}", "tool_names": "", "tools": ""}
+                            )
+
+        assert len(message_counts) == 3
+        assert message_counts[0] == message_counts[1] == message_counts[2], (
+            f"Message counts should be equal across invocations, got {message_counts}"
+        )
+
+    def test_sequential_invokes_no_duplicate_system_messages(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Each invoke must contain exactly one system message, not N."""
+        system_msg_counts: list[int] = []
+
+        def capture_and_finish() -> AgentFinish:
+            count = sum(1 for m in executor.messages if m.get("role") == "system")
+            system_msg_counts.append(count)
+            return AgentFinish(thought="done", output="ok", text="ok")
+
+        with patch.object(executor, "_invoke_loop", side_effect=capture_and_finish):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        for _ in range(5):
+                            executor.invoke({"input": "task", "tool_names": "", "tools": ""})
+
+        assert all(c == 1 for c in system_msg_counts), (
+            f"Expected 1 system message per invoke, got {system_msg_counts}"
+        )
+
+
+class TestCrewAgentExecutorAsyncStateReset:
+    """Tests that ainvoke also resets messages and iterations."""
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_resets_messages(self, executor: CrewAgentExecutor) -> None:
+        """Messages from a previous ainvoke must not leak into the next one."""
+        executor.messages = [
+            {"role": "system", "content": "old system prompt"},
+            {"role": "user", "content": "old task prompt"},
+            {"role": "assistant", "content": "old response"},
+        ]
+        executor.iterations = 7
+
+        with patch.object(
+            executor,
+            "_ainvoke_loop",
+            new_callable=AsyncMock,
+            return_value=AgentFinish(thought="done", output="result", text="done"),
+        ):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        await executor.ainvoke(
+                            {"input": "new task", "tool_names": "", "tools": ""}
+                        )
+
+        system_msgs = [m for m in executor.messages if m.get("role") == "system"]
+        assert len(system_msgs) == 1, (
+            f"Expected exactly 1 system message after ainvoke, got {len(system_msgs)}"
+        )
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_resets_iterations(self, executor: CrewAgentExecutor) -> None:
+        """Iterations must be reset to 0 at the start of each ainvoke."""
+        executor.iterations = 42
+
+        with patch.object(
+            executor,
+            "_ainvoke_loop",
+            new_callable=AsyncMock,
+            return_value=AgentFinish(thought="done", output="result", text="done"),
+        ):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        await executor.ainvoke(
+                            {"input": "task", "tool_names": "", "tools": ""}
+                        )
+
+    @pytest.mark.asyncio
+    async def test_sequential_ainvokes_no_message_accumulation(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Calling ainvoke multiple times must not accumulate messages."""
+        message_counts: list[int] = []
+
+        async def capture_and_finish() -> AgentFinish:
+            message_counts.append(len(executor.messages))
+            return AgentFinish(thought="done", output="result", text="done")
+
+        with patch.object(executor, "_ainvoke_loop", side_effect=capture_and_finish):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        for i in range(3):
+                            await executor.ainvoke(
+                                {"input": f"task {i}", "tool_names": "", "tools": ""}
+                            )
+
+        assert len(message_counts) == 3
+        assert message_counts[0] == message_counts[1] == message_counts[2], (
+            f"Message counts should be equal across invocations, got {message_counts}"
+        )
+
+    @pytest.mark.asyncio
+    async def test_sequential_ainvokes_no_duplicate_system_messages(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Each ainvoke must contain exactly one system message, not N."""
+        system_msg_counts: list[int] = []
+
+        async def capture_and_finish() -> AgentFinish:
+            count = sum(1 for m in executor.messages if m.get("role") == "system")
+            system_msg_counts.append(count)
+            return AgentFinish(thought="done", output="ok", text="ok")
+
+        with patch.object(executor, "_ainvoke_loop", side_effect=capture_and_finish):
+            with patch.object(executor, "_create_short_term_memory"):
+                with patch.object(executor, "_create_long_term_memory"):
+                    with patch.object(executor, "_create_external_memory"):
+                        for _ in range(5):
+                            await executor.ainvoke(
+                                {"input": "task", "tool_names": "", "tools": ""}
+                            )
+
+        assert all(c == 1 for c in system_msg_counts), (
+            f"Expected 1 system message per ainvoke, got {system_msg_counts}"
+        )

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/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",