chore(deps-dev): bump types-aiofiles

Bumps [types-aiofiles](https://github.com/typeshed-internal/stub_uploader) from 24.1.0.20250822 to 25.1.0.20251011.
- [Commits](https://github.com/typeshed-internal/stub_uploader/commits)

---
updated-dependencies:
- dependency-name: types-aiofiles
  dependency-version: 25.1.0.20251011
  dependency-type: direct:development
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/dependabot.yml

@@ -5,7 +5,12 @@
 
 version: 2
 updates:
-  - package-ecosystem: uv # See documentation for possible values
-    directory: "/" # Location of package manifests
+  - package-ecosystem: uv
+    directory: "/"
     schedule:
       interval: "weekly"
+    groups:
+      security-updates:
+        applies-to: security-updates
+        patterns:
+          - "*"

.python-version

@@ -0,0 +1 @@
+3.13

lib/crewai-tools/src/crewai_tools/generate_tool_specs.py

@@ -8,8 +8,9 @@ from typing import Any
 
 from crewai.tools.base_tool import BaseTool, EnvVar
 from pydantic import BaseModel
+from pydantic.fields import FieldInfo
 from pydantic.json_schema import GenerateJsonSchema
-from pydantic_core import PydanticOmit
+from pydantic_core import PydanticOmit, PydanticUndefined
 
 from crewai_tools import tools
 
@@ -44,6 +45,9 @@ class ToolSpecExtractor:
             schema = self._unwrap_schema(core_schema)
             fields = schema.get("schema", {}).get("fields", {})
 
+            # Use model_fields to get defaults (handles both default and default_factory)
+            model_fields = tool_class.model_fields
+
             tool_info = {
                 "name": tool_class.__name__,
                 "humanized_name": self._extract_field_default(
@@ -54,9 +58,9 @@ class ToolSpecExtractor:
                 ).strip(),
                 "run_params_schema": self._extract_params(fields.get("args_schema")),
                 "init_params_schema": self._extract_init_params(tool_class),
-                "env_vars": self._extract_env_vars(fields.get("env_vars")),
-                "package_dependencies": self._extract_field_default(
-                    fields.get("package_dependencies"), fallback=[]
+                "env_vars": self._extract_env_vars_from_model_fields(model_fields),
+                "package_dependencies": self._extract_package_deps_from_model_fields(
+                    model_fields
                 ),
             }
 
@@ -103,10 +107,27 @@ class ToolSpecExtractor:
             return {}
 
     @staticmethod
-    def _extract_env_vars(
-        env_vars_field: dict[str, Any] | None,
+    def _get_field_default(field: FieldInfo | None) -> Any:
+        """Get default value from a FieldInfo, handling both default and default_factory."""
+        if not field:
+            return None
+
+        default_value = field.default
+        if default_value is PydanticUndefined or default_value is None:
+            if field.default_factory:
+                return field.default_factory()
+            return None
+
+        return default_value
+
+    @staticmethod
+    def _extract_env_vars_from_model_fields(
+        model_fields: dict[str, FieldInfo],
     ) -> list[dict[str, Any]]:
-        if not env_vars_field:
+        default_value = ToolSpecExtractor._get_field_default(
+            model_fields.get("env_vars")
+        )
+        if not default_value:
             return []
 
         return [
@@ -116,10 +137,22 @@ class ToolSpecExtractor:
                 "required": env_var.required,
                 "default": env_var.default,
             }
-            for env_var in env_vars_field.get("schema", {}).get("default", [])
+            for env_var in default_value
             if isinstance(env_var, EnvVar)
         ]
 
+    @staticmethod
+    def _extract_package_deps_from_model_fields(
+        model_fields: dict[str, FieldInfo],
+    ) -> list[str]:
+        default_value = ToolSpecExtractor._get_field_default(
+            model_fields.get("package_dependencies")
+        )
+        if not isinstance(default_value, list):
+            return []
+
+        return default_value
+
     @staticmethod
     def _extract_init_params(tool_class: type[BaseTool]) -> dict[str, Any]:
         ignored_init_params = [
@@ -152,7 +185,7 @@ class ToolSpecExtractor:
 
 
 if __name__ == "__main__":
-    output_file = Path(__file__).parent / "tool.specs.json"
+    output_file = Path(__file__).parent.parent.parent / "tool.specs.json"
     extractor = ToolSpecExtractor()
 
     extractor.extract_all_tools()

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/__init__.py

@@ -13,16 +13,10 @@ from crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder impor
 from crewai_tools.tools.crewai_platform_tools.crewai_platform_tools import (
     CrewaiPlatformTools,
 )
-from crewai_tools.tools.crewai_platform_tools.file_hook import (
-    process_file_markers,
-    register_file_processing_hook,
-)
 
 
 __all__ = [
     "CrewAIPlatformActionTool",
     "CrewaiPlatformToolBuilder",
     "CrewaiPlatformTools",
-    "process_file_markers",
-    "register_file_processing_hook",
 ]

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py

@@ -2,8 +2,6 @@
 
 import json
 import os
-import re
-import tempfile
 from typing import Any
 
 from crewai.tools import BaseTool
@@ -16,26 +14,6 @@ from crewai_tools.tools.crewai_platform_tools.misc import (
     get_platform_integration_token,
 )
 
-_FILE_MARKER_PREFIX = "__CREWAI_FILE__"
-
-_MIME_TO_EXTENSION = {
-    "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx",
-    "application/vnd.ms-excel": ".xls",
-    "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx",
-    "application/msword": ".doc",
-    "application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx",
-    "application/vnd.ms-powerpoint": ".ppt",
-    "application/pdf": ".pdf",
-    "image/png": ".png",
-    "image/jpeg": ".jpg",
-    "image/gif": ".gif",
-    "image/webp": ".webp",
-    "text/plain": ".txt",
-    "text/csv": ".csv",
-    "application/json": ".json",
-    "application/zip": ".zip",
-}
-
 
 class CrewAIPlatformActionTool(BaseTool):
     action_name: str = Field(default="", description="The name of the action")
@@ -93,18 +71,10 @@ class CrewAIPlatformActionTool(BaseTool):
                 url=api_url,
                 headers=headers,
                 json=payload,
-                timeout=300,
-                stream=True,
+                timeout=60,
                 verify=os.environ.get("CREWAI_FACTORY", "false").lower() != "true",
             )
 
-            content_type = response.headers.get("Content-Type", "")
-
-            # Check if response is binary (non-JSON)
-            if "application/json" not in content_type:
-                return self._handle_binary_response(response)
-
-            # Normal JSON response
             data = response.json()
             if not response.ok:
                 if isinstance(data, dict):
@@ -121,49 +91,3 @@ class CrewAIPlatformActionTool(BaseTool):
 
         except Exception as e:
             return f"Error executing action {self.action_name}: {e!s}"
-
-    def _handle_binary_response(self, response: requests.Response) -> str:
-        """Handle binary streaming response from the API.
-
-        Streams the binary content to a temporary file and returns a marker
-        that can be processed by the file hook to inject the file into the
-        LLM context.
-
-        Args:
-            response: The streaming HTTP response with binary content.
-
-        Returns:
-            A file marker string in the format:
-            __CREWAI_FILE__:filename:content_type:file_path
-        """
-        content_type = response.headers.get("Content-Type", "application/octet-stream")
-
-        filename = self._extract_filename_from_headers(response.headers)
-
-        extension = self._get_file_extension(content_type, filename)
-
-        with tempfile.NamedTemporaryFile(
-            delete=False, suffix=extension, prefix="crewai_"
-        ) as tmp_file:
-            for chunk in response.iter_content(chunk_size=8192):
-                tmp_file.write(chunk)
-            tmp_path = tmp_file.name
-
-        return f"{_FILE_MARKER_PREFIX}:{filename}:{content_type}:{tmp_path}"
-
-    def _extract_filename_from_headers(
-        self, headers: requests.structures.CaseInsensitiveDict
-    ) -> str:
-        content_disposition = headers.get("Content-Disposition", "")
-        if content_disposition:
-            match = re.search(r'filename="?([^";\s]+)"?', content_disposition)
-            if match:
-                return match.group(1)
-        return "downloaded_file"
-
-    def _get_file_extension(self, content_type: str, filename: str) -> str:
-        if "." in filename:
-            return "." + filename.rsplit(".", 1)[-1]
-
-        base_content_type = content_type.split(";")[0].strip()
-        return _MIME_TO_EXTENSION.get(base_content_type, "")

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tools.py

@@ -6,9 +6,6 @@ from crewai_tools.adapters.tool_collection import ToolCollection
 from crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder import (
     CrewaiPlatformToolBuilder,
 )
-from crewai_tools.tools.crewai_platform_tools.file_hook import (
-    register_file_processing_hook,
-)
 
 
 logger = logging.getLogger(__name__)
@@ -25,8 +22,6 @@ def CrewaiPlatformTools(  # noqa: N802
     Returns:
         A list of BaseTool instances for platform actions
     """
-    register_file_processing_hook()
-
     builder = CrewaiPlatformToolBuilder(apps=apps)
 
     return builder.tools()  # type: ignore

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/file_hook.py

@@ -1,132 +0,0 @@
-"""File processing hook for CrewAI Platform Tools.
-
-This module provides a hook that processes file markers returned by platform tools
-and injects the files into the LLM context for native file handling.
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from crewai.hooks.tool_hooks import ToolCallHookContext
-
-logger = logging.getLogger(__name__)
-
-_FILE_MARKER_PREFIX = "__CREWAI_FILE__"
-
-_hook_registered = False
-
-
-def process_file_markers(context: ToolCallHookContext) -> str | None:
-    """Process file markers in tool results and inject files into context.
-
-    This hook detects file markers returned by platform tools (e.g., download_file)
-    and converts them into FileInput objects that are attached to the hook context.
-    The agent executor will then inject these files into the tool message for
-    native LLM file handling.
-
-    The marker format is:
-        __CREWAI_FILE__:filename:content_type:file_path
-
-    Args:
-        context: The tool call hook context containing the tool result.
-
-    Returns:
-        A human-readable message if a file was processed, None otherwise.
-    """
-    result = context.tool_result
-
-    if not result or not result.startswith(_FILE_MARKER_PREFIX):
-        return None
-
-    try:
-        parts = result.split(":", 3)
-        if len(parts) < 4:
-            logger.warning(f"Invalid file marker format: {result[:100]}")
-            return None
-
-        _, filename, content_type, file_path = parts
-
-        if not os.path.isfile(file_path):
-            logger.error(f"File not found: {file_path}")
-            return f"Error: Downloaded file not found at {file_path}"
-
-        try:
-            from crewai_files import File
-        except ImportError:
-            logger.warning(
-                "crewai_files not installed. File will not be attached to LLM context."
-            )
-            return (
-                f"Downloaded file: {filename} ({content_type}). "
-                f"File saved at: {file_path}. "
-                "Note: Install crewai_files for native LLM file handling."
-            )
-
-        file = File(source=file_path, content_type=content_type, filename=filename)
-
-        context.files = {filename: file}
-
-        file_size = os.path.getsize(file_path)
-        size_str = _format_file_size(file_size)
-
-        return f"Downloaded file: {filename} ({content_type}, {size_str}). File is attached for LLM analysis."
-
-    except Exception as e:
-        logger.exception(f"Error processing file marker: {e}")
-        return f"Error processing downloaded file: {e}"
-
-
-def _format_file_size(size_bytes: int) -> str:
-    """Format file size in human-readable format.
-
-    Args:
-        size_bytes: Size in bytes.
-
-    Returns:
-        Human-readable size string.
-    """
-    if size_bytes < 1024:
-        return f"{size_bytes} bytes"
-    elif size_bytes < 1024 * 1024:
-        return f"{size_bytes / 1024:.1f} KB"
-    elif size_bytes < 1024 * 1024 * 1024:
-        return f"{size_bytes / (1024 * 1024):.1f} MB"
-    else:
-        return f"{size_bytes / (1024 * 1024 * 1024):.1f} GB"
-
-
-def register_file_processing_hook() -> bool:
-    """Register the file processing hook globally.
-
-    This function should be called once during application initialization
-    to enable automatic file injection for platform tools.
-
-    Returns:
-        True if the hook was registered, False if it was already registered
-        or if registration failed.
-    """
-    global _hook_registered
-
-    if _hook_registered:
-        logger.debug("File processing hook already registered")
-        return False
-
-    try:
-        from crewai.hooks import register_after_tool_call_hook
-
-        register_after_tool_call_hook(process_file_markers)
-        _hook_registered = True
-        logger.info("File processing hook registered successfully")
-        return True
-    except ImportError:
-        logger.warning(
-            "crewai.hooks not available. File processing hook not registered."
-        )
-        return False
-    except Exception as e:
-        logger.exception(f"Failed to register file processing hook: {e}")
-        return False

lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/stagehand_tool.py

@@ -4,7 +4,7 @@ import os
 import re
 from typing import Any
 
-from crewai.tools import BaseTool
+from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, Field
 
 
@@ -137,7 +137,21 @@ class StagehandTool(BaseTool):
     - 'observe': For finding elements in a specific area
     """
     args_schema: type[BaseModel] = StagehandToolSchema
-    package_dependencies: list[str] = Field(default_factory=lambda: ["stagehand"])
+    package_dependencies: list[str] = Field(default_factory=lambda: ["stagehand<=0.5.9"])
+    env_vars: list[EnvVar] = Field(
+        default_factory=lambda: [
+            EnvVar(
+                name="BROWSERBASE_API_KEY",
+                description="API key for Browserbase services",
+                required=False,
+            ),
+            EnvVar(
+                name="BROWSERBASE_PROJECT_ID",
+                description="Project ID for Browserbase services",
+                required=False,
+            ),
+        ]
+    )
 
     # Stagehand configuration
     api_key: str | None = None

lib/crewai-tools/tests/test_generate_tool_specs.py

@@ -23,23 +23,26 @@ class MockTool(BaseTool):
     )
     my_parameter: str = Field("This is default value", description="What a description")
     my_parameter_bool: bool = Field(False)
+    # Use default_factory like real tools do (not direct default)
     package_dependencies: list[str] = Field(
-        ["this-is-a-required-package", "another-required-package"], description=""
+        default_factory=lambda: ["this-is-a-required-package", "another-required-package"]
+    )
+    env_vars: list[EnvVar] = Field(
+        default_factory=lambda: [
+            EnvVar(
+                name="SERPER_API_KEY",
+                description="API key for Serper",
+                required=True,
+                default=None,
+            ),
+            EnvVar(
+                name="API_RATE_LIMIT",
+                description="API rate limit",
+                required=False,
+                default="100",
+            ),
+        ]
     )
-    env_vars: list[EnvVar] = [
-        EnvVar(
-            name="SERPER_API_KEY",
-            description="API key for Serper",
-            required=True,
-            default=None,
-        ),
-        EnvVar(
-            name="API_RATE_LIMIT",
-            description="API rate limit",
-            required=False,
-            default="100",
-        ),
-    ]
 
 
 @pytest.fixture

lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tools.py

@@ -2,7 +2,6 @@ import unittest
 from unittest.mock import Mock, patch
 
 from crewai_tools.tools.crewai_platform_tools import CrewaiPlatformTools
-from crewai_tools.tools.crewai_platform_tools import file_hook
 
 
 class TestCrewaiPlatformTools(unittest.TestCase):
@@ -114,64 +113,3 @@ class TestCrewaiPlatformTools(unittest.TestCase):
             with self.assertRaises(ValueError) as context:
                 CrewaiPlatformTools(apps=["github"])
             assert "No platform integration token found" in str(context.exception)
-
-    @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
-    @patch(
-        "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
-    )
-    @patch(
-        "crewai_tools.tools.crewai_platform_tools.crewai_platform_tools.register_file_processing_hook"
-    )
-    def test_crewai_platform_tools_registers_file_hook(
-        self, mock_register_hook, mock_get
-    ):
-        mock_response = Mock()
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = {"actions": {"github": []}}
-        mock_get.return_value = mock_response
-
-        CrewaiPlatformTools(apps=["github"])
-        mock_register_hook.assert_called_once()
-
-
-class TestFileHook(unittest.TestCase):
-    def setUp(self):
-        file_hook._hook_registered = False
-
-    def tearDown(self):
-        file_hook._hook_registered = False
-
-    @patch("crewai.hooks.register_after_tool_call_hook")
-    def test_register_hook_is_idempotent(self, mock_register):
-        """Test hook registration succeeds once and is idempotent."""
-        assert file_hook.register_file_processing_hook() is True
-        assert file_hook._hook_registered is True
-        mock_register.assert_called_once_with(file_hook.process_file_markers)
-
-        # Second call should return False and not register again
-        assert file_hook.register_file_processing_hook() is False
-        mock_register.assert_called_once()
-
-    def test_process_file_markers_ignores_non_file_results(self):
-        """Test that non-file-marker results return None."""
-        test_cases = [
-            None,  # Empty result
-            "Regular tool output",  # Non-marker
-            "__CREWAI_FILE__:incomplete",  # Invalid format (missing parts)
-        ]
-        for tool_result in test_cases:
-            mock_context = Mock()
-            mock_context.tool_result = tool_result
-            assert file_hook.process_file_markers(mock_context) is None
-
-    def test_format_file_size(self):
-        """Test file size formatting across units."""
-        cases = [
-            (500, "500 bytes"),
-            (1024, "1.0 KB"),
-            (1536, "1.5 KB"),
-            (1024 * 1024, "1.0 MB"),
-            (1024 * 1024 * 1024, "1.0 GB"),
-        ]
-        for size_bytes, expected in cases:
-            assert file_hook._format_file_size(size_bytes) == expected

lib/crewai/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     # Core Dependencies
     "pydantic~=2.11.9",
-    "openai~=1.83.0",
+    "openai>=1.83.0,<3",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
@@ -78,7 +78,7 @@ voyageai = [
     "voyageai~=0.3.5",
 ]
 litellm = [
-    "litellm~=1.74.9",
+    "litellm>=1.74.9,<3",
 ]
 bedrock = [
     "boto3~=1.40.45",

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -930,10 +930,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             "name": func_name,
             "content": result,
         }
-
-        if after_hook_context.files:
-            tool_message["files"] = after_hook_context.files
-
         self.messages.append(tool_message)
 
         # Log the tool execution

lib/crewai/src/crewai/cli/constants.py

@@ -1,10 +1,13 @@
+from typing import Any
+
+
 DEFAULT_CREWAI_ENTERPRISE_URL = "https://app.crewai.com"
 CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER = "workos"
 CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE = "client_01JNJQWBJ4SPFN3SWJM5T7BDG8"
 CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID = "client_01JYT06R59SP0NXYGD994NFXXX"
 CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN = "login.crewai.com"
 
-ENV_VARS = {
+ENV_VARS: dict[str, list[dict[str, Any]]] = {
     "openai": [
         {
             "prompt": "Enter your OPENAI API key (press Enter to skip)",
@@ -112,7 +115,7 @@ ENV_VARS = {
 }
 
 
-PROVIDERS = [
+PROVIDERS: list[str] = [
     "openai",
     "anthropic",
     "gemini",
@@ -127,7 +130,7 @@ PROVIDERS = [
     "sambanova",
 ]
 
-MODELS = {
+MODELS: dict[str, list[str]] = {
     "openai": [
         "gpt-4",
         "gpt-4.1",

lib/crewai/src/crewai/cli/create_crew.py

@@ -3,6 +3,7 @@ import shutil
 import sys
 
 import click
+import tomli
 
 from crewai.cli.constants import ENV_VARS, MODELS
 from crewai.cli.provider import (
@@ -13,7 +14,31 @@ from crewai.cli.provider import (
 from crewai.cli.utils import copy_template, load_env_vars, write_env_file
 
 
-def create_folder_structure(name, parent_folder=None):
+def get_reserved_script_names() -> set[str]:
+    """Get reserved script names from pyproject.toml template.
+
+    Returns:
+        Set of reserved script names that would conflict with crew folder names.
+    """
+    package_dir = Path(__file__).parent
+    template_path = package_dir / "templates" / "crew" / "pyproject.toml"
+
+    with open(template_path, "r") as f:
+        template_content = f.read()
+
+    template_content = template_content.replace("{{folder_name}}", "_placeholder_")
+    template_content = template_content.replace("{{name}}", "placeholder")
+    template_content = template_content.replace("{{crew_name}}", "Placeholder")
+
+    template_data = tomli.loads(template_content)
+    script_names = set(template_data.get("project", {}).get("scripts", {}).keys())
+    script_names.discard("_placeholder_")
+    return script_names
+
+
+def create_folder_structure(
+    name: str, parent_folder: str | None = None
+) -> tuple[Path, str, str]:
     import keyword
     import re
 
@@ -51,6 +76,14 @@ def create_folder_structure(name, parent_folder=None):
             f"Project name '{name}' would generate invalid Python module name '{folder_name}'"
         )
 
+    reserved_names = get_reserved_script_names()
+    if folder_name in reserved_names:
+        raise ValueError(
+            f"Project name '{name}' would generate folder name '{folder_name}' which is reserved. "
+            f"Reserved names are: {', '.join(sorted(reserved_names))}. "
+            "Please choose a different name."
+        )
+
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
 
     class_name = re.sub(r"[^a-zA-Z0-9_]", "", class_name)
@@ -114,7 +147,9 @@ def create_folder_structure(name, parent_folder=None):
     return folder_path, folder_name, class_name
 
 
-def copy_template_files(folder_path, name, class_name, parent_folder):
+def copy_template_files(
+    folder_path: Path, name: str, class_name: str, parent_folder: str | None
+) -> None:
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"
 
@@ -155,7 +190,12 @@ def copy_template_files(folder_path, name, class_name, parent_folder):
             copy_template(src_file, dst_file, name, class_name, folder_path.name)
 
 
-def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
+def create_crew(
+    name: str,
+    provider: str | None = None,
+    skip_provider: bool = False,
+    parent_folder: str | None = None,
+) -> None:
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
     env_vars = load_env_vars(folder_path)
     if not skip_provider:
@@ -189,7 +229,9 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
             if selected_provider is None:  # User typed 'q'
                 click.secho("Exiting...", fg="yellow")
                 sys.exit(0)
-            if selected_provider:  # Valid selection
+            if selected_provider and isinstance(
+                selected_provider, str
+            ):  # Valid selection
                 break
             click.secho(
                 "No provider selected. Please try again or press 'q' to exit.", fg="red"

lib/crewai/src/crewai/cli/provider.py

@@ -1,8 +1,10 @@
 from collections import defaultdict
+from collections.abc import Sequence
 import json
 import os
 from pathlib import Path
 import time
+from typing import Any
 
 import certifi
 import click
@@ -11,16 +13,15 @@ import requests
 from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
 
 
-def select_choice(prompt_message, choices):
-    """
-    Presents a list of choices to the user and prompts them to select one.
+def select_choice(prompt_message: str, choices: Sequence[str]) -> str | None:
+    """Presents a list of choices to the user and prompts them to select one.
 
     Args:
-    - prompt_message (str): The message to display to the user before presenting the choices.
-    - choices (list): A list of options to present to the user.
+        prompt_message: The message to display to the user before presenting the choices.
+        choices: A list of options to present to the user.
 
     Returns:
-    - str: The selected choice from the list, or None if the user chooses to quit.
+        The selected choice from the list, or None if the user chooses to quit.
     """
 
     provider_models = get_provider_data()
@@ -52,16 +53,14 @@ def select_choice(prompt_message, choices):
         )
 
 
-def select_provider(provider_models):
-    """
-    Presents a list of providers to the user and prompts them to select one.
+def select_provider(provider_models: dict[str, list[str]]) -> str | None | bool:
+    """Presents a list of providers to the user and prompts them to select one.
 
     Args:
-    - provider_models (dict): A dictionary of provider models.
+        provider_models: A dictionary of provider models.
 
     Returns:
-    - str: The selected provider
-    - None: If user explicitly quits
+        The selected provider, None if user explicitly quits, or False if no selection.
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
     all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
@@ -80,16 +79,15 @@ def select_provider(provider_models):
     return provider.lower() if provider else False
 
 
-def select_model(provider, provider_models):
-    """
-    Presents a list of models for a given provider to the user and prompts them to select one.
+def select_model(provider: str, provider_models: dict[str, list[str]]) -> str | None:
+    """Presents a list of models for a given provider to the user and prompts them to select one.
 
     Args:
-    - provider (str): The provider for which to select a model.
-    - provider_models (dict): A dictionary of provider models.
+        provider: The provider for which to select a model.
+        provider_models: A dictionary of provider models.
 
     Returns:
-    - str: The selected model, or None if the operation is aborted or an invalid selection is made.
+        The selected model, or None if the operation is aborted or an invalid selection is made.
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
 
@@ -107,16 +105,17 @@ def select_model(provider, provider_models):
     )
 
 
-def load_provider_data(cache_file, cache_expiry):
-    """
-    Loads provider data from a cache file if it exists and is not expired. If the cache is expired or corrupted, it fetches the data from the web.
+def load_provider_data(cache_file: Path, cache_expiry: int) -> dict[str, Any] | None:
+    """Loads provider data from a cache file if it exists and is not expired.
+
+    If the cache is expired or corrupted, it fetches the data from the web.
 
     Args:
-    - cache_file (Path): The path to the cache file.
-    - cache_expiry (int): The cache expiry time in seconds.
+        cache_file: The path to the cache file.
+        cache_expiry: The cache expiry time in seconds.
 
     Returns:
-    - dict or None: The loaded provider data or None if the operation fails.
+        The loaded provider data or None if the operation fails.
     """
     current_time = time.time()
     if (
@@ -137,32 +136,31 @@ def load_provider_data(cache_file, cache_expiry):
     return fetch_provider_data(cache_file)
 
 
-def read_cache_file(cache_file):
-    """
-    Reads and returns the JSON content from a cache file. Returns None if the file contains invalid JSON.
+def read_cache_file(cache_file: Path) -> dict[str, Any] | None:
+    """Reads and returns the JSON content from a cache file.
 
     Args:
-    - cache_file (Path): The path to the cache file.
+        cache_file: The path to the cache file.
 
     Returns:
-    - dict or None: The JSON content of the cache file or None if the JSON is invalid.
+        The JSON content of the cache file or None if the JSON is invalid.
     """
     try:
         with open(cache_file, "r") as f:
-            return json.load(f)
+            data: dict[str, Any] = json.load(f)
+            return data
     except json.JSONDecodeError:
         return None
 
 
-def fetch_provider_data(cache_file):
-    """
-    Fetches provider data from a specified URL and caches it to a file.
+def fetch_provider_data(cache_file: Path) -> dict[str, Any] | None:
+    """Fetches provider data from a specified URL and caches it to a file.
 
     Args:
-    - cache_file (Path): The path to the cache file.
+        cache_file: The path to the cache file.
 
     Returns:
-    - dict or None: The fetched provider data or None if the operation fails.
+        The fetched provider data or None if the operation fails.
     """
     ssl_config = os.environ["SSL_CERT_FILE"] = certifi.where()
 
@@ -180,36 +178,39 @@ def fetch_provider_data(cache_file):
     return None
 
 
-def download_data(response):
-    """
-    Downloads data from a given HTTP response and returns the JSON content.
+def download_data(response: requests.Response) -> dict[str, Any]:
+    """Downloads data from a given HTTP response and returns the JSON content.
 
     Args:
-    - response (requests.Response): The HTTP response object.
+        response: The HTTP response object.
 
     Returns:
-    - dict: The JSON content of the response.
+        The JSON content of the response.
     """
     total_size = int(response.headers.get("content-length", 0))
     block_size = 8192
-    data_chunks = []
+    data_chunks: list[bytes] = []
+    bar: Any
     with click.progressbar(
         length=total_size, label="Downloading", show_pos=True
-    ) as progress_bar:
+    ) as bar:
         for chunk in response.iter_content(block_size):
             if chunk:
                 data_chunks.append(chunk)
-                progress_bar.update(len(chunk))
+                bar.update(len(chunk))
     data_content = b"".join(data_chunks)
-    return json.loads(data_content.decode("utf-8"))
+    result: dict[str, Any] = json.loads(data_content.decode("utf-8"))
+    return result
 
 
-def get_provider_data():
-    """
-    Retrieves provider data from a cache file, filters out models based on provider criteria, and returns a dictionary of providers mapped to their models.
+def get_provider_data() -> dict[str, list[str]] | None:
+    """Retrieves provider data from a cache file.
+
+    Filters out models based on provider criteria, and returns a dictionary of providers
+    mapped to their models.
 
     Returns:
-    - dict or None: A dictionary of providers mapped to their models or None if the operation fails.
+        A dictionary of providers mapped to their models or None if the operation fails.
     """
     cache_dir = Path.home() / ".crewai"
     cache_dir.mkdir(exist_ok=True)

lib/crewai/src/crewai/cli/version.py

@@ -1,6 +1,107 @@
+"""Version utilities for CrewAI CLI."""
+
+from collections.abc import Mapping
+from datetime import datetime, timedelta
+from functools import lru_cache
 import importlib.metadata
+import json
+from pathlib import Path
+from typing import Any, cast
+from urllib import request
+from urllib.error import URLError
+
+import appdirs
+from packaging.version import InvalidVersion, parse
+
+
+@lru_cache(maxsize=1)
+def _get_cache_file() -> Path:
+    """Get the path to the version cache file.
+
+    Cached to avoid repeated filesystem operations.
+    """
+    cache_dir = Path(appdirs.user_cache_dir("crewai"))
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    return cache_dir / "version_cache.json"
 
 
 def get_crewai_version() -> str:
-    """Get the version number of CrewAI running the CLI"""
+    """Get the version number of CrewAI running the CLI."""
     return importlib.metadata.version("crewai")
+
+
+def _is_cache_valid(cache_data: Mapping[str, Any]) -> bool:
+    """Check if the cache is still valid, less than 24 hours old."""
+    if "timestamp" not in cache_data:
+        return False
+
+    try:
+        cache_time = datetime.fromisoformat(str(cache_data["timestamp"]))
+        return datetime.now() - cache_time < timedelta(hours=24)
+    except (ValueError, TypeError):
+        return False
+
+
+def get_latest_version_from_pypi(timeout: int = 2) -> str | None:
+    """Get the latest version of CrewAI from PyPI.
+
+    Args:
+        timeout: Request timeout in seconds.
+
+    Returns:
+        Latest version string or None if unable to fetch.
+    """
+    cache_file = _get_cache_file()
+    if cache_file.exists():
+        try:
+            cache_data = json.loads(cache_file.read_text())
+            if _is_cache_valid(cache_data):
+                return cast(str | None, cache_data.get("version"))
+        except (json.JSONDecodeError, OSError):
+            pass
+
+    try:
+        with request.urlopen(
+            "https://pypi.org/pypi/crewai/json", timeout=timeout
+        ) as response:
+            data = json.loads(response.read())
+            latest_version = cast(str, data["info"]["version"])
+
+            cache_data = {
+                "version": latest_version,
+                "timestamp": datetime.now().isoformat(),
+            }
+            cache_file.write_text(json.dumps(cache_data))
+
+            return latest_version
+    except (URLError, json.JSONDecodeError, KeyError, OSError):
+        return None
+
+
+def check_version() -> tuple[str, str | None]:
+    """Check current and latest versions.
+
+    Returns:
+        Tuple of (current_version, latest_version).
+        latest_version is None if unable to fetch from PyPI.
+    """
+    current = get_crewai_version()
+    latest = get_latest_version_from_pypi()
+    return current, latest
+
+
+def is_newer_version_available() -> tuple[bool, str, str | None]:
+    """Check if a newer version is available.
+
+    Returns:
+        Tuple of (is_newer, current_version, latest_version).
+    """
+    current, latest = check_version()
+
+    if latest is None:
+        return False, current, None
+
+    try:
+        return parse(latest) > parse(current), current, latest
+    except (InvalidVersion, TypeError):
+        return False, current, latest

lib/crewai/src/crewai/events/types/llm_events.py

@@ -10,6 +10,7 @@ class LLMEventBase(BaseEvent):
     from_task: Any | None = None
     from_agent: Any | None = None
     model: str | None = None
+    call_id: str
 
     def __init__(self, **data: Any) -> None:
         if data.get("from_task"):

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -1,11 +1,14 @@
+import os
 import threading
-from typing import Any, ClassVar
+from typing import Any, ClassVar, cast
 
 from rich.console import Console
 from rich.live import Live
 from rich.panel import Panel
 from rich.text import Text
 
+from crewai.cli.version import is_newer_version_available
+
 
 class ConsoleFormatter:
     tool_usage_counts: ClassVar[dict[str, int]] = {}
@@ -35,6 +38,39 @@ class ConsoleFormatter:
             padding=(1, 2),
         )
 
+    def _show_version_update_message_if_needed(self) -> None:
+        """Show version update message if a newer version is available.
+
+        Only displays when verbose mode is enabled and not running in CI/CD.
+        """
+        if not self.verbose:
+            return
+
+        if os.getenv("CI", "").lower() in ("true", "1"):
+            return
+
+        try:
+            is_newer, current, latest = is_newer_version_available()
+            if is_newer and latest:
+                message = f"""A new version of CrewAI is available!
+
+Current version: {current}
+Latest version:  {latest}
+
+To update, run: uv sync --upgrade-package crewai"""
+
+                panel = Panel(
+                    message,
+                    title="✨ Update Available ✨",
+                    border_style="yellow",
+                    padding=(1, 2),
+                )
+                self.console.print(panel)
+                self.console.print()
+        except Exception:  # noqa: S110
+            # Silently ignore errors in version check - it's non-critical
+            pass
+
     def _show_tracing_disabled_message_if_needed(self) -> None:
         """Show tracing disabled message if tracing is not enabled."""
         from crewai.events.listeners.tracing.utils import (
@@ -176,9 +212,10 @@ To enable tracing, do any one of these:
         if not self.verbose:
             return
 
-        # Reset the crew completion event for this new crew execution
         ConsoleFormatter.crew_completion_printed.clear()
 
+        self._show_version_update_message_if_needed()
+
         content = self.create_status_content(
             "Crew Execution Started",
             crew_name,
@@ -237,6 +274,8 @@ To enable tracing, do any one of these:
 
     def handle_flow_started(self, flow_name: str, flow_id: str) -> None:
         """Show flow started panel."""
+        self._show_version_update_message_if_needed()
+
         content = Text()
         content.append("Flow Started\n", style="blue bold")
         content.append("Name: ", style="white")
@@ -885,7 +924,7 @@ To enable tracing, do any one of these:
 
             is_a2a_delegation = False
             try:
-                output_data = json.loads(formatted_answer.output)
+                output_data = json.loads(cast(str, formatted_answer.output))
                 if isinstance(output_data, dict):
                     if output_data.get("is_a2a") is True:
                         is_a2a_delegation = True

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -814,10 +814,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 "name": func_name,
                 "content": result,
             }
-
-            if after_hook_context.files:
-                tool_message["files"] = after_hook_context.files
-
             self.state.messages.append(tool_message)
 
             # Log the tool execution

lib/crewai/src/crewai/hooks/tool_hooks.py

@@ -5,7 +5,6 @@ from typing import TYPE_CHECKING, Any
 from crewai.events.event_listener import event_listener
 from crewai.hooks.types import AfterToolCallHookType, BeforeToolCallHookType
 from crewai.utilities.printer import Printer
-from crewai.utilities.types import FileInput
 
 
 if TYPE_CHECKING:
@@ -35,9 +34,6 @@ class ToolCallHookContext:
         crew: Crew instance (may be None)
         tool_result: Tool execution result (only set for after_tool_call hooks).
             Can be modified by returning a new string from after_tool_call hook.
-        files: Optional dictionary of files to attach to the tool message.
-            Can be set by after_tool_call hooks to inject files into the LLM context.
-            These files will be formatted according to the LLM provider's requirements.
     """
 
     def __init__(
@@ -68,7 +64,6 @@ class ToolCallHookContext:
         self.task = task
         self.crew = crew
         self.tool_result = tool_result
-        self.files: dict[str, FileInput] | None = None
 
     def request_human_input(
         self,

lib/crewai/src/crewai/llm.py

@@ -37,7 +37,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, get_current_call_id, llm_call_context
 from crewai.llms.constants import (
     ANTHROPIC_MODELS,
     AZURE_MODELS,
@@ -770,7 +770,7 @@ class LLM(BaseLLM):
                 chunk_content = None
                 response_id = None
 
-                if hasattr(chunk,'id'):
+                if hasattr(chunk, "id"):
                     response_id = chunk.id
 
                 # Safely extract content from various chunk formats
@@ -827,7 +827,7 @@ class LLM(BaseLLM):
                                         available_functions=available_functions,
                                         from_task=from_task,
                                         from_agent=from_agent,
-                                        response_id=response_id
+                                        response_id=response_id,
                                     )
 
                                     if result is not None:
@@ -849,7 +849,8 @@ class LLM(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             call_type=LLMCallType.LLM_CALL,
-                            response_id=response_id
+                            response_id=response_id,
+                            call_id=get_current_call_id(),
                         ),
                     )
             # --- 4) Fallback to non-streaming if no content received
@@ -1015,7 +1016,10 @@ class LLM(BaseLLM):
             crewai_event_bus.emit(
                 self,
                 event=LLMCallFailedEvent(
-                    error=str(e), from_task=from_task, from_agent=from_agent
+                    error=str(e),
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    call_id=get_current_call_id(),
                 ),
             )
             raise Exception(f"Failed to get streaming response: {e!s}") from e
@@ -1048,7 +1052,8 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     call_type=LLMCallType.TOOL_CALL,
-                    response_id=response_id
+                    response_id=response_id,
+                    call_id=get_current_call_id(),
                 ),
             )
 
@@ -1476,7 +1481,8 @@ class LLM(BaseLLM):
                             chunk=chunk_content,
                             from_task=from_task,
                             from_agent=from_agent,
-                            response_id=response_id
+                            response_id=response_id,
+                            call_id=get_current_call_id(),
                         ),
                     )
 
@@ -1619,7 +1625,12 @@ class LLM(BaseLLM):
                 logging.error(f"Error executing function '{function_name}': {e}")
                 crewai_event_bus.emit(
                     self,
-                    event=LLMCallFailedEvent(error=f"Tool execution error: {e!s}"),
+                    event=LLMCallFailedEvent(
+                        error=f"Tool execution error: {e!s}",
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        call_id=get_current_call_id(),
+                    ),
                 )
                 crewai_event_bus.emit(
                     self,
@@ -1669,108 +1680,117 @@ class LLM(BaseLLM):
             ValueError: If response format is not supported
             LLMContextLengthExceededError: If input exceeds model's context limit
         """
-        crewai_event_bus.emit(
-            self,
-            event=LLMCallStartedEvent(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                model=self.model,
-            ),
-        )
+        with llm_call_context() as call_id:
+            crewai_event_bus.emit(
+                self,
+                event=LLMCallStartedEvent(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    model=self.model,
+                    call_id=call_id,
+                ),
+            )
 
-        # --- 2) Validate parameters before proceeding with the call
-        self._validate_call_params()
+            # --- 2) Validate parameters before proceeding with the call
+            self._validate_call_params()
 
-        # --- 3) Convert string messages to proper format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        # --- 4) Handle O1 model special case (system messages not supported)
-        if "o1" in self.model.lower():
-            for message in messages:
-                if message.get("role") == "system":
-                    msg_role: Literal["assistant"] = "assistant"
-                    message["role"] = msg_role
+            # --- 3) Convert string messages to proper format if needed
+            if isinstance(messages, str):
+                messages = [{"role": "user", "content": messages}]
+            # --- 4) Handle O1 model special case (system messages not supported)
+            if "o1" in self.model.lower():
+                for message in messages:
+                    if message.get("role") == "system":
+                        msg_role: Literal["assistant"] = "assistant"
+                        message["role"] = msg_role
 
-        if not self._invoke_before_llm_call_hooks(messages, from_agent):
-            raise ValueError("LLM call blocked by before_llm_call hook")
+            if not self._invoke_before_llm_call_hooks(messages, from_agent):
+                raise ValueError("LLM call blocked by before_llm_call hook")
 
-        # --- 5) Set up callbacks if provided
-        with suppress_warnings():
-            if callbacks and len(callbacks) > 0:
-                self.set_callbacks(callbacks)
-            try:
-                # --- 6) Prepare parameters for the completion call
-                params = self._prepare_completion_params(messages, tools)
-                # --- 7) Make the completion call and handle response
-                if self.stream:
-                    result = self._handle_streaming_response(
-                        params=params,
-                        callbacks=callbacks,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        response_model=response_model,
-                    )
-                else:
-                    result = self._handle_non_streaming_response(
-                        params=params,
-                        callbacks=callbacks,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        response_model=response_model,
-                    )
-
-                if isinstance(result, str):
-                    result = self._invoke_after_llm_call_hooks(
-                        messages, result, from_agent
-                    )
-
-                return result
-            except LLMContextLengthExceededError:
-                # Re-raise LLMContextLengthExceededError as it should be handled
-                # by the CrewAgentExecutor._invoke_loop method, which can then decide
-                # whether to summarize the content or abort based on the respect_context_window flag
-                raise
-            except Exception as e:
-                unsupported_stop = "Unsupported parameter" in str(
-                    e
-                ) and "'stop'" in str(e)
-
-                if unsupported_stop:
-                    if (
-                        "additional_drop_params" in self.additional_params
-                        and isinstance(
-                            self.additional_params["additional_drop_params"], list
+            # --- 5) Set up callbacks if provided
+            with suppress_warnings():
+                if callbacks and len(callbacks) > 0:
+                    self.set_callbacks(callbacks)
+                try:
+                    # --- 6) Prepare parameters for the completion call
+                    params = self._prepare_completion_params(messages, tools)
+                    # --- 7) Make the completion call and handle response
+                    if self.stream:
+                        result = self._handle_streaming_response(
+                            params=params,
+                            callbacks=callbacks,
+                            available_functions=available_functions,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_model=response_model,
                         )
-                    ):
-                        self.additional_params["additional_drop_params"].append("stop")
                     else:
-                        self.additional_params = {"additional_drop_params": ["stop"]}
+                        result = self._handle_non_streaming_response(
+                            params=params,
+                            callbacks=callbacks,
+                            available_functions=available_functions,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_model=response_model,
+                        )
 
-                    logging.info("Retrying LLM call without the unsupported 'stop'")
+                    if isinstance(result, str):
+                        result = self._invoke_after_llm_call_hooks(
+                            messages, result, from_agent
+                        )
 
-                    return self.call(
-                        messages,
-                        tools=tools,
-                        callbacks=callbacks,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        response_model=response_model,
+                    return result
+                except LLMContextLengthExceededError:
+                    # Re-raise LLMContextLengthExceededError as it should be handled
+                    # by the CrewAgentExecutor._invoke_loop method, which can then decide
+                    # whether to summarize the content or abort based on the respect_context_window flag
+                    raise
+                except Exception as e:
+                    unsupported_stop = "Unsupported parameter" in str(
+                        e
+                    ) and "'stop'" in str(e)
+
+                    if unsupported_stop:
+                        if (
+                            "additional_drop_params" in self.additional_params
+                            and isinstance(
+                                self.additional_params["additional_drop_params"], list
+                            )
+                        ):
+                            self.additional_params["additional_drop_params"].append(
+                                "stop"
+                            )
+                        else:
+                            self.additional_params = {
+                                "additional_drop_params": ["stop"]
+                            }
+
+                        logging.info("Retrying LLM call without the unsupported 'stop'")
+
+                        return self.call(
+                            messages,
+                            tools=tools,
+                            callbacks=callbacks,
+                            available_functions=available_functions,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_model=response_model,
+                        )
+
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallFailedEvent(
+                            error=str(e),
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            call_id=get_current_call_id(),
+                        ),
                     )
-
-                crewai_event_bus.emit(
-                    self,
-                    event=LLMCallFailedEvent(
-                        error=str(e), from_task=from_task, from_agent=from_agent
-                    ),
-                )
-                raise
+                    raise
 
     async def acall(
         self,
@@ -1808,43 +1828,54 @@ class LLM(BaseLLM):
             ValueError: If response format is not supported
             LLMContextLengthExceededError: If input exceeds model's context limit
         """
-        crewai_event_bus.emit(
-            self,
-            event=LLMCallStartedEvent(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                model=self.model,
-            ),
-        )
+        with llm_call_context() as call_id:
+            crewai_event_bus.emit(
+                self,
+                event=LLMCallStartedEvent(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    model=self.model,
+                    call_id=call_id,
+                ),
+            )
 
-        self._validate_call_params()
+            self._validate_call_params()
 
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
+            if isinstance(messages, str):
+                messages = [{"role": "user", "content": messages}]
 
-        # Process file attachments asynchronously before preparing params
-        messages = await self._aprocess_message_files(messages)
+            # Process file attachments asynchronously before preparing params
+            messages = await self._aprocess_message_files(messages)
 
-        if "o1" in self.model.lower():
-            for message in messages:
-                if message.get("role") == "system":
-                    msg_role: Literal["assistant"] = "assistant"
-                    message["role"] = msg_role
+            if "o1" in self.model.lower():
+                for message in messages:
+                    if message.get("role") == "system":
+                        msg_role: Literal["assistant"] = "assistant"
+                        message["role"] = msg_role
 
-        with suppress_warnings():
-            if callbacks and len(callbacks) > 0:
-                self.set_callbacks(callbacks)
-            try:
-                params = self._prepare_completion_params(
-                    messages, tools, skip_file_processing=True
-                )
+            with suppress_warnings():
+                if callbacks and len(callbacks) > 0:
+                    self.set_callbacks(callbacks)
+                try:
+                    params = self._prepare_completion_params(
+                        messages, tools, skip_file_processing=True
+                    )
 
-                if self.stream:
-                    return await self._ahandle_streaming_response(
+                    if self.stream:
+                        return await self._ahandle_streaming_response(
+                            params=params,
+                            callbacks=callbacks,
+                            available_functions=available_functions,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_model=response_model,
+                        )
+
+                    return await self._ahandle_non_streaming_response(
                         params=params,
                         callbacks=callbacks,
                         available_functions=available_functions,
@@ -1852,52 +1883,50 @@ class LLM(BaseLLM):
                         from_agent=from_agent,
                         response_model=response_model,
                     )
+                except LLMContextLengthExceededError:
+                    raise
+                except Exception as e:
+                    unsupported_stop = "Unsupported parameter" in str(
+                        e
+                    ) and "'stop'" in str(e)
 
-                return await self._ahandle_non_streaming_response(
-                    params=params,
-                    callbacks=callbacks,
-                    available_functions=available_functions,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    response_model=response_model,
-                )
-            except LLMContextLengthExceededError:
-                raise
-            except Exception as e:
-                unsupported_stop = "Unsupported parameter" in str(
-                    e
-                ) and "'stop'" in str(e)
+                    if unsupported_stop:
+                        if (
+                            "additional_drop_params" in self.additional_params
+                            and isinstance(
+                                self.additional_params["additional_drop_params"], list
+                            )
+                        ):
+                            self.additional_params["additional_drop_params"].append(
+                                "stop"
+                            )
+                        else:
+                            self.additional_params = {
+                                "additional_drop_params": ["stop"]
+                            }
 
-                if unsupported_stop:
-                    if (
-                        "additional_drop_params" in self.additional_params
-                        and isinstance(
-                            self.additional_params["additional_drop_params"], list
+                        logging.info("Retrying LLM call without the unsupported 'stop'")
+
+                        return await self.acall(
+                            messages,
+                            tools=tools,
+                            callbacks=callbacks,
+                            available_functions=available_functions,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_model=response_model,
                         )
-                    ):
-                        self.additional_params["additional_drop_params"].append("stop")
-                    else:
-                        self.additional_params = {"additional_drop_params": ["stop"]}
 
-                    logging.info("Retrying LLM call without the unsupported 'stop'")
-
-                    return await self.acall(
-                        messages,
-                        tools=tools,
-                        callbacks=callbacks,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        response_model=response_model,
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallFailedEvent(
+                            error=str(e),
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            call_id=get_current_call_id(),
+                        ),
                     )
-
-                crewai_event_bus.emit(
-                    self,
-                    event=LLMCallFailedEvent(
-                        error=str(e), from_task=from_task, from_agent=from_agent
-                    ),
-                )
-                raise
+                    raise
 
     def _handle_emit_call_events(
         self,
@@ -1925,6 +1954,7 @@ class LLM(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
+                call_id=get_current_call_id(),
             ),
         )
 

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

@@ -7,11 +7,15 @@ in CrewAI, including common functionality for native SDK implementations.
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Generator
+from contextlib import contextmanager
+import contextvars
 from datetime import datetime
 import json
 import logging
 import re
 from typing import TYPE_CHECKING, Any, Final
+import uuid
 
 from pydantic import BaseModel
 
@@ -50,6 +54,32 @@ DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096
 DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
 
+_current_call_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
+    "_current_call_id", default=None
+)
+
+
+@contextmanager
+def llm_call_context() -> Generator[str, None, None]:
+    """Context manager that establishes an LLM call scope with a unique call_id."""
+    call_id = str(uuid.uuid4())
+    token = _current_call_id.set(call_id)
+    try:
+        yield call_id
+    finally:
+        _current_call_id.reset(token)
+
+
+def get_current_call_id() -> str:
+    """Get current call_id from context"""
+    call_id = _current_call_id.get()
+    if call_id is None:
+        logging.warning(
+            "LLM event emitted outside call context - generating fallback call_id"
+        )
+        return str(uuid.uuid4())
+    return call_id
+
 
 class BaseLLM(ABC):
     """Abstract base class for LLM implementations.
@@ -351,6 +381,7 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
+                call_id=get_current_call_id(),
             ),
         )
 
@@ -374,6 +405,7 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
+                call_id=get_current_call_id(),
             ),
         )
 
@@ -394,6 +426,7 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
+                call_id=get_current_call_id(),
             ),
         )
 
@@ -428,6 +461,7 @@ class BaseLLM(ABC):
                 from_agent=from_agent,
                 call_type=call_type,
                 response_id=response_id,
+                call_id=get_current_call_id(),
             ),
         )
 

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

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, TypeGuard, cast
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -266,35 +266,46 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            # Emit call started event
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                # Emit call started event
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            # Format messages for Anthropic
-            formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages
-            )
+                # Format messages for Anthropic
+                formatted_messages, system_message = (
+                    self._format_messages_for_anthropic(messages)
+                )
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            # Prepare completion parameters
-            completion_params = self._prepare_completion_params(
-                formatted_messages, system_message, tools
-            )
+                # Prepare completion parameters
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, system_message, tools
+                )
 
-            effective_response_model = response_model or self.response_format
+                effective_response_model = response_model or self.response_format
 
-            # Handle streaming vs non-streaming
-            if self.stream:
-                return self._handle_streaming_completion(
+                # Handle streaming vs non-streaming
+                if self.stream:
+                    return self._handle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return self._handle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -302,21 +313,13 @@ class AnthropicCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return self._handle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"Anthropic API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"Anthropic API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def acall(
         self,
@@ -342,28 +345,37 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages
-            )
+                formatted_messages, system_message = (
+                    self._format_messages_for_anthropic(messages)
+                )
 
-            completion_params = self._prepare_completion_params(
-                formatted_messages, system_message, tools
-            )
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, system_message, tools
+                )
 
-            effective_response_model = response_model or self.response_format
+                effective_response_model = response_model or self.response_format
 
-            if self.stream:
-                return await self._ahandle_streaming_completion(
+                if self.stream:
+                    return await self._ahandle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return await self._ahandle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -371,21 +383,13 @@ class AnthropicCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return await self._ahandle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"Anthropic API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"Anthropic API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _prepare_completion_params(
         self,

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

@@ -43,7 +43,7 @@ try:
     )
 
     from crewai.events.types.llm_events import LLMCallType
-    from crewai.llms.base_llm import BaseLLM
+    from crewai.llms.base_llm import BaseLLM, llm_call_context
 
 except ImportError:
     raise ImportError(
@@ -293,32 +293,44 @@ class AzureCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            # Emit call started event
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-            effective_response_model = response_model or self.response_format
+        with llm_call_context():
+            try:
+                # Emit call started event
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            # Format messages for Azure
-            formatted_messages = self._format_messages_for_azure(messages)
+                effective_response_model = response_model or self.response_format
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                # Format messages for Azure
+                formatted_messages = self._format_messages_for_azure(messages)
 
-            # Prepare completion parameters
-            completion_params = self._prepare_completion_params(
-                formatted_messages, tools, effective_response_model
-            )
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            # Handle streaming vs non-streaming
-            if self.stream:
-                return self._handle_streaming_completion(
+                # Prepare completion parameters
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, tools, effective_response_model
+                )
+
+                # Handle streaming vs non-streaming
+                if self.stream:
+                    return self._handle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return self._handle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -326,16 +338,8 @@ class AzureCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return self._handle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            return self._handle_api_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
+            except Exception as e:
+                return self._handle_api_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
 
     async def acall(  # type: ignore[return]
         self,
@@ -361,25 +365,35 @@ class AzureCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-            effective_response_model = response_model or self.response_format
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages = self._format_messages_for_azure(messages)
+                effective_response_model = response_model or self.response_format
 
-            completion_params = self._prepare_completion_params(
-                formatted_messages, tools, effective_response_model
-            )
+                formatted_messages = self._format_messages_for_azure(messages)
 
-            if self.stream:
-                return await self._ahandle_streaming_completion(
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, tools, effective_response_model
+                )
+
+                if self.stream:
+                    return await self._ahandle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return await self._ahandle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -387,16 +401,8 @@ class AzureCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return await self._ahandle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            self._handle_api_error(e, from_task, from_agent)
+            except Exception as e:
+                self._handle_api_error(e, from_task, from_agent)
 
     def _prepare_completion_params(
         self,

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

@@ -11,7 +11,7 @@ from pydantic import BaseModel
 from typing_extensions import Required
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -378,77 +378,90 @@ class BedrockCompletion(BaseLLM):
         """Call AWS Bedrock Converse API."""
         effective_response_model = response_model or self.response_format
 
-        try:
-            # Emit call started event
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-
-            # Format messages for Converse API
-            formatted_messages, system_message = self._format_messages_for_converse(
-                messages
-            )
-
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
-            # Prepare request body
-            body: BedrockConverseRequestBody = {
-                "inferenceConfig": self._get_inference_config(),
-            }
-
-            # Add system message if present
-            if system_message:
-                body["system"] = cast(
-                    "list[SystemContentBlockTypeDef]",
-                    cast(object, [{"text": system_message}]),
+        with llm_call_context():
+            try:
+                # Emit call started event
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
                 )
 
-            # Add tool config if present or if messages contain tool content
-            # Bedrock requires toolConfig when messages have toolUse/toolResult
-            if tools:
-                tool_config: ToolConfigurationTypeDef = {
-                    "tools": cast(
-                        "Sequence[ToolTypeDef]",
-                        cast(object, self._format_tools_for_converse(tools)),
-                    )
+                # Format messages for Converse API
+                formatted_messages, system_message = self._format_messages_for_converse(
+                    messages
+                )
+
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
+
+                # Prepare request body
+                body: BedrockConverseRequestBody = {
+                    "inferenceConfig": self._get_inference_config(),
                 }
-                body["toolConfig"] = tool_config
-            elif self._messages_contain_tool_content(formatted_messages):
-                # Create minimal toolConfig from tool history in messages
-                tools_from_history = self._extract_tools_from_message_history(
-                    formatted_messages
-                )
-                if tools_from_history:
-                    body["toolConfig"] = cast(
-                        "ToolConfigurationTypeDef",
-                        cast(object, {"tools": tools_from_history}),
+
+                # Add system message if present
+                if system_message:
+                    body["system"] = cast(
+                        "list[SystemContentBlockTypeDef]",
+                        cast(object, [{"text": system_message}]),
                     )
 
-            # Add optional advanced features if configured
-            if self.guardrail_config:
-                guardrail_config: GuardrailConfigurationTypeDef = cast(
-                    "GuardrailConfigurationTypeDef", cast(object, self.guardrail_config)
-                )
-                body["guardrailConfig"] = guardrail_config
+                # Add tool config if present or if messages contain tool content
+                # Bedrock requires toolConfig when messages have toolUse/toolResult
+                if tools:
+                    tool_config: ToolConfigurationTypeDef = {
+                        "tools": cast(
+                            "Sequence[ToolTypeDef]",
+                            cast(object, self._format_tools_for_converse(tools)),
+                        )
+                    }
+                    body["toolConfig"] = tool_config
+                elif self._messages_contain_tool_content(formatted_messages):
+                    # Create minimal toolConfig from tool history in messages
+                    tools_from_history = self._extract_tools_from_message_history(
+                        formatted_messages
+                    )
+                    if tools_from_history:
+                        body["toolConfig"] = cast(
+                            "ToolConfigurationTypeDef",
+                            cast(object, {"tools": tools_from_history}),
+                        )
 
-            if self.additional_model_request_fields:
-                body["additionalModelRequestFields"] = (
-                    self.additional_model_request_fields
-                )
+                # Add optional advanced features if configured
+                if self.guardrail_config:
+                    guardrail_config: GuardrailConfigurationTypeDef = cast(
+                        "GuardrailConfigurationTypeDef",
+                        cast(object, self.guardrail_config),
+                    )
+                    body["guardrailConfig"] = guardrail_config
 
-            if self.additional_model_response_field_paths:
-                body["additionalModelResponseFieldPaths"] = (
-                    self.additional_model_response_field_paths
-                )
+                if self.additional_model_request_fields:
+                    body["additionalModelRequestFields"] = (
+                        self.additional_model_request_fields
+                    )
 
-            if self.stream:
-                return self._handle_streaming_converse(
+                if self.additional_model_response_field_paths:
+                    body["additionalModelResponseFieldPaths"] = (
+                        self.additional_model_response_field_paths
+                    )
+
+                if self.stream:
+                    return self._handle_streaming_converse(
+                        formatted_messages,
+                        body,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return self._handle_converse(
                     formatted_messages,
                     body,
                     available_functions,
@@ -457,26 +470,17 @@ class BedrockCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return self._handle_converse(
-                formatted_messages,
-                body,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
+            except Exception as e:
+                if is_context_length_exceeded(e):
+                    logging.error(f"Context window exceeded: {e}")
+                    raise LLMContextLengthExceededError(str(e)) from e
 
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-
-            error_msg = f"AWS Bedrock API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+                error_msg = f"AWS Bedrock API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def acall(
         self,
@@ -514,69 +518,80 @@ class BedrockCompletion(BaseLLM):
                 'Install with: uv add "crewai[bedrock-async]"'
             )
 
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-
-            formatted_messages, system_message = self._format_messages_for_converse(
-                messages
-            )
-
-            body: BedrockConverseRequestBody = {
-                "inferenceConfig": self._get_inference_config(),
-            }
-
-            if system_message:
-                body["system"] = cast(
-                    "list[SystemContentBlockTypeDef]",
-                    cast(object, [{"text": system_message}]),
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
                 )
 
-            # Add tool config if present or if messages contain tool content
-            # Bedrock requires toolConfig when messages have toolUse/toolResult
-            if tools:
-                tool_config: ToolConfigurationTypeDef = {
-                    "tools": cast(
-                        "Sequence[ToolTypeDef]",
-                        cast(object, self._format_tools_for_converse(tools)),
-                    )
+                formatted_messages, system_message = self._format_messages_for_converse(
+                    messages
+                )
+
+                body: BedrockConverseRequestBody = {
+                    "inferenceConfig": self._get_inference_config(),
                 }
-                body["toolConfig"] = tool_config
-            elif self._messages_contain_tool_content(formatted_messages):
-                # Create minimal toolConfig from tool history in messages
-                tools_from_history = self._extract_tools_from_message_history(
-                    formatted_messages
-                )
-                if tools_from_history:
-                    body["toolConfig"] = cast(
-                        "ToolConfigurationTypeDef",
-                        cast(object, {"tools": tools_from_history}),
+
+                if system_message:
+                    body["system"] = cast(
+                        "list[SystemContentBlockTypeDef]",
+                        cast(object, [{"text": system_message}]),
                     )
 
-            if self.guardrail_config:
-                guardrail_config: GuardrailConfigurationTypeDef = cast(
-                    "GuardrailConfigurationTypeDef", cast(object, self.guardrail_config)
-                )
-                body["guardrailConfig"] = guardrail_config
+                # Add tool config if present or if messages contain tool content
+                # Bedrock requires toolConfig when messages have toolUse/toolResult
+                if tools:
+                    tool_config: ToolConfigurationTypeDef = {
+                        "tools": cast(
+                            "Sequence[ToolTypeDef]",
+                            cast(object, self._format_tools_for_converse(tools)),
+                        )
+                    }
+                    body["toolConfig"] = tool_config
+                elif self._messages_contain_tool_content(formatted_messages):
+                    # Create minimal toolConfig from tool history in messages
+                    tools_from_history = self._extract_tools_from_message_history(
+                        formatted_messages
+                    )
+                    if tools_from_history:
+                        body["toolConfig"] = cast(
+                            "ToolConfigurationTypeDef",
+                            cast(object, {"tools": tools_from_history}),
+                        )
 
-            if self.additional_model_request_fields:
-                body["additionalModelRequestFields"] = (
-                    self.additional_model_request_fields
-                )
+                if self.guardrail_config:
+                    guardrail_config: GuardrailConfigurationTypeDef = cast(
+                        "GuardrailConfigurationTypeDef",
+                        cast(object, self.guardrail_config),
+                    )
+                    body["guardrailConfig"] = guardrail_config
 
-            if self.additional_model_response_field_paths:
-                body["additionalModelResponseFieldPaths"] = (
-                    self.additional_model_response_field_paths
-                )
+                if self.additional_model_request_fields:
+                    body["additionalModelRequestFields"] = (
+                        self.additional_model_request_fields
+                    )
 
-            if self.stream:
-                return await self._ahandle_streaming_converse(
+                if self.additional_model_response_field_paths:
+                    body["additionalModelResponseFieldPaths"] = (
+                        self.additional_model_response_field_paths
+                    )
+
+                if self.stream:
+                    return await self._ahandle_streaming_converse(
+                        formatted_messages,
+                        body,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return await self._ahandle_converse(
                     formatted_messages,
                     body,
                     available_functions,
@@ -585,26 +600,17 @@ class BedrockCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return await self._ahandle_converse(
-                formatted_messages,
-                body,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
+            except Exception as e:
+                if is_context_length_exceeded(e):
+                    logging.error(f"Context window exceeded: {e}")
+                    raise LLMContextLengthExceededError(str(e)) from e
 
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-
-            error_msg = f"AWS Bedrock API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+                error_msg = f"AWS Bedrock API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _handle_converse(
         self,

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

@@ -10,7 +10,7 @@ from typing import TYPE_CHECKING, Any, Literal, cast
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -293,33 +293,45 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-            self.tools = tools
-            effective_response_model = response_model or self.response_format
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+                self.tools = tools
+                effective_response_model = response_model or self.response_format
 
-            formatted_content, system_instruction = self._format_messages_for_gemini(
-                messages
-            )
+                formatted_content, system_instruction = (
+                    self._format_messages_for_gemini(messages)
+                )
 
-            messages_for_hooks = self._convert_contents_to_dict(formatted_content)
+                messages_for_hooks = self._convert_contents_to_dict(formatted_content)
 
-            if not self._invoke_before_llm_call_hooks(messages_for_hooks, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                if not self._invoke_before_llm_call_hooks(
+                    messages_for_hooks, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            config = self._prepare_generation_config(
-                system_instruction, tools, effective_response_model
-            )
+                config = self._prepare_generation_config(
+                    system_instruction, tools, effective_response_model
+                )
 
-            if self.stream:
-                return self._handle_streaming_completion(
+                if self.stream:
+                    return self._handle_streaming_completion(
+                        formatted_content,
+                        config,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return self._handle_completion(
                     formatted_content,
                     config,
                     available_functions,
@@ -328,29 +340,20 @@ class GeminiCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return self._handle_completion(
-                formatted_content,
-                config,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except APIError as e:
-            error_msg = f"Google Gemini API error: {e.code} - {e.message}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
-        except Exception as e:
-            error_msg = f"Google Gemini API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except APIError as e:
+                error_msg = f"Google Gemini API error: {e.code} - {e.message}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
+            except Exception as e:
+                error_msg = f"Google Gemini API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def acall(
         self,
@@ -376,28 +379,38 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-            self.tools = tools
-            effective_response_model = response_model or self.response_format
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+                self.tools = tools
+                effective_response_model = response_model or self.response_format
 
-            formatted_content, system_instruction = self._format_messages_for_gemini(
-                messages
-            )
+                formatted_content, system_instruction = (
+                    self._format_messages_for_gemini(messages)
+                )
 
-            config = self._prepare_generation_config(
-                system_instruction, tools, effective_response_model
-            )
+                config = self._prepare_generation_config(
+                    system_instruction, tools, effective_response_model
+                )
 
-            if self.stream:
-                return await self._ahandle_streaming_completion(
+                if self.stream:
+                    return await self._ahandle_streaming_completion(
+                        formatted_content,
+                        config,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return await self._ahandle_completion(
                     formatted_content,
                     config,
                     available_functions,
@@ -406,29 +419,20 @@ class GeminiCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return await self._ahandle_completion(
-                formatted_content,
-                config,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except APIError as e:
-            error_msg = f"Google Gemini API error: {e.code} - {e.message}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
-        except Exception as e:
-            error_msg = f"Google Gemini API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except APIError as e:
+                error_msg = f"Google Gemini API error: {e.code} - {e.message}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
+            except Exception as e:
+                error_msg = f"Google Gemini API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _prepare_generation_config(
         self,

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

@@ -17,7 +17,7 @@ from openai.types.responses import Response
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -382,23 +382,35 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages = self._format_messages(messages)
+                formatted_messages = self._format_messages(messages)
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            if self.api == "responses":
-                return self._call_responses(
+                if self.api == "responses":
+                    return self._call_responses(
+                        messages=formatted_messages,
+                        tools=tools,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        response_model=response_model,
+                    )
+
+                return self._call_completions(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -407,22 +419,13 @@ class OpenAICompletion(BaseLLM):
                     response_model=response_model,
                 )
 
-            return self._call_completions(
-                messages=formatted_messages,
-                tools=tools,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_model=response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"OpenAI API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"OpenAI API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _call_completions(
         self,
@@ -479,20 +482,30 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages = self._format_messages(messages)
+                formatted_messages = self._format_messages(messages)
 
-            if self.api == "responses":
-                return await self._acall_responses(
+                if self.api == "responses":
+                    return await self._acall_responses(
+                        messages=formatted_messages,
+                        tools=tools,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        response_model=response_model,
+                    )
+
+                return await self._acall_completions(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -501,22 +514,13 @@ class OpenAICompletion(BaseLLM):
                     response_model=response_model,
                 )
 
-            return await self._acall_completions(
-                messages=formatted_messages,
-                tools=tools,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_model=response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"OpenAI API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"OpenAI API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def _acall_completions(
         self,

lib/crewai/tests/agents/test_async_agent_executor.py

@@ -235,8 +235,13 @@ class TestAsyncAgentExecutor:
         mock_crew: MagicMock, mock_tools_handler: MagicMock
     ) -> None:
         """Test that multiple ainvoke calls can run concurrently."""
+        max_concurrent = 0
+        current_concurrent = 0
+        lock = asyncio.Lock()
 
         async def create_and_run_executor(executor_id: int) -> dict[str, Any]:
+            nonlocal max_concurrent, current_concurrent
+
             executor = CrewAgentExecutor(
                 llm=mock_llm,
                 task=mock_task,
@@ -252,7 +257,13 @@ class TestAsyncAgentExecutor:
             )
 
             async def delayed_response(*args: Any, **kwargs: Any) -> str:
-                await asyncio.sleep(0.05)
+                nonlocal max_concurrent, current_concurrent
+                async with lock:
+                    current_concurrent += 1
+                    max_concurrent = max(max_concurrent, current_concurrent)
+                await asyncio.sleep(0.01)
+                async with lock:
+                    current_concurrent -= 1
                 return f"Thought: Done\nFinal Answer: Result from executor {executor_id}"
 
             with patch(
@@ -273,19 +284,15 @@ class TestAsyncAgentExecutor:
                                         }
                                     )
 
-        import time
-
-        start = time.time()
         results = await asyncio.gather(
             create_and_run_executor(1),
             create_and_run_executor(2),
             create_and_run_executor(3),
         )
-        elapsed = time.time() - start
 
         assert len(results) == 3
         assert all("output" in r for r in results)
-        assert elapsed < 0.15, f"Expected concurrent execution, took {elapsed}s"
+        assert max_concurrent > 1, f"Expected concurrent execution, max concurrent was {max_concurrent}"
 
 
 class TestAsyncLLMResponseHelper:

lib/crewai/tests/cassettes/utilities/test_llm_call_events_share_call_id.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"messages":[{"role":"user","content":"Say hi"}],"model":"gpt-4o-mini"}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '71'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
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lib/crewai/tests/cli/test_create_crew.py

@@ -296,6 +296,23 @@ def test_create_folder_structure_folder_name_validation():
                 shutil.rmtree(folder_path)
 
 
+def test_create_folder_structure_rejects_reserved_names():
+    """Test that reserved script names are rejected to prevent pyproject.toml conflicts."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        reserved_names = ["test", "train", "replay", "run_crew", "run_with_trigger"]
+
+        for reserved_name in reserved_names:
+            with pytest.raises(ValueError, match="which is reserved"):
+                create_folder_structure(reserved_name, parent_folder=temp_dir)
+
+            with pytest.raises(ValueError, match="which is reserved"):
+                create_folder_structure(f"{reserved_name}/", parent_folder=temp_dir)
+
+            capitalized = reserved_name.capitalize()
+            with pytest.raises(ValueError, match="which is reserved"):
+                create_folder_structure(capitalized, parent_folder=temp_dir)
+
+
 @mock.patch("crewai.cli.create_crew.create_folder_structure")
 @mock.patch("crewai.cli.create_crew.copy_template")
 @mock.patch("crewai.cli.create_crew.load_env_vars")

lib/crewai/tests/cli/test_version.py

@@ -1,10 +1,20 @@
 """Test for version management."""
 
+from datetime import datetime, timedelta
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
 from crewai import __version__
-from crewai.cli.version import get_crewai_version
+from crewai.cli.version import (
+    _get_cache_file,
+    _is_cache_valid,
+    get_crewai_version,
+    get_latest_version_from_pypi,
+    is_newer_version_available,
+)
 
 
-def test_dynamic_versioning_consistency():
+def test_dynamic_versioning_consistency() -> None:
     """Test that dynamic versioning provides consistent version across all access methods."""
     cli_version = get_crewai_version()
     package_version = __version__
@@ -15,3 +25,186 @@ def test_dynamic_versioning_consistency():
     # Version should not be empty
     assert package_version is not None
     assert len(package_version.strip()) > 0
+
+
+class TestVersionChecking:
+    """Test version checking utilities."""
+
+    def test_get_crewai_version(self) -> None:
+        """Test getting current crewai version."""
+        version = get_crewai_version()
+        assert isinstance(version, str)
+        assert len(version) > 0
+
+    def test_get_cache_file(self) -> None:
+        """Test cache file path generation."""
+        cache_file = _get_cache_file()
+        assert isinstance(cache_file, Path)
+        assert cache_file.name == "version_cache.json"
+
+    def test_is_cache_valid_with_fresh_cache(self) -> None:
+        """Test cache validation with fresh cache."""
+        cache_data = {"timestamp": datetime.now().isoformat(), "version": "1.0.0"}
+        assert _is_cache_valid(cache_data) is True
+
+    def test_is_cache_valid_with_stale_cache(self) -> None:
+        """Test cache validation with stale cache."""
+        old_time = datetime.now() - timedelta(hours=25)
+        cache_data = {"timestamp": old_time.isoformat(), "version": "1.0.0"}
+        assert _is_cache_valid(cache_data) is False
+
+    def test_is_cache_valid_with_missing_timestamp(self) -> None:
+        """Test cache validation with missing timestamp."""
+        cache_data = {"version": "1.0.0"}
+        assert _is_cache_valid(cache_data) is False
+
+    @patch("crewai.cli.version.Path.exists")
+    @patch("crewai.cli.version.request.urlopen")
+    def test_get_latest_version_from_pypi_success(
+        self, mock_urlopen: MagicMock, mock_exists: MagicMock
+    ) -> None:
+        """Test successful PyPI version fetch."""
+        # Mock cache not existing to force fetch from PyPI
+        mock_exists.return_value = False
+
+        mock_response = MagicMock()
+        mock_response.read.return_value = b'{"info": {"version": "2.0.0"}}'
+        mock_urlopen.return_value.__enter__.return_value = mock_response
+
+        version = get_latest_version_from_pypi()
+        assert version == "2.0.0"
+
+    @patch("crewai.cli.version.Path.exists")
+    @patch("crewai.cli.version.request.urlopen")
+    def test_get_latest_version_from_pypi_failure(
+        self, mock_urlopen: MagicMock, mock_exists: MagicMock
+    ) -> None:
+        """Test PyPI version fetch failure."""
+        from urllib.error import URLError
+
+        # Mock cache not existing to force fetch from PyPI
+        mock_exists.return_value = False
+
+        mock_urlopen.side_effect = URLError("Network error")
+
+        version = get_latest_version_from_pypi()
+        assert version is None
+
+    @patch("crewai.cli.version.get_crewai_version")
+    @patch("crewai.cli.version.get_latest_version_from_pypi")
+    def test_is_newer_version_available_true(
+        self, mock_latest: MagicMock, mock_current: MagicMock
+    ) -> None:
+        """Test when newer version is available."""
+        mock_current.return_value = "1.0.0"
+        mock_latest.return_value = "2.0.0"
+
+        is_newer, current, latest = is_newer_version_available()
+        assert is_newer is True
+        assert current == "1.0.0"
+        assert latest == "2.0.0"
+
+    @patch("crewai.cli.version.get_crewai_version")
+    @patch("crewai.cli.version.get_latest_version_from_pypi")
+    def test_is_newer_version_available_false(
+        self, mock_latest: MagicMock, mock_current: MagicMock
+    ) -> None:
+        """Test when no newer version is available."""
+        mock_current.return_value = "2.0.0"
+        mock_latest.return_value = "2.0.0"
+
+        is_newer, current, latest = is_newer_version_available()
+        assert is_newer is False
+        assert current == "2.0.0"
+        assert latest == "2.0.0"
+
+    @patch("crewai.cli.version.get_crewai_version")
+    @patch("crewai.cli.version.get_latest_version_from_pypi")
+    def test_is_newer_version_available_with_none_latest(
+        self, mock_latest: MagicMock, mock_current: MagicMock
+    ) -> None:
+        """Test when PyPI fetch fails."""
+        mock_current.return_value = "1.0.0"
+        mock_latest.return_value = None
+
+        is_newer, current, latest = is_newer_version_available()
+        assert is_newer is False
+        assert current == "1.0.0"
+        assert latest is None
+
+
+class TestConsoleFormatterVersionCheck:
+    """Test version check display in ConsoleFormatter."""
+
+    @patch("crewai.events.utils.console_formatter.is_newer_version_available")
+    @patch.dict("os.environ", {"CI": ""})
+    def test_version_message_shows_when_update_available_and_verbose(
+        self, mock_check: MagicMock
+    ) -> None:
+        """Test version message shows when update available and verbose enabled."""
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        mock_check.return_value = (True, "1.0.0", "2.0.0")
+
+        formatter = ConsoleFormatter(verbose=True)
+        with patch.object(formatter.console, "print") as mock_print:
+            formatter._show_version_update_message_if_needed()
+            assert mock_print.call_count == 2
+
+    @patch("crewai.events.utils.console_formatter.is_newer_version_available")
+    def test_version_message_hides_when_verbose_false(
+        self, mock_check: MagicMock
+    ) -> None:
+        """Test version message hidden when verbose disabled."""
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        mock_check.return_value = (True, "1.0.0", "2.0.0")
+
+        formatter = ConsoleFormatter(verbose=False)
+        with patch.object(formatter.console, "print") as mock_print:
+            formatter._show_version_update_message_if_needed()
+            mock_print.assert_not_called()
+
+    @patch("crewai.events.utils.console_formatter.is_newer_version_available")
+    def test_version_message_hides_when_no_update_available(
+        self, mock_check: MagicMock
+    ) -> None:
+        """Test version message hidden when no update available."""
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        mock_check.return_value = (False, "2.0.0", "2.0.0")
+
+        formatter = ConsoleFormatter(verbose=True)
+        with patch.object(formatter.console, "print") as mock_print:
+            formatter._show_version_update_message_if_needed()
+            mock_print.assert_not_called()
+
+    @patch("crewai.events.utils.console_formatter.is_newer_version_available")
+    @patch.dict("os.environ", {"CI": "true"})
+    def test_version_message_hides_in_ci_environment(
+        self, mock_check: MagicMock
+    ) -> None:
+        """Test version message hidden when running in CI/CD."""
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        mock_check.return_value = (True, "1.0.0", "2.0.0")
+
+        formatter = ConsoleFormatter(verbose=True)
+        with patch.object(formatter.console, "print") as mock_print:
+            formatter._show_version_update_message_if_needed()
+            mock_print.assert_not_called()
+
+    @patch("crewai.events.utils.console_formatter.is_newer_version_available")
+    @patch.dict("os.environ", {"CI": "1"})
+    def test_version_message_hides_in_ci_environment_with_numeric_value(
+        self, mock_check: MagicMock
+    ) -> None:
+        """Test version message hidden when CI=1."""
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        mock_check.return_value = (True, "1.0.0", "2.0.0")
+
+        formatter = ConsoleFormatter(verbose=True)
+        with patch.object(formatter.console, "print") as mock_print:
+            formatter._show_version_update_message_if_needed()
+            mock_print.assert_not_called()

lib/crewai/tests/test_streaming.py

@@ -217,6 +217,7 @@ class TestCrewKickoffStreaming:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="Hello ",
+                        call_id="test-call-id",
                     ),
                 )
                 crewai_event_bus.emit(
@@ -224,6 +225,7 @@ class TestCrewKickoffStreaming:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="World!",
+                        call_id="test-call-id",
                     ),
                 )
                 return mock_output
@@ -284,6 +286,7 @@ class TestCrewKickoffStreaming:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="",
+                        call_id="test-call-id",
                         tool_call=ToolCall(
                             id="call-123",
                             function=FunctionCall(
@@ -364,6 +367,7 @@ class TestCrewKickoffStreamingAsync:
                 LLMStreamChunkEvent(
                     type="llm_stream_chunk",
                     chunk="Async ",
+                    call_id="test-call-id",
                 ),
             )
             crewai_event_bus.emit(
@@ -371,6 +375,7 @@ class TestCrewKickoffStreamingAsync:
                 LLMStreamChunkEvent(
                     type="llm_stream_chunk",
                     chunk="Stream!",
+                    call_id="test-call-id",
                 ),
             )
             return mock_output
@@ -451,6 +456,7 @@ class TestFlowKickoffStreaming:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="Flow ",
+                        call_id="test-call-id",
                     ),
                 )
                 crewai_event_bus.emit(
@@ -458,6 +464,7 @@ class TestFlowKickoffStreaming:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="output!",
+                        call_id="test-call-id",
                     ),
                 )
                 return "done"
@@ -545,6 +552,7 @@ class TestFlowKickoffStreamingAsync:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="Async flow ",
+                        call_id="test-call-id",
                     ),
                 )
                 await asyncio.sleep(0.01)
@@ -553,6 +561,7 @@ class TestFlowKickoffStreamingAsync:
                     LLMStreamChunkEvent(
                         type="llm_stream_chunk",
                         chunk="stream!",
+                        call_id="test-call-id",
                     ),
                 )
                 await asyncio.sleep(0.01)
@@ -686,6 +695,7 @@ class TestStreamingEdgeCases:
                         type="llm_stream_chunk",
                         chunk="Task 1",
                         task_name="First task",
+                        call_id="test-call-id",
                     ),
                 )
                 return mock_output

lib/crewai/tests/test_task_guardrails.py

@@ -249,6 +249,8 @@ def test_guardrail_emits_events(sample_agent):
 
     result = task.execute_sync(agent=sample_agent)
 
+    crewai_event_bus.flush(timeout=10.0)
+
     with condition:
         success = condition.wait_for(
             lambda: len(started_guardrail) >= 2 and len(completed_guardrail) >= 2,
@@ -267,6 +269,8 @@ def test_guardrail_emits_events(sample_agent):
 
     task.execute_sync(agent=sample_agent)
 
+    crewai_event_bus.flush(timeout=10.0)
+
     with condition:
         success = condition.wait_for(
             lambda: len(started_guardrail) >= 3 and len(completed_guardrail) >= 3,

lib/crewai/tests/utilities/test_events.py

@@ -984,8 +984,8 @@ def test_streaming_fallback_to_non_streaming():
     def mock_call(messages, tools=None, callbacks=None, available_functions=None):
         nonlocal fallback_called
         # Emit a couple of chunks to simulate partial streaming
-        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1", response_id = "Id"))
-        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2", response_id = "Id"))
+        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1", response_id="Id", call_id="test-call-id"))
+        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2", response_id="Id", call_id="test-call-id"))
 
         # Mark that fallback would be called
         fallback_called = True
@@ -1041,7 +1041,7 @@ def test_streaming_empty_response_handling():
     def mock_call(messages, tools=None, callbacks=None, available_functions=None):
         # Emit a few empty chunks
         for _ in range(3):
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="",response_id="id"))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="", response_id="id", call_id="test-call-id"))
 
         # Return the default message for empty responses
         return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request."
@@ -1280,6 +1280,105 @@ def test_llm_emits_event_with_lite_agent():
     assert set(all_agent_id) == {str(agent.id)}
 
 
+# ----------- CALL_ID CORRELATION TESTS -----------
+
+
+@pytest.mark.vcr()
+def test_llm_call_events_share_call_id():
+    """All events from a single LLM call should share the same call_id."""
+    import uuid
+
+    events = []
+    condition = threading.Condition()
+
+    @crewai_event_bus.on(LLMCallStartedEvent)
+    def on_start(source, event):
+        with condition:
+            events.append(event)
+            condition.notify()
+
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_complete(source, event):
+        with condition:
+            events.append(event)
+            condition.notify()
+
+    llm = LLM(model="gpt-4o-mini")
+    llm.call("Say hi")
+
+    with condition:
+        success = condition.wait_for(lambda: len(events) >= 2, timeout=10)
+    assert success, "Timeout waiting for LLM events"
+
+    # Behavior: all events from the call share the same call_id
+    assert len(events) == 2
+    assert events[0].call_id == events[1].call_id
+    # call_id should be a valid UUID
+    uuid.UUID(events[0].call_id)
+
+
+@pytest.mark.vcr()
+def test_streaming_chunks_share_call_id_with_call():
+    """Streaming chunks should share call_id with started/completed events."""
+    events = []
+    condition = threading.Condition()
+
+    @crewai_event_bus.on(LLMCallStartedEvent)
+    def on_start(source, event):
+        with condition:
+            events.append(event)
+            condition.notify()
+
+    @crewai_event_bus.on(LLMStreamChunkEvent)
+    def on_chunk(source, event):
+        with condition:
+            events.append(event)
+            condition.notify()
+
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_complete(source, event):
+        with condition:
+            events.append(event)
+            condition.notify()
+
+    llm = LLM(model="gpt-4o-mini", stream=True)
+    llm.call("Say hi")
+
+    with condition:
+        # Wait for at least started, some chunks, and completed
+        success = condition.wait_for(lambda: len(events) >= 3, timeout=10)
+    assert success, "Timeout waiting for streaming events"
+
+    # Behavior: all events (started, chunks, completed) share the same call_id
+    call_ids = {e.call_id for e in events}
+    assert len(call_ids) == 1
+
+
+@pytest.mark.vcr()
+def test_separate_llm_calls_have_different_call_ids():
+    """Different LLM calls should have different call_ids."""
+    call_ids = []
+    condition = threading.Condition()
+
+    @crewai_event_bus.on(LLMCallStartedEvent)
+    def on_start(source, event):
+        with condition:
+            call_ids.append(event.call_id)
+            condition.notify()
+
+    llm = LLM(model="gpt-4o-mini")
+    llm.call("Say hi")
+    llm.call("Say bye")
+
+    with condition:
+        success = condition.wait_for(lambda: len(call_ids) >= 2, timeout=10)
+    assert success, "Timeout waiting for LLM call events"
+
+    # Behavior: each call has its own call_id
+    assert len(call_ids) == 2
+    assert call_ids[0] != call_ids[1]
+
+
 # ----------- HUMAN FEEDBACK EVENTS -----------
 
 

pyproject.toml

@@ -28,7 +28,7 @@ dev = [
     "boto3-stubs[bedrock-runtime]==1.40.54",
     "types-psycopg2==2.9.21.20251012",
     "types-pymysql==1.1.0.20250916",
-    "types-aiofiles~=24.1.0",
+    "types-aiofiles~=25.1.0",
 ]
 
 

uv.lock

@@ -51,7 +51,7 @@ dev = [
     { name = "pytest-timeout", specifier = "==2.4.0" },
     { name = "pytest-xdist", specifier = "==3.8.0" },
     { name = "ruff", specifier = "==0.14.7" },
-    { name = "types-aiofiles", specifier = "~=24.1.0" },
+    { name = "types-aiofiles", specifier = "~=25.1.0" },
     { name = "types-appdirs", specifier = "==1.4.*" },
     { name = "types-psycopg2", specifier = "==2.9.21.20251012" },
     { name = "types-pymysql", specifier = "==1.1.0.20250916" },
@@ -1294,10 +1294,10 @@ requires-dist = [
     { name = "json-repair", specifier = "~=0.25.2" },
     { name = "json5", specifier = "~=0.10.0" },
     { name = "jsonref", specifier = "~=1.1.0" },
-    { name = "litellm", marker = "extra == 'litellm'", specifier = "~=1.74.9" },
+    { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9,<3" },
     { name = "mcp", specifier = "~=1.23.1" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = "~=0.1.94" },
-    { name = "openai", specifier = "~=1.83.0" },
+    { name = "openai", specifier = ">=1.83.0,<3" },
     { name = "openpyxl", specifier = "~=3.1.5" },
     { name = "openpyxl", marker = "extra == 'openpyxl'", specifier = "~=3.1.5" },
     { name = "opentelemetry-api", specifier = "~=1.34.0" },
@@ -8282,11 +8282,11 @@ wheels = [
 
 [[package]]
 name = "types-aiofiles"
-version = "24.1.0.20250822"
+version = "25.1.0.20251011"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/19/48/c64471adac9206cc844afb33ed311ac5a65d2f59df3d861e0f2d0cad7414/types_aiofiles-24.1.0.20250822.tar.gz", hash = "sha256:9ab90d8e0c307fe97a7cf09338301e3f01a163e39f3b529ace82466355c84a7b", size = 14484, upload-time = "2025-08-22T03:02:23.039Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/6c/6d23908a8217e36704aa9c79d99a620f2fdd388b66a4b7f72fbc6b6ff6c6/types_aiofiles-25.1.0.20251011.tar.gz", hash = "sha256:1c2b8ab260cb3cd40c15f9d10efdc05a6e1e6b02899304d80dfa0410e028d3ff", size = 14535, upload-time = "2025-10-11T02:44:51.237Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8e/5e6d2215e1d8f7c2a94c6e9d0059ae8109ce0f5681956d11bb0a228cef04/types_aiofiles-24.1.0.20250822-py3-none-any.whl", hash = "sha256:0ec8f8909e1a85a5a79aed0573af7901f53120dd2a29771dd0b3ef48e12328b0", size = 14322, upload-time = "2025-08-22T03:02:21.918Z" },
+    { url = "https://files.pythonhosted.org/packages/71/0f/76917bab27e270bb6c32addd5968d69e558e5b6f7fb4ac4cbfa282996a96/types_aiofiles-25.1.0.20251011-py3-none-any.whl", hash = "sha256:8ff8de7f9d42739d8f0dadcceeb781ce27cd8d8c4152d4a7c52f6b20edb8149c", size = 14338, upload-time = "2025-10-11T02:44:50.054Z" },
 ]
 
 [[package]]