Merge branch 'main' into gl/fix/hitl-flow-plot

.github/dependabot.yml

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

.python-version

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

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

@@ -8,9 +8,8 @@ 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, PydanticUndefined
+from pydantic_core import PydanticOmit
 
 from crewai_tools import tools
 
@@ -45,9 +44,6 @@ 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(
@@ -58,9 +54,9 @@ 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_from_model_fields(model_fields),
-                "package_dependencies": self._extract_package_deps_from_model_fields(
-                    model_fields
+                "env_vars": self._extract_env_vars(fields.get("env_vars")),
+                "package_dependencies": self._extract_field_default(
+                    fields.get("package_dependencies"), fallback=[]
                 ),
             }
 
@@ -107,27 +103,10 @@ class ToolSpecExtractor:
             return {}
 
     @staticmethod
-    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],
+    def _extract_env_vars(
+        env_vars_field: dict[str, Any] | None,
     ) -> list[dict[str, Any]]:
-        default_value = ToolSpecExtractor._get_field_default(
-            model_fields.get("env_vars")
-        )
-        if not default_value:
+        if not env_vars_field:
             return []
 
         return [
@@ -137,22 +116,10 @@ class ToolSpecExtractor:
                 "required": env_var.required,
                 "default": env_var.default,
             }
-            for env_var in default_value
+            for env_var in env_vars_field.get("schema", {}).get("default", [])
             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 = [
@@ -185,7 +152,7 @@ class ToolSpecExtractor:
 
 
 if __name__ == "__main__":
-    output_file = Path(__file__).parent.parent.parent / "tool.specs.json"
+    output_file = Path(__file__).parent / "tool.specs.json"
     extractor = ToolSpecExtractor()
 
     extractor.extract_all_tools()

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

@@ -1,17 +1,12 @@
 from datetime import datetime
-import json
 import os
 import time
-from typing import Annotated, Any, ClassVar, Literal
+from typing import Any, ClassVar
 
 from crewai.tools import BaseTool, EnvVar
-from dotenv import load_dotenv
 from pydantic import BaseModel, Field
-from pydantic.types import StringConstraints
 import requests
 
-load_dotenv()
-
 
 def _save_results_to_file(content: str) -> None:
     """Saves the search results to a file."""
@@ -20,72 +15,37 @@ def _save_results_to_file(content: str) -> None:
         file.write(content)
 
 
-FreshnessPreset = Literal["pd", "pw", "pm", "py"]
-FreshnessRange = Annotated[
-    str, StringConstraints(pattern=r"^\d{4}-\d{2}-\d{2}to\d{4}-\d{2}-\d{2}$")
-]
-Freshness = FreshnessPreset | FreshnessRange
-SafeSearch = Literal["off", "moderate", "strict"]
-
-
 class BraveSearchToolSchema(BaseModel):
-    """Input for BraveSearchTool"""
+    """Input for BraveSearchTool."""
 
-    query: str = Field(..., description="Search query to perform")
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-    )
-    search_language: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return. Actual number may be less.",
-    )
-    offset: int | None = Field(
-        default=None, description="Skip the first N result sets/pages. Max is 9."
-    )
-    safesearch: SafeSearch | None = Field(
-        default=None,
-        description="Filter out explicit content. Options: off/moderate/strict",
-    )
-    spellcheck: bool | None = Field(
-        default=None,
-        description="Attempt to correct spelling errors in the search query.",
-    )
-    freshness: Freshness | None = Field(
-        default=None,
-        description="Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
-    )
-    text_decorations: bool | None = Field(
-        default=None,
-        description="Include markup to highlight search terms in the results.",
-    )
-    extra_snippets: bool | None = Field(
-        default=None,
-        description="Include up to 5 text snippets for each page if possible.",
-    )
-    operators: bool | None = Field(
-        default=None,
-        description="Whether to apply search operators (e.g., site:example.com).",
+    search_query: str = Field(
+        ..., description="Mandatory search query you want to use to search the internet"
     )
 
 
-# TODO: Extend support to additional endpoints (e.g., /images, /news, etc.)
 class BraveSearchTool(BaseTool):
-    """A tool that performs web searches using the Brave Search API."""
+    """BraveSearchTool - A tool for performing web searches using the Brave Search API.
 
-    name: str = "Brave Search"
+    This module provides functionality to search the internet using Brave's Search API,
+    supporting customizable result counts and country-specific searches.
+
+    Dependencies:
+        - requests
+        - pydantic
+        - python-dotenv (for API key management)
+    """
+
+    name: str = "Brave Web Search the internet"
     description: str = (
-        "A tool that performs web searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
+        "A tool that can be used to search the internet with a search_query."
     )
     args_schema: type[BaseModel] = BraveSearchToolSchema
     search_url: str = "https://api.search.brave.com/res/v1/web/search"
+    country: str | None = ""
     n_results: int = 10
     save_file: bool = False
+    _last_request_time: ClassVar[float] = 0
+    _min_request_interval: ClassVar[float] = 1.0  # seconds
     env_vars: list[EnvVar] = Field(
         default_factory=lambda: [
             EnvVar(
@@ -95,9 +55,6 @@ class BraveSearchTool(BaseTool):
             ),
         ]
     )
-    # Rate limiting parameters
-    _last_request_time: ClassVar[float] = 0
-    _min_request_interval: ClassVar[float] = 1.0  # seconds
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -116,64 +73,19 @@ class BraveSearchTool(BaseTool):
                 self._min_request_interval - (current_time - self._last_request_time)
             )
         BraveSearchTool._last_request_time = time.time()
-
-        # Construct and send the request
         try:
-            # Maintain both "search_query" and "query" for backwards compatibility
-            query = kwargs.get("search_query") or kwargs.get("query")
-            if not query:
-                raise ValueError("Query is required")
-
-            payload = {"q": query}
-
-            if country := kwargs.get("country"):
-                payload["country"] = country
-
-            if search_language := kwargs.get("search_language"):
-                payload["search_language"] = search_language
-
-            # Fallback to deprecated n_results parameter if no count is provided
-            count = kwargs.get("count")
-            if count is not None:
-                payload["count"] = count
-            else:
-                payload["count"] = self.n_results
-
-            # Offset may be 0, so avoid truthiness check
-            offset = kwargs.get("offset")
-            if offset is not None:
-                payload["offset"] = offset
-
-            if safesearch := kwargs.get("safesearch"):
-                payload["safesearch"] = safesearch
+            search_query = kwargs.get("search_query") or kwargs.get("query")
+            if not search_query:
+                raise ValueError("Search query is required")
 
             save_file = kwargs.get("save_file", self.save_file)
-            if freshness := kwargs.get("freshness"):
-                payload["freshness"] = freshness
+            n_results = kwargs.get("n_results", self.n_results)
 
-            # Boolean parameters
-            spellcheck = kwargs.get("spellcheck")
-            if spellcheck is not None:
-                payload["spellcheck"] = spellcheck
+            payload = {"q": search_query, "count": n_results}
 
-            text_decorations = kwargs.get("text_decorations")
-            if text_decorations is not None:
-                payload["text_decorations"] = text_decorations
+            if self.country != "":
+                payload["country"] = self.country
 
-            extra_snippets = kwargs.get("extra_snippets")
-            if extra_snippets is not None:
-                payload["extra_snippets"] = extra_snippets
-
-            operators = kwargs.get("operators")
-            if operators is not None:
-                payload["operators"] = operators
-
-            # Limit the result types to "web" since there is presently no
-            # handling of other types like "discussions", "faq", "infobox",
-            # "news", "videos", or "locations".
-            payload["result_filter"] = "web"
-
-            # Setup Request Headers
             headers = {
                 "X-Subscription-Token": os.environ["BRAVE_API_KEY"],
                 "Accept": "application/json",
@@ -185,32 +97,25 @@ class BraveSearchTool(BaseTool):
             response.raise_for_status()  # Handle non-200 responses
             results = response.json()
 
-            # TODO: Handle other result types like "discussions", "faq", etc.
-            web_results_items = []
             if "web" in results:
-                web_results = results["web"]["results"]
-
-                for result in web_results:
-                    url = result.get("url")
-                    title = result.get("title")
-                    # If, for whatever reason, this entry does not have a title
-                    # or url, skip it.
-                    if not url or not title:
+                results = results["web"]["results"]
+                string = []
+                for result in results:
+                    try:
+                        string.append(
+                            "\n".join(
+                                [
+                                    f"Title: {result['title']}",
+                                    f"Link: {result['url']}",
+                                    f"Snippet: {result['description']}",
+                                    "---",
+                                ]
+                            )
+                        )
+                    except KeyError:  # noqa: PERF203
                         continue
-                    item = {
-                        "url": url,
-                        "title": title,
-                    }
-                    description = result.get("description")
-                    if description:
-                        item["description"] = description
-                    snippets = result.get("extra_snippets")
-                    if snippets:
-                        item["snippets"] = snippets
 
-                    web_results_items.append(item)
-
-            content = json.dumps(web_results_items)
+            content = "\n".join(string)
         except requests.RequestException as e:
             return f"Error performing search: {e!s}"
         except KeyError as e:

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, EnvVar
+from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
 
@@ -137,21 +137,7 @@ 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<=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,
-            ),
-        ]
-    )
+    package_dependencies: list[str] = Field(default_factory=lambda: ["stagehand"])
 
     # Stagehand configuration
     api_key: str | None = None

lib/crewai-tools/tests/test_generate_tool_specs.py

@@ -23,26 +23,23 @@ 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(
-        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",
-            ),
-        ]
+        ["this-is-a-required-package", "another-required-package"], description=""
     )
+    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/brave_search_tool_test.py

@@ -1,9 +1,7 @@
-import json
 from unittest.mock import patch
 
-import pytest
-
 from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
+import pytest
 
 
 @pytest.fixture
@@ -32,43 +30,16 @@ def test_brave_tool_search(mock_get, brave_tool):
     }
     mock_get.return_value.json.return_value = mock_response
 
-    result = brave_tool.run(query="test")
+    result = brave_tool.run(search_query="test")
     assert "Test Title" in result
     assert "http://test.com" in result
 
 
-@patch("requests.get")
-def test_brave_tool(mock_get):
-    mock_response = {
-        "web": {
-            "results": [
-                {
-                    "title": "Brave Browser",
-                    "url": "https://brave.com",
-                    "description": "Brave Browser description",
-                }
-            ]
-        }
-    }
-    mock_get.return_value.json.return_value = mock_response
-
-    tool = BraveSearchTool(n_results=2)
-    result = tool.run(query="Brave Browser")
-    assert result is not None
-
-    # Parse JSON so we can examine the structure
-    data = json.loads(result)
-    assert isinstance(data, list)
-    assert len(data) >= 1
-
-    # First item should have expected fields: title, url, and description
-    first = data[0]
-    assert "title" in first
-    assert first["title"] == "Brave Browser"
-    assert "url" in first
-    assert first["url"] == "https://brave.com"
-    assert "description" in first
-    assert first["description"] == "Brave Browser description"
+def test_brave_tool():
+    tool = BraveSearchTool(
+        n_results=2,
+    )
+    tool.run(search_query="ChatGPT")
 
 
 if __name__ == "__main__":

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,<3",
+    "openai~=1.83.0",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
@@ -78,7 +78,7 @@ voyageai = [
     "voyageai~=0.3.5",
 ]
 litellm = [
-    "litellm>=1.74.9,<3",
+    "litellm~=1.74.9",
 ]
 bedrock = [
     "boto3~=1.40.45",

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py

@@ -16,7 +16,6 @@ from crewai.agents.agent_adapters.openai_agents.protocols import (
 )
 from crewai.tools import BaseTool
 from crewai.utilities.import_utils import require
-from crewai.utilities.pydantic_schema_utils import force_additional_properties_false
 from crewai.utilities.string_utils import sanitize_tool_name
 
 
@@ -136,9 +135,7 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
         for tool in tools:
             schema: dict[str, Any] = tool.args_schema.model_json_schema()
 
-            schema = force_additional_properties_false(schema)
-
-            schema.update({"type": "object"})
+            schema.update({"additionalProperties": False, "type": "object"})
 
             openai_tool: OpenAIFunctionTool = cast(
                 OpenAIFunctionTool,

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

@@ -1,13 +1,10 @@
-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: dict[str, list[dict[str, Any]]] = {
+ENV_VARS = {
     "openai": [
         {
             "prompt": "Enter your OPENAI API key (press Enter to skip)",
@@ -115,7 +112,7 @@ ENV_VARS: dict[str, list[dict[str, Any]]] = {
 }
 
 
-PROVIDERS: list[str] = [
+PROVIDERS = [
     "openai",
     "anthropic",
     "gemini",
@@ -130,7 +127,7 @@ PROVIDERS: list[str] = [
     "sambanova",
 ]
 
-MODELS: dict[str, list[str]] = {
+MODELS = {
     "openai": [
         "gpt-4",
         "gpt-4.1",

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

@@ -3,7 +3,6 @@ import shutil
 import sys
 
 import click
-import tomli
 
 from crewai.cli.constants import ENV_VARS, MODELS
 from crewai.cli.provider import (
@@ -14,31 +13,7 @@ from crewai.cli.provider import (
 from crewai.cli.utils import copy_template, load_env_vars, write_env_file
 
 
-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]:
+def create_folder_structure(name, parent_folder=None):
     import keyword
     import re
 
@@ -76,14 +51,6 @@ def create_folder_structure(
             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)
@@ -147,9 +114,7 @@ def create_folder_structure(
     return folder_path, folder_name, class_name
 
 
-def copy_template_files(
-    folder_path: Path, name: str, class_name: str, parent_folder: str | None
-) -> None:
+def copy_template_files(folder_path, name, class_name, parent_folder):
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"
 
@@ -190,12 +155,7 @@ def copy_template_files(
             copy_template(src_file, dst_file, name, class_name, folder_path.name)
 
 
-def create_crew(
-    name: str,
-    provider: str | None = None,
-    skip_provider: bool = False,
-    parent_folder: str | None = None,
-) -> None:
+def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
     env_vars = load_env_vars(folder_path)
     if not skip_provider:
@@ -229,9 +189,7 @@ def create_crew(
             if selected_provider is None:  # User typed 'q'
                 click.secho("Exiting...", fg="yellow")
                 sys.exit(0)
-            if selected_provider and isinstance(
-                selected_provider, str
-            ):  # Valid selection
+            if selected_provider:  # 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

@@ -1,10 +1,8 @@
 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
@@ -13,15 +11,16 @@ import requests
 from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
 
 
-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.
+def select_choice(prompt_message, choices):
+    """
+    Presents a list of choices to the user and prompts them to select one.
 
     Args:
-        prompt_message: The message to display to the user before presenting the choices.
-        choices: A list of options to present to the user.
+    - 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.
 
     Returns:
-        The selected choice from the list, or None if the user chooses to quit.
+    - str: The selected choice from the list, or None if the user chooses to quit.
     """
 
     provider_models = get_provider_data()
@@ -53,14 +52,16 @@ def select_choice(prompt_message: str, choices: Sequence[str]) -> str | None:
         )
 
 
-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.
+def select_provider(provider_models):
+    """
+    Presents a list of providers to the user and prompts them to select one.
 
     Args:
-        provider_models: A dictionary of provider models.
+    - provider_models (dict): A dictionary of provider models.
 
     Returns:
-        The selected provider, None if user explicitly quits, or False if no selection.
+    - str: The selected provider
+    - None: If user explicitly quits
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
     all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
@@ -79,15 +80,16 @@ def select_provider(provider_models: dict[str, list[str]]) -> str | None | bool:
     return provider.lower() if provider else False
 
 
-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.
+def select_model(provider, provider_models):
+    """
+    Presents a list of models for a given provider to the user and prompts them to select one.
 
     Args:
-        provider: The provider for which to select a model.
-        provider_models: A dictionary of provider models.
+    - provider (str): The provider for which to select a model.
+    - provider_models (dict): A dictionary of provider models.
 
     Returns:
-        The selected model, or None if the operation is aborted or an invalid selection is made.
+    - str: The selected model, or None if the operation is aborted or an invalid selection is made.
     """
     predefined_providers = [p.lower() for p in PROVIDERS]
 
@@ -105,17 +107,16 @@ def select_model(provider: str, provider_models: dict[str, list[str]]) -> str |
     )
 
 
-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.
+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.
 
     Args:
-        cache_file: The path to the cache file.
-        cache_expiry: The cache expiry time in seconds.
+    - cache_file (Path): The path to the cache file.
+    - cache_expiry (int): The cache expiry time in seconds.
 
     Returns:
-        The loaded provider data or None if the operation fails.
+    - dict or None: The loaded provider data or None if the operation fails.
     """
     current_time = time.time()
     if (
@@ -136,31 +137,32 @@ def load_provider_data(cache_file: Path, cache_expiry: int) -> dict[str, Any] |
     return fetch_provider_data(cache_file)
 
 
-def read_cache_file(cache_file: Path) -> dict[str, Any] | None:
-    """Reads and returns the JSON content from a 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.
 
     Args:
-        cache_file: The path to the cache file.
+    - cache_file (Path): The path to the cache file.
 
     Returns:
-        The JSON content of the cache file or None if the JSON is invalid.
+    - dict or None: The JSON content of the cache file or None if the JSON is invalid.
     """
     try:
         with open(cache_file, "r") as f:
-            data: dict[str, Any] = json.load(f)
-            return data
+            return json.load(f)
     except json.JSONDecodeError:
         return None
 
 
-def fetch_provider_data(cache_file: Path) -> dict[str, Any] | None:
-    """Fetches provider data from a specified URL and caches it to a file.
+def fetch_provider_data(cache_file):
+    """
+    Fetches provider data from a specified URL and caches it to a file.
 
     Args:
-        cache_file: The path to the cache file.
+    - cache_file (Path): The path to the cache file.
 
     Returns:
-        The fetched provider data or None if the operation fails.
+    - dict or None: The fetched provider data or None if the operation fails.
     """
     ssl_config = os.environ["SSL_CERT_FILE"] = certifi.where()
 
@@ -178,39 +180,36 @@ def fetch_provider_data(cache_file: Path) -> dict[str, Any] | None:
     return None
 
 
-def download_data(response: requests.Response) -> dict[str, Any]:
-    """Downloads data from a given HTTP response and returns the JSON content.
+def download_data(response):
+    """
+    Downloads data from a given HTTP response and returns the JSON content.
 
     Args:
-        response: The HTTP response object.
+    - response (requests.Response): The HTTP response object.
 
     Returns:
-        The JSON content of the response.
+    - dict: The JSON content of the response.
     """
     total_size = int(response.headers.get("content-length", 0))
     block_size = 8192
-    data_chunks: list[bytes] = []
-    bar: Any
+    data_chunks = []
     with click.progressbar(
         length=total_size, label="Downloading", show_pos=True
-    ) as bar:
+    ) as progress_bar:
         for chunk in response.iter_content(block_size):
             if chunk:
                 data_chunks.append(chunk)
-                bar.update(len(chunk))
+                progress_bar.update(len(chunk))
     data_content = b"".join(data_chunks)
-    result: dict[str, Any] = json.loads(data_content.decode("utf-8"))
-    return result
+    return json.loads(data_content.decode("utf-8"))
 
 
-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.
+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.
 
     Returns:
-        A dictionary of providers mapped to their models or None if the operation fails.
+    - dict or None: 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

@@ -1,107 +1,6 @@
-"""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

@@ -10,7 +10,6 @@ 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

@@ -1,14 +1,11 @@
-import os
 import threading
-from typing import Any, ClassVar, cast
+from typing import Any, ClassVar
 
 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]] = {}
@@ -38,39 +35,6 @@ 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 (
@@ -212,10 +176,9 @@ 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,
@@ -274,8 +237,6 @@ 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")
@@ -924,7 +885,7 @@ To enable tracing, do any one of these:
 
             is_a2a_delegation = False
             try:
-                output_data = json.loads(cast(str, formatted_answer.output))
+                output_data = json.loads(formatted_answer.output)
                 if isinstance(output_data, dict):
                     if output_data.get("is_a2a") is True:
                         is_a2a_delegation = True

lib/crewai/src/crewai/flow/flow.py

@@ -513,11 +513,17 @@ class FlowMeta(type):
                         and attr_value.__is_router__
                     ):
                         routers.add(attr_name)
-                        possible_returns = get_possible_return_constants(attr_value)
-                        if possible_returns:
-                            router_paths[attr_name] = possible_returns
+                        if (
+                            hasattr(attr_value, "__router_paths__")
+                            and attr_value.__router_paths__
+                        ):
+                            router_paths[attr_name] = attr_value.__router_paths__
                         else:
-                            router_paths[attr_name] = []
+                            possible_returns = get_possible_return_constants(attr_value)
+                            if possible_returns:
+                                router_paths[attr_name] = possible_returns
+                            else:
+                                router_paths[attr_name] = []
 
                 # Handle start methods that are also routers (e.g., @human_feedback with emit)
                 if (

lib/crewai/src/crewai/flow/visualization/assets/interactive.js

@@ -1025,7 +1025,7 @@ class TriggeredByHighlighter {
 
           const isAndOrRouter = edge.dashes || edge.label === "AND";
           const highlightColor = isAndOrRouter
-            ? "{{ CREWAI_ORANGE }}"
+            ? (edge.color?.color || "{{ CREWAI_ORANGE }}")
             : getComputedStyle(document.documentElement).getPropertyValue('--edge-or-color').trim();
 
           const updateData = {
@@ -1080,7 +1080,7 @@ class TriggeredByHighlighter {
           // Keep the original edge color instead of turning gray
           const isAndOrRouter = edge.dashes || edge.label === "AND";
           const baseColor = isAndOrRouter
-            ? "{{ CREWAI_ORANGE }}"
+            ? (edge.color?.color || "{{ CREWAI_ORANGE }}")
             : getComputedStyle(document.documentElement).getPropertyValue('--edge-or-color').trim();
 
           // Convert color to rgba with opacity for vis.js
@@ -1142,7 +1142,7 @@ class TriggeredByHighlighter {
 
         const defaultColor =
           edge.dashes || edge.label === "AND"
-            ? "{{ CREWAI_ORANGE }}"
+            ? (edge.color?.color || "{{ CREWAI_ORANGE }}")
             : getComputedStyle(document.documentElement).getPropertyValue('--edge-or-color').trim();
         const currentOpacity = edge.opacity !== undefined ? edge.opacity : 1.0;
         const currentWidth =
@@ -1253,7 +1253,7 @@ class TriggeredByHighlighter {
 
         const defaultColor =
           edge.dashes || edge.label === "AND"
-            ? "{{ CREWAI_ORANGE }}"
+            ? (edge.color?.color || "{{ CREWAI_ORANGE }}")
             : getComputedStyle(document.documentElement).getPropertyValue('--edge-or-color').trim();
         const currentOpacity = edge.opacity !== undefined ? edge.opacity : 1.0;
         const currentWidth =
@@ -2370,7 +2370,7 @@ class NetworkManager {
       this.edges.forEach((edge) => {
         let edgeColor;
         if (edge.dashes || edge.label === "AND") {
-          edgeColor = "{{ CREWAI_ORANGE }}";
+          edgeColor = edge.color?.color || "{{ CREWAI_ORANGE }}";
         } else {
           edgeColor = orEdgeColor;
         }

lib/crewai/src/crewai/flow/visualization/builder.py

@@ -129,7 +129,7 @@ def _create_edges_from_condition(
     edges: list[StructureEdge] = []
 
     if isinstance(condition, str):
-        if condition in nodes:
+        if condition in nodes and condition != target:
             edges.append(
                 StructureEdge(
                     source=condition,
@@ -140,7 +140,7 @@ def _create_edges_from_condition(
             )
     elif callable(condition) and hasattr(condition, "__name__"):
         method_name = condition.__name__
-        if method_name in nodes:
+        if method_name in nodes and method_name != target:
             edges.append(
                 StructureEdge(
                     source=method_name,
@@ -163,7 +163,7 @@ def _create_edges_from_condition(
                     is_router_path=False,
                 )
                 for trigger in triggers
-                if trigger in nodes
+                if trigger in nodes and trigger != target
             )
         else:
             for sub_cond in conditions_list:
@@ -196,9 +196,34 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
             node_metadata["type"] = "start"
             start_methods.append(method_name)
 
+        if (
+            hasattr(method, "__human_feedback_config__")
+            and method.__human_feedback_config__
+        ):
+            config = method.__human_feedback_config__
+            node_metadata["is_human_feedback"] = True
+            node_metadata["human_feedback_message"] = config.message
+
+            if config.emit:
+                node_metadata["human_feedback_emit"] = list(config.emit)
+
+            if config.llm:
+                llm_str = (
+                    config.llm
+                    if isinstance(config.llm, str)
+                    else str(type(config.llm).__name__)
+                )
+                node_metadata["human_feedback_llm"] = llm_str
+
+            if config.default_outcome:
+                node_metadata["human_feedback_default_outcome"] = config.default_outcome
+
         if hasattr(method, "__is_router__") and method.__is_router__:
             node_metadata["is_router"] = True
-            node_metadata["type"] = "router"
+            if "is_human_feedback" not in node_metadata:
+                node_metadata["type"] = "router"
+            else:
+                node_metadata["type"] = "human_feedback"
             router_methods.append(method_name)
 
             if method_name in flow._router_paths:
@@ -317,7 +342,7 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
                     is_router_path=False,
                 )
                 for trigger_method in methods
-                if str(trigger_method) in nodes
+                if str(trigger_method) in nodes and str(trigger_method) != listener_name
             )
         elif is_flow_condition_dict(condition_data):
             edges.extend(

lib/crewai/src/crewai/flow/visualization/renderers/interactive.py

@@ -81,6 +81,7 @@ class JSExtension(Extension):
 
 
 CREWAI_ORANGE = "#FF5A50"
+HITL_BLUE = "#4A90E2"
 DARK_GRAY = "#333333"
 WHITE = "#FFFFFF"
 GRAY = "#666666"
@@ -225,6 +226,7 @@ def render_interactive(
     nodes_list: list[dict[str, Any]] = []
     for name, metadata in dag["nodes"].items():
         node_type: str = metadata.get("type", "listen")
+        is_human_feedback: bool = metadata.get("is_human_feedback", False)
 
         color_config: dict[str, Any]
         font_color: str
@@ -241,6 +243,17 @@ def render_interactive(
             }
             font_color = "var(--node-text-color)"
             border_width = 3
+        elif node_type == "human_feedback":
+            color_config = {
+                "background": "var(--node-bg-router)",
+                "border": HITL_BLUE,
+                "highlight": {
+                    "background": "var(--node-bg-router)",
+                    "border": HITL_BLUE,
+                },
+            }
+            font_color = "var(--node-text-color)"
+            border_width = 3
         elif node_type == "router":
             color_config = {
                 "background": "var(--node-bg-router)",
@@ -266,16 +279,57 @@ def render_interactive(
 
         title_parts: list[str] = []
 
-        type_badge_bg: str = (
-            CREWAI_ORANGE if node_type in ["start", "router"] else DARK_GRAY
-        )
+        display_type = node_type
+        type_badge_bg: str
+        if node_type == "human_feedback":
+            type_badge_bg = HITL_BLUE
+            display_type = "HITL"
+        elif node_type in ["start", "router"]:
+            type_badge_bg = CREWAI_ORANGE
+        else:
+            type_badge_bg = DARK_GRAY
+
         title_parts.append(f"""
             <div style="border-bottom: 1px solid rgba(102,102,102,0.15); padding-bottom: 8px; margin-bottom: 10px;">
                 <div style="font-size: 13px; font-weight: 700; color: {DARK_GRAY}; margin-bottom: 6px;">{name}</div>
-                <span style="display: inline-block; background: {type_badge_bg}; color: white; padding: 2px 8px; border-radius: 4px; font-size: 10px; font-weight: 600; text-transform: uppercase; letter-spacing: 0.5px;">{node_type}</span>
+                <span style="display: inline-block; background: {type_badge_bg}; color: white; padding: 2px 8px; border-radius: 4px; font-size: 10px; font-weight: 600; text-transform: uppercase; letter-spacing: 0.5px;">{display_type}</span>
             </div>
         """)
 
+        if is_human_feedback:
+            feedback_msg = metadata.get("human_feedback_message", "")
+            if feedback_msg:
+                title_parts.append(f"""
+                    <div style="margin-bottom: 8px;">
+                        <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">👤 Human Feedback</div>
+                        <div style="background: rgba(74,144,226,0.08); padding: 6px 8px; border-radius: 4px; font-size: 11px; color: {DARK_GRAY}; border: 1px solid rgba(74,144,226,0.2); line-height: 1.4;">{feedback_msg}</div>
+                    </div>
+                """)
+
+            if metadata.get("human_feedback_emit"):
+                emit_options = metadata["human_feedback_emit"]
+                emit_items = "".join(
+                    [
+                        f'<li style="margin: 3px 0;"><code style="background: rgba(74,144,226,0.08); padding: 2px 6px; border-radius: 3px; font-size: 10px; color: {HITL_BLUE}; border: 1px solid rgba(74,144,226,0.2); font-weight: 600;">{opt}</code></li>'
+                        for opt in emit_options
+                    ]
+                )
+                title_parts.append(f"""
+                    <div style="margin-bottom: 8px;">
+                        <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">Outcomes</div>
+                        <ul style="list-style: none; padding: 0; margin: 0;">{emit_items}</ul>
+                    </div>
+                """)
+
+            if metadata.get("human_feedback_llm"):
+                llm_model = metadata["human_feedback_llm"]
+                title_parts.append(f"""
+                    <div style="margin-bottom: 8px;">
+                        <div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 3px; font-weight: 600;">LLM</div>
+                        <span style="display: inline-block; background: rgba(102,102,102,0.08); padding: 3px 8px; border-radius: 4px; font-size: 10px; color: {DARK_GRAY}; border: 1px solid rgba(102,102,102,0.12);">{llm_model}</span>
+                    </div>
+                """)
+
         if metadata.get("condition_type"):
             condition = metadata["condition_type"]
             if condition == "AND":
@@ -309,7 +363,7 @@ def render_interactive(
                 </div>
             """)
 
-        if metadata.get("router_paths"):
+        if metadata.get("router_paths") and not is_human_feedback:
             paths = metadata["router_paths"]
             paths_items = "".join(
                 [
@@ -365,7 +419,11 @@ def render_interactive(
         edge_dashes: bool | list[int] = False
 
         if edge["is_router_path"]:
-            edge_color = CREWAI_ORANGE
+            source_node = dag["nodes"].get(edge["source"], {})
+            if source_node.get("is_human_feedback", False):
+                edge_color = HITL_BLUE
+            else:
+                edge_color = CREWAI_ORANGE
             edge_dashes = [15, 10]
             if "router_path_label" in edge:
                 edge_label = edge["router_path_label"]
@@ -417,6 +475,7 @@ def render_interactive(
     css_content = css_content.replace("'{{ DARK_GRAY }}'", DARK_GRAY)
     css_content = css_content.replace("'{{ GRAY }}'", GRAY)
     css_content = css_content.replace("'{{ CREWAI_ORANGE }}'", CREWAI_ORANGE)
+    css_content = css_content.replace("'{{ HITL_BLUE }}'", HITL_BLUE)
 
     css_output_path.write_text(css_content, encoding="utf-8")
 
@@ -430,6 +489,7 @@ def render_interactive(
     js_content = js_content.replace("{{ DARK_GRAY }}", DARK_GRAY)
     js_content = js_content.replace("{{ GRAY }}", GRAY)
     js_content = js_content.replace("{{ CREWAI_ORANGE }}", CREWAI_ORANGE)
+    js_content = js_content.replace("{{ HITL_BLUE }}", HITL_BLUE)
     js_content = js_content.replace("'{{ nodeData }}'", dag_nodes_json)
     js_content = js_content.replace("'{{ dagData }}'", dag_full_json)
     js_content = js_content.replace("'{{ nodes_list_json }}'", json.dumps(nodes_list))
@@ -441,6 +501,7 @@ def render_interactive(
 
     html_content = template.render(
         CREWAI_ORANGE=CREWAI_ORANGE,
+        HITL_BLUE=HITL_BLUE,
         DARK_GRAY=DARK_GRAY,
         WHITE=WHITE,
         GRAY=GRAY,

lib/crewai/src/crewai/flow/visualization/types.py

@@ -21,6 +21,11 @@ class NodeMetadata(TypedDict, total=False):
     class_signature: str
     class_name: str
     class_line_number: int
+    is_human_feedback: bool
+    human_feedback_message: str
+    human_feedback_emit: list[str]
+    human_feedback_llm: str
+    human_feedback_default_outcome: str
 
 
 class StructureEdge(TypedDict, total=False):

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, get_current_call_id, llm_call_context
+from crewai.llms.base_llm import BaseLLM
 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:
@@ -849,8 +849,7 @@ class LLM(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             call_type=LLMCallType.LLM_CALL,
-                            response_id=response_id,
-                            call_id=get_current_call_id(),
+                            response_id=response_id
                         ),
                     )
             # --- 4) Fallback to non-streaming if no content received
@@ -1016,10 +1015,7 @@ class LLM(BaseLLM):
             crewai_event_bus.emit(
                 self,
                 event=LLMCallFailedEvent(
-                    error=str(e),
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    call_id=get_current_call_id(),
+                    error=str(e), from_task=from_task, from_agent=from_agent
                 ),
             )
             raise Exception(f"Failed to get streaming response: {e!s}") from e
@@ -1052,8 +1048,7 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     call_type=LLMCallType.TOOL_CALL,
-                    response_id=response_id,
-                    call_id=get_current_call_id(),
+                    response_id=response_id
                 ),
             )
 
@@ -1481,8 +1476,7 @@ class LLM(BaseLLM):
                             chunk=chunk_content,
                             from_task=from_task,
                             from_agent=from_agent,
-                            response_id=response_id,
-                            call_id=get_current_call_id(),
+                            response_id=response_id
                         ),
                     )
 
@@ -1625,12 +1619,7 @@ 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}",
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        call_id=get_current_call_id(),
-                    ),
+                    event=LLMCallFailedEvent(error=f"Tool execution error: {e!s}"),
                 )
                 crewai_event_bus.emit(
                     self,
@@ -1680,117 +1669,108 @@ class LLM(BaseLLM):
             ValueError: If response format is not supported
             LLMContextLengthExceededError: If input exceeds model's context limit
         """
-        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,
-                ),
-            )
+        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,
+            ),
+        )
 
-            # --- 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
-                            )
-                        ):
-                            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(),
-                        ),
+        # --- 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,
                     )
-                    raise
+                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
+                        )
+                    ):
+                        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
+                    ),
+                )
+                raise
 
     async def acall(
         self,
@@ -1828,54 +1808,43 @@ class LLM(BaseLLM):
             ValueError: If response format is not supported
             LLMContextLengthExceededError: If input exceeds model's context limit
         """
-        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,
-                ),
-            )
+        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,
+            ),
+        )
 
-            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(
-                            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(
+                if self.stream:
+                    return await self._ahandle_streaming_response(
                         params=params,
                         callbacks=callbacks,
                         available_functions=available_functions,
@@ -1883,50 +1852,52 @@ 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)
 
-                    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"]
-                            }
+                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)
 
-                        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,
+                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"]}
 
-                    crewai_event_bus.emit(
-                        self,
-                        event=LLMCallFailedEvent(
-                            error=str(e),
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            call_id=get_current_call_id(),
-                        ),
+                    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,
                     )
-                    raise
+
+                crewai_event_bus.emit(
+                    self,
+                    event=LLMCallFailedEvent(
+                        error=str(e), from_task=from_task, from_agent=from_agent
+                    ),
+                )
+                raise
 
     def _handle_emit_call_events(
         self,
@@ -1954,7 +1925,6 @@ 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

@@ -7,15 +7,11 @@ 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
 
@@ -54,32 +50,6 @@ 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.
@@ -381,7 +351,6 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
-                call_id=get_current_call_id(),
             ),
         )
 
@@ -405,7 +374,6 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
-                call_id=get_current_call_id(),
             ),
         )
 
@@ -426,7 +394,6 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 model=self.model,
-                call_id=get_current_call_id(),
             ),
         )
 
@@ -461,7 +428,6 @@ 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, llm_call_context
+from crewai.llms.base_llm import BaseLLM
 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 (
@@ -266,46 +266,35 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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,
-                )
+        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(
-                        completion_params,
-                        available_functions,
-                        from_task,
-                        from_agent,
-                        effective_response_model,
-                    )
-
-                return self._handle_completion(
+            # Handle streaming vs non-streaming
+            if self.stream:
+                return self._handle_streaming_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -313,13 +302,21 @@ class AnthropicCompletion(BaseLLM):
                     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
+            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
 
     async def acall(
         self,
@@ -345,37 +342,28 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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,
-                )
+        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(
-                        completion_params,
-                        available_functions,
-                        from_task,
-                        from_agent,
-                        effective_response_model,
-                    )
-
-                return await self._ahandle_completion(
+            if self.stream:
+                return await self._ahandle_streaming_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -383,13 +371,21 @@ class AnthropicCompletion(BaseLLM):
                     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
+            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
 
     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, llm_call_context
+    from crewai.llms.base_llm import BaseLLM
 
 except ImportError:
     raise ImportError(
@@ -293,44 +293,32 @@ class AzureCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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,
-                )
+        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
 
-                effective_response_model = response_model or self.response_format
+            # Format messages for Azure
+            formatted_messages = self._format_messages_for_azure(messages)
 
-                # Format messages for Azure
-                formatted_messages = self._format_messages_for_azure(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, tools, effective_response_model
+            )
 
-                # 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(
+            # Handle streaming vs non-streaming
+            if self.stream:
+                return self._handle_streaming_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -338,8 +326,16 @@ class AzureCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            except Exception as e:
-                return self._handle_api_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
+            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]
 
     async def acall(  # type: ignore[return]
         self,
@@ -365,35 +361,25 @@ class AzureCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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,
-                )
+        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
 
-                effective_response_model = response_model or self.response_format
+            formatted_messages = self._format_messages_for_azure(messages)
 
-                formatted_messages = self._format_messages_for_azure(messages)
+            completion_params = self._prepare_completion_params(
+                formatted_messages, tools, effective_response_model
+            )
 
-                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(
+            if self.stream:
+                return await self._ahandle_streaming_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -401,8 +387,16 @@ class AzureCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            except Exception as e:
-                self._handle_api_error(e, from_task, from_agent)
+            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)
 
     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, llm_call_context
+from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -378,90 +378,77 @@ class BedrockCompletion(BaseLLM):
         """Call AWS Bedrock Converse API."""
         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,
+        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}]),
                 )
 
-                # 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 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)),
+                    )
                 }
-
-                # Add system message if present
-                if system_message:
-                    body["system"] = cast(
-                        "list[SystemContentBlockTypeDef]",
-                        cast(object, [{"text": system_message}]),
+                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 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}),
-                        )
+            # 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 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_request_fields:
+                body["additionalModelRequestFields"] = (
+                    self.additional_model_request_fields
+                )
 
-                if self.additional_model_request_fields:
-                    body["additionalModelRequestFields"] = (
-                        self.additional_model_request_fields
-                    )
+            if self.additional_model_response_field_paths:
+                body["additionalModelResponseFieldPaths"] = (
+                    self.additional_model_response_field_paths
+                )
 
-                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(
+            if self.stream:
+                return self._handle_streaming_converse(
                     formatted_messages,
                     body,
                     available_functions,
@@ -470,17 +457,26 @@ class BedrockCompletion(BaseLLM):
                     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
+            return self._handle_converse(
+                formatted_messages,
+                body,
+                available_functions,
+                from_task,
+                from_agent,
+                effective_response_model,
+            )
 
-                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
+        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
 
     async def acall(
         self,
@@ -518,80 +514,69 @@ class BedrockCompletion(BaseLLM):
                 'Install with: uv add "crewai[bedrock-async]"'
             )
 
-        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,
+        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}]),
                 )
 
-                formatted_messages, system_message = self._format_messages_for_converse(
-                    messages
-                )
-
-                body: BedrockConverseRequestBody = {
-                    "inferenceConfig": self._get_inference_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)),
+                    )
                 }
-
-                if system_message:
-                    body["system"] = cast(
-                        "list[SystemContentBlockTypeDef]",
-                        cast(object, [{"text": system_message}]),
+                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 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.guardrail_config:
+                guardrail_config: GuardrailConfigurationTypeDef = cast(
+                    "GuardrailConfigurationTypeDef", cast(object, self.guardrail_config)
+                )
+                body["guardrailConfig"] = guardrail_config
 
-                if self.guardrail_config:
-                    guardrail_config: GuardrailConfigurationTypeDef = cast(
-                        "GuardrailConfigurationTypeDef",
-                        cast(object, self.guardrail_config),
-                    )
-                    body["guardrailConfig"] = guardrail_config
+            if self.additional_model_request_fields:
+                body["additionalModelRequestFields"] = (
+                    self.additional_model_request_fields
+                )
 
-                if self.additional_model_request_fields:
-                    body["additionalModelRequestFields"] = (
-                        self.additional_model_request_fields
-                    )
+            if self.additional_model_response_field_paths:
+                body["additionalModelResponseFieldPaths"] = (
+                    self.additional_model_response_field_paths
+                )
 
-                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(
+            if self.stream:
+                return await self._ahandle_streaming_converse(
                     formatted_messages,
                     body,
                     available_functions,
@@ -600,17 +585,26 @@ class BedrockCompletion(BaseLLM):
                     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
+            return await self._ahandle_converse(
+                formatted_messages,
+                body,
+                available_functions,
+                from_task,
+                from_agent,
+                effective_response_model,
+            )
 
-                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
+        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
 
     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, llm_call_context
+from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -293,45 +293,33 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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
+        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(
-                        formatted_content,
-                        config,
-                        available_functions,
-                        from_task,
-                        from_agent,
-                        effective_response_model,
-                    )
-
-                return self._handle_completion(
+            if self.stream:
+                return self._handle_streaming_completion(
                     formatted_content,
                     config,
                     available_functions,
@@ -340,20 +328,29 @@ class GeminiCompletion(BaseLLM):
                     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
+            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
 
     async def acall(
         self,
@@ -379,38 +376,28 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        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
+        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(
-                        formatted_content,
-                        config,
-                        available_functions,
-                        from_task,
-                        from_agent,
-                        effective_response_model,
-                    )
-
-                return await self._ahandle_completion(
+            if self.stream:
+                return await self._ahandle_streaming_completion(
                     formatted_content,
                     config,
                     available_functions,
@@ -419,20 +406,29 @@ class GeminiCompletion(BaseLLM):
                     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
+            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
 
     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, llm_call_context
+from crewai.llms.base_llm import BaseLLM
 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 (
@@ -382,35 +382,23 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        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,
-                )
+        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(
-                        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(
+            if self.api == "responses":
+                return self._call_responses(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -419,13 +407,22 @@ class OpenAICompletion(BaseLLM):
                     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
+            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
 
     def _call_completions(
         self,
@@ -482,30 +479,20 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        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,
-                )
+        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(
-                        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(
+            if self.api == "responses":
+                return await self._acall_responses(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -514,13 +501,22 @@ class OpenAICompletion(BaseLLM):
                     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
+            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
 
     async def _acall_completions(
         self,
@@ -1525,16 +1521,13 @@ class OpenAICompletion(BaseLLM):
     ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to OpenAI function calling format."""
         from crewai.llms.providers.utils.common import safe_tool_conversion
-        from crewai.utilities.pydantic_schema_utils import (
-            force_additional_properties_false,
-        )
 
         openai_tools = []
 
         for tool in tools:
             name, description, parameters = safe_tool_conversion(tool, "OpenAI")
 
-            openai_tool: dict[str, Any] = {
+            openai_tool = {
                 "type": "function",
                 "function": {
                     "name": name,
@@ -1544,11 +1537,10 @@ class OpenAICompletion(BaseLLM):
             }
 
             if parameters:
-                params_dict = (
-                    parameters if isinstance(parameters, dict) else dict(parameters)
-                )
-                params_dict = force_additional_properties_false(params_dict)
-                openai_tool["function"]["parameters"] = params_dict
+                if isinstance(parameters, dict):
+                    openai_tool["function"]["parameters"] = parameters  # type: ignore
+                else:
+                    openai_tool["function"]["parameters"] = dict(parameters)
 
             openai_tools.append(openai_tool)
         return openai_tools

lib/crewai/src/crewai/utilities/pydantic_schema_utils.py

@@ -127,36 +127,6 @@ def add_key_in_dict_recursively(
     return d
 
 
-def force_additional_properties_false(d: Any) -> Any:
-    """Force additionalProperties=false on all object-type dicts recursively.
-
-    OpenAI strict mode requires all objects to have additionalProperties=false.
-    This function overwrites any existing value to ensure compliance.
-
-    Also ensures objects have properties and required arrays, even if empty,
-    as OpenAI strict mode requires these for all object types.
-
-    Args:
-        d: The dictionary/list to modify.
-
-    Returns:
-        The modified dictionary/list.
-    """
-    if isinstance(d, dict):
-        if d.get("type") == "object":
-            d["additionalProperties"] = False
-            if "properties" not in d:
-                d["properties"] = {}
-            if "required" not in d:
-                d["required"] = []
-        for v in d.values():
-            force_additional_properties_false(v)
-    elif isinstance(d, list):
-        for i in d:
-            force_additional_properties_false(i)
-    return d
-
-
 def fix_discriminator_mappings(schema: dict[str, Any]) -> dict[str, Any]:
     """Replace '#/$defs/...' references in discriminator.mapping with just the model name.
 
@@ -308,7 +278,13 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
     """
     json_schema = model.model_json_schema(ref_template="#/$defs/{model}")
 
-    json_schema = force_additional_properties_false(json_schema)
+    json_schema = add_key_in_dict_recursively(
+        json_schema,
+        key="additionalProperties",
+        value=False,
+        criteria=lambda d: d.get("type") == "object"
+        and "additionalProperties" not in d,
+    )
 
     json_schema = resolve_refs(json_schema)
 
@@ -402,9 +378,6 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
     """
     effective_root = root_schema or json_schema
 
-    json_schema = force_additional_properties_false(json_schema)
-    effective_root = force_additional_properties_false(effective_root)
-
     if "allOf" in json_schema:
         json_schema = _merge_all_of_schemas(json_schema["allOf"], effective_root)
         if "title" not in json_schema and "title" in (root_schema or {}):

lib/crewai/tests/agents/test_async_agent_executor.py

@@ -235,13 +235,8 @@ 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,
@@ -257,13 +252,7 @@ class TestAsyncAgentExecutor:
             )
 
             async def delayed_response(*args: Any, **kwargs: Any) -> str:
-                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
+                await asyncio.sleep(0.05)
                 return f"Thought: Done\nFinal Answer: Result from executor {executor_id}"
 
             with patch(
@@ -284,15 +273,19 @@ 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 max_concurrent > 1, f"Expected concurrent execution, max concurrent was {max_concurrent}"
+        assert elapsed < 0.15, f"Expected concurrent execution, took {elapsed}s"
 
 
 class TestAsyncLLMResponseHelper:

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

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

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-      - '0'
-      x-stainless-runtime:
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-      x-stainless-runtime-version:
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-    uri: https://api.openai.com/v1/chat/completions
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lib/crewai/tests/cli/test_create_crew.py

@@ -296,23 +296,6 @@ 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

@@ -1,20 +1,10 @@
 """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_cache_file,
-    _is_cache_valid,
-    get_crewai_version,
-    get_latest_version_from_pypi,
-    is_newer_version_available,
-)
+from crewai.cli.version import get_crewai_version
 
 
-def test_dynamic_versioning_consistency() -> None:
+def test_dynamic_versioning_consistency():
     """Test that dynamic versioning provides consistent version across all access methods."""
     cli_version = get_crewai_version()
     package_version = __version__
@@ -25,186 +15,3 @@ def test_dynamic_versioning_consistency() -> None:
     # 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_flow_visualization.py

@@ -8,6 +8,7 @@ from pathlib import Path
 import pytest
 
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.human_feedback import human_feedback
 from crewai.flow.visualization import (
     build_flow_structure,
     visualize_flow_structure,
@@ -667,4 +668,180 @@ def test_no_warning_for_properly_typed_router(caplog):
     # No warnings should be logged
     warning_messages = [r.message for r in caplog.records if r.levelno >= logging.WARNING]
     assert not any("Could not determine return paths" in msg for msg in warning_messages)
-    assert not any("Found listeners waiting for triggers" in msg for msg in warning_messages)
+    assert not any("Found listeners waiting for triggers" in msg for msg in warning_messages)
+
+
+def test_human_feedback_node_metadata():
+    """Test that human feedback nodes have correct metadata."""
+    from typing import Literal
+
+    class HITLFlow(Flow):
+        """Flow with human-in-the-loop feedback."""
+
+        @start()
+        @human_feedback(
+            message="Please review the output:",
+            emit=["approved", "rejected"],
+            llm="gpt-4o-mini",
+        )
+        def review_content(self) -> Literal["approved", "rejected"]:
+            return "approved"
+
+        @listen("approved")
+        def on_approved(self):
+            return "published"
+
+        @listen("rejected")
+        def on_rejected(self):
+            return "discarded"
+
+    flow = HITLFlow()
+    structure = build_flow_structure(flow)
+
+    review_node = structure["nodes"]["review_content"]
+    assert review_node["is_human_feedback"] is True
+    assert review_node["type"] == "human_feedback"
+    assert review_node["human_feedback_message"] == "Please review the output:"
+    assert review_node["human_feedback_emit"] == ["approved", "rejected"]
+    assert review_node["human_feedback_llm"] == "gpt-4o-mini"
+
+
+def test_human_feedback_visualization_includes_hitl_data():
+    """Test that visualization includes human feedback data in HTML."""
+    from typing import Literal
+
+    class HITLFlow(Flow):
+        """Flow with human-in-the-loop feedback."""
+
+        @start()
+        @human_feedback(
+            message="Please review the output:",
+            emit=["approved", "rejected"],
+            llm="gpt-4o-mini",
+        )
+        def review_content(self) -> Literal["approved", "rejected"]:
+            return "approved"
+
+        @listen("approved")
+        def on_approved(self):
+            return "published"
+
+    flow = HITLFlow()
+    structure = build_flow_structure(flow)
+
+    html_file = visualize_flow_structure(structure, "test_hitl.html", show=False)
+    html_path = Path(html_file)
+
+    js_file = html_path.parent / f"{html_path.stem}_script.js"
+    js_content = js_file.read_text(encoding="utf-8")
+
+    assert "HITL" in js_content
+    assert "Please review the output:" in js_content
+    assert "approved" in js_content
+    assert "rejected" in js_content
+    assert "#4A90E2" in js_content
+
+
+def test_human_feedback_without_emit_metadata():
+    """Test that human feedback without emit has correct metadata."""
+
+    class HITLSimpleFlow(Flow):
+        """Flow with simple human feedback (no routing)."""
+
+        @start()
+        @human_feedback(message="Please provide feedback:")
+        def review_step(self):
+            return "content"
+
+        @listen(review_step)
+        def next_step(self):
+            return "done"
+
+    flow = HITLSimpleFlow()
+    structure = build_flow_structure(flow)
+
+    review_node = structure["nodes"]["review_step"]
+    assert review_node["is_human_feedback"] is True
+    assert "is_router" not in review_node or review_node["is_router"] is False
+    assert review_node["type"] == "start"
+    assert review_node["human_feedback_message"] == "Please provide feedback:"
+
+
+def test_human_feedback_with_default_outcome():
+    """Test that human feedback with default outcome includes it in metadata."""
+    from typing import Literal
+
+    class HITLDefaultFlow(Flow):
+        """Flow with human feedback that has a default outcome."""
+
+        @start()
+        @human_feedback(
+            message="Review this:",
+            emit=["approved", "needs_work"],
+            llm="gpt-4o-mini",
+            default_outcome="needs_work",
+        )
+        def review(self) -> Literal["approved", "needs_work"]:
+            return "approved"
+
+        @listen("approved")
+        def on_approved(self):
+            return "published"
+
+        @listen("needs_work")
+        def on_needs_work(self):
+            return "revised"
+
+    flow = HITLDefaultFlow()
+    structure = build_flow_structure(flow)
+
+    review_node = structure["nodes"]["review"]
+    assert review_node["is_human_feedback"] is True
+    assert review_node["human_feedback_default_outcome"] == "needs_work"
+
+
+def test_mixed_router_and_human_feedback():
+    """Test flow with both regular routers and human feedback routers."""
+    from typing import Literal
+
+    class MixedFlow(Flow):
+        """Flow with both regular routers and HITL."""
+
+        @start()
+        def init(self):
+            return "initialized"
+
+        @router(init)
+        def auto_decision(self) -> Literal["path_a", "path_b"]:
+            return "path_a"
+
+        @listen("path_a")
+        @human_feedback(
+            message="Review this step:",
+            emit=["continue", "stop"],
+            llm="gpt-4o-mini",
+        )
+        def human_review(self) -> Literal["continue", "stop"]:
+            return "continue"
+
+        @listen("continue")
+        def proceed(self):
+            return "done"
+
+        @listen("stop")
+        def halt(self):
+            return "halted"
+
+    flow = MixedFlow()
+    structure = build_flow_structure(flow)
+
+    auto_node = structure["nodes"]["auto_decision"]
+    assert auto_node["type"] == "router"
+    assert auto_node["is_router"] is True
+    assert "is_human_feedback" not in auto_node or auto_node["is_human_feedback"] is False
+
+    human_node = structure["nodes"]["human_review"]
+    assert human_node["type"] == "human_feedback"
+    assert human_node["is_router"] is True
+    assert human_node["is_human_feedback"] is True
+    assert human_node["human_feedback_message"] == "Review this step:"

lib/crewai/tests/test_streaming.py

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

@@ -249,8 +249,6 @@ 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,
@@ -269,8 +267,6 @@ 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", call_id="test-call-id"))
-        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2", response_id="Id", call_id="test-call-id"))
+        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"))
 
         # 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", call_id="test-call-id"))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="",response_id="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."
@@ -1280,105 +1280,6 @@ 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~=25.1.0",
+    "types-aiofiles~=24.1.0",
 ]
 
 

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 = "~=25.1.0" },
+    { name = "types-aiofiles", specifier = "~=24.1.0" },
     { 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,<3" },
+    { name = "litellm", marker = "extra == 'litellm'", specifier = "~=1.74.9" },
     { name = "mcp", specifier = "~=1.23.1" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = "~=0.1.94" },
-    { name = "openai", specifier = ">=1.83.0,<3" },
+    { name = "openai", specifier = "~=1.83.0" },
     { 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 = "25.1.0.20251011"
+version = "24.1.0.20250822"
 source = { registry = "https://pypi.org/simple" }
-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" }
+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" }
 wheels = [
-    { 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" },
+    { 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" },
 ]
 
 [[package]]