wip

- Convert enum constraints to Literal types
- Handle format constraints (date, date-time)
- Preserve original MCP tool names for server calls

.github/dependabot.yml

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

.python-version

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

lib/crewai-tools/src/crewai_tools/adapters/mcp_adapter.py

@@ -2,29 +2,95 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
 import logging
 from typing import TYPE_CHECKING, Any
 
 from crewai.tools import BaseTool
+from crewai.utilities.pydantic_schema_utils import create_model_from_schema
+from crewai.utilities.string_utils import sanitize_tool_name
+from pydantic import BaseModel
 
 from crewai_tools.adapters.tool_collection import ToolCollection
 
 
-logger = logging.getLogger(__name__)
-
 if TYPE_CHECKING:
     from mcp import StdioServerParameters
-    from mcpadapt.core import MCPAdapt
-    from mcpadapt.crewai_adapter import CrewAIAdapter
+    from mcp.types import CallToolResult, TextContent, Tool
+    from mcpadapt.core import MCPAdapt, ToolAdapter
+
+
+logger = logging.getLogger(__name__)
 
 
 try:
     from mcp import StdioServerParameters
-    from mcpadapt.core import MCPAdapt
-    from mcpadapt.crewai_adapter import CrewAIAdapter
+    from mcp.types import CallToolResult, TextContent, Tool
+    from mcpadapt.core import MCPAdapt, ToolAdapter
+
+    class CrewAIToolAdapter(ToolAdapter):
+        """Adapter that creates CrewAI tools with properly normalized JSON schemas.
+
+        This adapter bypasses mcpadapt's model creation which adds invalid null values
+        to field schemas, instead using CrewAI's own schema utilities.
+        """
+
+        def adapt(
+            self,
+            func: Callable[[dict[str, Any] | None], CallToolResult],
+            mcp_tool: Tool,
+        ) -> BaseTool:
+            """Adapt a MCP tool to a CrewAI tool.
+
+            Args:
+                func: The function to call when the tool is invoked.
+                mcp_tool: The MCP tool definition to adapt.
+
+            Returns:
+                A CrewAI BaseTool instance.
+            """
+            tool_name = sanitize_tool_name(mcp_tool.name)
+            tool_description = mcp_tool.description or ""
+            args_model = create_model_from_schema(mcp_tool.inputSchema)
+
+            class CrewAIMCPTool(BaseTool):
+                name: str = tool_name
+                description: str = tool_description
+                args_schema: type[BaseModel] = args_model
+
+                def _run(self, **kwargs: Any) -> Any:
+                    result = func(kwargs)
+                    if len(result.content) == 1:
+                        first_content = result.content[0]
+                        if isinstance(first_content, TextContent):
+                            return first_content.text
+                        return str(first_content)
+                    return str(
+                        [
+                            content.text
+                            for content in result.content
+                            if isinstance(content, TextContent)
+                        ]
+                    )
+
+                def _generate_description(self) -> None:
+                    schema = self.args_schema.model_json_schema()
+                    schema.pop("$defs", None)
+                    self.description = (
+                        f"Tool Name: {self.name}\n"
+                        f"Tool Arguments: {schema}\n"
+                        f"Tool Description: {self.description}"
+                    )
+
+            return CrewAIMCPTool()
+
+        async def async_adapt(self, afunc: Any, mcp_tool: Tool) -> Any:
+            """Async adaptation is not supported by CrewAI."""
+            raise NotImplementedError("async is not supported by the CrewAI framework.")
 
     MCP_AVAILABLE = True
-except ImportError:
+except ImportError as e:
+    logger.debug(f"MCP packages not available: {e}")
     MCP_AVAILABLE = False
 
 
@@ -34,9 +100,6 @@ class MCPServerAdapter:
     Note: tools can only be accessed after the server has been started with the
         `start()` method.
 
-    Attributes:
-        tools: The CrewAI tools available from the MCP server.
-
     Usage:
         # context manager + stdio
         with MCPServerAdapter(...) as tools:
@@ -89,7 +152,9 @@ class MCPServerAdapter:
         super().__init__()
         self._adapter = None
         self._tools = None
-        self._tool_names = list(tool_names) if tool_names else None
+        self._tool_names = (
+            [sanitize_tool_name(name) for name in tool_names] if tool_names else None
+        )
 
         if not MCP_AVAILABLE:
             import click
@@ -100,7 +165,7 @@ class MCPServerAdapter:
                 import subprocess
 
                 try:
-                    subprocess.run(["uv", "add", "mcp crewai-tools[mcp]"], check=True)  # noqa: S607
+                    subprocess.run(["uv", "add", "mcp crewai-tools'[mcp]'"], check=True)  # noqa: S607
 
                 except subprocess.CalledProcessError as e:
                     raise ImportError("Failed to install mcp package") from e
@@ -112,7 +177,7 @@ class MCPServerAdapter:
         try:
             self._serverparams = serverparams
             self._adapter = MCPAdapt(
-                self._serverparams, CrewAIAdapter(), connect_timeout
+                self._serverparams, CrewAIToolAdapter(), connect_timeout
             )
             self.start()
 
@@ -124,13 +189,13 @@ class MCPServerAdapter:
                     logger.error(f"Error during stop cleanup: {stop_e}")
             raise RuntimeError(f"Failed to initialize MCP Adapter: {e}") from e
 
-    def start(self):
+    def start(self) -> None:
         """Start the MCP server and initialize the tools."""
-        self._tools = self._adapter.__enter__()
+        self._tools = self._adapter.__enter__()  # type: ignore[union-attr]
 
-    def stop(self):
+    def stop(self) -> None:
         """Stop the MCP server."""
-        self._adapter.__exit__(None, None, None)
+        self._adapter.__exit__(None, None, None)  # type: ignore[union-attr]
 
     @property
     def tools(self) -> ToolCollection[BaseTool]:
@@ -152,12 +217,19 @@ class MCPServerAdapter:
             return tools_collection.filter_by_names(self._tool_names)
         return tools_collection
 
-    def __enter__(self):
-        """Enter the context manager. Note that `__init__()` already starts the MCP server.
-        So tools should already be available.
+    def __enter__(self) -> ToolCollection[BaseTool]:
+        """Enter the context manager.
+
+        Note that `__init__()` already starts the MCP server,
+        so tools should already be available.
         """
         return self.tools
 
-    def __exit__(self, exc_type, exc_value, traceback):
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: Any,
+    ) -> None:
         """Exit the context manager."""
-        return self._adapter.__exit__(exc_type, exc_value, traceback)
+        self._adapter.__exit__(exc_type, exc_value, traceback)  # type: ignore[union-attr]

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

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

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

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

lib/crewai-tools/tests/test_generate_tool_specs.py

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

lib/crewai/pyproject.toml

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

lib/crewai/src/crewai/agent/core.py

@@ -24,6 +24,7 @@ from pydantic import (
 )
 from typing_extensions import Self
 
+from crewai.agent.json_schema_converter import JSONSchemaConverter
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -118,6 +119,8 @@ MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30
 MCP_DISCOVERY_TIMEOUT: Final[int] = 15
 MCP_MAX_RETRIES: Final[int] = 3
 
+_passthrough_exceptions: tuple[type[Exception], ...] = ()
+
 # Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
 _mcp_schema_cache: dict[str, Any] = {}
 _cache_ttl: Final[int] = 300  # 5 minutes
@@ -479,6 +482,8 @@ class Agent(BaseAgent):
                     ),
                 )
                 raise e
+            if isinstance(e, _passthrough_exceptions):
+                raise
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 crewai_event_bus.emit(
@@ -711,6 +716,8 @@ class Agent(BaseAgent):
                     ),
                 )
                 raise e
+            if isinstance(e, _passthrough_exceptions):
+                raise
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 crewai_event_bus.emit(
@@ -1172,6 +1179,7 @@ class Agent(BaseAgent):
             tools = []
             for tool_def in tools_list:
                 tool_name = tool_def.get("name", "")
+                original_tool_name = tool_def.get("original_name", tool_name)
                 if not tool_name:
                     continue
 
@@ -1193,6 +1201,7 @@ class Agent(BaseAgent):
                         tool_name=tool_name,
                         tool_schema=tool_schema,
                         server_name=server_name,
+                        original_tool_name=original_tool_name,
                     )
                     tools.append(native_tool)
                 except Exception as e:
@@ -1207,26 +1216,63 @@ class Agent(BaseAgent):
             raise RuntimeError(f"Failed to get native MCP tools: {e}") from e
 
     def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]:
-        """Get tools from CrewAI AMP MCP marketplace."""
-        # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name"
+        """Get tools from CrewAI AMP MCP via crewai-oauth service.
+
+        Fetches MCP server configuration with tokens injected from crewai-oauth,
+        then uses _get_native_mcp_tools to connect and discover tools.
+        """
+        # Parse: "crewai-amp:mcp-slug" or "crewai-amp:mcp-slug#tool_name"
         amp_part = amp_ref.replace("crewai-amp:", "")
         if "#" in amp_part:
-            mcp_name, specific_tool = amp_part.split("#", 1)
+            mcp_slug, specific_tool = amp_part.split("#", 1)
         else:
-            mcp_name, specific_tool = amp_part, None
+            mcp_slug, specific_tool = amp_part, None
 
-        # Call AMP API to get MCP server URLs
-        mcp_servers = self._fetch_amp_mcp_servers(mcp_name)
+        # Fetch MCP config from crewai-oauth (with tokens injected)
+        mcp_config_dict = self._fetch_amp_mcp_config(mcp_slug)
 
-        tools = []
-        for server_config in mcp_servers:
-            server_ref = server_config["url"]
-            if specific_tool:
-                server_ref += f"#{specific_tool}"
-            server_tools = self._get_external_mcp_tools(server_ref)
-            tools.extend(server_tools)
+        if not mcp_config_dict:
+            self._logger.log(
+                "warning", f"Failed to fetch MCP config for '{mcp_slug}' from crewai-oauth"
+            )
+            return []
 
-        return tools
+        # Convert dict to MCPServerConfig (MCPServerHTTP or MCPServerSSE)
+        config_type = mcp_config_dict.get("type", "http")
+
+        if config_type == "sse":
+            mcp_config = MCPServerSSE(
+                url=mcp_config_dict["url"],
+                headers=mcp_config_dict.get("headers"),
+                cache_tools_list=mcp_config_dict.get("cache_tools_list", False),
+            )
+        else:
+            mcp_config = MCPServerHTTP(
+                url=mcp_config_dict["url"],
+                headers=mcp_config_dict.get("headers"),
+                streamable=mcp_config_dict.get("streamable", True),
+                cache_tools_list=mcp_config_dict.get("cache_tools_list", False),
+            )
+
+        # Apply tool filter if specific tool requested
+        if specific_tool:
+            from crewai.mcp.filters import create_static_tool_filter
+
+            mcp_config.tool_filter = create_static_tool_filter(
+                allowed_tool_names=[specific_tool]
+            )
+
+        # Use native MCP tools to connect and discover tools
+        try:
+            tools, client = self._get_native_mcp_tools(mcp_config)
+            if client:
+                self._mcp_clients.append(client)
+            return tools
+        except Exception as e:
+            self._logger.log(
+                "warning", f"Failed to get MCP tools from '{mcp_slug}': {e}"
+            )
+            return []
 
     @staticmethod
     def _extract_server_name(server_url: str) -> str:
@@ -1383,6 +1429,9 @@ class Agent(BaseAgent):
                     }
                 return schemas
 
+    # Shared JSON Schema converter instance
+    _schema_converter: JSONSchemaConverter = JSONSchemaConverter()
+
     def _json_schema_to_pydantic(
         self, tool_name: str, json_schema: dict[str, Any]
     ) -> type:
@@ -1395,77 +1444,62 @@ class Agent(BaseAgent):
         Returns:
             Pydantic BaseModel class
         """
-        from pydantic import Field, create_model
+        return self._schema_converter.json_schema_to_pydantic(tool_name, json_schema)
 
-        properties = json_schema.get("properties", {})
-        required_fields = json_schema.get("required", [])
+    def _fetch_amp_mcp_config(self, mcp_slug: str) -> dict[str, Any] | None:
+        """Fetch MCP server configuration from crewai-oauth service.
 
-        field_definitions: dict[str, Any] = {}
+        Returns MCPServerConfig dict with tokens injected, ready for use with
+        _get_native_mcp_tools.
 
-        for field_name, field_schema in properties.items():
-            field_type = self._json_type_to_python(field_schema)
-            field_description = field_schema.get("description", "")
-
-            is_required = field_name in required_fields
-
-            if is_required:
-                field_definitions[field_name] = (
-                    field_type,
-                    Field(..., description=field_description),
-                )
-            else:
-                field_definitions[field_name] = (
-                    field_type | None,
-                    Field(default=None, description=field_description),
-                )
-
-        model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return]
-
-    def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
-        """Convert JSON Schema type to Python type.
+        Environment variables:
+            CREWAI_OAUTH_URL: Base URL of crewai-oauth service
+            CREWAI_OAUTH_API_KEY: API key for authenticating with crewai-oauth
 
         Args:
-            field_schema: JSON Schema field definition
+            mcp_slug: The MCP server slug (e.g., "notion-mcp-abc123")
 
         Returns:
-            Python type
+            Dict with type, url, headers, streamable, cache_tools_list, or None if failed.
         """
+        import os
 
-        json_type = field_schema.get("type")
+        import requests
 
-        if "anyOf" in field_schema:
-            types: list[type] = []
-            for option in field_schema["anyOf"]:
-                if "const" in option:
-                    types.append(str)
-                else:
-                    types.append(self._json_type_to_python(option))
-            unique_types = list(set(types))
-            if len(unique_types) > 1:
-                result: Any = unique_types[0]
-                for t in unique_types[1:]:
-                    result = result | t
-                return result  # type: ignore[no-any-return]
-            return unique_types[0]
+        try:
+            endpoint = f"http://localhost:8787/mcps/{mcp_slug}/config"
+            response = requests.get(
+                endpoint,
+                headers={"Authorization": "Bearer 6b327f9ebe62726590f8de8f624cf018ad4765fecb7373f9db475a940ad546d0"},
+                timeout=30,
+            )
 
-        type_mapping: dict[str | None, type] = {
-            "string": str,
-            "number": float,
-            "integer": int,
-            "boolean": bool,
-            "array": list,
-            "object": dict,
-        }
+            if response.status_code == 200:
+                return response.json()
+            elif response.status_code == 400:
+                error_data = response.json()
+                self._logger.log(
+                    "warning",
+                    f"MCP '{mcp_slug}' is not connected: {error_data.get('error_description', 'Unknown error')}",
+                )
+                return None
+            elif response.status_code == 404:
+                self._logger.log(
+                    "warning", f"MCP server '{mcp_slug}' not found in crewai-oauth"
+                )
+                return None
+            else:
+                self._logger.log(
+                    "warning",
+                    f"Failed to fetch MCP config from crewai-oauth: HTTP {response.status_code}",
+                )
+                return None
 
-        return type_mapping.get(json_type, Any)
-
-    @staticmethod
-    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
-        """Fetch MCP server configurations from CrewAI AMP API."""
-        # TODO: Implement AMP API call to "integrations/mcps" endpoint
-        # Should return list of server configs with URLs
-        return []
+        except requests.exceptions.RequestException as e:
+            self._logger.log(
+                "warning", f"Failed to connect to crewai-oauth: {e}"
+            )
+            return None
 
     @staticmethod
     def get_multimodal_tools() -> Sequence[BaseTool]:

lib/crewai/src/crewai/agent/json_schema_converter.py

@@ -0,0 +1,399 @@
+from typing import Any, Literal, Type, Union, get_args
+from pydantic import Field, create_model
+from pydantic.fields import FieldInfo
+import datetime
+import uuid
+
+
+class JSONSchemaConverter:
+    """Converts JSON Schema definitions to Python/Pydantic types."""
+
+    def json_schema_to_pydantic(
+        self, tool_name: str, json_schema: dict[str, Any]
+    ) -> Type[Any]:
+        """Convert JSON Schema to Pydantic model for tool arguments.
+
+        Args:
+            tool_name: Name of the tool (used for model naming)
+            json_schema: JSON Schema dict with 'properties', 'required', etc.
+
+        Returns:
+            Pydantic BaseModel class
+        """
+        properties = json_schema.get("properties", {})
+        required_fields = json_schema.get("required", [])
+
+        model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
+        return self._create_pydantic_model(model_name, properties, required_fields)
+
+    def _json_type_to_python(
+        self, field_schema: dict[str, Any], field_name: str = "Field"
+    ) -> Type[Any]:
+        """Convert JSON Schema type to Python type, handling nested structures.
+
+        Args:
+            field_schema: JSON Schema field definition
+            field_name: Name of the field (used for nested model naming)
+
+        Returns:
+            Python type (may be a dynamically created Pydantic model for objects/arrays)
+        """
+        if not field_schema:
+            return Any
+
+        # Handle $ref if needed
+        if "$ref" in field_schema:
+            # You might want to implement reference resolution here
+            return Any
+
+        # Handle enum constraint - create Literal type
+        if "enum" in field_schema:
+            return self._handle_enum(field_schema)
+
+        # Handle different schema constructs in order of precedence
+        if "allOf" in field_schema:
+            return self._handle_allof(field_schema, field_name)
+
+        if "anyOf" in field_schema or "oneOf" in field_schema:
+            return self._handle_union_schemas(field_schema, field_name)
+
+        json_type = field_schema.get("type")
+
+        if isinstance(json_type, list):
+            return self._handle_type_union(json_type)
+
+        if json_type == "array":
+            return self._handle_array_type(field_schema, field_name)
+
+        if json_type == "object":
+            return self._handle_object_type(field_schema, field_name)
+
+        # Handle format for string types
+        if json_type == "string" and "format" in field_schema:
+            return self._get_formatted_type(field_schema["format"])
+
+        return self._get_simple_type(json_type)
+
+    def _get_formatted_type(self, format_type: str) -> Type[Any]:
+        """Get Python type for JSON Schema format constraint.
+
+        Args:
+            format_type: JSON Schema format string (date, date-time, email, etc.)
+
+        Returns:
+            Appropriate Python type for the format
+        """
+        format_mapping: dict[str, Type[Any]] = {
+            "date": datetime.date,
+            "date-time": datetime.datetime,
+            "time": datetime.time,
+            "email": str,  # Could use EmailStr from pydantic
+            "uri": str,
+            "uuid": str,  # Could use UUID
+            "hostname": str,
+            "ipv4": str,
+            "ipv6": str,
+        }
+        return format_mapping.get(format_type, str)
+
+    def _handle_enum(self, field_schema: dict[str, Any]) -> Type[Any]:
+        """Handle enum constraint by creating a Literal type.
+
+        Args:
+            field_schema: Schema containing enum values
+
+        Returns:
+            Literal type with enum values
+        """
+        enum_values = field_schema.get("enum", [])
+
+        if not enum_values:
+            return str
+
+        # Filter out None values for the Literal type
+        non_null_values = [v for v in enum_values if v is not None]
+
+        if not non_null_values:
+            return type(None)
+
+        # Create Literal type with enum values
+        # For strings, create Literal["value1", "value2", ...]
+        if all(isinstance(v, str) for v in non_null_values):
+            literal_type = Literal[tuple(non_null_values)]  # type: ignore[valid-type]
+            # If null is in enum, make it optional
+            if None in enum_values:
+                return literal_type | None  # type: ignore[return-value]
+            return literal_type  # type: ignore[return-value]
+
+        # For mixed types or non-strings, fall back to the base type
+        json_type = field_schema.get("type", "string")
+        return self._get_simple_type(json_type)
+
+    def _handle_allof(
+        self, field_schema: dict[str, Any], field_name: str
+    ) -> Type[Any]:
+        """Handle allOf schema composition by merging all schemas.
+
+        Args:
+            field_schema: Schema containing allOf
+            field_name: Name for the generated model
+
+        Returns:
+            Merged Pydantic model or basic type
+        """
+        merged_properties: dict[str, Any] = {}
+        merged_required: list[str] = []
+        found_type: str | None = None
+
+        for sub_schema in field_schema["allOf"]:
+            # Collect type information
+            if sub_schema.get("type"):
+                found_type = sub_schema.get("type")
+
+            # Merge properties
+            if sub_schema.get("properties"):
+                merged_properties.update(sub_schema["properties"])
+
+            # Merge required fields
+            if sub_schema.get("required"):
+                merged_required.extend(sub_schema["required"])
+
+            # Handle nested anyOf/oneOf - merge properties from all variants
+            for union_key in ("anyOf", "oneOf"):
+                if union_key in sub_schema:
+                    for variant in sub_schema[union_key]:
+                        if variant.get("properties"):
+                            # Merge variant properties (will be optional)
+                            for prop_name, prop_schema in variant["properties"].items():
+                                if prop_name not in merged_properties:
+                                    merged_properties[prop_name] = prop_schema
+
+        # If we found properties, create a merged object model
+        if merged_properties:
+            return self._create_pydantic_model(
+                field_name, merged_properties, merged_required
+            )
+
+        # Fallback: return the found type or dict
+        if found_type == "object":
+            return dict
+        elif found_type == "array":
+            return list
+        return dict  # Default for complex allOf
+
+    def _handle_union_schemas(
+        self, field_schema: dict[str, Any], field_name: str
+    ) -> Type[Any]:
+        """Handle anyOf/oneOf union schemas.
+
+        Args:
+            field_schema: Schema containing anyOf or oneOf
+            field_name: Name for nested types
+
+        Returns:
+            Union type combining all options
+        """
+        key = "anyOf" if "anyOf" in field_schema else "oneOf"
+        types: list[Type[Any]] = []
+
+        for option in field_schema[key]:
+            if "const" in option:
+                # For const values, use string type
+                # Could use Literal[option["const"]] for more precision
+                types.append(str)
+            else:
+                types.append(self._json_type_to_python(option, field_name))
+
+        return self._build_union_type(types)
+
+    def _handle_type_union(self, json_types: list[str]) -> Type[Any]:
+        """Handle union types from type arrays.
+
+        Args:
+            json_types: List of JSON Schema type strings
+
+        Returns:
+            Union of corresponding Python types
+        """
+        type_mapping: dict[str, Type[Any]] = {
+            "string": str,
+            "number": float,
+            "integer": int,
+            "boolean": bool,
+            "null": type(None),
+            "array": list,
+            "object": dict,
+        }
+
+        types = [type_mapping.get(t, Any) for t in json_types]
+        return self._build_union_type(types)
+
+    def _handle_array_type(
+        self, field_schema: dict[str, Any], field_name: str
+    ) -> Type[Any]:
+        """Handle array type with typed items.
+
+        Args:
+            field_schema: Schema with type="array"
+            field_name: Name for item types
+
+        Returns:
+            list or list[ItemType]
+        """
+        items_schema = field_schema.get("items")
+        if items_schema:
+            item_type = self._json_type_to_python(items_schema, f"{field_name}Item")
+            return list[item_type]  # type: ignore[valid-type]
+        return list
+
+    def _handle_object_type(
+        self, field_schema: dict[str, Any], field_name: str
+    ) -> Type[Any]:
+        """Handle object type with properties.
+
+        Args:
+            field_schema: Schema with type="object"
+            field_name: Name for the generated model
+
+        Returns:
+            Pydantic model or dict
+        """
+        properties = field_schema.get("properties")
+        if properties:
+            required_fields = field_schema.get("required", [])
+            return self._create_pydantic_model(field_name, properties, required_fields)
+
+        # Object without properties (e.g., additionalProperties only)
+        return dict
+
+    def _create_pydantic_model(
+        self,
+        field_name: str,
+        properties: dict[str, Any],
+        required_fields: list[str],
+    ) -> Type[Any]:
+        """Create a Pydantic model from properties.
+
+        Args:
+            field_name: Base name for the model
+            properties: Property schemas
+            required_fields: List of required property names
+
+        Returns:
+            Dynamically created Pydantic model
+        """
+        model_name = f"Generated_{field_name}_{uuid.uuid4().hex[:8]}"
+        field_definitions: dict[str, Any] = {}
+
+        for prop_name, prop_schema in properties.items():
+            prop_type = self._json_type_to_python(prop_schema, prop_name.title())
+            prop_description = self._build_field_description(prop_schema)
+            is_required = prop_name in required_fields
+
+            if is_required:
+                field_definitions[prop_name] = (
+                    prop_type,
+                    Field(..., description=prop_description),
+                )
+            else:
+                field_definitions[prop_name] = (
+                    prop_type | None,
+                    Field(default=None, description=prop_description),
+                )
+
+        return create_model(model_name, **field_definitions)  # type: ignore[return-value]
+
+    def _build_field_description(self, prop_schema: dict[str, Any]) -> str:
+        """Build a comprehensive field description including constraints.
+
+        Args:
+            prop_schema: Property schema with description and constraints
+
+        Returns:
+            Enhanced description with format, enum, and other constraints
+        """
+        parts: list[str] = []
+
+        # Start with the original description
+        description = prop_schema.get("description", "")
+        if description:
+            parts.append(description)
+
+        # Add format constraint
+        format_type = prop_schema.get("format")
+        if format_type:
+            parts.append(f"Format: {format_type}")
+
+        # Add enum constraint (if not already handled by Literal type)
+        enum_values = prop_schema.get("enum")
+        if enum_values:
+            enum_str = ", ".join(repr(v) for v in enum_values)
+            parts.append(f"Allowed values: [{enum_str}]")
+
+        # Add pattern constraint
+        pattern = prop_schema.get("pattern")
+        if pattern:
+            parts.append(f"Pattern: {pattern}")
+
+        # Add min/max constraints
+        minimum = prop_schema.get("minimum")
+        maximum = prop_schema.get("maximum")
+        if minimum is not None:
+            parts.append(f"Minimum: {minimum}")
+        if maximum is not None:
+            parts.append(f"Maximum: {maximum}")
+
+        min_length = prop_schema.get("minLength")
+        max_length = prop_schema.get("maxLength")
+        if min_length is not None:
+            parts.append(f"Min length: {min_length}")
+        if max_length is not None:
+            parts.append(f"Max length: {max_length}")
+
+        # Add examples if available
+        examples = prop_schema.get("examples")
+        if examples:
+            examples_str = ", ".join(repr(e) for e in examples[:3])  # Limit to 3
+            parts.append(f"Examples: {examples_str}")
+
+        return ". ".join(parts) if parts else ""
+
+    def _get_simple_type(self, json_type: str | None) -> Type[Any]:
+        """Map simple JSON Schema types to Python types.
+
+        Args:
+            json_type: JSON Schema type string
+
+        Returns:
+            Corresponding Python type
+        """
+        simple_type_mapping: dict[str | None, Type[Any]] = {
+            "string": str,
+            "number": float,
+            "integer": int,
+            "boolean": bool,
+            "null": type(None),
+        }
+
+        return simple_type_mapping.get(json_type, Any)
+
+    def _build_union_type(self, types: list[Type[Any]]) -> Type[Any]:
+        """Build a union type from a list of types.
+
+        Args:
+            types: List of Python types to combine
+
+        Returns:
+            Union type or single type if only one unique type
+        """
+        # Remove duplicates while preserving order
+        unique_types = list(dict.fromkeys(types))
+
+        if len(unique_types) == 1:
+            return unique_types[0]
+
+        # Build union using | operator
+        result = unique_types[0]
+        for t in unique_types[1:]:
+            result = result | t
+        return result  # type: ignore[no-any-return]

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

@@ -16,6 +16,7 @@ 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
 
 
@@ -135,7 +136,9 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
         for tool in tools:
             schema: dict[str, Any] = tool.args_schema.model_json_schema()
 
-            schema.update({"additionalProperties": False, "type": "object"})
+            schema = force_additional_properties_false(schema)
+
+            schema.update({"type": "object"})
 
             openai_tool: OpenAIFunctionTool = cast(
                 OpenAIFunctionTool,

lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -4,7 +4,6 @@ import time
 from typing import TYPE_CHECKING
 
 from crewai.agents.parser import AgentFinish
-from crewai.events.event_listener import event_listener
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.utilities.converter import ConverterError
@@ -138,52 +137,3 @@ class CrewAgentExecutorMixin:
                     content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
                     color="bold_yellow",
                 )
-
-    def _ask_human_input(self, final_answer: str) -> str:
-        """Prompt human input with mode-appropriate messaging.
-
-        Note: The final answer is already displayed via the AgentLogsExecutionEvent
-        panel, so we only show the feedback prompt here.
-        """
-        from rich.panel import Panel
-        from rich.text import Text
-
-        formatter = event_listener.formatter
-        formatter.pause_live_updates()
-
-        try:
-            # Training mode prompt (single iteration)
-            if self.crew and getattr(self.crew, "_train", False):
-                prompt_text = (
-                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
-                    "This will be used to train better versions of the agent.\n"
-                    "Please provide detailed feedback about the result quality and reasoning process."
-                )
-                title = "🎓 Training Feedback Required"
-            # Regular human-in-the-loop prompt (multiple iterations)
-            else:
-                prompt_text = (
-                    "Provide feedback on the Final Result above.\n\n"
-                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
-                    "• Otherwise, provide specific improvement requests.\n"
-                    "• You can provide multiple rounds of feedback until satisfied."
-                )
-                title = "💬 Human Feedback Required"
-
-            content = Text()
-            content.append(prompt_text, style="yellow")
-
-            prompt_panel = Panel(
-                content,
-                title=title,
-                border_style="yellow",
-                padding=(1, 2),
-            )
-            formatter.console.print(prompt_panel)
-
-            response = input()
-            if response.strip() != "":
-                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
-            return response
-        finally:
-            formatter.resume_live_updates()

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

@@ -19,6 +19,7 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
+from crewai.core.providers.human_input import ExecutorContext, get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
@@ -175,15 +176,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         return self.llm.supports_stop_words() if self.llm else False
 
-    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
-        """Execute the agent with given inputs.
+    def _setup_messages(self, inputs: dict[str, Any]) -> None:
+        """Set up messages for the agent execution.
 
         Args:
             inputs: Input dictionary containing prompt variables.
-
-        Returns:
-            Dictionary with agent output.
         """
+        provider = get_provider()
+        if provider.setup_messages(cast(ExecutorContext, cast(object, self))):
+            return
+
         if "system" in self.prompt:
             system_prompt = self._format_prompt(
                 cast(str, self.prompt.get("system", "")), inputs
@@ -197,6 +199,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
             self.messages.append(format_message_for_llm(user_prompt))
 
+        provider.post_setup_messages(cast(ExecutorContext, cast(object, self)))
+
+    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
+        """Execute the agent with given inputs.
+
+        Args:
+            inputs: Input dictionary containing prompt variables.
+
+        Returns:
+            Dictionary with agent output.
+        """
+        self._setup_messages(inputs)
+
         self._inject_multimodal_files(inputs)
 
         self._show_start_logs()
@@ -970,18 +985,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Dictionary with agent output.
         """
-        if "system" in self.prompt:
-            system_prompt = self._format_prompt(
-                cast(str, self.prompt.get("system", "")), inputs
-            )
-            user_prompt = self._format_prompt(
-                cast(str, self.prompt.get("user", "")), inputs
-            )
-            self.messages.append(format_message_for_llm(system_prompt, role="system"))
-            self.messages.append(format_message_for_llm(user_prompt))
-        else:
-            user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
-            self.messages.append(format_message_for_llm(user_prompt))
+        self._setup_messages(inputs)
 
         await self._ainject_multimodal_files(inputs)
 
@@ -1491,7 +1495,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return prompt.replace("{tools}", inputs["tools"])
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """Process human feedback.
+        """Process human feedback via the configured provider.
 
         Args:
             formatted_answer: Initial agent result.
@@ -1499,17 +1503,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Final answer after feedback.
         """
-        output_str = (
-            formatted_answer.output
-            if isinstance(formatted_answer.output, str)
-            else formatted_answer.output.model_dump_json()
-        )
-        human_feedback = self._ask_human_input(output_str)
-
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
-
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        provider = get_provider()
+        return provider.handle_feedback(formatted_answer, self)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1519,74 +1514,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         return bool(self.crew and self.crew._train)
 
-    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
-    ) -> AgentFinish:
-        """Process training feedback.
+    def _format_feedback_message(self, feedback: str) -> LLMMessage:
+        """Format feedback as a message for the LLM.
 
         Args:
-            initial_answer: Initial agent output.
-            feedback: Training feedback.
+            feedback: User feedback string.
 
         Returns:
-            Improved answer.
+            Formatted message dict.
         """
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
+        return format_message_for_llm(
+            self._i18n.slice("feedback_instructions").format(feedback=feedback)
         )
-        improved_answer = self._invoke_loop()
-        self._handle_crew_training_output(improved_answer)
-        self.ask_for_human_input = False
-        return improved_answer
-
-    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
-    ) -> AgentFinish:
-        """Process regular feedback iteratively.
-
-        Args:
-            current_answer: Current agent output.
-            initial_feedback: Initial user feedback.
-
-        Returns:
-            Final answer after iterations.
-        """
-        feedback = initial_feedback
-        answer = current_answer
-
-        while self.ask_for_human_input:
-            # If the user provides a blank response, assume they are happy with the result
-            if feedback.strip() == "":
-                self.ask_for_human_input = False
-            else:
-                answer = self._process_feedback_iteration(feedback)
-                output_str = (
-                    answer.output
-                    if isinstance(answer.output, str)
-                    else answer.output.model_dump_json()
-                )
-                feedback = self._ask_human_input(output_str)
-
-        return answer
-
-    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process single feedback iteration.
-
-        Args:
-            feedback: User feedback.
-
-        Returns:
-            Updated agent response.
-        """
-        self.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        return self._invoke_loop()
 
     @classmethod
     def __get_pydantic_core_schema__(

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

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

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

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

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

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

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

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

lib/crewai/src/crewai/core/__init__.py

@@ -0,0 +1 @@
+"""Core crewAI components and interfaces."""

lib/crewai/src/crewai/core/providers/__init__.py

@@ -0,0 +1 @@
+"""Provider interfaces for extensible crewAI components."""

lib/crewai/src/crewai/core/providers/content_processor.py

@@ -0,0 +1,78 @@
+"""Content processor provider for extensible content processing."""
+
+from contextvars import ContextVar
+from typing import Any, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class ContentProcessorProvider(Protocol):
+    """Protocol for content processing during task execution."""
+
+    def process(self, content: str, context: dict[str, Any] | None = None) -> str:
+        """Process content before use.
+
+        Args:
+            content: The content to process.
+            context: Optional context information.
+
+        Returns:
+            The processed content.
+        """
+        ...
+
+
+class NoOpContentProcessor:
+    """Default processor that returns content unchanged."""
+
+    def process(self, content: str, context: dict[str, Any] | None = None) -> str:
+        """Return content unchanged.
+
+        Args:
+            content: The content to process.
+            context: Optional context information (unused).
+
+        Returns:
+            The original content unchanged.
+        """
+        return content
+
+
+_content_processor: ContextVar[ContentProcessorProvider | None] = ContextVar(
+    "_content_processor", default=None
+)
+
+_default_processor = NoOpContentProcessor()
+
+
+def get_processor() -> ContentProcessorProvider:
+    """Get the current content processor.
+
+    Returns:
+        The registered content processor or the default no-op processor.
+    """
+    processor = _content_processor.get()
+    if processor is not None:
+        return processor
+    return _default_processor
+
+
+def set_processor(processor: ContentProcessorProvider) -> None:
+    """Set the content processor for the current context.
+
+    Args:
+        processor: The content processor to use.
+    """
+    _content_processor.set(processor)
+
+
+def process_content(content: str, context: dict[str, Any] | None = None) -> str:
+    """Process content using the registered processor.
+
+    Args:
+        content: The content to process.
+        context: Optional context information.
+
+    Returns:
+        The processed content.
+    """
+    return get_processor().process(content, context)

lib/crewai/src/crewai/core/providers/human_input.py

@@ -0,0 +1,304 @@
+"""Human input provider for HITL (Human-in-the-Loop) flows."""
+
+from __future__ import annotations
+
+from contextvars import ContextVar, Token
+from typing import TYPE_CHECKING, Protocol, runtime_checkable
+
+
+if TYPE_CHECKING:
+    from crewai.agent.core import Agent
+    from crewai.agents.parser import AgentFinish
+    from crewai.crew import Crew
+    from crewai.llms.base_llm import BaseLLM
+    from crewai.task import Task
+    from crewai.utilities.types import LLMMessage
+
+
+class ExecutorContext(Protocol):
+    """Context interface for human input providers to interact with executor."""
+
+    task: Task | None
+    crew: Crew | None
+    messages: list[LLMMessage]
+    ask_for_human_input: bool
+    llm: BaseLLM
+    agent: Agent
+
+    def _invoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop and return the result."""
+        ...
+
+    def _is_training_mode(self) -> bool:
+        """Check if training mode is active."""
+        ...
+
+    def _handle_crew_training_output(
+        self,
+        result: AgentFinish,
+        human_feedback: str | None = None,
+    ) -> None:
+        """Handle training output."""
+        ...
+
+    def _format_feedback_message(self, feedback: str) -> LLMMessage:
+        """Format feedback as a message."""
+        ...
+
+
+@runtime_checkable
+class HumanInputProvider(Protocol):
+    """Protocol for human input handling.
+
+    Implementations handle the full feedback flow:
+    - Sync: prompt user, loop until satisfied
+    - Async: raise exception for external handling
+    """
+
+    def setup_messages(self, context: ExecutorContext) -> bool:
+        """Set up messages for execution.
+
+        Called before standard message setup. Allows providers to handle
+        conversation resumption or other custom message initialization.
+
+        Args:
+            context: Executor context with messages list to modify.
+
+        Returns:
+            True if messages were set up (skip standard setup),
+            False to use standard setup.
+        """
+        ...
+
+    def post_setup_messages(self, context: ExecutorContext) -> None:
+        """Called after standard message setup.
+
+        Allows providers to modify messages after standard setup completes.
+        Only called when setup_messages returned False.
+
+        Args:
+            context: Executor context with messages list to modify.
+        """
+        ...
+
+    def handle_feedback(
+        self,
+        formatted_answer: AgentFinish,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Handle the full human feedback flow.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+
+        Raises:
+            Exception: Async implementations may raise to signal external handling.
+        """
+        ...
+
+    @staticmethod
+    def _get_output_string(answer: AgentFinish) -> str:
+        """Extract output string from answer.
+
+        Args:
+            answer: The agent's finished answer.
+
+        Returns:
+            String representation of the output.
+        """
+        if isinstance(answer.output, str):
+            return answer.output
+        return answer.output.model_dump_json()
+
+
+class SyncHumanInputProvider(HumanInputProvider):
+    """Default synchronous human input via terminal."""
+
+    def setup_messages(self, context: ExecutorContext) -> bool:
+        """Use standard message setup.
+
+        Args:
+            context: Executor context (unused).
+
+        Returns:
+            False to use standard setup.
+        """
+        return False
+
+    def post_setup_messages(self, context: ExecutorContext) -> None:
+        """No-op for sync provider.
+
+        Args:
+            context: Executor context (unused).
+        """
+
+    def handle_feedback(
+        self,
+        formatted_answer: AgentFinish,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Handle feedback synchronously with terminal prompts.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        feedback = self._prompt_input(context.crew)
+
+        if context._is_training_mode():
+            return self._handle_training_feedback(formatted_answer, feedback, context)
+
+        return self._handle_regular_feedback(formatted_answer, feedback, context)
+
+    @staticmethod
+    def _handle_training_feedback(
+        initial_answer: AgentFinish,
+        feedback: str,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Process training feedback (single iteration).
+
+        Args:
+            initial_answer: The agent's initial answer.
+            feedback: Human feedback string.
+            context: Executor context for callbacks.
+
+        Returns:
+            Improved answer after processing feedback.
+        """
+        context._handle_crew_training_output(initial_answer, feedback)
+        context.messages.append(context._format_feedback_message(feedback))
+        improved_answer = context._invoke_loop()
+        context._handle_crew_training_output(improved_answer)
+        context.ask_for_human_input = False
+        return improved_answer
+
+    def _handle_regular_feedback(
+        self,
+        current_answer: AgentFinish,
+        initial_feedback: str,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Process regular feedback with iteration loop.
+
+        Args:
+            current_answer: The agent's current answer.
+            initial_feedback: Initial human feedback string.
+            context: Executor context for callbacks.
+
+        Returns:
+            Final answer after all feedback iterations.
+        """
+        feedback = initial_feedback
+        answer = current_answer
+
+        while context.ask_for_human_input:
+            if feedback.strip() == "":
+                context.ask_for_human_input = False
+            else:
+                context.messages.append(context._format_feedback_message(feedback))
+                answer = context._invoke_loop()
+                feedback = self._prompt_input(context.crew)
+
+        return answer
+
+    @staticmethod
+    def _prompt_input(crew: Crew | None) -> str:
+        """Show rich panel and prompt for input.
+
+        Args:
+            crew: The crew instance for context.
+
+        Returns:
+            User input string from terminal.
+        """
+        from rich.panel import Panel
+        from rich.text import Text
+
+        from crewai.events.event_listener import event_listener
+
+        formatter = event_listener.formatter
+        formatter.pause_live_updates()
+
+        try:
+            if crew and getattr(crew, "_train", False):
+                prompt_text = (
+                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
+                    "This will be used to train better versions of the agent.\n"
+                    "Please provide detailed feedback about the result quality and reasoning process."
+                )
+                title = "🎓 Training Feedback Required"
+            else:
+                prompt_text = (
+                    "Provide feedback on the Final Result above.\n\n"
+                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
+                    "• Otherwise, provide specific improvement requests.\n"
+                    "• You can provide multiple rounds of feedback until satisfied."
+                )
+                title = "💬 Human Feedback Required"
+
+            content = Text()
+            content.append(prompt_text, style="yellow")
+
+            prompt_panel = Panel(
+                content,
+                title=title,
+                border_style="yellow",
+                padding=(1, 2),
+            )
+            formatter.console.print(prompt_panel)
+
+            response = input()
+            if response.strip() != "":
+                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
+            return response
+        finally:
+            formatter.resume_live_updates()
+
+
+_provider: ContextVar[HumanInputProvider | None] = ContextVar(
+    "human_input_provider",
+    default=None,
+)
+
+
+def get_provider() -> HumanInputProvider:
+    """Get the current human input provider.
+
+    Returns:
+        The current provider, or a new SyncHumanInputProvider if none set.
+    """
+    provider = _provider.get()
+    if provider is None:
+        initialized_provider = SyncHumanInputProvider()
+        set_provider(initialized_provider)
+        return initialized_provider
+    return provider
+
+
+def set_provider(provider: HumanInputProvider) -> Token[HumanInputProvider | None]:
+    """Set the human input provider for the current context.
+
+    Args:
+        provider: The provider to use.
+
+    Returns:
+        Token that can be used to reset to previous value.
+    """
+    return _provider.set(provider)
+
+
+def reset_provider(token: Token[HumanInputProvider | None]) -> None:
+    """Reset the provider to its previous value.
+
+    Args:
+        token: Token returned from set_provider.
+    """
+    _provider.reset(token)

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

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

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

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

lib/crewai/src/crewai/llm.py

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

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

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

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

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, TypeGuard, cast
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -266,35 +266,46 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            # Emit call started event
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                # Emit call started event
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            # Format messages for Anthropic
-            formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages
-            )
+                # Format messages for Anthropic
+                formatted_messages, system_message = (
+                    self._format_messages_for_anthropic(messages)
+                )
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            # Prepare completion parameters
-            completion_params = self._prepare_completion_params(
-                formatted_messages, system_message, tools
-            )
+                # Prepare completion parameters
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, system_message, tools, available_functions
+                )
 
-            effective_response_model = response_model or self.response_format
+                effective_response_model = response_model or self.response_format
 
-            # Handle streaming vs non-streaming
-            if self.stream:
-                return self._handle_streaming_completion(
+                # Handle streaming vs non-streaming
+                if self.stream:
+                    return self._handle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return self._handle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -302,21 +313,13 @@ class AnthropicCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return self._handle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"Anthropic API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"Anthropic API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def acall(
         self,
@@ -342,28 +345,37 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Chat completion response or tool call result
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages
-            )
+                formatted_messages, system_message = (
+                    self._format_messages_for_anthropic(messages)
+                )
 
-            completion_params = self._prepare_completion_params(
-                formatted_messages, system_message, tools
-            )
+                completion_params = self._prepare_completion_params(
+                    formatted_messages, system_message, tools, available_functions
+                )
 
-            effective_response_model = response_model or self.response_format
+                effective_response_model = response_model or self.response_format
 
-            if self.stream:
-                return await self._ahandle_streaming_completion(
+                if self.stream:
+                    return await self._ahandle_streaming_completion(
+                        completion_params,
+                        available_functions,
+                        from_task,
+                        from_agent,
+                        effective_response_model,
+                    )
+
+                return await self._ahandle_completion(
                     completion_params,
                     available_functions,
                     from_task,
@@ -371,27 +383,20 @@ class AnthropicCompletion(BaseLLM):
                     effective_response_model,
                 )
 
-            return await self._ahandle_completion(
-                completion_params,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"Anthropic API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"Anthropic API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _prepare_completion_params(
         self,
         messages: list[LLMMessage],
         system_message: str | None = None,
         tools: list[dict[str, Any]] | None = None,
+        available_functions: dict[str, Any] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Anthropic messages API.
 
@@ -399,6 +404,8 @@ class AnthropicCompletion(BaseLLM):
             messages: Formatted messages for Anthropic
             system_message: Extracted system message
             tools: Tool definitions
+            available_functions: Available functions for tool calling. When provided
+                with a single tool, tool_choice is automatically set to force tool use.
 
         Returns:
             Parameters dictionary for Anthropic API
@@ -424,7 +431,13 @@ class AnthropicCompletion(BaseLLM):
 
         # Handle tools for Claude 3+
         if tools and self.supports_tools:
-            params["tools"] = self._convert_tools_for_interference(tools)
+            converted_tools = self._convert_tools_for_interference(tools)
+            params["tools"] = converted_tools
+
+            if available_functions and len(converted_tools) == 1:
+                tool_name = converted_tools[0].get("name")
+                if tool_name and tool_name in available_functions:
+                    params["tool_choice"] = {"type": "tool", "name": tool_name}
 
         if self.thinking:
             if isinstance(self.thinking, AnthropicThinkingConfig):
@@ -726,15 +739,11 @@ class AnthropicCompletion(BaseLLM):
                     )
                     return list(tool_uses)
 
-                # Handle tool use conversation flow internally
-                return self._handle_tool_use_conversation(
-                    response,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         content = ""
         thinking_blocks: list[ThinkingBlock] = []
@@ -935,14 +944,12 @@ class AnthropicCompletion(BaseLLM):
                 if not available_functions:
                     return list(tool_uses)
 
-                return self._handle_tool_use_conversation(
-                    final_message,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                # Execute first tool and return result directly
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         full_response = self._apply_stop_words(full_response)
 
@@ -1001,6 +1008,41 @@ class AnthropicCompletion(BaseLLM):
 
         return tool_results
 
+    def _execute_first_tool(
+        self,
+        tool_uses: list[ToolUseBlock | BetaToolUseBlock],
+        available_functions: dict[str, Any],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> Any | None:
+        """Execute the first tool from the tool_uses list and return its result.
+
+        This is used when available_functions is provided, to directly execute
+        the tool and return its result (matching OpenAI behavior for use cases
+        like reasoning_handler).
+
+        Args:
+            tool_uses: List of tool use blocks from Claude's response
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            The result of the first tool execution, or None if execution failed
+        """
+        tool_use = tool_uses[0]
+        function_name = tool_use.name
+        function_args = cast(dict[str, Any], tool_use.input)
+
+        return self._handle_tool_execution(
+            function_name=function_name,
+            function_args=function_args,
+            available_functions=available_functions,
+            from_task=from_task,
+            from_agent=from_agent,
+        )
+
+    # TODO: we drop this
     def _handle_tool_use_conversation(
         self,
         initial_response: Message | BetaMessage,
@@ -1216,14 +1258,11 @@ class AnthropicCompletion(BaseLLM):
                     )
                     return list(tool_uses)
 
-                return await self._ahandle_tool_use_conversation(
-                    response,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         content = ""
         if response.content:
@@ -1404,14 +1443,11 @@ class AnthropicCompletion(BaseLLM):
                 if not available_functions:
                     return list(tool_uses)
 
-                return await self._ahandle_tool_use_conversation(
-                    final_message,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         full_response = self._apply_stop_words(full_response)
 

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

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

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

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

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

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

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

@@ -17,7 +17,7 @@ from openai.types.responses import Response
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -382,23 +382,35 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages = self._format_messages(messages)
+                formatted_messages = self._format_messages(messages)
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
+                if not self._invoke_before_llm_call_hooks(
+                    formatted_messages, from_agent
+                ):
+                    raise ValueError("LLM call blocked by before_llm_call hook")
 
-            if self.api == "responses":
-                return self._call_responses(
+                if self.api == "responses":
+                    return self._call_responses(
+                        messages=formatted_messages,
+                        tools=tools,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        response_model=response_model,
+                    )
+
+                return self._call_completions(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -407,22 +419,13 @@ class OpenAICompletion(BaseLLM):
                     response_model=response_model,
                 )
 
-            return self._call_completions(
-                messages=formatted_messages,
-                tools=tools,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_model=response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"OpenAI API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"OpenAI API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     def _call_completions(
         self,
@@ -479,20 +482,30 @@ class OpenAICompletion(BaseLLM):
         Returns:
             Completion response or tool call result.
         """
-        try:
-            self._emit_call_started_event(
-                messages=messages,
-                tools=tools,
-                callbacks=callbacks,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
+        with llm_call_context():
+            try:
+                self._emit_call_started_event(
+                    messages=messages,
+                    tools=tools,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-            formatted_messages = self._format_messages(messages)
+                formatted_messages = self._format_messages(messages)
 
-            if self.api == "responses":
-                return await self._acall_responses(
+                if self.api == "responses":
+                    return await self._acall_responses(
+                        messages=formatted_messages,
+                        tools=tools,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        response_model=response_model,
+                    )
+
+                return await self._acall_completions(
                     messages=formatted_messages,
                     tools=tools,
                     available_functions=available_functions,
@@ -501,22 +514,13 @@ class OpenAICompletion(BaseLLM):
                     response_model=response_model,
                 )
 
-            return await self._acall_completions(
-                messages=formatted_messages,
-                tools=tools,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_model=response_model,
-            )
-
-        except Exception as e:
-            error_msg = f"OpenAI API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            except Exception as e:
+                error_msg = f"OpenAI API call failed: {e!s}"
+                logging.error(error_msg)
+                self._emit_call_failed_event(
+                    error=error_msg, from_task=from_task, from_agent=from_agent
+                )
+                raise
 
     async def _acall_completions(
         self,
@@ -1521,13 +1525,16 @@ 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 = {
+            openai_tool: dict[str, Any] = {
                 "type": "function",
                 "function": {
                     "name": name,
@@ -1537,10 +1544,11 @@ class OpenAICompletion(BaseLLM):
             }
 
             if parameters:
-                if isinstance(parameters, dict):
-                    openai_tool["function"]["parameters"] = parameters  # type: ignore
-                else:
-                    openai_tool["function"]["parameters"] = dict(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
 
             openai_tools.append(openai_tool)
         return openai_tools

lib/crewai/src/crewai/mcp/client.py

@@ -420,6 +420,7 @@ class MCPClient:
         return [
             {
                 "name": sanitize_tool_name(tool.name),
+                "original_name": tool.name,
                 "description": getattr(tool, "description", ""),
                 "inputSchema": getattr(tool, "inputSchema", {}),
             }

lib/crewai/src/crewai/task.py

@@ -31,6 +31,7 @@ from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.core.providers.content_processor import process_content
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
@@ -496,6 +497,7 @@ class Task(BaseModel):
         tools: list[BaseTool] | None = None,
     ) -> TaskOutput:
         """Execute the task synchronously."""
+        self.start_time = datetime.datetime.now()
         return self._execute_core(agent, context, tools)
 
     @property
@@ -536,6 +538,7 @@ class Task(BaseModel):
     ) -> None:
         """Execute the task asynchronously with context handling."""
         try:
+            self.start_time = datetime.datetime.now()
             result = self._execute_core(agent, context, tools)
             future.set_result(result)
         except Exception as e:
@@ -548,6 +551,7 @@ class Task(BaseModel):
         tools: list[BaseTool] | None = None,
     ) -> TaskOutput:
         """Execute the task asynchronously using native async/await."""
+        self.start_time = datetime.datetime.now()
         return await self._aexecute_core(agent, context, tools)
 
     async def _aexecute_core(
@@ -566,8 +570,6 @@ class Task(BaseModel):
                     f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
                 )
 
-            self.start_time = datetime.datetime.now()
-
             self.prompt_context = context
             tools = tools or self.tools or []
 
@@ -579,6 +581,8 @@ class Task(BaseModel):
                 tools=tools,
             )
 
+            self._post_agent_execution(agent)
+
             if not self._guardrails and not self._guardrail:
                 pydantic_output, json_output = self._export_output(result)
             else:
@@ -661,8 +665,6 @@ class Task(BaseModel):
                     f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
                 )
 
-            self.start_time = datetime.datetime.now()
-
             self.prompt_context = context
             tools = tools or self.tools or []
 
@@ -674,6 +676,8 @@ class Task(BaseModel):
                 tools=tools,
             )
 
+            self._post_agent_execution(agent)
+
             if not self._guardrails and not self._guardrail:
                 pydantic_output, json_output = self._export_output(result)
             else:
@@ -741,6 +745,9 @@ class Task(BaseModel):
         finally:
             clear_task_files(self.id)
 
+    def _post_agent_execution(self, agent: BaseAgent) -> None:
+        pass
+
     def prompt(self) -> str:
         """Generates the task prompt with optional markdown formatting.
 
@@ -863,6 +870,11 @@ Follow these guidelines:
         except ValueError as e:
             raise ValueError(f"Error interpolating description: {e!s}") from e
 
+        self.description = process_content(self.description, {"task": self})
+        self._original_expected_output = process_content(
+            self._original_expected_output, {"task": self}
+        )
+
         try:
             self.expected_output = interpolate_only(
                 input_string=self._original_expected_output, inputs=inputs

lib/crewai/src/crewai/tools/mcp_native_tool.py

@@ -27,14 +27,16 @@ class MCPNativeTool(BaseTool):
         tool_name: str,
         tool_schema: dict[str, Any],
         server_name: str,
+        original_tool_name: str | None = None,
     ) -> None:
         """Initialize native MCP tool.
 
         Args:
             mcp_client: MCPClient instance with active session.
-            tool_name: Original name of the tool on the MCP server.
+            tool_name: Name of the tool (may be prefixed).
             tool_schema: Schema information for the tool.
             server_name: Name of the MCP server for prefixing.
+            original_tool_name: Original name of the tool on the MCP server.
         """
         # Create tool name with server prefix to avoid conflicts
         prefixed_name = f"{server_name}_{tool_name}"
@@ -57,7 +59,7 @@ class MCPNativeTool(BaseTool):
 
         # Set instance attributes after super().__init__
         self._mcp_client = mcp_client
-        self._original_tool_name = tool_name
+        self._original_tool_name = original_tool_name or tool_name
         self._server_name = server_name
         # self._logger = logging.getLogger(__name__)
 

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

@@ -19,9 +19,10 @@ from collections.abc import Callable
 from copy import deepcopy
 import datetime
 import logging
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Union
+from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union
 import uuid
 
+import jsonref  # type: ignore[import-untyped]
 from pydantic import (
     UUID1,
     UUID3,
@@ -69,6 +70,21 @@ else:
         EmailStr = str
 
 
+class JsonSchemaInfo(TypedDict):
+    """Inner structure for JSON schema metadata."""
+
+    name: str
+    strict: Literal[True]
+    schema: dict[str, Any]
+
+
+class ModelDescription(TypedDict):
+    """Return type for generate_model_description."""
+
+    type: Literal["json_schema"]
+    json_schema: JsonSchemaInfo
+
+
 def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
     """Recursively resolve all local $refs in the given JSON Schema using $defs as the source.
 
@@ -127,6 +143,102 @@ 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
+
+
+OPENAI_SUPPORTED_FORMATS: Final[
+    set[Literal["date-time", "date", "time", "duration"]]
+] = {
+    "date-time",
+    "date",
+    "time",
+    "duration",
+}
+
+
+def strip_unsupported_formats(d: Any) -> Any:
+    """Remove format annotations that OpenAI strict mode doesn't support.
+
+    OpenAI only supports: date-time, date, time, duration.
+    Other formats like uri, email, uuid etc. cause validation errors.
+
+    Args:
+        d: The dictionary/list to modify.
+
+    Returns:
+        The modified dictionary/list.
+    """
+    if isinstance(d, dict):
+        format_value = d.get("format")
+        if (
+            isinstance(format_value, str)
+            and format_value not in OPENAI_SUPPORTED_FORMATS
+        ):
+            del d["format"]
+        for v in d.values():
+            strip_unsupported_formats(v)
+    elif isinstance(d, list):
+        for i in d:
+            strip_unsupported_formats(i)
+    return d
+
+
+def ensure_type_in_schemas(d: Any) -> Any:
+    """Ensure all schema objects in anyOf/oneOf have a 'type' key.
+
+    OpenAI strict mode requires every schema to have a 'type' key.
+    Empty schemas {} in anyOf/oneOf are converted to {"type": "object"}.
+
+    Args:
+        d: The dictionary/list to modify.
+
+    Returns:
+        The modified dictionary/list.
+    """
+    if isinstance(d, dict):
+        for key in ("anyOf", "oneOf"):
+            if key in d:
+                schema_list = d[key]
+                for i, schema in enumerate(schema_list):
+                    if isinstance(schema, dict) and schema == {}:
+                        schema_list[i] = {"type": "object"}
+                    else:
+                        ensure_type_in_schemas(schema)
+        for v in d.values():
+            ensure_type_in_schemas(v)
+    elif isinstance(d, list):
+        for item in d:
+            ensure_type_in_schemas(item)
+    return d
+
+
 def fix_discriminator_mappings(schema: dict[str, Any]) -> dict[str, Any]:
     """Replace '#/$defs/...' references in discriminator.mapping with just the model name.
 
@@ -263,7 +375,49 @@ def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
     return schema
 
 
-def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
+def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
+    """Remove null type from anyOf/type arrays.
+
+    Pydantic generates `T | None` for optional fields, which creates schemas with
+    null in the type. However, for MCP tools, optional fields should be omitted
+    entirely rather than sent as null. This function strips null from types.
+
+    Args:
+        schema: JSON schema dictionary.
+
+    Returns:
+        Modified schema with null types removed.
+    """
+    if isinstance(schema, dict):
+        if "anyOf" in schema:
+            any_of = schema["anyOf"]
+            non_null = [opt for opt in any_of if opt.get("type") != "null"]
+            if len(non_null) == 1:
+                schema.pop("anyOf")
+                schema.update(non_null[0])
+            elif len(non_null) > 1:
+                schema["anyOf"] = non_null
+
+        type_value = schema.get("type")
+        if isinstance(type_value, list) and "null" in type_value:
+            non_null_types = [t for t in type_value if t != "null"]
+            if len(non_null_types) == 1:
+                schema["type"] = non_null_types[0]
+            elif len(non_null_types) > 1:
+                schema["type"] = non_null_types
+
+        for value in schema.values():
+            if isinstance(value, dict):
+                strip_null_from_types(value)
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict):
+                        strip_null_from_types(item)
+
+    return schema
+
+
+def generate_model_description(model: type[BaseModel]) -> ModelDescription:
     """Generate JSON schema description of a Pydantic model.
 
     This function takes a Pydantic model class and returns its JSON schema,
@@ -274,17 +428,13 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
         model: A Pydantic model class.
 
     Returns:
-        A JSON schema dictionary representation of the model.
+        A ModelDescription with JSON schema representation of the model.
     """
     json_schema = model.model_json_schema(ref_template="#/$defs/{model}")
 
-    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 = force_additional_properties_false(json_schema)
+    json_schema = strip_unsupported_formats(json_schema)
+    json_schema = ensure_type_in_schemas(json_schema)
 
     json_schema = resolve_refs(json_schema)
 
@@ -292,6 +442,7 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
     json_schema = fix_discriminator_mappings(json_schema)
     json_schema = convert_oneof_to_anyof(json_schema)
     json_schema = ensure_all_properties_required(json_schema)
+    json_schema = strip_null_from_types(json_schema)
 
     return {
         "type": "json_schema",
@@ -376,14 +527,19 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         >>> person.name
         'John'
     """
+    json_schema = dict(jsonref.replace_refs(json_schema, proxies=False))
+
     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 {}):
             json_schema["title"] = (root_schema or {}).get("title")
 
-    model_name = json_schema.get("title", "DynamicModel")
+    model_name = json_schema.get("title") or "DynamicModel"
     field_definitions = {
         name: _json_schema_to_pydantic_field(
             name, prop, json_schema.get("required", []), effective_root
@@ -391,9 +547,11 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         for name, prop in (json_schema.get("properties", {}) or {}).items()
     }
 
+    effective_config = __config__ or ConfigDict(extra="forbid")
+
     return create_model_base(
         model_name,
-        __config__=__config__,
+        __config__=effective_config,
         __base__=__base__,
         __module__=__module__,
         __validators__=__validators__,
@@ -572,8 +730,10 @@ def _json_schema_to_pydantic_type(
         any_of_schemas = json_schema.get("anyOf", []) + json_schema.get("oneOf", [])
     if any_of_schemas:
         any_of_types = [
-            _json_schema_to_pydantic_type(schema, root_schema)
-            for schema in any_of_schemas
+            _json_schema_to_pydantic_type(
+                schema, root_schema, name_=f"{name_ or 'Union'}Option{i}"
+            )
+            for i, schema in enumerate(any_of_schemas)
         ]
         return Union[tuple(any_of_types)]  # noqa: UP007
 
@@ -609,7 +769,7 @@ def _json_schema_to_pydantic_type(
         if properties:
             json_schema_ = json_schema.copy()
             if json_schema_.get("title") is None:
-                json_schema_["title"] = name_
+                json_schema_["title"] = name_ or "DynamicModel"
             return create_model_from_schema(json_schema_, root_schema=root_schema)
         return dict
     if type_ == "null":

lib/crewai/tests/agents/test_agent.py

@@ -703,6 +703,8 @@ def test_agent_definition_based_on_dict():
 # test for human input
 @pytest.mark.vcr()
 def test_agent_human_input():
+    from crewai.core.providers.human_input import SyncHumanInputProvider
+
     # Agent configuration
     config = {
         "role": "test role",
@@ -720,7 +722,7 @@ def test_agent_human_input():
         human_input=True,
     )
 
-    # Side effect function for _ask_human_input to simulate multiple feedback iterations
+    # Side effect function for _prompt_input to simulate multiple feedback iterations
     feedback_responses = iter(
         [
             "Don't say hi, say Hello instead!",  # First feedback: instruct change
@@ -728,16 +730,16 @@ def test_agent_human_input():
         ]
     )
 
-    def ask_human_input_side_effect(*args, **kwargs):
+    def prompt_input_side_effect(*args, **kwargs):
         return next(feedback_responses)
 
-    # Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
+    # Patch both _prompt_input on provider and _invoke_loop to avoid real API/network calls.
     with (
         patch.object(
-            CrewAgentExecutor,
-            "_ask_human_input",
-            side_effect=ask_human_input_side_effect,
-        ) as mock_human_input,
+            SyncHumanInputProvider,
+            "_prompt_input",
+            side_effect=prompt_input_side_effect,
+        ) as mock_prompt_input,
         patch.object(
             CrewAgentExecutor,
             "_invoke_loop",
@@ -749,7 +751,7 @@ def test_agent_human_input():
 
         # Assertions to ensure the agent behaves correctly.
         # It should have requested feedback twice.
-        assert mock_human_input.call_count == 2
+        assert mock_prompt_input.call_count == 2
         # The final result should be processed to "Hello"
         assert output.strip().lower() == "hello"
 

lib/crewai/tests/agents/test_async_agent_executor.py

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

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_tool_execution_returns_tool_result_directly.yaml

@@ -0,0 +1,102 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Calculate 5 +
+      3 using the simple_calculator tool with operation ''add''."}],"model":"claude-3-5-haiku-20241022","stream":false,"tool_choice":{"type":"tool","name":"simple_calculator"},"tools":[{"name":"simple_calculator","description":"Perform
+      simple math operations","input_schema":{"type":"object","properties":{"operation":{"type":"string","enum":["add","multiply"],"description":"The
+      operation to perform"},"a":{"type":"integer","description":"First number"},"b":{"type":"integer","description":"Second
+      number"}},"required":["operation","a","b"]}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '608'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.73.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01Q2F83aAeqqTCxsd8WpZjK7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BW4XkHnhRVM5JZsvoaQKw5","name":"simple_calculator","input":{"operation":"add","a":5,"b":3}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":498,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":67,"service_tier":"standard"}}'
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Security-Policy:
+      - CSP-FILTERED
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 03 Feb 2026 23:26:35 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-02-03T23:26:34Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '1228'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_tool_execution_with_available_functions.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Create a simple
+      plan to say hello. Use the create_reasoning_plan tool."}],"model":"claude-3-5-haiku-20241022","stream":false,"tool_choice":{"type":"tool","name":"create_reasoning_plan"},"tools":[{"name":"create_reasoning_plan","description":"Create
+      a structured reasoning plan for completing a task","input_schema":{"type":"object","properties":{"plan":{"type":"string","description":"High-level
+      plan description"},"steps":{"type":"array","items":{"type":"object"},"description":"List
+      of steps to execute"},"ready":{"type":"boolean","description":"Whether the plan
+      is ready to execute"}},"required":["plan","steps","ready"]}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '684'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.73.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01HLuGgGRFseMdhTYAhkKtfz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GQAUFHffGzMd3ufA6YRMZF","name":"create_reasoning_plan","input":{"plan":"Say
+        hello in a friendly and straightforward manner","steps":[{"description":"Take
+        a deep breath","action":"Pause and relax"},{"description":"Smile","action":"Prepare
+        a warm facial expression"},{"description":"Greet the person","action":"Say
+        ''Hello!''"},{"description":"Wait for response","action":"Listen and be ready
+        to continue conversation"}],"ready":true}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":513,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":162,"service_tier":"standard"}}'
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Security-Policy:
+      - CSP-FILTERED
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 03 Feb 2026 23:26:38 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-02-03T23:26:35Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '2994'
+    status:
+      code: 200
+      message: OK
+version: 1

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

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"messages":[{"role":"user","content":"Say hi"}],"model":"gpt-4o-mini"}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '71'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 1.83.0
+      x-stainless-read-timeout:
+      - X-STAINLESS-READ-TIMEOUT-XXX
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.0
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: "{\n  \"id\": \"chatcmpl-D2HpUSxS5LeHwDTELElWlC5CDMzmr\",\n  \"object\":
+        \"chat.completion\",\n  \"created\": 1769437564,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+        \"assistant\",\n        \"content\": \"Hi there! How can I assist you today?\",\n
+        \       \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\":
+        null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+        9,\n    \"completion_tokens\": 10,\n    \"total_tokens\": 19,\n    \"prompt_tokens_details\":
+        {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+        \"default\",\n  \"system_fingerprint\": \"fp_29330a9688\"\n}\n"
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 26 Jan 2026 14:26:05 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - SET-COOKIE-XXX
+      Strict-Transport-Security:
+      - STS-XXX
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - X-CONTENT-TYPE-XXX
+      access-control-expose-headers:
+      - ACCESS-CONTROL-XXX
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - OPENAI-ORG-XXX
+      openai-processing-ms:
+      - '460'
+      openai-project:
+      - OPENAI-PROJECT-XXX
+      openai-version:
+      - '2020-10-01'
+      x-envoy-upstream-service-time:
+      - '477'
+      x-openai-proxy-wasm:
+      - v0.1
+      x-ratelimit-limit-requests:
+      - X-RATELIMIT-LIMIT-REQUESTS-XXX
+      x-ratelimit-limit-tokens:
+      - X-RATELIMIT-LIMIT-TOKENS-XXX
+      x-ratelimit-remaining-requests:
+      - X-RATELIMIT-REMAINING-REQUESTS-XXX
+      x-ratelimit-remaining-tokens:
+      - X-RATELIMIT-REMAINING-TOKENS-XXX
+      x-ratelimit-reset-requests:
+      - X-RATELIMIT-RESET-REQUESTS-XXX
+      x-ratelimit-reset-tokens:
+      - X-RATELIMIT-RESET-TOKENS-XXX
+      x-request-id:
+      - X-REQUEST-ID-XXX
+    status:
+      code: 200
+      message: OK
+version: 1

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

@@ -0,0 +1,215 @@
+interactions:
+- request:
+    body: '{"messages":[{"role":"user","content":"Say hi"}],"model":"gpt-4o-mini"}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '71'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 1.83.0
+      x-stainless-read-timeout:
+      - X-STAINLESS-READ-TIMEOUT-XXX
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.0
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: "{\n  \"id\": \"chatcmpl-D2HpStmyOpe9DrthWBlDdMZfVMJ1u\",\n  \"object\":
+        \"chat.completion\",\n  \"created\": 1769437562,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+        \"assistant\",\n        \"content\": \"Hi! How can I assist you today?\",\n
+        \       \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\":
+        null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+        9,\n    \"completion_tokens\": 9,\n    \"total_tokens\": 18,\n    \"prompt_tokens_details\":
+        {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+        \"default\",\n  \"system_fingerprint\": \"fp_29330a9688\"\n}\n"
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 26 Jan 2026 14:26:02 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - SET-COOKIE-XXX
+      Strict-Transport-Security:
+      - STS-XXX
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - X-CONTENT-TYPE-XXX
+      access-control-expose-headers:
+      - ACCESS-CONTROL-XXX
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - OPENAI-ORG-XXX
+      openai-processing-ms:
+      - '415'
+      openai-project:
+      - OPENAI-PROJECT-XXX
+      openai-version:
+      - '2020-10-01'
+      x-envoy-upstream-service-time:
+      - '434'
+      x-openai-proxy-wasm:
+      - v0.1
+      x-ratelimit-limit-requests:
+      - X-RATELIMIT-LIMIT-REQUESTS-XXX
+      x-ratelimit-limit-tokens:
+      - X-RATELIMIT-LIMIT-TOKENS-XXX
+      x-ratelimit-remaining-requests:
+      - X-RATELIMIT-REMAINING-REQUESTS-XXX
+      x-ratelimit-remaining-tokens:
+      - X-RATELIMIT-REMAINING-TOKENS-XXX
+      x-ratelimit-reset-requests:
+      - X-RATELIMIT-RESET-REQUESTS-XXX
+      x-ratelimit-reset-tokens:
+      - X-RATELIMIT-RESET-TOKENS-XXX
+      x-request-id:
+      - X-REQUEST-ID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages":[{"role":"user","content":"Say bye"}],"model":"gpt-4o-mini"}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '72'
+      content-type:
+      - application/json
+      cookie:
+      - COOKIE-XXX
+      host:
+      - api.openai.com
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 1.83.0
+      x-stainless-read-timeout:
+      - X-STAINLESS-READ-TIMEOUT-XXX
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.0
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: "{\n  \"id\": \"chatcmpl-D2HpS1DP0Xd3tmWt5PBincVrdU7yw\",\n  \"object\":
+        \"chat.completion\",\n  \"created\": 1769437562,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+        \"assistant\",\n        \"content\": \"Goodbye! If you have more questions
+        in the future, feel free to reach out. Have a great day!\",\n        \"refusal\":
+        null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n
+        \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+        9,\n    \"completion_tokens\": 23,\n    \"total_tokens\": 32,\n    \"prompt_tokens_details\":
+        {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+        \"default\",\n  \"system_fingerprint\": \"fp_29330a9688\"\n}\n"
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 26 Jan 2026 14:26:03 GMT
+      Server:
+      - cloudflare
+      Strict-Transport-Security:
+      - STS-XXX
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - X-CONTENT-TYPE-XXX
+      access-control-expose-headers:
+      - ACCESS-CONTROL-XXX
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - OPENAI-ORG-XXX
+      openai-processing-ms:
+      - '964'
+      openai-project:
+      - OPENAI-PROJECT-XXX
+      openai-version:
+      - '2020-10-01'
+      x-envoy-upstream-service-time:
+      - '979'
+      x-openai-proxy-wasm:
+      - v0.1
+      x-ratelimit-limit-requests:
+      - X-RATELIMIT-LIMIT-REQUESTS-XXX
+      x-ratelimit-limit-tokens:
+      - X-RATELIMIT-LIMIT-TOKENS-XXX
+      x-ratelimit-remaining-requests:
+      - X-RATELIMIT-REMAINING-REQUESTS-XXX
+      x-ratelimit-remaining-tokens:
+      - X-RATELIMIT-REMAINING-TOKENS-XXX
+      x-ratelimit-reset-requests:
+      - X-RATELIMIT-RESET-REQUESTS-XXX
+      x-ratelimit-reset-tokens:
+      - X-RATELIMIT-RESET-TOKENS-XXX
+      x-request-id:
+      - X-REQUEST-ID-XXX
+    status:
+      code: 200
+      message: OK
+version: 1

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

@@ -0,0 +1,143 @@
+interactions:
+- request:
+    body: '{"messages":[{"role":"user","content":"Say hi"}],"model":"gpt-4o-mini","stream":true,"stream_options":{"include_usage":true}}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '125'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 1.83.0
+      x-stainless-read-timeout:
+      - X-STAINLESS-READ-TIMEOUT-XXX
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.0
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: 'data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"role":"assistant","content":"","refusal":null},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"rVIyGQF2E"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"Hi"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"ZGVqV7ZDm"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"!"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"vnfm7IxlIB"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        How"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"o8F35ZZ"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        can"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"kiBzGe3"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        I"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"cbGT2RWgx"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        assist"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"DtxR"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        you"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"6y6Co8J"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"
+        today"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"SZOmm"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{"content":"?"},"logprobs":null,"finish_reason":null}],"usage":null,"obfuscation":"s9Bc0HqlPg"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}],"usage":null,"obfuscation":"u9aar"}
+
+
+        data: {"id":"chatcmpl-D2HpUGTvIFKBsR9Xd6XRT4AuFXzbz","object":"chat.completion.chunk","created":1769437564,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_29330a9688","choices":[],"usage":{"prompt_tokens":9,"completion_tokens":9,"total_tokens":18,"prompt_tokens_details":{"cached_tokens":0,"audio_tokens":0},"completion_tokens_details":{"reasoning_tokens":0,"audio_tokens":0,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"obfuscation":"5hudm8ySqh39"}
+
+
+        data: [DONE]
+
+
+        '
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Type:
+      - text/event-stream; charset=utf-8
+      Date:
+      - Mon, 26 Jan 2026 14:26:04 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - SET-COOKIE-XXX
+      Strict-Transport-Security:
+      - STS-XXX
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - X-CONTENT-TYPE-XXX
+      access-control-expose-headers:
+      - ACCESS-CONTROL-XXX
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - OPENAI-ORG-XXX
+      openai-processing-ms:
+      - '260'
+      openai-project:
+      - OPENAI-PROJECT-XXX
+      openai-version:
+      - '2020-10-01'
+      x-envoy-upstream-service-time:
+      - '275'
+      x-openai-proxy-wasm:
+      - v0.1
+      x-ratelimit-limit-requests:
+      - X-RATELIMIT-LIMIT-REQUESTS-XXX
+      x-ratelimit-limit-tokens:
+      - X-RATELIMIT-LIMIT-TOKENS-XXX
+      x-ratelimit-remaining-requests:
+      - X-RATELIMIT-REMAINING-REQUESTS-XXX
+      x-ratelimit-remaining-tokens:
+      - X-RATELIMIT-REMAINING-TOKENS-XXX
+      x-ratelimit-reset-requests:
+      - X-RATELIMIT-RESET-REQUESTS-XXX
+      x-ratelimit-reset-tokens:
+      - X-RATELIMIT-RESET-TOKENS-XXX
+      x-request-id:
+      - X-REQUEST-ID-XXX
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cli/test_create_crew.py

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

lib/crewai/tests/cli/test_version.py

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

lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -45,85 +45,6 @@ def test_anthropic_completion_is_used_when_claude_provider():
 
 
 
-def test_anthropic_tool_use_conversation_flow():
-    """
-    Test that the Anthropic completion properly handles tool use conversation flow
-    """
-    from unittest.mock import Mock, patch
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
-    from anthropic.types.tool_use_block import ToolUseBlock
-
-    # Create AnthropicCompletion instance
-    completion = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
-
-    # Mock tool function
-    def mock_weather_tool(location: str) -> str:
-        return f"The weather in {location} is sunny and 75°F"
-
-    available_functions = {"get_weather": mock_weather_tool}
-
-    # Mock the Anthropic client responses
-    with patch.object(completion.client.messages, 'create') as mock_create:
-        # Mock initial response with tool use - need to properly mock ToolUseBlock
-        mock_tool_use = Mock(spec=ToolUseBlock)
-        mock_tool_use.type = "tool_use"
-        mock_tool_use.id = "tool_123"
-        mock_tool_use.name = "get_weather"
-        mock_tool_use.input = {"location": "San Francisco"}
-
-        mock_initial_response = Mock()
-        mock_initial_response.content = [mock_tool_use]
-        mock_initial_response.usage = Mock()
-        mock_initial_response.usage.input_tokens = 100
-        mock_initial_response.usage.output_tokens = 50
-
-        # Mock final response after tool result - properly mock text content
-        mock_text_block = Mock()
-        mock_text_block.type = "text"
-        # Set the text attribute as a string, not another Mock
-        mock_text_block.configure_mock(text="Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature.")
-
-        mock_final_response = Mock()
-        mock_final_response.content = [mock_text_block]
-        mock_final_response.usage = Mock()
-        mock_final_response.usage.input_tokens = 150
-        mock_final_response.usage.output_tokens = 75
-
-        # Configure mock to return different responses on successive calls
-        mock_create.side_effect = [mock_initial_response, mock_final_response]
-
-        # Test the call
-        messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
-        result = completion.call(
-            messages=messages,
-            available_functions=available_functions
-        )
-
-        # Verify the result contains the final response
-        assert "beautiful day in San Francisco" in result
-        assert "sunny skies" in result
-        assert "75°F" in result
-
-        # Verify that two API calls were made (initial + follow-up)
-        assert mock_create.call_count == 2
-
-        # Verify the second call includes tool results
-        second_call_args = mock_create.call_args_list[1][1]  # kwargs of second call
-        messages_in_second_call = second_call_args["messages"]
-
-        # Should have original user message + assistant tool use + user tool result
-        assert len(messages_in_second_call) == 3
-        assert messages_in_second_call[0]["role"] == "user"
-        assert messages_in_second_call[1]["role"] == "assistant"
-        assert messages_in_second_call[2]["role"] == "user"
-
-        # Verify tool result format
-        tool_result = messages_in_second_call[2]["content"][0]
-        assert tool_result["type"] == "tool_result"
-        assert tool_result["tool_use_id"] == "tool_123"
-        assert "sunny and 75°F" in tool_result["content"]
-
-
 def test_anthropic_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Anthropic provider
@@ -874,6 +795,125 @@ def test_anthropic_function_calling():
 # =============================================================================
 
 
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_tool_execution_with_available_functions():
+    """
+    Test that Anthropic provider correctly executes tools when available_functions is provided.
+
+    This specifically tests the fix for double llm_call_completed emission - when
+    available_functions is provided, _handle_tool_execution is called which already
+    emits llm_call_completed, so the caller should not emit it again.
+
+    The test verifies:
+    1. The tool is called with correct arguments
+    2. The tool result is returned directly (not wrapped in conversation)
+    3. The result is valid JSON matching the tool output format
+    """
+    import json
+
+    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
+
+    # Simple tool that returns a formatted string
+    def create_reasoning_plan(plan: str, steps: list, ready: bool) -> str:
+        """Create a reasoning plan with steps."""
+        return json.dumps({"plan": plan, "steps": steps, "ready": ready})
+
+    tools = [
+        {
+            "name": "create_reasoning_plan",
+            "description": "Create a structured reasoning plan for completing a task",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "plan": {
+                        "type": "string",
+                        "description": "High-level plan description"
+                    },
+                    "steps": {
+                        "type": "array",
+                        "items": {"type": "object"},
+                        "description": "List of steps to execute"
+                    },
+                    "ready": {
+                        "type": "boolean",
+                        "description": "Whether the plan is ready to execute"
+                    }
+                },
+                "required": ["plan", "steps", "ready"]
+            }
+        }
+    ]
+
+    result = llm.call(
+        messages=[{"role": "user", "content": "Create a simple plan to say hello. Use the create_reasoning_plan tool."}],
+        tools=tools,
+        available_functions={"create_reasoning_plan": create_reasoning_plan}
+    )
+
+    # Verify result is valid JSON from the tool
+    assert result is not None
+    assert isinstance(result, str)
+
+    # Parse the result to verify it's valid JSON
+    parsed_result = json.loads(result)
+    assert "plan" in parsed_result
+    assert "steps" in parsed_result
+    assert "ready" in parsed_result
+
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_tool_execution_returns_tool_result_directly():
+    """
+    Test that when available_functions is provided, the tool result is returned directly
+    without additional LLM conversation (matching OpenAI behavior for reasoning_handler).
+    """
+    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
+
+    call_count = 0
+
+    def simple_calculator(operation: str, a: int, b: int) -> str:
+        """Perform a simple calculation."""
+        nonlocal call_count
+        call_count += 1
+        if operation == "add":
+            return str(a + b)
+        elif operation == "multiply":
+            return str(a * b)
+        return "Unknown operation"
+
+    tools = [
+        {
+            "name": "simple_calculator",
+            "description": "Perform simple math operations",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "operation": {
+                        "type": "string",
+                        "enum": ["add", "multiply"],
+                        "description": "The operation to perform"
+                    },
+                    "a": {"type": "integer", "description": "First number"},
+                    "b": {"type": "integer", "description": "Second number"}
+                },
+                "required": ["operation", "a", "b"]
+            }
+        }
+    ]
+
+    result = llm.call(
+        messages=[{"role": "user", "content": "Calculate 5 + 3 using the simple_calculator tool with operation 'add'."}],
+        tools=tools,
+        available_functions={"simple_calculator": simple_calculator}
+    )
+
+    # Tool should have been called exactly once
+    assert call_count == 1, f"Expected tool to be called once, got {call_count}"
+
+    # Result should be the direct tool output
+    assert result == "8", f"Expected '8' but got '{result}'"
+
+
 @pytest.mark.vcr()
 def test_anthropic_agent_kickoff_structured_output_without_tools():
     """

lib/crewai/tests/test_flow_human_input_integration.py

@@ -2,6 +2,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 from crewai.events.event_listener import event_listener
+from crewai.core.providers.human_input import SyncHumanInputProvider
 
 
 class TestFlowHumanInputIntegration:
@@ -24,14 +25,9 @@ class TestFlowHumanInputIntegration:
     @patch("builtins.input", return_value="")
     def test_human_input_pauses_flow_updates(self, mock_input):
         """Test that human input pauses Flow status updates."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -39,7 +35,7 @@ class TestFlowHumanInputIntegration:
             patch.object(formatter, "pause_live_updates") as mock_pause,
             patch.object(formatter, "resume_live_updates") as mock_resume,
         ):
-            result = executor._ask_human_input("Test result")
+            result = provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
@@ -49,14 +45,9 @@ class TestFlowHumanInputIntegration:
     @patch("builtins.input", side_effect=["feedback", ""])
     def test_multiple_human_input_rounds(self, mock_input):
         """Test multiple rounds of human input with Flow status management."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -75,10 +66,10 @@ class TestFlowHumanInputIntegration:
                 formatter, "resume_live_updates", side_effect=track_resume
             ),
         ):
-            result1 = executor._ask_human_input("Test result 1")
+            result1 = provider._prompt_input(crew)
             assert result1 == "feedback"
 
-            result2 = executor._ask_human_input("Test result 2")
+            result2 = provider._prompt_input(crew)
             assert result2 == ""
 
             assert len(pause_calls) == 2
@@ -103,14 +94,9 @@ class TestFlowHumanInputIntegration:
 
     def test_pause_resume_exception_handling(self):
         """Test that resume is called even if exception occurs during human input."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -122,21 +108,16 @@ class TestFlowHumanInputIntegration:
             ),
         ):
             with pytest.raises(KeyboardInterrupt):
-                executor._ask_human_input("Test result")
+                provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
 
     def test_training_mode_human_input(self):
         """Test human input in training mode."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = True
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = True
 
         formatter = event_listener.formatter
 
@@ -146,7 +127,7 @@ class TestFlowHumanInputIntegration:
             patch.object(formatter.console, "print") as mock_console_print,
             patch("builtins.input", return_value="training feedback"),
         ):
-            result = executor._ask_human_input("Test result")
+            result = provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
@@ -161,4 +142,4 @@ class TestFlowHumanInputIntegration:
                 for call in call_args
                 if call[0]
             )
-            assert training_panel_found
+            assert training_panel_found

lib/crewai/tests/test_streaming.py

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

lib/crewai/tests/test_task_guardrails.py

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

lib/crewai/tests/utilities/test_events.py

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

pyproject.toml

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

uv.lock

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