wip

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

lib/crewai/pyproject.toml

@@ -14,7 +14,7 @@ dependencies = [
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
-    "regex~=2026.1.15",
+    "regex~=2024.9.11",
     # Telemetry and Monitoring
     "opentelemetry-api~=1.34.0",
     "opentelemetry-sdk~=1.34.0",
@@ -36,7 +36,7 @@ dependencies = [
     "json5~=0.10.0",
     "portalocker~=2.7.0",
     "pydantic-settings~=2.10.1",
-    "mcp~=1.26.0",
+    "mcp~=1.23.1",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
 ]

lib/crewai/src/crewai/__init__.py

@@ -4,7 +4,6 @@ import urllib.request
 import warnings
 
 from crewai.agent.core import Agent
-from crewai.agent.planning_config import PlanningConfig
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
@@ -81,7 +80,6 @@ __all__ = [
     "Flow",
     "Knowledge",
     "LLMGuardrail",
-    "PlanningConfig",
     "Process",
     "Task",
     "TaskOutput",

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

@@ -24,7 +24,7 @@ from pydantic import (
 )
 from typing_extensions import Self
 
-from crewai.agent.planning_config import PlanningConfig
+from crewai.agent.json_schema_converter import JSONSchemaConverter
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -213,23 +213,13 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    planning_config: PlanningConfig | None = Field(
-        default=None,
-        description="Configuration for agent planning before task execution.",
-    )
-    planning: bool = Field(
+    reasoning: bool = Field(
         default=False,
         description="Whether the agent should reflect and create a plan before executing a task.",
     )
-    reasoning: bool = Field(
-        default=False,
-        description="[DEPRECATED: Use planning_config instead] Whether the agent should reflect and create a plan before executing a task.",
-        deprecated=True,
-    )
     max_reasoning_attempts: int | None = Field(
         default=None,
-        description="[DEPRECATED: Use planning_config.max_attempts instead] Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
-        deprecated=True,
+        description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
     embedder: EmbedderConfig | None = Field(
         default=None,
@@ -296,26 +286,8 @@ class Agent(BaseAgent):
         if self.allow_code_execution:
             self._validate_docker_installation()
 
-        # Handle backward compatibility: convert reasoning=True to planning_config
-        if self.reasoning and self.planning_config is None:
-            import warnings
-
-            warnings.warn(
-                "The 'reasoning' parameter is deprecated. Use 'planning_config=PlanningConfig()' instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            self.planning_config = PlanningConfig(
-                max_attempts=self.max_reasoning_attempts,
-            )
-
         return self
 
-    @property
-    def planning_enabled(self) -> bool:
-        """Check if planning is enabled for this agent."""
-        return self.planning_config is not None or self.planning
-
     def _setup_agent_executor(self) -> None:
         if not self.cache_handler:
             self.cache_handler = CacheHandler()
@@ -391,11 +363,7 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        # Only call handle_reasoning for legacy CrewAgentExecutor
-        # For AgentExecutor, planning is handled in AgentExecutor.generate_plan()
-        if self.executor_class is not AgentExecutor:
-            handle_reasoning(self, task)
-
+        handle_reasoning(self, task)
         self._inject_date_to_task(task)
 
         if self.tools_handler:
@@ -632,10 +600,7 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        if self.executor_class is not AgentExecutor:
-            handle_reasoning(
-                self, task
-            )  # we need this till CrewAgentExecutor migrates to AgentExecutor
+        handle_reasoning(self, task)
         self._inject_date_to_task(task)
 
         if self.tools_handler:
@@ -1214,6 +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
 
@@ -1235,6 +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:
@@ -1249,26 +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:
@@ -1425,6 +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:
@@ -1437,77 +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/agent/planning_config.py

@@ -1,83 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-
-class PlanningConfig(BaseModel):
-    """Configuration for agent planning/reasoning before task execution.
-
-    This allows users to customize the planning behavior including prompts,
-    iteration limits, and the LLM used for planning.
-
-    Note: To disable planning, don't pass a planning_config or set planning=False
-    on the Agent. The presence of a PlanningConfig enables planning.
-
-    Attributes:
-        max_attempts: Maximum number of planning refinement attempts.
-            If None, will continue until the agent indicates readiness.
-        max_steps: Maximum number of steps in the generated plan.
-        system_prompt: Custom system prompt for planning. Uses default if None.
-        plan_prompt: Custom prompt for creating the initial plan.
-        refine_prompt: Custom prompt for refining the plan.
-        llm: LLM to use for planning. Uses agent's LLM if None.
-
-    Example:
-        ```python
-        from crewai import Agent
-        from crewai.agent.planning_config import PlanningConfig
-
-        # Simple usage
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            planning_config=PlanningConfig(),
-        )
-
-        # Customized planning
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            planning_config=PlanningConfig(
-                max_attempts=3,
-                max_steps=10,
-                plan_prompt="Create a focused plan for: {description}",
-                llm="gpt-4o-mini",  # Use cheaper model for planning
-            ),
-        )
-        ```
-    """
-
-    max_attempts: int | None = Field(
-        default=None,
-        description=(
-            "Maximum number of planning refinement attempts. "
-            "If None, will continue until the agent indicates readiness."
-        ),
-    )
-    max_steps: int = Field(
-        default=20,
-        description="Maximum number of steps in the generated plan.",
-        ge=1,
-    )
-    system_prompt: str | None = Field(
-        default=None,
-        description="Custom system prompt for planning. Uses default if None.",
-    )
-    plan_prompt: str | None = Field(
-        default=None,
-        description="Custom prompt for creating the initial plan.",
-    )
-    refine_prompt: str | None = Field(
-        default=None,
-        description="Custom prompt for refining the plan.",
-    )
-    llm: str | Any | None = Field(
-        default=None,
-        description="LLM to use for planning. Uses agent's LLM if None.",
-    )
-
-    model_config = {"arbitrary_types_allowed": True}

lib/crewai/src/crewai/agent/utils.py

@@ -28,20 +28,13 @@ if TYPE_CHECKING:
 
 
 def handle_reasoning(agent: Agent, task: Task) -> None:
-    """Handle the reasoning/planning process for an agent before task execution.
-
-    This function checks if planning is enabled for the agent and, if so,
-    creates a plan that gets appended to the task description.
-
-    Note: This function is used by CrewAgentExecutor (legacy path).
-    For AgentExecutor, planning is handled in AgentExecutor.generate_plan().
+    """Handle the reasoning process for an agent before task execution.
 
     Args:
         agent: The agent performing the task.
         task: The task to execute.
     """
-    # Check if planning is enabled using the planning_enabled property
-    if not getattr(agent, "planning_enabled", False):
+    if not agent.reasoning:
         return
 
     try:
@@ -50,13 +43,13 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
             AgentReasoningOutput,
         )
 
-        planning_handler = AgentReasoning(agent=agent, task=task)
-        planning_output: AgentReasoningOutput = (
-            planning_handler.handle_agent_reasoning()
+        reasoning_handler = AgentReasoning(task=task, agent=agent)
+        reasoning_output: AgentReasoningOutput = (
+            reasoning_handler.handle_agent_reasoning()
         )
-        task.description += f"\n\nPlanning:\n{planning_output.plan.plan}"
+        task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
     except Exception as e:
-        agent._logger.log("error", f"Error during planning: {e!s}")
+        agent._logger.log("error", f"Error during reasoning process: {e!s}")
 
 
 def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:

lib/crewai/src/crewai/agents/agent_adapters/base_agent_adapter.py

@@ -37,10 +37,9 @@ class BaseAgentAdapter(BaseAgent, ABC):
             tools: Optional list of BaseTool instances to be configured
         """
 
-    @abstractmethod
-    def configure_structured_output(self, task: Any) -> None:
+    def configure_structured_output(self, structured_output: Any) -> None:
         """Configure the structured output for the specific agent implementation.
 
         Args:
-            task: The task object containing output format specifications.
+            structured_output: The structured output to be configured
         """

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

@@ -814,7 +814,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 agent_key=agent_key,
             ),
         )
-        error_event_emitted = False
 
         track_delegation_if_needed(func_name, args_dict, self.task)
 
@@ -897,7 +896,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                             error=e,
                         ),
                     )
-                    error_event_emitted = True
         elif max_usage_reached and original_tool:
             # Return error message when max usage limit is reached
             result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
@@ -925,20 +923,20 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     color="red",
                 )
 
-        if not error_event_emitted:
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageFinishedEvent(
-                    output=result,
-                    tool_name=func_name,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                    started_at=started_at,
-                    finished_at=datetime.now(),
-                ),
-            )
+        # Emit tool usage finished event
+        crewai_event_bus.emit(
+            self,
+            event=ToolUsageFinishedEvent(
+                output=result,
+                tool_name=func_name,
+                tool_args=args_dict,
+                from_agent=self.agent,
+                from_task=self.task,
+                agent_key=agent_key,
+                started_at=started_at,
+                finished_at=datetime.now(),
+            ),
+        )
 
         # Append tool result message
         tool_message: LLMMessage = {
@@ -1009,7 +1007,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             raise
 
         if self.ask_for_human_input:
-            formatted_answer = await self._ahandle_human_feedback(formatted_answer)
+            formatted_answer = self._handle_human_feedback(formatted_answer)
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
@@ -1508,20 +1506,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         provider = get_provider()
         return provider.handle_feedback(formatted_answer, self)
 
-    async def _ahandle_human_feedback(
-        self, formatted_answer: AgentFinish
-    ) -> AgentFinish:
-        """Process human feedback asynchronously via the configured provider.
-
-        Args:
-            formatted_answer: Initial agent result.
-
-        Returns:
-            Final answer after feedback.
-        """
-        provider = get_provider()
-        return await provider.handle_feedback_async(formatted_answer, self)
-
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
 

lib/crewai/src/crewai/cli/plus_api.py

@@ -1,8 +1,6 @@
-import os
 from typing import Any
 from urllib.parse import urljoin
-
-import httpx
+import os
 import requests
 
 from crewai.cli.config import Settings
@@ -35,11 +33,7 @@ class PlusAPI:
         if settings.org_uuid:
             self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
 
-        self.base_url = (
-            os.getenv("CREWAI_PLUS_URL")
-            or str(settings.enterprise_base_url)
-            or DEFAULT_CREWAI_ENTERPRISE_URL
-        )
+        self.base_url = os.getenv("CREWAI_PLUS_URL") or str(settings.enterprise_base_url) or DEFAULT_CREWAI_ENTERPRISE_URL
 
     def _make_request(
         self, method: str, endpoint: str, **kwargs: Any
@@ -55,10 +49,8 @@ class PlusAPI:
     def get_tool(self, handle: str) -> requests.Response:
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
 
-    async def get_agent(self, handle: str) -> httpx.Response:
-        url = urljoin(self.base_url, f"{self.AGENTS_RESOURCE}/{handle}")
-        async with httpx.AsyncClient() as client:
-            return await client.get(url, headers=self.headers)
+    def get_agent(self, handle: str) -> requests.Response:
+        return self._make_request("GET", f"{self.AGENTS_RESOURCE}/{handle}")
 
     def publish_tool(
         self,

lib/crewai/src/crewai/context.py

@@ -43,23 +43,3 @@ def platform_context(integration_token: str) -> Generator[None, Any, None]:
         yield
     finally:
         _platform_integration_token.reset(token)
-
-
-_current_task_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
-    "current_task_id", default=None
-)
-
-
-def set_current_task_id(task_id: str | None) -> contextvars.Token[str | None]:
-    """Set the current task ID in the context. Returns a token for reset."""
-    return _current_task_id.set(task_id)
-
-
-def reset_current_task_id(token: contextvars.Token[str | None]) -> None:
-    """Reset the current task ID to its previous value."""
-    _current_task_id.reset(token)
-
-
-def get_current_task_id() -> str | None:
-    """Get the current task ID from the context."""
-    return _current_task_id.get()

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

@@ -2,9 +2,7 @@
 
 from __future__ import annotations
 
-import asyncio
 from contextvars import ContextVar, Token
-import sys
 from typing import TYPE_CHECKING, Protocol, runtime_checkable
 
 
@@ -48,21 +46,13 @@ class ExecutorContext(Protocol):
         ...
 
 
-class AsyncExecutorContext(ExecutorContext, Protocol):
-    """Extended context for executors that support async invocation."""
-
-    async def _ainvoke_loop(self) -> AgentFinish:
-        """Invoke the agent loop asynchronously and return the result."""
-        ...
-
-
 @runtime_checkable
 class HumanInputProvider(Protocol):
     """Protocol for human input handling.
 
     Implementations handle the full feedback flow:
     - Sync: prompt user, loop until satisfied
-    - Async: use non-blocking I/O and async invoke loop
+    - Async: raise exception for external handling
     """
 
     def setup_messages(self, context: ExecutorContext) -> bool:
@@ -96,7 +86,7 @@ class HumanInputProvider(Protocol):
         formatted_answer: AgentFinish,
         context: ExecutorContext,
     ) -> AgentFinish:
-        """Handle the full human feedback flow synchronously.
+        """Handle the full human feedback flow.
 
         Args:
             formatted_answer: The agent's current answer.
@@ -110,25 +100,6 @@ class HumanInputProvider(Protocol):
         """
         ...
 
-    async def handle_feedback_async(
-        self,
-        formatted_answer: AgentFinish,
-        context: AsyncExecutorContext,
-    ) -> AgentFinish:
-        """Handle the full human feedback flow asynchronously.
-
-        Uses non-blocking I/O for user prompts and async invoke loop
-        for agent re-execution.
-
-        Args:
-            formatted_answer: The agent's current answer.
-            context: Async executor context for callbacks.
-
-        Returns:
-            The final answer after feedback processing.
-        """
-        ...
-
     @staticmethod
     def _get_output_string(answer: AgentFinish) -> str:
         """Extract output string from answer.
@@ -145,7 +116,7 @@ class HumanInputProvider(Protocol):
 
 
 class SyncHumanInputProvider(HumanInputProvider):
-    """Default human input provider with sync and async support."""
+    """Default synchronous human input via terminal."""
 
     def setup_messages(self, context: ExecutorContext) -> bool:
         """Use standard message setup.
@@ -186,33 +157,6 @@ class SyncHumanInputProvider(HumanInputProvider):
 
         return self._handle_regular_feedback(formatted_answer, feedback, context)
 
-    async def handle_feedback_async(
-        self,
-        formatted_answer: AgentFinish,
-        context: AsyncExecutorContext,
-    ) -> AgentFinish:
-        """Handle feedback asynchronously without blocking the event loop.
-
-        Args:
-            formatted_answer: The agent's current answer.
-            context: Async executor context for callbacks.
-
-        Returns:
-            The final answer after feedback processing.
-        """
-        feedback = await self._prompt_input_async(context.crew)
-
-        if context._is_training_mode():
-            return await self._handle_training_feedback_async(
-                formatted_answer, feedback, context
-            )
-
-        return await self._handle_regular_feedback_async(
-            formatted_answer, feedback, context
-        )
-
-    # ── Sync helpers ──────────────────────────────────────────────────
-
     @staticmethod
     def _handle_training_feedback(
         initial_answer: AgentFinish,
@@ -265,62 +209,6 @@ class SyncHumanInputProvider(HumanInputProvider):
 
         return answer
 
-    # ── Async helpers ─────────────────────────────────────────────────
-
-    @staticmethod
-    async def _handle_training_feedback_async(
-        initial_answer: AgentFinish,
-        feedback: str,
-        context: AsyncExecutorContext,
-    ) -> AgentFinish:
-        """Process training feedback asynchronously (single iteration).
-
-        Args:
-            initial_answer: The agent's initial answer.
-            feedback: Human feedback string.
-            context: Async 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 = await context._ainvoke_loop()
-        context._handle_crew_training_output(improved_answer)
-        context.ask_for_human_input = False
-        return improved_answer
-
-    async def _handle_regular_feedback_async(
-        self,
-        current_answer: AgentFinish,
-        initial_feedback: str,
-        context: AsyncExecutorContext,
-    ) -> AgentFinish:
-        """Process regular feedback with async iteration loop.
-
-        Args:
-            current_answer: The agent's current answer.
-            initial_feedback: Initial human feedback string.
-            context: Async 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 = await context._ainvoke_loop()
-                feedback = await self._prompt_input_async(context.crew)
-
-        return answer
-
-    # ── I/O ───────────────────────────────────────────────────────────
-
     @staticmethod
     def _prompt_input(crew: Crew | None) -> str:
         """Show rich panel and prompt for input.
@@ -374,79 +262,6 @@ class SyncHumanInputProvider(HumanInputProvider):
         finally:
             formatter.resume_live_updates()
 
-    @staticmethod
-    async def _prompt_input_async(crew: Crew | None) -> str:
-        """Show rich panel and prompt for input without blocking the event loop.
-
-        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 = await _async_readline()
-            if response.strip() != "":
-                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
-            return response
-        finally:
-            formatter.resume_live_updates()
-
-
-async def _async_readline() -> str:
-    """Read a line from stdin using the event loop's native I/O.
-
-    Falls back to asyncio.to_thread on platforms where piping stdin
-    is unsupported.
-
-    Returns:
-        The line read from stdin, with trailing newline stripped.
-    """
-    loop = asyncio.get_running_loop()
-    try:
-        reader = asyncio.StreamReader()
-        protocol = asyncio.StreamReaderProtocol(reader)
-        await loop.connect_read_pipe(lambda: protocol, sys.stdin)
-        raw = await reader.readline()
-        return raw.decode().rstrip("\n")
-    except (OSError, NotImplementedError, ValueError):
-        return await asyncio.to_thread(input)
-
 
 _provider: ContextVar[HumanInputProvider | None] = ContextVar(
     "human_input_provider",

lib/crewai/src/crewai/crew.py

@@ -751,8 +751,6 @@ class Crew(FlowTrackable, BaseModel):
             for after_callback in self.after_kickoff_callbacks:
                 result = after_callback(result)
 
-            result = self._post_kickoff(result)
-
             self.usage_metrics = self.calculate_usage_metrics()
 
             return result
@@ -766,9 +764,6 @@ class Crew(FlowTrackable, BaseModel):
             clear_files(self.id)
             detach(token)
 
-    def _post_kickoff(self, result: CrewOutput) -> CrewOutput:
-        return result
-
     def kickoff_for_each(
         self,
         inputs: list[dict[str, Any]],
@@ -941,8 +936,6 @@ class Crew(FlowTrackable, BaseModel):
             for after_callback in self.after_kickoff_callbacks:
                 result = after_callback(result)
 
-            result = self._post_kickoff(result)
-
             self.usage_metrics = self.calculate_usage_metrics()
 
             return result
@@ -1188,9 +1181,6 @@ class Crew(FlowTrackable, BaseModel):
             self.manager_agent = manager
         manager.crew = self
 
-    def _get_execution_start_index(self, tasks: list[Task]) -> int | None:
-        return None
-
     def _execute_tasks(
         self,
         tasks: list[Task],
@@ -1207,9 +1197,6 @@ class Crew(FlowTrackable, BaseModel):
         Returns:
             CrewOutput: Final output of the crew
         """
-        custom_start = self._get_execution_start_index(tasks)
-        if custom_start is not None:
-            start_index = custom_start
 
         task_outputs: list[TaskOutput] = []
         futures: list[tuple[Task, Future[TaskOutput], int]] = []
@@ -1318,10 +1305,8 @@ class Crew(FlowTrackable, BaseModel):
         if files:
             supported_types: list[str] = []
             if agent and agent.llm and agent.llm.supports_multimodal():
-                provider = (
-                    getattr(agent.llm, "provider", None)
-                    or getattr(agent.llm, "model", None)
-                    or "openai"
+                provider = getattr(agent.llm, "provider", None) or getattr(
+                    agent.llm, "model", "openai"
                 )
                 api = getattr(agent.llm, "api", None)
                 supported_types = get_supported_content_types(provider, api)
@@ -1517,7 +1502,6 @@ class Crew(FlowTrackable, BaseModel):
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         self.token_usage = self.calculate_usage_metrics()
-        crewai_event_bus.flush()
         crewai_event_bus.emit(
             self,
             CrewKickoffCompletedEvent(
@@ -2027,13 +2011,7 @@ class Crew(FlowTrackable, BaseModel):
     @staticmethod
     def _show_tracing_disabled_message() -> None:
         """Show a message when tracing is disabled."""
-        from crewai.events.listeners.tracing.utils import (
-            has_user_declined_tracing,
-            should_suppress_tracing_messages,
-        )
-
-        if should_suppress_tracing_messages():
-            return
+        from crewai.events.listeners.tracing.utils import has_user_declined_tracing
 
         console = Console()
 

lib/crewai/src/crewai/events/__init__.py

@@ -195,7 +195,6 @@ __all__ = [
     "ToolUsageFinishedEvent",
     "ToolUsageStartedEvent",
     "ToolValidateInputErrorEvent",
-    "_extension_exports",
     "crewai_event_bus",
 ]
 
@@ -211,29 +210,14 @@ _AGENT_EVENT_MAPPING = {
     "LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
 }
 
-_extension_exports: dict[str, Any] = {}
-
 
 def __getattr__(name: str) -> Any:
-    """Lazy import for agent events and registered extensions."""
+    """Lazy import for agent events to avoid circular imports."""
     if name in _AGENT_EVENT_MAPPING:
         import importlib
 
         module_path = _AGENT_EVENT_MAPPING[name]
         module = importlib.import_module(module_path)
         return getattr(module, name)
-
-    if name in _extension_exports:
-        import importlib
-
-        value = _extension_exports[name]
-        if isinstance(value, str):
-            module_path, _, attr_name = value.rpartition(".")
-            if module_path:
-                module = importlib.import_module(module_path)
-                return getattr(module, attr_name)
-            return importlib.import_module(value)
-        return value
-
     msg = f"module {__name__!r} has no attribute {name!r}"
     raise AttributeError(msg)

lib/crewai/src/crewai/events/base_events.py

@@ -63,7 +63,6 @@ class BaseEvent(BaseModel):
     parent_event_id: str | None = None
     previous_event_id: str | None = None
     triggered_by_event_id: str | None = None
-    started_event_id: str | None = None
     emission_sequence: int | None = None
 
     def to_json(self, exclude: set[str] | None = None) -> Serializable:

lib/crewai/src/crewai/events/event_bus.py

@@ -227,39 +227,6 @@ class CrewAIEventsBus:
 
         return decorator
 
-    def off(
-        self,
-        event_type: type[BaseEvent],
-        handler: Callable[..., Any],
-    ) -> None:
-        """Unregister an event handler for a specific event type.
-
-        Args:
-            event_type: The event class to stop listening for
-            handler: The handler function to unregister
-        """
-        with self._rwlock.w_locked():
-            if event_type in self._sync_handlers:
-                existing_sync = self._sync_handlers[event_type]
-                if handler in existing_sync:
-                    self._sync_handlers[event_type] = existing_sync - {handler}
-                    if not self._sync_handlers[event_type]:
-                        del self._sync_handlers[event_type]
-
-            if event_type in self._async_handlers:
-                existing_async = self._async_handlers[event_type]
-                if handler in existing_async:
-                    self._async_handlers[event_type] = existing_async - {handler}
-                    if not self._async_handlers[event_type]:
-                        del self._async_handlers[event_type]
-
-            if event_type in self._handler_dependencies:
-                self._handler_dependencies[event_type].pop(handler, None)
-                if not self._handler_dependencies[event_type]:
-                    del self._handler_dependencies[event_type]
-
-            self._execution_plan_cache.pop(event_type, None)
-
     def _call_handlers(
         self,
         source: Any,
@@ -407,8 +374,7 @@ class CrewAIEventsBus:
                 if popped is None:
                     handle_empty_pop(event_type_name)
                 else:
-                    popped_event_id, popped_type = popped
-                    event.started_event_id = popped_event_id
+                    _, popped_type = popped
                     expected_start = VALID_EVENT_PAIRS.get(event_type_name)
                     if expected_start and popped_type and popped_type != expected_start:
                         handle_mismatch(event_type_name, popped_type, expected_start)
@@ -570,52 +536,24 @@ class CrewAIEventsBus:
             ...     # Do stuff...
             ... # Handlers are cleared after the context
         """
-        with self._rwlock.r_locked():
-            saved_sync: dict[type[BaseEvent], frozenset[SyncHandler]] = dict(
-                self._sync_handlers
-            )
-            saved_async: dict[type[BaseEvent], frozenset[AsyncHandler]] = dict(
-                self._async_handlers
-            )
-            saved_deps: dict[type[BaseEvent], dict[Handler, list[Depends[Any]]]] = {
-                event_type: dict(handlers)
-                for event_type, handlers in self._handler_dependencies.items()
-            }
-
-        for event_type, sync_handlers in saved_sync.items():
-            for sync_handler in sync_handlers:
-                self.off(event_type, sync_handler)
-
-        for event_type, async_handlers in saved_async.items():
-            for async_handler in async_handlers:
-                self.off(event_type, async_handler)
+        with self._rwlock.w_locked():
+            prev_sync = self._sync_handlers
+            prev_async = self._async_handlers
+            prev_deps = self._handler_dependencies
+            prev_cache = self._execution_plan_cache
+            self._sync_handlers = {}
+            self._async_handlers = {}
+            self._handler_dependencies = {}
+            self._execution_plan_cache = {}
 
         try:
             yield
         finally:
-            with self._rwlock.r_locked():
-                current_sync = dict(self._sync_handlers)
-                current_async = dict(self._async_handlers)
-
-            for event_type, cur_sync in current_sync.items():
-                orig_sync = saved_sync.get(event_type, frozenset())
-                for new_handler in cur_sync - orig_sync:
-                    self.off(event_type, new_handler)
-
-            for event_type, cur_async in current_async.items():
-                orig_async = saved_async.get(event_type, frozenset())
-                for new_async_handler in cur_async - orig_async:
-                    self.off(event_type, new_async_handler)
-
-            for event_type, sync_handlers in saved_sync.items():
-                for sync_handler in sync_handlers:
-                    deps = saved_deps.get(event_type, {}).get(sync_handler)
-                    self._register_handler(event_type, sync_handler, deps)
-
-            for event_type, async_handlers in saved_async.items():
-                for async_handler in async_handlers:
-                    deps = saved_deps.get(event_type, {}).get(async_handler)
-                    self._register_handler(event_type, async_handler, deps)
+            with self._rwlock.w_locked():
+                self._sync_handlers = prev_sync
+                self._async_handlers = prev_async
+                self._handler_dependencies = prev_deps
+                self._execution_plan_cache = prev_cache
 
     def shutdown(self, wait: bool = True) -> None:
         """Gracefully shutdown the event loop and wait for all tasks to finish.

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -797,13 +797,7 @@ class TraceCollectionListener(BaseEventListener):
         from rich.console import Console
         from rich.panel import Panel
 
-        from crewai.events.listeners.tracing.utils import (
-            has_user_declined_tracing,
-            should_suppress_tracing_messages,
-        )
-
-        if should_suppress_tracing_messages():
-            return
+        from crewai.events.listeners.tracing.utils import has_user_declined_tracing
 
         console = Console()
 

lib/crewai/src/crewai/events/listeners/tracing/utils.py

@@ -1,4 +1,3 @@
-from collections.abc import Callable
 from contextvars import ContextVar, Token
 from datetime import datetime
 import getpass
@@ -27,35 +26,6 @@ logger = logging.getLogger(__name__)
 
 _tracing_enabled: ContextVar[bool | None] = ContextVar("_tracing_enabled", default=None)
 
-_first_time_trace_hook: ContextVar[Callable[[], bool] | None] = ContextVar(
-    "_first_time_trace_hook", default=None
-)
-
-_suppress_tracing_messages: ContextVar[bool] = ContextVar(
-    "_suppress_tracing_messages", default=False
-)
-
-
-def set_suppress_tracing_messages(suppress: bool) -> object:
-    """Set whether to suppress tracing-related console messages.
-
-    Args:
-        suppress: True to suppress messages, False to show them.
-
-    Returns:
-        A token that can be used to restore the previous value.
-    """
-    return _suppress_tracing_messages.set(suppress)
-
-
-def should_suppress_tracing_messages() -> bool:
-    """Check if tracing messages should be suppressed.
-
-    Returns:
-        True if messages should be suppressed, False otherwise.
-    """
-    return _suppress_tracing_messages.get()
-
 
 def should_enable_tracing(*, override: bool | None = None) -> bool:
     """Determine if tracing should be enabled.
@@ -437,13 +407,10 @@ def truncate_messages(
 def should_auto_collect_first_time_traces() -> bool:
     """True if we should auto-collect traces for first-time user.
 
+
     Returns:
         True if first-time user AND telemetry not disabled AND tracing not explicitly enabled, False otherwise.
     """
-    hook = _first_time_trace_hook.get()
-    if hook is not None:
-        return hook()
-
     if _is_test_environment():
         return False
 
@@ -465,9 +432,6 @@ def prompt_user_for_trace_viewing(timeout_seconds: int = 20) -> bool:
     if _is_test_environment():
         return False
 
-    if should_suppress_tracing_messages():
-        return False
-
     try:
         import threading
 

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

@@ -9,7 +9,7 @@ class ReasoningEvent(BaseEvent):
     type: str
     attempt: int = 1
     agent_role: str
-    task_id: str | None = None
+    task_id: str
     task_name: str | None = None
     from_task: Any | None = None
     agent_id: str | None = None

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

@@ -16,7 +16,7 @@ class ToolUsageEvent(BaseEvent):
     tool_name: str
     tool_args: dict[str, Any] | str
     tool_class: str | None = None
-    run_attempts: int = 0
+    run_attempts: int | None = None
     delegations: int | None = None
     agent: Any | None = None
     task_name: str | None = None
@@ -26,7 +26,7 @@ class ToolUsageEvent(BaseEvent):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
-    def __init__(self, **data: Any) -> None:
+    def __init__(self, **data):
         if data.get("from_task"):
             task = data["from_task"]
             data["task_id"] = str(task.id)
@@ -96,10 +96,10 @@ class ToolExecutionErrorEvent(BaseEvent):
     type: str = "tool_execution_error"
     tool_name: str
     tool_args: dict[str, Any]
-    tool_class: Callable[..., Any]
+    tool_class: Callable
     agent: Any | None = None
 
-    def __init__(self, **data: Any) -> None:
+    def __init__(self, **data):
         super().__init__(**data)
         # Set fingerprint data from the agent
         if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:

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

@@ -1,4 +1,3 @@
-from contextvars import ContextVar
 import os
 import threading
 from typing import Any, ClassVar, cast
@@ -11,36 +10,6 @@ from rich.text import Text
 from crewai.cli.version import is_newer_version_available
 
 
-_disable_version_check: ContextVar[bool] = ContextVar(
-    "_disable_version_check", default=False
-)
-
-_suppress_console_output: ContextVar[bool] = ContextVar(
-    "_suppress_console_output", default=False
-)
-
-
-def set_suppress_console_output(suppress: bool) -> object:
-    """Set whether to suppress all console output.
-
-    Args:
-        suppress: True to suppress output, False to show it.
-
-    Returns:
-        A token that can be used to restore the previous value.
-    """
-    return _suppress_console_output.set(suppress)
-
-
-def should_suppress_console_output() -> bool:
-    """Check if console output should be suppressed.
-
-    Returns:
-        True if output should be suppressed, False otherwise.
-    """
-    return _suppress_console_output.get()
-
-
 class ConsoleFormatter:
     tool_usage_counts: ClassVar[dict[str, int]] = {}
 
@@ -77,15 +46,9 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        if _disable_version_check.get():
-            return
-
         if os.getenv("CI", "").lower() in ("true", "1"):
             return
 
-        if os.getenv("CREWAI_DISABLE_VERSION_CHECK", "").lower() in ("true", "1"):
-            return
-
         try:
             is_newer, current, latest = is_newer_version_available()
             if is_newer and latest:
@@ -113,12 +76,8 @@ To update, run: uv sync --upgrade-package crewai"""
         from crewai.events.listeners.tracing.utils import (
             has_user_declined_tracing,
             is_tracing_enabled_in_context,
-            should_suppress_tracing_messages,
         )
 
-        if should_suppress_tracing_messages():
-            return
-
         if not is_tracing_enabled_in_context():
             if has_user_declined_tracing():
                 message = """Info: Tracing is disabled.
@@ -170,8 +129,6 @@ To enable tracing, do any one of these:
 
     def print(self, *args: Any, **kwargs: Any) -> None:
         """Print to console. Simplified to only handle panel-based output."""
-        if should_suppress_console_output():
-            return
         # Skip blank lines during streaming
         if len(args) == 0 and self._is_streaming:
             return
@@ -528,9 +485,6 @@ To enable tracing, do any one of these:
         if not self.verbose:
             return
 
-        if should_suppress_console_output():
-            return
-
         self._is_streaming = True
         self._last_stream_call_type = call_type
 

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

@@ -18,7 +18,6 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
-from crewai.core.providers.human_input import get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.listeners.tracing.utils import (
     is_tracing_enabled_in_context,
@@ -32,8 +31,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.flow.flow import Flow, StateProxy, listen, or_, router, start
-from crewai.flow.types import FlowMethodName
+from crewai.flow.flow import Flow, listen, or_, router, start
 from crewai.hooks.llm_hooks import (
     get_after_llm_call_hooks,
     get_before_llm_call_hooks,
@@ -43,12 +41,7 @@ from crewai.hooks.tool_hooks import (
     get_after_tool_call_hooks,
     get_before_tool_call_hooks,
 )
-from crewai.hooks.types import (
-    AfterLLMCallHookCallable,
-    AfterLLMCallHookType,
-    BeforeLLMCallHookCallable,
-    BeforeLLMCallHookType,
-)
+from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
 from crewai.utilities.agent_utils import (
     convert_tools_to_openai_schema,
     enforce_rpm_limit,
@@ -68,7 +61,6 @@ from crewai.utilities.agent_utils import (
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.i18n import I18N, get_i18n
-from crewai.utilities.planning_types import PlanStep, TodoItem, TodoList
 from crewai.utilities.printer import Printer
 from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
@@ -102,13 +94,6 @@ class AgentReActState(BaseModel):
     ask_for_human_input: bool = Field(default=False)
     use_native_tools: bool = Field(default=False)
     pending_tool_calls: list[Any] = Field(default_factory=list)
-    plan: str | None = Field(default=None, description="Generated execution plan")
-    plan_ready: bool = Field(
-        default=False, description="Whether agent is ready to execute"
-    )
-    todos: TodoList = Field(
-        default_factory=TodoList, description="Todo list for tracking plan execution"
-    )
 
 
 class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
@@ -206,12 +191,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         self._instance_id = str(uuid4())[:8]
 
-        self.before_llm_call_hooks: list[
-            BeforeLLMCallHookType | BeforeLLMCallHookCallable
-        ] = []
-        self.after_llm_call_hooks: list[
-            AfterLLMCallHookType | AfterLLMCallHookCallable
-        ] = []
+        self.before_llm_call_hooks: list[BeforeLLMCallHookType] = []
+        self.after_llm_call_hooks: list[AfterLLMCallHookType] = []
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
 
@@ -226,71 +207,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             )
         self._state = AgentReActState()
 
-    @property
-    def messages(self) -> list[LLMMessage]:
-        """Delegate to state for ExecutorContext conformance."""
-        return self._state.messages
-
-    @messages.setter
-    def messages(self, value: list[LLMMessage]) -> None:
-        """Delegate to state for ExecutorContext conformance."""
-        if self._flow_initialized and hasattr(self, "_state_lock"):
-            with self._state_lock:
-                self._state.messages = value
-        else:
-            self._state.messages = value
-
-    @property
-    def ask_for_human_input(self) -> bool:
-        """Delegate to state for ExecutorContext conformance."""
-        return self._state.ask_for_human_input
-
-    @ask_for_human_input.setter
-    def ask_for_human_input(self, value: bool) -> None:
-        """Delegate to state for ExecutorContext conformance."""
-        self._state.ask_for_human_input = value
-
-    def _invoke_loop(self) -> AgentFinish:
-        """Invoke the agent loop and return the result.
-
-        Required by ExecutorContext protocol.
-        """
-        self._state.iterations = 0
-        self._state.is_finished = False
-        self._state.current_answer = None
-
-        self.kickoff()
-
-        answer = self._state.current_answer
-        if not isinstance(answer, AgentFinish):
-            raise RuntimeError("Agent loop did not produce a final answer")
-        return answer
-
-    async def _ainvoke_loop(self) -> AgentFinish:
-        """Invoke the agent loop asynchronously and return the result.
-
-        Required by AsyncExecutorContext protocol.
-        """
-        self._state.iterations = 0
-        self._state.is_finished = False
-        self._state.current_answer = None
-
-        await self.akickoff()
-
-        answer = self._state.current_answer
-        if not isinstance(answer, AgentFinish):
-            raise RuntimeError("Agent loop did not produce a final answer")
-        return answer
-
-    def _format_feedback_message(self, feedback: str) -> LLMMessage:
-        """Format feedback as a message for the LLM.
-
-        Required by ExecutorContext protocol.
-        """
-        return format_message_for_llm(
-            self._i18n.slice("feedback_instructions").format(feedback=feedback)
-        )
-
     def _ensure_flow_initialized(self) -> None:
         """Ensure Flow.__init__() has been called.
 
@@ -382,10 +298,18 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         Flow initialization is deferred to prevent event emission during agent setup.
         Returns the temporary state until invoke() is called.
         """
-        if self._flow_initialized and hasattr(self, "_state_lock"):
-            return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
         return self._state
 
+    @property
+    def messages(self) -> list[LLMMessage]:
+        """Compatibility property for mixin - returns state messages."""
+        return self._state.messages
+
+    @messages.setter
+    def messages(self, value: list[LLMMessage]) -> None:
+        """Set state messages."""
+        self._state.messages = value
+
     @property
     def iterations(self) -> int:
         """Compatibility property for mixin - returns state iterations."""
@@ -397,67 +321,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         self._state.iterations = value
 
     @start()
-    def generate_plan(self) -> None:
-        """Generate execution plan if planning is enabled.
-
-        This is the entry point for the agent execution flow. If planning is
-        enabled on the agent, it generates a plan before execution begins.
-        The plan is stored in state and todos are created from the steps.
-        """
-        if not getattr(self.agent, "planning_enabled", False):
-            return
-
-        try:
-            from crewai.utilities.reasoning_handler import AgentReasoning
-
-            if self.task:
-                planning_handler = AgentReasoning(agent=self.agent, task=self.task)
-            else:
-                # For kickoff() path - use input text directly, no Task needed
-                input_text = getattr(self, "_kickoff_input", "")
-                planning_handler = AgentReasoning(
-                    agent=self.agent,
-                    description=input_text or "Complete the requested task",
-                    expected_output="Complete the task successfully",
-                )
-
-            output = planning_handler.handle_agent_reasoning()
-
-            self.state.plan = output.plan.plan
-            self.state.plan_ready = output.plan.ready
-
-            if self.state.plan_ready and output.plan.steps:
-                self._create_todos_from_plan(output.plan.steps)
-
-            # Backward compatibility: append plan to task description
-            # This can be removed in Phase 2 when plan execution is implemented
-            if self.task and self.state.plan:
-                self.task.description += f"\n\nPlanning:\n{self.state.plan}"
-
-        except Exception as e:
-            if hasattr(self.agent, "_logger"):
-                self.agent._logger.log("error", f"Error during planning: {e!s}")
-
-    def _create_todos_from_plan(self, steps: list[PlanStep]) -> None:
-        """Convert plan steps into trackable todo items.
-
-        Args:
-            steps: List of PlanStep objects from the reasoning handler.
-        """
-        todos: list[TodoItem] = []
-        for step in steps:
-            todo = TodoItem(
-                step_number=step.step_number,
-                description=step.description,
-                tool_to_use=step.tool_to_use,
-                depends_on=step.depends_on,
-                status="pending",
-            )
-            todos.append(todo)
-
-        self.state.todos = TodoList(items=todos)
-
-    @listen(generate_plan)
     def initialize_reasoning(self) -> Literal["initialized"]:
         """Initialize the reasoning flow and emit agent start logs."""
         self._show_start_logs()
@@ -553,14 +416,15 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             raise
 
     @listen("continue_reasoning_native")
-    def call_llm_native_tools(self) -> None:
+    def call_llm_native_tools(
+        self,
+    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
         """Execute LLM call with native function calling.
 
         Always calls the LLM so it can read reflection prompts and decide
         whether to provide a final answer or request more tools.
 
-        Note: This is a listener, not a router. The route_native_tool_result
-        router fires after this to determine the next step based on state.
+        Returns routing decision based on whether tool calls or final answer.
         """
         try:
             # Clear pending tools - LLM will decide what to do next after reading
@@ -590,7 +454,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             if isinstance(answer, list) and answer and self._is_tool_call_list(answer):
                 # Store tool calls for sequential processing
                 self.state.pending_tool_calls = list(answer)
-                return  # Router will check pending_tool_calls
+
+                return "native_tool_calls"
 
             if isinstance(answer, BaseModel):
                 self.state.current_answer = AgentFinish(
@@ -600,7 +465,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
                 self._invoke_step_callback(self.state.current_answer)
                 self._append_message_to_state(answer.model_dump_json())
-                return  # Router will check current_answer
+                return "native_finished"
 
             # Text response - this is the final answer
             if isinstance(answer, str):
@@ -611,7 +476,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
                 self._invoke_step_callback(self.state.current_answer)
                 self._append_message_to_state(answer)
-                return  # Router will check current_answer
+
+                return "native_finished"
 
             # Unexpected response type, treat as final answer
             self.state.current_answer = AgentFinish(
@@ -621,12 +487,13 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             )
             self._invoke_step_callback(self.state.current_answer)
             self._append_message_to_state(str(answer))
-            # Router will check current_answer
+
+            return "native_finished"
 
         except Exception as e:
             if is_context_length_exceeded(e):
                 self._last_context_error = e
-                return  # Router will check _last_context_error
+                return "context_error"
             if e.__class__.__module__.startswith("litellm"):
                 raise e
             handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
@@ -639,22 +506,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             return "execute_tool"
         return "agent_finished"
 
-    @router(call_llm_native_tools)
-    def route_native_tool_result(
-        self,
-    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
-        """Route based on LLM response for native tool calling.
-
-        Checks state set by call_llm_native_tools to determine next step.
-        This router is needed because only router return values trigger
-        downstream listeners.
-        """
-        if self._last_context_error is not None:
-            return "context_error"
-        if self.state.pending_tool_calls:
-            return "native_tool_calls"
-        return "native_finished"
-
     @listen("execute_tool")
     def execute_tool_action(self) -> Literal["tool_completed", "tool_result_is_final"]:
         """Execute the tool action and handle the result."""
@@ -838,7 +689,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                     agent_key=agent_key,
                 ),
             )
-            error_event_emitted = False
 
             track_delegation_if_needed(func_name, args_dict, self.task)
 
@@ -914,7 +764,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                                 error=e,
                             ),
                         )
-                        error_event_emitted = True
             elif max_usage_reached and original_tool:
                 # Return error message when max usage limit is reached
                 result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
@@ -943,20 +792,20 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                         color="red",
                     )
 
-            if not error_event_emitted:
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageFinishedEvent(
-                        output=result,
-                        tool_name=func_name,
-                        tool_args=args_dict,
-                        from_agent=self.agent,
-                        from_task=self.task,
-                        agent_key=agent_key,
-                        started_at=started_at,
-                        finished_at=datetime.now(),
-                    ),
-                )
+            # Emit tool usage finished event
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageFinishedEvent(
+                    output=result,
+                    tool_name=func_name,
+                    tool_args=args_dict,
+                    from_agent=self.agent,
+                    from_task=self.task,
+                    agent_key=agent_key,
+                    started_at=started_at,
+                    finished_at=datetime.now(),
+                ),
+            )
 
             # Append tool result message
             tool_message: LLMMessage = {
@@ -1012,11 +861,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         self.state.iterations += 1
         return "initialized"
 
-    @listen(or_("initialized", "tool_completed", "native_tool_completed"))
+    @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""
-        if self._flow_initialized:
-            self._discard_or_listener(FlowMethodName("continue_iteration"))
         return "check_iteration"
 
     @router(or_(initialize_reasoning, continue_iteration))
@@ -1144,10 +991,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self.state.is_finished = False
             self.state.use_native_tools = False
             self.state.pending_tool_calls = []
-            self.state.plan = None
-            self.state.plan_ready = False
-
-            self._kickoff_input = inputs.get("input", "")
 
             if "system" in self.prompt:
                 prompt = cast("SystemPromptResult", self.prompt)
@@ -1232,10 +1075,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self.state.is_finished = False
             self.state.use_native_tools = False
             self.state.pending_tool_calls = []
-            self.state.plan = None
-            self.state.plan_ready = False
-
-            self._kickoff_input = inputs.get("input", "")
 
             if "system" in self.prompt:
                 prompt = cast("SystemPromptResult", self.prompt)
@@ -1266,7 +1105,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
 
             if self.state.ask_for_human_input:
-                formatted_answer = await self._ahandle_human_feedback(formatted_answer)
+                formatted_answer = self._handle_human_feedback(formatted_answer)
 
             self._create_short_term_memory(formatted_answer)
             self._create_long_term_memory(formatted_answer)
@@ -1480,22 +1319,17 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         Returns:
             Final answer after feedback.
         """
-        provider = get_provider()
-        return provider.handle_feedback(formatted_answer, self)
+        output_str = (
+            str(formatted_answer.output)
+            if isinstance(formatted_answer.output, BaseModel)
+            else formatted_answer.output
+        )
+        human_feedback = self._ask_human_input(output_str)
 
-    async def _ahandle_human_feedback(
-        self, formatted_answer: AgentFinish
-    ) -> AgentFinish:
-        """Process human feedback asynchronously and refine answer.
+        if self._is_training_mode():
+            return self._handle_training_feedback(formatted_answer, human_feedback)
 
-        Args:
-            formatted_answer: Initial agent result.
-
-        Returns:
-            Final answer after feedback.
-        """
-        provider = get_provider()
-        return await provider.handle_feedback_async(formatted_answer, self)
+        return self._handle_regular_feedback(formatted_answer, human_feedback)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1505,6 +1339,101 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         """
         return bool(self.crew and self.crew._train)
 
+    def _handle_training_feedback(
+        self, initial_answer: AgentFinish, feedback: str
+    ) -> AgentFinish:
+        """Process training feedback and generate improved answer.
+
+        Args:
+            initial_answer: Initial agent output.
+            feedback: Training feedback.
+
+        Returns:
+            Improved answer.
+        """
+        self._handle_crew_training_output(initial_answer, feedback)
+        self.state.messages.append(
+            format_message_for_llm(
+                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+            )
+        )
+
+        # Re-run flow for improved answer
+        self.state.iterations = 0
+        self.state.is_finished = False
+        self.state.current_answer = None
+
+        self.kickoff()
+
+        # Get improved answer from state
+        improved_answer = self.state.current_answer
+        if not isinstance(improved_answer, AgentFinish):
+            raise RuntimeError(
+                "Training feedback iteration did not produce final answer"
+            )
+
+        self._handle_crew_training_output(improved_answer)
+        self.state.ask_for_human_input = False
+        return improved_answer
+
+    def _handle_regular_feedback(
+        self, current_answer: AgentFinish, initial_feedback: str
+    ) -> AgentFinish:
+        """Process regular feedback iteratively until user is satisfied.
+
+        Args:
+            current_answer: Current agent output.
+            initial_feedback: Initial user feedback.
+
+        Returns:
+            Final answer after iterations.
+        """
+        feedback = initial_feedback
+        answer = current_answer
+
+        while self.state.ask_for_human_input:
+            if feedback.strip() == "":
+                self.state.ask_for_human_input = False
+            else:
+                answer = self._process_feedback_iteration(feedback)
+                output_str = (
+                    str(answer.output)
+                    if isinstance(answer.output, BaseModel)
+                    else answer.output
+                )
+                feedback = self._ask_human_input(output_str)
+
+        return answer
+
+    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
+        """Process a single feedback iteration and generate updated response.
+
+        Args:
+            feedback: User feedback.
+
+        Returns:
+            Updated agent response.
+        """
+        self.state.messages.append(
+            format_message_for_llm(
+                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+            )
+        )
+
+        # Re-run flow
+        self.state.iterations = 0
+        self.state.is_finished = False
+        self.state.current_answer = None
+
+        self.kickoff()
+
+        # Get answer from state
+        answer = self.state.current_answer
+        if not isinstance(answer, AgentFinish):
+            raise RuntimeError("Feedback iteration did not produce final answer")
+
+        return answer
+
     @classmethod
     def __get_pydantic_core_schema__(
         cls, _source_type: Any, _handler: GetCoreSchemaHandler

lib/crewai/src/crewai/flow/async_feedback/__init__.py

@@ -28,8 +28,6 @@ Example:
     ```
 """
 
-from typing import Any
-
 from crewai.flow.async_feedback.providers import ConsoleProvider
 from crewai.flow.async_feedback.types import (
     HumanFeedbackPending,
@@ -43,15 +41,4 @@ __all__ = [
     "HumanFeedbackPending",
     "HumanFeedbackProvider",
     "PendingFeedbackContext",
-    "_extension_exports",
 ]
-
-_extension_exports: dict[str, Any] = {}
-
-
-def __getattr__(name: str) -> Any:
-    """Support extensions via dynamic attribute lookup."""
-    if name in _extension_exports:
-        return _extension_exports[name]
-    msg = f"module {__name__!r} has no attribute {name!r}"
-    raise AttributeError(msg)

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

@@ -7,14 +7,7 @@ for building event-driven workflows with conditional execution and routing.
 from __future__ import annotations
 
 import asyncio
-from collections.abc import (
-    Callable,
-    ItemsView,
-    Iterator,
-    KeysView,
-    Sequence,
-    ValuesView,
-)
+from collections.abc import Callable, Sequence
 from concurrent.futures import Future
 import copy
 import inspect
@@ -52,7 +45,6 @@ from crewai.events.listeners.tracing.utils import (
     has_user_declined_tracing,
     set_tracing_enabled,
     should_enable_tracing,
-    should_suppress_tracing_messages,
 )
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
@@ -416,132 +408,6 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition
     return {"type": AND_CONDITION, "conditions": processed_conditions}
 
 
-class LockedListProxy(Generic[T]):
-    """Thread-safe proxy for list operations.
-
-    Wraps a list and uses a lock for all mutating operations.
-    """
-
-    def __init__(self, lst: list[T], lock: threading.Lock) -> None:
-        self._list = lst
-        self._lock = lock
-
-    def append(self, item: T) -> None:
-        with self._lock:
-            self._list.append(item)
-
-    def extend(self, items: list[T]) -> None:
-        with self._lock:
-            self._list.extend(items)
-
-    def insert(self, index: int, item: T) -> None:
-        with self._lock:
-            self._list.insert(index, item)
-
-    def remove(self, item: T) -> None:
-        with self._lock:
-            self._list.remove(item)
-
-    def pop(self, index: int = -1) -> T:
-        with self._lock:
-            return self._list.pop(index)
-
-    def clear(self) -> None:
-        with self._lock:
-            self._list.clear()
-
-    def __setitem__(self, index: int, value: T) -> None:
-        with self._lock:
-            self._list[index] = value
-
-    def __delitem__(self, index: int) -> None:
-        with self._lock:
-            del self._list[index]
-
-    def __getitem__(self, index: int) -> T:
-        return self._list[index]
-
-    def __len__(self) -> int:
-        return len(self._list)
-
-    def __iter__(self) -> Iterator[T]:
-        return iter(self._list)
-
-    def __contains__(self, item: object) -> bool:
-        return item in self._list
-
-    def __repr__(self) -> str:
-        return repr(self._list)
-
-    def __bool__(self) -> bool:
-        return bool(self._list)
-
-
-class LockedDictProxy(Generic[T]):
-    """Thread-safe proxy for dict operations.
-
-    Wraps a dict and uses a lock for all mutating operations.
-    """
-
-    def __init__(self, d: dict[str, T], lock: threading.Lock) -> None:
-        self._dict = d
-        self._lock = lock
-
-    def __setitem__(self, key: str, value: T) -> None:
-        with self._lock:
-            self._dict[key] = value
-
-    def __delitem__(self, key: str) -> None:
-        with self._lock:
-            del self._dict[key]
-
-    def pop(self, key: str, *default: T) -> T:
-        with self._lock:
-            return self._dict.pop(key, *default)
-
-    def update(self, other: dict[str, T]) -> None:
-        with self._lock:
-            self._dict.update(other)
-
-    def clear(self) -> None:
-        with self._lock:
-            self._dict.clear()
-
-    def setdefault(self, key: str, default: T) -> T:
-        with self._lock:
-            return self._dict.setdefault(key, default)
-
-    def __getitem__(self, key: str) -> T:
-        return self._dict[key]
-
-    def __len__(self) -> int:
-        return len(self._dict)
-
-    def __iter__(self) -> Iterator[str]:
-        return iter(self._dict)
-
-    def __contains__(self, key: object) -> bool:
-        return key in self._dict
-
-    def keys(self) -> KeysView[str]:
-        return self._dict.keys()
-
-    def values(self) -> ValuesView[T]:
-        return self._dict.values()
-
-    def items(self) -> ItemsView[str, T]:
-        return self._dict.items()
-
-    def get(self, key: str, default: T | None = None) -> T | None:
-        return self._dict.get(key, default)
-
-    def __repr__(self) -> str:
-        return repr(self._dict)
-
-    def __bool__(self) -> bool:
-        return bool(self._dict)
-
-
 class StateProxy(Generic[T]):
     """Proxy that provides thread-safe access to flow state.
 
@@ -556,13 +422,7 @@ class StateProxy(Generic[T]):
         object.__setattr__(self, "_proxy_lock", lock)
 
     def __getattr__(self, name: str) -> Any:
-        value = getattr(object.__getattribute__(self, "_proxy_state"), name)
-        lock = object.__getattribute__(self, "_proxy_lock")
-        if isinstance(value, list):
-            return LockedListProxy(value, lock)
-        if isinstance(value, dict):
-            return LockedDictProxy(value, lock)
-        return value
+        return getattr(object.__getattribute__(self, "_proxy_state"), name)
 
     def __setattr__(self, name: str, value: Any) -> None:
         if name in ("_proxy_state", "_proxy_lock"):
@@ -1732,6 +1592,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 reset_emission_counter()
                 reset_last_event_id()
 
+            # Emit FlowStartedEvent and log the start of the flow.
             if not self.suppress_flow_events:
                 future = crewai_event_bus.emit(
                     self,
@@ -1742,10 +1603,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ),
                 )
                 if future:
-                    try:
-                        await asyncio.wrap_future(future)
-                    except Exception:
-                        logger.warning("FlowStartedEvent handler failed", exc_info=True)
+                    self._event_futures.append(future)
                 self._log_flow_event(
                     f"Flow started with ID: {self.flow_id}", color="bold magenta"
                 )
@@ -1837,12 +1695,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             final_output = self._method_outputs[-1] if self._method_outputs else None
 
-            if self._event_futures:
-                await asyncio.gather(
-                    *[asyncio.wrap_future(f) for f in self._event_futures]
-                )
-                self._event_futures.clear()
-
             if not self.suppress_flow_events:
                 future = crewai_event_bus.emit(
                     self,
@@ -1854,12 +1706,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ),
                 )
                 if future:
-                    try:
-                        await asyncio.wrap_future(future)
-                    except Exception:
-                        logger.warning(
-                            "FlowFinishedEvent handler failed", exc_info=True
-                        )
+                    self._event_futures.append(future)
+
+            if self._event_futures:
+                await asyncio.gather(
+                    *[asyncio.wrap_future(f) for f in self._event_futures]
+                )
+                self._event_futures.clear()
 
             if not self.suppress_flow_events:
                 trace_listener = TraceCollectionListener()
@@ -1934,14 +1787,40 @@ class Flow(Generic[T], metaclass=FlowMeta):
             await self._execute_listeners(start_method_name, result, finished_event_id)
             # Then execute listeners for the router result (e.g., "approved")
             router_result_trigger = FlowMethodName(str(result))
-            listener_result = (
-                self.last_human_feedback
-                if self.last_human_feedback is not None
-                else result
-            )
-            await self._execute_listeners(
-                router_result_trigger, listener_result, finished_event_id
+            listeners_for_result = self._find_triggered_methods(
+                router_result_trigger, router_only=False
             )
+            if listeners_for_result:
+                # Pass the HumanFeedbackResult if available
+                listener_result = (
+                    self.last_human_feedback
+                    if self.last_human_feedback is not None
+                    else result
+                )
+                racing_group = self._get_racing_group_for_listeners(
+                    listeners_for_result
+                )
+                if racing_group:
+                    racing_members, _ = racing_group
+                    other_listeners = [
+                        name
+                        for name in listeners_for_result
+                        if name not in racing_members
+                    ]
+                    await self._execute_racing_listeners(
+                        racing_members,
+                        other_listeners,
+                        listener_result,
+                        finished_event_id,
+                    )
+                else:
+                    tasks = [
+                        self._execute_single_listener(
+                            listener_name, listener_result, finished_event_id
+                        )
+                        for listener_name in listeners_for_result
+                    ]
+                    await asyncio.gather(*tasks)
         else:
             await self._execute_listeners(start_method_name, result, finished_event_id)
 
@@ -2147,14 +2026,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 router_input = router_result_to_feedback.get(
                     str(current_trigger), current_result
                 )
-                (
-                    router_result,
-                    current_triggering_event_id,
-                ) = await self._execute_single_listener(
+                current_triggering_event_id = await self._execute_single_listener(
                     router_name, router_input, current_triggering_event_id
                 )
+                # After executing router, the router's result is the path
+                router_result = (
+                    self._method_outputs[-1] if self._method_outputs else None
+                )
                 if router_result:  # Only add non-None results
-                    router_results.append(FlowMethodName(str(router_result)))
+                    router_results.append(router_result)
                     # If this was a human_feedback router, map the outcome to the feedback
                     if self.last_human_feedback is not None:
                         router_result_to_feedback[str(router_result)] = (
@@ -2194,14 +2074,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
                             racing_members,
                             other_listeners,
                             listener_result,
-                            current_triggering_event_id,
+                            triggering_event_id,
                         )
                     else:
                         tasks = [
                             self._execute_single_listener(
-                                listener_name,
-                                listener_result,
-                                current_triggering_event_id,
+                                listener_name, listener_result, triggering_event_id
                             )
                             for listener_name in listeners_triggered
                         ]
@@ -2384,7 +2262,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         listener_name: FlowMethodName,
         result: Any,
         triggering_event_id: str | None = None,
-    ) -> tuple[Any, str | None]:
+    ) -> str | None:
         """Executes a single listener method with proper event handling.
 
         This internal method manages the execution of an individual listener,
@@ -2397,9 +2275,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 used for causal chain tracking.
 
         Returns:
-            A tuple of (listener_result, event_id) where listener_result is the return
-            value of the listener method and event_id is the MethodExecutionFinishedEvent
-            id, or (None, None) if skipped during resumption.
+            The event_id of the MethodExecutionFinishedEvent emitted by this listener,
+            or None if events are suppressed.
 
         Note:
             - Inspects method signature to determine if it accepts the trigger result
@@ -2425,7 +2302,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         ):
                             # This conditional start was executed, continue its chain
                             await self._execute_start_method(start_method_name)
-                return (None, None)
+                return None
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(listener_name)
             # Also clear from fired OR listeners for cyclic flows
@@ -2463,7 +2340,46 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 listener_name, listener_result, finished_event_id
             )
 
-            return (listener_result, finished_event_id)
+            # If this listener is also a router (e.g., has @human_feedback with emit),
+            # we need to trigger listeners for the router result as well
+            if listener_name in self._routers and listener_result is not None:
+                router_result_trigger = FlowMethodName(str(listener_result))
+                listeners_for_result = self._find_triggered_methods(
+                    router_result_trigger, router_only=False
+                )
+                if listeners_for_result:
+                    # Pass the HumanFeedbackResult if available
+                    feedback_result = (
+                        self.last_human_feedback
+                        if self.last_human_feedback is not None
+                        else listener_result
+                    )
+                    racing_group = self._get_racing_group_for_listeners(
+                        listeners_for_result
+                    )
+                    if racing_group:
+                        racing_members, _ = racing_group
+                        other_listeners = [
+                            name
+                            for name in listeners_for_result
+                            if name not in racing_members
+                        ]
+                        await self._execute_racing_listeners(
+                            racing_members,
+                            other_listeners,
+                            feedback_result,
+                            finished_event_id,
+                        )
+                    else:
+                        tasks = [
+                            self._execute_single_listener(
+                                name, feedback_result, finished_event_id
+                            )
+                            for name in listeners_for_result
+                        ]
+                        await asyncio.gather(*tasks)
+
+            return finished_event_id
 
         except Exception as e:
             # Don't log HumanFeedbackPending as an error - it's expected control flow
@@ -2710,8 +2626,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
     @staticmethod
     def _show_tracing_disabled_message() -> None:
         """Show a message when tracing is disabled."""
-        if should_suppress_tracing_messages():
-            return
 
         console = Console()
 

lib/crewai/src/crewai/hooks/llm_hooks.py

@@ -3,12 +3,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, cast
 
 from crewai.events.event_listener import event_listener
-from crewai.hooks.types import (
-    AfterLLMCallHookCallable,
-    AfterLLMCallHookType,
-    BeforeLLMCallHookCallable,
-    BeforeLLMCallHookType,
-)
+from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
 from crewai.utilities.printer import Printer
 
 
@@ -154,12 +149,12 @@ class LLMCallHookContext:
             event_listener.formatter.resume_live_updates()
 
 
-_before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = []
-_after_llm_call_hooks: list[AfterLLMCallHookType | AfterLLMCallHookCallable] = []
+_before_llm_call_hooks: list[BeforeLLMCallHookType] = []
+_after_llm_call_hooks: list[AfterLLMCallHookType] = []
 
 
 def register_before_llm_call_hook(
-    hook: BeforeLLMCallHookType | BeforeLLMCallHookCallable,
+    hook: BeforeLLMCallHookType,
 ) -> None:
     """Register a global before_llm_call hook.
 
@@ -195,7 +190,7 @@ def register_before_llm_call_hook(
 
 
 def register_after_llm_call_hook(
-    hook: AfterLLMCallHookType | AfterLLMCallHookCallable,
+    hook: AfterLLMCallHookType,
 ) -> None:
     """Register a global after_llm_call hook.
 
@@ -222,9 +217,7 @@ def register_after_llm_call_hook(
     _after_llm_call_hooks.append(hook)
 
 
-def get_before_llm_call_hooks() -> list[
-    BeforeLLMCallHookType | BeforeLLMCallHookCallable
-]:
+def get_before_llm_call_hooks() -> list[BeforeLLMCallHookType]:
     """Get all registered global before_llm_call hooks.
 
     Returns:
@@ -233,7 +226,7 @@ def get_before_llm_call_hooks() -> list[
     return _before_llm_call_hooks.copy()
 
 
-def get_after_llm_call_hooks() -> list[AfterLLMCallHookType | AfterLLMCallHookCallable]:
+def get_after_llm_call_hooks() -> list[AfterLLMCallHookType]:
     """Get all registered global after_llm_call hooks.
 
     Returns:
@@ -243,7 +236,7 @@ def get_after_llm_call_hooks() -> list[AfterLLMCallHookType | AfterLLMCallHookCa
 
 
 def unregister_before_llm_call_hook(
-    hook: BeforeLLMCallHookType | BeforeLLMCallHookCallable,
+    hook: BeforeLLMCallHookType,
 ) -> bool:
     """Unregister a specific global before_llm_call hook.
 
@@ -269,7 +262,7 @@ def unregister_before_llm_call_hook(
 
 
 def unregister_after_llm_call_hook(
-    hook: AfterLLMCallHookType | AfterLLMCallHookCallable,
+    hook: AfterLLMCallHookType,
 ) -> bool:
     """Unregister a specific global after_llm_call hook.
 

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

@@ -3,12 +3,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any
 
 from crewai.events.event_listener import event_listener
-from crewai.hooks.types import (
-    AfterToolCallHookCallable,
-    AfterToolCallHookType,
-    BeforeToolCallHookCallable,
-    BeforeToolCallHookType,
-)
+from crewai.hooks.types import AfterToolCallHookType, BeforeToolCallHookType
 from crewai.utilities.printer import Printer
 
 
@@ -117,12 +112,12 @@ class ToolCallHookContext:
 
 
 # Global hook registries
-_before_tool_call_hooks: list[BeforeToolCallHookType | BeforeToolCallHookCallable] = []
-_after_tool_call_hooks: list[AfterToolCallHookType | AfterToolCallHookCallable] = []
+_before_tool_call_hooks: list[BeforeToolCallHookType] = []
+_after_tool_call_hooks: list[AfterToolCallHookType] = []
 
 
 def register_before_tool_call_hook(
-    hook: BeforeToolCallHookType | BeforeToolCallHookCallable,
+    hook: BeforeToolCallHookType,
 ) -> None:
     """Register a global before_tool_call hook.
 
@@ -159,7 +154,7 @@ def register_before_tool_call_hook(
 
 
 def register_after_tool_call_hook(
-    hook: AfterToolCallHookType | AfterToolCallHookCallable,
+    hook: AfterToolCallHookType,
 ) -> None:
     """Register a global after_tool_call hook.
 
@@ -189,9 +184,7 @@ def register_after_tool_call_hook(
     _after_tool_call_hooks.append(hook)
 
 
-def get_before_tool_call_hooks() -> list[
-    BeforeToolCallHookType | BeforeToolCallHookCallable
-]:
+def get_before_tool_call_hooks() -> list[BeforeToolCallHookType]:
     """Get all registered global before_tool_call hooks.
 
     Returns:
@@ -200,9 +193,7 @@ def get_before_tool_call_hooks() -> list[
     return _before_tool_call_hooks.copy()
 
 
-def get_after_tool_call_hooks() -> list[
-    AfterToolCallHookType | AfterToolCallHookCallable
-]:
+def get_after_tool_call_hooks() -> list[AfterToolCallHookType]:
     """Get all registered global after_tool_call hooks.
 
     Returns:
@@ -212,7 +203,7 @@ def get_after_tool_call_hooks() -> list[
 
 
 def unregister_before_tool_call_hook(
-    hook: BeforeToolCallHookType | BeforeToolCallHookCallable,
+    hook: BeforeToolCallHookType,
 ) -> bool:
     """Unregister a specific global before_tool_call hook.
 
@@ -238,7 +229,7 @@ def unregister_before_tool_call_hook(
 
 
 def unregister_after_tool_call_hook(
-    hook: AfterToolCallHookType | AfterToolCallHookCallable,
+    hook: AfterToolCallHookType,
 ) -> bool:
     """Unregister a specific global after_tool_call hook.
 

lib/crewai/src/crewai/knowledge/source/utils/__init__.py

@@ -1 +0,0 @@
-"""Knowledge source utilities."""

lib/crewai/src/crewai/knowledge/source/utils/source_helper.py

@@ -1,70 +0,0 @@
-"""Helper utilities for knowledge sources."""
-
-from typing import Any, ClassVar
-
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
-
-
-class SourceHelper:
-    """Helper class for creating and managing knowledge sources."""
-
-    SUPPORTED_FILE_TYPES: ClassVar[list[str]] = [
-        ".csv",
-        ".pdf",
-        ".json",
-        ".txt",
-        ".xlsx",
-        ".xls",
-    ]
-
-    _FILE_TYPE_MAP: ClassVar[dict[str, type[BaseKnowledgeSource]]] = {
-        ".csv": CSVKnowledgeSource,
-        ".pdf": PDFKnowledgeSource,
-        ".json": JSONKnowledgeSource,
-        ".txt": TextFileKnowledgeSource,
-        ".xlsx": ExcelKnowledgeSource,
-        ".xls": ExcelKnowledgeSource,
-    }
-
-    @classmethod
-    def is_supported_file(cls, file_path: str) -> bool:
-        """Check if a file type is supported.
-
-        Args:
-            file_path: Path to the file.
-
-        Returns:
-            True if the file type is supported.
-        """
-        return file_path.lower().endswith(tuple(cls.SUPPORTED_FILE_TYPES))
-
-    @classmethod
-    def get_source(
-        cls, file_path: str, metadata: dict[str, Any] | None = None
-    ) -> BaseKnowledgeSource:
-        """Create appropriate KnowledgeSource based on file extension.
-
-        Args:
-            file_path: Path to the file.
-            metadata: Optional metadata to attach to the source.
-
-        Returns:
-            The appropriate KnowledgeSource instance.
-
-        Raises:
-            ValueError: If the file type is not supported.
-        """
-        if not cls.is_supported_file(file_path):
-            raise ValueError(f"Unsupported file type: {file_path}")
-
-        lower_path = file_path.lower()
-        for ext, source_cls in cls._FILE_TYPE_MAP.items():
-            if lower_path.endswith(ext):
-                return source_cls(file_path=[file_path], metadata=metadata)
-
-        raise ValueError(f"Unsupported file type: {file_path}")

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/project/crew_base.py

@@ -27,8 +27,6 @@ if TYPE_CHECKING:
     from crewai import Agent, Task
     from crewai.agents.cache.cache_handler import CacheHandler
     from crewai.crews.crew_output import CrewOutput
-    from crewai.hooks.llm_hooks import LLMCallHookContext
-    from crewai.hooks.tool_hooks import ToolCallHookContext
     from crewai.project.wrappers import (
         CrewInstance,
         OutputJsonClass,
@@ -36,8 +34,6 @@ if TYPE_CHECKING:
     )
     from crewai.tasks.task_output import TaskOutput
 
-_post_initialize_crew_hooks: list[Callable[[Any], None]] = []
-
 
 class AgentConfig(TypedDict, total=False):
     """Type definition for agent configuration dictionary.
@@ -270,9 +266,6 @@ class CrewBaseMeta(type):
         instance.map_all_agent_variables()
         instance.map_all_task_variables()
 
-        for hook in _post_initialize_crew_hooks:
-            hook(instance)
-
         original_methods = {
             name: method
             for name, method in cls.__dict__.items()
@@ -492,61 +485,47 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
             if has_agent_filter:
                 agents_filter = hook_method._filter_agents
 
-                def make_filtered_before_llm(
-                    bound_fn: Callable[[LLMCallHookContext], bool | None],
-                    agents_list: list[str],
-                ) -> Callable[[LLMCallHookContext], bool | None]:
-                    def filtered(context: LLMCallHookContext) -> bool | None:
+                def make_filtered_before_llm(bound_fn, agents_list):
+                    def filtered(context):
                         if context.agent and context.agent.role not in agents_list:
                             return None
                         return bound_fn(context)
 
                     return filtered
 
-                before_llm_hook = make_filtered_before_llm(bound_hook, agents_filter)
+                final_hook = make_filtered_before_llm(bound_hook, agents_filter)
             else:
-                before_llm_hook = bound_hook
+                final_hook = bound_hook
 
-            register_before_llm_call_hook(before_llm_hook)
-            instance._registered_hook_functions.append(
-                ("before_llm_call", before_llm_hook)
-            )
+            register_before_llm_call_hook(final_hook)
+            instance._registered_hook_functions.append(("before_llm_call", final_hook))
 
         if hasattr(hook_method, "is_after_llm_call_hook"):
             if has_agent_filter:
                 agents_filter = hook_method._filter_agents
 
-                def make_filtered_after_llm(
-                    bound_fn: Callable[[LLMCallHookContext], str | None],
-                    agents_list: list[str],
-                ) -> Callable[[LLMCallHookContext], str | None]:
-                    def filtered(context: LLMCallHookContext) -> str | None:
+                def make_filtered_after_llm(bound_fn, agents_list):
+                    def filtered(context):
                         if context.agent and context.agent.role not in agents_list:
                             return None
                         return bound_fn(context)
 
                     return filtered
 
-                after_llm_hook = make_filtered_after_llm(bound_hook, agents_filter)
+                final_hook = make_filtered_after_llm(bound_hook, agents_filter)
             else:
-                after_llm_hook = bound_hook
+                final_hook = bound_hook
 
-            register_after_llm_call_hook(after_llm_hook)
-            instance._registered_hook_functions.append(
-                ("after_llm_call", after_llm_hook)
-            )
+            register_after_llm_call_hook(final_hook)
+            instance._registered_hook_functions.append(("after_llm_call", final_hook))
 
         if hasattr(hook_method, "is_before_tool_call_hook"):
             if has_tool_filter or has_agent_filter:
                 tools_filter = getattr(hook_method, "_filter_tools", None)
                 agents_filter = getattr(hook_method, "_filter_agents", None)
 
-                def make_filtered_before_tool(
-                    bound_fn: Callable[[ToolCallHookContext], bool | None],
-                    tools_list: list[str] | None,
-                    agents_list: list[str] | None,
-                ) -> Callable[[ToolCallHookContext], bool | None]:
-                    def filtered(context: ToolCallHookContext) -> bool | None:
+                def make_filtered_before_tool(bound_fn, tools_list, agents_list):
+                    def filtered(context):
                         if tools_list and context.tool_name not in tools_list:
                             return None
                         if (
@@ -559,28 +538,22 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
 
                     return filtered
 
-                before_tool_hook = make_filtered_before_tool(
+                final_hook = make_filtered_before_tool(
                     bound_hook, tools_filter, agents_filter
                 )
             else:
-                before_tool_hook = bound_hook
+                final_hook = bound_hook
 
-            register_before_tool_call_hook(before_tool_hook)
-            instance._registered_hook_functions.append(
-                ("before_tool_call", before_tool_hook)
-            )
+            register_before_tool_call_hook(final_hook)
+            instance._registered_hook_functions.append(("before_tool_call", final_hook))
 
         if hasattr(hook_method, "is_after_tool_call_hook"):
             if has_tool_filter or has_agent_filter:
                 tools_filter = getattr(hook_method, "_filter_tools", None)
                 agents_filter = getattr(hook_method, "_filter_agents", None)
 
-                def make_filtered_after_tool(
-                    bound_fn: Callable[[ToolCallHookContext], str | None],
-                    tools_list: list[str] | None,
-                    agents_list: list[str] | None,
-                ) -> Callable[[ToolCallHookContext], str | None]:
-                    def filtered(context: ToolCallHookContext) -> str | None:
+                def make_filtered_after_tool(bound_fn, tools_list, agents_list):
+                    def filtered(context):
                         if tools_list and context.tool_name not in tools_list:
                             return None
                         if (
@@ -593,16 +566,14 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
 
                     return filtered
 
-                after_tool_hook = make_filtered_after_tool(
+                final_hook = make_filtered_after_tool(
                     bound_hook, tools_filter, agents_filter
                 )
             else:
-                after_tool_hook = bound_hook
+                final_hook = bound_hook
 
-            register_after_tool_call_hook(after_tool_hook)
-            instance._registered_hook_functions.append(
-                ("after_tool_call", after_tool_hook)
-            )
+            register_after_tool_call_hook(final_hook)
+            instance._registered_hook_functions.append(("after_tool_call", final_hook))
 
     instance._hooks_being_registered = False
 

lib/crewai/src/crewai/project/wrappers.py

@@ -72,8 +72,6 @@ class CrewInstance(Protocol):
     __crew_metadata__: CrewMetadata
     _mcp_server_adapter: Any
     _all_methods: dict[str, Callable[..., Any]]
-    _registered_hook_functions: list[tuple[str, Callable[..., Any]]]
-    _hooks_being_registered: bool
     agents: list[Agent]
     tasks: list[Task]
     base_directory: Path

lib/crewai/src/crewai/task.py

@@ -31,7 +31,6 @@ from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.context import reset_current_task_id, set_current_task_id
 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 (
@@ -562,7 +561,6 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task asynchronously."""
-        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -650,7 +648,6 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
-            reset_current_task_id(task_id_token)
 
     def _execute_core(
         self,
@@ -659,7 +656,6 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
-        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -748,7 +744,6 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
-            reset_current_task_id(task_id_token)
 
     def _post_agent_execution(self, agent: BaseAgent) -> None:
         pass

lib/crewai/src/crewai/tasks/hallucination_guardrail.py

@@ -6,7 +6,6 @@ Classes:
     HallucinationGuardrail: Placeholder guardrail that validates task outputs.
 """
 
-from collections.abc import Callable
 from typing import Any
 
 from crewai.llm import LLM
@@ -14,36 +13,32 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.logger import Logger
 
 
-_validate_output_hook: Callable[..., tuple[bool, Any]] | None = None
-
-
 class HallucinationGuardrail:
     """Placeholder for the HallucinationGuardrail feature.
 
     Attributes:
-        context: Optional reference context that outputs would be checked against.
+        context: The reference context that outputs would be checked against.
         llm: The language model that would be used for evaluation.
         threshold: Optional minimum faithfulness score that would be required to pass.
         tool_response: Optional tool response information that would be used in evaluation.
 
     Examples:
-        >>> # Basic usage without context (uses task expected_output as context)
-        >>> guardrail = HallucinationGuardrail(llm=agent.llm)
-
-        >>> # With context for reference
+        >>> # Basic usage with default verdict logic
         >>> guardrail = HallucinationGuardrail(
-        ...     llm=agent.llm,
         ...     context="AI helps with various tasks including analysis and generation.",
+        ...     llm=agent.llm,
         ... )
 
         >>> # With custom threshold for stricter validation
         >>> strict_guardrail = HallucinationGuardrail(
+        ...     context="Quantum computing uses qubits in superposition.",
         ...     llm=agent.llm,
-        ...     threshold=8.0,  # Require score >= 8 to pass
+        ...     threshold=8.0,  # Would require score >= 8 to pass in enterprise version
         ... )
 
         >>> # With tool response for additional context
         >>> guardrail_with_tools = HallucinationGuardrail(
+        ...     context="The current weather data",
         ...     llm=agent.llm,
         ...     tool_response="Weather API returned: Temperature 22°C, Humidity 65%",
         ... )
@@ -51,17 +46,16 @@ class HallucinationGuardrail:
 
     def __init__(
         self,
+        context: str,
         llm: LLM,
-        context: str | None = None,
         threshold: float | None = None,
         tool_response: str = "",
     ):
         """Initialize the HallucinationGuardrail placeholder.
 
         Args:
+            context: The reference context that outputs would be checked against.
             llm: The language model that would be used for evaluation.
-            context: Optional reference context that outputs would be checked against.
-                If not provided, the task's expected_output will be used as context.
             threshold: Optional minimum faithfulness score that would be required to pass.
             tool_response: Optional tool response information that would be used in evaluation.
         """
@@ -84,17 +78,16 @@ class HallucinationGuardrail:
     def __call__(self, task_output: TaskOutput) -> tuple[bool, Any]:
         """Validate a task output against hallucination criteria.
 
+        In the open source, this method always returns that the output is valid.
+
         Args:
             task_output: The output to be validated.
 
         Returns:
             A tuple containing:
-                - True if validation passed, False otherwise
-                - The raw task output if valid, or error feedback if invalid
+                - True
+                - The raw task output
         """
-        if callable(_validate_output_hook):
-            return _validate_output_hook(self, task_output)
-
         self._logger.log(
             "warning",
             "Premium hallucination detection skipped (use for free at https://app.crewai.com)\n",

lib/crewai/src/crewai/tasks/llm_guardrail.py

@@ -1,10 +1,6 @@
-import asyncio
-from collections.abc import Coroutine
-import inspect
 from typing import Any
 
 from pydantic import BaseModel, Field
-from typing_extensions import TypeIs
 
 from crewai.agent import Agent
 from crewai.lite_agent_output import LiteAgentOutput
@@ -12,13 +8,6 @@ from crewai.llms.base_llm import BaseLLM
 from crewai.tasks.task_output import TaskOutput
 
 
-def _is_coroutine(
-    obj: LiteAgentOutput | Coroutine[Any, Any, LiteAgentOutput],
-) -> TypeIs[Coroutine[Any, Any, LiteAgentOutput]]:
-    """Check if obj is a coroutine for type narrowing."""
-    return inspect.iscoroutine(obj)
-
-
 class LLMGuardrailResult(BaseModel):
     valid: bool = Field(
         description="Whether the task output complies with the guardrail"
@@ -73,10 +62,7 @@ class LLMGuardrail:
         - If the Task result complies with the guardrail, saying that is valid
         """
 
-        kickoff_result = agent.kickoff(query, response_format=LLMGuardrailResult)
-        if _is_coroutine(kickoff_result):
-            return asyncio.run(kickoff_result)
-        return kickoff_result
+        return agent.kickoff(query, response_format=LLMGuardrailResult)
 
     def __call__(self, task_output: TaskOutput) -> tuple[bool, Any]:
         """Validates the output of a task based on specified criteria.

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -903,7 +903,7 @@ class Telemetry:
                         {
                             "id": str(task.id),
                             "description": task.description,
-                            "output": task.output.raw if task.output else "",
+                            "output": task.output.raw_output,
                         }
                         for task in crew.tasks
                     ]
@@ -923,9 +923,6 @@ class Telemetry:
             value: The attribute value.
         """
 
-        if span is None:
-            return
-
         def _operation() -> None:
             return span.set_attribute(key, value)
 

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/tools/tool_usage.py

@@ -270,7 +270,6 @@ class ToolUsage:
         result = None  # type: ignore
         should_retry = False
         available_tool = None
-        error_event_emitted = False
 
         try:
             if self.tools_handler and self.tools_handler.cache:
@@ -409,7 +408,6 @@ class ToolUsage:
 
                 except Exception as e:
                     self.on_tool_error(tool=tool, tool_calling=calling, e=e)
-                    error_event_emitted = True
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
@@ -437,7 +435,7 @@ class ToolUsage:
                 result = self._format_result(result=result)
 
         finally:
-            if started_event_emitted and not error_event_emitted:
+            if started_event_emitted:
                 self.on_tool_use_finished(
                     tool=tool,
                     tool_calling=calling,
@@ -502,7 +500,6 @@ class ToolUsage:
         result = None  # type: ignore
         should_retry = False
         available_tool = None
-        error_event_emitted = False
 
         try:
             if self.tools_handler and self.tools_handler.cache:
@@ -641,7 +638,6 @@ class ToolUsage:
 
                 except Exception as e:
                     self.on_tool_error(tool=tool, tool_calling=calling, e=e)
-                    error_event_emitted = True
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
@@ -669,7 +665,7 @@ class ToolUsage:
                 result = self._format_result(result=result)
 
         finally:
-            if started_event_emitted and not error_event_emitted:
+            if started_event_emitted:
                 self.on_tool_use_finished(
                     tool=tool,
                     tool_calling=calling,

lib/crewai/src/crewai/translations/en.json

@@ -58,14 +58,9 @@
     }
   },
   "reasoning": {
-    "initial_plan": "You are {role}. Create a focused execution plan using only the essential steps needed.",
-    "refine_plan": "You are {role}. Refine your plan to address the specific gap while keeping it minimal.",
-    "create_plan_prompt": "You are {role}.\n\nTask: {description}\n\nExpected output: {expected_output}\n\nAvailable tools: {tools}\n\nCreate a focused plan with ONLY the essential steps needed. Most tasks require just 2-5 steps. Do NOT pad with unnecessary steps like \"review\", \"verify\", \"document\", or \"finalize\" unless explicitly required.\n\nFor each step, specify the action and which tool to use (if any).\n\nConclude with:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
-    "refine_plan_prompt": "Your plan:\n{current_plan}\n\nYou indicated you're not ready. Address the specific gap while keeping the plan minimal.\n\nConclude with READY or NOT READY."
-  },
-  "planning": {
-    "system_prompt": "You are a strategic planning assistant. Create minimal, effective execution plans. Prefer fewer steps over more.",
-    "create_plan_prompt": "Create a focused execution plan for the following task:\n\n## Task\n{description}\n\n## Expected Output\n{expected_output}\n\n## Available Tools\n{tools}\n\n## Planning Principles\nFocus on WHAT needs to be accomplished, not HOW. Group related actions into logical units. Fewer steps = better. Most tasks need 3-6 steps. Hard limit: {max_steps} steps.\n\n## Step Types (only these are valid):\n1. **Tool Step**: Uses a tool to gather information or take action\n2. **Output Step**: Synthesizes prior results into the final deliverable (usually the last step)\n\n## Rules:\n- Each step must either USE A TOOL or PRODUCE THE FINAL OUTPUT\n- Combine related tool calls: \"Research A, B, and C\" = ONE step, not three\n- Combine all synthesis into ONE final output step\n- NO standalone \"thinking\" steps (review, verify, confirm, refine, analyze) - these happen naturally between steps\n\nFor each step: State the action, specify the tool (if any), and note dependencies.\n\nAfter your plan, state READY or NOT READY.",
-    "refine_plan_prompt": "Your previous plan:\n{current_plan}\n\nYou indicated you weren't ready. Refine your plan to address the specific gap.\n\nKeep the plan minimal - only add steps that directly address the issue.\n\nConclude with READY or NOT READY as before."
+    "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
+    "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
+    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
+    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
   }
 }

lib/crewai/src/crewai/types/hitl.py

@@ -0,0 +1,37 @@
+"""Human-in-the-loop (HITL) type definitions.
+
+This module provides type definitions for human-in-the-loop interactions
+in crew executions.
+"""
+
+from typing import TypedDict
+
+
+class HITLResumeInfo(TypedDict, total=False):
+    """HITL resume information passed from flow to crew.
+
+    Attributes:
+        task_id: Unique identifier for the task.
+        crew_execution_id: Unique identifier for the crew execution.
+        task_key: Key identifying the specific task.
+        task_output: Output from the task before human intervention.
+        human_feedback: Feedback provided by the human.
+        previous_messages: History of messages in the conversation.
+    """
+
+    task_id: str
+    crew_execution_id: str
+    task_key: str
+    task_output: str
+    human_feedback: str
+    previous_messages: list[dict[str, str]]
+
+
+class CrewInputsWithHITL(TypedDict, total=False):
+    """Crew inputs that may contain HITL resume information.
+
+    Attributes:
+        _hitl_resume: Optional HITL resume information for continuing execution.
+    """
+
+    _hitl_resume: HITLResumeInfo

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -42,8 +42,6 @@ if TYPE_CHECKING:
     from crewai.llm import LLM
     from crewai.task import Task
 
-_create_plus_client_hook: Callable[[], Any] | None = None
-
 
 class SummaryContent(TypedDict):
     """Structure for summary content entries.
@@ -93,11 +91,7 @@ def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
 
     for tool in tools:
         if isinstance(tool, CrewAITool):
-            structured_tool = tool.to_structured_tool()
-            structured_tool.current_usage_count = 0
-            if structured_tool._original_tool:
-                structured_tool._original_tool.current_usage_count = 0
-            tools_list.append(structured_tool)
+            tools_list.append(tool.to_structured_tool())
         else:
             raise ValueError("Tool is not a CrewStructuredTool or BaseTool")
 
@@ -824,15 +818,12 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
     if from_repository:
         import importlib
 
-        if callable(_create_plus_client_hook):
-            client = _create_plus_client_hook()
-        else:
-            from crewai.cli.authentication.token import get_auth_token
-            from crewai.cli.plus_api import PlusAPI
+        from crewai.cli.authentication.token import get_auth_token
+        from crewai.cli.plus_api import PlusAPI
 
-            client = PlusAPI(api_key=get_auth_token())
+        client = PlusAPI(api_key=get_auth_token())
         _print_current_organization()
-        response = asyncio.run(client.get_agent(from_repository))
+        response = client.get_agent(from_repository)
         if response.status_code == 404:
             raise AgentRepositoryError(
                 f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization."

lib/crewai/src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 from pydantic import BaseModel, Field, InstanceOf
 from rich.box import HEAVY_EDGE
@@ -36,13 +36,7 @@ class CrewEvaluator:
         iteration: The current iteration of the evaluation.
     """
 
-    def __init__(
-        self,
-        crew: Crew,
-        eval_llm: InstanceOf[BaseLLM] | str | None = None,
-        openai_model_name: str | None = None,
-        llm: InstanceOf[BaseLLM] | str | None = None,
-    ) -> None:
+    def __init__(self, crew: Crew, eval_llm: InstanceOf[BaseLLM]) -> None:
         self.crew = crew
         self.llm = eval_llm
         self.tasks_scores: defaultdict[int, list[float]] = defaultdict(list)
@@ -92,9 +86,7 @@ class CrewEvaluator:
         """
         self.iteration = iteration
 
-    def print_crew_evaluation_result(
-        self, token_usage: list[dict[str, Any]] | None = None
-    ) -> None:
+    def print_crew_evaluation_result(self) -> None:
         """
         Prints the evaluation result of the crew in a table.
         A Crew with 2 tasks using the command crewai test -n 3
@@ -212,7 +204,7 @@ class CrewEvaluator:
                 CrewTestResultEvent(
                     quality=quality_score,
                     execution_duration=current_task.execution_duration,
-                    model=getattr(self.llm, "model", str(self.llm)),
+                    model=self.llm.model,
                     crew_name=self.crew.name,
                     crew=self.crew,
                 ),

lib/crewai/src/crewai/utilities/planning_types.py

@@ -1,103 +0,0 @@
-"""Types for agent planning and todo tracking."""
-
-from __future__ import annotations
-
-from typing import Literal
-from uuid import uuid4
-
-from pydantic import BaseModel, Field
-
-
-# Todo status type
-TodoStatus = Literal["pending", "running", "completed"]
-
-
-class PlanStep(BaseModel):
-    """A single step in the reasoning plan."""
-
-    step_number: int = Field(description="Step number (1-based)")
-    description: str = Field(description="What to do in this step")
-    tool_to_use: str | None = Field(
-        default=None, description="Tool to use for this step, if any"
-    )
-    depends_on: list[int] = Field(
-        default_factory=list, description="Step numbers this step depends on"
-    )
-
-
-class TodoItem(BaseModel):
-    """A single todo item representing a step in the execution plan."""
-
-    id: str = Field(default_factory=lambda: str(uuid4()))
-    step_number: int = Field(description="Order of this step in the plan (1-based)")
-    description: str = Field(description="What needs to be done")
-    tool_to_use: str | None = Field(
-        default=None, description="Tool to use for this step, if any"
-    )
-    status: TodoStatus = Field(default="pending", description="Current status")
-    depends_on: list[int] = Field(
-        default_factory=list, description="Step numbers this depends on"
-    )
-    result: str | None = Field(
-        default=None, description="Result after completion, if any"
-    )
-
-
-class TodoList(BaseModel):
-    """Collection of todos for tracking plan execution."""
-
-    items: list[TodoItem] = Field(default_factory=list)
-
-    @property
-    def current_todo(self) -> TodoItem | None:
-        """Get the currently running todo item."""
-        for item in self.items:
-            if item.status == "running":
-                return item
-        return None
-
-    @property
-    def next_pending(self) -> TodoItem | None:
-        """Get the next pending todo item."""
-        for item in self.items:
-            if item.status == "pending":
-                return item
-        return None
-
-    @property
-    def is_complete(self) -> bool:
-        """Check if all todos are completed."""
-        return len(self.items) > 0 and all(
-            item.status == "completed" for item in self.items
-        )
-
-    @property
-    def pending_count(self) -> int:
-        """Count of pending todos."""
-        return sum(1 for item in self.items if item.status == "pending")
-
-    @property
-    def completed_count(self) -> int:
-        """Count of completed todos."""
-        return sum(1 for item in self.items if item.status == "completed")
-
-    def get_by_step_number(self, step_number: int) -> TodoItem | None:
-        """Get a todo by its step number."""
-        for item in self.items:
-            if item.step_number == step_number:
-                return item
-        return None
-
-    def mark_running(self, step_number: int) -> None:
-        """Mark a todo as running by step number."""
-        item = self.get_by_step_number(step_number)
-        if item:
-            item.status = "running"
-
-    def mark_completed(self, step_number: int, result: str | None = None) -> None:
-        """Mark a todo as completed by step number."""
-        item = self.get_by_step_number(step_number)
-        if item:
-            item.status = "completed"
-            if result:
-                item.result = result

lib/crewai/src/crewai/utilities/printer.py

@@ -4,8 +4,6 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Final, Literal, NamedTuple
 
-from crewai.events.utils.console_formatter import should_suppress_console_output
-
 
 if TYPE_CHECKING:
     from _typeshed import SupportsWrite
@@ -79,8 +77,6 @@ class Printer:
             file: A file-like object (stream); defaults to the current sys.stdout.
             flush: Whether to forcibly flush the stream.
         """
-        if should_suppress_console_output():
-            return
         if isinstance(content, str):
             content = [ColoredText(content, color)]
         print(

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -1,13 +1,10 @@
-"""Handles planning/reasoning for agents before task execution."""
-
-from __future__ import annotations
-
 import json
 import logging
-from typing import TYPE_CHECKING, Any, Final, Literal, cast
+from typing import Any, Final, Literal, cast
 
 from pydantic import BaseModel, Field
 
+from crewai.agent import Agent
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
@@ -15,30 +12,14 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningStartedEvent,
 )
 from crewai.llm import LLM
-from crewai.utilities.llm_utils import create_llm
-from crewai.utilities.planning_types import PlanStep
+from crewai.task import Task
 from crewai.utilities.string_utils import sanitize_tool_name
 
 
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.agent.planning_config import PlanningConfig
-    from crewai.task import Task
-
-
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.agent.planning_config import PlanningConfig
-    from crewai.task import Task
-
-
 class ReasoningPlan(BaseModel):
     """Model representing a reasoning plan for a task."""
 
     plan: str = Field(description="The detailed reasoning plan for the task.")
-    steps: list[PlanStep] = Field(
-        default_factory=list, description="Structured steps to execute"
-    )
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
 
@@ -48,63 +29,24 @@ class AgentReasoningOutput(BaseModel):
     plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
 
 
-# Aliases for backward compatibility
-PlanningPlan = ReasoningPlan
-AgentPlanningOutput = AgentReasoningOutput
-
-
 FUNCTION_SCHEMA: Final[dict[str, Any]] = {
     "type": "function",
     "function": {
         "name": "create_reasoning_plan",
-        "description": "Create or refine a reasoning plan for a task with structured steps",
+        "description": "Create or refine a reasoning plan for a task",
         "parameters": {
             "type": "object",
             "properties": {
                 "plan": {
                     "type": "string",
-                    "description": "A brief summary of the overall plan.",
-                },
-                "steps": {
-                    "type": "array",
-                    "description": "List of discrete steps to execute the plan",
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "step_number": {
-                                "type": "integer",
-                                "description": "Step number (1-based)",
-                            },
-                            "description": {
-                                "type": "string",
-                                "description": "What to do in this step",
-                            },
-                            "tool_to_use": {
-                                "type": ["string", "null"],
-                                "description": "Tool to use for this step, or null if no tool needed",
-                            },
-                            "depends_on": {
-                                "type": "array",
-                                "items": {"type": "integer"},
-                                "description": "Step numbers this step depends on (empty array if none)",
-                            },
-                        },
-                        "required": [
-                            "step_number",
-                            "description",
-                            "tool_to_use",
-                            "depends_on",
-                        ],
-                        "additionalProperties": False,
-                    },
+                    "description": "The detailed reasoning plan for the task.",
                 },
                 "ready": {
                     "type": "boolean",
                     "description": "Whether the agent is ready to execute the task.",
                 },
             },
-            "required": ["plan", "steps", "ready"],
-            "additionalProperties": False,
+            "required": ["plan", "ready"],
         },
     },
 }
@@ -112,101 +54,41 @@ FUNCTION_SCHEMA: Final[dict[str, Any]] = {
 
 class AgentReasoning:
     """
-    Handles the agent planning/reasoning process, enabling an agent to reflect
-    and create a plan before executing a task.
+    Handles the agent reasoning process, enabling an agent to reflect and create a plan
+    before executing a task.
 
     Attributes:
-        task: The task for which the agent is planning (optional).
-        agent: The agent performing the planning.
-        config: The planning configuration.
-        llm: The language model used for planning.
+        task: The task for which the agent is reasoning.
+        agent: The agent performing the reasoning.
+        llm: The language model used for reasoning.
         logger: Logger for logging events and errors.
-        description: Task description or input text for planning.
-        expected_output: Expected output description.
     """
 
-    def __init__(
-        self,
-        agent: Agent,
-        task: Task | None = None,
-        *,
-        description: str | None = None,
-        expected_output: str | None = None,
-    ) -> None:
-        """Initialize the AgentReasoning with an agent and optional task.
+    def __init__(self, task: Task, agent: Agent) -> None:
+        """Initialize the AgentReasoning with a task and an agent.
 
         Args:
-            agent: The agent performing the planning.
-            task: The task for which the agent is planning (optional).
-            description: Task description or input text (used if task is None).
-            expected_output: Expected output (used if task is None).
+            task: The task for which the agent is reasoning.
+            agent: The agent performing the reasoning.
         """
-        self.agent = agent
         self.task = task
-        # Use task attributes if available, otherwise use provided values
-        self._description = description or (
-            task.description if task else "Complete the requested task"
-        )
-        self._expected_output = expected_output or (
-            task.expected_output if task else "Complete the task successfully"
-        )
-        self.config = self._get_planning_config()
-        self.llm = self._resolve_llm()
+        self.agent = agent
+        self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
 
-    @property
-    def description(self) -> str:
-        """Get the task/input description."""
-        return self._description
-
-    @property
-    def expected_output(self) -> str:
-        """Get the expected output."""
-        return self._expected_output
-
-    def _get_planning_config(self) -> PlanningConfig:
-        """Get the planning configuration from the agent.
-
-        Returns:
-            The planning configuration, using defaults if not set.
-        """
-        from crewai.agent.planning_config import PlanningConfig
-
-        if self.agent.planning_config is not None:
-            return self.agent.planning_config
-        # Fallback for backward compatibility
-        return PlanningConfig(
-            max_attempts=getattr(self.agent, "max_reasoning_attempts", None),
-        )
-
-    def _resolve_llm(self) -> LLM:
-        """Resolve which LLM to use for planning.
-
-        Returns:
-            The LLM to use - either from config or the agent's LLM.
-        """
-        if self.config.llm is not None:
-            if isinstance(self.config.llm, LLM):
-                return self.config.llm
-            return create_llm(self.config.llm)
-        return cast(LLM, self.agent.llm)
-
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """Public method for the planning process that creates and refines a plan
-        for the task until the agent is ready to execute it.
+        """Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
 
         Returns:
-            AgentReasoningOutput: The output of the agent planning process.
+            AgentReasoningOutput: The output of the agent reasoning process.
         """
-        task_id = str(self.task.id) if self.task else "kickoff"
-
-        # Emit a planning started event (attempt 1)
+        # Emit a reasoning started event (attempt 1)
         try:
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningStartedEvent(
                     agent_role=self.agent.role,
-                    task_id=task_id,
+                    task_id=str(self.task.id),
                     attempt=1,
                     from_task=self.task,
                 ),
@@ -216,13 +98,13 @@ class AgentReasoning:
             pass
 
         try:
-            output = self._execute_planning()
+            output = self.__handle_agent_reasoning()
 
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningCompletedEvent(
                     agent_role=self.agent.role,
-                    task_id=task_id,
+                    task_id=str(self.task.id),
                     plan=output.plan.plan,
                     ready=output.plan.ready,
                     attempt=1,
@@ -233,77 +115,71 @@ class AgentReasoning:
 
             return output
         except Exception as e:
-            # Emit planning failed event
+            # Emit reasoning failed event
             try:
                 crewai_event_bus.emit(
                     self.agent,
                     AgentReasoningFailedEvent(
                         agent_role=self.agent.role,
-                        task_id=task_id,
+                        task_id=str(self.task.id),
                         error=str(e),
                         attempt=1,
                         from_task=self.task,
                         from_agent=self.agent,
                     ),
                 )
-            except Exception as event_error:
-                logging.error(f"Error emitting planning failed event: {event_error}")
+            except Exception as e:
+                logging.error(f"Error emitting reasoning failed event: {e}")
 
             raise
 
-    def _execute_planning(self) -> AgentReasoningOutput:
-        """Execute the planning process.
+    def __handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """Private method that handles the agent reasoning process.
 
         Returns:
-            The output of the agent planning process.
+            The output of the agent reasoning process.
         """
-        plan, steps, ready = self._create_initial_plan()
-        plan, steps, ready = self._refine_plan_if_needed(plan, steps, ready)
+        plan, ready = self.__create_initial_plan()
 
-        reasoning_plan = ReasoningPlan(plan=plan, steps=steps, ready=ready)
+        plan, ready = self.__refine_plan_if_needed(plan, ready)
+
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
         return AgentReasoningOutput(plan=reasoning_plan)
 
-    def _create_initial_plan(self) -> tuple[str, list[PlanStep], bool]:
-        """Creates the initial plan for the task.
+    def __create_initial_plan(self) -> tuple[str, bool]:
+        """Creates the initial reasoning plan for the task.
 
         Returns:
-            A tuple of the plan summary, list of steps, and whether the agent is ready.
+            The initial plan and whether the agent is ready to execute the task.
         """
-        planning_prompt = self._create_planning_prompt()
-        planning_prompt = self._create_planning_prompt()
+        reasoning_prompt = self.__create_reasoning_prompt()
 
         if self.llm.supports_function_calling():
-            plan, steps, ready = self._call_with_function(
-                planning_prompt, "create_plan"
-            )
-            return plan, steps, ready
-
-        response = self._call_llm_with_prompt(
-            prompt=planning_prompt,
-            plan_type="create_plan",
+            plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
+            return plan, ready
+        response = _call_llm_with_reasoning_prompt(
+            llm=self.llm,
+            prompt=reasoning_prompt,
+            task=self.task,
+            reasoning_agent=self.agent,
+            backstory=self.__get_agent_backstory(),
+            plan_type="initial_plan",
         )
 
-        plan, ready = self._parse_planning_response(str(response))
-        return plan, [], ready  # No structured steps from text parsing
+        return self.__parse_reasoning_response(str(response))
 
-    def _refine_plan_if_needed(
-        self, plan: str, steps: list[PlanStep], ready: bool
-    ) -> tuple[str, list[PlanStep], bool]:
-        """Refines the plan if the agent is not ready to execute the task.
+    def __refine_plan_if_needed(self, plan: str, ready: bool) -> tuple[str, bool]:
+        """Refines the reasoning plan if the agent is not ready to execute the task.
 
         Args:
-            plan: The current plan.
-            steps: The current list of steps.
+            plan: The current reasoning plan.
             ready: Whether the agent is ready to execute the task.
 
         Returns:
-            The refined plan, steps, and whether the agent is ready to execute.
+            The refined plan and whether the agent is ready to execute the task.
         """
-
         attempt = 1
-        max_attempts = self.config.max_attempts
-        task_id = str(self.task.id) if self.task else "kickoff"
-        current_attempt = attempt + 1
+        max_attempts = self.agent.max_reasoning_attempts
 
         while not ready and (max_attempts is None or attempt < max_attempts):
             # Emit event for each refinement attempt
@@ -312,82 +188,62 @@ class AgentReasoning:
                     self.agent,
                     AgentReasoningStartedEvent(
                         agent_role=self.agent.role,
-                        task_id=task_id,
-                        attempt=current_attempt,
+                        task_id=str(self.task.id),
+                        attempt=attempt + 1,
                         from_task=self.task,
                     ),
                 )
             except Exception:  # noqa: S110
                 pass
 
-            refine_prompt = self._create_refine_prompt(plan)
-            refine_prompt = self._create_refine_prompt(plan)
+            refine_prompt = self.__create_refine_prompt(plan)
 
             if self.llm.supports_function_calling():
-                plan, steps, ready = self._call_with_function(
-                    refine_prompt, "refine_plan"
-                )
+                plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
             else:
-                response = self._call_llm_with_prompt(
+                response = _call_llm_with_reasoning_prompt(
+                    llm=self.llm,
                     prompt=refine_prompt,
+                    task=self.task,
+                    reasoning_agent=self.agent,
+                    backstory=self.__get_agent_backstory(),
                     plan_type="refine_plan",
                 )
-                plan, ready = self._parse_planning_response(str(response))
-                steps = []  # No structured steps from text parsing
-
-            # Emit completed event for this refinement attempt
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningCompletedEvent(
-                        agent_role=self.agent.role,
-                        task_id=task_id,
-                        plan=plan,
-                        ready=ready,
-                        attempt=current_attempt,
-                        from_task=self.task,
-                        from_agent=self.agent,
-                    ),
-                )
-            except Exception:  # noqa: S110
-                pass
+                plan, ready = self.__parse_reasoning_response(str(response))
 
             attempt += 1
 
             if max_attempts is not None and attempt >= max_attempts:
                 self.logger.warning(
-                    f"Agent planning reached maximum attempts ({max_attempts}) "
-                    "without being ready. Proceeding with current plan."
+                    f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
                 )
                 break
 
-        return plan, steps, ready
+        return plan, ready
 
-    def _call_with_function(
-        self, prompt: str, plan_type: Literal["create_plan", "refine_plan"]
-    ) -> tuple[str, list[PlanStep], bool]:
-        """Calls the LLM with function calling to get a plan.
+    def __call_with_function(self, prompt: str, prompt_type: str) -> tuple[str, bool]:
+        """Calls the LLM with function calling to get a reasoning plan.
 
         Args:
             prompt: The prompt to send to the LLM.
-            plan_type: The type of plan being created.
+            prompt_type: The type of prompt (initial_plan or refine_plan).
 
         Returns:
-            A tuple containing the plan summary, list of steps, and whether the agent is ready.
+            A tuple containing the plan and whether the agent is ready.
         """
-        self.logger.debug(f"Using function calling for {plan_type} planning")
+        self.logger.debug(f"Using function calling for {prompt_type} reasoning")
 
         try:
-            system_prompt = self._get_system_prompt()
+            system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory(),
+            )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(
-                plan: str,
-                steps: list[dict[str, Any]] | None = None,
-                ready: bool = True,
-            ) -> str:
-                """Return the planning result in JSON string form."""
-                return json.dumps({"plan": plan, "steps": steps or [], "ready": ready})
+            def _create_reasoning_plan(plan: str, ready: bool = True) -> str:
+                """Return the reasoning plan result in JSON string form."""
+                return json.dumps({"plan": plan, "ready": ready})
 
             response = self.llm.call(
                 [
@@ -399,33 +255,19 @@ class AgentReasoning:
                 from_task=self.task,
                 from_agent=self.agent,
             )
+
+            self.logger.debug(f"Function calling response: {response[:100]}...")
+
             try:
                 result = json.loads(response)
                 if "plan" in result and "ready" in result:
-                    # Parse steps from the response
-                    steps: list[PlanStep] = []
-                    raw_steps = result.get("steps", [])
-                    try:
-                        for step_data in raw_steps:
-                            step = PlanStep(
-                                step_number=step_data.get("step_number", 0),
-                                description=step_data.get("description", ""),
-                                tool_to_use=step_data.get("tool_to_use"),
-                                depends_on=step_data.get("depends_on", []),
-                            )
-                            steps.append(step)
-                    except Exception as step_error:
-                        self.logger.warning(
-                            f"Failed to parse step: {step_data}, error: {step_error}"
-                        )
-                    return result["plan"], steps, result["ready"]
+                    return result["plan"], result["ready"]
             except (json.JSONDecodeError, KeyError):
                 pass
 
             response_str = str(response)
             return (
                 response_str,
-                [],
                 "READY: I am ready to execute the task." in response_str,
             )
 
@@ -435,7 +277,13 @@ class AgentReasoning:
             )
 
             try:
-                system_prompt = self._get_system_prompt()
+                system_prompt = self.agent.i18n.retrieve(
+                    "reasoning", prompt_type
+                ).format(
+                    role=self.agent.role,
+                    goal=self.agent.goal,
+                    backstory=self.__get_agent_backstory(),
+                )
 
                 fallback_response = self.llm.call(
                     [
@@ -449,165 +297,78 @@ class AgentReasoning:
                 fallback_str = str(fallback_response)
                 return (
                     fallback_str,
-                    [],
                     "READY: I am ready to execute the task." in fallback_str,
                 )
             except Exception as inner_e:
                 self.logger.error(f"Error during fallback text parsing: {inner_e!s}")
                 return (
                     "Failed to generate a plan due to an error.",
-                    [],
                     True,
                 )  # Default to ready to avoid getting stuck
 
-    def _call_llm_with_prompt(
-        self,
-        prompt: str,
-        plan_type: Literal["create_plan", "refine_plan"],
-    ) -> str:
-        """Calls the LLM with the planning prompt.
-
-        Args:
-            prompt: The prompt to send to the LLM.
-            plan_type: The type of plan being created.
-
-        Returns:
-            The LLM response.
+    def __get_agent_backstory(self) -> str:
         """
-        system_prompt = self._get_system_prompt()
-
-        response = self.llm.call(
-            [
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": prompt},
-            ],
-            from_task=self.task,
-            from_agent=self.agent,
-        )
-        return str(response)
-
-    def _get_system_prompt(self) -> str:
-        """Get the system prompt for planning.
+        Safely gets the agent's backstory, providing a default if not available.
 
         Returns:
-            The system prompt, either custom or from i18n.
-        """
-        if self.config.system_prompt is not None:
-            return self.config.system_prompt
-
-        # Try new "planning" section first, fall back to "reasoning" for compatibility
-        try:
-            return self.agent.i18n.retrieve("planning", "system_prompt")
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "initial_plan").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-            )
-
-    def _get_agent_backstory(self) -> str:
-        """Safely gets the agent's backstory, providing a default if not available.
-
-        Returns:
-            The agent's backstory or a default value.
+            str: The agent's backstory or a default value.
         """
         return getattr(self.agent, "backstory", "No backstory provided")
 
-    def _create_planning_prompt(self) -> str:
-        """Creates a prompt for the agent to plan the task.
+    def __create_reasoning_prompt(self) -> str:
+        """
+        Creates a prompt for the agent to reason about the task.
 
         Returns:
-            The planning prompt.
+            str: The reasoning prompt.
         """
-        available_tools = self._format_available_tools()
+        available_tools = self.__format_available_tools()
 
-        # Use custom prompt if provided
-        if self.config.plan_prompt is not None:
-            return self.config.plan_prompt.format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-                max_steps=self.config.max_steps,
-            )
+        return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory(),
+            description=self.task.description,
+            expected_output=self.task.expected_output,
+            tools=available_tools,
+        )
 
-        # Try new "planning" section first
-        try:
-            return self.agent.i18n.retrieve("planning", "create_plan_prompt").format(
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-                max_steps=self.config.max_steps,
-            )
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-            )
-
-    def _format_available_tools(self) -> str:
-        """Formats the available tools for inclusion in the prompt.
+    def __format_available_tools(self) -> str:
+        """
+        Formats the available tools for inclusion in the prompt.
 
         Returns:
-            Comma-separated list of tool names.
+            str: Comma-separated list of tool names.
         """
         try:
-            # Try task tools first, then agent tools
-            tools = []
-            if self.task:
-                tools = self.task.tools or []
-            if not tools:
-                tools = getattr(self.agent, "tools", []) or []
-            if not tools:
-                return "No tools available"
-            return ", ".join([sanitize_tool_name(tool.name) for tool in tools])
+            return ", ".join(
+                [sanitize_tool_name(tool.name) for tool in (self.task.tools or [])]
+            )
         except (AttributeError, TypeError):
             return "No tools available"
 
-    def _create_refine_prompt(self, current_plan: str) -> str:
-        """Creates a prompt for the agent to refine its plan.
+    def __create_refine_prompt(self, current_plan: str) -> str:
+        """
+        Creates a prompt for the agent to refine its reasoning plan.
 
         Args:
-            current_plan: The current plan.
+            current_plan: The current reasoning plan.
 
         Returns:
-            The refine prompt.
+            str: The refine prompt.
         """
-        # Use custom prompt if provided
-        if self.config.refine_prompt is not None:
-            return self.config.refine_prompt.format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                current_plan=current_plan,
-                max_steps=self.config.max_steps,
-            )
-
-        # Try new "planning" section first
-        try:
-            return self.agent.i18n.retrieve("planning", "refine_plan_prompt").format(
-                current_plan=current_plan,
-            )
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                current_plan=current_plan,
-            )
+        return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory(),
+            current_plan=current_plan,
+        )
 
     @staticmethod
-    def _parse_planning_response(response: str) -> tuple[str, bool]:
-        """Parses the planning response to extract the plan and readiness.
+    def __parse_reasoning_response(response: str) -> tuple[str, bool]:
+        """
+        Parses the reasoning response to extract the plan and whether
+        the agent is ready to execute the task.
 
         Args:
             response: The LLM response.
@@ -619,13 +380,25 @@ class AgentReasoning:
             return "No plan was generated.", False
 
         plan = response
-        ready = "READY: I am ready to execute the task." in response
+        ready = False
+
+        if "READY: I am ready to execute the task." in response:
+            ready = True
 
         return plan, ready
 
+    def _handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """
+        Deprecated method for backward compatibility.
+        Use handle_agent_reasoning() instead.
 
-# Alias for backward compatibility
-AgentPlanning = AgentReasoning
+        Returns:
+            AgentReasoningOutput: The output of the agent reasoning process.
+        """
+        self.logger.warning(
+            "The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
+        )
+        return self.handle_agent_reasoning()
 
 
 def _call_llm_with_reasoning_prompt(
@@ -636,9 +409,7 @@ def _call_llm_with_reasoning_prompt(
     backstory: str,
     plan_type: Literal["initial_plan", "refine_plan"],
 ) -> str:
-    """Deprecated: Calls the LLM with the reasoning prompt.
-
-    This function is kept for backward compatibility.
+    """Calls the LLM with the reasoning prompt.
 
     Args:
         llm: The language model to use.
@@ -646,7 +417,7 @@ def _call_llm_with_reasoning_prompt(
         task: The task for which the agent is reasoning.
         reasoning_agent: The agent performing the reasoning.
         backstory: The agent's backstory.
-        plan_type: The type of plan being created.
+        plan_type: The type of plan being created ("initial_plan" or "refine_plan").
 
     Returns:
         The LLM response.

lib/crewai/src/crewai/utilities/serialization.py

@@ -19,7 +19,6 @@ def to_serializable(
     exclude: set[str] | None = None,
     max_depth: int = 5,
     _current_depth: int = 0,
-    _ancestors: set[int] | None = None,
 ) -> Serializable:
     """Converts a Python object into a JSON-compatible representation.
 
@@ -32,7 +31,6 @@ def to_serializable(
         exclude: Set of keys to exclude from the result.
         max_depth: Maximum recursion depth. Defaults to 5.
         _current_depth: Current recursion depth (for internal use).
-        _ancestors: Set of ancestor object ids for cycle detection (for internal use).
 
     Returns:
         Serializable: A JSON-compatible structure.
@@ -43,29 +41,16 @@ def to_serializable(
     if exclude is None:
         exclude = set()
 
-    if _ancestors is None:
-        _ancestors = set()
-
     if isinstance(obj, (str, int, float, bool, type(None))):
         return obj
     if isinstance(obj, uuid.UUID):
         return str(obj)
     if isinstance(obj, (date, datetime)):
         return obj.isoformat()
-
-    object_id = id(obj)
-    if object_id in _ancestors:
-        return f"<circular_ref:{type(obj).__name__}>"
-    new_ancestors = _ancestors | {object_id}
-
     if isinstance(obj, (list, tuple, set)):
         return [
             to_serializable(
-                item,
-                exclude=exclude,
-                max_depth=max_depth,
-                _current_depth=_current_depth + 1,
-                _ancestors=new_ancestors,
+                item, max_depth=max_depth, _current_depth=_current_depth + 1
             )
             for item in obj
         ]
@@ -76,7 +61,6 @@ def to_serializable(
                 exclude=exclude,
                 max_depth=max_depth,
                 _current_depth=_current_depth + 1,
-                _ancestors=new_ancestors,
             )
             for key, value in obj.items()
             if key not in exclude
@@ -87,16 +71,12 @@ def to_serializable(
                 obj=obj.model_dump(exclude=exclude),
                 max_depth=max_depth,
                 _current_depth=_current_depth + 1,
-                _ancestors=new_ancestors,
             )
         except Exception:
             try:
                 return {
                     _to_serializable_key(k): to_serializable(
-                        v,
-                        max_depth=max_depth,
-                        _current_depth=_current_depth + 1,
-                        _ancestors=new_ancestors,
+                        v, max_depth=max_depth, _current_depth=_current_depth + 1
                     )
                     for k, v in obj.__dict__.items()
                     if k not in (exclude or set())

lib/crewai/tests/agents/agent_adapters/test_base_agent_adapter.py

@@ -51,10 +51,6 @@ class ConcreteAgentAdapter(BaseAgentAdapter):
         # Dummy implementation for MCP tools
         return []
 
-    def configure_structured_output(self, task: Any) -> None:
-        # Dummy implementation for structured output
-        pass
-
     async def aexecute_task(
         self,
         task: Any,

lib/crewai/tests/agents/test_agent.py

@@ -1456,7 +1456,7 @@ def test_agent_execute_task_with_tool():
     )
 
     result = agent.execute_task(task)
-    assert "test query" in result
+    assert "you should always think about what to do" in result
 
 
 @pytest.mark.vcr()
@@ -1475,9 +1475,9 @@ def test_agent_execute_task_with_custom_llm():
     )
 
     result = agent.execute_task(task)
-    assert "Artificial minds" in result
-    assert "Code and circuits" in result
-    assert "Future undefined" in result
+    assert "In circuits they thrive" in result
+    assert "Artificial minds awake" in result
+    assert "Future's coded drive" in result
 
 
 @pytest.mark.vcr()

lib/crewai/tests/agents/test_agent_executor.py

@@ -25,18 +25,6 @@ class TestAgentReActState:
         assert state.current_answer is None
         assert state.is_finished is False
         assert state.ask_for_human_input is False
-        # Planning state fields
-        assert state.plan is None
-        assert state.plan_ready is False
-
-    def test_state_with_plan(self):
-        """Test AgentReActState initialization with planning fields."""
-        state = AgentReActState(
-            plan="Step 1: Do X\nStep 2: Do Y",
-            plan_ready=True,
-        )
-        assert state.plan == "Step 1: Do X\nStep 2: Do Y"
-        assert state.plan_ready is True
 
     def test_state_with_values(self):
         """Test AgentReActState initialization with values."""
@@ -489,249 +477,3 @@ class TestFlowInvoke:
 
         assert result == {"output": "Done"}
         assert len(executor.state.messages) >= 2
-
-
-class TestAgentExecutorPlanning:
-    """Test planning functionality in AgentExecutor with real agent kickoff."""
-
-    @pytest.mark.vcr()
-    def test_agent_kickoff_with_planning_stores_plan_in_state(self):
-        """Test that Agent.kickoff() with planning enabled stores plan in executor state."""
-        from crewai import Agent, PlanningConfig
-        from crewai.llm import LLM
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help solve simple math problems",
-            backstory="A helpful assistant that solves math problems step by step",
-            llm=llm,
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        # Execute kickoff with a simple task
-        result = agent.kickoff("What is 2 + 2?")
-
-        # Verify result
-        assert result is not None
-        assert "4" in str(result)
-
-    @pytest.mark.vcr()
-    def test_agent_kickoff_without_planning_skips_plan_generation(self):
-        """Test that Agent.kickoff() without planning skips planning phase."""
-        from crewai import Agent
-        from crewai.llm import LLM
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help solve simple math problems",
-            backstory="A helpful assistant",
-            llm=llm,
-            # No planning_config = no planning
-            verbose=False,
-        )
-
-        # Execute kickoff
-        result = agent.kickoff("What is 3 + 3?")
-
-        # Verify we get a result
-        assert result is not None
-        assert "6" in str(result)
-
-    @pytest.mark.vcr()
-    def test_planning_disabled_skips_planning(self):
-        """Test that planning=False skips planning."""
-        from crewai import Agent
-        from crewai.llm import LLM
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help solve simple math problems",
-            backstory="A helpful assistant",
-            llm=llm,
-            planning=False,  # Explicitly disable planning
-            verbose=False,
-        )
-
-        result = agent.kickoff("What is 5 + 5?")
-
-        # Should still complete successfully
-        assert result is not None
-        assert "10" in str(result)
-
-    def test_backward_compat_reasoning_true_enables_planning(self):
-        """Test that reasoning=True (deprecated) still enables planning."""
-        import warnings
-        from crewai import Agent
-        from crewai.llm import LLM
-
-        llm = LLM("gpt-4o-mini")
-
-        with warnings.catch_warnings(record=True):
-            warnings.simplefilter("always")
-            agent = Agent(
-                role="Test Agent",
-                goal="Complete tasks",
-                backstory="A helpful agent",
-                llm=llm,
-                reasoning=True,  # Deprecated but should still work
-                verbose=False,
-            )
-
-        # Should have planning_config created from reasoning=True
-        assert agent.planning_config is not None
-        assert agent.planning_enabled is True
-
-    @pytest.mark.vcr()
-    def test_executor_state_contains_plan_after_planning(self):
-        """Test that executor state contains plan after planning phase."""
-        from crewai import Agent, PlanningConfig
-        from crewai.llm import LLM
-        from crewai.experimental.agent_executor import AgentExecutor
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help solve simple math problems",
-            backstory="A helpful assistant that solves math problems step by step",
-            llm=llm,
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        # Track executor for inspection
-        executor_ref = [None]
-        original_invoke = AgentExecutor.invoke
-
-        def capture_executor(self, inputs):
-            executor_ref[0] = self
-            return original_invoke(self, inputs)
-
-        with patch.object(AgentExecutor, "invoke", capture_executor):
-            result = agent.kickoff("What is 7 + 7?")
-
-        # Verify result
-        assert result is not None
-
-        # If we captured an executor, check its state
-        if executor_ref[0] is not None:
-            # After planning, state should have plan info
-            assert hasattr(executor_ref[0].state, "plan")
-            assert hasattr(executor_ref[0].state, "plan_ready")
-
-    @pytest.mark.vcr()
-    def test_planning_creates_minimal_steps_for_multi_step_task(self):
-        """Test that planning creates only necessary steps for a multi-step task.
-
-        This task requires exactly 3 dependent steps:
-        1. Identify the first 3 prime numbers (2, 3, 5)
-        2. Sum them (2 + 3 + 5 = 10)
-        3. Multiply by 2 (10 * 2 = 20)
-
-        The plan should reflect these dependencies without unnecessary padding.
-        """
-        from crewai import Agent, PlanningConfig
-        from crewai.llm import LLM
-        from crewai.experimental.agent_executor import AgentExecutor
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Math Tutor",
-            goal="Solve multi-step math problems accurately",
-            backstory="An expert math tutor who breaks down problems step by step",
-            llm=llm,
-            planning_config=PlanningConfig(max_attempts=1, max_steps=10),
-            verbose=False,
-        )
-
-        # Track the plan that gets generated
-        captured_plan = [None]
-        original_invoke = AgentExecutor.invoke
-
-        def capture_plan(self, inputs):
-            result = original_invoke(self, inputs)
-            captured_plan[0] = self.state.plan
-            return result
-
-        with patch.object(AgentExecutor, "invoke", capture_plan):
-            result = agent.kickoff(
-                "Calculate the sum of the first 3 prime numbers, then multiply that result by 2. "
-                "Show your work for each step."
-            )
-
-        # Verify result contains the correct answer (20)
-        assert result is not None
-        assert "20" in str(result)
-
-        # Verify a plan was generated
-        assert captured_plan[0] is not None
-
-        # The plan should be concise - this task needs ~3 steps, not 10+
-        plan_text = captured_plan[0]
-        # Count steps by looking for numbered items or bullet points
-        import re
-
-        step_pattern = r"^\s*\d+[\.\):]|\n\s*-\s+"
-        steps = re.findall(step_pattern, plan_text, re.MULTILINE)
-        # Plan should have roughly 3-5 steps, not fill up to max_steps
-        assert len(steps) <= 6, f"Plan has too many steps ({len(steps)}): {plan_text}"
-
-    @pytest.mark.vcr()
-    def test_planning_handles_sequential_dependency_task(self):
-        """Test planning for a task where step N depends on step N-1.
-
-        Task: Convert 100 Celsius to Fahrenheit, then round to nearest 10.
-        Step 1: Apply formula (C * 9/5 + 32) = 212
-        Step 2: Round 212 to nearest 10 = 210
-
-        This tests that the planner recognizes sequential dependencies.
-        """
-        from crewai import Agent, PlanningConfig
-        from crewai.llm import LLM
-        from crewai.experimental.agent_executor import AgentExecutor
-
-        llm = LLM("gpt-4o-mini")
-
-        agent = Agent(
-            role="Unit Converter",
-            goal="Accurately convert between units and apply transformations",
-            backstory="A precise unit conversion specialist",
-            llm=llm,
-            planning_config=PlanningConfig(max_attempts=1, max_steps=10),
-            verbose=False,
-        )
-
-        captured_plan = [None]
-        original_invoke = AgentExecutor.invoke
-
-        def capture_plan(self, inputs):
-            result = original_invoke(self, inputs)
-            captured_plan[0] = self.state.plan
-            return result
-
-        with patch.object(AgentExecutor, "invoke", capture_plan):
-            result = agent.kickoff(
-                "Convert 100 degrees Celsius to Fahrenheit, then round the result to the nearest 10."
-            )
-
-        assert result is not None
-        # 100C = 212F, rounded to nearest 10 = 210
-        assert "210" in str(result) or "212" in str(result)
-
-        # Plan should exist and be minimal (2-3 steps for this task)
-        assert captured_plan[0] is not None
-        plan_text = captured_plan[0]
-
-        import re
-
-        step_pattern = r"^\s*\d+[\.\):]|\n\s*-\s+"
-        steps = re.findall(step_pattern, plan_text, re.MULTILINE)
-        assert len(steps) <= 5, f"Plan should be minimal ({len(steps)} steps): {plan_text}"

lib/crewai/tests/agents/test_agent_reasoning.py

@@ -1,345 +1,240 @@
-"""Tests for planning/reasoning in agents."""
+"""Tests for reasoning in agents."""
 
-import warnings
+import json
 
 import pytest
 
-from crewai import Agent, PlanningConfig, Task
+from crewai import Agent, Task
 from crewai.llm import LLM
 
 
-# =============================================================================
-# Tests for PlanningConfig configuration (no LLM calls needed)
-# =============================================================================
+@pytest.fixture
+def mock_llm_responses():
+    """Fixture for mock LLM responses."""
+    return {
+        "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
+        "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
+        "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
+        "execution": "4",
+    }
 
 
-def test_planning_config_default_values():
-    """Test PlanningConfig default values."""
-    config = PlanningConfig()
-
-    assert config.max_attempts is None
-    assert config.max_steps == 20
-    assert config.system_prompt is None
-    assert config.plan_prompt is None
-    assert config.refine_prompt is None
-    assert config.llm is None
-
-
-def test_planning_config_custom_values():
-    """Test PlanningConfig with custom values."""
-    config = PlanningConfig(
-        max_attempts=5,
-        max_steps=15,
-        system_prompt="Custom system",
-        plan_prompt="Custom plan: {description}",
-        refine_prompt="Custom refine: {current_plan}",
-        llm="gpt-4",
-    )
-
-    assert config.max_attempts == 5
-    assert config.max_steps == 15
-    assert config.system_prompt == "Custom system"
-    assert config.plan_prompt == "Custom plan: {description}"
-    assert config.refine_prompt == "Custom refine: {current_plan}"
-    assert config.llm == "gpt-4"
-
-
-def test_agent_with_planning_config_custom_prompts():
-    """Test agent with PlanningConfig using custom prompts."""
-    llm = LLM("gpt-4o-mini")
-
-    custom_system_prompt = "You are a specialized planner."
-    custom_plan_prompt = "Plan this task: {description}"
-
-    agent = Agent(
-        role="Test Agent",
-        goal="To test custom prompts",
-        backstory="I am a test agent.",
-        llm=llm,
-        planning_config=PlanningConfig(
-            system_prompt=custom_system_prompt,
-            plan_prompt=custom_plan_prompt,
-            max_steps=10,
-        ),
-        verbose=False,
-    )
-
-    # Just test that the agent is created properly
-    assert agent.planning_config is not None
-    assert agent.planning_config.system_prompt == custom_system_prompt
-    assert agent.planning_config.plan_prompt == custom_plan_prompt
-    assert agent.planning_config.max_steps == 10
-
-
-def test_agent_with_planning_config_disabled():
-    """Test agent with PlanningConfig disabled."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Test Agent",
-        goal="To test disabled planning",
-        backstory="I am a test agent.",
-        llm=llm,
-        planning=False,
-        verbose=False,
-    )
-
-    # Planning should be disabled
-    assert agent.planning_enabled is False
-
-
-def test_planning_enabled_property():
-    """Test the planning_enabled property on Agent."""
-    llm = LLM("gpt-4o-mini")
-
-    # With planning_config enabled
-    agent_with_planning = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-        planning=True,
-    )
-    assert agent_with_planning.planning_enabled is True
-
-    # With planning_config disabled
-    agent_disabled = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-        planning=False,
-    )
-    assert agent_disabled.planning_enabled is False
-
-    # Without planning_config
-    agent_no_planning = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-    )
-    assert agent_no_planning.planning_enabled is False
-
-
-# =============================================================================
-# Tests for backward compatibility with reasoning=True (no LLM calls)
-# =============================================================================
-
-
-def test_agent_with_reasoning_backward_compat():
-    """Test agent with reasoning=True (backward compatibility)."""
-    llm = LLM("gpt-4o-mini")
-
-    # This should emit a deprecation warning
-    with warnings.catch_warnings(record=True):
-        warnings.simplefilter("always")
-        agent = Agent(
-            role="Test Agent",
-            goal="To test the reasoning feature",
-            backstory="I am a test agent created to verify the reasoning feature works correctly.",
-            llm=llm,
-            reasoning=True,
-            verbose=False,
-        )
-
-    # Should have created a PlanningConfig internally
-    assert agent.planning_config is not None
-    assert agent.planning_enabled is True
-
-
-def test_agent_with_reasoning_and_max_attempts_backward_compat():
-    """Test agent with reasoning=True and max_reasoning_attempts (backward compatibility)."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning(mock_llm_responses):
+    """Test agent with reasoning."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
-        backstory="I am a test agent.",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        max_reasoning_attempts=5,
-        verbose=False,
-    )
-
-    # Should have created a PlanningConfig with max_attempts
-    assert agent.planning_config is not None
-    assert agent.planning_config.max_attempts == 5
-
-
-# =============================================================================
-# Tests for Agent.kickoff() with planning (uses AgentExecutor)
-# =============================================================================
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_with_planning():
-    """Test Agent.kickoff() with planning enabled generates a plan."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems step by step",
-        backstory="A helpful math tutor",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1),
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 15 + 27?")
-
-    assert result is not None
-    assert "42" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_without_planning():
-    """Test Agent.kickoff() without planning skips plan generation."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
-        llm=llm,
-        # No planning_config = no planning
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 8 * 7?")
-
-    assert result is not None
-    assert "56" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_with_planning_disabled():
-    """Test Agent.kickoff() with planning explicitly disabled via planning=False."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
-        llm=llm,
-        planning=False,  # Explicitly disable planning
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 100 / 4?")
-
-    assert result is not None
-    assert "25" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_multi_step_task_with_planning():
-    """Test Agent.kickoff() with a multi-step task that benefits from planning."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Tutor",
-        goal="Solve multi-step math problems",
-        backstory="An expert tutor who explains step by step",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1, max_steps=5),
-        verbose=False,
-    )
-
-    # Task requires: find primes, sum them, then double
-    result = agent.kickoff(
-        "Find the first 3 prime numbers, add them together, then multiply by 2."
-    )
-
-    assert result is not None
-    # First 3 primes: 2, 3, 5 -> sum = 10 -> doubled = 20
-    assert "20" in str(result)
-
-
-# =============================================================================
-# Tests for Agent.execute_task() with planning (uses CrewAgentExecutor)
-# These test the legacy path via handle_reasoning()
-# =============================================================================
-
-
-@pytest.mark.vcr()
-def test_agent_execute_task_with_planning():
-    """Test Agent.execute_task() with planning via CrewAgentExecutor."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful math tutor",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1),
-        verbose=False,
+        verbose=True,
     )
 
     task = Task(
-        description="What is 9 + 11?",
-        expected_output="A number",
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
         agent=agent,
     )
 
+    agent.llm.call = lambda messages, *args, **kwargs: (
+        mock_llm_responses["ready"]
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages)
+        else mock_llm_responses["execution"]
+    )
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    assert "20" in str(result)
-    # Planning should be appended to task description
-    assert "Planning:" in task.description
+    assert result == mock_llm_responses["execution"]
+    assert "Reasoning Plan:" in task.description
 
 
-@pytest.mark.vcr()
-def test_agent_execute_task_without_planning():
-    """Test Agent.execute_task() without planning."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
+    """Test agent with reasoning that requires refinement."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        verbose=False,
+        reasoning=True,
+        max_reasoning_attempts=2,
+        verbose=True,
     )
 
     task = Task(
-        description="What is 12 * 3?",
-        expected_output="A number",
+        description="Complex math task: What's the derivative of x²?",
+        expected_output="The answer should be a mathematical expression.",
         agent=agent,
     )
 
+    call_count = [0]
+
+    def mock_llm_call(messages, *args, **kwargs):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            if call_count[0] == 1:
+                return mock_llm_responses["not_ready"]
+            return mock_llm_responses["ready_after_refine"]
+        return "2x"
+
+    agent.llm.call = mock_llm_call
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    assert "36" in str(result)
-    # No planning should be added
-    assert "Planning:" not in task.description
+    assert result == "2x"
+    assert call_count[0] == 2  # Should have made 2 reasoning calls
+    assert "Reasoning Plan:" in task.description
 
 
-@pytest.mark.vcr()
-def test_agent_execute_task_with_planning_refine():
-    """Test Agent.execute_task() with planning that requires refinement."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning_max_attempts_reached():
+    """Test agent with reasoning that reaches max attempts without being ready."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
-        role="Math Tutor",
-        goal="Solve complex math problems step by step",
-        backstory="An expert tutor",
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        planning_config=PlanningConfig(max_attempts=2),
-        verbose=False,
+        reasoning=True,
+        max_reasoning_attempts=2,
+        verbose=True,
     )
 
     task = Task(
-        description="Calculate the area of a circle with radius 5 (use pi = 3.14)",
-        expected_output="The area as a number",
+        description="Complex math task: Solve the Riemann hypothesis.",
+        expected_output="A proof or disproof of the hypothesis.",
         agent=agent,
     )
 
+    call_count = [0]
+
+    def mock_llm_call(messages, *args, **kwargs):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
+        return "This is an unsolved problem in mathematics."
+
+    agent.llm.call = mock_llm_call
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    # Area = pi * r^2 = 3.14 * 25 = 78.5
-    assert "78" in str(result) or "79" in str(result)
-    assert "Planning:" in task.description
+    assert result == "This is an unsolved problem in mathematics."
+    assert (
+        call_count[0] == 2
+    )  # Should have made exactly 2 reasoning calls (max_attempts)
+    assert "Reasoning Plan:" in task.description
+
+
+def test_agent_reasoning_error_handling():
+    """Test error handling during the reasoning process."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+    )
+
+    task = Task(
+        description="Task that will cause an error",
+        expected_output="Output that will never be generated",
+        agent=agent,
+    )
+
+    call_count = [0]
+
+    def mock_llm_call_error(*args, **kwargs):
+        call_count[0] += 1
+        if call_count[0] <= 2:  # First calls are for reasoning
+            raise Exception("LLM error during reasoning")
+        return "Fallback execution result"  # Return a value for task execution
+
+    agent.llm.call = mock_llm_call_error
+
+    result = agent.execute_task(task)
+
+    assert result == "Fallback execution result"
+    assert call_count[0] > 2  # Ensure we called the mock multiple times
+
+
+@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
+def test_agent_with_function_calling():
+    """Test agent with reasoning using function calling."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True,
+    )
+
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent,
+    )
+
+    agent.llm.supports_function_calling = lambda: True
+
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return json.dumps(
+                {"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
+            )
+        return "4"
+
+    agent.llm.call = mock_function_call
+
+    result = agent.execute_task(task)
+
+    assert result == "4"
+    assert "Reasoning Plan:" in task.description
+    assert "I'll solve this simple math problem: 2+2=4." in task.description
+
+
+@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
+def test_agent_with_function_calling_fallback():
+    """Test agent with reasoning using function calling that falls back to text parsing."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True,
+    )
+
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent,
+    )
+
+    agent.llm.supports_function_calling = lambda: True
+
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
+        return "4"
+
+    agent.llm.call = mock_function_call
+
+    result = agent.execute_task(task)
+
+    assert result == "4"
+    assert "Reasoning Plan:" in task.description
+    assert "Invalid JSON that will trigger fallback" in task.description

lib/crewai/tests/agents/test_lite_agent.py

@@ -606,10 +606,9 @@ def test_lite_agent_with_invalid_llm():
 
 
 @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
-@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
 @patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get")
 @pytest.mark.vcr()
-def test_agent_kickoff_with_platform_tools(mock_get, mock_post):
+def test_agent_kickoff_with_platform_tools(mock_get):
     """Test that Agent.kickoff() properly integrates platform tools with LiteAgent"""
     mock_response = Mock()
     mock_response.raise_for_status.return_value = None
@@ -633,15 +632,6 @@ def test_agent_kickoff_with_platform_tools(mock_get, mock_post):
     }
     mock_get.return_value = mock_response
 
-    # Mock the platform tool execution
-    mock_post_response = Mock()
-    mock_post_response.ok = True
-    mock_post_response.json.return_value = {
-        "success": True,
-        "issue_url": "https://github.com/test/repo/issues/1"
-    }
-    mock_post.return_value = mock_post_response
-
     agent = Agent(
         role="Test Agent",
         goal="Test goal",

lib/crewai/tests/cassettes/agents/TestAgentExecutorPlanning.test_agent_kickoff_with_planning_stores_plan_in_state.yaml

@@ -1,234 +0,0 @@
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-      Create minimal, effective execution plans. Prefer fewer steps over more."},{"role":"user","content":"Create
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-      Expected Output\nComplete the task successfully\n\n## Available Tools\nNo tools
-      available\n\n## Instructions\nCreate ONLY the essential steps needed to complete
-      this task. Use the MINIMUM number of steps required - do NOT pad your plan with
-      unnecessary steps. Most tasks need only 2-5 steps.\n\nFor each step:\n- State
-      the specific action to take\n- Specify which tool to use (if any)\n\nDo NOT
-      include:\n- Setup or preparation steps that are obvious\n- Verification steps
-      unless critical\n- Documentation or cleanup steps unless explicitly required\n-
-      Generic steps like \"review results\" or \"finalize output\"\n\nAfter your plan,
-      state:\n- \"READY: I am ready to execute the task.\" if the plan is complete\n-
-      \"NOT READY: I need to refine my plan because [reason].\" if you need more thinking"}],"model":"gpt-4o-mini","tool_choice":"auto","tools":[{"type":"function","function":{"name":"create_reasoning_plan","description":"Create
-      or refine a reasoning plan for a task","strict":true,"parameters":{"type":"object","properties":{"plan":{"type":"string","description":"The
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lib/crewai/tests/cassettes/agents/test_native_tool_calling_error_handling.yaml

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

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lib/crewai/tests/cassettes/utilities/TestAzureStructuredPlanning.test_azure_research_workflow_generates_steps.yaml

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-      x-stainless-package-version:
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-      x-stainless-read-timeout:
-      - X-STAINLESS-READ-TIMEOUT-XXX
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
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lib/crewai/tests/cli/test_plus_api.py

@@ -1,8 +1,6 @@
 import os
 import unittest
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
-
-import pytest
+from unittest.mock import ANY, MagicMock, patch
 
 from crewai.cli.plus_api import PlusAPI
 
@@ -70,6 +68,37 @@ class TestPlusAPI(unittest.TestCase):
         )
         self.assertEqual(response, mock_response)
 
+    @patch("crewai.cli.plus_api.PlusAPI._make_request")
+    def test_get_agent(self, mock_make_request):
+        mock_response = MagicMock()
+        mock_make_request.return_value = mock_response
+
+        response = self.api.get_agent("test_agent_handle")
+        mock_make_request.assert_called_once_with(
+            "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+        )
+        self.assertEqual(response, mock_response)
+
+    @patch("crewai.cli.plus_api.Settings")
+    @patch("requests.Session.request")
+    def test_get_agent_with_org_uuid(self, mock_make_request, mock_settings_class):
+        mock_settings = MagicMock()
+        mock_settings.org_uuid = self.org_uuid
+        mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL')
+        mock_settings_class.return_value = mock_settings
+        # re-initialize Client
+        self.api = PlusAPI(self.api_key)
+
+        mock_response = MagicMock()
+        mock_make_request.return_value = mock_response
+
+        response = self.api.get_agent("test_agent_handle")
+
+        self.assert_request_with_org_id(
+            mock_make_request, "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+        )
+        self.assertEqual(response, mock_response)
+
     @patch("crewai.cli.plus_api.PlusAPI._make_request")
     def test_get_tool(self, mock_make_request):
         mock_response = MagicMock()
@@ -309,49 +338,3 @@ class TestPlusAPI(unittest.TestCase):
             custom_api.base_url,
             "https://custom-url-from-env.com",
         )
-
-
-@pytest.mark.asyncio
-@patch("httpx.AsyncClient")
-async def test_get_agent(mock_async_client_class):
-    api = PlusAPI("test_api_key")
-    mock_response = MagicMock()
-    mock_client_instance = AsyncMock()
-    mock_client_instance.get.return_value = mock_response
-    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
-
-    response = await api.get_agent("test_agent_handle")
-
-    mock_client_instance.get.assert_called_once_with(
-        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
-        headers=api.headers,
-    )
-    assert response == mock_response
-
-
-@pytest.mark.asyncio
-@patch("httpx.AsyncClient")
-@patch("crewai.cli.plus_api.Settings")
-async def test_get_agent_with_org_uuid(mock_settings_class, mock_async_client_class):
-    org_uuid = "test-org-uuid"
-    mock_settings = MagicMock()
-    mock_settings.org_uuid = org_uuid
-    mock_settings.enterprise_base_url = os.getenv("CREWAI_PLUS_URL")
-    mock_settings_class.return_value = mock_settings
-
-    api = PlusAPI("test_api_key")
-
-    mock_response = MagicMock()
-    mock_client_instance = AsyncMock()
-    mock_client_instance.get.return_value = mock_response
-    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
-
-    response = await api.get_agent("test_agent_handle")
-
-    mock_client_instance.get.assert_called_once_with(
-        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
-        headers=api.headers,
-    )
-    assert "X-Crewai-Organization-Id" in api.headers
-    assert api.headers["X-Crewai-Organization-Id"] == org_uuid
-    assert response == mock_response

lib/crewai/tests/events/test_event_context.py

@@ -177,40 +177,4 @@ class TestTriggeredByScope:
                 raise ValueError("test error")
         except ValueError:
             pass
-        assert get_triggering_event_id() is None
-
-
-def test_agent_scope_preserved_after_tool_error_event() -> None:
-    from crewai.events import crewai_event_bus
-    from crewai.events.types.tool_usage_events import (
-        ToolUsageErrorEvent,
-        ToolUsageStartedEvent,
-    )
-
-    push_event_scope("crew-1", "crew_kickoff_started")
-    push_event_scope("task-1", "task_started")
-    push_event_scope("agent-1", "agent_execution_started")
-
-    crewai_event_bus.emit(
-        None,
-        ToolUsageStartedEvent(
-            tool_name="test_tool",
-            tool_args={},
-            agent_key="test_agent",
-        )
-    )
-
-    crewai_event_bus.emit(
-        None,
-        ToolUsageErrorEvent(
-            tool_name="test_tool",
-            tool_args={},
-            agent_key="test_agent",
-            error=ValueError("test error"),
-        )
-    )
-
-    crewai_event_bus.flush()
-
-    assert get_current_parent_id() == "agent-1"
-
+        assert get_triggering_event_id() is None

lib/crewai/tests/memory/test_external_memory.py

@@ -308,7 +308,6 @@ def test_external_memory_search_events(
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "limit": 3,
@@ -331,7 +330,6 @@ def test_external_memory_search_events(
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "results": [],
@@ -392,7 +390,6 @@ def test_external_memory_save_events(
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "saving value",
         "metadata": {"task": "test_task"},
@@ -414,7 +411,6 @@ def test_external_memory_save_events(
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "saving value",
         "metadata": {"task": "test_task"},

lib/crewai/tests/memory/test_long_term_memory.py

@@ -74,7 +74,6 @@ def test_long_term_memory_save_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test_task",
         "metadata": {"task": "test_task", "quality": 0.5},
@@ -95,7 +94,6 @@ def test_long_term_memory_save_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test_task",
         "metadata": {
@@ -155,7 +153,6 @@ def test_long_term_memory_search_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test query",
         "limit": 5,
@@ -178,7 +175,6 @@ def test_long_term_memory_search_events(long_term_memory):
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test query",
         "results": None,

lib/crewai/tests/memory/test_short_term_memory.py

@@ -85,7 +85,6 @@ def test_short_term_memory_search_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "limit": 3,
@@ -108,7 +107,6 @@ def test_short_term_memory_search_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "results": [],
@@ -166,7 +164,6 @@ def test_short_term_memory_save_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test value",
         "metadata": {"task": "test_task"},
@@ -188,7 +185,6 @@ def test_short_term_memory_save_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
-        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test value",
         "metadata": {"task": "test_task"},

lib/crewai/tests/test_human_feedback_integration.py

@@ -157,176 +157,6 @@ class TestMultiStepFlows:
 
         assert execution_order == ["generate", "review", "finalize"]
 
-    def test_chained_router_feedback_steps(self):
-        """Test that a router outcome can trigger another router method.
-
-        Regression test: @listen("outcome") combined with @human_feedback(emit=...)
-        creates a method that is both a listener and a router. The flow must find
-        and execute it when the upstream router emits the matching outcome.
-        """
-        execution_order: list[str] = []
-
-        class ChainedRouterFlow(Flow):
-            @start()
-            @human_feedback(
-                message="First review:",
-                emit=["approved", "rejected"],
-                llm="gpt-4o-mini",
-            )
-            def draft(self):
-                execution_order.append("draft")
-                return "draft content"
-
-            @listen("approved")
-            @human_feedback(
-                message="Final review:",
-                emit=["publish", "revise"],
-                llm="gpt-4o-mini",
-            )
-            def final_review(self, prev: HumanFeedbackResult):
-                execution_order.append("final_review")
-                return "final content"
-
-            @listen("rejected")
-            def on_rejected(self, prev: HumanFeedbackResult):
-                execution_order.append("on_rejected")
-                return "rejected"
-
-            @listen("publish")
-            def on_publish(self, prev: HumanFeedbackResult):
-                execution_order.append("on_publish")
-                return "published"
-
-            @listen("revise")
-            def on_revise(self, prev: HumanFeedbackResult):
-                execution_order.append("on_revise")
-                return "revised"
-
-        flow = ChainedRouterFlow()
-
-        with (
-            patch.object(
-                flow,
-                "_request_human_feedback",
-                side_effect=["looks good", "ship it"],
-            ),
-            patch.object(
-                flow,
-                "_collapse_to_outcome",
-                side_effect=["approved", "publish"],
-            ),
-        ):
-            result = flow.kickoff()
-
-        assert execution_order == ["draft", "final_review", "on_publish"]
-        assert result == "published"
-        assert len(flow.human_feedback_history) == 2
-        assert flow.human_feedback_history[0].outcome == "approved"
-        assert flow.human_feedback_history[1].outcome == "publish"
-
-    def test_chained_router_rejected_path(self):
-        """Test that a start-router outcome routes to a non-router listener."""
-        execution_order: list[str] = []
-
-        class ChainedRouterFlow(Flow):
-            @start()
-            @human_feedback(
-                message="Review:",
-                emit=["approved", "rejected"],
-                llm="gpt-4o-mini",
-            )
-            def draft(self):
-                execution_order.append("draft")
-                return "draft"
-
-            @listen("approved")
-            @human_feedback(
-                message="Final:",
-                emit=["publish", "revise"],
-                llm="gpt-4o-mini",
-            )
-            def final_review(self, prev: HumanFeedbackResult):
-                execution_order.append("final_review")
-                return "final"
-
-            @listen("rejected")
-            def on_rejected(self, prev: HumanFeedbackResult):
-                execution_order.append("on_rejected")
-                return "rejected"
-
-        flow = ChainedRouterFlow()
-
-        with (
-            patch.object(
-                flow, "_request_human_feedback", return_value="bad"
-            ),
-            patch.object(
-                flow, "_collapse_to_outcome", return_value="rejected"
-            ),
-        ):
-            result = flow.kickoff()
-
-        assert execution_order == ["draft", "on_rejected"]
-        assert result == "rejected"
-        assert len(flow.human_feedback_history) == 1
-        assert flow.human_feedback_history[0].outcome == "rejected"
-
-    def test_router_and_non_router_listeners_for_same_outcome(self):
-        """Test that both router and non-router listeners fire for the same outcome."""
-        execution_order: list[str] = []
-
-        class MixedListenerFlow(Flow):
-            @start()
-            @human_feedback(
-                message="Review:",
-                emit=["approved", "rejected"],
-                llm="gpt-4o-mini",
-            )
-            def draft(self):
-                execution_order.append("draft")
-                return "draft"
-
-            @listen("approved")
-            @human_feedback(
-                message="Final:",
-                emit=["publish", "revise"],
-                llm="gpt-4o-mini",
-            )
-            def router_listener(self, prev: HumanFeedbackResult):
-                execution_order.append("router_listener")
-                return "final"
-
-            @listen("approved")
-            def plain_listener(self, prev: HumanFeedbackResult):
-                execution_order.append("plain_listener")
-                return "logged"
-
-            @listen("publish")
-            def on_publish(self, prev: HumanFeedbackResult):
-                execution_order.append("on_publish")
-                return "published"
-
-        flow = MixedListenerFlow()
-
-        with (
-            patch.object(
-                flow,
-                "_request_human_feedback",
-                side_effect=["approve it", "publish it"],
-            ),
-            patch.object(
-                flow,
-                "_collapse_to_outcome",
-                side_effect=["approved", "publish"],
-            ),
-        ):
-            flow.kickoff()
-
-        assert "draft" in execution_order
-        assert "router_listener" in execution_order
-        assert "plain_listener" in execution_order
-        assert "on_publish" in execution_order
-
 
 class TestStateManagement:
     """Tests for state management with human feedback."""

lib/crewai/tests/tools/test_tool_usage.py

@@ -10,9 +10,7 @@ from crewai import Agent, Task
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import (
     ToolSelectionErrorEvent,
-    ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
     ToolValidateInputErrorEvent,
 )
 from crewai.tools import BaseTool
@@ -746,78 +744,3 @@ def test_tool_usage_finished_event_with_cached_result():
     assert isinstance(event.started_at, datetime.datetime)
     assert isinstance(event.finished_at, datetime.datetime)
     assert event.type == "tool_usage_finished"
-
-
-def test_tool_error_does_not_emit_finished_event():
-    from crewai.tools.tool_calling import ToolCalling
-
-    class FailingTool(BaseTool):
-        name: str = "Failing Tool"
-        description: str = "A tool that always fails"
-
-        def _run(self, **kwargs) -> str:
-            raise ValueError("Intentional failure")
-
-    failing_tool = FailingTool().to_structured_tool()
-
-    mock_agent = MagicMock()
-    mock_agent.key = "test_agent_key"
-    mock_agent.role = "test_agent_role"
-    mock_agent._original_role = "test_agent_role"
-    mock_agent.verbose = False
-    mock_agent.fingerprint = None
-    mock_agent.i18n.tools.return_value = {"name": "Add Image"}
-    mock_agent.i18n.errors.return_value = "Error: {error}"
-    mock_agent.i18n.slice.return_value = "Available tools: {tool_names}"
-
-    mock_task = MagicMock()
-    mock_task.delegations = 0
-    mock_task.name = "Test Task"
-    mock_task.description = "A test task"
-    mock_task.id = "test-task-id"
-
-    mock_action = MagicMock()
-    mock_action.tool = "failing_tool"
-    mock_action.tool_input = "{}"
-
-    tool_usage = ToolUsage(
-        tools_handler=MagicMock(cache=None, last_used_tool=None),
-        tools=[failing_tool],
-        task=mock_task,
-        function_calling_llm=None,
-        agent=mock_agent,
-        action=mock_action,
-    )
-
-    started_events = []
-    error_events = []
-    finished_events = []
-    error_received = threading.Event()
-
-    @crewai_event_bus.on(ToolUsageStartedEvent)
-    def on_started(source, event):
-        if event.tool_name == "failing_tool":
-            started_events.append(event)
-
-    @crewai_event_bus.on(ToolUsageErrorEvent)
-    def on_error(source, event):
-        if event.tool_name == "failing_tool":
-            error_events.append(event)
-            error_received.set()
-
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def on_finished(source, event):
-        if event.tool_name == "failing_tool":
-            finished_events.append(event)
-
-    tool_calling = ToolCalling(tool_name="failing_tool", arguments={})
-    tool_usage.use(calling=tool_calling, tool_string="Action: failing_tool")
-
-    assert error_received.wait(timeout=5), "Timeout waiting for error event"
-    crewai_event_bus.flush()
-
-    assert len(started_events) >= 1, "Expected at least one ToolUsageStartedEvent"
-    assert len(error_events) >= 1, "Expected at least one ToolUsageErrorEvent"
-    assert len(finished_events) == 0, (
-        "ToolUsageFinishedEvent should NOT be emitted after ToolUsageErrorEvent"
-    )

lib/crewai/tests/utilities/test_planning_types.py

@@ -1,389 +0,0 @@
-"""Tests for planning types (PlanStep, TodoItem, TodoList)."""
-
-import pytest
-from uuid import UUID
-
-from crewai.utilities.planning_types import (
-    PlanStep,
-    TodoItem,
-    TodoList,
-    TodoStatus,
-)
-
-
-class TestPlanStep:
-    """Tests for the PlanStep model."""
-
-    def test_plan_step_with_required_fields(self):
-        """Test PlanStep creation with only required fields."""
-        step = PlanStep(
-            step_number=1,
-            description="Research the topic",
-        )
-
-        assert step.step_number == 1
-        assert step.description == "Research the topic"
-        assert step.tool_to_use is None
-        assert step.depends_on == []
-
-    def test_plan_step_with_all_fields(self):
-        """Test PlanStep creation with all fields."""
-        step = PlanStep(
-            step_number=2,
-            description="Search for information",
-            tool_to_use="search_tool",
-            depends_on=[1],
-        )
-
-        assert step.step_number == 2
-        assert step.description == "Search for information"
-        assert step.tool_to_use == "search_tool"
-        assert step.depends_on == [1]
-
-    def test_plan_step_with_multiple_dependencies(self):
-        """Test PlanStep with multiple dependencies."""
-        step = PlanStep(
-            step_number=4,
-            description="Synthesize results",
-            depends_on=[1, 2, 3],
-        )
-
-        assert step.depends_on == [1, 2, 3]
-
-    def test_plan_step_requires_step_number(self):
-        """Test that step_number is required."""
-        with pytest.raises(ValueError):
-            PlanStep(description="Missing step number")
-
-    def test_plan_step_requires_description(self):
-        """Test that description is required."""
-        with pytest.raises(ValueError):
-            PlanStep(step_number=1)
-
-    def test_plan_step_serialization(self):
-        """Test PlanStep can be serialized to dict."""
-        step = PlanStep(
-            step_number=1,
-            description="Test step",
-            tool_to_use="test_tool",
-            depends_on=[],
-        )
-
-        data = step.model_dump()
-        assert data["step_number"] == 1
-        assert data["description"] == "Test step"
-        assert data["tool_to_use"] == "test_tool"
-        assert data["depends_on"] == []
-
-
-class TestTodoItem:
-    """Tests for the TodoItem model."""
-
-    def test_todo_item_with_required_fields(self):
-        """Test TodoItem creation with only required fields."""
-        todo = TodoItem(
-            step_number=1,
-            description="First task",
-        )
-
-        assert todo.step_number == 1
-        assert todo.description == "First task"
-        assert todo.status == "pending"
-        assert todo.tool_to_use is None
-        assert todo.depends_on == []
-        assert todo.result is None
-        # ID should be auto-generated
-        assert todo.id is not None
-        # Verify it's a valid UUID
-        UUID(todo.id)
-
-    def test_todo_item_with_all_fields(self):
-        """Test TodoItem creation with all fields."""
-        todo = TodoItem(
-            id="custom-id-123",
-            step_number=2,
-            description="Second task",
-            tool_to_use="search_tool",
-            status="running",
-            depends_on=[1],
-            result="Task completed",
-        )
-
-        assert todo.id == "custom-id-123"
-        assert todo.step_number == 2
-        assert todo.description == "Second task"
-        assert todo.tool_to_use == "search_tool"
-        assert todo.status == "running"
-        assert todo.depends_on == [1]
-        assert todo.result == "Task completed"
-
-    def test_todo_item_status_values(self):
-        """Test all valid status values."""
-        for status in ["pending", "running", "completed"]:
-            todo = TodoItem(
-                step_number=1,
-                description="Test",
-                status=status,
-            )
-            assert todo.status == status
-
-    def test_todo_item_auto_generates_unique_ids(self):
-        """Test that each TodoItem gets a unique auto-generated ID."""
-        todo1 = TodoItem(step_number=1, description="Task 1")
-        todo2 = TodoItem(step_number=2, description="Task 2")
-
-        assert todo1.id != todo2.id
-
-    def test_todo_item_serialization(self):
-        """Test TodoItem can be serialized to dict."""
-        todo = TodoItem(
-            step_number=1,
-            description="Test task",
-            status="pending",
-        )
-
-        data = todo.model_dump()
-        assert "id" in data
-        assert data["step_number"] == 1
-        assert data["description"] == "Test task"
-        assert data["status"] == "pending"
-
-
-class TestTodoList:
-    """Tests for the TodoList model."""
-
-    @pytest.fixture
-    def empty_todo_list(self):
-        """Create an empty TodoList."""
-        return TodoList()
-
-    @pytest.fixture
-    def sample_todo_list(self):
-        """Create a TodoList with sample items."""
-        return TodoList(
-            items=[
-                TodoItem(step_number=1, description="Step 1", status="completed"),
-                TodoItem(step_number=2, description="Step 2", status="running"),
-                TodoItem(step_number=3, description="Step 3", status="pending"),
-                TodoItem(step_number=4, description="Step 4", status="pending"),
-            ]
-        )
-
-    def test_empty_todo_list(self, empty_todo_list):
-        """Test empty TodoList properties."""
-        assert empty_todo_list.items == []
-        assert empty_todo_list.current_todo is None
-        assert empty_todo_list.next_pending is None
-        assert empty_todo_list.is_complete is False
-        assert empty_todo_list.pending_count == 0
-        assert empty_todo_list.completed_count == 0
-
-    def test_current_todo_property(self, sample_todo_list):
-        """Test current_todo returns the running item."""
-        current = sample_todo_list.current_todo
-        assert current is not None
-        assert current.step_number == 2
-        assert current.status == "running"
-
-    def test_current_todo_returns_none_when_no_running(self):
-        """Test current_todo returns None when no running items."""
-        todo_list = TodoList(
-            items=[
-                TodoItem(step_number=1, description="Step 1", status="completed"),
-                TodoItem(step_number=2, description="Step 2", status="pending"),
-            ]
-        )
-        assert todo_list.current_todo is None
-
-    def test_next_pending_property(self, sample_todo_list):
-        """Test next_pending returns the first pending item."""
-        next_item = sample_todo_list.next_pending
-        assert next_item is not None
-        assert next_item.step_number == 3
-        assert next_item.status == "pending"
-
-    def test_next_pending_returns_none_when_no_pending(self):
-        """Test next_pending returns None when no pending items."""
-        todo_list = TodoList(
-            items=[
-                TodoItem(step_number=1, description="Step 1", status="completed"),
-                TodoItem(step_number=2, description="Step 2", status="completed"),
-            ]
-        )
-        assert todo_list.next_pending is None
-
-    def test_is_complete_property_when_complete(self):
-        """Test is_complete returns True when all items completed."""
-        todo_list = TodoList(
-            items=[
-                TodoItem(step_number=1, description="Step 1", status="completed"),
-                TodoItem(step_number=2, description="Step 2", status="completed"),
-            ]
-        )
-        assert todo_list.is_complete is True
-
-    def test_is_complete_property_when_not_complete(self, sample_todo_list):
-        """Test is_complete returns False when items are pending."""
-        assert sample_todo_list.is_complete is False
-
-    def test_is_complete_false_for_empty_list(self, empty_todo_list):
-        """Test is_complete returns False for empty list."""
-        assert empty_todo_list.is_complete is False
-
-    def test_pending_count(self, sample_todo_list):
-        """Test pending_count returns correct count."""
-        assert sample_todo_list.pending_count == 2
-
-    def test_completed_count(self, sample_todo_list):
-        """Test completed_count returns correct count."""
-        assert sample_todo_list.completed_count == 1
-
-    def test_get_by_step_number(self, sample_todo_list):
-        """Test get_by_step_number returns correct item."""
-        item = sample_todo_list.get_by_step_number(3)
-        assert item is not None
-        assert item.step_number == 3
-        assert item.description == "Step 3"
-
-    def test_get_by_step_number_returns_none_for_missing(self, sample_todo_list):
-        """Test get_by_step_number returns None for non-existent step."""
-        item = sample_todo_list.get_by_step_number(99)
-        assert item is None
-
-    def test_mark_running(self, sample_todo_list):
-        """Test mark_running changes status correctly."""
-        sample_todo_list.mark_running(3)
-        item = sample_todo_list.get_by_step_number(3)
-        assert item.status == "running"
-
-    def test_mark_running_does_nothing_for_missing(self, sample_todo_list):
-        """Test mark_running handles missing step gracefully."""
-        # Should not raise an error
-        sample_todo_list.mark_running(99)
-
-    def test_mark_completed(self, sample_todo_list):
-        """Test mark_completed changes status correctly."""
-        sample_todo_list.mark_completed(3)
-        item = sample_todo_list.get_by_step_number(3)
-        assert item.status == "completed"
-        assert item.result is None
-
-    def test_mark_completed_with_result(self, sample_todo_list):
-        """Test mark_completed with result."""
-        sample_todo_list.mark_completed(3, result="Task output")
-        item = sample_todo_list.get_by_step_number(3)
-        assert item.status == "completed"
-        assert item.result == "Task output"
-
-    def test_mark_completed_does_nothing_for_missing(self, sample_todo_list):
-        """Test mark_completed handles missing step gracefully."""
-        # Should not raise an error
-        sample_todo_list.mark_completed(99, result="Some result")
-
-    def test_todo_list_workflow(self):
-        """Test a complete workflow through TodoList."""
-        # Create a todo list with 3 items
-        todo_list = TodoList(
-            items=[
-                TodoItem(
-                    step_number=1,
-                    description="Research",
-                    tool_to_use="search_tool",
-                ),
-                TodoItem(
-                    step_number=2,
-                    description="Analyze",
-                    depends_on=[1],
-                ),
-                TodoItem(
-                    step_number=3,
-                    description="Report",
-                    depends_on=[1, 2],
-                ),
-            ]
-        )
-
-        # Initial state
-        assert todo_list.pending_count == 3
-        assert todo_list.completed_count == 0
-        assert todo_list.is_complete is False
-
-        # Start first task
-        todo_list.mark_running(1)
-        assert todo_list.current_todo.step_number == 1
-        assert todo_list.next_pending.step_number == 2
-
-        # Complete first task
-        todo_list.mark_completed(1, result="Research done")
-        assert todo_list.current_todo is None
-        assert todo_list.completed_count == 1
-
-        # Start and complete second task
-        todo_list.mark_running(2)
-        todo_list.mark_completed(2, result="Analysis complete")
-        assert todo_list.completed_count == 2
-
-        # Start and complete third task
-        todo_list.mark_running(3)
-        todo_list.mark_completed(3, result="Report generated")
-
-        # Final state
-        assert todo_list.is_complete is True
-        assert todo_list.pending_count == 0
-        assert todo_list.completed_count == 3
-        assert todo_list.current_todo is None
-        assert todo_list.next_pending is None
-
-
-class TestTodoFromPlanStep:
-    """Tests for converting PlanStep to TodoItem."""
-
-    def test_convert_plan_step_to_todo_item(self):
-        """Test converting a PlanStep to TodoItem."""
-        step = PlanStep(
-            step_number=1,
-            description="Search for information",
-            tool_to_use="search_tool",
-            depends_on=[],
-        )
-
-        todo = TodoItem(
-            step_number=step.step_number,
-            description=step.description,
-            tool_to_use=step.tool_to_use,
-            depends_on=step.depends_on,
-            status="pending",
-        )
-
-        assert todo.step_number == step.step_number
-        assert todo.description == step.description
-        assert todo.tool_to_use == step.tool_to_use
-        assert todo.depends_on == step.depends_on
-        assert todo.status == "pending"
-
-    def test_convert_multiple_plan_steps_to_todo_list(self):
-        """Test converting multiple PlanSteps to a TodoList."""
-        steps = [
-            PlanStep(step_number=1, description="Step 1", tool_to_use="tool1"),
-            PlanStep(step_number=2, description="Step 2", depends_on=[1]),
-            PlanStep(step_number=3, description="Step 3", depends_on=[1, 2]),
-        ]
-
-        todos = []
-        for step in steps:
-            todo = TodoItem(
-                step_number=step.step_number,
-                description=step.description,
-                tool_to_use=step.tool_to_use,
-                depends_on=step.depends_on,
-                status="pending",
-            )
-            todos.append(todo)
-
-        todo_list = TodoList(items=todos)
-
-        assert len(todo_list.items) == 3
-        assert todo_list.pending_count == 3
-        assert todo_list.items[0].tool_to_use == "tool1"
-        assert todo_list.items[1].depends_on == [1]
-        assert todo_list.items[2].depends_on == [1, 2]

lib/crewai/tests/utilities/test_structured_planning.py

@@ -1,698 +0,0 @@
-"""Tests for structured planning with steps and todo generation.
-
-These tests verify that the planning system correctly generates structured
-PlanStep objects and converts them to TodoItems across different LLM providers.
-"""
-
-import json
-import os
-from unittest.mock import MagicMock, Mock, patch
-
-import pytest
-
-from crewai import Agent, PlanningConfig, Task
-from crewai.llm import LLM
-from crewai.utilities.planning_types import PlanStep, TodoItem, TodoList
-from crewai.utilities.reasoning_handler import (
-    FUNCTION_SCHEMA,
-    AgentReasoning,
-    ReasoningPlan,
-)
-
-
-class TestFunctionSchema:
-    """Tests for the FUNCTION_SCHEMA used in structured planning."""
-
-    def test_schema_has_required_structure(self):
-        """Test that FUNCTION_SCHEMA has the correct structure."""
-        assert FUNCTION_SCHEMA["type"] == "function"
-        assert "function" in FUNCTION_SCHEMA
-        assert FUNCTION_SCHEMA["function"]["name"] == "create_reasoning_plan"
-
-    def test_schema_parameters_structure(self):
-        """Test that parameters have correct structure."""
-        params = FUNCTION_SCHEMA["function"]["parameters"]
-        assert params["type"] == "object"
-        assert "properties" in params
-        assert "required" in params
-
-    def test_schema_has_plan_property(self):
-        """Test that schema includes plan property."""
-        props = FUNCTION_SCHEMA["function"]["parameters"]["properties"]
-        assert "plan" in props
-        assert props["plan"]["type"] == "string"
-
-    def test_schema_has_steps_property(self):
-        """Test that schema includes steps array property."""
-        props = FUNCTION_SCHEMA["function"]["parameters"]["properties"]
-        assert "steps" in props
-        assert props["steps"]["type"] == "array"
-
-    def test_schema_steps_items_structure(self):
-        """Test that steps items have correct structure."""
-        items = FUNCTION_SCHEMA["function"]["parameters"]["properties"]["steps"]["items"]
-        assert items["type"] == "object"
-        assert "properties" in items
-        assert "required" in items
-        assert "additionalProperties" in items
-        assert items["additionalProperties"] is False
-
-    def test_schema_step_properties(self):
-        """Test that step items have all required properties."""
-        step_props = FUNCTION_SCHEMA["function"]["parameters"]["properties"]["steps"]["items"]["properties"]
-
-        assert "step_number" in step_props
-        assert step_props["step_number"]["type"] == "integer"
-
-        assert "description" in step_props
-        assert step_props["description"]["type"] == "string"
-
-        assert "tool_to_use" in step_props
-        # tool_to_use should be nullable
-        assert step_props["tool_to_use"]["type"] == ["string", "null"]
-
-        assert "depends_on" in step_props
-        assert step_props["depends_on"]["type"] == "array"
-
-    def test_schema_step_required_fields(self):
-        """Test that step required fields are correct."""
-        required = FUNCTION_SCHEMA["function"]["parameters"]["properties"]["steps"]["items"]["required"]
-        assert "step_number" in required
-        assert "description" in required
-        assert "tool_to_use" in required
-        assert "depends_on" in required
-
-    def test_schema_has_ready_property(self):
-        """Test that schema includes ready property."""
-        props = FUNCTION_SCHEMA["function"]["parameters"]["properties"]
-        assert "ready" in props
-        assert props["ready"]["type"] == "boolean"
-
-    def test_schema_top_level_required(self):
-        """Test that top-level required fields are correct."""
-        required = FUNCTION_SCHEMA["function"]["parameters"]["required"]
-        assert "plan" in required
-        assert "steps" in required
-        assert "ready" in required
-
-    def test_schema_top_level_additional_properties(self):
-        """Test that additionalProperties is False at top level."""
-        params = FUNCTION_SCHEMA["function"]["parameters"]
-        assert params["additionalProperties"] is False
-
-
-class TestReasoningPlan:
-    """Tests for the ReasoningPlan model with structured steps."""
-
-    def test_reasoning_plan_with_empty_steps(self):
-        """Test ReasoningPlan can be created with empty steps."""
-        plan = ReasoningPlan(
-            plan="Simple plan",
-            steps=[],
-            ready=True,
-        )
-
-        assert plan.plan == "Simple plan"
-        assert plan.steps == []
-        assert plan.ready is True
-
-    def test_reasoning_plan_with_steps(self):
-        """Test ReasoningPlan with structured steps."""
-        steps = [
-            PlanStep(step_number=1, description="First step", tool_to_use="tool1"),
-            PlanStep(step_number=2, description="Second step", depends_on=[1]),
-        ]
-
-        plan = ReasoningPlan(
-            plan="Multi-step plan",
-            steps=steps,
-            ready=True,
-        )
-
-        assert plan.plan == "Multi-step plan"
-        assert len(plan.steps) == 2
-        assert plan.steps[0].step_number == 1
-        assert plan.steps[1].depends_on == [1]
-
-
-class TestAgentReasoningWithMockedLLM:
-    """Tests for AgentReasoning with mocked LLM responses."""
-
-    @pytest.fixture
-    def mock_agent(self):
-        """Create a mock agent for testing."""
-        agent = MagicMock()
-        agent.role = "Test Agent"
-        agent.goal = "Test goal"
-        agent.backstory = "Test backstory"
-        agent.verbose = False
-        agent.planning_config = PlanningConfig()
-        agent.i18n = MagicMock()
-        agent.i18n.retrieve.return_value = "Test prompt: {description}"
-        # Mock the llm attribute
-        agent.llm = MagicMock()
-        agent.llm.supports_function_calling.return_value = True
-        return agent
-
-    def test_parse_steps_from_function_response(self, mock_agent):
-        """Test that steps are correctly parsed from LLM function response."""
-        # Mock the LLM response with structured steps
-        mock_response = json.dumps({
-            "plan": "Research and analyze",
-            "steps": [
-                {
-                    "step_number": 1,
-                    "description": "Search for information",
-                    "tool_to_use": "search_tool",
-                    "depends_on": [],
-                },
-                {
-                    "step_number": 2,
-                    "description": "Analyze results",
-                    "tool_to_use": None,
-                    "depends_on": [1],
-                },
-            ],
-            "ready": True,
-        })
-
-        mock_agent.llm.call.return_value = mock_response
-
-        handler = AgentReasoning(
-            agent=mock_agent,
-            task=None,
-            description="Test task",
-            expected_output="Test output",
-        )
-
-        # Call the function parsing method
-        plan, steps, ready = handler._call_with_function(
-            prompt="Test prompt",
-            plan_type="create_plan",
-        )
-
-        assert plan == "Research and analyze"
-        assert len(steps) == 2
-        assert steps[0].step_number == 1
-        assert steps[0].tool_to_use == "search_tool"
-        assert steps[1].depends_on == [1]
-        assert ready is True
-
-    def test_parse_steps_handles_missing_optional_fields(self, mock_agent):
-        """Test that missing optional fields are handled correctly."""
-        mock_response = json.dumps({
-            "plan": "Simple plan",
-            "steps": [
-                {
-                    "step_number": 1,
-                    "description": "Do something",
-                    "tool_to_use": None,
-                    "depends_on": [],
-                },
-            ],
-            "ready": True,
-        })
-
-        mock_agent.llm.call.return_value = mock_response
-
-        handler = AgentReasoning(
-            agent=mock_agent,
-            task=None,
-            description="Test task",
-            expected_output="Test output",
-        )
-
-        plan, steps, ready = handler._call_with_function(
-            prompt="Test prompt",
-            plan_type="create_plan",
-        )
-
-        assert len(steps) == 1
-        assert steps[0].tool_to_use is None
-        assert steps[0].depends_on == []
-
-    def test_parse_steps_with_missing_fields_uses_defaults(self, mock_agent):
-        """Test that steps with missing fields get default values."""
-        mock_response = json.dumps({
-            "plan": "Plan with step missing fields",
-            "steps": [
-                {"step_number": 1, "description": "Valid step", "tool_to_use": None, "depends_on": []},
-                {"step_number": 2},  # Missing description, tool_to_use, depends_on
-                {"step_number": 3, "description": "Another valid", "tool_to_use": None, "depends_on": []},
-            ],
-            "ready": True,
-        })
-
-        mock_agent.llm.call.return_value = mock_response
-
-        handler = AgentReasoning(
-            agent=mock_agent,
-            task=None,
-            description="Test task",
-            expected_output="Test output",
-        )
-
-        plan, steps, ready = handler._call_with_function(
-            prompt="Test prompt",
-            plan_type="create_plan",
-        )
-
-        # All 3 steps should be parsed, with defaults for missing fields
-        assert len(steps) == 3
-        assert steps[0].step_number == 1
-        assert steps[0].description == "Valid step"
-        assert steps[1].step_number == 2
-        assert steps[1].description == ""  # Default value
-        assert steps[2].step_number == 3
-
-
-class TestTodoCreationFromPlan:
-    """Tests for converting plan steps to todo items."""
-
-    def test_create_todos_from_plan_steps(self):
-        """Test creating TodoList from PlanSteps."""
-        steps = [
-            PlanStep(
-                step_number=1,
-                description="Research competitors",
-                tool_to_use="search_tool",
-                depends_on=[],
-            ),
-            PlanStep(
-                step_number=2,
-                description="Analyze data",
-                tool_to_use=None,
-                depends_on=[1],
-            ),
-            PlanStep(
-                step_number=3,
-                description="Generate report",
-                tool_to_use="write_tool",
-                depends_on=[1, 2],
-            ),
-        ]
-
-        # Convert steps to todos (mirroring agent_executor._create_todos_from_plan)
-        todos = []
-        for step in steps:
-            todo = TodoItem(
-                step_number=step.step_number,
-                description=step.description,
-                tool_to_use=step.tool_to_use,
-                depends_on=step.depends_on,
-                status="pending",
-            )
-            todos.append(todo)
-
-        todo_list = TodoList(items=todos)
-
-        assert len(todo_list.items) == 3
-        assert todo_list.pending_count == 3
-        assert todo_list.completed_count == 0
-
-        # Verify todo properties match step properties
-        assert todo_list.items[0].description == "Research competitors"
-        assert todo_list.items[0].tool_to_use == "search_tool"
-        assert todo_list.items[1].depends_on == [1]
-        assert todo_list.items[2].depends_on == [1, 2]
-
-
-# =============================================================================
-# Provider-Specific Integration Tests (VCR recorded)
-# =============================================================================
-
-
-# Common test tools used across provider tests
-def create_research_tools():
-    """Create research tools for testing structured planning."""
-    from crewai.tools import tool
-
-    @tool
-    def web_search(query: str) -> str:
-        """Search the web for information on a given topic.
-
-        Args:
-            query: The search query to look up.
-
-        Returns:
-            Search results as a string.
-        """
-        # Simulated search results for testing
-        return f"Search results for '{query}': Found 3 relevant articles about the topic including market analysis, competitor data, and industry trends."
-
-    @tool
-    def read_website(url: str) -> str:
-        """Read and extract content from a website URL.
-
-        Args:
-            url: The URL of the website to read.
-
-        Returns:
-            The extracted content from the website.
-        """
-        # Simulated website content for testing
-        return f"Content from {url}: This article discusses key insights about the topic including market size ($50B), growth rate (15% YoY), and major players in the industry."
-
-    @tool
-    def generate_report(title: str, findings: str) -> str:
-        """Generate a structured report based on research findings.
-
-        Args:
-            title: The title of the report.
-            findings: The research findings to include.
-
-        Returns:
-            A formatted report string.
-        """
-        return f"# {title}\n\n## Executive Summary\n{findings}\n\n## Conclusion\nBased on the analysis, the market shows strong growth potential."
-
-    return web_search, read_website, generate_report
-
-
-RESEARCH_TASK = """Research the current state of the AI agent market:
-1. Search for recent information about AI agents and their market trends
-2. Read detailed content from a relevant industry source
-3. Generate a brief report summarizing the key findings
-
-Use the available tools for each step."""
-
-
-class TestOpenAIStructuredPlanning:
-    """Integration tests for OpenAI structured planning with research workflow."""
-
-    @pytest.mark.vcr()
-    def test_openai_research_workflow_generates_steps(self):
-        """Test that OpenAI generates structured plan steps for a research task."""
-        web_search, read_website, generate_report = create_research_tools()
-        llm = LLM(model="gpt-4o")
-
-        agent = Agent(
-            role="Research Analyst",
-            goal="Conduct thorough research and produce insightful reports",
-            backstory="An experienced analyst skilled at gathering information and synthesizing findings into actionable insights.",
-            llm=llm,
-            tools=[web_search, read_website, generate_report],
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        result = agent.kickoff(RESEARCH_TASK)
-
-        # Verify result exists
-        assert result is not None
-        assert result.raw is not None
-        # The result should contain some report-like content
-        assert len(str(result.raw)) > 50
-
-
-class TestAnthropicStructuredPlanning:
-    """Integration tests for Anthropic structured planning with research workflow."""
-
-    @pytest.fixture(autouse=True)
-    def mock_anthropic_api_key(self):
-        """Mock API key if not set."""
-        if "ANTHROPIC_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_anthropic_research_workflow_generates_steps(self):
-        """Test that Anthropic generates structured plan steps for a research task."""
-        web_search, read_website, generate_report = create_research_tools()
-        llm = LLM(model="anthropic/claude-sonnet-4-20250514")
-
-        agent = Agent(
-            role="Research Analyst",
-            goal="Conduct thorough research and produce insightful reports",
-            backstory="An experienced analyst skilled at gathering information and synthesizing findings into actionable insights.",
-            llm=llm,
-            tools=[web_search, read_website, generate_report],
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        result = agent.kickoff(RESEARCH_TASK)
-
-        # Verify result exists
-        assert result is not None
-        assert result.raw is not None
-        # The result should contain some report-like content
-        assert len(str(result.raw)) > 50
-
-
-class TestGeminiStructuredPlanning:
-    """Integration tests for Google Gemini structured planning with research workflow."""
-
-    @pytest.fixture(autouse=True)
-    def mock_google_api_key(self):
-        """Mock API key if not set."""
-        if "GOOGLE_API_KEY" not in os.environ and "GEMINI_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_gemini_research_workflow_generates_steps(self):
-        """Test that Gemini generates structured plan steps for a research task."""
-        web_search, read_website, generate_report = create_research_tools()
-        llm = LLM(model="gemini/gemini-2.5-flash")
-
-        agent = Agent(
-            role="Research Analyst",
-            goal="Conduct thorough research and produce insightful reports",
-            backstory="An experienced analyst skilled at gathering information and synthesizing findings into actionable insights.",
-            llm=llm,
-            tools=[web_search, read_website, generate_report],
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        result = agent.kickoff(RESEARCH_TASK)
-
-        # Verify result exists
-        assert result is not None
-        assert result.raw is not None
-        # The result should contain some report-like content
-        assert len(str(result.raw)) > 50
-
-
-class TestAzureStructuredPlanning:
-    """Integration tests for Azure OpenAI structured planning with research workflow."""
-
-    @pytest.fixture(autouse=True)
-    def mock_azure_credentials(self):
-        """Mock Azure credentials for tests."""
-        if "AZURE_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {
-                "AZURE_API_KEY": "test-key",
-                "AZURE_ENDPOINT": "https://test.openai.azure.com"
-            }):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_azure_research_workflow_generates_steps(self):
-        """Test that Azure OpenAI generates structured plan steps for a research task."""
-        web_search, read_website, generate_report = create_research_tools()
-        llm = LLM(model="azure/gpt-4o")
-
-        agent = Agent(
-            role="Research Analyst",
-            goal="Conduct thorough research and produce insightful reports",
-            backstory="An experienced analyst skilled at gathering information and synthesizing findings into actionable insights.",
-            llm=llm,
-            tools=[web_search, read_website, generate_report],
-            planning_config=PlanningConfig(max_attempts=1),
-            verbose=False,
-        )
-
-        result = agent.kickoff(RESEARCH_TASK)
-
-        # Verify result exists
-        assert result is not None
-        assert result.raw is not None
-        # The result should contain some report-like content
-        assert len(str(result.raw)) > 50
-
-
-# =============================================================================
-# Unit Tests with Mocked LLM Providers
-# =============================================================================
-
-
-class TestStructuredPlanningWithMockedProviders:
-    """Unit tests with mocked LLM providers for faster execution."""
-
-    def _create_mock_plan_response(self, steps_data):
-        """Helper to create mock plan response."""
-        return json.dumps({
-            "plan": "Test plan",
-            "steps": steps_data,
-            "ready": True,
-        })
-
-    def test_openai_mock_structured_response(self):
-        """Test parsing OpenAI structured response."""
-        steps_data = [
-            {"step_number": 1, "description": "Search", "tool_to_use": "search", "depends_on": []},
-            {"step_number": 2, "description": "Analyze", "tool_to_use": None, "depends_on": [1]},
-        ]
-
-        response = self._create_mock_plan_response(steps_data)
-        parsed = json.loads(response)
-
-        assert len(parsed["steps"]) == 2
-        assert parsed["steps"][0]["tool_to_use"] == "search"
-        assert parsed["steps"][1]["depends_on"] == [1]
-
-    def test_anthropic_mock_structured_response(self):
-        """Test parsing Anthropic structured response (same format)."""
-        steps_data = [
-            {"step_number": 1, "description": "Research", "tool_to_use": "web_search", "depends_on": []},
-            {"step_number": 2, "description": "Summarize", "tool_to_use": None, "depends_on": [1]},
-            {"step_number": 3, "description": "Report", "tool_to_use": "write_file", "depends_on": [1, 2]},
-        ]
-
-        response = self._create_mock_plan_response(steps_data)
-        parsed = json.loads(response)
-
-        assert len(parsed["steps"]) == 3
-        assert parsed["steps"][2]["depends_on"] == [1, 2]
-
-    def test_gemini_mock_structured_response(self):
-        """Test parsing Gemini structured response (same format)."""
-        steps_data = [
-            {"step_number": 1, "description": "Gather data", "tool_to_use": "data_tool", "depends_on": []},
-            {"step_number": 2, "description": "Process", "tool_to_use": None, "depends_on": [1]},
-        ]
-
-        response = self._create_mock_plan_response(steps_data)
-        parsed = json.loads(response)
-
-        assert len(parsed["steps"]) == 2
-        assert parsed["ready"] is True
-
-    def test_azure_mock_structured_response(self):
-        """Test parsing Azure OpenAI structured response (same format as OpenAI)."""
-        steps_data = [
-            {"step_number": 1, "description": "Initialize", "tool_to_use": None, "depends_on": []},
-            {"step_number": 2, "description": "Execute", "tool_to_use": "executor", "depends_on": [1]},
-            {"step_number": 3, "description": "Finalize", "tool_to_use": None, "depends_on": [1, 2]},
-        ]
-
-        response = self._create_mock_plan_response(steps_data)
-        parsed = json.loads(response)
-
-        assert len(parsed["steps"]) == 3
-        assert parsed["steps"][0]["tool_to_use"] is None
-
-
-class TestTodoListIntegration:
-    """Integration tests for TodoList with plan execution simulation."""
-
-    def test_full_plan_execution_workflow(self):
-        """Test complete workflow from plan to todos to execution."""
-        # Simulate plan steps from LLM
-        plan_steps = [
-            PlanStep(
-                step_number=1,
-                description="Research the topic",
-                tool_to_use="search_tool",
-                depends_on=[],
-            ),
-            PlanStep(
-                step_number=2,
-                description="Compile findings",
-                tool_to_use=None,
-                depends_on=[1],
-            ),
-            PlanStep(
-                step_number=3,
-                description="Generate summary",
-                tool_to_use="summarize_tool",
-                depends_on=[1, 2],
-            ),
-        ]
-
-        # Convert to todos (like agent_executor._create_todos_from_plan)
-        todos = [
-            TodoItem(
-                step_number=step.step_number,
-                description=step.description,
-                tool_to_use=step.tool_to_use,
-                depends_on=step.depends_on,
-                status="pending",
-            )
-            for step in plan_steps
-        ]
-        todo_list = TodoList(items=todos)
-
-        # Verify initial state
-        assert todo_list.pending_count == 3
-        assert todo_list.is_complete is False
-
-        # Simulate execution
-        for i in range(1, 4):
-            todo_list.mark_running(i)
-            assert todo_list.current_todo.step_number == i
-            todo_list.mark_completed(i, result=f"Step {i} completed")
-
-        # Verify final state
-        assert todo_list.is_complete is True
-        assert todo_list.completed_count == 3
-        assert all(item.result is not None for item in todo_list.items)
-
-    def test_dependency_aware_execution(self):
-        """Test that dependencies are respected in execution order."""
-        steps = [
-            PlanStep(step_number=1, description="Base step", depends_on=[]),
-            PlanStep(step_number=2, description="Depends on 1", depends_on=[1]),
-            PlanStep(step_number=3, description="Depends on 1", depends_on=[1]),
-            PlanStep(step_number=4, description="Depends on 2 and 3", depends_on=[2, 3]),
-        ]
-
-        todos = [
-            TodoItem(
-                step_number=s.step_number,
-                description=s.description,
-                depends_on=s.depends_on,
-            )
-            for s in steps
-        ]
-        todo_list = TodoList(items=todos)
-
-        # Helper to check if dependencies are satisfied
-        def can_execute(todo: TodoItem) -> bool:
-            for dep in todo.depends_on:
-                dep_todo = todo_list.get_by_step_number(dep)
-                if dep_todo and dep_todo.status != "completed":
-                    return False
-            return True
-
-        # Step 1 has no dependencies
-        assert can_execute(todo_list.items[0]) is True
-
-        # Steps 2 and 3 depend on 1 (not yet done)
-        assert can_execute(todo_list.items[1]) is False
-        assert can_execute(todo_list.items[2]) is False
-
-        # Complete step 1
-        todo_list.mark_completed(1)
-
-        # Now steps 2 and 3 can execute
-        assert can_execute(todo_list.items[1]) is True
-        assert can_execute(todo_list.items[2]) is True
-
-        # Step 4 still can't (depends on 2 and 3)
-        assert can_execute(todo_list.items[3]) is False
-
-        # Complete steps 2 and 3
-        todo_list.mark_completed(2)
-        todo_list.mark_completed(3)
-
-        # Now step 4 can execute
-        assert can_execute(todo_list.items[3]) is True

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