Merge branch 'lorenze/feat/plan-execute-pattern' of github.com:crewAIInc/crewAI into lorenze/feat/plan-execute-pattern

* feat: introduce PlanningConfig for enhanced agent planning capabilities

This update adds a new PlanningConfig class to manage agent planning configurations, allowing for customizable planning behavior before task execution. The existing reasoning parameter is deprecated in favor of this new configuration, ensuring backward compatibility while enhancing the planning process. Additionally, the Agent class has been updated to utilize this new configuration, and relevant utility functions have been adjusted accordingly. Tests have been added to validate the new planning functionality and ensure proper integration with existing agent workflows.

* dropping redundancy

* fix test

* revert handle_reasoning here

* refactor: update reasoning handling in Agent class

This commit modifies the Agent class to conditionally call the handle_reasoning function based on the executor class being used. The legacy CrewAgentExecutor will continue to utilize handle_reasoning, while the new AgentExecutor will manage planning internally. Additionally, the PlanningConfig class has been referenced in the documentation to clarify its role in enabling or disabling planning. Tests have been updated to reflect these changes and ensure proper functionality.

* improve planning prompts

* matching

* refactor: remove default enabled flag from PlanningConfig in Agent class

* more cassettes

* fix test

* feat: enhance agent planning with structured todo management

This commit introduces a new planning system within the AgentExecutor class, allowing for the creation of structured todo items from planning steps. The TodoList and TodoItem models have been added to facilitate tracking of plan execution. The reasoning plan now includes a list of steps, improving the clarity and organization of agent tasks. Additionally, tests have been added to validate the new planning functionality and ensure proper integration with existing workflows.

* refactor: update planning prompt and remove deprecated methods in reasoning handler

* improve planning prompt

* improve handler

* linted

* linted

lib/crewai/src/crewai/__init__.py

@@ -4,6 +4,7 @@ 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
@@ -80,6 +81,7 @@ __all__ = [
     "Flow",
     "Knowledge",
     "LLMGuardrail",
+    "PlanningConfig",
     "Process",
     "Task",
     "TaskOutput",

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

@@ -24,6 +24,7 @@ from pydantic import (
 )
 from typing_extensions import Self
 
+from crewai.agent.planning_config import PlanningConfig
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -212,13 +213,23 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    reasoning: bool = Field(
+    planning_config: PlanningConfig | None = Field(
+        default=None,
+        description="Configuration for agent planning before task execution.",
+    )
+    planning: 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="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
+        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,
     )
     embedder: EmbedderConfig | None = Field(
         default=None,
@@ -285,8 +296,26 @@ 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()
@@ -362,7 +391,11 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        handle_reasoning(self, task)
+        # 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)
+
         self._inject_date_to_task(task)
 
         if self.tools_handler:
@@ -599,7 +632,10 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        handle_reasoning(self, task)
+        if self.executor_class is not AgentExecutor:
+            handle_reasoning(
+                self, task
+            )  # we need this till CrewAgentExecutor migrates to AgentExecutor
         self._inject_date_to_task(task)
 
         if self.tools_handler:

lib/crewai/src/crewai/agent/planning_config.py

@@ -0,0 +1,83 @@
+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

@@ -28,13 +28,20 @@ if TYPE_CHECKING:
 
 
 def handle_reasoning(agent: Agent, task: Task) -> None:
-    """Handle the reasoning process for an agent before task execution.
+    """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().
 
     Args:
         agent: The agent performing the task.
         task: The task to execute.
     """
-    if not agent.reasoning:
+    # Check if planning is enabled using the planning_enabled property
+    if not getattr(agent, "planning_enabled", False):
         return
 
     try:
@@ -43,13 +50,13 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
             AgentReasoningOutput,
         )
 
-        reasoning_handler = AgentReasoning(task=task, agent=agent)
-        reasoning_output: AgentReasoningOutput = (
-            reasoning_handler.handle_agent_reasoning()
+        planning_handler = AgentReasoning(agent=agent, task=task)
+        planning_output: AgentReasoningOutput = (
+            planning_handler.handle_agent_reasoning()
         )
-        task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
+        task.description += f"\n\nPlanning:\n{planning_output.plan.plan}"
     except Exception as e:
-        agent._logger.log("error", f"Error during reasoning process: {e!s}")
+        agent._logger.log("error", f"Error during planning: {e!s}")
 
 
 def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:

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
+    task_id: str | None = None
     task_name: str | None = None
     from_task: Any | None = None
     agent_id: str | None = None

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

@@ -68,6 +68,7 @@ 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
@@ -101,6 +102,13 @@ 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):
@@ -389,6 +397,67 @@ 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()
@@ -1075,6 +1144,10 @@ 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)
@@ -1159,6 +1232,10 @@ 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)

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

@@ -4,7 +4,6 @@ import json
 import logging
 import os
 from typing import TYPE_CHECKING, Any, TypedDict
-from urllib.parse import urlparse
 
 from pydantic import BaseModel
 from typing_extensions import Self
@@ -176,51 +175,11 @@ class AzureCompletion(BaseLLM):
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
         )
 
-        self.is_azure_openai_endpoint = self._is_azure_openai_deployment_endpoint(
-            self.endpoint
+        self.is_azure_openai_endpoint = (
+            "openai.azure.com" in self.endpoint
+            and "/openai/deployments/" in self.endpoint
         )
 
-    @staticmethod
-    def _parse_endpoint_url(endpoint: str):
-        parsed_endpoint = urlparse(endpoint)
-        if parsed_endpoint.hostname:
-            return parsed_endpoint
-
-        # Support endpoint values without a URL scheme.
-        return urlparse(f"https://{endpoint}")
-
-    @staticmethod
-    def _is_azure_openai_hostname(endpoint: str) -> bool:
-        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
-        hostname = parsed_endpoint.hostname or ""
-        labels = [label for label in hostname.lower().split(".") if label]
-
-        return len(labels) >= 3 and labels[-3:] == ["openai", "azure", "com"]
-
-    @staticmethod
-    def _get_endpoint_path_segments(endpoint: str) -> list[str]:
-        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
-        return [segment for segment in parsed_endpoint.path.split("/") if segment]
-
-    @staticmethod
-    def _is_azure_openai_deployment_endpoint(endpoint: str) -> bool:
-        if not AzureCompletion._is_azure_openai_hostname(endpoint):
-            return False
-
-        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
-        return len(path_segments) >= 3 and path_segments[:2] == [
-            "openai",
-            "deployments",
-        ]
-
-    @staticmethod
-    def _is_azure_openai_deployments_collection(endpoint: str) -> bool:
-        if not AzureCompletion._is_azure_openai_hostname(endpoint):
-            return False
-
-        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
-        return path_segments == ["openai", "deployments"]
-
     @staticmethod
     def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.
@@ -235,12 +194,10 @@ class AzureCompletion(BaseLLM):
         Returns:
             Validated and potentially corrected endpoint URL
         """
-        if AzureCompletion._is_azure_openai_hostname(
-            endpoint
-        ) and not AzureCompletion._is_azure_openai_deployment_endpoint(endpoint):
+        if "openai.azure.com" in endpoint and "/openai/deployments/" not in endpoint:
             endpoint = endpoint.rstrip("/")
 
-            if not AzureCompletion._is_azure_openai_deployments_collection(endpoint):
+            if not endpoint.endswith("/openai/deployments"):
                 deployment_name = model.replace("azure/", "")
                 endpoint = f"{endpoint}/openai/deployments/{deployment_name}"
                 logging.info(f"Constructed Azure OpenAI endpoint URL: {endpoint}")

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

@@ -58,9 +58,14 @@
     }
   },
   "reasoning": {
-    "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].\""
+    "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."
   }
 }

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

@@ -0,0 +1,103 @@
+"""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/reasoning_handler.py

@@ -1,10 +1,13 @@
+"""Handles planning/reasoning for agents before task execution."""
+
+from __future__ import annotations
+
 import json
 import logging
-from typing import Any, Final, Literal, cast
+from typing import TYPE_CHECKING, 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,
@@ -12,14 +15,30 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningStartedEvent,
 )
 from crewai.llm import LLM
-from crewai.task import Task
+from crewai.utilities.llm_utils import create_llm
+from crewai.utilities.planning_types import PlanStep
 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.")
 
 
@@ -29,24 +48,63 @@ 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",
+        "description": "Create or refine a reasoning plan for a task with structured steps",
         "parameters": {
             "type": "object",
             "properties": {
                 "plan": {
                     "type": "string",
-                    "description": "The detailed reasoning plan for the task.",
+                    "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,
+                    },
                 },
                 "ready": {
                     "type": "boolean",
                     "description": "Whether the agent is ready to execute the task.",
                 },
             },
-            "required": ["plan", "ready"],
+            "required": ["plan", "steps", "ready"],
+            "additionalProperties": False,
         },
     },
 }
@@ -54,41 +112,101 @@ FUNCTION_SCHEMA: Final[dict[str, Any]] = {
 
 class AgentReasoning:
     """
-    Handles the agent reasoning process, enabling an agent to reflect and create a plan
-    before executing a task.
+    Handles the agent planning/reasoning process, enabling an agent to reflect
+    and create a plan before executing a task.
 
     Attributes:
-        task: The task for which the agent is reasoning.
-        agent: The agent performing the reasoning.
-        llm: The language model used for reasoning.
+        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.
         logger: Logger for logging events and errors.
+        description: Task description or input text for planning.
+        expected_output: Expected output description.
     """
 
-    def __init__(self, task: Task, agent: Agent) -> None:
-        """Initialize the AgentReasoning with a task and an agent.
+    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.
 
         Args:
-            task: The task for which the agent is reasoning.
-            agent: The agent performing the reasoning.
+            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).
         """
-        self.task = task
         self.agent = agent
-        self.llm = cast(LLM, agent.llm)
+        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.logger = logging.getLogger(__name__)
 
-    def handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
+    @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:
-            AgentReasoningOutput: The output of the agent reasoning process.
+            The planning configuration, using defaults if not set.
         """
-        # Emit a reasoning started event (attempt 1)
+        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.
+
+        Returns:
+            AgentReasoningOutput: The output of the agent planning process.
+        """
+        task_id = str(self.task.id) if self.task else "kickoff"
+
+        # Emit a planning started event (attempt 1)
         try:
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningStartedEvent(
                     agent_role=self.agent.role,
-                    task_id=str(self.task.id),
+                    task_id=task_id,
                     attempt=1,
                     from_task=self.task,
                 ),
@@ -98,13 +216,13 @@ class AgentReasoning:
             pass
 
         try:
-            output = self.__handle_agent_reasoning()
+            output = self._execute_planning()
 
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningCompletedEvent(
                     agent_role=self.agent.role,
-                    task_id=str(self.task.id),
+                    task_id=task_id,
                     plan=output.plan.plan,
                     ready=output.plan.ready,
                     attempt=1,
@@ -115,71 +233,77 @@ class AgentReasoning:
 
             return output
         except Exception as e:
-            # Emit reasoning failed event
+            # Emit planning failed event
             try:
                 crewai_event_bus.emit(
                     self.agent,
                     AgentReasoningFailedEvent(
                         agent_role=self.agent.role,
-                        task_id=str(self.task.id),
+                        task_id=task_id,
                         error=str(e),
                         attempt=1,
                         from_task=self.task,
                         from_agent=self.agent,
                     ),
                 )
-            except Exception as e:
-                logging.error(f"Error emitting reasoning failed event: {e}")
+            except Exception as event_error:
+                logging.error(f"Error emitting planning failed event: {event_error}")
 
             raise
 
-    def __handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """Private method that handles the agent reasoning process.
+    def _execute_planning(self) -> AgentReasoningOutput:
+        """Execute the planning process.
 
         Returns:
-            The output of the agent reasoning process.
+            The output of the agent planning process.
         """
-        plan, ready = self.__create_initial_plan()
+        plan, steps, ready = self._create_initial_plan()
+        plan, steps, ready = self._refine_plan_if_needed(plan, steps, ready)
 
-        plan, ready = self.__refine_plan_if_needed(plan, ready)
-
-        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
+        reasoning_plan = ReasoningPlan(plan=plan, steps=steps, ready=ready)
         return AgentReasoningOutput(plan=reasoning_plan)
 
-    def __create_initial_plan(self) -> tuple[str, bool]:
-        """Creates the initial reasoning plan for the task.
+    def _create_initial_plan(self) -> tuple[str, list[PlanStep], bool]:
+        """Creates the initial plan for the task.
 
         Returns:
-            The initial plan and whether the agent is ready to execute the task.
+            A tuple of the plan summary, list of steps, and whether the agent is ready.
         """
-        reasoning_prompt = self.__create_reasoning_prompt()
+        planning_prompt = self._create_planning_prompt()
+        planning_prompt = self._create_planning_prompt()
 
         if self.llm.supports_function_calling():
-            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, 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",
         )
 
-        return self.__parse_reasoning_response(str(response))
+        plan, ready = self._parse_planning_response(str(response))
+        return plan, [], ready  # No structured steps from text parsing
 
-    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.
+    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.
 
         Args:
-            plan: The current reasoning plan.
+            plan: The current plan.
+            steps: The current list of steps.
             ready: Whether the agent is ready to execute the task.
 
         Returns:
-            The refined plan and whether the agent is ready to execute the task.
+            The refined plan, steps, and whether the agent is ready to execute.
         """
+
         attempt = 1
-        max_attempts = self.agent.max_reasoning_attempts
+        max_attempts = self.config.max_attempts
+        task_id = str(self.task.id) if self.task else "kickoff"
+        current_attempt = attempt + 1
 
         while not ready and (max_attempts is None or attempt < max_attempts):
             # Emit event for each refinement attempt
@@ -188,62 +312,82 @@ class AgentReasoning:
                     self.agent,
                     AgentReasoningStartedEvent(
                         agent_role=self.agent.role,
-                        task_id=str(self.task.id),
-                        attempt=attempt + 1,
+                        task_id=task_id,
+                        attempt=current_attempt,
                         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, ready = self.__call_with_function(refine_prompt, "refine_plan")
+                plan, steps, ready = self._call_with_function(
+                    refine_prompt, "refine_plan"
+                )
             else:
-                response = _call_llm_with_reasoning_prompt(
-                    llm=self.llm,
+                response = self._call_llm_with_prompt(
                     prompt=refine_prompt,
-                    task=self.task,
-                    reasoning_agent=self.agent,
-                    backstory=self.__get_agent_backstory(),
                     plan_type="refine_plan",
                 )
-                plan, ready = self.__parse_reasoning_response(str(response))
+                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
 
             attempt += 1
 
             if max_attempts is not None and attempt >= max_attempts:
                 self.logger.warning(
-                    f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
+                    f"Agent planning reached maximum attempts ({max_attempts}) "
+                    "without being ready. Proceeding with current plan."
                 )
                 break
 
-        return plan, ready
+        return plan, steps, ready
 
-    def __call_with_function(self, prompt: str, prompt_type: str) -> tuple[str, bool]:
-        """Calls the LLM with function calling to get a reasoning plan.
+    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.
 
         Args:
             prompt: The prompt to send to the LLM.
-            prompt_type: The type of prompt (initial_plan or refine_plan).
+            plan_type: The type of plan being created.
 
         Returns:
-            A tuple containing the plan and whether the agent is ready.
+            A tuple containing the plan summary, list of steps, and whether the agent is ready.
         """
-        self.logger.debug(f"Using function calling for {prompt_type} reasoning")
+        self.logger.debug(f"Using function calling for {plan_type} planning")
 
         try:
-            system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self.__get_agent_backstory(),
-            )
+            system_prompt = self._get_system_prompt()
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            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})
+            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})
 
             response = self.llm.call(
                 [
@@ -255,19 +399,33 @@ 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:
-                    return result["plan"], result["ready"]
+                    # 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"]
             except (json.JSONDecodeError, KeyError):
                 pass
 
             response_str = str(response)
             return (
                 response_str,
+                [],
                 "READY: I am ready to execute the task." in response_str,
             )
 
@@ -277,13 +435,7 @@ class AgentReasoning:
             )
 
             try:
-                system_prompt = self.agent.i18n.retrieve(
-                    "reasoning", prompt_type
-                ).format(
-                    role=self.agent.role,
-                    goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory(),
-                )
+                system_prompt = self._get_system_prompt()
 
                 fallback_response = self.llm.call(
                     [
@@ -297,78 +449,165 @@ 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 __get_agent_backstory(self) -> str:
-        """
-        Safely gets the agent's backstory, providing a default if not available.
+    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:
-            str: The agent's backstory or a default value.
+            The LLM response.
+        """
+        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.
+
+        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.
         """
         return getattr(self.agent, "backstory", "No backstory provided")
 
-    def __create_reasoning_prompt(self) -> str:
-        """
-        Creates a prompt for the agent to reason about the task.
+    def _create_planning_prompt(self) -> str:
+        """Creates a prompt for the agent to plan the task.
 
         Returns:
-            str: The reasoning prompt.
+            The planning prompt.
         """
-        available_tools = self.__format_available_tools()
+        available_tools = self._format_available_tools()
 
-        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,
-        )
+        # 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,
+            )
 
-    def __format_available_tools(self) -> str:
-        """
-        Formats the available tools for inclusion in the prompt.
+        # 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.
 
         Returns:
-            str: Comma-separated list of tool names.
+            Comma-separated list of tool names.
         """
         try:
-            return ", ".join(
-                [sanitize_tool_name(tool.name) for tool in (self.task.tools or [])]
-            )
+            # 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])
         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 reasoning plan.
+    def _create_refine_prompt(self, current_plan: str) -> str:
+        """Creates a prompt for the agent to refine its plan.
 
         Args:
-            current_plan: The current reasoning plan.
+            current_plan: The current plan.
 
         Returns:
-            str: The refine prompt.
+            The refine prompt.
         """
-        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,
-        )
+        # 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,
+            )
 
     @staticmethod
-    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.
+    def _parse_planning_response(response: str) -> tuple[str, bool]:
+        """Parses the planning response to extract the plan and readiness.
 
         Args:
             response: The LLM response.
@@ -380,25 +619,13 @@ class AgentReasoning:
             return "No plan was generated.", False
 
         plan = response
-        ready = False
-
-        if "READY: I am ready to execute the task." in response:
-            ready = True
+        ready = "READY: I am ready to execute the task." in response
 
         return plan, ready
 
-    def _handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """
-        Deprecated method for backward compatibility.
-        Use handle_agent_reasoning() instead.
 
-        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()
+# Alias for backward compatibility
+AgentPlanning = AgentReasoning
 
 
 def _call_llm_with_reasoning_prompt(
@@ -409,7 +636,9 @@ def _call_llm_with_reasoning_prompt(
     backstory: str,
     plan_type: Literal["initial_plan", "refine_plan"],
 ) -> str:
-    """Calls the LLM with the reasoning prompt.
+    """Deprecated: Calls the LLM with the reasoning prompt.
+
+    This function is kept for backward compatibility.
 
     Args:
         llm: The language model to use.
@@ -417,7 +646,7 @@ 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 ("initial_plan" or "refine_plan").
+        plan_type: The type of plan being created.
 
     Returns:
         The LLM response.

lib/crewai/tests/agents/test_agent.py

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

lib/crewai/tests/agents/test_agent_executor.py

@@ -25,6 +25,18 @@ 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."""
@@ -477,3 +489,249 @@ 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

@@ -1,240 +1,345 @@
-"""Tests for reasoning in agents."""
+"""Tests for planning/reasoning in agents."""
 
-import json
+import warnings
 
 import pytest
 
-from crewai import Agent, Task
+from crewai import Agent, PlanningConfig, Task
 from crewai.llm import LLM
 
 
-@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",
-    }
+# =============================================================================
+# Tests for PlanningConfig configuration (no LLM calls needed)
+# =============================================================================
 
 
-def test_agent_with_reasoning(mock_llm_responses):
-    """Test agent with reasoning."""
-    llm = LLM("gpt-3.5-turbo")
+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")
 
     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.",
+        backstory="I am a test agent.",
         llm=llm,
         reasoning=True,
-        verbose=True,
+        max_reasoning_attempts=5,
+        verbose=False,
     )
 
-    task = Task(
-        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 == mock_llm_responses["execution"]
-    assert "Reasoning Plan:" in task.description
+    # Should have created a PlanningConfig with max_attempts
+    assert agent.planning_config is not None
+    assert agent.planning_config.max_attempts == 5
 
 
-def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
-    """Test agent with reasoning that requires refinement."""
-    llm = LLM("gpt-3.5-turbo")
+# =============================================================================
+# 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="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        role="Math Assistant",
+        goal="Help solve math problems step by step",
+        backstory="A helpful math tutor",
         llm=llm,
-        reasoning=True,
-        max_reasoning_attempts=2,
-        verbose=True,
+        planning_config=PlanningConfig(max_attempts=1),
+        verbose=False,
     )
 
-    task = Task(
-        description="Complex math task: What's the derivative of x²?",
-        expected_output="The answer should be a mathematical expression.",
-        agent=agent,
-    )
+    result = agent.kickoff("What is 15 + 27?")
 
-    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 == "2x"
-    assert call_count[0] == 2  # Should have made 2 reasoning calls
-    assert "Reasoning Plan:" in task.description
+    assert result is not None
+    assert "42" in str(result)
 
 
-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")
+@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="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        role="Math Assistant",
+        goal="Help solve math problems",
+        backstory="A helpful assistant",
         llm=llm,
-        reasoning=True,
-        max_reasoning_attempts=2,
-        verbose=True,
+        # No planning_config = no planning
+        verbose=False,
     )
 
-    task = Task(
-        description="Complex math task: Solve the Riemann hypothesis.",
-        expected_output="A proof or disproof of the hypothesis.",
-        agent=agent,
-    )
+    result = agent.kickoff("What is 8 * 7?")
 
-    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 == "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
+    assert result is not None
+    assert "56" in str(result)
 
 
-def test_agent_reasoning_error_handling():
-    """Test error handling during the reasoning process."""
-    llm = LLM("gpt-3.5-turbo")
+@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="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        role="Math Assistant",
+        goal="Help solve math problems",
+        backstory="A helpful assistant",
         llm=llm,
-        reasoning=True,
+        planning=False,  # Explicitly disable planning
+        verbose=False,
     )
 
-    task = Task(
-        description="Task that will cause an error",
-        expected_output="Output that will never be generated",
-        agent=agent,
-    )
+    result = agent.kickoff("What is 100 / 4?")
 
-    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
+    assert result is not None
+    assert "25" in str(result)
 
 
-@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")
+@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="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        role="Math Tutor",
+        goal="Solve multi-step math problems",
+        backstory="An expert tutor who explains step by step",
         llm=llm,
-        reasoning=True,
-        verbose=True,
+        planning_config=PlanningConfig(max_attempts=1, max_steps=5),
+        verbose=False,
     )
 
-    task = Task(
-        description="Simple math task: What's 2+2?",
-        expected_output="The answer should be a number.",
-        agent=agent,
+    # Task requires: find primes, sum them, then double
+    result = agent.kickoff(
+        "Find the first 3 prime numbers, add them together, then multiply by 2."
     )
 
-    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
+    assert result is not None
+    # First 3 primes: 2, 3, 5 -> sum = 10 -> doubled = 20
+    assert "20" in str(result)
 
 
-@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")
+# =============================================================================
+# 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="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        role="Math Assistant",
+        goal="Help solve math problems",
+        backstory="A helpful math tutor",
         llm=llm,
-        reasoning=True,
-        verbose=True,
+        planning_config=PlanningConfig(max_attempts=1),
+        verbose=False,
     )
 
     task = Task(
-        description="Simple math task: What's 2+2?",
-        expected_output="The answer should be a number.",
+        description="What is 9 + 11?",
+        expected_output="A number",
         agent=agent,
     )
 
-    agent.llm.supports_function_calling = lambda: True
+    result = agent.execute_task(task)
 
-    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"
+    assert result is not None
+    assert "20" in str(result)
+    # Planning should be appended to task description
+    assert "Planning:" in task.description
 
-    agent.llm.call = mock_function_call
+
+@pytest.mark.vcr()
+def test_agent_execute_task_without_planning():
+    """Test Agent.execute_task() without planning."""
+    llm = LLM("gpt-4o-mini")
+
+    agent = Agent(
+        role="Math Assistant",
+        goal="Help solve math problems",
+        backstory="A helpful assistant",
+        llm=llm,
+        verbose=False,
+    )
+
+    task = Task(
+        description="What is 12 * 3?",
+        expected_output="A number",
+        agent=agent,
+    )
 
     result = agent.execute_task(task)
 
-    assert result == "4"
-    assert "Reasoning Plan:" in task.description
-    assert "Invalid JSON that will trigger fallback" in task.description
+    assert result is not None
+    assert "36" in str(result)
+    # No planning should be added
+    assert "Planning:" not 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")
+
+    agent = Agent(
+        role="Math Tutor",
+        goal="Solve complex math problems step by step",
+        backstory="An expert tutor",
+        llm=llm,
+        planning_config=PlanningConfig(max_attempts=2),
+        verbose=False,
+    )
+
+    task = Task(
+        description="Calculate the area of a circle with radius 5 (use pi = 3.14)",
+        expected_output="The area as a number",
+        agent=agent,
+    )
+
+    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

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

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

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+      - '0'
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+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
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lib/crewai/tests/llms/azure/test_azure.py

@@ -958,34 +958,6 @@ def test_azure_endpoint_detection_flags():
         assert llm_other.is_azure_openai_endpoint == False
 
 
-def test_azure_endpoint_detection_ignores_spoofed_urls():
-    """
-    Test that endpoint detection does not trust spoofed host/path substrings
-    """
-    with patch.dict(os.environ, {
-        "AZURE_API_KEY": "test-key",
-        "AZURE_ENDPOINT": (
-            "https://evil.example.com/?redirect="
-            "https://test.openai.azure.com/openai/deployments/gpt-4"
-        ),
-    }):
-        llm_query_spoof = LLM(model="azure/gpt-4")
-        assert llm_query_spoof.is_azure_openai_endpoint == False
-        assert "model" in llm_query_spoof._prepare_completion_params(
-            messages=[{"role": "user", "content": "test"}]
-        )
-
-    with patch.dict(os.environ, {
-        "AZURE_API_KEY": "test-key",
-        "AZURE_ENDPOINT": "https://test.openai.azure.com.evil/openai/deployments/gpt-4",
-    }):
-        llm_host_spoof = LLM(model="azure/gpt-4")
-        assert llm_host_spoof.is_azure_openai_endpoint == False
-        assert "model" in llm_host_spoof._prepare_completion_params(
-            messages=[{"role": "user", "content": "test"}]
-        )
-
-
 def test_azure_improved_error_messages():
     """
     Test that improved error messages are provided for common HTTP errors

lib/crewai/tests/utilities/test_planning_types.py

@@ -0,0 +1,389 @@
+"""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

@@ -0,0 +1,698 @@
+"""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