Merge branch 'main' into joaomdmoura/log

* Add MCP integration documentation and update enterprise docs

* Update MCP integration docs with code syntax improvements

* Standardize documentation structure and add reasoning docs

docs/concepts/agents.mdx

@@ -21,7 +21,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
 CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
-![Visual Agent Builder Screenshot](../images/enterprise/crew-studio-quickstart)
+![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
 The Visual Agent Builder enables:
 - Intuitive agent configuration with form-based interfaces

docs/concepts/cli.mdx

@@ -4,7 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
-# CrewAI CLI Documentation
+## Overview
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 

docs/concepts/collaboration.mdx

@@ -4,7 +4,7 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
 icon: screen-users
 ---
 
-## Collaboration Fundamentals      
+## Overview    
 
 Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
 

docs/concepts/crews.mdx

@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 icon: people-group
 ---
 
-## What is a Crew?
+## Overview
 
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
@@ -117,6 +117,12 @@ class YourCrewName:
         )
 ```
 
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={"any": "input here"})
+```
+
 <Note>
 Tasks will be executed in the order they are defined.
 </Note>
@@ -184,6 +190,11 @@ class YourCrewName:
             verbose=True
         )
 ```
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={})
+```
 
 In this example:
 

docs/concepts/event-listener.mdx

@@ -4,7 +4,7 @@ description: 'Tap into CrewAI events to build custom integrations and monitoring
 icon: spinner
 ---
 
-# Event Listeners
+## Overview
 
 CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
 
@@ -21,7 +21,7 @@ When specific actions occur in CrewAI (like a Crew starting execution, an Agent
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
 CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
-![Prompt Tracing Dashboard](../images/enterprise/prompt-tracing.png)
+![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
 
 With Prompt Tracing you can:
 - View the complete history of all prompts sent to your LLM
@@ -224,6 +224,15 @@ CrewAI provides a wide range of events that you can listen for:
 - **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
 - **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
 
+### Knowledge Events
+
+- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
+- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
+- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
+- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
+- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
+- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
+
 ### Flow Events
 
 - **FlowCreatedEvent**: Emitted when a Flow is created

docs/concepts/flows.mdx

@@ -4,7 +4,7 @@ description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
 ---
 
-## Introduction
+## Overview
 
 CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
 

docs/concepts/knowledge.mdx

@@ -4,7 +4,7 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---
 
-## What is Knowledge?
+## Overview
 
 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
 Think of it as giving your agents a reference library they can consult while working.
@@ -36,12 +36,15 @@ CrewAI supports various types of knowledge sources out of the box:
 
 ## Supported Knowledge Parameters
 
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
+<ParamField body="sources" type="List[BaseKnowledgeSource]" required="Yes"> 
+    List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content. 
+</ParamField>
+<ParamField body="collection_name" type="str"> 
+    Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to \"knowledge\" if not provided. 
+</ParamField>
+<ParamField body="storage" type="Optional[KnowledgeStorage]"> 
+Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. 
+</ParamField>
 
 <Tip>
 Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
@@ -432,6 +435,46 @@ Query rewriting happens transparently using a system prompt that instructs the L
   This mechanism is fully automatic and requires no configuration from users. The agent's LLM is used to perform the query rewriting, so using a more capable LLM can improve the quality of rewritten queries.
 </Tip>
 
+## Knowledge Events
+
+CrewAI emits events during the knowledge retrieval process that you can listen for using the event system. These events allow you to monitor, debug, and analyze how knowledge is being retrieved and used by your agents.
+
+### Available Knowledge Events
+
+- **KnowledgeRetrievalStartedEvent**: Emitted when an agent starts retrieving knowledge from sources
+- **KnowledgeRetrievalCompletedEvent**: Emitted when knowledge retrieval is completed, including the query used and the retrieved content
+- **KnowledgeQueryStartedEvent**: Emitted when a query to knowledge sources begins
+- **KnowledgeQueryCompletedEvent**: Emitted when a query completes successfully
+- **KnowledgeQueryFailedEvent**: Emitted when a query to knowledge sources fails
+- **KnowledgeSearchQueryFailedEvent**: Emitted when a search query fails
+
+### Example: Monitoring Knowledge Retrieval
+
+```python
+from crewai.utilities.events import (
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+
+class KnowledgeMonitorListener(BaseEventListener):
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
+        def on_knowledge_retrieval_started(source, event):
+            print(f"Agent '{event.agent.role}' started retrieving knowledge")
+            
+        @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
+        def on_knowledge_retrieval_completed(source, event):
+            print(f"Agent '{event.agent.role}' completed knowledge retrieval")
+            print(f"Query: {event.query}")
+            print(f"Retrieved {len(event.retrieved_knowledge)} knowledge chunks")
+
+# Create an instance of your listener
+knowledge_monitor = KnowledgeMonitorListener()
+```
+
+For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
+
 ### Example
 
 ```python

docs/concepts/llms.mdx

@@ -4,9 +4,10 @@ description: 'A comprehensive guide to configuring and using Large Language Mode
 icon: 'microchip-ai'
 ---
 
-<Note>
-  CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
-</Note>
+## Overview
+
+CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+
 
 ## What are LLMs?
 
@@ -677,18 +678,24 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai import LLM
-    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
+    from crewai.utilities.events import (
+      LLMStreamChunkEvent
+    )
+    from crewai.utilities.events.base_event_listener import BaseEventListener
 
-    class MyEventHandler(EventHandler):
-        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
-            # Process each chunk as it arrives
-            print(f"Received chunk: {event.chunk}")
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
+              # Process each chunk as it arrives
+              print(f"Received chunk: {event.chunk}")
 
-    # Register the event handler
-    from crewai.utilities.events import crewai_event_bus
-    crewai_event_bus.register_handler(MyEventHandler())
+    my_listener = MyCustomListener()
     ```
+
+    <Tip>
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+    </Tip>
   </Tab>
 </Tabs>
 
@@ -785,6 +792,24 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
+  
+  <Accordion title="Drop Additional Parameters">
+    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
+    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
+
+    ```python
+    from crewai import LLM
+    import os
+
+    os.environ["OPENAI_API_KEY"] = "<api-key>"
+
+    o3_llm = LLM(
+        model="o3",
+        drop_params=True,
+        additional_drop_params=["stop"]
+    )
+    ```
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/concepts/memory.mdx

@@ -4,7 +4,7 @@ description: Leveraging memory systems in the CrewAI framework to enhance agent
 icon: database
 ---
 
-## Introduction to Memory Systems in CrewAI
+## Overview
 
 The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. 
 This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember, 

docs/concepts/planning.mdx

@@ -1,10 +1,10 @@
 ---
 title: Planning
 description: Learn how to add planning to your CrewAI Crew and improve their performance.
-icon: brain
+icon: ruler-combined
 ---
 
-## Introduction
+## Overview
 
 The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, 
 all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.

docs/concepts/processes.mdx

@@ -4,7 +4,8 @@ description: Detailed guide on workflow management through processes in CrewAI,
 icon: bars-staggered
 ---
 
-## Understanding Processes
+## Overview
+
 <Tip>
   Processes orchestrate the execution of tasks by agents, akin to project management in human teams. 
   These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.

docs/concepts/reasoning.mdx

@@ -0,0 +1,147 @@
+---
+title: Reasoning
+description: "Learn how to enable and use agent reasoning to improve task execution."
+icon: brain
+---
+
+## Overview
+
+Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
+
+## Usage
+
+To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze complex datasets and provide insights",
+    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
+    reasoning=True,  # Enable reasoning
+    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
+)
+```
+
+## How It Works
+
+When reasoning is enabled, before executing a task, the agent will:
+
+1. Reflect on the task and create a detailed plan
+2. Evaluate whether it's ready to execute the task
+3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
+4. Inject the reasoning plan into the task description before execution
+
+This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
+
+## Configuration Options
+
+<ParamField body="reasoning" type="bool" default="False"> 
+    Enable or disable reasoning 
+</ParamField>
+
+<ParamField body="max_reasoning_attempts" type="int" default="None"> 
+    Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
+</ParamField>
+
+## Example
+
+Here's a complete example:
+
+```python
+from crewai import Agent, Task, Crew
+
+# Create an agent with reasoning enabled
+analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst.",
+    reasoning=True,
+    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
+)
+
+# Create a task
+analysis_task = Task(
+    description="Analyze the provided sales data and identify key trends.",
+    expected_output="A report highlighting the top 3 sales trends.",
+    agent=analyst
+)
+
+# Create a crew and run the task
+crew = Crew(agents=[analyst], tasks=[analysis_task])
+result = crew.kickoff()
+
+print(result)
+```
+
+## Error Handling
+
+The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
+
+Here's how to handle potential errors in your code:
+
+```python
+from crewai import Agent, Task
+import logging
+
+# Set up logging to capture any reasoning errors
+logging.basicConfig(level=logging.INFO)
+
+# Create an agent with reasoning enabled
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    reasoning=True,
+    max_reasoning_attempts=3
+)
+
+# Create a task
+task = Task(
+    description="Analyze the provided sales data and identify key trends.",
+    expected_output="A report highlighting the top 3 sales trends.",
+    agent=agent
+)
+
+# Execute the task
+# If an error occurs during reasoning, it will be logged and execution will continue
+result = agent.execute_task(task)
+```
+
+## Example Reasoning Output
+
+Here's an example of what a reasoning plan might look like for a data analysis task:
+
+```
+Task: Analyze the provided sales data and identify key trends.
+
+Reasoning Plan:
+I'll analyze the sales data to identify the top 3 trends.
+
+1. Understanding of the task:
+   I need to analyze sales data to identify key trends that would be valuable for business decision-making.
+
+2. Key steps I'll take:
+   - First, I'll examine the data structure to understand what fields are available
+   - Then I'll perform exploratory data analysis to identify patterns
+   - Next, I'll analyze sales by time periods to identify temporal trends
+   - I'll also analyze sales by product categories and customer segments
+   - Finally, I'll identify the top 3 most significant trends
+
+3. Approach to challenges:
+   - If the data has missing values, I'll decide whether to fill or filter them
+   - If the data has outliers, I'll investigate whether they're valid data points or errors
+   - If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
+
+4. Use of available tools:
+   - I'll use data analysis tools to explore and visualize the data
+   - I'll use statistical tools to identify significant patterns
+   - I'll use knowledge retrieval to access relevant information about sales analysis
+
+5. Expected outcome:
+   A concise report highlighting the top 3 sales trends with supporting evidence from the data.
+
+READY: I am ready to execute the task.
+```
+
+This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.

docs/concepts/tasks.mdx

@@ -4,7 +4,7 @@ description: Detailed guide on managing and creating tasks within the CrewAI fra
 icon: list-check
 ---
 
-## Overview of a Task
+## Overview
 
 In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
 
@@ -15,7 +15,7 @@ Tasks within CrewAI can be collaborative, requiring multiple agents to work toge
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
 CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
-![Task Builder Screenshot](../images/enterprise/crew-studio-quickstart.png)
+![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
 The Visual Task Builder enables:
 - Drag-and-drop task creation

docs/concepts/testing.mdx

@@ -4,7 +4,7 @@ description: Learn how to test your CrewAI Crew and evaluate their performance.
 icon: vial
 ---
 
-## Introduction
+## Overview
 
 Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
 

docs/concepts/tools.mdx

@@ -4,7 +4,7 @@ description: Understanding and leveraging tools within the CrewAI framework for
 icon: screwdriver-wrench
 ---
 
-## Introduction
+## Overview
 
 CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
@@ -18,8 +18,6 @@ enabling everything from simple searches to complex interactions and effective t
 <Note type="info" title="Enterprise Enhancement: Tools Repository">
 CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
 
-![Tools Repository Screenshot](../images/enterprise/tools-repository.png)
-
 The Enterprise Tools Repository includes:
 - Pre-built connectors for popular enterprise systems
 - Custom tool creation interface

docs/concepts/training.mdx

@@ -4,7 +4,7 @@ description: Learn how to train your CrewAI agents by giving them feedback early
 icon: dumbbell
 ---
 
-## Introduction
+## Overview
 
 The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.

docs/docs.json

@@ -74,6 +74,7 @@
               "concepts/collaboration",
               "concepts/training",
               "concepts/memory",
+              "concepts/reasoning",
               "concepts/planning",
               "concepts/testing",
               "concepts/cli",
@@ -129,6 +130,7 @@
               "tools/seleniumscrapingtool",
               "tools/snowflakesearchtool",
               "tools/spidertool",
+              "tools/stagehandtool",
               "tools/txtsearchtool",
               "tools/visiontool",
               "tools/weaviatevectorsearchtool",
@@ -138,6 +140,12 @@
               "tools/youtubevideosearchtool"
             ]
           },
+          {
+            "group": "MCP Integration",
+            "pages": [
+              "mcp/crewai-mcp-integration"
+            ] 
+          },
           {
             "group": "Agent Monitoring & Observability",
             "pages": [

docs/enterprise/introduction.mdx

@@ -64,14 +64,14 @@ CrewAI Enterprise extends the power of the open-source framework with features d
       Sign Up
     </Card>
   </Step>
-  <Step title="Create your first crew">
-    Use code or Crew Studio to create your crew
+  <Step title="Build your first crew">
+    Use code or Crew Studio to build your crew
     <Card 
-      title="Create Crew" 
+      title="Build Crew" 
       icon="paintbrush"
-      href="/enterprise/guides/create-crew"
+      href="/enterprise/guides/build-crew"
     >
-      Create Crew
+      Build Crew
     </Card>
   </Step>
   <Step title="Deploy your crew">

docs/guides/advanced/customizing-prompts.mdx

@@ -4,8 +4,6 @@ description: Dive deeper into low-level prompt customization for CrewAI, enablin
 icon: message-pen
 ---
 
-# Customizing Prompts at a Low Level
-
 ## Why Customize Prompts?
 
 Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here’s why you might want to take advantage of this deeper control:

docs/guides/advanced/fingerprinting.mdx

@@ -4,8 +4,6 @@ description: Learn how to use CrewAI's fingerprinting system to uniquely identif
 icon: fingerprint
 ---
 
-# Fingerprinting in CrewAI
-
 ## Overview
 
 Fingerprints in CrewAI provide a way to uniquely identify and track components throughout their lifecycle. Each `Agent`, `Crew`, and `Task` automatically receives a unique fingerprint when created, which cannot be manually overridden.

docs/guides/agents/crafting-effective-agents.mdx

@@ -4,8 +4,6 @@ description: Learn best practices for designing powerful, specialized AI agents
 icon: robot
 ---
 
-# Crafting Effective Agents
-
 ## The Art and Science of Agent Design
 
 At the heart of CrewAI lies the agent - a specialized AI entity designed to perform specific roles within a collaborative framework. While creating basic agents is simple, crafting truly effective agents that produce exceptional results requires understanding key design principles and best practices.

docs/guides/concepts/evaluating-use-cases.mdx

@@ -4,8 +4,6 @@ description: Learn how to assess your AI application needs and choose the right
 icon: scale-balanced
 ---
 
-# Evaluating Use Cases for CrewAI
-
 ## Understanding the Decision Framework
 
 When building AI applications with CrewAI, one of the most important decisions you'll make is choosing the right approach for your specific use case. Should you use a Crew? A Flow? A combination of both? This guide will help you evaluate your requirements and make informed architectural decisions.

docs/guides/crews/first-crew.mdx

@@ -4,8 +4,6 @@ description: Step-by-step tutorial to create a collaborative AI team that works
 icon: users-gear
 ---
 
-# Build Your First Crew
-
 ## Unleashing the Power of Collaborative AI
 
 Imagine having a team of specialized AI agents working together seamlessly to solve complex problems, each contributing their unique skills to achieve a common goal. This is the power of CrewAI - a framework that enables you to create collaborative AI systems that can accomplish tasks far beyond what a single AI could achieve alone.

docs/guides/flows/first-flow.mdx

@@ -4,8 +4,6 @@ description: Learn how to create structured, event-driven workflows with precise
 icon: diagram-project
 ---
 
-# Build Your First Flow
-
 ## Taking Control of AI Workflows with Flows
 
 CrewAI Flows represent the next level in AI orchestration - combining the collaborative power of AI agent crews with the precision and flexibility of procedural programming. While crews excel at agent collaboration, flows give you fine-grained control over exactly how and when different components of your AI system interact.

docs/guides/flows/mastering-flow-state.mdx

@@ -4,8 +4,6 @@ description: A comprehensive guide to managing, persisting, and leveraging state
 icon: diagram-project
 ---
 
-# Mastering Flow State Management
-
 ## Understanding the Power of State in Flows
 
 State management is the backbone of any sophisticated AI workflow. In CrewAI Flows, the state system allows you to maintain context, share data between steps, and build complex application logic. Mastering state management is essential for creating reliable, maintainable, and powerful AI applications.

docs/mcp/crewai-mcp-integration.mdx

@@ -0,0 +1,229 @@
+---
+title: 'MCP Servers as Tools in CrewAI'
+description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
+icon: 'plug'
+---
+
+## Overview
+
+The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers. 
+The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents. 
+This gives your crews access to a vast ecosystem of functionalities. For now, we support **Standard Input/Output** (Stdio) and **Server-Sent Events** (SSE) transport mechanisms. 
+
+<Info>
+We will also be integrating **Streamable HTTP** transport in the near future. 
+Streamable HTTP is designed for efficient, bi-directional communication over a single HTTP connection.
+</Info>
+
+## Installation
+
+Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
+
+```shell
+uv pip install 'crewai-tools[mcp]'
+```
+
+### Integrating MCP Tools with `MCPServerAdapter`
+
+The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. 
+It supports different transport mechanisms, primarily **Stdio** (for local servers) and **SSE** (Server-Sent Events).You have two main options for managing the connection lifecycle:
+
+### Option 1: Fully Managed Connection (Recommended)
+
+Using a Python context manager (`with` statement) is the recommended approach. It automatically handles starting and stopping the connection to the MCP server.
+
+**For a local Stdio-based MCP server:**
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import MCPServerAdapter
+from mcp import StdioServerParameters
+import os
+
+server_params=StdioServerParameters(
+    command="uxv", # Or your python3 executable i.e. "python3"
+    args=["mock_server.py"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+with MCPServerAdapter(server_params) as tools:
+    print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
+
+    # Example: Using the tools from the Stdio MCP server in a CrewAI Agent
+    agent = Agent(
+        role="Web Information Retriever",
+        goal="Scrape content from a specified URL.",
+        backstory="An AI that can fetch and process web page data via an MCP tool.",
+        tools=tools,
+        verbose=True,
+    )
+    task = Task(
+        description="Scrape content from a specified URL.",
+        expected_output="Scraped content from the specified URL.",
+        agent=agent,
+    )
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+    )
+    result = crew.kickoff()
+    print(result)
+```
+
+**For a remote SSE-based MCP server:**
+
+```python
+from crewai_tools import MCPServerAdapter
+from crewai import Agent, Task, Crew
+
+server_params = {"url": "http://localhost:8000/sse"}
+
+with MCPServerAdapter(server_params) as tools:
+    print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
+
+    # Example: Using the tools from the SSE MCP server in a CrewAI Agent
+    agent = Agent(
+        role="Web Information Retriever",
+        goal="Scrape content from a specified URL.",
+        backstory="An AI that can fetch and process web page data via an MCP tool.",
+        tools=tools,
+        verbose=True,
+    )
+    task = Task(
+        description="Scrape content from a specified URL.",
+        expected_output="Scraped content from the specified URL.",
+        agent=agent,
+    )
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+    )
+    result = crew.kickoff()
+    print(result)
+```
+
+### Option 2: More control over the MCP server connection lifecycle
+
+If you need finer-grained control over the MCP server connection lifecycle, you can instantiate `MCPServerAdapter` directly and manage its `start()` and `stop()` methods.
+
+<Info>
+You **MUST** call `mcp_server_adapter.stop()` to ensure the connection is closed and resources are released. Using a `try...finally` block is highly recommended.
+</Info>
+
+#### Stdio Transport Example (Manual)
+
+```python
+from mcp import StdioServerParameters
+from crewai_tools import MCPServerAdapter
+from crewai import Agent, Task, Crew
+import os
+
+stdio_params = StdioServerParameters(
+    command="uvx", # Or your python3 executable i.e. "python3"
+    args=["--quiet", "your-mcp-server@0.1.3"],
+    env={"UV_PYTHON": "3.12", **os.environ},
+)
+
+mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
+try:
+    mcp_server_adapter.start() # Manually start the connection
+    tools = mcp_server_adapter.tools
+    print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
+
+    # Use 'tools' with your Agent, Task, Crew setup as in Option 1
+    agent = Agent(
+        role="Medical Researcher", 
+        goal="Find recent studies on a given topic using PubMed.", 
+        backstory="An AI assistant specialized in biomedical literature research.", 
+        tools=tools,
+        verbose=True 
+    )
+    
+    task = Task(
+        description="Search for recent articles on 'crispr gene editing'.",
+        expected_output="A summary of the top 3 recent articles.",
+        agent=agent
+    )
+    
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+        process=Process.sequential
+    )
+    
+    result = crew.kickoff()
+    print(result)
+finally:
+    print("Stopping Stdio MCP server connection (manual)...")
+    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
+```
+
+
+#### SSE Transport Example (Manual)
+
+```python
+from crewai_tools import MCPServerAdapter
+from crewai import Agent, Task, Crew, Process
+from mcp import StdioServerParameters
+
+
+server_params = {"url": "http://localhost:8000/sse"}
+
+try:
+    mcp_server_adapter = MCPServerAdapter(server_params)
+    mcp_server_adapter.start()
+    tools = mcp_server_adapter.tools
+    print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
+
+    agent = Agent(
+        role="Medical Researcher", 
+        goal="Find recent studies on a given topic using PubMed.", 
+        backstory="An AI assistant specialized in biomedical literature research.", 
+        tools=tools,
+        verbose=True 
+    )
+    
+    task = Task(
+        description="Search for recent articles on 'crispr gene editing'.",
+        expected_output="A summary of the top 3 recent articles.",
+        agent=agent
+    )
+    
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True,
+        process=Process.sequential
+    )
+    
+    result = crew.kickoff()
+    print(result)
+finally:
+    print("Stopping SSE MCP server connection (manual)...")
+    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
+```
+
+## Staying Safe with MCP
+<Warning>
+Always ensure that you trust an MCP Server before using it.
+</Warning>
+
+#### Security Warning: DNS Rebinding Attacks
+SSE transports can be vulnerable to DNS rebinding attacks if not properly secured. 
+To prevent this:
+
+1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
+2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
+3. **Implement proper authentication** for all SSE connections
+
+Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
+
+For more details, see the [MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations) documentation.
+
+### Limitations
+*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`. 
+Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
+*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/tools/stagehandtool.mdx

@@ -0,0 +1,244 @@
+---
+title: Stagehand Tool
+description: Web automation tool that integrates Stagehand with CrewAI for browser interaction and automation
+icon: hand
+---
+
+
+# Overview
+
+The `StagehandTool` integrates the [Stagehand](https://docs.stagehand.dev/get_started/introduction) framework with CrewAI, enabling agents to interact with websites and automate browser tasks using natural language instructions.
+
+## Overview
+
+Stagehand is a powerful browser automation framework built by Browserbase that allows AI agents to:
+
+- Navigate to websites
+- Click buttons, links, and other elements
+- Fill in forms
+- Extract data from web pages
+- Observe and identify elements
+- Perform complex workflows
+
+The StagehandTool wraps the Stagehand Python SDK to provide CrewAI agents with browser control capabilities through three core primitives:
+
+1. **Act**: Perform actions like clicking, typing, or navigating
+2. **Extract**: Extract structured data from web pages
+3. **Observe**: Identify and analyze elements on the page
+
+## Prerequisites
+
+Before using this tool, ensure you have:
+
+1. A [Browserbase](https://www.browserbase.com/) account with API key and project ID
+2. An API key for an LLM (OpenAI or Anthropic Claude)
+3. The Stagehand Python SDK installed
+
+Install the required dependency:
+
+```bash
+pip install stagehand-py
+```
+
+## Usage
+
+### Basic Implementation
+
+The StagehandTool can be implemented in two ways:
+
+#### 1. Using Context Manager (Recommended)
+<Tip>
+  The context manager approach is recommended as it ensures proper cleanup of resources even if exceptions occur.
+</Tip>
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import StagehandTool
+from stagehand.schemas import AvailableModel
+
+# Initialize the tool with your API keys using a context manager
+with StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",  # OpenAI or Anthropic API key
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,  # Optional: specify which model to use
+) as stagehand_tool:
+    # Create an agent with the tool
+    researcher = Agent(
+        role="Web Researcher",
+        goal="Find and summarize information from websites",
+        backstory="I'm an expert at finding information online.",
+        verbose=True,
+        tools=[stagehand_tool],
+    )
+
+    # Create a task that uses the tool
+    research_task = Task(
+        description="Go to https://www.example.com and tell me what you see on the homepage.",
+        agent=researcher,
+    )
+
+    # Run the crew
+    crew = Crew(
+        agents=[researcher],
+        tasks=[research_task],
+        verbose=True,
+    )
+
+    result = crew.kickoff()
+    print(result)
+```
+
+#### 2. Manual Resource Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import StagehandTool
+from stagehand.schemas import AvailableModel
+
+# Initialize the tool with your API keys
+stagehand_tool = StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
+)
+
+try:
+    # Create an agent with the tool
+    researcher = Agent(
+        role="Web Researcher",
+        goal="Find and summarize information from websites",
+        backstory="I'm an expert at finding information online.",
+        verbose=True,
+        tools=[stagehand_tool],
+    )
+
+    # Create a task that uses the tool
+    research_task = Task(
+        description="Go to https://www.example.com and tell me what you see on the homepage.",
+        agent=researcher,
+    )
+
+    # Run the crew
+    crew = Crew(
+        agents=[researcher],
+        tasks=[research_task],
+        verbose=True,
+    )
+
+    result = crew.kickoff()
+    print(result)
+finally:
+    # Explicitly clean up resources
+    stagehand_tool.close()
+```
+
+## Command Types
+
+The StagehandTool supports three different command types for specific web automation tasks:
+
+### 1. Act Command
+
+The `act` command type (default) enables webpage interactions like clicking buttons, filling forms, and navigation.
+
+```python
+# Perform an action (default behavior)
+result = stagehand_tool.run(
+    instruction="Click the login button", 
+    url="https://example.com",
+    command_type="act"  # Default, so can be omitted
+)
+
+# Fill out a form
+result = stagehand_tool.run(
+    instruction="Fill the contact form with name 'John Doe', email 'john@example.com', and message 'Hello world'", 
+    url="https://example.com/contact"
+)
+```
+
+### 2. Extract Command
+
+The `extract` command type retrieves structured data from webpages.
+
+```python
+# Extract all product information
+result = stagehand_tool.run(
+    instruction="Extract all product names, prices, and descriptions", 
+    url="https://example.com/products",
+    command_type="extract"
+)
+
+# Extract specific information with a selector
+result = stagehand_tool.run(
+    instruction="Extract the main article title and content", 
+    url="https://example.com/blog/article",
+    command_type="extract",
+    selector=".article-container"  # Optional CSS selector
+)
+```
+
+### 3. Observe Command
+
+The `observe` command type identifies and analyzes webpage elements.
+
+```python
+# Find interactive elements
+result = stagehand_tool.run(
+    instruction="Find all interactive elements in the navigation menu", 
+    url="https://example.com",
+    command_type="observe"
+)
+
+# Identify form fields
+result = stagehand_tool.run(
+    instruction="Identify all the input fields in the registration form", 
+    url="https://example.com/register",
+    command_type="observe",
+    selector="#registration-form"
+)
+```
+
+## Configuration Options
+
+Customize the StagehandTool behavior with these parameters:
+
+```python
+stagehand_tool = StagehandTool(
+    api_key="your-browserbase-api-key",
+    project_id="your-browserbase-project-id",
+    model_api_key="your-llm-api-key",
+    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
+    dom_settle_timeout_ms=5000,  # Wait longer for DOM to settle
+    headless=True,  # Run browser in headless mode
+    self_heal=True,  # Attempt to recover from errors
+    wait_for_captcha_solves=True,  # Wait for CAPTCHA solving
+    verbose=1,  # Control logging verbosity (0-3)
+)
+```
+
+## Best Practices
+
+1. **Be Specific**: Provide detailed instructions for better results
+2. **Choose Appropriate Command Type**: Select the right command type for your task
+3. **Use Selectors**: Leverage CSS selectors to improve accuracy
+4. **Break Down Complex Tasks**: Split complex workflows into multiple tool calls
+5. **Implement Error Handling**: Add error handling for potential issues
+
+## Troubleshooting
+
+
+Common issues and solutions:
+
+- **Session Issues**: Verify API keys for both Browserbase and LLM provider
+- **Element Not Found**: Increase `dom_settle_timeout_ms` for slower pages
+- **Action Failures**: Use `observe` to identify correct elements first
+- **Incomplete Data**: Refine instructions or provide specific selectors
+
+
+## Additional Resources
+
+For questions about the CrewAI integration:
+- Join Stagehand's [Slack community](https://stagehand.dev/slack)
+- Open an issue in the [Stagehand repository](https://github.com/browserbase/stagehand)
+- Visit [Stagehand documentation](https://docs.stagehand.dev/)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.120.0"
+version = "0.120.1"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.120.0"
+__version__ = "0.120.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -119,6 +119,14 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
+    reasoning: bool = Field(
+        default=False,
+        description="Whether the agent should reflect and create a plan before executing a task.",
+    )
+    max_reasoning_attempts: Optional[int] = Field(
+        default=None,
+        description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
+    )
     embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
@@ -225,6 +233,21 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
+        if self.reasoning:
+            try:
+                from crewai.utilities.reasoning_handler import AgentReasoning, AgentReasoningOutput
+                
+                reasoning_handler = AgentReasoning(task=task, agent=self)
+                reasoning_output: AgentReasoningOutput = reasoning_handler.handle_agent_reasoning()
+                
+                # Add the reasoning plan to the task description
+                task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
+            except Exception as e:
+                if hasattr(self, '_logger'):
+                    self._logger.log("error", f"Error during reasoning process: {str(e)}")
+                else:
+                    print(f"Error during reasoning process: {str(e)}")
+            
         if self.tools_handler:
             self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.0,<1.0.0"
+    "crewai[tools]>=0.120.1,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.0,<1.0.0",
+    "crewai[tools]>=0.120.1,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.0"
+    "crewai[tools]>=0.120.1"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -161,7 +161,7 @@ def tree_find_and_replace(directory, find, replace):
         for filename in files:
             filepath = os.path.join(path, filename)
 
-            with open(filepath, "r") as file:
+            with open(filepath, "r", encoding="utf-8", errors="ignore") as file:
                 contents = file.read()
             with open(filepath, "w") as file:
                 file.write(contents.replace(find, replace))

src/crewai/task.py

@@ -135,6 +135,10 @@ class Task(BaseModel):
         description="Whether the task should have a human review the final answer of the agent",
         default=False,
     )
+    markdown: Optional[bool] = Field(
+        description="Whether the task should instruct the agent to return the final answer formatted in Markdown",
+        default=False,
+    )
     converter_cls: Optional[Type[Converter]] = Field(
         description="A converter class used to export structured output",
         default=None,
@@ -522,10 +526,14 @@ class Task(BaseModel):
         return guardrail_result
 
     def prompt(self) -> str:
-        """Prompt the task.
-
+        """Generates the task prompt with optional markdown formatting.
+        
+        When the markdown attribute is True, instructions for formatting the
+        response in Markdown syntax will be added to the prompt.
+        
         Returns:
-            Prompt of the task.
+            str: The formatted prompt string containing the task description,
+                 expected output, and optional markdown formatting instructions.
         """
         tasks_slices = [self.description]
 
@@ -533,6 +541,17 @@ class Task(BaseModel):
             expected_output=self.expected_output
         )
         tasks_slices = [self.description, output]
+        
+        if self.markdown:
+            markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.
+Follow these guidelines:
+- Use # for headers
+- Use ** for bold text
+- Use * for italic text
+- Use - or * for bullet points
+- Use `code` for inline code
+- Use ```language for code blocks"""
+            tasks_slices.append(markdown_instruction)
         return "\n".join(tasks_slices)
 
     def interpolate_inputs_and_add_conversation_history(

src/crewai/telemetry/telemetry.py

@@ -64,7 +64,7 @@ class Telemetry:
     attribute in the Crew class.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.ready: bool = False
         self.trace_set: bool = False
 

src/crewai/translations/en.json

@@ -51,5 +51,11 @@
       "description": "See image to understand its content, you can optionally ask a question about the image",
       "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe."
     }
+  },
+  "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\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.\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.\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].\""
   }
 }

src/crewai/utilities/events/event_listener.py

@@ -56,6 +56,11 @@ from .tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from .reasoning_events import (
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+)
 
 
 class EventListener(BaseEventListener):
@@ -406,5 +411,30 @@ class EventListener(BaseEventListener):
                 self.formatter.current_crew_tree,
             )
 
+        # ----------- REASONING EVENTS -----------
+
+        @crewai_event_bus.on(AgentReasoningStartedEvent)
+        def on_agent_reasoning_started(source, event: AgentReasoningStartedEvent):
+            self.formatter.handle_reasoning_started(
+                self.formatter.current_agent_branch,
+                event.attempt,
+                self.formatter.current_crew_tree,
+            )
+
+        @crewai_event_bus.on(AgentReasoningCompletedEvent)
+        def on_agent_reasoning_completed(source, event: AgentReasoningCompletedEvent):
+            self.formatter.handle_reasoning_completed(
+                event.plan,
+                event.ready,
+                self.formatter.current_crew_tree,
+            )
+
+        @crewai_event_bus.on(AgentReasoningFailedEvent)
+        def on_agent_reasoning_failed(source, event: AgentReasoningFailedEvent):
+            self.formatter.handle_reasoning_failed(
+                event.error,
+                self.formatter.current_crew_tree,
+            )
+
 
 event_listener = EventListener()

src/crewai/utilities/events/event_types.py

@@ -43,6 +43,19 @@ from .tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from .reasoning_events import (
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+)
+from .knowledge_events import (
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
 
 EventTypes = Union[
     CrewKickoffStartedEvent,
@@ -74,4 +87,13 @@ EventTypes = Union[
     LLMStreamChunkEvent,
     LLMGuardrailStartedEvent,
     LLMGuardrailCompletedEvent,
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeSearchQueryFailedEvent,
 ]

src/crewai/utilities/events/reasoning_events.py

@@ -0,0 +1,31 @@
+from crewai.utilities.events.base_events import BaseEvent
+
+
+class AgentReasoningStartedEvent(BaseEvent):
+    """Event emitted when an agent starts reasoning about a task."""
+
+    type: str = "agent_reasoning_started"
+    agent_role: str
+    task_id: str
+    attempt: int = 1  # The current reasoning/refinement attempt
+
+
+class AgentReasoningCompletedEvent(BaseEvent):
+    """Event emitted when an agent finishes its reasoning process."""
+
+    type: str = "agent_reasoning_completed"
+    agent_role: str
+    task_id: str
+    plan: str
+    ready: bool
+    attempt: int = 1
+
+
+class AgentReasoningFailedEvent(BaseEvent):
+    """Event emitted when the reasoning process fails."""
+
+    type: str = "agent_reasoning_failed"
+    agent_role: str
+    task_id: str
+    error: str
+    attempt: int = 1

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

@@ -4,6 +4,7 @@ from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text
 from rich.tree import Tree
+from rich.live import Live
 
 
 class ConsoleFormatter:
@@ -15,10 +16,17 @@ class ConsoleFormatter:
     current_method_branch: Optional[Tree] = None
     current_lite_agent_branch: Optional[Tree] = None
     tool_usage_counts: Dict[str, int] = {}
+    current_reasoning_branch: Optional[Tree] = None  # Track reasoning status
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
         self.verbose = verbose
+        # Live instance to dynamically update a Tree renderable (e.g. the Crew tree)
+        # When multiple Tree objects are printed sequentially we reuse this Live
+        # instance so the previous render is replaced instead of writing a new one.
+        # Once any non-Tree renderable is printed we stop the Live session so the
+        # final Tree persists on the terminal.
+        self._live: Optional[Live] = None
 
     def create_panel(self, content: Text, title: str, style: str = "blue") -> Panel:
         """Create a standardized panel with consistent styling."""
@@ -59,7 +67,7 @@ class ConsoleFormatter:
         label.append(f"{prefix} ", style=f"{style} bold")
         label.append(name, style=style)
         if status:
-            label.append("\n    Status: ", style="white")
+            label.append("\nStatus: ", style="white")
             label.append(status, style=f"{style} bold")
         tree.label = label
 
@@ -68,7 +76,46 @@ class ConsoleFormatter:
         return parent.add(Text(text, style=style))
 
     def print(self, *args, **kwargs) -> None:
-        """Print to console with consistent formatting if verbose is enabled."""
+        """Custom print that replaces consecutive Tree renders.
+
+        * If the argument is a single ``Tree`` instance, we either start a
+          ``Live`` session (first tree) or update the existing one (subsequent
+          trees). This results in the tree being rendered in-place instead of
+          being appended repeatedly to the log.
+
+        * A blank call (no positional arguments) is ignored while a Live
+          session is active so it does not prematurely terminate the tree
+          rendering.
+
+        * Any other renderable will terminate the Live session (if one is
+          active) so the last tree stays on screen and the new content is
+          printed normally.
+        """
+
+        # Case 1: updating / starting live Tree rendering
+        if len(args) == 1 and isinstance(args[0], Tree):
+            tree = args[0]
+
+            if not self._live:
+                # Start a new Live session for the first tree
+                self._live = Live(tree, console=self.console, refresh_per_second=4)
+                self._live.start()
+            else:
+                # Update existing Live session
+                self._live.update(tree, refresh=True)
+            return  # Nothing else to do
+
+        # Case 2: blank line while a live session is running – ignore so we
+        # don't break the in-place rendering behaviour
+        if len(args) == 0 and self._live:
+            return
+
+        # Case 3: printing something other than a Tree → terminate live session
+        if self._live:
+            self._live.stop()
+            self._live = None
+
+        # Finally, pass through to the regular Console.print implementation
         self.console.print(*args, **kwargs)
 
     def print_panel(
@@ -156,7 +203,7 @@ class ConsoleFormatter:
 
         task_content = Text()
         task_content.append(f"📋 Task: {task_id}", style="yellow bold")
-        task_content.append("\n   Status: ", style="white")
+        task_content.append("\nStatus: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 
         task_branch = None
@@ -196,11 +243,17 @@ class ConsoleFormatter:
         # Update tree label
         for branch in crew_tree.children:
             if str(task_id) in str(branch.label):
+                # Build label without introducing stray blank lines
                 task_content = Text()
+                # First line: Task ID
                 task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
-                task_content.append("\n   Assigned to: ", style="white")
+
+                # Second line: Assigned to
+                task_content.append("\nAssigned to: ", style="white")
                 task_content.append(agent_role, style=style)
-                task_content.append("\n   Status: ", style="white")
+
+                # Third line: Status
+                task_content.append("Status: ", style="white")
                 task_content.append(status_text, style=f"{style} bold")
                 branch.label = task_content
                 self.print(crew_tree)
@@ -219,18 +272,16 @@ class ConsoleFormatter:
         if not self.verbose or not task_branch or not crew_tree:
             return None
 
-        agent_branch = task_branch.add("")
-        self.update_tree_label(
-            agent_branch, "🤖 Agent:", agent_role, "green", "In Progress"
-        )
+        # Instead of creating a separate Agent node, we treat the task branch
+        # itself as the logical agent branch so that Reasoning/Tool nodes are
+        # nested under the task without an extra visual level.
 
-        self.print(crew_tree)
-        self.print()
+        # Store the task branch as the current_agent_branch for future nesting.
+        self.current_agent_branch = task_branch
 
-        # Set the current_agent_branch attribute directly
-        self.current_agent_branch = agent_branch
-
-        return agent_branch
+        # No additional tree modification needed; return the task branch so
+        # caller logic remains unchanged.
+        return task_branch
 
     def update_agent_status(
         self,
@@ -240,19 +291,10 @@ class ConsoleFormatter:
         status: str = "completed",
     ) -> None:
         """Update agent status in the tree."""
-        if not self.verbose or agent_branch is None or crew_tree is None:
-            return
-
-        self.update_tree_label(
-            agent_branch,
-            "🤖 Agent:",
-            agent_role,
-            "green",
-            "✅ Completed" if status == "completed" else "❌ Failed",
-        )
-
-        self.print(crew_tree)
-        self.print()
+        # We no longer render a separate agent branch, so this method simply
+        # updates the stored branch reference (already the task branch) without
+        # altering the tree. Keeping it a no-op avoids duplicate status lines.
+        return
 
     def create_flow_tree(self, flow_name: str, flow_id: str) -> Optional[Tree]:
         """Create and initialize a flow tree."""
@@ -265,7 +307,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\n    ID: ", style="white")
+        flow_label.append("\nID: ", style="white")
         flow_label.append(flow_id, style="blue")
 
         flow_tree = Tree(flow_label)
@@ -280,7 +322,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\n    ID: ", style="white")
+        flow_label.append("\nID: ", style="white")
         flow_label.append(flow_id, style="blue")
         flow_tree.label = flow_label
 
@@ -394,12 +436,22 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
+        # Parent for tool usage: LiteAgent > Agent > Task
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        # Render full crew tree when available for consistent live updates
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
 
         if branch_to_use is None or tree_to_use is None:
-            return None
+            # If we don't have a valid branch, default to crew_tree if provided
+            if crew_tree is not None:
+                branch_to_use = tree_to_use = crew_tree
+            else:
+                return None
 
         # Update tool usage count
         self.tool_usage_counts[tool_name] = self.tool_usage_counts.get(tool_name, 0) + 1
@@ -418,10 +470,9 @@ class ConsoleFormatter:
             "yellow",
         )
 
-        # Only print if this is a new tool usage
-        if tool_branch not in branch_to_use.children:
-            self.print(tree_to_use)
-            self.print()
+        # Print updated tree immediately
+        self.print(tree_to_use)
+        self.print()
 
         return tool_branch
 
@@ -435,8 +486,8 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
         if tree_to_use is None:
             return
 
@@ -467,8 +518,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
 
         if tool_branch:
             self.update_tree_label(
@@ -496,12 +547,22 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
+        # Parent for tool usage: LiteAgent > Agent > Task
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        # Render full crew tree when available for consistent live updates
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
 
         if branch_to_use is None or tree_to_use is None:
-            return None
+            # If we don't have a valid branch, default to crew_tree if provided
+            if crew_tree is not None:
+                branch_to_use = tree_to_use = crew_tree
+            else:
+                return None
 
         # Only add thinking status if we don't have a current tool branch
         if self.current_tool_branch is None:
@@ -523,24 +584,33 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None or tree_to_use is None:
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
+        if tree_to_use is None:
             return
 
-        # Remove the thinking status node when complete, but only if it exists
+        # Remove the thinking status node when complete
         if "Thinking" in str(tool_branch.label):
-            try:
-                # Check if the node is actually in the children list
-                if tool_branch in branch_to_use.children:
-                    branch_to_use.children.remove(tool_branch)
-                    self.print(tree_to_use)
-                    self.print()
-            except Exception:
-                # If any error occurs during removal, just continue without removing
-                pass
+            parents = [
+                self.current_lite_agent_branch,
+                self.current_agent_branch,
+                self.current_task_branch,
+                tree_to_use,
+            ]
+            removed = False
+            for parent in parents:
+                if isinstance(parent, Tree) and tool_branch in parent.children:
+                    parent.children.remove(tool_branch)
+                    removed = True
+                    break
+
+            # Clear pointer if we just removed the current_tool_branch
+            if self.current_tool_branch is tool_branch:
+                self.current_tool_branch = None
+
+            if removed:
+                self.print(tree_to_use)
+                self.print()
 
     def handle_llm_call_failed(
         self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -549,8 +619,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
 
         # Update tool branch if it exists
         if tool_branch:
@@ -592,7 +662,7 @@ class ConsoleFormatter:
         test_label = Text()
         test_label.append("🧪 Test: ", style="blue bold")
         test_label.append(crew_name or "Crew", style="blue")
-        test_label.append("\n    Status: ", style="white")
+        test_label.append("\nStatus: ", style="white")
         test_label.append("In Progress", style="yellow")
 
         test_tree = Tree(test_label)
@@ -614,7 +684,7 @@ class ConsoleFormatter:
             test_label = Text()
             test_label.append("✅ Test: ", style="green bold")
             test_label.append(crew_name or "Crew", style="green")
-            test_label.append("\n    Status: ", style="white")
+            test_label.append("\nStatus: ", style="white")
             test_label.append("Completed", style="green bold")
             flow_tree.label = test_label
 
@@ -703,7 +773,7 @@ class ConsoleFormatter:
             lite_agent_label = Text()
             lite_agent_label.append("🤖 LiteAgent: ", style="cyan bold")
             lite_agent_label.append(lite_agent_role, style="cyan")
-            lite_agent_label.append("\n    Status: ", style="white")
+            lite_agent_label.append("\nStatus: ", style="white")
             lite_agent_label.append("In Progress", style="yellow")
 
             lite_agent_tree = Tree(lite_agent_label)
@@ -742,7 +812,7 @@ class ConsoleFormatter:
         lite_agent_label = Text()
         lite_agent_label.append(f"{prefix} ", style=f"{style} bold")
         lite_agent_label.append(lite_agent_role, style=style)
-        lite_agent_label.append("\n    Status: ", style="white")
+        lite_agent_label.append("Status: ", style="white")
         lite_agent_label.append(status_text, style=f"{style} bold")
         lite_agent_branch.label = lite_agent_label
 
@@ -797,7 +867,7 @@ class ConsoleFormatter:
         tree_to_use = branch_to_use or crew_tree
 
         if branch_to_use is None or tree_to_use is None:
-            # If we don't have a valid branch, use crew_tree as the branch if available
+            # If we don't have a valid branch, default to crew_tree if provided
             if crew_tree is not None:
                 branch_to_use = tree_to_use = crew_tree
             else:
@@ -982,3 +1052,124 @@ class ConsoleFormatter:
             "Knowledge Search Failed", "Search Error", "red", Error=error
         )
         self.print_panel(error_content, "Search Error", "red")
+
+    # ----------- AGENT REASONING EVENTS -----------
+
+    def handle_reasoning_started(
+        self,
+        agent_branch: Optional[Tree],
+        attempt: int,
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
+        """Handle agent reasoning started (or refinement) event."""
+        if not self.verbose:
+            return None
+
+        # Prefer LiteAgent > Agent > Task branch as the parent for reasoning
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        # We always want to render the full crew tree when possible so the
+        # Live view updates coherently. Fallbacks: crew tree → branch itself.
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+
+        if branch_to_use is None:
+            # Nothing to attach to, abort
+            return None
+
+        # Reuse existing reasoning branch if present
+        reasoning_branch = self.current_reasoning_branch
+        if reasoning_branch is None:
+            reasoning_branch = branch_to_use.add("")
+            self.current_reasoning_branch = reasoning_branch
+
+        # Build label text depending on attempt
+        status_text = (
+            f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
+        )
+        self.update_tree_label(reasoning_branch, "🧠", status_text, "blue")
+
+        self.print(tree_to_use)
+        self.print()
+
+        return reasoning_branch
+
+    def handle_reasoning_completed(
+        self,
+        plan: str,
+        ready: bool,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        """Handle agent reasoning completed event."""
+        if not self.verbose:
+            return
+
+        reasoning_branch = self.current_reasoning_branch
+        tree_to_use = (
+            self.current_crew_tree
+            or self.current_lite_agent_branch
+            or self.current_task_branch
+            or crew_tree
+        )
+
+        style = "green" if ready else "yellow"
+        status_text = "Reasoning Completed" if ready else "Reasoning Completed (Not Ready)"
+
+        if reasoning_branch is not None:
+            self.update_tree_label(reasoning_branch, "✅", status_text, style)
+
+        if tree_to_use is not None:
+            self.print(tree_to_use)
+
+        # Show plan in a panel (trim very long plans)
+        if plan:
+            plan_panel = Panel(
+                Text(plan, style="white"),
+                title="🧠 Reasoning Plan",
+                border_style=style,
+                padding=(1, 2),
+            )
+            self.print(plan_panel)
+
+        self.print()
+
+        # Clear stored branch after completion
+        self.current_reasoning_branch = None
+
+    def handle_reasoning_failed(
+        self,
+        error: str,
+        crew_tree: Optional[Tree],
+    ) -> None:
+        """Handle agent reasoning failure event."""
+        if not self.verbose:
+            return
+
+        reasoning_branch = self.current_reasoning_branch
+        tree_to_use = (
+            self.current_crew_tree
+            or self.current_lite_agent_branch
+            or self.current_task_branch
+            or crew_tree
+        )
+
+        if reasoning_branch is not None:
+            self.update_tree_label(reasoning_branch, "❌", "Reasoning Failed", "red")
+
+        if tree_to_use is not None:
+            self.print(tree_to_use)
+
+        # Error panel
+        error_content = self.create_status_content(
+            "Reasoning Failed",
+            "Error",
+            "red",
+            Error=error,
+        )
+        self.print_panel(error_content, "Reasoning Error", "red")
+
+        # Clear stored branch after failure
+        self.current_reasoning_branch = None

src/crewai/utilities/reasoning_handler.py

@@ -0,0 +1,387 @@
+import logging
+import json
+from typing import Tuple, cast
+
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.utilities import I18N
+from crewai.llm import LLM
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.reasoning_events import (
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+)
+
+
+class ReasoningPlan(BaseModel):
+    """Model representing a reasoning plan for a task."""
+    plan: str = Field(description="The detailed reasoning plan for the task.")
+    ready: bool = Field(description="Whether the agent is ready to execute the task.")
+
+
+class AgentReasoningOutput(BaseModel):
+    """Model representing the output of the agent reasoning process."""
+    plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
+
+
+class ReasoningFunction(BaseModel):
+    """Model for function calling with reasoning."""
+    plan: str = Field(description="The detailed reasoning plan for the task.")
+    ready: bool = Field(description="Whether the agent is ready to execute the task.")
+
+
+class AgentReasoning:
+    """
+    Handles the agent reasoning process, enabling an agent to reflect and create a plan
+    before executing a task.
+    """
+    def __init__(self, task: Task, agent: Agent):
+        if not task or not agent:
+            raise ValueError("Both task and agent must be provided.")
+        self.task = task
+        self.agent = agent
+        self.llm = cast(LLM, agent.llm)
+        self.logger = logging.getLogger(__name__)
+        self.i18n = I18N()
+
+    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.
+
+        Returns:
+            AgentReasoningOutput: The output of the agent reasoning process.
+        """
+        # Emit a reasoning started event (attempt 1)
+        try:
+            crewai_event_bus.emit(
+                self.agent,
+                AgentReasoningStartedEvent(
+                    agent_role=self.agent.role,
+                    task_id=str(self.task.id),
+                    attempt=1,
+                ),
+            )
+        except Exception:
+            # Ignore event bus errors to avoid breaking execution
+            pass
+
+        try:
+            output = self.__handle_agent_reasoning()
+
+            # Emit reasoning completed event
+            try:
+                crewai_event_bus.emit(
+                    self.agent,
+                    AgentReasoningCompletedEvent(
+                        agent_role=self.agent.role,
+                        task_id=str(self.task.id),
+                        plan=output.plan.plan,
+                        ready=output.plan.ready,
+                        attempt=1,
+                    ),
+                )
+            except Exception:
+                pass
+
+            return output
+        except Exception as e:
+            # Emit reasoning failed event
+            try:
+                crewai_event_bus.emit(
+                    self.agent,
+                    AgentReasoningFailedEvent(
+                        agent_role=self.agent.role,
+                        task_id=str(self.task.id),
+                        error=str(e),
+                        attempt=1,
+                    ),
+                )
+            except Exception:
+                pass
+
+            raise
+
+    def __handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """
+        Private method that handles the agent reasoning process.
+
+        Returns:
+            AgentReasoningOutput: The output of the agent reasoning process.
+        """
+        plan, ready = self.__create_initial_plan()
+
+        plan, ready = self.__refine_plan_if_needed(plan, ready)
+
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
+        return AgentReasoningOutput(plan=reasoning_plan)
+
+    def __create_initial_plan(self) -> Tuple[str, bool]:
+        """
+        Creates the initial reasoning plan for the task.
+
+        Returns:
+            Tuple[str, bool]: The initial plan and whether the agent is ready to execute the task.
+        """
+        reasoning_prompt = self.__create_reasoning_prompt()
+
+        if self.llm.supports_function_calling():
+            plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
+            return plan, ready
+        else:
+            system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory()
+            )
+
+            response = self.llm.call(
+                [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": reasoning_prompt}
+                ]
+            )
+
+            return self.__parse_reasoning_response(str(response))
+
+    def __refine_plan_if_needed(self, plan: str, ready: bool) -> Tuple[str, bool]:
+        """
+        Refines the reasoning plan if the agent is not ready to execute the task.
+
+        Args:
+            plan: The current reasoning plan.
+            ready: Whether the agent is ready to execute the task.
+
+        Returns:
+            Tuple[str, bool]: The refined plan and whether the agent is ready to execute the task.
+        """
+        attempt = 1
+        max_attempts = self.agent.max_reasoning_attempts
+
+        while not ready and (max_attempts is None or attempt < max_attempts):
+            # Emit event for each refinement attempt
+            try:
+                crewai_event_bus.emit(
+                    self.agent,
+                    AgentReasoningStartedEvent(
+                        agent_role=self.agent.role,
+                        task_id=str(self.task.id),
+                        attempt=attempt + 1,
+                    ),
+                )
+            except Exception:
+                pass
+
+            refine_prompt = self.__create_refine_prompt(plan)
+
+            if self.llm.supports_function_calling():
+                plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
+            else:
+                system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
+                    role=self.agent.role,
+                    goal=self.agent.goal,
+                    backstory=self.__get_agent_backstory()
+                )
+
+                response = self.llm.call(
+                    [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": refine_prompt}
+                    ]
+                )
+                plan, ready = self.__parse_reasoning_response(str(response))
+
+            attempt += 1
+
+            if max_attempts is not None and attempt >= max_attempts:
+                self.logger.warning(
+                    f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
+                )
+                break
+
+        return plan, 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.
+
+        Args:
+            prompt: The prompt to send to the LLM.
+            prompt_type: The type of prompt (initial_plan or refine_plan).
+
+        Returns:
+            Tuple[str, bool]: A tuple containing the plan and whether the agent is ready.
+        """
+        self.logger.debug(f"Using function calling for {prompt_type} reasoning")
+
+        function_schema = {
+            "type": "function",
+            "function": {
+                "name": "create_reasoning_plan",
+                "description": "Create or refine a reasoning plan for a task",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "plan": {
+                            "type": "string",
+                            "description": "The detailed reasoning plan for the task."
+                        },
+                        "ready": {
+                            "type": "boolean",
+                            "description": "Whether the agent is ready to execute the task."
+                        }
+                    },
+                    "required": ["plan", "ready"]
+                }
+            }
+        }
+
+        try:
+            system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory()
+            )
+
+            # Prepare a simple callable that just returns the tool arguments as JSON
+            def _create_reasoning_plan(plan: str, ready: bool):  # noqa: N802
+                """Return the reasoning plan result in JSON string form."""
+                return json.dumps({"plan": plan, "ready": ready})
+
+            response = self.llm.call(
+                [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ],
+                tools=[function_schema],
+                available_functions={"create_reasoning_plan": _create_reasoning_plan},
+            )
+
+            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"]
+            except (json.JSONDecodeError, KeyError):
+                pass
+
+            response_str = str(response)
+            return response_str, "READY: I am ready to execute the task." in response_str
+
+        except Exception as e:
+            self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
+
+            try:
+                system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+                    role=self.agent.role,
+                    goal=self.agent.goal,
+                    backstory=self.__get_agent_backstory()
+                )
+
+                fallback_response = self.llm.call(
+                    [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": prompt}
+                    ]
+                )
+
+                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: {str(inner_e)}")
+                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.
+
+        Returns:
+            str: 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.
+
+        Returns:
+            str: The reasoning prompt.
+        """
+        available_tools = self.__format_available_tools()
+
+        return self.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
+        )
+
+    def __format_available_tools(self) -> str:
+        """
+        Formats the available tools for inclusion in the prompt.
+
+        Returns:
+            str: Comma-separated list of tool names.
+        """
+        try:
+            return ', '.join([tool.name for tool in (self.task.tools or [])])
+        except (AttributeError, TypeError):
+            return "No tools available"
+
+    def __create_refine_prompt(self, current_plan: str) -> str:
+        """
+        Creates a prompt for the agent to refine its reasoning plan.
+
+        Args:
+            current_plan: The current reasoning plan.
+
+        Returns:
+            str: The refine prompt.
+        """
+        return self.i18n.retrieve("reasoning", "refine_plan_prompt").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory(),
+            current_plan=current_plan
+        )
+
+    def __parse_reasoning_response(self, response: str) -> Tuple[str, bool]:
+        """
+        Parses the reasoning response to extract the plan and whether
+        the agent is ready to execute the task.
+
+        Args:
+            response: The LLM response.
+
+        Returns:
+            Tuple[str, bool]: The plan and whether the agent is ready to execute the task.
+        """
+        if not response:
+            return "No plan was generated.", False
+
+        plan = response
+        ready = False
+
+        if "READY: I am ready to execute the task." in response:
+            ready = True
+
+        return plan, ready
+
+    def _handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """
+        Deprecated method for backward compatibility.
+        Use handle_agent_reasoning() instead.
+
+        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()

tests/agent_reasoning_test.py

@@ -0,0 +1,261 @@
+"""Tests for reasoning in agents."""
+
+import json
+import pytest
+
+from crewai import Agent, Task
+from crewai.llm import LLM
+from crewai.utilities.reasoning_handler import AgentReasoning
+
+
+@pytest.fixture
+def mock_llm_responses():
+    """Fixture for mock LLM responses."""
+    return {
+        "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
+        "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
+        "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
+        "execution": "4"
+    }
+
+
+def test_agent_with_reasoning(mock_llm_responses):
+    """Test agent with reasoning."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent
+    )
+    
+    agent.llm.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
+
+
+def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
+    """Test agent with reasoning that requires refinement."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="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,
+        max_reasoning_attempts=2,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Complex math task: What's the derivative of x²?",
+        expected_output="The answer should be a mathematical expression.",
+        agent=agent
+    )
+    
+    call_count = [0]
+    
+    def mock_llm_call(messages, *args, **kwargs):
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            if call_count[0] == 1:
+                return mock_llm_responses["not_ready"]
+            else:
+                return mock_llm_responses["ready_after_refine"]
+        else:
+            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
+
+
+def test_agent_with_reasoning_max_attempts_reached():
+    """Test agent with reasoning that reaches max attempts without being ready."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="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,
+        max_reasoning_attempts=2,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Complex math task: Solve the Riemann hypothesis.",
+        expected_output="A proof or disproof of the hypothesis.",
+        agent=agent
+    )
+    
+    call_count = [0]
+    
+    def mock_llm_call(messages, *args, **kwargs):
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
+        else:
+            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
+
+
+def test_agent_reasoning_input_validation():
+    """Test input validation in AgentReasoning."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True
+    )
+    
+    with pytest.raises(ValueError, match="Both task and agent must be provided"):
+        AgentReasoning(task=None, agent=agent)
+    
+    task = Task(
+        description="Simple task",
+        expected_output="Simple output"
+    )
+    with pytest.raises(ValueError, match="Both task and agent must be provided"):
+        AgentReasoning(task=task, agent=None)
+
+
+def test_agent_reasoning_error_handling():
+    """Test error handling during the reasoning process."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True
+    )
+    
+    task = Task(
+        description="Task that will cause an error",
+        expected_output="Output that will never be generated",
+        agent=agent
+    )
+    
+    call_count = [0]
+    
+    def mock_llm_call_error(*args, **kwargs):
+        call_count[0] += 1
+        if call_count[0] <= 2:  # First calls are for reasoning
+            raise Exception("LLM error during reasoning")
+        return "Fallback execution result"  # Return a value for task execution
+    
+    agent.llm.call = mock_llm_call_error
+    
+    result = agent.execute_task(task)
+    
+    assert result == "Fallback execution result"
+    assert call_count[0] > 2  # Ensure we called the mock multiple times
+
+
+def test_agent_with_function_calling():
+    """Test agent with reasoning using function calling."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent
+    )
+    
+    agent.llm.supports_function_calling = lambda: True
+    
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return json.dumps({
+                "plan": "I'll solve this simple math problem: 2+2=4.",
+                "ready": True
+            })
+        else:
+            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
+
+
+def test_agent_with_function_calling_fallback():
+    """Test agent with reasoning using function calling that falls back to text parsing."""
+    llm = LLM("gpt-3.5-turbo")
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent
+    )
+    
+    agent.llm.supports_function_calling = lambda: True
+    
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
+        else:
+            return "4"
+    
+    agent.llm.call = mock_function_call
+    
+    result = agent.execute_task(task)
+    
+    assert result == "4"
+    assert "Reasoning Plan:" in task.description
+    assert "Invalid JSON that will trigger fallback" in task.description

tests/test_markdown_task.py

@@ -0,0 +1,96 @@
+"""Test the markdown attribute in Task class."""
+
+import pytest
+from pydantic import BaseModel
+
+from crewai import Agent, Task
+
+
+@pytest.mark.parametrize(
+    "markdown_enabled,should_contain_instructions",
+    [
+        (True, True),
+        (False, False),
+    ],
+)
+def test_markdown_option_in_task_prompt(markdown_enabled, should_contain_instructions):
+    """Test that markdown flag correctly controls the inclusion of markdown formatting instructions."""
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher specialized in providing well-formatted content.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Research advances in AI in 2023",
+        expected_output="A summary of key AI advances in 2023",
+        markdown=markdown_enabled,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert "Research advances in AI in 2023" in prompt
+    assert "A summary of key AI advances in 2023" in prompt
+    
+    if should_contain_instructions:
+        assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+        assert "Use # for headers" in prompt
+        assert "Use ** for bold text" in prompt
+    else:
+        assert "Your final answer MUST be formatted in Markdown syntax." not in prompt
+
+
+def test_markdown_with_empty_description():
+    """Test markdown formatting with empty description."""
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="",
+        expected_output="A summary",
+        markdown=True,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert prompt.strip() != ""
+    assert "A summary" in prompt
+    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+
+
+def test_markdown_with_complex_output_format():
+    """Test markdown with JSON output format to ensure compatibility."""
+    
+    class ResearchOutput(BaseModel):
+        title: str
+        findings: list[str]
+    
+    researcher = Agent(
+        role="Researcher",
+        goal="Research a topic",
+        backstory="You're a researcher.",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Research topic",
+        expected_output="Research results",
+        markdown=True,
+        output_json=ResearchOutput,
+        agent=researcher,
+    )
+
+    prompt = task.prompt()
+    
+    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
+    assert "Research topic" in prompt
+    assert "Research results" in prompt

tests/utilities/test_converter.py

@@ -1,5 +1,4 @@
 import json
-import os
 from typing import Dict, List, Optional
 from unittest.mock import MagicMock, Mock, patch
 
@@ -19,6 +18,8 @@ from crewai.utilities.converter import (
     validate_model,
 )
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+# Tests for enums
+from enum import Enum
 
 
 @pytest.fixture(scope="module")
@@ -359,7 +360,7 @@ def test_convert_with_instructions():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_2_model():
-    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
+    llm = LLM(model="openrouter/meta-llama/llama-3.2-3b-instruct")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
@@ -431,7 +432,7 @@ def test_converter_error_handling():
     )
 
     with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
+        converter.to_pydantic()
 
     assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
 
@@ -515,10 +516,6 @@ def test_converter_with_list_field():
     assert output.items == [1, 2, 3]
 
 
-# Tests for enums
-from enum import Enum
-
-
 def test_converter_with_enum():
     class Color(Enum):
         RED = "red"
@@ -565,7 +562,7 @@ def test_converter_with_ambiguous_input():
     )
 
     with pytest.raises(ConverterError) as exc_info:
-        output = converter.to_pydantic()
+        converter.to_pydantic()
 
     assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
 

uv.lock

@@ -738,7 +738,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.120.0"
+version = "0.120.1"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },