Add reasoning attribute to Agent class (#2866)

* Add reasoning attribute to Agent class

Co-Authored-By: Joe Moura <joao@crewai.com>

* Address PR feedback: improve type hints, error handling, refactor reasoning handler, and enhance tests and docs

Co-Authored-By: Joe Moura <joao@crewai.com>

* Implement function calling for reasoning and move prompts to translations

Co-Authored-By: Joe Moura <joao@crewai.com>

* Simplify function calling implementation with better error handling

Co-Authored-By: Joe Moura <joao@crewai.com>

* Enhance system prompts to leverage agent context (role, goal, backstory)

Co-Authored-By: Joe Moura <joao@crewai.com>

* Fix lint and type-checker issues

Co-Authored-By: Joe Moura <joao@crewai.com>

* Enhance system prompts to better leverage agent context

Co-Authored-By: Joe Moura <joao@crewai.com>

* Fix backstory access in reasoning handler for Python 3.12 compatibility

Co-Authored-By: Joe Moura <joao@crewai.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>

docs/concepts/reasoning.mdx

@@ -0,0 +1,140 @@
+---
+title: "Agent Reasoning"
+---
+
+# Agent Reasoning
+
+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.
+
+## How to Use Agent Reasoning
+
+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
+
+- `reasoning` (bool): Enable or disable reasoning (default: False)
+- `max_reasoning_attempts` (int, optional): Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready.
+
+## 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.