Update LiteAgent documentation for clarity and consistency; replace WebsiteSearchTool with SerperDevTool, and improve formatting in examples.

docs/concepts/lite-agent.mdx

@@ -9,13 +9,15 @@ icon: feather
 A `LiteAgent` is a streamlined version of CrewAI's Agent, designed for simpler, standalone tasks that don't require the full complexity of a crew-based workflow. It's perfect for quick automations, single-purpose tasks, or when you need a lightweight solution.
 
 <Tip>
-Think of a LiteAgent as a specialized worker that excels at individual tasks. While regular Agents are team players in a crew, LiteAgents are solo performers optimized for specific operations.
+  Think of a LiteAgent as a specialized worker that excels at individual tasks.
+  While regular Agents are team players in a crew, LiteAgents are solo
+  performers optimized for specific operations.
 </Tip>
 
 ## LiteAgent Attributes
 
 | Attribute                        | Parameter         | Type                   | Description                                                     |
-| :------------------------------------ | :----------------- | :---------------------- | :--------------------------------------------------------------------------------------------- |
+| :------------------------------- | :---------------- | :--------------------- | :-------------------------------------------------------------- |
 | **Role**                         | `role`            | `str`                  | Defines the agent's function and expertise.                     |
 | **Goal**                         | `goal`            | `str`                  | The specific objective that guides the agent's actions.         |
 | **Backstory**                    | `backstory`       | `str`                  | Provides context and personality to the agent.                  |
@@ -31,7 +33,7 @@ Here's a simple example of creating and using a standalone LiteAgent:
 ```python
 from typing import List, cast
 
-from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
+from crewai_tools import SerperDevTool
 from pydantic import BaseModel, Field
 
 from crewai.lite_agent import LiteAgent
@@ -50,7 +52,7 @@ critic = LiteAgent(
     role="Movie Critic",
     goal="Provide insightful movie reviews",
     backstory="You are an experienced film critic known for balanced, thoughtful reviews.",
-    tools=[WebsiteSearchTool()],
+    tools=[SerperDevTool()],
     verbose=True,
     response_format=MovieReview,
 )
@@ -65,6 +67,7 @@ Review the movie 'Inception'. Include:
 
 result = critic.kickoff(query)
 
+
 # Access the structured output
 review = cast(MovieReview, result.pydantic)
 print(f"\nMovie Review: {review.title}")
@@ -79,6 +82,7 @@ for con in review.cons:
 ```
 
 This example demonstrates the core features of a LiteAgent:
+
 - Structured output using Pydantic models
 - Tool integration with WebSearchTool
 - Simple execution with `kickoff()`
@@ -88,7 +92,7 @@ This example demonstrates the core features of a LiteAgent:
 
 For more complex scenarios, you can integrate LiteAgents into a Flow. Here's an example of a market research flow:
 
-```python
+````python
 from typing import List
 from pydantic import BaseModel, Field
 from crewai.flow.flow import Flow, start, listen
@@ -188,9 +192,10 @@ agent = LiteAgent(
     tools=[SerperDevTool(), CalculatorTool()],
     verbose=True
 )
-```
+````
 
 ### 4. Async Support
+
 LiteAgents support asynchronous execution through the `kickoff_async` method, making them suitable for non-blocking operations in your application.
 
 ## Response Formatting
@@ -224,6 +229,7 @@ print(f"Relevance: {result.pydantic.relevance_score}")
 When using `response_format`, the agent's response will be available in two forms:
 
 1. **Raw Response**: Access the unstructured string response
+
    ```python
    result = await agent.kickoff_async("Analyze the market")
    print(result.raw)  # Original LLM response
@@ -234,4 +240,3 @@ When using `response_format`, the agent's response will be available in two form
    print(result.pydantic)  # Parsed response as Pydantic model
    print(result.pydantic.dict())  # Convert to dictionary
    ```
-