Merge pull request #473 from emmanuel-ferdman/main

Resolve Python Logger warnings

.gitignore

@@ -1,3 +1,4 @@
+workspace.code-workspace
 .vscode
 crewai
 .cache
@@ -66,4 +67,6 @@ agents/praisonaiagents/build
 agents/praisonaiagents/dist
 agents/praisonaiagents/praisonaiagents.egg-info
 .codegpt
-.praison
+.praison
+# Local Netlify folder
+.netlify

README.md

@@ -20,7 +20,7 @@
 
 </div>
 
-PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AutoGen, and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
+PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
 
 <div align="center">
   <a href="https://docs.praison.ai">
@@ -548,7 +548,7 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
 
 ## Other Features
 
-- 🔄 Use CrewAI or AutoGen Framework
+- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
 - 💻 Chat with ENTIRE Codebase
 - 🎨 Interactive UIs
 - 📄 YAML-based Configuration

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.76 gunicorn markdown
+RUN pip install flask praisonai==2.1.1 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api-reference/index.mdx

@@ -30,7 +30,7 @@ Features:
 - Built-in tools
 - Structured workflows
 
-### AutoGen Integration
+### AG2 (Formerly AutoGen) Integration
 Requires installation with:
 ```bash
 pip install "praisonai[autogen]"

docs/api.mdx

@@ -23,7 +23,7 @@ from praisonai.auto import AutoGenerator
 ### praisonai.agents_generator
 Framework-specific agent generation and orchestration:
 - CrewAI support (requires `praisonai[crewai]`)
-- AutoGen support (requires `praisonai[autogen]`)
+- AG2 (Formerly AutoGen) support (requires `praisonai[autogen]`)
 
 ```python
 from praisonai.agents_generator import AgentsGenerator
@@ -51,7 +51,7 @@ pip install praisonai
 
 # Framework-specific installations
 pip install "praisonai[crewai]"    # Install with CrewAI support
-pip install "praisonai[autogen]"   # Install with AutoGen support
+pip install "praisonai[autogen]"   # Install with AG2 support
 pip install "praisonai[crewai,autogen]"  # Install both frameworks
 
 # Additional features
@@ -71,9 +71,9 @@ When installing with `pip install "praisonai[crewai]"`, you get:
 - Task delegation capabilities
 - Sequential and parallel task execution
 
-### AutoGen
+### AG2 (Formerly AutoGen)
 When installing with `pip install "praisonai[autogen]"`, you get:
-- AutoGen framework support
+- AG2 framework support
 - PraisonAI tools integration
 - Multi-agent conversation capabilities
 - Code execution environment

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.76 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.1.1 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/course/agents/01-introduction.mdx

@@ -0,0 +1,71 @@
+---
+title: "Introduction to AI Agents"
+description: "Welcome to the beginner's course on AI Agents"
+icon: "book-open"
+---
+
+# Introduction to AI Agents
+
+Welcome to the beginners' course on AI Agents! This course is designed to help you understand and build your own AI agents, even if you have little to no programming experience.
+
+## What Are AI Agents?
+
+An AI agent is a software program that can:
+- Understand information from its environment
+- Make decisions based on that information
+- Take actions to achieve specific goals
+
+Think of an AI agent like a helpful assistant that can perform tasks for you automatically.
+
+```mermaid
+graph TD
+    A[Perceive Information] --> B[Make Decisions]
+    B --> C[Take Actions]
+    C --> A
+```
+
+## Examples in Daily Life
+
+You might already be using AI agents without realizing it:
+
+- **Virtual Assistants**: Siri, Alexa, and Google Assistant are AI agents that respond to your voice commands
+- **Recommendation Systems**: Netflix, YouTube, and Amazon use AI agents to suggest content you might like
+- **Smart Home Devices**: Devices that adjust temperature or lighting based on your preferences
+
+## What You'll Learn in This Course
+
+Throughout this course, you will:
+
+1. Understand the basic concepts of AI agents
+2. Learn about different types of AI agents
+3. Explore how AI agents make decisions
+4. Discover how to build simple AI agents without complex programming
+5. Create your own functional AI agents
+
+## Who This Course Is For
+
+This course is perfect for:
+- Complete beginners with no prior knowledge of AI
+- Non-technical professionals interested in how AI can solve problems
+- Students curious about artificial intelligence
+- Anyone who wants to understand and use AI agents in practical ways
+
+Let's begin our journey into the fascinating world of AI agents!
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="600px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
+<IframeWithAutoHeight />
+
+<CardGroup cols={1}>
+  <Card title="Ready to Start?" icon="rocket">
+    Continue to the next lesson to learn about the different types of AI agents.
+  </Card>
+</CardGroup>

docs/course/agents/02-types-of-agents.mdx

@@ -0,0 +1,113 @@
+---
+title: "Types of AI Agents"
+description: "Understanding the different categories of AI agents"
+icon: "sitemap"
+---
+
+# Types of AI Agents
+
+AI agents come in different forms, each designed for specific purposes and with varying levels of complexity. Understanding these types will help you choose the right approach for your needs.
+
+## Simple Reflex Agents
+
+These are the most basic type of AI agents that operate using simple if-then rules.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="lightbulb">
+    - React directly to current input only
+    - No memory of past events
+    - No prediction of future states
+    - Use predefined rules to determine actions
+  </Card>
+</CardGroup>
+
+**Real-world example**: A basic thermostat that turns heating on when temperature drops below a set point.
+
+```mermaid
+graph LR
+    A[Sensor Input] --> B{If-Then Rules}
+    B --> C[Action]
+```
+
+## Model-Based Agents
+
+These agents maintain an internal model of their environment to make better decisions.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="brain">
+    - Build and maintain an internal representation of the world
+    - Can work with partial information
+    - Can consider "what if" scenarios
+    - Make decisions based on their understanding of how the world works
+  </Card>
+</CardGroup>
+
+**Real-world example**: A GPS navigation system that models roads and traffic conditions.
+
+## Goal-Based Agents
+
+These agents make decisions based on how their actions will help achieve specific goals.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="bullseye">
+    - Consider future consequences of actions
+    - Evaluate which actions will lead toward goals
+    - Can plan sequences of actions
+    - Choose actions that maximize goal attainment
+  </Card>
+</CardGroup>
+
+**Real-world example**: A chess-playing AI that plans moves to achieve checkmate.
+
+## Utility-Based Agents
+
+These agents choose actions that maximize a specific utility (or happiness) function.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="chart-line">
+    - Assign a utility value to different possible outcomes
+    - Consider probability and risk
+    - Choose actions that maximize expected utility
+    - Can balance competing objectives
+  </Card>
+</CardGroup>
+
+**Real-world example**: An investment robo-advisor that balances risk and return.
+
+## Learning Agents
+
+These agents improve their performance over time through experience.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="graduation-cap">
+    - Learn from past experiences
+    - Adapt to changing environments
+    - Improve decision-making over time
+    - Can discover new strategies
+  </Card>
+</CardGroup>
+
+**Real-world example**: Recommendation systems that learn your preferences over time.
+
+## Understanding Agent Complexity
+
+<Tip>
+As you move from Simple Reflex Agents toward Learning Agents, both capability and complexity increase. For beginners, starting with simpler agent types is often best.
+</Tip>
+
+## Which Agent Type Should You Build?
+
+The best type of agent for your project depends on:
+
+1. **The problem complexity**: Simple problems may only need reflex agents
+2. **Available data**: Learning agents need training data
+3. **Required adaptability**: Will your agent need to adapt to new situations?
+4. **Available resources**: More complex agents require more computational power
+
+In this course, we'll focus primarily on goal-based and simple learning agents as they provide a good balance of capability and complexity for beginners.
+
+<CardGroup cols={1}>
+  <Card title="Next Steps" icon="arrow-right">
+    In the next lesson, we'll explore how agents process information and make decisions.
+  </Card>
+</CardGroup>

docs/course/agents/03-agent-architecture.mdx

@@ -0,0 +1,157 @@
+---
+title: "Agent Architecture"
+description: "Understanding how AI agents are structured"
+icon: "layer-group"
+---
+
+# Agent Architecture
+
+Understanding how AI agents are structured will help you build more effective agents. This lesson covers the fundamental components of an agent's architecture.
+
+## Basic Components of an AI Agent
+
+Every AI agent, regardless of complexity, has these basic components:
+
+```mermaid
+graph TD
+    A[Sensors/Input] --> B[Processing Unit]
+    B --> C[Decision-Making]
+    C --> D[Actions/Output]
+```
+
+### 1. Input (Sensors)
+
+This is how agents receive information from their environment.
+
+<CardGroup cols={1}>
+  <Card title="Examples of Input" icon="eye">
+    - Text input from users
+    - Data from databases
+    - Image or audio input
+    - API responses
+    - Sensor readings (in physical agents)
+  </Card>
+</CardGroup>
+
+### 2. Processing Unit
+
+This component processes information and converts it into a format the agent can understand.
+
+<CardGroup cols={1}>
+  <Card title="Processing Functions" icon="gears">
+    - Data cleaning and transformation
+    - Feature extraction
+    - Context building
+    - Information retrieval
+    - Pattern recognition
+  </Card>
+</CardGroup>
+
+### 3. Decision-Making Core
+
+The "brain" of the agent that determines what actions to take.
+
+<CardGroup cols={1}>
+  <Card title="Decision Components" icon="brain">
+    - Language models (like GPT-4)
+    - Rule systems
+    - Planning algorithms
+    - Knowledge base
+    - Memory systems
+  </Card>
+</CardGroup>
+
+### 4. Output (Actions)
+
+The actions the agent can perform to achieve its goals.
+
+<CardGroup cols={1}>
+  <Card title="Action Examples" icon="hand">
+    - Generating text responses
+    - Creating visual content
+    - Making API calls
+    - Controlling other systems
+    - Updating databases
+  </Card>
+</CardGroup>
+
+## The Agent Loop
+
+Agents operate in a continuous loop:
+
+```mermaid
+graph LR
+    A[Perceive] --> B[Process]
+    B --> C[Decide]
+    C --> D[Act]
+    D --> A
+```
+
+This cycle allows agents to continuously:
+1. Gather information
+2. Update their understanding
+3. Make new decisions
+4. Take appropriate actions
+
+## PraisonAI Agent Architecture
+
+In the PraisonAI framework, agents follow a specific architecture:
+
+<CardGroup cols={1}>
+  <Card title="PraisonAI Agent Components" icon="puzzle-piece">
+    - **Instructions**: Defines the agent's purpose and behavior
+    - **Language Model**: Powers the agent's intelligence (e.g., GPT-4)
+    - **Memory**: Stores context and previous interactions
+    - **Tools**: Specialized capabilities an agent can use
+  </Card>
+</CardGroup>
+
+### Simple Agent Structure
+
+```python
+from praisonaiagents import Agent
+
+# Create a simple agent
+research_agent = Agent(
+    instructions="Research the latest developments in renewable energy",
+    name="ResearchAgent"
+)
+
+# Start the agent
+research_agent.start()
+```
+
+## Understanding Agent Communication
+
+Multi-agent systems allow agents to communicate with each other:
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+    C --> A
+```
+
+Each agent can:
+- Pass information to other agents
+- Request assistance from specialized agents
+- Collaborate on complex tasks
+
+## Key Takeaways
+
+<CardGroup cols={2}>
+  <Card title="Component Importance" icon="check">
+    Each component plays a vital role in the agent's functionality
+  </Card>
+  <Card title="Agent Customization" icon="gear">
+    You can customize each component based on your specific needs
+  </Card>
+  <Card title="Component Balance" icon="scale-balanced">
+    A well-designed agent balances all components effectively
+  </Card>
+  <Card title="Continuous Improvement" icon="arrow-trend-up">
+    Agents can be improved by enhancing individual components
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to define effective instructions for your AI agents.

docs/course/agents/04-agent-instructions.mdx

@@ -0,0 +1,187 @@
+---
+title: "Creating Effective Agent Instructions"
+description: "How to write clear instructions for your AI agents"
+icon: "file-lines"
+---
+
+# Creating Effective Agent Instructions
+
+The instructions you give to your AI agent are crucial to its performance. Well-crafted instructions lead to agents that understand their purpose and execute tasks effectively.
+
+## Why Instructions Matter
+
+Instructions serve as the agent's "job description" and guide its behavior:
+
+<CardGroup cols={2}>
+  <Card title="Clarity" icon="glasses">
+    Clear instructions reduce confusion and misinterpretation
+  </Card>
+  <Card title="Focus" icon="bullseye">
+    Specific instructions keep the agent on task
+  </Card>
+  <Card title="Boundaries" icon="border-all">
+    Good instructions define what an agent should and shouldn't do
+  </Card>
+  <Card title="Personality" icon="face-smile">
+    Instructions can shape how an agent communicates
+  </Card>
+</CardGroup>
+
+## Elements of Effective Instructions
+
+### 1. Role Definition
+
+Tell the agent who it is and what its expertise should be.
+
+<CodeGroup>
+```text Good Example
+You are a financial analyst with expertise in market trends and investment strategies.
+```
+
+```text Weak Example
+You are a helper.
+```
+</CodeGroup>
+
+### 2. Purpose and Goals
+
+Clearly state what the agent should accomplish.
+
+<CodeGroup>
+```text Good Example
+Your goal is to analyze company quarterly reports and provide actionable investment insights.
+```
+
+```text Weak Example
+Help with finance stuff.
+```
+</CodeGroup>
+
+### 3. Process Guidelines
+
+Explain how the agent should approach tasks.
+
+<CodeGroup>
+```text Good Example
+First analyze the data trends, then identify key risk factors, and finally make recommendations based on the client's risk tolerance.
+```
+
+```text Weak Example
+Just do a good analysis.
+```
+</CodeGroup>
+
+### 4. Response Format
+
+Specify how you want information presented.
+
+<CodeGroup>
+```text Good Example
+Present your analysis in a structured format with: Summary, Key Findings, Risk Assessment, and Recommendations.
+```
+
+```text Weak Example
+Tell me what you find.
+```
+</CodeGroup>
+
+## Instructions Template
+
+Here's a template you can use to create effective agent instructions:
+
+```
+You are a [ROLE] specializing in [EXPERTISE AREAS].
+
+Your goal is to [MAIN OBJECTIVE].
+
+When approaching tasks:
+1. [FIRST STEP/APPROACH]
+2. [SECOND STEP/APPROACH]
+3. [THIRD STEP/APPROACH]
+
+Present your results in [FORMAT PREFERENCES].
+
+Additional guidelines:
+- [SPECIFIC BEHAVIOR 1]
+- [SPECIFIC BEHAVIOR 2]
+- [SPECIFIC BEHAVIOR 3]
+```
+
+## Examples in PraisonAI
+
+Let's look at how instructions are implemented in PraisonAI:
+
+<CodeGroup>
+```python Simple Example
+from praisonaiagents import Agent
+
+research_agent = Agent(
+    instructions="You are a research assistant specializing in climate science. Your goal is to find the latest research on rising sea levels and summarize key findings in bullet points."
+)
+
+research_agent.start()
+```
+
+```python Detailed Example
+from praisonaiagents import Agent
+
+content_agent = Agent(
+    name="ContentCreator",
+    instructions="""
+    You are a content creation specialist with expertise in digital marketing.
+    
+    Your goal is to create engaging blog content that drives traffic and conversions.
+    
+    When creating content:
+    1. Research the target keyword thoroughly
+    2. Create an attention-grabbing headline
+    3. Develop well-structured content with clear sections
+    4. Include actionable takeaways for readers
+    
+    Present content in markdown format with proper headings, bullet points, and emphasis where appropriate.
+    
+    Additional guidelines:
+    - Maintain a conversational but professional tone
+    - Include 2-3 relevant statistics to support key points
+    - Add a call-to-action at the end of each piece
+    """
+)
+
+content_agent.start("Create a blog post about sustainable living tips")
+```
+</CodeGroup>
+
+## Common Instruction Mistakes
+
+<CardGroup cols={2}>
+  <Card title="Being Too Vague" icon="cloud">
+    Vague instructions lead to unpredictable outputs
+  </Card>
+  <Card title="Information Overload" icon="bomb">
+    Too many details can confuse the agent's priorities
+  </Card>
+  <Card title="Contradictory Guidelines" icon="arrows-split-up-and-left">
+    Conflicting instructions create confusion
+  </Card>
+  <Card title="Missing Context" icon="puzzle-piece-missing">
+    Insufficient background information leads to generic outputs
+  </Card>
+</CardGroup>
+
+## Refining Your Instructions
+
+<Tip>
+Good instructions often require iteration. Start with basic instructions, review the agent's performance, and refine as needed.
+</Tip>
+
+Effective instruction improvement follows this cycle:
+
+```mermaid
+graph TD
+    A[Write Initial Instructions] --> B[Test Agent Performance]
+    B --> C[Identify Issues/Gaps]
+    C --> D[Refine Instructions]
+    D --> B
+```
+
+In the next lesson, we'll explore how agents use tools to extend their capabilities.

docs/course/agents/05-agent-tools.mdx

@@ -0,0 +1,183 @@
+---
+title: "Agent Tools"
+description: "Understanding how tools extend agent capabilities"
+icon: "screwdriver-wrench"
+---
+
+# Agent Tools
+
+Tools allow AI agents to interact with the world and perform specific actions. Think of tools as the "hands" of your agent that let it accomplish tasks beyond just generating text.
+
+## What Are Agent Tools?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="toolbox">
+    Tools are functions or capabilities that agents can access to perform specific actions, retrieve information, or interact with external systems.
+  </Card>
+</CardGroup>
+
+Just as humans use tools to extend their physical abilities, AI agents use digital tools to extend their capabilities.
+
+## Why Tools Matter
+
+Tools transform agents from simple chat interfaces into powerful assistants that can:
+
+<CardGroup cols={2}>
+  <Card title="Access Information" icon="database">
+    Retrieve data from various sources
+  </Card>
+  <Card title="Perform Calculations" icon="calculator">
+    Analyze numbers and data accurately
+  </Card>
+  <Card title="Create Content" icon="pen-to-square">
+    Generate images, code, or other media
+  </Card>
+  <Card title="Control Systems" icon="gears">
+    Interact with other software or hardware
+  </Card>
+</CardGroup>
+
+## Common Types of Tools
+
+### 1. Information Retrieval Tools
+
+These tools help agents access information beyond their training data.
+
+**Examples:**
+- Web search tools
+- Database query tools
+- Document retrieval tools
+
+```python
+# Example of a simple web search tool
+from praisonaiagents import Agent
+
+def web_search(query):
+    # Simplified example
+    return f"Results for: {query}"
+
+agent = Agent(
+    instructions="Research assistant",
+    tools=[web_search]
+)
+```
+
+### 2. Computation Tools
+
+These tools perform calculations or data processing.
+
+**Examples:**
+- Calculator tools
+- Data analysis tools
+- Conversion tools
+
+### 3. Content Creation Tools
+
+These tools help generate or modify various types of content.
+
+**Examples:**
+- Image generation tools
+- Code writing tools
+- Document formatting tools
+
+### 4. External API Tools
+
+These tools connect to third-party services.
+
+**Examples:**
+- Weather API tools
+- Translation API tools
+- E-commerce API tools
+
+## How Tools Work in PraisonAI
+
+In the PraisonAI framework, tools follow a simple pattern:
+
+1. Tools are defined as functions
+2. Tools are registered with an agent
+3. The agent decides when to use appropriate tools
+4. Tool results are incorporated into the agent's reasoning
+
+```mermaid
+graph TD
+    A[Agent Receives Task] --> B{Needs a Tool?}
+    B -->|Yes| C[Select Appropriate Tool]
+    C --> D[Execute Tool]
+    D --> E[Process Tool Results]
+    E --> F[Continue Task]
+    B -->|No| F
+```
+
+## Creating Basic Tools
+
+Here's a simple example of creating and using tools in PraisonAI:
+
+```python
+from praisonaiagents import Agent
+
+# Define a simple calculator tool
+def calculator(expression):
+    try:
+        return str(eval(expression))
+    except:
+        return "Error: Could not calculate"
+
+# Create an agent with the tool
+math_agent = Agent(
+    instructions="You are a math tutor helping students solve problems",
+    tools=[calculator]
+)
+
+# Use the agent
+math_agent.start("Calculate 24 * 17 and explain the steps")
+```
+
+## Tool Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Descriptions" icon="comment">
+    Provide detailed descriptions so the agent knows when to use each tool
+  </Card>
+  <Card title="Error Handling" icon="shield">
+    Make tools robust with proper error handling
+  </Card>
+  <Card title="Focused Purpose" icon="bullseye">
+    Each tool should do one thing well
+  </Card>
+  <Card title="Input Validation" icon="check-double">
+    Validate inputs to prevent misuse
+  </Card>
+</CardGroup>
+
+## Building a Toolbox
+
+As you advance, you'll want to create a collection of tools your agent can use:
+
+```python
+from praisonaiagents import Agent, Tool
+
+# Define multiple tools
+def weather(location):
+    return f"Weather in {location}: Sunny, 75°F"
+
+def translator(text, target_language):
+    return f"Translated to {target_language}: {text} (placeholder)"
+
+def reminder(message, time):
+    return f"Reminder set for {time}: {message}"
+
+# Create tool collection
+tools = [
+    Tool(name="weather", function=weather, description="Get weather for a location"),
+    Tool(name="translator", function=translator, description="Translate text"),
+    Tool(name="reminder", function=reminder, description="Set a reminder")
+]
+
+# Create agent with multiple tools
+assistant = Agent(
+    instructions="You are a helpful personal assistant",
+    tools=tools
+)
+```
+
+In the next lesson, we'll explore how agents use memory to maintain context across interactions.

docs/course/agents/06-agent-memory.mdx

@@ -0,0 +1,167 @@
+---
+title: "Agent Memory"
+description: "Understanding how agents maintain context and learn"
+icon: "brain"
+---
+
+# Agent Memory
+
+Memory enables AI agents to remember past interactions, maintain context, and provide more coherent responses over time. Without memory, agents would treat each interaction as if it was their first.
+
+## Why Memory Matters
+
+<CardGroup cols={2}>
+  <Card title="Context Awareness" icon="comments">
+    Remembers previous parts of a conversation
+  </Card>
+  <Card title="Personalization" icon="user-gear">
+    Recalls user preferences and history
+  </Card>
+  <Card title="Continuous Learning" icon="graduation-cap">
+    Builds on past experiences
+  </Card>
+  <Card title="Task Persistence" icon="list-check">
+    Maintains progress on long-running tasks
+  </Card>
+</CardGroup>
+
+## Types of Agent Memory
+
+### 1. Short-Term Memory (Conversation Memory)
+
+This type of memory stores recent interactions within a session.
+
+```mermaid
+graph LR
+    A[User Input 1] --> B[Agent Response 1]
+    B --> C[User Input 2]
+    C --> D[Agent Response 2]
+    D --> E[User Input 3]
+```
+
+The agent can refer back to earlier messages in the same conversation.
+
+### 2. Long-Term Memory
+
+This allows agents to remember information across different sessions.
+
+<CardGroup cols={1}>
+  <Card title="Use Cases" icon="clock">
+    - Remembering user preferences
+    - Recalling previous tasks completed
+    - Storing learned information
+    - Maintaining relationship history
+  </Card>
+</CardGroup>
+
+### 3. Working Memory
+
+This is where agents process current information and combine it with retrieved memories.
+
+```mermaid
+graph TD
+    A[Current Input] --> D[Working Memory]
+    B[Short-Term Memory] --> D
+    C[Long-Term Memory] --> D
+    D --> E[Agent Response]
+```
+
+## How Memory Works in PraisonAI
+
+In the PraisonAI framework, memory is handled automatically for basic cases and can be customized for more advanced needs:
+
+```python
+from praisonaiagents import Agent
+
+# Basic agent with default memory handling
+agent = Agent(
+    instructions="You are a helpful assistant that remembers our conversations"
+)
+
+# The conversation history is maintained automatically
+agent.start("My name is Alex")
+agent.continue("What's my name?")# Note: TODO: This Feature yet to be developed  # Agent will remember "Alex"
+```
+
+## Memory Limitations
+
+<Tip>
+All AI memories have limitations. They aren't perfect and may occasionally forget or misremember information.
+</Tip>
+
+Common limitations include:
+
+<CardGroup cols={2}>
+  <Card title="Memory Capacity" icon="database">
+    Limit to how much information can be stored
+  </Card>
+  <Card title="Context Window" icon="window">
+    Only a portion of memory fits in the active context
+  </Card>
+  <Card title="Memory Decay" icon="hourglass-half">
+    Older memories may become less accessible
+  </Card>
+  <Card title="Memory Retrieval" icon="magnifying-glass">
+    Finding the right memory at the right time
+  </Card>
+</CardGroup>
+
+## Implementing Persistent Memory
+
+For more advanced applications, you can implement custom memory systems:
+
+```python
+# Note: TODO: This Feature yet to be tested
+from praisonaiagents import Agent, Memory
+
+# Create a simple memory store
+memory = Memory()
+
+# Add information to memory
+memory.add("user_name", "Alex")
+memory.add("favorite_color", "blue")
+
+# Create agent with this memory
+agent = Agent(
+    instructions="You are a personal assistant",
+    memory=memory
+)
+
+# The agent can now access these memories
+agent.start("What's my favorite color?")  # Agent should respond "blue"
+```
+
+## Memory Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Prioritize Important Info" icon="star">
+    Not everything needs to be remembered
+  </Card>
+  <Card title="Verify When Uncertain" icon="question">
+    Have agents confirm uncertain memories
+  </Card>
+  <Card title="Structured Storage" icon="folder-tree">
+    Organize memories by categories
+  </Card>
+  <Card title="Regular Updates" icon="arrows-rotate">
+    Update memories when information changes
+  </Card>
+</CardGroup>
+
+## Memory in Multi-Agent Systems
+
+In systems with multiple agents, memory can be:
+
+1. **Private**: Each agent has its own memories
+2. **Shared**: Agents have access to a common memory store
+3. **Hybrid**: Some memories are private, others are shared
+
+```mermaid
+graph TD
+    A[Agent 1] --> B[Private Memory 1]
+    C[Agent 2] --> D[Private Memory 2]
+    A -.-> E[Shared Memory]
+    C -.-> E
+```
+
+In the next lesson, we'll learn about how to set up multiple agents to work together effectively.

docs/course/agents/07-multi-agent-systems.mdx

@@ -0,0 +1,171 @@
+---
+title: "Multi-Agent Systems"
+description: "How multiple agents can work together"
+icon: "users"
+---
+
+# Multi-Agent Systems
+
+Multi-agent systems allow multiple AI agents to work together, each handling different parts of a complex task. This approach mirrors how human teams collaborate, with each member contributing their specialized skills.
+
+## Why Use Multiple Agents?
+
+<CardGroup cols={2}>
+  <Card title="Specialization" icon="star">
+    Each agent can focus on its area of expertise
+  </Card>
+  <Card title="Complex Tasks" icon="puzzle-piece">
+    Break down difficult problems into manageable pieces
+  </Card>
+  <Card title="Scalability" icon="arrows-up-down">
+    Add more agents as tasks become more complex
+  </Card>
+  <Card title="Diverse Perspectives" icon="lightbulb">
+    Different agents can approach problems differently
+  </Card>
+</CardGroup>
+
+## Basic Multi-Agent Architecture
+
+```mermaid
+graph LR
+    A[Research Agent] --> B[Analysis Agent]
+    B --> C[Writing Agent]
+    C --> D[Final Output]
+```
+
+In this example:
+1. The Research Agent gathers information
+2. The Analysis Agent processes and interprets the data
+3. The Writing Agent creates the final content
+
+## Multi-Agent Patterns
+
+### 1. Pipeline Pattern
+
+Agents work in sequence, with each agent handling a specific stage of the process.
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+```
+
+**Example Use Case**: Content creation where one agent researches, another drafts, and a third edits.
+
+### 2. Expert Panel Pattern
+
+Multiple specialist agents work in parallel on the same problem, then their outputs are combined.
+
+```mermaid
+graph TD
+    Task --> A[Expert Agent 1]
+    Task --> B[Expert Agent 2]
+    Task --> C[Expert Agent 3]
+    A --> D[Coordination Agent]
+    B --> D
+    C --> D
+    D --> Result
+```
+
+**Example Use Case**: Financial analysis where different experts analyze market trends, economic indicators, and company performance.
+
+### 3. Hierarchical Pattern
+
+A manager agent delegates tasks to worker agents and coordinates their efforts.
+
+```mermaid
+graph TD
+    A[Manager Agent] --> B[Worker Agent 1]
+    A --> C[Worker Agent 2]
+    A --> D[Worker Agent 3]
+    B --> A
+    C --> A
+    D --> A
+```
+
+**Example Use Case**: Project management where a coordinator assigns tasks and integrates results.
+
+## Implementing Multi-Agent Systems in PraisonAI
+
+Here's a simple example of creating a multi-agent system:
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+# Create individual agents
+research_agent = Agent(
+    name="Researcher",
+    instructions="Research the latest trends in renewable energy"
+)
+
+analysis_agent = Agent(
+    name="Analyst",
+    instructions="Analyze the research findings and identify key insights"
+)
+
+writing_agent = Agent(
+    name="Writer", 
+    instructions="Create a clear, engaging report based on the analysis"
+)
+
+# Create a multi-agent system
+agents = PraisonAIAgents(
+    agents=[research_agent, analysis_agent, writing_agent]
+)
+
+# Start the agents
+agents.start()
+```
+
+## Agent Communication
+
+For agents to work together effectively, they need to communicate. This happens through:
+
+<CardGroup cols={2}>
+  <Card title="Output Passing" icon="arrow-right">
+    One agent's output becomes another's input
+  </Card>
+  <Card title="Shared Memory" icon="database">
+    Agents can access common information
+  </Card>
+  <Card title="Message Exchange" icon="comments">
+    Agents can send specific messages to each other
+  </Card>
+  <Card title="Coordination Protocols" icon="traffic-light">
+    Rules that determine how agents interact
+  </Card>
+</CardGroup>
+
+## Challenges in Multi-Agent Systems
+
+<CardGroup cols={2}>
+  <Card title="Coordination" icon="shuffle">
+    Ensuring agents work together smoothly
+  </Card>
+  <Card title="Conflicting Goals" icon="arrows-split-up-and-left">
+    Managing agents with different objectives
+  </Card>
+  <Card title="Information Transfer" icon="exchange">
+    Passing information effectively between agents
+  </Card>
+  <Card title="Resource Management" icon="microchip">
+    Allocating computational resources efficiently
+  </Card>
+</CardGroup>
+
+## Designing Effective Multi-Agent Systems
+
+<Tip>
+Start simple with just 2-3 agents and a clear workflow before scaling to more complex systems.
+</Tip>
+
+Key principles for effective design:
+
+1. **Clear Role Definition**: Each agent should have a specific and well-defined role
+2. **Minimized Dependencies**: Reduce complex interdependencies between agents
+3. **Standardized Communication**: Use consistent formats for information exchange
+4. **Failure Handling**: Plan for cases where an agent fails to complete its task
+5. **Performance Monitoring**: Track how well each agent and the overall system performs
+
+In the next lesson, we'll explore how to create effective agent workflows using the Process component.

docs/course/agents/08-agent-process.mdx

@@ -0,0 +1,152 @@
+---
+title: "Agent Process"
+description: "Understanding agent workflows and processes"
+icon: "diagram-project"
+---
+
+# Agent Process
+
+The Process component in AI agent systems defines how agents work together and the flow of information between them. Think of it as the "playbook" that coordinates how agents complete tasks.
+
+## What is an Agent Process?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="route">
+    A process is a structured workflow that defines the sequence of steps, decision points, and information flow between agents to complete a task.
+  </Card>
+</CardGroup>
+
+## Process Flow Basics
+
+A simple process flow might look like this:
+
+```mermaid
+graph LR
+    A[Start] --> B[Gather Information]
+    B --> C[Process Information]
+    C --> D[Make Decision]
+    D --> E[Take Action]
+    E --> F[Report Results]
+```
+
+Each step could be handled by a different agent or by the same agent in different stages.
+
+## Types of Process Flows
+
+### 1. Sequential Process
+
+Agents work in a straight line, one after another.
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+    C --> D[Complete]
+```
+
+**Best for**: Tasks with clear, linear stages
+
+### 2. Parallel Process
+
+Multiple agents work simultaneously on different parts of a task.
+
+```mermaid
+graph TD
+    A[Start] --> B[Agent 1]
+    A --> C[Agent 2]
+    A --> D[Agent 3]
+    B --> E[Combine Results]
+    C --> E
+    D --> E
+    E --> F[Complete]
+```
+
+**Best for**: Tasks that can be broken into independent pieces
+
+### 3. Conditional Process
+
+The workflow changes based on decisions or conditions.
+
+```mermaid
+graph TD
+    A[Start] --> B{Decision Point}
+    B -->|Condition A| C[Agent 1]
+    B -->|Condition B| D[Agent 2]
+    C --> E[Complete]
+    D --> E
+```
+
+**Best for**: Tasks requiring different approaches based on circumstances
+
+## Implementing Process in PraisonAI
+
+Here's a simple example of creating a process in PraisonAI:
+
+```python
+from praisonaiagents import Agent, Process, PraisonAIAgents
+
+# Create agents
+research_agent = Agent(
+    name="Researcher",
+    instructions="Research the topic and gather key information about AI"
+)
+
+writing_agent = Agent(
+    name="Writer",
+    instructions="Create content based on the research"
+)
+
+editing_agent = Agent(
+    name="Editor",
+    instructions="Review and improve the content"
+)
+
+# Create the multi-agent system with the process
+agents = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent],
+    process="hierarchical"
+)
+
+# Start the process
+agents.start()
+```
+
+## Process Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Step Definitions" icon="list-check">
+    Define exactly what each step should accomplish
+  </Card>
+  <Card title="Error Handling" icon="bug">
+    Include steps for handling unexpected situations
+  </Card>
+  <Card title="Feedback Loops" icon="repeat">
+    Allow agents to request clarification when needed
+  </Card>
+  <Card title="Modular Design" icon="cubes">
+    Create reusable process components
+  </Card>
+</CardGroup>
+
+<Tip>
+Start with simple processes and gradually add complexity as you become more comfortable with the system.
+</Tip>
+
+## Monitoring Process Execution
+
+It's important to track how your processes are running:
+
+```python
+agents = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent],
+    process=content_process,
+    verbose=True  # Enables detailed logging
+)
+```
+
+This will provide information about:
+- Which step is currently running
+- The inputs and outputs of each step
+- Any errors or issues encountered
+
+In the next lesson, we'll explore how to use knowledge bases to enhance agent capabilities.

docs/course/agents/09-knowledge-bases.mdx

@@ -0,0 +1,190 @@
+---
+title: "Knowledge Bases"
+description: "Enhancing agents with specialized knowledge"
+icon: "book"
+---
+
+# Knowledge Bases
+
+Knowledge bases provide agents with specialized information beyond their training data. They help agents answer specific questions, follow guidelines, or work within defined domains.
+
+## What is a Knowledge Base?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="database">
+    A knowledge base is a structured collection of information that an agent can access to answer questions or make decisions.
+  </Card>
+</CardGroup>
+
+Think of a knowledge base as a specialized reference library for your agent.
+
+## Why Use Knowledge Bases?
+
+<CardGroup cols={2}>
+  <Card title="Domain Expertise" icon="user-graduate">
+    Provide specialized information for specific fields
+  </Card>
+  <Card title="Current Information" icon="clock">
+    Include data beyond the agent's training cutoff
+  </Card>
+  <Card title="Custom Guidelines" icon="list">
+    Define rules and policies for the agent to follow
+  </Card>
+  <Card title="Consistent Responses" icon="equals">
+    Ensure agents provide standardized answers
+  </Card>
+</CardGroup>
+
+## Types of Knowledge Bases
+
+### 1. Document Collections
+
+Text documents, articles, guides, or manuals that agents can reference.
+
+```mermaid
+graph TD
+    A[Query] --> B[Search Documents]
+    B --> C[Find Relevant Content]
+    C --> D[Generate Response]
+```
+
+### 2. Structured Data
+
+Databases, tables, or other structured formats that organize information systematically.
+
+<CardGroup cols={1}>
+  <Card title="Examples" icon="table">
+    - Product catalogs with specifications
+    - Customer information databases
+    - Statistical data collections
+    - Reference tables for compliance information
+  </Card>
+</CardGroup>
+
+### 3. Vector Databases
+
+Special databases that store information as numerical representations (vectors) for semantic search.
+
+```mermaid
+graph TD
+    A[User Question] --> B[Convert to Vector]
+    B --> C[Find Similar Vectors]
+    C --> D[Retrieve Related Information]
+    D --> E[Generate Answer]
+```
+
+## Implementing Knowledge Bases in PraisonAI
+
+Here's an example of how to use a knowledge base with an agent:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"]
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Advanced Configuration
+
+For more control over the knowledge base, you can specify a configuration:
+
+```python
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Knowledge Retrieval Process
+
+When an agent uses a knowledge base, this typical process occurs:
+
+```mermaid
+graph LR
+    A[User Question] --> B[Query Processing]
+    B --> C[Search Knowledge Base]
+    C --> D[Rank Results]
+    D --> E[Synthesize Answer]
+```
+
+1. **Query Processing**: The user's question is analyzed
+2. **Search**: The system searches the knowledge base for relevant information
+3. **Ranking**: Results are ranked by relevance
+4. **Synthesis**: The agent creates an answer using the retrieved information
+
+## Best Practices for Knowledge Bases
+
+<CardGroup cols={2}>
+  <Card title="Keep Information Current" icon="arrows-rotate">
+    Regularly update your knowledge base
+  </Card>
+  <Card title="Organize Logically" icon="folder-tree">
+    Structure information in intuitive categories
+  </Card>
+  <Card title="Prioritize Quality" icon="star">
+    Focus on accurate, high-quality information
+  </Card>
+  <Card title="Include Examples" icon="list">
+    Add examples to illustrate complex concepts
+  </Card>
+</CardGroup>
+
+## When to Use Knowledge Bases
+
+Knowledge bases are particularly valuable when:
+
+1. Your agent needs to reference specific information that may change over time
+2. You need to ensure consistent answers to common questions
+3. Your agent needs to follow specific guidelines or protocols
+4. You want to provide expertise in specialized domains
+
+<Tip>
+Start with a small knowledge base focusing on the most important information, then expand as needed.
+</Tip>
+
+## Creating a Simple Knowledge Base
+
+For beginners, you can start with a simple text-based knowledge base:
+
+```
+# Company FAQ Knowledge Base
+
+## Return Policy
+Our return policy allows customers to return products within 30 days of purchase for a full refund.
+
+## Shipping Information
+Standard shipping takes 3-5 business days. Express shipping takes 1-2 business days.
+
+## Product Warranty
+All products come with a 1-year limited warranty covering manufacturing defects.
+```
+
+Save this as a text file and add it to your knowledge base:
+
+```python
+kb = KnowledgeBase()
+kb.add_document("company_faq.txt")
+```
+
+In the next lesson, we'll explore how agents handle tasks and the task management process.

docs/course/agents/10-agent-tasks.mdx

@@ -0,0 +1,274 @@
+---
+title: "Agent Tasks"
+description: "Understanding how agents handle and manage tasks"
+icon: "clipboard-list"
+---
+
+# Agent Tasks
+
+Tasks are the specific units of work that AI agents perform. Understanding how to define and manage tasks is essential for creating effective agent systems.
+
+## What is an Agent Task?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="list-check">
+    A task is a specific, well-defined piece of work assigned to an agent with clear inputs, expected outputs, and success criteria.
+  </Card>
+</CardGroup>
+
+Just as human jobs are broken into specific responsibilities, agent systems work best when complex goals are divided into manageable tasks.
+
+## Anatomy of a Task
+
+A well-defined task includes:
+
+<CardGroup cols={2}>
+  <Card title="Objective" icon="bullseye">
+    What the task should accomplish
+  </Card>
+  <Card title="Inputs" icon="arrow-right-to-bracket">
+    Information provided to complete the task
+  </Card>
+  <Card title="Process" icon="gears">
+    Steps to follow or approach to take
+  </Card>
+  <Card title="Outputs" icon="arrow-right-from-bracket">
+    Expected results or deliverables
+  </Card>
+</CardGroup>
+
+## Task Types
+
+### 1. Information Tasks
+
+These tasks involve finding, analyzing, or summarizing information.
+
+**Examples:**
+- Research a topic
+- Summarize a document
+- Extract data from text
+- Answer questions
+
+### 2. Creation Tasks
+
+These tasks involve generating new content.
+
+**Examples:**
+- Write an article
+- Create a marketing plan
+- Generate images (with appropriate tools)
+- Design a workflow
+
+### 3. Analysis Tasks
+
+These tasks involve evaluating information and drawing conclusions.
+
+**Examples:**
+- Analyze data trends
+- Review content for quality
+- Evaluate options
+- Identify patterns
+
+### 4. Interaction Tasks
+
+These tasks involve communicating or working with users or other systems.
+
+**Examples:**
+- Answer customer questions
+- Guide users through processes
+- Collect information from users
+- Notify about events
+
+## Creating Tasks in PraisonAI
+
+### Task Flow
+
+The typical lifecycle of a task follows this pattern:
+
+```mermaid
+graph LR
+    A[Define Task] --> B[Assign to Agent]
+    B --> C[Execute Task]
+    C --> D[Return Results]
+    D --> E[Evaluate Success]
+```
+
+### Task Configuration
+
+<CodeGroup>
+  ```python Basic
+  task = Task(
+      description="Research AI trends",
+      expected_output="Summary report",
+      agent=research_agent
+  )
+  ```
+  ```python Advanced
+  task = Task(
+      description="Analyze market data",
+      expected_output="Detailed analysis",
+      agent=analyst_agent,
+      tools=[AnalysisTool()],
+      output_file="analysis.md",
+      async_execution=True
+  )
+  ```
+</CodeGroup>
+
+# Understanding Tasks
+<br />
+```mermaid
+graph LR
+    %% Task Flow Steps
+    Start([▶ Start]) --> TaskDef
+    TaskDef --> AgentAssign
+    AgentAssign --> Exec
+    Exec --> Output([✓ Output])
+    
+    %% Define subgraphs for each step
+    subgraph TaskDef[" "]
+        TD_Icon[📋 Task Definition]
+        TD_Desc[Description & Requirements]
+        
+        TD_Icon --- TD_Desc
+        
+        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph AgentAssign[" "]
+        AA_Icon[🤖 Agent Assignment]
+        AA_Desc[Matching & Delegation]
+        
+        AA_Icon --- AA_Desc
+        
+        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Exec[" "]
+        E_Icon[⚙️ Execution]
+        E_Desc[Processing & Tools]
+        
+        E_Icon --- E_Desc
+        
+        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Style for containers
+    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br /><br />
+```mermaid
+graph LR
+    %% Task States
+    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
+    InProgress --> Completed([✅ Completed])
+    InProgress --> Failed([❌ Failed])
+    
+    %% Styling
+    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
+    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
+    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
+## Task Types
+
+<Steps>
+  <Step title="Basic Task">
+    Simple, single-operation tasks with clear inputs and outputs
+  </Step>
+  <Step title="Decision Task">
+    Tasks involving choices and conditional paths
+    ```python
+    decision_task = Task(
+        type="decision",
+        conditions={
+            "success": ["next_task"],
+            "failure": ["error_task"]
+        }
+    )
+    ```
+  </Step>
+  <Step title="Loop Task">
+    Repetitive operations over collections
+    ```python
+    loop_task = Task(
+        type="loop",
+        items=data_list,
+        operation="process_item"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Task Relationships
+
+<Warning>
+  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
+</Warning>
+
+### Context Sharing
+```python
+task_a = Task(name="research")
+task_b = Task(
+    name="analyze",
+    context=[task_a]  # Uses task_a's output
+)
+```
+
+## Task Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Be Specific" icon="magnifying-glass">
+    Clearly define what the task should accomplish
+  </Card>
+  <Card title="One Goal Per Task" icon="check-double">
+    Each task should focus on a single objective
+  </Card>
+  <Card title="Provide Context" icon="circle-info">
+    Include necessary background information
+  </Card>
+  <Card title="Define Success" icon="trophy">
+    Specify what a successful outcome looks like
+  </Card>
+</CardGroup>
+
+<Tip>
+Break complex goals into multiple smaller tasks rather than creating one large, complex task.
+</Tip>
+
+## Common Task Mistakes
+
+<CardGroup cols={2}>
+  <Card title="Vague Instructions" icon="question">
+    Unclear about what the agent should do
+  </Card>
+  <Card title="Too Many Objectives" icon="list-ol">
+    Asking for too many things in one task
+  </Card>
+  <Card title="Missing Context" icon="puzzle-piece-missing">
+    Not providing necessary background information
+  </Card>
+  <Card title="Unrealistic Expectations" icon="mountain-sun">
+    Asking for tasks beyond agent capabilities
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll learn about creating your first complete agent application.

docs/course/agents/11-creating-your-first-agent.mdx

@@ -0,0 +1,172 @@
+---
+title: "Creating Your First Agent"
+description: "A step-by-step guide to building your first AI agent"
+icon: "robot"
+---
+
+# Creating Your First Agent
+
+In this lesson, we'll walk through the process of building a simple AI agent. We'll focus on practical implementation without complex programming.
+
+## What We'll Build
+
+We'll create a basic research assistant agent that can:
+- Understand simple instructions
+- Respond to questions
+- Provide helpful information
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+1. A basic understanding of the concepts from previous lessons
+2. Python installed on your computer (if following the code examples)
+3. An OpenAI API key (or access to another supported language model)
+
+## Step 1: Installation
+
+First, install the PraisonAI package:
+
+```bash
+pip install praisonaiagents
+```
+
+## Step 2: Setup Your Environment
+
+Create a file called `first_agent.py` and set up your API key:
+
+```python
+import os
+from praisonaiagents import Agent
+
+# Set your API key
+# Option 1: Set it in your environment (recommended)
+# export OPENAI_API_KEY=your_key_here
+
+# Option 2: Set it in your code (not recommended for production)
+# os.environ["OPENAI_API_KEY"] = "your_key_here"
+```
+
+## Step 3: Define Your Agent
+
+Now, let's create a simple agent:
+
+```python
+# Create a research assistant agent
+research_assistant = Agent(
+    name="ResearchAssistant",
+    instructions="""
+    You are a helpful research assistant that provides clear, 
+    accurate information on various topics. When answering:
+    
+    1. Be concise and to the point
+    2. Explain complex concepts in simple terms
+    3. When appropriate, organize information with bullet points
+    4. If you don't know something, admit it instead of guessing
+    """
+)
+```
+
+## Step 4: Start Your Agent
+
+Now let's use our agent to answer a question:
+
+```python
+# Start the agent with a specific query
+response = research_assistant.start("Explain how solar panels work in simple terms")
+
+# Print the response
+print(response)
+```
+
+## Step 5: Run Your Agent
+
+Save the file and run it from your terminal:
+
+```bash
+python first_agent.py
+```
+
+You should see the agent's response explaining how solar panels work in simple terms.
+
+## Experimenting with Your Agent
+
+Try changing the question to see how your agent responds to different topics:
+
+```python
+# Try different questions
+response = research_assistant.start("What are the benefits of exercise?")
+# or
+response = research_assistant.start("How does artificial intelligence work?")
+```
+
+## Modifying Agent Behavior
+
+You can change how your agent behaves by adjusting its instructions:
+
+```python
+# Create an agent with different instructions
+creative_writer = Agent(
+    name="CreativeWriter",
+    instructions="""
+    You are a creative writer that specializes in short, engaging stories.
+    Your writing should:
+    
+    1. Include vivid descriptions
+    2. Have interesting characters
+    3. Include some dialogue
+    4. Have a clear beginning, middle, and end
+    """
+)
+
+# Ask for a story
+story = creative_writer.start("Write a short story about a lost dog finding its way home")
+print(story)
+```
+
+## Examining How It Works
+
+What's happening behind the scenes:
+
+1. The agent receives your instructions and query
+2. The language model (like GPT-4) processes the information
+3. The model generates a response based on the instructions and query
+4. The response is returned to you
+
+```mermaid
+graph TD
+    A[Your Instructions] --> B[Agent]
+    C[Your Query] --> B
+    B --> D[Language Model]
+    D --> E[Generated Response]
+    E --> F[Output to You]
+```
+
+## Common Challenges
+
+If you encounter issues, check these common problems:
+
+<CardGroup cols={2}>
+  <Card title="API Key Issues" icon="key">
+    Make sure your API key is set correctly
+  </Card>
+  <Card title="Connection Problems" icon="wifi">
+    Check your internet connection
+  </Card>
+  <Card title="Unclear Instructions" icon="circle-question">
+    Try making your instructions more specific
+  </Card>
+  <Card title="Rate Limits" icon="hand">
+    You might be making too many API calls
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+Now that you've created your first agent, you can:
+- Experiment with different instructions
+- Try more complex queries
+- Add specialized capabilities (we'll cover this in upcoming lessons)
+- Create agents for specific use cases
+
+Congratulations on creating your first AI agent! In the next lesson, we'll explore how to add tools to enhance your agent's capabilities.

docs/course/agents/12-adding-tools-to-agents.mdx

@@ -0,0 +1,275 @@
+---
+title: "Adding Tools to Agents"
+description: "Enhancing your agents with specialized capabilities"
+icon: "screwdriver-wrench"
+---
+
+# Adding Tools to Agents
+
+In this lesson, we'll learn how to enhance your agents by adding tools. Tools allow agents to perform specific actions beyond just generating text, making them much more useful for real-world tasks.
+
+## What You'll Learn
+
+By the end of this lesson, you'll understand:
+- What agent tools are and why they're useful
+- How to create simple tools for your agents
+- How to integrate tools with your agents
+- Best practices for tool design
+
+## Understanding Agent Tools
+
+Tools give your agents the ability to:
+- Perform calculations
+- Access specific information
+- Interact with external systems
+- Generate different types of content
+
+<Tabs>
+
+<Tab title="Code">
+<Steps>
+
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
+
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+<CodeGroup>
+```python Single Agent
+from praisonaiagents import Agent
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Perform internet searches to collect relevant information.",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+# 3. Start Agent
+search_agent.start("Search about AI job trends in 2025")
+```
+
+```python Multiple Agents
+from praisonaiagents import Agent, PraisonAIAgents
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Search about AI job trends in 2025",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+blog_agent = Agent(
+    instructions="Write a blog article based on the previous agent's search results."
+)
+
+# 3. Start Agents
+agents = PraisonAIAgents(agents=[search_agent, blog_agent])
+agents.start()
+```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
+
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+
+## Creating Custom Tool
+<Steps>
+<Step>
+Create any function that you want to use as a tool, that performs a specific task.
+```python
+from duckduckgo_search import DDGS
+
+def internet_search_tool(query: str):
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+```
+</Step>
+<Step>
+Assign the tool to an agent
+```python
+    data_agent = Agent(
+        instructions="Search about AI job trends in 2025",
+        tools=[internet_search_tool], # <-- Tool Assignment
+    )
+```
+</Step>
+</Steps>
+
+<Card title="That's it!">
+<Check>You have created a custom tool and assigned it to an agent.</Check>
+</Card>
+
+## When Does the Agent Use Tools?
+
+The agent decides when to use tools based on:
+1. The nature of the user's request
+2. The tools available to it
+3. The instructions you've provided
+
+If your agent isn't using tools when expected, you might need to:
+- Make your instructions more explicit about when to use tools
+- Provide more descriptive tool definitions
+- Ensure the tools are appropriate for the task
+
+## Tool Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Specific Purpose" icon="bullseye">
+    Each tool should have a clear, specific purpose
+  </Card>
+  <Card title="Error Handling" icon="shield-check">
+    Tools should handle errors gracefully
+  </Card>
+  <Card title="Descriptive Names" icon="tag">
+    Use clear names that indicate functionality
+  </Card>
+  <Card title="Clear Documentation" icon="file-lines">
+    Include detailed descriptions and examples
+  </Card>
+</CardGroup>
+
+## Real-World Tool Examples
+
+In production systems, you might create tools for:
+
+<CardGroup cols={2}>
+  <Card title="Database Access" icon="database">
+    Retrieve or store information in databases
+  </Card>
+  <Card title="API Integration" icon="link">
+    Connect to external services
+  </Card>
+  <Card title="File Operations" icon="folder">
+    Read from or write to files
+  </Card>
+  <Card title="Image Generation" icon="image">
+    Create images based on descriptions
+  </Card>
+</CardGroup>
+
+## Advanced Tool Concepts
+
+As you become more comfortable with tools, you can explore:
+- Tools that call other tools
+- Tools with complex parameters
+- Tools that maintain state
+- Dynamically generated tools
+
+In the next lesson, we'll explore how to create a multi-agent system for more complex tasks.

docs/course/agents/13-building-multi-agent-system.mdx

@@ -0,0 +1,218 @@
+---
+title: "Building a Multi-Agent System"
+description: "Creating a system with multiple cooperative agents"
+icon: "users"
+---
+
+# Building a Multi-Agent System
+
+In this lesson, we'll create a simple multi-agent system where several agents work together to accomplish a task. This approach allows you to divide complex problems into smaller, more manageable pieces.
+
+## What We'll Build
+
+We'll create a content creation system with three specialized agents:
+1. A research agent to gather information
+2. A writing agent to create content
+3. An editing agent to improve and refine the content
+
+## Step 1: Setting Up
+
+First, let's import the necessary modules and set up our environment:
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+# Make sure your API key is set in your environment:
+# export OPENAI_API_KEY=your_openai_key
+```
+
+## Step 2: Creating Specialized Agents
+
+Now, let's create our three specialized agents:
+
+```python
+# Research Agent
+research_agent = Agent(
+    name="Researcher",
+    instructions="""
+    You are a research specialist who excels at finding relevant information.
+    Your job is to:
+    1. Thoroughly research the given topic
+    2. Identify key facts, statistics, and insights
+    3. Organize the information in a clear, structured format
+    4. Provide accurate information with no fabrications
+    5. Focus on the most relevant and current information
+    """
+)
+
+# Writing Agent
+writing_agent = Agent(
+    name="Writer",
+    instructions="""
+    You are a content writer who creates engaging, reader-friendly content.
+    Your job is to:
+    1. Use the research provided to create well-structured content
+    2. Write in a clear, engaging style
+    3. Include an attention-grabbing introduction
+    4. Organize the content with appropriate headings
+    5. Conclude with a summary and call-to-action when appropriate
+    """
+)
+
+# Editing Agent
+editing_agent = Agent(
+    name="Editor",
+    instructions="""
+    You are an editor who refines and improves content.
+    Your job is to:
+    1. Correct any grammatical or spelling errors
+    2. Improve clarity and flow
+    3. Ensure consistency in tone and style
+    4. Check for logical structure and organization
+    5. Enhance readability for the target audience
+    """
+)
+```
+
+## Step 3: Creating the Multi-Agent System
+
+Now, let's combine these agents into a multi-agent system:
+
+```python
+# Create a multi-agent system
+content_team = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent]
+)
+```
+
+## Step 4: Setting Up the Workflow
+
+Next, we need to define how our agents will work together:
+
+```python
+# Define a topic
+topic = "The Benefits of Regular Exercise"
+
+# Start the multi-agent process
+results = content_team.start(
+    f"""
+    Task: Create a comprehensive blog post about {topic}
+    
+    Process:
+    1. Researcher: Research {topic} and provide key information including:
+       - Main benefits of exercise (physical and mental)
+       - Recommended exercise guidelines
+       - Scientific studies supporting the benefits
+       - Common misconceptions
+    
+    2. Writer: Use the research to create a 500-word blog post about {topic} with:
+       - An engaging introduction
+       - Clear sections with headings
+       - Practical advice for readers
+       - A compelling conclusion
+    
+    3. Editor: Review and improve the blog post by:
+       - Correcting any errors
+       - Enhancing clarity and flow
+       - Ensuring a consistent tone
+       - Making the content more engaging
+    """
+)
+
+# Print the final result
+print(results)
+```
+
+## Understanding the Workflow
+
+Let's examine how the multi-agent system works:
+
+```mermaid
+graph TD
+    A[Start] --> B[Research Agent]
+    B --> C[Writing Agent]
+    C --> D[Editing Agent]
+    D --> E[Final Output]
+```
+
+1. The research agent gathers and organizes relevant information
+2. The writing agent uses that research to create the initial content
+3. The editing agent refines and improves the content
+4. The final polished content is returned
+
+## Customizing Agent Interactions
+
+You can customize how agents interact by modifying the process instructions. For example, you could add feedback loops:
+
+```python
+process_with_feedback = """
+Task: Create a comprehensive blog post about {topic}
+
+Process:
+1. Researcher: Research {topic} and provide key information
+
+2. Writer: Use the research to create a blog post
+
+3. Editor: Review the blog post and provide feedback
+
+4. Writer: Revise the blog post based on the editor's feedback
+
+5. Editor: Make final improvements to the revised blog post
+"""
+```
+
+## Alternative Agent Configurations
+
+You can organize multi-agent systems in different ways:
+
+### Parallel Processing
+
+```python
+parallel_process = """
+Task: Create a marketing campaign for a new product
+
+Process:
+1. Market Researcher: Research the target market and competitors
+   Content Researcher: Research the product features and benefits
+   [These tasks happen in parallel]
+
+2. Strategy Agent: Use both research results to create a marketing strategy
+
+3. Content Creator: Create the marketing materials based on the strategy
+"""
+```
+
+### Expert Panel
+
+```python
+expert_panel = """
+Task: Evaluate a business idea
+
+Process:
+1. Financial Expert: Analyze financial viability
+   Market Expert: Analyze market potential
+   Technical Expert: Analyze technical feasibility
+   [These assessments happen in parallel]
+
+2. Coordinator: Compile all expert opinions into a final recommendation
+"""
+```
+
+## Best Practices for Multi-Agent Systems
+
+<CardGroup cols={2}>
+  <Card title="Clear Role Definition" icon="user-tag">
+    Define exactly what each agent is responsible for
+  </Card>
+  <Card title="Explicit Handoffs" icon="arrow-right-arrow-left">
+    Specify how information passes between agents
+  </Card>
+  <Card title="Avoid Redundancy" icon="copy">
+    Don't have multiple agents do the same work
+  </Card>
+  <Card title="Include Error Handling" icon="triangle-exclamation">
+    Plan for cases where an agent might fail
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll learn how to create a conversational agent that can maintain context across multiple interactions.

docs/course/agents/14-conversational-agents.mdx

@@ -0,0 +1,193 @@
+---
+title: "Building Conversational Agents"
+description: "Creating agents that maintain context in conversations"
+icon: "comments"
+---
+
+# Building Conversational Agents
+
+In this lesson, we'll learn how to create conversational agents that can maintain context across multiple interactions. This is essential for creating assistants that feel natural to interact with.
+
+## What Makes a Good Conversational Agent?
+
+A good conversational agent should:
+- Remember previous parts of the conversation
+- Understand context and references
+- Provide coherent, relevant responses
+- Maintain a consistent personality
+
+## Creating a Simple Conversational Agent
+
+Let's build a basic conversational agent:
+
+```python
+# Note: TODO: This Feature yet to be developed
+from praisonaiagents import Agent
+
+# Create a conversational agent
+chat_agent = Agent(
+    name="Conversational Assistant",
+    instructions="""
+    You are a friendly, helpful conversational assistant.
+    
+    When chatting with users:
+    1. Maintain a warm, friendly tone
+    2. Remember information shared earlier in the conversation
+    3. Ask clarifying questions when needed
+    4. Keep responses concise but informative
+    5. Be helpful while respecting boundaries
+    """
+)
+
+# Start a conversation
+response = chat_agent.start("Hi there! My name is Jamie.")
+print(response)
+
+# Continue the conversation
+response = chat_agent.continue("What can you help me with today?")
+print(response)
+
+# The agent should remember the user's name
+response = chat_agent.continue("Can you remember my name?")
+print(response)
+```
+
+## How Context Works
+
+PraisonAI automatically manages conversation context for you:
+
+```mermaid
+graph TD
+    A[User Message 1] --> B[Agent]
+    B --> C[Response 1]
+    D[User Message 2] --> E[Agent + Context]
+    E --> F[Response 2]
+    G[User Message 3] --> H[Agent + More Context]
+    H --> I[Response 3]
+```
+
+The agent maintains a memory of the conversation, allowing it to refer back to previous messages.
+
+## Conversation Settings
+
+You can customize how your conversational agent behaves:
+
+```python
+# Create an agent with specific conversation settings
+custom_chat_agent = Agent(
+    name="Custom Chat Assistant",
+    instructions="You are a helpful assistant",
+    memory_size=10,  # Remember the last 10 messages
+    temperature=0.7  # Slightly more creative in responses
+)
+```
+
+## Handling Different Types of Conversations
+
+Different use cases require different conversation styles:
+
+<CardGroup cols={2}>
+  <Card title="Customer Support" icon="headset">
+    - Professional and helpful tone
+    - Focus on problem resolution
+    - Remember customer details
+  </Card>
+  <Card title="Educational Tutor" icon="graduation-cap">
+    - Patient and explanatory
+    - Builds on previous lessons
+    - Adjusts to learning pace
+  </Card>
+  <Card title="Personal Assistant" icon="calendar">
+    - Remembers preferences
+    - Provides reminders
+    - Handles scheduling
+  </Card>
+  <Card title="Companion Chat" icon="user-group">
+    - More casual tone
+    - Focuses on engagement
+    - Shows personality
+  </Card>
+</CardGroup>
+
+## Example: Customer Support Agent
+
+Let's create a specialized customer support agent:
+
+```python
+support_agent = Agent(
+    name="Support Agent",
+    instructions="""
+    You are a customer support specialist for a tech company.
+    
+    When helping customers:
+    1. Greet them professionally
+    2. Show empathy for their issues
+    3. Ask for necessary information to troubleshoot
+    4. Provide clear step-by-step solutions
+    5. Confirm if the issue is resolved
+    6. End with an offer for additional help
+    
+    Remember product details and customer information throughout the conversation.
+    """
+)
+
+# Example conversation
+print(support_agent.start("Hi, I'm having trouble logging into my account."))
+print(support_agent.continue("My username is user123."))
+print(support_agent.continue("I've tried resetting my password but I'm not receiving the email."))
+```
+
+## Building Multi-Turn Conversations
+
+To create engaging multi-turn conversations, consider:
+
+1. **Appropriate follow-up questions**: Keep the conversation flowing
+2. **Acknowledgment**: Reference what the user has said
+3. **Progressive disclosure**: Don't overwhelm with information at once
+4. **Conversation repair**: Gracefully handle misunderstandings
+
+## Example: Multi-Turn Educational Conversation
+
+```python
+tutor_agent = Agent(
+    name="Math Tutor",
+    instructions="""
+    You are a patient, encouraging math tutor.
+    
+    When teaching:
+    1. Gauge the student's current understanding
+    2. Explain concepts using clear, simple language
+    3. Provide examples to illustrate points
+    4. Ask questions to check understanding
+    5. Give positive reinforcement for progress
+    
+    Adapt your explanations based on the student's responses.
+    """
+)
+
+# Example tutoring session
+print(tutor_agent.start("Can you help me understand algebra?"))
+print(tutor_agent.continue("I'm struggling with equations like 2x + 5 = 13"))
+print(tutor_agent.continue("So I subtract 5 from both sides first?"))
+print(tutor_agent.continue("Then I divide by 2 to get x = 4?"))
+print(tutor_agent.continue("Could you give me another example to practice?"))
+```
+
+## Conversation Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Transitions" icon="arrow-right">
+    Signal when changing topics
+  </Card>
+  <Card title="Manageable Chunks" icon="layer-group">
+    Break complex information into smaller pieces
+  </Card>
+  <Card title="Engagement Checks" icon="hand">
+    Periodically confirm understanding
+  </Card>
+  <Card title="Personality Consistency" icon="masks-theater">
+    Maintain a consistent tone and style
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build agents that can search and retrieve information from external sources.

docs/course/agents/15-research-agents.mdx

@@ -0,0 +1,216 @@
+---
+title: "Building Research Agents"
+description: "Creating agents that can gather and analyze information"
+icon: "magnifying-glass"
+---
+
+# Building Research Agents
+
+In this lesson, we'll build agents specialized in gathering, analyzing, and summarizing information. Research agents are valuable for quickly obtaining insights on various topics.
+
+## What is a Research Agent?
+
+A research agent is designed to:
+- Find relevant information on specific topics
+- Analyze and synthesize data
+- Present findings in a clear, structured format
+- Answer questions based on gathered information
+
+## Creating a Basic Research Agent
+
+Let's start by building a simple research agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic research agent
+research_agent = Agent(
+    name="ResearchAgent",
+    instructions="""
+    You are a research specialist who finds and summarizes information.
+    
+    When researching topics:
+    1. Focus on finding accurate, current information
+    2. Organize findings in a logical structure
+    3. Identify key points and insights
+    4. Cite sources when available
+    5. Present information in a clear, concise format
+    """
+)
+
+# Use the research agent
+research_results = research_agent.start("Research the impact of renewable energy on reducing carbon emissions")
+print(research_results)
+```
+
+## Adding Web Search Capability
+
+To make our research agent more powerful, let's add web search capability:
+
+```python
+from praisonaiagents import Agent, Tool
+
+# Define a web search function (simulated for this example)
+def web_search(query):
+    """
+    Simulated web search function.
+    In a real application, this would connect to a search API.
+    
+    Args:
+        query (str): The search query
+        
+    Returns:
+        str: Search results
+    """
+    # This is just a simulation - in a real application, you'd call a search API
+    if "renewable energy" in query.lower():
+        return """
+        Search results for 'renewable energy carbon emissions':
+        
+        1. According to the IEA, renewable energy prevented 2.1 billion tonnes of CO2 emissions in 2022.
+        
+        2. Solar and wind power generate electricity with 95-98% lower carbon emissions than coal-based electricity.
+        
+        3. The IPCC reports that renewable energy could deliver more than half of the emission reductions needed by 2030.
+        """
+    else:
+        return f"Search results for '{query}' (simulated data)"
+
+# Create the web search tool
+search_tool = Tool(
+    name="web_search",
+    function=web_search,
+    description="Search the web for current information on a topic"
+)
+
+# Create a research agent with web search capability
+enhanced_research_agent = Agent(
+    name="EnhancedResearchAgent",
+    instructions="""
+    You are a research specialist who finds and summarizes information.
+    
+    When researching topics:
+    1. Use the web_search tool to find current information
+    2. Analyze and synthesize the search results
+    3. Organize findings in a logical structure
+    4. Identify key points and insights
+    5. Present information in a clear, concise format
+    """,
+    tools=[search_tool]
+)
+
+# Use the enhanced research agent
+enhanced_results = enhanced_research_agent.start("What is the impact of renewable energy on carbon emissions?")
+print(enhanced_results)
+```
+
+## Research Agent with Structured Output
+
+For more organized research results, we can instruct the agent to provide structured output:
+
+```python
+structured_research_agent = Agent(
+    name="StructuredResearchAgent",
+    instructions="""
+    You are a research specialist who finds and organizes information.
+    
+    When researching topics:
+    1. Use available tools to find current information
+    2. Analyze and synthesize the findings
+    
+    Always structure your response in this format:
+    
+    ## Summary
+    [Brief 2-3 sentence overview of the topic]
+    
+    ## Key Facts
+    - [Important fact 1]
+    - [Important fact 2]
+    - [Important fact 3]
+    
+    ## Analysis
+    [Deeper analysis of the information, including implications]
+    
+    ## Sources
+    [List of sources if available]
+    """,
+    tools=[search_tool]
+)
+
+# Use the structured research agent
+structured_results = structured_research_agent.start("Research the latest advancements in quantum computing")
+print(structured_results)
+```
+
+## Specialized Research Agents
+
+Different research tasks require different approaches. Here are some specialized research agents:
+
+### Comparison Research Agent
+
+```python
+comparison_agent = Agent(
+    name="ComparisonAgent",
+    instructions="""
+    You are a research specialist who compares different options or topics.
+    
+    When comparing items:
+    1. Identify the key criteria for comparison
+    2. Research each option thoroughly
+    3. Create a clear side-by-side comparison
+    4. Highlight advantages and disadvantages of each option
+    5. Provide a balanced conclusion
+    
+    Present your findings in a structured table format when appropriate.
+    """,
+    tools=[search_tool]
+)
+
+# Use the comparison agent
+comparison_results = comparison_agent.start("Compare solar and wind energy for residential use")
+print(comparison_results)
+```
+
+### Literature Review Agent
+
+```python
+literature_agent = Agent(
+    name="LiteratureAgent",
+    instructions="""
+    You are a research specialist who reviews academic and professional literature.
+    
+    When reviewing literature:
+    1. Identify key papers, articles, and studies on the topic
+    2. Summarize the main findings and methodologies
+    3. Identify trends, agreements, and contradictions
+    4. Evaluate the strength of evidence
+    5. Highlight gaps in current research
+    
+    Structure your review in a clear, academic format.
+    """,
+    tools=[search_tool]
+)
+
+# Use the literature review agent
+literature_results = literature_agent.start("Review recent research on artificial intelligence in healthcare")
+print(literature_results)
+```
+
+## Research Process Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Define Scope" icon="bullseye">
+    Set clear boundaries for the research
+  </Card>
+  <Card title="Verify Information" icon="check-double">
+    Cross-check facts from multiple sources
+  </Card>
+  <Card title="Consider Bias" icon="scale-balanced">
+    Be aware of potential biases in sources
+  </Card>
+  <Card title="Prioritize Relevance" icon="filter">
+    Focus on information most relevant to the query
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build agents that can assist with creative tasks like content creation.

docs/course/agents/16-content-creation-agents.mdx

@@ -0,0 +1,252 @@
+---
+title: "Content Creation Agents"
+description: "Building agents that generate various types of content"
+icon: "pen-to-square"
+---
+
+# Content Creation Agents
+
+Content creation agents can help generate various types of content, from blog posts to marketing materials. In this lesson, we'll learn how to build effective content creation agents.
+
+## What is a Content Creation Agent?
+
+A content creation agent is designed to:
+- Generate written content based on specific requirements
+- Structure content in appropriate formats
+- Adapt tone and style to match the target audience
+- Create content that achieves specific goals (inform, persuade, entertain)
+
+## Creating a Basic Content Creation Agent
+
+Let's start with a simple content creation agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic content creation agent
+content_agent = Agent(
+    name="ContentCreator",
+    instructions="""
+    You are a versatile content creator who specializes in writing engaging material.
+    
+    When creating content:
+    1. Understand the purpose and target audience
+    2. Create a logical structure with clear sections
+    3. Use an engaging, appropriate writing style
+    4. Include a strong introduction and conclusion
+    5. Format content for easy readability
+    """
+)
+
+# Use the content creation agent
+blog_post = content_agent.start(
+    """
+    Create a blog post about the benefits of meditation.
+    Target audience: Busy professionals
+    Length: Approximately 500 words
+    Tone: Informative but conversational
+    """
+)
+print(blog_post)
+```
+
+## Specialized Content Creation Agents
+
+Different types of content require different approaches. Let's create specialized agents for various content needs:
+
+### Blog Post Agent
+
+```python
+blog_agent = Agent(
+    name="BlogWriter",
+    instructions="""
+    You are a blog post writer who creates engaging, informative articles.
+    
+    When writing blog posts:
+    1. Create attention-grabbing headlines
+    2. Start with a compelling introduction
+    3. Use subheadings to organize content
+    4. Include relevant examples and evidence
+    5. Incorporate a clear call-to-action at the end
+    
+    Format your content with proper Markdown formatting including:
+    - Headings (## for main sections, ### for subsections)
+    - Bullet points for lists
+    - *Italic* for emphasis
+    - **Bold** for important points
+    - > Blockquotes for notable quotes
+    """
+)
+
+# Use the blog agent
+blog_content = blog_agent.start(
+    """
+    Topic: 5 Ways to Improve Your Productivity
+    Target audience: Remote workers
+    Style: Practical, actionable advice
+    Length: 800 words
+    Include: Tips, examples, and research-backed information
+    """
+)
+print(blog_content)
+```
+
+### Social Media Content Agent
+
+```python
+social_media_agent = Agent(
+    name="SocialMediaCreator",
+    instructions="""
+    You are a social media content creator who crafts engaging posts.
+    
+    When creating social media content:
+    1. Create attention-grabbing, concise content
+    2. Adapt to the specific platform requirements
+    3. Include relevant hashtags when appropriate
+    4. Craft content that encourages engagement
+    5. Consider visual elements that would pair well with the text
+    
+    For each platform, follow these guidelines:
+    - Twitter/X: 280 characters max, punchy, relevant hashtags
+    - LinkedIn: Professional tone, industry insights, 1-3 paragraphs
+    - Instagram: Visual-focused descriptions, emoji-friendly, hashtags
+    - Facebook: Conversational, can be longer, question-based engagement
+    """
+)
+
+# Use the social media agent
+social_content = social_media_agent.start(
+    """
+    Create posts for Twitter, LinkedIn, and Facebook announcing:
+    
+    Event: Free Webinar on "Future of AI in Business"
+    Date: March 15, 2025
+    Time: 2:00 PM EST
+    Registration link: example.com/webinar
+    Key speakers: Dr. Jane Smith, AI Ethics Expert & Tom Johnson, Business AI Implementation Specialist
+    """
+)
+print(social_content)
+```
+
+### Email Campaign Agent
+
+```python
+email_agent = Agent(
+    name="EmailWriter",
+    instructions="""
+    You are an email marketing specialist who creates effective email campaigns.
+    
+    When writing marketing emails:
+    1. Create compelling subject lines that increase open rates
+    2. Write engaging opening lines that hook the reader
+    3. Keep content concise and focused on a single main message
+    4. Include clear calls-to-action
+    5. Use a tone appropriate for the brand and audience
+    
+    Structure your emails with:
+    - Subject line
+    - Greeting
+    - Opening hook
+    - Main content (2-3 paragraphs maximum)
+    - Call-to-action
+    - Sign-off
+    """
+)
+
+# Use the email agent
+email_content = email_agent.start(
+    """
+    Create a promotional email for:
+    
+    Product: Premium Fitness Subscription
+    Key benefit: Personalized workout plans and nutrition advice
+    Special offer: 30% off first 3 months
+    Target audience: Health-conscious professionals aged 25-45
+    CTA: Sign up for the discounted offer
+    Tone: Motivational but not aggressive
+    """
+)
+print(email_content)
+```
+
+## Content Creation with Templates
+
+Templates can help create more consistent content. Let's build an agent that works with templates:
+
+```python
+template_agent = Agent(
+    name="TemplateWriter",
+    instructions="""
+    You are a content creator who works with templates to create consistent content.
+    
+    When filling a template:
+    1. Maintain the overall structure of the template
+    2. Replace placeholder text with appropriate content
+    3. Ensure consistency in tone and style
+    4. Adapt content to the specific requirements
+    5. Review for coherence and flow
+    
+    Always preserve formatting elements like headings, lists, and emphasis.
+    """
+)
+
+# Example template
+product_review_template = """
+# {Product Name} Review: {Brief Opinion}
+
+## Quick Summary
+**Rating:** {Rating}/10
+**Price:** {Price Range}
+**Recommended For:** {Target User}
+
+## What We Liked
+- {Positive Point 1}
+- {Positive Point 2}
+- {Positive Point 3}
+
+## What Could Be Better
+- {Negative Point 1}
+- {Negative Point 2}
+
+## Bottom Line
+{Conclusion Paragraph}
+
+"""
+
+# Use the template agent
+filled_template = template_agent.start(
+    f"""
+    Fill the following template for a product review:
+    
+    {product_review_template}
+    
+    Product: Wireless Noise-Cancelling Headphones XZ200
+    Key features: 30-hour battery life, active noise cancellation, voice assistant integration
+    Price: $249.99
+    Target audience: Commuters and office workers
+    Notable pros: Sound quality, comfort, battery life
+    Notable cons: Price, occasional Bluetooth connectivity issues
+    """
+)
+print(filled_template)
+```
+
+## Content Creation Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Know Your Audience" icon="users">
+    Understand who will consume the content
+  </Card>
+  <Card title="Clear Purpose" icon="bullseye">
+    Define what the content should achieve
+  </Card>
+  <Card title="Consistent Voice" icon="comment">
+    Maintain a consistent tone and style
+  </Card>
+  <Card title="Readability" icon="eye">
+    Make content easy to scan and understand
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build AI agents that can analyze and work with data.

docs/course/agents/17-data-analysis-agents.mdx

@@ -0,0 +1,333 @@
+---
+title: "Data Analysis Agents"
+description: "Creating agents that analyze and interpret data"
+icon: "chart-line"
+---
+
+# Data Analysis Agents
+
+Data analysis agents can help process, analyze, and extract insights from data. In this lesson, we'll learn how to build agents that can work with data effectively.
+
+## What is a Data Analysis Agent?
+
+A data analysis agent is designed to:
+- Process and interpret data
+- Identify patterns and trends
+- Generate insights and recommendations
+- Present findings in an understandable format
+
+## Creating a Basic Data Analysis Agent
+
+Let's start by building a simple data analysis agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic data analysis agent
+data_analysis_agent = Agent(
+    name="DataAnalyst",
+    instructions="""
+    You are a data analysis specialist who interprets and explains data.
+    
+    When analyzing data:
+    1. First understand what the data represents
+    2. Identify key patterns, trends, or anomalies
+    3. Generate meaningful insights
+    4. Explain findings in simple, clear language
+    5. Provide actionable recommendations when appropriate
+    """
+)
+
+# Use the data analysis agent with sample data
+sample_data = """
+Monthly Sales Data (2024):
+January: $12,500
+February: $13,200
+March: $15,800
+April: $14,300
+May: $16,700
+June: $18,900
+"""
+
+analysis = data_analysis_agent.start(
+    f"""
+    Analyze the following sales data and provide insights:
+    
+    {sample_data}
+    
+    Questions to answer:
+    1. What is the overall sales trend?
+    2. Which month had the highest sales?
+    3. What is the average monthly sales?
+    4. What recommendations would you make based on this data?
+    """
+)
+print(analysis)
+```
+
+## Adding Data Processing Tools
+
+Let's enhance our agent with basic data processing capabilities:
+
+```python
+from praisonaiagents import Agent, Tool
+
+# Define a simple calculation tool
+def calculate_statistics(data_string):
+    """
+    Calculate basic statistics from a string of numbers.
+    
+    Args:
+        data_string (str): A string with numbers separated by commas
+        
+    Returns:
+        str: Statistical summary
+    """
+    try:
+        # Convert string to list of numbers
+        data = [float(x.strip()) for x in data_string.split(',')]
+        
+        # Calculate statistics
+        total = sum(data)
+        average = total / len(data)
+        minimum = min(data)
+        maximum = max(data)
+        
+        return f"""
+        Statistical summary:
+        - Count: {len(data)}
+        - Sum: {total}
+        - Average: {average:.2f}
+        - Minimum: {minimum}
+        - Maximum: {maximum}
+        - Range: {maximum - minimum}
+        """
+    except Exception as e:
+        return f"Error processing data: {str(e)}"
+
+# Create the tool
+stats_tool = Tool(
+    name="calculate_statistics",
+    function=calculate_statistics,
+    description="Calculate basic statistics from a list of numbers (comma-separated)"
+)
+
+# Create an agent with the statistics tool
+enhanced_data_agent = Agent(
+    name="EnhancedDataAnalyst",
+    instructions="""
+    You are a data analysis specialist who interprets and explains data.
+    
+    When analyzing data:
+    1. First understand what the data represents
+    2. Use the calculate_statistics tool for numerical data
+    3. Identify key patterns, trends, or anomalies
+    4. Generate meaningful insights
+    5. Explain findings in simple, clear language
+    6. Provide actionable recommendations when appropriate
+    """,
+    tools=[stats_tool]
+)
+
+# Use the enhanced data agent
+numerical_data = "12500, 13200, 15800, 14300, 16700, 18900"
+enhanced_analysis = enhanced_data_agent.start(
+    f"""
+    Analyze the following monthly sales data for the first half of 2024:
+    
+    {numerical_data}
+    
+    Provide a complete analysis with trends and recommendations.
+    """
+)
+print(enhanced_analysis)
+```
+
+## Specialized Data Analysis Agents
+
+Different analysis tasks require specialized approaches. Let's create some specialized agents:
+
+### Market Trend Analysis Agent
+
+```python
+market_analysis_agent = Agent(
+    name="MarketAnalyst",
+    instructions="""
+    You are a market trend analyst who specializes in identifying patterns and making predictions.
+    
+    When analyzing market data:
+    1. Identify long-term trends and short-term patterns
+    2. Compare data points to industry benchmarks
+    3. Identify potential causes for changes
+    4. Assess future implications
+    5. Provide strategic recommendations
+    
+    Present your analysis with clear sections for:
+    - Overall Market Assessment
+    - Key Trends Identified
+    - Growth Opportunities
+    - Risk Factors
+    - Strategic Recommendations
+    """,
+    tools=[stats_tool]
+)
+
+# Use the market analysis agent
+market_data = """
+Industry Growth Rates (2021-2024):
+2021: 3.2%
+2022: 2.8%
+2023: 4.5%
+2024: 5.1%
+
+Our Company Growth:
+2021: 2.9%
+2022: 3.1%
+2023: 4.8%
+2024: 6.3%
+
+Competitor A Growth:
+2021: 3.5%
+2022: 3.7%
+2023: 4.2%
+2024: 4.5%
+
+Competitor B Growth:
+2021: 3.0%
+2022: 2.5%
+2023: 5.0%
+2024: 5.8%
+"""
+
+market_analysis = market_analysis_agent.start(
+    f"""
+    Analyze the following market and company growth data:
+    
+    {market_data}
+    
+    Provide a comprehensive market analysis and strategic recommendations.
+    """
+)
+print(market_analysis)
+```
+
+### Customer Data Analysis Agent
+
+```python
+customer_analysis_agent = Agent(
+    name="CustomerAnalyst",
+    instructions="""
+    You are a customer data analyst who specializes in understanding customer behavior.
+    
+    When analyzing customer data:
+    1. Identify customer segments and patterns
+    2. Analyze purchasing behavior
+    3. Evaluate customer satisfaction metrics
+    4. Identify opportunities to improve customer experience
+    5. Recommend strategies for customer retention and growth
+    
+    Present your analysis with sections for:
+    - Customer Segmentation
+    - Behavior Patterns
+    - Key Insights
+    - Recommended Actions
+    """,
+    tools=[stats_tool]
+)
+
+# Use the customer analysis agent
+customer_data = """
+Customer Segments by Age:
+18-24: 15% of customers (Avg. Purchase: $45)
+25-34: 32% of customers (Avg. Purchase: $78)
+35-44: 28% of customers (Avg. Purchase: $92)
+45-54: 18% of customers (Avg. Purchase: $85)
+55+: 7% of customers (Avg. Purchase: $65)
+
+Customer Satisfaction:
+Very Satisfied: 42%
+Satisfied: 35%
+Neutral: 15%
+Dissatisfied: 6%
+Very Dissatisfied: 2%
+
+Repeat Purchase Rate:
+One-time: 40%
+2-5 purchases: 35%
+6-10 purchases: 15%
+10+ purchases: 10%
+"""
+
+customer_analysis = customer_analysis_agent.start(
+    f"""
+    Analyze the following customer data:
+    
+    {customer_data}
+    
+    Provide insights on customer segments, behavior patterns, and recommendations to improve customer retention.
+    """
+)
+print(customer_analysis)
+```
+
+## Data Visualization Recommendations
+
+While our agents can't directly create visualizations, they can recommend appropriate visualization types:
+
+```python
+visualization_agent = Agent(
+    name="VisualizationAdvisor",
+    instructions="""
+    You are a data visualization specialist who helps select the best ways to visually represent data.
+    
+    When recommending visualizations:
+    1. Understand the nature of the data (categorical, numerical, time-series, etc.)
+    2. Consider the key relationships or patterns to highlight
+    3. Recommend the most appropriate chart or graph type
+    4. Explain why your recommendation is effective
+    5. Provide suggestions for layout, color scheme, and labeling
+    
+    Common visualization types to consider:
+    - Line charts (for trends over time)
+    - Bar charts (for comparing categories)
+    - Pie charts (for showing composition, use sparingly)
+    - Scatter plots (for showing relationships between variables)
+    - Heatmaps (for showing patterns in complex data)
+    - Box plots (for showing distributions)
+    """
+)
+
+# Use the visualization agent
+viz_recommendations = visualization_agent.start(
+    """
+    I have the following data and need visualization recommendations:
+    
+    1. Monthly sales data for the past year
+    2. Customer satisfaction ratings across 5 product categories
+    3. Market share compared to 3 competitors
+    4. Customer age distribution
+    
+    What visualizations would you recommend for each of these datasets and why?
+    """
+)
+print(viz_recommendations)
+```
+
+## Data Analysis Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Data Quality" icon="shield-check">
+    Ensure data is accurate and complete
+  </Card>
+  <Card title="Context Matters" icon="circle-info">
+    Consider external factors affecting data
+  </Card>
+  <Card title="Clear Communication" icon="comments">
+    Present findings in understandable terms
+  </Card>
+  <Card title="Actionable Insights" icon="bolt">
+    Focus on insights that lead to action
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build AI agents that can assist with customer support tasks.

docs/course/agents/18-customer-support-agents.mdx

@@ -0,0 +1,260 @@
+---
+title: "Customer Support Agents"
+description: "Building agents that handle customer inquiries and issues"
+icon: "headset"
+---
+
+# Customer Support Agents
+
+Customer support agents can handle inquiries, resolve issues, and provide assistance to users. In this lesson, we'll learn how to build effective customer support agents.
+
+## What is a Customer Support Agent?
+
+A customer support agent is designed to:
+- Answer frequently asked questions
+- Troubleshoot common problems
+- Provide information about products or services
+- Collect information for support tickets
+- Guide users through processes or procedures
+
+## Creating a Basic Customer Support Agent
+
+Let's start by building a simple customer support agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic customer support agent
+support_agent = Agent(
+    name="SupportAgent",
+    instructions="""
+    You are a helpful customer support representative for a software company.
+    
+    When helping customers:
+    1. Greet them professionally and warmly
+    2. Understand their issue completely before attempting to solve it
+    3. Provide clear, step-by-step solutions when possible
+    4. Maintain a friendly, patient tone throughout
+    5. If you cannot solve the issue, explain how to escalate it
+    
+    Our software product is a productivity app with features for:
+    - Task management
+    - Calendar scheduling
+    - Note-taking
+    - Team collaboration
+    """
+)
+
+# Use the support agent
+response = support_agent.start("I'm having trouble syncing my calendar with my phone. Can you help?")
+print(response)
+```
+
+## Adding a Knowledge Base
+
+Let's enhance our agent with a simple knowledge base to answer common questions:
+
+```python
+from praisonaiagents import Agent, KnowledgeBase
+
+# Create a simple knowledge base
+faq_kb = KnowledgeBase()
+
+# Add FAQ content (in a real scenario, you would load this from files)
+faq_kb.add_text("""
+# Frequently Asked Questions
+
+## Account Issues
+
+### How do I reset my password?
+To reset your password:
+1. Go to the login page
+2. Click on "Forgot Password"
+3. Enter your email address
+4. Follow the instructions sent to your email
+
+### How do I change my email address?
+To change your email address:
+1. Log in to your account
+2. Go to Settings > Account
+3. Click on "Change Email"
+4. Enter your new email and confirm with your password
+
+## Subscription and Billing
+
+### What subscription plans do you offer?
+We offer three plans:
+- Basic: $9.99/month - Includes core features for individuals
+- Pro: $19.99/month - Includes advanced features and team capabilities for up to 5 users
+- Enterprise: Custom pricing - Full feature set with advanced security and support
+
+### How do I cancel my subscription?
+To cancel your subscription:
+1. Log in to your account
+2. Go to Settings > Billing
+3. Click "Cancel Subscription"
+4. Follow the confirmation steps
+
+## Technical Issues
+
+### Why isn't my calendar syncing?
+Common reasons for calendar sync issues:
+1. Outdated app version - Try updating the app
+2. Account permission issues - Check sync permissions in Settings > Integrations
+3. Internet connectivity problems - Ensure you have a stable connection
+
+### How do I export my data?
+To export your data:
+1. Go to Settings > Data
+2. Click "Export Data"
+3. Choose the data types you want to export
+4. Select your preferred format (CSV or JSON)
+5. Click "Export" and save the file
+""")
+
+# Create an agent with the knowledge base
+kb_support_agent = Agent(
+    name="KnowledgeBaseAgent",
+    instructions="""
+    You are a helpful customer support representative with access to our FAQ knowledge base.
+    
+    When helping customers:
+    1. Greet them professionally and warmly
+    2. Search the knowledge base for relevant information
+    3. Provide clear, accurate answers based on the knowledge base
+    4. If the knowledge base doesn't cover the question, provide general helpful guidance
+    5. Maintain a friendly, supportive tone throughout
+    """,
+    knowledge_base=faq_kb
+)
+
+# Use the knowledge base support agent
+kb_response = kb_support_agent.start("How do I cancel my subscription?")
+print(kb_response)
+```
+
+## Creating a Troubleshooting Agent
+
+Let's create an agent specialized in technical troubleshooting:
+
+```python
+troubleshooting_agent = Agent(
+    name="TroubleshootingAgent",
+    instructions="""
+    You are a technical support specialist who helps users troubleshoot problems.
+    
+    When troubleshooting:
+    1. Ask clarifying questions to understand the exact issue
+    2. Identify the most likely causes of the problem
+    3. Provide step-by-step solutions, starting with the simplest fixes
+    4. Explain what each step does and why it might help
+    5. Confirm if the issue is resolved or suggest next steps
+    
+    Common troubleshooting areas:
+    - Login and account access issues
+    - App installation and updates
+    - Feature functionality problems
+    - Data synchronization
+    - Performance issues
+    """
+)
+
+# Use the troubleshooting agent
+troubleshooting_response = troubleshooting_agent.start("The app keeps crashing when I try to open it on my iPhone")
+print(troubleshooting_response)
+```
+
+## Building a Conversational Support Agent
+
+Let's create a support agent that can handle multi-turn conversations:
+
+```python
+conversational_support = Agent(
+    name="ConversationalSupport",
+    instructions="""
+    You are a customer support representative who handles support conversations.
+    
+    When handling support conversations:
+    1. Maintain context throughout the conversation
+    2. Ask follow-up questions when needed
+    3. Verify understanding before providing solutions
+    4. Be patient with users who may be frustrated
+    5. Summarize the conversation and next steps at the end
+    
+    Our product is a mobile banking app with features for:
+    - Account balance checking
+    - Money transfers
+    - Bill payments
+    - Mobile check deposits
+    - Budget tracking
+    """
+)
+
+# Start a support conversation
+initial_response = conversational_support.start("I can't find where to deposit checks in the app")
+print("Initial Response:", initial_response)
+
+# Continue the conversation
+follow_up_response = conversational_support.continue("I'm using version 2.5 of the app on Android")
+print("Follow-up Response:", follow_up_response)
+
+# Further continuation
+resolution_response = conversational_support.continue("I found the deposit button now, but it's giving me an error when I try to take a photo")
+print("Resolution Response:", resolution_response)
+```
+
+## Support Ticket Creation Agent
+
+Let's create an agent that can gather information for support tickets:
+
+```python
+ticket_agent = Agent(
+    name="TicketCreationAgent",
+    instructions="""
+    You are a support ticket creation specialist who gathers the necessary information to create support tickets.
+    
+    When creating a ticket:
+    1. Collect the customer's name and contact information
+    2. Identify the category of the issue
+    3. Gather a detailed description of the problem
+    4. Determine the severity level
+    5. Collect relevant technical details (device, software version, etc.)
+    6. Organize all information in a structured ticket format
+    
+    Present the final ticket in a clearly formatted structure.
+    """
+)
+
+# Use the ticket creation agent
+ticket = ticket_agent.start(
+    """
+    Create a support ticket for the following issue:
+    
+    Customer: Jane Smith
+    Email: [email protected]
+    Issue: Cannot access premium features after payment
+    Details: Completed payment yesterday, received confirmation email, but premium features are still locked in the app
+    Using: iPhone 13, app version 3.2.1
+    """
+)
+print(ticket)
+```
+
+## Customer Support Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Empathy First" icon="heart">
+    Show understanding before offering solutions
+  </Card>
+  <Card title="Clear Communication" icon="comment">
+    Use simple language, avoid jargon
+  </Card>
+  <Card title="Consistent Responses" icon="equals">
+    Provide consistent answers to similar questions
+  </Card>
+  <Card title="Know Escalation Paths" icon="arrow-up-right">
+    Have clear processes for escalating complex issues
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build AI agents that can assist with personal productivity tasks.

docs/course/agents/19-personal-assistant-agents.mdx

@@ -0,0 +1,270 @@
+---
+title: "Personal Assistant Agents"
+description: "Building agents that help with personal tasks and productivity"
+icon: "calendar-check"
+---
+
+# Personal Assistant Agents
+
+Personal assistant agents can help users manage tasks, organize information, and increase productivity. In this lesson, we'll learn how to build effective personal assistant agents.
+
+## What is a Personal Assistant Agent?
+
+A personal assistant agent is designed to:
+- Manage tasks and reminders
+- Organize information
+- Answer questions
+- Provide recommendations
+- Help with personal productivity
+
+## Creating a Basic Personal Assistant Agent
+
+Let's start by building a simple personal assistant agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic personal assistant agent
+assistant_agent = Agent(
+    name="PersonalAssistant",
+    instructions="""
+    You are a helpful personal assistant who helps users manage tasks and information.
+    
+    When assisting users:
+    1. Be concise and efficient in your responses
+    2. Prioritize user requests based on importance and urgency
+    3. Maintain a helpful, friendly tone
+    4. Remember important information the user shares
+    5. Proactively suggest relevant information when appropriate
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the personal assistant agent
+response = assistant_agent.start("I need to plan a meeting with my team next Tuesday at 2 PM")
+print(response)
+```
+
+## Task Management Assistant
+
+Let's create an agent specialized in task management:
+
+```python
+task_agent = Agent(
+    name="TaskManager",
+    instructions="""
+    You are a task management assistant who helps users organize and prioritize tasks.
+    
+    When managing tasks:
+    1. Help identify and clarify tasks
+    2. Assist with prioritization
+    3. Break down complex tasks into manageable steps
+    4. Track deadlines and progress
+    5. Provide reminders and follow-ups
+    
+    For each task, collect:
+    - Task description
+    - Priority (High, Medium, Low)
+    - Deadline (if applicable)
+    - Dependencies (other tasks that must be completed first)
+    - Estimated time required
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the task management agent
+task_response = task_agent.start(
+    """
+    I need help organizing these tasks for the week:
+    
+    - Prepare quarterly report (due Friday)
+    - Meet with client about new project (Wednesday, 2 PM)
+    - Review team's project proposals
+    - Finish budget planning for next quarter
+    - Send feedback on marketing materials
+    """
+)
+print(task_response)
+```
+
+## Information Organization Assistant
+
+Let's create an agent that helps organize information:
+
+```python
+info_agent = Agent(
+    name="InformationOrganizer",
+    instructions="""
+    You are an information organization assistant who helps users structure and categorize information.
+    
+    When organizing information:
+    1. Identify the main categories and topics
+    2. Create logical groupings
+    3. Suggest clear labels and headers
+    4. Establish relationships between different pieces of information
+    5. Present information in a structured, easily navigable format
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the information organization agent
+info_response = info_agent.start(
+    """
+    Help me organize my research notes on climate change:
+    
+    - Rising sea levels affecting coastal cities
+    - Carbon dioxide levels increasing annually
+    - Renewable energy adoption statistics by country
+    - Impact of deforestation on carbon sequestration
+    - Climate policy differences between EU and US
+    - Economic impacts of extreme weather events
+    - Public opinion polls on climate action
+    - Arctic ice sheet measurements over 50 years
+    - Agricultural adaptations to changing climate
+    """
+)
+print(info_response)
+```
+
+## Daily Planning Assistant
+
+Let's create an agent that helps with daily planning:
+
+```python
+planning_agent = Agent(
+    name="DailyPlanner",
+    instructions="""
+    You are a daily planning assistant who helps users organize their day efficiently.
+    
+    When helping with daily planning:
+    1. Prioritize tasks based on importance and deadlines
+    2. Consider time constraints and commitments
+    3. Suggest efficient scheduling
+    4. Allow for breaks and transitions
+    5. Balance work tasks with personal needs
+    
+    Create a structured daily plan with time blocks and priorities.
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the daily planning agent
+planning_response = planning_agent.start(
+    """
+    Help me plan my day tomorrow. I have:
+    
+    - A team meeting from 9:30-10:30 AM
+    - A report due by end of day
+    - Emails to catch up on (about 1 hour's worth)
+    - Lunch with a colleague at 12:30 PM
+    - A dentist appointment at 3:00 PM (will take about 1 hour including travel)
+    - Need to prepare a presentation for Friday
+    
+    I typically work from 8:30 AM to 5:30 PM and prefer to have short breaks between tasks.
+    """
+)
+print(planning_response)
+```
+
+## Decision Support Assistant
+
+Let's create an agent that helps with decision-making:
+
+```python
+decision_agent = Agent(
+    name="DecisionSupport",
+    instructions="""
+    You are a decision support assistant who helps users make informed choices.
+    
+    When supporting decisions:
+    1. Help clarify the decision to be made
+    2. Identify relevant criteria and factors
+    3. Present pros and cons for each option
+    4. Ask questions to understand preferences and priorities
+    5. Provide a structured analysis without making the final decision
+    
+    Present options with balanced information to help the user make their own informed choice.
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the decision support agent
+decision_response = decision_agent.start(
+    """
+    I'm trying to decide whether to buy a new laptop now or wait 3 months. 
+    
+    Current situation:
+    - My current laptop is 4 years old and running slow
+    - It still works but has battery issues
+    - I use it daily for work (programming and video calls)
+    - I've heard new models are coming out in 3 months
+    - My budget is around $1,500
+    
+    What should I consider in making this decision?
+    """
+)
+print(decision_response)
+```
+
+## Recommendation Assistant
+
+Let's create an agent that provides personalized recommendations:
+
+```python
+recommendation_agent = Agent(
+    name="RecommendationAssistant",
+    instructions="""
+    You are a recommendation assistant who provides personalized suggestions.
+    
+    When making recommendations:
+    1. Understand the user's preferences and requirements
+    2. Consider constraints (budget, time, location, etc.)
+    3. Provide diverse options that match the criteria
+    4. Explain why each recommendation might be suitable
+    5. Give enough detail for the user to evaluate options
+    
+    For each recommendation, include:
+    - A brief description
+    - Key features or highlights
+    - Why it matches the user's needs
+    """,
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+# Use the recommendation agent
+recommendation_response = recommendation_agent.start(
+    """
+    Can you recommend some books for me? I enjoy:
+    
+    - Science fiction and fantasy
+    - Books with complex, morally gray characters
+    - Intricate world-building
+    - Books that explore philosophical themes
+    
+    I've recently read and enjoyed:
+    - "The Fifth Season" by N.K. Jemisin
+    - "Dune" by Frank Herbert
+    - "The Lies of Locke Lamora" by Scott Lynch
+    """
+)
+print(recommendation_response)
+```
+
+## Personal Assistant Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Respect Privacy" icon="lock">
+    Be careful with personal information
+  </Card>
+  <Card title="Customize Interactions" icon="sliders">
+    Adapt to the user's preferences
+  </Card>
+  <Card title="Clear Communication" icon="comment">
+    Keep responses direct and easy to understand
+  </Card>
+  <Card title="Contextual Awareness" icon="puzzle-piece">
+    Remember relevant context from previous interactions
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to deploy and share your AI agents with others.

docs/course/agents/20-deploying-agents.mdx

@@ -0,0 +1,342 @@
+---
+title: "Deploying Agents"
+description: "How to deploy and share your AI agents with others"
+icon: "rocket"
+---
+
+# Deploying Agents
+
+After creating your AI agents, the next step is deploying them for use by yourself or others. In this final lesson, we'll explore different deployment options and best practices.
+
+## Deployment Options
+
+There are several ways to deploy your AI agents:
+
+### 1. Local Deployment
+
+Running your agents locally is the simplest approach:
+
+```python
+from praisonaiagents import Agent
+
+def run_local_agent():
+    agent = Agent(
+        name="SimpleAgent",
+        instructions="You are a helpful assistant that answers questions concisely.",
+        llm="gpt-4o-mini"  # Using the specified model
+    )
+    
+    while True:
+        user_input = input("Ask a question (type 'exit' to quit): ")
+        if user_input.lower() == 'exit':
+            break
+        
+        response = agent.start(user_input)
+        print("\nAgent response:")
+        print(response)
+        print("\n" + "-"*50 + "\n")
+
+if __name__ == "__main__":
+    run_local_agent()
+```
+
+### 2. Web Application Deployment
+
+You can deploy your agents as part of a web application:
+
+```python
+from flask import Flask, request, jsonify
+from praisonaiagents import Agent
+import os
+
+app = Flask(__name__)
+
+# Create agent once at startup
+support_agent = Agent(
+    name="SupportAgent",
+    instructions="You are a customer support agent that helps users with product questions.",
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+@app.route('/api/support', methods=['POST'])
+def get_support():
+    data = request.json
+    user_query = data.get('query', '')
+    
+    if not user_query:
+        return jsonify({"error": "No query provided"}), 400
+    
+    try:
+        response = support_agent.start(user_query)
+        return jsonify({"response": response})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(debug=True, port=5000)
+```
+
+### 3. Serverless Function Deployment
+
+For scalable, event-driven deployments, serverless functions work well:
+
+```python
+# Example AWS Lambda function
+import json
+from praisonaiagents import Agent
+
+# Initialize agent outside the handler for reuse across invocations
+agent = Agent(
+    name="ServerlessAgent",
+    instructions="You provide concise, helpful responses to user questions.",
+    llm="gpt-4o-mini"  # Using the specified model
+)
+
+def lambda_handler(event, context):
+    try:
+        # Get the user query from the event
+        body = json.loads(event.get('body', '{}'))
+        user_query = body.get('query', '')
+        
+        if not user_query:
+            return {
+                'statusCode': 400,
+                'body': json.dumps({'error': 'No query provided'})
+            }
+        
+        # Process with the agent
+        response = agent.start(user_query)
+        
+        return {
+            'statusCode': 200,
+            'body': json.dumps({'response': response})
+        }
+    except Exception as e:
+        return {
+            'statusCode': 500,
+            'body': json.dumps({'error': str(e)})
+        }
+```
+
+## Environment Variables and Security
+
+When deploying agents, it's crucial to handle API keys and secrets securely:
+
+```python
+import os
+from dotenv import load_dotenv
+from praisonaiagents import Agent
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Access API key from environment variable
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Create agent with API key
+agent = Agent(
+    name="SecureAgent",
+    instructions="You are a helpful assistant.",
+    llm="gpt-4o-mini",  # Using the specified model
+    api_key=api_key  # Pass API key securely
+)
+```
+
+## Creating a Simple Chat Interface
+
+Here's a simple command-line chat interface for your agent:
+
+```python
+import os
+from praisonaiagents import Agent
+
+def create_chat_interface():
+    # Initialize agent
+    agent = Agent(
+        name="ChatAgent",
+        instructions="""
+        You are a conversational assistant that maintains context throughout the conversation.
+        Respond in a helpful, concise manner.
+        """,
+        llm="gpt-4o-mini"  # Using the specified model
+    )
+    
+    print("Chat with AI Assistant (type 'exit' to quit)")
+    print("-" * 50)
+    
+    # Start conversation
+    conversation_active = True
+    first_message = True
+    
+    while conversation_active:
+        # Get user input
+        user_message = input("You: ")
+        
+        # Check if user wants to exit
+        if user_message.lower() == 'exit':
+            print("Goodbye!")
+            conversation_active = False
+            continue
+        
+        # Get agent response
+        try:
+            if first_message:
+                response = agent.start(user_message)
+                first_message = False
+            else:
+                response = agent.continue(user_message)
+            
+            print("\nAssistant:", response)
+            print("\n" + "-" * 50)
+        except Exception as e:
+            print(f"Error: {str(e)}")
+    
+if __name__ == "__main__":
+    create_chat_interface()
+```
+
+## Scaling Considerations
+
+As you deploy agents for wider use, consider these scaling factors:
+
+<CardGroup cols={2}>
+  <Card title="Rate Limiting" icon="gauge-high">
+    Implement rate limiting to manage API usage and costs
+  </Card>
+  <Card title="Caching" icon="database">
+    Cache common responses to improve performance and reduce API calls
+  </Card>
+  <Card title="Error Handling" icon="triangle-exclamation">
+    Implement robust error handling for API failures
+  </Card>
+  <Card title="Monitoring" icon="chart-line">
+    Set up monitoring for usage patterns and performance issues
+  </Card>
+</CardGroup>
+
+## Best Practices for Deployment
+
+### 1. Testing Before Deployment
+
+Always test your agents thoroughly before deployment:
+
+```python
+def test_agent_functionality():
+    """Test basic agent functionality with various inputs"""
+    agent = Agent(
+        name="TestAgent",
+        instructions="You are a helpful assistant for testing.",
+        llm="gpt-4o-mini"  # Using the specified model
+    )
+    
+    test_cases = [
+        "What is artificial intelligence?",
+        "How do I reset my password?",
+        "Tell me about machine learning"
+    ]
+    
+    for test_case in test_cases:
+        print(f"\nTesting: {test_case}")
+        response = agent.start(test_case)
+        print(f"Response: {response[:100]}...")  # Print first 100 chars
+        
+        # Add assertions or validation logic here
+        assert len(response) > 0, "Response should not be empty"
+```
+
+### 2. Documentation
+
+Create clear documentation for users of your agent:
+
+```python
+"""
+# Customer Support Agent API
+
+This API provides access to an AI customer support agent
+that can answer questions about our products.
+
+## Authentication
+
+Include your API key in the request header:
+```
+Authorization: Bearer YOUR_API_KEY
+```
+
+## Endpoints
+
+POST /api/support
+- Request body: {"query": "Your question here"}
+- Response: {"response": "Agent's answer"}
+
+## Example
+
+```python
+import requests
+
+response = requests.post(
+    "https://api.example.com/api/support",
+    headers={"Authorization": "Bearer YOUR_API_KEY"},
+    json={"query": "How do I reset my password?"}
+)
+
+print(response.json())
+```
+"""
+```
+
+### 3. Version Control
+
+Implement version control for your agents to track changes:
+
+```python
+class VersionedAgent:
+    def __init__(self, name, version, instructions):
+        self.name = name
+        self.version = version
+        self.agent = Agent(
+            name=f"{name}_v{version}",
+            instructions=instructions,
+            llm="gpt-4o-mini"  # Using the specified model
+        )
+    
+    def get_response(self, query):
+        response = self.agent.start(query)
+        return {
+            "agent_name": self.name,
+            "version": self.version,
+            "response": response,
+            "timestamp": datetime.now().isoformat()
+        }
+```
+
+## Conclusion
+
+Congratulations on completing the AI Agents Course! You've learned how to:
+
+1. Understand different types of AI agents and their architectures
+2. Create effective agent instructions and tools
+3. Implement memory and context for your agents
+4. Build specialized agents for various tasks
+5. Create multi-agent systems
+6. Deploy your agents for real-world use
+
+As AI agent technology continues to evolve, keep experimenting with new capabilities and use cases. Remember that the best agents are those that effectively solve real problems for users while being trustworthy, reliable, and helpful.
+
+We hope this course has provided you with the knowledge and skills to build powerful AI agents that enhance productivity and creativity!
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Join the Community" icon="users">
+    Participate in discussions and share your agent projects
+  </Card>
+  <Card title="Stay Updated" icon="newspaper">
+    Follow updates in AI agent technology and new features
+  </Card>
+  <Card title="Share Your Feedback" icon="comment">
+    Let us know how you're using agents and what you'd like to learn next
+  </Card>
+  <Card title="Build Real Solutions" icon="lightbulb">
+    Apply your knowledge to build solutions for real-world problems
+  </Card>
+</CardGroup>

docs/developers/wrapper.mdx

@@ -65,14 +65,14 @@ def basic(): # Basic Mode
 def advanced(): # Advanced Mode with options
     praisonai = PraisonAI(
         agent_file="agents.yaml",
-        framework="autogen",
+        framework="autogen", # use AG2 framework (Formerly AutoGen)
     )
     praisonai.run()
     
 def auto(): # Full Automatic Mode
     praisonai = PraisonAI(
         auto="Create a movie script about car in mars",
-        framework="autogen"
+        framework="autogen" # use AG2 framework
     )
     print(praisonai.framework)
     praisonai.run()

docs/framework/autogen.mdx

@@ -1,12 +1,13 @@
 ---
-title: "AutoGen with PraisonAI"
-description: "Guide for using AutoGen framework with PraisonAI, including installation, setup, and multi-agent conversation capabilities"
+title: "AG2 with PraisonAI"
+description: "Guide for using AG2 framework with PraisonAI, including installation, setup, and multi-agent conversation capabilities"
 icon: "robot"
 ---
 
-# AutoGen with PraisonAI
+# AG2 with PraisonAI
 
-Low-code solution to run AutoGen with integrated tools and features.
+Low-code solution to run AG2 with integrated tools and features.
+AutoGen is being evolved into AG2, check the new look [here](https://docs.ag2.ai/docs/Home/)
 
 ## Installation
 
@@ -16,7 +17,7 @@ pip install "praisonai[autogen]"
 ```
 
 This installation includes:
-- AutoGen framework
+- AG2 framework
 - PraisonAI tools integration
 - Multi-agent conversation capabilities
 - Code execution environment
@@ -27,7 +28,7 @@ This installation includes:
 # Set your OpenAI API key
 export OPENAI_API_KEY=xxxxxxxxxx
 
-# Initialize with AutoGen
+# Initialize with AG2
 praisonai --framework autogen --init "Create a Movie Script About Cat in Mars"
 
 # Run the agents

docs/home.mdx

@@ -1,7 +1,7 @@
 ---
 title: "Praison AI"
 sidebarTitle: "Home"
-description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Self Reflection, combining PraisonAI Agents, AutoGen, and CrewAI for building multi-agent LLM systems"
+description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Self Reflection, combining PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI for building multi-agent LLM systems"
 icon: "house"
 openGraph:
   url: "/"
@@ -46,7 +46,7 @@ seo:
 </div>
 
 <Tip>
-  PraisonAI combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
+  PraisonAI combines PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
 </Tip>
 
 ## Key Features
@@ -56,7 +56,7 @@ seo:
     Automated creation and management of AI agents with self-reflection capabilities
   </Card>
   <Card title="Framework Integration" icon="puzzle-piece">
-    Seamless integration with CrewAI and AutoGen frameworks
+    Seamless integration with CrewAI and AG2 frameworks
   </Card>
   <Card title="LLM Support" icon="brain">
     Support for 100+ Language Learning Models
@@ -301,8 +301,8 @@ seo:
   <Card title="CrewAI Framework" icon="users-gear" href="/framework/crewai">
     Build collaborative AI teams with CrewAI integration
   </Card>
-  <Card title="AutoGen Framework" icon="robot" href="/framework/autogen">
-    Create autonomous agent networks using AutoGen
+  <Card title="AG2 Framework" icon="robot" href="/framework/autogen">
+    Create autonomous agent networks using AG2 (Formerly AutoGen)
   </Card>
   <Card title="Multimodal Agents" icon="icons" href="/framework/multimodalagents">
     Work with agents that can process text, images, and other data types
@@ -316,7 +316,7 @@ seo:
 
 <CardGroup cols={3}>
   <Card title="Multi Agents UI" icon="users" href="/ui/ui">
-    Work with CrewAI or AutoGen multi-agent systems
+    Work with CrewAI or AG2 multi-agent systems
   </Card>
   <Card title="Chat Interface" icon="comments" href="/ui/chat">
     Chat with 100+ LLMs using a single AI Agent

docs/index.mdx

@@ -1,7 +1,7 @@
 ---
 title: "Praison AI"
 sidebarTitle: "Home"
-description: "PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AutoGen, and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration."
+description: "PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration."
 icon: "house"
 openGraph:
   url: "/"
@@ -45,7 +45,7 @@ seo:
     Automated creation and management of AI agents with self-reflection capabilities
   </Card>
   <Card title="Framework Integration" icon="puzzle-piece">
-    Seamless integration with CrewAI and AutoGen frameworks
+    Seamless integration with CrewAI and AG2 frameworks
   </Card>
   <Card title="LLM Support" icon="brain">
     Support for 100+ Language Learning Models
@@ -114,27 +114,53 @@ agents.start()
     </Steps>
   </Tab>
   <Tab title="No Code">
-    <Tip>
-        PraisonAI combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
-    </Tip>
     <Steps>
       <Step title="Install Package">
+        Install the No Code PraisonAI Package:
         ```bash
-        pip install praisonai
+        pip install praisonaiagents
         ```
       </Step>
+
       <Step title="Set API Key">
         ```bash
-        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        export OPENAI_API_KEY=your_openai_key
         ```
       </Step>
-      <Step title="Auto Mode">
+
+      <Step title="Create Config">
+        Create `agents.yaml`:
+
+    <CodeGroup>
+      ```yaml Single Agent
+      roles:
+        summarise_agent:
+          instructions: Summarise Photosynthesis
+      ```
+
+      ```yaml Multiple Agents
+      roles:
+        diet_agent:
+          instructions: Give me 5 healthy food recipes
+        blog_agent:
+          instructions: Write a blog post about the food recipes
+      ```
+    </CodeGroup>
+
+<Note>
+You can automatically create `agents.yaml` using:
+```bash
+praisonai --init "your task description"
+```
+</Note>
+
+      </Step>
+
+      <Step title="Run Agents">
+        Execute your config:
         ```bash
-        praisonai --auto create a movie script about Robots in Mars
+        praisonai agents.yaml
         ```
-
-        This will automatically create required agents and complete the task
-
       </Step>
     </Steps>
   </Tab>
@@ -180,8 +206,83 @@ agents.start();
       </Step>
     </Steps>
   </Tab>
+  <Tab title="TypeScript">
+    <Steps>
+      <Step title="Install Package">
+        <CodeGroup>
+        ```bash npm
+        npm install praisonai
+        ```
+        ```bash yarn
+        yarn add praisonai
+        ```
+        </CodeGroup>
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.ts` file
+
+        ## Code Example
+
+<CodeGroup>
+```javascript Single Agent
+import { Agent } from 'praisonai';
+
+const agent = new Agent({ 
+  instructions: `You are a creative writer who writes short stories with emojis.`,
+  name: "StoryWriter"
+});
+
+agent.start("Write a story about a time traveler")
+```
+
+```javascript Multi Agents
+import { Agent, PraisonAIAgents } from 'praisonai';
+
+const storyAgent = new Agent({
+  instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
+  name: "StoryAgent"
+});
+
+const summaryAgent = new Agent({
+  instructions: "Summarize the provided AI story in one sentence with emojis.",
+  name: "SummaryAgent"
+});
+
+const agents = new PraisonAIAgents({
+  agents: [storyAgent, summaryAgent]
+});
+
+agents.start()
+```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        npx ts-node app.ts
+        ```
+      </Step>
+    </Steps>
+  </Tab>
 </Tabs>
 
+## Playground
+
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="600px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
 ## AI Agents Flow
 
 ```mermaid
@@ -610,8 +711,8 @@ flowchart LR
   <Card title="CrewAI Framework" icon="users-gear" href="/framework/crewai">
     Build collaborative AI teams with CrewAI integration
   </Card>
-  <Card title="AutoGen Framework" icon="robot" href="/framework/autogen">
-    Create autonomous agent networks using AutoGen
+  <Card title="AG2 Framework" icon="robot" href="/framework/autogen">
+    Create autonomous agent networks using AG2 (Formerly AutoGen)
   </Card>
   <Card title="Multimodal Agents" icon="icons" href="/framework/multimodalagents">
     Work with agents that can process text, images, and other data types
@@ -625,7 +726,7 @@ flowchart LR
 
 <CardGroup cols={3}>
   <Card title="Multi Agents UI" icon="users" href="/ui/ui">
-    Work with CrewAI or AutoGen multi-agent systems
+    Work with CrewAI or AG2 multi-agent systems
   </Card>
   <Card title="Chat Interface" icon="comments" href="/ui/chat">
     Chat with 100+ LLMs using a single AI Agent

docs/installation.mdx

@@ -14,6 +14,9 @@ pip install praisonai
 ```bash JavaScript
 npm install praisonai
 ```
+```bash TypeScript
+npm install praisonai
+```
 </RequestExample>
 
 <Note>
@@ -109,6 +112,22 @@ Follow these steps to set up PraisonAI in your development environment.
         </Step>
     </Steps>
   </Tab>
+  <Tab title="TypeScript">
+    <Steps>
+        <Step title="Install PraisonAI">
+            Install the PraisonAI package:
+            ```bash
+            npm install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_openai_key
+            ```
+        </Step>
+    </Steps>
+  </Tab>
 </Tabs>
 
 Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)

docs/introduction.mdx

@@ -248,8 +248,83 @@ agents.start();
       </Step>
     </Steps>
   </Tab>
+  <Tab title="TypeScript">
+    <Steps>
+      <Step title="Install Package">
+        <CodeGroup>
+        ```bash npm
+        npm install praisonai
+        ```
+        ```bash yarn
+        yarn add praisonai
+        ```
+        </CodeGroup>
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.ts` file
+
+        ## Code Example
+
+<CodeGroup>
+```javascript Single Agent
+import { Agent } from 'praisonai';
+
+const agent = new Agent({ 
+  instructions: `You are a creative writer who writes short stories with emojis.`,
+  name: "StoryWriter"
+});
+
+agent.start("Write a story about a time traveler")
+```
+
+```javascript Multi Agents
+import { Agent, PraisonAIAgents } from 'praisonai';
+
+const storyAgent = new Agent({
+  instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
+  name: "StoryAgent"
+});
+
+const summaryAgent = new Agent({
+  instructions: "Summarize the provided AI story in one sentence with emojis.",
+  name: "SummaryAgent"
+});
+
+const agents = new PraisonAIAgents({
+  agents: [storyAgent, summaryAgent]
+});
+
+agents.start()
+```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        npx ts-node app.ts
+        ```
+      </Step>
+    </Steps>
+  </Tab>
 </Tabs>
 
+## Playground
+
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="600px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
 ## Key Features
 
 <CardGroup cols={3}>

docs/js/customtools.mdx

@@ -0,0 +1,50 @@
+---
+title: "Custom Tools for TypeScript AI Agents"
+sidebarTitle: "Custom Tools"
+description: "Learn how to create custom tools for TypeScript AI Agents"
+icon: "toolbox"
+---
+
+## Single Agent
+
+```typescript
+import { Agent } from 'praisonai';
+
+async function getWeather(location: string) {
+  console.log(`Getting weather for ${location}...`);
+  return `${Math.floor(Math.random() * 30)}°C`;
+}
+
+const agent = new Agent({ 
+  instructions: `You provide the current weather for requested locations.`,
+  name: "DirectFunctionAgent",
+  tools: [getWeather]
+});
+
+agent.start("What's the weather in Paris, France?");
+``` 
+
+## Multi Agents
+
+```typescript
+import { Agent } from 'praisonai';
+
+async function getWeather(location: string) {
+  console.log(`Getting weather for ${location}...`);
+  return `${Math.floor(Math.random() * 30)}°C`;
+}
+
+async function getTime(location: string) {
+  console.log(`Getting time for ${location}...`);
+  const now = new Date();
+  return `${now.getHours()}:${now.getMinutes()}`;
+}
+
+const agent = new Agent({ 
+  instructions: `You provide the current weather and time for requested locations.`,
+  name: "DirectFunctionAgent",
+  tools: [getWeather, getTime]
+});
+
+agent.start("What's the weather and time in Paris, France and Tokyo, Japan?");
+``` 

docs/js/typescript.mdx

@@ -264,3 +264,34 @@ agents.start()
     ```
   </Step>
 </Steps>
+
+## Tool Calls Examples
+
+<AccordionGroup>
+  <Accordion title="Direct Function Tools" icon="code" defaultOpen>
+    Create an agent with directly registered function tools:
+
+```typescript
+import { Agent } from 'praisonai';
+
+async function getWeather(location: string) {
+  console.log(`Getting weather for ${location}...`);
+  return `${Math.floor(Math.random() * 30)}°C`;
+}
+
+async function getTime(location: string) {
+  console.log(`Getting time for ${location}...`);
+  const now = new Date();
+  return `${now.getHours()}:${now.getMinutes()}`;
+}
+
+const agent = new Agent({ 
+  instructions: `You provide the current weather and time for requested locations.`,
+  name: "DirectFunctionAgent",
+  tools: [getWeather, getTime]
+});
+
+agent.start("What's the weather and time in Paris, France and Tokyo, Japan?");
+```
+  </Accordion>
+</AccordionGroup>

docs/mcp/airbnb.mdx

@@ -0,0 +1,112 @@
+---
+title: "Airbnb MCP Integration"
+sidebarTitle: "Airbnb"
+description: "Guide for integrating Airbnb booking capabilities with PraisonAI agents using MCP"
+icon: "airbnb"
+---
+
+## Add Airbnb Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `airbnb_search.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+        )
+
+        search_agent.start("I want to book an apartment in Paris for 2 nights. 03/28 - 03/30 for 2 adults")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python airbnb_search.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Gradio UI 
+
+```python
+from praisonaiagents import Agent, MCP
+import gradio as gr
+
+def search_airbnb(query):
+    agent = Agent(
+        instructions="You help book apartments on Airbnb.",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+    )
+    result = agent.start(query)
+    return f"## Airbnb Search Results\n\n{result}"
+
+demo = gr.Interface(
+    fn=search_airbnb,
+    inputs=gr.Textbox(placeholder="I want to book an apartment in Paris for 2 nights..."),
+    outputs=gr.Markdown(),
+    title="Airbnb Booking Assistant",
+    description="Enter your booking requirements below:"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Accommodation Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Booking Details" icon="calendar">
+    Specify dates, guests, and location preferences in natural language.
+  </Card>
+  <Card title="NPM Package" icon="js">
+    Leverages the official Airbnb MCP server package.
+  </Card>
+</CardGroup>

docs/mcp/anthropic.mdx

@@ -0,0 +1,93 @@
+---
+title: "Anthropic MCP Integration"
+sidebarTitle: "Anthropic"
+description: "Guide for integrating Anthropic's Claude models with PraisonAI agents using MCP"
+icon: "a"
+---
+
+## Add Anthropic Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your Anthropic API key as an environment variable in your terminal:
+        ```zsh
+        export ANTHROPIC_API_KEY=your_anthropic_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `anthropic_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get API key from environment variable
+        anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY")
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="anthropic/claude-3-7-sonnet-20250219",
+            tools=MCP(
+                command="npx",
+                args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
+                env={"ANTHROPIC_API_KEY": anthropic_api_key}
+            )
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python anthropic_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Anthropic API key
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Claude 3.7 Sonnet" icon="brain">
+    Leverage Anthropic's powerful Claude 3.7 Sonnet model.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Environment Variables" icon="key">
+    Securely pass API keys using environment variables.
+  </Card>
+</CardGroup>

docs/mcp/aws-kb-retrieval.mdx

@@ -0,0 +1,84 @@
+---
+title: "AWS KB Retrieval MCP Integration"
+sidebarTitle: "AWS KB Retrieval"
+description: "Guide for integrating AWS Knowledge Base retrieval capabilities with PraisonAI agents using MCP"
+icon: "aws"
+---
+
+## Add AWS KB Retrieval Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[AWS KB Retrieval MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF9900,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Keys">
+        Set your AWS credentials as environment variables in your terminal:
+        ```bash
+        export AWS_ACCESS_KEY_ID=your_aws_access_key_id_here
+        export AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key_here
+        export AWS_REGION=your_aws_region_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `aws_kb_retrieval.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get AWS credentials from environment
+        aws_access_key = os.getenv("AWS_ACCESS_KEY_ID")
+        aws_secret_key = os.getenv("AWS_SECRET_ACCESS_KEY")
+        aws_region = os.getenv("AWS_REGION")
+
+        # Use a single string command with AWS KB Retrieval configuration
+        aws_kb_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with AWS Knowledge Base.
+            Use the available tools when relevant to retrieve and process AWS information.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-aws-kb-retrieval",
+                    env={
+                        "AWS_ACCESS_KEY_ID": aws_access_key,
+                        "AWS_SECRET_ACCESS_KEY": aws_secret_key,
+                        "AWS_REGION": aws_region
+                    })
+        )
+
+        aws_kb_agent.start("Search AWS documentation about EC2 instances")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python aws_kb_retrieval.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - AWS credentials (Access Key ID, Secret Access Key, and Region)
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/bravesearch.mdx

@@ -0,0 +1,77 @@
+---
+title: "Brave Search MCP Integration"
+sidebarTitle: "Brave Search"
+description: "Guide for integrating Brave Search capabilities with PraisonAI agents using MCP"
+icon: "searchengin"
+---
+
+## Add Brave Search Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Brave Search MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4169E1,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your Brave Search API key as an environment variable in your terminal:
+        ```bash
+        export BRAVE_API_KEY=your_brave_api_key_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+        
+        You can obtain a Brave Search API key from [Brave Search API](https://brave.com/search/api/).
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `brave_search.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Use the API key from environment or set it directly
+        brave_api_key = os.getenv("BRAVE_API_KEY") or "your_brave_api_key_here"
+
+        # Use a single string command with environment variables
+        search_agent = Agent(
+            instructions="""You are a helpful assistant that can search the web for information.
+            Use the available tools when relevant to answer user questions.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-brave-search", env={"BRAVE_API_KEY": brave_api_key})
+        )
+
+        search_agent.start("Search more information about AI News")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python brave_search.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Brave Search API key
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/custom-python-client.mdx

@@ -0,0 +1,103 @@
+---
+title: "Custom Python MCP Client"
+sidebarTitle: "Custom Python Client"
+description: "Guide for creating a client to interact with a custom Python MCP server"
+icon: "person-digging"
+---
+
+## Custom Python MCP Client
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Client[Python MCP Client]
+    Client --> Server[Python MCP Server]
+    Server --> Client
+    Client --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Client fill:#3776AB,color:#fff
+    style Server fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Overview
+
+The Custom Python MCP Client demonstrates how to integrate a custom Python MCP server with a PraisonAI agent. This client connects to a stock price MCP server to retrieve real-time stock information.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Install the required packages:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set Up the Server">
+        First, set up the Custom Python MCP Server.
+    </Step>
+    <Step title="Create Custom Python Client">
+        Save the code above to a file named `custom-python-client.py`.
+```python
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools = MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/custom-python-server.py")
+)
+
+# NOTE: Python Path replace with yours: /Users/praison/miniconda3/envs/mcp/bin/python
+# NOTE: custom-python-server.py file path, replace it with yours: /Users/praison/stockprice/custom-python-server.py
+
+agent.start("What is the stock price of Tesla?")
+```
+    </Step>
+    <Step title="Run the Client">
+        Execute the client script:
+        ```bash
+        python custom-python-client.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - praisonaiagents and mcp packages
+  - A properly configured custom Python MCP server
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Environment Variables
+
+For better security and flexibility, you can modify the client to use environment variables:
+
+```python
+import os
+from praisonaiagents import Agent, MCP
+
+# Get paths from environment variables or use defaults
+python_path = os.getenv("PYTHON_PATH", "/path/to/python")
+server_path = os.getenv("SERVER_PATH", "/path/to/server.py")
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP(f"{python_path} {server_path}")
+)
+
+agent.start("What is the stock price of Tesla?")
+```
+
+This approach allows you to set the paths using environment variables:
+
+```bash
+export PYTHON_PATH=/Users/praison/miniconda3/envs/mcp/bin/python
+export SERVER_PATH=/Users/praison/stockprice/app.py
+```

docs/mcp/custom-python-server.mdx

@@ -0,0 +1,86 @@
+---
+title: "Custom Python MCP Server"
+sidebarTitle: "Custom Python Server"
+description: "Guide for creating a custom Python MCP server for stock price retrieval"
+icon: "server"
+---
+
+## Custom Python MCP Server
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Client[Python MCP Client]
+    Client --> Server[Python MCP Server]
+    Server --> Client
+    Client --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Client fill:#3776AB,color:#fff
+    style Server fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Overview
+
+The Custom Python MCP Server is a simple implementation of the Model Context Protocol (MCP) that provides stock price information using the yfinance library. This server can be used with PraisonAI agents to retrieve real-time stock prices.
+
+## Server Implementation
+
+Below is the complete implementation of the custom Python MCP server:
+
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Install the required packages:
+        ```bash
+        pip install yfinance mcp
+        ```
+    </Step>
+    <Step title="Save the Server Code">
+        Save the code above to a file named `custom-python-server.py`.
+```python
+import yfinance as yf
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("stock_prices")
+
+@mcp.tool()
+async def get_stock_price(ticker: str) -> str:
+    """Get the current stock price for a given ticker symbol.
+    
+    Args:
+        ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
+        
+    Returns:
+        Current stock price as a string
+    """
+    if not ticker:
+        return "No ticker provided"
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+        current_price = info.get('currentPrice') or info.get('regularMarketPrice')
+        if not current_price:
+            return f"Could not retrieve price for {ticker}"
+        return f"${current_price:.2f}"
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+if __name__ == "__main__":
+    mcp.run(transport='stdio')
+```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - yfinance package
+  - mcp package
+</Note>

docs/mcp/custom.mdx

@@ -0,0 +1,168 @@
+---
+title: "Custom Python MCP Server"
+sidebarTitle: "Custom Python MCP"
+description: "Guide for creating and using custom Python MCP servers with PraisonAI agents"
+icon: "python"
+---
+
+# Custom Python MCP Server
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Custom Python MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Create MCP Server">
+        Create a new file `app.py` with your custom MCP server implementation:
+```python
+import yfinance as yf
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("stock_prices")
+
+@mcp.tool()
+async def get_stock_price(ticker: str) -> str:
+    """Get the current stock price for a given ticker symbol.
+    
+    Args:
+        ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
+        
+    Returns:
+        Current stock price as a string
+    """
+    if not ticker:
+        return "No ticker provided"
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+        current_price = info.get('currentPrice') or info.get('regularMarketPrice')
+        if not current_price:
+            return f"Could not retrieve price for {ticker}"
+        return f"${current_price:.2f}"
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+if __name__ == "__main__":
+    mcp.run(transport='stdio')
+```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Install the required dependencies in a conda environment:
+        ```bash
+        pip install yfinance mcp
+        ```
+    </Step>
+
+    <Step title="Create Agent Integration">
+        Create a new file `stock_agent.py` with the following code:
+```python
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools = MCP("/path/to/python /path/to/app.py")
+)
+
+# NOTE: Replace with your actual Python path and app.py file path
+
+agent.start("What is the stock price of Tesla?")
+```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python stock_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Conda for environment management
+  - yfinance package for stock data
+  - mcp-python-sdk for MCP server implementation
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Custom Tools" icon="wrench">
+    Create your own custom tools with Python.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Type Hints" icon="code">
+    Strong typing for better code reliability.
+  </Card>
+  <Card title="Async Support" icon="bolt">
+    Built-in support for asynchronous functions.
+  </Card>
+</CardGroup>
+
+## Implementation Details
+
+### FastMCP Class
+
+The `FastMCP` class from the `mcp-python-sdk` package provides a simple way to create MCP servers in Python:
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+# Create an MCP server with a name
+mcp = FastMCP("my_tools")
+
+# Define a tool using the @mcp.tool decorator
+@mcp.tool()
+async def my_tool(param1: str, param2: int) -> str:
+    """Tool description with clear documentation.
+    
+    Args:
+        param1: Description of param1
+        param2: Description of param2
+        
+    Returns:
+        Description of the return value
+    """
+    # Tool implementation
+    return f"Processed {param1} with {param2}"
+
+# Run the server with stdio transport
+if __name__ == "__main__":
+    mcp.run(transport='stdio')
+```
+
+### Agent Integration
+
+To use your custom MCP server with PraisonAI agents, use the `MCP` class to specify the command to run your Python script:
+
+```python
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="Agent instructions",
+    llm="gpt-4o-mini",
+    tools=MCP(
+        command="python",  # Or full path to Python
+        args=["path/to/your/mcp_server.py"]  # Path to your MCP server script
+    )
+)
+```

docs/mcp/everart.mdx

@@ -0,0 +1,76 @@
+---
+title: "Everart MCP Integration"
+sidebarTitle: "Everart"
+description: "Guide for integrating Everart AI art generation capabilities with PraisonAI agents using MCP"
+icon: "palette"
+---
+
+## Add Everart Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Everart MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF6B6B,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your Everart API key as an environment variable in your terminal:
+        ```bash
+        export EVERART_API_KEY=your_everart_api_key_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `everart_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get Everart API key from environment
+        everart_api_key = os.getenv("EVERART_API_KEY")
+
+        # Use a single string command with Everart configuration
+        everart_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with Everart.
+            Use the available tools when relevant to generate and manage art.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-everart",
+                    env={"EVERART_API_KEY": everart_api_key})
+        )
+
+        everart_agent.start("Generate an artistic image of a sunset")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python everart_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Everart API key
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/filesystem.mdx

@@ -0,0 +1,77 @@
+---
+title: "Filesystem MCP Integration"
+sidebarTitle: "Filesystem"
+description: "Guide for integrating filesystem operations with PraisonAI agents using MCP"
+icon: "folder-open"
+---
+
+## Add Filesystem Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Filesystem MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FFA500,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `filesystem_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Define allowed directories for filesystem access
+        allowed_dirs = [
+            "/Users/username/Desktop",
+            "/path/to/other/allowed/dir"
+        ]
+
+        # Use a single string command with allowed directories
+        filesystem_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with the filesystem.
+            Use the available tools when relevant to manage files and directories.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-filesystem", args=allowed_dirs)
+        )
+
+        filesystem_agent.start("List files in the allowed directories")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python filesystem_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+  - Read/write access to the specified directories
+</Note>

docs/mcp/gdrive.mdx

@@ -0,0 +1,76 @@
+---
+title: "Google Drive MCP Integration"
+sidebarTitle: "Google Drive"
+description: "Guide for integrating Google Drive file management with PraisonAI agents using MCP"
+icon: "google-drive"
+---
+
+## Add Google Drive Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Google Drive MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#0F9D58,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set up Google Drive Credentials">
+        Set your Google Drive credentials path as an environment variable in your terminal:
+        ```bash
+        export GDRIVE_CREDENTIALS_PATH=path/to/your/gcp-oauth.keys.json
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `gdrive_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get the credentials path from environment
+        gdrive_credentials = os.getenv("GDRIVE_CREDENTIALS_PATH", "servers/gcp-oauth.keys.json")
+
+        # Use a single string command with Google Drive configuration
+        gdrive_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with Google Drive.
+            Use the available tools when relevant to manage files and folders.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-gdrive",
+                    env={"GDRIVE_CREDENTIALS_PATH": gdrive_credentials})
+        )
+
+        gdrive_agent.start("List files in my Google Drive")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python gdrive_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Google Drive API credentials (OAuth keys)
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/gemini.mdx

@@ -0,0 +1,93 @@
+---
+title: "Gemini MCP Integration"
+sidebarTitle: "Gemini"
+description: "Guide for integrating Google's Gemini models with PraisonAI agents using MCP"
+icon: "google"
+---
+
+## Add Gemini Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your Google API key as an environment variable in your terminal:
+        ```zsh
+        export GOOGLE_API_KEY=your_google_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `gemini_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get API key from environment variable
+        google_api_key = os.environ.get("GOOGLE_API_KEY")
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="gemini/gemini-2.5-pro-exp-03-25",
+            tools=MCP(
+                command="npx",
+                args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
+                env={"GOOGLE_API_KEY": google_api_key}
+            )
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python gemini_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Google API key for Gemini models
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Gemini 2.5 Pro" icon="brain">
+    Leverage Google's advanced Gemini 2.5 Pro model.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Environment Variables" icon="key">
+    Securely pass API keys using environment variables.
+  </Card>
+</CardGroup>

docs/mcp/github.mdx

@@ -0,0 +1,76 @@
+---
+title: "GitHub MCP Integration"
+sidebarTitle: "GitHub"
+description: "Guide for integrating GitHub repository management with PraisonAI agents using MCP"
+icon: "github"
+---
+
+## Add GitHub Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[GitHub MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#181717,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your GitHub Personal Access Token as an environment variable in your terminal:
+        ```bash
+        export GITHUB_PERSONAL_ACCESS_TOKEN=your_github_token_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `github_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Use the API key from environment or set it directly
+        github_token = os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN")
+
+        # Use a single string command with environment variables
+        github_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with GitHub.
+            Use the available tools when relevant to answer user questions.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-github", 
+                    env={"GITHUB_PERSONAL_ACCESS_TOKEN": github_token})
+        )
+
+        github_agent.start("List my GitHub repositories")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python github_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - GitHub Personal Access Token
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/gitlab.mdx

@@ -0,0 +1,81 @@
+---
+title: "GitLab MCP Integration"
+sidebarTitle: "GitLab"
+description: "Guide for integrating GitLab repository management with PraisonAI agents using MCP"
+icon: "gitlab"
+---
+
+## Add GitLab Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[GitLab MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FC6D26,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Keys">
+        Set your GitLab credentials as environment variables in your terminal:
+        ```bash
+        export GITLAB_PERSONAL_ACCESS_TOKEN=your_gitlab_token_here
+        export GITLAB_API_URL=https://gitlab.com/api/v4
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `gitlab_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Use the API token and URL from environment or set directly
+        gitlab_token = os.getenv("GITLAB_PERSONAL_ACCESS_TOKEN")
+        gitlab_api_url = os.getenv("GITLAB_API_URL", "https://gitlab.com/api/v4")
+
+        # Use a single string command with environment variables
+        gitlab_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with GitLab.
+            Use the available tools when relevant to answer user questions.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-gitlab", 
+                    env={
+                        "GITLAB_PERSONAL_ACCESS_TOKEN": gitlab_token,
+                        "GITLAB_API_URL": gitlab_api_url
+                    })
+        )
+
+        gitlab_agent.start("List my GitLab projects")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python gitlab_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - GitLab Personal Access Token
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/google-maps.mdx

@@ -0,0 +1,76 @@
+---
+title: "Google Maps MCP Integration"
+sidebarTitle: "Google Maps"
+description: "Guide for integrating Google Maps location services with PraisonAI agents using MCP"
+icon: "map"
+---
+
+## Add Google Maps Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Google Maps MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4285F4,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your Google Maps API key as an environment variable in your terminal:
+        ```bash
+        export GOOGLE_MAPS_API_KEY=your_google_maps_api_key_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `google_maps_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get the API key from environment
+        maps_api_key = os.getenv("GOOGLE_MAPS_API_KEY")
+
+        # Use a single string command with Google Maps configuration
+        maps_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with Google Maps.
+            Use the available tools when relevant to handle location-based queries.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-google-maps",
+                    env={"GOOGLE_MAPS_API_KEY": maps_api_key})
+        )
+
+        maps_agent.start("Find nearby restaurants in London")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python google_maps_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Google Maps API key
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/groq.mdx

@@ -0,0 +1,85 @@
+---
+title: "Groq MCP Integration"
+sidebarTitle: "Groq"
+description: "Guide for integrating Groq models with PraisonAI agents using MCP"
+icon: "bolt"
+---
+
+## Add Groq Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your Groq API key as an environment variable in your terminal:
+        ```bash
+        export GROQ_API_KEY=your_groq_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `groq_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="groq/llama-3.2-90b-vision-preview",
+            tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python groq_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Groq API key
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Ultra-Fast Inference" icon="bolt">
+    Utilize Groq's high-performance LPU inference for rapid responses.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Advanced Models" icon="brain">
+    Access to Llama 3.2 90B and other powerful models.
+  </Card>
+</CardGroup>

docs/mcp/memory.mdx

@@ -0,0 +1,75 @@
+---
+title: "Memory MCP Integration"
+sidebarTitle: "Memory"
+description: "Guide for integrating memory storage capabilities with PraisonAI agents using MCP"
+icon: "memory"
+---
+
+## Add Memory Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Memory MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4B0082,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `memory_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get the memory file path from environment
+        memory_file_path = os.getenv("MEMORY_FILE_PATH", "/path/to/custom/memory.json")
+
+        # Use a single string command with Memory configuration
+        memory_agent = Agent(
+            instructions="""You are a helpful assistant that can store and retrieve information.
+            Use the available tools when relevant to manage memory operations.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-memory",
+                    env={"MEMORY_FILE_PATH": memory_file_path})
+        )
+
+        memory_agent.start("Store this conversation in memory")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python memory_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+  - Write access to the specified memory file path
+</Note>

docs/mcp/mistral.mdx

@@ -0,0 +1,93 @@
+---
+title: "Mistral MCP Integration"
+sidebarTitle: "Mistral"
+description: "Guide for integrating Mistral models with PraisonAI agents using MCP"
+icon: "wind"
+---
+
+## Add Mistral Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your Mistral API key as an environment variable in your terminal:
+        ```zsh
+        export MISTRAL_API_KEY=your_mistral_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `mistral_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get API key from environment variable
+        mistral_api_key = os.environ.get("MISTRAL_API_KEY")
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="mistral/mistral-large-latest",
+            tools=MCP(
+                command="npx",
+                args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
+                env={"MISTRAL_API_KEY": mistral_api_key}
+            )
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python mistral_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Mistral API key
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Mistral Large" icon="brain">
+    Leverage Mistral's powerful large language model.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Environment Variables" icon="key">
+    Securely pass API keys using environment variables.
+  </Card>
+</CardGroup>

docs/mcp/ollama-python.mdx

@@ -0,0 +1,90 @@
+---
+title: "Ollama Python MCP Integration"
+sidebarTitle: "Ollama Python"
+description: "Guide for integrating Ollama models with Python MCP servers using PraisonAI agents"
+icon: "python"
+---
+
+## Add Ollama Python Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Stock Price MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set Up Ollama">
+        Make sure you have Ollama installed and running locally:
+        ```bash
+        # Install Ollama from https://ollama.ai/
+        # Pull the llama3.2 model
+        ollama pull llama3.2
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `ollama_stock.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        stock_agent = Agent(
+            instructions="""You are a Stock Price Assistant.""",
+            llm="ollama/llama3.2",
+            tools=MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/app.py")
+        )
+
+        # NOTE: Python Path replace with yours: /Users/praison/miniconda3/envs/mcp/bin/python
+        # NOTE: app.py file path, replace it with yours: /Users/praison/stockprice/app.py
+
+        stock_agent.start("What is the Stock Price of Apple?")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[llm]" yfinance mcp
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python ollama_stock.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Ollama installed and running locally
+  - yfinance and mcp packages installed
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Local LLM" icon="server">
+    Run models locally using Ollama without relying on external APIs.
+  </Card>
+  <Card title="Stock Data" icon="chart-line">
+    Retrieve real-time stock price information using the yfinance library.
+  </Card>
+  <Card title="Python MCP" icon="python">
+    Use Python-based MCP servers for custom tool functionality.
+  </Card>
+  <Card title="Privacy-Focused" icon="shield-alt">
+    Keep sensitive data local with on-device inference.
+  </Card>
+</CardGroup>

docs/mcp/ollama.mdx

@@ -0,0 +1,87 @@
+---
+title: "Ollama MCP Integration"
+sidebarTitle: "Ollama"
+description: "Guide for integrating Ollama models with PraisonAI agents using MCP"
+icon: "dragon"
+---
+
+## Add Ollama Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set Up Ollama">
+        Make sure you have Ollama installed and running locally:
+        ```bash
+        # Install Ollama from https://ollama.ai/
+        # Pull the llama3.2 model
+        ollama pull llama3.2
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `ollama_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="ollama/llama3.2",
+            tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python ollama_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Ollama installed and running locally
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Local LLM" icon="server">
+    Run models locally using Ollama without relying on external APIs.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Privacy-Focused" icon="shield-alt">
+    Keep sensitive data local with on-device inference.
+  </Card>
+</CardGroup>

docs/mcp/openai.mdx

@@ -0,0 +1,128 @@
+---
+title: "OpenAI MCP Integration"
+sidebarTitle: "OpenAI"
+description: "Guide for integrating OpenAI models with PraisonAI agents using MCP"
+icon: "folder-open"
+---
+
+## Add OpenAI Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```zsh
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `openai_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get API key from environment variable
+        openai_api_key = os.environ.get("OPENAI_API_KEY")
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="gpt-4o-mini",
+            tools=MCP(
+                command="npx",
+                args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
+                env={"OPENAI_API_KEY": openai_api_key}
+            )
+        )
+
+        search_agent.start("I want to book an apartment in Paris for 2 nights. 03/28 - 03/30 for 2 adults")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```zsh
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```zsh
+        python openai_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Gradio UI 
+
+```python
+from praisonaiagents import Agent, MCP
+import gradio as gr
+import os
+
+# Get API key from environment variable
+openai_api_key = os.environ.get("OPENAI_API_KEY")
+
+def search_airbnb(query):
+    agent = Agent(
+        instructions="You help book apartments on Airbnb.",
+        llm="gpt-4o-mini",
+        tools=MCP(
+            command="npx",
+            args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
+            env={"OPENAI_API_KEY": openai_api_key}
+        )
+    )
+    result = agent.start(query)
+    return f"## Airbnb Search Results\n\n{result}"
+
+demo = gr.Interface(
+    fn=search_airbnb,
+    inputs=gr.Textbox(placeholder="I want to book an apartment in Paris for 2 nights..."),
+    outputs=gr.Markdown(),
+    title="Airbnb Booking Assistant",
+    description="Enter your booking requirements below:"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="GPT-4o-mini" icon="brain">
+    Uses OpenAI's efficient GPT-4o-mini model for optimal performance.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Environment Variables" icon="key">
+    Securely pass API keys using environment variables.
+  </Card>
+</CardGroup>

docs/mcp/openrouter.mdx

@@ -0,0 +1,85 @@
+---
+title: "OpenRouter MCP Integration"
+sidebarTitle: "OpenRouter"
+description: "Guide for integrating OpenRouter models with PraisonAI agents using MCP"
+icon: "network-wired"
+---
+
+## Add OpenRouter Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your OpenRouter API key as an environment variable in your terminal:
+        ```bash
+        export OPENROUTER_API_KEY=your_openrouter_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `openrouter_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="openrouter/google/gemini-2.0-flash-exp:free",
+            tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python openrouter_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenRouter API key
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Model Flexibility" icon="layer-group">
+    Access to various models through OpenRouter's unified API.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="Cost Effective" icon="money-bill">
+    Use free tier models like Gemini Flash through OpenRouter.
+  </Card>
+</CardGroup>

docs/mcp/perplexity.mdx

@@ -0,0 +1,91 @@
+---
+title: "Perplexity MCP Integration"
+sidebarTitle: "Perplexity"
+description: "Guide for integrating Perplexity search with PraisonAI agents using MCP"
+icon: "magnifying-glass"
+---
+
+## Add Perplexity Search Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Perplexity MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4B0082,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your Perplexity API key as an environment variable in your terminal:
+        ```bash
+        export PERPLEXITY_API_KEY=your_perplexity_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `perplexity_search.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get API key from environment variable
+        api_key = os.getenv("PERPLEXITY_API_KEY")
+
+        agent = Agent(
+            instructions="You are a helpful assistant that can search the web for information. Use the available tools when relevant to answer user questions.",
+            llm="gpt-4o-mini",
+            tools=MCP("uvx perplexity-mcp", 
+                env={"PERPLEXITY_API_KEY": api_key, "PERPLEXITY_MODEL": "sonar" })
+        )
+        
+        result = agent.start("What is the latest news on AI?, Pass only the query parameter to the tool")
+
+        print(result)
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python perplexity_search.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Perplexity API key
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Web Search" icon="globe">
+    Search the web for real-time information using Perplexity's powerful search API.
+  </Card>
+  <Card title="Sonar Model" icon="radar">
+    Utilize Perplexity's Sonar model for high-quality search results.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Environment Variables" icon="key">
+    Securely pass API keys using environment variables.
+  </Card>
+</CardGroup>

docs/mcp/postgres.mdx

@@ -0,0 +1,71 @@
+---
+title: "PostgreSQL MCP Integration"
+sidebarTitle: "PostgreSQL"
+description: "Guide for integrating PostgreSQL database operations with PraisonAI agents using MCP"
+icon: "database"
+---
+
+## Add PostgreSQL Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[PostgreSQL MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#336791,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set up PostgreSQL">
+        Ensure you have PostgreSQL running locally or specify your PostgreSQL connection URL.
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `postgres_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # PostgreSQL connection string
+        postgres_url = "postgresql://localhost/mydb"
+
+        # Use a single string command with PostgreSQL configuration
+        postgres_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with PostgreSQL databases.
+            Use the available tools when relevant to manage database operations.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-postgres", args=[postgres_url])
+        )
+
+        postgres_agent.start("List all tables in the database")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python postgres_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - PostgreSQL server running locally or remotely
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/puppeteer.mdx

@@ -0,0 +1,71 @@
+---
+title: "Puppeteer MCP Integration"
+sidebarTitle: "Puppeteer"
+description: "Guide for integrating web automation capabilities with PraisonAI agents using Puppeteer MCP"
+icon: "chrome"
+---
+
+## Add Puppeteer Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Puppeteer MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#00B4FF,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `puppeteer_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Use a single string command with Puppeteer configuration
+        puppeteer_agent = Agent(
+            instructions="""You are a helpful assistant that can automate web browser interactions.
+            Use the available tools when relevant to perform web automation tasks.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-puppeteer")
+        )
+
+        puppeteer_agent.start("Navigate to example.com and take a screenshot")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python puppeteer_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+  - Chrome or Chromium browser installed on your system
+</Note>

docs/mcp/redis.mdx

@@ -0,0 +1,71 @@
+---
+title: "Redis MCP Integration"
+sidebarTitle: "Redis"
+description: "Guide for integrating Redis database operations with PraisonAI agents using MCP"
+icon: "database"
+---
+
+## Add Redis Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Redis MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#DC382D,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set up Redis">
+        Ensure you have Redis running locally or specify your Redis connection URL.
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `redis_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Redis connection string
+        redis_url = "redis://localhost:6379"
+
+        # Use a single string command with Redis configuration
+        redis_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with Redis.
+            Use the available tools when relevant to manage Redis operations.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-redis", args=[redis_url])
+        )
+
+        redis_agent.start("Set a key-value pair in Redis")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python redis_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Redis server running locally or remotely
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/sequential-thinking.mdx

@@ -0,0 +1,70 @@
+---
+title: "Sequential Thinking MCP Integration"
+sidebarTitle: "Sequential Thinking"
+description: "Guide for integrating sequential thinking capabilities with PraisonAI agents using MCP"
+icon: "brain"
+---
+
+## Add Sequential Thinking Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Sequential Thinking MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#6A5ACD,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `sequential_thinking.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Use a single string command with Sequential Thinking configuration
+        sequential_agent = Agent(
+            instructions="""You are a helpful assistant that can break down complex problems.
+            Use the available tools when relevant to perform step-by-step analysis.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+        )
+
+        sequential_agent.start("Break down the process of making a cup of tea")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python sequential_thinking.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/slack.mdx

@@ -0,0 +1,81 @@
+---
+title: "Slack MCP Integration"
+sidebarTitle: "Slack"
+description: "Guide for integrating Slack messaging capabilities with PraisonAI agents using MCP"
+icon: "slack"
+---
+
+## Add Slack Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Slack MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4A154B,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Keys">
+        Set your Slack credentials as environment variables in your terminal:
+        ```bash
+        export SLACK_BOT_TOKEN=your_slack_bot_token_here
+        export SLACK_TEAM_ID=your_slack_team_id_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `slack_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get Slack credentials from environment
+        slack_token = os.getenv("SLACK_BOT_TOKEN")
+        slack_team_id = os.getenv("SLACK_TEAM_ID")
+
+        # Use a single string command with Slack configuration
+        slack_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with Slack.
+            Use the available tools when relevant to manage Slack communications.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-slack",
+                    env={
+                        "SLACK_BOT_TOKEN": slack_token,
+                        "SLACK_TEAM_ID": slack_team_id
+                    })
+        )
+
+        slack_agent.start("Send a message to the general channel")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python slack_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - Slack Bot Token and Team ID
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/sse.mdx

@@ -0,0 +1,248 @@
+---
+title: "MCP SSE Integration"
+sidebarTitle: "MCP SSE"
+description: "Guide for integrating Server-Sent Events (SSE) with PraisonAI agents using MCP"
+icon: "server"
+---
+
+## Add SSE Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[SSE MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#4169E1,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Create a client file">
+
+```python
+from praisonaiagents import Agent, MCP
+
+search_agent = Agent(
+    instructions="""You are a weather agent that can provide weather information for a given city.""",
+    llm="gpt-4o-mini",
+    tools=MCP("http://localhost:8080/sse")
+)
+
+search_agent.start("What is the weather in London?")
+```
+
+    </Step>
+    <Step title="Set Up SSE MCP Server">
+
+```python
+# python mcp-sse-direct-server.py --host 127.0.0.1 --port 8080
+from typing import Any
+import httpx
+from mcp.server.fastmcp import FastMCP
+from starlette.applications import Starlette
+from mcp.server.sse import SseServerTransport
+from starlette.requests import Request
+from starlette.routing import Mount, Route
+from mcp.server import Server
+import uvicorn
+import argparse
+import logging
+import os
+import inspect
+
+# Set up logging based on environment variable
+log_level = os.environ.get("LOGLEVEL", "info").upper()
+logging.basicConfig(level=getattr(logging, log_level))
+logger = logging.getLogger("mcp-server")
+
+# Initialize FastMCP server for simple tools (SSE)
+mcp = FastMCP("simple-tools")
+
+@mcp.tool()
+async def get_greeting(name: str) -> str:
+    """Get a personalized greeting.
+
+    Args:
+        name: Name of the person to greet
+    """
+    logger.debug(f"get_greeting called with name: {name}")
+    return f"Hello, {name}! Welcome to our MCP SSE server."
+
+@mcp.tool()
+async def get_weather(city: str) -> str:
+    """Get a simulated weather report for a city.
+
+    Args:
+        city: Name of the city
+    """
+    logger.debug(f"get_weather called with city: {city}")
+    # This is a mock implementation
+    weather_data = {
+        "Paris": "Sunny with a temperature of 22°C",
+        "London": "Rainy with a temperature of 15°C",
+        "New York": "Cloudy with a temperature of 18°C",
+        "Tokyo": "Clear skies with a temperature of 25°C",
+        "Sydney": "Partly cloudy with a temperature of 20°C"
+    }
+    
+    return weather_data.get(city, f"Weather data not available for {city}")
+
+def create_starlette_app(mcp_server: Server, *, debug: bool = False) -> Starlette:
+    """Create a Starlette application that can serve the provided mcp server with SSE."""
+    sse = SseServerTransport("/messages/")
+
+    async def handle_sse(request: Request) -> None:
+        logger.debug(f"SSE connection request received from {request.client}")
+        async with sse.connect_sse(
+                request.scope,
+                request.receive,
+                request._send,  # noqa: SLF001
+        ) as (read_stream, write_stream):
+            await mcp_server.run(
+                read_stream,
+                write_stream,
+                mcp_server.create_initialization_options(),
+            )
+
+    return Starlette(
+        debug=debug,
+        routes=[
+            Route("/sse", endpoint=handle_sse),
+            Mount("/messages/", app=sse.handle_post_message),
+        ],
+    )
+
+if __name__ == "__main__":
+    mcp_server = mcp._mcp_server  # noqa: WPS437
+    
+    parser = argparse.ArgumentParser(description='Run MCP SSE-based server')
+    parser.add_argument('--host', default='localhost', help='Host to bind to')
+    parser.add_argument('--port', type=int, default=8080, help='Port to listen on')
+    args = parser.parse_args()
+
+    print(f"Starting MCP SSE server on {args.host}:{args.port}")
+    
+    # Hardcode the tool names since we know what they are
+    tool_names = ["get_greeting", "get_weather"]
+    print(f"Available tools: {', '.join(tool_names)}")
+    
+    # Bind SSE request handling to MCP server
+    starlette_app = create_starlette_app(mcp_server, debug=True)
+
+    uvicorn.run(starlette_app, host=args.host, port=args.port) 
+```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[llm]" mcp starlette uvicorn httpx
+        ```
+    </Step>
+    <Step title="Export API Key">
+        ```bash
+        export OPENAI_API_KEY="your_api_key"
+        ```
+    </Step>
+
+    <Step title="Run the Server and Agent">
+        First, start the SSE server:
+        ```bash
+        python mcp-sse-direct-server.py --host 127.0.0.1 --port 8080
+        ```
+        
+        Then, in a new terminal, run the agent:
+        ```bash
+        python weather_agent.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - MCP server dependencies
+</Note>
+
+## Alternative LLM Integrations
+
+### Using Groq with SSE
+
+```python
+from praisonaiagents import Agent, MCP
+
+weather_agent = Agent(
+    instructions="""You are a weather agent that can provide weather information for a given city.""",
+    llm="groq/llama-3.2-90b-vision-preview",
+    tools=MCP("http://localhost:8080/sse")
+)
+
+weather_agent.start("What is the weather in London?")
+```
+
+### Using Ollama with SSE
+
+```python
+from praisonaiagents import Agent, MCP
+
+weather_agent = Agent(
+    instructions="""You are a weather agent that can provide weather information for a given city.""",
+    llm="ollama/llama3.2",
+    tools=MCP("http://localhost:8080/sse")
+)
+
+weather_agent.start("What is the weather in London? Use get_weather tool, city is the required parameter.")
+```
+
+## Gradio UI Integration
+
+Create a Gradio UI for your weather service:
+
+```python
+from praisonaiagents import Agent, MCP
+import gradio as gr
+
+def get_weather_info(query):
+    weather_agent = Agent(
+        instructions="""You are a weather agent that can provide weather information for a given city.""",
+        llm="gpt-4o-mini",
+        tools=MCP("http://localhost:8080/sse")
+    )
+
+    result = weather_agent.start(query)
+    return f"## Weather Information\n\n{result}"
+
+demo = gr.Interface(
+    fn=get_weather_info,
+    inputs=gr.Textbox(placeholder="What's the weather in London?"),
+    outputs=gr.Markdown(),
+    title="Weather MCP Agent",
+    description="Ask about the weather in any major city:"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Real-time Updates" icon="bolt">
+    Receive server-sent events in real-time from your AI agent.
+  </Card>
+  <Card title="Multi-Agent Support" icon="users">
+    Combine SSE with other MCP tools for complex workflows.
+  </Card>
+  <Card title="Multiple LLM Options" icon="brain">
+    Use with OpenAI, Groq, Ollama, or other supported LLMs.
+  </Card>
+  <Card title="Gradio UI" icon="window">
+    Create user-friendly interfaces for your SSE integrations.
+  </Card>
+</CardGroup>

docs/mcp/stdio.mdx

@@ -0,0 +1,221 @@
+---
+title: "MCP STDIO Integration"
+sidebarTitle: "MCP STDIO"
+description: "Guide for integrating Standard Input/Output (STDIO) with PraisonAI agents using MCP"
+icon: "terminal"
+---
+
+## Add STDIO Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[STDIO MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#000000,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Create a client file">
+        Create a new file `calculator_client.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        calculator_agent = Agent(
+            instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
+            llm="gpt-4o-mini",
+            tools=MCP("python calculator_server.py")
+        )
+
+        calculator_agent.start("What is 25 * 16?")
+        ```
+    </Step>
+
+    <Step title="Set Up STDIO MCP Server">
+        Create a file `calculator_server.py` with the following code:
+        ```python
+        # calculator_server.py
+        from mcp.server.fastmcp import FastMCP
+        import logging
+        import sys
+
+        # Set up logging
+        logging.basicConfig(level=logging.INFO, filename="calculator_server.log")
+        logger = logging.getLogger("calculator-server")
+
+        # Initialize FastMCP server for simple tools
+        mcp = FastMCP("calculator-tools")
+
+        @mcp.tool()
+        def add(a: float, b: float) -> float:
+            """Add two numbers.
+
+            Args:
+                a: First number
+                b: Second number
+            
+            Returns:
+                The sum of a and b
+            """
+            logger.info(f"Adding {a} + {b}")
+            return a + b
+
+        @mcp.tool()
+        def subtract(a: float, b: float) -> float:
+            """Subtract b from a.
+
+            Args:
+                a: First number
+                b: Second number
+            
+            Returns:
+                The result of a - b
+            """
+            logger.info(f"Subtracting {b} from {a}")
+            return a - b
+
+        @mcp.tool()
+        def multiply(a: float, b: float) -> float:
+            """Multiply two numbers.
+
+            Args:
+                a: First number
+                b: Second number
+            
+            Returns:
+                The product of a and b
+            """
+            logger.info(f"Multiplying {a} * {b}")
+            return a * b
+
+        @mcp.tool()
+        def divide(a: float, b: float) -> float:
+            """Divide a by b.
+
+            Args:
+                a: First number (numerator)
+                b: Second number (denominator)
+            
+            Returns:
+                The result of a / b
+            """
+            if b == 0:
+                raise ValueError("Cannot divide by zero")
+            logger.info(f"Dividing {a} / {b}")
+            return a / b
+
+        if __name__ == "__main__":
+            # Run the MCP server using STDIO transport
+            mcp.run()
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[llm]" mcp
+        ```
+    </Step>
+    <Step title="Export API Key">
+        ```bash
+        export OPENAI_API_KEY="your_api_key"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Run the agent which will automatically start the calculator server:
+        ```bash
+        python calculator_client.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - MCP package
+</Note>
+
+## Alternative LLM Integrations
+
+### Using Groq with STDIO
+
+```python
+from praisonaiagents import Agent, MCP
+
+calculator_agent = Agent(
+    instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
+    llm="groq/llama-3.2-90b-vision-preview",
+    tools=MCP("python calculator_server.py")
+)
+
+calculator_agent.start("What is 144 divided by 12?")
+```
+
+### Using Ollama with STDIO
+
+```python
+from praisonaiagents import Agent, MCP
+
+calculator_agent = Agent(
+    instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
+    llm="ollama/llama3.2",
+    tools=MCP("python calculator_server.py")
+)
+
+calculator_agent.start("What is 15 + 27? Use the add tool with parameters a and b.")
+```
+
+## Gradio UI Integration
+
+Create a Gradio UI for your calculator service:
+
+```python
+from praisonaiagents import Agent, MCP
+import gradio as gr
+
+def calculate(query):
+    calculator_agent = Agent(
+        instructions="""You are a calculator agent that can perform basic arithmetic operations.""",
+        llm="gpt-4o-mini",
+        tools=MCP("python calculator_server.py")
+    )
+
+    result = calculator_agent.start(query)
+    return f"## Calculation Result\n\n{result}"
+
+demo = gr.Interface(
+    fn=calculate,
+    inputs=gr.Textbox(placeholder="What is 25 * 16?"),
+    outputs=gr.Markdown(),
+    title="Calculator MCP Agent",
+    description="Ask any arithmetic question:"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Simple Integration" icon="plug">
+    Use standard input/output for easy integration with any tool or service.
+  </Card>
+  <Card title="Cross-Platform" icon="laptop">
+    Works on any operating system that supports Python.
+  </Card>
+  <Card title="Multiple LLM Options" icon="brain">
+    Use with OpenAI, Groq, Ollama, or other supported LLMs.
+  </Card>
+  <Card title="Gradio UI" icon="window">
+    Create user-friendly interfaces for your STDIO integrations.
+  </Card>
+</CardGroup>

docs/mcp/stockprice.mdx

@@ -0,0 +1,153 @@
+---
+title: "Stock Price MCP Integration"
+sidebarTitle: "Stock Price"
+description: "Guide for integrating stock price retrieval capabilities with PraisonAI agents using MCP"
+icon: "chart-line"
+---
+
+## Add Stock Price Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[Stock Price MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Create a conda environment and install the required packages:
+        ```bash
+        zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda create -n windsurf python=3.10 -y"
+        zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && pip install praisonaiagents mcp yfinance"
+        ```
+    </Step>
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create the MCP Server">
+        Create a new file `stock_price_server.py` with the following code:
+        ```python
+        import yfinance as yf
+        from mcp.server.fastmcp import FastMCP
+
+        mcp = FastMCP("stock_prices")
+
+        @mcp.tool()
+        async def get_stock_price(ticker: str) -> str:
+            """Get the current stock price for a given ticker symbol.
+            
+            Args:
+                ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
+                
+            Returns:
+                Current stock price as a string
+            """
+            if not ticker:
+                return "No ticker provided"
+            try:
+                stock = yf.Ticker(ticker)
+                info = stock.info
+                current_price = info.get('currentPrice') or info.get('regularMarketPrice')
+                if not current_price:
+                    return f"Could not retrieve price for {ticker}"
+                return f"${current_price:.2f}"
+                
+            except Exception as e:
+                return f"Error: {str(e)}"
+
+        if __name__ == "__main__":
+            mcp.run(transport='stdio')
+        ```
+    </Step>
+
+    <Step title="Create the Agent">
+        Create a new file `stock_price_agent.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get the path to your Python interpreter and the server file
+        python_path = os.getenv("PYTHON_PATH", "/path/to/your/python")
+        server_path = os.getenv("SERVER_PATH", "/path/to/your/stock_price_server.py")
+
+        # Create the agent with the stock price MCP tool
+        agent = Agent(
+            instructions="""You are a helpful assistant that can check stock prices.
+            Use the available tools when relevant to answer user questions.""",
+            llm="gpt-4o-mini",
+            tools=MCP(f"{python_path} {server_path}")
+        )
+
+        agent.start("What is the stock price of Tesla?")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && python stock_price_agent.py"
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - yfinance package
+  - mcp-python-sdk package
+  - praisonaiagents package
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Gradio UI Example
+
+You can also create a simple web UI for your stock price agent using Gradio:
+
+```python
+from praisonaiagents import Agent, MCP
+import gradio as gr
+import os
+
+# Get the path to your Python interpreter and the server file
+python_path = os.getenv("PYTHON_PATH", "/path/to/your/python")
+server_path = os.getenv("SERVER_PATH", "/path/to/your/stock_price_server.py")
+
+# Create the agent with the stock price MCP tool
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP(f"{python_path} {server_path}")
+)
+
+def chat(message, history):
+    return agent.chat(message)
+
+demo = gr.ChatInterface(
+    chat,
+    title="Stock Price Assistant",
+    description="Ask about any stock price and get real-time information",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+Install Gradio with:
+```bash
+zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && pip install gradio"
+```

docs/mcp/whatsapp.mdx

@@ -0,0 +1,185 @@
+---
+title: "WhatsApp MCP Integration"
+sidebarTitle: "WhatsApp"
+description: "Guide for integrating WhatsApp messaging with PraisonAI agents using MCP"
+icon: "whatsapp"
+---
+
+## Add WhatsApp Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[WhatsApp MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#25D366,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set Up WhatsApp MCP Server">
+        Clone and set up the WhatsApp MCP server:
+        ```bash
+        git clone https://github.com/lharries/whatsapp-mcp.git
+        cd whatsapp-mcp
+        cd whatsapp-bridge
+        go run main.go
+        ```
+    </Step>
+    <Step title="Create a file">
+        Create a new file `whatsapp_message.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        whatsapp_agent = Agent(
+            instructions="Whatsapp Agent",
+            llm="gpt-4o-mini",
+            tools=MCP("python /path/to/whatsapp-mcp/whatsapp-mcp-server/main.py")
+        )
+
+        whatsapp_agent.start("Send Hello to Mervin Praison")
+        ```
+        
+        Note: Replace `/path/to/whatsapp-mcp` with the actual path to your WhatsApp MCP server.
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have the required packages installed:
+        ```bash
+        pip install "praisonaiagents[llm]" mcp gradio
+        ```
+    </Step>
+    <Step title="Export API Key">
+        ```bash
+        export OPENAI_API_KEY="your_api_key"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python whatsapp_message.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - WhatsApp MCP server set up and configured
+</Note>
+
+## Multi-Agent Integration
+
+You can also combine WhatsApp with other MCP tools, such as Airbnb search:
+
+```python
+from praisonaiagents import Agent, Agents, MCP
+
+airbnb_agent = Agent(
+    instructions="""Search for Apartments in Paris for 2 nights on Airbnb. 04/28 - 04/30 for 2 adults""",
+    llm="gpt-4o-mini",
+    tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+)
+
+whatsapp_agent = Agent(
+    instructions="""Send AirBnb Search Result to 'Mervin Praison'""",
+    llm="gpt-4o-mini",
+    tools=MCP("python /path/to/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+agents = Agents(agents=[airbnb_agent, whatsapp_agent])
+
+agents.start()
+```
+
+## Alternative LLM Integrations
+
+### Using Groq with WhatsApp
+
+```python
+from praisonaiagents import Agent, MCP
+
+whatsapp_agent = Agent(
+    instructions="Whatsapp Agent",
+    llm="groq/llama-3.2-90b-vision-preview",
+    tools=MCP("python /path/to/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+whatsapp_agent.start("Send Hello to Mervin Praison")
+```
+
+### Using Ollama with WhatsApp
+
+```python
+from praisonaiagents import Agent, MCP
+
+whatsapp_agent = Agent(
+    instructions="Whatsapp Agent",
+    llm="ollama/llama3.2",
+    tools=MCP("python /path/to/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+whatsapp_agent.start("Send Hello to Mervin Praison. Use send_message tool, recipient and message are the required parameters.")
+```
+
+## Gradio UI Integration
+
+Create a Gradio UI for your WhatsApp and Airbnb integration:
+
+```python
+from praisonaiagents import Agent, Agents, MCP
+import gradio as gr
+
+def search_airbnb(query):
+    airbnb_agent = Agent(
+        instructions=query+" on Airbnb",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+    )
+
+    whatsapp_agent = Agent(
+        instructions="""Send AirBnb Search Result to 'Mervin Praison'. Don't include Phone Number in Response, but include the AirBnb Search Result""",
+        llm="gpt-4o-mini",
+        tools=MCP("python /path/to/whatsapp-mcp/whatsapp-mcp-server/main.py")
+    )
+
+    agents = Agents(agents=[airbnb_agent, whatsapp_agent])
+
+    result = agents.start()
+    return f"## Airbnb Search Results\n\n{result}"
+
+demo = gr.Interface(
+    fn=search_airbnb,
+    inputs=gr.Textbox(placeholder="I want to book an apartment in Paris for 2 nights..."),
+    outputs=gr.Markdown(),
+    title="WhatsApp MCP Agent",
+    description="Enter your booking requirements below:"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="WhatsApp Messaging" icon="message">
+    Send messages to WhatsApp contacts directly from your AI agent.
+  </Card>
+  <Card title="Multi-Agent Support" icon="users">
+    Combine WhatsApp with other MCP tools for complex workflows.
+  </Card>
+  <Card title="Multiple LLM Options" icon="brain">
+    Use with OpenAI, Groq, Ollama, or other supported LLMs.
+  </Card>
+  <Card title="Gradio UI" icon="window">
+    Create user-friendly interfaces for your WhatsApp integrations.
+  </Card>
+</CardGroup>

docs/mcp/xai.mdx

@@ -0,0 +1,85 @@
+---
+title: "xAI MCP Integration"
+sidebarTitle: "xAI"
+description: "Guide for integrating XAI's Grok model with PraisonAI agents using MCP"
+icon: "robot"
+---
+
+## Add XAI Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Tool[Airbnb MCP]
+    Tool --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF5A5F,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Set API Key">
+        Set your XAI API key as an environment variable in your terminal:
+        ```bash
+        export XAI_API_KEY=your_xai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `xai_airbnb.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+
+        search_agent = Agent(
+            instructions="""You help book apartments on Airbnb.""",
+            llm="xai/grok-2-latest",
+            tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
+        )
+
+        search_agent.start("MUST USE airbnb_search Tool to Search. Search for Apartments in Paris for 2 nights. 04/28 - 04/30 for 2 adults. All Your Preference")
+        ```
+    </Step>
+
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python xai_airbnb.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - XAI API key (for the Grok model)
+</Note>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Grok Integration" icon="robot">
+    Uses XAI's Grok model for advanced natural language understanding.
+  </Card>
+  <Card title="MCP Integration" icon="plug">
+    Seamless integration with Model Context Protocol.
+  </Card>
+  <Card title="Airbnb Search" icon="hotel">
+    Search for accommodations on Airbnb with natural language queries.
+  </Card>
+  <Card title="NPM Package" icon="js">
+    Leverages the official Airbnb MCP server package.
+  </Card>
+</CardGroup>

docs/mint.json

@@ -75,9 +75,17 @@
         "name": "Tools",
         "url": "/tools"
     },
+    {
+        "name": "MCP",
+        "url": "/mcp"
+    },
     {
         "name": "JS",
         "url": "/js"
+    },
+    {
+        "name": "Course",
+        "url": "/course"
     }
   ],
   "navigation": [
@@ -154,6 +162,31 @@
         "models/other"
       ]
     },
+    {
+      "group": "Course",
+      "pages": [
+        "course/agents/01-introduction",
+        "course/agents/02-types-of-agents",
+        "course/agents/03-agent-architecture",
+        "course/agents/04-agent-instructions",
+        "course/agents/05-agent-tools",
+        "course/agents/06-agent-memory",
+        "course/agents/07-multi-agent-systems",
+        "course/agents/08-agent-process",
+        "course/agents/09-knowledge-bases",
+        "course/agents/10-agent-tasks",
+        "course/agents/11-creating-your-first-agent",
+        "course/agents/12-adding-tools-to-agents",
+        "course/agents/13-building-multi-agent-system",
+        "course/agents/14-conversational-agents",
+        "course/agents/15-research-agents",
+        "course/agents/16-content-creation-agents",
+        "course/agents/17-data-analysis-agents",
+        "course/agents/18-customer-support-agents",
+        "course/agents/19-personal-assistant-agents",
+        "course/agents/20-deploying-agents"
+      ]
+    },
     {
       "group": "Examples",
       "pages": [
@@ -201,11 +234,61 @@
         "agents/recommendation"
       ]
     },
+    {
+      "group": "MCP",
+      "pages": [
+        "mcp/airbnb",
+        "mcp/stdio",
+        "mcp/sse",
+        "mcp/ollama",
+        "mcp/groq",
+        "mcp/openrouter",
+        "mcp/xai",
+        "mcp/openai",
+        "mcp/anthropic",
+        "mcp/gemini",
+        "mcp/mistral",
+        "mcp/whatsapp",
+        "mcp/perplexity",
+        "mcp/ollama-python",
+        "mcp/bravesearch",
+        "mcp/custom",
+        "mcp/sequential-thinking",
+        "mcp/filesystem",
+        "mcp/github",
+        "mcp/gdrive",
+        "mcp/gitlab",
+        "mcp/google-maps",
+        "mcp/memory",
+        "mcp/redis",
+        "mcp/stockprice",
+        "mcp/postgres",
+        "mcp/puppeteer",
+        "mcp/everart",
+        "mcp/slack",
+        "mcp/custom-python-client",
+        "mcp/custom-python-server"
+      ]
+    },
     {
       "group": "Tools",
       "pages": [
         "tools/tools",
         "tools/langchain_tools",
+        {
+          "group": "External Tools",
+          "icon": "subscript",
+          "pages": [
+            "tools/external/serp-api",
+            "tools/external/brave-search",
+            "tools/external/google-trends",
+            "tools/external/exa-search",
+            "tools/external/wikipedia-search",
+            "tools/external/duckduckgo-search",
+            "tools/external/google-serper-search",
+            "tools/external/serp-search"
+          ]
+        },
         "tools/duckduckgo_tools",
         "tools/arxiv_tools",
         "tools/calculator_tools",
@@ -244,7 +327,14 @@
       "group": "JavaScript",
       "pages": [
         "js/js",
-        "js/typescript",
+        {
+          "group": "TypeScript Agents",
+          "icon": "subscript",
+          "pages": [
+            "js/typescript",
+            "js/customtools"
+          ]
+        },
         "js/nodejs",
         "js/typescript-async",
         "js/development"
@@ -254,6 +344,7 @@
       "group": "Other Features",
       "pages": [
         "train",
+        "playground",
         "framework/crewai",
         "framework/autogen",
         "framework/praisonaiagents"

docs/netlify.toml

@@ -0,0 +1,4 @@
+[build]
+  command = "npm install && npm run build"
+  publish = "dist"
+  base = "docs" 

docs/nocode/auto.mdx

@@ -1,10 +1,10 @@
 ---
 title: "Auto Generation Mode"
-description: "Guide to PraisonAI's automatic agent generation feature, supporting both CrewAI and AutoGen frameworks for automated task execution"
+description: "Guide to PraisonAI's automatic agent generation feature, supporting both CrewAI and AG2 frameworks for automated task execution"
 icon: "robot"
 ---
 
-PraisonAI supports automatic agent generation with PraisonAI Agents, CrewAI and AutoGen frameworks.
+PraisonAI supports automatic agent generation with PraisonAI Agents, CrewAI and AG2 frameworks.
 
 ## Installation
 
@@ -17,7 +17,7 @@ pip install praisonai
 # For CrewAI
 pip install "praisonai[crewai]"
 
-# For AutoGen
+# For AG2 (Formerly AutoGen)
 pip install "praisonai[autogen]"
 
 # For both frameworks
@@ -36,7 +36,7 @@ praisonai --auto "create a movie script about Dog in Moon"
 praisonai --framework crewai --auto "create a movie script about Dog in Moon"
 ```
 
-### With AutoGen
+### With AG2
 ```bash
 praisonai --framework autogen --auto "create a movie script about Dog in Moon"
 ```
@@ -49,7 +49,7 @@ praisonai --framework autogen --auto "create a movie script about Dog in Moon"
 - Built-in tools integration
 - Structured agent-task relationships
 
-### AutoGen
+### AG2 (Formerly AutoGen)
 - Multi-agent conversation capabilities
 - Code execution environment
 - Built-in tools integration

docs/nocode/installation.mdx

@@ -28,13 +28,13 @@ Includes:
 - Task delegation capabilities
 - Sequential and parallel task execution
 
-### AutoGen Support
+### AG2 Support
 ```bash
 pip install "praisonai[autogen]"
 ```
 
 Includes:
-- AutoGen framework
+- AG2 framework
 - PraisonAI tools integration
 - Multi-agent conversation capabilities
 - Code execution environment

docs/nocode/introduction.mdx

@@ -21,7 +21,7 @@ icon: "hand-wave"
     Create and orchestrate sophisticated multi-agent systems with ease
   </Card>
   <Card title="Framework Integration" icon="puzzle-piece">
-    Seamlessly integrates AutoGen and CrewAI capabilities
+    Seamlessly integrates AG2 and CrewAI capabilities
   </Card>
   <Card title="Low-Code Design" icon="code">
     Build complex AI systems with minimal coding required

docs/nocode/tldr.mdx

@@ -1,6 +1,6 @@
 ---
 title: "TL;DR"
-description: "Quick start guide for PraisonAI with CrewAI, AutoGen, and UI setup instructions"
+description: "Quick start guide for PraisonAI with CrewAI, AG2 (Formerly AutoGen), and UI setup instructions"
 icon: "bolt"
 ---
 
@@ -32,10 +32,10 @@ praisonai --framework crewai --init "create a movie script about dog in moon"
 praisonai --framework crewai
 ```
 
-## Quick Start with AutoGen
+## Quick Start with AG2
 
 ```bash
-# Install with AutoGen support
+# Install with AG2 support
 pip install "praisonai[autogen]"
 
 # Set your API key

docs/package.json

@@ -0,0 +1,14 @@
+{
+    "name": "praisonai-docs",
+    "version": "1.0.0",
+    "description": "PraisonAI Documentation",
+    "scripts": {
+      "dev": "mintlify dev",
+      "start": "mintlify dev",
+      "build": "mkdir -p dist && find . -type f -not -path \"./dist/*\" -not -path \"./node_modules/*\" -not -path \"./.git/*\" -not -path \"./.*\" -not -name \"*.json\" -not -name \"*.md\" -not -name \"*.yaml\" -not -name \"*.yml\" -not -name \"*.toml\" -not -name \"*.lock\" | while read file; do mkdir -p \"dist/$(dirname \"$file\")\" && cp \"$file\" \"dist/$file\"; done"
+    },
+    "dependencies": {
+      "mintlify": "^4.0.0"
+    }
+  }
+  

docs/playground.mdx

@@ -0,0 +1,33 @@
+---
+title: "Praison AI Agents Playground"
+sidebarTitle: "Playground"
+description: "Interactive environment to test and run your AI Agents"
+icon: "code"
+---
+
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="800px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
+## How to Use the Playground
+
+1. The code editor is pre-loaded with a simple example using Praison AI Agents
+2. Modify the code as needed for your experiments
+3. Click "Run" to execute the code and see the results
+4. Use the playground to test concepts from the course or your own ideas
+
+<CardGroup cols={2}>
+  <Card title="API Documentation" icon="book" href="https://docs.praison.ai">
+    Reference the Praison AI Agents documentation for more examples
+  </Card>
+  <Card title="Back to Course" icon="arrow-left" href="/course/agents/01-introduction">
+    Return to the AI Agents course
+  </Card>
+</CardGroup>

docs/quickstart.mdx

@@ -167,6 +167,68 @@ agents.start();
       </Step>
     </Steps>
   </Tab>
+  <Tab title="TypeScript">
+    <Steps>
+      <Step title="Install Package">
+        <CodeGroup>
+        ```bash npm
+        npm install praisonai
+        ```
+        ```bash yarn
+        yarn add praisonai
+        ```
+        </CodeGroup>
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.ts` file
+
+        ## Code Example
+
+<CodeGroup>
+```javascript Single Agent
+import { Agent } from 'praisonai';
+
+const agent = new Agent({ 
+  instructions: `You are a creative writer who writes short stories with emojis.`,
+  name: "StoryWriter"
+});
+
+agent.start("Write a story about a time traveler")
+```
+
+```javascript Multi Agents
+import { Agent, PraisonAIAgents } from 'praisonai';
+
+const storyAgent = new Agent({
+  instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
+  name: "StoryAgent"
+});
+
+const summaryAgent = new Agent({
+  instructions: "Summarize the provided AI story in one sentence with emojis.",
+  name: "SummaryAgent"
+});
+
+const agents = new PraisonAIAgents({
+  agents: [storyAgent, summaryAgent]
+});
+
+agents.start()
+```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        npx ts-node app.ts
+        ```
+      </Step>
+    </Steps>
+  </Tab>
 </Tabs>
 
 <Note>
@@ -176,6 +238,19 @@ agents.start();
 - For other LLM providers, see [Models](/models)
 </Note>
 
+## Playground
+
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="600px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
 # Advanced
 
 ## Providing Detailed Tasks to Agents (Optional)

docs/requirements.in

@@ -5,8 +5,20 @@ mkdocs
 markdown-include
 mkdocs-material
 mkdocs-jupyter
+mkdocs-glightbox
 pymdown-extensions
 Pygments
+mkdocstrings 
+mkdocs-apidoc 
+mkdocstrings-python
+mkdocs-jupyter
 pdoc3
+rich
+pyautogen
+crewai
+gradio
+duckduckgo_search
+praisonai_tools
+open-interpreter
 xmlrunner
-unittest2
+unittest2

docs/requirements.txt

@@ -1,24 +0,0 @@
-sphinx
-sphinx_rtd_theme
-sphinx-autodoc-typehints
-mkdocs
-markdown-include
-mkdocs-material
-mkdocs-jupyter
-mkdocs-glightbox
-pymdown-extensions
-Pygments
-mkdocstrings 
-mkdocs-apidoc 
-mkdocstrings-python
-mkdocs-jupyter
-pdoc3
-rich
-pyautogen
-crewai
-gradio
-duckduckgo_search
-praisonai_tools
-open-interpreter
-xmlrunner
-unittest2

docs/tools/custom.mdx

@@ -71,7 +71,7 @@ To run the PraisonAI tool, simply type the following command in your terminal:
 praisonai
 ```
 
-If you want to run the `autogen` framework, use:
+If you want to run the AG2 framework (Formerly AutoGen), use:
 
 ```bash
 praisonai --framework autogen

docs/tools/external/agentql-toolkit.mdx

@@ -0,0 +1,52 @@
+---
+title: "AgentQL structured data extraction Tool"
+description: "Guide for using the AgentQL structured data extraction with PraisonAI agents."
+icon: "code"
+---
+
+## Overview
+
+AgentQL is a tool that allows you to extract structured data from webpages using AI Agents.
+
+```bash
+pip install langchain_agentql langchain-community
+```
+
+```bash
+os.environ["AGENTQL_API_KEY"] = "your_api_key_here"
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_agentql.tools import ExtractWebDataTool
+from dotenv import load_dotenv
+
+import os
+
+os.environ["AGENTQL_API_KEY"] = os.getenv('AGENTQL_API_KEY')
+
+def extract_web_data_tool(url, query):
+    agentql_tool = ExtractWebDataTool().invoke(
+        {
+            "url": url,
+            "prompt": query,
+        },)
+    return agentql_tool
+
+# Create agent with web extraction instructions
+orchestration_agent = Agent(
+    instructions="""Extract All 37 products from the url https://www.colorbarcosmetics.com/bestsellers along with its name, overview, description, price and additional information by recursively clicking on each product""",
+    tools=[extract_web_data_tool]
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(agents=[orchestration_agent])
+agents.start()
+```
+
+## Getting Started
+
+1. Get your AgentQL API key from [AgentQL Dashboard](https://agentql.com)
+2. Set the API key in your environment variables
+3. Install the required dependencies
+4. Use the example code to start extracting structured data

docs/tools/external/azure-code-interpreter.mdx

@@ -0,0 +1,28 @@
+---
+title: "Azure Container Apps dynamic sessions Code runtime Tool"
+description: "Guide for using the Azure Container Apps dynamic sessions based code Interpreter tool with PraisonAI agents."
+icon: "code"
+---
+
+## Overview
+
+The Azure Container Apps dynamic sessions based code Interpreter tool is a tool that allows you to execute and run development environments using the AI Agents.
+
+```bash
+pip install langchain-azure-dynamic-sessions langchain-openai langchainhub langchain langchain-community
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+import getpass
+from langchain_azure_dynamic_sessions import SessionsPythonREPLTool
+
+POOL_MANAGEMENT_ENDPOINT = getpass.getpass()
+
+coder_agent = Agent(instructions="""word = "strawberry"
+                                    count = word.count("r")
+                                    print(f"There are {count}'R's in the word 'Strawberry'")""", tools=[SessionsPythonREPLTool])
+
+agents = PraisonAIAgents(agents=[coder_agent])
+agents.start()
+```

docs/tools/external/bearly-code-interpreter.mdx

@@ -0,0 +1,25 @@
+---
+title: "Bearly Code Interpreter Tool"
+description: "Guide for using the Bearly Code Interpreter tool with PraisonAI agents."
+icon: "code"
+---
+
+## Overview
+
+The Bearly Code Interpreter tool is a tool that allows you to execute various programming languages using the AI Agents.
+
+```bash
+pip install langchain-community
+export BEARLY_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools import BearlyInterpreterTool
+
+coder_agent = Agent(instructions="""for i in range(0,10):
+                                        print(f'The number is {i}')""", tools=[BearlyInterpreterTool])
+
+agents = PraisonAIAgents(agents=[coder_agent])
+agents.start()
+```

docs/tools/external/brave-search.mdx

@@ -0,0 +1,38 @@
+---
+title: "BraveSearch Tool"
+description: "Guide for using the BraveSearch tool with PraisonAI agents."
+icon: "magnifying-glass"
+---
+
+## Overview
+
+The BraveSearch tool is a tool that allows you to search the web using the BraveSearch API.
+
+```bash
+pip install langchain-community google-search-results
+``` 
+
+```bash
+export BRAVE_SEARCH_API=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools import BraveSearch
+import os
+
+
+def search_brave(query: str):
+    """Searches using BraveSearch and returns results."""
+    api_key = os.environ['BRAVE_SEARCH_API']
+    tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})
+    return tool.run(query)
+
+data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[search_brave])
+editor_agent = Agent(instructions="Write a blog article")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```
+
+Generate your BraveSearch API key from [BraveSearch](https://brave.com/search/api/)

docs/tools/external/duckduckgo-search.mdx

@@ -0,0 +1,24 @@
+---
+title: "DuckDuckGo Search Tool"
+description: "Guide for using the DuckDuckGo Search tool with PraisonAI agents."
+icon: "duck"
+---
+
+## Overview
+
+The DuckDuckGo Search tool is a tool that allows you to search the web using the DuckDuckGo search engine.
+
+```bash
+pip install praisonaiagents
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+
+data_agent = Agent(instructions="Search and Read Research Papers on DNA Mutation", tools=[duckduckgo])
+editor_agent = Agent(instructions="Write a scientifically researched outcome and findings about DNA Mutation")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/exa-search.mdx

@@ -0,0 +1,35 @@
+---
+title: "Exa Search Tool"
+description: "Guide for using the Exa Search tool with PraisonAI agents."
+icon: "magnifying-glass-chart"
+---
+
+## Overview
+
+The Exa Search tool is a tool that allows you to search the web and retrieve content using the Exa API.
+
+```bash
+pip install praisonaiagents exa-py
+export EXA_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from exa_py import Exa
+import os
+
+exa = Exa(api_key=os.environ["EXA_API_KEY"])
+
+def search_and_contents(query: str):
+    """Search for webpages based on the query and retrieve their contents."""
+    # This combines two API endpoints: search and contents retrieval
+    return str(exa.search_and_contents(
+        query, use_autoprompt=False, num_results=5, text=True, highlights=True
+    ))
+
+data_agent = Agent(instructions="Find the latest jobs for Video Editor in New York at startups", tools=[search_and_contents])
+editor_agent = Agent(instructions="Curate the available jobs at startups and their email for the candidate to apply based on his skills on Canva, Adobe Premiere Pro, and Adobe After Effects")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent], process='hierarchical')
+agents.start()
+```

docs/tools/external/google-search.mdx

@@ -0,0 +1,32 @@
+---
+title: "GoogleSearch Tool"
+description: "Guide for using the GoogleSearch tool with PraisonAI agents."
+icon: "magnifying-glass"
+---
+
+## Overview
+The GoogleSearch tool is a tool that allows you to search the web using the GoogleSearch API.
+
+```bash
+pip install langchain-community langchain-google-community
+``` 
+
+```bash
+os.environ["GOOGLE_CSE_ID"] = "YOUR_API_KEY"
+os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY"
+```
+
+```python
+import os 
+from langchain_google_community import GoogleSearchAPIWrapper
+from praisonaiagents import Agent, PraisonAIAgents
+
+data_agent = Agent(instructions="Search about best places to visit in India during Summer", tools=[GoogleSearchAPIWrapper])
+editor_agent = Agent(instructions="Write a blog article")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent], process='hierarchical')
+agents.start()
+```
+
+Generate your GoogleSearch API key from Google CloudConsole by clicking on [Create Credentials](https://console.cloud.google.com/apis/credentials)
+To get a CSE ID, Create a Google Programmable Search Engine [here](https://programmablesearchengine.google.com/)
+From the Overview of your newly created search engine copy the "Search engine ID"

docs/tools/external/google-serper-search.mdx

@@ -0,0 +1,33 @@
+---
+title: "Google Serper Search Tool"
+description: "Guide for using the Google Serper Search tool with PraisonAI agents."
+icon: "google"
+---
+
+## Overview
+
+The Google Serper Search tool is a tool that allows you to search the web using the Google Serper API.
+
+```bash
+pip install langchain-community python-dotenv
+export SERPER_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import GoogleSerperAPIWrapper
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+os.environ['SERPER_API_KEY'] = os.getenv('SERPER_API_KEY')
+
+search = GoogleSerperAPIWrapper()
+
+data_agent = Agent(instructions="Suggest me top 5 most visited websites for Dosa Recipe", tools=[search])
+editor_agent = Agent(instructions="List out the websites with their url and a short description")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/google-trends.mdx

@@ -0,0 +1,34 @@
+---
+title: "Google Trends Tool"
+description: "Guide for using the Google Trends tool with PraisonAI agents."
+icon: "arrow-trend-up"
+---
+
+## Overview
+
+The Google Trends tool is a tool that allows you to search the web using the Google Trends API.
+
+```bash
+pip install langchain-community google-search-results
+```
+
+```bash
+export SERPAPI_API_KEY=your_api_key_here
+```
+
+```python
+from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(
+    instructions="Research trending topics related to AI",
+    tools=[GoogleTrendsAPIWrapper]
+)
+
+summarise_agent = Agent(
+    instructions="Summarise findings from the research agent",
+)
+
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```

docs/tools/external/jina-code-interpreter.mdx

@@ -0,0 +1,26 @@
+---
+title: "Jina Code Interpreter Tool"
+description: "Guide for using the Jina Code Interpreter tool with PraisonAI agents."
+icon: "code"
+---
+
+## Overview
+
+The Jina Code Interpreter tool is a tool that allows you to execute various programming languages using the AI Agents.
+
+```bash
+pip install langchain-community rizaio
+export RIZA_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools.riza.command import ExecPython
+
+coder_agent = Agent(instructions="""word = "strawberry"
+                                    count = word.count("r")
+                                    print(f"There are {count}'R's in the word 'Strawberry'")""", tools=[ExecPython])
+
+agents = PraisonAIAgents(agents=[coder_agent])
+agents.start()
+```

docs/tools/external/jina-search.mdx

@@ -0,0 +1,40 @@
+---
+title: "JinaSearch Tool"
+description: "Guide for using the JinaSearch tool with PraisonAI agents."
+icon: "magnifying-glass"
+---
+
+## Overview
+The JinaSearch tool is a tool that allows you to search the web using the JinaSearch API.
+
+```bash
+pip install langchain-community
+``` 
+
+```bash
+os.environ["JINA_API_KEY"]= "YOUR_API_KEY"
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools import JinaSearch
+import os 
+
+def invoke_jina_search(query: str):
+    JinaSearchTool = JinaSearch()
+    model_generated_tool_call = {
+        "args": {"query": query},
+        "id": "1",
+        "name": JinaSearchTool.name,
+        "type": "tool_call",
+    }
+    tool_msg = JinaSearchTool.invoke(model_generated_tool_call)
+    return(tool_msg.content[:1000])
+
+data_agent = Agent(instructions="Find 10 websites where I can learn coding for free", tools=[invoke_jina_search])
+editor_agent = Agent(instructions="write a listicle blog ranking the best websites. The blog should contain a proper intro and conclusion")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent], process='hierarchical')
+agents.start()
+```
+
+Register on Jina Generate your JinaSearch API key from [Jina Platform](https://jina.ai/api-dashboard/key-manager)

docs/tools/external/searchapi-search.mdx

@@ -0,0 +1,24 @@
+---
+title: "SearchApi Tool"
+description: "Guide for using the SearchApi tool with PraisonAI agents."
+icon: "searchengine"
+---
+
+## Overview
+
+The SearchApi tool is a tool that allows you to search the web using the SearchApi.
+
+```bash
+export SEARCHAPI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import SearchApiAPIWrapper
+
+data_agent = Agent(instructions="I am looking for the top google searches of 2025", tools=[SearchApiAPIWrapper])
+editor_agent = Agent(instructions="Analyze the data and derive insights")
+
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/serp-api.mdx

@@ -0,0 +1,26 @@
+---
+title: "SerpAPI Tool"
+description: "Guide for using the SerpAPI tool with PraisonAI agents."
+icon: "searchengin"
+---
+
+## Overview
+
+The SerpAPI tool is a tool that allows you to search the web using the SerpAPI.
+
+```bash
+pip install langchain-community google-search-results
+export SERPAPI_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import SerpAPIWrapper
+
+data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[SerpAPIWrapper])
+editor_agent = Agent(instructions="Write a blog article")
+
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/serp-search.mdx

@@ -0,0 +1,25 @@
+---
+title: "SerpSearch Tool"
+description: "Guide for using the SerpSearch tool with PraisonAI agents."
+icon: "searchengin"
+---
+
+## Overview
+
+The SerpSearch tool is a tool that allows you to search the web using the SerpAPI.
+
+```bash
+pip install langchain-community google-search-results
+export SERPAPI_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import SerpAPIWrapper
+
+data_agent = Agent(instructions="Search about decline of recruitment across various industries with the rise of AI", tools=[SerpAPIWrapper])
+editor_agent = Agent(instructions="Write a blog article pointing out the jobs most at rish due to the rise of AI")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/tavily-search.mdx

@@ -0,0 +1,35 @@
+---
+title: "TavilySearch Tool"
+description: "Guide for using the TavilySearch tool with PraisonAI agents."
+icon: "searchengine"
+---
+
+## Overview
+
+The TavilySearch tool is a tool that allows you to search the web using the TavilySearch.
+
+```bash
+pip install "langchain-community>=0.2.11" tavily-python
+export TAVILY_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools import TavilySearchResults
+
+def search_tool(query: str):
+    tool = TavilySearchResults(
+        max_results=5,
+        search_depth="advanced",
+        include_answer=True,
+        include_raw_content=True,
+        include_images=True
+    )
+    return tool.run(query)
+
+data_agent = Agent(instructions="I am looking for the top google searches on AI tools of 2025", tools=[search_tool])
+editor_agent = Agent(instructions="Analyze the data and rank the tools based on their popularity")
+
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/external/wikipedia-search.mdx

@@ -0,0 +1,24 @@
+---
+title: "Wikipedia Search Tool"
+description: "Guide for using the Wikipedia Search tool with PraisonAI agents."
+icon: "wikipedia-w"
+---
+
+## Overview
+
+The Wikipedia Search tool is a tool that allows you to search and retrieve information from Wikipedia.
+
+```bash
+pip install langchain-community
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import WikipediaAPIWrapper
+
+data_agent = Agent(instructions="Gather all of Messi's record in LaLiga", tools=[WikipediaAPIWrapper])
+summarise_agent = Agent(instructions="Summarize the data into a well structured format")
+agents = PraisonAIAgents(agents=[data_agent, summarise_agent])
+agents.start()
+```

docs/tools/external/you-search.mdx

@@ -0,0 +1,25 @@
+---
+title: "YouSearchAPI Tool"
+description: "Guide for using the YouSearchAPI tool with PraisonAI agents."
+icon: "searchengine"
+---
+
+## Overview
+
+The YouSearchAPI tool is a tool that allows you to search the web using the YouSearchAPI.
+
+```bash
+pip install langchain-community
+export YDC_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities.you import YouSearchAPIWrapper
+
+data_agent = Agent(instructions="Gather the weather data for Barcelona", tools=[YouSearchAPIWrapper])
+editor_agent = Agent(instructions="Breifly describe the weather in Barcelona")
+
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```

docs/tools/langchain_tools.mdx

@@ -7,6 +7,217 @@ icon: "link-simple"
 
 ## Quick Start
 
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the required packages:
+            ```bash
+            pip install praisonaiagents langchain-community wikipedia youtube-search
+            ```
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+<CodeGroup>
+  ```python Single Tool
+from praisonaiagents import Agent
+from langchain_community.utilities import WikipediaAPIWrapper
+
+wiki_agent = Agent(
+    instructions="You are a wikipedia research Agent",
+    tools=[WikipediaAPIWrapper]
+)
+
+wiki_agent.start("Research 'Artificial Intelligence' on Wikipedia")
+```
+
+```python Multiple Tools
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_community.tools import YouTubeSearchTool
+
+youtube_agent = Agent(
+    instructions="Search for information about 'AI advancements' on YouTube",
+    tools=[YouTubeSearchTool]
+)
+
+wiki_agent = Agent(
+    instructions="Research 'Artificial Intelligence' on Wikipedia",
+    tools=[WikipediaAPIWrapper]
+)
+
+agents = PraisonAIAgents(agents=[youtube_agent, wiki_agent])
+agents.start()
+```
+</CodeGroup>
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            (Upcoming Feature)
+            Install the PraisonAI package:
+            ```bash
+            pip install "praisonai"
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+roles:
+  researcher:
+    name: SearchAgent
+    role: Research Assistant
+    goal: Search for information from multiple sources
+    backstory: I am an AI assistant that can search YouTube and Wikipedia.
+    tools:
+      - youtube_search
+      - wikipedia
+    tasks:
+      search_task:
+        name: search_task
+        description: Search for information about 'AI advancements' on both YouTube and Wikipedia
+        expected_output: Combined information from YouTube videos and Wikipedia articles
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys)
+  - LangChain compatible tools and utilities
+</Note>
+
+## Understanding LangChain Integration
+
+<Card title="What is LangChain Integration?" icon="question">
+  LangChain integration enables agents to:
+  - Use LangChain's extensive tool ecosystem
+  - Access various data sources and APIs
+  - Leverage pre-built utilities and wrappers
+  - Combine multiple tools in a single agent
+  - Extend agent capabilities with community tools
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Tool Integration" icon="plug">
+    Seamlessly use LangChain tools with PraisonAI agents.
+  </Card>
+  <Card title="Multiple Sources" icon="database">
+    Access various data sources through LangChain utilities.
+  </Card>
+  <Card title="Community Tools" icon="users">
+    Leverage the extensive LangChain community ecosystem.
+  </Card>
+  <Card title="Custom Tools" icon="wrench">
+    Create and integrate custom LangChain tools.
+  </Card>
+</CardGroup>
+
+## LangChain Tools with Wrappers
+
+<Note>
+  - LangChain tools with wrappers can be directly used as Tools without modification.
+  - PraisonAI will automatically detect the tool and use it.
+  - Some exceptions apply where it doesn't work.
+</Note>
+
+### Example Wrapper Usage
+
+```bash
+pip install langchain-community google-search-results
+export SERPAPI_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.utilities import SerpAPIWrapper
+
+data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[SerpAPIWrapper])
+editor_agent = Agent(instructions="Write a blog article")
+
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```
+
+### Example without Wrapper Usage
+
+```bash
+pip install langchain-community 
+export BRAVE_SEARCH_API=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
+```
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+from langchain_community.tools import BraveSearch
+import os
+
+def search_brave(query: str):
+    """Searches using BraveSearch and returns results."""
+    api_key = os.environ['BRAVE_SEARCH_API']
+    tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})
+    return tool.run(query)
+
+data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[search_brave])
+editor_agent = Agent(instructions="Write a blog article")
+agents = PraisonAIAgents(agents=[data_agent, editor_agent])
+agents.start()
+```
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Memory Integration" icon="brain" href="../concepts/memory">
+    Learn how to combine LangChain tools with agent memory
+  </Card>
+  <Card title="Custom Tools" icon="wrench" href="./tools">
+    Create your own custom tools for agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.
+</Note>
+
+## With More Detailed Instructions
+
 <Tabs>
   <Tab title="Code">
     <Steps>
@@ -164,56 +375,4 @@ praisonai agents.yaml
         </Step>
     </Steps>
   </Tab>
-</Tabs>
-
-<Note>
-  **Requirements**
-  - Python 3.10 or higher
-  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys)
-  - LangChain compatible tools and utilities
-</Note>
-
-## Understanding LangChain Integration
-
-<Card title="What is LangChain Integration?" icon="question">
-  LangChain integration enables agents to:
-  - Use LangChain's extensive tool ecosystem
-  - Access various data sources and APIs
-  - Leverage pre-built utilities and wrappers
-  - Combine multiple tools in a single agent
-  - Extend agent capabilities with community tools
-</Card>
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Tool Integration" icon="plug">
-    Seamlessly use LangChain tools with PraisonAI agents.
-  </Card>
-  <Card title="Multiple Sources" icon="database">
-    Access various data sources through LangChain utilities.
-  </Card>
-  <Card title="Community Tools" icon="users">
-    Leverage the extensive LangChain community ecosystem.
-  </Card>
-  <Card title="Custom Tools" icon="wrench">
-    Create and integrate custom LangChain tools.
-  </Card>
-</CardGroup>
-
-
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card title="Memory Integration" icon="brain" href="../concepts/memory">
-    Learn how to combine LangChain tools with agent memory
-  </Card>
-  <Card title="Custom Tools" icon="wrench" href="./tools">
-    Create your own custom tools for agents
-  </Card>
-</CardGroup>
-
-<Note>
-  For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.
-</Note>
+</Tabs>

docs/tools/tools.mdx

@@ -45,25 +45,172 @@ flowchart TB
 
 Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.
 
+<Tabs>
+
+<Tab title="Code">
+<Steps>
+
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
+
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+<CodeGroup>
+```python Single Agent
+from praisonaiagents import Agent
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Perform internet searches to collect relevant information.",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+# 3. Start Agent
+search_agent.start("Search about AI job trends in 2025")
+```
+
+```python Multiple Agents
+from praisonaiagents import Agent, PraisonAIAgents
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Search about AI job trends in 2025",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+blog_agent = Agent(
+    instructions="Write a blog article based on the previous agent's search results."
+)
+
+# 3. Start Agents
+agents = PraisonAIAgents(agents=[search_agent, blog_agent])
+agents.start()
+```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
+
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+
 ## Creating Custom Tool
 <Steps>
 <Step>
 Create any function that you want to use as a tool, that performs a specific task.
 ```python
 from duckduckgo_search import DDGS
-from typing import List, Dict
 
-# Tool Implementation
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search using DuckDuckGo
-    
-    Args:
-        query (str): The search query string
-        
-    Returns:
-        List[Dict]: List of search results containing title, URL, and snippet
-    """
+def internet_search_tool(query: str):
     results = []
     ddgs = DDGS()
     for result in ddgs.text(keywords=query, max_results=5):
@@ -79,11 +226,8 @@ def internet_search_tool(query: str) -> List[Dict]:
 Assign the tool to an agent
 ```python
     data_agent = Agent(
-        name="DataCollector",
-        role="Search Specialist",
-        goal="Perform internet searches to collect relevant information.",
-        backstory="Expert in finding and organising internet data.",
-        tools=[internet_search_tool], ## Add the tool to the agent i.e the function name
+        instructions="Search about AI job trends in 2025",
+        tools=[internet_search_tool], # <-- Tool Assignment
     )
 ```
 </Step>
@@ -93,7 +237,7 @@ Assign the tool to an agent
 <Check>You have created a custom tool and assigned it to an agent.</Check>
 </Card>
 
-## Implementing Tools Full Code Example
+## Creating Custom Tool with Detailed Instructions
 <Tabs>
 
 <Tab title="Code">
@@ -119,19 +263,9 @@ pip install praisonaiagents duckduckgo-search
 ```python
 from praisonaiagents import Agent, Task, PraisonAIAgents
 from duckduckgo_search import DDGS
-from typing import List, Dict
 
 # 1. Tool Implementation
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search using DuckDuckGo
-    
-    Args:
-        query (str): The search query string
-        
-    Returns:
-        List[Dict]: List of search results containing title, URL, and snippet
-    """
+def internet_search_tool(query: str):
     results = []
     ddgs = DDGS()
     for result in ddgs.text(keywords=query, max_results=5):

docs/ui/chat.mdx

@@ -18,7 +18,7 @@ icon: "comments"
 
 | Interface | Description | URL |
 |---|---|---|
-| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
+| **UI** | Multi Agents such as CrewAI or AG2 | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
 | **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](https://docs.praison.ai/ui/chat) |
 | **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](https://docs.praison.ai/ui/code) |
 

docs/ui/code.mdx

@@ -20,7 +20,7 @@ PraisonAI Code helps you to interact with your whole codebase using the power of
 
 | Interface | Description | URL |
 |---|---|---|
-| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
+| **UI** | Multi Agents such as CrewAI or AG2 | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
 | **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](https://docs.praison.ai/ui/chat) |
 | **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](https://docs.praison.ai/ui/code) |