Merge pull request #456 from MervinPraison/develop

Increment version to 0.0.77 in `pyproject.toml` and update `markitdow…

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==2.0.81 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==2.0.81 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/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
@@ -711,8 +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
@@ -726,7 +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/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/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

@@ -75,6 +75,10 @@
         "name": "Tools",
         "url": "/tools"
     },
+    {
+        "name": "MCP",
+        "url": "/mcp"
+    },
     {
         "name": "JS",
         "url": "/js"
@@ -230,6 +234,41 @@
         "agents/recommendation"
       ]
     },
+    {
+      "group": "MCP",
+      "pages": [
+        "mcp/airbnb",
+        "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": [

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/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/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) |
 

docs/ui/overview.mdx

@@ -11,7 +11,7 @@ icon: "browser"
 |---|---|---|
 | **Streamlit** | Create interactive web apps with Python | [https://docs.praison.ai/ui/streamlit](/ui/streamlit) |
 | **Gradio** | Build machine learning web apps rapidly | [https://docs.praison.ai/ui/gradio](/ui/gradio) |
-| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](/ui/ui) |
+| **UI** | Multi Agents such as CrewAI or AG2 | [https://docs.praison.ai/ui/ui](/ui/ui) |
 | **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](/ui/chat) |
 | **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](/ui/code) |
 

examples/mcp/airbnb-mcp.py

@@ -0,0 +1,9 @@
+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")

examples/mcp/anthropic-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+search_agent = Agent(
+    instructions="""You help book apartments on Airbnb.""",
+    llm="anthropic/claude-3-7-sonnet-20250219",
+    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")

examples/mcp/aws-kb-retrieval-mcp.py

@@ -0,0 +1,22 @@
+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") 

examples/mcp/bravesearch-mcp.py

@@ -0,0 +1,15 @@
+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")
+
+# 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")

examples/mcp/custom-python-client.py

@@ -0,0 +1,13 @@
+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/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
+
+agent.start("What is the stock price of Tesla?")

examples/mcp/custom-python-server.py

@@ -0,0 +1,30 @@
+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')

examples/mcp/databutton-mcp.py

@@ -0,0 +1,15 @@
+from praisonaiagents import Agent, MCP
+import os
+
+# Databutton API key
+databutton_api_key = os.getenv("DATABUTTON_API_KEY")
+
+# Create databutton agent
+databutton_agent = Agent(
+    instructions="""You are a helpful assistant that can interact with Databutton.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP("uvx databutton-app-mcp@latest", env={"DATABUTTON_API_KEY": databutton_api_key})
+)
+
+databutton_agent.start("Get the current stock price for AAPL, Tesla, and Amazon")

examples/mcp/everart-mcp.py

@@ -0,0 +1,16 @@
+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") 

examples/mcp/fetch-mcp.py

@@ -0,0 +1,13 @@
+from praisonaiagents import Agent, MCP
+import os
+
+# pip install mcp-server-fetch
+# Use a single string command with Fetch configuration
+fetch_agent = Agent(
+    instructions="""You are a helpful assistant that can fetch and process web content.
+    Use the available tools when relevant to retrieve and convert web pages to markdown.""",
+    llm="gpt-4o-mini",
+    tools=MCP("python -m mcp_server_fetch")
+)
+
+fetch_agent.start("Fetch and convert the content from https://example.com to markdown") 

examples/mcp/filesystem-mcp.py

@@ -0,0 +1,18 @@
+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") 

examples/mcp/gdrive-mcp.py

@@ -0,0 +1,16 @@
+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") 

examples/mcp/gemini-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+search_agent = Agent(
+    instructions="""You help book apartments on Airbnb.""",
+    llm="gemini/gemini-2.5-pro-exp-03-25",
+    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")

examples/mcp/git-mcp.py

@@ -0,0 +1,23 @@
+from praisonaiagents import Agent, MCP
+import os
+
+# pip install mcp-server-git
+# Get Git credentials from environment
+git_username = os.getenv("GIT_USERNAME")
+git_email = os.getenv("GIT_EMAIL")
+git_token = os.getenv("GIT_TOKEN")  # For private repos
+
+# Use a single string command with Git configuration
+git_agent = Agent(
+    instructions="""You are a helpful assistant that can perform Git operations.
+    Use the available tools when relevant to manage repositories, commits, and branches.""",
+    llm="gpt-4o-mini",
+    tools=MCP("python -m mcp_server_git",
+              env={
+                  "GIT_USERNAME": git_username,
+                  "GIT_EMAIL": git_email,
+                  "GIT_TOKEN": git_token
+              })
+)
+
+git_agent.start("Clone and analyze the repository at https://github.com/modelcontextprotocol/servers") 

examples/mcp/github-mcp.py

@@ -0,0 +1,15 @@
+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") 

examples/mcp/gitlab-mcp.py

@@ -0,0 +1,20 @@
+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") 

examples/mcp/google-maps-mcp.py

@@ -0,0 +1,16 @@
+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") 

examples/mcp/groq-mcp.py

@@ -0,0 +1,9 @@
+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")

examples/mcp/memory-mcp.py

@@ -0,0 +1,16 @@
+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") 

examples/mcp/mistral-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+search_agent = Agent(
+    instructions="""You help book apartments on Airbnb.""",
+    llm="mistral/mistral-large-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")

examples/mcp/ollama-python.py

@@ -0,0 +1,12 @@
+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?")

examples/mcp/ollama.py

@@ -0,0 +1,9 @@
+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")

examples/mcp/openai-mcp.py

@@ -0,0 +1,9 @@
+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")

examples/mcp/openrouter-mcp.py

@@ -0,0 +1,9 @@
+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")

examples/mcp/perplexity-mcp.py

@@ -0,0 +1,14 @@
+from praisonaiagents import Agent, MCP
+import os
+
+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)

examples/mcp/postgres-mcp.py

@@ -0,0 +1,15 @@
+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") 

examples/mcp/puppeteer-mcp.py

@@ -0,0 +1,12 @@
+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") 

examples/mcp/redis-mcp.py

@@ -0,0 +1,15 @@
+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") 

examples/mcp/sentry-mcp.py

@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, MCP
+import os
+
+# pip install mcp-server-sentry
+# Get Sentry auth token from environment
+sentry_token = os.getenv("SENTRY_AUTH_TOKEN")
+
+# Use a single string command with Sentry configuration
+sentry_agent = Agent(
+    instructions="""You are a helpful assistant that can analyze Sentry error reports.
+    Use the available tools when relevant to inspect and debug application issues.""",
+    llm="gpt-4o-mini",
+    tools=MCP("python -m mcp_server_sentry --auth-token", args=[sentry_token])
+)
+
+sentry_agent.start("Analyze the most recent critical error in Sentry") 

examples/mcp/sequential-thinking-mcp.py

@@ -0,0 +1,12 @@
+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") 

examples/mcp/slack-mcp.py

@@ -0,0 +1,20 @@
+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") 

examples/mcp/time-mcp.py

@@ -0,0 +1,13 @@
+from praisonaiagents import Agent, MCP
+import os
+
+# pip install mcp-server-time
+# Use a single string command with Time Server configuration
+time_agent = Agent(
+    instructions="""You are a helpful assistant that can handle time-related operations.
+    Use the available tools when relevant to manage timezone conversions and time information.""",
+    llm="gpt-4o-mini",
+    tools=MCP("python -m mcp_server_time --local-timezone=America/New_York")
+)
+
+time_agent.start("Get the current time in New York and convert it to UTC") 

examples/mcp/whatapp-groq-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+whatsapp_agent = Agent(
+    instructions="Whatsapp Agent",
+    llm="groq/llama-3.2-90b-vision-preview",
+    tools=MCP("python /Users/praison/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+whatsapp_agent.start("Send Hello to Mervin Praison")

examples/mcp/whatapp-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+whatsapp_agent = Agent(
+    instructions="Whatsapp Agent",
+    llm="gpt-4o-mini",
+    tools=MCP("python /Users/praison/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+whatsapp_agent.start("Send Hello to Mervin Praison")

examples/mcp/whatapp-multi-agents-mcp.py

@@ -0,0 +1,17 @@
+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 /Users/praison/whatsapp-mcp/whatsapp-mcp-server/main.py")
+)
+
+agents = Agents(agents=[airbnb_agent, whatsapp_agent])
+
+agents.start()

examples/mcp/whatapp-ollama-mcp.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent, MCP
+
+whatsapp_agent = Agent(
+    instructions="Whatsapp Agent",
+    llm="ollama/llama3.2",
+    tools=MCP("python /Users/praison/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.")

examples/mcp/whatsapp-mcp-ui.py

@@ -0,0 +1,31 @@
+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 /Users/praison/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()

examples/mcp/xai-mcp.py

@@ -0,0 +1,9 @@
+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")

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.81.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.1.1.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.81 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.1.1 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

praisonai/inbuilt_tools/__init__.py

@@ -1,4 +1,4 @@
-# Only try to import autogen_tools if either CrewAI or AutoGen is available
+# Only try to import autogen_tools if either CrewAI or AG2 is available
 CREWAI_AVAILABLE = False
 AUTOGEN_AVAILABLE = False
 PRAISONAI_TOOLS_AVAILABLE = False

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.81"
+version = "2.1.1"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,10 +12,11 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.67",
+    "praisonaiagents>=0.0.71",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
+    "mcp==1.6.0",
 ]
 
 [project.optional-dependencies]
@@ -84,7 +85,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.81"
+version = "2.1.1"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -102,10 +103,11 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.67"
+praisonaiagents = ">=0.0.71"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
+mcp = "==1.6.0"
 pyautogen = {version = ">=0.2.19", optional = true}
 crewai = {version = ">=0.32.0", optional = true}
 praisonai-tools = {version = ">=0.0.7", optional = true}

setup.sh

@@ -12,7 +12,7 @@ print_message() {
 print_usage() {
     echo "Usage: $0 [-u|--ui] [-c|--chat] [-d|--code] [-r|--realtime] [-a|--all] [--run] [--quick] [--model MODEL] [--base URL]"
     echo "Options:"
-    echo "  -u, --ui       Install UI dependencies for Multi Agents (CrewAI/AutoGen)"
+    echo "  -u, --ui       Install UI dependencies for Multi Agents (CrewAI/AG2)"
     echo "  -c, --chat     Install Chat interface for single AI Agent"
     echo "  -d, --code     Install Code interface for codebase interaction"
     echo "  -r, --realtime Install Realtime voice interaction interface"
@@ -97,8 +97,8 @@ run_ui_interface() {
             if [ -z "$prompt" ]; then
                 prompt="create a movie script about dog in moon"
             fi
-            read -p "Use AutoGen framework? (y/N): " use_autogen
-            if [[ $use_autogen =~ ^[Yy]$ ]]; then
+            read -p "Use AG2 (Formerly AutoGen) framework? (y/N): " use_ag2
+            if [[ $use_ag2 =~ ^[Yy]$ ]]; then
                 praisonai --framework autogen --init "$prompt"
             else
                 praisonai --init "$prompt"

src/praisonai-agents/.gitignore

@@ -84,4 +84,5 @@ build
 *.mp4 
 *.png 
 graph.py
-chroma_db/
+chroma_db/
+.qodo

src/praisonai-agents/README.md

@@ -0,0 +1,83 @@
+# MCP SSE Server and Client Implementation
+
+This project demonstrates a working pattern for SSE-based MCP (Model Context Protocol) servers and clients. It consists of three main components:
+
+1. **server.py**: An SSE-based MCP server that provides simple tools
+2. **client.py**: A standalone client that connects to the server and uses its tools with Claude
+3. **mcp-sse.py**: A client using praisonaiagents that connects to the server and uses its tools with OpenAI
+
+## Tools Provided by the Server
+
+The server implements two simple tools:
+
+- **get_greeting**: Returns a personalized greeting for a given name
+- **get_weather**: Returns simulated weather data for a given city
+
+## Setup and Usage
+
+### Prerequisites
+
+Make sure you have the required packages installed:
+
+```bash
+pip install praisonaiagents mcp httpx starlette uvicorn anthropic python-dotenv
+```
+
+### Running the Server
+
+First, start the MCP SSE server:
+
+```bash
+python server.py
+```
+
+By default, the server runs on 0.0.0.0:8080, but you can customize the host and port:
+
+```bash
+python server.py --host 127.0.0.1 --port 8081
+```
+
+### Running the Standalone Client
+
+The standalone client uses Claude to interact with the MCP server tools:
+
+```bash
+# Set your Anthropic API key
+export ANTHROPIC_API_KEY=your_api_key_here
+
+# Run the client
+python client.py http://0.0.0.0:8080/sse
+```
+
+You'll see a prompt where you can type queries for Claude to process using the MCP tools.
+
+### Running the praisonaiagents Client
+
+The praisonaiagents client uses OpenAI to interact with the MCP server tools:
+
+```bash
+# Set your OpenAI API key
+export OPENAI_API_KEY=your_api_key_here
+
+# Run the client
+python mcp-sse.py
+```
+
+This will automatically send a query about the weather in Paris to the agent.
+
+## How It Works
+
+1. The server exposes MCP tools via an SSE endpoint
+2. Clients connect to this endpoint and discover available tools
+3. When a user makes a query, the client:
+   - For client.py: Uses Claude to determine which tool to call
+   - For mcp-sse.py: Uses OpenAI to determine which tool to call
+4. The client executes the tool call against the server
+5. The result is returned to the user
+
+This pattern allows for decoupled processes where the MCP server can run independently of clients, making it suitable for cloud-native applications.
+
+## Customizing
+
+- To add more tools to the server, define new functions with the `@mcp.tool()` decorator in `server.py`
+- To change the client's behavior, update the instructions and query in `mcp-sse.py` 

src/praisonai-agents/groq-mcp.py

@@ -0,0 +1,9 @@
+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")

src/praisonai-agents/mcp-llama4.py

@@ -0,0 +1,12 @@
+from praisonaiagents import Agent, MCP
+import os
+
+brave_api_key = os.getenv("BRAVE_API_KEY")
+
+research_agent = Agent(
+    instructions="Research Agent",
+    llm="groq/meta-llama/llama-4-scout-17b-16e-instruct",
+    tools=MCP("npx -y @modelcontextprotocol/server-brave-search", env={"BRAVE_API_KEY": brave_api_key})
+)
+
+research_agent.start("What is the latest research on the topic of AI and its impact on society?")

src/praisonai-agents/mcp-mini-airbnb.py

@@ -0,0 +1,9 @@
+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")

src/praisonai-agents/mcp-mini-bravesearch.py

@@ -0,0 +1,15 @@
+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")
+
+# 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")

src/praisonai-agents/mcp-npx-brave.py

@@ -1,24 +1,18 @@
 from praisonaiagents import Agent, MCP
+import os
 
+brave_api_key = os.getenv("BRAVE_API_KEY")
+
+# Pass the environment variable directly to the MCP server
 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(
         command="npx",
-        args=[
-            "-y", 
-            "@smithery/cli@latest", 
-            "install", 
-            "@smithery-ai/brave-search", 
-            "--client", 
-            "claude", 
-            "--config", 
-            '{"braveApiKey":"BSANfDaqLKO9wq7e08mrPth9ZlJvKtc"}'
-        ],
-        timeout=30,  # 3 minutes for brave-search
-        debug=True    # Enable detailed logging
+        args=["-y", "@modelcontextprotocol/server-brave-search"],
+        env={"BRAVE_API_KEY": brave_api_key}
     )
 )
 
-search_agent.start("Search more information about Praison AI")
+search_agent.start("Search more information about AI News")

src/praisonai-agents/mcp-npx-mini-airbnb-stockprice.py

@@ -0,0 +1,18 @@
+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")
+
+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/app.py")
+)
+
+agent.start("What is the stock price of Tesla?")