> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI MCP Integration

> Guide for integrating OpenAI models with PraisonAI agents using MCP

## Add OpenAI Tool to AI Agent

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    pip install praisonaiagents
    ```
  </Step>

  <Step title="Run the Agent">
    Execute your script:

    ```zsh theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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>