File: //opt/PraisonAI/docs/mcp/groq.mdx
---
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>