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Update to version 2.1.1
- Incremented version number to 2.1.1 in `pyproject.toml`, `praisonai.rb`, and `uv.lock` to reflect recent changes. - Updated dependency version for `praisonaiagents` to 0.0.71 in `pyproject.toml` and `uv.lock`. - Modified Dockerfile and deployment scripts to install the updated version of `praisonai`. - Added new documentation files for various MCP integrations, including Anthropic, Gemini, Groq, Mistral, Ollama, OpenAI, and XAI. - Introduced example scripts for each MCP integration to facilitate user understanding and implementation. - Enhanced the LLM class to process tool results based on the original user query for improved response accuracy.
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‎docker/Dockerfile‎

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FROM python:3.11-slim
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WORKDIR /app
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COPY . .
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RUN pip install flask praisonai==2.1.0 gunicorn markdown
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RUN pip install flask praisonai==2.1.1 gunicorn markdown
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EXPOSE 8080
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CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

‎docs/api/praisonai/deploy.html‎

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file.write(&#34;FROM python:3.11-slim\n&#34;)
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file.write(&#34;WORKDIR /app\n&#34;)
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file.write(&#34;COPY . .\n&#34;)
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file.write(&#34;RUN pip install flask praisonai==2.1.0 gunicorn markdown\n&#34;)
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file.write(&#34;RUN pip install flask praisonai==2.1.1 gunicorn markdown\n&#34;)
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file.write(&#34;EXPOSE 8080\n&#34;)
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file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
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‎docs/mcp/anthropic.mdx‎

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

‎docs/mcp/gemini.mdx‎

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

‎docs/mcp/groq.mdx‎

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

‎docs/mcp/mistral.mdx‎

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

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