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Merged
MervinPraison merged 1 commit into from
Apr 1, 2025
Merged

Update to version 2.1.1 #442

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2 changes: 1 addition & 1 deletion docker/Dockerfile
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install flask praisonai==2.1.0 gunicorn markdown
RUN pip install flask praisonai==2.1.1 gunicorn markdown
EXPOSE 8080
CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
2 changes: 1 addition & 1 deletion docs/api/praisonai/deploy.html
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Expand Up @@ -110,7 +110,7 @@ <h2 id="raises">Raises</h2>
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==2.1.0 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;)

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93 changes: 93 additions & 0 deletions docs/mcp/anthropic.mdx
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---
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>
93 changes: 93 additions & 0 deletions docs/mcp/gemini.mdx
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---
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>
85 changes: 85 additions & 0 deletions docs/mcp/groq.mdx
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@@ -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")
)
Comment on lines +39 to +42
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@gemini-code-assist gemini-code-assist bot Apr 1, 2025

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high

The Groq API key is not being passed to the MCP server. This is inconsistent with other integrations. You should pass the Groq API key as an environment variable to the MCP server.

tools=MCP(
                command="npx",
                args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
                env={"GROQ_API_KEY": os.environ.get("GROQ_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 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>
93 changes: 93 additions & 0 deletions docs/mcp/mistral.mdx
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@@ -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>
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