SmolAgents + MCP servers

This project demonstrates building an AI assistant using the SmolAgents library that can perform web searches and scrape/crawl websites by integrating with the Brave Search and the Crawl4ai Model Context Protocols (MCP) servers.

Requirements

Before running this project, ensure you have the following dependencies installed:

• smolagents
• python-dotenv
• mcp
• @modelcontextprotocol/server-brave-search (installed globally via npm/npx)
• litellm

You can install the Python packages using pip:

pip install -r requirements.txt

Ensure you have Node.js and npm/npx and uv installed to run the MCP servers.

Environment Variables

Create a .env file in the root directory of the project and add the following variables:

BRAVE_API_KEY=your_brave_api_key
OPENAI_API_KEY=your_openai_api_key

Replace your_brave_api_key and your_openai_api_key with your actual API keys.

Usage

1. Clone the repository or download the script (smolagent_with_mcp.py).

2. Navigate to the project directory in your terminal.

3. Run the script:

   python smolagent_with_mcp.py
   

4. Interact with the assistant by typing your search queries. Type exit or quit to terminate the session.

the code encapsulates the agent initialization and the main interaction loop within a Python class (SmolAgentWithMCP). This object-oriented approach offers modularity and makes it easier to extend the agent's capabilities, such as integrating additional MCP servers and their tools.

Notes

• Ensure that npx is installed and available in your system's PATH, as it is required to run the Brave Search MCP server.
• The ToolCallingAgent in SmolAgents is designed to output tool calls in a structured format (like JSON) which are then executed by the environment (in this case, the script's main loop using the MCPClient).

Adding More MCP Servers

The smolagent_with_mcp.py file is structured to allow for easily adding more MCP servers and their tools in a modular way. To add a new MCP server:

1. Define the StdioServerParameters for the new server. This involves specifying the command to run the server, its arguments, and any necessary environment variables (similar to how brave_server_params and crawl4ai_server_params are defined).

2. Add the newly defined server parameters object to the server_list in the main function. The BraveSearchAgentApp class will automatically initialize the new server and include its tools in the agent's capabilities.

Example:

# Define parameters for a new server (replace with actual command and args)
new_server_params = StdioServerParameters(
    command="your_server_command",
    args=["arg1", "arg2"],
    env={"YOUR_API_KEY": os.getenv("YOUR_API_KEY")},
)

def main():
    # Define the list of MCP server parameters
    server_list = [
        brave_server_params,
        crawl4ai_server_params,
        new_server_params # Add the new server parameters here
        # Add other server parameters here as needed
    ]
    app = BraveSearchAgentApp(server_list)
    app.run()

if __name__ == "__main__":
    main()

MCP used in the project:

https://playbooks.com/mcp/ritvij14-crawl4ai https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search

License

This project is licensed under the MIT License. See the LICENSE file for more details.