SmolAgentWithMCP

Overview

SmolAgentWithMCP is a Python-based AI agent framework that connects multiple Model Context Protocol (MCP) tool servers and orchestrates them with a large language model (LLM). With support for Brave Search and other MCP-compatible tools, it enables powerful, tool-augmented question-answering workflows. The agent leverages smolagents, LiteLLM, and MCP protocol to offer flexible, extensible, and modern AI tooling.

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Use Case

This project is ideal for developers and researchers who want to:

• Integrate multiple tool servers (e.g., Brave Search, custom MCP crawlers) into a single AI agent.
• Enable conversational AI agents to use external tools for enhanced answering capabilities.
• Prototype and deploy advanced AI workflows with minimal setup.
• Extend the agent with custom tools using the MCP protocol.

Example Scenarios:

• An assistant that answers queries using live web search data and custom knowledge bases.
• A research agent that leverages both public APIs and private datasets/tools.
• An automation bot that can interact with external services via MCP-enabled tools.

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Features

• Multi-tool orchestration: Connects to multiple MCP tool servers.
• Tool-calling agent: Uses LLM to decide when and how to call tools.
• Configurable via .env: Supports multiple API keys and environment settings.
• Async workflow: Efficient asynchronous tool management.
• Extensible: Easily add new MCP tools or change LLM models.

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Project Structure

- smolagentwithmcp.py         # Main agent source code
- requirements.txt            # Python dependencies
- .env.example                # Example env file
- README.md                   # Project documentation

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Setup & Installation

1. Clone the repository:

   git clone https://github.com/ashishpatel26/SmolAgentWithMCP.git
   cd SmolAgentWithMCP

2. Install dependencies:

   pip install -r requirements.txt

3. Configure environment variables:

   • Copy .env.example to .env and fill in your API keys.

   cp .env.example .env

4. Run the agent:

   python smolagentwithmcp.py

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Code Workflow Diagram

flowchart TD
    subgraph User Interaction
        U([User]) -->|Query| AGENT[SmolAgentWithMCP]
    end
    AGENT -->|Loads| MCP1[MCP Tool Server 1: Brave Search]
    AGENT -->|Loads| MCP2[MCP Tool Server 2: Custom]
    AGENT -->|Initializes| LLM[LiteLLM Model]
    U -->|Input| AGENT
    AGENT -->|Decides tool usage| TOOLCALL[ToolCallingAgent]
    TOOLCALL -->|Calls| MCP1
    TOOLCALL -->|Calls| MCP2
    MCP1 -->|Returns Data| TOOLCALL
    MCP2 -->|Returns Data| TOOLCALL
    TOOLCALL -->|Generates Answer| AGENT
    AGENT -->|Output| U

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How It Works

1. Initialization: Loads MCP tool servers (like Brave Search, custom crawlers) and configures LLM.
2. User Query: Accepts user input via terminal.
3. Tool Orchestration: Determines which tools to call for the query.
4. Execution: Calls tools, gathers results, and generates a final response using the LLM.
5. Async Management: Handles multiple tool collections and steps asynchronously.
6. Output: Returns the answer to the user interactively.

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Configuration

Edit .env to set your API keys:

BRAVE_API_KEY=your_brave_search_api_key
OPENAI_API_KEY=your_openai_api_key

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Requirements

See requirements.txt:

• smolagents
• python-dotenv
• mcp
• litellm

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License

MIT License. See LICENSE for details.

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Contributing

Contributions welcome! Please open issues or PRs for feature requests, bug fixes, or enhancements.

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Credits

• SmolAgents
• LiteLLM
• Model Context Protocol