SmolAgentWithMCP

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SmolAgentWithMCP

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SmolAgentWithMCP enables developers to build powerful AI agents that integrate multiple MCP tool servers, like Brave Search, with LLMs for enhanced, real-time question-answering and tool orchestration.

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# 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](https://github.com/smol-ai/smolagents), [LiteLLM](https://github.com/BerriAI/litellm), and MCP protocol to offer flexible, extensible, and modern AI tooling.

---

## 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.

---

## 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.

---

## Project Structure

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

---

## Setup & Installation

1. **Clone the repository:**
   ```bash
   git clone https://github.com/ashishpatel26/SmolAgentWithMCP.git
   cd SmolAgentWithMCP
   ```

2. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

3. **Configure environment variables:**
   - Copy `.env.example` to `.env` and fill in your API keys.
   ```bash
   cp .env.example .env
   ```

4. **Run the agent:**
   ```bash
   python smolagentwithmcp.py
   ```

---

## Code Workflow Diagram

---

## 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.

---

## Configuration

Edit `.env` to set your API keys:

```dotenv
BRAVE_API_KEY=your_brave_search_api_key
OPENAI_API_KEY=your_openai_api_key
```

---

## Requirements

See [`requirements.txt`](./requirements.txt):

- `smolagents`
- `python-dotenv`
- `mcp`
- `litellm`

---

## License

MIT License. See [LICENSE](LICENSE) for details.

---

## Contributing

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

---

## Credits

- [SmolAgents](https://github.com/smol-ai/smolagents)
- [LiteLLM](https://github.com/BerriAI/litellm)
- [Model Context Protocol](https://github.com/modelcontextprotocol)

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