Otter Bridge

🚀 OtterBridge

OtterBridge is a lightweight and flexible server that connects applications to various large language model providers. Adhering to the principles of simplicity and composability outlined in Anthropic's guide to building efficient agents, OtterBridge offers a clean interface for connecting to LLMs while remaining adaptable to different use cases.

📦 File Structure

├── .env.example           # Example file for environment variables
├── .gitignore             # File for excluding files from git
├── LICENSE                # Open-source license (e.g., MIT, Apache)
├── README.md              # Project documentation
├── server.py              # MCP server implementation (previously named fastmcp_server.py)
├── requirements.txt       # Python dependencies
└── src/                   # Source code directory
    ├── __init__.py        # Package initialization file
    └── services/          # Services directory
        ├── __init__.py    # Initialization code for services
        └── ollama.py      # Ollama service implementation

✨ Features

• Provider-agnostic: Designed to work with multiple LLM providers (currently supports Ollama, with ChatGPT and Claude coming soon).
• Simple and composable design: Follows best practices for building LLM agents.
• Lightweight server: Built with FastMCP for a reliable and efficient server implementation.
• Model management: Easily access information and functionality about available models.

🤔 Why "OtterBridge"?

Just as otters create connections between riverbanks, OtterBridge builds a seamless bridge between your applications and various LLM providers. As adaptable and resourceful as otters, OtterBridge accommodates different LLM backends while providing a consistent interface.

🚦 Prerequisites

Before installing OtterBridge, you need to:

1. Install and run Ollama (https://ollama.ai/download) on the default port.
2. Install uv (https://github.com/astral-sh/uv) for Python package management.

📦 Installation

1. Clone this repository:

git clone https://github.com/yourusername/otterbridge.git
cd otterbridge

2. Install dependencies using uv:

uv add -r requirements.txt

3. Create a .env file based on the provided .env.example:

cp .env.example .env

4. Configure your environment variables in the .env file.

💻 Integration with Claude Desktop

For users of Claude Desktop, you need to add OtterBridge to your Claude Desktop configuration:

1. Open your Claude Desktop configuration file.
2. Add the following configuration (adjust the path to match your local installation):

"otterbridge": {
    "command": "uv",
    "args": [
        "--directory",
        "C:\\Path\\To\\Your\\otterbridge",
        "run",
        "server.py"
    ]
}

💻 Usage Examples

Basic Usage

Starting the Server

OtterBridge can be started in two ways:

1. Manually for testing purposes:

uv run server:app --host 0.0.0.0 --port 8000

2. In a production environment:

uv run server:app --host 0.0.0.0 --port 8000 --reload off

📚 Documentation

Please refer to the API documentation to learn how to use the interfaces provided by OtterBridge.

🤝 Contribution Guidelines

Contributions are welcome! Feel free to submit a Pull Request.

Development Guidelines

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/amazing-feature).
3. Commit your changes (git commit -m 'Add some amazing feature').
4. Push to the branch (git push origin feature/amazing-feature).
5. Open a Pull Request.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• MCP (Model Context Protocol) for the server framework.
• Ollama for local LLM hosting support.
• Anthropic's Guide to Building Effective Agents for architectural inspiration.