# OmniXy

> MCP Client

OmniXy is a universal Model Context Protocol client in Python, enabling seamless integration with any LLM. It supports structured reasoning, robust prompt management, and adheres to the MCP specification for consistency.

## Overview

- **Category:** AI
- **Language:** Python
- **Stars:** 1
- **Forks:** 0
- **Owner:** MaTriXy
- **GitHub:** https://github.com/MaTriXy/OmniXy
- **Created:** 2025-02-25T13:33:40+00:00
- **Updated:** 2025-03-08T15:39:30+00:00
- **Source:** https://model-context-protocol.com/clients/universal-model-context-protocol-python-client

## Setup

## Setup

1. **Installation**:

   ```bash
   # Using uv (Python)
   uv venv
   source .venv/bin/activate
   uv pip install -r requirements.txt
   ```

2. **Configuration**:
   - Set up your LLM provider credentials (e.g., Anthropic API key) in a secure vault or environment variables.

3. Copy the `.env.example` file to `.env` and fill in your API keys:

    ```bash
    cp .env.example .env
    # Edit .env with your API keys and settings
    ```

4. Install test dependencies:

```bash
uv pip install pytest pytest-cov pytest-asyncio
```

5. Set up the development environment:

```bash
# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -r requirements.txt

# Install development dependencies
uv pip install pytest pytest-cov pytest-asyncio ruff black mypy
```

## Tools

## Available Tools

1.  Vendor-Agnostic (Easily switch between or integrate multiple LLM backends.)
2.  Standards-Based (Leverages the Model Context Protocol to ensure consistency in request/response structures.)
3.  Extensible (New LLM providers can be added via a driver pattern without refactoring the core.)
4.  Reasoning-Capable (Orchestrate multi-step chain-of-thought processing for complex tasks.)
5.  Secure & Scalable (Incorporates best practices for authentication, concurrency, caching, and data handling.)
6.  Type-Safe (Uses Pydantic for data validation, serialization, and documentation.)