semvec/pypi-packages huggingface dataset:
deepglue 0.1.14
deepglue: A utility package for deep learning projects. Keeping things together.
License: MIT
Requires Python: >=3.9

# Deep Glue

<img src="https://raw.githubusercontent.com/EricThomson/deepglue/main/docs/images/deep_glue_logo.png" alt="deepglue logo" align="right" width="150">

Utilities for pytorch-based deep learning projects. Keeping the useful stuff together in one place.

The philosophy of deepglue is to provide simple functionality that makes deep learning projects easier. There are lots of functions we tend to replicate across different projects, and deepglue puts them together in one place. 

### Version: Pre-Alpha :construction: 
deepglue is quickly evolving. If something breaks or seems awry, please ask: open an issue or discussion thread because you probably aren't wrong. 

## Installing and importing
    pip install deepglue
    import deepglue as dg

## Learn more
- [Read the docs](https://deepglue.readthedocs.io/en/latest/) :book:
- [Demo notebook](https://github.com/EricThomson/deepglue/tree/main/demos) :rocket: 
- [Project roadmap](https://github.com/EricThomson/deepglue/issues/1) :world_map:
- [Contribute to deepglue](CONTRIBUTING.md) :hammer:
mcpturbo 1.0.0
MCPTurbo - Multi-agent Communication Protocol
License: MIT
Requires Python: >=3.9
Keywords: mcpturbo, mcp, multi-agent, communication, protocol, ai, automation, bots, bots-ai, bots-automation, bots-communication, bots-protocol, bots-ai-automation, bots-ai-communication, bots-ai-protocol, bots-ai-automation-communication, bots-ai-automation-communication-protocol

# 🤖 Mcpturbo 

MCPTurbo - library for multi-agent communication protocol

## 📋 Overview

mcpturbo-ai, mcpturbo-core, mcpturbo-agents, mcpturbo-generators, mcpturbo-templates, mcpturbo-cli, mcpturbo-cloud, mcpturbo-web, mcpturbo-enterprise

## 📦 Installation

```bash
pip install mcpturbo
```

## 🚀 Quick Start

```python
# Basic usage example
from mcpturbo import mcpturbo.ai, mcpturbo.agents, mcpturbo.generators, mcpturbo.templates, mcpturbo.cli, mcpturbo.cloud, mcpturbo.web, mcpturbo.enterprise

# Initialize component
component = mcpturbo.ai()

# Use component
result = await component.execute()
print(f"Result: {result}")
```

## 🔧 Configuration

```python
# Configuration example
config = {
    "setting1": "value1",
    "setting2": "value2"
}

component = main_component(**config)
```

## 📚 Documentation

- **[API Reference](https://mcpturbo.dev/docs/)**
- **[Examples](https://github.com/fmonfasani/mcpturbo/tree/main/examples)**
- **[Contributing](https://github.com/fmonfasani/mcpturbo/blob/main/CONTRIBUTING.md)**

## 🔗 Related Projects

- **[MCPTurbo Core](https://github.com/fmonfasani/mcpturbo-core)** - Core protocol
- **[MCPTurbo CLI](https://github.com/fmonfasani/mcpturbo-cli)** - Command line interface
- **[MCPTurbo Complete](https://github.com/fmonfasani/mcpturbo-complete)** - Full installation

## 🤝 Contributing

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 - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Part of the MCPTurbo ecosystem
- Built with modern Python best practices
- Inspired by the need for intelligent agent coordination

---

<div align="center">
Made with ❤️ by the MCPTurbo Team
</div>