iotagents 0.0.1
iotagents - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503)

# **IoTAgents**
[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)

**IoTAgents** is a comprehensive framework designed to seamlessly integrate IoT data with advanced AI agents, enabling the effortless parsing, processing, and utilization of IoT data streams. By bridging the gap between IoT devices and LLM-powered agents, IoTAgents empowers businesses to harness the full potential of their IoT ecosystems, transforming raw data into actionable insights and intelligent automation.

**IoTAgents** is an open-source framework that simplifies the integration of IoT data with large language model (LLM) agents. As IoT devices generate vast amounts of data, efficiently parsing and utilizing this data has become increasingly critical. IoTAgents streamlines this process by providing tools and APIs to effortlessly connect, process, and manage IoT data streams, making them readily available for LLM-powered agents to analyze, understand, and act upon.

## Why IoTAgents?

The explosion of IoT devices in industries such as manufacturing, healthcare, agriculture, and smart cities has led to a significant increase in the amount of data being generated. However, this data is often siloed, underutilized, and difficult to integrate with existing AI-driven systems. **IoTAgents** was created to address these challenges by offering a framework that:

- **Simplifies Data Parsing**: Effortlessly connects to IoT data streams, extracting and normalizing data for immediate use.
- **Enables Seamless Integration**: Provides a seamless interface for LLM agents to access and interact with IoT data, allowing for real-time decision-making and automation.
- **Enhances Scalability**: Built to handle large-scale IoT deployments, ensuring that your AI agents can keep up with the data influx.
- **Accelerates Innovation**: By making IoT data more accessible, IoTAgents accelerates the development of innovative solutions across various industries.

## Features

- **IoT Data Connectors**: Easily connect to popular IoT platforms and protocols, including MQTT, CoAP, HTTP, and more.
- **Data Normalization**: Automatically parse and normalize data from various IoT devices, making it uniform and ready for analysis.
- **LLM Integration**: Integrate seamlessly with leading LLM frameworks, enabling your agents to process IoT data efficiently.
- **Real-Time Processing**: Handle real-time data streams, allowing for immediate insights and actions.
- **Scalable Architecture**: Designed to scale with your IoT deployments, from a single device to millions.

## Getting Started

### Prerequisites

- Python 3.8 or later
- An IoT platform or devices generating data
- An LLM framework (e.g., OpenAI GPT, Anthropic Claude)

### Installation

```bash
pip install iotagents
```

### Quick Start

1. **Connect to IoT Data Streams**:
   ```python
   from iotagents import IoTConnector

   connector = IoTConnector("mqtt://broker.hivemq.com")
   data_stream = connector.subscribe("iot/topic")
   ```

2. **Parse and Normalize Data**:
   ```python
   from iotagents import DataParser

   parser = DataParser()
   parsed_data = parser.parse(data_stream)
   ```

3. **Integrate with LLM Agents**:
   ```python
   from iotagents import LLMInterface

   llm_agent = LLMInterface("your-llm-model")
   response = llm_agent.process(parsed_data)
   print(response)
   ```

## Use Cases

### 1. **Smart Manufacturing**
   - Monitor and analyze data from IoT-enabled machines in real-time.
   - Predict maintenance needs and optimize production schedules using AI agents.

### 2. **Healthcare**
   - Process data from wearable devices and smart sensors to provide real-time health insights.
   - Enable AI agents to assist in patient monitoring and diagnostics.

### 3. **Agriculture**
   - Analyze environmental data from IoT sensors to optimize crop management.
   - Automate irrigation and fertilization based on AI-driven insights.

### 4. **Smart Cities**
   - Integrate traffic, weather, and energy data to optimize city operations.
   - Allow AI agents to manage and respond to real-time events.

## Contributing

We welcome contributions from the community! Please see our [contributing guidelines](CONTRIBUTING.md) for more details.

## License

IoTAgents is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

### Why IoTAgents Exists

IoTAgents was born out of the necessity to bridge the gap between the ever-growing world of IoT devices and the capabilities of LLM agents. As industries increasingly rely on IoT for data-driven insights, the need to efficiently manage, parse, and utilize this data has never been more crucial. Traditional systems often struggle to keep up with the volume and complexity of IoT data, leading to missed opportunities for automation and innovation.

By creating a framework that seamlessly integrates IoT data with powerful LLM agents, IoTAgents empowers organizations to unlock the full potential of their IoT ecosystems. Whether it's optimizing industrial processes, improving patient care, or making cities smarter, IoTAgents provides the tools needed to turn raw IoT data into intelligent actions.

The future of automation lies in the hands of AI agents, and IoTAgents is the key to making that future a reality.