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.
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.
- 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.
- Python 3.8 or later
- An IoT platform or devices generating data
- An LLM framework (e.g., OpenAI GPT, Anthropic Claude)
pip install iotagentsfrom iotagents.main import (
map_to_pandas,
map_to_json,
map_to_csv,
map_to_text,
map_to_sql,
map_to_xml,
map_to_markdown,
map_to_graph,
map_to_heatmap_data,
)
iot_data = [
{
"Device ID": "001",
"Device Type": "Temperature",
"Data Type": "Temperature",
"Timestamp": "2024-08-23 10:00:00",
"Location": "Warehouse A",
"Sensor Reading": 22.5,
"Unit": "°C",
"Status": "Active",
"Description": "Temperature inside Warehouse A",
},
{
"Device ID": "002",
"Device Type": "Humidity",
"Data Type": "Humidity",
"Timestamp": "2024-08-23 10:01:00",
"Location": "Warehouse B",
"Sensor Reading": 55.3,
"Unit": "%",
"Status": "Active",
"Description": "Humidity level inside Warehouse B",
},
]
# Examples of calling the functions
print(map_to_pandas(iot_data))
print(map_to_json(iot_data))
print(map_to_csv(iot_data))
print(map_to_text(iot_data))
print(map_to_sql(iot_data))
print(map_to_xml(iot_data))
print(map_to_markdown(iot_data))
print(map_to_graph(iot_data))
print(map_to_heatmap_data(iot_data))- Monitor and analyze data from IoT-enabled machines in real-time.
- Predict maintenance needs and optimize production schedules using AI agents.
- Process data from wearable devices and smart sensors to provide real-time health insights.
- Enable AI agents to assist in patient monitoring and diagnostics.
- Analyze environmental data from IoT sensors to optimize crop management.
- Automate irrigation and fertilization based on AI-driven insights.
- Integrate traffic, weather, and energy data to optimize city operations.
- Allow AI agents to manage and respond to real-time events.
We welcome contributions from the community! Please see our contributing guidelines for more details.
IoTAgents is licensed under the MIT License. See the LICENSE file for more details.