Welcome to SecAI! This project focuses on creating a smart graph for networks of autonomous AI agents. The aim is to facilitate communication and coordination among AI agents in various environments. By leveraging advanced algorithms and deep learning techniques, SecAI provides a robust framework for developing intelligent systems.
The project is designed for researchers, developers, and enthusiasts interested in artificial intelligence, machine learning, and networked systems. Whether you're building a complex AI application or exploring new research avenues, SecAI can serve as a powerful tool in your arsenal.
For the latest updates and releases, check out our Releases section.
- Graph Representation: Utilize a flexible graph structure to model relationships between AI agents.
- Asynchronous Communication: Support for non-blocking communication between agents, enhancing performance and scalability.
- Debugging Tools: Integrated debugging features to simplify troubleshooting and optimization.
- Deep Learning Integration: Built-in support for deep learning frameworks to enhance agent capabilities.
- Visual Diagrams: Generate visual representations of agent interactions and network structures.
- SQL Database Support: Store and retrieve agent data efficiently using SQL.
- LLM Compatibility: Integrate with large language models for advanced natural language processing tasks.
To get started with SecAI, follow these steps:
-
Clone the Repository:
git clone https://github.com/Dukemunene1/secai.git cd secai -
Install Dependencies:
Ensure you have Python and pip installed. Then run:
pip install -r requirements.txt
-
Download the Latest Release:
For the latest features and improvements, download the latest release from our Releases section. Follow the instructions provided there to execute the files.
Once you have installed SecAI, you can start using it in your projects. Here’s a basic example of how to create a network of AI agents:
from secai import Agent, Network
# Create a network
network = Network()
# Create agents
agent1 = Agent(name="Agent 1")
agent2 = Agent(name="Agent 2")
# Add agents to the network
network.add_agent(agent1)
network.add_agent(agent2)
# Establish communication
agent1.send_message("Hello from Agent 1!")
response = agent2.receive_message()
print(response)SecAI also allows for more advanced configurations, such as setting up asynchronous communication and integrating with deep learning models. Here’s how you can do that:
from secai import AsyncAgent
async_agent = AsyncAgent(name="Async Agent")
await async_agent.perform_task("Analyze data")Explore the documentation for more detailed examples and use cases.
We welcome contributions from the community! If you want to contribute to SecAI, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/YourFeature). - Make your changes and commit them (
git commit -m 'Add your feature'). - Push to the branch (
git push origin feature/YourFeature). - Open a Pull Request.
Please ensure that your code follows our coding standards and includes appropriate tests.
SecAI is licensed under the MIT License. See the LICENSE file for more details.
For questions or feedback, feel free to reach out:
- Email: [email protected]
- GitHub: Dukemunene1
Thank you for your interest in SecAI! We hope you find it useful for your projects. For the latest updates and releases, visit our Releases section.
We would like to thank the contributors and the community for their support. Your feedback helps us improve SecAI and make it better for everyone.
Explore these resources to deepen your understanding of the concepts behind SecAI.
SecAI aims to bridge the gap between autonomous AI agents and effective communication. By providing a smart graph structure, we enable developers to create sophisticated AI networks. We encourage you to experiment with SecAI and share your findings with the community. Your contributions can help shape the future of this project.
For the latest features and updates, don’t forget to check our Releases section.