p3ngu1nzz / agentnet

AgentNet is a state-of-the-art peer-to-peer networking framework designed for decentralized agent systems. It utilizes Vulkan for high-performance graphics rendering, enabling efficient communication and data sharing among distributed agents.

AgentNet

AgentNet is a sophisticated peer-to-peer networking framework that empowers distributed agents to communicate and share data with high efficiency. Leveraging Vulkan for graphics rendering, AgentNet is built for performance and scalability, providing a robust platform for decentralized agent interactions.

Getting Started

These instructions will guide you through getting a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following installed:

• Vulkan SDK
• CMake
• A C++ compiler like GCC or Clang
• Access to Llama sources (see below)

Llama Sources

To build this project from source, you will need access to the Llama sources. Follow these steps to download them:

1. Visit the Llama GitHub repository and follow the instructions to register and accept the license¹.
2. Once approved, you will receive a custom URL via email from Meta.
3. Clone the Llama repository and run the download.sh script with the custom URL provided. (Requires Linux or MacOS)

Installation

Follow these steps to set up your development environment:

1. Clone the repository:

git clone https://github.com/K-Rawson/AgentNet.git

2. Navigate to the project directory:

cd AgentNet

3. Create a build directory:

mkdir build && cd build

4. Run CMake to configure the project:

cmake ..

5. Build the project:

cmake --build .

Running the Tests

To run the automated tests for this system, use the following command:

ctest

Deployment

For deployment, additional steps may be required, such as setting up a server environment or configuring network settings.

Built With

• Vulkan - A low-overhead, cross-platform 3D graphics and compute API.
• CMake - An open-source, cross-platform family of tools designed to build, test, and package software.
• Llama - For building AgentNet llama models from source. (Advanced)

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• K-Rawson - Initial work - AgentNet

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Tonic-AI

Please cite this project as:

Rawson, K. (2024). AgentNet: An Innovative AI Framework for Gaming. [https://github.com/K-Rawson/AgentNet](https://github.com/K-Rawson/AgentNet)

agentnet's Issues

Performance Benchmarking

benchmarking the performance of the chat completion feature could be useful. This would involve measuring response times, memory usage, and CPU/GPU utilization to ensure the feature performs well under different loads.

User Feedback Collection

Implement a system for collecting user feedback on the new chat completion feature. This could be through surveys, direct feedback in the application, or monitoring usage patterns.

Error Handling and Logging

Plan for robust error handling and logging mechanisms. This will help in diagnosing issues during development and after deployment.

Future Roadmap

Discuss and plan the future roadmap of your project post-integration of the LLaMa models. Consider what features or improvements could come next.

Documentation Update

update the project’s documentation to reflect the integration of the LLaMa models and any new functionalities or changes in the workflow.

Testing Framework

consider setting up a testing framework for the new chat completion feature. This could include unit tests, integration tests, and end-to-end tests to ensure the feature works correctly and is robust against various input scenarios.

Compliance Check

Ensure that your use of the LLaMa models complies with Meta’s licensing and any other relevant regulations or guidelines.

Integrate LLaMa Model for Chat Completion in AgentNetTerm

Issue Description

We're exploring the integration of the LLaMa model into our AgentNetTerm console application to enable chat completion capabilities. This task involves setting up the LLaMa model within our C++ environment, ensuring compatibility, and creating a seamless user experience for chat interactions.

Goals:

• Successfully integrate the LLaMa model with our existing C++ project.
• Develop a chat interface that can handle input and output for chat completion.
• Implement an inference engine to process and respond to user inputs using the LLaMa model.

Steps:

• Review llamacpp documentation and examples for integration guidance.
• Set up the necessary environment for running the LLaMa model.
• Convert and place the downloaded LLaMa models into the llama sources directory.
• Build and test the integration with a simple chat completion example.
• Document the process and any issues encountered for team reference.

Expected Outcome:

A proof of concept demonstrating the LLaMa model's chat completion in our AgentNetTerm application, paving the way for more advanced AI features in our project.

Continuous Integration/Continuous Deployment (CI/CD)

If you haven’t already, set up CI/CD pipelines to automate the testing and deployment of your application as new code is merged.

Community Engagement

discuss strategies for engaging with your project’s community. This could include forums, social media, or other platforms where users and developers can interact.

Security Review

Given that you’re working with AI models, it’s important to review the security aspects of your application, especially how user data is handled and stored.