tinyAGI

Overview

Welcome to the TinyAgi- repository! This project is an attempt to build and explore the capabilities of Agentic Systems and LLMs by making various tools, applying recent research.

Learning Objectives

• Explore Agentic Frameworks: Delve into the architecture and functionality of agentic systems, gaining a comprehensive understanding of their capabilities.
• Build and Integrate Tools: Learn how to create and integrate various tools that enhance the functionality of AI agents.
• Experiment with Research: Engage with cutting-edge research in AI, applying it to practical tasks to deepen your knowledge and skills.

Project Structure

The project consists of several key components:

• main.py: The main entry point for the application, where tasks are decomposed and processed using a Hugging Face API client. Update this file with your own tasks to run the application on your specific needs.
• agent.py: Contains the Agent class, which defines the behavior of the AI agent, including how it processes tasks and generates executable Python code.
• toolbox/: A folder containing all the tools that agent can be used for.

Getting Started

To get started with tinyAgi, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Memomer/TinyAgi-.git
   cd tinyAgi

2. Set Up Environment: Ensure you have Python installed and set up a virtual environment. Install the required dependencies:

   pip install -r requirements.txt

3. Configure API Access: Create a .env file in the root directory and add your Hugging Face API key:

   HUGGINGFACE_API_KEY=your_api_key_here
   

4. Run the Application: Execute the main script to start decomposing tasks:

   python agents_hf/main.py

To be added

• Custom Tool Creation: Easily create and integrate custom tools tailored to your specific needs. The project includes an inbuilt video editing tool located in the editor folder.
• Multi-Modal Support: The framework supports various input and output modalities, allowing for a more versatile interaction with tasks.
• Transcription Capabilities: Built-in support for transcribing audio and video content into text, facilitating easier task decomposition and processing.
• Task Customization: Update main.py with your own tasks to run the application on custom inputs, allowing for personalized experimentation.
• More Features to be Added: The project is continuously evolving, with plans to introduce additional features and tools in the future.

Contributing

We welcome contributions! If you have ideas for improvements or new features, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Thanks to the open-source community and the researchers whose work inspires this project. Let's learn and innovate together!