AutoGroq is a tool designed to simplify the creation and management of AI agent teams for complex tasks, targeting developers and users who want to leverage multi-agent AI without extensive manual configuration. It dynamically generates specialized agent teams based on user-defined project requirements, enabling efficient problem-solving and task execution.

How It Works

AutoGroq operates by taking a user's natural language request and dynamically generating a tailored team of AI agents, each with specialized expertise. This approach contrasts with traditional methods that require pre-defining agent roles and skills. The system then facilitates interaction through a natural conversation flow, with features like code snippet extraction to a "Whiteboard" and advanced prompt rephrasing to ensure clarity and accuracy.

Quick Start & Requirements

• Install: Follow provided instructions for Autogen and Miniconda. Clone the repository, create and activate a conda environment (conda create -n AutoGroq python=3.11, conda activate AutoGroq), then run pip install -r requirements.txt and streamlit run c:\AutoGroq\AutoGroq\main.py.
• Prerequisites: Python 3.11, Miniconda, Git. Requires API keys for supported LLMs (Groq, ChatGPT, Ollama, etc.) configured in config_local.py.
• Resources: Setup involves environment creation and dependency installation, estimated to take a few minutes.
• Links: AutoGroq Live Demo, Video Tutorials

Highlighted Details

• Dynamic generation of expert AI agents and workflows based on user needs.
• Natural conversation flow with context-aware dialogue.
• Code snippet extraction to a dedicated "Whiteboard" section.
• Support for multiple LLMs (Groq, ChatGPT, Ollama) and custom provider integration.
• Bulk file upload for agents, skills, and workflows into Autogen.

Maintenance & Community

The project is actively maintained by J. Gravelle. Community feedback and contributions are encouraged via the GitHub repository.

Licensing & Compatibility

Released under the MIT License. The license permits commercial use, modification, and distribution, with the condition that original copyright and authorship notices are retained and modifications are clearly indicated.

Limitations & Caveats

The project is described as "groundbreaking" and has a rapidly growing user base, but it is primarily a Streamlit application, which may have performance implications for very large-scale or production-critical deployments. Specific details on scalability or robustness under heavy load are not provided.