AIConfig is an open-source framework designed to streamline the development of production-grade generative AI applications. It targets developers and researchers by separating AI configurations—prompts, models, and parameters—from application code, enabling version control, evaluation, and rapid prototyping. This separation simplifies application code and facilitates iterative AI development.

How It Works

AIConfig uses a config-based approach, storing AI logic in JSON-serializable files. This allows prompts, model parameters, and chaining logic to be managed independently of application code. The framework provides SDKs for Python and Node.js, enabling seamless integration into existing applications. A key feature is the AIConfig Editor, available as a VS Code extension or a standalone web application, which offers a visual interface for creating, editing, and testing AI configurations.

Quick Start & Requirements

• Python: pip3 install python-aiconfig
• Node.js: npm install aiconfig or yarn add aiconfig
• Prerequisites: Requires an OpenAI API key (export OPENAI_API_KEY='your-key').
• Editor: VS Code extension or run aiconfig edit --aiconfig-path=<your-config>.json.
• Documentation: Getting Started Tutorial

Highlighted Details

• Model-agnostic and multimodal SDKs supporting text, image, and audio models.
• Supports various models including OpenAI, Gemini, LLaMA, and Hugging Face models.
• Extensible architecture for custom model parsers and callback event handlers.
• Visual editor for rapid prototyping and iteration on prompts and model settings.

Maintenance & Community

The project releases new versions weekly and welcomes contributions. Community engagement is encouraged via Discord (#aiconfig channel) and GitHub issues. A public roadmap is available.

Licensing & Compatibility

The project appears to be licensed under Apache 2.0, allowing for commercial use and integration with closed-source applications.

Limitations & Caveats

While designed to be model-agnostic, out-of-the-box support is limited to specific models; others require custom ModelParser implementation. The project is under active development with weekly releases, suggesting potential for breaking changes.