AIConfig is an open-source framework designed to simplify the development and management of generative AI applications by separating AI logic from application code. The framework allows prompts, model configurations, and parameters to be stored as structured configuration files that can be version controlled and managed independently from the rest of the software system. This approach improves collaboration between developers, prompt engineers, and machine learning practitioners by turning prompt logic into a reusable and editable artifact. AIConfig supports multiple model providers and modalities, enabling developers to experiment with different models without rewriting application logic. The configuration format is JSON-serializable and integrates with tools such as Python and Node SDKs, allowing the same configuration file to be used across multiple environments.
Features
- Configuration-driven framework for managing generative AI prompts and models
- JSON-based format for storing prompts, parameters, and model metadata
- Model-agnostic architecture supporting multiple AI providers and modalities
- Notebook-style editor for experimenting with prompts and model settings
- Python and Node SDKs for integrating AIConfig into applications
- Version-controlled AI artifacts enabling evaluation and reproducibility