jintra-aether 1.0.0

A lightweight, extensible framework for structured content authoring and validation with AI assistance

Aether Framework

Human-readable, AI-interpretable, machine-consumable.

Aether is a lightweight, schema-driven framework for structured content authoring and validation with AI assistance. It enables humans and AI to collaborate on creating, validating, and distributing structured content.

Features

• ✅ Schema Validation - JSON Schema Draft 7 with custom Aether extensions (media, markdown)
• ✅ Entity Collections - Hierarchical content organization with automatic reference resolution
• ✅ Bundle Distribution - Package complete projects as portable .aetherpack archives
• ✅ AI Integration - Native support for Cursor AI and Model Context Protocol (MCP)
• ✅ CLI & Python API - Use from command line or integrate into Python applications
• ✅ Comprehensive - Project scaffolding, introspection, validation context, and more

Installation

pip install aether-py

Quick Start

Define a Schema

Create blog.aether:

id: blog
spec_version: "1.0.0"
version: "1.0.0"
title: Blog

structure:
  posts:
    type: array
    items:
      $ref: "#/objects/Post"

objects:
  Post:
    type: object
    properties:
      title: { type: string }
      content: { type: markdown }
      author: { type: string }
      published: { type: boolean }

Create Content

# Create project structure
aether scaffold blog.aether my-blog --examples

# Validate project
aether validate-project my-blog

# Bundle for distribution
aether bundle my-blog -o blog.aetherpack

Use in Python

import aether

# Load and validate
schema = aether.load_schema("blog.aether")
project = aether.open_bundle("blog.aetherpack")
aether.validate_project(project, schema)

# Access resolved content
for post in project['posts']:
    print(f"Title: {post['title']}")
    print(f"Author: {post['author']}")

Use Cases

📚 Documentation & Reports

• Technical documentation with automatic cross-references
• Corporate reports with validated data structures
• Static site generators with schema-driven content

🎨 Interactive Digital Experiences

• Educational simulations with validated entity systems
• Narrative platforms with branching storylines
• Interactive media with decision trees

🧠 Knowledge Management

• Portable content packages for AI agents
• Validated knowledge bases
• Schema-driven data distribution

Documentation

• 📖 Getting Started
• 📘 Concepts - Schemas, entities, collections
• 🔧 CLI Reference - Command-line tools
• 🐍 Python API - Library reference
• 🤖 AI Integration - Cursor AI & MCP
• 📄 Whitepaper - Vision and architecture

Examples

Check out the examples/ directory for complete working projects:

• aether-docs - Self-documenting: Aether's own documentation as an Aether project

Development

# Clone repository
git clone https://github.com/jintra-jayce/aether.git
cd aether

# Install dependencies
pip install -e .

# Run tests
pytest

VS Code Extension

For the best development experience, install aether-vscode:

• Real-time schema validation
• IntelliSense for entity references
• AI-powered content assistance
• Visual project navigation

License

Functional Source License, Version 1.1, MIT Future License (FSL-1.1-MIT)

• Free to use for any purpose except creating competing products
• Automatically converts to MIT License after 2 years (January 1, 2027)
• See LICENSE for full terms

Contributing

Contributions welcome! Please read CONTRIBUTING.md before submitting pull requests.

Contact

• Issues: GitHub Issues
• Email: [e-mail]
• License Questions: [e-mail]

Acknowledgments

Copyright © 2025 Jintra Software Solutions LLC