Org-SuperTag enhances Org-mode by transforming its traditional tagging system into an intelligent knowledge management engine. It allows tags to carry structured data, automate tasks, and uncover connections through AI, benefiting users who want to supercharge their note-taking and knowledge management workflows within Emacs.

How It Works

Org-SuperTag redefines tags as data tables, nodes as data records with unique IDs, and queries as data analysis tools. Each tag can have defined fields and types, while each Org heading becomes a data record. This structure enables automated actions based on tags and supports complex relational queries and visualizations, offering a more dynamic and interconnected knowledge base than standard Org-mode.

Quick Start & Requirements

1. Installation: Clone the repository (git clone https://github.com/yibie/org-supertag.git ~/org-supertag). Set up the Python backend using cd ~/org-supertag/simtag && sh ./setup_uv.sh. Configure Emacs Lisp as shown in the README, ensuring org-supertag-bridge-python-command points to the correct Python executable within the cloned repository's .venv.
2. Prerequisites: Emacs, Git, Python. AI features utilize Ollama by default.
3. Setup: Requires cloning the repository and configuring Emacs Lisp.

Highlighted Details

• Tags function as structured data tables with definable fields and types.
• Nodes (Org headings) automatically receive unique IDs and structured storage.
• Supports AI-driven features like tag suggestions, node conversations, and natural language queries.
• Includes diverse views: Kanban, Discover, AI Chat, and interactive Query Buffer.

Maintenance & Community

• Documentation: Detailed documentation is available.
• Community: Discussions can be found via Community Discussions link.
• Issue Feedback: Users can provide feedback through the Issue Feedback link.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

• The AI Workflow System is noted as "Not yet practical."
• Some previously complex features have been simplified or removed for a more focused user experience.
• The README does not specify a license, which could be a concern for commercial adoption.