SANE - Smart AI Note Evolution

by Adam Chen
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New Plugin

Description

This plugin has not been manually reviewed by Obsidian staff. Evolve your notes intelligently with AI. When you add/edit notes, SANE finds the 3 most relevant notes and enhances them with smart tags, keywords, links, and summaries.

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Latest Version

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Changelog

README file from

Github

SANE — Smart AI Note Evolution

Official Website | Full Documentation

An Obsidian plugin that automatically enhances your notes using AI. When you add or edit a note, SANE finds the most relevant notes in your vault and enriches them with tags, keywords, links, and summaries via YAML frontmatter.

Features

  • Multiple AI providers — OpenAI, Google AI, Grok, Azure OpenAI, and local LLMs (Ollama)
  • Semantic similarity — finds related notes using embeddings, not keyword matching
  • Flexible triggers — process immediately, after a delay, on a schedule, or manually
  • Targeted scope — limit processing to a specific folder
  • Cost controls — daily budget limits and real-time cost tracking
  • Reversible — all generated fields are prefixed with sane_ and can be removed at any time

Quick Start

  1. Install — search for "SANE" in Obsidian Community Plugins (or install manually from GitHub releases)
  2. Configure — choose an AI provider and add your API key in Settings > SANE
  3. Set scope — specify a target folder to limit which notes are processed (recommended)
  4. Initialize — run "Initialize all notes" to process your existing notes
  5. Write — SANE processes notes automatically as you add or edit them

Example Output

SANE adds frontmatter to the top of processed notes:

sane_tags: ["machine-learning", "neural-networks"]
sane_keywords: ["backpropagation", "training data"]
sane_links: ["Introduction to AI", "Neural Network Basics"]
sane_summary: "A guide to understanding neural networks and their applications."
sane_version: "1.0"
created_at: "2025/5/5 19:59:11"
modified_at: "2025/6/29 12:15:12"

Your original note content is never modified.

Documentation

Full usage documentation, configuration reference, and local LLM setup guide are available at the project documentation site.

Support

License

MIT