A local, privacy-first AI agent for Obsidian powered by Ollama. Chat, write notes, search your vault, summarize videos and articles, and digitize handwritten notes — everything runs on your machine. No cloud accounts, no API keys, no telemetry. Completely free and open source.
| Command | What it does |
|---|---|
/write <topic> |
Generate a structured note with AI-suggested filename, tags, and body |
/find <query> |
Natural-language search across your vault |
/summarize <url> |
Summarize a YouTube video or web article |
/clip <url> |
Fetch, summarize, and save a URL to your Clips folder |
/clip long <url> |
Clip with detailed notes — ideal for long commentaries or reads |
/clip learn <url> |
Clip with detailed notes plus a study guide popup — ideal for lectures |
/read |
Summarize or ask a question about the active note |
/handwriting |
OCR a photo of handwritten notes and save as a vault note |
| Free chat | Stream a conversation with any Ollama model |
All commands are also available from the Command Palette (Cmd/Ctrl+P).
- Ollama running locally (
ollama serve) - At least one model pulled, e.g.
ollama pull gemma3:4b - Desktop only (uses Node.js child_process for the Vizier server)
- Install Ollama and start it:
ollama serve - Pull a model:
ollama pull gemma3:4b(or any model you prefer) - Set the model name in Settings → Vizier → Default model
These features require a small local Python server (vizier_server.py) that runs alongside Obsidian.
One-click setup: Open the Command Palette and run "Vizier: Setup / start Vizier server". The modal will detect Python 3, create a virtual environment, install dependencies, and start the server automatically.
On first use of /handwriting, you will be prompted to download the OCR model files (~1.5 GB including PyTorch). This is a one-time download.
Manual setup (fallback):
cd <vault>/.obsidian/plugins/vizier
python3 -m venv .venv
.venv/bin/pip install youtube-transcript-api
.venv/bin/python3 vizier_server.pyThe server runs on http://127.0.0.1:11435. You can change this in settings
Articles are fetched via Jina AI Reader (r.jina.ai), a free public service that returns clean markdown from any URL. No API key required. The only data sent is the URL you provide.
| Setting | Default | Description |
|---|---|---|
| Ollama URL | http://localhost:11434 |
Base URL of your Ollama instance |
| Default model | gemma3:4b |
Model used for all AI tasks |
| Vizier server URL | http://127.0.0.1:11435 |
URL of the local Vizier server |
| Clips folder | Clips |
Vault folder where /clip saves notes |
| AI notes folder | (empty) | Vault folder where /write saves notes (empty = vault root) |
| Handwritten notes folder | (empty) | Vault folder where /handwriting saves notes |
git clone https://github.com/your-username/vizier
cd vizier
npm install --legacy-peer-deps
npm run build # production
npm run dev # watch modeRelease artifacts: main.js, manifest.json, styles.css.
Do I need an internet connection?
Only for /summarize//clip (fetches the article/transcript) and article fetching via Jina. All AI inference runs fully offline via Ollama.
What model should I use?
gemma3:4b is a good default — fast, capable, and runs on most machines. For better quality on longer documents try gemma3:12b or mistral. Any Ollama-compatible model works.
The Vizier server won't start — what do I check?
Make sure Python 3 is installed (python3 --version). If setup fails mid-way, delete the .venv folder inside the plugin directory and run "Vizier: Setup / start Vizier server" again.
OCR results are inaccurate — can I improve them?
Results depend on image quality. Ensure the photo is well-lit with minimal skew. After OCR, Vizier automatically runs a cleanup pass through your Ollama model to fix spacing and punctuation.
Port 11435 is already in use.
Change the Vizier server URL in settings to use a different port (e.g. http://127.0.0.1:11436), then restart the server.
- All AI inference runs locally via Ollama — nothing leaves your machine.
- The only outbound requests are to
r.jina.aiwhen fetching articles (the URL you provide is sent). - YouTube transcripts are fetched locally via
youtube-transcript-api— no third-party service. - No analytics, no telemetry, no cloud sync.