Install and quick start

ThoughtDAG turns LLM conversations into an editable graph. Each node contains a question and answer; incoming wires determine what the model sees next.

Desktop app recommended

The desktop app provides the complete experience: local session discovery, Session Atlas, automatic folder backup, bundled search and tools, and desktop updates.

Download ThoughtDAG · All releases

Install the desktop app

macOS

Install with Homebrew:

brew install --cask thoughtdag

The macOS build is signed and notarized. You can also download it from the official download page.

Windows and Linux

Choose the correct installer on the download page. Windows builds are not signed yet, so SmartScreen may show a warning.

See the interface in under a minute

The recording below shows the actual app: asking from a document, removing an unwanted context path, zooming from full answers to the map, and opening backup/export controls.

First launch: choose where to start

The first screen offers four entry points. You can inspect examples and existing work before deciding whether to connect a model.

The red labels and dashed leaders are documentation overlays, not part of the app. Open the unannotated screenshot.

1. Ask a question: after connecting a model, start the graph from a real question.
2. Add material: drop in a PDF, image, or another file. Opening and reading need no model; asking and recognition do.
3. Open local agent sessions: enter Session Atlas. Viewing a mirror graph needs no model.
4. Connect a model: enable answer generation, image recognition, and condensing.

If you only want to learn the interface, Load example canvas at the bottom of the page needs no setup.

Connect your first model

When you need generation, choose Connect a model. One connection is enough to begin; you do not need to configure every option.

The red callouts are documentation overlays. Open the unannotated screenshot.

• Fastest path: use the one-time authorization in the interface.
• Existing API key: choose its provider preset and paste the key.
• Local model: choose Ollama, or use a custom endpoint.

The flow is: choose a connection → authorize or enter a key → fetch models → select models → save. The window states where connection information is kept; see Connect a model for providers, subscriptions, local models, and developer configuration.

Continue

1. Load the example canvas or ask your first real question.
2. Open the interface overview to learn the toolbar, nodes, wires, and floating panel.
3. To continue local agent work, see source and handoff setup.

Browse all guides by interface area →

Other ways to run ThoughtDAG

Browser demo

The hosted demo is useful for a quick look, but it is a feature subset and cannot access local desktop sessions or folder backup.

Run from source

npm install
npm run server
npm run dev

Open http://localhost:5173. You can configure an OpenAI-compatible endpoint inside the app, so an .env file is optional.