Your thinking deserves a map. An infinite canvas where LLM conversations grow into an editable thought graph.
no install, no signup; the example canvas needs no key
中文 · Quick start · More capabilities · Models & subscriptions · Cost & privacy
Wires are the context. What the model sees is exactly what wires into the node. Editing the graph edits the model's memory.
One principle behind every gesture: the human in the loop, the model on the wires. No autonomous agent redraws your graph.
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The model sees only what wires in. Delete the noise edge, ask again, and the same prompt returns a clean answer. Reproduce it in chapter ③ of the example canvas. |
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Select a passage, ask right there. The answer lands on the canvas with its page number, and the p.N chip jumps back to the page. Finish the paper, and the map is drawn. |
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Merge nodes into one higher conclusion; weave highlights into a summary. The graph folds inward instead of sprawling. The human refines in the loop. |
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Highlights are your judgment, not the model's. Check any subset and weave one passage where every sentence traces back. |
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Full cards, takeaway plaques, an icon skeleton: three semantic tiers, every step badged ✕ ⚖ ↩ ?. The detours are part of the map. |
# Online: app.thoughtdag.workers.dev (example canvas needs no key)
# Local:
npm install
npm run server # LLM proxy :3001
npm run dev # → localhost:5173
# No .env? Connect any OpenAI-compatible endpoint inside the appThe first launch opens a seeded example canvas: four chapters around one everyday question (why saved articles stay unread), including a reading loop with a real embedded PDF. Environment variables, free keys and configuration details → docs/setup.md
| Capability | What it does |
|---|---|
| 📤 Read-only share | One link carries the whole graph: no account, no server storage |
| 🧭 Staleness & replay | Upstream edits mark the answers they invalidate; replay in dependency order, token estimate first |
| 🧪 Paradigms | Human-machine workflows saved as files; change the input, replay the experiment |
| 🔌 Any model | Per-node pins that follow the line; image requests reroute to vision models automatically |
| 🔒 Local-first | Automatic folder backup writes real files; point it at a synced folder for cross-device |
Full feature list (60+, grouped by area) → docs/features.md
Zhipu · Qwen · OpenAI · Anthropic · Google · DeepSeek · Kimi · OpenRouter · Ollama, or any OpenAI-compatible endpoint. Requests with images reroute to vision models automatically. Environment variables and default models → docs/setup.md
Already paying for a subscription? It plugs in. A ChatGPT plan connects through a one-command local bridge (with ThoughtDAG running locally). GLM Coding and Kimi Code plans issue real API keys: pick the preset, paste the key, done. Setup for all three → docs/setup.md#subscriptions
- The free model tier covers every feature; a local Ollama runs fully offline
- On the hosted demo, model traffic runs browser-direct: keys never touch the server
- PDFs never leave your machine; only extracted text travels when you ask
- The backup format stays backward compatible; Markdown export is the permanent escape hatch