ThoughtDAG turns LLM chats into editable graph maps

ThoughtDAG is a web application that reimagines LLM conversations as editable graphs. Instead of a linear chat thread, users work on an infinite canvas where nodes represent ideas or answers, and wires (edges) represent the context flowing from one node into another. The foundational rule is simple: the model sees only what wires into the node it is processing. This means users can delete a wire, ask the same question again, and receive a different answer because the model's input has changed—making reasoning transparent and reproducible.

The tool includes several workflows. Users can select a passage from a PDF, ask a question right there, and receive an answer that lands on the canvas with a page-number badge; clicking that badge jumps back to the source. As users read a paper, they can build a map of their thinking in parallel. They can also merge nodes into one higher-level conclusion, compress sprawling graphs by weaving highlights into a summary, and view the graph at three semantic tiers: full cards, takeaway plaques, and an icon skeleton. Every step is badged with symbols (✕ for deletion, ⚖ for balance, ↩ for redo, ? for question) to indicate operations and dependencies.

ThoughtDAG supports multiple AI models—OpenAI, Anthropic, Google, DeepSeek, Qwen, Zhipu, Kimi, OpenRouter, Ollama, and any OpenAI-compatible endpoint. Per-node model pins allow users to select different models for different nodes; image requests automatically route to vision models. The application is local-first: users can enable automatic folder backup to write graphs as real files, and pointing it at a synced folder enables cross-device access. PDFs never leave the user's machine; only extracted text travels when the user asks a question. The tool offers read-only sharing—one link carries the entire graph with no account or server storage required—and backward-compatible exports to Markdown for permanent portability.

Access is free: the hosted demo at app.thoughtdag.workers.dev requires no signup or API key for the example canvas, and local installation via npm is available. The free model tier covers every feature, and users can run Ollama locally for fully offline operation. Model traffic on the hosted demo runs browser-direct, so keys never touch the server. A full feature list exceeds 60 capabilities, grouped by functional area and documented in the project's setup and features guides.

ThoughtDAG addresses a gap in how LLM conversations are typically managed: chat interfaces are linear and ephemeral, making it difficult to inspect, verify, or systematically refine reasoning. By representing conversations as directed acyclic graphs, the tool makes the flow of context explicit and editable. The core principle—"wires are the context"—means the human retains full visibility and control over what information the model receives at each step. This design philosophy applies across features: merging nodes represents human judgment about which ideas compress into a higher-level insight, weaving highlights into cited prose ensures every sentence traces back to the user's own marked passages, not the model's selection, and replaying upstream edits with token estimates lets users reason about the cost and validity of changes.

The tool's support for reading PDFs directly into maps and its local-first architecture (automatic folder backup, PDFs never leaving the user's machine) suggest an intent to keep knowledge work grounded in primary sources and user control. The read-only share feature enables collaboration without requiring accounts or server-side storage, and backward-compatible export to Markdown provides an escape hatch independent of the ThoughtDAG platform itself.

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