ThoughtDAG MCP server

ThoughtDAG

AI conversations that branch on an infinite canvas.

Each exchange becomes a node. Wires are the context. Explore a side question, connect useful paths, and choose what the model sees next.

Download · Website · Docs · 中文

0.5 update · CLI · Harness · Desktop · How it works · How it differs · Research

New in 0.5 · ThoughtDAG × Jev

Bring relevant past conversations into the question you are asking now.

• Find earlier work. The local index searches supported agent sessions and ThoughtDAG canvases. Topic dossiers collect decisions and open questions with links back to their sources.
• Select what belongs. The optional Jev decision layer helps identify topics and rank relevant excerpts. Your chosen language model develops the answer.
• Check what comes back. With recall enabled, the context panel lists the dossiers and excerpts added to a request. Inspect their sources or exclude individual items before continuing.

In a small relevance-selection pilot, Jev's median was 391 ms versus 24,813 ms for our GLM adapter. These are selection-stage timings, not end-to-end search or answer times.

What the timing measures

Six runs per engine over the same 14 synthetic excerpts. Median selection latency: 391 ms for Jev-1.13 and 24,813 ms for the GLM-5.3-Flash adapter with default reasoning. These are different inference paths, not a controlled ranking of model speed. Retrieval and answer generation are excluded; this does not measure whole-product speed or accuracy gains.

Without a decision model, recall falls back to rules. The System 1 / System 2-style split describes software roles here: quick relevance decisions, then answer and dossier generation. It is not a claim about human cognition.

Set up history and recall · Configure Jev

Find past context from the command line

Remember a file, a phrase or a URL, but not the session? Search local conversations and jump to the matching turn, without opening the desktop app.

npx thoughtdag why src/lib/api.ts           # conversations about this file
npx thoughtdag find "a phrase you remember" # matching conversation turns
npx thoughtdag topics                       # topics in your local index

For regular use: npm install -g thoughtdag. Run thoughtdag setup mcp to expose read-only history tools to your agent. Retrieve the relevant turns rather than replaying a whole session. CLI guide →

Inside DeepSeek Harness

Switch between chat and ThoughtDAG's graph inside the harness. Choose the context on the canvas; the harness runs the next turn.

dsh plugin --profile web add dsh-thoughtdag
dsh web

The plugin bundles the canvas and memory layer. Requires Node 22.19+ (22.x) or 24+, and DeepSeek Harness 0.1.2-rc.1 or later. Plugin guide →

Shortened. The full README is on GitHub.