chenxiachan / thoughtdag

# 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.
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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.
 [code] 
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.
In the DeepSeek Harness desktop app (0.2.0-rc.2 or later): open **Plugins**, search for `dsh-thoughtdag`, install it, and restart Harness. A release published within the last 24 hours needs its version named, `dsh-thoughtdag@ ` (pnpm holds back such versions by default). For the web profile, from the command line:
 [code] 
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 →
With the web switch on, the harness's own models search through Harness's web search. The search service and its billing are set in Harness under **Plugins › Built-in