thoughtdag

项目详情摘要

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 pa