Yes, I have seen several adjacent approaches. Microsoft Huabu explores spatial interaction around research materials, while LLM Canvas and tldraw’s branching-chat experiments explore visual conversation trees.
Many workflow canvases also use nodes and edges, but they usually represent execution pipelines. The specific interaction I am testing is more narrowly about context: an edge changes what the model receives, while removing it keeps the earlier work visible but excludes it from the next inference.
I would be interested in other projects I may have missed.
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I vibe coded/sketched a similar idea for the obsidian canvas. https://github.com/ff6347/obsidian-canvas-context it is more a research artifact then a real product/tool.
I think I gave up at some point because it does not integrate with my agent workflow. I also try to keep my second brain free of AI generated text.
But it was a nice experiment. The biggest barrier is for me that it happens in a different space than the agents I run and it does not scratch an urgent itch. I think a bidirectional integration is crucial.
It would be wonderful if you included a section like "ThoughtDAG vs X" in the README, where you then compare ThoughtDAG against these other approaches/tools, and explain with some clear concise words how it's different than those. For extra bonus-points, also explicitly list where ThoughtDAG falls short (today?) and compare to them in that manner too :)
Thank you for your suggestion. I've added a "How ThoughtDAG differs" section to the README. Rather than listing specific products one by one, I ultimately chose to compare them based on interaction methods because the functional boundaries of many products are still evolving.
If you have time to take another look, I'd also like to know if the differences are clear and concise enough now.