Hi HN, I built ThoughtDAG around one rule: wires are the context.
Each question and answer is a node. When you ask from a node, only its wired upstream nodes are included in the model request. Delete an edge, regenerate, and that branch leaves the model's actual context, not just the visualization.
The interface is intentionally human-controlled. I'm testing whether explicit context control is useful for long-running research, or whether most people would rather delegate memory selection to retrieval.
It is MIT licensed, local-first, supports Ollama and OpenAI-compatible endpoints, and includes PDF clipping with page provenance.
GitHub: https://github.com/chenxiachan/thoughtdag
I'd especially appreciate criticism of the interaction model and onboarding.
have you seen other people or project on the same idea ? manipulation history and exploration space of LLM seems to be quite important
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.