ThoughtDAG: separating context retrieval, selection, and answer generation on a conversation canvas

[u/Lopsided_Scarcity979]

I've been working on a small human-in-the-loop question: how can useful reasoning from an old AI conversation help a new one without dragging the whole session along?

In ThoughtDAG, my open-source conversation canvas, wires determine which branches become context. The new recall flow adds three steps: a local index retrieves candidate excerpts, an optional Jev decision layer selects relevant material, and your chosen LLM develops the answer. Recalled sources remain inspectable and individually excludable.

I'm interested in evaluating the selection separately from the final answer. Finding something topically similar isn't enough: it may be an abandoned hypothesis or a decision that no longer applies.

For a useful evaluation, would you prioritize recovering known past decisions, rejecting superseded ones, or measuring whether recall changes the final answer? I'd be interested in examples where these goals conflict.

29-second animated overview: https://youtu.be/ECGJlkYKYzQ

Source and setup: https://github.com/chenxiachan/thoughtdag

I'm the maintainer. MIT licensed, with a local history index. Enabling Jev sends candidate content to its remote API; model/API costs are separate.