Editable LLM context

Don’t just read chat history. Edit what the model actually sees.

ThoughtDAG turns every question-and-answer exchange into a node, and every connection into context. Remove one edge, ask the same question again, and that branch is gone from the request.

Open the live demo ↗ Explore the interactive story ↓

The hosted demo presents the interaction model. The local setup adds the full PDF workflow—including clipping passages and figures into source-linked nodes—plus web search and MCP.

THOUGHTDAG PRODUCT STORY 0:32

The graph is not a picture of context. The graph is the context.

ThoughtDAG walks the incoming edges of a node, orders the relevant ancestors, and builds the message sequence sent to the selected model. The structure stays visible, editable, and inspectable.

Branch

Explore another interpretation without overwriting the path that led you here.

Prune

Keep a useful detour on the canvas while excluding it from the next request.

Merge

Bring selected evidence and reasoning paths back together in one answer.

Inspect

Preview what the model will receive before generation. No hidden memory selection.

CHAT HIDES CONTEXT.
THE GRAPH IS THE CONTEXT.
Linear conversation
Editable context graph
Same prompt · different context
AI conversation 87 messages
Compare three research paths.
Start with the first. Its advantage is…
What if the core hypothesis fails?
Consider another explanation…
Also, what should I eat tonight?
There is a new restaurant nearby.

The history is here. Which parts enter the next request?

research-paper.pdfp.7

Results

The effect appears only in the experimental condition.
Selected from the page
Clipped passageresearch-paper.pdf · p.7

The effect appears only in the experimental condition

Source linked · not wired yet
Asked from sourceresearch-paper.pdf · p.7

What does this evidence actually mean?

The source is in context
Unrelated branchdetour

What should I eat tonight?

This history should not enter the research summary.

Still connected
Polluted summary 3 sources

Research summary… also, consider hot pot for dinner.

The prompt stayed the same. Polluted context changed the answer.

Includes unrelated branch
Will send1,284 tokens

Preview what the model will receive

Incoming ancestors:

Research question Evidence A Dinner detour

After deleting the orange edge: −47 tokens

Context diff−47 tok

The dinner detour left context

Same prompt · regenerate
Same promptask again

Give me a bullet-point summary

The words are identical. Only one edge changed.

Reproducible context
Clean answer 2 sources

One: record the database version. Two: use independent reviewers. Three: resolve conflicts with a third reviewer.

The unrelated dinner suggestion is gone.

Answer updated in place
One ruleThoughtDAG

Wires are context.

No hidden memory selector. What the model sees, why, and what was removed stay visible in the graph.

Visible Editable Inspectable
01 · The problem

Chat history is long. Context is still invisible.

The interface shows what was said, not which history enters the next request.

02 · Externalize

Ask from the source. Clip what matters.

Ask from a selected passage, or turn a passage or figure into its own source-linked node. Provenance stays attached; context remains yours to wire.

03 · Inspect

Before sending, inspect what the model will read.

Preview source nodes, order, and token count. Context is no longer a hidden decision.

04 · Edit

Delete one edge. Ask the same question again.

The removed branch really leaves the request. The answer changes with the context.

05 · The protocol

Most canvases organize information. ThoughtDAG edits context.

You decide what enters and leaves. The graph is the context protocol before generation.

1 / 5 Invisible context