Chat interfaces hide the true context that an LLM reads, making it hard to control what information is actually fed to the model. ThoughtDAG tackles this by turning the conversation into an editable graph where each node is a source‑linked passage and each edge represents a logical flow into the next prompt. The graph is the context protocol, so nothing is hidden in a black‑box memory selector.

With LLM interactions, you can clip exactly what matters, preview token counts—1,284 tokens in the example—, and reorder or delete edges before sending. In one demo, removing an unrelated dinner suggestion changed the answer, proving that the context you see is the context the model receives. Because the graph is inspectable, you can run a “regenerate” or “ask again” without accidentally pulling in irrelevant branches.

The system supports 87 messages of dialogue while keeping the context clean. It also offers a simple protocol: record the database version, use independent reviewers, and resolve conflicts with a third reviewer, ensuring reproducible, auditable conversations.

ThoughtDAG’s visual, editable interface empowers developers and researchers to debug LLM outputs, iterate faster, and maintain high‑quality, traceable knowledge bases in complex conversational systems.