Developer Builds Open-Source Tool to Make LLM Conversation Context Manually Editable

A developer has created ThoughtDAG, an open-source prototype that reimagines how users interact with large language models by representing conversations as a directed acyclic graph on an infinite canvas. Each question-and-answer exchange becomes a node, and the edges between nodes directly determine which prior exchanges are included in the model's next request. Users can branch, merge, or prune conversation paths, giving them explicit control over what the model 'remembers' at any point. The tool runs entirely in the browser using IndexedDB, requires no account or hosted database, and supports both local inference via Ollama and OpenAI-compatible endpoints. Built with React, TypeScript, and the Vercel AI SDK under an MIT license, the project raises the open question of whether users prefer manual context control over automated memory management by agents or retrieval systems.