I built an open-source Ollama canvas where the wires are the actual context

Most graph-based LLM interfaces use a canvas as a visual layer over what is still a linear chat. I wanted the graph itself to determine what Ollama receives. ThoughtDAG has one rule: wires are the context. Each node is one question/answer exchange. When you ask from a node, ThoughtDAG walks only its upstream edges and builds the model input from those ancestors. For example, if a research branch and an unrelated dinner branch both feed into a summary node, both appear in the answer. Delete the dinner edge, regenerate the same prompt, and that context disappears. So this is not an autonomous agent or a visual workflow runner. The graph is a human-controlled way to inspect, branch, merge, and prune an LLM's actual memory. It currently supports: - Ollama for fully local/offline inference - arbitrary OpenAI-compatible endpoints - branching and merging conversation paths - local PDF reading with page-linked citations - per-node model selection - browser-local IndexedDB storage - local file backup plus JSON and Markdown export There are no accounts, telemetry, or hosted databases. It is MIT licensed. It is still an early prototype and currently runs from source; Docker packaging is not ready yet. Repo: https://github.com/chenxiachan/thoughtdag What I would especially like to learn from local-model users: does explicit manual context control become more useful with smaller context windows, or do you prefer automatic memory/RAG? Which Ollama model and context length would you test first? submitted by /u/Lopsided_Scarcity979 [link] [comments]

Related

• Give any Ollama-compatible client session memory + a shared knowledge wiki by swapping the chat URL
• Kwipu, a fully-local MCP server that turns your Obsidian/Markdown notes into a queryable knowledge graph (runs on Ollama)
• Fabritorio: a local-first visual canvas for building and running AI agents against your own models (MIT, self-hosted)

Source: r/ollama | 2026-07-26