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DAG-chat
A visual DAG for AI chats. Branch and merge.
DAG-chat renders LLM conversations as a DAG. Branch from any message, explore tangents, and merge threads back into your flow. The front-end visualizes the topology like a mind map — you see the structure of your thinking, not just a wall of text. Works with any OpenAI-compatible API, runs in the browser, and self-hosts in minutes. I built this because linear chat UIs don't match how my brain works.
Hey PH 👋
Backend engineer here. Built DAG-chat because my brain doesn't think in straight lines — it branches. This is a web UI that renders LLM chats as a DAG: branch anywhere, explore tangents, merge them back. Front-end draws it like a mind map.
Try it:
git clone https://github.com/ZM-BAD/DAG-ch... → add API key → npm run dev
One honest question: After months of using this, I can't tell if the branch/merge flow feels like a superpower or just extra complexity. Would love your unfiltered take.
Demo video in the gallery if you want a preview before cloning.
About DAG-chat on Product Hunt
“A visual DAG for AI chats. Branch and merge.”
DAG-chat was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #142 on the daily leaderboard. DAG-chat renders LLM conversations as a DAG. Branch from any message, explore tangents, and merge threads back into your flow. The front-end visualizes the topology like a mind map — you see the structure of your thinking, not just a wall of text. Works with any OpenAI-compatible API, runs in the browser, and self-hosts in minutes. I built this because linear chat UIs don't match how my brain works.
On the analytics side, DAG-chat competes within Open Source, User Experience and Artificial Intelligence — topics that collectively have 900.7k followers on Product Hunt. The dashboard above tracks how DAG-chat performed against the three products that launched closest to it on the same day.
Who hunted DAG-chat?
DAG-chat was hunted by 周铭. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of DAG-chat including community comment highlights and product details, visit the product overview.
周铭