Instead of a vertical scroll, you get a tree-based workflow. Every prompt and response acts as a node that you can branch out from. If you have a prompt that gets you 80% of the way there, you don't have to start over; you just create a new branch from that specific node to iterate on the remaining 20%. This spatial approach makes it much easier to visualize the evolution of a thought process or a piece of code.
How the canvas workflow actually functions #
The core concept here is non-linear progression. In a typical chat, if you ask an LLM to "write a Python script" and then "add error handling," you are locked into that specific sequence. With a canvas-based AI workflow, you can do something much more powerful:
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Branching: You can take a single successful response and split it into three different directions simultaneously to see which path yields the best result.
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Model Comparison: Since you can bring your own API keys, you aren't stuck with one provider. You can actually run different models against the same prompt node to compare outputs side-by-side in the same visual space.
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Regeneration: If a specific response is subpar, you can regenerate just that node without disrupting the rest of your logic tree.
Technical setup and provider support #
One thing I appreciate is that this isn't another subscription-based wrapper that eats your margin. It's a "bring your own keys" (BYOK) model, which is essential for power users who want to control their own costs and latency. The tool currently supports four major providers:
For anyone doing heavy-duty prompt engineering or building complex LLM agents, the ability to map out the decision tree visually is a massive advantage. It turns a conversation into a structured map of logic. If you are tired of the "scroll and pray" method of interacting with models, exploring a spatial UI might be the logical next step for your development process. Goodfire just released a tool to peek inside the AI black box 1h ago
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a library of Claude prompt techniques, with plenty of directly applicable cases.