How To Use Rivet Ai
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What it is
Rivet treats LLM workflows the way Unreal treats game logic: as a graph of nodes you manipulate visually. You wire prompts, branching, loops, subgraphs, and tool calls on a canvas, debug each execution step by step, and then ship the graph as a file your production app loads at runtime.
Install / set up
Open Rivet, create a new project, and drop a Chat node onto the canvas. Wire a Text node into its prompt input, put “Say hello” in the text, and hit Run. The execution log on the right shows each node’s input, output, and token usage.
First run
Rivet is JS/TS first. The Node runtime is solid; Python users have to call the headless runner as a subprocess or reimplement the graph, because there is no official Python SDK. If your backend is Python-heavy, weigh this before adopting.
Everyday workflows
Graph files merge poorly in git. Two people editing the same graph will produce conflicts in the JSON that are hard to resolve by hand. Coordinate on who edits which graph, lean on subgraphs to split ownership, and review diffs in Rivet itself rather than in GitHub’s line-by-line view.
Gotchas and tips
JS/TS developers who want a designer-grade IDE for prompts, and teams where a PM or designer co-authors logic with an engineer. If you’ve outgrown plain LangChain code but don’t want a hosted platform, Rivet is the local-first sweet spot.