WorkflowsApr 7, 2026·3 min read

Rivet — Visual AI Prompt Workflow IDE

Visual IDE for designing and debugging AI prompt chains. Drag-and-drop nodes for LLM calls, conditionals, loops, and data transforms with real-time execution preview.

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Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

  1. Download from rivet.ironcladapp.com
  2. Open the app and create a new project
  3. Drag a "Chat" node onto the canvas and connect to "Text" output
  4. Click "Run" to execute

What is Rivet?

Rivet is a visual IDE for designing, debugging, and testing AI prompt chains. Instead of writing prompt code, you build workflows by connecting nodes — LLM calls, conditionals, loops, data transforms, and subgraphs — with a real-time execution preview that shows intermediate results at every step.

Answer-Ready: Rivet is a visual IDE for AI prompt chain development by Ironclad. Build, debug, and test LLM workflows with drag-and-drop nodes, real-time execution preview, and team collaboration. Open-source with 3k+ GitHub stars.

Best for: Teams designing complex AI prompt chains who need visual debugging. Works with: OpenAI, Anthropic, Google, any OpenAI-compatible API. Setup time: Under 5 minutes.

Core Features

1. Visual Node Editor

[User Input] → [Chat Node (Claude)] → [If/Else] → [Output A]
                                            ↓
                                        [Output B]

Node types:

  • Chat: LLM call with model selection
  • Text: Static or template text
  • If/Else: Conditional branching
  • Loop: Iterate over arrays
  • Code: Custom JavaScript
  • Subgraph: Reusable sub-workflows
  • Extract: Parse JSON/regex from LLM output

2. Real-Time Debugging

Click "Run" and see results at every node:

Node 1 (Input): "Summarize this article about AI"
Node 2 (Chat):  "This article discusses three key trends..."
Node 3 (If):    condition=true → taking branch A
Node 4 (Output): "Summary: This article discusses..."

3. Prompt Versioning

  • Save graph versions with descriptions
  • Compare outputs between versions
  • Roll back to any previous version

4. Team Collaboration

  • Share graphs as .rivet-project files
  • Export as TypeScript for production integration
  • Git-friendly project format

5. TypeScript Integration

import { runGraph } from '@ironclad/rivet-node';

const outputs = await runGraph(project, {
  graph: 'main',
  inputs: { userMessage: 'Hello!' },
  context: {},
  openAiKey: process.env.OPENAI_API_KEY,
});

console.log(outputs.response);

6. Built-In Nodes

Category Nodes
AI Chat, Text Completion, Embeddings
Logic If/Else, Switch, Loop, Match
Data JSON Parse, Regex, Split, Join
I/O User Input, HTTP Request, Read File
Flow Subgraph, Delay, Race, Parallel

Use Cases

Use Case How
Prompt chaining Multi-step LLM workflows
A/B testing Compare prompt variants visually
Guardrails Add validation nodes between LLM calls
RAG prototyping Build retrieval + generation pipelines
Team review Share visual graphs for prompt review

FAQ

Q: Is Rivet free? A: Yes, open-source under MIT license. Desktop app is free.

Q: Can I use it with Claude? A: Yes, Anthropic Claude is fully supported as a chat node provider.

Q: How does it compare to Langflow? A: Rivet focuses on prompt design and debugging with fine-grained node control. Langflow is more about assembling LangChain components.

🙏

Source & Thanks

Created by Ironclad. Licensed under MIT.

Ironclad/rivet — 3k+ stars

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