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Alex Spinov
Alex Spinov

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Rivet Has a Free API: A Visual Programming IDE for Building AI Agent Graphs

Rivet is a visual programming environment for building, testing, and debugging AI agent workflows. You create AI pipelines by connecting nodes in a graph — like Node-RED but for LLM applications.

Why Rivet Matters

Building AI agents in code means blind debugging: you cannot see intermediate outputs, token usage, or where the pipeline fails. Rivet visualizes every step.

What you get for free:

  • Visual graph editor for AI pipelines
  • Real-time execution visualization
  • Support for OpenAI, Anthropic, and local models
  • Subgraphs (reusable AI components)
  • Built-in prompt testing and evaluation
  • TypeScript SDK for production deployment
  • Node types: LLM, text, code, conditions, loops

Quick Start

# Install the desktop app from rivet.ironcladapp.com
# Or use the TypeScript SDK
npm install @ironclad/rivet-node
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Visual Graph Components

[User Input] → [Prompt Template] → [Claude/GPT] → [Extract JSON] → [Output]
                     ↑                    |
              [System Prompt]      [If Error] → [Retry with different prompt]
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Each node is configurable:

  • Chat Node: Call any LLM (GPT-4, Claude, Llama)
  • Text Node: Template with variables
  • Code Node: Run JavaScript for transformations
  • If/Else Node: Conditional branching
  • Loop Node: Iterate over arrays
  • Subgraph Node: Reuse entire pipelines

TypeScript SDK (Production)

import { runGraph, loadProjectFromFile } from "@ironclad/rivet-node";

// Load your visual graph
const project = await loadProjectFromFile("my-agent.rivet-project");

// Run it programmatically
const result = await runGraph(project, {
  graph: "Main",
  inputs: {
    userMessage: { type: "string", value: "Analyze this data..." },
  },
  context: {},
  openAiKey: process.env.OPENAI_API_KEY,
});

console.log(result.output.value);
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Node Types

Node Purpose
Chat Call LLM (GPT, Claude, Llama)
Text Template strings with variables
Prompt System/user/assistant messages
Extract JSON Parse structured output
Code Run JavaScript
If/Else Conditional logic
Loop Iterate over arrays
Subgraph Reuse pipelines
HTTP Call external APIs
Vector Store RAG retrieval

Links


Building AI agent pipelines? Check out my developer tools on Apify or email spinov001@gmail.com for custom solutions.

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