Rivet is an open-source visual AI programming environment and TypeScript library by Ironclad for creating, debugging, and deploying AI agents using a graph-based interface where complex LLM prompt chains are designed as visual node networks. It solves the challenge of making AI agent development accessible and debuggable by providing a visual IDE where developers can see the flow of data through every node in real-time, inspect inputs and outputs at each step, and iterate on agent logic without writing boilerplate code. Rivet transforms AI agent development from a text-editing exercise into a visual engineering discipline where complex interactions between prompts, tools, and data sources become intuitive to understand and modify.
Rivet provides a node-based editor for building prompt chains and agent workflows, real-time visualization of data flow with input and output inspection at every node, team collaboration features for designing, debugging, and sharing AI agent graphs, and a TypeScript library (Rivet Core) for running graphs in production applications. The visual approach makes it easy to understand complex multi-step agent workflows, identify bottlenecks, debug failures, and optimize prompt sequences without diving into code. Rivet supports integration with multiple LLM providers and can call into application code and vice versa through its extensible plugin architecture.
Rivet targets AI engineers, product teams, and enterprises building complex LLM-powered applications who need a visual development environment for designing, testing, and iterating on AI agent workflows. Ironclad uses Rivet internally for their Contract AI product, demonstrating its production readiness for enterprise legal document processing at scale. Rivet is particularly valuable for teams where multiple stakeholders need to understand and contribute to AI agent design, as the visual graph representation makes agent logic transparent and accessible to both technical and non-technical team members, enabling faster iteration and more effective collaboration.
