Rivet: Visual IDE for building, testing, and debugging AI agent workflows using a node-graph interface by Ironclad.
A visual node-based editor for building AI workflows — drag and drop to create complex AI logic without writing code.
Rivet is a visual AI development IDE that enables teams to design, debug, and deploy complex LLM prompt graphs using a node-based interface, available completely free as an open-source project under the MIT license. It's built for engineering teams, prompt engineers, and AI developers who need to move beyond code-only prompt chains into collaborative, visual agent orchestration.
Developed and battle-tested internally at Ironclad — the leading digital contracting platform that powers legal teams' contract review and obligations workflows — Rivet emerged from Ironclad's own struggles building AI agents programmatically. The platform centers on three pillars: visualize and build (create complex chains for production applications, not just prototypes), debug remotely (observe prompt chain execution in real-time inside your own application via a remote executor), and collaborate (graphs are stored as plain YAML files, making them compatible with Git version control and standard code review workflows). This YAML-based architecture is a deliberate differentiator: unlike SaaS-locked visual builders, Rivet graphs live in your repository alongside your application code.
Based on our analysis of 870+ AI tools in our directory, Rivet occupies a distinct niche as a desktop-first, open-source visual programming environment specifically aimed at production LLM deployments. Production adoption includes Ironclad (for AI contract workflows), Attentive, Bento (for AI-powered product experiences), and AssemblyAI (which integrated its audio transcription and understanding models into the Rivet ecosystem). Compared to cloud-hosted alternatives like LangChain's LangSmith or Flowise, Rivet's desktop-app + SDK architecture prioritizes developer control, data privacy, and tight integration with existing codebases, while trading off the instant-shareability of browser-based tools. With zero licensing cost, real-time debugging, and public endorsements from CTOs at multiple production AI companies, Rivet is particularly suited to mid-size engineering teams shipping agentic features where observability and code review discipline matter.
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Rivet is a standout open-source visual IDE for teams building production LLM agents, offering a unique combination of node-graph editing, real-time remote debugging, and Git-friendly YAML graph storage — all at zero cost under the MIT license. Best suited for engineering teams that want visual prompt chain development with the same code review discipline they apply to application code.
Rivet provides a desktop programming environment where LLM prompt chains are built as connected nodes instead of code. This makes complex agent logic immediately legible to both engineers and non-engineers, and — per Ironclad's CTO Todd Berman — lets teams create complex chains in 'drastically less time than it would take in other environments.'
Rivet's remote executor connects the desktop IDE to a running production application, letting developers watch prompt chain execution live as real traffic flows through. Teddy Coleman, CTO at a mortgage servicing company, credits this visualization with making it 'easy to see what the AI is doing' — a capability he cites as instrumental in launching their virtual mortgage servicing agent.
Every Rivet graph is stored as a plain YAML file rather than opaque database rows in a SaaS backend. This means graphs can be committed to Git, diffed in pull requests, and reviewed with the same tooling your team already uses for application code. It's a deliberate design choice to make prompt engineering a first-class citizen in a normal engineering workflow.
Rivet is released under the permissive MIT license, with full source code available on GitHub. Teams can fork, self-host, audit, or extend the tool without usage fees or vendor approval. Compared to the other visual AI builders in our directory that charge seat-based or usage-based pricing, this is a significant cost and control advantage for enterprise adopters.
A TypeScript/Node.js SDK lets your application load and execute Rivet YAML graphs at runtime, so the same graph the team designs in the IDE is what ships to production. This eliminates the drift between 'prototype prompt' and 'production prompt' that often plagues teams using notebook-based prompt development, and it keeps API keys and data inside your own infrastructure.
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