Why it matters
- Step-through debugging at the node level is unique among visual LLM builders — identify exactly which prompt or transformation causes failures without guessing.
- Production-grade origin: Ironclad built Rivet to power their own AI contract analysis product — it's not a toy, it handles real enterprise workloads.
- TypeScript export means visual prototypes can become production code — no rewriting from visual to code.
- Open source and desktop-based means no data leaves your machine during development — important for sensitive document processing workflows.
Key capabilities
- Visual workflow editor: Node-based canvas for connecting LLM calls, prompts, and data transformations.
- Step-through debugging: Pause execution at any node, inspect inputs/outputs, understand failures precisely.
- Multi-model support: OpenAI (GPT-4o), Anthropic (Claude), and other providers via API key.
- Prompt templates: Parameterized prompts with variable injection and Handlebars templating.
- Conditional branching: If/else logic based on LLM output content or classification.
- Parallel execution: Run multiple nodes simultaneously for faster pipeline execution.
- Loop nodes: Iterative processing (map over a list, retry until success).
- TypeScript export: Export workflows to TypeScript code for production deployment.
- Collaborative graphs: Share
.rivet-projectfiles with team members.
Technical notes
- License: MIT (open source)
- GitHub: github.com/Ironclad/rivet (3K+ stars)
- Platform: Desktop app; macOS, Windows, Linux
- LLMs: OpenAI, Anthropic, and extensible to others
- Export: TypeScript; JavaScript; embedded SDK
- Install: Download from rivet.ironcladapp.com or GitHub releases
- Created by: Ironclad (AI contract intelligence platform)
Ideal for
- Engineers building complex LLM pipelines who need visual debugging to understand multi-step workflow failures.
- Teams prototyping AI workflows visually before implementing in code — reduces back-and-forth between design and engineering.
- Organizations working with sensitive data who need local-first development tools that don't send data to SaaS platforms.
Not ideal for
- Non-technical users who want a no-code workflow builder — Rivet requires understanding of LLM concepts and API keys.
- Web-based collaborative environments — Rivet is a desktop app; teams need to share project files manually.
- Simple prompt engineering — for single prompts, a Playground (OpenAI/Anthropic) is simpler.