What is Rivet?
Rivet is the open-source visual programming environment designed specifically for building AI agents powered by Large Language Models (LLMs). If you're a developer or team looking to move beyond simple prompts and create robust, production-ready AI applications, Rivet provides the visual tools and integrated capabilities you need to effectively design, debug, and deploy complex LLM prompt graphs directly within your own applications.
Core Capabilities
Rivet empowers you to build sophisticated AI agent logic with confidence and efficiency.
🧠 Visualize and Build Complex Logic: Move past code-based prompt engineering limitations. Rivet's node-based editor allows you to visually construct intricate AI prompt chains and agent workflows. This clarity makes it easier to understand data flow, manage complex logic, and build applications suitable for production environments, not just prototypes.
🔍 Real-time & Remote Debugging: Gain unprecedented insight into your AI agent's execution. Rivet offers live debugging within the application, letting you see the input, output, and AI responses for every node in real-time. It also supports remote debugging, enabling you to diagnose issues with AI chains running in your production environment.
🤝 Streamlined Team Collaboration: Rivet graphs are saved as standard YAML files. This allows your team to easily version control your AI logic using familiar tools like Git and integrate graph reviews into your existing code review workflows, fostering effective collaboration.
🛠️ Execute Graphs in Your Application: Design your AI agent logic visually in the Rivet Application, then run it seamlessly within your Node or TypeScript application using the Rivet Core/Node libraries. This provides a simple API for integrating sophisticated AI capabilities directly into your existing projects.
🧩 Comprehensive Node Library: Access a rich library of essential node types (including Text, Chat, Match, Loop Controller, Extract YAML/JSON, and External Call) to execute common functionalities. Easily connect these nodes to build diverse and powerful AI workflows.
How Rivet Solves Your Problems
Building reliable, complex AI agents programmatically can be challenging, often lacking visibility and making collaboration difficult. Rivet directly addresses these pain points:
Eliminating the Black Box: Instead of guessing how your prompt chains are behaving, Rivet's visual editor and real-time debugger let you see exactly what's happening at each step, making it simple to identify and fix issues.
Simplifying Complexity: Visually mapping out complex logic with nodes and wires is significantly more intuitive than managing nested code structures, especially as your AI agent's capabilities grow. This reduces development time and cognitive load.
Enabling Production Readiness: Rivet provides the tools needed for rigorous development – from integrated testing within the app to remote debugging for live systems – ensuring your AI agents are robust and reliable when deployed.
Facilitating Teamwork: By saving graphs as standard, version-controllable files, Rivet allows your team to build, review, and manage AI agent logic collaboratively, just like any other part of your codebase.
Why Choose Rivet?
Developed and used internally by Ironclad Research for their own AI products (like Ironclad Contract AI), Rivet is built by practitioners solving real-world AI agent challenges. Its focus on a visual interface, powerful debugging, and seamless application integration via open-source libraries makes it a compelling choice for teams serious about building production-grade AI applications with LLMs.
Conclusion
Rivet provides the clarity, control, and collaborative foundation you need to build sophisticated AI agents effectively. By offering a visual development environment, robust debugging tools, and easy integration into your existing applications, Rivet helps you unlock the full potential of LLMs for your products.
More information on Rivet
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Source: Similarweb (Sep 24, 2025)
Rivet was manually vetted by our editorial team and was first featured on 2023-09-08.
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