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The world's first end-to-end AI platform for automating MLOps, LLM development, and agentic workflows.

The Open-Source Visual AI Programming Environment

Rivet is an open-source visual programming environment designed for building AI agents with LLMs. It allows users to visually create, debug, and collaborate on complex LLM prompt graphs. Rivet enables teams to design, debug, and deploy these graphs within their own environments, bridging the gap between prototyping and production-ready applications. It simplifies the development of AI agents by providing an intuitive visual interface, a powerful debugger, and remote execution capabilities. Teams can version Rivet graphs as YAML files, facilitating collaboration and code review. Originally built and used by Ironclad, Rivet addresses the challenges of programmatically constructing AI agents, making it easier to iterate and build robust AI solutions.
Rivet is an open-source visual programming environment designed for building AI agents with LLMs.
Explore all tools that specialize in node-based prompt design. This domain focus ensures Rivet delivers optimized results for this specific requirement.
Explore all tools that specialize in real-time debugging. This domain focus ensures Rivet delivers optimized results for this specific requirement.
Explore all tools that specialize in yaml-based graph storage. This domain focus ensures Rivet delivers optimized results for this specific requirement.
Enables users to create AI agent workflows using a visual graph interface, reducing complexity and improving understanding.
Allows users to observe the execution of prompt chains in real-time, enabling faster identification and resolution of issues.
Rivet graphs are stored as YAML files, facilitating version control, collaboration, and code review.
Follow the Getting Started guide
Integrate Rivet into your Node or TypeScript application
Experiment with the Rivet example application
All Set
Ready to go
Verified feedback from other users.
"Users praise Rivet for its visual programming environment, collaborative capabilities, and ease of use in building complex AI agents."
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