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The open-source visual AI programming environment for building and debugging LLM agents.

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Tracked since2026
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The Bottom Line

Entry price

Free, no paid tier

Biggest pro

Simplifies the creation of complex AI agent chains visually

Biggest con

Requires some technical knowledge for integration and deployment

TL;DR - Rivet

  • Visual programming environment for building AI agents with LLMs.
  • Enables debugging of prompt chains in real-time within applications.
  • Facilitates collaboration and version control through YAML-based graphs.
Pricing: Free forever
Best for: Individuals & startups

What is Rivet?

Editorial review
Rivet is an open-source visual programming environment designed for building and debugging AI agents powered by Large Language Models (LLMs). It allows users to visually construct complex prompt graphs, iterate on their designs, and then run these graphs directly within their applications. This tool is particularly useful for teams looking to design, debug, and collaborate on sophisticated LLM prompt chains, and then deploy them in their own environments. Developed and used by Ironclad, Rivet addresses the challenges of programmatically building AI agents by providing a visual interface, an easy-to-use debugger, and a remote executor. Its core benefit lies in enabling faster iteration, better understanding of agentic logic, and seamless team collaboration, as graphs are stored as YAML files for version control and code review. Rivet is ideal for developers and teams who want to create robust and reliable AI applications, moving beyond mere prototyping to production-ready solutions.

Pros & Cons

Pros

  • Simplifies the creation of complex AI agent chains visually
  • Provides powerful debugging capabilities for LLM workflows
  • Enhances team collaboration through version-controlled YAML graphs
  • Allows for rapid iteration and deployment of AI applications
  • Open-source nature fosters community contributions and transparency

Cons

  • Requires some technical knowledge for integration and deployment
  • Primarily focused on LLM prompt graphs, may not cover all AI development needs

Preview

Key Features

Visual programming environment for LLM prompt graphsReal-time remote debugging of prompt chainsYAML-based graph storage for version controlIntegration with Node.js and TypeScript applicationsSupport for complex AI agent logicOpen-source availability

Pricing

Free

Rivet is completely free to use with no hidden costs.

View pricing

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Rivet FAQ

How does Rivet assist in building AI agents?

Rivet provides an open-source visual programming environment that allows users to construct complex prompt graphs for AI agents. It enables faster iteration and a clearer understanding of agentic logic, moving beyond prototyping to production-ready solutions.

What kind of user benefits most from Rivet?

Rivet is ideal for developers and teams who need to create robust and reliable AI applications, particularly those focused on building and debugging AI agents powered by Large Language Models. It is designed for collaborative environments where prompt chains need to be designed, debugged, and deployed.

How does Rivet compare to Relevance AI?

Rivet differentiates itself by being an open-source visual programming environment specifically for building and debugging LLM agents, emphasizing version control and collaborative development through YAML graphs. It focuses on providing powerful debugging capabilities and a remote executor for AI agent workflows.

What are the primary limitations of Rivet?

Rivet primarily focuses on LLM prompt graphs, meaning it may not cover all AI development needs beyond this scope. It also requires some technical knowledge for effective integration and deployment of the AI applications built within it.

Does Rivet include a free tier?

Rivet is free to use, as it is an open-source visual AI programming environment. There is no paid plan required to access its features for building and debugging LLM agents.

How does Rivet facilitate team collaboration on AI projects?

Rivet enhances team collaboration by storing prompt graphs as YAML files, which allows for easy version control and code review. This structure supports seamless sharing and iteration among team members working on sophisticated LLM prompt chains.

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