Design and Deploy Complex AI Agent Workflows Visually

Building sophisticated AI agents with large language models is hard. You're stuck writing complex code to manage prompts, workflow logic, and debugging issues you can't see. When your team needs to collaborate on these AI workflows, the complexity multiplies, leaving you frustrated and slowing down your entire project.

What is Rivet

Rivet is an open-source visual programming environment that lets you build AI agents with large language models through an intuitive drag-and-drop interface. It transforms the complex process of designing LLM-based applications into visual prompt graphs that are easy to understand, debug, and collaborate on. Once perfected, these workflows can be deployed directly into your applications, turning sophisticated AI ideas into reality without coding expertise.

Key Capabilities

Visual Prompt Graph Editor : Create complex AI workflows by connecting nodes in a visual interface, making it easier to understand relationships between different components and logic flows.

Real-Time Debugging Tools : Observe the execution of your prompt chains in real-time, allowing you to pinpoint issues and optimize performance before deployment.

Team Collaboration : Store your Rivet graphs as simple YAML files that can be versioned in repositories and reviewed using standard code review tools, enhancing team coordination.

Remote Execution : Run your designed workflows directly in your application environment without needing to reimplement the logic, saving development time and reducing errors.

Integrated Testing : Use Trivet, Rivet's companion testing library, to validate graph functionality programmatically, ensuring your AI agents perform consistently.

Perfect For

A product team at a legal tech company needed to build an AI agent to answer complex questions about contract data. With Rivet, they visualized the entire prompt chain, quickly identified reasoning issues, and deployed a functioning solution in just weeks instead of months.

A small development team was struggling to iterate on their customer support chatbot due to complex prompt sequences. After switching to Rivet, they could see exactly how each message flowed through the system, allowing non-technical team members to suggest and implement improvements.

Worth Considering

Rivet is best for teams with at least some technical understanding of LLM concepts, even if they don't have coding expertise. While it excels at design and debugging, deployment options are somewhat limited, requiring additional work to move workloads to production at scale. For larger enterprises, you may need to supplement Rivet with additional tools for monitoring and scaling. As an open-source solution, Rivet is completely free to use.

Also Consider

LangFlow: Better for users who prefer a web-based interface with built-in deployment capabilities and want to stay within the LangChain ecosystem.

Flowise: Good choice for teams looking for a low-code platform with more UI components and pre-built templates for common AI applications.

LangSmith: Consider when advanced monitoring, evaluation, and production-grade observability are critical requirements for your LLM applications.

Bottom Line

Rivet transforms the complex task of building AI agents into a visual, collaborative process accessible to both technical and non-technical team members. For organizations looking to rapidly prototype and iterate on LLM-powered workflows without extensive coding, Rivet offers an impressive balance of power and accessibility that can significantly accelerate your AI development process.