Rivet

The Open-Source Visual AI Programming Environment

Free

Description

Rivet is a visual programming environment designed for building AI agents using Large Language Models (LLMs). It enables teams to effectively design, debug, and collaborate on complex LLM prompt graphs, which can then be run directly within their applications. The platform supports iteration on these prompt graphs and their deployment in the user's own environment, moving beyond mere prototyping to production-ready solutions.

Developed and utilized by Ironclad Research, Rivet addresses the challenges of programmatic AI agent construction by offering a visual interface, an easy-to-use debugger, and a remote executor. This combination empowers teams to work together on increasingly sophisticated and powerful LLM prompt graphs, which are stored as YAML files for easy versioning and review.

Key Features

Visual Construction: Build complex prompt chains visually for production-grade AI applications.
Remote Debugging: Observe real-time execution of prompt chains within your live application for thorough analysis.
Collaborative Development: Manage and version Rivet graphs as YAML files, enabling team collaboration and code reviews.
Open-Source Platform: Access and contribute to a free, open-source visual AI programming environment.
Direct Execution: Run prompt graphs directly in Node or TypeScript applications using a remote executor for seamless integration.

Use Cases

Developing AI agents with Large Language Models.
Designing and iterating on complex LLM prompt graphs.
Debugging AI agent behavior in real-time within applications.
Facilitating team collaboration on AI development projects.
Deploying robust AI agents into production environments.
Building virtual assistants, such as contract management AI.
Creating and refining AI-powered features for software products.

Frequently Asked Questions

What is Rivet?

Rivet is an open-source visual programming environment for building AI agents with LLMs. It allows teams to design, debug, and collaborate on complex LLM prompt graphs, and then run them directly in their applications.

What are the key benefits of using Rivet?

Rivet enables users to visualize and build complex AI chains for production, not just prototypes. It offers remote debugging capabilities to observe prompt chain execution in real-time. Additionally, Rivet facilitates collaboration as its graphs are YAML files, allowing for version control and standard code review practices.