Question: I need a solution that simplifies the process of constructing and debugging Large Language Model prompt graphs for my team.

Rivet is a visual programming environment for building AI agents using Large Language Models (LLMs). It lets teams build, debug and collaborate on complex LLM prompt graphs with a visual interface. Rivet can also be used to visualize and construct complex chains, debug remotely in real time and collaborate through versioning Rivet graphs as YAML files in a team repository. It can be integrated with Node and TypeScript applications and runs on MacOS, Linux and Windows.

Another good option is Langtail, which offers a collection of tools for debugging, testing and deploying LLM prompts. Langtail features include fine-tuning prompts with variables, running tests to prevent unexpected app behavior, and deploying prompts as API endpoints. It also comes with a no-code playground for writing and running prompts, adjustable parameters and detailed logging. Langtail is designed to help teams collaborate and build more reliable AI products.

For a more complete platform, you might want to look at GradientJ. This full-stack platform lets LLM teams build next-generation AI applications. It offers tools for ideating, building and managing LLM-native applications, including a team collaboration mechanism and an app-building canvas that learns from users. GradientJ is designed to support a variety of use cases and to free up engineering time and promote seamless teamwork.

Last, Prompt Studio is a team collaboration environment for building LLMs. It includes a collaborative text editor, customizable templates, testing and iteration tools, and a managed AI backend for deployment. Prompt Studio is good for automating legal document conformance checks, creating reusable AI features, and integrating AI into existing workflows, making AI development more efficient and collaborative for both technical and non-technical users.