Rivet is a visual programming environment for building AI agents with LLMs. Iterate on your prompt graphs, then run them directly in your application. Rivet was born out of our need at Ironclad to build complex AI agents within our existing application.
Hi Product Hunt! I'm Cai, the technical co-founder of Ironclad.
We're really excited to launch Rivet! This is our first open-source software initiative at Ironclad, and it's been transformative to our work with LLMs and AI agents. Our hope is that Rivet can be similarly transformative for others, and become part the emerging ecosystem of LLM and agent tooling!
We'd love for you to try it out, and hear what you think! Are there parts of it that feel particularly compelling? Things it would be cool to push on?
Love that Ironclad is creating and sharing these tools with the greater community. Congrats on the launch!
Very cool, congrats on the launch Cai and team! Who's the target ICP - mid/late stage enterprises or startups/tinkerers? Or both?
Great work on the launch, Ironclad team! 🥳 Can't wait to use this for more complex generative pipelines.
Curious: what was the motivation for providing a Tauri desktop app for the initial release? Are there workflows present in the desktop version that make development easier?
Super cool. What features do you think companies should build with agents using Rivet?
Kudos on launching Rivet, it sounds like a groundbreaking tool in the LLM and AI agent ecosystem! Being open-source is such a power move - it truly lets a community sculpt the best version of a product. Given that we often deal with video production for SaaS companies, I'm eager to explore if there's a space where Rivet could revolutionize the way we interact with LLMs in this domain. Keep up the awesome work, and can't wait to see how Rivet evolves with community insights! 🌟
What is the maintenance plan for this? I see that you're primarily working on the Ironclad contract AI. Would be curious if these are things you've thought about:
- When prompts get really long or potentially contain long content, how to prioritize the contents to fit within the context
- How you might create an integrated evaluation flow within Rivet (there are so many edge case errors in AI agent dev and you'd want to cover as many as you can!)
Looks like a cool product! Will have to try it out on my AI projects.
Could you share more about the specific use cases or industries where Rivet has proven to be particularly valuable? Additionally, what are some of the key features that set Rivet apart from other visual programming environments for AI agents?
This kind of innovation is essential as AI integration becomes increasingly important in various applications and industries.
