So we sifted through 97K posts from the OpenAI Community to confirm and find people with similar problems (& we did). It turns out that there are a handful of low-level problems that everybody who is building an AI app that works well needs to solve: statefulness to manage conversations interaction interface between multiple agents and multiple users. robust document search dynamic tool calling from different apps swapping out different LLMs/models while preserving state,

And that's exactly what we ended up making Julep to be.

We built this as a prototype to solve our problems, and now we want to lower the barrier for someone to create functional and production-ready LLM apps.

For more advanced stuff; We're also working on a task specification that defines a task execution framework for LLMs using a YAML format. It includes task triggers, available tools, debugging options, input and output schemas, and workflows. The main workflow is the entry point, with conditional transitions, error handling, and mapping operations for parallel processing.

We want to make a Supabase-like backend for AI apps with a great DX. So if you're interested in this stuff or want a platform that you can make design decisions for, specifically for yourself, please submit PRs, become a maintainer, or join the Discord to check out cool examples we're building!