Sample: Starter Kit for RAG + Agents with CrewAI
Why Build a Starter Kit for RAG + Agents?
Let’s be honest: every developer who’s played with LLMs gets that rush of “wow” from the first working demo. But the real headaches show up when you need to stitch LLMs into something production-grade: an app that can pull in real data, coordinate multi-step logic, and more. Suddenly, you’re not just writing single prompts. You’re coordinating between multiple prompts, managing queues, adding vector databases, orchestrating workers, and trying to get things back to the user in real-time. We've found that CrewAI (coordinating prompts, agents, tools) + Django (building an api, managing data), with a bit of Celery (orchestrating workers/async tasks), is a really nice set of tools for this. We're also going to use Django Channels (real-time updates) to push updates back to the user. And of course, we'll use Defang to deploy all that to the cloud.
If this sounds familiar (or if you're dreading the prospect of dealing with it), you’re the target audience for this sample. Instead of slogging through weeks of configuration and permissions hell, you get a ready-made template that runs on your laptop, then scales—unchanged—to Defang’s Playground, and finally to your own AWS or GCP account. All the gnarly infra is abstracted, so you can focus on getting as much value as possible out of that magical combo of CrewAI and Django.
You can find it here.