Hey everyone! đź‘‹
As Python developers, we’ve all been there: you spin up a beautiful, high-performance FastAPI backend, and then you hit the wall. You have to spend the next three hours coding repetitive CRUD endpoints, wiring up authentication boilerplate, and stitching together a frontend dashboard just to see your data or build an internal admin tool.
I got tired of doing this manually, so I built VeloIQ—an open-source, AI-native framework designed to turn your Python model definitions into full-stack production applications in minutes.
🚀 The 30-Second Workflow
Instead of writing boilerplate, VeloIQ lets you scaffold a full application directly from your terminal. Here is what the end-to-end flow looks like:
You define your database models using SQLModel (or SQLAlchemy), run our initialization command, and the framework auto-generates a type-safe FastAPI backend paired with a headless, highly responsive React frontend (powered by Refine + AntD).
The speed of No-Code, but with the pure control of Pro-Code.
🤖 Built for the "Vibe Coding" Era
We didn't just build VeloIQ for humans; we built it for the AI coding assistants you are already using.
The framework root includes a fully optimized llms.txt context file. This means if you are using Cursor, Windsurf, Claude, or Copilot, your AI assistant can instantly read the entire layout architecture of VeloIQ. It won't hallucinate legacy syntax or break structural states—it knows exactly how to write VeloIQ code for you on the first try.
🛡️ Enterprise AI Scaling: Enter IQVigilant
Building rapid full-stack tools is step one. Step two is making sure those tools are completely safe when you hook them up to autonomous AI agents.
To solve the data compliance problem, we also engineered a premium companion extension called IQVigilant.
While text-based network firewalls try to intercept words, IQVigilant operates directly inside your application’s live database memory. It hooks into the SQLAlchemy before_flush lifecycle loop. If an autonomous agent hallucinates a destructive database execution or violates an enterprise business logic chain, IQVigilant catches it in-memory and triggers a clean transaction rollback before the corruption hits your production tables. Plus, it features native integration for enterprise AI stacks like IBM WatsonX.ai.
📦 Try It Out Today!
The framework is open-source, fully public, and ready for you to break.
- Documentation & Quickstart: Check out veloiq.dev
- Source Code: Star us on GitHub
I would love to hear your thoughts! How are you currently handling the "messy boilerplate" parts of your FastAPI backends, and how are you securing your database layers from AI tool-calls? Drop a comment below!
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