About
Delegate real engineering work. Keep control.
SuperQwen is a developer-first, self-hosted agentic AI platform. It turns plain-language instructions into planned, executed, tested and reviewable code changes, using an open Qwen model running on your own hardware.
Why SuperQwen#
Most coding assistants stop after generating code. You still have to check whether it runs, whether the tests pass, and whether it touched something it shouldn't have. And your source code usually leaves your network to get there.
SuperQwen keeps going until there's evidence. It inspects your repository, makes a plan, applies changes inside an isolated workspace, runs your real test suite, and hands you a diff with the results. Inference happens on a model you host, and every risky action can wait for your approval.
Mission#
- ▸Convert developer instructions into structured, executable plans.
- ▸Enable repository-aware coding, testing, debugging and refactoring.
- ▸Keep inference and sensitive source code inside your infrastructure.
- ▸Support tool-driven execution with iterative verification.
- ▸Provide transparent execution traces and reproducible results.
- ▸Offer configurable autonomy without sacrificing security.
Principles#
Local control
Model inference and execution run on infrastructure you control.
Explicit tools
Tools execute only through application-controlled, schema-validated interfaces.
Defined outcomes
Every task has a goal, a state and completion criteria.
Verify, don't assume
Generated code is validated by real tests, not by the model's own opinion.
Bounded autonomy
Autonomous execution always stays within defined policies and budgets.
Observable failure
Failures are recorded and clearly reported, never hidden.
Replaceable parts
Model providers and execution backends can be swapped behind adapters.
Smallest reliable design
One reliable agent before a swarm of them.
Who it's for#
Software developers, engineering teams, platform engineers, and technical users who want repository-aware automation without handing their code or their infrastructure to a third party. The first focus is coding: understanding a codebase, fixing bugs, implementing features, refactoring, writing tests and documentation. The long-term goal is a general-purpose agent platform built on the same controlled loop.