I built a local-first AI dev pipeline because existing tools weren’t good enough. QonQrete v0.5.0 Beta is here.

## The pain that pushed me to build this

I was frustrated with AI tooling that:

- Runs mostly in the cloud

- Treats privacy as an afterthought

- Gives me almost no control over how and where code is executed

I wanted something that lives on my own machine, works with multiple LLM providers, and treats security + reproducibility as first-class concerns.

So I started hacking on my own system to orchestrate LLMs locally… and it snowballed into QonQrete.

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## What I built

QonQrete is a local-first, multi-agent AI “construction system” that plans, writes, and reviews code inside a sandboxed workspace.

The core pipeline uses 3 agents:

- InstruQtor – planning: breaks down the task, designs the steps

- ConstruQtor – building: writes/edits code inside a controlled repo

- InspeQtor – reviewing: does code review, sanity checks, and suggests fixes

Each agent can run with your choice of:

- GPT

- Gemini

- Claude

- DeepSeek

- (or other OpenAI-compatible APIs)

You keep everything on your own machine and wire in the models you like.

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## Why it’s different (at least for me)

A few things that make QonQrete feel different from the usual “AI coding assistant”:

- Local-first: the whole construction loop runs on your machine

- No cloud lock-in: bring your own APIs and swap them out per agent

- Sandbox execution: work happens inside a controlled project folder / “qoreyard”

- Multi-LLM per pipeline step: planning, coding, and reviewing can each use different models

- Human-in-the-loop or autonomous: you can approve steps, or let it run more hands-off

- Very dev-oriented: it’s built for people who live in terminals and care about git history, diffs, and reproducible runs more than pretty UIs

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## My personal journey with it

This started as a quick experiment to reduce boilerplate in my own projects.

I just wanted a smarter way to:

- Plan features

- Write the boring bits

- Keep everything inside git

But as I kept iterating, it turned into a full system where multiple AI agents collaborate in a structured way. I didn’t expect it to work as well as it does — or to drag me this deep into agent orchestration, prompt chaining, and local-first workflows.

It’s still early, but I’m using it on real projects now and trying to harden it as I go.

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## What I’d love feedback on

If you use AI in your programming workflow:

- What’s missing for you right now?

- Would you trust a local multi-agent system to modify your repo if you can see every diff and approve steps?

- What features would make something like this actually useful in your day-to-day dev life?

Honest criticism is very welcome — I’d rather hear “this is overkill” or “I’d only use it if X/Y existed” than polite silence.

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## Repo (open-source, still early)

If you want to poke around the code or try it:

👉 GitHub: https://github.com/illdynamics/qonqrete

It’s still a v0.5.0 beta, but I’d love to hear from anyone who:

- Cares about local-first + privacy-conscious AI dev tooling

- Plays with multi-agent setups

- Has strong opinions about how AI should (or shouldn’t) touch their codebase

Happy to answer any questions here and share more details about the architecture or trade-offs if people are curious.