Maestro App Factory
If you’re looking for a general-purpose agent framework, you’ll probably want something like CrewAI or LangChain. But if you’re building robust software and want a system that reflects actual engineering best practices out of the box—welcome to the Maestro App Factory.
The Maestro App Factory is an AI-powered application development tool that orchestrates multiple agents to build production-ready software. Rather than relying on a single “super developer” agent, it distributes work across specialized roles with explicit workflow boundaries, reviews, and coordination mechanisms.
Why a Team Instead of a Single Agent?
LLMs are trained on human work and tend to reproduce human work patterns—good and bad. If you want consistent results, you’ll get farther by structuring the work like the best engineering teams do. That’s why Maestro separates concerns: AI handles reasoning and code generation, while the software handles state management, orchestration, and enforcing constraints through deterministic workflows.
How It Works
Maestro organizes AI agents into distinct roles that mirror a real development team. The PM gathers requirements through interactive interviews and generates structured specifications, adapting to whatever level of technical detail you’re comfortable with. The Architect reviews those specs, breaks them into stories with dependency graphs, dispatches work to coders, and conducts code reviews. Coders work in parallel, each implementing individual stories in isolated Docker containers—they plan, code, test, and submit for review before their work gets merged.
There’s also a dedicated Hotfix Coder for urgent work that needs to bypass the normal queue without interrupting ongoing development.
What Makes It Different
Maestro ships as a single binary—no Python dependency hell to deal with. It supports multiple LLM providers including OpenAI, Anthropic, Google, and Ollama, so you can mix and match models based on what each role needs. A built-in knowledge graph maintains architectural consistency across stories, serving as institutional memory for your project.
All coder work runs in Docker containers with security hardening, and state persists in SQLite so you can recover from crashes without losing progress. If you need to work offline, Airplane mode lets you run entirely local using Gitea and Ollama.
Getting Started
Install via Homebrew on Mac or Linux:
brew tap SnapdragonPartners/tap
brew install --cask maestro
You’ll need Docker running, API keys for your preferred LLM provider, and a GitHub token with push/PR/merge permissions. Once that’s set up, just run maestro and open the web UI at http://localhost:8080 to start your first project.
Is It Right for You?
Maestro shines when you’re building production-quality applications and want engineering discipline baked in—parallel implementation, process consistency, and institutional memory that persists across stories. It’s probably not the right choice if you prefer rapid prototyping with hands-on steering or real-time interactive coding sessions.