agent-browser

AI Maestro

I was running 35 AI agents across multiple terminals and became the human mailman between them. So I built AI Maestro.

The OS for AI-first organizations — orchestrate any AI agent with persistent memory, agent-to-agent messaging, and multi-machine support.

Quick Start · Features · Documentation · Contributing

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The Story

I gave an AI agent a real task — not autocomplete, a real engineering problem. It checked the code, read the logs, queried the database, and came back with the answer. That was the moment. This thing can actually work.

Within a week I was running 35 agents across terminals. They were productive, but they couldn't talk to each other. I became the human message bus — copying context from one terminal, pasting into another. I was the bottleneck in my own AI team.

So I built AI Maestro — one dashboard to see every agent, on every machine, with persistent memory and direct agent-to-agent communication. Today I run 80+ agents across multiple computers, building real companies with them every day.

What makes this different:

• Works with any AI agent — Claude Code, Codex, Aider, Cursor, OpenClaw, Hermes, Droid, or any terminal-based agent. We don't lock you in.
• Multi-machine from day one — Peer mesh network with no central server. Nobody else does this.
• Agents that communicate — The Agent Messaging Protocol (AMP) lets agents coordinate directly. You orchestrate, they collaborate.
• Yours, entirely — MIT licensed. No account, no telemetry, no per-seat pricing, no hosted tier we upsell you to. Your code and your agents stay on your machines.

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The Mental Model

AI Maestro is an operating system for an AI-first company. Not a task runner — a place where a standing team works.

An agent here is closer to an employee than to a job. It has a name, a face, a memory that survives the session, an inbox — and it owns something: a product, a repository, a process, a customer. Its memory and code graph are indexed against what it owns, which is why it gets better at that thing over months rather than starting cold every morning.

Because agents own things, they don't share a working copy. Two agents that need the same repository each clone it, work on their own branch, and integrate through git — push, pull request, review, merge — exactly like two engineers on a team.

That's deliberate, and it follows from the one thing that defines this product: your agents run on different machines. A shared checkout needs a shared filesystem. Git worktrees — the isolation primitive the single-machine agent IDEs are built on — are several working directories over one .git store on one disk, so they stop working the moment your backend agent is on a Linux box and your iOS agent is on a Mac. A clone is the only primitive that survives the move. It's why transferring an agent to another host clones its repos to the destination: an agent's repositories travel with the agent.

The full model →

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Quick Start

curl -fsSL https://raw.githubusercontent.com/23blocks-OS/ai-maestro/main/scripts/remote-install.sh | sh

This installs everything you need:

• AI Maestro dashboard and service
• Agent messaging system (AMP)
• Claude Code plugin with 5 skills and 32 CLI scripts

Time: 5-10 minutes · Requires: Node.js 18+, tmux

Windows: Install WSL2 first (PowerShell as Administrator, then restart), then open Ubuntu from the Start menu and run the curl command there:

wsl --install

New to WSL? WSL is a Linux system with its own disk, so its home folder starts empty. Keep agents there (~/agents/<name>, the default), not in C:\ folders, which are much slower from WSL. Open their files from Windows at \\wsl.localhost\Ubuntu\home\<you>\agents. Windows in 5 minutes · Full Windows guide

Linux: Ensure build tools are installed: sudo apt install tmux build-essential

git clone https://github.com/23blocks-OS/ai-maestro.git
cd ai-maestro
yarn install
yarn dev

See QUICKSTART.md for detailed setup options.

Dashboard opens at http://localhost:23000

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Features

Every feature was born from running a real AI-first organization. We built them in the order we needed them.

One Dashboard

I had 35 terminals and couldn't tell which was which.

See and manage all your AI agents in one place. Create agents from the UI with a guided wizard, organize them with smart naming (project-backend-api becomes a 3-level tree with auto-coloring), and switch between any agent with a click. Four deployment modes: tmux (local), Docker (containerized), AWS EC2 (dedicated instance), and AWS ECS Fargate (serverless). Auto-discovers tmux sessions, Docker containers, cloud deployments, and standalone agents.

Any Machine

My Mac Mini was sitting there idle. What if I ran agents on that too?

A peer mesh network where every machine is equal. Add a computer, it joins the mesh. Every agent on every machine, visible from one dashboard. Use each machine for what it's best at — Mac for iOS builds, Linux for Docker, cloud for heavy compute. No central server required.

Worker machines can run headless (yarn headless) — the full API and agent runtime with no UI, in about 100MB of RAM. Run the dashboard where you sit; run agents wherever the compute is.

Agent Messaging

I was the mailman — copying messages between agents because they couldn't talk to each other.

The Agent Messaging Protocol (AMP) gives your agents email-like communication. Priority levels, message types, cryptographic signatures, and push notifications. Tell your agent "send a message to backend about the deployment" — it just works. Agents coordinate directly while you manage the big picture.

Before AMP: You copy research from one terminal, paste into another, repeat 50 times a day. With AMP: "Research agent, send your findings to the writing agent." Done.

Gateways

A friend in Singapore wanted his agents to talk to mine. But I didn't want to give him access to my network.

Connect your AI agents to Slack, Discord, Email, and WhatsApp through organizational gateways. Smart routing (@AIM:agent-name), thread-aware responses, and content security with 34 prompt injection patterns detected at the gateway — before any agent sees the message.

Persistent Memory

Every morning, my agents woke up with amnesia.

Long-term memory is a skill you switch on per agent. Every night each agent's conversations become memory: short statements of what it learned, each backed by the passages it came from, and an entity graph of the things it works with and how they relate (runs on, depends on, stores data in, deploys to), kept current, with ended relations marked. Claude Code deletes transcripts after 30 days; AI Maestro rebuilds that history from the age