ai-maestro

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

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.

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 →

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, then run the curl command inside Ubuntu:

wsl --install

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

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.

Three layers of intelligence that grow over time: Memory (agents remember past conversations and decisions), Code Graph (interactive visualization of your entire codebase with delta indexing), and Documentation (auto-generated, searchable docs from your code). Agents get smarter the longer they work with you.

Work Coordination

Talking isn't working. I needed agents to coordinate on actual deliverables.

Assemble agents into teams, run meetings in split-pane war rooms, and track tasks on a full Kanban board with drag-and-drop, dependencies, and 5 status columns. Cross-machine teams work seamlessly. This is project management for your AI workforce.

Scheduled Work

Some jobs shouldn't wait for me to remember to ask.

Give an agent its own schedule — morning triage, a nightly dependency check, a Monday report. The timer belongs to the agent, not the machine, so a schedule travels with the agent when it moves hosts, and fires when that agent goes idle instead of interrupting it mid-task.

Agent Presence

At 80 agents, they all looked the same.

Custom avatars, personality profiles, and roles for every agent. When an agent has a face and a job title, you instinctively assign it the right work — just like a real team.

Agents can also speak and be seen: a voice pipeline with your choice of TTS provider, and live animated faces that move while the agent talks. Open a call with an agent and it looks back at you. Nobody else is doing this, and once you've reviewed a plan by listening to it on a walk, the terminal feels like a downgrade.

One caveat worth stating plainly: true lip-sync — mouth movement driven by the actual audio — needs the OpenAI or ElevenLabs voice and an API key. The default browser voice (web-speech) renders straight to your speakers and exposes no audio stream to measure, so the face animates from a synthetic envelope instead. It looks alive; it is not tracking the words. That is a limit of the Web Speech API, not something we plan to work around.

Agent Deployment

One agent on my laptop. Another on EC2. A third on Fargate. All in the same dashboard.

Four ways to run agents, each for a different need:

Cloud agents are Terraform-managed. EC2 installs Node.js, tmux, and AI CLIs directly on ARM64 hardware (no Docker overhead). ECS auto-builds your Docker image, pushes to ECR, and runs on Fargate. Both deploy with one command from the dashboard or CLI.

# EC2: dedicated instance with SSL
aimaestro-agent.sh create my-api --ec2 \
  --domain api.example.com --ssl-email admin@example.com --key-name my-key

# ECS Fargate: serverless (auto-builds image)
aimaestro-agent.sh create worker --ecs

Meet Lola — Your First Hire

Every AI-first organization starts with one agent. Lola is yours — a batteries-included Chief of Staff framework built for AI Maestro. She handles email triage, semantic memory, task management, and content security out of the box. Install the platform, deploy Lola, and you have your first employee on day one.

# Clone and deploy Lola on AI Maestro
git clone https://github.com/23blocks-OS/lolabot.git
cd lolabot && ./setup.sh

The LolaBot Ecosystem:

• LolaBot Framework — Open-source agent framework. Build your own Lola.
• lolabots.com — The LolaBot Factory. Pre-built agent templates, one-click deploy.

Learn more about Lola →

The Agent Ecosystem

Every agent is built from five dimensions:

Open Protocols — AI Maestro is built on three open standards: