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

wsl --install

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

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:

• AMP — Agent Messaging Protocol. Email-like communication between agents with cryptographic signatures.
• AID — Agent Identity. Portable cryptographic identity for agents across platforms.
• AAP — Agent Actions Protocol. Standardized tool execution and workflow triggers.

AI Maestro is the stage. Pick personalities, give them skills, and run them from one dashboard.

Read the full ecosystem guide →

Who Is This For

Founders building AI-first organizations. You're the CEO, your agents are the team. Solo operators, agency owners, and anyone running a business where AI agents do the work and you drive the strategy. Start with Lola, add specialists, scale your AI workforce.

Developers running multiple AI agents. If you have 3+ agents and you're switching between terminals, losing context, and playing messenger — this is for you. Works with Claude Code, Codex, Aider, Cursor, or any terminal-based AI.

Teams coordinating AI-assisted work. Multiple developers, multiple agents, multiple machines. One dashboard. Agent-to-agent messaging replaces you as the bottleneck.

Creators and operators who want to connect AI agents to the outside world through Slack, Discord, or Email — without exposing their infrastructure.

Screenshots

Code Graph — Interactive codebase visualization

Agent Inbox — Direct agent-to-agent messaging

Documentation

New here?

• Quick Start Guide — Get up and running
• Core Concepts — Understand how it works
• Use Cases — Real examples of what people build

Going deeper:

• Multi-Machine Setup · Network Access
• Cloud Deployment (AWS) · Docker Agents
• Agent Messaging Guide · Architecture
• Intelligence Guide · Code Graph
• Operations Guide

Troubleshooting:

• Common Issues · Security · Windows Installation

Open Protocols:

• AMP · AID · AAP

Extending:

• Plugin Development · API Reference · Ecosystem Guide

What's Next

• Create an agent from a repo URL — clone and staff it in one step, instead of cloning by hand and pointing an agent at the folder
• Agent search and filtering across the entire mesh
• Agent playback — time-travel through agent sessions
• Performance analytics dashboard

See the full roadmap and join the discussion.

Contributing

We love contributions. See CONTRIBUTING.md for guidelines.

• Report a bug
• Request a feature

License

MIT — see LICENSE. Free for any purpose, including commercial.

FAQ

Do I need to use Claude Code? No. AI Maestro works with any terminal-based AI agent — Claude Code, Codex, Aider, Cursor, OpenClaw, Hermes, Droid, or your own scripts. We're agent-agnostic.

Does it work on Linux / Windows? Yes. macOS and Linux natively. Windows via WSL2 — see the Windows guide.

Can agents on different machines talk to each other? Yes. The Agent Messaging Protocol (AMP) handles cross-machine messaging with cryptographic signatures and push notifications. No central server required.

What are AID and AAP? AID (Agent Identity) gives each agent a portable cryptographic identity. AAP (Agent Actions Protocol) standardizes how agents execute tools and trigger workflows. Together with AMP, they form the open protocol stack that AI Maestro is built on.

What is the Canvas skill? Canvas lets agents generate visual artifacts — diagrams, charts, and interactive documents — directly from conversations. It's part of the plugin system and available to any agent.

How is this different from just using tmux? tmux gives you terminals. AI Maestro gives you an organization — persistent memory, agent-to-agent messaging, team coordination, Kanban boards, multi-machine mesh, cloud deployment, and gateway integrations. tmux is the foundation; AI Maestro is the building.

What is LolaBot? LolaBot is an open-source agent framework — a batteries-included Chief of Staff that handles email, memory, tasks, and security. The LolaBot Factory offers pre-built agent templates for one-click deployment.

What does it cost? Nothing. MIT licensed, free for any purpose including commercial — no seats, no usage fees, no paid tier. You bring your own agent subscriptions (Claude Code, Codex, whatever you already pay for) and AI Maestro just runs them. Note that some tools in this space are source-available rather than open source, under licenses that forbid offering them as a service; MIT has no such restriction.

Do you collect telemetry? No. No analytics SDK, no account, no login, no phone-home. The only telemetry in the product is the agent metrics shown on your own dashboard, and those post to localhost:23000 — your machine. Point them at a central collector only if you choose to run one.

How does this compare to the parallel-agent IDEs? Different job, and deliberately so. Tools like Orca, Paseo, Superset and Conductor run several agents on one repository on one machine, each in an isolated git worktree, then diff the results so you can merge the best one. It's a disposable fan-out: five attempts at the same task, keep one, throw away four. They're good at it. If that's the workflow you want today, use one of them.

AI Maestro is built for the case after that one — a standing team of agents that own things, remember their work, message each other, and run across every machine you have. We don't use worktrees because worktrees can't leave the machine they're on: they're several working directories over one .git store on one disk. Our agents each clone what they own and integrate through pull requests, like a human team, which is the only model that works when the backend agent is on Linux and the iOS agent is on a Mac. See the mental model.

Can I run several agents on the same repository? Yes — give each one its own clone. Backend owns the API, frontend owns the web app, infra owns the Terraform, even inside a monorepo. They coordinate over AMP and merge through git. For a large repo, git clone --filter=blob:none or --reference to a local mirror keeps the disk cost down. And an individual agent is free to use git worktree inside its own clone — worktrees are a fine tool within an agent, just not how we isolate agents from each other.

Is there a hosted / cloud version? Not yet. AI Maestro runs on your machines. You own your data, your agents, and your infrastructure.

How do I add more agents? Create them from the dashboard UI, the CLI (aimaestro-agent.sh create), or just start a new tmux session — AI Maestro auto-discovers it.