AI Agent Orchestrator with Skills System - Give AI Agents superpowers: memory search, code graph queries, agent-to-agent messaging. Manage Claude, Codex or any AI Agent from one dashboard. Move Agents between computers and locations
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.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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ai-memory
ai-orchestration
autonomous-agents
claude-code
claude-code-plugins
claude-code-skills
clawdbot
code-graph
developer-tools
multi-agent-systems
openclaw
pair-programming
plugin-builder
tmux
What makes this different:
Works with any AI agent — Claude Code, Aider, Cursor, Copilot, your own scripts. 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.
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:
Every feature was born from a real problem. 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, organize them with smart naming (project-backend-api becomes a 3-level tree with auto-coloring), and switch between any agent with a click. Auto-discovers your existing tmux sessions.
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.
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.
Agent Identity
At 80 agents, they all looked the same.
Custom avatars, personality profiles, and visual presence for every agent. When an agent has a face and a role, you instinctively assign it the right work — just like a real team.
Who Is This For
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, Aider, Cursor, GitHub Copilot, 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.
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