AI Maestro
Orchestrate terminal-based AI agents across machines with a unified dashboard, direct messaging, memory, and deployment controls.
What does this agent do, and when should you use it?
AI Maestro is a self-hosted orchestration platform that manages terminal-based AI agents from one web dashboard. It discovers tmux sessions, Docker containers, cloud deployments, and standalone agents, with local tmux, Docker, AWS EC2, and AWS ECS Fargate execution modes. Its Agent Messaging Protocol (AMP) provides direct agent-to-agent communication with priorities, message types, cryptographic signatures, and push notifications. The platform also includes persistent memory, an incrementally indexed interactive Code Graph, and auto-generated searchable code documentation. Teams can coordinate in split-pane meetings and track work in a five-column Kanban board with dependencies, while machines connect through a peer mesh without a central server.
After installation, AI Maestro serves a dashboard at http://localhost:23000 for viewing and switching agents, creating agents through a guided UI, and organizing them into named trees. It auto-discovers tmux sessions, Docker containers, cloud deployments, and standalone agents; agents can exchange prioritized, signed AMP messages directly. It builds and delta-indexes a Code Graph for a codebase, exposes an interactive visualization, generates searchable documentation, and retains past conversations and decisions in its memory layer. The aimaestro-agent.sh create command creates agents, with --ec2 for dedicated EC2 deployment and --ecs for ECS Fargate deployment.
- A developer running several Claude Code, Codex, or other terminal agents can replace manual terminal switching and context relaying with one dashboard.
- An engineering team with Mac and Linux machines can place iOS builds on Mac hosts and Docker workloads on Linux while viewing their agents together.
- A multi-project team that needs isolation and resource limits can run agents in Docker containers while retaining centralized discovery and management.
- An operations team needing always-on or burst workers can deploy agents through aimaestro-agent.sh create using the --ec2 or --ecs modes.
- A team with research, writing, and backend agents can use AMP to send findings across machines instead of copying them between terminals.
- A lead coordinating agent deliverables can use teams, split-pane meetings, and a dependency-aware Kanban board to assign and track work.
What are this agent's strengths and limitations?
- Unifies four agent execution modes—tmux, Docker, EC2, and ECS Fargate—in one dashboard instead of managing only a single terminal environment.
- Its peer mesh makes agents visible across machines without requiring a central server, fitting mixed Mac, Linux, and cloud fleets.
- AMP explicitly includes message priorities, message types, cryptographic signatures, and push notifications for direct agent coordination.
- Beyond session management, it combines persistent memory, delta-indexed Code Graphs, searchable documentation, and dependency-aware Kanban coordination.
- The quick installation requires Node.js 18+ and tmux; Windows support is through WSL2 only.
- Cloud modes depend on AWS: EC2 and ECS Fargate are Terraform-managed, and ECS builds an image and pushes it to ECR.
- The project states that there is no hosted cloud version yet, so adopters operate their own machines, agents, and infrastructure.
- Although the README lists many terminal agents as supported, it does not document adapter configuration, permission models, or feature differences for each one.
How do you install or deploy this agent?
The documented runtime requirements are Node.js 18+ and tmux. For the quick install, run: curl -fsSL https://raw.githubusercontent.com/23blocks-OS/ai-maestro/main/scripts/remote-install.sh | sh. It installs the dashboard, service, AMP, and a Claude Code plugin; then open http://localhost:23000. No installation credentials are documented. A manual path is: git clone https://github.com/23blocks-OS/ai-maestro.git && cd ai-maestro && yarn install && yarn dev. On Windows, install WSL2 first and run the installer inside Ubuntu.
How do you use this agent?
Open http://localhost:23000 and use the dashboard to inspect discovered agents or create new ones. From the CLI, use aimaestro-agent.sh create to create an agent; the documented EC2 example is aimaestro-agent.sh create my-api --ec2 --domain api.example.com --ssl-email [email protected] --key-name my-key, and the ECS Fargate example is aimaestro-agent.sh create worker --ecs. After creation, organize agents in the dashboard, view their cross-machine status, and have them communicate through AMP.
How does this agent compare with similar options?
Compared with tmux alone, AI Maestro adds agent discovery, persistent memory, AMP messaging, a multi-machine mesh, task boards, team coordination, and cloud deployment management on top of terminal sessions.