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cli-agent-orchestrator

awslabs · awslabs/cli-agent-orchestrator

This orchestrator allows AI coding agents like Claude Code and Cursor CLI to work together in parallel or sequence within isolated tmux sessions.

Multi-agent orchestration for AI coding CLIs — Claude Code, Kiro, Codex, and more, coordinated in isolated tmux sessions

1.4k stars291License: Apache-2.0Updated 12 days ago0 opens

Preview

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What you can do with it

  • Delegate complex coding tasks to a team of specialized AI agents.
  • Automate multi-step development workflows by sequencing different AI CLIs.
  • Compare outputs from multiple coding agents on the same task for better reliability.

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From the README

CLI Agent Orchestrator (CAO)

English | 简体中文

CLI Agent Orchestrator (CAO) coordinates multiple AI coding CLIs so a supervisor can delegate work to specialist agents in parallel or sequence.

📚 Documentation — guides, reference, and two interactive courses.

What CAO does

CAO runs a local cao-server, starts provider CLIs in isolated terminal sessions, and gives a supervisor tools for coordinating workers. The agents remain full CLI processes with their native authentication and capabilities. See CODEBASE.md for the runtime architecture and package layout.

Prerequisites

Install:

The focused provider guides contain installation, authentication, and provider-specific behavior.

Install CAO

Install the current main branch as a uv tool:

uv tool install git+https://github.com/awslabs/cli-agent-orchestrator.git@main --upgrade
cao --help

For a tagged release, install cli-agent-orchestrator from PyPI. See DEVELOPMENT.md for a source checkout. For container-based installation, see the devcontainer feature.

To update an existing CAO installation:

cao update

See Updating CAO for source-aware behavior and edge cases.

First supervisor launch

The unqualified commands below use CAO's default Kiro CLI provider. If you installed a different provider, follow its focused guide above for the provider override while keeping the same sequence.

  1. Install the built-in supervisor profile:

    cao install code_supervisor
    
  2. In terminal A, start the local server and leave it running:

    cao-server
    
  3. In terminal B, change to the project directory the agents should work in, then launch the supervisor:

    cd /path/to/your/project
    cao launch --agents code_supervisor
    
  4. Observe the supervisor in the attached launch terminal, open the Web UI at http://localhost:9889, or follow the tmux guide to attach to its session.

  5. Stop the named session when finished:

    cao shutdown --session {session-name}
    

    To stop every CAO session instead, run cao shutdown --all.

Where to go next

Operate CAO

  • Control-plane selection: choose the Web UI, shell CLI, operations MCP server, or plugins.
  • Web UI and MCP Apps: browser and host-rendered fleet interfaces.
  • Flows and workflows: scheduled runs and multi-step pipelines.
  • Skills: install, scope, and author reusable agent guidance.
  • Memory and self-learning: persistent cross-session memory, and the opt-in loop that turns workflow outcomes into lessons and promoted instructions.
  • AI-DLC portfolio example: coordinate parallel AI-DLC intents across repositories and isolated worktrees.
  • Tool restrictions: roles, allowlists, and provider enforcement.
  • Kubernetes deployment: run a supervisor and worker fleet on Amazon EKS, with shared workspace, per-pod state, and credential delivery.
  • Updating CAO: update an installed uv tool.

Configure and integrate

Contribute

Contributing

See CONTRIBUTING.md and DEVELOPMENT.md before submitting changes. Documentation changes must also follow the documentation maintenance rule.

License

Thi

View source on GitHub