Apache License 2.0

项目介绍

awslabs/cli-agent-orchestrator 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 Apache License 2.0 许可证,目前获得 1.5k 个 Star、317 次 Fork。

仓库全名
awslabs/cli-agent-orchestrator
开源许可证
Apache License 2.0
Star 数
1.5k
Fork 数
317
未关闭 Issue
139

README 摘要

/prefers-color-scheme swap, since that navy contrasts at only 1.77:1 on GitHub's dark theme, and PyPI allows into visible tag soup depending on how it sanitizes. The same constraint means the banner cannot be swapped per theme either. It is light, matching PyPI and GitHub's light theme, and it shows as a bright panel on GitHub's dark theme. That is a deliberate trade, not an oversight. Keep in sync with…

完整内容见 GitHub 原始 README 或本页 。

常见问题

cli-agent-orchestrator 用什么语言开发?

主要使用 Python 编写,采用 Apache License 2.0 许可证,目前有 1.5k 个 Star。

cli-agent-orchestrator 可以免费商用吗?

仓库采用 Apache License 2.0 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 cli-agent-orchestrator 的源码?

可以直接克隆仓库:git clone https://github.com/awslabs/cli-agent-orchestrator.git,默认分支为 main;安装与运行步骤见仓库 README。

cli-agent-orchestrator 最近还在维护吗?

仓库最近一次更新为 7 小时前,当前有 139 个未关闭 Issue;Star 数为 1.5k,可作为社区活跃度的参考。

简要分析

热度分
2163
价值分
1003
活跃状态
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语言占比

CSS
HTML
JavaScript
Jinja
Makefile
Python
Rust
Shell
TypeScript

Release

README

CLI Agent Orchestrator (CAO)

English | 简体中文

PyPI version Python versions Ask DeepWiki

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.
  • Launch decisions: local routing settings and content-free records.
  • Skills: install, scope, and author reusable agent guidance.
  • Skills lifecycle example: install, discover, load, apply, update, and remove a reusable skill in an isolated home.
  • 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.
  • Cross-zone robot transport: coordinate simulated transport, measured custody handoffs, and independent checks.
  • Hierarchical robot factory: a supervisor and three team-manager agents run nine Open Duck Mini robots on a simulated make, assemble, and ship line, and balance its bottleneck.
  • Self-hosted decision model: run the open JevK5-4B decision model locally or on AWS (CDK), behind a loopback-only /v1/systemone API.
  • 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

This project is licensed under the Apache License 2.0. See LICENSE.

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