aidlc-workflows README

The project's README from github.com/awslabs/aidlc-workflows. As one markdown file: README.md.

AI-DLC - one core, many harnesses

AI-DLC (AI-Driven Development Life Cycle) turns AI coding assistants into structured, verifiable software-delivery workflows. One harness-neutral core runs natively in Claude Code, Kiro CLI, Kiro IDE, Codex CLI, Cursor, opencode, and GitHub Copilot.

version license

The Quick Start below installs the latest stable AI-DLC release.

Quick Start

1. Install AI-DLC

macOS, Linux, or WSL:

curl -fsSL https://github.com/awslabs/aidlc-workflows/releases/latest/download/install.sh | sh

Windows PowerShell:

irm https://github.com/awslabs/aidlc-workflows/releases/latest/download/install.ps1 | iex

The installer adds the native aidlc command and every harness runtime. Bun and Node.js are not required. If your shell cannot find aidlc, follow the PATH instruction printed by the installer or start a new shell.

Prefer to manage the project files manually? Install the matching native aidlc command, download aidlc-runtime-X.Y.Z.tar.gz from the release, and copy runtime/<harness>/ into your project.

2. Configure a project

From the project root, select the harness you use:

cd /path/to/your-project
aidlc config --harness claude
aidlc doctor

Replace claude with kiro, kiro-ide, codex, cursor, opencode, or copilot. Running aidlc config without --harness starts the interactive setup when a terminal is available.

3. Start a workflow

Open your harness in the configured project and describe the work:

/aidlc Build a REST API for inventory management

Codex CLI uses $aidlc instead of /aidlc. AI-DLC selects a workflow from the request, asks for missing decisions, and stops at approval gates before moving forward.

For provider setup, trust prompts, and harness-specific prerequisites, use the guide in the table below. The complete walkthrough is in Getting Started.

Pick your harness

Harness Configure Open Invoke Guide
Claude Code aidlc config --harness claude claude /aidlc Getting Started
Kiro CLI >= 2.6 aidlc config --harness kiro kiro-cli chat /aidlc Kiro CLI
Kiro IDE aidlc config --harness kiro-ide Open the project /aidlc Kiro IDE
Codex CLI >= 0.145.0 aidlc config --harness codex codex $aidlc Codex CLI
Cursor aidlc config --harness cursor Open Cursor or run agent /aidlc Cursor
opencode >= 1.17 aidlc config --harness opencode opencode /aidlc opencode
GitHub Copilot CLI >= 1.0.74 / VS Code >= 1.130 aidlc config --harness copilot Copilot CLI or VS Code /aidlc GitHub Copilot

Model-provider setup belongs to the harness. Claude Code and the shipped Codex configuration default to Amazon Bedrock; GitHub Copilot uses GitHub sign-in or BYOK; Kiro, Cursor, and opencode use their configured provider. The methodology itself is provider-independent.

AI-DLC works best with capable reasoning models. The current recommended model is Claude Opus 4.8.

Why AI-DLC

Ad-hoc AI coding loses context as projects grow. AI-DLC keeps requirements, decisions, implementation, tests, and operational work connected through one audited lifecycle:

Start with Workflow Profiles to compare Classic, Express, and the focused workflows. See the AI-DLC Workflows 2.0 Specification for the architecture and methodology.

[!IMPORTANT] Generative AI can make mistakes. Review generated output and costs before acting on them. See the AWS Responsible AI Policy.

Documentation

Guide Use it when
Getting Started Installing, configuring, and running your first workflow
User Guide Using workflows, profiles, agents, knowledge, and approval gates
Harness guides Handling provider, trust, and runtime differences
Install and Lifecycle Updating, pinning, installing offline, using mirrors, or uninstalling
Harness Engineer Guide Reshaping stages, agents, rules, sensors, and knowledge
Developer Reference Changing the engine, hooks, packaging, or tests

Repository Layout

Edit core/ or harness/<name>/, never generated dist* output.

Development

Install dependencies and generate every harness:

bun install --frozen-lockfile
bun scripts/package.ts

Useful commands:

bun scripts/package.ts <name>     # generate one harness
bun scripts/package.ts --check    # determinism guard
bun tests/run-tests.ts --ci       # smoke, unit, and integration
bun tests/run-tests.ts --release  # full release acceptance

See the Contributing Guide for the complete development workflow and Porting to a New Harness to add another runtime.

Troubleshooting

Run aidlc doctor from the project root first. Common fixes:

Symptom Fix
aidlc is not found Apply the PATH instruction printed by the installer or start a new shell
Project/runtime version skew Finish the active workflow, then run aidlc config
Codex hooks do not run Trust the project hooks as described in the Codex guide
Bedrock access fails Enable the configured models and verify AWS credentials and region
Plugin stages disappear after refresh Run /aidlc plugin sync
Refreshed skills do not take effect Start a new harness session

See Troubleshooting for diagnostic and recovery procedures.

References

aidlc-workflows (awslabs/aidlc-workflows)

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Aidlc-workflows
这是什么？AI-DLC 可以把 AI 编程助手变成结构化、可验证的软件交付工作流，并在 Claude Code、Kiro、Codex CLI、Cursor、opencode 和 GitHub Copilot 上运行同一个与 harness 无关的核心。
这是独立应用，还是可以接入我现有的编程代理？它不是独立应用，而是一个与 harness 无关的工作流引擎（包含 14 个代理、33 个阶段、95 种事件的审计记录），会以一组规则/命令的形式安装到你已在使用的代理中，例如 Claude Code、Codex 或 Cursor。
我该怎么安装并启动一个工作流？运行 macOS/Linux/WSL 安装脚本或 Windows PowerShell 安装程序以获取 aidlc 命令，然后在项目根目录下运行 'aidlc config --harness claude'（或 kiro、codex、cursor、opencode、copilot），打开对应的 harness，并调用 '/aidlc'（在 Codex 中为 '$aidlc'），同时描述你要做的工作。
支持哪些编程代理？我需要自己配置模型提供方吗？支持的 harness 包括 Claude Code、Kiro CLI/IDE、Codex CLI（>=0.145.0）、Cursor、opencode（>=1.17）以及 GitHub Copilot CLI/VS Code；模型提供方按 harness 单独配置，其中 Claude Code 和 Codex 默认使用 Amazon Bedrock，Copilot 则使用 GitHub 登录或 BYOK（自带密钥）。
它会自己运行，还是我需要一直盯着？AI-DLC 在阶段之间推进前会在人工审批关卡处暂停，README 也明确提醒，在据此采取行动之前，应先检查生成的输出和成本。
AI-DLC 推荐使用哪个模型以获得最佳效果？README 目前推荐 Claude Opus 4.8，认为它最适合 AI-DLC 中依赖大量推理的工作流步骤。
既然可以直接和 AI 编程代理聊天，我为什么还要用这个？随着项目变大，临时性的 AI 编程会丢失上下文；AI-DLC 则通过一个可审计的生命周期，把需求、决策、实现、测试和运维串联起来，该生命周期包含 5 个主要阶段、33 个阶段步骤、14 个代理和 11 种工作流配置。
它自带哪些类型的工作流配置？共有 11 种工作流配置，涵盖新功能、缺陷修复、基础设施、安全、概念验证、企业级交付以及其他常见工作，其中包括轻量的 'Classic' 和 'Express' 版本。
如果 aidlc 命令或某个 harness 集成出了问题，我该怎么办？先在项目根目录运行 'aidlc doctor'；常见的修复方法包括重新执行安装程序给出的 PATH 配置指令、版本不一致后重新运行 'aidlc config'、在 Codex 中信任项目 hooks，或在刷新后启动新的 harness 会话。
What are similar projects?orca, pstack-claude, pstack, munder-difflin, no-mistakes, vicoa, operator-memory, compound-engineering-plugin
Which topics is it in?harness-engineering, github-copilot, aws, harness, opencode, workflow, workflow-automation, cursor, coding-agent, codex, agent, claude-code
Creator
awslabs
Size
108.2 MB
Stars
5.4K
Updated
CommitOct 17Releasev2.11.0Oct 08
Age
11mo
Commits · 90 Days