aidlc

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.

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. On Windows, it installs for the current account and automatically registers the bin directory in User PATH. Run it from a normal PowerShell window; one opened with "Run as administrator" gets a warning and a prompt, since installing as administrator is less safe. Open a new terminal if another session cannot find aidlc. To skip both persistent and current-process PATH changes, use -NoModifyPath. Windows uninstall removes only the User PATH entry recorded as installer-owned. On macOS, Linux, or WSL, follow the installer's PATH instruction if needed.

Cannot install a native executable, or prefer to manage the project files manually? Install Bun, download aidlc-copy-runtime-X.Y.Z.tar.gz from the release, and copy the complete runtime/<harness>/ directory into your project. This path does not require the native aidlc command.

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. If you use Kiro IDE's own terminal in a project folder you have not trusted yet, Kiro first asks whether you trust it. Choose Trust Folder & Continue only for your own project or one you have checked, because trusting lets the folder's .kiro hooks run commands on your machine; otherwise choose Cancel and review the folder first (see First run).

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. In Kiro IDE, first choose aidlc in the chat panel's agent picker. 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

Model-provider setup belongs to the harness. Shipped project configuration keeps the provider and model already selected by the user. aidlc config providers can apply Amazon Bedrock settings on supported project surfaces or record manual setup for other harnesses. Kiro CLI and Kiro IDE need no provider answer because model access comes with Kiro. The methodology itself is provider-independent.

Recommended Model

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:

• 5 phases and 33 stages from initialization through operation
• 14 agents: 11 domain experts, 2 reviewers, and an adaptive composer
• 11 workflow profiles for features, bug fixes, infrastructure, security, proofs of concept, enterprise delivery, and other common work
• Human approval gates and source-bound review evidence
• 107-event audit trail plus persistent state, team knowledge, and learned rules
• The same deterministic engine across every supported harness

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

Repository Layout

• core/ - hand-authored, harness-neutral methodology and engine
• core/tools/ - 80 aidlc-*.ts engine and authoring tools
• harness/<name>/ - thin, harness-specific manifests and integrations
• plugins/<name>/ - optional AIDLC plugins
• scripts/ - packaging, binary, installer, and release tooling
• tests/ - smoke, unit, integration, and end-to-end tests
• docs/ - user, harness-engineering, and developer documentation
• dist/ and dist-release/ - generated, ignored local outputs

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](docs/h