aidlc-workflows
SHA-256AI-Driven Development Life Cycle: harness-neutral workflow rules that give Claude Code, Cursor, Copilot, Codex, Kiro, and opencode a structured, auditable delivery process with approval gates.
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v2.11.0 · 86.6 MB
AWS's open-source framework that turns AI coding assistants into structured, auditable software-delivery workflows running identically across seven harnesses.
Core Features
- One harness-neutral core runs natively in Claude Code, Kiro CLI/IDE, Codex CLI, Cursor, opencode, and GitHub Copilot
- Full lifecycle coverage: 5 phases and 33 stages from initialization through operations
- 14 agents working together: 11 domain experts, 2 reviewers, and an adaptive composer
- Human approval gates with source-bound review evidence before work moves forward
- 120-event audit trail plus persistent state, team knowledge, and learned rules
What It Can't Do
- •It is not a standalone AI tool; you need a supported AI coding assistant (Claude Code, Cursor, Copilot, etc.) with its own model access. Strong reasoning models matter: Claude Opus 4.8 is recommended, and weaker models noticeably degrade workflow quality. In Kiro IDE, only choose Trust Folder & Continue for projects you trust, because trusting lets the folder's hooks run commands on your machine. On Windows, reopen the terminal after installation or the aidlc command will not be found; Bun is only needed for the manual copy-runtime path. Generative AI can make mistakes, so review generated output and costs before acting on them.
Use Cases
- Teams that want disciplined, auditable AI-assisted delivery instead of ad-hoc prompting sessions
- Organizations where members use different AI coding tools but need one consistent methodology
- Large or long-running projects where requirements, decisions, code, and tests must stay connected as context grows
- Enterprises that require human approval checkpoints and a full audit trail for compliance
AI-DLC (AI-Driven Development Life Cycle) is an open-source framework from AWS Labs that converts ad-hoc AI coding sessions into structured, verifiable software-delivery workflows. A single harness-neutral core runs natively in Claude Code, Kiro CLI/IDE, Codex CLI, Cursor, opencode, and GitHub Copilot, so teams keep one consistent process no matter which AI tool they use. The methodology spans 5 phases and 33 stages from initialization through operations, orchestrated by 14 agents (11 domain experts, 2 reviewers, and an adaptive composer) with 11 workflow profiles for features, bug fixes, infrastructure, security, proofs of concept, and enterprise delivery. Human approval gates, source-bound review evidence, and a 120-event audit trail keep requirements, decisions, code, and tests connected and traceable as projects grow. Compared with GitHub's Spec Kit, which focuses mainly on specification-driven planning, AI-DLC covers the entire lifecycle including operations and ships a native cross-platform installer. Installation is a one-line script on macOS, Linux, WSL, or Windows, and the permissive MIT-0 license allows use without attribution. Claude Opus 4.8 is the recommended model.
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Getting Started
Download installer
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Install the software
Double-click the downloaded installer and follow the prompts
Step 1: Run the one-line installer script in a terminal (curl on macOS/Linux/WSL, or the irm PowerShell command on Windows)
Step 2: In your project folder, run aidlc config --harness claude (swap claude for kiro, codex, cursor, opencode, or copilot)
Step 3: Run aidlc doctor to verify the environment and configuration
- Step 1: Run the one-line installer script in a terminal (curl on macOS/Linux/WSL, or the irm PowerShell command on Windows)
- Step 2: In your project folder, run aidlc config --harness claude (swap claude for kiro, codex, cursor, opencode, or copilot)
- Step 3: Run aidlc doctor to verify the environment and configuration
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SHA256 Checksum
47352efbb2bf49abb9956d04a6abcb8f4db623c7fb7c93b8ec40b1fe3de3aec9This checksum is extracted from the GitHub Release page. Verify file integrity after download.
All SHA-256 checksums on this platform are extracted from the project's official GitHub Release page, without any modification. You can independently verify them on the GitHub Releases page.
Open Source Transparency
View GitHub SourceUninstall Info
On Windows, the uninstaller removes only the User PATH entry recorded as installer-owned. On macOS/Linux/WSL, delete the installed files and revert the PATH change shown during installation. Full uninstall steps are in the official Install and Lifecycle guide.
No Extra Dependencies
Ready to use after download. No additional runtime required.
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