aidlc-workflows
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents
Links
README
From the repo.
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. If your shell cannot find aidlc, follow the PATH
instruction printed by the installer or start a new shell.
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.
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. 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
- 105-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
| 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 |
| Development and Releases | Taking a PR through AI review, preview testing, and stable publication |
| Developer Reference | Changing the engine, hooks, packaging, or tests |
Repository Layout
core/- hand-authored, harness-neutral methodology and enginecore/tools/- 75 aidlc-*.ts engine and authoring toolsharness/<name>/- thin, harness-specific manifests and integrationsplugins/<name>/- optional AIDLC pluginsscripts/- packaging, binary, installer, and release toolingtests/- smoke, unit, integration, and end-to-end testsdocs/- user, harness-engineering, and developer documentationdist/anddist-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 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
Collected info
- ★ 4,825 stars
- ⎇ 876 forks
- Language: TypeScript
- Source updated: 9/25/2026
Config for your environment
Replace {MCP_ENDPOINT_URL} with this MCP’s endpoint URL (from its repo or docs above). No API key — you connect directly.
Tool
OS
Config file: ~/.cursor/mcp.json
{
"mcpServers": {
"mcp-server": {
"url": "{MCP_ENDPOINT_URL}"
}
}
}Paste into mcpServers in the config file. Restart Cursor after saving.
If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.