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    awslabs

    awslabs/aidlc-workflows

    #814 this week

    AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents

    automation
    TypeScript
    MIT-0
    5.4K stars
    967 forks
    5.4K GitHub watchers
    Updated Oct 18, 2026
    View on GitHub

    Build with Backblaze B2

    SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    Ranks #21 among Automation repositories tracked this week, and #21 over the last 30 days; #814 of 2,794 repositories GStars tracks this week (top 29%).

    5 releases in the last 90 days, the latest today, with 73 contributors on GitHub.

    Use Cases & Benefits

    • Implements AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents across multiple development harnesses.
    • Provides a harness-neutral core enabling consistent, verifiable, and self-correcting AI-driven software development workflows with approval gates and audit trails.
    • Use for orchestrating complex AI-assisted software development lifecycles with multiple domain expert agents and adaptive workflow composition.
    • Use for integrating AI-DLC methodology into various IDEs and CLIs like Kiro, Claude Code, Codex, Cursor, opencode, and GitHub Copilot seamlessly.
    • Use for enterprises requiring structured, traceable, and adaptive AI coding workflows with human-in-the-loop approvals and persistent learning from corrections.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT No Attribution

    Public-domain style: no conditions on use.

    Community files

    • README: present
    • Contributing guide: present
    • Code of conduct: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    AI-DLC Workflows 2.0 is GA on this repository's default main branch. Use main for new installations and upgrades. It turns AI agents into verifiable, self-correcting engineering workflows from one harness-neutral core rendered natively across Claude Code, Kiro IDE, Kiro CLI, Codex CLI, Cursor, opencode, and GitHub Copilot.

    A native implementation of the AI-DLC methodology (AI-Driven Development Life Cycle) that runs on many harnesses from one source of truth — today Claude Code, Kiro IDE, Kiro CLI, Codex CLI, Cursor, opencode, and GitHub Copilot, and any capable harness you port it to. Run a full software-development lifecycle with a 14-agent roster — 11 domain experts, 2 review-only agents, and the adaptive-workflows composer — working through a 33-stage workflow, with you approving every gate.

    The methodology lives once, in a harness-neutral core/; each harness adds a thin surface that decides how it shows up on that harness. So you edit the methodology in one place, and every harness distribution is generated from it — no harness gets special treatment. (See Repository layout for how the pieces fit together.)

    To learn more about AI-DLC, read this blog post and the Method Definition Paper it references.

    Excerpt from the awslabs/aidlc-workflows README on GitHub (MIT-0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Sep 4: +23Sep 5: +28Sep 6: +14Sep 7: +21Sep 8: +28Sep 9: +36Sep 10: +48Sep 11: +39Sep 12: +14Sep 13: +13Sep 14: +17Sep 15: +15Oct 18: +16

    +1,053 stars over 45 days across 16 snapshot days; best day +48 on Sep 10.

    Forks

    967+198

    Open issues

    324+195

    Compared with peers in Automation

    Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth

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