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Experimental version.This is the latest in-development version of AI-Implement. Features may change without notice and behavior is not guaranteed. Switch to the latest stable version here.
AI-Implement is an orchestration service that connects your Linear or Jira workspace to one or more GitHub repositories. You mark an issue with the AI-Implement label (Linear) or set its AI-Implement Status to Ready (Jira), and within minutes Claude Code checks out the right repo, reads your codebase, implements the ticket, opens a pull request, and posts a gap analysis comparing what was built to what you asked for. Your team reviews everything in the tools they already use — Linear or Jira, plus GitHub — without learning a new interface.

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Who it’s for

AI-Implement is built for software teams, not individual developers. If you already manage work in Linear or Jira and want well-specified tickets to turn into PRs without manually opening them yourself, this is designed for you. You’ll get the most value if:
  • Your team runs tickets through Linear or Jira and wants AI output to land in your existing review process.
  • You have focused, well-specified issues where the requirement fits in a single pass.
  • You’re comfortable operating a small Node.js service on Fly.io (or similar infrastructure).
  • You want Claude to run inside your own CI, with your own secrets, against your own provider.
AI-Implement works best with clearly scoped tickets. Claude handles focused, well-specified issues well and struggles with sprawling or vague ones. The quality of the ticket is the quality of the prompt.

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What you need to get started

Before you can run AI-Implement, you’ll need accounts and credentials across four services:
  • Linear or Jira Cloud workspace — where your issues live and where the AI-Implement label or two custom fields (AI-Implement Status and AI-Implement Repo) are applied
  • GitHub App — authenticates the orchestrator to dispatch workflows and open PRs
  • Fly.io account — where the orchestrator service runs (a single shared-cpu-1x machine is enough)
  • Anthropic API key — or an AWS Bedrock setup with the appropriate IAM role

Quick start

Get from zero to your first AI-generated PR in under 30 minutes.

How it works

Understand the full lifecycle from ticket to merged PR.

Prerequisites

Everything you need to set up before deploying the orchestrator.

Customize prompts

Tailor the WORKFLOW.md and PLANNING.md templates for your stack.

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Key concepts

The ticket is the prompt. Writing well-specified tickets is something teams already know how to do. AI-Implement uses that skill — and the cross-issue structure in your ticket backlog — instead of asking anyone to learn prompt engineering. The PR is the work product. Every run produces a pull request, a gap analysis comment, and a ticket-system status change. Reviewers see exactly what was attempted and where it fell short of the spec. One orchestrator, many repos. A single AI-Implement service can dispatch workflows across multiple repos in multiple GitHub organizations. Project mappings — each one connecting a Linear team or Jira JQL to a GitHub repo — are managed in the admin UI. Gap-fill on demand. After reviewing a PR, comment /ai-implement on it to send Claude back in for a second pass on the same branch. The gap analysis comment updates automatically.