15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T756,At AWeber our move from GitLab to GitHub demanded a tool that could migrate hundreds of repositories and automatically transform CI pipelines. That effort produced imbi-automations, a workflow engine capable of applying coordinated changes that leverage AI across our entire codebase.

Imbi is our operational source of truth. As an open-source system, it maps every project, service, database, and dependency into an interconnected graph. It tracks technical debt, enforces standards, and serves as a single control plane for configuration, project state, deployments, and operational change.

Imbi Automations leverages this graph to target projects by language, version, or type, executing workflows that combine AI with traditional tooling to enforce consistency at scale. It updates project metadata, identifies deficiencies, and monitors compliance with our standards.

We've used Imbi Automations to:

- Migrate several hundred projects from GitLab to GitHub, automating the transition from GitLab CI to GitHub Actions. Finish our Python 3.9 to 3.12 migration by updating all projects to use the latest syntax, tooling, and project standards.

- Scan all projects leveraging Claude Code, creating comprehensive AGENTS.md files for every project to ensure Agent readiness to work on project related tasks.

- Automate the scanning of our projects for standards compliance, updating Imbi project facts with the results.

- Update base Docker images across all projects in minutes instead of months.

What we're focused on next:

- Python 3.14 upgrade (January 2026): one to two sprints instead of months

- JavaScript → TypeScript: bulk migration instead of individual rewrites

- AWS infrastructure, security scanning, automated incident response: all candidates for AI-driven workflows

- Standards compliance: continuous reporting a

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