Green CI Optimizer

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  Code execution

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  Optimized Automated Merge request

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  Gitlab project

Inspiration

CI/CD pipelines silently waste compute—reinstalling dependencies, running redundant jobs, and executing inefficient workflows. While existing tools suggest improvements, developers still spend time manually applying them.

We built Green CI Optimizer to eliminate this friction by moving from advisory tools to autonomous optimization—where the system not only detects inefficiencies but fixes them automatically.

What it does

Green CI Optimizer is an autonomous GitLab agent that:

Analyzes .gitlab-ci.yml for inefficiencies Detects issues like: Missing caching Redundant job execution Unoptimized job structure Generates an optimized pipeline configuration Automatically creates a Merge Request with improvements applied Key Differentiator

Unlike existing tools that suggest improvements, Green CI Optimizer takes action—applying optimizations directly and reducing manual effort.

Output Optimized CI configuration Automated Merge Request Compute and time savings

How we built it

The system is implemented as a GitLab-integrated automation agent with a simple, reliable pipeline:

Architecture Analyzer Module Parses .gitlab-ci.yml Identifies inefficiencies using rule-based detection Optimization Engine Applies targeted improvements (e.g., caching, deduplication) Execution Layer Uses GitLab APIs to: Create a new branch Commit optimized CI file Open a Merge Request

Stack

Python (core logic) PyYAML (CI parsing) GitLab REST API (automation layer)

Challenges we ran into

Variability in CI configurations Pipelines differ significantly across projects, requiring flexible parsing logic. Avoiding unsafe modifications Automated changes must not break pipelines. We restricted optimizations to safe, high-confidence transformations. Balancing simplicity and impact We focused on a minimal, reliable feature set rather than overengineering. Accomplishments that we're proud of Built a fully autonomous optimization workflow (no manual steps required) Successfully integrated with GitLab to create real Merge Requests automatically Designed a system that delivers visible, measurable improvements Achieved a clean, reproducible demo with immediate impact

What we learned

The real value of AI in development lies in automation of repetitive workflows, not just assistance Simple, well-executed solutions outperform complex, unreliable ones Developers trust tools that are predictable, transparent, and actionable

What's next for Green CI Optimizer

Advanced optimization rules Parallel job execution Dependency-aware scheduling Learning system Improve recommendations based on past pipeline behavior Expanded ecosystem support Support for multiple languages and build systems Sustainability metrics dashboard Track compute savings and efficiency improvements over time

Built With

• gitlab
• python
• pyyaml
• restapi