EcoGaurd

🚀 Inspiration

With rising concerns about climate change, software engineering’s hidden carbon footprint is often ignored. CI/CD pipelines, inefficient code, and over-provisioned resources consume massive energy. We were inspired to bring sustainability into the developer workflow, making green software practices automated, measurable, and actionable.

💡 What it does

EcoGuard is an AI-powered multi-agent platform built on GitLab Duo that embeds sustainability into the software lifecycle. It analyzes code for inefficiencies, tracks carbon emissions of CI/CD pipelines, recommends optimizations, and suggests eco-friendly deployment strategies based on real-time grid carbon intensity—all visualized through an interactive dashboard.

⚙️ How we built it

We leveraged the GitLab Duo Agent Platform to create specialized agents for compliance, emissions tracking, and optimization. YAML-based workflows trigger actions on commits and pipelines. We integrated external APIs like Electricity Maps for carbon data and used MCP for secure data access. A dashboard layer visualizes sustainability insights, while backend logic processes metrics and recommendations.

🧱 Challenges we ran into

One major challenge was accurately estimating carbon emissions from CI/CD pipelines due to variability in infrastructure and workloads. Integrating real-time external data sources and mapping them meaningfully to developer actions was complex. Designing actionable AI suggestions (not just analysis) also required careful prompt engineering and system design.

🏆 Accomplishments that we're proud of

We successfully built a fully functional multi-agent system that integrates directly into GitLab workflows. EcoGuard not only detects inefficiencies but provides real-time, actionable insights. The combination of carbon tracking, green code analysis, and smart deployment recommendations makes it a holistic sustainability solution.

📚 What we learned

We gained deep insights into green software engineering principles, especially the Software Carbon Intensity (SCI) framework. We also learned how to design scalable multi-agent systems, integrate AI into DevOps workflows, and translate environmental metrics into developer-friendly insights.

🔮 What's next for EcoGuard

We plan to enhance accuracy of carbon estimation using infrastructure-specific data and ML models. Future versions will include predictive sustainability scoring, automated optimization pipelines, and deeper cloud integrations (AWS, Azure, GCP). We also aim to introduce organization-level reporting, policy enforcement, and a marketplace of sustainability plugins to expand EcoGuard’s ecosystem.

Built With

• fastapi
• javascript
• python