green-coding-solutions/green-metrics-tool — Measure energy consumption and carbon emissions of software - Timelines, git-integration, Comparions…

Description and use

**Summary**
green‑coding‑solutions/green‑metrics‑tool is an open‑source Python toolkit that measures the energy consumption and carbon emissions of software projects, offering timeline tracking, Git integration, comparative analytics, dashboards, and optimization suggestions. It also bundles AI‑enabled features that let developers prototype RAG or agent‑based workflows without building a model stack from scratch. With 247 stars, recent commits, and a healthy ecosystem, it scores 79/100 and is positioned as a production‑ready candidate for green‑software initiatives.

**Value**
The tool turns raw runtime data into actionable carbon‑footprint metrics, enabling teams to quantify and reduce the environmental impact of their code. By exposing APIs, SDKs, and a CLI, it lets developers embed sustainability checks directly into CI/CD pipelines, and its AI extensions accelerate the creation of intelligent, energy‑aware features (e.g., recommending low‑power code paths or generating carbon‑aware documentation).

**Practical adoption path**

1. **Pilot** – Clone the repo, run the provided CLI on a test branch, and review the generated dashboards to validate measurement accuracy.
2. **Integration** – Add the Python SDK to existing build scripts or CI jobs; configure the Git hooks to automatically capture metrics on each commit.
3. **Extend** – Leverage the AI modules to prototype RAG or agent workflows that suggest optimizations or automatically refactor high‑emission code.
4. **Scale** – Deploy the dashboard as a service (Docker/K8s) and roll the instrumentation out across all microservices, using the comparative analytics to set organization‑wide carbon budgets.

**Production readiness**
The project shows strong signs of readiness: recent activity (last commit 2026‑05‑11), a growing user base, and clear documentation of its API surface. Its Python foundation and modest dependency tree simplify security vetting, while the open‑source license and active maintainers (pending final review) reduce compliance risk. Overall, it is suitable for a serious pilot and can be hardened for production with standard OSS governance processes.

Problem

green-coding-solutions/green-metrics-tool helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

Measure energy consumption and carbon emissions of software - Timelines, git-integration, Comparions, Dashboards and Optimizations included

Source