EcoLogits is a Python library that estimates the environmental footprint (energy, GHG emissions, abiotic resource depletion, primary energy, water consumption) of generative AI model inference accessed through provider APIs. It integrates with official client libraries for OpenAI, Anthropic, Cohere, Mistral AI, HuggingFace Hub, Google GenAI, and LiteLLM by monkey-patching their core methods using `wrapt`. When a user makes an API call, the library intercepts the request, measures latency, looks up model characteristics and electricity mix data, and attaches an `ImpactsOutput` object to the response containing usage and embodied impact estimates with uncertainty ranges. Architecturally, the library is organized around a provider instrumentor pattern: each provider has a tracer class that wraps sync/async and streaming/non-streaming methods. Impact calculations are performed by a Directed Acyclic Graph (DAG) engine that resolves interdependent computations (energy, GWP, ADPe, PE, WCF) from regression coefficients and data files. The library also supports video generation impact estimation via a standalone function. An optional OpenTelemetry integration exports metrics to an OTLP endpoint. Key entry points are `EcoLogits.init()` to enable tracers for selected providers, and `video_impacts()` for video generation. External dependencies include provider SDKs (openai, anthropic, etc.), `wrapt` for wrapping, `pydantic` for data models, and optionally `opentelemetry-api` and `opentelemetry-sdk`. The library is published on PyPI and documented with MkDocs. While primarily a software library, EcoLogits has a strong data/research character: its methodology is based on Life Cycle Assessment principles, relies on curated JSON data files (models, electricity mixes, video models) that require regular updates, and is published in the Journal of Open Source Software. The codebase demonstrates high craftsmanship with clean abstractions, extensive testing, and a well-documented methodology.
Sitespeed.io is a mature, open-source web performance testing and monitoring tool that drives real browsers (Chrome, Firefox, Edge, Safari, including mobile) to collect a comprehensive set of performance metrics. It measures Core Web Vitals (LCP, INP, CLS, TTFB, FCP), visual metrics (Speed Index, filmstrip), HAR waterfalls, CPU long tasks, third-party requests, accessibility violations (via axe-core), and sustainability metrics (CO₂ estimates). The tool is used for one-off audits, CI/CD performance budgets, and continuous production monitoring, with results output as rich HTML reports, JSON, JUnit/TAP, and time-series data sent to Graphite/InfluxDB/Grafana. The architecture is plugin-based: a core message queue orchestrates a pipeline of plugins (setup → url → summarize → render). Built-in plugins handle browser testing (via Browsertime), HAR analysis (PageXray), best-practice advice (Coach), accessibility, sustainability, statistical comparison (Mann-Whitney U), performance budgets, and result storage (S3, GCS, SCP, Rsync). The CLI is built with yargs and supports ~600 options organized into topical modules. The tool is primarily delivered as a Docker image (multi-arch amd64/arm64) and an npm package. Key entry points are `bin/sitespeed.js` (CLI), `lib/sitespeed.js` (programmatic API), and `docker/scripts/start.sh` (Docker entrypoint). External dependencies include Browsertime, Coach Core, PageXray, axe-core, and the third-party-web dataset. The project has extensive CI/CD (GitHub Actions), a documentation site built with Eleventy, and a release workflow that publishes npm packages and Docker images with Sigstore signatures and SBOMs.
Scaphandre is a Rust-based CLI tool for monitoring electric power and energy consumption of hosts and processes. It reads RAPL (Running Average Power Limit) energy counters from Linux sysfs or Windows MSR, computes per-process power attribution using CPU usage ratios, and exports metrics via multiple backends: Prometheus (HTTP scrape or push gateway), JSON, stdout, Riemann, Warp10, and QEMU for virtual machines. The architecture is modular, built on `Sensor` and `Exporter` traits, allowing easy extension. It is packaged for Docker, Kubernetes (Helm chart), Linux (RPM/DEB), and Windows (installer with custom driver). The project is production-ready, actively maintained, and backed by extensive documentation and CI/CD pipelines. It is intended for infrastructure sustainability monitoring and carbon footprint tracking.
co2.js is a JavaScript library by the Green Web Foundation that calculates the carbon footprint of digital activities, such as data transfer and web page visits. It provides multiple emission models (OneByte, Sustainable Web Design v3/v4) and supports per-byte and per-visit calculations with optional grid intensity and green hosting factors. The library also includes utilities to check if a domain is hosted on green energy via an API or local database, and to validate carbon.txt files. It is published on npm and JSR, supports Node.js, browsers, and Deno, and bundles static grid intensity data from Ember, UNFCCC, and Electricity Maps. The architecture is modular, with separate model classes, hosting check modules, and helper utilities, making components independently testable and potentially reusable in other sustainability-focused projects. The repository also contains data generation scripts for updating grid intensity data automatically via GitHub Actions.