Star History is a web application that visualizes GitHub repository star growth over time through interactive charts and embeddable SVG badges. The system consists of a Next.js 14 frontend with D3-based chart rendering, a Hono backend that generates SVG charts and Open Graph cards on demand, and a shared code library used by both. A data pipeline (gh/) fetches repository metadata from the GitHub API and weekly activity stats from BigQuery (GitHub Archive), stores them in SQLite, and exports JSON files consumed by the frontend for leaderboards, weekly rankings, and star count pyramids. The application also includes a blog with curated open-source project roundups, a sponsor system, and a Chrome extension for quick chart viewing. Architecturally, the frontend uses URL hash-based state management to persist chart configurations (repos, timeline mode, log scale) and supports embedding via iframe or SVG. The backend caches star data, SVG charts, and OG cards using LRU caches with TTL and size limits, and rotates GitHub API tokens with cooldown to handle rate limits. The shared chart utilities (axis drawing, labels, legend, watermark) are used both server-side (via JSDOM) and client-side, demonstrating a clean separation of rendering logic. External dependencies include the GitHub REST API, BigQuery for weekly stats, GH Archive for event data, D3.js for chart rendering, JSDOM and Satori for server-side SVG/OG card generation, and SVGO for SVG optimization. The project is deployed with Docker containers (frontend on Cloudflare Pages, backend on GKE) and uses GitHub Actions for weekly data refreshes and deployments. While primarily an application, the repo has a strong data pipeline component that ingests, transforms, and exports structured data about GitHub repositories. This hybrid nature is evident in the gh/ directory, which functions as an ETL pipeline, and the blog content that analyzes open-source trends using star history data.
This repository is an 'awesome list' curated by the TeleAI-UAGI team, aggregating open-source and closed-source products, papers, benchmarks, tutorials, and surveys on memory systems for LLM/MLLM agents. It does not contain application business logic or runnable services; its primary output is a meticulously structured README.md that serves as a reference catalog. The accompanying Python scripts automate two maintenance tasks: (1) checking that open-source entries are ordered by GitHub star count and respecting a 100-star boundary for emerging projects, and (2) generating self-hosted star-history SVG charts. The scripts are well-crafted, with robust parsing, GitHub API integration, and careful edge-case handling (grace bands, orphan detection), making them reusable for other awesome-list maintainers. The repository's value lies in its curated content rather than its code, but the supporting infrastructure demonstrates solid engineering principles.