This repository provides the `starred` Python CLI tool, which generates and maintains an Awesome-style Markdown list from a GitHub user's starred repositories. The tool queries the GitHub GraphQL API to fetch all starred repos (with pagination), groups them by programming language or repository topic, and outputs a formatted Markdown document. It can optionally sort categories alphabetically, filter by topic popularity, include private repositories, and directly push the generated file to a target GitHub repository using the GitHub REST API. The architecture is a simple single-command CLI with two source modules: `starred/starred.py` (Click CLI entry point and Markdown generation) and `starred/githubgql.py` (a self-contained GraphQL client for fetching starred repos). External dependencies include `click`, `requests`, `github3.py`, `gql`, and `aiohttp`. The code is clean, follows a straightforward functional design, and integrates well with GitHub's APIs. While it lacks unit tests, the CI runs an integration test against the real GitHub API.
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.